Content uploaded by Richard Ferguson
Author content
All content in this area was uploaded by Richard Ferguson on Jul 03, 2014
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
V o l . 19, 4 (2010) 269–356
AGRICULTURAL AND FOOD SCIENCE
The Scientic Agricultural Society
of Finland
MTT Agrifood Research Finland www.mtt./afs
AGRICULTURAL AND FOOD SCIENCE
AGRICULTURAL AND FOOD SCIENCE
Vol. 19, No. 4, 2010
Contents
Hansson, H., Ferguson, R. and Olofsson, C.
Understanding the diversication and specialization of farm businesses 269
Värv, S., Kantanen, J. and Viinalass, H.
Microsatellite, blood group and transferrin protein diversity of Estonian dairy cattle breeds 284
Rybarczyk, A., Kmieć, M., Szaruga, R., Napierała, F. and Terman, A.
The effect of calpastatin polymorphism and its interaction with
RYR1
genotypes on carcass and meat quality
of crossbred pigs
294
Martínez-Fernández, A., Soldado, A., Vicente, F., Martínez, A. and de la Roza-Delgado, B.
Wilting and inoculation of
Lactobacillus buchneri
on intercropped triticale-fava silage: effects on nutritive,
fermentative and aerobic stability characteristics
302
Alakukku, L., Ristolainen, A., Sarikka, I. and Hurme, T.
Surface water ponding on clayey soils managed by conventional and conservation tillage in boreal conditions 313
Uusi-Kämppä, J. and Mattila, P.K.
Nitrogen losses from grass ley after slurry application - surface broadcasting vs. injection 327
Peltonen-Sainio, P. and Jauhiainen, L.
Cultivar improvement and environmental variability in yield removed nitrogen of spring cereals and rapeseed
in northern growing conditions according to a long-term dataset
341
Contents Vol. 19 (2010) 354
Acknowledgement of referees 356
ISSN electronic edition 1795-1895
V o l . 1 9 , N o . 4 , 2 0 1 0
Agricultural Economics
Agricultural Engineering
Animal Science
Environmental Science
Food Science
Horticulture
Plant and Soil Science
AGRICULTURAL AND FOOD SCIENCE
Agricultural and Food Science publishes original reports on agriculture and food
research. The papers, which are of international interest but feature a northern
perspective, cover a wide range of topics in basic and applied research. Submissions
are internationally refereed. Review articles and research notes will also be considered.
Readers are welcome to send their comments to the journal (Letter to the Editor).
Publishers
The Scientic Agricultural Society of Finland
MTT Agrifood Research Finland
Editorial board
Juha Helenius - Editor
Tapani Alatossava
Paula Elomaa
Xavier Irz
Eila Turtola
Pekka Uimari
Kaisa Kuoppala - Associate editor
Address
Agricultural and Food Science
Editorial Oce
MTT Agrifood Research Finland
FI-31600 Jokioinen, Finland
http://www.mtt./afs
From volume 19 the publication of Agricultural and Food Science is available as an Open
Access journal, free of charge to the user. The issues and articles published since volume
11 have also been transferred to the Open Access mode. Publishing continues in electronic
format only, available on the journal's web-pages (www.mtt./afs), or via database links or
where the journal is indexed. The printing of special issues will still be considered should the
applicant oer to provide the resources.
Single printed issues up to volume 18 may be obtained from Bookstore Tiedekirja, Kirkkokatu
14, FI-00170 Helsinki, Finland, e-mail: tiedekirja@tsv.. The price of a single issue is 17 +
VAT within the EU.
The journal is indexed by the following databases: AGRIS, BIOSIS, CAB
Abstracts,Cambridge Scientic Abstracts, Current Awareness in Biological Sciences, Current
Contents, FSTA, Pascal, Scopus, Science Citation Index, ISI Web of Science and Ingenta.
Copyright: Agricultural and Food Science
Printed in Finland by Tampereen Yliopistopaino - Juvenes Print LTD
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
269
© Agricultural and Food Science
Manuscript received October 2009
Understanding the diversification and specialization
of farm businesses
Helena Hansson*, Richard Ferguson and Christer Olofsson
Department of Economics, Swedish University of Agricultural Sciences, PO Box 7013, SE-75007 Uppsala, Sweden
*e-mail: Helena.Hansson@ekon.slu.se
This paper addresses the development of farm businesses in Sweden, 2000–2007, with regard to their
specialization in single farm enterprises, diversied agricultural production and diversication with new
income-generating ventures. Furthermore, regression analysis is used to study the impact of farm charac-
teristics on the observed specialization and diversication. The study is based a panel data set of about 900
farms participating in the Swedish Agricultural Economics Survey. Results show that farms are increasingly
engaging in diversied activities, though in most rms these activities make only minor contributions to
total revenue. Results also show that the degrees of specialization and diversication are inuenced by
characteristics of rms’ business structure, nancial and demographic conditions. These results contribute
to the understanding of farm business development, as well as show the need for policy makers and farm
advisors to consider the differences between farms pursuing different development strategies in their efforts
to inuence behavior.
Key-words: Business structure, demographic conditions, diversication, nancial conditions, specializa-
tion, Sweden
Introduction
Small rms are recognized as important engines in
local economies (Davidsson et al. 1999), and the
establishment of competitive rural businesses is a
major goal both in the European Union Rural De-
velopment Program 2007 – 2013 and in the Swedish
Rural Development Program 2007 – 2013. In rural
areas, small rms are often traditional farm busi-
nesses, as exemplied in Sweden, where 96 percent
of rural rms have less than 10 employees, and over
one quarter are commercial farm businesses (Nils-
son et al. 2009, Statistics Sweden, 2008). Currently,
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
270
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
271
however, traditional farming is going through major
structural changes in many countries, where farm
businesses are facing increasing competition, new
emerging markets, such as agricultural tourism and
bio-energy, and increasingly volatile prices. There
are clear trends of traditional farm businesses exit-
ing agricultural production, and/or merging with
other farms, resulting in fewer and larger businesses
(e.g. Statistics Sweden 2002, 2006, 2008; Tauer
and Mishra 2006; MacDonald et al. 2007). In this
business climate, continued farm business survival
may depend largely on the strategic choice of ei-
ther focusing on a specialized line of production,
to increase margins and/or scale, or diversifying
into new ventures, to supplement business income.
In the European Union Rural Development Pro-
gram, 2007-2013, the diversication of farm busi-
nesses is identied as a prioritized area. Accord-
ingly, in the Swedish Rural Development Program,
2007 – 2013, diversication of rural rms is named
as one of four central goals. However, at the same
time, with technological development continuing to
offer ways to improve production efciency, spe-
cialization within a single farm enterprise is another
plausible development strategy for individual rms
to pursue.
This raises the question of how farm businesses
are developing over time. To what extent are farms
specializing in single enterprises, versus diversify-
ing business income through additional agricultural
enterprises, and/or non-agricultural ventures? Fur-
thermore, if policy and advisory services are to be
effective, it would be valuable to know if there are
underlying factors that inuence the direction and
degree of development.
Much of the more recent research on the stra-
tegic development of farm businesses has focused
largely on patterns of diversication into non-agri-
cultural activities, leaving out analysis of alterna-
tive strategies for farm business development, such
as specialization in single production enterprises
and/or diversication with other primary agricultur-
al productions. A number of authors have assessed
the inuence of selected farm characteristics on
the probability of observing certain diversication
activities (e.g. McNally 2001; Chaplin et al. 2004;
Barbieri and Mahoney, 2009), but at the same time,
the diversied activities have been measured in a
binary variable, reducing the possibility of analysis
of the contributions that activities are making to
businesses. Furthermore, with the notable excep-
tion of McNally (2001), little previous research on
farm diversication has used panel data, limiting
assessment of development patterns over time.
The focus in this study lies in tracing devel-
opment patterns over time and in exploring how
the characteristics of a farm business, such as
business structure, nancial conditions and demo-
graphic characteristics, inuence strategic choices
of single-venture specialization and diversication
within and outside of conventional agricultural
activities. With this focus, two distinct aims are
addressed: First, the pattern of farm business de-
velopment through specialization within single pro-
duction enterprises, diversication within primary
agricultural production and diversication into new
income-generating ventures in Swedish farms dur-
ing 2000 – 2007 is described. Second, the impact of
business structure, including the extent of existing
diversication/specialization, farm size, number of
employees, and business form; nancial conditions,
including liquidity, solidity, return and possibilities
for internal capitalization; and demographic char-
acteristics, including gender and age, on the extent
to which farmers specialize in single agricultural
ventures (specialization), diversify their activities
within conventional agriculture (agricultural diver-
sication) and/or diversify into activities outside of
conventional agriculture (non-agricultural diversi-
cation) is assessed.
This study contributes to the literature by in-
creasing the understanding of the business devel-
opment process in farms in three particular ways:
First, the study is not limited to investigating di-
versication into non-traditional agricultural pro-
duction, but considers also the alternative business
development strategies of specialization in single
agricultural enterprise and diversication within
traditional agricultural production. Second, the
study investigates the impact of farm characteris-
tics on the extent to which certain strategies are
exhibited, rather than merely the occurrence of a
particular strategy. Third, as the study is based on
panel data, it provides an analysis of the evolution
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
270
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
271
of development strategies over time. In addition
to contributions to academic understanding, the
ndings in this study provide advisors and policy
makers with information about the heterogeneous
behavior of farm businesses that can lead to better
targeted support and policy.
Theoretical background
Definitions
The concept of diversication among farm busi-
nesses has long received attention from researchers,
yet there is little consensus on a single denition.
Much of earlier research focused on diversication
as a means of reducing risk, dening diversication
as the development of multiple production ventures
within the farm business unit, perhaps but not neces-
sarily using the farm’s existing resources, resulting
in additional distinct marketed outputs (e.g. Johnson,
1967; Heady, 1952). This basic denition is still
used in current research (e.g. Mishra et al. 2004;
Sumner and Wolf, 2002), though increasingly focus
is on diversication as an entrepreneurial reaction
to change rather than risk management.
Much of the literature is unclear in the distinc-
tion between the farm business, the farmer, and the
farm household. As Gasson et al. (1988) points out,
the business, the individual, and the family are par-
ticularly intertwined in family farms, and the three
levels inuence one another in management deci-
sions. In the general business literature, however,
diversication refers to multiple activities within
a single business enterprise (Robson et al. 1993).
This denition should not be confused with the con-
cept of pluriactivity, which refers to the activities of
the farmer, and includes off-farm work as a “diver-
sied” source of income; nor with the concept of
portfolio entrepreneurship, which refers to a single
entrepreneur holding multiple separate businesses
(Alsos and Carter 2006, Alsos et al. 2003, Carter
1999). While these two concepts are similar with
the concept of diversication in that multiple sourc-
es of revenue and possible synergy and conict
between the different ventures’ resource demands,
the concept of diversication differs by being ex-
clusively focused on multiple income generating
activities within a single business entity.
Another point of inconsistency in the litera-
ture concerns exactly what additional ventures to
consider as diversication. In the early literature,
any venture that provided additional streams of
income that could balance the risks is considered
diversication (Johnson 1967, Heady 1952). Thus
additional ventures in conventional agricultural
production, such as dairy, pork, poultry and/or crop
production, are means of diversication. Much of
the more recent literature, however, limit the con-
cept of diversication to gainful activities that
take place outside the primary production of food
and ber, such as contract machine services, food
processing, or summer cottage rentals, and therein
exclude multiple agricultural production activities
as diversied ventures (e.g. Barbieri and Mahoney
2009, Turner et al. 2003, Ilbery 1991, Slee 1987).
Barbieri and Mahoney (2009) identify seven
different types of diversication, including non-
traditional crops, livestock, and/or practices; alter-
native marketing schemes; tourism and recreation;
lease and rental of resources; contract machine
services; value-added processing; and preserva-
tion, education and consulting services. On a more
general level, Ilbery (1991) notes that diversica-
tion can be either structural, including activities ori-
ented outward from the farm towards the public, or
agricultural, including activities focused on farm-
ing and the various types of farm work (but which
are different from traditional farming). Ilbery’s
structural diversication includes ventures such
as tourism, value-adding to products and renting
out of land and buildings, whereas his agricultural
diversication includes ventures such as contract
eld work.
These examples from the literature show that
while there is variation in exact denitions, dif-
ferent types of diversication can be identied,
depending on the level of conceptualization. In
the present study, the concept of diversication is
differentiated at three different levels. At the most
aggregate level, specialized versus differentiated
sources of revenue are considered.
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
272
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
273
The concept of specialization has not received
attention in the literature to the same extent as the
concept of diversication, though the literature
measuring the degree of specialization has often
focused on the proportion of total farm revenue
that is obtained from the main farm enterprise or
from the enterprise of interest (e.g. Hansson 2007,
Hadley 2006, Brümmer 2001) and this strategy is
followed in this study.
At the next level, the study distinguishes be-
tween diversication within versus outside con-
ventional agriculture. Diversication within con-
ventional agriculture implies multiple sources of
revenue from primary agricultural production for
which there are established industries, such as pork
or wheat. Diversication outside conventional agri-
culture implies revenue-generating activities based
on non-conventional agricultural production in ad-
dition to one or more conventional enterprises. High
specialization in one farm enterprise has repeatedly
been found in empirical studies to decrease techni-
cal and/or economic efciency, concepts intimately
related to protability (e.g. Hansson 2007, Hadley
2006, Brümmer 2001). Reasons elaborated in the
literature include that high dependency on a single
farm enterprise makes the farm more vulnerable
to changing market conditions. Thus, rms with
multiple conventional enterprises can reduce risk,
just as farms with non-conventional ventures can.
Finally, a distinction is made between two types
of diversication activities outside conventional ag-
riculture: Activities based on new markets for exist-
ing farm resources are differentiated from activities
based on new value-added products and services.
These two alternatives reect different expres-
sions of dynamic capability (Eisenhardt and Martin
2000, Teece et al. 1997), where on the one hand
the rm redirects existing resources in a relatively
unchanged form and on the other hand modies or
creates new resources. In activities based on new
markets for existing resources, such as providing
contract work and renting out of farm buildings
and equipment, the farm’s resources are used in
essentially the same way, but revenues are attained
from new markets. Activities based on value-added
products and services, such as food processing and
agricultural tourism, involve a more innovative
process, where resources are re-formed, acquired
or created to build increased customer value in a
new product or service.
Review of related research
Previous research on farm diversication related
to this study can be divided into two general types.
On the one hand there are studies that have taken a
more general approach, seeking to identify patterns
of diversication and/or indentifying factors associ-
ated with diversication. On the other hand there
are studies investigating particular types of diver-
sication, such as small-scale foods or agricultural
tourism, where venture-specic issues come more
into focus. The aim in the present study is clearly
related to the rst of these two types.
McNally (2001) investigated patterns of agri-
cultural diversication over the time period 1988—
1997 in England and Wales. The most frequently
reported diversication strategy was hirework,
though there was a signicant increase in the oc-
currences of renting out of farm buildings over the
studied period as well. The study also showed that
the total number of diversied farms was rather sta-
ble over time and that smaller farm size, and/or a
livestock based production generally lowered the
likelihood of diversication. At the same time, Mc-
Nally found that diversication activities based on
the traditional farm resources played only a minor
role in the development of business income.
In a benchmark study intended to contribute to
policy development, Turner et al. (2003) reviewed
and updated research on farm diversication in
England during the 1990s. They reported that farm
diversication increased during the studied period,
with over 53 percent of the surveyed farms report-
ing diversied activities. They also found that lager
farms have more resources to direct toward diver-
sied activities, and that certain types of primary
production on farms – grain and crop production
and “mixed” production, rather than dairy, beef and
sheep production – were more apt to diversify. The
study found diversied activities to be relatively
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
272
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
273
protable, and in contrast to McNally (2001), to
be a signicant contribution to total farm income.
Chaplin et al. (2004) found that, in the three
central European countries – Czech Republic, Hun-
gary and Poland – the level and effect of diversi-
cation is relatively small. Relating the probability
of observing a diversied farm to farm and farmer
characteristics, the study found that farm and farmer
characteristics inuenced diversication decisions
differently in the different countries. For example,
the proportion of unearned income signicantly
negatively affected diversication decisions in the
Czech Republic and Poland, but not in Hungary.
An exception to this heterogeneity, however, was
level of education, where a higher level of education
positively affected the probability of diversication
in all three countries.
Focusing on the goals underlying decisions to
diversify, Barbieri and Mahoney (2009) concluded
that reduction of uncertainty and risk, followed by
growth and market services, and enhancement of
nancial conditions were the most important goals
motivating diversication decisions among farm-
ers and ranchers in Texas. Farm and farmer char-
acteristics underlying these goals, and thus also af-
fecting decisions to diversify were the number of
generations in the farm, household gross income,
distance to urban areas and the number of full time
employees.
While Barbieri and Mahoney (2009) do not
make the differentiation between diversication
for reasons of opportunity-pull versus necessity-
push (Brockhaus 1980), the goals that they iden-
tify include both proactive responses to a detected
prospect and reactive responses to unsatisfactory
conditions. Rantamäki-Lahtinen (2009) reports
that diversication can be both necessity- and
opportunity-driven, and that the availability of idle
resources can affect the direction of new activities.
Firms with more agriculturally related diversi-
cation often exploited unused physical resources,
whereas rms with more non-agriculturally related
diversication made more use of the rm’s compe-
tence resources.
In contrast to these more general studies of di-
versication, Sharpley and Vass (2006) and Nilsson
(2002) both investigated specically diversication
through agricultural tourism in greater detail. Apart
from the need or desire of extra income being a
major driving force for establishing an agricultural
tourism enterprise in north-eastern England, Shar-
pley and Vass (2006) found that the perceived ben-
ets from working at home were also important mo-
tivating factors. Nilsson (2002) found that gender of
the main entrepreneur plays a central role in devel-
opment of agricultural tourism ventures, where the
vast majority of the studied ventures had women in
central positions. These studies show that there is
reason to consider the type of venture when analyz-
ing factors that inuence diversication decisions.
Despite the range and contributions of the above
sampling of the literature, there are two recurring
deciencies in most of the existing research on farm
diversication. First, little attention has been paid
to understanding diversication as one of multiple
paths for business development. A farm that dedi-
cated resources to a diversied activity will then not
have those resources available for alternative uses.
Second, the literature shows an inconsistent, often
unclear distinction between the farm business, farm
family income, and the farm manager, often mixing
business diversication and a farmer’s puriactivity.
While it is well established that these three levels
are intimately interconnected (Gasson et al. 1988),
a distinct focus on the level of the farm business is
necessary to advance understanding of farm busi-
ness development.
Hypotheses about factors associated with
diversification and specialization
The interest in this study lies in exploring how
fundamental rm characteristics, including business
structure, nancial conditions, and demographic
characteristics are associated with the degree of
exhibited specialization in single farm enterprise,
diversication within agriculture and diversication
outside conventional agriculture of the farm busi-
nesses. It is assumed that farmers have economic
motives in the management of their farm businesses;
hence, that they aim to generate at least a satisfactory
income from their farm activities and, all else being
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
274
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
275
equal, they prefer more income to less. Furthermore,
it is assumed that farmers want to make good use of
their existing farm resources, such as available labor,
farm buildings and equipment, in generating income.
These assumptions are well in line with ndings
of Alsos et al. (2003) who found that motivations
behind farmers’ decisions to diversify their farms
included a desire to stay at the farm, for which a
protable farm would be a long-term condition, and
to make use of existing resources. These assumptions
are also well in line with ndings of Barbieri and
Mahoney (2009) who concluded that the generation
of income and enhanced nancial conditions were
among the most important goals in farm diversica-
tion. These assumptions also support a rationale for
specialization, where the focused use of resources
can lead to gains from economies of scale and other
competitive advantages (Porter, 1985).
This underlying view of the management be-
havior in farm rms together with the given dataset
provides a basis for formulating a set of hypoth-
eses that propose the inuence of specic factors of
business structure, nancial conditions and demo-
graphic characteristics on rms’ specialization and
diversication within- and outside agriculture. The
specic factors that are identied in the hypotheses
in Table 1 are all aspects that dene existing pre-
conditions of a farm and thus, under the assumption
Table 1. Hypotheses (H) of business structure, nancial and demographic factors affecting farm business diversica-
tion and specialization.
Supporting references
Business structure
H:B1 Previous specialization and diversication will inuence the extent of fu-
ture specialization and diversication, as strategic development tends to be
path-dependent.
Teece et al. 1997
H:B2 The presence of employees will be associated with specialization, as employees
are typically hired for specialized skills.
Mugera and Bitsch 2005
H:B3 Farms with signicant seasonal variation in labor needs, or with production that is
not intensive-labor, will become more diversied, as they are apt to have unused
resources.
Turner et al. 2003, McNally
2001; Ilbery 1991
H:B4 Larger farms, given the proportionally higher value of unused resources, are more
likely to diversify their production, as the lost value from unexploited opportuni-
ty will be greater.
Mishra et al. 2004, McNally
2001; Ilbery 1991
H:B5 Incorporated companies will be more diversied in ventures outside traditional agri-
cultural production, as the potential losses from failure in new ventures will be limited.
McNally 2001, Cressy and
Olofsson 1997
Financial structure
H:F1 Weaker liquidity ratios will increase in the degree of diversication, as farmers
search for alternative sources of farm income.
McNally 2001
H:F2 Stronger solidity and return on assets will encourage increased specialization, as
farmers reinvest in a protable enterprise.
Porter 1985
H:F3 Income from forestry in a preceding period will be positively associated with di-
versication, especially outside agriculture, as such internal capital can reduce risk
exposure in new venture investments.
Cressy and Olofsson 1997
Demographic characteristics
H:D1 Diversication into value-added activities is positively associated with women,
whereas diversication into new market activities is positively associated with men.
Nilsson 2002
H:D2 Farms managed by older farmers will be more specialized or diversied with-
in conventional agriculture, whereas farms managed by younger farmers will be
more diversied into enterprises outside of conventional agriculture.
McNally 2001
H:D3 Farms in areas with higher production costs are more likely to develop diversied
enterprises outside of conventional agriculture.
Turner et al. 2003
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
274
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
275
of rational behavior, create a context for strategic
decisions.
The study data
This study is based on a dataset compiled by Sta-
tistics Sweden called the Agricultural Economics
Survey. The annual survey collects data from a panel
of about 800-900 Swedish farm businesses with a
size of at least 8 European Size Units (ESU1), and
queries on income statements and balance sheets,
labor, land holdings, farm equipment, animals and
inventories. Stratied sampling of the approximately
30 000 qualifying farms is used to ensure good
representation of geographic location, production,
and size. The smaller farms that are excluded are
estimated to provide less than 800 standardized
hours of labor, and therein the dataset is assumed
to be representative of Swedish farms in general
(excluding very small holdings, which are arguably
not true commercial businesses). The dataset uses
a rotating panel, where a fraction of the sample is
replaced each year.
The Agricultural Economics Survey database
is maintained primarily for Sweden’s participation
in the European data network, Farm Accounting
Data Network (FADN), which collects standard-
ized farm accounting data from the member states
to support EU policy and research. The Swedish
dataset has limited missing values and good con-
sistency between years. While the response rate of
entering rms is only about 50 percent, responses
in following years are good, and a non-response
analysis has indicated little bias in the data (Jans-
son, 2008).
The minimum 8 ESU limit means that slightly
over half of all registered farms in Sweden are
not included in the survey population. While the
excluded farms are likely homes valued by their
occupants and contribute to rural life-style and
community, they are estimated to require less than
800 standardized hours of labor, suggesting that
the owner-managers of these small holdings are
more dependent on off-farm income than on their
farm business’ commercial success. Lagerkvist et
al. (2007) found that reliance on off-farm income
affects the nancial management of farm business-
es, suggesting there is reason to consider part-time
farm businesses separately from the more com-
mercially reliant farms in the survey population.
As the focus of the current study is on the strategic
development of farm businesses, and not farm fam-
ily households nor rural communities, the size limit
is a desirable restriction of the total population.
Working definitions
The degree of specialization is dened by the share
of total farm revenue coming from the main farm
enterprise. Diversication is dened by activities
in addition to the main enterprise that produce farm
income, and can be venture within agriculture,
ventures based on new markets for existing farm
resources, or ventures based on value-added farm
products and services, as described above.
The degree of diversication is measured by
the share of total farm revenue coming from each
of the diversication types. Thus, diversication
within agriculture includes all revenue from tradi-
tional agricultural production, apart from the main
enterprise. Diversication based on new markets
for existing farm resources, includes activities such
as contract work and renting out of farm buildings
and xed equipment, and diversication based on
added-value farm products, includes activities such
as, food processing, direct marketing produce and
agricultural tourism.
1 ESU is based on standardized gross margins. 1 ESU = 1200 euro. 1 ESU corresponds to approximately 1.3 hectares or 1 cow
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
276
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
277
Results and discussion
The development of diversification and
specialization 2000 – 2007
Table 2 shows the development of diversication
and specialization 2000 – 2007 in the sampled
farms. The gures in Table 2 show that the degree
of specialization is fairly consistent, with about
three-quarters of total revenue coming from the
main farm enterprise. However, the slight drop
between 76.5 percent in 2000 and 75.4 percent in
2007 hides much of the stronger negative trend that
can be clearly seen during 2000 – 2006. In light of
the signicant crop price increases in 2007, it can
be concluded that production on farms is becom-
ing less specialized, though market conditions at
the end of the studied period counteracted a more
signicant restructuring of farm income.
Turning to the diversied activities, Table 2
shows that the majority of farms have diversied
agricultural production, with 72 percent of farms
reporting income from multiple agricultural ven-
tures in 2000, increasing to 74 percent in 2007. The
share of total revenue obtained from these ventures
has been quite steady during the studied period,
uctuating for rms with such activities not more
than one percentage point around the average of
16.5 percent.
Regarding diversication outside of conven-
tional agriculture, Table 2 shows that there has
been a more substantial increase in the number of
farms reporting income from both new market ven-
tures and added-value ventures, with new market
ventures increasing from 68 percent to 75 percent,
and added-value ventures increasing from none in
2000 to 5 percent of rms in 2007. While there are
no grounds for suggesting causality, this change is
well in line with public policy goals.
The role that diversication outside conven-
tional agriculture is playing in farms’ total revenue
is more mixed. On the one hand, diversication
into new market ventures fairly well follows the
inverse of the pattern seen in specialization, with a
gradual increase from 12.3 percent of revenues in
2000 to a top of 15.2 percent in 2006, followed by
Table 2. The development of diversication and specialization among the sampled farms 2000 – 2007.
2000
n = 889
2001
n = 854
2002
n = 878
2003
n = 892
2004
n = 897
2005
n = 883
2006
n = 864
2007
n = 902
Share of
rms re-
porting
revenue
Share of
total rev-
enue (re-
port-
ing rms
only)
Share of
rms re-
porting
revenue
Share of
total rev-
enue (re-
port-
ing rms
only)
Share of
rms re-
porting
revenue
Share of
total rev-
enue (re-
port-
ing rms
only)
Share of
rms re-
porting
revenue
Share of
total rev-
enue (re-
port-
ing rms
only)
Share of
rms re-
porting
revenue
Share of
total rev-
enue (re-
port-
ing rms
only)
Share of
rms re-
porting
revenue
Share of
total rev-
enue (re-
port-
ing rms
only)
Share of
rms re-
porting
revenue
Share of
total rev-
enue (re-
port-
ing rms
only)
Share of
rms re-
porting
revenue
Share of
total rev-
enue (re-
port-
ing rms
only)
Specialized in
main enterprise
100% 0.765 100% 0.763 100% 0.760 100% 0.744 100% 0.736 100% 0.740 100% 0.735 100% 0.754
Diversication
in agriculture
72% 0.164 69% 0.162 71% 0.162 71% 0.173 74% 0.175 70% 0.160 71% 0.156 74% 0.164
Diversication
based on new
markets
68% 0.123 68% 0.130 68% 0.133 69% 0.137 71% 0.139 73% 0.147 74% 0.152 75% 0.121
Diversication
based on add-
ed-value
products
0% --- 2% 0.216 2% 0.183 4% 0.120 4% 0.082 4% 0.095 5% 0.130 5% 0.114
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
276
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
277
a signicant drop to 12.1 percent in 2007. Again,
the crop price increases in 2007 may explain much
of the fall. On the other hand, the pattern of change
in share of revenue from diversication based on
added-value products is much more erratic, begin-
ning with no revenues reported in 2000, followed
by the top share of revenue already in 2001 of 21.6
percent, ending in 2007 at 11.4 percent.
While perhaps 2 percent of farms generating 20
percent of their income from diversied added-val-
ue enterprises is noteworthy for that small group,
for farm businesses in general the average frac-
tion of total revenue obtained from diversication
activities during the studied period is fairly small,
which is much in line with earlier ndings, such as
McNally (2001). In particular, the average fraction
of total farm revenue originating from diversica-
tion activities involving value-adding for all farms
in the study never exceeds 0.6 percent, suggesting
that such diversication, while perhaps signicant
for a single business, has only marginal effects on
total farm revenues in general. On the other hand
diversication into new markets contributed to at
the most 11.3 percent of total revenues in the whole
group, suggesting that this type of diversication
outside conventional agriculture may be of more
signicance to the farmers.
Influence of factors associated with
diversification and specialization
Moving to the second part of the study, hypotheses
of the inuence of factors associated with extent of
specialization and diversication that have been
outlined above have been tested in regression analy-
ses of the dataset. To capture causal dependency,
values for the variables measuring business structure
and nancial conditions were taken from the year
preceding an observed diversication or specializa-
tion measurement, meaning that a sequence of two
consecutive years of data for a farm are needed for
a usable observation. In addition to the variables
identied in the hypotheses, a variable accounting
for the time trend is also included in the analyses.
After removal of observations with missing values
created by the rotating panel, the total number of
observations in the regression analyses is 6049.
Because the dependent variables are censored, in
the case of the extent of specialization at 1 and in
the cases of diversication at 0, the Tobit model
was used. The Tobit model is dened as follows in
the case where the censoring occurs at 1:
(1)
where εi ~N(0,σ2) and the β:s are the parameters
for the explanatory variables. In the case where the
censoring occurs at 0, the Tobit model is dened in
the following way:
(2)
The regression results are presented in Table 3,
along with notes on whether or not the hypotheses
are accepted.
Business structure
Results clearly show the inuence of business
structure on rms’ specialization and diversication,
where it can be seen that the degree of specializa-
tion and diversication in a previous year positively
affects the degree of specialization and diversica-
tion in a following year, as expected, giving clear
support to hypothesis H:B1. This shows that farm
strategies tend to follow a trajectory, where farms
that are more specialized tend to continue to be so
in the future and farms that have diversied in a
particular way tend to continue with that diversi-
cation in the future. This nding is not surprising,
as both specialization and diversication require
long-term investments and process development
that have a lock-in effect.
It is also interesting to note the cross-effects
between the considered farm strategies. For in-
stance, the degree of diversication in new market
activities in a preceding year has a signicant and
12
1
11
,,....2,1,
**
*
*
iii
ii
ii
i
i
yifyy
yify
nixy
(1) 349
where ),0(~ 2
N
i and the s:
are the parameters for the explanatory variables. In the case 350
where the censoring occurs at 0, the Tobit model is defined in the following way: 351
0
00
,,....2,1,
**
*
*
iii
ii
ii
i
i
yifyy
yify
nixy
(2) 352
The regression results are presented in Table 3, along with notes on whether or not the 353
hypotheses are accepted. 354
*** Table 3 about here *** 355
4.2.1 Business structure 356
Results clearly show the influence of business structure on firms’ specialization and 357
diversification, where it can be seen that the degree of specialization and diversification in a 358
previous year positively affects the degree of specialization and diversification in a following 359
year, as expected, giving clear support to hypothesis H:B1. This shows that farm strategies 360
tend to follow a trajectory, where farms that are more specialized tend to continue to be so in 361
the future and farms that have diversified in a particular way tend to continue with that 362
diversification in the future. This finding is not surprising, as both specialization and 363
diversification require long-term investments and process development that have a lock-in 364
effect.365
It is also interesting to note the cross-effects between the considered farm strategies. For 366
instance, the degree of diversification in new market activities in a preceding year has a 367
significant and negative impact on specialization in agriculture in a following year. Moreover 368
the degree of specialization in a preceding year has a significant negative impact on both 369
types of diversification outside conventional agriculture. This suggests that farms that develop 370
diversified activities related to new markets and/or added-value products have made a 371
strategic decision to move away from traditional farming. The cross effects hence suggest that 372
at some point in time farms make a strategic choice in the direction of their development; 373
12
1
11
,,....2,1,
**
*
*
iii
ii
ii
i
i
yifyy
yify
nixy
(1) 349
where ),0(~ 2
N
i and the s:
are the parameters for the explanatory variables. In the case 350
where the censoring occurs at 0, the Tobit model is defined in the following way: 351
0
00
,,....2,1,
**
*
*
iii
ii
ii
i
i
yifyy
yify
nixy
(2) 352
The regression results are presented in Table 3, along with notes on whether or not the 353
hypotheses are accepted. 354
*** Table 3 about here *** 355
4.2.1 Business structure 356
Results clearly show the influence of business structure on firms’ specialization and 357
diversification, where it can be seen that the degree of specialization and diversification in a 358
previous year positively affects the degree of specialization and diversification in a following 359
year, as expected, giving clear support to hypothesis H:B1. This shows that farm strategies 360
tend to follow a trajectory, where farms that are more specialized tend to continue to be so in 361
the future and farms that have diversified in a particular way tend to continue with that 362
diversification in the future. This finding is not surprising, as both specialization and 363
diversification require long-term investments and process development that have a lock-in 364
effect.365
It is also interesting to note the cross-effects between the considered farm strategies. For 366
instance, the degree of diversification in new market activities in a preceding year has a 367
significant and negative impact on specialization in agriculture in a following year. Moreover 368
the degree of specialization in a preceding year has a significant negative impact on both 369
types of diversification outside conventional agriculture. This suggests that farms that develop 370
diversified activities related to new markets and/or added-value products have made a 371
strategic decision to move away from traditional farming. The cross effects hence suggest that 372
at some point in time farms make a strategic choice in the direction of their development; 373
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
278
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
279
Table 3. Regression results of the effects of hypothesized variables on specialization and diversication.
Specialization in
agriculture
Diversication
within conven-
tional agriculture
Diversication
outside conven-
tional agriculture:
new markets
Diversication out-
side convention-
al agriculture: value
added
Concerned
hypothesis
Constant 0.261*** -0.106*** 0.066** -0.389***
Degree of specialization in agriculture previous year 0.734*** -0.012 -0.080*** -0.173** H:B1-Accepted
Degree of diversication within traditional agriculture previous year -0.000 0.700*** -0.011 -0.155
Degree of diversication outside traditional agriculture:
new markets previous year
-0.132*** -0.023 0.879*** -0.027
Degree of diversication outside traditional agriculture:
added-value previous year
0.032 -0.046 -0.038 1.405***
Employees in previous year (1=yes; 0=no) -0.003 -0.009 0.001 -0.004 H:B2-Not accepted
Revenue from crop production in previous year (1=yes; 0=no) -0.011*** 0.014*** 0.019*** 0.016 H:B3-Accepted
Revenue from pig production in previous year (1=yes; 0=no) -0.010** 0.083*** -0.009* -0.032
Revenue from poultry production in previous year (1=yes; 0=no) -0.007 0.028*** 0.001 0.128***
Revenue from dairy production in previous year (1=yes; 0=no) -0.002 0.059*** -0.022*** -0.042**
Revenue from beef production in previous year (1=yes; 0=no) -0.028*** 0.081*** 0.011** 0.057**
Revenue from sheep production in previous year (1=yes; 0=no) -0.024*** 0.045*** -0.004 0.028
Farm size (revenue, normalized around sample mean) 0.002 0.002* 0.003*** 0.023*** H:B4-Accepted
Business form (1=limited company, 0=not) 0.006 -0.003 -0.010* -0.019 H:B5-Not accepted
Liquidity ratio previous year 0.000*** -0.000 -0.000*** -0.001 H:F1-Accepted
Solidity previous year -0.005 0.004 0.001 -0.001 H: F2-Unclear
Return on assets previous year 0.024 -0.007 -0.013 -0.154***
Result from forest previous year (normalized around sample mean) -0.001 -0.000 0.001** 0.006** H: F3-Accepted
Gender (1=male; 0=female) -0.030*** 0.023*** 0.013 0.025 H: D1-Accepted
Spouse’s work previous year (hours, normalized around sample mean) 0.001 0.001 -0.001 0.011***
Age in 2009 0.000 -0.000*** -0.001*** -0.000 H: D2-Unclear
Geographic location (1= Northern Sweden; 0=elsewhere) 0.000 -0.005 -0.008 0.031 H: D3-Not accepted
Linear time trend -0.002** 0.003*** 0.000 0.015***
Fit statistics Log likelihood:
4168; Prob > LR
0.000; McKelvey
& Zavoina’s
R-square: 0.637
Log likelihood:
3153; Prob
> LR 0.000;
McKeelvey
& Zaviona’s
R-square: 0.651
Log likelihood:
2316; Prob > LR
0.000; McKelvey
& Zaviona’s
R-square: 0.642
Log likelihood: -396;
Prob > LR 0.000;
McKelvey &
Zaviona’s R-square:
0.201
*** indicates statistical signicance at the 1% level or less; ** indicates statistical signicance at the 5% level or less; * indicates statistical signicance at the 10% level or less. Statistical
signicance is based on robust standard errors.
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
278
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
279
negative impact on specialization in agriculture in
a following year. Moreover the degree of speciali-
zation in a preceding year has a signicant nega-
tive impact on both types of diversication outside
conventional agriculture. This suggests that farms
that develop diversied activities related to new
markets and/or added-value products have made
a strategic decision to move away from traditional
farming. The cross effects hence suggest that at
some point in time farms make a strategic choice in
the direction of their development; choosing either
a strategy that is centered on conventional farm-
ing, where specialization in a single enterprise will
play the leading role, perhaps supported with some
secondary diversied agricultural activities, or a
strategy that moves away from conventional farm-
ing, where the rm’s resources are redirected or
further developed to generate income in new ways.
Results further show that the presence of em-
ployees at the farms does not signicantly inuence
rms’ specialization or diversication strategies,
which speaks against hypothesis H:B2. It may be
that the presence of employees neither contributes
to nor deters diversication per se, but rather is an
effect of the size and work load at the farm. Alter-
natively, though the study data did not enable an
analysis of the use of employee labor in specialized
versus diversied ventures within a rm, it is pos-
sible that the presence of employees can support
rm development by both facilitating increased
specialization and/or by providing labor for new
activities, and that these two opposing stimulus
hide the effects of one another in the analysis.
Results show the inuence of different types
of farm production on exhibited specialization
and diversication. In Table 3 it can be seen that
more specialized farms are signicantly less likely
to obtain revenue from crop, pig, beef and sheep
production. Furthermore, the extent of diversica-
tion within conventional agriculture can be seen
to be positively inuenced by crop, pig, poultry,
dairy, beef and sheep production. Results also show
that dairy production, which can be considered as
labor intensive livestock production, has a signi-
cant negative inuence on both types of diversi-
cation outside traditional agriculture. Similarly,
pig production has a signicant negative impact on
new market activities. In contrast, farm production
which is more seasonal (e.g. crop production) or
less labor intensive (e.g. beef production), show
a positive association with diversication outside
conventional agriculture. These results support hy-
pothesis H:B3, and suggest that the availability and
use of labor is playing an important role in the for-
mulation of development strategies. These results
are in line with ndings of McNally (2001), who
reported similar ndings for diversication choices
of farmers in England and Wales, as well as with
Ilbery (1991) who found that farms with extensive
livestock production are the more diversied. This
evidence suggests that there may be reason to tar-
get policy encouraging farm diversication outside
agriculture towards crop farms or farms with less
labor intensive livestock production.
In support of hypothesis H:B4, the results show
that a larger farm size had a signicant impact on
the degree of diversication outside traditional ag-
riculture. One can also note, however, that larger
farms show a greater degree of diversication
within conventional agriculture. These results
are in line with ndings of McNally (2001), who
reported that the probability of being involved in
hirework or renting out of farm buildings is lower
in smaller farms. The results are also in line with
ndings of Ilbery (1991) who found that farms
with alternative enterprises tend to be larger than
the average farms. The results are reasonable and
expected, as larger farms are likely to have more
valuable idle resources that can generate income if
put to use, and thus their degree of diversication
should be larger.
Contrary to what was expected in hypothesis
H:B5, business form, measured by whether the
farm is an incorporated company or not, is nega-
tively associated with the extent of diversication
related to new markets, and is otherwise not sig-
nicantly associated with the studied development
alternatives. These ndings contradict ndings by
McNally (2001), which may reect cultural dif-
ferences in business between England, Wales and
Sweden. A possible explanation of these results is
that once farmers have incorporated one company,
they have the knowledge to set up additional lim-
ited corporations, and consequently may choose
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
280
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
281
to operate new ventures in separate companies.
Though such portfolio behavior has been identied
in general entrepreneurship literature (e.g Westhead
and Wright 1999), starting multiple businesses as
an alternative of farm business diversication is an
interesting issue to be further explored.
Financial conditions
Regarding the nancial conditions of the farm,
results show that the liquidity ratio in a preceding
year is positively associated with a farm’s degree
of specialization in the following year, whereas, in
accordance with hypothesis H:F1, it is negatively
associated with the degree of diversication in new
market enterprises. The return on assets in a preced-
ing year is negatively associated with the degree of
diversication though added-value enterprises. The
results involving liquidity ratio and return on assets
suggest that the degree of specialization increases
when financial conditions are more favorable,
whereas the degree of diversication increases when
nancial conditions are less favorable, as expected.
Similar conclusions were made by McNally (2001),
suggesting that diversication is triggered by a need
to improve nancial conditions rather than strong
nancial conditions stimulating diversication: Us-
ing Brockhaus’ (1980) terminology, one might say
that rms are initially pushed into diversication by
the unfavorable economic conditions, rather than
pulled by opportunity. The inuence of the liquidity
ratio on the degree of specialization supports this
reasoning, where farms with more favorable con-
ditions focus on increasing what they are already
doing rather than seeking new business strategies.
Interestingly, the solidity previous year has
no statistically signicant inuence on any of the
considered strategies. In combination with the re-
sults on liquidity and return on assets, this suggests
that it is the possibilities of the farms to generate
income and cash ows that maters, not how the
assets are nanced. In support of hypothesis H:F3,
results show that economic returns from forestry in
a previous year is positively associated with both
types of diversication outside traditional agricul-
ture, indicating that farmers may be funding diver-
sication with internally generated resources. If in
fact so, this nding may reect a control aversion
(Cressy and Olofsson, 1997), where rms are re-
luctant to diversify if it infringes upon their inde-
pendence. This is an interesting issue for further
research.
Demographic conditions
Table 3 reveals a number of signicant effects of
demographic conditions on the degree of rms’
specialization and diversication. Surprisingly,
businesses headed by women are found to have
a higher degree of specialization, suggesting a
more concentrated business strategy. At the same
time, male gender is positively associated with a
higher degree of diversication within agriculture,
suggesting that men are more apt to look within
conventional agricultural production for supple-
mentary income. While diversication into activities
outside conventional agriculture does not appear
to have a gender biased based on the gender of the
main farm operator, there is a positive association
between spouse’s labor in the rm and value-added
diversication. With the vast majority of main farm
operators being men, this suggests that women are
playing a signicant role in diversication into
value-added enterprises, giving partial support to
hypothesis H:D1. These results conrm the sug-
gestions by Nilsson (2002), who concluded that
gender is of central importance for diversication
into agricultural tourism, but particularly in light
of the contrasting nding of greater specialization
in women-headed farms, the impact of gender on
farm business development is a question that merits
further research.
The age of the farm operator also appears to
affect farms’ diversication. The results show a
negative association with the extent of diversi-
cation within agriculture and with diversication
related to new markets. This indicates that younger
operators are more apt to include revenues from
any of the studied diversied activities in their total
farm income, thus supporting hypothesis H:D2. A
similar impact of the age of the farm operator on
the probability of being engaged in contract work
(which involves nding new markets for existing
resources) was found by McNally (2001).
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
280
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
281
Trends over time
A linear time trend variable was included in the
analysis as a control variable, and can be seen to have
a signicant effect in all cases except for the degree
of diversication based on new markets. Interest-
ingly all coefcients related to diversication are
positive, suggesting that the farmers are becoming
more diversied over time, whereas the coefcient
associated with the extent of specialization is nega-
tive, indicating a decrease in specialization over
time. These trends are consistent with evidence from
the descriptive statistics presented above.
Conclusions
The aim in this study has been two-fold: First, there
has been an interest in tracing the development
of commercial farm businesses in Sweden with
regard to their degree of specialization in single
farm enterprises, their degree of diversication
within conventional agriculture and their degree of
diversication outside conventional agriculture over
the period 2000 to 2007. Second, there has been an
interest in exploring the impact of business structure,
nancial conditions, and demographic conditions
on the degree of specialization and diversication,
both within and outside of traditional agricultural
production.
With respect to the rst aim, clear evidence has
been found of a trend towards increasing diversi-
cation into activities outside conventional agri-
cultural production among Swedish farms. Results
thus indicate that farm business development dur-
ing the observed period has been in line with rural
development policy goals with many farms having
diversied revenues. At the same time, however, it
should be noted that the observed diversication
activities play on average only a minor role in to-
tal farm revenue, particularly with regard to value-
added activities, indicating that most farms remain
strongly dependent on their main farm enterprise.
Regarding the second aim, evidence has been
found of signicant effects on rms’ specialization
and diversication in all three of the studied cat-
egories, business structure, nancial conditions and
demographic conditions: While previously more-
specialized farms and more-diversied farms tend
to follow their respective trajectories in subsequent
development, factors such as farm size and type
of production appear to inuence choices of spe-
cialization and different types of diversication.
There is evidence that farms seek to exploit their
available resources, such as the greater tendency
toward diversication into non-farming enterprises
by farms with signicant variation in labor needs,
such as crop farms and certain types of livestock
production.
It is also notable that farms’ access to internal
sources of nancing, such as return from forestry,
have a signicantly positive impact on the extent of
both types of diversication outside conventional
agriculture. While on the one hand this may fur-
ther expression of the interest in exploiting rm
resources, it may also indicate that farm diversica-
tion is tempered by control aversion (Cressy and
Olofsson, 1997), where the use of existing internal
resources is preferred.
Results also show that farms experiencing fa-
vorable nancial conditions, measured in terms of
higher liquidity ratio, tend to increase their special-
ization in following years. The opposite was also
generally the case, where less favorable nancial
conditions, i.e. lower liquidity and lower returns
on assets, were associated with diversication into
activities outside conventional agriculture in fol-
lowing years. On the one hand, this may reect an
economic rationality, where successful enterprise
stimulates further investments. However, the re-
sults also give reason to question whether diversi-
cation is an entrepreneurial act stimulated by the
pull of a market opportunity (Brockhaus, 1980), or
rather a defensive response to the push of unsatis-
factory nances.
When comparing the estimated equations re-
lated to diversication outside conventional ag-
riculture, it is apparent that the two types of di-
versication activities are not always affected the
same way by the same variables. It is not surprising
that diversication activities based on generating
new income from existing farm resources – such
as performing contract eld work for neighbors or
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
282
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
283
renting out an unused building – are marked by
different conditions than diversication activities
that require the acquisition and/or development
of new resources – such as building a dairy for
cheese-making or developing a bed-and-breakfast.
Diversication through new marketing of existing
farm resources is more likely in rms with main
enterprise using adaptable resources, such as crop
production versus poultry production. Diversica-
tion through new value-added products and serv-
ices appears to be more dependent upon the active
participation of a partner, but also is more common
in rms facing lower returns.
It is important to recall that these conclusions
are based on a study of commercial farm businesses
greater than 8 ESU, and therein should not be as-
sumed to be valid in smaller part-time farms. The
evidence presented suggests that strategic develop-
ment in commercial farms is moderated by a degree
of risk aversion. Smaller part-time farms may have
greater opportunity to be subsidize by the farm-
ers’ pluriactivity, and therein face a different risk
situation.
This study shows a clear need for further study
of commercial farm business development. There
is evidence that rms that are becoming more spe-
cialized differ in a number of ways from farms that
are diversifying. Furthermore, there are indications
of distinct differences between rms that diversify
by nding new markets for their existing products
and unused resources, and rms that diversify by
developing new value-added products and services.
If these differences are more fully understood, they
can be taken into consideration for more effective
design of agricultural policy and farm development
advice.
Acknowledgement. We are grateful to the Editors and
three anonymous Reviewers for valuable comments. This
study was nanced by The Swedish Research Council for
Environment, Agricultural Sciences and Spatial Planning,
Formas, which is gratefully acknowledged.
References
Alsos G. & Carter S. 2006. Multiple business ownership
in the Norwegian farm sector: Resource transfer and
performance consequences. Journal of Rural Studies
22: 313–322.
Alsos, G., Ljunggren, E. & Pettersen, L. 2003. Farm-based
entrepreneurs: What triggers the start-up of new busi-
ness activities? Journal of Small Business and Enter-
prise Development 10: 435–443.
Barbieri, C. & Mahoney, E. 2009. Why is diversication
an attractive farm adjustment strategy? Insights from
Texas farmers and ranchers. Journal of Rural Stud-
ies 25: 58–66.
Brockhaus, R. 1980. The effect of job dissatisfaction on
the decision to start a business. Journal of Small Busi -
ness Management 18: 37–43.
Brümmer, B. 2001. Estimating condence intervals for tech-
nical efciency: the case of private farms in Slovenia. Eu-
ropean Review of Agricultural Economics 28: 285 –306.
Carter, S. 1999. Multiple business ownership in the farm
sector: assessing the enterprise and employment con-
tributions of farmers in Cambridgeshire. Journal of Ru-
ral Studies 15: 417–429.
Chaplin, H., Davidova, S. & Gorton, M. 2004. Agricultural
adjustment and the diversication of farm households
and corporate farms in Central Europe. Journal of Ru-
ral Studies 20: 61–77.
Cressy, R. & Olofsson, C. 1997. The Financial Conditions
for SMEs - a Swedish case. “Financing SMEs”, in Small
Business Ec onomics, 9: 179–194
Davidsson, P., Lindmark, L. & Olofsson, C. 1999. SMEs
and job creation during a recession and recovery. In Z.
Acs, B. Carlsson and C. Karlsson (eds.): Entrepreneur-
ship, small and medium-sized enter prises and the mac-
ro economy, Cambridge University Press, Cambridge.
p. 286–309.
Eisenhardt, K. & Martin, J. 2000. Dynamic capabilities:
What are they? Stra tegic Managemen t Journal 21:
1105–21.
Gasson, R. Crow, G. Errington, A. Hutson, J. Marsden, T.
& Winter, D. 1988. The farm as a family business: A re-
view. Journal of Agricultural Economics 39: 1–41.
Hadley, D. 2006. Patterns in technical efciency and tech-
nical change at the farm-level in England and Wales,
1982–2002. Journal of Agricultural Economics 57: 81–
100.
Hansson, H. 2007. Strategy factors as drivers and restraints
on dairy farm per formance: Evidence from Sweden. Ag-
ricultural Systems 94: 726 –737.
Heady E. 1952. Diversication in resource allocation and
minimization of income variability. Journal of Farm Eco -
nomics 34: 482–496
AGRICULTURAL AND FOOD SCIENCE
Hansson, H. et al. The diversication and specialization of farm businesses
282
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 269–283.
283
Ilbery, B. W. 1991. Farm Diversication as an adjustment
strategy on the urban fringe of the West Midlands. Jour-
nal of Rural S tudies 7: 207–218.
Jansson, K. (ed.) 2008. Jordbruksekonomiska undersök-
ningen 2006, korrigerad 2008–03–13: Slutlig statistik
(The 2006 Farm Economic Survey), report JO 40 SM
0801. Statistiska meddelanden. Statistics Sweden and
The Swedish Board of Agriculture, Jönköping. 41 p.
Johnson S. 1967. Reexamination of the farm diversica-
tion problem. Journal of Farm Economics 49: 610–621
Lagerkvist, C J., Larsén, K. & Olson, K. D., 2007. Off-
farm Income and Farm Capital Accumulation: A Farm-
Level Analysis. Agricultural Finance Review. Fall 2007:
243–257.
MacDonald, J,M., O’Donoghue, E.J, McBride, W.D., Ne-
hring, R.F., Sandretto, C.L. & Mosheim, R. 2007. Prof-
its, Costs, and the Changing Structure of Dairy Farm-
ing. Economic Research report no 47. USDA Economic
Research Service, Washington D.C. 41 p.
McNally, S. 2001. Farm diversication in England and
Wales – what can we learn from the farm business sur-
vey? Journal of Rural Studies 17: 247–257.
Mugera, A. & V. Bitsch. 2005. Managing labor on dairy
farms: A resource-based persoective with evidence from
case studies. International Food and Agribusiness Man-
agement Review 8: 79–98.
Mishra, A. El-Osta, H. & Sandretto, C. 2004. Factors af-
fecting farm enterprise diversication. Agricultural Fi-
nance Review 64: 151–166
Nilsson, J., Petersson, M., & Pettersson, L. 2009. Före-
tagandet på landsbygden - Stad eller land, gör det nå-
gon skillnad? (Business in rural communities – City or
countryside, does it make any difference?), SJV rap-
port 2009:2. Swedish Board of Agriculture, Jönköping.
Nilsson, P. 2002. Staying on Farms. An Ideological Back-
ground. Annals of Tourism Research 29: 7–24.
Porter, M. 1985. Competitive advantages - Creating and
sustaining superior performance. Free Press: New
York. 557 p.
Rantamäki-Lahtinen, L. 2009. The success of the diversi-
ed farm - Resource-based view. Agricultural and Food
Science 18, supplement 1, pp. 1–134. (PhD dissertation).
Robson, Paul J. A., C. Gallagher, & M. Daly. 1993. Diver-
sication strategy and practice in small rms. Interna-
tion Small Business Journal 11: 37–53.
Sharpley, R. & Vass, A. 2006. Tourism, farming and di-
versication: An attitudinal study. Tourism Management
27: 1040–1052.
Slee, B. 1987. Alternative Farm Enterprises, Farming press
Ltd., Wiltshire. 210 p.
Statistics Sweden. 2002. Jordbruksstatistisk årsbok 2002.
Statistics Sweden, Örebro.
Statistics Sweden. 2006. Jordbruksstatistisk årsbok 2006.
Statistics Sweden, Örebro.
Statistics Sweden. 2008. Jordbruksstatistisk årsbok 2008.
Statistics Sweden, Örebro.
Sumner D. & Wolf C. 2002. Diversication, vertical integra-
tion, and the regional pattern of dairy farm size. Review
of Agricultural Economics 24: 442–457
Tauer, L. & Mishra, A. 2006. Dairy Farm Cost Ef ciency.
Journal of Dairy Science 89: 4937–4943.
Teece, D.J., Pisano, G. & Shuen, A. 1997. Dynamic capa-
bilities and strategic management. Strategic Manage-
ment Journal 18: 509–533.
Turner, M., Whitehead, D., Barr, D., Fogerty, M., Errington,
A., Lobley, M. & Reed M. 2003. Farm Diversication Ac-
tivities: Benchmarking study 2002 - Final report to DE-
FRA. U. of Exeter & U. of Plymouth. Retreived 2010–06–
15 from <http://www.defra.gov.uk/evidence/ economics/
foodfarm/reports/farmdiv/index.htm>.
Westhead, P. & Wright, M. 1999. Contributions of novice,
portfolio and serial founders located in rural and urban
areas. Regional Studies 33: 157–173.
AGRICULTURAL AND FOOD SCIENCE
Vol. 19 (2009): 284–293.
284
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
285
© Agricultural and Food Science
Manuscript received August 2009
Microsatellite, blood group and transferrin protein
diversity of Estonian dairy cattle breeds
Sirje Värv1*, Juha Kantanen2 and Haldja Viinalass1
1Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Kreutzwaldi 1,
EE-51014 Tartu, Estonia, *e-mail: sirje.varv@emu.ee
2Biotechnology and Food Research, MTT Agrifood Research Finland, FI-31600 Jokioinen, Finland
This study investigates genetic diversity within and among three Estonian dairy cattle breeds (Estonian
Native, Estonian Red and Estonian Holstein). A total of 36 markers (25 microsatellites, 10 blood group
systems and transferrin protein) were investigated and the within-breed diversity was quantied by expected
heterozygosity, number of private alleles and mean allelic richness. The population structure was studied
by computing the inbreeding coefcients, breed differentiation and relationships were investigated with
random drift-based measures and a factorial correspondence analysis. In addition, a neighbour-joining
tree was drawn summarising allele sharing distances for 195 individuals of the Estonian breeds, Western
Finncattle, and Danish Jersey. The Estonian breeds displayed generally similar levels of within-population
diversity. Depending on the set of markers used 6.2 or 4.3% of the total genetic variation can be explained
by differences among the breeds. Construction of the tree for individuals revealed a distinctive pattern of
grouping for Estonian Holstein, Estonian Red and Danish Jersey, but Estonian Native and Western Finncattle
appeared on the same branches. This indicates that the gene pool of Estonian Native largely overlaps with
that of Western Finncattle. However, our genetic marker analysis shows that the three Estonian breeds are
genetically differentiated, suggesting that the current gene pool of Estonian dairy cattle is diverse.
Key-words: blood group, cattle, genetic diversity, microsatellite, transferrin
Introduction
Population genetic structures of domestic cattle
breeds are greatly inuenced by human activities.
Different ancestral and demographic histories can
generate dissimilar patterns of genetic variation
within and among breeds, which can be effectively
measured using genetic marker analysis (Ibeagha-
Awemu et al. 2004; Li et al. 2007; Mao et al. 2007).
Three dairy cattle breeds of different origins and
AGRICULTURAL AND FOOD SCIENCE
Vol. 19 (2009): 284–293.
284
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
285
census sizes are currently raised in Estonia. Estonian
Native Cattle are of old Estonian origin and were
ofcially recognised in 1914 when the herd book for
the breed was established. The present population
(500 breeding females) has been inuenced by the
use of Western Finncattle A.I. bulls during the 20th
Century. Since 1995 a conservation programme
for Estonian Native Cattle has been implemented,
including the collection of embryos for an ex situ
gene bank for the breed. The Estonian Holstein
Breed, which comprises 75% of the dairy cattle
population (98 500 dairy cows in 2009) in Estonia,
and the Estonian Red (21 000 breeding females)
are the main dairy cattle breeds in Estonia. Their
herd books were established in 1885. The Estonian
Holstein originally descended from Dutch Friesian
cattle, with a marked introduction of international
Holstein semen since the late 1970s. The Estonian
Red Cattle contain genes sourced from a broad
Angeln and Danish Red base. Estonian breeders,
being incorporated with the European Red Dairy
Breed Association, are using genetic material focal
to all European red cattle breeds.
Tapio et al. (2006) analysed genetic diversity
using microsatellite markers in 35 North European
cattle breeds, including Estonian Native and Es-
tonian Red. Their study showed that the Estonian
Native Cattle share common ancestries with the
Finnish and Scandinavian native breeds, while the
Estonian Red belongs to the group of Baltic red
breeds. In addition, Tapio et al. (2006) demonstrated
that the Estonian Native Cattle are of a high value
in the conservation of cattle genetic resources when
the prioritisation of cattle breeds is based simultane-
ously on within- and between-breed components
of genetic diversity. However, that study did not
include the Estonian Holstein Cattle.
Here we study for the rst time the molecular
genetic diversity of this breed and increase the
number of microsatellites used to analyse all Es-
tonian dairy cattle breeds. In addition, we analyse
erythrocyte antigen (EA) systems (or blood groups)
and blood protein in the Estonian dairy breeds. Our
attempt is to apply genotypes in the blood group
systems to make more diverse use of them by typing
also the parents and/or offspring of the studied in-
dividuals. In studies where genetic data have come
from typings of one generation, simplication and
modication of the mode of inheritance are typical-
ly needed (Blott et al. 1998; Kantanen et al. 1999).
In the blood-group systems EAA, EAB, EAC and
EAS, the antigenic factors form complexes that are
inherited as haplotypic blocks. Also a recessive al-
lele segregates in these complex blood groups and
in bi-allelic systems EAJ, EAL, EAM, and EAZ.
Previously, Arranz et al. (1996), Moazami-
Goudarzi et al. (1997) and Kantanen et al. (2000)
compared microsatellites and biochemical markers
in cattle, and e.g. Barker et al. (1997), Luís et al.
(2007) and Tapio et al. (2003) studied polymor-
phisms in these two types of markers in water buf-
faloes, horses and sheep, respectively. These two
marker types typically give congruent results for
population divergence (Arranz et al. 1996; Luís et
al. 2007). The aim of the present study is to analyse
genetic diversity and differentiation of the three Es-
tonian dairy cattle breeds by comparing genotypic
and allelic data of microsatellites and blood groups
- protein markers. In addition, we investigate the di-
vergence of Estonian Native Breed from the breeds
(Western Finncattle, Jersey and the two other Esto-
nian breeds) that have had genetic inuence on the
gene pool of the breed in order to identify animals
with the most pure Estonian origins.
Materials and Methods
Sampling
We collected blood samples from 40 Estonian Na-
tive, 40 Estonian Red and 34 Estonian Holstein
cattle. The sampled individuals originated from
14, 7 and 17 farms located in distinct regions of
Estonia. Animals were pre-selected using pedigree
data kept by the Estonian Animal Recording Center.
For each animal three generations were considered
to avoid sampling closely related animals. Sires of
Estonian Native and Estonian Red animals were of
Estonian origin and therefore our samples represent
characteristic present-day types of these breeds.
For Estonian Holstein, in turn, it was difcult
AGRICULTURAL AND FOOD SCIENCE
Värv, S. et al. Diversity among the Estonian dairy breeds
286
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
287
to nd animals sired by the old type of Estonian
Black-and-White Cattle and the animals included
in the present study were descended from modern
international Holstein bulls.
DNA extraction and marker analysis
For this study, DNA from the blood samples of Esto-
nian Holstein Cattle was extracted using a Genomic
DNA Purication Kit (MBI Fermentas, Lithuania).
For the Estonian Native and Estonian Red Cattle,
DNA samples for the typing of microsatellites were
available from a previous study (Tapio et al. 2006).
The individuals were analysed for 25 microsat-
ellites, 10 blood group loci, and the transferrin pro-
tein locus. Data for the following 20 microsatellites
were available for the Estonian Native and Estonian
Red breeds (Tapio et al. 2006): BM1818, BM1824,
BM2113, CSSM66, ETH003, ETH010, ETH152,
ETH225, HEL005, HEL01, HEL09, HEL13, IL-
STS005, ILSTS006, INRA005, INRA023, INRA032,
INRA035, INRA037, and INRA063. In addition to
these microsatellites, we typed a further ve micros-
atellites: TGLA053, TGLA122, TGLA126, TGLA227,
and SPS115. Moreover, these additional markers are
recommended by the International Society for Ani-
mal Genetics (ISAG) and the Food and Agriculture
Organization of the United Nations (FAO) for cat-
tle genetic diversity studies. The Estonian Holstein
Cattle, which were not included in the previous mi-
crosatellite study by Tapio et al. (2006), were typed
for all 25 microsatellites. We carried out the PCR
reactions to amplify microsatellite loci in a 25 μl
reaction mixture including 7.5 – 20 pmol of each
primer, 200 μM of each dNTP, DynaZyme™- buffer
(Finnzymes, Finland), 50 ng of DNA template, and 1
U of DynaZyme™II DNA polymerase (Finnzymes).
Annealing temperatures in the PCR for different mi-
crosatellites ranged from 55 to 58 ºC and the ampli-
ed products were separated on a MegaBACE™ 500
DNA Sequencer (Amersham Biosciences, UK). The
consistency in size of microsatellite alleles was as-
sured by comparison with control samples available
from the study of Tapio et al. (2006).
A total of 60 erythrocyte antigenic (EA) factors
for the 10 systems were typed. These were: A1, and
A2 in the blood group system A (EAA); B1, B2, G1,
G2, G3, I1, I2, K, O1, O2, P1, P2, Q, T1, T2, Y2, A’,
B’, D’, E’2, F’1, F’2, G’, I’1, J’2, K’, O’2, P’, Q’, Y’,
B’’, G’’ (EAB); C1, C2, E, R1, R2, W, X1, X2, C’,
L’ (EAC); F and V (EAF); J (EAJ); L (EAL); M
(EAM); S, U
1
, U
2
, H’, U’, H’’, U’’ (EAS); Z (EAZ);
S’ and R’ (EAR’). In the antigenic factor detection,
the internationally accepted haemolysis test using
monospecic reagents was used, the suitability
of which was examined by biannual comparison
tests organised by the ISAG in 1993 – 2004. For
the blood group systems EAF and EAR’, codo-
minance operates, while for other EA systems a
recessive allele segregates. In addition, genes con-
trolling the determination of erythrocyte antigens
for multifactor systems are closely linked and are
inherited as haplotypic complexes that determine
the phenotypic appearance of several antigenic
factors or a single antigen. Determination of the
antigen complexes (considered here as alleles) in
the genotypes of EAA, EAB, EAC, and EAS was
carried out using family analysis. Thus, parents of
all the 114 individuals were typed in our study to
determine their genotypes.
Horizontal polyacrylamide gel electrophoresis
was used to separate transferrin (TF) alleles (A, D1,
D2, E), as described by Juneja and Gahne (1987).
Estonian Native and Estonian Red were typed also
for amylase 1 (AMY1), amylase 2 (AMY2) and
ceruloplasmin (CP) proteins (unpublished data)
using starch gel electrophoresis (Smithies 1955).
These proteins were not typed for Estonian Hol-
stein. We used these additional data only in the
calculation of the within-population inbreeding
coefcient (see results).
Statistical analysis
In the statistical analysis, the microsatellite and
biochemical marker (the 10 blood group systems
and transferrin, denoted here and henceforth as EA
systems/TF) data sets were examined separately.
Locus-wise deviations from Hardy-Weinberg equi-
AGRICULTURAL AND FOOD SCIENCE
Värv, S. et al. Diversity among the Estonian dairy breeds
286
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
287
librium (HWE) and pair-wise linkage disequilibrium
(LD) between loci within each breed were computed
using GENEPOP v.3.4 (Raymond and Rousset 1995a)
with the following parameters of the Markov Chain
Method: dememorization = 10 000, batches = 1 000
and iterations = 10 000. In the breed-wise LD tests,
the frequency of signicant results (p < 0.05) and
the signicance of pooled p-values of the exact tests
using Fisher’s method for combining probabilities
(Raymond and Rousset 1995b) was reported for mi-
crosatellites and EA systems/TF separately. Basic di-
versity indices, i.e. the unbiased estimates of expected
heterozygosity, the number of private alleles and the
allelic richness, were calculated and the calculation
of the allelic richness was based on 22 (microsatel-
lite data) and 15 individuals (EA systems/TF). The
within-population diversity estimates were derived
using FSTAT v.2.9.3 (Goudet 2001). This program was
also used to compute within-population inbreeding
coefcients (f) (Weir and Cockerham 1984).
Genetic differentiation was computed using
the variance based method (θ) of Weir and Cock-
erham (1984) in FSTAT v. 2.9.3.2 (Goudet 2001).
The signicance of θ-estimates was determined
with 5 000 permutations. Moreover, the pattern of
population differentiation was described by a facto-
rial correspondence analysis of the individual mul-
tilocus scores using GENETIX4.05 (www.genetix.
univ-montp2.fr/genetix/genetix.htm). The popula-
tion clusters derived from the factorial correspond-
ence analysis are identied graphically (Lebart et al.
1984). The rst two major components were plotted
on a scatter diagram for the three cattle breeds. In ad-
dition, Chord genetic distances (Cavalli-Sforza and
Edwards 1967) between the breeds were computed
using GENETIX4.05.
We conducted an additional genetic differentia-
tion analysis by calculating the allele sharing distanc-
es (Bowcock et al. 1994) between 195 individuals
of the three Estonian breeds and Danish Jersey and
Western Finncattle using the data for 19 microsatel-
lites (INRA035 was excluded, see results). The data
for Western Finncattle and Danish Jersey, the breeds
which the Estonian Native Cattle Breed Society has
used for upgrading of the Estonian Native, were ob-
tained from the study of Tapio et al. (2006). Based on
the allele sharing distance matrix, a neighbour-join-
ing tree was constructed using SplitsTree4 V4.11.3
software (Huson and Bryant 2006).
Results
Markers
All markers were polymorphic across the breeds
(Table 1). A total of 209 microsatellite alleles and 122
blood group and transferrin alleles were detected.
The number of microsatellite alleles per single
locus ranged from 2 (ILSTS005) to 15 (TGLA053
and TGLA122), and that of EA systems/TF alleles
from 2 (EAJ, EAL, EAM, EAF, EAR’, and EAZ)
to 59 (EAB). The average expected heterozygos-
ity for the microsatellite loci was 0.70, and for the
biochemical markers 0.41. EAB and EAC displayed
higher levels of expected heterozygosity than any
microsatellite marker (Table 1).
In the blood group systems, the genotyping was
not totally successful due to discrimination difcul-
ties between probable homozygotes for a dominant
allele and heterozygous genotypes for blood groups
where a recessive allele was segregating. The EAA
and EAS were the most difcult markers to deter-
mine an individual’s genotype from the antigenic
phenotypes, with an overall genotyping success
of 76 and 53%, respectively. In Estonian Native,
the genotyping success at dominant marker loci
ranged from 55 (EAS) to 90% (EAL), in Estonian
Red from 58 (EAC) to 93% (EAJ) and in Estonian
Holstein from 45 (EAS) to 92% (EAB). The most
complex locus, EAB, was genotyped for 80 and
85% of Estonian Red and Estonian Native Cattle
individuals, respectively.
Nine of a total of 75 (12%) independent tests
for Hardy-Weinberg equilibrium (HWE) at the mi-
crosatellite loci were rejected at p < 0.05. When
results of the microsatellite loci were pooled across
the breeds, INRA035 showed signicant (p < 0.05,
adjusted with a Bonferroni correction) deviation
from HWE. This marker showed also a high posi-
tive f value (Weir and Cockerham 1984; Table 1).
The Mendelian inheritance of microsatellite alleles
AGRICULTURAL AND FOOD SCIENCE
Värv, S. et al. Diversity among the Estonian dairy breeds
288
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
289
was not investigated in this study, but we assume
that the deciency of heterozygotes at INRA035
was due to the presence of non-amplifying null
alleles and therefore we excluded INRA035 from
further analysis.
In the data set of EA systems/TF, one deviation
(transferrin in the Estonian Native) from HWE (3%
of the independent tests) was recorded (p < 0.05).
When the results were pooled and a Bonferroni
correction applied to adjust the signicance levels,
none of the biochemical markers showed deviation
from HWE.
Genetic diversity and population
structure of the breeds
The within-population genetic diversity and popula-
tion structure estimates are given in Table 2. The
breeds showed similar levels of within-population
diversity in terms of expected heterozygosity and
allelic richness on the basis of the microsatellite
data. For the EA systems/TF data, however, Estonian
Holstein displayed lower within-population diversity
than the two other breeds. Our data sets indicated
that the number of private alleles was highest in
Estonian Red (20 microsatellite alleles over 24
loci and 28 of EAB, EAC and EAS alleles totally).
The within-breed population structure was in-
vestigated by computing linkage disequilibrium es-
timates and inbreeding coefcients for the breeds.
For the Estonian Red and Estonian Holstein, the
frequency for linkage disequilibrium was less than
5%, while in Estonian Native this frequency was
slightly more than expected by chance (microsatel-
lite data). No linkage disequilibrium was detected
in the EA system/TF data and pooled p -values
from locus-by-locus pair-wise comparison did not
indicate any signicant deviations from linkage
equilibrium proportions. Within-population in-
breeding estimates (f) based on microsatellite data
did not deviate signicantly from zero. However,
the negative f-estimate obtained from the EA sys-
tems/TF analysis suggested the inuence of out-
breeding in Estonian Native Cattle (95% CI for f
[-0.163, -0.086]).
Table 1. Microsatellites and Erythrocyte antigen sys-
tems/Transferrin protein analysed in the present study,
number of alleles (Na) detected, Nei’s gene diversity
(H) and f estimates calculated according to Weir and
Cockerham (1984).
Marker Na H f
BM1818 8 0.651 0.033
BM1824 5 0.751 0.036
BM2113 8 0.814 0.053
CSSM66 9 0.816 -0.022
ETH003 7 0.791 0.016
ETH010 10 0.810 -0.026
ETH152 8 0.743 0.055
ETH225 9 0.867 0.052
HEL001 9 0.720 0.068
HEL005 9 0.740 0.085
HEL009 12 0.731 0.004
HEL013 7 0.669 -0.152
ILSTS005 2 0.574 0.066
ILSTS006 9 0.797 0.030
INRA005 4 0.575 -0.255
INRA023 9 0.803 -0.043
INRA032 5 0.659 0.045
INRA035 6 0.579 0.255
INRA037 11 0.719 -0.009
INRA063 6 0.646 -0.198
SPS115 6 0.682 0.022
TGLA053 15 0.860 0.006
TGLA122 15 0.803 0.005
TGLA126 8 0.730 0.025
TGLA227 12 0.870 -0.023
EAA 3 0.328 -0.178
EAB 59 0.967 -0.020
EAC 39 0.941 -0.060
EAF 2 0.322 -0.162
EAJ 2 0.213 -0.124
EAL 2 0.092 -0.068
EAM 2 0.036 -0.021
EAR' 2 0.280 -0.103
EAS 5 0.441 0.016
EAZ 2 0.194 -0.107
TF 4 0.680 0.027
AGRICULTURAL AND FOOD SCIENCE
Värv, S. et al. Diversity among the Estonian dairy breeds
288
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
289
We calculated within-population inbreeding co-
efcients for Estonian Native and Estonian Red,
including three additional codominantly inherited
blood protein loci (AMY1, AMY2 and CP) in the
data set. Data on these proteins for the Estonian
Holstein are not available. We obtained -0.050 and
0.004 for f-estimates, with 95% condence inter-
vals [-0.134, 0.062] and [-0.054, 0.054], respec-
tively, suggesting that our estimate for the Estonian
Native presented in Table 2 was not robust.
Genetic differentiation
The overall θ estimate (Weir and Cockerham 1984)
for the microsatellite data was 0.062 (95% CI [0.045,
0.080]) and for the EA systems/TF data, 0.043 (95%
CI [0.021, 0.067]). All pair-wise θ comparisons
were statistically signicantly different from zero
(p <0.05) when computed from the microsatellite
data (Table 3). For the EA systems/TF data, the
respective pair-wise θ estimates were: 0.051 (p <
0.05), 0.040 (p = 0.06) and 0.043 (p < 0.05).
In the factorial correspondence analysis of the
microsatellite data, the rst two principal compo-
nents (PCs) explained 61.7 and 38.3% of the total
variation, and in the analysis of the EA system/TF
data, 53.7 and 46.3%, respectively (Fig. 1a and 1b).
The two-dimensional plot constructed from the mi-
crosatellite data indicated discrete grouping of the
three Estonian cattle breeds with only two Estonian
Native and one Estonian Red animals not being as-
signed to their source population. Based on the EA
systems/TF data the demarcation within Estonian
Red, Estonian Native and Estonian Holstein clusters
was lower than on the microsatellite-based plot, but
still discriminated the breeds. However, individuals
with intermediate component scores indicated the
probable outbred origin of these animals.
Table 2. Within-population diversity values and population structure derived from the microsatellite loci and EA systems/
TF data. Mean expected unbiased heterozygosity (Hexp), allelic richness (R), number of private alleles (A), the frequency
of signicant (P < 0.05) pair-wise linkage disequilibrium test (LD%), the pooled exact P-values in the LD-tests (χ2) and
within-population inbreeding coefcient (f) with 95% condence intervals (95% CI) are shown.
Breed
Microsatellite data EA systems/TF data
Hexp R A LD% χ2f (95% CI) Hexp R A LD% χ2f (95% CI)
Est.
Native
Est. Red
Est.
Holstein
0.715
0.699
0.694
6.01
5.97
5.87
16
20
15
5.4
4.7
3.3
573.1 NS
511.6 NS
481.2 NS
-0.017
[-0.065, 0.003]
0.026
[-0.022, 0.048]
-0.016
[-0.076, 0.009]
0.404
0.405
0.361
4.94
5.14
4.53
26
28
16
0
0
0
63.9 NS
37.9 NS
49.8 NS
-0.107
[-0.163, -0.086]
-0.010
[-0.083, 0.031]
-0.034
[-0.119, 0.009]
The signicance of pooled P-values of the exact tests in LD analysis using Fisher’s method:
NS Not Signicant
Table 3. Pair-wise θ and chord distances between the breeds based on microsatellite (given above the diagonal) and
EA systems/TF (below the diagonal)
Breed Chord distance θ
Estonian Native
Estonian Red
Estonian Holstein
1
2
3
1
0.061
0.046
2
0.058
0.046
3
0.095
0.075
1
0.051
0.040
2
0.052
0.043
3
0.078
0.059
AGRICULTURAL AND FOOD SCIENCE
Värv, S. et al. Diversity among the Estonian dairy breeds
290
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
291
A)
B)
Figure1. Plotted representation of three breed clusters as defined by Analysis of Factorial
Correspondence: analysis based on A) microsatellites; B) EA systems/TF data.
Estonian
Native
Estonian
Red
Estonian
Holstein
Estonian
Red
Estonian
Holstein
Estonian
Native
A)
B)
Figure1. Plotted representation of three breed clusters as defined by Analysis of Factorial
Correspondence: analysis based on A) microsatellites; B) EA systems/TF data.
Estonian
Native
Estonian
Red
Estonian
Holstein
Estonian
Red
Estonian
Holstein
Estonian
Native
Fig. 1. Plotted representation of three breed clusters as dened by Analysis of Factorial Correspondence: analysis based
on a) microsatellites; b) EA systems/TF data.
The analysis of the genetic distances based
on the proportion of shared alleles between indi-
viduals (Fig. 2) conrmed the close relationship
of Estonian Native with Western Finncattle. The
grouping of 195 cattle revealed a large mixed group
of individuals from the Estonian Native and West-
ern Finncattle and distinct branches of Estonian
Red, Estonian Holstein and Danish Jersey cattle
conrming the discrete grouping of the Estonian
breeds found in the factorial correspondence analy-
sis.
Discussion
As suggested by our genetic marker analyses, the
Estonian Native, Estonian Red and Estonian Holstein
breeds are genetically divergent populations among
which the gene ow appears to be restricted. The
divergence indicates that the present-day gene pool
of Estonian dairy cattle is diverse. The θ estimates
(Weir and Cockerham 1984) showed that the Es-
tonian cattle breeds are signicantly differentiated
and the factorial correspondence analysis (Fig. 1)
and allele sharing distances (Fig. 2) conrm the
grouping of individuals graphically according to
their breed origin. However, as seen in Figure 2 the
present Estonian Native cattle population forms an
overlapped gene pool with Western Finncattle and
it was not possible to determine a special Estonian
Native group among the analysed individuals.
This nding diminishes the conservation value of
the Estonian Native cattle among the North Euro-
pean cattle breeds (Tapio et al. 2006) in terms of
genetic uniqueness, but despite this the breed can
be considered as an important gene reservoir for
agro-biodiversity in the Estonian context.
As shown by the microsatellite data, 6.2% of
the total genetic variation of the Estonian dairy
cattle can be explained by differences among the
breeds. The level of genetic differentiation among
European cattle breeds has been slightly higher,
around 10% (MacHugh et al. 1998; Kantanen et
al. 2000), than the present estimate, but estimates
have typically been based on a larger set of breeds,
from a wider geographic region, than covered in
the present study. The current subdivision of the
Estonian dairy breeds at the microsatellite loci is
comparable to the extent of genetic differentiation
among 18 French, Spanish, and Portuguese cattle
breeds (a proportion of 7% among the breeds ac-
cording to Cañón et al. 2001). Our conclusion re-
a) b)
AGRICULTURAL AND FOOD SCIENCE
Värv, S. et al. Diversity among the Estonian dairy breeds
290
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
291
garding their genetic divergence, is in agreement
with the previous study by Tapio et al. (2006),
which showed that the North European native, Red
and Holstein-Friesian breeds form discrete breed
groups.
The EA/TF data indicated a lower level of
genetic differentiation among the breeds (4.3%
of the total genetic variation) than the microsatel-
lites. This could have been due to the lower num-
ber of alleles found at the biochemical markers,
which may increase the probability that alleles are
identical by state but not identical by descent, but
partly also due to the lower number of EA sys-
tem/TF markers analysed in the present study. The
standardised genetic differentiation measure G’ST
presented by Hedrick (2005) allows a more ap-
propriate comparison between loci with different
mutation rates. We obtained overall G’
ST
values
of 0.20 for microsatellites (20% of the maximum
possible) and 0.06 for EA system/transferrin (6%
of the maximum possible). Our data indicate that
microsatellites are more valuable markers for di-
agnostics in breed differentiation and individual
assignment analysis of dairy cattle. In addition to
microsatellites, the two highly polymorphic blood
group systems, EAB and EAC, were found to dis-
criminate cattle breeds more efciently than other
blood groups and even microsatellite loci (G’
ST
0.80 at EAB and 0.54 at EAC). These loci add valu-
able information for breed differentiation studies
including for the private alleles found (e.g. alleles
of the EAB Y
2
D’G’, B
1
G
2
KA’, B
1
G
2
KE’F’
2
and
I2G’Q’ in the Estonian Native breed and BP’ and
O2QJ’K’O’ in the Estonian Red breed).
Although recent demographic histories of the
Estonian dairy cattle breeds differ considerably, the
breeds in general show a similar degree of intra-
breed genetic variation (Table 2). The estimates for
the Estonian breeds at the microsatellite and at the
EA systems/TF markers are comparable with those
presented for other European cattle breeds (Kan-
tanen et al. 2000; Li et al. 2007; Tapio et al. 2006).
The molecular diversity of the Estonian Holstein
breed was measured for the rst time in the present
study and we found that this effectively selected
breed shows similar levels of within-population
variation as e.g. Finnish, Russian and French Hol-
stein or Black-and-White cattle populations (Li et
Fig. 2. Neighbour-joining tree
showing relationships between
cattle of five breeds (Estonian
Native, Western Finncattle,
Estonian Red, Estonian Holstein,
and Danish Jersey) constructed
by SplitsTree4 using the allele
sharing distances (Bowcock et al.
1994). The individuals of Estonian
Native and Western Finncattle
located at the same branches
on the figure are marked with
large circles and small squares,
respectively.
AGRICULTURAL AND FOOD SCIENCE
Värv, S. et al. Diversity among the Estonian dairy breeds
292
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
293
al. 2007; Maudet et al. 2002; Tapio et al. 2006;
19 or 20 common microsatellites with our study).
The present f-estimates calculated from the mic-
rosatellite data (Table 2) do not suggest effects of
inbreeding or outbreeding in the Estonian breeds.
However, the biochemical marker data point to-
wards outbreeding in Estonian Native Cattle.
When additional protein loci were included in the
analysis, the outbreeding was less apparent. We
speculate that the test statistics did not reveal an
outbreeding effect in the Estonian breeds (although
they have been inuenced by other breeds) because
genetically closely related breeds have been used
for crossing.
The present microsatellite and biochemical
marker data gave inconsistent results on breed re-
lationships (Table 3; Fig. 1). The Estonian breeds
did not show a fragmented population structure
(Table 2), which could have been one source of
discrepancy, as reported by Tapio et al. (2003).
The microsatellite data may provide more reliable
results on between-breed diversity compared with
documented breed histories (Kantanen et al. 2000;
Rendo et al. 2004; Wiener et al. 2004; Tapio et al.
2006). As pointed out by Bowcock et al. (1994),
markers with a large number of alleles typically
show less biased estimates than those based on
low-polymorphic markers. On the other hand, we
typed more microsatellite markers than biochemi-
cal markers, which increases the reliability of
analysis (Takezaki and Nei 1996).
Our genetic marker analysis indicated that the
Estonian dairy cattle gene pool is variable. The
breeds have diverged as reected also by the rela-
tively high number of private alleles detected in
each breed. However, the future trends may threat-
en this diversity in the Estonian dairy cattle gene
pool. For example, the old type of Estonian Red
studied here is rapidly disappearing due to cross-
breeding with Red Holstein and with other Euro-
pean red cattle. We conclude that the DNA samples
and genotyping data collected in the present study
will be of value in future studies examining, for
example, temporal changes in the genetic diversity
of Estonian dairy cattle breeds.
Acknowledgements. This work was supported by the
Nordic Council of Ministers and the Nordic Genetic
Resource Center (NordGen), targeted nancing of
research project SF1080022s07 and applied research
of the Ministry of Agriculture of Estonia.
References
Arranz, J. J., Bayón, Y. and San Primitivo, F. 1996. Compar-
ison of protein markers and microsatellites in differentia-
tion of cattle populations. Animal Genetics 27: 415–419.
Barker, J.S.F., Moore, S.S., Hetzel, D.J. S., Evans, D., Tan,
S.G. and Byrne, K. 1997. Genetic diversity of Asian wa-
ter buffalo (Bubalus bubalis): microsatellite variation
and a comparison with protein-coding loci. Animal Ge-
netics 28: 103 –115.
Blott, S.C., Williams J.L. and Haley, C.S. 1998. Genetic re-
lationship among European cattle breeds. Animal Ge-
netics 29: 273 –282.
Bowcock, A., M., Ruiz-Linares, A., Tomfohrde, J., Minch,
E., Kidd, J.R. and Cavalli-Sforza, L.L. 1994. High reso-
lution of human evolutionary trees with polymorphic mi-
crosatellites. Nature 368: 455– 457.
Cañón, J., Alexandrino, P., Bessa, I., Carleos, C., Carrete-
ro, J., Dunner, S., Ferran, N., Garcia, D., Jordana, J.,
Laloė, D., Pereira, A., Sanchez, A. and Moazami-Gou-
darzi, K. 2001. Genetic diversity measures of local Eu-
ropean beef cattle breeds for conservation purposes.
Genetics Selection Evolution 33: 311–332.
Cavalli-Sforza, L.L. and Edwards, A.W.F. 1967. Phyloge-
netic analysis: models and estimation procedures. Amer-
ican Journal of Human Genetics 19: 233–257.
Goudet, J. 2001. FSTAT, a program to estimate and test
gene diversities and xation indices (version 2.9.3).
Available from http://www.unil.ch/izea/softwares/fstat.
html.
Hedrick, P.W. 2005. A standardized genetic differentiation
measure. Evolution 59: 1633-1638.
Huson, D.H. and Bryant D. 2006. Application of Phyloge-
netic Networks in Evolutionary Studies. Molecular Biol -
ogy and Evolution 23: 254–267. Software available from
www.splitstree.org.
Ibeagha-Awemu, E. M., Jäger, S. and Erchardt, G. 2004.
Polymorphisms in blood proteins of B os indicus and Bos
taurus cattle breeds of Cameroon and Nigeria, and de-
scription of new albumin variants. Biochemical Genet-
ics 42: 181–197.
Juneja, R.K. and Gahne, B. 1987. Simultaneous pheno-
typing of pig plasma α-proteaseinhibitors (PI1, PO1A,
PO1B, PI2) and four other proteins (PO2, TF, CP, HPX)
by a simple method of 2D horizontal electrophoresis.
Animal Genetics 18: 197–211.
Kantanen, J., Olsaker, I., Adalsteinsson, S., Sandberg, K.,
Eythorsdottir, E., Pirhonen, K. and Holm, L.-E. 1999.
Temporal changes in genetic variation of Northern Eu-
ropean cattle breeds. Animal Genetics 30: 16–27.
AGRICULTURAL AND FOOD SCIENCE
Värv, S. et al. Diversity among the Estonian dairy breeds
292
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 284–293.
293
Kantanen, J., Olsaker, I., Holm, L.-E., Lien, S., Vilkki, J.,
Brusgaard, K., Eythorsdottir, E., Danell, B. and Adalsteis-
son, S. 2000. Genetic diversity and population structure
of 20 North European cattle breeds. Jour nal of Hered-
ity 91: 446–457.
Lebart, L., Morineau, A. and Warwick, K.M. 1984. Multi-
variate Descriptive Statistical analysis: Correspondence
Analysis and Related Techniques for Large Matrices.
John Wiley ansd Sons, New York.
Li, M.H., Tapio, I., Vilkki, J., Ivanova, Z., Kiselyova, T.,
Marzanov, N., Ćinkulov, M., Stonajović, S., Ammosov,
I., Popov, R. and Kantanen, J. 2007. The genetic struc-
ture of cattle population (Bos taurus) in northern Eura-
sia and the neighbouring Near Eastern regions: impli-
cations for breeding strategies and conservation. Mo-
lecular Ecology 16: 3839–3853.
Luís, C., Juras, R., Oom, M.M. and Cothran, E.G. 2007.
Genetic diversity and relationship of Portugese and oth-
er horse breeds based on protein and microsatellite loci
variation. Animal Genetics 38: 20–27.
MacHugh, D.E., Loftus, R.T., Cunningham, P. and Bradley,
D.G. 1998. Genetic structure of seven European cattle
breeds assessed using 20 microsatellite markers. Ani-
mal Genetics 29: 333–340.
Mao, Y., Chang, H., Yang, Z., Zhang, L., Xu, M., Sun, W.,
Chang, G and Song, G. 2007. Genetic structure and dif-
ferentiation of three Chinese indigenous cattle popula-
tions. Biochemical Genetics 45: 195–209.
Maudet, C., Luikart, G., and Taberlet, P. 2002. Genetic di-
versity and assignment tests among seven French cat-
tle breeds based on microsatellite DNA analysis. J our-
nal of Animal Science. 80: 942–950.
Moazami-Goudarzi, K., Laloë, D., Furet, J.P. and Gro-
sclaude, F. 1997. Analysis of genetic relationships be-
tween 10 cattle breeds with 17 microsatellites. Animal
Genetics 28: 338–345.
Raymond, M. and Rousset, F. 1995a. Genepop (Version
3.1d, updated version of Genepop v. 1.2): population ge-
netics software for exact tests and ecumenicism. Jour-
nal of Heredity 86: 248–249.
Raymond, M. and Rousset, F. 1995b. An exact test for pop-
ulation differentiation. Evoution 49: 1280 –1283.
Rendo, F., Iriondo, M., Jugo, B.M., Aguirre, A., Mazón, L.I.,
Vicario, A., Gómez, M. and Estonba, A. 2004. Analysis
of the genetic structure of endangered bovine breeds
from the western Pyrenees using DNA microsatellite
markers. Biochemical Genetics 42: 99–108.
Smithies, O. 1955. Zone electrophoresis in starch gels:
Group variation in the serum proteins of normal human
adults. Biochemical Journal 61: 629.
Takezaki, N. and Nei, M. 1996. Genetic distances and re-
construction of phylogenetic trees from microsatellite
DNA. Genetics 144: 389–399.
Tapio, I., Värv, S., Bennewitz, J., Maleviciute, J., Fimland,
E., Grislis, Z., Meuwissen, T.H.E., Miceikiene, I., Olsa-
ker, I., Viinalass, H., Vilkki, J. and Kantanen, J. 2006.
Prioritization for conservation of Northern European cat-
tle breeds based on analysis of microsatellite data. Con-
servation Biology 20: 1768–1779.
Tapio, M., Miceikiene, I., Vilkki, J. and Kantanen, J. 2003.
Comparison of microsatellite and blood protein diversi-
ty in sheep: inconsistencies in fragmented breeds. Mo -
lecular Ecology 12: 2045–2056.
Weir, B.C. and Cockerham, C.C. 1984. Estimating F-sta-
tistics for the analysis of population structure. Evolution
38: 1358–1370.
Wiener, P., Burton, D. and Williams, J.L. 2004. Breed rela-
tionships and denition in British cattle: a genetic analy-
sis. Heredity 93: 597–602.
AGRICULTURAL AND FOOD SCIENCE
Vol. 19 (2010): 294–301.
294
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 294–301.
295
© Agricultural and Food Science
Manuscript received April 2010
The effect of calpastatin polymorphism and its
interaction with
RYR1
genotypes on carcass and
meat quality of crossbred pigs
Artur Rybarczyk1*, Marek Kmieć2, Roman Szaruga1, Filip Napierała2 and Arkadiusz Terman2
1Department of Livestock Product Evaluation, West Pomeranian University of Technology,
Doktora Judyma 24 st., 71-466, Szczecin, Poland
2Department of Genetics and Animal Breeding, West Pomeranian University of Technology,
Doktora Judyma 6 st., 71-466, Szczecin, Poland
*e-mail: Artur.Rybarczyk@zut.edu.pl
The aim of the study was to establish the relationship between a calpastatin gene (CAST) polymorphism,
the ryanodine receptor gene (RYR1) polymorphism and carcass/meat quality traits in crossbred pigs. No
signicant differences in the analyzed pigs were found between genotypes CC and CT at the locus RYR1 and
CD and DD at the locus CAST/MspI in terms of carcass and meat quality. However, a signicant association
of the CAST/ApaLI polymorphism with carcass quality and meat marbling were observed. The carcasses of
AB pigs had signicantly higher carcass percentage of lean meat, thinner backfat and thicker muscle, as well
as lower meat marbling, as compared with the BB pigs. Furthermore, interactions CAST/MspI × RYR1 and
CAST/ApaLI × RYR1 were found signicant in relation to all the studied carcass traits. The results presented
here imply that the CAST gene recognized with ApaLI may be considered as important in terms of the way
it affects porcine carcass quality traits. Moreover, the research has revealed a relationship between CAST
and RYR1 genotypes as regards formation of carcass traits in pigs. Follow-up studies, however, should be
carried out on larger populations representing all possible CAST genotypes.
Key-words: pigs, CAST gene, RYR1 gene, carcass quality, meat quality
AGRICULTURAL AND FOOD SCIENCE
Vol. 19 (2010): 294–301.
294
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 294–301.
295
Introduction
Intensive efforts on improvement of swine percent-
age of lean meat with use of Pietrain pigs resulted
in a number of issues, especially those linked to a
high frequency of the stress syndrome gene (RYR1
T
)
observed in this breed, which is associated with
occurrence of pale, soft, and exudative (PSE) meat
(Fiedler et al. 2001). Moreover, research shows,
despite belonging to very different breeds, pigs of
the same genotype for the RYR1 gene not only do
exhibit a considerable variability in carcass lean
content, but also provide meat of varying quality.
This may be an effect of other genes that possibly
affect both carcass traits and meat quality, modi-
fying the effect of the RYR1 (Koćwin-Podsiadła
and Kurył 2003).
Calpastatin (CAST) is the endogenous inhibi-
tor involved in regulation of calpain activity in
muscle cells. Activity of calpains and calpastatin
relies on appropriate concentration of calcium
ions in the cell (Murachi 1989). Moreover, calp-
astatin activity is strongly correlated with muscle
growth rate, proteolytic processes, and immediate
post mortem changes in the muscle (Goll et al.
1998), which affects many quality traits of the
meat (Koćwin-Podsiadła et al. 2003; Melody et
al. 2004).
The calpastatin gene has been mapped near the
centromere of SSC 2 in the region q2.1-q2.4. The
calpastatin molecule consists of domain L, cod-
ed by exons 2 to 8, and four repetitive domains,
each of which is coded by exons 9-14 (Stearns
et al. 2005). Polymorphisms in the calpastatin
gene (CAST), identied in the intron with 3 re-
striction enzymes, HinfI, MspI, and RsaI, were
rst reported by Ernst et al. (1998). Ciobanu et
al. (2004), on the other hand, identied a CAST
gene polymorphism at domains L, 1, and 4, recog-
nized using ApaLI, Hpy188I, and PvuII enzymes.
Chromosome 2 may be associated with the QTL
related to tenderness and shear force because the
QTL afliated with the traits were relatively close
in position and shared similar effects (Kurył et al.
2003; Ciobanu et al. 2004). The QTL associated
with fat percent was within the same marker inter-
val as the QTL related to shear force, suggesting
pleiotropic or linked QTL (Malek et al. 2001).
Koćwin-Podsiadła et al. (2003) and Krzęcio et al.
(2004) demonstrated that a number of meat qual-
ity traits signicantly depended on the animal’s
genotype at the locus CAST. The authors state that
such correlation can be used in swine selection
for better carcass and meat quality. In our studies,
on the same cross pigs, in which to identify cal-
pastatin gene (CAST) polymorphism were used re-
striction enzymes HinfI and Hpy188I, was found
signicant association between calpastatin gene
(CAST/HinfI) and meat quality traits (Rybarczyk
et al. 2010).
The aim of the study was to nd a possible
relationship between variants of the calpastatin
gene (CAST/MspI and CAST/ApaLI), the ryanod-
ine receptor gene (RYR1) and carcass/meat quality
traits in crossbred pigs sired by Pietrain boars.
Material and methods
Material
The experiment was carried out on 125 pigs (76
gilts and 49 barrows) from a pig farm located in
Mecklemburg-Vorpommern (Germany). The study
comprised offspring from crossing German Lan-
drace × German Large White and also Leicoma
× German Large White sows with Pietrain boars,
kept under similar environmental conditions and
fed with a balanced feed-mix ad libitum. All sub-
jects destined for the experiment were conveyed in
one group to the “Agryf” Meat Plant in Szczecin
(Poland) in the evening after 4 hours transportation
from a distance of 250 km, and slaughtered on the
next day in the morning.
Slaughter value
After animal CO2 stunning during the slaughter,
blood was collected for identication of CAST and
AGRICULTURAL AND FOOD SCIENCE
Rybarczyk, A. et al. Effects of CAST and RYR1 genotypes on carcass and meat quality
296
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 294–301.
297
RYR1 genotypes. Subsequently, carcass percentage
of lean meat was measured, hot carcass weight,
as well as the thickness of the longissimus dorsi
(LD) muscle and back fat between the 3rd and 4th
rib, 7 cm from the line of carcass partition into
the left-hand side of the carcass half, by means of
an optic-needle CGM apparatus (Sydel, France).
Mean percentage of lean meat amounted to 55.39
± 0.40 and hot carcass weight to 87.75 ± 0.55 kg
(mean value and standard error).
Meat quality and basic meat chemical
composition
Two hours after slaughter during carcass cooling,
electric conductivity (EC2) was measured in the
longissimus dorsi muscle, between the 4th and
5th lumbar vertebrae of the right-hand side of
the carcass half using an LF-Star MATTHÄUS
conductometer. After 24 hours of carcass cooling,
meat samples from the longissimus dorsi muscle
were collected from the 1st to-4th lumbar vertebrae
section (longgisimus lumborum) of the right-hand
side of the carcass half. 24 hours from the slaughter,
meat pH
24
value (Elmetron CP-311 pH-meter) and
the volume of drip loss from the muscle tissue were
determined according to Honikel (1987).
Within 48 hours after slaughter, minced meat
samples were measured for pH in water solution,
and meat colour traits, i.e. L* (lightness), a* (red-
ness) and b* (yellowness), were established by
means of a HunterLab Mini Scan XE Plus 45/0
with light illuminant D65 and observer 10º (CIE
1976). Meat water-holding capacity (WHC) was
determined according to Grau and Hamm (1952)
as modied by Pohja and Niinivaara (1957), as
well as thermal drip from a difference of meat
sample weight before and after heating in a water
bath at 85
º
C for 10 minutes. Water-soluble protein
content was determined by Kotik method (1974).
Marbling (the degree of intramuscular fatness)
was determined by a trained 5 person team of
panellists, using a 1-5 point scale (1 point – slight
muscle fatness; 5 points – strong muscle fatness).
The basic meat chemical composition, i.e. total
protein, fat, ash and dry matter (AOAC 2003),
was estimated.
Genotyping
Genomic DNA was extracted from blood using a
Master Pure kit (Epicentre Technologies). Geno-
types RYR1, CAST/MspI and CAST/ApaLI were
identied by PCR/RFLP method according to Fujii
et al. (1991), Ernst et al. (1998) and Ciobanu et al.
(2004), respectively.
Statistical analysis
A statistical analysis was performed to compare
carcass and meat quality traits and basic chemi-
cal composition of meat between pigs of different
CAST and RYR1 genotypes, using the least squares
method of the GLM procedure (Statistica 8.0 PL)
according to the following linear model:
Yijkl = μ + ai + bj + ck + bcjk + β (xijkl – x) + eijkl
where:
Yijkl - trait measured,
μ - the overall mean,
ai - the effect of sex (i = 1, 2),
bj - the effect of RYR1 genotype (j = CT, CC),
c
k
- the effect of CAST/MspI genotype (k = CD, DD)
or CAST/ApaLI genotype (k = AB, BB);
bc
jk
- interaction (RYR1 × CAST/MspI or CAST/
ApaLI genotype),
β - linear regression coefcient for hot carcass
weight;
x
ijkl
- hot carcass weight of ijkl-th individual included
as covariable;
x
- mean for hot carcass weight;
eijkl - the random error.
AGRICULTURAL AND FOOD SCIENCE
Rybarczyk, A. et al. Effects of CAST and RYR1 genotypes on carcass and meat quality
296
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 294–301.
297
A detailed comparison of mean least squares
(LSQ) for the analysed CAST and RYR1 genotypes
was done using a Tukey’s test.
Results
The frequencies CAST/MspI, CAST/ApaLI, and
RYR1 of alleles and genotypes in Pietrain-sired
pigs are presented in Table 1. The signicances of
association between the genotypes of calpastatin
Table 1. The frequency of CAST and RYR1 alleles and genotypes in analysed pigs.
(n = 125) CAST/MspICAST/ApaLI RYR1
CC CD DD AA AB BB CC CT TT
No. of animals - 68 57 - 95 30 71 54 -
Frequency of alleles C = 0.27 D = 0.73 A = 0.38 B = 0.62 C = 0.78 T = 0.22
Frequency of genotypes (%) - 54.4 45.6 - 76.0 24.0 56.8 43.2 -
Table 2. The LSQ means of analysed traits and relationship between genotypes at the loci CAST/MspI and CAST/ApaLI
and RYR1 for carcass and meat quality in pigs.
Trait LSQ SE CAST/MspICAST/ApaLI RYR1 CAST/MspI
× RYR1
CAST/ApaLI
× RYR1
Slaughter value
Lean meat content (%) 55.39 0.39 n.s. p=0.011 n.s. p=0.006 p=0.007
Backfat thickness (mm) 14.90 0.38 n.s. p=0.026 n.s. p=0.037 p=0.006
Muscle thickness (mm) 56.64 0.58 n.s. p=0.035 n.s. p=0.014 p=0.047
Basic chemical composition
Total protein (%) 22.40 0.06 n.s. n.s. n.s. n.s. n.s.
Fat (%) 2.52 0.05 n.s. n.s. n.s. n.s. n.s.
Ash (%) 1.18 0.01 n.s. n.s. n.s. n.s. n.s.
Dry matter (%) 26.10 0.07 n.s. n.s. n.s. n.s. n.s.
Marbling (score) 1.28 0.04 n.s. p=0.020 n.s. n.s. n.s.
Meat quality
pH24 5.66 0.01 n.s. n.s. n.s. n.s. n.s.
pH48 5.57 0.01 n.s. n.s. n.s. n.s. n.s.
EC2 (mS/cm) 3.08 0.12 n.s. n.s. n.s. n.s. n.s.
L* 54.74 0.30 n.s. n.s. n.s. n.s. n.s.
a* 9.33 0.11 n.s. n.s. n.s. n.s. n.s.
b* 16.81 0.12 n.s. n.s. n.s. n.s. n.s.
Drip loss (%) 7.65 0.23 n.s. n.s. n.s. n.s. n.s.
WHC (% of free water) 17.42 0.44 n.s. n.s. n.s. n.s. n.s.
Thermal drip (%) 25.88 0.25 n.s. n.s. n.s. n.s. n.s.
Water-soluble protein (%) 8.22 0.08 n.s. n.s. n.s. n.s. n.s.
n.s. - statistically not signicant.
AGRICULTURAL AND FOOD SCIENCE
Rybarczyk, A. et al. Effects of CAST and RYR1 genotypes on carcass and meat quality
298
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 294–301.
299
(CAST/MspI and CAST/ApaLI), RYR1 and carcass
and meat quality traits of the pigs are presented in
Table 2.
We did not nd any signicant differences be-
tween the genotypes CC and CT of the RYR1 in
terms of carcass percentage of lean meat, backfat
and LD muscle thickness, as well as in meat quality
and basic chemical composition determined in the
longissimus lumborum muscle.
Signicant differences were found in carcass
slaughter performance and meat marbling between
pigs of genotypes AB and BB at the locus CAST/
ApaLI (Table 3). The AB pigs had signicantly
higher percentage of lean meat, thinner backfat,
thicker LD muscle, and lower marbling of the meat
as compared with the BB genotype (p ≤ 0.05). No
signicant association, however, was found be-
tween the CAST/ApaLI genotype and meat qual-
ity traits of the pigs. Moreover, signicant CAST/
ApaLI × RYR1 interactions were found in terms of
carcass quality. Signicantly higher percentage of
lean meat and thinner backfat (p ≤ 0.01), as well
as thicker LD muscle (p ≤ 0.05), were found in the
AB/CT, AB/CC, and BB/CT genotypes in relation
to BB/CC pigs (Table 4).
No signicant effect of the CAST/MspI poly-
morphism on carcass and meat quality or meat
basic chemical composition was observed. Signif-
icant CAST/MspI × RYR1 interactions, however,
were found as regards carcass quality of the pigs.
Table 3. The relationship between genotypes at the CAST/ApaLI locus and carcass quality traits in pigs.
Item AB BB
No. of animals 95 30
Lean meat content (%) 55.93a ± 0.43 53.70b ± 0.91
Backfat thickness (mm) 14.46a ± 0.40 16.30b ± 0.92
Muscle thickness (mm) 57.28a ± 0.66 54.57b ± 1.14
Marbling (score) 1.24a ± 0.04 1.43b ± 0.10
Results in the table are given as LSQ mean ± standard error
a,b Mean values marked by different small letters differ signicantly at p≤0.05.
Table 4. Effect of interaction between CAST and RYR1 genotypes and carcass quality traits in pigs.
Item CAST/ApaLI and RYR1 genotypes
AB/CT BB/CT AB/CC BB/CC
No. of animals 34 20 61 10
Lean meat content (%) 56.18A ± 0.66 55.28A ± 0.81 55.78A ± 0.56 50.56B ± 1.88
Backfat thickness (mm) 13.94A ± 0.66 14.80A ± 0.81 14.75A ± 0.51 19.30B ± 1.97
Muscle thickness (mm) 56.53a ± 0.90 56.40a ± 1.32 57.70a ± 0.90 50.90b ± 1.70
CAST/MspI and RYR1 genotypes
CD/CT DD/CT CD/CC DD/CC
No. of animals 31 23 37 34
Lean meat content (%) 56.16A ± 0.75 55.41AB ± 0.65 53.61B ± 0.85 56.61A ± 0.71
Backfat thickness (mm) 14.13a ± 0.75 14.43ab ± 0.67 16.43b ± 0.82 14.26ab ± 0.67
Muscle thickness (mm) 57.26ab ± 1.07 55.43ab ± 0.95 54.78a ± 1.12 58.88b ± 1.22
Results in the table are given as LSQ mean ± standard error
A,B Mean values marked by different capital letters differ signicantly at p ≤ 0.01.
a,b Mean values marked by different small letters differ signicantly at p ≤ 0.05.
AGRICULTURAL AND FOOD SCIENCE
Rybarczyk, A. et al. Effects of CAST and RYR1 genotypes on carcass and meat quality
298
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 294–301.
299
The DD/CC and CD/CT pigs had signicantly
higher percentage of lean meat (p ≤ 0.01) and thin-
ner backfat (p ≤ 0.05) as compared with the CD/
CC genotype. Moreover, DD/CC pigs had a signi-
cantly thicker LD in relation to CD/CC (p ≤ 0.05).
Discussion
The presented study on Pietrain-sired pigs of un-
known family structure did not reveal signicant
differences in carcass or meat quality between CC
and CT genotype at the locus RYR1. Results by
other authors who studied carcass and meat quality
in the same genotypes of pigs with Pietrain genes
are not unambiguous. Busk et al. (2000) stated that
the carcasses of CT pigs contained more lean and
had worse meat quality as compared with the CC
pigs. Kusec et al. (2005) did not observe signicant
differences in butchery value of carcasses between
the CC and CT pigs; the authors found, however,
that meat quality of heterozygous pigs (CT) was
worse. Also Krzęcio et al. (2005) and Kuhn et al.
(2005) report poorer quality of meat obtained from
CT pigs. On the other hand, Koćwin-Podsiadła et al.
(2003) did not nd signicant differences in meat
quality between the discussed RYR1 genotypes.
The analysis of CAST/MspI genotypes frequen-
cy in the studied crossbred pigs sired by Pietrain
boars revealed two genotypes, CD and DD, which
was also reported by Kurył et al. (2003) in pigs
obtained from the program TORHYB [Pietrain ×
(Polish Landrace × Polish Large White)] as well
as in Polish Landrace pigs. In both the cited report
and studies by Ernst et al. (1998), Pietrain pigs
were monomorphic at the locus CAST/MspI and
were of DD genotype. All three possible genotypes
were observed in Yorkshire and Large White pigs
(Ernst et al., 1998), as well as in Stamboek (Dutch
Large White × Dutch Landrace) and Złotnicka
Spotted (Kurył et al. 2003).
We did not observe signicant differences in
carcass quality between pigs of CD and DD geno-
types at the locus CAST/MspI. Conversely, Kurył et
al. (2003) demonstrated a relationship between the
CAST/MspI polymorphism and some carcass qual-
ity traits in RYR1T-free Stamboek hogs. DD pigs
had thinner backfat at some measurement points
and a larger rib eye area in relation to CC pigs.
Also Koćwin-Podsiadła et al. (2004) observed that
crossbred, RYR1
T
-free fatteners revealed signicant
effect of association between the CAST/MspI geno-
type and ve out of 19 analyzed carcass traits. The
authors report that activity of a given molecular
type of calpastatin depends on the muscle, since
BB-genotype pigs at CAST/MspI had a larger ham
weight, whereas larger loins were cut from AA pigs.
The analyzed pigs did not show a signicant
association between the CAST/MspI genotype and
meat quality as well as basic chemical composition
of meat in the longissimus lumborum muscle. Also
Koćwin-Podsiadła et al. (2003) and Kurył et al.
(2004), who studied crossbreds sired by Duroc ×
Pietrain boars, failed to demonstrate a relationship
between the CAST/MspI genotype and meat quality
traits, except for loin efciency during smoking.
Krzęcio et al. (2005), on the other hand, observed
signicant effects of the CAST/MspI polymorphism
on the level of lactic acid in the longissimus lumbo-
rum muscle tissue 45 minutes post mortem, index
of energy metabolism intensity R1, water holding
capacity (WHC), drip loss from muscle tissue at 48
and 96 hours post mortem, as well as meat protein
and water content. Moreover, Koćwin-Podsiadła
and Kurył (2003) demonstrate that polymorphism
at the locus CAST/MspI relatively strongly corre-
lates with the incidence of increased drip loss from
meat in the group of pigs with the gene RYR1T.
We have demonstrated here a signicant asso-
ciation between the CAST/ApaLI genotype and car-
cass quality traits and meat marbling. Bonforroni’s
correction revealed a signicant linkage between
the CAST/ApaLI and percentage of lean meat. It is
known that the proteolytic calpain-calpastatin sys-
tem has its role in the processes of muscle growth
and development. Results of experiments show
that the calpain system is important for the proper
development of the skeletal muscles. An increased
rate of skeletal muscles development may be a re-
sult of a reduced rate of protein degradation in the
muscle, which is associated with a reduced activ-
ity of the calpain system resulting mainly from a
AGRICULTURAL AND FOOD SCIENCE
Rybarczyk, A. et al. Effects of CAST and RYR1 genotypes on carcass and meat quality
300
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 294–301.
301
considerable increase in calpastatin activity (Goll
et al. 1998). The presented results may indicate that
calpastatin as calpains inhibitor may behave differ-
ently depending on the genetic variant. Moreover,
analysis found signicant evidence of QTL on SSC
2 associated with backfat thickness, longissimus
muscle area and fat percent (Malek et al. 2001;
Stearns at al. 2005).
The analysis of the pigs revealed signicant
effect of interactions, CAST/ApaLI × RYR1 and
CAST/MspI × RYR1, in relation to carcass quality
traits only, i.e. percentage of lean meat, backfat and
LD muscle thickness. Bonforroni’s correction re-
vealed signicant interactions between CAST/MspI
× RYR1 in relation to percentage of lean meat and
muscle thickness, as well as between CAST/ApaLI
× RYR1 in relation to percentage of lean meat and
backfat thickness. On the other hand, Krzęcio et
al. (2005), who studied a group of crossbred pigs,
observed a signicant effect of interaction between
the RYR1 and CAST/MspI genotype for muscle tis-
sue acidity 24 hours post mortem and drip loss from
the LL tissue 48 hours post mortem. In the studies
on crossbred pigs sired by Duroc × Pietrain boars,
an interaction between the CAST/MspI and RYR1
genotypes was signicant only for loin efciency
during smoking (Koćwin-Podsiadła et al. 2003)
and drip loss measured at 48 hours post mortem
(Kurył et al. 2004). The authors (Kurył et al. 2004;
Krzęcio et al. 2005) conclude that the frequency of
meat with high drip loss in pigs free of the stress
sensibility gene (genotype CC at the locus RYR1),
as well as that of normal meat in carriers of the
gene (genotype CT at the locus RYR1) may be a
result of a joint modifying effect of the CAST gene
on the post mortem changes in the muscle tissue.
Conclusions
No signicant differences in the analyzed pigs sired
by Pietrain boars were found between genotypes
CC and CT at the RYR1 locus and CD and DD at
the CAST/MspI locus in terms of carcass and meat
quality. However, a signicant inuence was found
of the CAST/ApaLI polymorphism on carcass qual-
ity and meat marbling. The carcasses of AB pigs
had signicantly higher percentage of lean meat,
thinner backfat, and thicker LD muscle, as well as
lower meat marbling, as compared with the BB pigs.
Furthermore, interactions CAST/MspI × RYR1 and
CAST/ApaLI × RYR1 were found signicant in rela-
tion to all the studied carcass traits, i.e. percentage
of lean meat, backfat thickness, and LD thickness.
The results presented here imply that the CAST
gene recognized with ApaLI may be considered
as important in terms of the way it affects porcine
carcass quality traits. Moreover, research has re-
vealed an association between CAST and RYR1
genotypes as regards formation of carcass traits in
pigs. Follow-up studies, however, should be carried
out on larger populations representing all possible
CAST genotypes.
References
AOAC 2003. Ofcial methods of analysis (17th ed.). Associ-
ation of Ofcial Analytical Chemists. Gaithersburg, USA.
Busk, H., Karlsson, A. & Hertel, S.H. 2000. Halothane gene
effect on carcass and meat quality by use of Duroc x Pi-
etrain Boars. In: Wenk, C., Fernandez J.A. & Dupuis, M.
(eds.). Quality of meat and fat in pigs as affected by ge-
netics and nutrition. Proceedings of the joint session of
the EAAP commissions on pig production, animal genet-
ics and animal nutrition, Zurich, Switzerland. Wagenin-
gen Pers. Wageningen, The Netherlands, p. 129–133.
Ciobanu, D.C., Bastiaansen, J.W.M., Lonergan, S.M.,
Thomsen, H., Dekkers, J.C.M., Plastow, G.S. & Roth-
schild, M.F. 2004. New alleles in calpastatin gene are
associated with meat quality traits in pigs. Journal of
Animal Science 82: 2829–2839.
CIE (Commission International de I’Eclairage) 1976. Color-
imetry.: Ofcial Recommendations of the Internation -
al Commission on Illumination. Publication CIE, 15 (E-
1.3.1). Bureau Central de la CIE, Paris, France.
Ernst, C.W., Robic, A., Yerle, M., Wang, L. & Rothschild,
M.F. 1998. Mapping of calpastatin and three microsat-
ellites to porcine chromosome 2q2.1-q2.4. Animal Ge-
netics 29: 212–215.
Fiedler, I., Kuhn, G., Hartung, M., Küchenmeister, U., Nürn-
berg, K., Rehfeldt, C., Huber, K. & Kłosowska, D. 2001.
Effects of the malignant hyperthermia syndrome (MHS)
on meat quality, muscle bre characteristics and meta-
bolic traits of the Longissimus muscle in Pietrain pigs.
Archiv Tierzucht, Dummerstorf 44: 203–217.
Fujii, J., Otsu, K., Zorzato, F., De Leon, S., Khana, V.K.,
Weiler, J.E., Obrien, P.J. & Maclennan, D.H. 1991.
AGRICULTURAL AND FOOD SCIENCE
Rybarczyk, A. et al. Effects of CAST and RYR1 genotypes on carcass and meat quality
300
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 294–301.
301
Identication of a mutation in porcine ryanodine recep-
tor associated with Malignant Hyperthermia. Science
253: 448 –451.
Goll, D.E., Thompson, V.F., Taylor, R.G. & Ouali, A. 1998.
The calpain system and skeletal muscle growth. Cana-
dian Journal of Animal Science 78: 503– 512.
Grau R. & Hamm R. 1952. Eine einfache Methode zur Be-
stimmung der Wasserbindung in Fleisch. Fleischwirt-
schaft 4: 295–297.
Honikel, K.O. 1987. pH and water binding of meat. Fleisch-
wirtschaft 67: 1098–1102.
Koćwin-Podsiadła, M. & Kurył, J. 2003. The effect of inter-
action between genotypes at loci CAST, RYR1 and RN
on pig carcass quality and pork traits – a review. Ani-
mal Scienc e Papers and Repor ts 21 (Suppl. 1): 61–75.
Koćwin-Podsiadła, M., Kurył, J., Krzęcio, E., Antosik, K.,
Zybert, A. & Sieczkowska, H. 2004. An association be-
tween genotype at the CAST (calpastatin) locus and car-
cass quality traits in porkers free of RYR1T allele. Animal
Science Papers and Repor ts 22: 497–505.
Koćwin-Podsiadła, M., Kurył, J., Krzęcio, E., Zybert, A. &
Przybylski, W. 2003. The interaction between calpastatin
and RYR1 genes for some pork quality traits. Meat Sci-
ence 65: 731–735.
Kotik, T. 1974. Zawartość białka w ekstraktach wodnych
mięsa jako wskaźnik jego jakości. (Protein content in
water extracts of meat as an index of its quality). Roc-
zniki Instytutu Przemysłu Mięsnego 12: 47–52. (in Pol-
ish with abstract in English)
Krzęcio, E., Koćwin-Podsiadła, M., Kurył, J., Antosik, K.,
Zybert, A., Sieczkowska, H., Pospiech, E., Łyczyński,
A. & Grześ, B. 2004. An association between genotype
at the CAST locus (calpastatin) and meat quality traits
in porkers free of RYR1T allele. Animal Science Papers
and Repor ts 22: 489–496.
Krzęcio, E., Kur ył, J., Koćwin-Podsiadła, M. & Monin, G.
2005. Association of calpastatin (CAST/MspI) polymor-
phism with meat quality parameters of fatteners and its
interaction with RYR1 genotypes. Journal Animal Breed-
ing and Genetics 122: 251–258.
Kuhn, G., Fiedler, I., Ender, K. & Mattes, W. 2005. Duroc
and Pietrain breedings in comparison. Fleischwirtschaft
85: 92–96.
Kurył, J., Kapelański, W., Pierzchała, M., Grajewska, S.
& Bocian, M. 2003. Preliminary observations on the ef-
fect of calpastatin gene (CAST) polymorphism on car-
cass traits in pigs. Animal Science Papers and Reports
21: 87–95.
Kurył, J., Krzęcio, E., Koćwin-Podsiadła, M. & Monin, G.
2004. The inuence of CAST and RYR1 genes poly-
morphism and their interactions on selected meat qual-
ity parameters in four-breed fatteners. Animal Science
Papers and Reports 22: 479–488.
Kusec, G., Baulain, U., Henning, M., Köhler, P. & Kallweit,
E. 2005. Fattening, carcass and meat quality traits of
hybrid pigs as inuenced by MHS genotype and feed-
ing systems. Archiv Tierzucht Dummerstorf 48: 40–49.
Malek, M., Dekkers, J.C.M., Lee, H.K., Baas, T.J., Prusa,
K., Huff-Lonergan, E. & Rothschild, M.F. 2001. A molec-
ular genome scan analysis to identify chromosomal re-
gions inuencing economic traits in the pig. II. Meat and
muscle composition. Mammalian Genome 12: 637–645.
Melody, J.L., Lonergan, S.M., Rowe, L.J., Huiatt, T.W.,
Maye,s M.S. & Huff-Lonergan, E. 2004. Early post mor-
tem biochemical factors inuence tenderness and wa-
ter-holding capacity of three porcine muscle. Journal of
Animal Science 82: 1195–1205.
Murachi, T. 1989. Intracellular regulatory system involv-
ing calpain and calpastatin. Biochemistry Internatio-
nal 18: 263–294.
Pohja, M.S. & Niinivaara, F.P. 1957. Die Bestimmung der
Wasserbindung des Fleisches mittels der Konstant-
drückmethode. Fleischwirtschaf t 9: 193–195.
Rybarczyk, A., Kmieć, M., Napierała, F., Natalczyk-Szym-
kowska, W. 2010. The effect of calpastatin polymor-
phism (CAST/Hinf I and CAST/Hpy188I) and its interac-
tion with RYR1 genotypes on carcass and pork quali-
ty of crossbred pigs. Animal Science Papers and Re-
ports 28: 253–260.
Stearns, T.M., Beever, J.E., Southey, B.R., Ellis, M., McK-
eith, F.K. & Rodriguez-Zas, S.L. 2005. Evaluation of ap-
proaches to detect quantitative trait loci for growth, car-
cass, and meat quality on swine chromosomes 2, 6, 13,
and 18. I. Univariate outbred F2 and sib-pair analyses.
Journal of Animal Science 83: 1481–1493.
AGRICULTURAL AND FOOD SCIENCE
Vol. 19 (2010): 302–312.
302
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
303
© Agricultural and Food Science
Manuscript received November 2009
Wilting and inoculation of
Lactobacillus buchneri
on
intercropped triticale-fava silage: effects on nutritive,
fermentative and aerobic stability characteristics
Adela Martínez-Fernández*, Ana Soldado, Fernando Vicente, Antonio Martínez
and Begoña de la Roza-Delgado
Department of Animal Nutrition, Grassland and Forages, Regional Institute of Research and
Agro-food Development (SERIDA), PO Box 13; E-33300 Villaviciosa (Asturias) Spain
*e-mail: admartinez@serida.org
This study investigated the effects of wilting and Lactobacillus buchneri inoculation on fermentation end
products, DM recovery, nutritive characteristics and aerobic stability in organically grown triticale-fava
bean intercrop silages. For this purpose, a bi-crop of triticale (× Triticosecale Wittm.) and fava bean (Vicia
faba L.) was established on an old low-input mixed sward (Lolium perenne-Trifolium repens). The asso-
ciation of triticale and fava bean in winter crops and wilting forages before ensiling improved ensilability
characteristics. Wilting for 24 hours before ensiling avoided efuent losses during the fermentation process
and reduced ammonia nitrogen production. Inoculation with Lactobacillus buchneri 40788, for a nal ap-
plication rate of 1×105 cfu g-1 of fresh forage ensiled in laboratory silos during 80 days, promoted a higher
CP concentration. Furthermore, it promoted changes in the concentration of fermentation end products,
decreasing lactic acid and increasing acetic and propionic acids. The effects of Lactobacillus buchneri
on aerobic stability were not conrmed in this study. Wilting depressed, but did not inhibit the activity of
Lactobacillus buchneri in the fermentation process.
Key-words: Lactobacillus buchneri, wilting, ensilability characteristics, efuent production, nutritive value,
organic management
AGRICULTURAL AND FOOD SCIENCE
Vol. 19 (2010): 302–312.
302
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
303
Introduction
In humid temperate areas with oceanic climatic
conditions, the most common rotation crops have
traditionally been Italian ryegrass-maize (Lolium
multiorum Lam.-Zea mays L.) due to their high
productivity. However, the negative effect on soil
fertility of this rotation has prompted the introduction
of new forage crops that provide an alternative to
Italian ryegrass as a winter crop. Some alternatives
have been mixtures of grasses with legumes which
are able to x nitrogen in the soil, thereby reducing
nitrogen requirements and associated with environ-
mental benets (Vanotti et al. 1997). Moreover,
farmers have become increasingly interested in
cereal-legume intercrops for winter-feeding in order
to improve the efciency of production systems.
In this respect, Adesogan et al. (2002) have shown
pea-wheat bi-crop silages to be both high yielding
and environmentally benign forages that promote
higher dry matter intake (DMI) and greater nitrogen
contents than grass silages. Our research group has
tested several legumes in association with winter
cereals, including pea-wheat bi-crops, obtaining
the best results when combining triticale-fava bean
(x Triticosecale Wittm.-Vicia faba L.) in rotation
with maize (Pedrol and Martínez 2003, Martínez-
Fernández et al. 2008). In this intercrop, the cereal
contributes dry matter and water soluble carbo-
hydrates that improve ensilability characteristics,
thus reducing efuent production and minimizing
environmental risks. However, this intercrop yields
a lower nutritive value than a legume crop alone.
As regards the triticale-fava bean intercrop, the
optimal time for mowing is at the initial grain stage
for triticale and immature legumes for fava bean
(Argamentería et al. 2004). In these phenological
stages, high energy and protein values are avail-
able as well as lower contents in DM (Arzani et al.
2004, Lloveras-Vilamanya 1987). For this reason,
pre-wilting the intercrop before ensiling seems to
be necessary. In fact, the higher the DM of the crop,
the lower the bacterial activity and the role of fer-
mentation acids in preservation (Wolford 1984).
Some biological additives, such as homofer-
mentative bacteria, improve fermentative activity
during the fermentation process, silage nutritive
value and animal response in terms of milk and
meat production. However, these additives can
reduce silage stability during storage and after
opening. These effects are due to the lactic acid
produced throughout the fermentation process,
which is metabolized by some species of yeast and
mould upon exposure to oxygen (Combs and Hoff-
man 2001), and reduced production of antifungal
factors (Kung et al. 2003). When air inltrates the
silage during storage, the growth of aerobic micro-
organisms is stimulated and the process of aerobic
deterioration is initiated, leading to dry matter loss-
es of feed, decreasing the feed nutritive value and
probably also reducing voluntary feed intake. Yeast
acid-tolerant and occasionally acetic acid-tolerant
bacteria are the main micro-organisms responsible
for consuming nutrients and fermentative residual
products, increasing temperature in the silage mass
and reducing dry matter and energy (Taylor et al.
2002, Reis et al. 2005).
In contrast, some commercial additives pro-
duced with heterofermentative Lactobacillus cul-
tures have demonstrated an ability to inhibit fungal
growth (Nishino and Hattori 2005) and have been
used to improve the aerobic stability of silages
after long-term storage (Kung and Ranjit 2001).
Their activity is related to the presence of acetic
acid, which inhibits the growth of specic species
of yeast responsible for heating upon exposure
to oxygen and also decreasing losses during the
fermentation process and improving animal pro-
duction (Kung et al. 2003). Different studies have
shown the efciency of Lactobacillus buchneri
40788, which, when added to fodder that has been
harvested, increases acetic fermentation and re-
duces fungal contamination, thereby improving the
aerobic stability of silage by reducing yeast growth
(Combs and Hoffman 2001, Kung and Ranjit 2001,
Kleinschmit and Kung 2006). The positive impact
of L. buchneri appears to be related to acetic acid
production. Yeast can be inhibited by the presence
of short-chain fatty acids, like acetic acid, which
penetrate by passive diffusion into cells and re-
lease hydrogen ions, thus decreasing intracellular
pH quickly and resulting in cell death (Ruser and
Kleinmans 2005).
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
304
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
305
The aim of this study was to determine the ef-
fects of wilting and Lactobacillus buchneri 40788
inoculation on fermentation end products, DM
recovery, nutritive value and aerobic stability in
triticale and fava bean intercrop silages grown or-
ganically under prevailing weather conditions in
wet temperate coastal areas.
Material and methods
Forages and silages
This study was conducted on an experimental farm
located in the North of Spain (latitude 43.23°N,
longitude 6.07°W, altitude 65 m above sea level,
Asturias, Spain). The average temperature for the
growing period was 10 °C (ranging between 1 to
16 °C) with 78% relative humidity and 425 mm
of rainfall.
A winter bi-crop of triticale and fava bean (x
Triticosecale Wittm. and Vicia faba L.) was grown
under organic conditions. This bi-crop was estab-
lished in February 2005, on an old low-input mixed
sward (Lolium perenne and Trifolium repens) pre-
viously used for grazing and without NPK inorgan-
ic fertilizers prior to and during this assay. Sowing
was carried out on an experimental single plot area
of 360 m2. The viable seeding rate was 159 grains
m
-2
for triticale and 26 seeds m
-2
for fava bean.
After 14 weeks of growth, plants were harvested
in May 2005 by direct cut method using a mower
of cutting bars. The growth stage of forages at the
time of harvest was milky grain stage for triticale
and pods with grain for fava bean (Fraser et al.
2001). At that time, the triticale-fava bean ratio
obtained was 6.3:1. The total yield obtained after
harvest was divided in two parts: 1) triticale-fava
bean after discarding the weeds from the existing
sward (TF), and 2) fava bean alone (F).
Before ensiling, TF and F samples were divided
in half, with one part being prepared for ensiling
in direct cut (D) and the other wilted for 24 hours
(W). All fractions to ensiling were cut at 2 cm us-
ing an ORGO precision chopper (Agro ORGA S.A,
Tarragona Spain) and after that ensiled with ad-
dition (A) or no addition (NA) of Lactobacillus
buchneri NCIMB strain 40788 (Lallemand Animal
Nutrition, BP 59, Cedex, France). The additive was
prepared diluting 200 g in 40 l of water, and ap-
plying one litre of this solution by ton of forage,
to obtain a nal application rate of 1 × 105 cfu g-1
of fresh forage.
All material was ensiled at room temperature
(20 ± 5 °C) in laboratory silos made of PVC cyl-
inders provided with bun valves to allow for gas
losses, and glass containers to store the evacuated
efuent. These laboratory silos have a capacity of
4 dm3 and a forage density of 650 kg m-3, accord-
ing to Martínez-Fernández and de la Roza-Delgado
(1997). The amount of plant material ensiled was
2.5 ± 0.25 kg per silo, and three replicates per treat-
ment. A total of 24 laboratory silages were made.
At the end of the fermentative process (80 days),
the silos were opened and sampled for analysis.
Efuent production was measured throughout the
process by weekly weighting.
Analytical methods
Forages
Two representative fresh forage samples for total
herbage mass were taken and later each one was
divided in two parts: TF and F (see forages and
silages section). All samples (in direct cut and
wilted) were analyzed in duplicate for ensilability
characteristics, including water soluble carbohy-
drates (WSC) following Hoffman (1937) and buffer
capacity (BC) using the methodology described by
Playne and McDonald (1966). The fermentabil-
ity coefcient (FC) was calculated according to
Schmidt et al. (1971), cited by Weissbach (1999).
Dry matter in forages (DM) was determined by
drying in an air-forced oven at 102 °C for 24 h. For
analytical determinations, a subsample from each
treatment was dried at 60°C and milled at 0.75 mm.
Samples were analyzed for ash and crude protein
(CP) according to AOAC (1984), neutral detergent
ber (NDF) (Van Soest et al. 1991) and cellulase
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
304
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
305
digestibility (Riveros and Argamentería 1987) to
estimate metabolizable energy (ME) by ARC (1980).
Silages
After 80 days silos were opened and three sub-
samples of each experimental silage were collected.
The rst sub-sample was pressed to obtain juice
extract to determine pH, ammonia-N, lactic acid
and volatile fatty acids. Ammonia-N was determined
by UV-Vis (Ammonium test, Merck, Germany).
Lactic acid and volatile fatty acids analyses were
performed by HPLC with a Water Alliance 2690
instrument equipped with a Waters 996 Photodiode
Array Detector Module (Waters, Milford, Massa-
chusetts) monitoring at 206 nm and drove by Mil-
lenium software. The juice extract were analyzed on
a Shodex RSpak KC-811 column (Waters), using a
mobile phase with 0.025 % phosphoric acid. Flow
rate was 1.0 ml min -1 and column temperature for
analyses was kept at 40ª C.
The second silage sub-sample was freeze-dried
to avoid loss of volatile compounds. After that,
samples were milled at 0.75 mm and analyzed for
DM, ash and CP (AOAC 1984), NDF (Van Soest
et al. 1991) and cellulase digestibility to estimate
metabolizable energy (ME) by ARC (1980).
The third silage sub-sample around 1.5±0.25 kg
was used for aerobic stability analysis, dened by
Moran et al. (1996) as the number of hours the si-
lage mass remained at the baseline
temperature be-
fore rising 2 °C. Following McEniry et al. (2007),
this sub-sample was placed in a polystyrene box
(60 × 40 × 15 cm) provided with no hermetic cov-
ers and exposed to the air in a room with a control-
led temperature of 20±1 ºC. Thermocouples were
placed in the middle of the silage in each box and
the temperature was automatically recorded each
hour for 240 h. Silage pH was measured directly
each day at different positions of the silages using
a portable electrode (Inlab 427, Mettler Toledo).
To calculate losses, the material was weighted
both in and out of the silos. The proportion of total
DM losses was calculated according to the follow-
ing expression:
Statistical Analysis
Forages data were analyzed to ANOVA by the gen-
eral linear procedure of SAS (SAS 1999). All silage
data were analyzed as a factorial design with type of
forage (S), wilting (W) and inoculation (A) as the
main factors using again the SAS (1999) general
linear models procedure. The model used was:
Y
ijkl
= µ +S
i
+W
j
+ A
k
+ SW
ij
+ SA
ik
+ WA
jk
+
SWAijk+ εijkl
where: Y
ijkl
= observation, µ = population mean,
Si = forage effect (i = 1 to 2), Wj = wilting effect (j
= 1 to 2), Ak = additive effect (k = 1 to 2), SWij =
interaction between forage and wilting effects, SAik
= interaction between forage and additive effects,
WA
jk
= interaction between wilting and additive
effects, SWAijk= interaction between forage, wilt-
ing and additive effects and εijkl = residual error.
Signicant differences were accepted if p ≤ 0.05.
Aerobic stability was evaluated using an ANO-
VA with repeated measures testing for statistical
signicance of pH and temperature data, collected
over 10 days and considering the type of forage,
treatment by wilting and additive as xed effects.
The statistical analyses were performed using the
SAS (1999) statistical package.
Results
The ensilability characteristics, chemical compo-
sition and estimated ME of fresh forages before
ensiling are shown in Table 1. DM was below 200
g kg-1 in fresh crops and was increased by wilting
to about 320 g kg-1. The bi-crop (TF) had higher
DM (upper than 20%) and lower CP (around 50%)
proportions than fava bean in direct cut and wilted
forages, respectively (p < 0.001). After wilting,
signicant differences were observed with respect
to chemical composition in both forages, with losses
in CP and NDF (p < 0.01).
×
×
×
−= 100
)(
)(
100
forage
silage
DMgForage
DMgSilage
Loss es
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
306
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
307
Fermentability coefcient was higher in TF than F
for direct cut and wilting respectively (p < 0.001),
as a consequence of the higher proportion of water
soluble carbohydrates (WSC) and lower buffer
capacity (BC) of the former. Furthermore, wilting
for 24 hours improved the ensilability conditions in
TF and F, increasing WSC and markedly reducing
BC. The interaction between the effect of forage
and wilting was also signicant (p < 0.001).
Table 2 shows the chemical and fermentative
characteristics on direct cut and wilted silages in-
oculated with or without Lactobacillus buchneri
at ensiling.
The average DM losses were around 20 % for
TF and F. In direct cut forages DM losses were
higher in F than TF, it is inversely related with for-
age DM content. These losses decrease in wilted
forages. As regards the additive effect, losses were
higher in both TF and F silages with additive com-
pared to those without inoculation (p < 0.001).
The total efuent production in direct cut with-
out additive was higher in F than TF forages (p <
0.05), in concordance with their lower DM content
(Table 2). In fact, twenty-four hours of wilting was
enough to avoid efuent production during the fer-
mentation period.
Wilting did not affect silage pH, although sig-
nicant differences were observed in terms of the
forage effect (p < 0.01). The pH was higher in F
silages. Wilting time did not affect the chemical
composition of silages, except for DM content (p <
0.001). The differences due to forage effect showed
higher values of ash and CP and a lower proportion
of DM and NDF in F than TF silages, respectively
(p < 0.001), when comparing the direct cut and
wilting effect. ME was not affected by the consid-
ered effects (S and W).
Attending fermentative characteristics, wilting
promoted a substantial decrease in the proportion
of NH
3
-N (p < 0.001) without any effect on CP
content, being ammonia synthesis lower in F than
TF silages (S × W, p < 0.05). TF had a lower pro-
portion of lactic and propionic acids and a higher
proportion of acetic acid than F, the lactic/acetic
acid ratio being lower in TF despite their better FC.
Addition of Lactobacillus buchneri at ensiling
increased pH (p < 0.001). The inoculation also in-
creased concentrations of ashes (p < 0.001), CP
(p < 0.01) and NDF (p < 0.01), while decreasing
metabolizable energy (p < 0.05).
As regards fermentative characteristics, the
concentration of lactic acid was lower in silages
Table 1: Ensilability characteristics, chemical composition and energy content of forages before ensiling.
Triticale-Fava bean Fava bean Signicance
Direct Wilting Direct Wilting s.e.m S W S×W
Ensilability characteristics
Dry matter (g kg-1) 206 361 183 280 22.4 *** *** ***
WSC (g kg-1 DM) 201 251 94 142 2.3 *** *** NS
BC (meq NaOH kg-1 DM) 218 127 319 221 40.0 *** *** NS
FC 28 52 21 33 1.9 *** *** ***
Chemical composition and estimated energy
Crude Protein (g kg-1 DM) 117 111 173 166 0.6 *** ** NS
NDF (g kg-1 DM) 550 534 519 491 2.1 *** ** NS
ME (MJ kg-1 DM) 9.5 9.5 9.3 8.9 0.03 *** * *
Signicant levels: *, ** and *** at p-values< 0.05, 0.01, 0.001, respectively. NS: p ≥ 0.05. s.e.m.: standard error mean; W: Wilting effect;
S: Forage effect; WSC: Water soluble carbohydrates; BC: Buffer capacity; FC: Fermentability coefcient; NDF: Neutral detergent bre;
ME: Metabolizable energy.
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
306
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
307
Table 2. Chemical and fermentative characteristics on direct cut and wilted silages inoculated with or without Lactobacillus buchneri at ensiling.
Parameter
Triticale-Fava bean Fava bean Signicance
Direct Wilting Direct Wilting
s.e.m. S W A S×W S×A W×A S×W×A
NA A NA A NA A NA A
pH 3.94 4.28 4.09 4.18 4.06 4.67 4.18 4.55 0.013 ** NS *** NS ** *** NS
Dry matter (g kg-1) 232 212 339 317 181 178 245 231 1.066 *** *** *** *** NS NS NS
Ash (g kg-1 DM) 49.3 55.6 46.4 54.7 55.7 61.3 60.2 62.0 0.400 *** NS *** * NS NS NS
Crude protein (g kg-1 DM) 125 137 119 134 185 213 200 212 2.325 *** NS ** NS NS NS NS
NDF (g kg-1 DM) 554 594 552 601 494 490 463 512 5.894 *** NS ** NS NS NS NS
ME (MJ kg-1 DM) 9.11 8.67 8.97 8.59 9.41 9.26 9.33 8.25 0.080 NS NS * NS NS NS NS
Ammonia-N (g NH3 kg-1 N) 38.1 35.9 13.1 12.0 28.7 30.5 10.3 25.4 0.711 NS *** * * NS *** *
DM losses (g kg-1) 163 218 145 222 206 258 165 215 4.680 *** NS *** NS NS NS NS
Efuent (l t-1) 80.5 52.9 0 0 96.6 43.2 0 0 3.391 NS *** * NS NS * NS
Lactic acid (g kg-1 DM) 66.6 24.5 38.9 29.4 96.5 23.9 82.7 24.8 1.050 *** *** *** NS *** NS *
Acetic acid (g kg-1 DM) 48.5 88.9 39.5 66.6 37.3 60.6 25.8 64.6 1.069 *** *** *** NS NS ** **
Propionic acid (g kg-1 DM) 1.67 3.93 2.33 4.77 2.83 20.4 8.60 10.8 0.221 *** NS *** NS * *** ***
Butyric acid (g kg-1 DM) 6.53 3.57 5.03 4.30 7.20 7.17 6.20 0.00 0.651 NS NS NS NS NS NS NS
Signicant levels: *, ** and *** at p-values< 0.05, 0.01, 0.001, respectively. NS: p ≥ 0.05. s.e.m.: standard error mean; S: Forage effect; W: Wilting effect; A: Additive effect
NA: No additive; DM: Dry matter; NDF: Neutral detergent bre; ME: Metabolizable energy.
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
308
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
309
treated with L. buchneri (p < 0.001). In contrast,
silages inoculated resulted in a marked increased in
the concentration of acetic (p < 0.001) and propi-
onic (p < 0.001) acids. The additive only increased
NH3-N synthesis in F, numerically but not statisti-
cally (S × A, p > 0.05).
Additive treatment increased pH in both for-
ages (A, p < 0.001) while the effect was more clear
in F (S × A, p < 0.01), pH was lower in TF than in F
(p < 0.01). Moreover, the additive showed a strong
inuence on lactic (p < 0.001) and propionic (p <
0.05) acids levels in F.
Wilting appeared to inhibit the effects of L.
buchneri on fermentation parameters because pro-
pionic acid (7.77 vs 12.2 g kg
-1
DM) and NH
3
-N
(18.7 vs 33.4 g NH3 kg-1N) were 40% less of those
produced in direct cut inoculated silages (W × A,
p < 0.001). Besides acetic acid (65.6 vs 74.7 g kg-
1
DM) decreased around 20% (W × A, p < 0.01)
without differences concerning lactic acid produc-
tion (27.1 vs 24.2 g kg-1DM).
Chemical parameters of silages were unaffected
by interaction among forage, additive and wilting
effects, whereas all fermentative characteristics
except butyric acid concentration, were affected by
this triple interaction. In this sense, in TF forages,
with higher ensilability than F, the additive action
was less effective reducing lactic acid (S × W × A, p
< 0.05) and increasing acetic (S × W × A, p < 0.01)
and propionic (S × W × A, p < 0.001) acids. In fava
bean (F), with lower ensilability than TF, the addi-
tive effects were better than TF, especially in direct
cut silages. In the other hand, the additive increases
the ammonium concentration in silages with high
proteolysis such as wilted fava bean silages (S × W
× A, p < 0.05).
Regardless aerobic stability, all the silages
involved in this study remained stable at least 10
days after opening. No heating above room tem-
perature (20 ± 1ºC) was observed during exposure
to air (Table 3). Nevertheless, it should be stressed
that according to the statistical analysis of repeated
Table 3. Changes in temperature during air exposure of silages depending on effects: forage, wilting and additive.
Day
Triticale-Fava bean Fava bean
Direct Wilting Direct Wilting
NA A NA A NA A NA A
1 18.04±0.2318.48±0.21 18.88±0.23 18.84±0.50 19.36±0.24 19.12±0.36 19.38±0.32 18.58±0.29
2 17.10±0.2016.84±0.19 17.56±0.18 17.36±0.40 16.98±0.43 17.80±0.25 17.82±0.27 18.12±0.16
3 18.42±0.1118.98±0.04 18.36±0.05 17.94±0.44 18.34±0.09 18.42±0.13 18.02±0.25 18.70±0.24
4 17.84±0.19 17.80±0.18 18.12±0.11 17.78±0.13 17.96±0.30 17.88±0.13 18.48±0.20 17.96±0.24
5 17.68±0.1917.82±0.17 18.20±0.16 17.92±0.47 18.22±0.08 18.00±0.20 17.94±0.44 17.96±0.33
6 17.62±0.1317.86±0.21 18.10±0.23 18.18±0.36 18.14±0.33 17.82±0.08 17.90±0.19 18.04±0.11
7 18.70±0.2418.10±0.21 18.34±0.09 19.08±0.41 18.28±0.13 18.24±0.27 18.98±0.24 18.86±0.15
8 18.70±0.3218.16±0.24 19.20±0.22 18.78±0.34 18.44±0.27 19.24±0.11 19.06±0.09 18.78±0.27
9 19.16±0.1818.34±0.10 18.90±0.35 19.06±0.28 18.14±0.21 17.90±0.23 18.22±0.33 18.20±0.46
10 19.74±0.1919.60±0.24 19.48±0.22 19.58±0.29 19.36±0.09 18.70±0.39 18.80±0.46 18.70±0.42
NA: No Additive; A: Additive.
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
308
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
309
measures in time, no effects were found with re-
spect to the additive. Marginal differences in nal
temperature were observed when comparing wilt-
ing and direct cut silages after exposure to air.
Figure 1 shows the evolution of pH over time.
pH was signicantly affected by the type of forage
(4.12 vs 4.41 for TF and F, respectively; p < 0001)
and inoculation (4.41 vs. 4.07 for silage with and
without additive, respectively; p < 0001). Further-
more, the forage-additive interaction (p < 0.001)
with the lowest pH values corresponded to TF for-
age without additive (pH = 3.98) and the highest
to F silages with additive (pH = 4.66). The W × A
interaction (p < 0.001) shows that wilting depresses
the L. buchneri effect related to pH evolution after
silages aerobic exposure.
Discussion
The current study provides evidence that adding L.
buchneri to direct-cut forages of triticale-fava bean
and fava bean alone at ensiling increases DM losses.
However, this effect decreases in wilted forages,
probably because the higher dry matter content in
wilted forages decreases the overall activity of L.
buchneri and therefore less acetic acid is produced.
This nding may be explained by the fact that the
addition of L. buchneri could have inhibited the
activity of yeasts that were probably accelerated by
wilting (Nishino and Touno 2005, Oude Elfernick
et al. 2001).
The DM content in wilted silages was clearly
lower after ensiling than before it. Similar effects
were found by Nishino and Touno (2005) on wilted
grass silages, and it was explained by gas losses
during fermentation process. Interaction in DM
content could be explained due to the fact that the
proportional increase in DM in F was higher than
in TF. When F was wilted before ensiling, the ash
content decreased due to leaf loss, while this effect
was the opposite in TF. The results of this study
showed that wilting forage for 24 hours prior to
ensiling reduced lactic and acetic fermentation re-
gardless of the type of forage.
Nishino and Touno (2005) indicated that DM
loss was signicantly increased when L. buchneri
was inoculated into direct-cut materials, in our
study this effect was numerically but not statisti-
cally signicance.
As expected, inoculation with L. buchneri at
ensiling altered the fermentation parameters by
causing an accumulation of acetic and propionic
acids via the metabolism of heterofermentative
Lactobacillus culture. These strains transform wa-
ter soluble carbohydrates into lactic acid at an early
stage of fermentation. This lactic acid is further
transformed into acetic and propionic acids, the
3.80
4.00
4.20
4.40
4.60
4.80
1 2 3 4 5 6 7 8 9 10
Days
TF-D-A F-D-A TF- W - A F-W-A
3.80
4.00
4.20
4.40
4.60
4.80
12345678910
pH pH
Days
TF-D-NA F- D - NA TF-W-NA F-W-NA
Figure 1. Changes in pH during aerobic deterioration of triticale-fava bean (TF) and fava bean (F) silages, ensiled di-
rectly (D) or wilted (W) and pre-treated with (A) or without (NA) Lactobacillus buchneri.
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
310
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
311
combined action of which has antifungal proper-
ties. However, the result of this metabolic process
is an increase in nal pH. In this experiment, the
pH of silages inoculated with L. buchneri was sig-
nicantly higher than that of silages which were not
inoculated. Moreover, the synthesis of acetic and
propionic acids rose during the fermentation proc-
ess with decreasing lactic acid content. Similar re-
sults have been reported with cereal silage by Kung
and Ranjit (2001) using different rate of inoculants;
alfalfa silage by Kung et al. (2003) after 56 days of
ensiling in laboratory silages; whole plant corn si-
lage (Kleinschmit and Kung 2006) or different crop
silages (Ruser and Kleinmans 2005). Nishino and
Touno (2005) have also reported that in direct-cut
grass silages pH was lower than 4.0 and inoculants
treated grass silages had pH values around 4.5. In
contrast, Wróbel (2008) also added commercial in-
oculants containing homo and heterofermentative
lactic acid bacteria to grass forages but found no
increases in the pH values of the resulting silage.
When forages were inoculated at ensiling with
L. buchneri, the nal silages contained lower con-
centrations of lactic acid than untreated forages.
Conversely, a marked increase in the concentra-
tions of acetic and propionic acid were observed.
In a previous study, Combs and Hoffman (2001)
reported that silages inoculated with an effective
dose (4 to 6 × 10 5 cfu g-1 of fresh material) of L.
buchneri had higher concentration of acetic acid
and lower levels of lactic acid than untreated silag-
es. Similar effects were found by Kung et al. (2003)
without differences among doses of inoculants.
The ammonia-N concentration of fava bean
silages were increased by inoculation treatment in
agreement with Kung et al. (2003) working with
alfalfa silages.
In the current study, all silages had very high
acetic acid concentrations, which have contributed
to the high aerobic stability of them. Although the
amount of acetic acid obtained as a result of the W
× A interaction (65.6 g kg-1 DM) did not reach the
values obtained in direct cut silages with additive
(74.7 g kg-1 DM), it did remain signicantly higher
than in direct cut silages without additive (42.9 g
kg-1 DM). Combs and Hoffman (2001) related the
benecial impact of L. buchneri to the production
of acetic acid. In fact, aerobic stability could be
improved because acetic acid inhibits growth of
specic species of yeast that are responsible for
heating upon exposure to air. Similar results have
also been reported by Taylor et al. (2002) and with
those obtained by Nishino and Touno (2005) with
Italian ryegrass and Festilolium.
Critics of using heterolactic acid bacteria as si-
lage inoculants suggest that high concentrations of
acetic acid in silages have had depressing effects on
dry matter intake (DMI) for lactating cows, but at
this time it is not clear whether enough acetic acid
will be produced in silages treated with L. buchneri
to affect feed intake (Combs and Hoffman 2001).
Recently, Wróbel (2008) reported that the additive
treatment using bacterial inoculants containing
homo and heterofermentative lactic acid bacteria,
in pre-wilted grass silages (about 450 g kg-1 DM),
and with very low concentrations of acetic acid
(ranged between 10.1–14.5 g kg
-1
DM), did not
affect silage intake and daily live weight gain of
heifers.
Inoculation with L. buchneri in forages to make
laboratory silages did not affect aerobic stability
after 10 days of air exposure. The chosen tempera-
ture for the experiment (20 + 1ºC) may possibly be
too low. In fact, during this study period, no heat-
ing above room temperature was observed in the
silages. These results are in agreement with those
obtained by Taylor et al. (2002), who reported that
the temperature of barley silages from laboratory
silos did not rise after exposure to air for seven
days, and by Combs and Hoffman (2001), who
explained that L. buchneri is unlikely to improve
silage quality in situations where silage has a his-
tory of being aerobically stable at feed out. Our
location was probably optimum to maintain silage
under stable conditions after exposure to air.
Conclusions
The results of this study show that the association
of triticale with fava bean for ensiling improves
ensilability characteristics when compared with
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
310
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 302–312.
311
fava bean alone. In fact, wilting forage for twenty-
four hours before ensiling also increases the fer-
mentability coefcient. Furthermore, this wilting
period is sufcient to avoid efuent losses during
the fermentation process and to reduce ammonia
nitrogen production without changes in CP content.
Lactobacillus buchneri promotes changes in
the amount of fermentation end products, leading
to acetate and propionate from lactic acid via the
metabolism of heterofermentative biological cul-
tures.
Wilting appeared to depress, but not inhibit
the effects of Lactobacillus buchneri on the fer-
mentation parameters. The inhibitory effects of
Lactobacillus buchneri on aerobic stability were
not conrmed in this study.
Acknowledgements. The authors wish to thank
the eld and laboratory staff of the Animal Nu-
trition Grassland and Forages Department at the
Regional Institute for Research and Agro-food
Development (SERIDA) for their assistance.This
study was nancially supported by INIA project
number RTA2006-00082
References
Adesogan, A.T., Salawu, M.B. & Deaville, E.R. 2002. The
effect on voluntary feed intake, in vivo digestibility and
nitrogen balance in sheep of feeding grass silage or pea–
wheat intercrops differing in pea to wheat ratio and matu-
rity. Animal Feed Science and Technology 96: 161–173.
AOAC. 1984. Ofcial Methods of Analysis Ed: Association
of Ofcial Agricultural Chemist. 14th ed. S. Williams (ed).
Association of Ofcial Analytical Chemistry, Arlington,
Virginia, USA. 1141 p.
ARC. 1980. The nutrient requirements of ruminant live-
stock. Commonwealth Agric. Bureaux. England. 351 p.
Argamentería, A., Roza, B. de la, Martínez-Fernández, A.
& Vicente, F. 2004. Yield of intercropped of triticale and
fava bean according to their developing state. Prelimi-
nary results. In: Lüscher, A., Jeangros, B., Kessler, W.
Huguenim O., Lobsiger M., Millar, N & Suter, D. (eds).
Land Use Systems in Grassland Dominated Regions.
Proceedings of 20th General Meeting of the European
Grassland Federation in June in Luzern, Switzerland.
Zurich. Swiss Grassland Society. p. 101.
Arzani, H., Zohdi, M., Fish, E., Zahedi Amiri, G.H., Nikkhah,
A. & Wester, D. 2004. Phenological effects on forage
quality of ve grass species. Rangeland Ecology & Man-
agement. 57(6): 624–629.
Combs, D.K. &. Hoffman, P.C. 2001. Lactobacillus buch-
neri for silage aerobic stability. In: 5th Arlington Dairy
day Proceedings. Dairy Science Department. Universi-
ty of Wisconsin, Madison, USA. p. 28–30.
Fraser, M.D., Fychan, R. & Jones, R. 2001. The effect of
harvest date and inoculation on the yield, fermentation
characteristics and feeding value of forage pea and
eld bean silages. Grass Forage Science 56: 218–230.
Hoffman, W S. 1937. A rapid photoelectric method for the
determination of glucose in blood and urine. Journal of
Biological Chemistr y 120: 51-55.
Kleinschmit, D.H. & Kung L. 2006. The effects of Lacto-
bacillus buchneri 40788 and Pediococcus pentosaceus
R1094 on the fermentation of corn silage. Journal of
Dairy Science 89: 3999–4004.
Kung, L. & Ranjit, N.K. 2001. The effect of Lactobacillus
buchneri and other additives on the fermentation and
aerobic stability of barley silage. Journal of Dairy Sci-
ence 84: 1149–1155.
Kung, L., Taylor, C.C., Lynch, M.P. & Neylon, J. M. 2003.
The effect of treating alfalfa with Lactobacillus buchner i
40788 on silage fermentation, aerobic stability, and nu-
tritive value for lactating dairy cows. Journal of Dairy
Science 86: 336–343.
Lloveras-Vilamanya, J. 1987. Forage production and qual-
ity of several crop rotation and pastures in northwestern
Spain. Grass Forage Science 42: 241–247.
McEniry, J., O’Kiely, P., Clipson, N.J.W., Forristal, P.D.
& Doyle, E.M. 2007. Manipulating the ensilage of wilt-
ed, unchopped grass through the use of additive treat-
ments. Irish Journal of Agricultural and Food Research
46: 77–91.
Martínez-Fernández, A., Vicente Mainar, F., Roza Delga-
do, B., & Argamentería Gutiérrez, A. 2008. Ensilado de
haboncillos en monocultivo o asociado a triticale. Pas -
tos. XXXVIII (1), 75-90. (In Spanish).
Martínez-Fernández, A. & de la Roza, B. 1997. Poder con-
taminante de los euentes de ensilados de hierba y rai-
grás italiano según materia seca del forraje inicial y adi-
tivo utilizado. In: Proceedings of XXXII Reunión Cientí-
ca de la Sociedad Española para el Estudio de los Pas-
tos in May in Sevilla, Spain. Sevilla: Junta de Andalucía,
p. 199-204. (In Spanish).
Moran, J. P., Weinberg, Z. G., Ashbell, G., Hen, Y. & Owen,
T. R. 1996. A comparison of two methods for the evalu-
ation of the aerobic stability of whole crop wheat silage.
In: Proceedings of. XI International Silage Conference.
Aberystwyth, UK, p. 162–163.
Nishino, N. & Hattori, H. 2005. The use of Lactobacillus
buchneri. Inoculation to decrease ethanol and 2,3-bu-
tanediol production in whole crop rice silage. In: Park,
R.S. & Stronge, M.D. (eds). Silage Production and Uti-
lisation. Proceedings of the 14th International Silage
Conference, Belfast, Northern Ireland. Wageningen Ac-
ademic Publishers, The Netherlands. p. 261.
Nishino, N. & Touno, E. 2005. Ensiling characteristics and
aerobic stability of direct-cut and wilted grass silag-
es inoculated with Lactobacillus casei or Lactobacillus
buchneri. Journal of the Science of Food and Agricul-
ture 85: 1882–1888.
Oude Elfernick, S.J.W.H., Krooneman. J., Gottschal, J.C.,
Spolestra, S.F., Faber, F. &, Driehuis, F. 2001. Anaero-
AGRICULTURAL AND FOOD SCIENCE
Martínez-Fernández. et al. Wilting and inoculation effects on cereal-legume silage characteristics
312
bic conversion of lactic acid to acetic acid and 1,2-pro-
panediol by Lactobacillus buchneri. Applied and Envi-
ronmental Microbiology 67:125–132.
Pedrol, N. & Martínez, A.2003. Asociaciones cereal-le-
guminosa en rotaciones forrajeras ecológicas de zo-
nas húmedas. In: Robles-Cruz, A.B. et al. (eds). Pas-
tos, desarrollo y conservación. Ptoccedings of XLIII Re-
unión Cientíca de la Sociedad Española para el Estu-
dio de los Pastos. Junta de Andalucía, Spain, p. 131–
136. (In Spanish).
Playne, M. J. & McDonald, P. 1966. The buffering constit-
uents of herbage and of silage. Journal of the Science
of Food and Agriculture 17: 264–268.
Reis, R.A., Almeida, E.O., Siqueira, G.R., Bernardes,
T.F., Janusckiewiez, E.R. & Roth, M.T.P. 2005. Microbi-
al changes and aerobic stability in high moisture maize
silages inoculated with Lactobacillus buchneri. In: Park,
R.S. & Stronge, M.D. (eds). Silage Production and Uti-
lisation. Proceedings of the 14th International Silage
Conference, Belfast, Northern Ireland. Wageningen Ac-
ademic Publishers, The Netherlands. p. 223.
Riveros, E. & Argamentería, A. 1987. Métodos enzimáticos
de la predicción de la digestibilidad in vivo de la mate-
ria orgánica de forrajes. I. Forrajes verdes. II. Henos. III
Ensilados y pajas. Avances en Producción Animal. 12:
49–75. (In Spanish).
Ruser, B. & Kleinmans, J. 2005. The effect of acetic acid
on the aerobic stability of silages and on intake. In: Park,
R.S. & Stronge, M.D. (eds). Silage Production and Uti-
lisation. Proceedings of the 14th International Silage
Conference, Belfast, Northern Ireland. Wageningen Ac-
ademic Publishers, The Netherlands. p. 231.
SAS. 1999. SAS Institute, SAS/STATTM, 1999. User’s
Guide. Release 8.2. SAS Institute, Inc. 10 Cary, NC.
Schmidt, L., Weissbach, F., Wemecke, K-D & Hein, E.
1971. Erarbeitung von Parametern für die Vorhersage
und Steuerung des Gärungsverlaufes bei der Grünfut-
ter silierung. Forschungsbericht, Oskar-Kellner- Institut
für Tierenährung Rostock. (In German).
Taylor, C.C., Ranjit, N.J., Mills, J.A., Neylon, J.M. & Kung,
L. 2002. The effect of treating whole plant barley with
Lactobac illus buchneri 40788 on silage fermentation,
aerobic stability, and nutritive value for lactating dairy
cows. Journal of Dairy Science 85: 1793–1800.
Vanotti, M.B., Bundy, L.G. and Peterson, A.E. 1997. Nitro-
gen fertilization and legume-cereal rotation effects on
soil productivity and organic matter dynamics in Wis-
consin, In: Paul, E.A. et al. (eds). Soil organic matter in
template agrosystems. CRC Press, Boca Raton, Flori-
da, USA, p.105–120.
Van Soest, P.J., Robertson, J.B. & Lewis, B.A. 1991. Meth-
ods of dietary ber, neutral detergent ber, and nonstarch
polysaccharides in relation to animal nutrition. J ournal
of Dairy Science 74: 3583–3597.
Weissbach, F. 1999. Consequences of grassland de-inten-
sication for ensilability and feeding value of herbage,
in: Landbauforschung Völkenrode, (ed.). Contributions
of Grassland and Forage Research to the development
of Systems of Sustainable Land Use. Institute of Crop
and Grassland Science of the Federal Agriculture Re-
search Centre (FAL), Braunschwig, Germany, p. 41–53.
Wolford, M.K. 1984. The silage fermen tati on, Marce l
Dekker, INC, New York, USA. 350 p.
Wróbel, B. 2008. Quality and aerobic stability of big-
bale silage treated with bacterial inoculants contain-
ing Lactobacillus buchneri. Grassland Science in Eu-
rope 13: 651-653.
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
313
© Agricultural and Food Science
Manuscript received August 2009
Surface water ponding on clayey soils managed
by conventional and conservation tillage in boreal
conditions
Laura Alakukku1,2*, Antti Ristolainen1, Ilkka Sarikka1 and Timo Hurme2
1MTT Agrifood Research Finland, Plant Production Research, 31600 Jokioinen, Finland
2University of Helsinki, Department of Agricultural Sciences, PO Box 28,00014 University of Helsinki, Finland,
3MTT Agrifood Research Finland, Services Unit, 31600 Jokioinen, Finland
*email: laura.alakukku@helsinki.
Surface water ponding and crop hampering due to soil wetness was monitored in order to evaluate the ef-
fects of conservation tillage practices and perennial grass cover on soil inltrability for ve years in situ
in gently sloping clayey elds. Thirteen experimental areas, each having three experimental elds, were
established in southern Finland. The elds belonged to: autumn mouldboard ploughing (AP), conservation
tillage (CT) and perennial grass in the crop rotation (PG). In the third year, direct drilled (DD) elds were
established in ve areas. Excluding PG, mainly spring cereals were grown in the elds. Location and sur-
face area of ponded water (in the spring and autumn) as well as hampered crop growth (during June−July)
were determined in each eld by using GPS devices and GIS programs. Surface water ponding or crop
hampering occurred when the amount of rainfall was clearly greater than the long-term average. The mean
of the relative area of the ponded surface water, indicating the risk of surface runoff, and hampered crop
growth was larger in the CT elds than in the AP elds. The differences between means were, however, not
statistically signicant. Complementary soil physical measurements are required to investigate the reasons
for the repeated surface water ponding.
Key-words: crop cover, direct drilling, grass cover, ploughing, stubble cultivation, zero tillage
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
314
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
315
Introduction
In boreal areas, growing annual crops by using con-
ventional tillage practice leaves the soil surface bare
outside the growing season, i.e., from September to
May. Bare soil surface increases the risk of erosion
because, especially in the autumn the tilled soil has
fresh, unstable shearing face, and is vulnerable to
rainfall and ponding water. Out of different soils in
Finland, clayey and silt soils have been evaluated to
be the most susceptible to water erosion (Ministry
of Agriculture and Forestry 2004). Relevant to this,
the main part of the surface runoff, erosion and
phosphorus loading from arable clayey elds into
surface waters occurs outside the growing season
during the autumn and spring runoffs (e.g. Turtola
and Jaakkola 1995, Puustinen et al. 2007).
Field practices that decrease or eliminate the
need for tillage and maintain crop residue on soil
surface are advocated worldwide due to their po-
tential benets in terms of erosion control. Conser-
vation autumn tillage practices, including shallow
stubble tillage and zero tillage, and perennial grass
cover are known to be advantageous regarding
erosion control in clayey soils also in boreal areas
as compared to conventional autumn ploughing
(Skøien 1988, Turtola and Paajanen 1995, Ulén
1997, Koskiaho et al. 2002, Puustinen et al. 2005).
However, contrary to the results above, Turtola et
al. (2007) found that the erosion from shallow au-
tumn stubble cultivated clayey soil (slope 2%) was
equally high to that from ploughed soil.
Changing tillage practice from mouldboard
ploughing to a depth of 20–25 cm to shallow stub-
ble tillage to a depth of 5–15 cm or zero tillage,
in which only the coulters of the sowing machine
disturb the soil surface, creates gradual changes in
soil physical properties relevant to eld hydrology.
One sign of the problems in the eld’s hydrology
is free water ponding on soil surface. Free water
tends to accumulate on the surface when the local
rainfall rate exceeds the soil inltrability. Surface
water ponding increases the risk of surface runoff,
erosion and nutrient losses from arable elds. In
fact, the share of the surface runoff from the to-
tal runoff was found to increase from 8–42% to
36–82% when the autumn ploughing was replaced
by shallow stubble cultivation or zero tilled stub-
ble or perennial grass cover on a gently sloping
clayey soil (Turtola and Jaakkola 1995, Turtola
et al. 2007). The excess of surface water on soil
surface and soil wetness due to the low inltration
rate and saturated hydraulic conductivity also en-
hance gaseous nitrogen loss (e.g. Ball et al. 2008)
and hamper the crop growth due to anaerobic soil
conditions, which further decrease the crop yield
and the nutrient uptake (Pitkänen 1994, Alakukku
et al. 2009). In addition, the low water inltration
into the soil delays eld operations due to lower
soil bearing capacity, which increases the risk of
soil puddling and compaction due to eld trafc
(e.g. Hamza and Anderson 2005). Especially at
and gently sloping clayey soils are sensitive to sur-
face water ponding.
Good water inltration capacity and high hy-
draulic conductivity of wet soil prevent surface
water ponding. For reduced or zero tillage, greater
inltration capacity and hydraulic conductiv-
ity have been reported compared to conventional
tillage (Arshad et al. 1999, McGarry et al. 2000,
Buczko et al. 2006). However, in several cases
also lower hydraulic conductivity and inltration
rate under conservation tillage in ne texture soils
were found (Gantzer and Blake 1978, Tebrügge
and Düring 1999, Lipiec et al. 2006, Withers et al.
2007). On the other hand, some studies have shown
nonsignicant differences in hydraulic properties
under different practices (e.g. Starr 1990) or in the
variation of inltration properties with time (Dunn
and Philips 1991).
Numerous eld experiments have been con-
ducted to evaluate the inuence of conservation
tillage on soil physical properties and crop growth
on the plot scale. However, few studies have been
carried out to investigate the spatial and temporal
variation of these issues on the eld scale. Changes
in the physical properties of the soil, critical to soil
hydrology and crop growth, are important because
they clearly affect practical farming and the poten-
tial of conservation tillage to reduce erosion and
nutrient loading. There is interest towards explor-
ing the effects of different tillage practices on the
eld scale because a clear increase in the adop-
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
314
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
315
tion of conservation tillage on clayey soil areas
has been documented since Finland joined the EU
in 1995. We addressed the question in southern
Finland by monitoring the surface water ponding
and crop growth in situ three times a year for ve
years in differently tilled gently sloping clayey soil
elds. There we used surface water ponding and
crop hampering due to soil wetness as indicators
for a low water inltration capacity and saturated
hydraulic conductivity of wet soil.
Material and methods
Experimental areas and tillage
treatments
In 2001, 13 experimental areas were established on
the elds of private farmers in southern Finland.
The main selection criteria were that the eld was
clayey soil and that a group of three closely located
elds with different tillage intensities was avail-
able (Fig. 1). Moreover, the intention was that the
soil type, slope and drainage system of the closely
located elds were similar to each other. The elds
were subsurface drained and the mean slope was
2% or less except for the 4% slope of the ploughed
eld in area 6.
The topsoil properties of the elds at the begin-
ning of the study are shown in Table 1. Yli-Halla
et al. (2000) and Lilja et al. (2006) have classied
the clayey soils of this area mainly as Vertic Cam-
bisols, Eutric Cambisols and Dystic Cambisols
according to the FAO (1998). The data of soil the
properties was collected from farmers who had
the soil analyses made according to the common
protocol in Finland,i.e., by estimating the soil type
and organic matter content, determined by a human
sensory test. Based on the pH and macronutrient
determinations (data not shown), the fertility of the
elds was close to the average in Finland.
60º
Ploughed Stubble
annually cultivated
Spring Spring
cereals cereals
Crop rotation Direct
including drilling
perennial Spring
grass cereals
23º
Fig. 1. Location of the ex-
periment al areas in south ern
Finland in 2001−2005. In each
area, three closely situated elds
were established in 2001. Direct
drilled elds were included in
the study in 2004.
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
316
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
317
Table 1. Soil type and organic matter content (SOM) for the topsoil layer of 0–20 cm of the experimental elds in the
beginning of the study in 2001. Areas 1−13 are located from west to east in Fig. 1. Fields on experimental areas: AP =
autumn ploughing, CT = conservation tillage, PG = perennial grass (green fallow, dried hay or hayseed) included in the
crop rotation, DD = direct drilling (zero tillage).
Area
Soil type SOMa)
AP CT PG DD b) AP CT PG DD
1 Sandy clay Sandy clay Sandy clay - rm rm m -
2 Clayey ne sand Clayey ne sand Clayey ne sand Sandy clay m vm m vm
3 Sandy clay Sandy clay Sandy clay m m/rm m
4 Clay loam Clay loam Sandy clay m m m
5 Sandy clay Clay loam Silt Clay loam m m vm rm
6 Loam Loam Loam m m m
7 Sandy clay Clay loam Clay loam rm m m
8 Gyttja clay Gyttja clay Clay loam m m rm
9 Silt Clay loam Silt m m m
10 Clay loam Clay loam Clay loam - rm rm rm -
11 Silty clay Silty clay Clay loam m m rm
12 Silty clay Silty clay Silty clay rm m/rm rm
13 Sandy clay Sandy clay Sandy clay Silt m m m m
aSOM for vm less than 0.03 g g-1, m: 0.03−0.059 g g-1, and rm: 0.06−0.119 g g-1
b -: data not available; empty cells: no DD eld in the area
In 2001, three experimental elds with different
tillage intensities were established in each selected
area. The treatments of experimental elds were:
annual autumn mouldboard ploughing (AP), con-
servation tillage (CT), and temporal zero tillage
including perennial grass (green fallow, dried hay
or hayseed) that was included in the crop rotation
(PG). In 2004, annually direct drilled (DD, zero
tillage) elds in areas 1, 2, 5, 10 and 13 were in-
cluded in the study because direct drilling of spring
cereals started to increase rapidly in Finland dur-
ing the study. In the same year, two additional CT
elds (in areas 5 and 9) were included in the study.
The CT treatment represented annual autumn stub-
ble cultivation with a cultivator, a s-tine harrow,
a rotary spade harrow or a disc harrow. In years
2001–2003, the plant cover for most of the PG
elds was perennial grass.
The original plan was that the AP and CT treat-
ments would be carried out in the same elds in
years 2000–2004. The CT treatment had been
started in 1999 in areas 1, 8, and 12, and started
in 2001 in areas 7 and 11. In other areas, CT had
been carried out for several years before the study.
In areas 2 and 13, the AP treatment was supposed
to be established by ploughing conservation tillage
elds but the farmers decided to continue the cur-
rent tillage practices until autumn 2003 (Table 2).
After the rainy summer and autumn of 2004, some
of the CT elds were ploughed (Table 2).
Cultivated crops and weather conditions
Mainly spring sown crops barley (Hordeum vulgare
L.), wheat (Triticum aestivum L.), oats (Avena sativa
L.) and spring oilseed rape (Brassica rapa L.) were
cultivated in the annually tilled elds. Annual crops
were grown in the same elds ve years before
the present study, excluding the AP elds in areas
2 and 12, and the CT elds in areas 8 and 12, in
which perennial grass was grown in rotation. Lin-
seed (Linum usitatissimum) was grown in area 2 in
2002 (CT), 2003 (AP, CT, PG) and 2004 (DD), and
camelina (Camelina sativa) in area 1 (AP) in 2005.
Winter cereals were grown occasionally (Table 2).
During the rst three years, perennial grass was
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
316
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
317
cultivated in the PG elds. In areas 5, 7, 11, 12 and
13, perennial grass was grown in the PG elds also
in years 2004 and 2005.
The elds were cultivated following common
farming practices in Finland. For spring sown crops
it usually meant autumn tillage (AP: ploughing to a
depth of 20–25 cm, CT: stubble cultivation (10–15
cm), DD: zero tillage), seedbed preparation (AP,
CT) before sowing, fertilizing and sowing with a
combined drill (AP, CT, DD), herbicide spraying
in summer, and harvesting. The PG treatment was
cut (green fallow) or harvested (for hay or seed)
annually.
The local weather conditions and soil moisture
status were evaluated by utilizing the data of the
closest weather stations of the Finnish Meteoro-
logical Institute and the closest groundwater sta-
tions of the Finnish Environment Institute (Fig.
2). The soil moisture status was obtained based on
the depth from the soil surface to the groundwater
table. During the experimental period, the mean
temperature was close to the long-term average or
higher in April, May, and from July to Septem-
ber. Except year 2002, the mean temperature of
June was lower than the long-term average (data
not shown). The precipitation was clearly less than
that on average, i.e. in years 1971−2000, in autumn
2002 and in the beginning of the year 2003 (Fig.
2a). Thus, the groundwater table was clearly lower
than the average from the end of the year 2002
to the beginning of the year 2004 (Fig. 2b). Even
though the annual precipitation in years 2004 and
2005 was larger than the long-term average, the
beginning of the year 2004 (January–April) was
again dry. The growing season (May–August) of
2004 was exceptionally rainy and the precipita-
Table 1. Soil type and organic matter content (SOM) for the topsoil layer of 0–20 cm of the experimental elds in the
beginning of the study in 2001. Areas 1−13 are located from west to east in Fig. 1. Fields on experimental areas: AP =
autumn ploughing, CT = conservation tillage, PG = perennial grass (green fallow, dried hay or hayseed) included in the
crop rotation, DD = direct drilling (zero tillage).
Area
Soil type SOMa)
AP CT PG DD b) AP CT PG DD
1 Sandy clay Sandy clay Sandy clay - rm rm m -
2 Clayey ne sand Clayey ne sand Clayey ne sand Sandy clay m vm m vm
3 Sandy clay Sandy clay Sandy clay m m/rm m
4 Clay loam Clay loam Sandy clay m m m
5 Sandy clay Clay loam Silt Clay loam m m vm rm
6 Loam Loam Loam m m m
7 Sandy clay Clay loam Clay loam rm m m
8 Gyttja clay Gyttja clay Clay loam m m rm
9 Silt Clay loam Silt m m m
10 Clay loam Clay loam Clay loam - rm rm rm -
11 Silty clay Silty clay Clay loam m m rm
12 Silty clay Silty clay Silty clay rm m/rm rm
13 Sandy clay Sandy clay Sandy clay Silt m m m m
aSOM for vm less than 0.03 g g-1, m: 0.03−0.059 g g-1, and rm: 0.06−0.119 g g-1
b -: data not available; empty cells: no DD eld in the area
Table 2. Tillage of the elds (AP = autumn ploughing, CT = conservation tillage, PG = perennial grass included in the
crop rotation, DD= direct drilling) of experimental areas in autumns 2000−2004. Observations were carried out in spring,
summer and autumn of years 2001−2005. Tillage: P = ploughing, SC = stubble cultivation, NTG = no tillage, grass cov-
er, NTS = no tillage, stubble cover.
Year Tillage Area
1 2 3 4 5 6 7 8 9 10 11 12 13
2000 AP SC NTS PaSCPPPPPPPPSC
CT SC SC SC SC SC SC P SC SC SC P SC SC
PG NTG NTG P NTG NTG NTG NTG NTG SC P NTG NTG NTG
2001 AP P NTS P P P P P P P P P P NTS
CT SC NTS SC NTS SC SC SC SC SC SC SC SCaNTS
PG NTG NTG NTG NTG NTG NTG NTG NTG P NTG NTG NTG NTG
2002 AP P NTS P P P P P P Pa/F P SCb SCbSCb
CT SC NTS SC NTSbSC SC SCb,c SC SC SC SC SC SC
PG NTG NTS NTG NTG NTG NTG NTG NTG P SC NTG NTG NTG
2003 AP P P NTG P P P P P P P P P
CT SC P P SC SC SC SC SC SC SC SC SC
PG NTG PaP NTG P NTG Pa P NTG NTG NTG NTG
DD NTS NTS NTS NTS NTSa
2004 AP P P NTG P P P P SC P P
CT SC NTS P SC P NTS SC/P P SC NTS
PG PaFd/NTG P NTG P NTG SC P NTS/P NTG NTG NTG
DD NTS NTS NTS NTS NTS
asown for winter wheat or rye
bsoil was too dry for plowing or stubble cultivation in autumn
cspring tillage
dF = bare, stubble or annual grass fallow
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
318
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
319
0
100
200
300
400
500
600
700
800
900
1000
2
001
2
0
02
2
0
03
2
004
20
05
1
9
71
-00
2
001
2
0
02
2
0
03
2
004
2
0
05
19
71
-00
20
01
20
02
2
0
03
2
0
04
2
0
05
19
71
-00
2
0
01
20
02
2
0
03
2
0
04
2
005
19
71
-00
2
001
20
02
20
03
2
004
2
005
1
971
-00
20
01
20
02
20
03
2
0
04
2
0
05
1
9
71
-00
I-IV V-VIII IX-XII
Turku Piikkiö Jokioinen Hyvinkää Porvoo Anjala
a)
b)
-160
-120
-80
-40
0
40
80
120
Month and year
Perniö Karkkila Siuntio Lammi Elimäki Valkeala Mean of sites
Annual value
─
long-term mean, cm
2
0
02
2
0
03
2
004
20
05
1
9
71
-00
2
001
2
0
02
2
0
03
2
004
2
0
05
19
71
-00
20
01
20
02
2
0
03
2
0
04
2
0
05
19
71
-00
2
0
01
20
02
2
0
03
2
0
04
2
005
19
71
-00
2
001
20
02
20
03
2
004
2
005
1
971
-00
20
01
20
02
20
03
2
0
04
2
0
05
1
9
71
-00
I-IV V-VIII IX-XII
2
0
02
2
0
03
2
004
20
05
1
9
71
-00
2
001
2
0
02
2
0
03
2
004
2
0
05
19
71
-00
20
01
20
02
2
0
03
2
0
04
2
0
05
19
71
-00
2
0
01
20
02
2
0
03
2
0
04
2
005
19
71
-00
2
001
20
02
20
03
2
004
2
005
1
9 -00
20
01
20
02
20
03
2
0
04
2
0
05
1
9
Year
Precipitation, mm
I-IV V-VIII IX-XII
4 8 12 4 8 12 4 8 12 4 8 12 4 8 12
Fig. 2. (a) Annual precipitation as a sum of three periods: January to April (I–IV), May to August (V–VIII) and September
to December (IX–XII) of Turku (60°31’ N, 22°15’ E), Piikkiö (60°26’ N, 22°31’ E), Jokioinen (60°49’ N, 23°31’ E),
Hyvinkää (60°38’ N, 24°51’ E), Porvoo (60°23’ N, 25°39’ E) and Anjala (60°41’ N, 26°47’ E) meteorological stations of
the Finnish Meteorological Institute in 2001–2005, and the mean of years 1971–2000 according to Drebs et al. (2002).
(b) The variation of the distance to the groundwater table from the soil surface at Perniö (60°12’ N, 23°07’ E), Karkkila
(60°32’ N, 24°12’ E), Siuntio (60°08’ N, 24°13’ E), Lammi (61°05’ N, 25°00’ E), Elimäki (60°43’ N, 26°27’ E) and
Valkeala (60°56’ N, 26°48’ E) in 2001–2005. The difference between the monthly and long-term average values is pre-
sented (annual data from the Finnish Environment Institute and long-term average from Soveri et al. (2001)).
tion was 38−100% higher than on average (Fig.
2a). In 2004, September and December were also
rainy. The distribution of rainfall was uneven, and
in January 2005, the precipitation was larger by
82−147% than on average. In the growing season
of 2005, only the precipitation of August was clear-
ly larger (by 70−130%) than the long-term average.
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
318
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
319
Field area covered or hampered by
ponded water
The share of the eld area covered by ponded water
was monitored in the springs (before tillage) and
autumns of 2001–2005 except in autumn 2002 when
the soil was very dry (Fig. 2b). The observations
were performed outside the growing season because
in Finnish conditions most of the surface runoff has
been found to occur in the spring and autumn (Tur-
tola and Jaakkola 1995, Puustinen et al. 2007). The
ground with a uniform cover of water was classied
as a puddle area, and the ground having water in the
depressions of rough soil surface was classied as
a depressional water area. In springs and autumns
of 2001–2003, both puddle and depressional water
areas were observed. In 2004–2005, only the areas
covered by puddles were recoreded.
In the growing seasons of 2001 and 2003–2005,
the proportion of the eld areas hampered by soil
wetness were determined in June–July before the
spring sown crop came into ear, because the plants
are sensitive to excess wetness in the early devel-
opmental stages (Stẹpniewski and Łabuda 1989).
Year 2004 was exceptional due to excess wetness,
and the observations were made therefore later, af-
ter the crop had ripened. If heavy rains and surface
water ponding were observed in any experimental
area, the size of the area where crop hampered due
to soil wetness was determined based on the color/
ripening differences (from light green to yellow
color/forced ripening in an overwet growing place
compared to dark green/still growing crop in a per-
meable place). The places covered or hampered
by surface water were observed by walking across
the eld from bottom headland to upper headland
with a distance of 40−50 m between the contiguous
routes. For consistency, only two persons made the
eld observations, the rst person in years 2001–
2003 and the second one in years 2004–2005.
The location and surface area of ponded wa-
ter and hampered crop was determined with GPS
devices by saving the readings given by the de-
vice on the edge of the places that were covered
by hampering or surface water. The eld data was
corrected after recordings by using the differential
correction which typically yields the accuracy of
0.5–1 m. The eld surveys were carried out with
Trimble ProXR (2001−03, Trimble Navigation
Ltd.), Garmin eTrex (only spring 2003, Garmin
Ltd.) and Trimble GeoXT (2004−05, Trimble Navi-
gation Ltd.) GPS receivers. The eld data was ana-
lyzed and stored using GIS programs (Pathnder
2.70 (2001−03), Pathnder ofce 3.10 (2004−05)
(Trimble Ltd.) and ArcView 3.2 (ESRI Inc.)). The
ratio of the surface area with water ponded on
ground or the area with hampered crop growth to
the total eld area was calculated.
Statistical analyses
The results of years 2001–2003 and of years 2004–
2005 were analyzed separately because only during
the rst three years, the AP, CT and PG elds were
tilled according to the original plan. The results of
areas 2 and 13 were, however, not included in the
data because the elds intended for AP were not
mouldboard ploughed (Table 2). The results from
years 2001–2003 consist of the total relative areas
covered by puddles and depressional water with
respect to the total eld area. For each year, the re-
sults of different observation times (spring, growing
season, autumn) were treated separately. The data of
spring 2001 was analyzed as a randomized complete
block design. In this analysis the experimental area
was considered as a blocking factor and the tillage
as a treatment factor. The analysis was performed
using a nonparametric simulation based approach
with simulation size 100000 (Berry 1997). The
nonparametric method was used because of the
low proportions of the observed surface ponded
area. For the other observations in 2001–2003, the
surface water ponding was detected only in some
elds due to the drought, and the data was not
analyzed statistically.
In 2004, six direct drilled and two additional
conservation tillage elds joined the study (Table
2). Some of the farmers also changed the tillage
practice, especially after the rainy growing season
of 2004 (Fig. 2, Table 2). Therefore the results of
years 2004 and 2005 were analyzed together, and
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
320
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
321
only the results of elds having the same tillage
treatment in both years (see Table 2) were included
in the data. The results of the relative area of pud-
dles to the total eld area were analyzed by uti-
lizing a randomized complete block design with
repeated measurements. The tillage treatment was
considered as the treatment factor, the experimen-
tal area as the blocking factor, and the year as the
repeated measure. The individual elds within each
area and the tillage treatment constituted the ex-
perimental unit. Thus the statistical model became:
Y
jklmj
=μ+area
i
+tillage
j
+area×tillage
ij
+eld
k(ij)
+year
l
+
area×yearil+tillage×yearjl+area×tillage×yearijl+εijklm
(1)
In equation (1), μ is a constant. The tillage
j
,
year
l
and tillage×year
jl
are xed effects. The area
i
,
,
area×tillage
ij
,
area×year
il
and
area×tillage×year
ijl
are random effects. The eld
k(ij)
is also a random
effect and it identies an individual eld within
area i and tillage j. The εijklm is the error term. The
random effects area
i
,
area×tillage
ij
,
area×year
il
,
area×tillage×yearijl, eldk(ij) and εijklm were all as-
sumed to be mutually independent and normally
distributed with zero means and variances σ2
a, σ2
at,
σ
2
ay
, σ
2
aty
, σ
2
f
and σ
2
ε
, respectively (Kuehl 2000).
The arcsine transformation was used to normalize
the skewed data. The estimated means were then
transformed back to the original scale of measure-
ment. REML was used as the estimation method
and degrees of freedom were calculated using the
Kenward-Roger method (Kenward and Roger
1997). Modelling was performed with the MIXED
procedure of SAS version 9.1.3 (SAS Institute Inc.,
Cary, NC, USA).
Results
Surface water ponding
An example of the results from one experimental
area is given in Fig. 3. Table 3 shows the results for
the share of the total area of ponded water during the
period of 2001– 2005 (puddles and depressional wa-
ter in years 2001–2003, and puddles in 2004–2005).
During the period from autumn 2001 to spring 2004,
the mean share of the ponding area was larger in
stubble cultivated elds than in ploughed or grass
Ploughed
Stubble
cultivated
Perennial
grass
Puddle
Depressional
water
Ploughed
Stubble
cultivated
Perennial
grass
Puddle
Depressional
water
Fig. 3. Areas covered by puddles
and depressional water in the elds
of area 4 in 2001–2002.
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
320
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
321
Table 3. Mean area of the ponded surface water on ground (sum of the areas of puddles and depressional water in 2001−03,
and the areas of puddles in 2004−05) with respect to the total eld area (%) in differently tilled elds in springs and au-
tumns 2001−2005. Tillage treatments repeated annually in 2001–2003 and in 2004–2005: AP = autumn ploughing, CT =
conservation tillage, PG = perennial grass in crop rotation (the results of zero tillage years were used in analyzes), DD=
direct drilling (zero tillage). For years 2001−2003, the means presented are arithmetic means, and for years 2004−2005,
the means presented are model based mean estimates transformed back to the original scale of measurement. The CI’s
are 95% condence intervals for the means.
April–May 2001 April 2002 April–May 2003
AP CT PG AP CT PG AP CT PG
Mean 2.4 1.6 0.4 0.3 0.5 0.05 0.2 1.1 0.2
Median 0.1 0.2 0 0 0 0 0 0 0
Min–Max 0–24 0–9.7 0–2.1 0–3.0 0–4.1 0–0.4 0–1.8 0–10 0–1.1
n/Na7/11 8/11 3/11 2/10 3/10 2/10 2/10 2/10 3/10
Pair AP/CT CT/PG AP/PG
p-value 0.84 0.44 0.84
September 2001 Autumn 2002bNovember 2003
AP CT PG AP CT PG AP CT PG
Mean 0.03 0.5 0.1 0 0.01 0.1
Median 0 0 0 0 0 0
Min–Max 0–0.2 0–4.0 0–0.8 0–0.1 0–0.8
n/N 2/11 4/11 2/11 0/11 1/11 3/11
April 2004 Mean
2004c
April–May 2005 Mean
2005c
AP CT PG DD AP CT PG DD
Mean 0.002 0.000 0.000 0.02 0.002 0.2 3.2 2.7 0.1 1.1
CI 0–0.02 0–0.02 0–0.01 0.001–0.07 0-0.01 0–1.6 0.3–9.0 0.3–7.5 0–2.2 0.3–2.6
n/N 1/12 1/5 0/7 3/6 6/12 3/5 4/6 2/6
PairdAP/CT AP/PG AP/DD AP/CT AP/PG AP/DD
p-value 0.54 0.46 0.19 0.07 0.09 0.87
November 2004 October–November 2005
AP CT PG DD AP CT PG DD
Mean 1.0 1.9 0.05 0.8 0.8 0.1 1.0 0.02 1.7 0.5
CI 0.1–2.8 0.2–5.6 0–1.4 0–3.4 0.1–2.0 0–0.8 0.01–3.4 0–0.7 0.3–4.3 0.05–1.3
n/N 9/11 4/5 2/3 3/5 5/12 3/5 0/6 5/6
anumber of elds having surface ponded water (n)/total number of experimental elds (N)
bno surface ponded water in dry conditions
cbetween years comparison: in spring p = 0.0008, and in autumn p = 0.53
din spring, interaction between the year and the tillage treatment p = 0.10, and in autumn p = 0.15
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
322
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
323
covered elds (Table 3). However, surface water
was observed only in a few elds because of the dry
conditions in southern Finland (Fig. 2). During the
period from autumn 2001 to autumn 2003, surface
water ponding was observed altogether in 8 out of
11 experimental areas (Table 3). For each different
treatment (AP, CT, PG), the ponding was observed
in ve elds. More than once in the same eld,
it was observed for 1, 3 and 4 ploughed, stubble
cultivated and perennial grass elds, respectively.
Since the more rainy season starting in summer
2004 (Fig. 2), puddles were found in several elds,
and the share of puddled area was clearly larger
than in earlier years (Table 3). During autumns
2004 and 2005, and spring 2005, the puddles were
observed at 92, 100, 67 and 83% of the ploughed,
stubble cultivated, perennial grass and direct drilled
elds, respectively. More than once in the same
eld the puddles were observed at 58, 60, 17 and
50% of the ploughed, stubble cultivated, perennial
grass and direct drilled elds, respectively.
In autumns 2004 and 2005, and in spring 2005,
the mean share of puddles from the total eld area
was larger in the stubble cultivated elds than in
the ploughed or grass covered elds (Table 3). In
spring 2005, the difference in the share of the pud-
dled area between the CT and AP treatments was
nearly statistically signicant (p = 0.07). The re-
sults of the direct drilled treatment were only indic-
ative because the zero tillage cultivation had been
carried out only for a few years in the same elds.
Crop hampering
In addition to direct measurements of surface puddle
areas, the effect of tillage treatment on surface water
ponding during the growing season was observed
by determining the share of crop hampered by soil
wetness (Table 4). In the exceptionally rainy grow-
ing season of 2004 (Fig. 2), crop hampered on each
Table 4. Area of the eld ground having crop hampering due to soil wetness as a ratio of the total eld area (%) in differently tilled elds
during growing seasons 2001 and 2003−2005. In 2002, no crop hampering was observed. Tillage treatments repeated annually in 2001–
2003 and in 2004–2005: AP = autumn ploughing, CT = conservation tillage, PG = perennial grass in crop rotation (the results of zero till-
age years were used in analyzes), DD= direct drilling (zero tillage). For years 2001−2003, the means presented are arithmetic means, and
for years 2004−2005, the means presented are model based mean estimates transformed back to the original scale of measurement. The
CI’s are 95% condence intervals for the means.
July 2001 June-July 2003
AP CT PG AP CT PG
Mean 0.9 1.2 3.0 0.01 1.2 0
Median 0 0 0 0 0 0
Min–Max 0–2.5 0–4.7 0–12 0–0.1 0–10
n/Na2/5 2/5 1/5 1/9 2/9 0/9
September 2004 Mean
2004
June–July 2005 Mean
2005
AP CT PG DD AP CT PG DD
Mean 1.5 3.9 1.3 2.6 2.2b0.2 0.1 0.01 0.4 0.1
CI 0.3–3.5 0.8–9.1 0.1–3.7 0.4–6.7 1.0–3.9 0–0.8 0–1.2 0–0.5 0–1.6 0–0.6
n/N 9/12 4/4 5/6 5/5 3/11 2/5 1/6 2/6
a number of elds having crop growth problems due to surface ponded water (n)/total number of experimental elds (N)
b signicant difference between years, p = 0.0002
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
322
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
323
stubble cultivated and direct drilled eld, and the
mean share of the eld area with growth problems
was larger in these elds than in the ploughed or
grass elds. The differences were, however, not
statistically signicant when the data of years 2004
and 2005 was analyzed together. In years 2001, 2003
and 2005, the mean areas hampered were small
compared to those in 2004, and growing problems
were observed only in some elds.
Discussion
Surface water ponding
During the experimental period of ve years, the
surface water ponding and the signs of soil wetness
in crop stand were monitored 13 times. The deter-
mination of the share of the water ponded areas to
that of the total eld area by using a GPS device and
GIS programs was found to be a practical method
for monitoring differences between various tillage
treatments in the eld scale in terrain conditions
in areas covered by puddles, depressional water or
poorly growing crop. The annual variation in the
precipitation and the distribution of the rainfall af-
fected the occurrence of ponded surface water. In
most observation times, surface water ponding or
signs in crop stand were found only in some elds.
On the other hand, taking into account all observa-
tion times, ponded water was found three times and
signs in crop stand once in more than 50% of the
elds. In each of these cases, the precipitation during
1 to 4 months before the determination was clearly
larger than the long-term average. According to our
results, surface water ponding or crop hampering
due to soil wetness occurred on clayey soils when
the amount of rainfall was considerable greater than
on average. In Finland, heavy rainstorms are rare
(Finnish Meteorological Institute 2007). Thus, a
moderate soil inltration capacity is sufcient for
avoiding surface water ponding.
We described the effect of rainfall on the sur-
face water ponding very roughly. For more detailed
examination, the rainfall should be determined dai-
ly in each area or eld. Likewise, more frequent
surface water ponding observations would have
improved the accuracy of results. In the future, au-
tomatic weather stations, located in experimental
areas, can improve the accuracy of observations.
Also making the observations of surface water
ponding during the winter time become relevant in
the future if the winters will become milder and the
precipitation in the autumn and winter will increase
as has been forecasted by IPCC (2007).
Surface water ponding and crop hampering
due to soil wetness were used as indicators for low
clayey soil inltration capacity and near saturated
hydraulic conductivity. According to our results,
for such purpose, observations from a period of
several years need to be available, depending on
the amount and distribution of rainfall as discussed
above. Also, the repeated identications of the pon-
ded water and crop hampering from a same eld
reduce the effect of the temporal variation on the
result. In addition, when comparing different elds
the differences in the crop sensitivity to the soil
wetness as well as different soil management prac-
tices need to be taken into account. For instance,
van Es et al. (1999) found that tillage and temporal
factors are important sources for the variation of
the inltrability of ne textured soils.
The surface water ponding occurred usually
year after year in the same areas of a eld. In many
elds, water gathered in the depressions, caused
by old furrows, and in other uneven spots on the
soil surface or in compact headland areas (see Fig.
3). The reasons for the surface water ponding or
soil wetness during a growing season were, how-
ever, not evident from our data. Soil sealing and
discontinuity in ow via active macroporosity are
soil physical properties that are critical to the inl-
tration capacity and hydraulic conductivity of wet
soil. Repeated surface water ponding can also be a
sign of poorly operating drainage system. Comple-
mentary soil physical measurements are required
to investigate reasons for the repeated surface
water ponding. In the eld scale, the soil quality
kits (described by e.g. Kukkonen et al. 2004), may
provide a practical method for examination of soil
properties.
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
324
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
325
Effects of tillage on surface water
ponding
Our results indicate that the inltration capacity or
the hydraulic conductivity of the wet stubble cul-
tivated clayey soil with gentle slope were slightly
lower than those of the mouldboard ploughed soil.
The mean relative area of the ponded surface water,
and the crop (mainly spring cereals) hampered due
to soil wetness were larger in the conservation tilled
elds than in the autumn mouldboard ploughed
elds. The difference was, however, statistically
signicant only in spring 2005. Our result is in
agreement with those by Lipiec et al. (2006), who
found that the cumulative inltration was reduced
by 36% in shallow harrowed silt loam soil compared
to mouldboard ploughed soil. Alakukku (1998) has
reported that the saturated hydraulic conductivity
of ne textured soils was less in the shallow stub-
ble cultivated topsoil layer of 20 cm than that of
mouldboard ploughed topsoil. On the other hand,
Buczko et al. (2006) have reported controversy
inltration results for silty soil. The inconsistencies
can be associated, for instance, with soil surface
sealing (e.g. McGarry et al. 2000), temporal water
storage capacity of wet soil (Alakukku 1998, Lipiec
et al. 2006), tillage timing (Withers et al. 2007) and
ow through active macropores made by soil fauna
(Pitkänen and Nuutinen 1998).
In general, the mean relative share of the sur-
face ponded water was less for the perennial grass
than for stubble cultivated soil. The differences in
the mean relative areas between the ploughed and
grass covered elds varied from one measurement
to the other. The water ponding determined on the
surface of the eld may have been underestimated
especially in perennial grass elds as compared to
the tilled soil. In perennial grass eld, crop residue
is accumulated on the soil surface forming a nota-
ble cover, which probably increases the temporal
water storage in soil surface even when no visible
water is recognized. On the other hand, Pietola et
al. (2006) found that under dry soil conditions inl-
tration into tilled clay soil was lower than to an ad-
jacent cracked soil under grass, but the difference
in the inltration rate between the tilled and grass
covered soil reduced when the soil reswelled in
wet conditions. Also Wienhold and Tanaka (2000)
found that the inltration rate of soil at 5 cm ten-
sion was higher in perennial hay than in tilled plots.
The saturated hydraulic conductivity of a clayey
soil, covered by perennial hay, was found by Jiang
et al. (2007) to be clearly greater in the 0–10 cm
layer than in mulch tilled soil.
Surface water ponding and low hydraulic con-
ductivity in wet conditions increase clearly the
risk for surface runoff and crop hampering due
to the soil wetness. Relevant to this, Turtola et al.
(2007) found that replacing mouldboard plough-
ing by shallow stubble cultivation or grass cover
increases the proportion of the surface runoff from
the total runoff from 16 to 44 or 60%, respectively,
in a gently sloping clayey soil having low saturated
hydraulic conductivity. In 2004, the spring cereal
yields in areas hampered due to the surface water
ponding were evidently very small and in several
places the crops failed. This is in accordance with
Pitkänen (1994) and Alakukku et al. (2009), who
found that in clayey soils the spring cereal yields
were lower in stubble cultivated or direct drilled
elds when the precipitation of June was clearly
larger than the long-term average. Arshad et al.
(1999) reported similar results for barley cultiva-
tion in a silty loam soil in a cold, semiarid region
of Canada.
Conclusions
A GPS devise was found to be a simple technique
to locate and monitor the spatial and temporal vari-
ations in areas of surface ponded water or poorly
growing crop.
Surface water ponding or crop hampering due
to soil wetness occurred in clayey soils when the
amount of rainfall was clearly larger than the long-
term average. Thus, observations from a period of
several years and/or several times during a year are
needed for reliable determinations of differences
between different soil management practices.
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
324
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 313–326.
325
Surface water ponding and crop hampering
due to soil wetness are indicators for low clayey
soil inltration capacity and hydraulic conductiv-
ity close to saturation. Surface water ponding and
low hydraulic conductivity of soil in wet conditions
increase clearly the risk for surface runoff. How-
ever, complementary soil physical measurements
are required to investigate the reasons for repeated
surface water ponding.
Acknowledgements. Financial support from the Ministry of
Agriculture and Forestry is gratefully acknowledged. We
acknowledge the assistance of Juha Eskelinen and Marja-
Liisa Westerlund in the elds. We are grateful to Risto Mäki
from the Finnish Environment Institute for supplying the
groundwater table data, and Anneli Nordlund and Pirkko
Karlsson from the Finnish Meteorological Institute for
delivering the weather data, and Berit Mannfors for revis-
ing the language.
References
Alakukku, L. 1998. Properties of compacted ne-textured
soils as affected by crop rotation and reduced tillage.
Soil & Tillage Research 47: 83– 89.
Alakukku, L., Ristolainen, A. & Salo, T. 2009. Grain Yield
and Nutirent Balance of Spring Cereals in Different Till-
age Systems. In: Proceedings of ISTRO 18th Triennial
Conference ‘Sustainable Agriculture’. June 15–19, 2009,
Izmir, Turkey . CD-ROM. T6-005-1-T6-005-7.
Arshad, M.A., Franzluebbers, A.J. & Azooz, R.H. 1999.
Component of surface soil structure under convention-
al and no-tillage in northwestern Canada. Soil & Tillage
Research 53: 41– 47.
Ball, B.C., Crichton, I. & Horgan, G.W. 2008. Dynamics of
upward and downward N2O and CO2 uxes in ploughed
or no-tilled soils in relation to water-lled pore space,
compaction and crop presence. Soil & Tillage Research
101:20–30.
Berry, J.J. 1997. The simulation approach to analyzing
the nonparametric randomized complete block design.
Observations: The Technical Journal for SAS Software
Users 6: 36 –45.
Buczko, U., Bens, O. & Hüttl, R.F. 2006. Tillage effects
on hydraulic properties and macroporosity in silty and
sandy soils. Soil Science Society of American Journal
70: 1998–2007.
Drebs, A., Nordlund, A., Karlsson, P., Helminen, J. & Ris-
sanen, P. 2002. Climatological statistics of Finland 1971–
2000. Finnish Meteorological Institute, Helsinki.
Dunn, G.H. & Phillips, R.E. 1991. Macroporosity of a well-
drained soil under no-till and conventional tillage. Soil
Science Society of Amer ican Journal 55: 817–823.
Finnish Meteorological Institute. 2007. Rankka sade, pal-
jonko sataa ja miten pitkän ajan sisällä. Cited 10 July
2007. Available on the Internet: http://www.fmi./saa/
tilastot_38.html. (In Finnish)
FAO 1998. World Reference Base for Soil Resources. Word
Soil Resources Report 84. FAO, Rome, Italy.
Gantzer, C.J. & Blake, G.R. 1978. Physical characteristics
of Le Sueur clay loam soil following no-till and conven-
tional tillage. Agronomy Journal 70: 853–857.
Hamza, M.A. & Anderson, W.K. 2005. Soil compaction in
cropping systems. A review of the nature, causes and
possible solutions. Soil & Tillage Research 82: 121–145.
IPCC. 2007. Climate Change 2007: Synthesis Repor t. 52
p. Cited 10 July 2009. Available: http://www.ipcc.ch/pdf/
assessment-report/ar4/syr/ar4_syr.pdf
Jiang, P., Anderson, S.H., Kirchen, N.R., Sadler, E.J. &
Sudduth, K.A. 2007. Landscape and conservation man-
agement effects on hydraulic properties of a claypan-
soil toposequence. Soil Science Society of American
Journal 71: 803–811.
Kenward, M.G. & Roger, J.H. 1997. Small sample inference
for xed effects from restricted maximum likelihood. Bi-
ometrics 53: 983–997.
Koskiaho, J., Kivisaari, S., Vermeulen, S., Kauppila, R.,
Kallio, K. & Puustinen, M. 2002. Reduced tillage: Inu-
ence on erosion and nutrient losses in a clayey eld in
southern Finland. Agricultural and Food Science in Fin-
land 11: 37–50.
Kuehl, R.O. 2000. Design of Experiments: Statistical Prin-
ciples of Research Design and Analysis, 2nd edition. Pa-
cic Grove, CA: Duxbury.
Kukkonen, S., Alakukku, L., Myllys, M. & Palojärvi, A. 2004.
Maan laadun arviointi tiloilla – Kirjallisuuskatsaus. Soil
quality on-farm assessment – a review. Maa- ja elin-
tarviketalous 63: 86 p. Cited 10 July 2007. Available:
http://w ww.mtt.fi/met /pdf/met63.pdf (In Finnish with
English summary)
Lilja, H., Uusitalo, R., Yli-Halla, M., Nevalainen, M.,
Väänänen, T. & Tamminen, P. 2006. Suomen maan-
nostietokanta. Soil maps at scale 1:250,000 and prop-
erties of soil, MTT:n selvityksiä 114: 1–69. (In Finnish
with English summary)
Lipiec, J., Kuś, J., Słowińska-Jurkiewicz, A. & Nosalewicz,
A. 2006. Soil porosity and water inltration as inuenced
by tillage methods. Soil & Tillage Research 89: 210-220.
McGarry, D., Bridge, B.J. & Radford, B.J. 2000. Contrast-
ing soil physical properties after zero and traditional till-
age of an alluvial soil in the semi-arid subtropics. Soil &
Tillage Research 53: 105–115.
Ministry of Agriculture and Forestry 2004. Horisontaali-
sen maaseudun kehittämisohjelman väliarviointi. Man-
ner-Suomi. MMM:n julkaisuja 1/2004. 273 p. + 27 app.
(In Finnish with English summary)
Pietola, L., Rasa, K., Räty, M., Uusi-Kämppä, J., Yli-Hal-
la, M., Horn, R., & Tippkötter, R. 2006. Management-in-
duced changes of soil physical properties in vegetated
buffer zones. In: Horn, R., Fleige, H., Peth, S. & Peng,
X. (eds.). Soil Management for Sustainability. Advanc-
es in Geoecology 38: 301–308.
Pitkänen, J. 1994. A long-term comparison of ploughing
and shallow tillage on the yield of spring cereals in Fin-
land. Proc eedings 13th International Conference Inter-
national Soil Tillage Research Organization, Aalborg,
AGRICULTURAL AND FOOD SCIENCE
Alakukku et al. Monitoring of surface water ponding on clayey soils
326
Denmark. July 24-29 1994. Volume II: 709–715.
Pitkänen, J. & Nuutinen, V. 1998. Earthworm contribution
to inltration and surface runoff after 15 years of differ-
ent soil management. Applied Soil Ecology 9: 411–415.
Puustinen, M., Koskiaho, J. & Peltonen, K. 2005. Inu-
ence of cultivation methods on suspended solids and
phosphorus concentrations in surface runoff on clayey
sloped elds in boreal climate. Agriculture, Ecosystems
& Environment 105: 565–579.
Puustinen, M., Tattari, S., Koskiaho, J. & Linjama, J. 2007.
Inuence of seasonal and annual hydrological varia-
tions on erosion and phosphorus transport from arable
areas in Finland. Soil and Tillage Research 93: 44–55.
Skøien, S. 1988. Soil erosion and runoff losses of phos-
phorus, effect of tillage and plant cover. Norsk landbruks-
forskning 2: 207–218.
Soveri, J., Mäkinen, R. & Peltonen, K. 2001. Pohjaveden
korkeuden ja laadun vaihteluista Suomessa 1975–1999.
Suomen ympäristö 420. 382 p. (In Finnish with English
summary).
Starr, J.L. 1990. Spatial and temporal variation of pond-
ed inltration. Soil Science Societ y of American Jour-
nal 54: 629 –636.
Stẹpniewski, W. & Łabuda, S. 1989. The inuence of 10
days’ ooding in seven development stages of spring
barley on its growth, yield, and N, P, K content and up-
take. Part I. Growth and yield. Polish Journal of Soil Sci-
ence 22: 93 –99.
Tebrügge, F. & Düring, R.-A. 1999. Reducing tillage inten-
sity – a review of results from a long-term study in Ger-
many. Soil & Tillage Research 53: 15–28.
Turtola, E., Alakukku, L., Uusitalo, R. & Kaseva, A. 2007.
Surface runoff, subsurface drainow and soil erosion as
affected by tillage in a clayey Finnish soil. Agricultural
and Food Science 16: 332–351.
Turtola, E. & Jaakkola, A. 1995. Loss of phosphorus by
surface runoff and leaching from a heavy clay soil un-
der barley and grass ley in Finland. Acta Agriculturae
Scandinavica 45: 159–165.
Turtola, E. & Paajanen, A. 1995. Inuence of improved
subsurface drainage on phosphorus losses and nitro-
gen leaching from a heavy clay soil. Agricultural Water
Management 28: 259–310.
Ulén, B. 1997. Nutrient losses by surface run-off from soils
with winter cover crops and spring-ploughed soils in the
south of Sweden. Soil & Tillage Research 44: 165–177.
Van Es, H.M., Ogde, C.B., Hill, R.L., Schindelbeck, R.R.
& Tsegaye, T. 1999. Integrated assessment of space,
time, and management-related variability of soil hydrau-
lic properties. Soil Science Society of American Jour-
nal 63: 1500 –1608.
Wienhold, B.J. & Tanaka, D.L. 2000. Haying, tillage, and
nitrogen fertilization inuences on inltration rates at a
Conservation Reserve Program site. S oil Science So-
ciety of American Journal 64: 379–381.
Withers, P.J.A., Hodgkinson, R.A., Bates, A. & Withers,
C.L. 2007. Soil cultivation effects on sediment and phos-
phorus mobilization in surface runoff from three con-
trasting soil types in England. Soil & Tillage Research
93: 438–451.
Yli-Halla, M., Mokma, D., Peltovuori, T. & Sippola, J.
2000. Suomalaisia maaproileja. Agricultural soil pro-
les in Finland and their classication. Maatalouden tut-
kimuskeskuksen julkaisuja. Sarja A 78, 105 p. (In Finn-
ish with English Summary)
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
327
© Agricultural and Food Science
Manuscript received July 2009
Introduction
While many small dairy farms have shut down milk
production, the livestock density and number of
livestock farms have increased in certain regions
in western and central Finland during recent dec-
ades. At present, most dairy farms prefer almost
continuous grass cultivation to crop rotation with
cereals and grasses. Consequently, slurry is spread
onto elds of silage grass instead of using earlier
methods where slurry was applied to cereal elds
before autumn ploughing or before spring tillage.
Due to the soil wetness and risk of soil compac-
Nitrogen losses from grass ley after slurry application
– surface broadcasting vs. injection
Jaana Uusi-Kämppä and Pasi K. Mattila
MTT Agrifood Research Finland, Plant Production Research, FI-31600 Jokioinen, Finland,
e-mail: jaana.uusi-kamppa@mtt.
As the livestock numbers on Finnish dairy farms have increased and most elds on dairy farms are under
grass, it has become common to spread cattle slurry over grasslands. To estimate environmental effects of
recurrent slurry applications, a 5-year eld study was performed to compare nitrogen (N) losses to water
and ammonia losses to air by volatilization, when cattle slurry was either surface broadcast or injected into
clay soil after grass cuttings. Slurry was spread on the grass in summer (1996–1997) or both in summer and
autumn (1998–2000). Biomass N uptake before grass harvesting and amount of soil mineral N in spring and
autumn were measured and eld N balances were calculated. Despite cool weather, up to one third of the
ammonium N of broadcast slurries was lost through ammonia volatilization after application in autumn, but
injection effectively prevented losses. The mean surface runoff losses of total N were negligible (0.3–4.6
kg ha-1 yr-1) with the highest loss of 13 kg ha-1 yr-1 measured after slurry broadcasting to wet soil in autumn
and followed with heavy rains. A substantial part (24–55%) of the applied mineral N was not recovered by
the foregoing measurements.
Key-words: Slurry application, grassland, surface runoff, nitrogen, nitrogen uptake, NH3 volatilization,
nitrogen balance, surface broadcasting, injection
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
328
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
329
tion in spring, however, only mineral fertilizer is
often surface applied to grass in the beginning of
the growing season whereas cattle slurry is applied
after the rst cut. If the growing season is extremely
rainy, it may not be possible to spread slurry with
heavy machinery on wet soils in summer. Then the
slurry tanks are emptied in the autumn to provide
storage capacity for the winter months.
In Finland, slurry is applied to grassland sur-
face either by conventional broadcasting or by
more recently adopted band spreading and trailing
shoe techniques, whereas injection is used to apply
slurry below the soil surface. Surface application
is an easy and cheap process but it leaves the ma-
nure prone to NH3 volatilization (Braschkat et al.
1997, Mattila and Joki-Tokola 2003) and surface
runoff (Turtola and Kemppainen 1998). Injection
of slurry might be better environmentally but it is
more expensive and more difcult than broadcast
application. Top-dress fertilization of grass elds
with mineral fertilizers is also a typical comple-
mentary method.
The purpose of this study was to compare two
different slurry application methods – surface
broadcasting and injection – on grass elds. The
former method is a cheap and commonly used prac-
tice on most dairy farms whereas the latter is con-
sidered as a difcult method to use, particularly on
stony soils. In this study, we investigated whether
slurry injection could be recommended in given
environmental conditions in boreal climates. Loss-
es of total nitrogen (TN), ammonium N (NH4
+-N,
hereafter NH4-N) and nitrate N (NO3
–-N, hereafter
NO3-N) to surface runoff water from the surface-
applied slurry were compared to losses from inject-
ed slurry or mineral fertilization on a grass eld.
Knowledge of ammonia (NH3) losses to air due to
the methods in cool autumn weather was also lack-
ing. Nitrogen uptake by grass was measured for N
balances. The amounts of soil mineral N (SMN;
NH4-N plus NO3-N) at different depths were also
measured and N balances were calculated to allow
an estimation of the risk for NO3 leaching.
Material and methods
The experimental field
The study was performed on an eight-plot ex-
perimental field (0.34 ha; Uusi-Kämppä and
Heinonen-Tanski 2008) located in Jokioinen, south
west Finland (60°49’N 23°30’E). The area had a
long-term (1971–2000) mean annual precipitation
of 607 mm and mean annual temperature of 4.3 °C,
with the mean temperatures of the coldest (Febru-
ary) and the warmest (July) months being -6.5 and
16.1 oC, respectively (Drebs et al. 2002). The soil
was classied as Typic Cryaquept (Soil Survey Staff
1996) containing 61% clay in the plough layer. The
concentrations of Ca, K, Mg and P in the plough
layer were at a satisfactory or good level.
The experimental plots with slopes of 0.9–1.7%
were isolated from each other by plastic lm to
a depth of 0.6 m and by soil banks. Uncultivated
10-m wide buffer zones were established at the
lower edge of the plots since buffers (mostly 3-m
or 15-m wide) are typical on Finnish elds. Ten-
metre wide buffer area in the upper edges of the
plots of total length of 70 m, and 0.5-m (1998–
2000) or 1.5-m (1996–1997) wide borders on both
sides of the plots were also untreated, with neither
soil nor plant sampling, nor slurry application, due
to difculties to drive and work with tractors and
spreaders on those areas on the narrow plots. The
grass ley on the experimental eld consisted for
the most part of timothy (Phleum pratense) and
meadow fescue (Festuca pratensis) sown in June
1995. The grass ley was cut twice a year, with the
rst cut always in June and the second cut in late
August (1996), September (1997, 1998) or early
October (2000).
Treatments and applications
The experimental treatments were as follows:
1.
Surface broadcasting (SB) of cattle slurry onto
the grass ley (three replicates);
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
328
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
329
2.
Injection (IN) of cattle slurry into the grass ley
(depth of 0.05–0.1 m; three replicates); and
3.
Mineral fertilization (MF) – top-dress fertiliza-
tion onto the grass ley (two replicates).
Slurry was applied annually to grass ley after
the rst cut in 1996–1997 (Phase I) and biannually
after the rst and the second cuts in 1998–2000
(Phase II). In Phase I, the application rates of min-
eral N (160 kg ha-1 yr-1 including NH4-N of slurry
and NH
4
-N and NO
3
-N of mineral fertilizer) and
total P (36 kg ha
-1
yr
-1
) represented the nutrient
amounts allowed by Finnish ‘good agricultural
practice’ and the average used on most Finnish
farms. In Phase II, the corresponding amounts were
230 and 66 kg ha-1 yr-1 for mineral N and total P,
respectively. In autumn, slurry amounts of 33–42 t
ha-1 were applied, although the maximum allowed
autumn slurry amount at that time was 30 t ha
-1
(Finlex 1998). In fact, 120–160 kg ha-1 more TN
in slurry was applied than allowed in the nitrate
directive (170 kg TN ha-1 yr-1) to detect possible
environmental risks due to over-dosing of manure.
More details about P applications in slurry and fer-
tilizers, slurry properties, and storage tanks have
been presented by Uusi-Kämppä and Heinonen-
Tanski (2008).
In Phase I (annual slurry application in June,
1996–1997), cattle slurry (34–61 t ha
-1
, Table 1)
was applied to an area of 3 m × 50 m by a “Vogel-
sang” spreader on slurry plots after the rst grass
cut in June. Slurry was either applied to the soil
surface with a band spreading unit equipped with
a small splash plate under each hose, or injected
with an injector that had 10 tines with 0.3 m spac-
ing, each equipped with a disc coulter and a press
wheel (Kapuinen 1998).
Table 1. Application dates, amended plot area, amount of slurry, and total nitrogen (TN) applications in slurry (s) and
mineral fertilizer (mf) in plots where slurry was surface broadcast (SB) or injected (IN) into soil and in mineral ferti-
lized (MF) plots. Values in parenthesis indicate the application rate of mineral N in slurry and mineral fertilizer.
Dates Area, Slurry rate, t ha-1 TN (mineral N) kg ha-1
m2(wet weight) SB IN MF
Annual slurry application (Study phase I)
14 May 1996 350 112 (112) mf 112 (112) mf 112 (112) mf
17–19 June 1996 150 34–37 134 (78) s 146 (85) s 81 (81) mf
12 May 1997 350 49 (49) mf 49 (49) mf 49 (49) mf
26–27 June 1997 150 61 148 (78) s 148 (78) s 80 (80) mf
Total 1996–1997 443 (317) 455 (324) 322 (322) mf
Mean 1996–1997 48 222 (159) 228 (162) 161 (161) mf
Biannual slurry application (Study phase II)
11 May 1998 350 48 (48) mf 48 (48) mf 48 (48) mf
29 June 1998 250 50–52 187 (94) s 194 (97) s 92 (92) mf
16 October 1998 250 38–42 140 (73) s 155 (80) s
11 May 1999 250 61 (61) mf 61 (61) mf 100 (100) mf
30 June 1999 250 59–62 209 (112) s 219 (118) s 100 (100) mf
27 October 1999 250 33–38 105 (58) s 120 (67) s
8 May 2000 250 69 (69) mf 69 (69) mf 100 (100) mf
21–22 June 2000 250 47–52 170 (94) s 188 (105) s 100 (100) mf
23 October 2000 250 33–36 119 (59) s 130 (64) s
Total 1998–2000 1108 (668) 1184 (709) 540 (540) mf
Mean 1998–2000 90 369 (223) 395 (236) 180 (180) mf
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
330
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
331
During Phase II (biannual slurry application
in June and October, 1998–2000) slurry was ap-
plied by a “Teho-Lotina” spreader that had an in-
jector with 0.47 m tine spacing and disc coulters
but no press wheels. Broadcast spreading was
carried out by holding the injector up while each
tine was equipped with a small splash plate. The
slurry amounts were slightly higher in the IN than
SB plots due to a lower driving speed during injec-
tion. In autumn 2000, the eld was ploughed three
days after the slurry application, when the ammonia
volatilization measurements had been nished.
Mineral fertilizer was spread by a “Juko” ferti-
lizer drill to all plots in spring and to MF plots after
the rst cut in June. In spring 1996, NK fertilizer
(20% N and 15% K; Table 1) was surface applied
to all plots. Since then ammonium nitrate fertilizer
(26% N) was spread in spring, except in spring 1999
and spring 2000 when NPK fertilizer (20% N, 4%
P and 7% K) was spread on the MF plots only. Due
to half of the NH4-N in slurry spread in autumn was
assumed to be available for plants in the following
spring (Ministry of Agriculture and Forestry 1998),
39 and 31 kg ha
-1
less fertilizer N was applied to
slurry plots than to MF plots in spring 1999 and in
spring 2000, respectively. On the MF plots, NPK
fertilizer (20% N, 4% P and 7% K) was surface
applied every summer, except in the rst summer,
when NPK fertilizer (18% N, 5% P, 10% K) was
spread.
Measurement of ammonia volatilization
Volatilization of NH
3
was measured after the autumn
applications of slurry (SB and IN) in 1999 and
2000 by the equilibrium concentration technique,
also called the “JTI method” (Svensson 1994). The
method uses passive diffusional NH3 samplers that
are placed on treated areas both in ambient air and
under ventilated chambers. The ammonia volatiliza-
tion rate in ambient air is calculated from the amounts
of NH
3
absorbed by the samplers. Air temperature is
used to calculate the diffusion coefcient of NH
3
. The
concentration of NH3 inside the chambers was used
as a measure of NH
3
volatilization potential without
the effect of varying wind conditions in ambient air.
Ammonia volatilization was measured in the
daytime starting at 5–15 min after the application
of slurry and lasting for 2.75–4 h divided into two
consecutive periods. On the following two days,
NH3 measurement began about 24 h and about 48 h
after the slurry application and lasted for 3.5–5 h on
each of the days. The measurement was carried out
in the three replicate plots of both SB and IN. Two
chambers and two ambient air sampler holders were
placed on each plot. Air temperature was measured
with a thermohygrograph at about 0.2 m height and
wind speed was measured with a cup anemometer
at 2 m height. The volatilization of NH
3
between
measurement periods was interpolated by calculat-
ing the average emission values before and after an
interval and correcting it based on the temperature
and wind speed that prevailed during the interval.
The procedure is described in detail by Malgeryd
(1996).
Water sampling and analyses
Surface and near-surface runoff (referred to hereafter
as surface runoff) to a depth of 0.3 m was collected
in a modied collector trench planned by Puustinen
(1994) at the lower end of each plot and fed by pipes
into 8 plastic tanks (2.0 m
3
) buried in the soil. Water
volume was measured by ow meters (Oy Tekno-
Monta Ab, JOT-company, 1992) and representative
subsamples were taken through samplers (Fig. 1) for
laboratory analyses when the tanks were emptied.
Water was sampled 16–27 times per year, with most
samplings in spring and autumn. The time interval
between water samplings in peak runoff periods
varied from a day to two weeks, depending on rains
and snowmelts.
The volume of runoff water was calculated from
the whole plot area, whereas the N losses were cal-
culated from the slurry applied source area. On the
border areas, the mean TN losses through surface
runoff were estimated to be negligible (ca 0.5 kg ha
-1
yr-1) according to the TN concentrations of surface
runoff water measured earlier on nearby plots under
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
330
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
331
unfertilized grass (Turtola and Paajanen 1995). In
spring 1997, the surface runoff results from one SB
plot and one MF plot were omitted, due to freezing
of the outlet pipes. Precipitation was measured at
Jokioinen Observatory, Finnish Meterological In-
stitute, situated 0.5 km from the eld.
Water samples were stored in polyethylene bot-
tles for periods from a few days to a few weeks
in the dark (4°C) before determining the nutrient
concentrations. The storage time probably did not
have a large impact on the concentrations of TN
and NO3-N, but the concentrations of NH4-N may
have decreased during the prolonged storage (Tur-
tola 1989). For the determinations of NH
4
-N and
NO3-N, the samples were ltered through a mem-
brane lter (0.2 µm) and analysed with a Skalar
autoanalyser according to Finnish standard methods
(SFS 3030, SFS 3032). The concentration of TN
was determined from unltered water samples by
oxidation of N compounds to NO3 in alkaline solu-
tion (SFS 3031).
Soil sampling and analyses
Because the drainage water was not measured, both
the amounts of NH4-N and NO3-N as well as their
sum (SMN) in the 0–60 cm soil layers were used to
indicate the risk of N leaching from the grass ley.
Soil samples were taken separately from each plot in
spring and autumn before the application of mineral
fertilizer or slurry (Uusi-Kämppä and Heinonen-
Tanski 2008). The samples taken in spring 1997
were omitted because the eld had been fertilized
a few days earlier.
Soil samples were frozen immediately after the
sampling. For NH
4
-N and NO
3
-N analyses, soils
were thawed overnight (4°C), and 40 ml of moist
soil was subsequently extracted with 100 ml of 2
M KCl for 16 hours (Sippola and Yläranta 1985).
After ltration, concentrations of NH4-N and NO3-
N were measured with a Skalar autoanalyser. The
concentrations of TN and carbon (C) were deter-
mined using the C-N-autoanalyser (LecoCN-2000,
Leco Corporation, St.Joseph, MI, USA).
Other samplings and calculations of nitro-
gen balances
Slurry samples were taken during spreading and
analysed for concentrations of TN (Kjeldahl) and
NH
4
-N as described by Mattila and Joki-Tokola
(2003).
Above-ground biomass was sampled before har-
vesting the grass. Samples (0.64 m
2
) were collected
from each plot so that the grass was cut leaving a
stubble of 1 cm. Plant samples were dried at 60°C
overnight for TN analysis with a LECO analyser
and at 105°C for dry matter (DM) determination.
Field N balance was estimated as the differ-
ence between N inputs and outputs (Equation 1).
The N uptake of grass, ammonia volatilization and
TN in surface runoff were considered as outputs
in the calculations. Ammonia volatilization from
summer-applied slurry was estimated to be 40% of
the applied NH
4
-N for surface application and 0.4%
for injection, based on the results of Mattila and
Joki-Tokola (2003). Volatilization from autumn-
applied slurry was taken from the results of the NH
3
measurements carried out in this study. Ammonia
volatilization from mineral fertilizer, in turn, was
estimated to be 1.6% of the applied N (Grönroos
et al. 2009).
Equation 1:
N balance = N (input) – N (output)
= (Nfertilizer + Nslurry) – (Ncrop + Nvolatilized NH3 + TNrunoff)
3
2
1
Fig. 1. Sampling of surface runoff: 1, water from the
collector tank ows through the ow meter; 2, the wa-
ter sample drips into a pail; 3, the rest of the water ows
through an outlet.
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
332
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
333
Statistical analyses
Amounts of grass yield and biomass N in grass as
well as the amounts of NO3-N, NH4-N and SMN in
the soil (0–0.6 m) were analysed statistically using
a mixed model, where treatment, sampling date and
their interactions were used as xed effects while
block, block x treatment and block x sampling date
were used as random effects. The soil data were
log-transformed before analysis because of skewed
distributions.
For statistical analyses of the surface runoff
results, log-transformation was used for the TN
and NH
4
-N values. The data from the two study
phases were analysed together using a mixed mod-
el whereby study, treatment, and their interactions
were used as xed effects, whereas block, block ×
treatment, and block × study were used as random
effects. Each block included two or three adjacent
plots with different treatments. Soil, plant and run-
off analyses were performed using an SAS/MIXED
procedure.
The effect of application technique on NH
3
concentration in chambers was studied for each
measurement period with analysis of variance ac-
cording to a randomized complete block arrange-
ment (Steel and Torrie 1981) with three replica-
tions. The effect was considered signicant with
p values <0.05. The analysis was carried out with
the GLM procedure of SAS statistical software ver-
sion 6.12.
Results and discussion
Dry Matter and Nitrogen Uptake
of Grass
In Phase I, the mean DM grass yields (8.0–9.3 t
ha-1 yr-1) and N uptakes (160–200 kg ha-1 yr-1) were
higher in the MF plots than in the slurry treated plots
(Table 2). There were no signicant differences in
the DM yields or N uptakes between treatments
in the rst cuts, probably since all the treatments
received the same amount of fertilizer N in spring.
In contrast, in the second cuts, the DM yields and
N uptakes were statistically (p < 0.05) lower in the
slurry treated plots than in the MF plots, although
the same amount of mineral N (ca 80 kg ha-1) was
spread in all treatments.
In Phase II (biannual slurry application), the
mean DM yields (5.5–7.0 t ha-1 yr-1) and N uptakes
(90–125 kg ha-1 yr-1) were lower than in Phase I,
although the mean applications of mineral N were
44, 53 and 20 kg ha
-1
yr
-1
higher on the SB, IN
and MF plots, respectively, than in Phase I. This
time, however, three-fourths of the applied mineral
N originated from cattle slurry on the SB and IN
plots, whereas in Phase I, half of the mineral N
was from slurry and half from mineral fertilizer. As
in Phase I, there were no statistical differences in
uptake between the treatments in the rst cuts, but
in the second cuts, the N uptake was statistically
higher (p < 0.05) in the MF and IN plots than in
the SB plots in 1998–1999.
Ammonia volatilization
The NH3 volatilization from SB was considerable,
which is indicated both by NH3 volatilization rates
in the ambient air and by NH3 concentrations in the
chambers (Table 3). Over IN, chamber concentra-
tions of NH3 were low and the volatilization rates
in ambient air were close to zero and often slightly
negative, which may indicate deposition of NH
3
that
drifted from SB. Despite this disturbance, it can
be concluded that the volatilization of NH3 from
injected slurry was small compared with broadcast
slurry. To obtain undisturbed results, slurry injection
and the subsequent NH3 measurement should have
been carried out before broadcasting. However,
different timing of the applications would have
compromised the comparison of SB and IN by
making a difference in the weather conditions at
application and during a few days thereafter.
There are also earlier studies showing that in-
jection of slurry into soil effectively prevents NH3
volatilization (e.g. Frost 1994, Dosch and Gutser
1996). Most of the previous work has been done
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
332
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
333
with mass balance or wind tunnel techniques, but
the JTI method used in this study has proven to
give results comparable with other methods (Mis-
selbrook et al. 2005b). Mattila and Joki-Tokola
(2003) used the same JTI equipment as in this
study, and measured negligible NH3 volatilization
from cattle slurry injected to ley in summer, and a
40% loss, on average, of NH
4
-N from broadcast
slurry. The results reported here indicate that sur-
face application in autumn may cause high losses
despite the lower temperature. In cooler weather,
the volatilization rate is lower, but total losses may
still be considerable, as also observed by Sommer
et al. (1991). The effect of temperature on NH
3
volatilization is interconnected with many other
factors such as solar radiation, air humidity, soil
moisture content and drying of manure after appli-
cation. Temperature as such has not always proven
an important factor in determining NH
3
volatili-
zation from applied manure (e.g. Braschkat et al.
1997, Sommer and Olesen 2000, Misselbrook et
al. 2005a).
On the SB plots, ammonia volatilization was
the largest measured single N ow into the envi-
ronment (15% of TN application and 24% of the
mineral N). The NH3 volatilization was highest on
the application day and decreased rapidly during
the following two days (Table 3). The decrease
is assumed to result from a rainfall and a reduc-
tion in the concentration of NH
4
-N in the slurry
although not measured after the application. Am-
monia volatilization was higher in 2000 than in
Table 2. Over-ground grass dry matter yields and biomass N. Percentage of biomass N from the previous mineral N ap-
plication is given in parenthesis.
Date of harvest Yield, kg ha-1 pBiomass N, kg ha-1 p
SB IN MF SB IN MF
Annual slurry application (Study phase I)
13 June 1996 4600 4600 5100 0.65 150 (134) 150 (134) 150 (134) 0.81
20 August 1996 3800a3400a4500b0.04 54a (69) 49a (58) 80b (99) 0.02
Total 1996 8400 8000 9600 0.12 204 (107) 199 (101) 230 (119) 0.16
23 June 1997 5000 4700 4500 0.36 85 (173) 86 (176) 96 (196) 0.81
24 September 1997 3300a3200a4500b0.03 37a (47) 37a (47) 71b (89) 0.02
Total 1997 8300 7900 9000 0.40 122 (96) 123 (97) 167 (129) 0.18
Mean 96–97 8400 8000 9300 163 (102) 161 (99) 199 (124)
Biannual slurry application (Study phase II)
19 June 1998 2700 3000 2500 0.11 59 (123) 63 (131) 52 (108) 0.11
4 September 1998 2300a2700b3600c<0.01 34a (36) 47b (48) 57b (62) 0.01
Total 1998 5000a5700b6100b0.03 93(65) 110 (76) 109 (78) 0.08
24 June 1999 4200 4000 4800 0.35 81 (60) 71 (50) 100 (100) 0.12
12 October 1999†700 1100 1100 0.11 14a (13) 30b (25) 36b (36) 0.03
Total 1999 4900 5100 5900 0.32 95 (39) 101 (39) 136 (68) 0.09
21 June 2000 3100 3000 3600 0.31 42 (33) 47 (35) 56 (56) 0.18
4 October 2000 3400 4100 5300 0.20 44 (47) 60 (57) 73 (73) 0.13
Total 2000 6500 7100 8900 0.22 86 (39) 107 (44) 129 (65) 0.13
Mean 98–00 5500 6000 7000 91 (48) 106 (53) 125 (70)
†The grass was not harvested.
Different letters in the same row indicate a signicant difference between treatments (p < 0.05).
SB = surface broadcasting of slurry, IN = slurry injection, MF = mineral fertilization
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
334
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
335
1999, which may have resulted at least partly from
higher ambient temperatures (Table 3) and a higher
DM content of the slurry: 6.9 and 8.3% in 1999 and
2000, respectively (Uusi-Kämppä and Heinonen-
Tanski 2008).
Amount of surface runoff
The mean annual precipitation during the experiment
was 626 mm (586–673 mm) which is near the long-
term (1971–2000) average of 607 mm. On our eld,
the surface runoff was 10–20% of the precipitation
(Table 4). The mean annual surface runoff (110
mm) in the Phase II was comparable to the surface
runoff (110 mm) on a nearby clay soil under timothy
and red clover in September 1992–August 1993
(Uusitalo et al. 2007). In Phase I, surface runoff (64
mm) was only half of that was measured in Phase
II. On a coarse-textured pasture soil, Saarijärvi et
al. (2007) measured surface runoff of 66–107 mm
which was around 40% of the total runoff and 15%
of the average precipitation in Eastern Finland. The
measured volumes of surface runoff on our eld
agreed quite well with these ndings, indicating that
there has been deep percolation (drainow) as well.
However, if the drainage system does not function
well or there is no drainage system, the surface
runoff can be multifold compared to volumes of
drainow from well-drained grass elds (Turtola
and Paajanen 1995, Bilotta et al. 2008).
Nitrogen losses in surface runoff
Owing to the relatively small amounts of fertilizer
and slurry N in Phase I and lack of heavy rainfall
after the slurry applications in summer, losses of TN,
NH
4
-N and NO
3
-N in surface runoff were negligible
from all treatments over the 18-month monitoring
period (Table 4). In fact, the volumes of surface
Table 3. Concentration of NH3 in chambers on surface broadcasting (SB) and injection (IN) plots, NH3 volatilization in
ambient air and weather conditions during the measurement periods.
Date Period NH3 concentration
µg m-3
pNH3 volatilization from SB
SB IN Volatilization rate
NH3-N, g ha-1 h-1
N loss,
% of NH4-N
Temperature,
°C
Wind,
m s-1
Precipitation,
mm
1999
27 Oct 1 7896a76b0.014 1230 20 6.0 2.8 1 (0.5)
27 Oct 2 4216a128b0.025 791 4.0 1.6 0
28 Oct 3 1400a40b0.002 220 2.5 1.3 0 (4)
29 Oct 4 593a29b0.000 201 10.0 2.9 0
2000
23 Oct 1 9657a103b0.032 1492 33 11.0 1.4 0
23 Oct 2 7476a105b0.019 920 8.0 1.3 0 (5)
24 Oct 3 730a55b0.016 154 9.0 3.8 <0.5 (5.5)
25 Oct 4 198a32b0.013 20 9.0 3.3 0
Superscripts denote statistically signicant differences. Volatilization from injected slurry is excluded, because it was close to zero and
may have been affected by NH3 drifting from broadcast slurry. Nitrogen loss values include measured emissions from all the four peri-
ods and estimated emissions during their intervals. Precipitation between the end of a measurement period and the start of the next peri-
od is in parenthesis.
SB = surface broadcasting of slurry, IN = slurry injection, MF = mineral fertilization.
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
334
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
335
runoff and N losses were higher before slurry ap-
plications during the snow melting in spring 1996.
In Phase II, over the 36-month monitoring
period the cumulative losses of NH
4
-N and TN,
5.2 kg ha-1 and 20 kg ha-1, respectively, were still
relatively small in surface runoff from the plots
with slurry broadcasting (Table 4). The TN losses
were small, although the slurry TN rates exceeded
the currently allowed maximum amount of 170 kg
ha-1 yr-1.
Injection further reduced the originally small
surface runoff losses of NH4-N and TN by 83% (p
< 0.001) and 34% (p < 0.01), respectively, com-
pared with surface broadcasting, although a little
more slurry TN (20–30 kg ha
-1
yr
-1
) was spread
on the IN plots. On a ne sandy soil, Turtola and
Kemppainen (1998) measured great annual N loss-
es in surface runoff from grass with autumn broad-
cast slurry, 16–36 kg ha-1 yr-1 and 7.7–22 kg ha-1
yr-1 for TN and NH4-N, respectively. In their study,
however, the amount of TN applied in autumn was
one-third higher and the volumes of surface run-
off were three times greater than in Phase II of
our study. In Norway, Uhlen (1978) reported that
surface runoff losses of TN and NH4-N were 8 and
4 kg ha-1, respectively, during the next 14 months
after autumn application of 60 t ha
-1
semi-liquid
cow manure (228 kg TN ha-1) to grass. On boreal
pastures, too, the annual losses of TN in surface
runoff were small (below 5 kg ha-1) in the study
of Saarijärvi (2008), although the pastures often
receive more N than silage grasses.
However, after slurry application (140–155 kg
TN ha-1) to wet soil on October 16, 1998, followed
with heavy rainfall (60 mm) and surface runoff (10
mm) during the next two weeks, the mean losses of
TN and NH4-N in surface runoff from the SB plots
were 9.3 and 3.5 kg ha-1over 2.5 months, respec-
tively (Fig. 2), being 47% of TN and 67% of NH4-
N losses over the whole 3-year study phase. During
three days after slurry application, incidental TN
losses were highest, at 6.8, 0.5 and 0.1 kg ha-1 from
the SB, IN and MF plots, respectively. Soon after
slurry application, the mean TN concentration in
surface runoff water was 92 mg l-1 for SB, but less
for IN (7.6 mg l-1) and MF (1.2 mg l-1; Fig. 3). Since
concentrations of NO
3
-N and NH
4
-N from SB plots
were < 0.1 mg l-1 and < 51.1 mg l-1, respectively, a
large part of TN was in organic form. In June 1998,
surface runoff (4 mm) from the grass stubble was
also high with high rainfall (99 mm) but N losses
Table 4. Precipitation and means of surface runoff and losses of total nitrogen, ammonium nitrogen and nitrate nitrogen
to surface runoff water.
Study period Precipitation,
mm
n†Surface runoff,
mm
Total nitrogen
kg ha-1
Ammonium
nitrogen
Nitrate nitrogen
SB IN MF SB IN MF SB IN MF SB IN MF
Annual slurry application (Study phase I)
1 Jan 1996–18 June 1996 204 11 67 71 63 4.6 3.9 4.6 0.7 0.8 0.9 2.1 1.2 1.5
19 June 1996–31 Dec 1997 1065 21 56 66 63 2.1 2.2 1.4 0.1 0.1 0.1 0.3 0.5 0.2
Total 1269 32 123 137 126 6.7 6.1 6.0 0.8 0.9 1.0 2.4 1.7 1.7
Biannual slurry application (Study phase II)
1 Jan 1998–16 Oct 1998 507 21 102 104 82 3.4 3.5 2.6 0.1 0.1 0.1 0.1 0.2 0.1
17 Oct 1998–31 Dec 1998 120 8 36 36 30 9.3 1.2 0.5 3.5 0.1 0.1 0.2 0.1 0.1
1 Jan 1999–30 June 1999 221 16 110 116 99 3.6 3.0 1.6 1.2 0.3 0.2 0.3 0.3 0.2
1 July 1999–20 Oct 2000 845 17 63 63 40 1.6 2.5 1.4 0.3 0.4 0.3 0.3 0.7 0.2
21 Oct 2000–31 Dec 2000 172 9 29 33 17 1.7 2.7 0.8 0.1 0 0 0.2 0.9 0.2
Total 1865 71 340 352 268 19.6 12.9 6.9 5.2 0.9 0.7 1.1 2.2 0.8
† n = number of samplings
SB = surface broadcasting of slurry, IN = slurry injection, MF = mineral fertilization
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
336
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
337
were negligible because slurry had not yet been
applied (Fig. 2).
While the N losses were small on our experi-
mental eld with a slope of 0.9–1.7%, losses may
be higher on steep slopes with heavy rainfalls soon
after slurry application. The surface application of
manure is no longer allowed on elds with an aver-
age slope of over 10% (Finlex 2000). Heathwaite
et al. (1998) have also shown that the 10-m un-
treated buffer zone below the source area applied
with cattle slurry reduced the TN load by 75% in
surface runoff. Thus in our study, the 10-m buffer
zone probably decreased nitrogen losses from all
treatments. At present, nitrogen losses from slurry
applied elds are mitigated, since the application
of N fertilizers (including slurry N) is not allowed
Fig. 2. Cumulative losses of to-
tal nitrogen in surface runoff
and periodic precipitation from
summer 1996 to autumn 2000.
Slurry applications are marked
by arrows. (Au, autumn; Sp,
spring; Su, summer)
Fig. 3. The average concentra-
tions of total nitrogen in sur-
face runoff during 1996–2000.
Slurry applications are marked
by arrows. The concentration
was off scale twice in broad-
cast plots.
0
100
200
300
400
Su 96
Au 96
Sp 97
Su 97
Au 97
Sp 98
Su 98
Au 98
Sp 99
Su 99
Au 99
Sp 00
Su 00
Au 00
Precipitation (mm)
0
3
6
9
12
15
18
21
24
Total nitrogen (kg ha-1) in surface runoff
Precipitation Broadcasting Injection Mineral fertilization
0
2
4
6
8
10
Total nitrogen (mg l
-1
) in surface runoff
Broadcasting
Injection
Mineral fertilization
1996 1997 1998 1999 2000
92 20
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
336
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
337
on areas closer than 5 metres to a watercourse. And
along the width of the next ve metres, surface
application of N fertilizers is prohibited if the eld
slope exceeds two per cent (Finlex 2000). Even
wider unmanured areas would be needed on eld
edges with steep slopes along lakes and rivers to
decrease direct N losses in surface runoff from
source elds to water.
The cumulative load of NO3-N in surface run-
off was small in all treatments (0.8–2.2 kg ha
-1
),
being highest in the IN plots over the 3-year study
phase. The small NO3-N losses in surface runoff
from grass are consistent with results from studies
of Uhlen (1978), Turtola and Kemppainen (1998),
Ridley et al. (2001), Smith et al (2001), and Saari-
järvi (2008). Ploughing of grass soil in October
2000 increased slightly losses of NO3-N and TN in
surface runoff but decreased NH4-N losses (Table
4).
Soil mineral nitrogen and
nitrogen leaching
Although in Phase II slurry was spread to the grass
ley in autumn, the SMN amounts measured in the
following spring were only slightly higher (0–30 kg
ha-1) or even lower (4–6 kg ha-1) than the amounts
measured in autumn before the slurry applications.
This demonstrates that the slurry N added in the
autumn (105–155 kg TN; 60–80 kg NH4-N) might
have volatilized, become converted to organic form
in the soil or leached. In the IN plots, however, the
SMN amounts in spring were signicantly higher (p
= 0.03) than in the SB plots, probably due to lower
NH
3
volatilization and slightly higher N input. Also
the NO3-N amounts were 6–7 kg ha-1 higher in the
IN plots compared to SB plots in May 1999 and in
October 1999 (p < 0.001). Cameron et al. (1996)
observed that NO
3
-N leaching was consistently
higher after subsurface injection of dairy pond
sludge compared to surface application. According
these results slurry injection may thus increase N
leaching from grass elds.
The summer season 1999 was fairly warm and
dry and therefore only one grass yield could be
harvested (Table 2). Hooda et al. (1998) and Sc-
holeeld et al. (1993) have reported that NO
3
-N
leaching is higher after a dry and warm summer
than after a wet and cool summer season, since in
dry conditions nitrication may be high whereas
denitrication and plant uptake of N can be lower
than during cool and wet years. In October 1999
and April 2000, the NO3-N amounts in soil were
3–7 kg ha-1 higher than measured at other times in
this study (Fig. 4) and, thus, there was a slightly
higher risk for NO
3
-N leaching from the grassland.
0
20
40
60
80
7.10.96
8.11.97
7.05.98
15.09.98
4.05.99
20.10.99
26.04.00
16.10.00
27.10.00
7.10.96
8.11.97
7.05.98
15.09.98
4.05.99
20.10.99
26.04.00
16.10.00
27.10.00
7.10.96
8.11.97
7.05.98
15.09.98
4.05.99
20.10.99
26.04.00
16.10.00
27.10.00
Broadcasting Injection Mineral fertilization
Soil mineral nitrogen (kg ha-1 )
NO -N
NH -N
4
3
16
19
45
28
16
11
26
16
0.5
16
19
45
11
28
16
26
16
0.5
16
19
45
11
43
16
43
16
18
Fig. 4. Amounts of soil mineral
nitrogen (±S.D.) at 0–60 cm dur-
ing 1996–2000. The number of
weeks passed between previous
slurry application and soil sam-
pling is shown inside of the bars.
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
338
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
339
Immediately after ploughing in October 2000,
the amount of SMN was greater in the plots where
slurry had been previously injected (p < 0.01) than
in those in which slurry had been broadcast (Fig.
4). In the SB plots, part of the slurry N was volatil-
ized as NH3 and therefore also the NH4-N amounts
in soil were smaller than in IN plots (p < 0.01).
The experiment continued for 5 years and
before that the eld had not received manure for
years, which increased the capacity of the soil to
retain excess slurry N. In this respect, the situation
is often different on animal farms, where the same
grass elds have been manured for decades. More-
over clay soil has a higher capacity for retaining
NH
4
-N than coarse textured soils. Since most Finn-
ish cattle farms are situated on areas with coarse
textured soils, the risk for higher N leaching losses
to water is more likely than in our study.
Nitrogen balance and fate of nitrogen
During the ve study years, the cumulative eld
TN surpluses were 687, 971 and 65 kg ha-1 in the
SB, IN and MF plots, respectively (Fig. 5). In Phase
I, TN balances were negative on the MF plots. In
Phase II, the amount of non-recovered N was ex-
tremely high, up to 58% (ca 210 kg ha-1 yr-1) and
72% (ca 280 kg ha-1 yr-1) of the TN input on the
SB and IN plots, respectively (Fig. 5). According
to Macdonald and Jones (2003), 20–70% of the N
inputs to agricultural systems may be unaccounted
for. Although denitrication was not measured it is
obvious that large part of organic N applied in slurry
was not mineralized and thus it was not recognized
as SMN. In Canada, Bittman et al. (2007) estimated
that ca 30% of applied manure-N was stored in soil
organic matter. A signicant amount of NH4-N in
slurry might also have been microbially immobi-
lized soon after application due to decomposition
of fatty acids in slurry (Kirchmann and Lundvall
1993, Sørensen and Amato 2002). According to the
results of Huss-Danell and Chaia (2007), over 30
kg N ha-1 can be incorporated into grass roots in
the northern part of Sweden. Pierzynski and Gehl
(2005) showed that some of the N saved from NH3
emissions may have been lost as N2O from slurry
injected elds. In a Finnish study, however, only
ca 0.7% of cattle slurry N incorporated with a disc
was lost as N2O uxes (Syväsalo et al. 2006, Perälä
et al. 2006). Ammonium can also be xed into clay
minerals or nitrate can be leached into subsurface
drains or ground water.
0
100
200
300
400
Input
Output
Input
Output
Input
Output
Input
Output
Input
Output
Input
Output
Broadcasting Injection Mineral
fertilization
Broadcasting Injection Mineral
fertilization
N input/output (kg ha
-1
)
Surface runoff Plant uptake Non-mineral N Mineral N
Phase I Phase II
NH volatilization
3
Fig. 5. Input of mineral N and
non-mineral N and output of N
(plant uptake, NH3 volatilization
and TN in surface runoff) during
study Phases I and II.
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
338
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 327–340.
339
Conclusions
Despite cool autumn weather, a considerable portion
(20–33%) of the surface-applied slurry NH
4
-N was
lost through ammonia volatilization within a few
days after application, but the injection of slurry
into the soil effectively prevented this. Nitrogen
losses in surface runoff from grass eld applied with
slurry were small during the ve study years, except
when heavy rainfall occurred after slurry application
in autumn. Although high slurry N amounts were
added to grass, nitrogen leaching risk was surpris-
ingly small from clay soil. If over-dosing of manure
would continue longer, however, the situation could
be different. When moderate slurry amounts (as in
Phase I) are applied in summer and by a technique
with low NH
3
emissions most of the N is kept within
the nutrient cycle of the farm. These study results
can be directly applied to clay soils, whereas on
coarse textured soils, the leaching losses may be
higher than in this study.
Acknowledgements. We are grateful to Mr. Risto
Tanni, Mr. Ari Seppänen, Mr. Aaro Närvänen, Mr.
Pekka Kivistö and Mr. Petri Kapuinen, Lic.Sc. (Agr.
Eng.) for their technical assistance during the experi-
ment. We thank biometrician Lauri Jauhiainen for
his statistical expertise. Critical comments and sug-
gestions by Prof. Eila Turtola, Adjunct Prof. Helvi
Heinonen-Tanski and the anonymous referees are
gratefully acknowledged. Financial support for this
study was provided by the Ministry of Agriculture
and Forestry.
References
Bilotta, G.S., Brazier, R.E., Haygarth, P.M., Macleod,
C.J.A., Butler, P., Granger, S., Krueger, T., Freer, J. &
Quinton J. 2008. Rethinking the contribution of drai-
ned and undrained grasslands to sediment-related wa-
ter quality problems. Journal of Environmental Quality
37: 906– 914.
Bittman, S., Kowalenko, C.G., Forge, T., Hunt, D.E., Bou-
naix, F. & Patni, N. 2007. Agronomic effects of multi-year
surface-banding of dairy slurry on grass. Bioresource
Technology 98: 3249–3258.
Braschkat, J., Mannheim, T. & Marschner, H. 1997. Estima-
tion of ammonia losses after application of liquid cattle
manure on grassland. Zeitschrift fűr Panzenernährung
und Bodenkunde 160: 117–123.
Cameron, K.C., Rate, A.W., Noonan, M.J., Moore, S.,
Smith, N.P. & Kerr L.E. 1996. Lysimeter study of the fate
of nutrients following subsurface injection and surface
application of dair y pond sludge to pasture. Agriculture,
Ecosystems & Environment 58: 187–197.
Dosch, P. & Gutser, R. 1996. Reducing N losses (NH3, N2O,
N2) and immobilization from slurry through optimized ap-
plication techniques. Fertilizer Research 43: 165–171.
Drebs, A., Nordlund, A., Karlsson, P., Helminen, J. & Ris-
sanen P. 2002. Climatological statistics of Finland 1971–
2000. Helsinki: Finnish Meteorological Institute.
Finlex. 1998. Decision of the Council of State on the re-
striction of discharge of nitrates from agriculture into
waters (219/1998). Issued in Helsinki, 19 March 1998.
Helsinki: Finlex. Cited 16 April 2009. Available on the
Internet: http://www.nlex.//laki/alkup/1998/19980219
(in Finnish)
Finlex. 2000. Government Dec ree on the restrictio n
of discharge of nitr ates from agri cultur e into waters
(931/200 0). Issued in Helsinki, 9 November 2000. Hel-
sinki: Finlex. Cited 13 March 2009. Available on the
Internet: http://www.nlex.//laki/kaannokset/2000/
en20000931.pdf
Frost, J.P. 1994. Effect of spreading method, application
rate and dilution on ammonia volatilization from cattle
slurry. Grass and Forage Science 49: 391–400.
Grönroos, J., Mattila, P., Perälä, P., Nousiainen, J., Regi-
na, K., Saarinen K. & Mikkola-Pusa, J. 2009. Develop-
ment of ammonia emission inventory in Finland. Revised
model for agriculture. Finnish Environment 8/2009. Hel-
sinki: Finnish Environment Institute. 60 p.
Heathwaite, A.L., Grif ths, P. & Parkinson, R.J. 1998. Ni-
trogen and phosphorus in runoff from grassland with buf-
fer strips following application of fertilizers and manures.
Soil Use and Management 14: 142–148.
Hooda, P.S., Moynagh, M., Svoboda, I.F. & Anderson, H.A.
1998. A comparative study of nitrate leaching from in-
tensively managed monoculture grass and grass-clo-
ver pastures. The Journal of Agricultural Science 131:
267–275.
Huss-Danell, K. & Chaia, E. 2007. N2 xation and nitro-
gen allocation to above and below ground plant parts
in red clower-grasslands. Plant and Soil 299: 215–226.
Kapuinen, P. 1998. A light liquid manure injector for grass-
lands in polar farming. In: Chastain, J.P. (Ed.) 4th Interna-
tional Dairy Housing Conference, Conference Proceed-
ings. American Society of Agricultural Engineers publi-
cation 01-98. St.Joseph, MI, USA: p. 346–354
Kirchmann, H. & Lundvall, A. 1993. Relationship between
N immobilization and volatile fatty acids in soil after ap-
plication of pig and cattle slurry. Biology and Fertility of
Soils. 15: 161–164.
Macdonald, A.J. & Jones, D.L. 2003. Missing N: is the so-
lution in dissolved N? In: Hatch, D.J. Controlling Nitro -
gen Flows and Losses. Wageningen: Wageningen Ac-
ademic Publishers. p. 600 –605.
Malgeryd, J. 1996. Åtgärder för att minska ammoniake-
AGRICULTURAL AND FOOD SCIENCE
Uusi-Kämppä, J. et al. Nitrogen losses from broadcast or injected slurry
340
missionerna vid spridning av stallgödsel (Measures to
reduce ammonia emissions following application of an-
imal manure). JTI- rapport, Lantbruk & Industri nr 229.
Uppsala: Swedish Institute of Agricultural Engineering.
(In Swedish with English summar y). 126 p.
Mattila, P.K. & Joki-Tokola. E. 2003. Effect of treatment
and application technique of cattle slurry on its utiliza-
tion by ley: I. Slurry properties and ammonia volatiliza-
tion. Nutrient Cycling in Agroecosystems 65: 221–230.
Ministry of Agriculture and Forestry. 1998. Perustuki maati-
loille. 1998 Maatalouden ympäristötuki. Helsinki: Ministry
of Agriculture and Forestry. 32 p. (In Finnish).
Misselbrook, T.H., Nicholson, F.A . & Chambers, B.J.
2005a. Predicting ammonia losses following the appli-
cation of livestock manure to land. Bioresource Tech-
nology 96: 159–168.
Misselbrook, T.H., Nicholson, F.A., Chambers, B.J. & John-
son, R.A. 2005b. Measuring ammonia emissions from
land applied manure: an intercomparison of commonly
used samplers and techniques. Environmental Polluti-
on 135: 389–397.
Perälä, P., Kapuinen, P., Esala, M., Tyynelä, S. & Regina,
K. 2006. Inuence of slurry and mineral fertiliser appli-
cation techniques on N2O and CH4 uxes from a barley
eld in southern Finland. Agriculture, Ecosystems & En-
vironment 117: 71–78.
Pierzynski, G.M. & Gehl, K.A. 2005. Plant nutrient issues
for sustainable land application. Journal of Environmen-
tal Quality 34: 18–28.
Puustinen, M. 1994. Effect of soil tillage on soil erosion
and nutrient transport in plough layer runoff. Publica-
tions of the Water and Environment Research Institute
17: 71–90. Helsinki, National Board of Waters and the
Environment, Finland.
Ridley, A.M., White, R.E., Helyar, K.R., Morrison, G.R.,
Heng, L.K. & Fisher, R. 2001. Nitrate leaching loss un-
der annual and perennial pastures with and without lime
on a duplex (texture contrast) soil in humid southeastern
Australia. European Journal of Soil Science 52: 237–252.
Saarijärvi, K. 2008. Nitrogen cycling on intensively man-
aged boreal dairy pastures. Doctoral Dissertation. Ag-
rifood Research Reports 134. Jokioinen: MTT Agrifood
Research Finland. 71 p. Cited 15 April 2010. Available
on the Internet: http://www.mtt./met/pdf/met134.pdf
Saarijärvi, K., Virkajärvi, P. & Heinonen-Tanski, H. 2007.
Nitrogen leaching and herbage production on intensive-
ly managed grass-clover pastures on sandy soil in Fin-
land. European Jour nal of Soil Science 58: 1382–1392.
Scholeeld, D., Tyson, K.C., Garwood, E.A., Armstrong,
A.C., Hawkins, J. & Stone, A.C. 1993. Nitrate leaching
from grazed grassland lysimeters: effects of fertilizer in-
put, eld drainage, age of sward and patterns of weath-
er. Journal of Soil Science 44: 601–613.
Sippola, J. & Yläranta, T. 1985. Mineral nitrogen reserves
in soil and nitrogen fertilization of barley. Annales Agri-
culturae Fenniae 24: 117–124.
Smith, K.A., Jackson, D.R. & Pepper, T.J. 2001. Nutrient
losses by surface run-off following the application of or-
ganic manures to arable land. 1. Nitrogen. Environmen-
tal Pollution 112: 41–51.
Soil Survey Staff, 1996. Keys to soil taxonomy. 7th ed.
Washington, DC: USDA, Natural Resources Conser-
vation Service.
Sommer, S.G. & Olesen, J.E. 2000. Modelling ammonia
volatilization from animal slurry applied with trail hoses
to cereals. Atmospheric Environment 34: 2361–2372.
Sommer, S.G., Olesen, J.E. & Christensen, B.T. 1991. Ef-
fects of temperature, wind speed and air humidity on am-
monia volatilization from surface applied cattle slurry. The
Journal of Agricultural Science 117: 91–100.
Sørensen, P. & Amato, M. 2002. Remineralisation and re-
sidual effects of N after application of pig slurry to soil.
European Journal of Agronomy 16: 81–95.
Steel, R.G.D. & Torrie, J.H. 1981. Principles and procedures
of statistics, 2nd ed. McGraw-Hill, Singapore.
Svensson, L., 1994. A new dynamic chamber technique
for measuring ammonia emissions from land-spread
manure and fertilizers. Acta Agriculturae Scandinavica,
Section B , Soil Plant Science 44: 35–46.
Syväsalo, E., Regina, K., Turtola, E., Lemola, R. & Esala
M. 2006. Fluxes of nitrous oxide and methane, and ni-
trogen leaching from organically and conventionally cul-
tivated sandy soil in western Finland. Agriculture, Eco -
systems & Environment 113: 342–348.
Turtola, E. 1989. Effect of storage and sulphuric acid addi-
tion on analytical results of nitrogen and phosphorus in
water samples containing eroded material. Aqua Fen-
nica 19(2): 109–112.
Turtola, E. & Kemppainen, E. 1998. Nitrogen and phospho-
rus losses in surface runoff and drainage water after ap-
plication of slurry and mineral fertilizer to perennial grass
ley. Agricultural and Food Science in Finland 7: 569–581.
Turtola, E. & Paajanen, A. 1995. Inuence of improved
subsurface drainage on phosphorus losses and nitro-
gen leaching from a heavy clay soil. Agricultural Water
Management 28: 295–310.
Uhlen, G. 1978. Nutrient leaching and surface runoff in eld
lysimeters on a cultivated soil. II. Effects of farm yard
manure spread on a frozen ground and mixed in the soil
on a water pollution. Scientic Reports of the Agricultur-
al University of Norway. 57(28): 1–23.
Uusi-Kämppä, J. & Heinonen-Tanski, H. 2008. Evaluating
slurry broadcasting and injection to ley for phosphorus
losses and fecal microorganisms in surface runoff. Jour-
nal of Environmental Qualit y 37: 2339–2350.
Uusitalo, R., Turtola, E. & Lemola, R. 2007. Phosphorus
losses from a subdrained clayey soil as affected by cul-
tivation practices. Agricultural and Food Science 16:
352–365.
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
341
© Agricultural and Food Science
Manuscript received November 2009
Cultivar improvement and environmental variability
in yield removed nitrogen of spring cereals and
rapeseed in northern growing conditions according
to a long-term dataset
Pirjo Peltonen-Sainio* and Lauri Jauhiainen
MTT Agrifood Research Finland, Plant Production Research, FI-31600 Jokioinen, Finland
*email pirjo.peltonen-sainio@mtt.
The balance between applied and harvested nitrogen (yield removed nitrogen, YRN %) is a recognized
indicator of the risk of N leaching. In this study we monitored the genetic improvements and environmen-
tal variability as well as differences among crop species (spring cereals and rapeseed) in YRN in order to
characterize changes that have occurred and environmental constraints associated with reducing N leaching
into the environment. MTT long-term multi-location eld experiments for spring cereals (Hordeum vulgare
L., Avena sativa L. and Triticum aestivum L.), turnip rape (Brassica rapa L.), and oilseed rape (B. napus L.)
were conducted in 1988–2008, covering each crop’s main production regions. Yield (kg ha-1) was recorded
and grain/seed nitrogen content (Ngrain, g kg-1) analyzed. Total yield N (Nyield, kg ha-1) was determined and
YRN (%) was calculated as a ratio between applied and harvested N. A mixed model was used to separate
genetic and environmental effects. Year and location had marked effects on YRN and Nyield. Average early
and/or late season precipitation was often most advantageous for Nyield in cereals, while in dry seasons N
uptake is likely restricted and in rainy seasons N leaching is often severe. Elevated temperatures during
early and/or late growth phases had more consistent, negative impacts on YRN and/or Nyield for all crops,
except oilseed rape. In addition to substantial variability caused by the environment, it was evident that
genetic improvements in YRN have taken place. Hence, YRN can be improved by cultivar selection and
through favouring crops with high YRN such as oat in crop rotations.
Key-words: nitrogen, growing conditions, cultivar, barley, oat, wheat, oilseed rape, turnip rape, yield,
protein content
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
342
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
343
Introduction
Risks associated with nitrogen leaching into natural
water systems is high in northern Europe and espe-
cially in Finland with its more than 100000 lakes,
14000 km of Baltic coastline (Peltonen-Sainio et
al. 2009d), and substantial annual precipitation
averaging 500–650 mm for 1970–2000 (Finnish
Meteorological Institute). While grasslands ensure
continuous ground cover in the central and northern
parts of Finland, spring-sown crops provide only
partial ground cover in the main production areas
in the south of the country. The capacity of spring
sown crops to utilize nitrogen (N) determines the
potential risk for N leaching in the major production
areas of Finland, with typical peaks in autumn and
winter (Syväsalo et al. 2006). Nitrogen surplus is
evident when the quantity of N applied is greater than
that used for production of crop biomass (Rankinen
et al. 2007). For this and economic reasons it is
essential that N application occurs when the crop
needs it, when it can be used for biomass production
and is harvested instead of remaining unused in the
soil (Peltonen-Sainio et al. 2009d). Yield removed
nitrogen (YRN, %) represents the ratio between
applied and harvested N.
Most N in harvested grains derives from N
translocated from senescing vegetative plant parts
(Cox et al. 1985, Papakosta and Gagianas 1991,
Bulman and Smith 1994). When available, N can
also be taken up from the soil during grain lling
(Cox et al. 1985). In northern Europe this occurs,
for example, when N is not taken up adequately
at pre-heading because of typical early summer
drought (Peltonen-Sainio et al. 2010), manure is
used or elevated late summer temperatures stim-
ulate excess N mobilization from soil (Rajala et
al. 2007). Typically N uptake values for fertilized
wheat (Triticum aestivum L.) and barley (Hordeum
vulgare L.) range from 20% to 100% of fertiliser
applied in temperate regions (Gauer et al. 1992,
Le Gouis et al. 2000, Sinebo et al. 2003, Noulas
et al. 2004). This indicates considerable induced
variability in N uptake according to growing con-
ditions and challenges sustainable and economic
fertilizer use.
Genetic variation in N uptake was reported for
cereals (Kelly et al. 1995, Singh and Arora 2001).
However, in wheat no consistent correlations be-
tween N uptake and year of cultivar release were
recorded (Slafer et al. 1990, Calderini et al. 1995,
Foulkes et al., 1998) in contrast to six-row bar-
ley (Bulman et al. 1993) and oat (Avena sativa L.)
(Wych and Stuthman 1983, Welch and Leggett
1997). Modern cultivars have high yield poten-
tials (Peltonen-Sainio et al. 2009b) associated with
improvements in many N-related traits (Muurinen
2007). Early vigorous growth can also enhance N
uptake as shown in modern wheat lines (Liao et
al. 2004 and 2006). Genetic variation and gains
were reported for other N traits that are important
for efcient N use (Woodend et al. 1986, Papako-
sta 1994, Singh and Arora 2001). Improvements
in key N traits are essential for efcient N uptake
and use.
In this study, using 20-year multi-location trial
datasets, we monitored the balance between genet-
ic improvements and environmental variability for
applied and harvested N in spring barley, oat and
wheat as well as turnip rape (Brassica rapa L.) and
oilseed rape (B. napus L.) in order to characterize
current position but also past changes in YRN. We
also assessed environmental constraints associated
with reducing N leaching into the environment for
spring cereal and rapeseed production systems.
Material and methods
Plant material, experimental design,
measurements and estimations
MTT long-term eld experiments for spring cereals
(barley, oat, and wheat), turnip rape, and oilseed
rape were conducted in 1988–2008 at 12–19 dif-
ferent locations in Finland according to crop and
production area. The experiments were part of
the MTT Ofcial Variety Trials and all followed
procedures specied for that purpose (Kangas et
al. 2005). In addition to MTT Agrifood Research
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
342
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
343
Finland, which has numerous regional research
units in Finland, some of the experiments were
organized by plant breeding companies and private
agricultural research stations.
All experiments were arranged as randomized
complete block designs or incomplete block de-
signs. Three to four replicates were used. Each
year the tested set of cultivars and breeding lines
changed, but long term-check cultivars were used.
Annual turnover of cultivars and breeding lines
was usually less than 20%, which made it possible
to separate effects of environment and genotype.
Plots were 7–10 m × 1.25 m, depending on location
and year. Seeding rate depended on crop, conform-
ing to the commonly used seeding rates in Finland.
Weeds were chemically controlled with commonly
used agents. Diseases were not routinely controlled
with fungicides to allow differences among entries
in disease resistance to be recorded. Fertilizer use
depended on cropping history, soil type and fertility
and was comparable with standard practices in Fin-
land. There was, however, no systematic reduction
or increase in N fertilizer use during the 20-year
study period.
Cereals and rapeseed were combine-harvested
and the grain/seed weighed (kg ha-1) after remov-
ing straw, weed seeds, and other particles. Grain/
seed moisture content was determined by weigh-
ing samples before and after oven drying, or more
recently by using a GAC II grain analysis computer
(DICKEY-john corporation, USA). Grain and seed
nitrogen content (Ngrain, g kg-1) were analyzed us-
ing the Kjeldahl-method. Yield and N content were
both adjusted to 0% moisture content. Total yield
N (N
yield
, kg ha
-1
) was calculated by multiplying
yield (kg ha-1) by Ngrain (%) and dividing by one
hundred.
Yield removed N (YRN, %) was calculated
by dividing Nyield (kg ha-1) by applied N fertilizer
rate (kg ha-1) and multiplying by one hundred. As
no unfertilised plots were included in these long-
term experiments, contribution of soil derived N
to YRN could not be distinguished. In addition
to YRN, we approximated the likely minimum to
maximum range of N use efciency (NUErange, kg
kg N-1) and N harvest index (NHIrange, %). Due to
absence of actual measurements of harvest index
(HI) for these long-term datasets, for above-ground
vegetative biomass (VEGE, kg ha-1) we estimated
ranges of HI documented for Finnish conditions
(Peltonen-Sainio et al. 2008, Hakala et al. 2009,
Pahkala et al. 2009). These were 0.44–0.60 for two-
row barley, 0.47–0.63 for six-row barley, 0.40–0.56
for oat, 0.35–0.48 for wheat, and 0.28–0.38 for tur-
nip rape and oilseed rape. Furthermore, due to lack
of information on N content of vegetative above-
ground biomass (N
vege
, g kg
-1
) in these experiments,
we used mean estimates of 0.58%, 0.43%, 0.41%,
0.42%, 0.90% and 0.90% for two- and six-row
barley, oat, wheat, turnip rape and rapeseed, re-
spectively. These estimates were based on results
from e.g. Muurinen et al. (2007), Peltonen-Sainio
et al. (2009c and unpublished crude data), and
Hocking et al. (1997). By this means, NUE
range
was estimated as yield divided by (N
grain
+N
vege
),
having minimum and maximum estimates for N
vege
according to HI range typical for each crop. Simi-
larly, NHI
range
was estimated as N
grain
divided by
(N
grain
+N
vege
) and multiplied by one hundred. As
NUErange and NHIrange were only rough estimates,
they were not necessarily included in statistical
analyses. Benchmarking with documented cereal
NHI values, showed our NHI
range
estimates to be
close to or even exceeding 80% (Spiertz and de
Vos 1983, Feil 1997, Noulas et al. 2004), though
NHI is strongly affected by e.g. weather conditions
(Feil, 1997).
Statistical analyses
The main purpose of the statistical analysis of yield
and nitrogen content was to estimate two effects:
genetic and environmental effects. A mixed model
technique was applied for this purpose using the fol-
lowing statistical model for each individual crop:
yijk= m + ai + bjk + eijk
where yij is the observed seed yield or nitrogen con-
tent of the ith cultivar in the jth location and kth year,
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
344
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
345
m is the intercept, ai is the effect of the ith cultivar,
bjk is the effect of the jkth experiment and eijk is the
normally distributed residual error.
Nitrogen traits of cultivars were compared us-
ing the estimated cultivar effects, ˆ
α
i
. Cultivars in-
cluded in 20 or more experiments contributed to
the comparison. For oilseed rape, limitation was
decreased to 10 experiments because the annual
number of trials was smaller than for other crops.
By this means, 60 two-row barley, 51 six-row bar-
ley, 65 oat, 44 wheat, 33 turnip rape and 22 oilseed
rape cultivars were compared.
During the next stage the estimated environ-
mental effects,
ˆ
β
jk
, were examined graphically by
drawing box-plots for all experimental sites and
years. Correlation analysis was performed to meas-
ure relationships between studied traits and vari-
ables. Correlation analysis was applied using the
estimated environmental effects.
Subsequently
ˆ
β
jk
values were used to compare dif-
ferent crops using the following mixed model and
the REML (Restricted Maximum Likelihood) es-
timation method:
ˆ
β
jkl = m + fl + gj + hk + ijk + jjl + kkl + ejkl
where
ˆ
β
jkl is the previously estimated environ-
mental effect or derivative of these estimates (Nyield,
N rate, YRN, NUE estimate, NHI estimate) for the
lth crop, m is the intercept, fl is the effect of the lth
crop while g
j
, h
k
, i
jk
, j
jl
, k
kl
, and e
jkl
are random
effects of location, year, location x year, crop x
location, crop x year, and residual, respectively.
The model assumes that all the random effects are
mutually independent. This model can estimate the
mutually comparable crop means despite not test-
ing the complete set of crops every year at all the
locations.
The precipitation during early (15 May to 31
June) and late growing seasons (1 July to 15 Aug.)
was calculated for each experiment from the data
of the Finnish Meteorological Institute. According
to precipitation, experiments were classied into
three categories: dry, average or rainy. Early sea-
sons with precipitation ≤55 mm, 56–104 mm and
≥105 mm were considered to be dry, average and
rainy (±5 mm depending on crop species), while in
late season ≤82 mm, 83–144 mm and ≥145 mm,
respectively (±10 mm depending on crop species).
The average condition contained 50% of experi-
ments, while dry and rainy only 25%. This clas-
sication was done for both seasons and relation-
ships between precipitation, YRN, and Nyield were
examined using following model:
yijk = m + wi + uj + tij + eijk
where yijk is the observed YRN or Nyield, m is the
intercept, w
i
is the effect of precipitation in the ear-
ly season (i=dry, average, or rainy), uj is the effect
of precipitation late in the season (j=dry, average,
or rainy), t
ij
is the interaction between two seasons,
and eijk is the residual error. The relationships be-
tween mean temperature and YRN and Nyield were
examined using the same procedure. All the statis-
tical analyses were done using SAS/MIXED and
SAS/CORR software (SAS 1999).
Results
Crop species differed signicantly in yield, Ngrain,
N
yield
, and YRN as well as in N fertilizer used (Table
1). Oat had superior yield, Nyield and YRN despite
receiving less N fertilizer than two- and six-row
barley. Turnip rape and oilseed rape contrasted with
oat. Their YRN was only close to half of that in oat,
although the N content in seeds clearly exceeded
that of cereals. Of the cereals wheat had the lowest
YRN. All crop yields were strongly and positively
associated with N
yield
and YRN, but were negatively
associated with grain or seed N content (Table 2).
Depending on crop, a 100 kg ha-1 increase in yield
resulted in 1.5–2.7 percentage unit increase in YRN
and 1.5–3.0 kg ha-1 increase in Nyield. In contrast,
YRN was positively and signicantly associated
with Ngrain (p < 0.001, r = 0.25) only for oat. Ap-
proximating the range for NUE and NHI suggested
that cereals clearly out-perform turnip rape and
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
344
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
345
oilseed rape: even the estimated maxima for NUE
and NHI of oil crops were lower than estimated
minima for NUE and NHI for any of the spring
cereals (Table 1).
In general, location x year was the dominant
source of variation associated with yield, N
grain
,
Nyield, and YRN (Table 3). Depending on the year,
the yield ranged from –799 to 533 kg ha-1 compared
with the mean yield over all years. Similarly Ngrain
ranged from –0.3 to 0.3 % units, Nyield from –7.8 to
7.2 kg ha-1, and YRN from –9 to 11% units. Varia-
tion due to location exceeded that for year only for
YRN,, where it was –15 to 25% units. High within
year and inter-annual variation for Nyield and YRN
(Fig. 1 and 2) emphasized comprehensive insta-
bility in both N traits and in all crops. The slight
differences in favour of wheat and rapeseed, which
seemed to be more stable than the other cereals, is
probably an artefact resulting from later maturing
species grown in more southerly regions than oat
and six- and two-row barley. Although total range
of variability between the lowest and highest re-
corded Nyield and YRN did not show any clear and
consistent trend of reduced within year variability,
for oat and two- and six-row barley, the recorded
values were more concentrated around their mean
and/or median in the latter than the former part
of the 20 year study period, especially regarding
N
yield
. There was no consistent tendency for im-
proved mean N
yield
and YRN over time. On the
other hand, despite marked variability in N traits
of cereals, years with exceptionally low YRN and
N
yield
were rare. Such years were 1998 and 1999 for
Table 1. Comparable crop means (standard errors of means in parentheses) for grain or seed yield, grain or seed N content
(Ngrain), N yield, N fertilizer application rate, yield removed N (YRN), and estimated ranges of N use efciency (NUE)
and N harvest index (NHI).
Crop Yield
(kg ha-1)
Ngrain
(%)
Nyield
(kg ha-1)
N rate
(kg ha-1)
YRN
(%)
NUE estimate
(kg kg-1 N)
NHI estimate
(%)
Min Max Min Max
Two-row barley 4990 (128) 1.9 (0.05) 81 (2.4) 89 (2.7) 95 (4.6) 42 50 69 81
Six-row barley 4740 (125) 2.0 (0.05) 80 (2.4) 89 (2.7) 93 (4.5) 46 52 78 87
Oat 5270 (127) 2.1 (0.05) 93 (2.4) 88 (2.7) 110 (4.5) 42 49 74 84
Wheat 4450 (139) 2.2 (0.05) 82 (2.6) 102 (2.9) 83 (5.0) 38 44 70 80
Turnip rape 1940 (138) 3.6 (0.05) 58 (2.6) 101 (2.9) 58 (4.9) 19 22 57 67
Oilseed rape 2020 (156) 3.7 (0.05) 63 (3.0) 105 (3.2) 61 (5.5) 19 22 58 68
p-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Table 2. Correlations of grain or seed yield (kg ha-1) with grain N content (Ngrain, %), N yield (kg ha-1) and yield removed
N (YRN, %), and the effect of increase in yield by 100 kg ha-1 on N traits for spring cereals and rapeseed according to
20 years multi-location Ofcial Variety Trials (1988–2008).
Crop Yield and Ngrain Yield and Nyield Yield and YRN
Correlation
coefcient
p-value Change
(% units)
Correlation
coefcient
p-value Change
(kg ha-1)
Correlation
coefcient
p-value Change
(% units)
Two-row barley –0.26 <0.001 –0.005 0.90 <0.001 1.5 0.70 <0.001 1.9
Six-row barley –0.25 <0.001 –0.005 0.89 <0.001 1.6 0.71 <0.001 2.0
Oat –0.14 0.02 –0.003 0.86 <0.001 1.7 0.63 <0.001 2.3
Wheat –0.35 <0.001 –0.009 0.87 <0.001 1.6 0.59 <0.001 1.5
Turnip rape –0.17 0.03 –0.011 0.95 <0.001 2.9 0.80 <0.001 2.7
Rapeseed –0.33 <0.01 –0.020 0.96 <0.001 3.0 0.79 <0.001 2.5
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
346
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
347
all cereals and for cereals other than wheat 1988
also. In contrast, such failures were more frequent
for turnip rape and oilseed rape (Fig. 1 and 2).
Many signicant effects were associated with
growing conditions through responses of YRN and
N
yield
to precipitation and temperature. Precipitation
effects at early and/or late season were frequently
recorded for N
yield
, though not in two-row barley
and turnip rape (Table 4). In the case of signicant
effects, the trend was that rainy early or late sea-
sons resulted in lower Nyield compared with below
average precipitation conditions, while there were
Table 3. Sources of variation for grain or seed yield, grain or seed N content (Ngrain), N yield, N fertilizer application rate,
and yield removed N (YRN).
Trait and source of variation Variance RatioaRange of variation compared to mean
caused by year and location (p-value)
Yield (kg ha-1):
Year 141579 0.19 –799 (<0.001) – 533 (<0.01)
Location 32474 0.04 –
Location × year 303299 0.40
Year × crop 25076 0.03
Location × crop 33829 0.04
Residual 760948 1.00
Ngrain (%):
Year 0.021 0.72 –0.3 (<0.001) – 0.3 (<0.001)
Location 0.012 0.39 –0.2 (0.01) – 0.2 (0.03)
Location × year 0.023 0.78
Year × crop 0.004 0.14
Location × crop 0.002 0.05
Residual 0.030 1.00
Nyield (kg ha-1):
Year 26.9 0.10 –7.8 (0.01) – 7.2 (0.03)
Location 29.5 0.11 0.0 – 7.0 (0.05)
Location × year 133.6 0.50
Year × crop 7.8 0.03
Location × crop 14.5 0.05
Residual 265.4 1.00
N rate (kg ha-1):
Year 0.0 0.00 –
Location 85.9 0.50 –16.9 (<0.001) – 18.9 (<0.001)
Location × year 25.4 0.15
Year × crop 0.0 0.00
Location × crop 38.5 0.22
Residual 172.6 1.00
YRN (%):
Year 0.4 0.06 –9 (0.03) – 11 (<0.01)
Location 1.8 0.26 –15 (0.08) – 25 (<0.001)
Location × year 1.9 0.28
Year × crop 0.0 0.00
Location × crop 1.0 0.14
Residual 7.0 1.00
acompared to residual
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
346
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
347
Nyield (kg ha-1)
0.08
0.46
0.38
0.08
0.08
0.46
0.38
0.08
0.11
0.41
0.39
0.09
0.11
0.41
0.39
0.09
79 80
82 80 92 95
86 82 62 55 69 56
0.11
0.48
0.37
0.04
0.11
0.48
0.37
0.04
0.13
0.39
0.35
0.13
0.13
0.39
0.35
0.13
0.08
0.36
0.48
0.08
0.08
0.36
0.48
0.08
0.21
0.43
0.30
0.06
0.21
0.43
0.30
0.06
200
150
100
50
0
200
150
100
50
0
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Two -row barley Six - row barley Oat
Wheat Turnip rape Oilseed rape
Yield removed N (%)
0.01
0.07
0.50
0.43
0.01
0.07
0.50
0.43
0.01
0.07
0.47
0. 44
0.01
0.07
0.47
0. 44
62 53 63 5387 78
99 92 98 94 113 114 0.03
0.07
0.63
0.27
0.03
0.07
0.63
0.27
0.01
0.02
0.40
0.57
0.01
0.02
0.40
0.57
0.01
0.10
0.59
0.31
0.01
0.10
0.59
0.31
0.01
0.22
0.57
0.19
300
250
200
150
100
50
300
250
200
150
100
50
0
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Two - row barley Six -row barley Oat
Wheat Turnip rape Oilseed rape
Fig. 1. Within year and between years variation in mean (asterisk), median (line within each box), standard deviation
(the lowest and highest limit of the box), minimum, and maximum (bottom and top segment of the line, respectively)
for N yield (kg ha-1) of spring cereals and rapeseed. Also frequencies (in right-hand side) for each of the four groups
having regular intervals between minimum and maximum N yield (shown with dash lines) are indicated as well as
mean N yields above an arrow for early (1988–1998) and late study years (1999–2008). Mean N fertilizer application
rates were 89 kg ha-1 for two- and six-row barley, 88 kg ha-1 for oat, 102 kg ha-1 for wheat, 101 kg ha-1 for turnip rape,
and 105 kg ha-1 for oilseed rape.
Fig. 2. Within year and between years variation in mean (asterisk), median (line within each box), standard deviation
(the lowest and highest limit of the box), minimum, and maximum (bottom and top segment of the line, respectively)
for yield removed N (YRN, %) of spring cereals and rapeseed. Also frequencies (in right-hand side) for each of the
four groups having regular intervals between minimum and maximum YRN (shown with dash lines) are indicated as
well as mean YRN above an arrow for early (1988–1998) and late study years (1999–2008). Mean N fertilizer
application rates were 89 kg ha-1 for two- and six-row barley, 88 kg ha-1 for oat, 102 kg ha-1 for wheat, 101 kg ha-1 for
turnip rape, and 105 kg ha-1- for oilseed rape.
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
348
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
349
no consistent differences in effect between dry
and average precipitation conditions for Nyield. For
YRN late season precipitation was close to signi-
cant, but only for six-row barley and oilseed rape
(Table 4). For six-row barley, high precipitation
markedly reduced YRN compared with average
conditions while for oilseed rape dry conditions
resulted in low YRN. No signicant interactions
between early or late season precipitation were re-
corded for any of the crops or for YRN or Nyield.
Temperature had very consistent and signicant
or close to signicant effect on YRN of two-row
barley (at late season), six-row barley (early and
late), oat (late), and turnip rape (early). YRN was
increased at low to average temperatures and at av-
erage to high temperatures (Table 5). Temperature
effects on Nyield were, however, dependent on crop
and time of season. Above average temperatures
Table 4. Signicant precipitation effects at early and late growing season on N yield and yield removed N (YRN) of
spring cereals and rapeseed. Seasons are grouped to be average, dry, or rainy and mean estimates of N trait for each con-
dition are shown with standard errors of the means in parentheses.
Trait and crop Signicance Mean estimate (s.e.) for N trait and condition
Early or late season p-value Dry Average Rainy
YRN (%):
Six-row barley Late 0.09 90 (4.8) 99 (2.9) 88 (4.8)
Oilseed rape Late 0.06 49 (4.7) 63 (3.4) 63 (5.3)
Nyield (kg ha-1):
Six-row barley Early 0.01 75 (3.0) 83 (1.9) 73 (3.2)
Six-row barley Late 0.04 77 (3.1) 82 (1.8) 73 (3.1)
Oat Early <0.01 88 (3.1) 97 (2.0) 87 (2.9)
Oat Late 0.03 94 (2.9) 94 (2.0) 84 (3.1)
Wheat Early 0.03 73 (4.4) 86 (2.5) 85 (4.0)
Oilseed rape Late 0.09 66 (6.1) 61 (3.3) 60 (5.2)
Table 5. Signicant temperature effects at early and late growing season on N yield and yield removed N (YRN) of spring
cereals and rapeseed. Seasons are grouped to have average, low, or high temperatures and mean estimates of N trait for
each condition are shown with standard errors of means in parentheses.
Trait and crop Signicance Mean estimate (s.e.) for N trait and temperature condition
Early or late season p-value Low Average High
YRN (%):
Two-row barley Late 0.06 100 (4.4) 95 (3.1) 85 (4.6)
Six-row barley Early 0.11 99 (4.3) 96 (3.0) 87 (4.1)
Six-row barley Late 0.03 100 (4.0) 97 (2.9) 85 (4.5)
Oat Late 0.11 118 (5.1) 111 (3.6) 102 (5.5)
Turnip rape Early 0.03 65 (3.4) 57 (2.3) 52 (3.4)
Nyield (kg ha-1):
Two-row barley Early 0.05 82 (3.0) 82 (2.3) 74 (2.8)
Six-row barley Early <0.01 81 (2.8) 83 (1.9) 71 (2.6)
Oat Early <0.01 91 (2.9) 96 (2.0) 84 (2.9)
Wheat Early <0.001 94 (3.8) 81 (2.5) 73 (4.3)
Turnip rape Early 0.05 64 (3.1) 57 (2.1) 53 (3.1)
Turnip rape Late 0.07 54 (3.1) 62 (2.1) 57 (3.1)
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
348
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
349
early during the season reduced Nyield of all cereal
species and turnip rape. For turnip rape, average
temperatures during late season were most advan-
tageous for Nyield.
Comprehensive differences among cultivars
were recorded for YRN and N
yield
(Table 6). We
found the highest relative derived differences
among cultivars for N traits in oilseed rape, for
which the ranges between the weakest and strong-
est cultivars were 31% units for YRN and 33 kg
ha
-1
for N
yield
. In contrast to oilseed rape, differ-
ences among turnip rape cultivars were more mod-
est: 14% units for YRN and 14 kg ha-1 for Nyield.
For cereals the range was greatest for oats, reaching
35% units for YRN and 29 kg ha-1 for Nyield. For
wheat it was 24% units and 24 kg ha
-1
, for two-row
barley 21% units and 19 kg ha-1, and for six-row
barley 26% units and 20 kg ha-1. When comparing
year of release for the top ve and bottom ve cul-
tivars, according to their YRN, it was evident that
in general, and for all crops, modern cultivars out-
performed the older ones (Table 7). This was par-
ticularly striking in six-row barley, wheat, turnip
Table 6. Cultivar differences for N yield and yield re-
moved N (YRN) in spring cereals and rapeseed (n=60
for two-row barley, n=51 for six-row barley, n=65 for
oat, n=44 for wheat, n=33 for turnip rape, and n=22 for
oilseed rape). Std, standard deviation of mean.
Crop Mean Std Std/
mean
Min Max
Nyield (kg ha-1):
Two-row barley 81 4.2 5.2 70 89
Six-row barley 80 4.9 6.1 70 90
Oat 93 4.5 4.9 74 103
Wheat 82 6.2 7.6 69 93
Turnip rape 59 3.2 5.4 52 66
Oilseed rape 69 9.7 14.0 52 85
YRN (%):
Two-row barley 94 4.8 5.1 83 104
Six-row barley 94 6.0 6.4 81 107
Oat 110 5.5 5.0 87 122
Wheat 82 6.4 7.8 69 93
Turnip rape 59 3.2 5.5 51 65
Oilseed rape 66 9.4 14.2 50 81
Table 7. Ranking of the ve top- and bottommost cultivars of spring cereals and rapeseed according to their yield removed N (YRN, %) (n=60 for two-row barley,
n=51 for six-row barley, n=65 for oat, n=44 for wheat, n=33 for turnip rape, and n=22 for oilseed rape) with year of release in Finland in parentheses.
Cultivar
ranking
Two-row barley Six-row barley Oat Wheat Turnip rape Oilseed rape
Cultivar YRN (year) Cultivar YRN (year) Cultivar YRN (year) Cultivar YRN (year) Cultivar YRN (year) Cultivar YRN (year)
Top:
1st Tolar 104 (2003) Vilde 107 (2005) Venla 122 (2007) Anniina 93 (2001) Cordelia 65 (2009) Trapper 81 (2009a)
2nd Justina 104 (2006) Tiril 106 (2006) Roope 120 (1996) Epos 93 (2007) Eos 64 (2007) Sheik 78 (2008)
3rd Tocada 102 (2006) Pilvi 103 (2005) Aslak 119 (1999) Bombona 93 (2008) Pouta 63 (2001) Ilves 77 (2008)
4th Ingmar 102 (2007) Einar 103 (2008) Fiia 116 (2002) Amaretto 91 (2003) Apollo 62 (2006) Merryl 73 (2008)
5th Xanadu 101 (2007) Gaute 102 (2003) Peppi 116 (2006) Picolo 90 (2006) Valo 61 (1996) Wildcat 73 (2002)
Bottom:
5th Mentor 92 (1998) Hjan Potra 87 (1983) Ivory 107 (2004) Runar 75 (1987) Kelta 57 (1991) Bullet 58 (1996)
4th Prestige 91 (2007) Hjan Eero 86 (1975) Revisor 106 (2001) Kadett 72 (1981) Kova 57 (1988) Ebony 58 (1996)
3rd Kymppi 90 (1986) Hjan Pokko 86 (1980) Salo 106 (1989) Hjan Tapio 71 (1980) Valtti 56 (1985) Bounty 57 (1992)
2nd Kustaa 85 (1979) Agneta 85 (1982) Karhu 103 (1985) Ruso 70 (1967) Nopsa 56 (1986) Topas 53 (1984)
1st Prisma 83 (1995) Hankkija-673 85 (1973) Lisbeth (naked) 87 (1994) Hjan Ulla 69 (1975) Ante 51 (1982) Varma 50 (1985)
aexpected
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
350
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
351
rape, and oilseed rape. In two-row barley the only
exception to this tendency was for cultivar Prestige,
which was only recently included on the National
List of Plant Varieties by the Finnish Plant Variety
Board. It has very low YRN. This was also true
for oat cultivars Ivory and Revisor. Even though
top cultivars of most crops were all released in the
2000s, oat differed by having two cultivars (Roope
and Aslak) released during the late 1990s in the top
YRN ranks, similarly to turnip rape cultivar Valo
(Table 7). Substantial genetic gains in YRN were
also evident for all crops when comparing the mean
YRN among decades based on introduced cultivars
(Table 8).
Discussion
Even though the environment had marked effects
on YRN and N
yield
of spring cereals, turnip rape, and
oilseed rape, it was also evident that cultivar differ-
ences (Table 6) and genetic improvements in N traits
were signicant. For example, when comparing a
large number of cultivars (ranging from 22 to 65,
depending on crop), according to their YRN the top
ve ranked cultivars were all released in Finland in
the 2000s, the only exception being two late oat cul-
tivars and a turnip rape cultivar, both released in the
1990s (Table 7). Moreover, only one two-row barley
cultivar and two oat cultivars from the 2000s were
among the ve bottommost cultivars according to
YRN comparisons. Results from additional analyses
indicated that improvements were consistent and
signicant over time (Table 8), demonstrating the
important role of plant breeding and cultivar selec-
tion in improving the balance between applied and
harvested N, thereby reducing the N leaching risk.
Bertholdsson and Stoy (1995) and Foulkes et al.
(1998) also reported that the most recent cultivars
were adapted to higher fertilizer application N rates
and they took up relatively more N from fertilizer
compared with older cultivars.
Improved yields were associated with genetic
improvements in YRN and N
yield
. On the other
hand, Ngrain was associated with YRN only in oat,
even though the top ranked (according to YRN)
wheat cultivar Anniina had only a moderate yield
(4580 kg ha-1 compared with 5090–5610 kg ha-1
for the other top ve cultivars), but exceptionally
high Ngrain (2.42% compared with 1.89–2.09% for
other top ve cultivars). Furthermore, crops with
higher mean yields had higher YRN (Table 1).
Consistent genetic gains in yield potential of all
these crops have taken place during recent years
in the northernmost European growing areas as
recently reported: by ca. 26–41 kg ha-1 y-1 depend-
ing on spring cereal and ca. 17 kg ha-1 y-1 for tur-
nip rape (Peltonen-Sainio et al. 2007 and 2009b).
Harvest index has increased substantially through
plant breeding, contributing to genetic yield gains,
whereas total above-ground biomass has remained
virtually unchanged (Austin et al. 1980, Bulman et
al. 1993). The impact of yield increase on increase
in YRN and Nyield was highest for turnip rape and
oilseed rape and lowest for wheat, in the case of
YRN, although for Nyield differences among cereals
Table 8. Mean of yield removed N (YRN, %) for spring cereal and rapeseed cultivars (n indicating their number) intro-
duced into the experiments during different decades.
Decade Two-row barley Six-row barley Oat Wheat Turnip rape Oilseed rape
n YRN n YRN n YRN n YRN n YRN n YRN
1970 2 85 8 89 3 111 6 72 2 54 1 53
1980 7 91 18 91 18 106 15 81 17 57 3 56
1990 29 93 10 96 33 111 16 84 8 60 10 62
2000 23 97 16 99 11 114 7 89 6 62 8 75
p-value <0.01 <0.001 <0.001 <0.001 <0.001 <0.001
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
350
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
351
were not signicant (Table 2). There was, how-
ever, a signicant and negative association between
yield and grain or seed N content for all crops: i.e.
under highly productive conditions crops yielded
relatively more per unit available, grain-allocated
N.
It is possible that differences in N fertilizer
rate of up to 15 kg N ha-1, depending on crop, in-
terfere with crop species comparisons. However,
differences in N use for different eld crops are
very typical of farming in Finland. Furthermore,
in these experiments, as is common farming prac-
tice, N was applied only at sowing and was ex-
pected to sustain growth for the entire period from
sowing to maturity (Peltonen-Sainio et al. 2009d).
Therefore, our results may represent the prevalent
conditions in Finnish elds, except that yields are
systematically higher for all crops grown in experi-
ments than when grown on-farm (Peltonen-Sainio
et al. 2009b). On the other hand, considering di-
rect comparisons among YRN values is justiable
only in the cases of oat, two-row, and six-row
barley, which all received N at 88–89 kg ha-1 and
for wheat and turnip rape receiving 101–102 kg N
ha-1. It was evident that oat was superior regarding
YRN, averaging 110 % and exceeding the values
for two- and six-row barleys by 15 and 13 per-
centage units, respectively. Furthermore, the later
maturing wheat and turnip rape differed even more.
YRN for wheat was 25 percent units higher than
that for turnip rape. Because we were only able to
compare quantities of applied and harvested N in
the long-term datasets, and had no information on
N mobilized from soil nor on N content of vegeta-
tive biomass, we estimated the likely range (min
to max for all experiments and cultivars) for NUE
and NHI. Although being only estimates, NUE and
NHI ranges were far lower for turnip rape than for
wheat or other spring cereals. These comparisons
and ndings highlight the advantageous role of oat
over barley in crop rotations when early maturity is
required, and that of wheat over turnip rape when
later maturity is possible (in southern regions),
solely considering better capacity to transfer ap-
plied N to harvested yield and reduce risk of N
leaching. Also Granlund et al. (2000) emphasized
with modelling the high risks of nitrate leaching
in turnip rape under Finnish conditions. Turnip
rape has, however, many prominent advantages as
a break-crop (Smith et al. 2004; Shahbaz et al.,
2006; Kirkegaard et al., 2008) especially in cereal
rotations as a sole non-cereal break-crop.
Even though marked differences among crop
species and cultivars were recorded, it was obvious
that because YRN and Nyield were highly variable
traits (Fig. 1 and 2), similarly as for grain yield,
(Peltonen-Sainio et al. 2009a), genetic improve-
ments were largely masked by variation attrib-
utable to growing conditions and because of the
large numbers of cultivars included in the annual
trials. In fact, for most crops Nyield and YRN were
higher at early than latter part of the study period
when averaged over years. Because year x crop
and location x crop interactions were not signi-
cant sources of variation for yield, N
grain
, N
yield
, and
YRN, compared with year, location, and their in-
teraction (Table 3), differences among crop species
often remained consistent despite large recorded
differences attributable to conditions. Within year
variability in YRN and Nyield ranged from modest
in 2001 and 2004 for two- and six-row barley to
substantial in 1989, 1990, and 2006 (Fig. 1 and
2). When considering the risks of an exceptionally
low YRN, associated with higher risks of N leach-
ing, we noticed that even though such years were
evident, they were rare for cereals, although sys-
tematically low YRN was evident for turnip rape
and oilseed rape. On the other hand, exceptionally
high YRN (even over 150%) were generally more
frequent for cereals than exceptionally low values,
indicating that soil-remobilized N was particularly
signicant in some experiments and resulted in ex-
cess uptake and N allocation to grains.
Water availability is a principal factor affecting
N uptake and utilization by a crop and our study
conrmed that Nyield depends on precipitation, and
occasionally YRN also (Table 4). Average early
and/or late season precipitation often beneted
N
yield
in cereals. Under dry conditions N uptake
is disrupted, while in rainy seasons N leaching
increases (Rankinen et al. 2007). There is thus
considerable variability in N losses attributable
to changes in weather conditions (Granlund et al.
2007). However, in this study elevated tempera-
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
352
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
353
tures during early and/or late growth phases had
a consistent negative impact on YRN and/or Nyield
for all crops except oilseed rape (Table 5). Ele-
vated temperatures, often coinciding with drought,
are critical for yield determination (Ugarte et al.
2007), as also demonstrated for the northernmost
European growing areas, where they result in yield
penalties of up to 160 kg ha-1 for spring cereals and
140 kg ha
-1
for oil crops for each degree rise in tem-
perature (Peltonen-Sainio et al. 2010). Therefore,
yield penalties caused by elevated temperatures are
likely to increase the challenge of climate change
regarding N leaching, in addition to the projected
increases in annual precipitation, milder winters,
higher soil temperatures, and increased N mobi-
lization from soil at northern latitudes (Peltonen-
Sainio et al. 2009d).
In conclusion, we found that inter-annual and
within year variation in YRN is marked. YRN can,
however, be improved through cultivar selection
and designing better crop rotations because modern
cultivars were generally superior to their predeces-
sors. However, elevated temperatures that cause
yield penalties for cereals and Brassica crops under
long-day conditions due to hastened development,
often resulted in reduced YRN.
Acknowledgements. The authors are grateful to the
numerous partners who participated in organizing the
MTT Ofcial Cultivar Trials. The work was nanced
by the Finnish Ministry of Agriculture and Forestry
and MTT Agrifood Research Finland as a part of
an on-going consortium project entitled Follow-
up of the effectiveness of the Agri-Environmental
Programme in Finland (MYTVAS 3).
References
Austin, R.B., Bingham, J., Blackwell, R.D., Evans, L.T.,
Ford, M.A., Morgan, C.L. & Taylor, M. 1980. Genetic im-
provements in winter wheat yields since 1900 and as-
sociated physiological changes. Journal of Agricultural
Science 94: 675–689.
Bertholdsson, N.O. & Stoy, V. 1995. Accumulation of bio-
mass and nitrogen during plant growth in highly diverg-
ing genotypes of winter wheat. Journal of Agronomy and
Crop Science 175: 167–182.
Bulman, P., Mather, D.E. & Smith, D.L. 1993. Genetic im-
provement of spring barley cultivars grown in eastern
Canada from 1910 to 1988. Euphytica 71: 35– 48.
Bulman, P. & Smith, D.L. 1994. Post-harvest uptake, re-
translocation, and partitioning in spring barley. Crop Sci-
ence 34: 977–984.
Calderini, D.F., Torres-León, S. & Slafer, G.A. 1995. Conse-
quences of wheat breeding on nitrogen and phosphorus
yield, grain nitrogen and phosphorus concentration and
associated traits. Annals of Botany 76: 315–322.
Cox, M.C., Qualset, C.O. & Rains, D.W. 1985. Genetic
variation for nitrogen assimilation and translocation in
wheat: II. Nitrogen assimilation in relation to grain yield
and protein. Crop Science 25: 435–440.
Feil, B. 1997. The inverse yield-protein relationship in
cereals: possibilities and limitation for genetically im-
proving the grain protein yield. Trends in Agronomy 1:
103–119.
Foulkes, M.J., Sylvester-Bradley, R. & Scott, R. 1998. Evi-
dence for differences between winter wheat cultivars in
acquisition of soil mineral nitrogen and uptake and uti-
lization of applied fertilizer nitrogen. Journal of Agricul -
tural Science 130: 29– 44.
Gauer, L.E., Grant, C.A., Gehl, D.T. & Bailey, L.D. 1992.
Effects of nitrogen fertilization on grain protein content,
nitrogen uptake and nitrogen use efciency of six spring
wheat (Triticum aestivum L.) cultivars in relation to esti-
mated moisture supply. Canadian J ournal of Plant Sci-
ence 72: 235 –241.
Granlund, K., Bärlund, I., Salo, T., Esala, M. & Posch, M.
2007. The effect of decreasing fertilization on agricul-
tural nitrogen leaching: a model study. Agricultural and
Food Science 16: 376–386.
Granlund, K., Rekolainen, S., Grönroos, S.J., Nikander,
A. & Laine, Y. 2000. Estimation of the impact of fertili-
sation rate on nitrate leaching in Finland using a math-
ematical simulation model. Agriculture, Ecosystem &
Environment 80: 1–13.
Hakala, K., Kontturi, M. & Pahkala, K. 2009. Field bio-
mass as global energy source. Agricultural and Food
Science 18: 347–365.
Hocking, P.J., Randall, P.J. & DeMarco, D. 1997. The re-
sponse of dryland canola to nitrogen fertilizer: partition-
ing and mobilization of dry matter and nitrogen, and ni-
trogen effects on yield components. Field Crops Re-
search 54: 201–220.
Kangas, A., Laine, A., Niskanen, M., Salo, Y., Vuorinen,
M., Jauhiainen, L. & Nikander, H. 2005. Results of of-
cial variety trials 1997–2005. MTT:n selvityksiä 83.
Kasvintuotanto. 192 p. ISBN 951–729–934–6. (Part-
ly in English).
Kelly, J.T., Bacon, R.K. & Wells, B.R. 1995. Genetic variabil-
ity in nitrogen utilization at four growth stages in soft red
winter wheat. Journal of Plant Nutrition 18: 969–982.
Kirkegaard, J., Christen, O., Krupinsky, J. & Layzell, D.
2008. Break crop benets in temperate wheat produc-
tion. Field Crops Research 107: 185–195.
Le Gouis, J., Béghin, D., Heumez, E. & Pluchard, P. 2000.
Genetic differences for nitrogen uptake and nitrogen uti-
lisation ef ciency in winter wheat. European Journal of
Agronomy 12: 163–173.
AGRICULTURAL AND FOOD SCIENCE
Peltonen-Sainio and Jauhiainen. Yield removed nitrogen of spring cereals and rapeseed
352
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 341–353.
353
Liao, M.T., Fillery, I.R.P. & Palta, J.A. 2004. Early vigorous
growth is a major factor inuencing nitrogen uptake in
wheat. Functional Plant Biology 31: 121–129.
Liao, M.T., Palta, J.A. & Fillery, I.R.P. 2006. Root charac-
teristics of vigorous wheat improve early nitrogen up-
take. Australian Journal of Agricultural Research 57:
1097–1107.
Muurinen, S. 2007. Nitrogen dynamics and nitrogen use ef-
ciency of spring cereals under Finnish growing condi-
tions. Ph.D. thesis, University of Helsinki, Department of
Applied Biology, Section of Crop Husbandry, Publication
no. 29. http://www.doria./handle/10024/4221
Muurinen, S., Kleemola, J. & Peltonen-Sainio, P. 2007. Ac-
cumulation and translocation of N in spring cereal culti-
vars differing in NUE. Agronomy Journal 99: 441–449.
Noulas, C., Stamp, P., Soldati, A. & Liedgens, M. 2004. Ni-
trogen use efciency of spring wheat genotypes under
eld and lysimeter conditions. Journal of Agronomy and
Crop Science 190: 111–118.
Pahkala, K., Hakala, K., Kontturi, M. & Niemeläinen, O.
200 9. Peltobiomassat globaalina energianlähteenä.
Maa- ja elintar viketalous 137. 53 p. ISBN 978–952–
487–214–0 (In Finnish)
Papakosta, D.K. 1994. Analysis of wheat cultivar in grain
yield, grain nitrogen yield and nitrogen utilization ef-
ciency. Journal of Agronomy and Crop Science 172:
305–316.
Papakosta, D.K. & Gagianas, A.A. 1991. Nitrogen and dry
matter accumulation, remobilization and losses for Med-
iterranean wheat during grain lling. Agronomy Journal
83: 864 –870.
Peltonen-Sainio, P., Jauhiainen, L. & Hakala, K. 2009a. Are
there climate change induced increases in variability of
major eld crops in the northernmost European condi-
tions? Agricultural and Food Science 18: 206 –226.
Peltonen-Sainio, P., Jauhiainen, L. & Hakala, K. 2010. Crop
responses to temperature and precipitation according
to long-term multi-location trials at high latitude condi-
tions. Journal of Agricultural Science, in press. Special
issue on Climate Change and Agriculture. doi:10.1017/
S0021859610000791
Peltonen-Sainio, P., Jauhiainen, L. & Hannukkala, A. 2007.
Declining rapeseed yields in Finland: how, why and what
next? Journal of Agricultural Science 145: 587–598.
Peltonen-Sainio, P., Jauhiainen, L. & Laurila, I.P. 2009b.
Cereal yield trends in northern European conditions:
Changes in yield potential and its realisation. Field Crops
Research 110: 85– 90.
Peltonen-Sainio, P., Jauhiainen, L., Rajala, A. & Muuri-
nen, S. 2009c. Tiller traits of spring cereals in tiller-de-
pressing long day conditions. Field Crops Research
113: 82– 89.
Peltonen-Sainio, P., Muurinen, S., Rajala, A. & Jauhiainen,
L. 2008. Variation in harvest index of modern spring bar-
ley, oat and wheat under northern conditions. Journal of
Agricultural Science 146: 35–47.
Peltonen-Sainio, P., Rajala, A., Känkänen, H. & Hakala,
K. 2009d. Improving farming systems in northern Eu-
ropean conditions. Pp: 71–97. In: V.O. Sadras. and D.
Calderini (ed.) Crop Physiology: Applications for Genet-
ic Improvement and Agronomy. Elsevier, Amsterdam,
The Netherlands.
Rajala, A., Peltonen-Sainio, P., Kauppila, R., Wilhelmson,
A., Reinikainen, P. & Kleemola, J. 2007. Within-eld
variation in grain yield, yield components and quality
traits of two-row barley. Journal of Agricultural Science
145: 445–454.
Rankinen, K., Granlund, K. & Rita, H. 2007. Simulated ni-
trogen leaching, nitrogen mass eld balances and their
correlation on four farms in south-western Finland dur-
ing the period 2000–2005. Agricultural and Food Sci-
ence 16: 387–406.
SAS Institute Inc., 1999. SAS/STAT User’s Guide, Version
8, Cary, NC: SAS Institute Inc. 3809 p.
Shahbaz, A.M., Oki, Y., Adachi, T., Murata & Y. & Khan,
M.H.R. 2006. Phosphorus starvation induced root-me-
diated pH changes in solublization and acquisition of
sparingly soluble P sources and organic acids exuda-
tion by Brassica cultivars. Soil Science and Plant Nutri-
tion 52: 623–633.
Sinebo, W., Gretzmacher, R. & Edelbauer, A. 2003. Gen-
otypic variation for nitrogen use efciency in Ethiopian
barley. Field Crops Research 85: 43 –60.
Singh, V.P. & Arora, A. 2001. Intraspecic variation in ni-
trogen uptake and nitrogen utilization efciency in wheat
(Triticum aestivum L.). Journal of Agronomy and Crop
Science 186: 239–244.
Slafer, G.A., Andrade, F.H. & Feingold, S.E. 1990. Genet-
ic improvement of bread wheat (Triticum aestivum L.) in
Argentina: Relationships between nitrogen and dry mat-
ter. Euphytica 50: 63–71.
Smith, B.J., Kirkegaard, J.A. & Howe, G.N. 2004. Impacts
of Brassica break-crops on soil biology and yield of fol-
lowing wheat crops. Australian Journal of Agricultural
Research 55: 1–11.
Spiertz, J.H.J. & de Vos, N.M. 1983. Agronomy and physi-
ological aspects of the role of nitrogen in yield formation
of cereals. Plant and Soil 75: 379–391.
Syväsalo, E., Regina, K., Turtola, E., Lemola, R. & Esala,
M. 2006. Fluxes of nitrous oxide and methane, and ni-
trogen leaching from organically and conventionally cul-
tivated sandy soil in western Finland. Agriculture, Eco -
system & Environment 113: 342–348.
Ugarte, C., Calderini, D.F. & Slafer, G.A. 2007. Grain weight
and grain number responsiveness to pre-anthesis tem-
perature in wheat, barley and triticale. Field Crops Re-
search 100: 240–248.
Welch, R.W. & Leggett, J.M. 1997. Nitrogen content, oil
content and oil composition of oat cultivars (A. sativa)
and wild Avena species in relation to nitrogen fertility,
yield and partitioning of assimilates. Journal of Cereal
Science 26: 105–120.
Woodend, J.J., Glass, A.D.M. & Person, C.O. 1986. In-
traspecic variation for nitrate uptake and nitrogen uti-
lization in wheat (T. aestivum L.) grown under nitrogen
stress. Journal of Plant Nutrition 9: 1213–1225.
Wych, R.D. & Stuthman, D.D. 1983. Genetic improvement
in Minnesota-adapted oat cultivars released since 1923.
Crop Science 23: 879–881.
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 354–355.
354
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 354–355.
355
Contents Vol. 19 (2010)
Lindqvist, I., Lindqvist, B., Tiilikkala, K., Hagner, M., Pent-
tinen, O -P., Pasanen, T. and Setälä, H. 2010. Birch tar
oil is an effective mollusc repellent: eld and laborato-
ry experiments using Arianta arbustorum (Gastropoda:
Helicidae) and Arion lusitanicus (Gastropoda: Arionidae).
Agricultural and Food Science 19: 1-12.
Hagner, M., Pasanen, T., Lindqvist, B., Lindqvist, I., Tiilikka-
la, K., Penttinen , O-P. and Setälä, H. 2010. Effects of
birch tar oils on soil organisms and plants. Agricultural
and Food Science 19: 13-23.
Hagner, M., Penttinen, O-P., Pasanen, T., Tiilikkala, K. and
Setälä, H. 2010. Acute toxicity of birch tar oil on aquat-
ic organisms. Agricultural and Food Science 19: 24-33.
Sooväli, P. and Koppel, M. 2010. Efcacy of fungicide te-
buconazole in barley varieties with different resistance
level. Agricultural and Food Science 19: 34-42.
Bonesmo, H., Nordang, L. and Davies, L. 2010. Tactical
decisions of concentrate level, slaughter age and car-
cass weight of bulls of ve beef breeds under Norwegian
conditions. Agricultural and Food Science 19: 101-115.
Huuskonen, A. and Joki-Tokola, E. 2010. Performance of
growing dairy bulls offered diets based on silages made
of whole-crop barley, whole-crop wheat, hairy vetch
and grass. Agricultural and Food Science 19: 116-126.
Bakal, S.B., Gedam, K.H. and Sharma, G.P. 2010. Drying
characteristics and kinetics of uidized bed dried potato.
Agricultural and Food Science 19: 127-135.
Słupski, J., Lisiewska, Z., Kmiecik, W., Gębczyński, P. and
Sobczyńska, L. 2010. The effect of processing on the
amino acid content in green cauliower. Agricultural and
Food Science 19: 136-143.
Kaukoranta, T., Tahvonen, R. and Ylämäki, A. 2010. Cli-
matic potential and risks for apple growing by 2040. Ag-
Vol 19 no 1
Tamm, K., Võsa, T., Loko, V., Kadaja, J., Vettik, R. and Olt,
J. 2010. The impact of distance to the farm compound
on the options for use of the cereal plot. Agricultural and
Food Science 19: 43-56.
Pradhan, S.K., Pitkänen, S. and Heinonen-Tanski, H. 2010.
Fertilizer value of urine in pumpkin (Cucurbita maxima
l.) cultivation. Agricultural and Food Science 19: 57-68.
Ochmian, I., Grajkowski, J. and Skupień, K. 2010. Effect
of substrate type on the eld performance and chemi-
cal composition of highbush blueberry cv. Patriot. Agri -
cultural and Food Science 19: 69-80.
Väre, M., Pietola, K. and Weiss, C.R. 2010. The irrelevance
of stated plans in predicting farm successions in Finland.
Agricultural and Food Science 19: 81-100.
Vol 19 no 2
ricultural and Food Science 19: 144-159.
Räty, M., Horn, R., Rasa, K., Yli-Halla, M. and Pietola, L.
2010. Compressive behaviour of the soil in buffer zones
under different management practices in nland. Agri-
cultural and Food Science 19: 160-172.
Mustonen, L., Wallius, E. and Hurme, T. 2010. Nitro -
gen fertilization and yield formation of potato during a
short growing period. Agricultural and Food Science
19: 173-183.
Bergjord, A.K ., Bonesmo, H. and Skjelvåg, A.O. 2010.
Model prediction of frost tolerance as related to winter
survival of wheat in nnish eld trials. Agr icultural and
Food Science 19: 184-192.
Žgajnar, J., Erjavec, E. and Kavčič, S. 2010. Multi-step beef
ration optimisation: application of linear and weighted
goal programming with a penalty function. Agricultural
and Food Science 19: 193-206.
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 354–355.
354
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 354–355.
355
Ghavi Hossein-Zadeh, N. and Ardalan, M. 2010. Compari-
son of different models for the estimation of genetic pa-
rameters of body weight traits in Moghani sheep. Agri-
cultural and Food Science 19: 207-213.
Huuskonen, A., Joki-Tokola, E., Honkavaara, M., Tuomisto,
L. and Kauppinen, R. 2010. Meat quality and fatty acid
prole of m. Longissimus dorsi of growing bulls under
insulated, uninsulated and outdoor housing conditions.
Agricultural and Food Science 19: 214-222.
Winiarska-Mieczan, A. and Kwiecień, M. 2010. The inu-
ence of raw grass pea (Lathyrus sativus l.) seeds on
growth performance and biochemical and haematolog-
ical parameters in the blood of grower-nisher pigs. Ag-
ricultural and Food Science 19: 223-232.
Hansson, H., Ferguson, R. and Olofsson, C. 2010. Under-
standing the diversication and specialization of farm
businesses. Agricultural and Food Science 19: 269–283.
Värv, S., Kantanen, J. and Viinalass, H. 2010. Microsatel-
lite, blood group and transferrin protein diversity of Es-
tonian dairy cattle breeds. Agricultural and Food Sci-
ence 19: 284–293.
Rybarczyk, A., Kmiec, M., Szaruga, R., Napierala, F. and
Terman, A. 2010. The effect of calpastatin polymorphism
and its interaction with RYR1 genotypes on carcass and
meat quality of crossbred pigs. Agr icultural and Food
Science 19: 294–301.
Martínez-Fernández, A., Soldado, A., Vicente, F., Martín-
ez, A. and de la Roza-Delgado, B. 2010. Wilting and in-
oculation of Lactobacillus buchneri on intercropped trit-
icale-fava silage: ef fects on nutritive, fermentative and
Vol 19 no 3
Świątkiewicz, S., Koreleski, J. and Arczewska-Włosek,
A. 2010. Egg performance, egg quality, and nutrient
utilization in laying hens fed diets with different levels
of rapeseed expeller cake. Agr icultural and Food Sci-
ence 19: 233-239.
Bañón, S., Bedia, M., Almela, E. and Martínez, P.J. 2010.
Improving the quality of dry-cured sausages using pork
from rustic breeds. Agricultural and Food Science 19:
240-251.
Nissinen, O., Kalliainen, P. and Jauhiainen, L. 2010. Devel-
opment of yield and nutritive value of timothy in prima-
ry growth and regrowth in northern growing conditions.
Agricultural and Food Science 19: 252-268.
Vol 19 no 4
aerobic stability characteristics. Agricultural and Food
Science 19: 302–312.
Alakukku, L., Ristolainen, A., Sarikka, I. and Hurme, T.
2010. Surface water ponding on clayey soils managed
by conventional and conservation tillage in boreal con-
ditions. Agricultural and Food Science 19: 313–326.
Uusi-Kämppä, J. and Mattila, P.K. 2010. Nitrogen losses
from grass ley after slurry application - surface broad-
casting vs. injection. Agricultural and Food Science 19:
327–340.
Peltonen-Sainio, P. and Jauhiainen, L. 2010. Cultivar im-
provement and environmental variability in yield removed
nitrogen of spring cereals and rapeseed in northern
growing conditions according to a long-term dataset.
Agricultural and Food Science 19: 341–353.
AGRICULTURAL AND FOOD SCIENCE
Vol. 19(2010): 356.
356
Acknowledgement of referees
Agricultural and Food Science expresses its sincere thanks to the following referees for their constructive
critical reviews of one or more manuscripts during the year 2010.
Andersson, B.
Andersson, L.
Archimede, H.
Arvidsson, J.
Banse, M.
Baugerød, H.
Berg, J.
Bhogal, A.
Blystad, D-R.
Botti, S.
Carlsson, G.
Chan, Y-K.
Cook, D.C.
Czajkowski, R.
Duvetter, T.
Eckersten, H.
Eichler-Löbermann, B.
Erhard, A.
Fraeye, I.
Glemnitz, M.
Hagman, J.
Halloran, J.
Hansen, E.
Holm, L.E.
Jones, D.R.
Keady, T.
Kenis, M.
Kent, M.P.
Kiritani, K.
Koop, A.L.
Laidlaw, S.
Lambdon, P.
Larrazabal, M.J.
Lundkvist A
Lenstra, J.A.
Liinamo, A-E.
Maleki, M.R.
Mattson, L.
McFeeters, R.
McKenney, D.
Meyers, W.
Mononen, J.
Møller, S.H.
Negrini, R.
Näsholm, A.
Petersen, J.
Plumed-Ferrer, C.
Rantamäki, P.
Rasco, B.
Rico, A.
Riley, H.
Saarela, I.
Santanen, A.
Saris, P.
Raussi, S.
Schepers, H.
Seppänen, M.
Sigvald, R.
Sormunen-Cristian, R.
Stavang, J.A.
Sutherland, J.
Therkildsen, M.
Trombetta, M.F.
Tsror, L.
Uimari, P.
Valkonen, J.
van der Velde, M.
Widmark, A-K.
Wiik, L.
Vinke, C.M.
Vähäoja, P.
V o l . 19, 4 (2010) 269–356
AGRICULTURAL AND FOOD SCIENCE
The Scientic Agricultural Society
of Finland
MTT Agrifood Research Finland www.mtt./afs
AGRICULTURAL AND FOOD SCIENCE
AGRICULTURAL AND FOOD SCIENCE
Vol. 19, No. 4, 2010
Contents
Hansson, H., Ferguson, R. and Olofsson, C.
Understanding the diversication and specialization of farm businesses 269
Värv, S., Kantanen, J. and Viinalass, H.
Microsatellite, blood group and transferrin protein diversity of Estonian dairy cattle breeds 284
Rybarczyk, A., Kmieć, M., Szaruga, R., Napierała, F. and Terman, A.
The effect of calpastatin polymorphism and its interaction with
RYR1
genotypes on carcass and meat quality
of crossbred pigs
294
Martínez-Fernández, A., Soldado, A., Vicente, F., Martínez, A. and de la Roza-Delgado, B.
Wilting and inoculation of
Lactobacillus buchneri
on intercropped triticale-fava silage: effects on nutritive,
fermentative and aerobic stability characteristics
302
Alakukku, L., Ristolainen, A., Sarikka, I. and Hurme, T.
Surface water ponding on clayey soils managed by conventional and conservation tillage in boreal conditions 313
Uusi-Kämppä, J. and Mattila, P.K.
Nitrogen losses from grass ley after slurry application - surface broadcasting vs. injection 327
Peltonen-Sainio, P. and Jauhiainen, L.
Cultivar improvement and environmental variability in yield removed nitrogen of spring cereals and rapeseed
in northern growing conditions according to a long-term dataset
341
Contents Vol. 19 (2010) 354
Acknowledgement of referees 356
ISSN electronic edition 1795-1895
V o l . 1 9 , N o . 4 , 2 0 1 0
Agricultural Economics
Agricultural Engineering
Animal Science
Environmental Science
Food Science
Horticulture
Plant and Soil Science
