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Relative nitrogen efficiency, a new indicator to assess crop livestock farming systems


Abstract and Figures

Improving nitrogen (N) efficiency is a priority for increasing food production while reducing its environmental impacts. N efficiency indicators are needed to achieve this goal, but current indicators have some limitations. In particular , current N efficiency indicators are not appropriate tools to compare farming systems with different types of production because animal N efficiency is, by nature, lower than crop N efficiency. A novel N efficiency indicator called " relative N efficiency " was developed to address this issue. It was calculated as the ratio of the actual N efficiency of the farming system to the weighted mean of the potential efficiency of each type of product output provided in literature reviews. Relative N efficiency was calculated for 557 farms of various types from France and Italy. The relative N efficiency indicator was validated by comparison with a statistical approach based on multiple linear regression. Statistical analysis showed that relative N efficiency was independent of production type and could therefore be used for unbiased comparison of different farming systems. Relative N efficiency was particularly interesting when comparing mixed farming systems with different proportions of animal and crop production.
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Relative nitrogen efficiency, a new indicator to assess crop
livestock farming systems
Olivier Godinot &Philippe Leterme &Françoise Vertès &
Philippe Faverdin &Matthieu Carof
Accepted: 9 January 2015 /Published online: 4 March 2015
#INRA and Springer-Verlag France 2015
Abstract Improving nitrogen (N) efficiency is a priority for
increasing food production while reducing its environmental
impacts. N efficiency indicators are needed to achieve this
goal, but current indicators have some limitations. In particu-
lar, current N efficiency indicators are not appropriate tools to
compare farming systems with different types of production
because animal N efficiency is, by nature, lower than crop N
efficiency. A novel N efficiency indicator called relative N
efficiencywas developed to address this issue. It was calcu-
lated as the ratio of the actual N efficiency of the farming
system to the weighted mean of the potential efficiency of
each type of product output provided in literature reviews.
Relative N efficiency was calculated for 557 farms of various
types from France and Italy. The relative N efficiency indica-
tor was validated by comparison with a statistical approach
based on multiple linear regression. Statistical analysis
showed that relative N efficiency was independent of produc-
tion type and could therefore be used for unbiased comparison
of different farming systems. Relative N efficiency was par-
ticularly interesting when comparing mixed farming systems
with different proportions of animal and crop production.
Keywords Relative nitrogen efficiency .Potential nitrogen
efficiency .Farming s ystem compari son .Indicator .Diagnosis
1 Introduction
Improving nitrogen (N) efficiency is a major way to increase
agricultural productivity while reducing environmental impacts
of agriculture (Spiertz 2010; Sutton et al. 2011). N use efficiency
can be defined as the ratio of N outputs to N inputs at the animal
(Van der Hoek 1998), crop (Oenema et al. 2009) or farm scale
(Aarts et al. 2000). It is the most widely used indicator to assess
the potential impact of farming practices on N efficiency and to
design more efficient farming systems (Simon et al. 2000;Powell
et al. 2010;Oenemaetal.2012). N eco-efficiency indicators at
thefarmscale(Halbergetal.2005; Nevens et al. 2006)usethe
same data to express production efficiency relatively to N losses
instead of N inputs. These indicators present several limitations,
such as the artificial improvement of efficiency due to purchased
feed or the non-consideration of soil N changes (Schröder et al.
2003). Recently, Godinot et al. (2014) proposed ways to correct
them. One important limitation not addressed in previous work is
that N efficiency indicators only allow comparison of farming
systems when they have a similar production type and intensity
(Godinot et al. 2014; Lebacq et al. 2012;Nevensetal.2006).
This limitation exists because crop production and animal
production do not have the same N efficiencies (Goulding
et al. 2008; Ramírez and Reheul 2009). Arable crops (there-
after named crops) and grasslands are primary producers that
use inorganic nutrients to produce biomass through photosyn-
thesis, while nearly all farm animals are primary consumers
that derive most nutrients and energy from plants. This differ-
ence in trophic level induces a systematic difference in nutri-
ent use efficiency. The N transferred from inorganic sources to
animal products is based on plant N efficiency, but also in-
cludes feed production losses at harvest and processing, feed
losses during conservation and consumption, and assimilation
O. Godinot :P. Leterme :F. Vert è s
Agrocampus Ouest, UMR1069 Sol Agro et hydrosystème
Spatialisation, F-35000 Rennes, France
O. Godinot :P. Leterme :F. Ver t è s :M. Carof
INRA, UMR1069 Sol Agro et hydrosystème Spatialisation,
F-35000 Rennes, France
P. Faverdin
INRA, UMR1348 PEGASE, F-35590 Saint-Gilles, France
P. Faverdin
Agrocampus Ouest, UMR1348 PEGASE,
F-35590 Saint-Gilles, France
M. Carof (*)
Agrocampus Ouest, UMR1069 Sol Agro et hydrosystème
Spatialisation, 65 rue de Saint-Brieuc, 35042 Rennes Cedex, France
Agron. Sustain. Dev. (2015) 35:857868
DOI 10.1007/s13593-015-0281-6
losses resulting in N excretion. Therefore, the N efficiency in
livestock systems is biologically lower than in cropping sys-
tems (Figure 1). This makes comparisons between farming
systems with different proportions of crops and livestock or
different types of livestock less meaningful for modifying
farm practices to increase N efficiency.
The aim of this study was to develop an indicator of N
efficiency that allows the relative efficiency of farming systems
that produce outputs of different trophic levels to be compared.
This would help farmers and advisors to compare the efficiency
of farming systems with different products, and could allow
policy makers to set efficiency objectives for all types of farm-
ing systems. The Materials and Methods section details the
methodology used for calculating this new indicator. A litera-
ture review provides references for potential efficiency of each
output. The indicator is then calculated for a sample of 557
farms with various types of crop and animal production. A
comparison of these results with a multiple linear regression
allows validating the selected potential efficiency values, and
thus the developed indicator. The interests of relative N effi-
ciency are presented and discussed in the third section, with a
focus on the significance and limits of this novel indicator. The
fourth section provides a summary and concluding remarks.
2.1 Presentation of the data
2.1.1 Farm sample
Data were obtained from a previous work by Simon et al.
(2000). The sample comprised 557 farms surveyed from 1989
to 1994 to calculate farm gate N balances (N inputs minus N
outputs at the farm scale) and N use efficiencies. It was
constructed to represent a large diversity of production types
and included farms that produced crops, milk, beef cattle, poul-
try, eggs, and/or pigs. Most farms had conventional production,
but 52 were organic, and 29 were defined as autonomousin
which farmers replaced inorganic fertilizers with legume crops.
They also represented a wide range of soils and climates, with
379 farms from western France (mostly cambisols, oceanic cli-
mate), 111 farms from northern Italy (mostly gleyic luvisols,
subtropical wet climate), 36 farms from northern France (mostly
haplic luvisols, oceanic climate), and 31 farms from eastern
France (mostly rendzic leptosols, semi-continental climate).
Such a large and diversified dataset was valuable for the meth-
odological developments proposed in this article. However, data
were collected over 20 years ago and cannot be considered
representative of current farming practices.
2.1.2 Estimation of N inputs and outputs and classification
of farming systems
System N efficiency (Godinot et al. 2014) is an N efficiency
indicator at the farming system scale. It is based on N use
efficiency, but considers net inputs and outputs, N used for
the production and transport of net inputs, as well as soil N
variations. These modifications make System N efficiency a
more relevant indicator than N use efficiency for farming
systems comparison. We, therefore, decided to base the
development of our relative efficiency indicator on System N
Most N flows needed to calculate system N efficiency were
available in the dataset. N outputs included manure, crops, and
animal products. N inputs consisted of feed and litter, manure
and inorganic fertilizers, purchased animals, and biological N
fixation. However, as they admit, Simon et al. (2000)likely
underestimated biological N fixation of grasslands in organic
and autonomous farms by assuming a constant 10 % of
above-ground dry matter as clover in grasslands of all farms.
Andrews et al. (2007) considered that in mixed perennial
ryegrass and white clover grasslands that receive no mineral
fertilizer, white clover was likely to stabilize at around 20 % of
above-ground dry matter. Since organic and autonomous farms
relied heavily on grass-clover mixtures in their grasslands, we
recalculated biological N fixation for these farms assuming
20 % of above-ground dry matter as clover in grasslands.
Atmospheric N deposition was estimated using national means
for 1990 from the EMEP/MSC-W model (EMEP 2014). This
led to total atmospheric N deposition of 13 kg N ha
for French
farms and 17.5 kg N ha
for Italian farms. Due to limited data
on soil management, soil N variations were estimated from on-
farm crop areas. Soils under annual crops were assumed to lose
70 kg N ha
, while grasslands were assumed to store
43 kg N ha
(values derived from Vleeshouwers and
Verhagen 2002 with a C:N ratio of 12). Seed N input and indi-
rect N losses from seed production and transport were calculated
Fig. 1 Chickens ina corn field.Animal products have,by nature, a lower
N efficiency than crops, which makes N efficiency comparisons between
different farming systems problematic (Credit D. Poulain)
858 O. Godinot et al.
according to Godinot et al. (2014). We used constants to repre-
sent small N inputs such as non-symbiotic N fixation by free-
living soil microorganisms, fuel combustion, and indirect N
losses for fuel production and transport (Godinot et al. 2014).
We calculated the indirect losses due to fertilizer production
based on the percentage of each inorganic fertilizer in the total
mass of inorganic fertilizers used in France from 1989 to 1994
(UNIFA 2014). Similarly, feed composition was estimated from
the percentage of each feedstuff in the total mass of main feed-
stuffs used in France in 19931994 (Castel and Pous 1998)to
approximate its indirect losses. Only a few dairy farms had net
animal inputs. For these farms, indirect losses from animal pro-
duction and transport were calculated using life cycle assess-
ment references. We assumed that no change in stock occurred
from year to year except for soil N.
Tab le 1presents direct and indirect N inputs and outputs for
the 557 farms.
We classified farming systems into nine categories accord-
ing to the composition of their net N outputs (Table 1). For
instance, the cropscategory was made of farming systems
with only net crop outputs (regardless of the different types of
crops), while the milkcategory gathered farming systems
Tabl e 1 Mean net annual N inputs and outputs from the nine farming system categories (kg N ha
agricultural area). Standard deviations are in
Beef cattle
Beef cattle
and pigs
Crops Crops
and milk
Milk Milk
and pigs
Pigs Poultry
Number of farms 47 35 13 24 53 299 36 30 20
Agricultural area 43 (25) 79 (38) 39 (23) 121 (157) 68 (37) 44 (25) 39 (16) 38 (26) 45 (18)
Net inputs
Atm. deposition 14 (2) 14 (2) 13 (0) 13 (0) 14 (2) 14 (2) 13 (0) 13 (0) 13 (0)
BNF 14 (22) 26 (29) 21 (47) 32 (35) 23 (32) 22 (31) 7 (17) 4 (12) 12 (20)
Cattle 0 (3) 0 (1)
Cattle indir. loss 2 (11) 0 (2)
Feed 69 (107) 321 (203) 98 (170) 302 (235) 918 (964) 292 (279)
Feed indir. loss 18 (28) 86 (54) 26 (45) 80 (62) 244 (257) 78 (74)
3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0) 3 (0)
Fuel indir. loss 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)
Inorg. fertilizer 100 (84) 99 (53) 70 (51) 97 (74) 100 (51) 114 (73) 123 (53) 83 (44) 103 (52)
Inorg. fertilizer
indir. loss
2 (1) 2 (1) 1 (1) 2 (1) 2 (1) 2 (1) 2 (1) 1 (1) 2 (1)
Manure 15 (70) 1 (4) 18 (78) 25 (70) 1 (4) 1 (26) 17 (72) 227 (394) 24 (63)
Seeds 1 (1) 2 (1) 1 (1) 3 (1) 1 (1) 1 (0) 1 (0) 2 (0) 1 (1)
Seeds indir. loss 1 (1) 1 (0) 2 (1) 2 (0) 1 (0) 1 (1) 1 (0) 2 (0) 2 (1)
Soil N fixation
5 (0) 5 (0) 5 (0) 5 (0) 5 (0) 5 (0) 5 (0) 5 (0) 5 (0)
Soil N change 16 (43) 31 (24) 37 (28) 61 (21) 22 (24) 7 (25) 17 (19) 66 (8) 29 (30)
Tot al net i npu ts
soil N change
258 (228) 184 (55) 543 (249) 242 (81) 172 (58) 295 (235) 538 (267) 1114 (832) 515 (320)
Net outputs
Beef cattle 30 (31) 6 (6) 9 (8) 4 (3) 7 (7) 6 (2) 7 (6)
Crops 49 (33) 101 (48) 29 (25)
Milk 15 (8) 43 (42) 36 (13) 20 (20)
Pigs 85 (51) 76 (66) 262 (256)
Eggs 27 (71)
Poultry 83 (115)
Total net outputs 30 (31) 56 (31) 95 (49) 101 (48) 48 (25) 50 (44) 118 (74) 262 (256) 138 (112)
Atm. atmospheric, BNF biological N fixation, indir. loss indirect N losses due to input production and transport to the farm, Inorg. inorganic
Constant value
Relative nitrogen efficiency, a new indicator to assess crop livestock 859
having net milk outputs as well as net cattle outputs from the
dairy herd. The pigcategory included farms producing pigs
only and farms producing pigs and crops, as the difference
between feed inputs and crop outputs always resulted in pos-
itive net feed input and zero net crop output. There was, thus,
no pig and cropscategory. In order to avoid categories with
Tabl e 2 Potential efficiencies of
main N flows in farming systems
and their sources
Values used in this study are
indicated in bold letters
w/ with
N flow Efficiency name Potential
External input to soil
(biological N
Input efficiency 100 % Eggleston et al. (2006)
Manure to soil
- Cattle grazing Manure efficiency 93 % Aarts et al. (2000)
- Cattle grazing 85 % Rotz (2004)
- Poultry 82 % Rotz (2004)
- Cattle at stable 79 % Rotz (2004)
-Pig 77 % Rotz (2004)
- Cattle grazing + stable 7684 % Steinshamn et al. (2004)
- Cattle grazing + stable 75 % Powell et al. (2010)
Soil to harvestable crop
- Grassland Crop efficiency 91 % Oenema et al. (2012)
- Arable crops, fruits 90 % Task Force on Reactive
Nitrogen (2011)
-Grassclover ley 89 % Steinshamn et al. (2004)
- Silage maize 88 % Zavattaro et al. (2012)
- Undefined crops 80 % Powell et al. (2010)
- Vegetables 80 % Task Force on Reactive
Nitrogen (2011)
- Grain maize 77 % Moll et al. (1982)
- Wheat (grain only) 69 % Górny et al. (2011)
Harvestable to harvested crop
- Silage maize Harvest efficiency 95 % Rotz et al. (2012)
- Cereals 93 % Rotz et al. (2012)
- Cereals and forages 89 % Steinshamn et al. (2004)
Harvested crop to feed
- Full diet w/ grazing Feed production efficiency 93 % Steinshamn et al. (2004)
-Fulldiet 86 % Aarts et al. (2000)
Feed to milk
- W/ dry period Feed-to-milk efficiency 30 % Chase (2004)
- W/ dry period 30 % Gourley et al. (2012)
- W/ dry period, confined 30 % Powell et al. (2010)
- W/ dry period, grazing 25 % Powell et al. (2010)
Feed to cattle Feed-to-cattle efficiency 17 % Micol et al. (2003), Biagini and
Lazzaroni (2013)
Feed to pig Feed-to-pig efficiency 41 % Cederberg and Flysjö (2004)
Feed to egg Feed-to-egg efficiency 40 % Singh et al. (2009)
39 % Rios et al. (2009)
Feed to poultry Feed-to-poultry efficiency 57 % Ebling et al. (2013)
860 O. Godinot et al.
a very small number of farms, we aggregated both specialized
poultry farms that only had net poultry and/or egg outputs, and
poultry farms combined with other net animal outputs (beef
cattle and/or milk) into a wider poultrycategory. Very im-
portant N flows per hectare in all categories including pig
production are explained by intensive pig production on small
agricultural areas. This was still common in France in the
years 1990 but has changed with the implementation of the
Nitrate Directive (91/676/EEC).
2.2 Development of the relative N efficiency indicator
2.2.1 Review of potential N efficiencies
Calculation of relative N efficiency is based on the po-
tential efficiency (i.e., the best efficiency that can be
attained in optimal conditions) of N transfers between
farm components (soil, crops, feed, animals). A review
of existing literature was performed to determine the
potential N efficiency for the main N flows in farming
systems (Table 2,Fig.2). N flow efficiency was calcu-
lated as the ratio of N outputs to N inputs. For the
purpose of this review, it was assumed that the N effi-
ciency of each flow was independent.
Biological N fixation was estimated to generate no
direct N loss in the latest IPCC guidelines (Eggleston
et al. 2006). This was also the case for atmospheric N
deposition, which although a serious environmental is-
sue, generates no direct emissions for the farming sys-
tem receiving it. Therefore, the potential input N effi-
ciency could be as high as 100 % (N flow: external
input to soil, Table 2).
Manure N produced by animals was calculated as the
difference between N in feed intake and N in animal prod-
ucts.AccordingtoRotz(2004), minimum N losses from
excretion to the soil were 21 % for cattle in tied stables,
15 % for grazing animals, 23 % for swine on slatted floors
with an enclosed slurry tank and deep injection, and 18 %
for poultry raised in cages. For the sake of generality, the
lowest value was used for all animal types, leading to a
77 % manure N efficiency (N flow: manure to soil,
Tab le 2). This value is similar to those proposed by other
authors for dairy herds (Steinshamn et al. 2004;Powell
et al. 2010). Harvest losses, crop residues, and manure are
not desired outputs; however, they can improve N effi-
ciency by replacing external inputs with recycled N
returned to the soil; thus, they were considered to be fully
recycled when calculating potential efficiency.
A potential crop efficiency of 90 % (N flow: soil to
harvestable crop, Table 2) is proposed by the Task
Force on Reactive Nitrogen (2011) for arable crops.
This value is close to other references for cereals and
grasslands (Table 2). As we did not find pertinent ref-
erences for some of the major crop types (oilseeds,
Fig. 2 Potential N efficiencies of
the main flows in farming
systems. Black arrows represent
N flows with their potential
efficiencies. Dashed arrows
represent the partition between
crops used as feed and those sold.
Gray shaded boxes are the net N
output data needed to calculate
potential efficiency at the farming
system scale. *Excreted N
efficiency calculated as 1-animal
N efficiency
Relative nitrogen efficiency, a new indicator to assess crop livestock 861
legumes, root crops), we chose to use this value for all
crops as a first estimate. Soil N stock variations were
cies, since we considered that the most efficient use of
N was to produce N outputs without decreasing soil N
Rotz et al. (2012) found minimal harvest N losses of
5 % of total yield, which gave a potential harvest N effi-
ciency of 95 % (N flow: harvestable to harvested crop,
Tab le 2).
Conservation and feeding losses were taken from
Aarts et al. (2000), who estimated minimum N losses
of 14 % from harvested crop to feed intake. This led to
a potential feed production N efficiency of 86 % (N
flow: harvested crop to feed, Table 2). We chose not to
use the higher reference based on grazing (Steinshamn
et al. 2004), as it could not be attained in some animal
farming systems.
The feed-to-milk N efficiency of dairy cows was cal-
culated from the highest reported feed-to-milk N efficien-
cy for a dairy herd (35.8 %; Chase 2004)torepresentthe
entire milking period. It was assumed that dairy cows
were in milk for 11 months and dry for 2 months with a
calving interval of 13 months. Therefore, an 11/13 coef-
ficient was applied to herd feed-to-milk N efficiency to
include the unproductive period of dry cows. A dairy
cattle was assumed to have a similar feed-to-cattle N ef-
ficiency as beef cattle and was therefore included in the
calculation of the feed-to-cattle N efficiency factor.
Similarly, dairy calves were also included in the feed-to-
cattle N efficiency. This resulted in a potential feed-to-
milk N efficiency of 30 % when including the dry period
(N flow: feed to milk, Table 2), close to the values found
in other studies (Table 2).
Feed-to-cattle N efficiency was calculated for a 16-
month-old animal by calculating a weighted mean of
feed-to-beef efficiencies at three stages of its life
According to Micol et al. (2003), the N efficiency of
a newborn 50-kg calf was 8.4 % due to its mothers
gestation and maintenance. The N efficiency of a 200-
kg calf before weaning was 16.7 %, including its
mothers milk production efficiency and maintenance
cost. The N efficiency of a weaned animal up to its
slaughter at 550 kg live weight was 20 % (Biagini
and Lazzaroni 2013). This resulted in a potential feed-
to-cattle N efficiency of 17 % from birth to slaughter,
including gestation and milk production for the calf (N
flow: feed to cattle, Table 2).
The feed-to-pig N efficiency (41 %; N flow: feed to
pig, Table 2) was taken from Cederberg and Flysjö
(2004) and included sows and piglets (Table 2). This po-
tential efficiency was not directly observed in an experi-
ment but was calculated by the authors based on the best
available techniques for improved feed-to-pig N
The feed-to-egg N efficiency was based on Singh et al.
(2009) for laying hens 2060 weeks old, with a mean feed
conversion ratio of 1.81 and a crude protein content of
16.5 % in feed. Feed-to-hen meat N efficiency was not
included in egg production. It was assumed to be similar
to feed-to-poultry N efficiency. This resulted in a potential
feed-to-egg N efficiency of 40 % (N flow: feed to egg,
Tab le 2).
Feed-to-poultry N efficiency was calculated from
Ebling et al. (2013) for a broiler reaching 3.65 kg live
weight in 47 days with a feed conversion ratio of 1.67
and a crude protein content of 20.8 % in feed. Egg pro-
duction was included in the feed-to-poultry N efficiency.
This led to a potential feed-to-poultry N efficiency of
57 % (N flow: feed to poultry, Table 2).
2.2.2 Calculation of potential N efficiency
Figure 2presents the data from Table 2in a graphical
manner, which can be more convenient to understand
the calculation methods for potential efficiency at the
farming system scale. Calculating potential N efficiency
begins with potential crop N efficiency. Based on poten-
tial N efficiency values (Table 2) and assuming a full
recycling of harvest residues, the external inputs neces-
sary to produce net crop output are calculated as:
external input ¼total inputharvest losses
total input ¼net crop output
harvest efficiency uptake efficiency input efficiency
harvest losses ¼net crop output
harvest efficiency 1harvest efficiencyðÞ:
Potential N efficiency for net crop production is therefore:
potential efficiency ¼net crop output
external input :
Solving these equations for one unit of net crop out-
put led to an external input of 1.11 and thus a potential
crop efficiency of 90 %.
862 O. Godinot et al.
The animal N efficiency perimeter is larger because animals
consume crops and produce animal products as well as manure
(Fig. 2). The calculation of external input is expressed as:
external input ¼total inputharvest lossesrecycled manure
total input ¼net animal output
feed efficiency feed production efficiency harvest efficiency uptake efficiency input efficiency
harvest losses ¼net animal output
feed efficiency feed production efficiency harvest efficiency 1harvest efficiencyðÞ
recycled manure ¼net animal output
feed efficiency
net animal output
manure efficiency
Potential N efficiency for net animal production can then
be calculated as:
potential efficiency ¼net animal output
external input :
This leads to potential N efficiencies of 26 % for cattle,
48 % for eggs, 39 % for milk, 49 % for pig, and 59 % for
poultry, including all steps from inputs to outputs as well as
full recycling of manure and harvest losses.
For a farming system producing more than one output, po-
tential N efficiency is calculated as the ratio of the sum of its net
outputs to the sum of their minimal external inputs. Minimal
external input for a given output is calculated as the ratio of net
output to its potential efficiency. For example, a farm in the
sample produced 30 kg N ha
net cattle output and
20 kg N ha
net crop output; its potential efficiency is therefore:
potential efficiency ¼30 þ20
0:26 þ20
Relative N efficiency can then be calculated as the ratio be-
tween observed system N efficiency and potential N efficiency:
relative N efficiency ¼system N efficiency=potential efficiency:
Using the previous example, a farm that produces
30 kg N ha
net cattle output and 20 kg N ha
net crop
output has a potential efficiency of 36 %. If its actual system
N efficiency is 20 %, its relative N efficiency is expressed as:
relative N efficiency ¼0:20=0:36 ¼55%:
Given its net production and observed efficiency, it attained
a relative N efficiency of 55 % of its potential efficiency,
which indicates room for improvement.
Regardless of the shares of animal and crop N in total N
output, the closer relative N efficiency is to 100 %, the
closer the farming system is to its potential efficiency.
The calculation of relative N efficiency for each of the
557 farms based on potential N efficiencies and each
farms net outputs allows comparisons of relative efficien-
cy among farms with different types of production.
2.3 Validation of relative N efficiency by the relative residual
input approach
As a novel indicator, relative N efficiency had to be
validated, especially concerning the choice of potential
efficiency values (Table 2). The analysis of residues
from a multiple linear regression between N inputs
and outputs was proposed as another method to estimate
farming systems efficiency without using potential effi-
ciency values. Our large sample made it possible to
calculate a multiple linear regression predicting net N
input from all net N outputs. The residue was calculated
as the difference between predicted net input (from
Relative nitrogen efficiency, a new indicator to assess crop livestock 863
regression based on net outputs) and measured net input
(from surveys):
residual net input ¼predicted net inputmeasured net input
Residual net input was then expressed as a fraction of pre-
dicted net N input:
relative residual input ¼residual netinput=predicted netinput
Relative residual input could be interpreted as an N ef-
ficiency indicator: a negative relative residual input indi-
cated a farming system that needed more input than what
the multiple linear regression estimated for a given net
output, and thus a farming system less efficient than the
average farmwith the same production. Conversely, a
positive relative residual input indicated a farming system
that used less net input than the multiple linear regression
estimated for a given production and that was therefore
more efficient.
Relative N efficiency and relative residual input were cal-
culated for the 557 farms. We compared the ranking of farm-
ing systems in order to determine whether both indicators
gave similar results.
2.4 Statistical analysis
All statistical tests were performed with the R software (R
Core Team 2014). Linear models of net N input from all com-
binations of net N outputs were calculated from the 557-farm
sample. The best model was selected with the Bayesian
Information Criterion (BIC).
Spearmans rank correlation coefficients and associated
pvalues were calculated for N efficiency indicators.
Analyses of variance were performed to compare the
mean of system N efficiency and relative N efficiency
indicators for each production category. The means of
system N efficiency and relative N efficiency were then
compared for each pair of categories to determine signif-
icant differences. The Games-Howell test was chosen for
pairwise comparisons of groups with unequal sizes and
unequal variances.
Sensitivity analysis was performed to asses the reliabil-
ity of the relative N efficiency indicator with uncertain
potential efficiency values. Potential efficiencies (Table 2)
were attributed normal distributions with a range of
±20 % from their baseline values. A set of 1000 random
combinations of potential efficiencies was generated.
Spearmans rank correlations were then calculated for the
nine farming systems (Table 1).
3 Results and discussion
3.1 Relative residual input approach for validating relative N
The linear model of net N input based on all N net outputs
(net out) was:
preticted net input ¼69:37 þ6:220net out cattle þ1:420net out crop
þ3:740net out egg þ4:460net out milk
þ3:620net out pig þ2:530net out poultry
The standard errors of estimates were, respectively, 6.99 for
the intercept, 0.34 for cattle, 0.16 for crops, 0.30 for eggs, 0.12
for milk, 0.05 for pig, and 0.16 for poultry net outputs. All
variables of the linear model were significant (p<0.001).
According to the BIC test, all variables were needed to obtain
the best linear model. The adjusted R
of the full model was
0.92 and was significant (F(6550)= 1054, p<0.001; RSE=
100). It was therefore considered a good estimator of net N
input. The high and significant adjusted R
of the model illus-
trated that net inputs and net outputs were strongly linked.
Moreover, from the small standard errors of estimates, we
concluded that variability was moderate for each output type.
The relatively high intercept value represented N inputs weak-
ly linked to production, such as atmospheric deposition, soil N
fixation, emissions from fuel consumption, and soil N change.
Spearmans rank correlation between relative N efficiency and
relative residual input was significant on the full dataset (rho=
0.81, p<0.001). Rank correlation between relative N efficiency
and relative residual input for each of the nine farm categories
ranged from 0.71 for the beef cattle category to 0.94 for the pig
category. The correlation was significant (p<0.001) for all cate-
gories. The strong correlation between these two indicators con-
firmed that the potential efficiency values (Table 2)usedtocal-
culate relative N efficiency were coherent with sample data.
In the multiple linear regression, the inverse of each esti-
mate corresponded to the mean observed N efficiency for each
output type. Therefore, observed efficiency was 16 % for cat-
tle output, 70 % for crop output, 27 % for egg output, 22 % for
milk output, 28 % for pig output, and 40 % for poultry output.
The ranking of output types was the same in our sample as the
values of potential efficiency found in the literature (Table 2),
corroborating our choices.
3.2 Main utility of relative N efficiency
Analysis of variance showed a significant effect of production
category on system N efficiency (F(8, 548)= 38.570, p<0.001).
Pairwise comparison of means revealed five overlapping groups
of comparable system N efficiency. Conversely, analysis of vari-
ance between production category and relative N efficiency was
864 O. Godinot et al.
not significant (F(8, 548)=1.517, p=0.148>0.05). We thus con-
clude that relative N efficiency can be used to compare the relative
efficiency of farming systems with different types of production.
All production categories were able to reach a high relative N
efficiency. The mean relative N efficiencies of all categories were
similar, ranging from 39 % for beef cattle and pig to 54 % for
poultry. This result showed that in our sample, relative N man-
agement was no better on crop farms than on beef cattle farms.
Relative N efficiency had high variability within each category
(boxplot whiskers, Fig. 3), especially beef cattle, milk and crop
productions. This was due to the large diversity in production
methods for these categories in our sample including conven-
tional, organic, and autonomousfarms in different regions.
Plotting system N efficiency versus relative N efficiency illus-
trates major differences in potential between production types
(Fig. 4). For instance, four different types of specialized farms in
the sample (crop, pig, dairy, and beef cattle) had the same system
N efficiency (14 %) but different relative N efficiencies (16, 28, 38,
and 56 %, respectively) based on what they produced. The farms
with the highest system N efficiencies produced crops (Fig. 4).
Conversely, the farms with the highest relative N efficiencies oc-
curred in all production types, not only crop farms, but also beef
cattle or dairy farms, which have lower inherent N efficiencies.
This indicator is also pertinent for comparing farming sys-
tems that produce the same or similar products in different
percentages. For example, two farms in the crop and milk
category produced net milk, meat and crop outputs and had
a system N efficiency of 29 %. With this indicator alone, one
would have concluded that they had the same N efficiency.
However, the two farms produced different percentages of
total N output in milk, cattle meat, and crops (46, 38, and
17 % vs. 19, 9, and 72 %, respectively), leading to greatly
different relative N efficiencies (80 and 44 %, respectively).
Fig. 3 Comparison of system N efficiency and relative N efficiency by production category. Diamonds represent the mean of each category. Twelve
outliers with relative N efficiency >100 % are not shown
Fig. 4 Comparison of system N efficiency and relative N efficiency for
the 557-farm sample. Farming systems with crops have greater system N
efficiency than livestock farming systems but not necessarily greater
relative N efficiency. Diagonal lines represent the relationship between
system N efficiency and relative N efficiency for specialized farming
systems with 100 % relative efficiency equal to potential efficiency
value for given output. Specialized dairy systems show some variation
around the diagonal due to the variable share of milk and meat outputs.
Mixed livestock is the sum of beef cattle and pig, milk and pig and
poultry; mixed livestock and crops is the sum of beef cattle and crops
and crops and milk. Twelve outliers with relative N efficiency >100 % are
not shown
Relative nitrogen efficiency, a new indicator to assess crop livestock 865
The mixed livestock and crop category (gathering beef and
crops and crops and milk categories, Fig. 4) illustrates the
great diversity of crop and livestock proportions in mixed
systems, from almost specialized beef cattle to almost special-
ized crops. Some farms of this category have both a higher
system N efficiency and a lower relative N efficiency than
other farms with less crops. In this situation, the use of relative
N efficiency is particularly interesting to compare N manage-
ment efficiency between farms with different outputs.
3.3 Relative N efficiency as a reliable diagnosis tool
Relative N efficiency helps to better estimate any farming sys-
temsroom for improvement. Within each category, some
farms lie below 30 % and others above 50 % of their potential
efficiency, which illustrates a large gap between actual and po-
tential efficiency for some farms. Therefore, relative N efficiency
can be a useful diagnostic tool to quickly assess which production
could be improved on a given farm (but not how to improve it).
In order to test the sensitivity of relative N efficiency to cho-
sen potential efficiency values, we checked the effect of ±20 %
changes of all potential efficiencies simultaneously on the rela-
tive N efficiency of the nine average farming systems described
in Table 1. Rank correlations were then calculated to determine
whether the variation of potential efficiency values had an im-
pact on the ranking of these nine average farming systems.
Observed rank correlations were greater than 0.95 between rel-
ative N efficiency of all animal farming systems except beef and
crops, and greater than 0.90 between all farming systems except
crops. Rank correlations between crops and other systems
ranged from 0.65 to 0.87. Therefore, a ±20 % change in potential
efficiency did not strongly affect the ranking of farming systems
and thus the interest of relative N efficiency for comparing them.
Potential efficiency was defined by references from litera-
ture for each output type. Another method could be to use the
calculated system N efficiency from the most efficient special-
ized farms of the sample. This might prove interesting when
studying productions whose potential efficiency references
are lacking (e.g., flowers, vine, etc.). It would also be adapted
for the study of farming systems in contexts where potential
efficiency cannot be attained due to soil, climate, or technical
limitations. However, it is less generic than the approach we
proposed, as potential efficiency would be defined from each
sample, which would make comparisons between studies im-
practical. Moreover, it would require a large number of spe-
cialized farms for correct potential efficiency definition, and
would thus not be pertinent for a small sample or a single
farm. Finally, as the estimation of some N inputs (soil N
change, biological N fixation) is uncertain, the most efficient
farming systems of a sample could also be underestimating
their inputs, which could skew the potential efficiency value.
Since the references we usedwere comforted by comparing
relative N efficiency to relative residual input, and since
±20 % uncertainty did not have profound effects on relative
N efficiency results, this indicator seems reliable.
Unlike a statistical approach, it can be calculated with a
small dataset or even for one farm, making it a convenient
tool for farm diagnosis. Calculating N efficiency for each net
output is simple and allows comparisons between breeds or
production methods that produce different proportions of co-
products such as milk and meat.
3.4 Limits of relative N efficiency
3.4.1 Limits due to estimation of N flows
Twelve outliers (2.2 % of the sample) had relative N efficiencies
greater than 100 %. Due to the high values used for potential N
efficiencies, it is unlikely that these incorrect results come from
efficient farming systems exceeding the indicators limits. It is
more probable that some N inputs were underestimated. This
hypothesis is strengthened by the fact that all outlier farms had
net inputs lower than the mean of 343 kg N ha
, and nine of them
were in the lowest 10 % of farms (below 95 kg N ha
). Eight of
them had over two thirds of permanent pasture in their agricultural
area, while two had over one third of temporary grasslands with
clover in their AA. A small underestimation in symbiotic fixation
or a small overestimation of soil N storage in these farms with low
inputs could thus have a large impact on relative N efficiency.
In our sample, most flows derived from purchases and sales
of products. For most inputs, this method had low uncertainty
(Oenema et al. 2003). For soil N changes and biological fixa-
tion by legumes, however, rough calculation rules were used
due to the lack of data for the former and to the large uncertainty
in the latter. Biological N fixation was already recognized as a
large source of uncertainty in farm N budgets (Nimmo et al.
2013; Payraudeau et al. 2007), while soil N change is usually
ignored due to its complexity. These variables were found to be
highly influential on system N efficiency in another study
(Godinot et al. 2014) and are likely to explain the presence of
12 outliers with RNE greater than 100 % in our sample.
Therefore, more work is needed to better estimate them to re-
duce uncertainty and avoid relative N efficiency aberrations.
3.4.2 Limits due to potential N efficiency values
The highest potential N efficiency values found in the literature
were used in this work. These do not consider production poten-
tial linked to local conditions such as soil fertility, climate, water
availability, pests, and weeds; nor do they consider input avail-
ability, crop and animal breed choices or farm equipment.
Moreover, actual N use efficiency at the system scale is substan-
tially lower than what can be achieved in research experiments
(Goulding et al. 2008). Therefore, the potential N efficiencies used
in this study should not be considered as realistic targets but rather
as initial maximum values to calculate relative N efficiency.
866 O. Godinot et al.
The same crop efficiency (soil to harvestable crop, Table 2)
was used for all crops, though plants have different N efficien-
cies. For instance, cereals are more N efficient than root crops
(Task Force on Reactive Nitrogen 2011). Since we could not
find references according to crop type, it appeared more sim-
ple and robust to use a single value. This could be improved in
further development of the indicator when references are
available. Similarly, only the highest value of harvest efficien-
cy (95 %) was used. It corresponds to silage maize whose
above-ground biomass is almost entirely harvested, while
most crops leave large amounts of residues in fields.
However, the assumption of recycling of crop residues when
calculating relative N efficiency moderates this issue. For in-
stance, harvesting 95 % of a crop and recycling 5 % leads to a
potential crop efficiency of 90 % (see section 2.2.2), while
harvesting 50 % and recycling 50 % (common for some veg-
etables) leads to a potential crop efficiency of 82 %. Moreover,
most crops relocate N into grains at maturity greatly increas-
ing their N harvest index compared to their biomass harvest
Net flows of animals were calculated by subtracting ani-
mals of each species purchased from those sold. However,
animal age has a major impact on N use efficiency: feed con-
version ratio usually decreases with age, but the needs of the
mother for pregnancy, maintenance, and milk production
greatly reduces the efficiency of young animals. Therefore,
considering all animals of the same species equal is an imper-
fect solution. This bias favors farming systems that buy young
animals instead of breeding them. To estimate the importance
of this bias to relative N efficiency, we compared pig farms
that only breed (n=10), only fatten (n=19), or do both (n=50).
No significant difference was found for relative N efficiency
between these groups (F(2, 76)=2.297; p=0.107 > 0.05). The
bias was therefore considered acceptable, and the indicator
was not modified to address this specific point. Animal effi-
ciency does not consider their feed and/or forage rations. It is
known that feed N content impacts feed conversion ratio
(Powell et al. 2010). For the sake of generality, a single value
was chosen for the potential efficiency of all rations for a
given animal product. Different animal breeds also have dif-
ferent feed conversion ratios, which were not considered in
this simple indicator.
All farm manure was considered recycled on cropping sys-
tems. Exporting it to other farms did not modify the indicator,
since it was then considered to be recycled in other farming
systems and treated as a negative fertilizer input. However,
best available techniques for manure storage and management
are not yet widespread. Moreover, manure spreading on soils
should not always be considered as manure recycling, as
losses can be very important when soil N status does not
require additional N input. Therefore, the assumption that
77 % of manure N is recycled into the soil seems highly
In spite of these limits, a ±20 % variation of potential effi-
ciencies did not greatly affect the ranking of average farming
systems, making relative N efficiency a perfectible but reliable
indicator. Differences in calculation perimeters between crops
and animal products, however, make uncertainty a bigger is-
sue when comparing crop farming systems and animal farm-
ing systems. Meta-analysis of published potential NUE refer-
ences would provide better estimates than the single reference
values used in this study, and would allow to express the level
of uncertainty of potential NUE on the results of relative N
efficiency (Doré et al. 2011).
4 Conclusion
Relative N efficiency is a novel indicator that compares ob-
served system N efficiency to a potential value that could be
attained for a similar combination of farm products. It thus
considers production type when calculating N efficiency at
the farming system scale, making relative comparisons possi-
ble among different farming systems. The main utility of this
indicator is to compare relative efficiencies of farms that pro-
duce products of different trophic levels, which is more useful
to farmers than the correct but unhelpful observation that pro-
ducing more crops and fewer animal products increases abso-
lute N efficiency. It is particularly useful for comparing mixed
farming systems to each other or to specialized systems.
Relative N efficiency is therefore a valuable diagnosis tool
to identify efficient N management in farming systems. It also
provides a simple assessment of the theoretical room for im-
provement of a given farm. However, the simplifying hypoth-
eses used to calculate it must be considered when comparing
results. This indicator is a useful step toward the identification
and development of efficient practices and systems for crop
and livestock production.
Acknowledgments The authors are grateful to Michelle and Michael S.
Corson for English proofreading. The authors also thank three anony-
mous reviewers for their constructive remarks and comments, which
greatly improved the quality of this article.
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... Hence, livestock production systems show a lower NUE compared to plant production systems. NUE further depends on the type of livestock and in mixed plant-livestock systems also on the proportion of livestock versus plants (Hoang and Alauddin, 2010;Godinot et al., 2015;Oenema et al., 2015;Groenestein et al., 2018;Quemada et al., 2020;Bhattacharyya et al., 2021). The plant production system presented here is biased since the further usage of the harvested fodder crops and the origin of manure is not considered. ...
... The LSU further primarily consists of pig farming (Tab. A3, Appendix), which reveals the second highest NUE among all livestock types Godinot et al., 2015;Groenestein et al., 2018). Obviously, NUE is limited by the potentially achievable NUE of the respective type of livestock and is influenced by the LSU density and thus the extent of the impact of the livestock production on NUE of mixed plant-livestock systems. ...
... Consequently, the agricultural N management in less favorable environmental conditions, such as in high mountainous regions, cannot be assessed with the same criteria as in regions better suited for agricultural production. It requires adapted targets, such as relative nitrogen efficiencies which take into account yield gaps defined as the difference between potentially achievable NUE and actual NUE within specific framework conditions (Godinot et al., 2015;Rattalino Edreira et al., 2021). The dependency of NUE on geographic and climatic conditions is also evident concerning large-scale weather patterns affecting NUE across regional borders. ...
Full-text available
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... Enfin, un levier important consiste à mobiliser les atouts associés à la diversité des différentes productions agricoles d'un territoire, à l'opposé de la spécialisation observée dans certaines régions françaises. En effet, les productions végétales ont le plus souvent une meilleure efficience que les productions animales, les volailles une meilleure efficience que les bovins, et les légumineuses une meilleure efficience que les fruits et légumes (Godinot et al., 2015). Par ailleurs, des gains d'efficience importants sont accessibles grâce à la complémentarité entre productions végétales et animales (valorisation de légumineuses fourragères par des ruminants, effluents d'élevages employés comme engrais, consommation de co-produits par les animaux…). ...
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... The FCE of the pigs (13.4%) is equivalent to that of the cattle in the diversified system. In this system, the FCE of sheep (6.8%) is lower than that of cattle due to the nature of the production (the potential efficiency of dairy systems is higher than that of suckler systems - Godinot et al., 2015). Similarly, the FCE of sheep is lower than that of pigs due to the physiology of the animals (the potential efficiency of pig systems is higher than that of ruminant systems - Godinot et al., 2015). ...
... In this system, the FCE of sheep (6.8%) is lower than that of cattle due to the nature of the production (the potential efficiency of dairy systems is higher than that of suckler systems - Godinot et al., 2015). Similarly, the FCE of sheep is lower than that of pigs due to the physiology of the animals (the potential efficiency of pig systems is higher than that of ruminant systems - Godinot et al., 2015). Finally, the FCE of arable land is greater for the diversified system than for the dairy system (16.8%, ...
Mixed farming systems are of interest in the search for sustainability because of their species diversity and the potential for synergy from integrating crops with livestock. However, their ability to maximize food production has been little addressed in the literature and deserves to be explored further. The issue of nutrient recycling raises questions about resource allocation between food crops, feed and animal products. This study, based on a whole farm system experiment conducted for approximately fifteen years in northeastern France, assesses the biotechnical processes and food production performance of two integrated mixed system configurations. These configurations differed both in their types of production (diversity in both livestock and crops) and in their overall strategies (striving for self-sufficiency vs. maximizing food-crop output). Taking a metabolic approach, the study evaluates biotechnical processes (by ecological network analysis and nutrient balances) and food production efficiency. Our results show that the configuration reared to maximizing food production is not the more productive but is the more efficient. In both cases, efficiency at the farm system scale is better than the efficiencies of each production. This confirms the importance of combining systemic and analytical approaches to better understand and act on the development of agroecological farming systems. We also show the importance, for a self-sufficient system, of having stocks in reserve to cope with unfavorable years. Finally, our study confirms the value of integrated mixed farming systems in terms of agroecology but highlights the need for (i) a closer consideration of their food production aspect and (ii) an analysis of the temporal dynamics of agrosystems and the trade-offs between food production and nutrient cycling.
... In agriculture, the NUE depends on many interrelated factors, such as the genetic potential of crops and animals, environmental conditions (soil, climate, weather, and yield potential), N fertilization (type and amount of fertilizer, application time, and technology), livestock production (stable system, pasture grazing, performance, feeding intensity, and slurry management), farm structure (animal stocking density, and crop rotation), and production method (conventional or organic, irrigation) [36][37][38][39]. The associated data are becoming increasingly digitally available. ...
... In livestock production (3) in Table 4, N efficiency and emissions depend on the animal species, animal performance, feed regime, and husbandry system, among other factors [36,121]. Data collection and documentation are performed in various digital systems, such as livestock management systems, systems for determining feeding requirements, and systems for calculating the quantity and storage capacity for manure. ...
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Data that are required for nutrient management are becoming increasingly available in digital format, leading to a high innovation potential for digital nitrogen (N) management applications. However, it is currently difficult for farmers to analyze, assess, and optimize N flows in their farms using the existing software. To improve digital N management, this study identified, evaluated, and systematized the requirements of stakeholders. Furthermore, digital farm N management tools with varying objectives in terms of system boundaries, data requirements, used methods and algorithms, performance, and practicality were appraised and categorized. According to the identified needs, the concept of a farm N management system (FNMS) software is presented which includes the following modules: (1) management of site and farm data, (2) determination of fertilizer requirements, (3) N balancing and cycles, (4) N turnover and losses, and (5) decision support. The aim of FNMS is to support farmers in their farming practices for increasing N efficiency and reducing environmentally harmful N surpluses. In this study, the conceptual requirements from the agricultural and computer science perspectives were determined as a basis for developing a consistent, scientifically sound, and user-friendly FNMS, especially applicable in European countries. This FNMS enables farmers and their advisors to make knowledge-based decisions based on comprehensive and integrated data.
... When combined, the entire system reached a high average NUE (62, 67, and 49% for HFSR, MFMR, and LFLR, respectively, for the entire period). These values are higher than reported for other mixed systems, which were ∼35-45% (Godinot, Leterme, Vertés, Faverdin, and Carof, Godinot et al.;Westhoek et al., 2014). However, in the last third of the time span analyzed here, system NUE values decreased considerably (51, 63, and 48%) due to greater N fertilizer use. ...
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The reintegration of crops with livestock systems is proposed as a way of improving the environmental impacts of food production globally, particularly the impact involving nitrogen (N). A detailed understanding of processes governing N fluxes and budgets is needed to design productive and efficient crop–livestock systems. This study aimed to investigate regional differences in N balance (NBAL, defined as all N inputs minus outputs), N use efficiency (NUE, defined as N outputs/inputs × 100), and N surplus (NSURP, defined as all N inputs minus only outputs in food products) in the rice–livestock system of Uruguay. Three regions across Uruguay are distinguished based on soil fertility and length of pasture rotation. The northern region has high soil fertility and short length of rotation (HFSR); the central region has medium soil fertility and medium length of rotation (MFMR); the eastern region has low fertility and long pasture rotation (LFLR). Results for the last 18 years show a very high NUE (90%) for the rice component in all rotations, associated with negative NBALs ranging from −35 kg N ha−1 yr−1 in HFSR to −3 kg N ha−1 yr−1 in LFLR. However, the livestock component, which overall had low animal productivity (<2 kg N ha−1 yr−1), had low NUE (<10%) but positive NBALs in all the rotations, sustaining N supply in the rice component. At the system level, NUE was high (60%) and NBAL was slightly positive in all rotations (from +2.8 kg N ha−1 yr−1 in HFSR to +8.5 kg N ha−1 yr−1 in LFLR). Because of a recent increase in the N fertilizer dose in rice, NSURP for the overall system was intermediate (40 kg N ha−1 yr−1) and should be monitored in the future. Efforts to improve the system's efficiency should focus on the livestock component.
... It is evident that the shift in the overall consumer behaviour towards patterns not involving the so-called production animal is needed in order to substantially alleviate the current nitrogen stress on the planet. Indeed, most crops achieve nitrogen uptake efficiencies of only 50% or less of what is provided, and animals convert at the very best only 30% of what they consume to high-value products (Congreves et al., 2021;Godinot et al., 2015). Microorganisms growing directly on carbohydrates and mineral nitrogen (as depicted in Figure 2) can have efficiencies of N uptake which are of the order of 100% (Pikaar et al., 2017;Upcraft et al., 2021). ...
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Nitrogen is the most crucial element in the production of nutritious feeds and foods. The production of reactive nitrogen by means of fossil fuel has thus far been able to guarantee the protein supply for the world population. Yet, the production and massive use of fertilizer nitrogen constitute a major threat in terms of environmental health and sustainability. It is crucial to promote consumer acceptance and awareness towards proteins produced by highly effective microorganisms, and their potential to replace proteins obtained with poor nitrogen efficiencies from plants and animals. The fact that reactive fertilizer nitrogen, produced by the Haber Bosch process, consumes a significant amount of fossil fuel worldwide is of concern. Moreover, recently, the prices of fossil fuels have increased the cost of reactive nitrogen by a factor of 3 to 5 times, while international policies are fostering the transition towards a more sustainable agro-ecology by reducing mineral fertilizers inputs and increasing organic farming. The combination of these pressures and challenges opens opportunities to use the reactive nitrogen nutrient more carefully. Time has come to effectively recover used nitrogen from secondary resources and to upgrade it to a legal status of fertilizer. Organic nitrogen is a slow-release fertilizer, it has a factor of 2.5 or higher economic value per unit nitrogen as fertilizer and thus adequate technologies to produce it, for instance by implementing photobiological processes, are promising. Finally, it appears wise to start the integration in our overall feed and food supply chains of the exceptional potential of biological nitrogen fixation. Nitrogen produced by the nitrogenase enzyme, either in the soil or in novel biotechnology reactor systems, deserves to have a 'renaissance' in the context of planetary governance in general and the increasing number of people who desire to be fed in a sustainable way in particular.
... Although NUE comparisons among systems may be confounded by differences in methods or defined boundaries, the NUE of this rice-livestock system (23.1%) is only slightly higher than values reported for other systems (Galloway and Cowling, 2002;Howarth et al., 2002). In integrated systems, NUE values between 35% (Godinot et al., 2015) and 45% (Westhoek et al., 2014) are attained only when the proportion of the crop component greatly exceeds the livestock. ...
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Over many decades there has been a global trend away from mixed farming and integrated crop-livestock systems to more-intensive single commodity systems. This has distorted local and global nutrient balances, resulting in environmental pollution as well as soil nutrient depletion. Future food systems should include integrated crop-livestock systems with tight nutrient budgets. For nitrogen (N), detailed understanding of processes, fluxes-including of gaseous forms-and budgets at a component level is needed to design productive systems with high N use efficiency (NUE) across the full nutrient chain. In Uruguay, a unique rice-livestock system has been practiced for over 50 years, attaining a high production level for rice (mean grain yields > 8 Mg ha − 1) and an average level for livestock (120 kg liveweight gain ha − 1 y − 1). The aim of this study was to quantify the components of the N balance and NUE of this system, so as to understand its long-term sustainability, and draw conclusions for other regions. Analysis of country-level statistics for each component over the last 16 years shows tight N balances of +3.49, +2.20 and +2.22 kg N ha − 1 yr − 1 for rice, livestock and the whole system, respectively. Based on average values of N retained in edible food products, NUE values were 65.7, 13.2 and 23.1% for rice, livestock and the whole system, respectively. While NUE of livestock was unchanged over the period, NUE of the rice component decreased due to increasing fertiliser use. Further gains in N efficiency are possible by better integrating the system components. Actions to increase system level NUE include raising pasture and livestock productivity and controlling the increasing use of N fertilisers in rice. Tightly integrated crop-livestock systems can play a significant role in reshaping global agriculture towards meeting food security, environmental and socioeconomic sustainability targets.
Technical Report
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The aim of deliverable D3.1 was to make an initial introductory review of research projects and the wider literature on the topic of mixed farming in Europe, to use as a go-to resource for the further work in the MIXED Project and parallel research projects. A literature review covering a large and broad topic such as ‘mixed farming systems’ is challenging, and the report makes recommendations towards a narrower discipline focused literature search approach. As a research field, mixed farming has increased significantly since 2015 and with a noteworthy leap since 2018. Most of the funding towards mixed farming research is provided by the European Commission and 80% of the research is carried out by 6 countries alone. The data obtained from the literature search shows that agroforestry and mixed farming approaches, which incorporate trees and bushes, hold most of the data. The projects identified within the report also show an overrepresentation of an agroforestry focus and especially research focusing on energy-crops is wanting. The project identification has, however, been supported by project partners and stakeholders, which may have caused a bias in the data. Twenty-two projects were discarded from the analysis, due to not disseminating their research in English. All were from France, which highlights a potential barrier for knowledgebase sharing.
Soil-water-air (SWA) contamination from soil-plant and animal-plant-soil systems have become a major concern for the sustainability of agroecosystems. Nutrient budgeting is one of the techniques that can simultaneously monitor and prevent SWA contamination, if employed in plot, field, regional or continental scale. This study presents nutrient budgeting/ balancing (NB) methods, their implications in SWA contamination prevention as well as factors and agricultural practices that affect NB most. Current global policies and regulations aimed at offsetting nutrient pollution are also discussed. We hypothesized that (i) ecosystems equipped with NB approaches can limit the entry of excess nutrients while (ii) policies and actions linked with NB techniques eventually yield in ecosystems preservation. We demonstrated that “nutrient surpluses” responsible for SWA contamination derived from crop-livestock systems can be simultaneously monitored and prevented by using different NBs. Nutrient surpluses are highly linked with excessive use of fertilizers and manures along with low nutrient utilization efficiency of plants or in animal farming systems. Atmospheric wet and dry depositions, nutrient loss through leaching, surface runoff or erosion and impact of management e.g., tillage or crop rotation that can enhance nutrient balance should be carefully estimated to prevent over- and underestimation in NBs. However, sustainable policy designing and implementation to prevent SWA contamination need global coordinated actions and may be sustainable if integrated with different NB approaches.
Conference Paper
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Extensive and sustainable productive systems, as the organic one, are reputed to have several positive effects on environmental and socio-economic aspects, but also some negative ones could be pointed out, especially regarding rearing and nitrogen efficiency. N excretion has been studied in two groups of Piemontese beef cattle (10 animals each) fed according conventional (2 kg/d of hay forage and 3-8 kg/d of concentrate) or organic (3.5-8 kg/d of hay forage, 60% of DM intake, and 2-3 kg/d of concentrate) farming systems during the growing and fattening period (200-550 kg live weight). Monthly individual weights, average daily weight gain (ADG), daily feed consumption, and feed conversion rate (FCR) were recorded, and after slaughter (at about 16 and 20 months of age, according to feeding system) the nitrogen balance was calculated as 2.7 % of weight gain (ERM/AB-DLO, 1999). The conventional rearing system showed better productive indices (ADG 0.96 vs. 0.85, P<0.01; FCR 6.41 vs. 9.18 kg DM/kg live weight, P<0.01; N-diet 131 vs. 140 g/d) and lower environmental impact considering individual nitrogen excretion (105 vs. 117 g/d) and efficiency (19.91 vs. 16.50 %, P<0.01) than the extensive ones. Moreover, the number of animals allowed per surface unit in the organic farming could considerably reduce the soil nitrogen supply, causing a progressive reduction of soil fertility and organic matter content especially in the Mediterranean country for their soil and climatic conditions. In conclusion, livestock show several environmental functions, both positive and negative, changing in accordance to intensity, rearing systems and geographical areas. So the higher N excretion in extensive farm system should be evaluated considering all functions developed from livestock, especially in marginal areas subject to environmental risk and socio-economic decline.
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Legume biological N fixation (BNF) is a large source of uncertainty in farm N budgets. This study sought to quantify the BNF-N input to two whole farm nitrogen budgets and establish a simple and accurate method for incorporating BNF values as inputs in whole farm N budgets. Nitrogen inputs and outputs as well as flows of N between animal and crop production components were determined for a dairy farm in New Brunswick (NB) and Prince Edward Island (PE) over a two year period. The 15N natural abundance method was used to determine the %N derived from the atmosphere (%Ndfa) through BNF at both sites. Red clover (Trifolium pratense) at the PE site derived 77 % of its N from BNF and alfalfa (Medicago sativa) collected at both the PE and NB farms derived 72 % of its N from BNF. Total BNF-N present in legume biomass from mixed forage fields measured with the 15N natural abundance method ranged from 39 to 116 kg N ha−1 year−1. A legume dry matter conversion model adjusted with %Ndfa and %N of red clover and alfalfa samples from both farm sites was selected to estimate BNF-N inputs from mixed forage fields on the farms. Averaged across the entire cropland area at each farm site, the BNF-N inputs ranged from 27 to 52 kg N ha−1 year−1. The farmgate BNF-N inputs are low in comparison to other studies, possibly due to low legume contents in forage fields. BNF accounted for 18–29 % of farmgate N inputs at the farms. Surpluses of N found at both farm sites ranged from 98 to 135 kg N ha−1 year−1, typical to the whole farm N budgets of similar dairy farms.
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Four Nutritional Programs (NP) used in the Brazilian poultry industry were tested in two broiler strains (Cobb 500 and Ross 308). NP varied in the concentrations of their main essential amino acids (AA) and were classified as Low, Regular, High and Mixed (high AA concentrations up to 21 days and regular concentrations after that). Minimum digestible Met+Cys/Lys, Thr/Lys, Arg/Lys, Ile/Lys, and Val/Lys ratios were 0.74, 0.64, 1.05, 0.65 and 0.75, respectively, in all NP, and no minimum amount of CP was fixed. There were no interactions between strain and NP for any of the evaluated responses. From 1 to 47 days of age, birds fed the Low NP presented lower average body weight and body weight gain (BWG). The high NP allowed for better feed conversion ratio (FCR), followed by the Regular and the Mixed NP. Ross 308 broilers were heavier, presenting worse FCR due to higher FI. Birds fed the High NP had lower carcass yield than those fed the Low NP. The Low and Regular NP had lower costs per WG when compared with the High NP. Low and Regular NP presented higher gross margin returns compared with the High NP. The Regular and Mixed NP are the most recommended, presenting intermediate performance and higher economic returns.
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Intensive livestock farming has raised issues about environmental impacts and food security during the past 20 years. As a consequence, there is a strong social demand for sustainable livestock systems. Sustainable livestock systems should indeed be environmentally friendly, economically viable for farmers, and socially acceptable, notably for animal welfare. For that goal, many sustainability indicators and methods have been developed at the farm level. The main challenge is using a transparent selection process to avoid assessment subjectivity. Here, we review typologies of sustainability indicators. We set guidelines for selecting indicators in a data-driven context, by reviewing selection criteria and discussing methodological issues. A case study is presented. The selected set of indicators mainly includes (1) environmental indicators focusing on farmer practices; (2) quantitative economic indicators; and (3) quantitative social indicators with a low degree of aggregation. The selection of indicators should consider (1) contextualization to determine purpose, scales, and stakeholders involved in the assessment; (2) the comparison of indicators based on various criteria, mainly data availability; and (3) the selection of a minimal, consistent, and sufficient set of indicators. Finally, we discuss the following issues: topics for which no indicators are measurable from available data should explicitly be mentioned in the results. A combination of means-based indicators could be used to assess a theme, but redundancy must be avoided. The unit used to express indicators influences the results and has therefore to be taken into account during interpretation. To compare farms from indicators, the influence of the structure on indicator values has to be carefully studied.
Quels sont les besoins protéiques des bovins producteurs de viande (mère allaitante et son veau, animaux en croissance et bovins à l'engrais) et, parallèlement, quels sont les rejets azotés dans l'environnement ? La synthèse des connaissances apportées par la physiologie animale, l'alimentation et des approches plus globales fournit des éléments intéressants. Les connaissances physiologiques sur l'utilisation de l'azote par l'animal ont permis de modéliser l'accrétion protéique chez les bovins en croissance et à l'engrais, de l'ingestion des rations à la fixation dans les différents tissus de l'animal. Le système français des PDI permet de raisonner les besoins de l'animal selon ses caractéristiques, et d'évaluer ses rejets azotés compte tenu des rations utilisées ; les cas d'excès de nutrition en azote ou d'épargne (recyclage de l'azote par le bovin) peuvent être pris en compte. Enfin, des bilans plus globaux permettent d'estimer, pour le cycle de production, l'azote ingéré, fixé ou rejeté. En production de viande, l'azote provient essentiellement des ressources fourragères et la part d'azote importée dans l'exploitation ne devient significative que dans le cas des systèmes les plus intensifs.
Reactive nitrogen (N) flows (all forms of N except N2) are greatly increasing worldwide. This is mainly due to the ever larger use of inorganic N fertilizers used to sustain the growing food production. N flows have major impacts on water, air and soil quality as well as on biodiversity and human health. Reconciling the objectives of feeding the world and preserving the environment is a great challenge for agriculture. One of the main ways to increase food production while reducing its detrimental effects is to increase the efficiency of N use.