Evaluation of Environmental and Economic Performance of Crop Production in Relation to Crop Rotation, Catch Crops, and Tillage

Abstract

Crop production constitutes a significant portion of the EU’s agricultural output and influences land use decisions. Various elements within the crop production system can significantly impact its outcomes. This paper aims to evaluate the environmental and economic performance of crop rotation, catch crops, and different tillage practices in Latvia by analyzing data from case studies, field trials, and field monitoring to identify the potential for improvement towards a more sustainable utilization of agricultural land. Environmental performance was evaluated by focusing on nitrogen use efficiency (NUE), as it is likely to play a significant role in assessing the environmental suitability of crop production according to the Platform on Sustainable Finance. For economic performance, gross margins were calculated. Crop rotation in Latvia tends to be monotonous, with wheat and oilseed rape dominating over 60% of the cultivated area due to their profitability. The findings of this study indicate that achieving a minimum NUE of 70% is challenging. Crop rotations including oilseed rape, particularly the common wheat–oilseed rape rotation, have an average NUE below the threshold, while proper use of catch crops may increase NUE by 7–9%. The three-year field trials on commercial farms yielded divergent findings about the impact of various tillage practices on NUE and gross margin. However, the field trials conducted on the farm practicing reduced tillage for over ten years show higher NUE compared to ploughing. The advantage of reduced tillage was supported by the obtained results indicating lower costs of agrotechnical operations, including less diesel consumption.

Full text

Citation: Auzins, A.; Leimane, I.;
Krievina, A.; Morozova, I.; Miglavs,
A.; Lakovskis, P. Evaluation of
Environmental and Economic
Performance of Crop Production in
Relation to Crop Rotation, Catch
Crops, and Tillage. Agriculture 2023,
13, 1539. https://doi.org/10.3390/
agriculture13081539
Academic Editor: Xinhua Yin
Received: 24 June 2023
Revised: 19 July 2023
Accepted: 29 July 2023
Published: 2 August 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
agriculture
Article
Evaluation of Environmental and Economic Performance of
Crop Production in Relation to Crop Rotation, Catch Crops,
and Tillage
Alberts Auzins 1, Ieva Leimane 1, *, Agnese Krievina 1, Inga Morozova 1, Andris Miglavs 2
and Peteris Lakovskis 1
1Institute of Agricultural Resources and Economics, LV-1039 Riga, Latvia; alberts.auzins@arei.lv (A.A.);
agnese.krievina@arei.lv (A.K.); inga.morozova@arei.lv (I.M.); peteris.lakovskis@arei.lv (P.L.)
2do Consult, Ltd., LV-1021 Riga, Latvia; andris.miglavs@gmail.com
*Correspondence: ieva.leimane@arei.lv; Tel.: +371-29384949
Abstract:
Crop production constitutes a significant portion of the EU’s agricultural output and
influences land use decisions. Various elements within the crop production system can significantly
impact its outcomes. This paper aims to evaluate the environmental and economic performance of
crop rotation, catch crops, and different tillage practices in Latvia by analyzing data from case studies,
field trials, and field monitoring to identify the potential for improvement towards a more sustainable
utilization of agricultural land. Environmental performance was evaluated by focusing on nitrogen
use efficiency (NUE), as it is likely to play a significant role in assessing the environmental suitability
of crop production according to the Platform on Sustainable Finance. For economic performance,
gross margins were calculated. Crop rotation in Latvia tends to be monotonous, with wheat and
oilseed rape dominating over 60% of the cultivated area due to their profitability. The findings of
this study indicate that achieving a minimum NUE of 70% is challenging. Crop rotations including
oilseed rape, particularly the common wheat–oilseed rape rotation, have an average NUE below the
threshold, while proper use of catch crops may increase NUE by 7–9%. The three-year field trials
on commercial farms yielded divergent findings about the impact of various tillage practices on
NUE and gross margin. However, the field trials conducted on the farm practicing reduced tillage
for over ten years show higher NUE compared to ploughing. The advantage of reduced tillage was
supported by the obtained results indicating lower costs of agrotechnical operations, including less
diesel consumption.
Keywords: crop rotation; catch crops; tillage; nitrogen use efficiency; gross margin; Latvia
1. Introduction
In an era marked by global challenges related to climate change, resource scarcity,
and food security concerns, sustainable agricultural practices have garnered increased
attention [
1
,
2
], and the interplay between environmental sustainability and economic
viability has come into focus in the field of agricultural research [
3
–
6
]. To tackle these
global challenges, European Union (EU) Member States including Latvia are looking for
new solutions to increase the sustainability of food systems, starting with agriculture as
a bioresource provider. Ambitious policy initiatives with the aim of sustainability in the
agrifood sector are revealed in the European Green Deal strategies: e.g., the From Farm
to Fork strategy, the EU Biodiversity Strategy for 2030, the EU Taxonomy for sustainable
activities, etc. [
7
]. The Farm to Fork strategy stipulates a fair, healthy, and environmentally
friendly food system while ensuring farmers’ livelihoods [
8
,
9
]. The strategy aims to ensure
that the food chain has a neutral or positive environmental impact; everyone has access
to sufficient, nutritious, and sustainable food; and the affordability of food is preserved
while generating fairer economic returns in the supply chain [
10
]. The new paradigm
Agriculture 2023,13, 1539. https://doi.org/10.3390/agriculture13081539 https://www.mdpi.com/journal/agriculture

Agriculture 2023,13, 1539 2 of 25
imposes strict requirements for agricultural production. To secure a social license to farm,
the role of agricultural producers in the transition to sustainable food systems is no longer
only to produce and secure nutritious and high-value raw materials for processing, they
also cannot harm the environment and have to consider social impacts. Furthermore, the
EU Taxonomy for sustainable activities (“green taxonomy”), as a classification system,
is aimed at making finance flows (investment, credits, etc.) consistent with a pathway
towards low greenhouse gas (GHG) emissions and climate-resilient development [
11
].
Therefore, alignment with the Taxonomy will not only reflect the sustainability of farmers
but also affect their access to finance (especially bank credit) and, likely, to public support
in the future.
Crop production accounts for a significant share (60%) of the agricultural goods output
within the EU, corresponding to 66% in Latvia (data from 2021 [
12
]). It is important to note
that crop production systems play a vital role in decisions concerning the utilization of
agricultural land. The combination of various elements such as crop rotation, catch crops,
and different tillage practices within crop production systems can significantly influence
both environmental and economic outcomes. Understanding the performance of each
element and the complex interaction among the elements is fundamental for designing
sustainable agricultural practices that promote long-term environmental benefits while
also ensuring economic resilience for crop farmers.
The aim of this paper is to assess the environmental and economic performance of
crop rotation, catch crops, and different tillage practices as elements of the crop production
systems in Latvia, using a comprehensive approach that integrates empirical data gathered
from case studies, field trials and field monitoring questionnaires, and literature studies to
identify the potential areas for improvement with the aim of a more sustainable utilization
of agricultural land. To achieve the research aim, several research questions were raised:
Q1: What are the nitrogen use efficiency (NUE) and gross margin of typical crop rotations in
Latvia? Q2: What is the impact of reduced tillage (min-till, strip-till, and no-till) on the NUE
and gross margin compared to conventional tillage (ploughing) in crop-producing farms in
Latvia? Q3: How can the composition of catch crop mixtures affect the performance of crop
rotations? Q4: What is the impact of catch crop mixtures on the NUE in a medium-term
crop rotation? The structure of this paper is organized based on the research questions.
The results of our study contribute to the broader scientific discourse on sustainable
development of agricultural practices. By expanding the understanding of the environ-
mental and economic performance of crop production system elements, we contribute
to the ongoing efforts to optimize resource utilization, reduce environmental impacts,
and enhance the resilience of agricultural systems. Our research findings can support
the development of digital tools for farmers and aid in decision making regarding farm
management, promoting the ability of farmers to adapt to new regulations and contributing
to the implementation of the EU Green Deal strategies.
Crop rotation involves the sequential cultivation of different crops in the same field
over a defined period. It is a traditional practice that provides benefits including pest and
disease management and nutrient cycling; furthermore, it can enhance soil fertility and
contribute to the long-term sustainability of agricultural production [
13
]. However, the
paradigm of input intensification and specialization that has contributed to large yield
gains in staple crops has also led to dramatic declines in crop diversity [
14
]. Approximately
1.2 mill. hectares (data from 2021 [
12
]) of arable land in Latvia are used for conventional
crop production. Wheat, oilseed rape, barley, and oats are the main crops that are cultivated
in Latvia, occupying approximately 75% of the total arable land. Structural changes can be
observed in the crop production in Latvia since 2015 (Figure 1): the areas for wheat and
oilseed rape have increased, mainly at the expense of perennial grass area. The combined
area where wheat or oilseed rape were cultivated exceeded 60% of the total in 2019.

Agriculture 2023,13, 1539 3 of 25
Agriculture 2023, 13, x FOR PEER REVIEW 3 of 25
Figure 1. Structure of conventional crop production area in Latvia and its dynamics in 2015 vs.
2019.
A spatially dynamic analysis conducted using the information on direct payments
for declared areas and crops in Latvia between 2015 and 2019 indicates that Latvia’s con-
ventional crop production tends to be monotonous. Typically, two or three different crops
are included in the rotation. There are fields comprising 3% of the total conventional ara-
ble land area where wheat was cultivated as a monoculture continuously for five years
within the 2015–2019 period (see Table 1). Only 16% of the total conventional arable land
area was not used for wheat cultivation at least once between 2015 and 2019.
Table 1. Crop rotation structure by crop areas in the total area of conventional arable land in Latvia
during the period of 2015–2019.
Number of
Year s of
Occurrence in
Crop Rotation
Share of Area with
Wheat, %
Share of Area with
Oilseed Rape, %
Share of Area with
Cereals
(Wheat Excluded), %
Share of Area with Pulses
and/or Legumes, %
0 16 53.6 5 77
1 16 36 9 20
2 21 10 12 2
3 27 0.4 28 1
4 18 0 33 0
5 3 0 13 0
Oilseed rape is cultivated on half of the total conventional arable land in Latvia. Typ-
ically, it is included in the crop rotation once within a five-year period; however, there are
fields (constituting 10% of the total conventional arable land area) where oilseed rape was
included in the crop rotation at least twice during a five-year crop rotation period. It
should be mentioned that pulses are not widely cultivated in Latvia. Spatially dynamic
analysis reveals that there are fields (encompassing 77% of the total conventional arable
land area) where pulses were not cultivated at all between 2015 and 2019.
The practice of monoculture is not commonly employed in Latvia. However, it can
be observed in all regions, particularly in the Zemgale region, which is of significant im-
portance for crop production due to its fertile soil, as well as in the Kurzeme region (Figure
2). Wheat is the most common crop cultivated as a monoculture, and maize (both for feed
44%
9%
10%
4%
3%
15%
6%
47%
14%
7%
5%
3%
11%
4%3%3% 3%
2015 (inner circle) vs 2019 (outer sircle) wheat
rape
barley
oats
rye
perennial grass in arable land
fallow
pulses
maize
others
Figure 1.
Structure of conventional crop production area in Latvia and its dynamics in 2015 vs. 2019.
A spatially dynamic analysis conducted using the information on direct payments
for declared areas and crops in Latvia between 2015 and 2019 indicates that Latvia’s
conventional crop production tends to be monotonous. Typically, two or three different
crops are included in the rotation. There are fields comprising 3% of the total conventional
arable land area where wheat was cultivated as a monoculture continuously for five years
within the 2015–2019 period (see Table 1). Only 16% of the total conventional arable land
area was not used for wheat cultivation at least once between 2015 and 2019.
Table 1.
Crop rotation structure by crop areas in the total area of conventional arable land in Latvia
during the period of 2015–2019.
Number of Years of
Occurrence in Crop
Rotation
Share of Area with
Wheat, %
Share of Area with
Oilseed Rape, %
Share of Area with
Cereals
(Wheat Excluded), %
Share of Area with
Pulses
and/or Legumes, %
0 16 53.6 5 77
1 16 36 9 20
2 21 10 12 2
3 27 0.4 28 1
4 18 0 33 0
5 3 0 13 0
Oilseed rape is cultivated on half of the total conventional arable land in Latvia.
Typically, it is included in the crop rotation once within a five-year period; however, there
are fields (constituting 10% of the total conventional arable land area) where oilseed rape
was included in the crop rotation at least twice during a five-year crop rotation period. It
should be mentioned that pulses are not widely cultivated in Latvia. Spatially dynamic
analysis reveals that there are fields (encompassing 77% of the total conventional arable
land area) where pulses were not cultivated at all between 2015 and 2019.
The practice of monoculture is not commonly employed in Latvia. However, it can
be observed in all regions, particularly in the Zemgale region, which is of significant
importance for crop production due to its fertile soil, as well as in the Kurzeme region
(Figure 2). Wheat is the most common crop cultivated as a monoculture, and maize (both
for feed and for energy production) follows as the second most widespread crop cultivated
as a monoculture.

Agriculture 2023,13, 1539 4 of 25
Agriculture 2023, 13, x FOR PEER REVIEW 4 of 25
and for energy production) follows as the second most widespread crop cultivated as a
monoculture.
(a)
(b) (c)
Figure 2. Share of monocultures in the average total conventional arable land in Latvia during 2015–
2019 (%): (a) total area with a monoculture; (b) area with wheat as a monoculture; (c) area with
maize as a monoculture.
Two-crop rotation is a widely adopted practice in the Latvian crop production sys-
tem, with only a few areas where it is not commonly applied (Figure 3). In the majority of
the country, two-crop rotation occupies less than 33% of the total conventional arable land.
However, there are certain areas where two-crop rotation is the dominant practice in crop
production, encompassing more than 50% of the total conventional arable land.
(a)
Figure 2.
Share of monocultures in the average total conventional arable land in Latvia during
2015–2019 (%): (
a
) total area with a monoculture; (
b
) area with wheat as a monoculture; (
c
) area with
maize as a monoculture.
Two-crop rotation is a widely adopted practice in the Latvian crop production system,
with only a few areas where it is not commonly applied (Figure 3). In the majority of the
country, two-crop rotation occupies less than 33% of the total conventional arable land.
However, there are certain areas where two-crop rotation is the dominant practice in crop
production, encompassing more than 50% of the total conventional arable land.
The selection of a crop rotation practice is influenced by various factors. Assuming that
the objective of crop farmers is to establish an optimal solution tailored to local conditions
and maximize the productivity potential of their fields, the prevalence of two-crop rotation
in Latvian crop production indicates that farmers, whenever possible, choose to cultivate
oilseed rape alongside wheat. In cases where this combination is not feasible, farmers
typically opt to grow wheat in combination with other cereal crops. The incorporation
of pulses and other protein crops into crop rotation is not yet widely adopted, resulting
in a missed opportunity to fix nitrogen from the atmosphere and enhance the long-term
sustainability of the crop production system.
The inclusion of catch crops as intermediate crops in crop production is a relatively
new phenomenon in Latvia. This is primary due to the prevailing practice of cultivating
winter crops as the main crops, as revealed by crop rotation analysis. However, considering
the crop rotation in the period between 2015 and 2019, the average sowing potential of
catch crops in conventional arable lands in Latvia was 272,980 ha per year. It is anticipated
that the availability of areas for catch crops will increase with the introduction of more
legumes or other spring crops in the crop rotation (Figure 4).

Agriculture 2023,13, 1539 5 of 25
Agriculture 2023, 13, x FOR PEER REVIEW 4 of 25
and for energy production) follows as the second most widespread crop cultivated as a
monoculture.
(a)
(b) (c)
Figure 2. Share of monocultures in the average total conventional arable land in Latvia during 2015–
2019 (%): (a) total area with a monoculture; (b) area with wheat as a monoculture; (c) area with
maize as a monoculture.
Two-crop rotation is a widely adopted practice in the Latvian crop production sys-
tem, with only a few areas where it is not commonly applied (Figure 3). In the majority of
the country, two-crop rotation occupies less than 33% of the total conventional arable land.
However, there are certain areas where two-crop rotation is the dominant practice in crop
production, encompassing more than 50% of the total conventional arable land.
(a)
Agriculture 2023, 13, x FOR PEER REVIEW 5 of 25
(b) (c)
Figure 3. Share of two-crop rotations in the average total conventional arable land in Latvia, during
2015–2019 (%): (a) total area with a two-crop rotation; (b) area with a two-crop rotation including
wheat; (c) area with a wheat–oilseed rape rotation.
The selection of a crop rotation practice is influenced by various factors. Assuming
that the objective of crop farmers is to establish an optimal solution tailored to local con-
ditions and maximize the productivity potential of their fields, the prevalence of two-crop
rotation in Latvian crop production indicates that farmers, whenever possible, choose to
cultivate oilseed rape alongside wheat. In cases where this combination is not feasible,
farmers typically opt to grow wheat in combination with other cereal crops. The incorpo-
ration of pulses and other protein crops into crop rotation is not yet widely adopted, re-
sulting in a missed opportunity to fix nitrogen from the atmosphere and enhance the long-
term sustainability of the crop production system.
The inclusion of catch crops as intermediate crops in crop production is a relatively
new phenomenon in Latvia. This is primary due to the prevailing practice of cultivating
winter crops as the main crops, as revealed by crop rotation analysis. However, consider-
ing the crop rotation in the period between 2015 and 2019, the average sowing potential
of catch crops in conventional arable lands in Latvia was 272,980 ha per year. It is antici-
pated that the availability of areas for catch crops will increase with the introduction of
more legumes or other spring crops in the crop rotation (Figure 4).
Figure 4. Potential area for catch crops in conventional arable land of Latvia (average ha annually)
according to crop rotation during the period of 2015–2019.
Recently, the focus on catch crops, particularly regarding their potential to enhance
N balance and NUE in crop rotation, has increased. In some European countries, for, ex-
ample Denmark, cultivation of catch crops is mandatory to absorb excess soil N during
the autumn season [15], following catch crops, a mandatory reduction in the amount of N
Figure 3.
Share of two-crop rotations in the average total conventional arable land in Latvia, during
2015–2019 (%): (
a
) total area with a two-crop rotation; (
b
) area with a two-crop rotation including
wheat; (c) area with a wheat–oilseed rape rotation.
Agriculture 2023, 13, x FOR PEER REVIEW 5 of 25
(b) (c)
Figure 3. Share of two-crop rotations in the average total conventional arable land in Latvia, during
2015–2019 (%): (a) total area with a two-crop rotation; (b) area with a two-crop rotation including
wheat; (c) area with a wheat–oilseed rape rotation.
The selection of a crop rotation practice is influenced by various factors. Assuming
that the objective of crop farmers is to establish an optimal solution tailored to local con-
ditions and maximize the productivity potential of their fields, the prevalence of two-crop
rotation in Latvian crop production indicates that farmers, whenever possible, choose to
cultivate oilseed rape alongside wheat. In cases where this combination is not feasible,
farmers typically opt to grow wheat in combination with other cereal crops. The incorpo-
ration of pulses and other protein crops into crop rotation is not yet widely adopted, re-
sulting in a missed opportunity to fix nitrogen from the atmosphere and enhance the long-
term sustainability of the crop production system.
The inclusion of catch crops as intermediate crops in crop production is a relatively
new phenomenon in Latvia. This is primary due to the prevailing practice of cultivating
winter crops as the main crops, as revealed by crop rotation analysis. However, consider-
ing the crop rotation in the period between 2015 and 2019, the average sowing potential
of catch crops in conventional arable lands in Latvia was 272,980 ha per year. It is antici-
pated that the availability of areas for catch crops will increase with the introduction of
more legumes or other spring crops in the crop rotation (Figure 4).
Figure 4. Potential area for catch crops in conventional arable land of Latvia (average ha annually)
according to crop rotation during the period of 2015–2019.
Recently, the focus on catch crops, particularly regarding their potential to enhance
N balance and NUE in crop rotation, has increased. In some European countries, for, ex-
ample Denmark, cultivation of catch crops is mandatory to absorb excess soil N during
the autumn season [15], following catch crops, a mandatory reduction in the amount of N
Figure 4.
Potential area for catch crops in conventional arable land of Latvia (average ha annually)
according to crop rotation during the period of 2015–2019.
Recently, the focus on catch crops, particularly regarding their potential to enhance
N balance and NUE in crop rotation, has increased. In some European countries, for,
example Denmark, cultivation of catch crops is mandatory to absorb excess soil N during
the autumn season [
15
], following catch crops, a mandatory reduction in the amount of N
fertilizer that can be applied to the succeeding crop is imposed [
16
]. The reduction accounts
for the N derived from the mineralization of the catch crop residues and thus compensates
for the residual effect of catch crops in the years after their incorporation [
17
]. There is also
a great interest in including leguminous catch crops, as they can, through their biological N
fixation, enhance the nitrogen supply to the subsequent crop and thus further reduce N

Agriculture 2023,13, 1539 6 of 25
fertilization rates [
17
]. The use of mixtures of non-leguminous and leguminous catch crops
has been promoted to better utilize the N fixed by legumes without the risk of increased
N losses via leaching from pure leguminous catch crops [
18
]. Reducing N input would
be vital for improving NUE, with a current loss of 40% of the total N input via leaching,
gaseous emissions, and runoff [19].
The choice of tillage type in crop production is also a meaningful decision that farmers
face. Conventional tillage involving deep ploughing and soil disruption has traditionally
been practiced for weed control and seedbed preparation [
20
]. However, reduced and no
till have gained popularity due to their potential benefits, including improved soil structure,
moisture retention, erosion control, and lower fuel consumption [
21
,
22
]. The selection of
tillage type depends on various factors, such as soil type, climate, crop rotation, weed pres-
sure, equipment availability, and farmers’ preferences [
21
]. Therefore, the development of
local knowledge in this field is critically important; especially nowadays, by understanding
how different tillage types can influence N balance and NUE, farmers can make informed
decisions to enhance NUE, reduce environmental impacts, and maximize crop productivity.
2. Materials and Methods
The main data source for this study is unpublished information and empirical data ob-
tained from Latvian crop farmers within the agricultural European Innovation Partnership
(EIP-AGRI) project “Progressive land cultivation system as the basis for environmentally
friendly and effective crop production”. During the implementation of the project, field
monitoring, field case studies, and field trials were carried out.
Field monitoring covered about 150 monitored fields belonging to the farms of the two
largest Latvian grain cooperatives (project partners), VAKS and Latraps. For each monitored
field, general data such as location, farm size, farm specialization, farming system, and
tillage system were collected through questionnaires. Soil characteristics including soil type,
soil organic matter, pH, etc., were also recorded. Additionally, data on production activities
and results, including crop information, agrotechnical operations, use of synthetic and
organic fertilizers, use of plant protection products, and yield parameters, were obtained
for four consecutive years (2018, 2019, 2020, and 2021) for the same farm field and reported
per 1 hectare (field monitoring questionnaire form provided in Supplementary Material).
The cooperatives were entrusted with the selection of farms for field monitoring, and
currently, the obtained information constitutes the largest dataset containing information
on field histories of the main crop production in Latvia. The dataset was used to analyze
and compare the environmental and economic performance of crop rotation, as well as
conventional tillage (ploughing) and non-inversion tillage.
Field case studies were carried out on six conventional crop farms to investigate
whether different types of tillage (ploughing and non-inversion tillage, including min-till,
strip-till, and no-till) result in different economic and environmental outcomes (Table 2).
The tillage trials were conducted for three consecutive years (2020–2022), with each farm
implementing at least two different types of tillage. Each farm applied its own crop rotation
and cultivation technology based on the cultivated main crop. The same agrotechnology
(fertilization and plant protection measures) was applied for all trial fields within a given
farm in order to assess the potential impact of tillage type on the crop yield. The trial
fields were established with a minimum size of 0.5 hectares. The farms involved in the
case studies were located in different regions of Latvia, encompassing soil with various
granulometric compositions. This included one farm with clay soil, while the remaining
farms had varying sandy clay soils. Considering the unique nature of each field case
study, an evaluation of environmental and economic performance associated with different
types of tillage was conducted individually for each case. However, to facilitate better
interpretation of the results, the cases were divided into three groups based on the type of
crop rotation implemented during the respective case studies: (1) crop rotation involving
only cereals; (2) crop rotation involving cereals, oilseed rape, and faba beans; (3) and crop
rotation including maize.

Agriculture 2023,13, 1539 7 of 25
Table 2. Characteristics of field case studies for different tillage types (2020–2022).
Field Case Study Ploughing Min-Till Strip-Till No-Till
Crop rotation involving only cereals
Field A X X - X
Crop rotation including maize
Field B - X - X
Field C X X - -
Crop rotation involving cereals, oilseed
rape, and faba beans
Field D X X - X
Field E * - X X -
Field F X X X -
* Ploughing has not been practiced for more than 10 years. X indicates the application of the respective
tillage type
.
Field trials were conducted to obtain empirical data for catch crop performance analy-
sis, with the focus on catch crop mixtures. Data for catch crop mixes were collected from
the field trials conducted over three years (2019–2021) in two locations—(1) the Institute of
Agricultural Resources and Economics Stende Research Center (Stende) in the Kurzeme
region and (2) the “Lielvaiceni” (Vitini) agricultural farm in the Zemgale region—both
located in the western part of Latvia. According to the Köppen climate classification, Latvia
has a mild continental humid climate. The catch crop mixtures were grown in arandomized
complete block design what included plots of 36 m
2
(3 m
×
12 m) in four replicates in
Stende and 0.1 ha plots in Vitini.
Agrometeorological condition characteristics were obtained from the Stende and
Saldus hydrometeorological stations. In the study, the hydrothermal coefficient (HTC)
was calculated for each month during the catch crop vegetation period (Figure 5). The
calculations were performed using the following formula [23]:
HTC = Σx/Σt×10, (1)
where Σx is the total precipitation for the period (mm), and Σt is the total temperature for
the period in which the average temperature exceeds 10 ◦C.
Agriculture 2023, 13, x FOR PEER REVIEW 8 of 25
Figure 5. Hydrothermal coefficient (HTC) from August to October 2019–2021.
Catch crop species were selected based on their ability to grow rapidly during the
autumn in a short-term fallow period until winter. The following species were included
in the mixtures: oats (Avena sativa L), ryegrass (Lolium multiflorum), rye (Secale cereale L),
mustard (Sinapis alba L), radish (Raphanus sativus var. longipinnatus L.), oilseed rape (Bras-
sica napus L.), buckwheat (Fagopyrum esculentum Moench), phacelia (Phacelia tanacetifolia
Benth.), crimson clover (Trifolium incarnatum), and vetch (Vicia sativa L./Vicia villosa Roth).
There were three non-legume-based mixtures ((1) oats and mustard; (2) mustard and rad-
ish; and (3) ryegrass, buckwheat, and phacelia) and three legume-based mixtures ((1)
ryegrass, crimson clover, and phacelia (not included in the mixture for all years); (2) oats,
vetch, and phacelia; and (3) rye, oilseed rape, and vetch/phacelia). The control was bare
fallow in both locations. The field was prepared, and catch crops were sown immediately
after the winter wheat harvesting: on 12 August 2019, 26 August 2020, and 6 August 2021
in Stende and on 11 August 2019, 13 August 2020, and 10 August 2021 in Vitini. The gran-
ulometric composition comprised sandy clay soils in both locations (Table 3).
Aboveground biomass and belowground biomass were collected from a 0.25 m
2
plot
per replicate for four replications. Samples were washed and dried, and the dry maer of
shoots and roots was weighed. In 2021, the total nitrogen and carbon contents of dried
samples were determined (separately in belowground and aboveground biomass) based
on the LVS ISO 13878:1998 and LVS ISO 10694:2006 methods, respectively. Correlation
analysis was employed to examine the relationships between the dry maer yield of
shoots and roots, as well as between the combined dry maer yield of shoots and roots
and HTC for each catch crop mixture by year (p ≤ 0.05 or p ≤ 0.01).
Table 3. Soil agrochemical characteristics and preceding crops for catch crop trials (2019–2021).
Location Stende Vitini
Year 2019 2020 2021 2019 2020 2021
pH
KCl
4.9–5.7 6.1 6.8 7.4 7.3–7.6 7
Organic matter, % 2.0–2.5 3.7 3.8–4.3 2.7 3.7–4.7 3
P
2
O
5,
mg kg
−1
158–219 238 42 165 75–201 230–370
K
2
O, mg kg
−1
169–182 107 128 131 130
Preceding crop Winter wheat Winter wheat
Following crop Spring barley Spring barley Field pea Faba beans
To study the performance of catch crop mixtures, a literature analysis was also con-
ducted to review the influence of combining catch crops on various aspects, including
providing a source of nitrogen (in the case of legumes), nitrogen scavenging, the availa-
bility of free phosphorus and potassium, erosion and weed control, topsoil loosening and
subsoiling effects, nematodes and disease suppression, and allelopathic effects on the soil
Figure 5. Hydrothermal coefficient (HTC) from August to October 2019–2021.
Ranges of values of this index were classified according to Sielianinov coefficient as
modified by Skowera et al. as [
24
]: extremely dry—HTC
≤
0.4,
very dry—0.4 < HTC ≤0.7
,
dry—0.7 < HTC
≤
1.0, relatively dry—1.0 < HTC
≤
1.3, optimal—1.3 < HTC
≤
1.6, rela-
tively humid—1.6 < HTC
≤
2.0, humid –2.0 < HTC
≤
2.5,
very humid—2.5 < HTC ≤3
, or
extremely humid—HTC > 3.0. The meteorological data for each month represent the mea-

Agriculture 2023,13, 1539 8 of 25
surements taken during the growing season, starting from the sowing date and continuing
until the catch crop termination date (biomass harvest date).
Catch crop species were selected based on their ability to grow rapidly during the
autumn in a short-term fallow period until winter. The following species were included
in the mixtures: oats (Avena sativa L.), ryegrass (Lolium multiflorum), rye (
Secale cereale L.
),
mustard (Sinapis alba L.), radish (Raphanus sativus var. longipinnatus L.), oilseed rape
(
Brassica napus L.
), buckwheat (Fagopyrum esculentum Moench), phacelia (Phacelia tanaceti-
folia Benth.), crimson clover (Trifolium incarnatum), and vetch (Vicia sativa L./Vicia villosa
Roth). There were three non-legume-based mixtures ((1) oats and mustard; (2) mustard
and radish; and (3) ryegrass, buckwheat, and phacelia) and three legume-based mixtures
((1) ryegrass, crimson clover, and phacelia (not included in the mixture for all years);
(2) oats, vetch, and phacelia; and (3) rye, oilseed rape, and vetch/phacelia). The control
was bare fallow in both locations. The field was prepared, and catch crops were sown
immediately after the winter wheat harvesting: on 12 August 2019, 26 August 2020, and
6 August 2021
in Stende and on 11 August 2019, 13 August 2020, and 10 August 2021
in Vitini. The granulometric composition comprised sandy clay soils in both locations
(Table 3).
Table 3. Soil agrochemical characteristics and preceding crops for catch crop trials (2019–2021).
Location Stende Vitini
Year 2019 2020 2021 2019 2020 2021
pHKCl 4.9–5.7 6.1 6.8 7.4 7.3–7.6 7
Organic matter, % 2.0–2.5 3.7 3.8–4.3 2.7 3.7–4.7 3
P2O5, mg kg−1158–219 238 42 165 75–201 230–370
K2O, mg kg−1169–182 107 128 131 130
Preceding crop Winter wheat Winter wheat
Following crop Spring barley Spring barley Field pea Faba beans
Aboveground biomass and belowground biomass were collected from a 0.25 m
2
plot
per replicate for four replications. Samples were washed and dried, and the dry matter
of shoots and roots was weighed. In 2021, the total nitrogen and carbon contents of dried
samples were determined (separately in belowground and aboveground biomass) based on
the LVS ISO 13878:1998 [
25
] and LVS ISO 10694:2006 [
26
] methods, respectively. Correlation
analysis was employed to examine the relationships between the dry matter yield of shoots
and roots, as well as between the combined dry matter yield of shoots and roots and HTC
for each catch crop mixture by year (p≤0.05 or p≤0.01).
To study the performance of catch crop mixtures, a literature analysis was also con-
ducted to review the influence of combining catch crops on various aspects, including
providing a source of nitrogen (in the case of legumes), nitrogen scavenging, the availabil-
ity of free phosphorus and potassium, erosion and weed control, topsoil loosening and
subsoiling effects, nematodes and disease suppression, and allelopathic effects on the soil
environment. A meta-analysis of the performance and potential advantages of catch crop
mixtures covering different scientific sources was conducted, including information from
studies conducted in the USA, Germany, Estonia, France, and other countries.
2.1. Measuring Environmental Performance
In this study, environmental performance was evaluated by focusing on nitrogen
balance because the nitrogen cycle plays a significant role in the agrifood sector due to its
direct impact on crop productivity, soil health, and environmental sustainability. Moreover,
the Platform on Sustainable Finance (PSF) has proposed nitrogen use efficiency (NUE) as
an indicator of nitrogen balance [
27
,
28
]. Thus, the NUE for a field was calculated and used

Agriculture 2023,13, 1539 9 of 25
as an indicator of N balance and environmental performance. According to the general
methodology, NUE was calculated as the ratio of N output to N input:
NUE = ΣN_output/ΣN_input, (2)
where
Σ
N_output is a field’s N output (kg N ha
−1
), and
Σ
N_input is a field’s N input
(kg N ha−1).
As the typical practice in Latvia is that straw and other aboveground plant above-
ground residues are left in the field, it was assumed that
Σ
N_output was comprised of N
removal by harvest. The N removal by harvest was assessed according to the crude protein
content. To calculate N content based on crude protein, the standard nitrogen-to-protein
conversion factor (Kjeldahl method; 5.7 in the case of winter wheat and spring wheat, and
6.25 for other crops) specified by ISO 20483:2013(E) [
29
] was used. If information on the
crude protein content was not available from the empirical project data, the nitrogen content
in dry matter derived from the values reported by K
¯
arkli
n
,
š and Ruža (see Table 4) [
30
]
was used.
Table 4. Nitrogen removal by harvest of different field crops.
Field Crop Product Dry Matter, % N Content, kg·t−1Dry Matter
Winter wheat Grain 86 22.0
Rye Grain 86 17.4
Winter barley Grain 86 20.3
Winter triticale Grain 86 18.6
Spring wheat Grain 86 25.3
Spring barley Grain 86 21.0
Oats Grain 86 18.1
Field peas and faba beans Seeds 86 45.7
Winter oilseed rape Seeds 92 29.1
Spring oilseed rape Seeds 92 38.3
Source: derived from K¯
arklin
,š and Ruža [30].
Σ
N_input was comprised of N inputs from seed, synthetic fertilizers, organic manure
(if applied), and biological N fixation (if relevant). If trials involved sowing catch crops
(grown as intermediate crops), the N inputs from their seeds were also considered. It was
assumed that the N content in seeds was the same as that in the harvested grains or seeds.
Biological N fixation was assessed by assuming that, on average, faba beans fix 72.6 kg of N
per ton of dry matter (DM) yield, field peas fix 46.7 kg of N per ton of DM yield, and spring
vetches fix 73.5 kg of N per ton of DM yield. These assumptions were derived from the
research project “Legume-supported cropping systems for Europe (Legume Futures)” [
31
].
According to the methodology proposed by the PSF, the changes in soil nitrogen content
were not considered as N input or output [27,28].
To exclude short-term effects that can affect the result for a single year, NUE was
calculated for a multiyear period. Such an approach complies with the recommendations
of the PSF, which suggest calculating NUE as a rolling average of three years [
27
,
28
]. As
the field monitoring involved a four-year period, NUE for crop rotations was calculated as
the average for four years (2018–2021). NUE for different tillage types was calculated as a
rolling average for a three-year period based on field case studies.
The NUE calculation results for the crop rotation considering the data from the field
monitoring dataset were first assessed by Piliksere, Auzins, and Aboltins [
32
]. However,
the analysis at that time was based on preliminary field monitoring data, and the available
dataset (particularly for 2021) was considerably smaller. Moreover, the previous research
did not consider the actual crude protein content to assess N input (only the values reported
by K¯
arklin
,š and Ruža were used).
It should be mentioned that the PSF has proposed that biological N fixation can
be excluded from N input to encourage the cultivation of legumes (pulses) [
28
]. If this

Agriculture 2023,13, 1539 10 of 25
proposal is accepted and incorporated in the technical screening criteria of the Taxonomy,
crop rotations including pulses will have an advantage over crop rotations without pulses.
Therefore, the assessment of the NUE for crop rotations was supplemented with calculations
in which biological N fixation was excluded. The laboratory tests conducted by the Institute
of Agricultural Resources and Economics within this project revealed that the N content
in winter rapeseeds was likely higher than the value reported by Karkli
n
,
š and Ruža.
According to these tests, the N content was 20% higher (90% confidence interval: 11–28%).
Thus, the assessment of the NUE for crop rotations was also supplemented by additional
calculations in which adjusted N content of winter rapeseed (by 20% higher) was used.
NUE for catch crop (intermediate crops) trials was calculated for two-year periods
(2019–2020, 2020–2021, and 2021–2022). These two-year periods consisted of the year before
catch crop cultivation and the year following catch crop cultivation. Such an approach
allowed for assessment of the effect of catch crops in reducing N losses (leaching) and
transferring N to the next cash crop. The average NUE was calculated as a simple average
of NUE for the periods of 2019–2020, 2020–2021, and 2021–2022. The effect of catch crops
on NUE was assessed by comparing the average NUE between a catch crop mixture and
the control group, calculating the relative difference:
RDNUEi = NUEcci/NUEcon −1, (3)
where RD
NUEi
is the relative difference for mixture i of catch crops, NUE
cci
is the average
NUE for the mixture i of catch crops, and NUE
con
is the average NUE for the control group.
The relative differences in NUE were calculatedusing both stubble after disc harrowing
and ploughing as the control group. Since the trials of 2019–2020 involved only stubble
after disc harrowing and did not include ploughing, the assessments and comparisons with
ploughing were conducted excluding the period of 2019–2020.
2.2. Measuring Economic Performance
For the study, the economic performance was evaluated as the gross margin, which
corresponds to the revenue minus variable costs. The gross margin is expressed in EU
per hectare (EUR ha
−1
). The average annual value of the gross margin is presented for a
four-year period (2018–2021).
Evaluation of the economic performance for crop rotation and different tillage types
was conducted based on the dataset of monitored fields (field monitoring) compiled during
the implementation of the project and the information gathered from the field case studies
(see Table 2). The revenue for each field andyear (2018, 2019, 2020, and 2021) was calculated
based on the crop yield in tonnes per ha (obtained from the farm questionnaires; see the
questionnaire form in Supplementary Materials) and the corresponding sales price of the
crop. The evaluation was conducted using constant 2020 prices, which were sourced from
the official agricultural producer price statistics [
33
]. Constant prices were used in the
evaluation to exclude the effects of market fluctuations and individual organizational
characteristics of each farm.
The costs for each field and year were obtained by aggregating the variable costs
for the following agrotechnical operations and raw materials from the questionnaires:
liming and liming material, ploughing, subsoiling, disc harrowing, levelling, cultivation,
combined soil preparation without sowing, sowing/planting of a main crop and a catch
crop, seed for the main crop and catch crop, combined soil preparation with sowing, roller
pressing, harrowing, application of synthetic fertilizers, application of plant protection
products, application of animal manure or digestate, harvesting, straw chopping, straw
pressing, crop rolling and chopping, and chemical crop termination (catch crops).
The total costs of agrotechnical operations for each field were determined by con-
sidering the number of times the activity was reported to have been performed in the
questionnaires and the associated price/cost per executed operation. However, the costs of
spreading organic manure were derived from the quantities of distributed organic manure
or digestate reported in the questionnaires and the corresponding spreading price/cost per

Agriculture 2023,13, 1539 11 of 25
tonne. The evaluation was carried out at constant 2020 prices. Information regarding the
price/cost of agrotechnical operations was obtained through direct communication with
the expert of the Latvian Rural Advisory and Training Centre (LRATC) [34].
The costs of seeds were calculated based on the sowing rate provided in the question-
naires and the corresponding seed price for the specific crop [
35
]. The costs of synthetic
fertilizers were determined by considering the quantity of nitrogen reported in the question-
naires and the corresponding value of nitrogen. The value of nitrogen was derived from the
prices of NPK complex fertilizers, ammonia nitrate, and ammonia sulphate fertilizers [
35
],
considering their respective nitrogen (N), phosphorus (P
2
O
5
), potassium (K
2
O), and sulfur
(S) contents. An equation was constructed to calculate the values of N, P
2
O
5
, K
2
O, and
S based on these factors. The costs of organic fertilizers and digestate were estimated
by considering the amount of organic manure or digestate used on each field and the
estimated quantities of N, P
2
O
5
, and K
2
O present in the manure [
36
] or in digestate (field
case studies), as well as the estimated value of the basic plant nutrients. Additionally, costs
associated with other fertilizers, as presented in terms of value in the questionnaires, were
included in the calculations. The costs of plant protection products (PPPs) were obtained
by summing the PPP items presented in the questionnaires, including herbicides, limacides,
fungicides, insecticides, retardants, desiccants, and biological PPPs. These costs were pro-
vided in monetary terms in the questionnaires. The cost of liming material was estimated
by considering the consumption of liming material reported in the questionnaires, as well
as the corresponding prices. For each agrotechnical operation, the consumption of diesel
was also estimated by the expert of LRATC [
34
], enabling the determination of the total
diesel consumption associated with the agrotechnical operations implemented for crop
production in the field.
For the purpose of grouping fields by crop rotations, only those fields from the
field monitoring dataset were selected for the subsequent analysis, where production did
not occur using organic farming methods and costs and revenue were greater than zero
in all four years (excluding the year with fallow land). When analyzing the economic
performance of different tillage types using data from field monitoring, a field was selected
for the ploughing system if ploughing had been conducted every year during the four-year
period. In the case of non-inversion tillage, it was required that the monitored field had
not been ploughed for at least three years within the four-year period, allowing for the
possibility of ploughing once in any of the four years. Analysis of the collected data was
performed using the R programming language (Excel files were provided as an input data
source: the total of four Excel files for the years 2018, 2019, 2020, and 2021). R served as a
tool for data verification, calculations, and grouping.
3. Results
3.1. Effect of Crop Rotation on NUE
The assessed average NUE and its confidence intervals for crop rotations within field
monitoring are presented in Table 5. When applying the assumptions regarding biological
nitrogen fixation described in Section 2.1, wheat, various cereals and green maize–other
field crop rotations demonstrated the highest average NUE. However, the confidence
intervals (CI) for various cereals and green maize–other field crops rotations were very
wide (even extremely wide). Thus, only wheat rotation showed a statistically significant
difference from the other crop rotations (except various cereals and green maize–other field
crop rotations). Rotation of various cereals also demonstrated statistically significantly
higher average NUE values than wheat–oilseed rape and wheat–oilseed rape—pulses
rotation, although this rotation did not outperform other crop rotations.

Agriculture 2023,13, 1539 12 of 25
Table 5. Average NUE for different crop rotations (for the period of 2018–2021).
Crop Rotation Number of Farm Fields * Average NUE, % CI for Average NUE **, %
Wheat 5 74.3 (71.2, 77.3)
Various species of cereals 11 74.7 (67.4, 81.9)
Wheat–oilseed rape 42 63.9 (60.7, 67.1)
If N content of winter rapeseed is adjusted 42 66.1 (62.9, 69.3)
Wheat–pulses 6 63.4 (58.8, 68.0)
If biological N fixation is excluded 6 81.2 (75.0, 87.3)
Wheat–oilseed rape–pulses 18 63.1 (59.9, 66.2)
If biological N fixation is excluded 18 82.1 (77.4, 86.7)
If N content of winter rapeseed is adjusted 18 65.4 (62.3, 68.5)
If biological fixation is excluded and N
content of winter rapeseed is adjusted 18 85.1 (80.5, 89.7)
Wheat–barley–oilseed rape 9 63.0 (57.0, 69.0)
If N content of winter rapeseed is adjusted 9 65.0 (58.6, 71.4)
Wheat–oilseed rape–fallow 7 61.4 (54.1, 68.8)
If N content of winter rapeseed is adjusted 7 65.2 (57.4, 73.0)
Green maize–other field crops 2 72.9 (47.8, 98.0)
* Fields where only synthetic fertilizers were applied. ** 90% confidence interval.
The low performance of crop rotations that include oilseed rape can be attributed
to the N content of rapeseed derived from the value reported by Karkli
n
,
š and Ruža (see
Section 2.1). As already mentioned, these values are probably too low. Although the
use of adjusted N content of winter rapeseed increased the average NUE, as well as the
lower bound confidence intervals, the increase was not significant. Therefore, these results
suggest that the inclusion of winter rape in crop rotation is highly likely to reduce the NUE
of crop rotation, even when assessed for a 4-year period.
The low performance of crop rotations that include pulses can be explained by the
possible overestimation of biological N fixation. Consequently, an additional assessment
of NUE was conducted by excluding biological N fixation. These results indicate signifi-
cantly higher average NUE values and lower bounds of confidence intervals. Therefore,
if the proposal by the PSF to exclude biological N fixation is applied, wheat–pulses and
wheat–oilseed
rape–pulses rotations demonstrate the highest average NUE. Moreover,
wheat–oilseed rape–pulses rotation statistically significantly outperforms wheat rotation.
3.2. Effect of Crop Rotation on Gross Margin
When evaluating the gross margin for the main types of crop rotations within field
monitoring, the highest average annual value was obtained in wheat–oilseed rape and
wheat–oilseed rape–pulses rotations, which are the most common crop rotations in the
monitored fields (see Table 6). Conversely, a lower average annual gross margin was
obtained in crop rotation of various cereals (excluding fields where wheat was grown as
a monoculture), which can be explained by the high proportion of summer crops in such
a rotation. Regionally, these fields are located in parts of Latvia that are less suitable for
wheat and rapeseed production. The difference between the gross margin of various cereals
and other types of crop rotations (except wheat) is statistically significant (90% confidence
intervals do not overlap). Another crop rotation with a statistically significant difference
relative to wheat–oilseed rape and wheat–oilseed rape–pulses rotations was wheat–pulses.

Agriculture 2023,13, 1539 13 of 25
Table 6.
Gross margin and consumption of PPP, N, and diesel by crop rotations in monitored farm
fields in 2018–2021.
Crop Rotation Number of
Farm Fields
Average Annual
Gross Margin,
EUR ha−1
Average Annual
Consumption of
PPPs, EUR ha−1
Average Annual
Consumption of
N, kg ha−1
Average Annual
Consumption of
Diesel, liter ha−1
Wheat 5 401 82 149 65
(217.4, 583.6) ** (39.1, 124.3) (101.7, 195.4) (59.2, 70.5)
Various species of cereals 15 183 36 110 61
(117.0, 248.8) (25.6, 45.8) (95.6, 124.5) (57.5, 64.4)
Wheat–oilseed rape 51 439 98 175 60
(401.3, 477.5) (91.2, 105.2) (168.7, 180.9) (58.4, 62.1)
Wheat–pulses 7 331 91 132 60
(294.7, 366.4) (59.5, 122.5) (102.8, 160.7) (52.8, 66.3)
Wheat–oilseed rape–pulses 19 444 89 136 59
(407.0, 480.5) (77.6, 101.0) (129.3, 142.2) (55.7, 61.9)
Wheat–barley–oilseed rape 11 358 87 165 60
(270.9, 445.6) (74.8, 98.4) (146.5, 183.5) (55.6, 63.5)
Wheat–oilseed rape–fallow * 7 425 88 159 67
(291.0, 559.6) (73.0, 103.3) (151.4, 165.9) (58.0, 76.8)
Green maize–other field crops
6 375 56 184 69
(298.4, 451.7) (38.4, 74.1) (131.9, 236.3) (50.2, 88.7)
* The average annual values were obtained based on the summed 4-year values by dividing them by 4. However,
for the wheat–rape–fallow rotation, the values were divided by 3 instead. ** Numbers in brackets show the lower
and upper bounds of the 90% confidence interval.
Upon summarizing the use of PPPs in different crop rotations (see Table 6), it was
observed that the highest amount of PPPs was used in the wheat–oilseed rape rotation,
followed by crop rotations that included pulses or oilseed rape. Lower annual average PPP
consumption was observed in various cereals, as well as in crop rotation in which maize
(for silage and green feed) was grown at least once in a 4-year period. The average annual
consumption of PPPs in crop rotations including oilseed rape or pulses does not differ statis-
tically significantly between these groups (the 90% confidence intervals overlap). However,
for most crop rotations that include oilseed rape or pulses (except for
wheat–pulses
and
wheat–oilseed rape–fallow rotations), PPP usage differs statistically significantly from that
associated with various cereals and green maize–other field crop rotations.
After evaluating the use of nitrogen (from synthetic fertilizers and estimated N
from manure), it was observed that the highest average annual usage occurred in green
maize–other
field crops and wheat–oilseed rape rotations (Table 6). These were closely
followed by wheat–barley–oilseed rape and wheat–oilseed rape–fallow rotations, as well
as wheat rotation. Lower nitrogen usage was observed when pulses were grown alongside
wheat or oilseed rape, as well as when various cereals (with a higher proportion of summer
crops) were included in the crop rotation. For the widely adopted wheat–oilseed rape
rotation, the average N consumption differs statistically significantly from all other crop
rotations, except for wheat and wheat–barley–oilseed rape and green maize–other field
crops. Meanwhile, the N consumption of wheat–oilseed rape–pulses rotation does not
differ statistically significantly from that of wheat, wheat–pulses, and green maize–other
field crops rotations. The average annual N consumption in the crop rotation of green
maize–other field crops differs statistically significantly only from the average N use of
various cereals. The average N use of wheat–pulses rotations also differs statistically signif-
icant only from the value in wheat–oilseed rape rotation. The average N value of various
cereals differs statistically significantly from all other crop rotations, except wheat and
wheat–pulses.

Agriculture 2023,13, 1539 14 of 25
The assessment of diesel consumption in different types of crop rotations shows
(
Table 6
) that the highest annual average is obtained in the crop rotation of green
maize–other
field crops, followed by wheat–oilseed rape–fallow and wheat rotations. In other plant
rotations, the average diesel consumption is quite similar. The higher average annual diesel
consumption in the wheat–oilseed rape–fallow rotation is explained by the fact that in some
monitored fields agrotechnical operations were performed during the fallow year which
increased the average fuel consumption over a 3-year period when revenue was generated.
As the 90% confidence intervals overlap, the average annual diesel consumption does not
differ statistically significantly between any of the crop rotation types. Along with costs,
diesel consumption also impacts the emissions of greenhouse gases (GHGs). As diesel
consumption increases, so does the amount of CO
2
emissions from agricultural transport
(fuel combustion). One liter of diesel emits about 2.65 kg CO
2
according to
Auzins et al.
(2021) [37].
3.3. Effect of Tillage Type on NUE
The assessed results of field case studies where different tillage types were applied are
presented in Table 7. Overall, these results show divergent outcomes regarding the effect of
tillage types on NUE. In almost all field trials, the average NUE for min-till was lower than
for ploughing (conventional tillage). However, one trial (field A) demonstrated that no-till
had a higher NUE compared to both ploughing and min-till. In contrast, field B and field
D had average NUE values for no-till was lower than for min-till and ploughing (field D).
Therefore, the findings do not suggest that lesser extent of tillage results in a higher NUE.
Table 7. Average NUE of different tillage types (for the period of 2020–2022), %.
Field Case Study Ploughing Min-Till Strip-Till No-Till
Crop rotation involving only cereals
Field A 57 51 – 59
Crop rotation including maize
Field B – 82 – 72
Field C 74 63 – –
Crop rotation involving cereals, oilseed rape,
and faba beans
Field D 70 70 – 64
Field E * – 80 82 –
Field F 75 74 72 –
* Ploughing has not been practiced for more than 10 years. “–“ indicates no application of the respective
tillage type.
However, the trials of field E demonstrated a NUE of 80% for min-till and 82% for
strip-till. These levels were significantly higher than those observed in the other cause
studies (except for field B in the case of min-till).
A possible interpretation of these findings is that almost all fields had undergone
ploughing before the trials. The exception was field E, which had not been ploughed for
more than 10 years. Consequently, the transitional processes in the soil associated with the
shift from ploughing to reduced tillage types could have affected NUE and overshadowed
the impact of other factors. The results of field E suggest that reduced tillage types can
outperform ploughing when practiced for an extended period of time (10 years or more).
3.4. Effect of Tillage Type on Gross Margin
Evaluation of the economic performance associated with different tillage types based
on the field case studies does not reveal homogeneous results (see Tables 8–10). This
indicates that economic performance is determined not only by the type of tillage but also
by various other factors. Considering that non-inversion tillage is a less energy-intensive

Agriculture 2023,13, 1539 15 of 25
practice, lower diesel consumption is observed in trials with min-till or no-till compared
to ploughing.
Table 8.
Gross margin and consumption of diesel by tillage type in crop rotation involving only
cereals according to field case studies in 2019–2021.
Field Case Study (Field A) Ploughing Min-till/
Ploughing—Difference in %
No-Till/
Ploughing—Difference in %
Average annual gross margin
(EUR ha−1)73 24 87
Average annual consumption
of diesel (l ha −1)65 −32 −43
Table 9.
Gross margin and consumption of diesel by tillage type in crop rotation including maize
according to field case studies in 2019–2021.
Field Case Study Field B Field C
Ploughing
Min-Till/
Ploughing—
Difference in %
No-Till/
Ploughing—
Difference in %
Min-Till
No-Till/
Min-Till—
Difference in %
Average annual gross
margin (EUR ha−1)387 31 27 297 −37
Average annual
consumption of diesel
(l ha−1)
88 −8−34 95 −19
Table 10.
Gross margin and consumption of diesel by tillage type in crop rotation involving cereals,
oilseed rape, and faba beans according to field case studies in 2019–2021.
Field Case
Studies Field D Field E * Field F
Ploughing
Min-Till/
Ploughing—
Difference
in %
No-Till/
Ploughing—
Difference
in %
Min-Till
Strip-Till/
Min-Till—
Difference
in %
Ploughing
Min-
Till/Ploughing—
Difference
in %
Strip-Till/
Ploughing—
Difference
in %
Average annual
gross margin
(EUR ha−1)381 −1−11 579 9 497 3 −5
Average annual
consumption of
diesel (l ha−1)
84 −21 −39 51 −10 68 −24 −29
* Ploughing has not been practiced for more than 10 years.
Evaluation of the economic performance associated with different tillage types for the
most common crop rotation within the field monitoring (wheat–oilseed rape) indicated
a higher gross margin for non-inversion tillage (Table 11). In part, this can be explained
by a higher share of monitored fields belonging to the Zemgale region, in which the most
fertile soils are located, as well as a higher proportion of winter wheat and winter oilseed
rape in the monitored farms practicing non-inversion tillage compared to those using
ploughing. Due to the insufficient number of fields belonging to other crop rotation types,
their economic analysis was not performed.

Agriculture 2023,13, 1539 16 of 25
Table 11.
Gross margin and consumption of PPPs, N, and diesel by tillage type in wheat–oilseed rape
rotation in monitored fields in 2018–2021.
Crop Rotation:
Wheat–Oilseed Rape
Tillage System
Number of
Farm Fields
Average Annual
Gross Margin,
EUR ha−1
Average Annual
Consumption of
PPPs, EUR ha−1
Average Annual
Consumption of
N, kg ha−1
Average Annual
Consumption of
Diesel, liter ha−1
Ploughing 14 377 89 177 67
(302.5, 452.0) * (73.2, 104.5) (161.1, 193.7) (64.7, 69.3)
Non-inversion tillage 21 487 91 167 55
(437.6, 537.1) (63.9, 118.7) (146.6, 187.8) (47.9, 61.4)
* Numbers in brackets show the lower and upper bound of the 90% confidence interval.
The average annual consumption of PPPs in the two tillage systems for wheat–oilseed
rape crop rotation was almost the same. The average annual N consumption was slightly
higher when practicing ploughing, while non-inversion tillage resulted in lower average
annual diesel consumption. Based on the 90% confidence intervals, diesel consumption
was the only indicator that showed a statistically significant difference between the two
tillage types.
3.5. Environmental and Economic Performance of Catch Crops
Catch crop management within the cropping system can be a beneficial strategy and an
addition to fulfilling specific agroecosystem function requirements. The literature presents
a wide range of variations regarding the potential of catch crops, suggesting their use
as parameters to better understand the effects of catch crops on soil and nutrient uptake.
However, the effectiveness of catch crops depends on factors such as plant species, soil
characteristics, climate conditions, biomass volume, and agronomic practices [38,39].
To better understand the potential of catch crop mixtures, expected results of catch
crop performance and their advantages are summarized in this study (Figure 6) based
on literature analysis. The literature analysis was incorporated in the study to present
arguments for making better decisions regarding the selection of the most appropriate
catch crop mixtures for practical implementation on Latvian farms. Selected mixtures
with different species can provide varying degrees of chemical, biological, and mechanical
impacts. All selected species in the mixtures are more adaptive to the climatic conditions
in Latvia characterized by a short growing season and rapid growth in the early stages
of development. According to the conducted analysis (Figure 6), the rye–oilseed rape–
phacelia/vetch mixture has the greatest impact on soil erosion, topsoil, allelopathy, the
release of P and K, and nitrogen accumulation (legumes). Mustard and radish have the
greatest impact on the subsoiling. Ryegrass, buckwheat, and phacelia have an excellent
impact on topsoil loosening and the release of P and K in available forms for plants. Three
legume-based mixtures include two types of plants with different N source possibilities. All
mixtures contain different species with specific natural diseases and nematode suppression
abilities in the soil, which, when incorporated in an appropriate crop rotation, can limit
these biological agents.

Agriculture 2023,13, 1539 17 of 25
Agriculture 2023, 13, x FOR PEER REVIEW 17 of 25
(a)
(b)
(c)
(d) (e) (f)
Figure 6. Performance and potential advantages of non-legume and legume-based catch crop mix-
tures. Value indicators: 1—poor; 2—fair; 3—good; 4—very good; 5—excellent [38–42]. * Phacelia
was not included in the mixture all years. (a) Mixture of oats and mustard; (b) mixture of mustard
and radish; (c) mixture of ryegrass, buckwheat, and phacelia; (d) mixture of ryegrass, crimson clo-
ver, and phacelia *; (e) mixture of oats, vetch, and phacelia; (f) mixture of rye, oilseed rape, phace-
lia/vetch.
According to the catch crop field trials conducted at two locations, the mustard–rad-
ish mixture in Stende yielded the highest average shoot DM yield of 0.91 t ha
–1
while sim-
ultaneously recording the lowest average root DM yield of 0.29 t ha
–1
compared to all other
mixtures grown there (see Table 12). In Vitini, the range of the average shoot DM yield
was 0.81 t ha
−1
from ryegrass, crimson clover, and phacelia to 1.71 t ha
−1
from the oat–
mustard mixture. The lowest average root DM yield in Vitini was 0.42 t ha
−1
from the mus-
tard–radish mixture (similarly to in Stende). However, studies in Estonia and Germany
revealed that the most optimal catch crop mixture is radish and mustard [43,44]. A statis-
tically significant (p ≤ 0.01, p ≤ 0.05) positive correlation confirmed the relationships be-
tween shoot and root DM yield calculated for each mixture using annual data. The ob-
tained results indicate that as shoot dry maer (DM) yield increases, root DM yield also
increases for all mixtures. However, depending on the year, the ryegrass–crimson clover–
phacelia mixture exhibited a higher root DM yield than shoot DM yield.
Depending on the year, the quantity of catch crop dry maer (DM) yield exhibited
great fluctuations, which were aributed to the hydrothermal conditions during the grow-
ing season from August to October. Our trials demonstrated that DM yield was negatively
correlated with the hydrothermal coefficient for each catch crop mixture. The results show
that extremely high humidity levels in the autumn had a negative impact on the DM yield
of plants. A significant (p ≤ 0.01, p ≤ 0.05) impact on yield loss in humid conditions was
Figure 6.
Performance and potential advantages of non-legume and legume-based catch crop mix-
tures. Value indicators: 1—poor; 2—fair; 3—good; 4—very good; 5—excellent [
40
–
44
]. * Phacelia was
not included in the mixture all years. (
a
) Mixture of oats and mustard; (
b
) mixture of mustard and
radish; (
c
) mixture of ryegrass, buckwheat, and phacelia; (
d
) mixture of ryegrass, crimson clover, and
phacelia *; (e) mixture of oats, vetch, and phacelia; (f) mixture of rye, oilseed rape, phacelia/vetch.
According to the catch crop field trials conducted at two locations, the mustard–
radish mixture in Stende yielded the highest average shoot DM yield of 0.91 t ha
−1
while
simultaneously recording the lowest average root DM yield of 0.29 t ha
−1
compared to
all other mixtures grown there (see Table 12). In Vitini, the range of the average shoot
DM yield was 0.81 t ha
−1
from ryegrass, crimson clover, and phacelia to 1.71 t ha
−1
from
the oat–mustard mixture. The lowest average root DM yield in Vitini was 0.42 t ha
−1
from the mustard–radish mixture (similarly to in Stende). However, studies in Estonia and
Germany revealed that the most optimal catch crop mixture is radish and mustard [
45
,
46
]. A
statistically significant (p
≤
0.01, p
≤
0.05) positive correlation confirmed the relationships
between shoot and root DM yield calculated for each mixture using annual data. The
obtained results indicate that as shoot dry matter (DM) yield increases, root DM yield
also increases for all mixtures. However, depending on the year, the ryegrass–crimson
clover–phacelia mixture exhibited a higher root DM yield than shoot DM yield.
Depending on the year, the quantity of catch crop dry matter (DM) yield exhibited great
fluctuations, which were attributed to the hydrothermal conditions during the growing
season from August to October. Our trials demonstrated that DM yield was negatively
correlated with the hydrothermal coefficient for each catch crop mixture. The results
show that extremely high humidity levels in the autumn had a negative impact on the
DM yield of plants. A significant (p
≤
0.01, p
≤
0.05) impact on yield loss in humid
conditions was observed for non-legume-based mixtures: oats–mustard, mustard–radish,
and ryegrass–buckwheat–phacelia.

Agriculture 2023,13, 1539 18 of 25
Table 12.
Average shoot and root dry matter (2019–2021), total N and C, and C: N ratio (2021) of
different catch crop mixtures.
Mix/Control Location Part of
Plant
Dry Matter
Yield,
t ha−1±SD
rdw/HTC rShdw/Rdw
Ntotal, g
kg−1
Ctotal, g
kg−1C:N
Control
SSh 0.37 ±0.19 −0.85 0.62 - - -
R 0.29 ±0.20 −0.1 - - -
VSh 0.75 ±0.42 −0.75 0.77 - - -
R 0.75 ±0.43 −0.99 *** - - -
Oats and
mustard
SSh 0.88 ±0.56 −0.95 ** 0.99 *** 36.82 441.86 12
R 0.38 ±0.20 −0.89 15.07 346.72 23
VSh 1.71 ±0.95 −0.97 ** 0.99 *** 29.35 410.92 14
R 0.57 ±0.27 −0.97 ** 14.68 337.55 23
Mustard and
radish
SSh 0.91 ±0.60 −0.94 0.97 ** 32.46 421.97 13
R 0.29 ±0.15 −0.83 14.14 367.76 26
VSh 1.18 ±0.81 −0.51 0.99 *** 31.46 377.55 12
R 0.42 ±0.25 −0.63 17.67 406.47 23
Ryegrass,
buckwheat, and
phacelia
SSh 0.60 ±0.34 −0.91 0.98 *** 25.53 383.00 15
R 0.39 ±0.02 −0.82 19.23 403.86 21
VSh 0.98 ±0.74 −0.58 0.99 *** 30.24 393.18 13
R 0.62 ±0.28 −0.54 18.18 345.35 19
Ryegrass,
crimson clover,
and phacelia *
SSh 0.67 ±0.43 −0.99 *** 0.83 36.72 440.66 12
R 0.79 ±0.34 −0.77 13.77 371.86 27
VSh 0.81 ±0.39 −0.89 0.88 26.06 364.86 14
R 0.80 ±0.47 −0.57 14.74 339.00 23
Oats, spring
vetch, and
phacelia
SSh 0.62 ±0.41 −0.99 *** 0.69 39.33 432.61 11
R 0.43 ±0.22 −0.64 15.70 314.07 20
VSh 1.23 ±0.94 −0.53 0.99 *** 28.45 426.79 15
R 0.55 ±0.25 −0.59 10.43 229.36 22
Rye, oilseed rape,
and winter
vetch/phacelia
SSh 0.52 ±0.31 −0.84 0.83 33.06 396.73 12
R 0.42 ±0.21 −0.38 22.31 401.56 18
VSh 1.03 ±0.59 −0.72 0.94 30.25 363.06 12
R 0.53 ±0.28 −0.92 20.37 387.08 19
S—Stende; V—Vitini; Sh—shoots; R—roots; SD—standard deviation; r
dm/HTC—
correlation coefficient between
plant dry matter yield and HTC; r
Shdm/Rdm
—correlation coefficient between shoot and root dry matter yield; *
Phacelia was not included in the mixture all year; ** correlation significant at p≤0.05; *** at p≤0.01.
This study confirms that in Latvia, the autumns have extremely high humidity that can
negatively affect soil productive capacity, for example, through N leaching. The average
total N uptake of catch crops was the highest in the shoot DM yield for all mixtures
(Table 12). In Stende, the highest total N content in shoot dry matter yield was obtained
from oats, vetch, and phacelia, while in Vitini, it was from mustard and radish. The highest
N content in root dry matter yield was observed in the rye–oilseed rape–vetch/phacelia
mixture, surpassing all other mixtures in both locations. The best total N balance between
shoot and root DM yield was observed in ryegrass–buckwheat–phacelia and rye–oilseed
rape–vetch/phacelia mixtures.
The highest C content of the root DM yield was observed for the mustard–radish
mixture in Vitini and for ryegrass–buckwheat–phacelia in Stende, while it was high in the
shoot DM yield and lower in the root DM yield for oats–vetch–phacelia in both locations
(Table 12).
A higher C:N ratio was observed in roots for all selected mixtures. Results confirm
that roots decompose slowly, leading to the later release of nitrogen and a longer-lasting

Agriculture 2023,13, 1539 19 of 25
effect. On the other hand, the shoot mass decomposes faster, providing additional nutrients
for the following main crop. Similar results have been confirmed in other experiments, such
as those conducted in Estonia [
47
]. The rate of organic matter decomposition is determined
not only by a plant species, its biomass volume, and C:N ratio but also by soil conditions
(temperature, moisture content, acidity, aeration, etc.) [
48
]. The humification rate of organic
matter from catch crops is estimated at 28%, which is much higher than in cereal straw
(11–14%) [
49
]. The lower C:N of the mixtures with legumes can thus potentially increase
the risk of N leaching [
48
]. Based on an experiment conducted in Switzerland and a linear
model that estimates the contributions of species identities and interactions to biomass
found that a combination of 24% of a legume cover crop and 76% of a non-legume cover
crop produced the highest biomass [50]. Field trials of catch crop mixtures yielded results
on their biomass and N content, serving for NUE calculations.
Relative differences between the average NUE for different catch crop mixtures and
stubble after disc harrowing are presented in Table 13. Overall, the results indicate that
catch crop mixes increased the average NUE. In Stende, the mixture of ryegrass, buckwheat,
and phacelia and the mixture radish/oil radish and mustard demonstrated the highest
positive impact on NUE. In Vitini, the highest positive impact was achieved by the mixture
of radish/oil radish and mustard; the mixture of oats and mustard; and the mixture of
ryegrass, buckwheat, and phacelia. However, it should be noted that two mixtures had a
negative impact on NUE according to Vitini field trials.
Table 13. Difference in average NUE compared to stubble after disc harrowing, %.
Tested Catch Crop Mixtures 2019–2020 2020–2021 2021–2022 Average
Field trials in Stende:
Rye, winter oilseed rape, and phacelia/winter vetch +5.9 +2.7 −0.8 +2.6
Oats, spring vetch, and phacelia +9.1 −1.4 +2.3 +3.3
Oats and mustard −3.7 +8.9 −0.7 +1.5
Radish and mustard +5.6 +10.5 −2.3 +4.6
Ryegrass, buckwheat, and phacelia +3.0 +12.1 +3.5 +6.2
Oats, crimson clover, and phacelia * +2.7 +0.9 −3.2 +0.2
Field trials in Vitini:
Rye, winter oilseed rape, and phacelia −3.9 +2.2 −0.1 −0.6
Oats, spring vetch, and phacelia +3.7 +2.1 +1.2 +2.3
Oats and mustard +11.3 +5.6 +1.0 +5.9
Radish and mustard +18.0 +4.7 −0.8 +7.3
Ryegrass, buckwheat, and phacelia +9.1 +5.7 −0.1 +4.9
Oats, crimson clover, and phacelia * −4.9 +1.3 −0.8 −1.5
* Phacelia was not included in the mixture all years.
Additionally, the field trials in Stende enabled us to compare the catch crop mixtures
with ploughing, which is a more typical alternative to catch crops (Table 14). These results
demonstrated a more convincing positive impact of catch crops on the average NUE. The
best-performing mixture was ryegrass, buckwheat, and phacelia.
Table 14. Difference in average NUE compared to ploughing (in Stende), %.
Tested Catch Crop Mixture 2020–2021 2021–2022 Average
Rye, winter oilseed rape, and phacelia/winter vetch
+8.5 +3.3 +5.9
Oats, spring vetch, and phacelia +4.1 +6.6 +5.4
Oats and mustard +15.0 +3.4 +9.2
Radish and mustard +16.7 +1.8 +9.2
Ryegrass, buckwheat, and phacelia +18.4 +7.8 +13.1
Oats, crimson clover, and phacelia * +6.6 +0.9 +3.7
* Phacelia was not included in the mixture all years.

Agriculture 2023,13, 1539 20 of 25
The capacity of catch crops to reduce N leaching is directly linked to their ability
(and the conditions) to generate biomass in which N is captured. It is also essential to
consider the economic aspects of the introduction of catch crops, which involve evaluation
of the costs associated with seeds, establishment operations, and, if necessary, termination
operations. Considering that catch crops are not harvested, the calculation of gross margin
is not applicable for the assessment of economic performance. However, within the project,
we evaluated the amount of N captured per hectare and the associated costs per unit of
captured N, resulting from the implementation of catch crops (Figure 7).
Agriculture 2023, 13, x FOR PEER REVIEW 20 of 25
Table 14. Difference in average NUE compared to ploughing (in Stende), %.
Tested Catch Crop Mixture 2020–2021 2021–2022 Average
Rye, winter oilseed rape, and phacelia/winter
vetch +8.5 +3.3 +5.9
Oats, spring vetch, and phacelia +4.1 +6.6 +5.4
Oats and mustard +15.0 +3.4 +9.2
Radish and mustard +16.7 +1.8 +9.2
Ryegrass, buckwheat, and phacelia +18.4 +7.8 +13.1
Oats, crimson clover, and phacelia * +6.6 +0.9 +3.7
* Phacelia was not included in the mixture all years.
The capacity of catch crops to reduce N leaching is directly linked to their ability (and
the conditions) to generate biomass in which N is captured. It is also essential to consider
the economic aspects of the introduction of catch crops, which involve evaluation of the
costs associated with seeds, establishment operations, and, if necessary, termination op-
erations. Considering that catch crops are not harvested, the calculation of gross margin
is not applicable for the assessment of economic performance. However, within the pro-
ject, we evaluated the amount of N captured per hectare and the associated costs per unit
of captured N, resulting from the implementation of catch crops (Figure 7).
Figure 7. N (kg ha−1) captured by catch crop mixtures and costs (EUR kg−1) per unit of captured N
according to field trials in Stende and Vitini in 2021. * Phacelia was not included in the mixture all
years.
The capacity of the catch crop mixtures to capture N varied depending on the trial
location and the composition of the mixture, ranging from 23 to 58 kg N ha−1. Generally,
higher levels of captured N were observed in the field trials conducted in Vitini compared
to the field trials in Stende. The mixture containing mustard and oats exhibited the best
ratio of captured N to cost per unit, whereas the mixtures of ryegrass, buckwheat, and
phacelia and oats, spring vetch, and phacelia had higher production costs and relatively
lower amounts of captured N.
58 44 42 40 33 4138 35 29 23 36 27
1.31
2.66
2.06
3.41
4.31
2.35
2.01
3.36
2.96
6.02
3.96
3.58
0
1
2
3
4
5
6
0
10
20
30
40
50
60
70
80
90
100
oats, mustard radish, mustard oats, spring vetch,
phacelia
ryegrass,
buckwheat,
phacelia
oats, crimson
clover, phacelia *
rye, winter rape,
phacelia/winter
vetch
EUR
kg
Captured N (kg ha¯¹), Vitini Captured N (kg ha¯¹), Stende
Costs per unit of captured N (EUR kg¯¹), Vitini Costs per unit of captured N (EUR kg¯¹), Stende
Figure 7.
N (kg ha
−1
) captured by catch crop mixtures and costs (EUR kg
−1
) per unit of captured
N according to field trials in Stende and Vitini in 2021. * Phacelia was not included in the mixture
all years.
The capacity of the catch crop mixtures to capture N varied depending on the trial
location and the composition of the mixture, ranging from 23 to 58 kg N ha
−1
. Generally,
higher levels of captured N were observed in the field trials conducted in Vitini compared
to the field trials in Stende. The mixture containing mustard and oats exhibited the best
ratio of captured N to cost per unit, whereas the mixtures of ryegrass, buckwheat, and
phacelia and oats, spring vetch, and phacelia had higher production costs and relatively
lower amounts of captured N.
4. Discussion
The PSF have proposed a minimum NUE of 70% for crop production as an important
criterion of substantial contribution to the protection and restoration of biodiversity and
ecosystems (the sixth environmental objective) within the framework of the EU Taxon-
omy [
27
,
28
]. Thus, NUE is likely to play a significant role in assessing the environmental
suitability of crop production. The findings of this study indicate that achieving the mini-
mum NUE is challenging. Only wheat rotation and various cereals rotation demonstrated
an average NUE above 70%. Green maize–other field crops rotation also showed an average
NUE above the threshold, although the confidence interval was extremely wide. When
excluding biological N fixation, crop rations including pulses (wheat–pulses, wheat–oilseed
rape–pulses) also exhibited an average NUE above the threshold. On the other hand,
crop rotations that include oilseed rape, particularly the common wheat–oilseed rape rota-
tion, had an average NUE below the threshold, even after adjusting for the N content of
winter rapeseed.

Agriculture 2023,13, 1539 21 of 25
Although the divergent results of the field case studies limited their interpretation from
the perspective of the minimum NUE criterion, they did demonstrate that the long-term
practice of reduced tillage supported a high NUE (above 70%), even when incorporating
pulses and oilseed rape in the crop rotation. The results of the field trials mainly indicated
the relative performance of catch crops. However, these results implied the potential
contribution of catch crops in achieving a minimum NUE of at least 70%. For example,
the mixture of oats, crimson clover, and phacelia demonstrated an increase of 13% in the
average NUE. Such an increase is considerable and can help to raise the NUE above the
threshold of 70%.
It should be mentioned that the divergent findings of this study regarding the effect of
tillage types on NUE are similar to the findings of other studies. For example, Smith and
Chalk reported that their results indicated a minimal impact of tillage on N mineralization,
immobilization, and NUE [
51
]. An
15
N labeling study conducted in Northern China
reported that long-term no-tillage with wheat straw incorporation alleviated N limitation
compared with conventional tillage and increased straw N recovered as particulate organic
matter N [
52
]. Although this study did not specifically focus on NUE, its findings implied
that no-till and, possibly, reduced tillage can increase NUE. Additionally, according to the
study by Price et al., reduced tillage and no-till can reduce NO
3-
leaching loss by up to
20% [53]. Therefore, this study, which is also referred to by the PSF, suggests that reduced
tillage and no-till have a meaningful potential to increase NUE.
We introduced a novel approach with respect to how to assess the environmental
performance of catch crops: calculation of field-level NUE for two-year period that covers
both the year before the catch crop and the year after the catch crop (see Section 2.1).
This approach has not been used in previous studies. This new approach can be easily
incorporated into typical field trials, as it requires hardly any additional operations and
data recording. This approach is quite general and straightforward. Therefore, it can be
implemented not only in Latvia or Baltic counties but also in other countries/regions. It
can also be applied to meta-analysis.
The Latvian Rural Advisory and Training Centre prepares annual gross margin calcu-
lations for the main agricultural products in Latvia [
35
]. These calculations are essential for
both newcomers and established farms, as they serve as a basis for planning, representing
the optimal production technology. For the past five years, the gross margin calculations
for field crops by the LRATC in collaboration with sector experts (farm managers) have
also included the results for reduced tillage systems.
Based on the presented gross margins calculated by LRARC, winter oilseed rape
consistently emerges as the most profitable crop. Winter wheat follows winter oilseed rape
as the next most profitable crop. In certain years, the gross margin of faba beans competes
with that of winter wheat, although its ranking is more volatile from year to year. The
leading crops in N consumption are winter wheat, winter oilseed rape, green maize, and
summer oilseed rape. The crops with the highest average consumption of PPPs are oilseed
rape, winter wheat, field peas, and faba beans. These results are generally in line with the
findings of our study for the monitored fields.
Regarding the difference between ploughing and reduced tillage, the results from
LRARC indicate that winter wheat in reduced tillage generated a slightly better result
(almost +20 EUR ha
−1
, on average, annually over five years, reaching +50 EUR ha
−1
in
2022). The main contribution to the higher gross margin was lower costs of agrotechnical
operations and less fertilizer used. In contrast, winter oilseed rape under reduced tillage
demonstrated poorer economic performance, consistently yielding a lower gross margin
(on average,
−
135 EUR ha
−1
annually in a five-year period). This result can be attributed
to higher costs of PPPs and fertilizer, despite lower costs of agrotechnical operations.
When considering equal planned yields for ploughing and reduced tillage instead of
yields associated with optimal technology and impacted by weather conditions, technologi-
cal crop models for Latvia have been developed within the EIP-Agri project “Development
of electronic farm management system” [
54
]. According to the latest version of these mod-

Agriculture 2023,13, 1539 22 of 25
els, the gross margin of winter wheat with a planned yield of 5 tonnes per ha under reduced
tillage is by about 20 EUR ha−1higher. This is due to the lower costs of agrotechnological
operations, as N consumption, fertilizer, and PPP costs are higher. When considering
winter oilseed rape with a planned yield of 3 tonnes per hectare, the gross margin under
reduced tillage is almost the same. Similar to winter wheat, this is achieved by reduced
agrotechnical operational expenses, despite higher fertilizer and PPP costs.
Crop production is undergoing significant changes and awaiting a new equilibrium.
The rising costs of resources, specifically nitrogen (N), have significantly altered the agricul-
tural landscape, compelling farmers to address issues that were previously considered less
important. While in the past, NUE was primarily associated with mitigating N leaching
and other environmental concerns, in today’s scenario, issues related to nitrogen use not
only impact public interests but also directly influence the economic viability of farms.
Consequently, the solutions and practices that tackle these challenges have become criti-
cally important and require the generation of new knowledge. This newfound significance
emphasizes the need for a deeper understanding of NUE issues that could support decision-
making processes both at the farm level and in the formulation of future policies.
5. Conclusions
Crop rotation in Latvia tends to be monotonous, with wheat and oilseed rape dominat-
ing over 60% of the cultivated conventional area due to their profitability. The incorporation
of pulses and other protein crops into crop rotation is not yet widely adopted, resulting in
a missed opportunity to fix nitrogen from the atmosphere.
The findings of this study indicate that achieving a minimum NUE of 70% (as proposed
by the PSF) is challenging. Crop rotations including oilseed rape, particularly the common
wheat–oilseed rape rotation, have an average NUE below the proposed threshold.
The use of catch crops as intermediate crops is not yet common in crop production in
Latvia, as revealed by crop rotation analysis, primarily due to the prevailing practice of
cultivating winter crops as main crops. However, it is anticipated that the availability of
areas for catch crops will increase with the introduction of more legumes or other spring
crops into the crop rotation. Three-year field trials demonstrate that proper use of catch
crops may increase nitrogen use efficiency (NUE) by up to 7–9%. These results imply
that catch crops may decrease the leaching of nitrogen from cropping systems, which
leads to less nitrogen pollution in the environment. Our literature review and field study
suggest that non-legume-based and legume-based catch crop mixtures may be a promising
approach to increase multiservices in cropping systems.
As in previous studies carried out by other researchers, the three-year field trials on
commercial farms, where various tillage practices were applied, yielded divergent findings
about the impact of conventional, reduced, and no-tillage practices on NUE. However,
the field trials in the farm that has practiced reduced tillage (min-till and strip-till) for
more than ten years, show a higher NUE for reduced tillage compared to ploughing. The
advantage of reduced tillage was also supported by the obtained results indicating lower
costs of agrotechnical operations, including less diesel consumption.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/agriculture13081539/s1, Figure S1: Farm field monitoring ques-
tionnaire form.
Author Contributions:
Conceptualization, I.L., A.A. and A.K.; methodology, A.A., A.K., I.M. and
I.L.; validation, A.A., A.K., I.L. and I.M.; formal analysis, A.A., A.K., I.M., I.L. and A.M.; investigation,
A.A., A.K., I.M., I.L. and A.M.; data curation, A.K., A.A., I.L., A.M. and I.M.; writing—original draft
preparation, A.A., A.K., I.L. and I.M.; writing—review and editing, A.K., I.L. and A.A.; visualization,
A.A., I.L., A.K., I.M., A.M. and P.L. All authors have read and agreed to the published version of
the manuscript.

Agriculture 2023,13, 1539 23 of 25
Funding:
This paper was funded by the agricultural European Innovation Partnership (EIP-AGRI)
project “Progressive land cultivation system as the basis for environmentally friendly and effective
crop production” (No. 19-00-A01612-000011).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: The authors thank S. Malecka (AREI) for technical support.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
References
1.
Camaréna, S. Artificial intelligence in the design of the transitions to sustainable food systems. J. Clean. Prod.
2020
,271, 122574.
[CrossRef]
2.
Fridman, D.; Koellner, T.; Kissinger, M. Exploring global interregional food system’s sustainability using the functional regions
typology. Glob. Environ. Chang. 2021,68, 102276. [CrossRef]
3.
Stoorvogel, J.J.; Antle, J.M.; Crissman, C.C.; Bowen, W. The trade-off analysis model: Integrated biophysical and economic
modeling of agricultural production systems. Agric. Syst. 2004,80, 43–66. [CrossRef]
4.
Vastola, A.; Zdruli, P.; D’Amico, M.; Pappalardo, G.; Viccaro, M.; Di Napoli, F.; Cozzi, M.; Romano, S. A comparative multidimen-
sional evaluation of conservation agriculture systems: A case study from a Mediterranean area of Southern Italy. Land Use Policy
2017,68, 326–333. [CrossRef]
5.
Špiˇcka, J.; Vintr, T.; Aulová, R.; Macháˇcková, J. Trade-off between the economic and environmental sustainability in Czech dual
farm structure. Agric. Econ. 2020,66, 243–250. [CrossRef]
6.
Deng, J.; Ni, H.; Zhang, Z.; Usman, S.; Yang, X.; Shen, Y.; Li, Y. Designing productive, energy-efficient, and environmentally
friendly production systems by replacing fallow period with annual forage cultivation on the Loess Plateau of China. J. Clean.
Prod. 2021,320, 128660. [CrossRef]
7.
European Parliament. Green Deal: Key to a Climate-Neutral and Sustainable EU. Available online: https://www.europarl.
europa.eu/news/en/headlines/society/20200618STO81513/green-deal-key-to-a-climate-neutral-and-sustainable-eu (accessed
on 15 June 2023).
8.
Marvin, H.J.; Bouzembrak, Y.; Van der Fels-Klerx, H.J.; Kempenaar, C.; Veerkamp, R.; Chauhan, A.; Stroosnijder, S.; Top, J.;
Simsek-Senel, G.; Vrolijk, H.; et al. Digitalisation and Artificial Intelligence for sustainable food systems. Trends Food Sci. Technol.
2022,120, 344–348. [CrossRef]
9.
European Parliament. Creating a Sustainable Food System: The EU’s Strategy. Available online: https://www.europarl.europa.
eu/news/en/headlines/priorities/climate-change/20200519STO79425/creating-a-sustainable-food-system-the-eu-s-strategy
(accessed on 15 June 2023).
10.
European Commission. Farm to Fork Strategy: For a Fair, Healthy and Environmentally-Friendly Food System. Available online:
https://food.ec.europa.eu/system/files/2020-05/f2f_action-plan_2020_strategy-info_en.pdf (accessed on 15 June 2023).
11.
European Parliament and the Council of the EU. Regulation 2020/852 (the Taxonomy Regulation). Available online: https:
//eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32020R0852 (accessed on 15 June 2023).
12. Eurostat. Agriculture. Available online: https://ec.europa.eu/eurostat/web/main/data/database (accessed on 15 June 2023).
13.
McDaniel, M.D.; Tiemann, L.K.; Grandy, A.S. Does agricultural crop diversity enhance soil microbial biomass and organic matter
dynamics? A meta-analysis. Ecol. Appl. 2014,24, 560–570. [CrossRef]
14.
Bowles, T.M.; Mooshammer, M.; Socolar, Y.; Calderón, F.; Cavigelli, M.A.; Culman, S.W.; Deen, W.; Drury, C.F.;
Garcia y Garcia, A.
;
Gaudin, A.C.M.; et al. Long-Term Evidence Shows that Crop-Rotation Diversification Increases Agricultural Resilience to Adverse
Growing Conditions in North America. One Earth 2020,2, 284–293. [CrossRef]
15.
Dalgaard, T.; Hansen, B.; Hasler, B.; Hertel, O.; Hutchings, N.J.; Jacobsen, B.H.; Jensen, L.S.; Kronvang, B.; Olesen, J.E.;
Schjørring, J.K.; et al.
Policies for agricultural nitrogen management—Trends, challenges and prospects for improved efficiency
in Denmark. Environ. Res. Lett. 2014,9, 115002. [CrossRef]
16.
Aronsson, H.; Hansen, E.M.; Thomsen, I.K.; Liu, J.; Øgaard, A.F.; Känkänen, H.; Ulén, B.J.J.O. The ability of cover crops to reduce
nitrogen and phosphorus losses from arable land in southern Scandinavia and Finland. J. Soil Water Conserv.
2016
,71, 41–55.
[CrossRef]
17.
Vogeler, I.; Hansen, E.M.; Thomsen, I.K. The effect of catch crops in spring barley on nitrate leaching and their fertilizer
replacement value. Agric. Ecosyst. Environ. 2023,343, 108282. [CrossRef]
18.
Thapa, R.; Mirsky, S.B.; Tully, K.L. Cover crops reduce nitrate leaching in agroecosystems: A global meta-analysis. J. Environ.
Qual. 2018,47, 1400–1411. [CrossRef]

Agriculture 2023,13, 1539 24 of 25
19.
Kros, J.; Hutchings, N.J.; Kristensen, I.T.; Kristensen, I.S.; Børgesen, C.D.; Voogd, J.C.; Dalgaard, T.; de Vries, W. A comparison of
disaggregated nitrogen budgets for Danish agriculture using Europe-wide and national approaches. Sci. Total Environ.
2018
,
643, 890–901. [CrossRef]
20. Lal, R. Restoring soil quality to mitigate soil degradation. Sustainability 2015,7, 5875–5895. [CrossRef]
21.
Derpsch, R.; Friedrich, T.; Kassam, A.; Hongwen, L. Current status of adoption of no-till farming in the world and some of its
main benefits. Int. J. Agric. Biol. Eng. 2010,3, 1–25.
22.
Pittelkow, C.M.; Liang, X.; Linquist, B.A.; van Groenigen, K.J.; Lee, J.; Lundy, M.E.; van Gestel, N.; Six, J.; Venterea, R.T.;
van Kessel, C.
Productivity limits and potentials of the principles of conservation agriculture. Nature
2015
,517, 365–368. [Cross-
Ref]
23. Selyaninov, G.T. About climate agricultural estimation. Proc. Agric. Meteorol. 1928,20, 165–177.
24.
Skowera, B.; J ˛edrszczyk, E.; Kopci´nska, J.; Ambroszczyk, A.M.; Kołton, A. The effects of hydrothermal conditions during
vegetation period on fruit quality of processing tomatoes. Pol. J. Environ. Stud. 2014,23, 195–202.
25.
LVS ISO 13878:1998; Soil Quality—Determination of Total Nitrogen Content by Dry Combustion (“Elemental Analysis”). 1st ed.
ISO: Geneva, Switzerland, 1998; p. 5.
26.
LVS ISO 10694:2006 A/L; Soil Quality—Determination of Organic and Total Carbon after Dry Combustion (Elementary Analysis).
ISO: Geneva, Switzerland, 2006.
27.
Platform on Sustainable Finance: Technical Working Group. Part B—Annex: Full list of Technical Screening Criteria. 2021.
Available online: https://finance.ec.europa.eu/document/download/f7383cf5-43b3-45f8-9c6d-ab9571918849_en?filename=21
0803-sustainable-finance-platform-report-technical-screening-criteria-taxonomy-annex_en.pdf (accessed on 8 June 2023).
28.
Platform on Sustainable Finance: Technical Working Group. Supplementary: Methodology and Technical Screening Criteria. 2022.
Available online: https://finance.ec.europa.eu/document/download/7599ea2d-975c-4b25-adca-de1d26533e99_en?filename=
221128-sustainable-finance-platform-technical-working-group_en.pdf (accessed on 8 June 2023).
29.
International Organization for Standardization. Cereals and Pulses—Determination of the Nitrogen Content and Calculation of the
Crude Protein Content—Kjeldahl Method, 2nd ed.; ISO: Geneva, Switzerland, 2013; p. 12.
30.
K
¯
arkli
n
,
š, A.; Ruža, A. Lauku Kult
¯
uraugu M
¯
eslošanas Normat
¯
ıvi (Fertiliser Recommendations for Agricultural Crops); Latvia University
of Agriculture: Jelgava, Latvia, 2013; pp. 7–8. (In Latvian)
31.
Baddeley, J.A.; Jones, S.; Topp, C.F.E.; Watson, C.A.; Helming, J.; Stoddard, F.L. Biological Nitrogen Fixation (BNF) by Legume
Crops in Europe. Legume Futures Report 1.5. 2013. Available online: https://www.legumehub.eu/wp-content/uploads/2021/0
6/Legume-Futures-Report-1.5.pdf (accessed on 8 June 2023).
32.
Piliksere, D.; Auzins, A.; Aboltins, A. Effect of Crop Rotation on Nitrogen Use Efficiency—Case Study of Latvia. In Proceedings
of the 21th International Scientific Conference Engineering for Rural Development Proceedings, Jelgava, Latvia, 25–27 May 2022;
pp. 400–406. [CrossRef]
33.
Central Statistical Bureau of Latvia. Purchase Prices of Agricultural Products 1995–2021. Available online: https://data.stat.gov.
lv/pxweb/en/OSP_PUB/START__NOZ__LA__LAC/LAC020/ (accessed on 10 December 2022).
34. Kazotnieks, J.; Latvian Rural Advisory and Training Centre, Ozolnieki, Latvia. Personal communication, 2021.
35.
Latvian Rural Advisory and Training Centre. Lauksaimniec
¯
ıbas Bruto Segumu Apr
¯
e
k
,
ini (Agricultural Gross Margin Calculations).
Available online: https://new.llkc.lv/lv/nozares/ekonomika/bruto-segumi (accessed on 16 June 2023). (In Latvian).
36.
K
¯
arkli
n
,
š, A.; L
¯
ıpen
¯
ıte, I. Apr
¯
e
k
,
inu Metodes Un Normat
¯
ıvi Augsnes Iekultiv
¯
ešanai Un M
¯
eslošanas L
¯
ıdzek
l
,
u Lietošanai. Rokasgr
¯
amata
(Calculation Methods and Standards for Cultivating Soil and Using Fertilizers. Handbook); Latvia University of Agriculture: Jelgava,
Latvia, 2019; p. 156. (In Latvian)
37. Auzins, A.; Kazotnieks, J.; Leimane, I.; Miglavs, A. Assessment of Carbon Dioxide Emissions from Different Tillage Systems. In
Proceedings of the 20th International Scientific Conference Engineering for Rural Development Proceedings, Jelgava, Latvia,
26–28 May 2021; pp. 1558–1562. [CrossRef]
38.
Wanic, M.; ˙
Zuk-Gołaszewska, K.; Orzech, K. Catch crops and the soil environment—A review of the literature. J. Elem.
2019
,
24, 31–45. [CrossRef]
39.
Burger, M.; Dumlao, M.R.; Wang, J.; Moradi, B.Z.; Horwath, W.R.; Silk, W.K. Cover Crop Development Related to Nitrate Uptake
and Cumulative Temperature. Crop Sci. 2017,57, 971–982. [CrossRef]
40.
Heuermann, D.; Gentsch, N.; Guggenberger, G.; Reinhold-Hurek, B.; Schweneker, D.; Feuerstein, U.; Heuermann, M.C.; Groß, J.;
Kümmerer, R.; Bauer, B.; et al. Catch crop mixtures have higher potential for nutrient carry-over than pure stands under changing
environments. Eur. J. Agron. 2022,136, 126504. [CrossRef]
41.
Magdoff, F.; van ES, H. Building Soils For Better Crops Ecological Management For Healthy Soils, 4th ed.; USDA (Sustainable
Agriculture, Research and Education Programme (SARE)): College Park, MD, USA, 2021; pp. 137–156. Available online:
https://www.sare.org/wp-content/uploads/Building-Soils-for-Better-Crops.pdf (accessed on 22 June 2023).
42.
LLI-49 Project. Optimal Catch Crop Solutions to Reduce Pollution in the Transboundary Venta and Lielupe River Basins,
Joint Report on Activity AT1.1. Catch Crops and Their Growing Potentials in Venta and Lielupe RBDs 2019. Available
online: https://2014-2020.latlit.eu/lli-49-optimal-catch-crop-solutions-to-reduce-pollution-in-the-transboundary-venta-and-
lielupe-river-basins/ (accessed on 22 June 2023).
43.
Couëdel, A.; Kirkegaard, J.; Alletto, L.; Justes, É. Crucifer—Legume cover crop mixtures for biocontrol: Towards a new multi-
service paradigm. Adv. Agron. 2019,157, 55–139.

Agriculture 2023,13, 1539 25 of 25
44.
Clark, A. Managing Cover Crops Profitably, 3rd ed.; USDA (Sustainable Agriculture, Research and Education Programme (SARE)):
College Park, MD, USA, 2012; pp. 66–145. Available online: https://www.sare.org/wp-content/uploads/Managing-Cover-
Crops-Profitably.pdf (accessed on 22 June 2023).
45.
Talgre, L.; Lauringson, E.; Makke, A.; Lauk, R. Biomass production and nutrient binding of catch crops. Zemdirbyste
2011
,
98, 251–258.
46.
Selzer, T.; Schubert, S. Nutrient uptake of catch crops under non-limiting growth conditions. Ann. Appl. Biol.
2019
,174, 179–189.
47.
Talgre, L. Biomass Production of Different Green Manure Crops and Their Effect on the Succeeding Crops Yield. Ph.D. Thesis,
Institute of Agricultural and Environmental Sciences Estonian University of Life Sciences, Tartu, Estonia, 8 March 2013.
48.
Böldt, M.; Taube, F.; Vogeler, I.; Reinsch, T.; Kluß, C.; Loges, R. Evaluating Different Catch Crop Strategies for Closing the Nitrogen
Cycle in Cropping Systems—Field Experiments and Modelling. Sustainability 2021,13, 394. [CrossRef]
49.
Thomsen, I.K.; Christensen, B.T. Yields of wheat and soil carbon and nitrogen contents following long-term incorporation of
barley straw and rye-grass catch crops. Soil Use Manag. 2004,20, 432–438. [CrossRef]
50.
Wendling, M.; Charles, R.; Herrera, J.; Amossé, C.; Jeangros, B.; Walter, A.; Büchi, L. Effect of species identity and diversity on
biomass production and its stability in cover crop mixtures. Agric. Ecosyst. Environ. 2019,281, 81–91. [CrossRef]
51.
Smith, C.J.; Chalk, P.M. The role of
15
N in tracing N dynamics in agro-ecosystems under alternative systems of tillage management:
A review. Soil Tillage Res. 2020,197, 10449. [CrossRef]
52.
Liu, X.; Dong, W.; Jia, S.; Liu, Q.; Li, Y.; Hossain, M.E.; Liu, E.; Kuzyakov, Y. Transformations of N derived from straw under
long-term conventional and no-tillage soils: A 15N labelling study. Sci. Total Environ. 2021,786, 147428. [CrossRef]
53.
Price, N.; Harris, J.P.; Taylor, M.; Williams, J.R.; Anthony, S.G.; Duethmann, D.; Gooday, R.D.; Lord, E.I.; Chambers, B.J.;
Chadwick, D.R.; et al. An Inventory of Mitigation Methods and Guide to their Effects on Diffuse Water Pollution, Green-
house Gas Emissions and Ammonia Emissions from Agriculture. DEFRA Project WQ0106. 2011, p. 22. Available online:
https://repository.rothamsted.ac.uk/item/98583/an-inventory-of-mitigation-methods-and-guide-to-their-effects-on-diffuse-
water-pollution-greenhouse-gas-emissions-and-ammonia-emissions-from-agriculture (accessed on 22 June 2023).
54.
Latvian Rural Advisory and Training Centre. Crop Technological Models. Unpublished data of the EIP-Agri project “Development
of electronic farm management system” (No. 18-00-A01612-000018).
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.