Quantifying the impact of sustainable farming practices on environment and climate : greenhouse gas emissions, carbon sequestration and nutrient loss data from meta-analysis.
Date: 2024-07-02
Institutions: Joint Research Centre
Subjects: climate change, common agricultural policy, farming sector, greenhouse gas, reduction of gas emissions, research report, sustainable agriculture, sustainable development

ISSN 1831-9424 

Quantifying the Impact of Sustainable 
Farming Practices on Environment and 
Climate 

Greenhouse gas emissions, carbon sequestration and nutrient loss data 
from meta-analysis 

Guerrero, I., Bielza Diaz-Caneja, M., Angileri, V., 
Assouline, M., Bosco, S., Catarino, R., Chen, M., Koeble, R., 
Lindner, S., Makowski, D., Montero Castaño, A., Perez-
Soba Aguilar, M., Schievano, A., Tamburini, G., Terres, J., 
Rega, C. 

2024 

 EUR 31985 EN 

 
 
  
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
publication do not necessarily reflect the position or opinion of the European Commission. Neither the European 
Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this 
publication. For information on the methodology and quality underlying the data used in this publication for which the 
source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations 
employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the 
part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or 
concerning the delimitation of its frontiers or boundaries. 

 Contact information  

Name: Irene Guerrero  

Address: Joint Research Centre, Via E. Fermi 2749, I-21027 Ispra, Italy.  

Email: irene.guerrero-fernandez@ec.europa.eu 

Address:  
EU Science Hub 
Email:  

Tel.: 
https://joint-research-centre.ec.europa.eu 

JRC137826 

EUR 31985 EN 

PDF  

ISBN 978-92-68-18610-7   ISSN 1831-9424   doi:10.2760/20814  

KJ-NA-31-985-EN-N 

Luxembourg: Publications Office of the European Union, 2024  

© European Union, 2024  

The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 December 2011 
on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of this document is authorised 
under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that 
reuse is allowed provided appropriate credit is given and any changes are indicated.  

For any use or reproduction of photos or other material that is not owned by the European Union permission must be sought directly from 
the copyright holders. 

- Cover page illustration, © European Union, 2014 

How to cite this report: European Commission, Joint Research Centre, Guerrero, I., Bielza Diaz-Caneja, M., Angileri, V., Assouline, M., Bosco, S., 
Catarino, R., Chen, M., Koeble, R., Lindner, S., Makowski, D., Montero Castaño, A., Perez-Soba Aguilar, M., Schievano, A., Tamburini, G., Terres, J. 
and Rega, C., Quantifying the Impact of Sustainable Farming Practices on Environment and Climate, Publications Office of the European Union, 
Luxembourg, 2024, https://data.europa.eu/doi/10.2760/20814, JRC137826. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ...................................................................................................................................................................................................... 1 

Acknowledgements .............................................................................................................................................................................. 2 

1  Introduction ....................................................................................................................................................................................... 3 

1.1  Purpose and scope of the document ........................................................................................................................ 4 

2  Context and methodological approach .............................................................................................................................. 5 

2.1  Systematic review of meta-analyses and result synthesis ......................................................................... 5 

2.2  Selection of synthesis papers for the extraction of quantitative data ................................................. 7 

3  Results .................................................................................................................................................................................................. 9 

3.1  Summary table ..................................................................................................................................................................... 9 

3.2  Agroforestry ......................................................................................................................................................................... 11 

3.2.1  Effects on soil organic carbon ..................................................................................................................... 11 

3.3  Organic farming systems ............................................................................................................................................. 12 

3.3.1  Effects on greenhouse gas emissions .................................................................................................... 12 

3.3.2  Effects on soil organic carbon ..................................................................................................................... 13 

3.3.2.1  Organic livestock systems ................................................................................................................... 13 

3.3.2.2  Organic cropping systems ................................................................................................................... 14 

3.3.3  Effects on nutrient leaching and run-off .............................................................................................. 16 

3.4  Low-ammonia emission techniques for mineral fertilisation ................................................................. 16 

3.4.1  Effects on ammonia emissions .................................................................................................................. 16 

3.4.2  Effects on the nutrient balance .................................................................................................................. 18 

3.4.3  Effects on nutrient leaching and run-off .............................................................................................. 21 

3.5  Enhanced Efficiency Fertilisers ................................................................................................................................. 22 

3.5.1  Effects on greenhouse gas emissions .................................................................................................... 22 

3.5.2  Effects on ammonia emissions .................................................................................................................. 23 

3.5.3  Effects on the nutrient balance .................................................................................................................. 25 

3.5.4  Effects on nutrient leaching and run-off .............................................................................................. 28 

3.6  Organic fertilisation ......................................................................................................................................................... 29 

3.6.1  Effects on greenhouse gas emissions .................................................................................................... 30 

3.6.2  Effects on soil organic carbon ..................................................................................................................... 30 

3.6.3  Effects on ammonia emissions .................................................................................................................. 31 

i 

 
 
3.6.4  Effects on the nutrient balance .................................................................................................................. 32 

3.6.5  Effects on nutrient leaching and run-off .............................................................................................. 33 

3.7  Green manuring ................................................................................................................................................................. 34 

3.7.1  Effects on greenhouse gas emissions .................................................................................................... 34 

3.7.2  Effects on soil organic carbon ..................................................................................................................... 35 

3.7.3  Effects on the nutrient balance .................................................................................................................. 36 

3.7.4  Effects on nutrient leaching and run-off .............................................................................................. 37 

3.8  Soil amendment with Biochar ................................................................................................................................... 38 

3.8.1  Effects on greenhouse gas emissions .................................................................................................... 38 

3.8.2  Effects on soil organic carbon ..................................................................................................................... 39 

3.8.3  Effects on ammonia emissions .................................................................................................................. 40 

3.8.4  Effects on the nutrient balance .................................................................................................................. 41 

3.8.5  Effects of nutrient leaching and run-off ............................................................................................... 42 

3.9  Soil amendments with lime and gypsum ........................................................................................................... 43 

3.9.1  Effects on soil organic carbon ..................................................................................................................... 43 

3.10 Manure management: manure land application techniques ................................................................... 44 

3.10.1  Effects on greenhouse gas emissions .................................................................................................... 45 

3.10.2  Effects on ammonia emissions .................................................................................................................. 46 

3.11 Manure management: manure storage techniques ...................................................................................... 47 

3.11.1  Effects on greenhouse gas emissions .................................................................................................... 47 

3.11.2  Effects on ammonia emissions .................................................................................................................. 49 

3.12 Manure management: manure processing techniques ............................................................................... 50 

3.12.1  Effects on greenhouse gas emissions .................................................................................................... 51 

3.12.2  Effects on ammonia emissions .................................................................................................................. 53 

3.13 Livestock housing techniques .................................................................................................................................... 54 

3.13.1  Effects on greenhouse gas emissions .................................................................................................... 54 

3.13.2  Effects on ammonia emissions .................................................................................................................. 55 

3.14 Livestock feeding techniques..................................................................................................................................... 59 

3.14.1  Effects on greenhouse gas emissions .................................................................................................... 59 

3.14.2  Effects on ammonia emissions .................................................................................................................. 65 

3.15 Landscape Features ........................................................................................................................................................ 68 

ii 

 
 
3.15.1  Effects on soil organic carbon ..................................................................................................................... 68 

3.15.2  Effects on nutrient leaching and runoff ................................................................................................ 70 

3.16 Fallowing ............................................................................................................................................................................... 72 

3.16.1  Effects on soil organic carbon ..................................................................................................................... 72 

3.17 Intercropping ....................................................................................................................................................................... 74 

3.17.1  Effects on soil organic carbon ..................................................................................................................... 75 

3.17.2  Effects on the nutrient balance .................................................................................................................. 75 

3.17.3  Effects on the nutrient balance .................................................................................................................. 79 

3.18 Crop rotation........................................................................................................................................................................ 80 

3.18.1  Effects on greenhouse gas emissions .................................................................................................... 80 

3.18.2  Effects on soil organic carbon ..................................................................................................................... 82 

3.19 Cover and catch crops .................................................................................................................................................... 83 

3.19.1  Effects on greenhouse gas emissions .................................................................................................... 83 

3.19.2  Effects on soil organic carbon ..................................................................................................................... 84 

3.19.3  Effects on the nutrient balance .................................................................................................................. 85 

3.19.4  Effects on nutrient leaching and runoff ................................................................................................ 85 

3.20 Leguminous (nitrogen-fixing) crops ....................................................................................................................... 87 

3.20.1  Effects on soil organic carbon ..................................................................................................................... 88 

3.21 Tillage practices: No tillage and reduced tillage ............................................................................................. 88 

3.21.1  Effects on greenhouse gas emissions .................................................................................................... 89 

3.21.2  Effects on soil organic carbon ..................................................................................................................... 92 

3.22 Crop residue management .......................................................................................................................................... 94 

3.22.1  Effects on GHG emissions ............................................................................................................................. 94 

3.22.2  Effects on soil organic carbon ..................................................................................................................... 95 

3.22.3  Effects on the nutrient balance .................................................................................................................. 96 

3.22.4  Effects on nutrient leaching and run-off .............................................................................................. 97 

3.23 Mulching ................................................................................................................................................................................. 99 

3.23.1  Effects on GHG emissions ............................................................................................................................. 99 

3.23.2  Effects on ammonia emissions ............................................................................................................... 102 

3.23.3  Effects on the nutrient balance ............................................................................................................... 103 

3.23.4  Effects on nutrient leaching and run-off ........................................................................................... 104 

iii 

 
 
3.24 Grassland conservation and restoration .......................................................................................................... 105 

3.24.1  Effects on greenhouse gas emissions ................................................................................................. 105 

3.24.2  Effects on soil organic carbon .................................................................................................................. 106 

3.24.3  Effect on nutrient leaching and runoff ................................................................................................ 108 

3.25 Grassland management ............................................................................................................................................ 108 

3.25.1  Effects on greenhouse gas emissions ................................................................................................. 108 

3.25.2  Effects on soil organic carbon .................................................................................................................. 111 

3.25.3  Effects on nutrient leaching and runoff ............................................................................................. 112 

3.26 Grazing ................................................................................................................................................................................. 113 

3.26.1  Effects on greenhouse gas emissions ................................................................................................. 114 

3.26.2  Effects on soil organic carbon .................................................................................................................. 115 

3.27 Peatland management ............................................................................................................................................... 116 

3.27.1  Effects on greenhouse gas emissions ................................................................................................. 117 

3.28 Peatland conservation ................................................................................................................................................ 118 

3.28.1  Effects on greenhouse gas emissions ................................................................................................. 118 

3.28.2  Effects on soil organic carbon .................................................................................................................. 120 

3.29 Peatland restoration .................................................................................................................................................... 121 

3.29.1  Effects on greenhouse gas emissions ................................................................................................. 121 

3.29.2  Effects on soil organic carbon .................................................................................................................. 123 

3.30 Wetland management ................................................................................................................................................ 124 

3.30.1  Effects on greenhouse gas emissions ................................................................................................. 124 

3.31 Wetland conservation.................................................................................................................................................. 125 

3.31.1  Effects on greenhouse gas emissions ................................................................................................. 125 

3.31.2  Effects on soil organic carbon .................................................................................................................. 127 

3.32 Wetland restoration...................................................................................................................................................... 128 

3.32.1  Effects on greenhouse gas emissions ................................................................................................. 129 

3.32.2  Effects on nutrient leaching and runoff ............................................................................................. 129 

3.33 No irrigation ...................................................................................................................................................................... 130 

3.33.1  Effects on greenhouse gas emissions ................................................................................................. 131 

3.33.2  Effects on nutrient leaching and run-off ........................................................................................... 131 

3.33.3  Effects on the nutrient balance ............................................................................................................... 132 

iv 

 
 
3.34 Water-saving irrigation practice in flooded lands ....................................................................................... 133 

3.34.1  Effects on greenhouse gas emissions ................................................................................................. 133 

3.34.2  Effects on soil organic carbon .................................................................................................................. 133 

3.34.3  Effects on ammonia emissions ............................................................................................................... 134 

3.34.4  Effects on the nutrient balance ............................................................................................................... 134 

3.34.5  Effects on nutrient leaching and run-off ........................................................................................... 135 

3.35 Water-saving irrigation practice in non-flooded lands............................................................................. 136 

3.35.1  Effects on greenhouse gas emissions ................................................................................................. 136 

3.35.2  Effects on the nutrient balance ............................................................................................................... 137 

3.35.3  Effects on nutrient leaching and run-off ........................................................................................... 138 

Conclusions ......................................................................................................................................................................................... 139 

References .......................................................................................................................................................................................... 140 

List of abbreviations and definitions ................................................................................................................................... 155 

List of figures .................................................................................................................................................................................... 157 

List of tables ...................................................................................................................................................................................... 158 

Annexes ................................................................................................................................................................................................ 165 

Annex 1. Metrics considered for the analysis of the effects of farm practices on the nutrient 
balance ........................................................................................................................................................................................... 165 

v 

 
 
Abstract  

Agriculture  plays  a  pivotal  role  in  meeting  global  food  demands  and  maintaining  socio-economic 
stability. However, some agricultural practices may negatively contribute to environmental concerns 
such as greenhouse gas emissions, nutrient losses, and biodiversity decline, impacting climate change, 
water quality and quantity, and ecosystem functioning. In response, the European Commission (EC) 
fosters the integration of environmental sustainability within agricultural policy frameworks, including 
the Common Agricultural Policy (CAP).  

In this context, identifying sustainable farming practices is crucial for achieving the EU's sustainability 
objectives,  in  particular  to  assess  the  environmental  and  climate  performance  of  the  agricultural 
sector. To support this, we present in this report a comprehensive collection of coefficients quantifying 
the  environmental  impacts  of  farming  practices.  Focusing  on  greenhouse  gas  emissions,  carbon 
sequestration, and nutrient losses, these coefficients are sourced from scientific articles, primarily 
meta-analyses, which have been reviewed through systematic literature analysis. They offer valuable 
insights into the effects of different agricultural management options, directly relevant to assess the 
likely impacts of CAP interventions on the environment and the climate.  

The report presents a collection of over 100 tables containing quantitative coefficients that assess 
the environmental impacts of 35 farming practices (ranging from single farming practices to cropping 
systems or conservation and restoration actions), offering clear links to specific scholar references, 
along with detailed contextual information. 

This  work  is  part  of  the  iMAP4agri  project,  commissioned  by  the  EC's  Directorate-General  for 
Agriculture  and  Rural  Development  (DG  AGRI)  to  the  Joint  Research  Centre  (JRC),  drawing  on 
experience  from  previous  CAP  periods  and  materials  developed  for  the  current  policy  cycle.  By 
synthesizing existing meta-analyses and applying a systematic review framework, we ensure a robust 
and transparent approach that can be implemented within the time and data availability constraints 
of policymaking processes.  

1 

 
 
 
Acknowledgements  

The authors would like to thank Manuel Beltran Miralles (JRC-Sevilla) and anonymous reviewersfor 
their valuable comments which certainly helped to improve this document. 

Authors 

Guerrero Irene1, Bielza Maria2, Angileri Vincenzo1, Assouline Michael1, Bosco Simona1, Catarino Rui1, 
Chen Mathilde3, Koeble Renate4, Lindner Sören5, Makowski David6, Montero-Castano Ana1, Perez-soba 
Marta1, Schievano Andrea1, Tamburini Giovanni7, Terres Jean Michel1, Rega Carlo8. 

1 European Commission, Joint Research Centre (JRC), Ispra, Italy 

2 Seidor Italy s.r.l., Milano, Italy. 

3 CIRAD, UMR PHIM, Montpellier, France. 

4 ARHS Developments, Belvaux, Luxembourg.  

5 Environment and Climate Hub (ECH). University of Vienna, Austria. 

6 University of Paris-Saclay. Paris, France. 

7 University of Bari, Italy.  

8 European Commission, Directorate-General for Agriculture and Rural Development (AGRI). Brussels, 
Belgium. 

2 

 
 
 
1 

Introduction  

Agriculture not only fulfils the basic  need  for food supply but also plays a role  in providing other 
ecosystem  services,  while  ensuring  the  socio-economic  stability  of  societies worldwide.  In  Europe, 
agriculture  is  intrinsically  linked  to  the  environment,  rural  development,  and  the  well-being  of  its 
citizens (Stoate et al. 2009). Recognizing the dual role of agriculture as both a driver and a mitigating 
factor to environmental change, the European Union (EU) has consistently fostered the integration of 
environmental sustainability within its agricultural policy framework (Dos Santos & Ahmad 2020).  

Agriculture  is  a  major  contributor  to  environmental  problems  such  as  greenhouse  gas  (GHG) 
emissions, nutrient losses or biodiversity declines, with  implications for climate change, water quality 
and  quantity,  and  ecosystem  functioning  (Campbell  et  al.  2017).  The  EU  has  recognized  these 
environmental  issues  and  is  enhancing  the  policy  to  support  environmentally  sustainable  farming 
practices.  Not  only through  the  CAP,  but  other  recent  EU  initiatives,  including  the  European  Green 
Deal1, the Farm to Fork Strategy2, the Biodiversity Strategy for 20303 and the proposal of the Nature 
Restoration Law4 reflect a strategy towards sustainability by setting targets for reducing the sector's 
environmental footprint. 

Sustainable farming practices are crucial for achieving the EU's objectives. The application of these 
practices may minimize impacts, such as GHG emissions and nutrient losses, by employing methods 
that enhance efficiency and promote ecological balance (Pretty 2018), thereby leading to substantial 
environmental benefits. 

The role of scientific knowledge in this context is multifaceted. Identifying sustainable agricultural 
practices  is  dependent  on  strong  scientific  evidence  from  global  research  and  observations.  Thus, 
research  provides  the  necessary  evidence  base  for  designing  policies  that  effectively  address  the 
environmental  challenges  of  agriculture  (Aznar-Sánchez  et  al.  2019).  It  also  supports  the 
implementation of these policies by offering technical guidance and best practices for farmers and 
land managers. Furthermore, ongoing scientific evaluation is essential for monitoring the outcomes 
of  policy  interventions,  contributing  to  their  refinement  and  optimization  through  the  policy  cycle 
(Head 2016).  

Nevertheless, all this knowledge, while comprehensive, requires to be effectively communicated to 
policymakers  for  evidence-based  decision.  Systematic  reviews  and  meta-analyses  (MAs)  are  key 
methods  for  knowledge  synthesis  in  various  scientific  fields.  They  constitute  valuable  tools  for 
converting complex data into actionable information, here applied for sustainable agricultural policy 
development  (Gurevitch  et  al.  2018).  A  systematic  review  involves  the  exhaustive  compilation, 
evaluation  and  synthesis  of  all  relevant  studies  dealing  with  a  specific  question.  It  is  based  on  a 

1 Communication from the Commission to the European Parliament, the Council, the European Economic and Social 

Committee and the Committee of the Regions. The European Green Deal COM(2019) 640 Final, 2019 

2 Communication from the Commission to the European Parliament, the Council, the European Economic and Social 

Committee and the Committee of the Regions. A Farm to Fork Strategy COM(2020) 381 Final, 2020 

3 Communication from the Commission to the European Parliament, the Council, the European Economic and Social 
Committee and the Committee of the Regions. EU Biodiversity Strategy for 2030 COM(2020) 380 Final, 2020 
4  Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on nature restoration COM(2022) 

304 Final, 2022 

3 

 
 
                                                 
 
detailed protocol limiting the bias and favouring a transparent and reproducible approach (Chalmers 
et al. 2002).  

Meta-analysis combines systematic review and statistical analysis and aims to provide quantitative 
information from a set of relevant primary studies (Lau 1997). This quantitative information can be 
critical  to  assess  the  environmental  impacts  of  agricultural  practices,  often  resolving  conflicting 
research  findings  and  considering  the  potential  variability  in  the  effectiveness  of  these  practices 
under diverse conditions. Moreover, by aggregating data from individual studies, meta-analysis can 
increase the accuracy and robustness of outcome estimates by enhancing the overall sample size 
(Makowski  et  al.  2019;  Philibert  et  al.  2012).  The  quantitative  coefficients  obtained  from  meta-
analysis  are valuable  not only  for  informing policy  decision-making  at various  levels,  such  as the 
design of targeted interventions, or the evaluation of their impacts, but also for filling data gaps. They 
can complement data from other sources or feed predictive models that aid in these tasks (Guerrero 
2023). The use of these coefficients can streamline policy development and ensure that interventions 
are grounded in comprehensive and robust empirical evidence. 

This  report  presents  a  collection  of  coefficients  quantifying  the  environmental  impacts  of  various 
farming practices, with a focus on their potential to increase carbon sequestration, and to reduce GHG 
emissions  and  nutrient  losses.  These  coefficients  are  sourced  from  scientific  articles,  specifically 
meta-analyses,  which  have  been  identified  through  systematic  literature  review.  They  have  been 
selected based on their quality and relevance  to provide decision support tools for evaluating the 

Adapted  for  publication,  this  project  deliverable  provides  clear  links  to  the  specific  bibliographic 
references accompanying each coefficient. Additionally, it includes detailed contextual information to 
aid  in  understanding  the  coefficients  and  their  caveats.  This  collection  offers  insights  into  how 
different  agricultural  management  options  can  affect  the  environment,  with  relevance  to  Impact 
indicators5 of the Common Agricultural Policy. 

1.1  Purpose and scope of the document 

The purpose of this document is to systematically compile and synthesize scientific evidence on the 
environmental  impacts  of  various  agricultural  practices.  By  presenting  a  collection  of  quantitative 
coefficients derived from peer-reviewed meta-analyses, this report aims to inform and support the 
decision-making  process  for  agricultural  policy  development  within  the  EU.  These  coefficients 
quantify the potential of farming practices to increase carbon sequestration and reduce greenhouse 
gas (GHG) emissions and nutrient losses, thereby providing decision support tools for evaluating the 
environmental performance of agricultural policies. 

The  scope  of  this  document  is  confined  to  the  environmental  impacts  of  a  group  of  selected 
agricultural  practices  and  management  options,  specifically  focusing  on  GHG  emissions,  nutrient 
losses,  and  carbon  sequestration.  This  document  does  not  present  an  exhaustive  list  of  farming 
practices and does not address other potential environmental and socio-economic impacts that may 
result from the application of these and other agricultural management options. 

5 More information on Impact indicators: https://agriculture.ec.europa.eu/common-agricultural-policy/cap-

overview/cmef_en#towardsthepmef 

4 

 
 
 
                                                 
 
2  Context and methodological approach  

This work is part of the developments carried out under the project Integrated Modelling Platform for 
Agro-economic  and  resource  Policy  analysis  (iMAP4agri) 6 ,  an  Administrative  Agreement 
-General  for  Agriculture  and  Rural 
Development (DG AGRI) to the Joint Research Centre (JRC). iMAP4agri provides scientific support and 
tools necessary for the implementation, monitoring, and evaluation of the Common Agricultural Policy 
(CAP) Strategic Plan regulation (Regulation (EU) 2021/2115), with a particular focus on achieving the 
CAP objectives related to environmental sustainability and climate change mitigation. 

The project aims to establish a comprehensive scientific understanding of the impacts that various 
farming practices have on the environment and climate. Drawing on experience from previous CAP 
periods  and  materials  developed  for  the  current  policy  cycle,  the  project  has  gathered  scientific 
evidence  of  the  positive  and  negative  environmental  impacts  of  selected  sustainable  farming 
practices, including their estimation through quantitative information.  

2.1  Systematic review of meta-analyses and result synthesis 

In  this  work,  building  the  scientific  evidence  base  is  grounded  on  a  large  systematic  review  of 
synthesis papers (meta-analysis and others). The starting point is the large number of meta-analyses 
published  in  agricultural  science  that  allow  to  explore  general  trends  from  large  numbers  of 
experimental studies and to identify key moderating factors. Thus, a methodological framework has 
been developed for assessing the impacts of farming practices on the environment and climate based 
on a systematic review of published meta-analyses. The framework helps to report the results and 
quality of meta-analysis in a rigorous and transparent manner. In addition, the framework can be 
implemented in a time-frame operational and compatible with the time constraints of policymaking. 

The proposed framework's primary goal is to collect and synthesize meta-analyses (MAs) on specific 
farming practices, as defined by the above mentioned policy needs. It consists of four steps: (1) a 
systematic  literature  search  to  compile  MAs,  (2)  screening  and  selecting  relevant  MAs  based  on 
predefined criteria, (3) extracting data and assessing the quality of selected MAs, and (4) generating 
reports on the impacts of farming practices. 

The search string for Step 1 includes keywords and synonyms pertinent to the farming practice and 
filters  out  unrelated  papers.  It  focuses  on  MAs  and  systematic  reviews  that  include  quantitative 
results,  excluding  unsystematic  reviews.  The  use  of  multiple  scientific  databases  ensures  a 
comprehensive coverage. 

In Step 2, the initial screening is based on titles and abstracts, and studies are categorized based on 
their relevance. Full-text screening follows for those deemed potentially relevant.  

Step 3's data extraction covers various aspects of the MAs, such as references, objectives, methods, 
experimental details, and significant results. These results are rated, and MAs are evaluated using 16 
quality criteria covering the following aspects: 

6 https://wikis.ec.europa.eu/display/IMAP/IMAP+Home+page 

5 

 
 
 
                                                 
 
─  Scoping: objective is specified 

─  Search: search databases are mentioned, search string is reported, and list of studies is reported 

─  Study selection: selection criteria are reported, number of selected studies at each step is reported 

─  Data extraction: method for data extraction is described 

─  Statistical analysis: quantitative results are provided, statistical method is described, individual 
effect  sizes  are  reported,  heterogeneity  is  analysed,  individual  studies  are  weighted,  and 
confidence intervals are presented 

─  Bias  and  uncertainty:  dataset  is  available,  funding  sources  are  reported,  publication  bias  is 

analysed 

Reporting in Step 4 delivers detailed, summarized, and synthetic information through different report 
types, offering a progressively synthesized view of the impacts, from detailed individual analyses to 
a broad overview in a general report.  

A synopsis matrix in this general report presents a comprehensive scoring of the farming practice for 
all impacts, informed by the data compiled in Step 3. From this matrix, the nature of the overall effect 
of each farming practice on different environmental impacts, such as greenhouse gas emissions or 
nutrient use efficiency, can be determined. For this report, the determination of the direction of the 
effect is done using an accepted fast-track method for synthesizing the results of MAs without using 
the original primary data: relative majority of first-order MAs results (Figure 1). This method consists 
on the vote counting of MAs results reporting positive, negative, and non-significant effect (Makowski 
et al. 2023).  

The final outputs of this work, as well as the intermediate results of each step, including search strings 
or  exclusion/inclusion  critera,  can  be  consulted  in  the  dedicated  wiki  space  and  in  the  published 
database (EC JRC 2023; Schievano et al. 2023).  

It is important to note at this stage that the results presented in all the reports (EC JRC 2023), the 
dataset  (Schievano  et  al.  2023)  and  this  document,  are  based  on  the  impacts  retrieved  from  the 
selected  meta-analyses  after  screening.  This  means  that  the  search  strings  used  did  not  include 
keywords  related  to  specific  or  expected  outcomes, so  the  impacts  collected have  been  extracted 
solely from what was reported by the authors. For presentation and policy adequacy, in particular 
with regard to the Impact Indicators of the Performance and Monitoring Evaluation Framework7, some 
of the metrics provided by the authors have been aggregated under the general nomenclatures that 
title  the  sections  of  the  report.  Some  are  straightforward,  such  as  the  case  of  greenhouse  gas 
emissions but others require some further conceptualization, as nutrient balance (see  Annex 1 for 
detailed information). 

It is also worth emphasizing that the work is based on synthesis literature, which does not prevent 
having  primary  studies  linking  these  practices  to  other  impacts  or  outcomes  not  reported  here. 
Besides,  this  document  focuses  only  on  results  obtained  through  synthesis  literature  review 
concerning  the  impact  of  the  selected  set  of  farming  practices  on  GHG  emissions,  carbon 
sequestration and nutrient losses. 

7 https://agriculture.ec.europa.eu/common-agricultural-policy/cap-overview/cmef_en#towardsthepmef 

6 

 
 
                                                 
 
2.2  Selection of synthesis papers for the extraction of quantitative data 

To extract relevant and suitable quantitative data, a method to select the most appropriate synthesis 
papers has been developed. It is intended to be simple, feasible and time-wise efficient. The method 
is based on a modified version of the quality score used in Step 3 of the above explained literature 
review framework (Figure 1). The original quality score evaluates the synthesis papers according to 
criteria covering three dimensions of the research synthesis procedure: i) literature review and studies 
selection, ii) quality of statistical analysis, and iii) potential bias (EC JRC 2023).  

Given  the  aim  of  this  selection  to  gather  robust  quantitative  information  on  the  environmental 
impacts of selected farming practices, we calculated a relevance score by assigning more weight to 
the criteria related to the statistical part (7 criteria out of 16 have been assigned 70% of the total 
weight).  This  ensures  the  statistical  quality  of  the  synthesis  articles,  without  discarding  the 
information on the quality provided by the other characteristics (30%).  

The relevance score is further complemented with the addition of five more components related to:  

─  the number of individual papers analysed in each synthesis paper, including a standardisation 
that gives higher value to the synthesis paper including more individual studies and weights 
the others according to this maximum.  

─  the year of publication, including a standardisation that gives higher value to the most recent 

paper and weights the others according to this last publication date.  

─  the inclusion of data from European Member States, giving higher value to those synthesis 

including European cases.  

─  the  relevance  of  crops  for  the  European  Union  agricultural  context,  giving  higher  value  to 
those  synthesis  including  two  or  more  EU  relevant  crops.  In  some  specific  cases  (i.e. 
agroforestry), this "bonus" is used to penalize comparators or interventions less pertinent to 
the policy.  

─  the  pertinence  of the impact  metric  to the policy  framework,  giving  higher value  to those 
output metrics more applicable to the policy context. This is needed because there are many 
different metrics used in systematic reviews to express effect sizes (indexes measuring the 
magnitude of the effects) and not all of them allow to derive a percentage or absolute change 
of the measured effect when applying the practice compared to control.  

All  these  values,  once  aggregated,  result  in  a  final  selection  score.  This  evaluation  and  selection 
exercise is repeated for each farming practice-impact pair for which a significant positive or negative 
effect has been found (Figure 1). The studies are ordered according to their selection score and the 
first  ranked  is  used  for  the  extraction  of  numerical  coefficients.  In  a  few  cases  when  not  all  the 
synthesis  papers  retrieved  in  the  systematic  review  report  unanimously  the  same  qualitative 
(positive/negative) effect (hence the overall effect is determined by vote counting), it may happen 
that the first ranked synthesis paper belongs to the minority of those reporting a different qualitative 
effect, e.g. negative when the majority of the other paper report positive. In this case and to ensure 
consistency,  the  highest  ranked  synthesis  paper  reporting  results  in  line  with  the  overall  effect  is 
selected for the extraction of data.   

From the subset of selected synthesis papers, corresponding quantitative information is extracted 
from texts, tables and figures provided in the publications. Usually, synthesis papers report the effect 
size i.e. a ratio of a certain metric affected by the practice compared to the same metric measured 
without  applying  it  (control  study).  For  example,  if  the  mean  effect  size  of  the  use  of  controlled 

7 

 
 
released fertilizers with regard to ammonia emission is 0.8, it means that the use of these techniques 
reduces ammonia emissions by 20% on average, compared to control (i.e. not using it, other things 
being  equal).  Effect  sizes,  whenever  possible,  are  transformed  to  percentage  of  change,  and 
confidence intervals and number of pairwise comparisons are provided when applicable and available.   

For a single combination of farming practice and its measured impact, a synthesis paper may report 
more  than  one  effect  size  based  on  different  factors  such  as  crop  type,  biogeographic  region,  or 
specific techniques used. This further stratified information is extracted when it can be relevant for 
the  policy.  In  this  cases,  the  stratified  results  can differ  in their  direction  (positive  or  negative)  or 
statistical significance from the overall result defined by relative majority of first-order MAs results, 
as discussed earlier. In some cases, data were extracted from two papers if they provide consistent 
but complementary information with regard to the examined environmental effects, e.g. by providing 
more detailed results with respect to specific sub-practices or geographic locations.   

Figure 1 Representation of the decision phases for the selection of meta-analyses for quantitative data 
extraction. Following a systematic review process in synthesis literature, the results are evaluated using the 
relative majority fast-track method. In cases where statistically significant majority results are determined, 
the most appropriate meta-analysis is selected for coefficient extraction. See text for further details.  

Source: Own elaboration.  

8 

 
 
 
 
 
 
3  Results 

3.1  Summary table 

The following table (Table 1) summarizes the results presented in this document. It displays, for each 
combination of farming practice and environmental impact, the number of coefficients extracted from 
selected synthesis papers, once the process described in the previous section is completed (Figure 1). 
These coefficients can represent overall values (i.e. average result considering all sub-groups) and/or 
disaggregated results regarding different sub-groups, which may be crop type, soil type, geographical 
area, etc. As explained earlier in the methodological approach, the selection of farming practices has 
been guided by the policy preferences and needs that contextualize the project. From all the farming 
practice-impact combinations found in the systematic literature review, only those determined to be 
statistically  significant  by  the  majority  of  results,  have  been  considered  for  quantitative  data 
extraction. Thus, the empty cells in the following summary table may reflect situations where  the 
practice predominantly does not present statistically significant effects or where the literature review 
did not retrieve results. For policy related preferences, the authors of this collection have decided not 
to include LCA (Life Cycle Assessment) results, as their adoption in European policy is not widespread 
(Sala  et  al.  2021),  therefore,  these  results  wer  not  taken  into  account  at  any  of  the  steps  of  the 
process.  

The  focus  is  on  data  concerning  GHG  emissions,  carbon  sequestration  and  nutrient  losses 
disaggregated  into  ammonia  emissions,  nutrient  leaching  and  run-off  and  the  effect  of  farming 
practices on the agricultural nutrient balance (more information on the nutrient balance impact can 
be  found  in  Annex  1).  For  the  coefficients  extracted  on  soil  organic  carbon  (SOC)  additional 
information on geographical area, time and soil depth, is provided as supporting material available 
at Bosco et al. 2024. 

Table  1 Summary of results from the  systematic literature review and study selection  process: Number of 
coefficients extracted for each combination of farming practice and environmental impact, based on statistically 
significant results and, in brackets, the corresponding section. Empty cells may indicate both situations of not 
statistically significant effects or of not retrieved results from literature review.  

Agroforestry 

Organic systems 

Low-ammonia mineral fertilisation  
techniques 

Enhanced Efficiency Fertilisers 

Organic fertilisation 

Green manuring 

Soil amendment with biochar 

Soil amendment with lime and gypsum 

Manure land application techniques 

Soil  
Organic 
Carbon 
6  
(3.2.1) 
26 
 (3.3.2) 

12 
 (3.6.2) 
7 
 (3.7.2) 
18 
 (3.8.2) 
12 
 (3.9.1) 

GHG  
emissions 

6  
(3.3.1) 

14 
 (3.5.1) 
1  
(3.6.1) 
12 
 (3.7.1) 
15 
 (3.8.1) 

8 
 (3.10.1) 

9 

Ammonia 
emissions 

Nutrient 
balance 

Nutrient 
leaching and 
run-off 

3 
 (3.3.3) 
6 
 (3.4.3) 
26 
 (3.5.4) 
10 
 (3.6.5) 
2 
 (3.7.4) 
5 
 (3.8.5) 

35 
 (3.4.2) 
83 
 (3.5.3) 
9  
(3.6.4) 
1 
 (3.7.3) 
8 
 (3.8.4) 

31 
 (3.4.1) 
17  
(3.5.2) 
4 
 (3.6.3) 

9 
 (3.8.3) 

5 
 (3.10.2) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
GHG  
emissions 

Soil  
Organic 
Carbon 

Ammonia 
emissions 

Nutrient 
balance 

Nutrient 
leaching and 
run-off 

Manure storage techniques 

Manure processing techniques 

Livestock housing techniques 

Livestock feeding techniques 

Landscape Features 

Fallowing 

Intercropping 

Crop rotation 

Cover and catch crops 

Leguminous crops 

Tillage practices 

Crop residue management 

Mulching 

Grassland conservation and restoration 

Grassland management 

Grazing 

Peatland management 

Peatland conservation 

Peatland restoration 

Wetland management 

Wetland conservation 

Wetland restoration 

No irrigation 

Water-saving irrigation in flooded lands 

Water-saving irrigation in non-flooded 
lands 

Source: own elaboration. 

15 
 (3.11.1) 
20 
 (3.12.1) 
6 
 (3.13.1) 
30  
(3.14.1) 

5 
 (3.18.1) 
8 
 (3.19.1) 

30 
 (3.21.1) 
10 
 (3.22.1) 
30 
 (3.23.1) 
1  
(3.24.1) 
18 
 (3.25.1) 
16 
 (3.26.1) 
5 
 (3.27.1) 
14 
 (3.28.1) 
9 
 (3.29.1) 
3 
 (3.30.1) 
17 
 (3.31.1) 
1  
(3.32.1) 
1  
(3.33.1) 
3  
(3.34.1) 
2  
(3.35.1) 

12 
 (3.11.2) 
10 
 (3.12.2) 
22 
 (3.13.2) 
8 
 (3.14.2) 

16 
 (3.15.2) 

34 
(3.17.2) 

4  
(3.17.2) 

2 
 (3.19.3) 

23  
(3.19.4) 

10 
(3.22.3) 
20 
(3.23.3) 

5 
 (3.23.2) 

23 
 (3.22.4) 
5 
 (3.23.4) 
1 
 (3.24.3) 
8 
 (3.25.3) 

9 
 (3.15.1) 
26 
(3.16.1) 
1 
 (3.17.1) 
13 
(3.18.2) 
7 
 (3.19.2) 
1 
 (3.20.1) 
15 
(3.21.2) 
12 
(3.22.2) 

16  
(3.24.2) 
4 
 (3.25.2) 
9 
 (3.26.2) 

2 
 (3.28.2) 
1  
(3.29.2) 

4 
 (3.31.2) 

1  
(3.34.2) 

2 
 (3.34.3) 

5 
 (3.32.2) 
4 
 (3.33.2) 
8  
(3.34.5) 
4  
(3.35.3) 

19  
(3.33.3) 
3 
 (3.34.4) 
4 
 (3.35.2) 

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.2  Agroforestry 

Agroforestry  is  a  type  of  land-use  system  where  perennial  woody  plants  (trees,  shrubs)  are 
deliberately  used  in  combination  with  agricultural  crops,  pastures  and/or  animals.  In  Europe,  six 
agroforestry practices are identified: silvoarable agroforestry, forest farming, riparian buffer strips, 
improved fallow, multipurpose trees and silvopasture. In the literature analysed, agroforestry impacts 
are compared to: (i) land use without trees, including cropland, pasture and, sometimes, fallow land; 
and  (ii)  forests,  either  natural  or  planted.  The  comparator  considered  in  the  present  deliverable  is 
cropland without trees, so just results related to this comparator are considered here. 

3.2.1  Effects on soil organic carbon 

The selected synthesis paper for the extraction of quantitative data is the study from Chatterjee et 
al. 2018. Data were extracted for two relevant agroecological regions: Mediterranean regions (located 
on the western sides of continents between the latitudes of 30 °N and 45 °N) and temperate regions 
(located in latitudes between 40 °N and 60 °N). The assessed metric is the percentage change in soil 
organic  carbon  stock  between  agroforestry  systems  and  cropland  without  trees,  measured  within 
different soil depth classes: 0-20, 0-40, 0-60 and 0-100 cm for Mediterranean regions and 0-20 and 
0 40 cm for temperate regions, as reported in the study (Table 2). 

The conversion from agriculture to agroforestry generally increased SOC stocks in both Mediterranean 
and temperate regions and at different soil depth classes. This increase was found significant at 0-
20  and  0-100  cm  for  Mediterranean  regions,  and  at  0-40  cm  for  temperate regions.  The  highest 
increase in SOC stock was found in the 0-20 cm soil depth class for Mediterranean regions (+43.51%; 
95% confidence interval (CI): 12.13%, 75.73%; number of samples or pairwise comparisons (Nc) = 
8), and in the 0-40 cm soil depth class for temperate regions (+18.64%; 95% CI: 12.99%, 25.14%; 
Nc=15). 

Table  2  Effect  of  agroforestry  on  carbon  sequestration  (%  change  in  SOC  stock)  in  Mediterranean  and 
temperate  regions.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of 
pairwise comparisons. 

Biogeographic regions 

Mediterranean regions 

Temperate regions 

Source: Chatterjee et al. 2018. 

soil 
depth 
(cm) 

0-20 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

12.13 

43.51 

75.73 

8 

Positive 

0-40 
0-60 
0-100 
0-20 
0-40 
other  Not available  Not available  Not available 

-3.00 
-12.50 
4.15 
-3.49 
12.99 

10.21 
8.5 
5.80 
1.45 
18.64 

21.92 
27.72 
7.77 
6.69 
25.14 

Non-significant 
Non-significant 
Positive 

15 
7 
6 
15   Non-significant 
15  

Positive 

11 

 
 
 
 
 
 
 
3.3  Organic farming systems 

Organic production is an overall system of farm management and food production that combines 
best  environmental  and  climate  action  practices,  a  high  level  of  biodiversity,  the  preservation  of 
natural resources and the application of high animal welfare standards and high production standards 
in  line  with  the  demand  of  a  growing  number  of  consumers  for  products  produced  using  natural 
substances  and  processes 8 .  Organic  farming  systems  have  been  examined  both  mixed  and 
considering cropping systems and livestock systems separately.  

3.3.1  Effects on greenhouse gas emissions 

Results of GHG using the Life Cycle Assessment method (LCA) have not been included in the analysis. 
For this reason, there are no results for organic mixed livestock and crop systems but only for organic 
cropping systems.  

The selected synthesis paper (Skinner et al. 2014) reports effects on GHG emissions from arable land 
and grasslands soils. Studies were conducted in the Northern hemisphere under temperate climate. 
All arable soils showed an average methane uptake, which was slightly higher (both area-scaled and 
yield-scaled) under organic than under non-organic management with a mean difference of 3.2 ± 2.5 
kg CO2 eq ha 1 yr 1 for organic management (8 comparisons; p = 0.01). The important exceptions to 
soils as a methane sink are rice paddies, waterlogged anaerobic systems, which are emitters of large 
amounts of methane produced by methanogenic Archaea. However, only one comparative study on 
rice  paddies  had  been  included  in  their  meta-analysis.  The  study  on  paddy  rice  cropping  showed 
pronounced methane emissions under both systems. Significantly more methane was emitted under 
organic compared to non-organic management with a mean difference of 950 kg CO2 eq ha 1 yr 1 
(Nc=3;  p  <  0.01).  The  authors  (Skinner  et  al.  2014)  also  found  that  across  all  annual  GHG 
measurements on arable and grassland use, soils under organic farming emit less N2O than the non-
organic counterparts. The impact for arable land was -497 ±162 kg CO2-eq. ha 1 yr 1 (p 0.001) (Table 
3). 

Table 3 Effect of organic systems on greenhouse gas emissions (change of emissions per hectare9). All figures 
are in kg CO2eq/ha/yr. CI_LOW: confidence interval  bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. 

GHG 

Systems 

CI_LOW 

CH4 

N2O 

Arable 
Rice paddies 

Arable + 
grassland 
Arable 
Grassland 
Rice-paddies 

-5.7  
+535  

-652  

-659  
-3622  
-1686  

Source: Skinner et al. 2014 

Mean 

-3.2  
+950  

-492  

-497  
-1091  
-646  

CI_HIGH 

-0.7  
+1365  

-332  

-335  
+1440  
+394  

Nc 

8 
3 

70 

67 
3 
3 

Effect 

Positive 
Negative 

Positive 

Positive 

Non-significant 
Non-significant 

8 https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32018R0848&from=EN 

9 The conversion factors applied are 298 kg CO2eq/kg N2O, 468,198 kg CO2eq/kg N2O-N, 25 kg CO2eq/kg CH4.. 

12 

 
 
 
 
                                                 
 
3.3.2  Effects on soil organic carbon 

Results are typically provided for soil organic carbon concentration, soil organic carbon stocks and 
soil carbon sequestration rates. SOC concentrations describe the organic carbon concentration on a 
weight by weight basis, SOC stocks on a weight by area basis, and C sequestration rates, on weight 
by area and elapsed time since conversion to organic (Schievano et al. 2023). 

3.3.2.1  Organic livestock systems 

The data provided in the selected paper (Gattinger et al. 2012) mainly cover top-soil and temperate 
zones, whereas only few data from tropical regions and subsoil horizons exist. Authors argue that, 
although these data clearly showed that organic management increased SOC, it is often stated that 
increased SOC stocks originate from massive imports of organic matter. To examine the potential 
impact  of  imported  organic  matter,  the  authors  analyse  a  subset  of  studies  representing  organic 
farming systems with 
mixed livestock crop production farms with forage crops in the crop rotation, such that the livestock 
can  be  fed  entirely  from  fodder  produced  on-farm.  In  such  systems,  no  import  of  organic  matter 
should occur. On the other hand, these systems could also be stockless farms that import organic 

Zero net input systems

analysis, the cut-off for a zero net input system was set at an amount of organic fertilisers applied 

This is lower than the maximum stocking rates that they consider to reflect a zero net input system. 
Adopting this conservative threshold, they may have neglected some studies that actually represent 
zero net input systems, but they could be sure that all organic systems with net inputs were excluded. 

Table 4 Effect of organic systems on carbon sequestration10 mostly for top soil and temperate zones. CI_LOW: 
confidence interval bottom. CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.  

Impact and metric 

Farm 
types 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

SOC concentration (g/kg) 

SOC stock (Mg C/ha) 

All farms 

1.2 

1.8 

2.4 

200 

Positive 

Zero net 
input 

0.4 

1.3 

2.2 

60 

Positive 

All farms 

2.4 

3.5 

4.6 

204 

Positive 

Zero net 
input 

0.52 

2.17 

3.81 

60 

Positive 

All farms 

0.24 

0.45 

0.66 

41 

Positive 

C sequestration rate (Mg C/ha/yr) 

Zero net 
input 

Source: Gattinger et al. 2012. 

-0.10 

0.27 

0.64 

19  Non-significant 

10 Results are provided for soil organic carbon concentration, for soil carbon stocks and carbon sequestration. SOC 

concentrations describe the organic carbon concentration on a weight by weight basis, SOC stocks on a weight by 
area basis, and C sequestration rates, on weight by area and elapsed time since conversion to organic. 

13 

 
 
                                                 
 
The selected synthesis paper (Gattinger et al. 2012) found significant differences and higher values 
r 1 

(systems with zero net input of organic matter, see previous paragraph), SOC stocks were 2.16 ± 1.65 
p < 0.01), and C sequestration rates were no longer significantly different from 
non-
r 1; P > 0.1). SOC concentration and stocks 
increase  with  time,  while  C  sequestration  rates  decrease  with  the  number  of  years.  Data  are  not 
provided in terms of % change, but considering typical SOC values in soils the absolute changes in 
SOC stocks are in the order of magnitude of 6-10% (Table 4). 

3.3.2.2  Organic cropping systems 

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis  papers  (Schievano  et  al.  2023),  the  overall  effect  is  positive.  Data  for  organic  cropping 
systems are extracted from the synthesis paper by Aguilera et al. 2013 (Table 5). Additional results 
from Smith et al. 2019 have been added as it differentiates SOC concentration from SOC stock and 
USA from European studies (Table 6)  

In  the  synthesis  paper  by  Aguilera  et  al.  2013  SOC  concentration  was  preferentially  chosen  for 
comparisons because it is considered to be a more direct measure of SOC, which is not influenced by 
soil volume and bulk density estimations. Shallow sampling was often the case in organic farming 
studies, where average sampling depth was just 19.2 cm. SOC increment in organic systems was 
greater  under  irrigation  than  under  rainfed  conditions  (25%  vs.  13%  increase  over  conventional, 
respectively). When analysed by crop type, the data shows that the best performing organic group is 
horticulture, where SOC is increased by 48%. Groups in Irrigation and Crop Type categories differed 
in the intensification of C input rate. Mean C inputs (additional C input over conventional) were 4.8 
ated and rainfed systems, respectively, and 6.1, 2.5 and 3.1 Mg C 

The  type  of  organic  input  employed  in  the  organic  system  also  influences  the  differences  found 
between  systems.  Compost,  either  applied  alone  or  in  combination  with  cover  crops,  is  the  input 
associated to highest increases in SOC (48% and 26.2% for compost alone and mixtures with cover 
d 
mixtures,  respectively).  Manure  application  obtains  poorer  results,  being  the  increase  in  C 
sequestration rate over conventional non-significant when this amendment is applied alone, although 
the  sample  size  is  lower  in  this  case  (9  paired  comparisons  versus  27  in  Compost  alone).  When 
manure is combined with cover crops, the increase is higher and significant (35.8% and 0.62 Mg C 

also studied in the general meta-analysis, where the number of studies was larger. Finally we have 
found some organic treatments where no organic inputs were applied, or at least no more than in 
their  conventional  counterpart.  These  treatments  were  the  only  ones  with  a  lower  SOC  level  than 
conventional, although the differences were not significant. The last variable studied was the type of 
experimental approach. The increase in SOC concentration and sequestration rate in organic plots was 
much more pronounced in plots of controlled expe

-significant). 

14 

 
 
 
Table 5 Effect of organic systems on carbon sequestration: change in SOC concentration or C stock and in C 
sequestration  rate.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of 
pairwise comparisons. 

Impact, metric and source 

Systems 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

All crops 

11.1% 

19.2% 

29.9% 

53 

Positive 

SOC concentration 
(or SOC stock if missing)  

Cereals 
rotation 

2.2% 

7.8% 

17.2% 

20 

Positive 

Source: Aguilera et al. 2013 

Horticulture 

26.7% 

48.3% 

74.5% 

Woody crops 

4.6% 

15.7% 

27.9% 

25 

35 

Positive 

Positive 

SOC concentration [%]  

Source: Smith et al. 2019 

SOC stock [kg C/ha] 

Source: Smith et al. 2019 

C sequestration rate  
(Change in Mg C/ha/yr) 
Source: Aguilera et al. 2013 

Source: references in the first column. 

All crops 

10.2 %  13.9 % 

17.3 % 

209 

Positive 

Annual 

12.7% 

16.2% 

20.9% 

138 

Positive 

Perennial 

-0.6% 

5.1% 

11.6% 

54  Non-significant 

All crops 

9.1 % 

11.6 % 

15 % 

209 

Positive 

Annual 

11.6% 

15% 

19.7% 

138 

Positive 

Perennial 

-1.7% 

24.6% 

8.5% 

54  Non-significant 

All crops 

0.54 

0.97 

1.48 

24 

Positive 

Cereals 
rotation 

0.30 

0.76 

1.44 

10 

Positive 

Horticulture 

0.46 

1.05 

Woody crops 

0.54 

0.92 

1.56 

1.58 

24 

10 

Positive 

Positive 

Interestingly, important differences have been found by Smith et al. 2019 between European and US 
studies (Table 6). USA studies showed higher effect (double) of organic farming on SOC concentration 
and SOC stock than the European ones.  

Table 6 Effect (% change) of organic systems on carbon sequestration by regions. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Impact, metric and source 

Region 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

SOC concentration [%]  

SOC stock [kg C/ha] 

Source: Smith et al. 2019 

All 

EU 

10.2 

13.9  

17.3  

209 

Positive 

4.8 

9.4 

13.9 

108 

Positive 

USA 

17.3 

24.6 

32.3 

47 

Positive 

All 

EU 

9.1  

5.3 

9.4 

11.6  

15  

209 

108 

47 

Positive 

Positive 

Positive 

13.9 

27.1 

USA 

13.9 

20.9 

15 

 
 
 
3.3.3  Effects on nutrient leaching and run-off 

The selected synthesis paper is the one by Mondelaers et al. 2009. Mean response ratios showed that 
nitrogen leaching per unit of area was 32% lower from organic farming compared to conventional 
farming (95% CI: -47%, -13%). These values are rather in line with those for arable land (cropping 
systems) (-35%; 95% CI: -54%, -8%) while for mixed livestock systems the effect on N leaching is 
not significant (Table 7).  

Table 7 Effect of organic systems on nitrogen leaching (% change of kg N per hectare).  CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top. 

N leaching 

Systems 

CI_LOW 

All 

Mixed livestock 
(without arable) 

Arable 

-47 

-55 

-54 

Mean 

-32.1 

-30.5 

-34.8 

CI_HIGH 

-13 

+8 

-8 

Effect 

Positive 

Non-significant 

Positive 

Source: Mondelaers et al. 2009. 

3.4  Low-ammonia emission techniques for mineral fertilisation  

Low-ammonia-emission techniques are fertilisation techniques used to reduce agricultural emissions 
of  ammonia  from  mineral  fertilisers.  They  include  several  practices  that  have  been  analysed 
separately, namely: 

─  Deep  placement:  the  fertiliser  is  placed  into  the  soil  at  greater  depth  than  in  conventional 

application. 

─ 

Irrigation after fertilisation. 

─  Split application: the total  amount  of fertiliser is applied through multiple  applications in time 

(usually 2 or 3). 

─  Use of specific amendments with sorbents (such as pyrite or zeolites) added to the applied 

fertiliser, to reduce NH3 volatilisation. 

─  Use of non-urea based fertilisers, like ammonium nitrate and ammonium sulphate.  

─  Leaving crop residues on soil. 

3.4.1  Effects on ammonia emissions  

The selected paper is Ti et al. 2019. However, we also include Pan et al. 2016 which includes all the 
techniques, comprising also use of sorbents and crop residues left on the soil (Table 8). 

Most of the mitigation strategies studied by Ti et al. 2019 were effective in reducing NH3 emission. 
For organic fertilisers, deep manure placement has the highest mitigation potential relative to other 
mitigation strategies, with reduction range of 93.8 to 99.7% relative to the control. Regarding mineral 
fertilisers, the fertiliser type and deep placement application are the most effective for reducing NH3 
emission.  The  application  of  the  following  chemical  fertilisers:  ammonium  nitrate,  ammonium 

16 

 
 
 
 
sulphate, urea phosphate, calcium ammonium nitrate, monoammonium phosphate, and diammonium 
phosphate  could  significantly  reduce  NH3  emission  by  88.3,  82.9,  76.2,  67.3,  51.9,  and  51.2%, 
respectively, compared with the use of urea. Deep placement of mineral fertilisers could reduce NH3 
emission by 48.0%. Irrigation right after fertilisation significantly reduced NH3 emission by 38.8%. 
Split application of fertilisers has no significant reduction effect according to Ti et al. 2019. Among 
crop types, significant reductions through fertiliser management (deep placement, split application 
and irrigation) were achieved in wheat and maize. 

Table 8 Effect (% change) of low-ammonia emission techniques on ammonia emissions. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top. 

Specific practice 

Subpractice or factor 

Overall for deep placement, 
split application & irrigation 
Source: Ti et al. 2019 

Deep placement 
Source: Ti et al. 2019 

Deep placement 
Source: Pan et al. 2016 

Irrigation after fertilisation  
Source: Ti et al. 2019 

Irrigation after fertilisation  
Source: Pan et al. 2016 

Use of sorbents 
Source: Pan et al. 2016 

Overall 

pastures 

rice  

maize 

wheat 

Overall 

Overall 

Overall 

Overall 

Overall 

zeolite 

pyrite 

CI_LO
W 

Mean  CI_HIGH 

Effect 

-46.8 

-39.8 

-31.4 

Positive 

-40.2 

-13.9 

-42.0 

-19.7 

-46.8 

-28.5 

25.3 

11.4 

-2.9 

-71.3 

-61.8 

-48.3 

Non-significant 

Non-significant 

Positive 

Positive 

-57.4 

-48.3 

-36.6 

Positive 

-66 

-54.7 

-41 

Positive 

-49.0 

-39 

-26 

Positive 

-50 

-34.5 

-17.3 

Positive 

-41.6 

-31.4 

-20.6 

Positive 

-59.0 

-43.5 

-26.1 

Positive 

-31.6 

-21.0 

-9.8 

Positive 

Use of non-urea fertilisers 
Source: Ti et al. 2019 

Use of non-urea fertilisers 
Source: Pan et al. 2016 

organic acid 

-20.8 

-15.9 

-11.0 

Positive 

ammonium nitrate 

-91.3 

-88.3 

ammonium sulphate 

-86.8 

-82.9 

urea phosphate 

-89.6 

-76.2 

-83.9 

-78.2 

-43.3 

Positive 

Positive 

Positive 

calcium ammonium 
nitrate 

monoammonium 
phosphate 

-81.0 

-67.3 

-41.6 

Positive 

-76.5 

-51.9 

-1.0 

Positive 

diammonium phosphate 

-68.5 

-51.2 

-23.9 

Positive 

urea ammonium nitrate 

ammonium bicarbonate 

-58.2 

-26.8 

1.2 

145.8 

Non-significant 

84.1 

364.7 

Non-significant 

Overall (17 types) 

-70.0 

-63.5 

-55.5 

Positive 

non-urea based 
fertilisers 

-80.6 

-74.5 

-67.1 

Positive 

17 

 
 
 
Specific practice 

Subpractice or factor 

urea-containing mixed 
fertilisers 

CI_LO
W 

Mean  CI_HIGH 

Effect 

-42.9 

-30.8 

-15.8 

Positive 

ammonium nitrate 

-94.2 

-87.9 

ammonium sulphate 

-86.5 

-78.8 

other 8 types 

other 4 types 

other 3 types 

- 

- 

- 

- 

- 

- 

-76.8 

-69.0 

- 

- 

- 

Positive 

Positive 

Positive 

Non-significant 

Negative 

Overall 

Overall 

4 times 

3 times 

Overall 

-34.0 

-1.6 

47.3 

Non-significant 

-20.6 

-32.3 

-12.9 

7.4 

11.3 

2.6 

33.5 

46.1 

21.9 

Non-significant 

Non-significant 

Non-significant 

6.7 

25.5 

50 

Negative 

Split application 
Source: Ti et al. 2019 

Split application 
Source: Pan et al. 2016 

Crop residues left on soils 
Source: Pan et al. 2016 

 Source: references in the first column. 

Pan  et  al.  2016  found that,  when  compared  to  urea  fertilisers, the  application  of  non-urea  based 
fertilisers and urea-containing mixed fertilisers significantly decreased NH3 volatilization by 74.5% 
and  30.8%,  respectively,  with  an  overall  reduction  of  63.5%.  Ammonium  nitrate  and  ammonium 
sulphate were the two most effective non-urea fertilisers in reducing NH3 volatilization (by 87.9% 
and 78.8%, respectively) relative to urea application. Deep placement significantly decreased NH3 
volatilization  through  incorporation  of  fertilisers  by 54.7%  when  compared  to  surface  application. 
Split applications of N fertiliser did not affect NH3 volatilization, regardless of splitting frequency. 
Irrigation significantly decreased NH3 volatilization by 34.5% compared to rainfed or supplementary 
(minimal) irrigation.  

Soil cover with crop residues significantly increased NH3 volatilization by 25.5%. Amendments with 
sorbent materials significantly reduced NH3 volatilization by 31.4%. In particular, zeolite decreased 
NH3 volatilization by 43.5%, followed by pyrite (20.9%) and organic acid (15.9%) when applied with 
fertilisers. 

3.4.2  Effects on the nutrient balance 

The selected synthesis paper (L. Xia et al. 2017) conducted a comprehensive meta-analysis for staple 
grain (rice, wheat, and maize) production in China. The authors assessed the responses to knowledge-
based N management practice of crop productivity (yield; NUE; aboveground N uptake), N2O emission 
and major Nr losses (NH3 emission, N leaching and runoff), and economic indicators (input cost, yield 
profit, and NEB). They focused on seven knowledge-based N management practices, including the 
applications of several  enhanced efficiency fertilisers techniques, increasing splitting frequency of 
fertiliser N application, deep placement of N  fertiliser, lower basal N fertiliser (BF) proportion, and 
optimal N rate based on soil N test. 

The  aboveground  N  uptake  was  significantly  increased  by  5.1%  increasing  splitting  frequency  of 
fertiliser N application. Acco

Table  9  it  can  be 

18 

 
 
observed that values for the different crops are rather similar, but not the values for soil pH: there is 
a positive effect for acidic and neutral soils (pH<8) but non-significant effect for alkaline soils.  

The paper by L. Xia et al. 2017 also provides results for nitrogen use efficiency (NUE) which they 

treatments with and without fertilisation by fertiliser 
authors have defined as fertiliser recovery (NR, see Annex 1).  

For deep placement, the selected paper for data extraction (Nkebiwe et al. 2016) provides results for 
nitrogen, phosphorus and potassium uptake and for all nutrients globally. We also report the results 
by L. Xia et al. 2017 for comparison with split application and because they provide an overall value 
for nitrogen fertilisation.  

Table  9  Effect  (%  change)  of  low-ammonia  emission  fertiliser  techniques  on  nutrient  uptake  and  nutrient 
recovery (NR). CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. 

Specific 
practice and 
source 

Split application 
or increased 
splitting 
frequency 

Source: L. Xia et 
al. 2017 

Factor and metric 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

Overall- uptake 

Overall- NUE (NR) 

Rice - uptake 

Wheat - uptake 

Maize - uptake 

Rice   NUE (NR) 

Wheat   NUE (NR) 

Maize   NUE (NR) 

- uptake 

6<pH<8 - uptake 

- uptake 

Overall above-
ground biomass 
content 

3.5 

20.5 

2.7 

1.0 

3.9 

25.4 

-3.9 

19.8 

5 

1.7 

-1.9 

5.1 

30 

5.6 

3.9 

6.3 

46.1 

10.2 

32.0 

7.3 

5.5 

1 

6.8 

41.5 

8.2 

7.1 

9.0 

75.9 

27.2 

46.6 

9.7 

9.4 

4 

52 

77 

11 

19 

22 

20 

16 

41 

10 

10 

11 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Non-significant 

Positive 

Positive 

Positive 

Non-significant 

10 

12 

14 

245 

Positive 

Nutrient (N, P, K 
& S) deep 
placement 

Source: 
Nkebiwe et al. 
2016 

Rapeseed 
(maximum) 

Maize 

Winter wheat 

Soybean 

Mixed grass species 

Winter rye 
(minimum) 

30 

9 

3 

1 

0 

36 

12 

7 

2 

0 

-9 

-3 

19 

43 

16 

11 

4 

0 

1 

36 

112 

57 

2 

3 

Positive 

Positive 

Positive 

Positive 

Non-significant 

16 

Non-significant 

 
 
 
 
Specific 
practice and 
source 

Nitrogen deep 
placement 

Source: 
Nkebiwe et al. 
2016 

Nitrogen deep 
placement 

Source: L. Xia et 
al. 2017 

Phosphorus 
deep placement 
vs. broadcast 

Source: 
Nkebiwe et al. 
2016 

Factor and metric 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

Overall - above-
ground biomass 
content 

Depth > 10 cm 

Depth 5-10 cm 

Depth 0 cm 

Overall- uptake 

Overall- NUE (NR) 

Rice - uptake 

Wheat - uptake 

Maize - uptake 

Rice   NUE (NR) 

Wheat   NUE (NR) 

Maize   NUE (NR) 

- uptake 

6<pH<8 - uptake 

- uptake 

Overall- above-
ground biomass 
content 

Depth > 10 cm 

Depth 5-10 cm 

Depth 0 cm 

NA 

NA 

NA 

NA 

Not available 

6 

4 

7 

4.8 

11.2 

4.7 

2.0 

2.0 

-4.3 

8.9 

9.4 

13.6 

3.9 

-0.7 

11 

4 

9 

8.3 

28.5 

13.4 

6.7 

6.4 

5.5 

23.0 

42.6 

26.2 

8 

2.9 

17 

10 

12 

12.2 

48.6 

24.9 

11.4 

10.9 

16.8 

34.7 

82.8 

39 

12.4 

6.6 

47 

80 

14 

39 

26 

10 

14 

15 

5 

6 

15 

6 

16 

17 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Non-significant 

Positive 

Positive 

Positive 

Positive 

Non-significant 

NA 

NA 

NA 

Not available 

5 

11 

4 

9 

20 

5 

12 

31 

6 

27 

25 

5 

Positive 

Positive 

Positive 

Source: references in the first column. 

According to Nkebiwe et al. 2016, the overall deep placement effect on crop uptake (above-ground 
biomass) content of all nutrients (NPK and S) was 11.9% (95% CI: 9.7,14.5; Nc= 245). Results are 
also split by nutrient type, crop, crop growth stage, fertiliser placement methods, fertiliser type and 
placement depth. By crop type only, relative placement effect on above-ground biomass nutrient (NPK 
and S) content for different crop species were in the following order: rapeseed (36.4%; 95% CI: 30.2%, 
43.3%; Nc= 36); turnip rape (30.3%; 95% CI: 28.1%, 32.9%; Nc= 2); sorghum(17.7%; 95% CI: 10.8%, 
26.4%; Nc= 12); maize (12.2%; 95% CI: 8.7%,16.1%; Nc= 112); cauliflower (12.2%; 95% CI: 9.1%
15.4%; Nc= 2); winter wheat(7.2%; 95% CI: 3.5% 11.1%; Nc= 57); soybean (2.2%; 95% CI: 0.6%
4.1%;  Nc=  2);  mixed  grass  species  (0.0%;  95%  CI:  0.0% 0.0%;  Nc=  3);  l
%, 0.8%; Nc= 16).  

17.4%, 13.2%; Nc= 2); w

; 95% CI: 

During vegetative growth, relative placement effect on nutrient (NPK and S) content in above-ground 
biomass showed the following decreasing trend according to placement depth: >10 cm (24.9%; 95% 
; 95% 
CI: 16.8%, 33.8%; Nc= 36); 5 10 cm (23.4%; 95% CI: 16.2%,32.5%; Nc= 51); s

20 

 
 
 
 
%,  6.3%;  Nc=  4)  as  compared  with  fertiliser  broadcast.  According  to  fertilizer  type  and 
CI: 
placement depth, there was also a tendency for the effect on uptake of N, P and K to increase with 
increasing placement depth (see Table 9).  

Nkebiwe et al. 2016 also analysed differences by: 

─  crop type and development stage vegetative and generative stages) 

─  eight  fertiliser  placement  methods  (e.g.  subsurface  shallow  band,  subsurface  deep  band, 

subsurface shallow point injection, subsurface deep point injection, etc.) 

─  fertiliser  type  (placement  of  a  combination  of  ammonium  or  urea  with  soluble  P  showed  a 
tendency  to  lead  to  stronger  effect  on  N  or  P  uptake  than  placement  of  ammonium,  urea  or 
soluble P not combined). 

3.4.3  Effects on nutrient leaching and run-off 

The selected synthesis paper (L. Xia et al. 2017) showed reduced N leaching and run-off for both 
deep  fertiliser  placement  compared  to  superficial  placement  and  for  split  application  techniques 
compared to single application. However, no evidence from other low-ammonia techniques other than 
from deep placement and split application has been found (Table 10). 

L. Xia et al. 2017 conducted a comprehensive meta-analysis for staple grain (rice, wheat, and maize) 
production in China. Their results indicate that increasing splitting frequency of fertiliser N application 
significantly reduced N leaching by 24.7% (95% CI: -46%, -3%) and N run-off by 36.5% (95% CI: -
46%-26%). Deep placement also significantly decreased N run-off by 15.5% (95% CI: -25%, -9.5%) 
while no values are available for N leaching. 

A stronger reduction in N runoff was shown in rice (45.7%) than wheat (24.5%) for increasing splitting 
frequency of fertiliser N application. Responses of N leaching and runoff were in general similar for 
different soil properties. 

Table  10  Effect  (%  change)  of  low-ammonia  emission  fertiliser  techniques  on  NO3-  leaching.  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. 

Specific practice 

Metric 

CI_LOW  Mean 

CI_HIGH 

Effect 

N leaching  all crops 

-46.2 

-24.7 

-3.0 

Split application or 
increased splitting 
frequency 

N runoff   all crops 

-45.9 

-36.5 

-26.1 

N runoff - rice 

-57.3 

-45.7 

-30.3 

N runoff - wheat 

-28.3 

-24.5 

-19.2 

N runoff - maize 

-57.2 

-37.5 

-10.6 

Positive 

Positive 

Positive 

Positive 

Positive 

Deep placement 

N leaching   all crops 

Source: : L. Xia et al. 2017 

N runoff   all crops 

NA 

-25 

NA 

-15.5 

NA 

-9.5 

Not available 

Positive 

21 

 
 
 
 
 
3.5  Enhanced Efficiency Fertilisers  

This  set  of  practices  comprises  different  specific  techniques  aimed  to  increase  the  nitrogen  use 
efficiency, i.e. the ratio of N uptake by crops over total N input (either mineral or organic). This can be 
achieved  by  slowing  down  the  rate  of  N  release  from  conventional  fertilisers  or  by  delaying  the 
chemical and biological N transformation processes occurring in the soil leading to N volatilization in 
the form of ammonia and/or nitrous dioxide (N2O) and N leaching and runoff. The specific techniques 
included in this review are:  

─  controlled-release  fertilisers,  which  use  partially  permeable  coating  material  or  encapsulating 

material to control N release.  

─  urease inhibitors, which delay urea hydrolysis thus lowering NH3 emission potential.  

─  nitrification inhibitors, which reduce the activities of nitrifying bacteria to decrease NO3 leaching 

and N2O emissions 

─  double inhibitors, combining nitrification inhibitors and urease inhibitors, they lower both NH3 and 

NO3/N2O losses.  

3.5.1  Effects on greenhouse gas emissions  

Enhanced efficiency fertilisers have an effect on N losses, therefore this practice is relevant for N2O 
emissions. The selected source of data is the synthesis paper by T. Li et al. 2018. The assessed metric 
is N2O emissions per hectare and year in production systems of major crops. Major crops or crop 
groups  considered  are:  pastures,  rice,  and  drylands,  which  include  vegetables,  maize  and  wheat. 
Results are summarised in Table 11. 

For controlled-release fertilisers (polymer-coated fertilisers), the mean effect on N2O emissions is 
close to -74% for grasslands and paddy rice areas, while it is much lower for drylands (-23%; 95% 
CI: -32%, -13%).  

For urease inhibitors, the synthesis paper (T. Li et al. 2018) found a reducing effect of -37% (95% CI: 
-59%,  -7%) for drylands only (wheat, maize, vegetables). Additional research is needed to further 
clarify or validate the effect of urease inhibitors on N2O emissions. Although not showed in the table, 
Fan et al. 2018 found a mean value for all soils of -24% (95% CI: -33%, -14%; Nc=65). However, 
they found significant differences depending on the soil pH, with a positive effect for alkaline soils (-
41%; 95% CI: -52%, -27%; Nc=43)  but  not significant effect  for acid soils (-12%; 95% CI: -24%, 
0.1%; Nc=22). This can be in relation with the not significant effect on grasslands, which in Europe 
are often located on acid soils. 

For nitrification inhibitors, there is a mean effect of almost -60% effect for grasslands and drylands. 
The  effect  is  lower  for  paddies  (-33%;  95%  CI:  -43%,  -23%).  Interestingly,  the  reduction  of  N2O 
emission  by  nitrification  inhibitors  was  more  important  with  higher  baseline  N2O  emission  (i.e., 
emissions from the conventional fertiliser treatment). For example, the average N2O reduction was 
67% for situations where baseline emissions exceeded 20 kg N/ha, compared to a 50% reduction for 
baseline  emissions  of  <1  kg  N/ha.  Differences  were  also  observed  between  different  types  of 
nitrification inhibitors used: from a mean decrease of -22.4% for Neem to a mean of -62.6% for 3,4-
Dimethylpyrazole phosphate (DMPP). 

For double inhibitors, a clear positive effect of approximately -50% reduction (95% CI: -55%,-41 and 
-44%) is observed for grassland and drylands.  

22 

 
 
Table 11 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification inhibitors, and double 
inhibitors on N2O emissions for major crop groups. CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top. 

Specific practice 

CI_LOW  Mean  CI_HIGH 

Effect 

Overall 

Grasslands 

-46.6 

-37.5 

-27.1 

-86.2 

-73.5 

-8.6 

Drylands (Wheat, maize, vegetables) 

-40.2 

-31 

-20.8 

Controlled-
release 
fertiliser 

Urease 
inhibitors 

Nitrification 
inhibitors 

Paddies 

Overall 

Grasslands 

Overall 

Grasslands 

Paddies 

Overall 

Grasslands 

Double 
Inhibitors 

Drylands (Wheat, maize, vegetables) 

-59.3 

-37.2 

-7.1 

Drylands (Wheat, maize, vegetables) 

-64 

-57.7 

-48.4 

Positive 

-86.6 

-74.5 

-51.0 

-39.9 

-20.6 

+1.1 

Non-significant 

-11.4 

+3.9 

+33.6 

Non-significant 

-60 

-56.6 

-52.8 

-61.7 

-58.2 

-54.5 

Positive 

-42.9 

-33 

-22.9 

Positive 

-53.5 

-48.8 

-43.7 

-55.3 

-50.1 

-43.8 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Drylands (Wheat, maize, vegetables) 

-55.5 

-48.8 

-41.2 

 Source: T. Li et al. 2018. 

3.5.2  Effects on ammonia emissions  

The source of data is the synthesis paper by Ti et al. 2019 for the first three techniques and T. Li et 
al. 2018 for double inhibitors. The assessed metric is ammonia (NH3) emission per hectare and year 
in production systems of major crops. Major crops or crop groups considered are: vegetables, pastures, 
rice, maize, wheat, and the aggregate of these ones for the first three techniques; and three main 
land  uses  (grasslands,  drylands  and  paddy)  for  double  inhibitors.  Extracted  data  are  presented  in 
Table 12.  

Table 12 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification inhibitors, and double 
inhibitors on NH3 emissions for major crop groups. CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top. 

Specific practice 

CI_LOW  Mean 

CI_HIGH 

Controlled-
release 
fertilisers 

Source: Ti 
et al. 2019 

Overall 

vegetables 

pastures 

rice  

maize 

wheat 

-58.0 

-53.1 

-60.6 

-43.9 

-65.8 

-53.3 

-76.0 

-63.2 

-60.6 

-43.1 

-47.8 

-19.5 

-34.9 

-43.5 

-16.5 

Effect 

Positive 

Positive 

Positive 

Positive 

Positive 

-80.3 

-33.2 

124.3 

Non-significant 

23 

 
 
Overall 

vegetables 

pastures 

rice  

maize 

wheat 

Overall 

Urease 
Inhibitors 

Source: Ti 
et al. 2019 

Nitrification 
inhibitors 

Source: Ti 
et al. 2019 

Double 
Inhibitors 

Source: T. 
Li et al. 
2018 

Specific practice 

CI_LOW  Mean 

CI_HIGH 

Effect 

Positive 

-63.3 

-56.80 

-48.8 

-99.3 

-51.9 

65.1 

Non-significant 

-86.5 

-75.8 

-79.9 

-72.7 

-55.7 

-37.7 

-56.1 

-60.6 

-11.8 

Positive 

Positive 

Positive 

-58.8 

-33.2 

9.0 

Non-significant 

16.5 

41.7 

77.4 

Negative 

Individual crop types/groups 

- 

- 

- 

Non-significant 

Overall 

Grasslands 

-64.1 

-52.7 

-48.1 

-36.8 

Drylands (wheat, maize, vegetables) 

-87.2 

-69.9 

-40.4 

-24.2 

-36.7 

Paddies 

-85.3 

-75.5 

-58.8 

Positive 

Positive 

Positive 

Positive 

 Source: Ti et al. 2019 and T. Li et al. 2018. 

For controlled-release fertilisers, the mean effect on NH3 emissions is a reduction of -53% (95% CI: 
-58.0%, -47.8%), when examining all crop groups together. This overall positive effect holds true for 
all examined crop groups when examined separately, with the exception of wheat for which a non-
significant effect was found.  

For urease inhibitors, the mean effect of NH3 emission is -56.8% (95% CI: -63.3%, -48.8%) when 
examining all crop groups together. This overall positive effect holds true for all crop groups when 
examined  separately,  except vegetables  and  wheat, for  which  a  non-significant  effect  was  found. 
Another  synthesis  paper  (T.  Li  et  al.  2018)  found  that  the  higher  the  baseline  NH3  emission,  the 
greater the percentage reduction by urease inhibitors. Additionally, areas with annual rainfall <800 
mm benefitted more from urease inhibitors, with a 70% reduction in NH3 emission, compared to a 
50%  reduction in  areas  with  higher  annual  rainfall. This is  consistent  with  the  aggregated  results 
showing  a  stronger  NH3  loss  reduction  effect  of  urease  inhibitors  on  in  rainfed  than  in  irrigated 
systems (T. Li et al. 2018). 

For nitrification inhibitors, results are significantly different from zero only when the overall effect is 
considered (i.e., when examining all crops together). In this  case, the effect is negative, as results 
indicate an average increase of ammonia emission of 41.7% (95% CI: 16.5% 77.4%). Results per 
crop groups are based on a limited number of studies so they are not shown.  

For double inhibitors, a clear positive effect for all examined land use groups is reported. In particular, 
in drylands the mean effect size is -70% (95% CI: -87.3%, -36.7%).  

In Europe, nitrogen losses as NH3 from fertilisers application are estimated to be on average 17.8 kg 
of N/ha (median), corresponding to circa 13% of the total applied N (Pan et al. 2016). Applying this 
baseline  value,  indicative  absolute  values  of  potential  reduction/increase  of  NH3  emissions  from 
fertiliser application due to the use of each technique is: 

─  Controlled-release fertilisers: -9.5 Kg N/ha/yr (95% CI: -10.3  8.5) 

24 

 
 
─  Urease inhibitors: -10.1 Kg N/ha/yr (95% CI: -11.3  8.7) 

─  Nitrification inhibitors: +7.4 Kg N/ha/yr (95% CI: 2.9  13.8) 

─  Double inhibitors (in drylands): -12.5 Kg N/ha/yr (95% CI: -15.2  -6.5) 

3.5.3  Effects on the nutrient balance 

Selected papers for data extraction presented in Table 13 and Table 14 are:  

─  L. Xia et al. 2017 for controlled-release fertilisers and urease inhibitors; 

─  Yang et al. 2016 and Sha et al. 2020 for nitrification inhibitors but as Yang et al. 2016 provide 
results for N uptake only for Dicyandiamide (DCD) and DMPP, we selected the overall value by 
Qiao et al. 2015.  

─  For double inhibitors no values on N uptake are available. The best paper among the two providing 

results on N fertiliser recovery (NR, see Annex 1) is Sha et al. 2020. 

Table 13 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification inhibitors and double 
inhibitors  on  N  use  efficiency  (crop  N fertiliser  recovery)  or N  plant  uptake, for  major  crop  groups.  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. 

Technique and 
source 

Crop or 
subtechnique 

Metric 

CI_LOW  Mean 

CI_HIGH 

Effect 

Controlled-release 
fertilisers  

Source: L. Xia et al. 
2017 

Overall  

Overall 

Rice  

Wheat 

Maize 

Rice 

Wheat 

Maize 

Overall 

Uptake 

9.0 

NUE (NR) 

29.6 

Uptake 

Uptake 

Uptake 

NUE (NR) 

NUE (NR) 

NUE (NR) 

7.3 

7.4 

7.9 

29.5 

28.1 

22.6 

NUE (NR) 

3.7 

Controlled-release 
fertilisers (polymer 
coated fertilisers) 

Source: T. Li et al. 
2018 

Maize & wheat 

NUE (NR) 

-10.5 

Vegetables 

NUE (NR) 

3.7 

Grasslands 

NUE (NR) 

-23.3 

Paddy 

NUE (NR) 

15.6 

10.9 

34.3 

11.4 

10.6 

10.7 

34.3 

36.3 

29.9 

8.7 

-2.1 

11.2 

-4.8 

26.4 

12.8 

39.5 

15.6 

13.8 

14 

39.4 

44.8 

37.5 

13.9 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

6.4 

Non-significant 

18.9 

16.3 

38.7 

Positive 

Non-significant 

Positive 

Urease inhibitors 

Source: L. Xia et al. 
2017 

Overall- NUE 
(NR) 

Rice 

Wheat 

Rice 

Wheat 

Maize 

NUE (NR) 

22 

31.3 

42.0 

Positive 

Uptake 

Uptake 

NUE (NR) 

NUE (NR) 

NUE (NR) 

3.2 

0.3 

-1.9 

15.9 

24.7 

5.8 

6.6 

7.1 

25.3 

39.6 

8.5 

12.8 

16.4 

39.5 

56.6 

Positive 

Positive 

Non-significant 

Positive 

Positive 

25 

 
 
Technique and 
source 

Crop or 
subtechnique 

Metric 

CI_LOW  Mean 

CI_HIGH 

Effect 

NUE (NR) 

18.7 

24.1 

29.8 

Positive 

Urease inhibitors 

Source: Sha et al. 
2020 

Urease inhibitors 

Source: T. Li et al. 
2018   very much 
dependent on 
inhibitor type 

Nitrification 
inhibitors 

Source: Qiao et al. 
2015 

Nitrification 
inhibitors 

Source: Sha et al. 
2020 

Nitrification 
inhibitors 

Source: T. Li et al. 
2018   depends a lot 
on inhibitor type 

Double Inhibitors 
Source: Sha et al. 
2020 

Overall 

Dryland 

Paddy 

Overall 

Paddy 

Overall 

Overall 

Cropland 

Cropland 

Pasture 

Pasture 

Overall 

Dryland 

Paddy 

Overall 

Paddy 

Overall 

Dryland 

Paddy 

Overall 

maize & wheat 

NUE (NR) 

Vegetables 

NUE (NR) 

Grasslands 

NUE (NR) 

NUE (NR) 

NUE (NR) 

NUE (NR) 

10.7 

24.5 

13.1 

4.9 

NA 

9.5 

9.3 

11 

34 

12 

22 

6 

65 

4 

NUE (NR) 

Uptake 

NUE (NR) 

Uptake 

NUE (NR) 

Uptake 

NUE (NR) 

NUE (NR) 

NUE (NR) 

-3.6 

NUE (NR) 

18.8 

NUE (NR) 

15.4 

Maize & wheat 

NUE (NR) 

-3.9 

Vegetables 

NUE (NR) 

-11.6 

Grasslands 

NUE (NR) 

22.5 

NUE (NR) 

NUE (NR) 

NUE (NR) 

NUE (NR) 

4.3 

29 

NA 

NA 

NUE (NR) 

15.3 

22.1 

16.5 

34.9 

20.1 

14.3 

NA 

17.9 

28.7 

15 

58 

18 

33 

12 

297 

10.5 

2.7 

33.2 

31.7 

12.7 

-2.1 

48.4 

11.1 

47.6 

NA 

NA 

Double Inhibitors 
Source: T. Li et al. 
2018 

Maize & wheat 

NUE (NR) 

Vegetables 

NUE (NR) 

Grasslands 

NUE (NR) 

Paddies 

NUE (NR) 

-4 

NA 

26.7 

18.9 

2.8 

NA 

32.7 

43.3 

Source: references in the first column. 

21.7 

45.2 

28.1 

28.4 

NA 

26.7 

53.6 

20 

93 

25 

46 

19 

923 

18 

Positive 

Positive 

Positive 

Positive 

Not available 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

10.2 

Non-significant 

52.1 

53.9 

41.9 

Positive 

Positive 

Non-significant 

8.3 

Non-significant 

86.1 

19.5 

75.4 

NA 

NA 

29.6 

10.2 

NA 

39.9 

76.3 

Positive 

Positive 

Positive 

Not available 

Not available 

Positive 

Non-significant 

Not available 

Positive 

Positive 

Controlled-release  fertilisers  increase  nitrogen  uptake  by  11%  (95%  CI:  9%,  13%);  N-(n-butyl) 
thiophosphoric triamide (NBPT) urease inhibitor by 6% (95% CI: 3.5%, 8.5%); nitrification inhibitors 
by 15% (95% CI: 11%, 20%); there were no results for double inhibitors. 

Given that some results depend greatly on the type of urease inhibitor used, we also provide results 
by inhibitor type (Table 14). For urease inhibitors, we only have a mean value for uptake from L. Xia 

26 

 
 
et al. 2017 of +6% for (NBTP) but values for  NR can vary between +30% of NBTP and +14% of 
hydroquinone (HQ) according to Sha et al. 2020.  

For nitrification inhibitors, the mean value has been found between +11 and +18% for DCD, while it 
is very low and non-significant for DMPP (T. Li et al. 2018; Qiao et al. 2015; Yang et al. 2016.) For 
double inhibitors there was not information on N uptake, but T. Li et al. 2018 repot high differences 
in  NUE(NR),  from  19%  (95%CI:  12%,  26%)  for  DCD+NBPT  to  62%  (95%CI:  32%,  97%)  for  other 
products.  

Table 14 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification inhibitors and double 
inhibitors on N use efficiency or N plant uptake for different inhibitor types. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top. 

Technique / source 

Subtechnique 

Metric 

CI_LOW  Mean  CI_HIGH 

Effect 

Controlled-release 
fertilisers  
Source: L. Xia et al. 
2017 

Controlled-release 
fertilisers  
Source: T. Li et al. 
2018 

Urease inhibitors 
Source: L. Xia et al. 
2017 

Urease inhibitors 
Source: Sha et al. 
2020 

Urease inhibitors 
Source: T. Li et al. 
2018   depends a lot 
on inhibitor type 

Nitrification inhibitors 
Source: Yang et al. 
2016 

Nitrification inhibitors 
Source: Qiao et al. 
2015 

NBTP 

HQ 

Overall 

NBPT 

PPD 

Limus 

DCD 

DMPP 

Overall 

Overall 

DCD 

DCD 

DMPP 

DMPP 

Overall 

Overall 

Overall (polymer 
coated fertiliser) 

NBPT 

Overall 

NBPT 

HQ 

Overall 

Phenyl-
phosphoryl-
diamine (PPD)  

Uptake 

9.0 

10.9 

12.8 

Positive 

NUE (NR) 

29.6 

34.3 

39.5 

Positive 

NUE (NR) 

3.7 

8.7 

13.9 

Positive 

Uptake 

NUE (NR) 

3.4 

22 

NUE (NR) 

29.3 

NUE (NR) 

5.7 

NUE (NR) 

18.7 

5.9 

31.3 

41.6 

15.7 

24.1 

8.5 

42.0 

55.4 

29.7 

29.8 

Positive 

Positive 

Positive 

Positive 

Positive 

NUE (NR) 

11.6 

20.5 

30.0 

Positive 

NUE (NR) 

22.2 

NUE (NR) 

NUE (NR) 

NUE (NR) 

5.3 

13.1 

10.3 

NUE (NR) 

-16.7 

NUE (NR) 

Uptake 

6.2 

8.2 

30.3 

14.2 

20.1 

17.2 

0.1 

20.9 

39.5 

26.8 

28.1 

24.7 

20.4 

40.5 

Positive 

Positive 

Positive 

Positive 

Non-significant 

Positive 

18.1 

19.1 

Positive 

Uptake 

-1.9 

1.9 

Uptake 

NUE (NR) 

Uptake 

NUE (NR) 

Uptake 

11 

34 

9 

25 

-5 

15 

58 

16 

90 

6 

6.5 

20 

93 

23 

226 

19 

NUE (NR) 

1949 

2611 

3487 

Non-significant 

Positive 

Positive 

Positive 

Positive 

Non-significant 
Outlier values, 
(only 2 obs.) 
Not available 

Non-significant 

Nitrapyrin 

Nitrapyrin 

Uptake 

NUE (NR) 

NA 

-10 

NA 

7 

NA 

32 

27 

 
 
Technique / source 

Subtechnique 

Metric 

CI_LOW  Mean  CI_HIGH 

Effect 

Organic NI 

Organic NI 

Overall 

DCD 

DMPP 

Nitrapyrin 

Overall 

DCD 

DMPP 

Else 

Uptake 

NUE (NR) 

NUE (NR) 

NUE (NR) 

NUE (NR) 

NUE (NR) 

NUE (NR) 

NUE (NR) 

NUE (NR) 

NUE (NR) 

16 

33 

4 

-1.4 

-4.1 

4.0 

15.4 

17.4 

-6.0 

4.6 

24 

50 

10.5 

11.4 

2.8 

13.8 

31.7 

34.4 

31.4 

12.2 

32 

70 

18 

26.6 

9.6 

28.4 

53.9 

59 

111 

20.7 

Positive 

Positive 

Positive 

Non-significant 

Non-significant 

Positive 

Positive 

Positive 

Non-significant 

Positive 

Overall 

NUE (NR) 

29 

47.6 

75.4 

Positive 

Overall 
DCD+NBPT 
Else 

NUE (NR) 
NUE (NR) 
NUE (NR) 

15.3 
12.3 
32.3 

22.1 
19 
61.9 

29.6 
26 
97.4 

Positive 
Positive 
Positive 

Nitrification inhibitors 
Source: Sha et al. 
2020 

Nitrification inhibitors 
Source: T. Li et al. 
2018  

Double Inhibitors 
Source: Sha et al. 
2020 

Double Inhibitors 
Source: T. Li et al. 
2018 

Source: references in the first column. 

3.5.4  Effects on nutrient leaching and run-off 

The selected synthesis paper for data extraction is T. Li et al. 2018 for all techniques. In the case of 
nitrification inhibitors, two synthesis papers are selected: Yang et al. 2016 and Qiao et al. 2015.  

Non-significant effect  on nitrate leaching was found for  urease inhibitors and  for double inhibitor 
overall, even though for double inhibitors a positive effect was found for grasslands and a negative 
one for cropland (dryland).  

Only two practices have proven effective to decrease NO3- leaching: controlled-release fertilisers by 
-66% (95 CI: -81%, -42%) and nitrification inhibitors by -57% (95% CI: -65%, -38% for DCD and -
63%, -52% for DMPP) (Table 15).  

Table 15 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification inhibitors and double 
inhibitors on NO3- leaching for major crop groups. CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top. 

Intervention and 
source 

Controlled-release 
fertilisers 

Source: T. Li et al. 
2018 

Urease inhibitors 

Source: T. Li et al. 
2018 

Factor 

CI_LOW 

Mean 

CI_HIGH 

Overall 

-81.3 

-65.9 

-41.8 

dryland 

-66.9 

-42.3 

paddy 

Overall 

dryland 

grasslands 

paddy 

-96.8 

-91.9 

-83.4 

-38.9 

-61.6 

-13.3 

NA 

NA 

-94.8 

NA 

28 

-20.2 

-83.6 

45.1 

19.3 

NA 

NA 

Effect 

Positive 

Positive 

Positive 

Non-significant 

Non-significant 

Non-significant 

Not available 

 
 
Intervention and 
source 

Factor 

CI_LOW 

Mean 

CI_HIGH 

Nitrification inhibitors 

Source: T. Li et al. 
2018 

Nitrification inhibitors 

Source: Yang et al. 
2016 

Overall   NO3- 

-50.4 

-44.5 

Dryland   NO3- 

-51.5 

-35.3 

Grasslands   NO3- 

-51.8 

-45.2 

Paddy   NO3- 

-58.3 

-52.3 

DCD   NO3- 

-64.8 

-56.9 

DMPP - NO3- 

-63.2 

-57.6 

DCD - DIN 

-47.7 

-38.0 

DMPP-DIN 

-53.0 

-46.9 

Overall   NO3- 

Overall - DIN 

Cropland   NO3- 

Pasture   NO3- 

Cropland - DIN 

Pasture - DIN 

DCD   NO3- 

DMPP - NO3- 

DCD - DIN 

DMPP-DIN 

Overall 

dryland 

-59 

-56 

-64 

-63 

-47 

-63 

-61 

-59 

-63 

-45 

-47 

-48 

-37 

-52 

-38 

-53 

-45 

-54 

-53 

-38 

-53.9 

-29.3 

17.9 

58 

grasslands 

-68.7 

-50.5 

Nitrification inhibitors 

Source: Qiao et al. 
2015 

Double inhibitors 
Source: T. Li et al. 
2018 

Source: references in the first column. 

3.6  Organic fertilisation 

-38.1 

-12.6 

-38.3 

-44.9 

-38.1 

-51.7 

-23.2 

-39.3 

-32 

-38 

+9 

-37 

-29 

-42 

-24 

-48 

-41 

-29 

4.2 

111.2 

-31.7 

Effect 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Non-significant 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Non-significant 

Negative 

Positive 

Organic fertilisation is the application to soils of plant or animal-derived materials containing organic 
forms of nutrients that soil microorganisms decompose, making them available for use by crops (FAO 
2009).  This  section  covers  the  application  of  organic  fertilisers,  alone  or  combined  with  mineral 
fertilisation,  from  different  animal  (cattle,  pig,  sheep,  poultry,  and  earthworms),  plant  and  mixed 
(municipal and agro-industrial waste) sources used both as composted and non-composted manures. 
The fertilisation with green manure or green manuring (organic fertilisation from crops grown on the 
same field as cover-crops) is addressed as a separate practice in section 3.7.  

To  assess  the  quantitative  effect  of  the  practice  on the  environment, the  used  comparator is  the 
application of mineral fertilisers alone.  

29 

 
 
 
3.6.1  Effects on greenhouse gas emissions 

The  selected  paper  for  data  extraction  is  Linquist  et  al.  2012,  that  reported  a  negative  effect  of 
organic fertilisation as compared to mineral fertilisation in rice systems. They found that farmyard 
manure forming all or part of the total N rate increased CH4 emissions by 26% (95% CI: 12%, 47%) 
when compared to a treatment receiving only urea N at the same total N rate. Wei et al. 2020 found 
a non-significant effect on CH4 emissions in maize systems (Table 16). 

Table  16  Effect (%  change) of organic  fertilisation as compared to mineral fertilisation on  GHG  emissions. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

GHG  Source 

Population  CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

CH4 

Linquist et al. 2012 

Rice 

Wei et al. 2020 

Maize 

12 

- 

26 

- 

47 

14 

Negative 

- 

- 

Non-significant 

Source: references in the second column. 

3.6.2  Effects on soil organic carbon 

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis  papers  (Schievano  et  al.  2023),  the  overall  effect  of  organic  fertilisation  compared  to 
mineral fertilisation is positive. The selected synthesis paper for data extraction in Table 17 is Y. Chen 
et al. 2018, a paper that covers a wide geographic spectrum (but includes in their analysis also green 
manure). 

The  authors  (Y.  Chen  et  al.  2018)  compare  the  effect  of  organic  amendments  (OA)  and  organic 
amendments with inorganic fertiliser (OA+IF) for different levels of inorganic N application rates, with 
the standard control of inorganic fertiliser. They also compare with a nil control of no fertilisation, but 
these results are not reported here. Overall, there was a significant mean relative increase in final 
SOC  content,  with  29%  (95%  CI:  27%,  31%)  with  the  use  of  OA  and  OA+IF  as  compared  to  the 
standard control. The variables that explained the greatest variation in organic carbon when compared 
with either nil or standard controls were (1) initial SOC, (2) climate, (3) initial total N, (4) soil texture, 
(5) soil order, and (6) initial soil pH. The type, rate, and duration of organic amendment application, 
as well as the inorganic N application rate added along with the organic amendment also explained 
a considerable amount of SOC variation. The types of organic amendments that were compared with 
the standard control are: straw (crop straw, straw husk, straw compost), manure (farmyard manure, 
livestock manure, manure-based materials such as composts) and green manure (green manure crops 
such as rape crop, lantana and some leguminous plants). Therefore, the results would be a bit higher 
if green manure were not included, the effect for animal manure being +39% (95% CI: 35%, 43%).  

It shall be noted that > 90% of the straw-treated soils received additional application of inorganic 
fertiliser compared with >71% of those treated with manure applied soils. The addition of organic 
amendments to soils located in warm regions (Mediterranean/subarid/arid, tropical and semi-tropical) 
had the greatest relative increases in final SOC (more than 30% when compared with the standard 
control),  whereas  relative  increases  were  below  these  threshold  values  in  soils  under  humid-
temperate and continental-type climates. 

30 

 
 
Table 17 Effect (% change) of organic fertilisation on soil organic carbon. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Overall 

Tropical 

CI_LOW  Mean  CI_HIGH 

Nc 

Effect 

27 

29 

31 

150 

Positive 

36.7 

45.0 

54.3 

Mediterranean, arid and semi-arid 

23.0 

31.3 

40.4 

Subtropical 

Continental 

27.6 

31.6 

35.5 

17.4 

22.8 

28.3 

Humid-temperate 

7.0 

13.9 

21.4 

Years of application 

20-10 

10 - 20 

Manure 

Straw 

Green manure* 

34 

19 

35 

8 

15 

38 

23 

39 

13 

23 

42 

27 

43 

19 

31 

18 

16 

68 

31 

17 

82 

70 

78 

33 

21 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Source: Y. Chen et al. 2018. *Please note that green manure is addressed in a specific independent section of the 
document. This value is shown here just to highlight that the overall value also includes green manure. 

3.6.3  Effects on ammonia emissions 

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis  papers  (Schievano  et  al.  2023),  the  overall  effect  of  organic  fertilisation  compared  to 
mineral  fertilisation  alone  is  positive,  with  a  significant  reduction  of  ammonia  (NH3)  emissions, 
although  Ti  et  al.  2019  found  that  the  significant  reduction  only  occurs  if  the  mineral  fertiliser 
substitution rate is 100%.  

The selected paper according to the established criteria is Ti et al. 2019 (Table 18). When comparing 
with 50, 75 and 100% substitution of synthetic N fertiliser by livestock manure (with an equivalent 
total N rate), only the 100% substitution significantly reduced NH3 emissions (by -67%; 95% CI: -
79%,  -47%).  The  retention  of  crop  residues,  such  as  wheat  straw  and  leaf  vegetable,  on  the  soil 
surface could increase NH3 emission by 22.3% on average, but it is not statistically significant as 
compared with the control. 

Table  18  Effect  (%  change)  of  organic  fertilisation  on  ammonia  emissions  as  compared  to  urea.  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. 

Specific practice 

CI_LOW 

Mean 

CI_HIGH 

Effect 

Residue retention (straw) 

-5.7 

Organic 
fertilisation 

Manure 100 % 

Manure 75 % 

Manure 50 % 

Source: Ti et al. 2019. 

-78.9 

-69.7 

-44.5 

31 

22.5 

-67.2 

-16.2 

-16.8 

59.4 

-46.9 

133.2 

25.6 

Non-significant 

Positive 

Non-significant 

Non-significant 

 
 
 
 
3.6.4  Effects on the nutrient balance 

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis papers (Schievano et al. 2023), the overall effect of organic fertilisation on N use efficiency 
is mostly negative but dependent on N fertilisation rates (Table 19).  

Wei  et  al.  2020  found  that  the  substitution  of  mineral  fertiliser  with  organic  fertiliser  had  non-
significant effect on nitrogen use efficiency in maize systems. However, the effect of substitution of 
mineral  fertilisation  with  organic  fertilisers  on  nitrogen  use  efficiency  tended  to  increase  with 
increasing  N  fertilisation  rate11.  The  low  fertilisation  rate  had  a  negative  impact  on  nitrogen  use 
iven that in EU the Nitrates Directive does not allow putting more than 170 kg 
N/ha, we can consider that for all regions/countries where this threshold is respected the effect would 
be  negative.  The  optimal  fertilisation  rate  (180-250  kg  N/ha  in  EU)  compromised  nitrogen  use 
efficiency to some extent. The high fertilisation rate significantly increased nitrogen use efficiency 
(8.77%). The authors did not take the effects of organic treatment (compost or digestate) into account 
due to the limited amount of available data on maize production. The studies included in their analysis 
included the following types of organic fertilisers: animal manure (47%), compost (37%), commercial 
organic fertiliser (e.g., industrially processed, standardized poultry or livestock manure; 9%), digestate 
(5%), slurry (2%); 

Huygens et al. 2020 cover all arable crops. They found that the response ratios of nitrogen utilization 
efficiency12 for organic fertilizers were lower than those of mineral fertilizers, but results depended 
on the type of organic fertilizer. Highest  values corresponded to manure-derived  N  fertilizers that 
were more mineral-like or were dominated by urea, an easily degradable mineral N precursor. The 
processed manure materials (with mineral-N/total-N ratio < 90% or TOC:TN > 3) showed a response 
ratio of nitrogen utilization efficiency value below 75% (significantly lower than mineral fertilizers), 
with the lowest values observed for materials of high organic matter content, such as compost and 
solid  digestate  fractions.  Manure  and  processed  manure  fertilizers  with  high  organic  content  are: 
Digestate liquid fraction (n=11); Raw manure (n=78); Acidified manure (n=1); Manure solid fraction 
(n=17); Digestate slurry (n=65); Manure liquid fraction (n=17); Digestate solid fraction (n=6); Compost 
(n=5). 

Table  19  Effect  (%  change)  of  organic  fertilisation  on  nitrogen  use  efficiency.  CI_LOW: confidence  interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Population 

Arable 
crops 

Metric and 
Source 

NUE (NR) 
Source: 
Huygens et al. 
2020 

Factors 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

Overall 

NA 

NA 

NA 

NA 

Negative 

Organic fertiliser type 

Raw manure 

-34.4 

-28.7 

-22.7 

78 

Negative 

11 The N fertilisation rate for maize production was categorized as low, optimal, or high, where these categories were 
region-specific: the optimal N rates for Africa, Asia, and other regions were 50 80, 150 210, and 180 250 kg N 

12 Plant N use efficiency was calculated as the difference in N uptake between fertilised and unfertilised plants, expressed 
relative to the fertiliser application rate. As the NUE was corrected based on the N uptake of a blank without fertiliser, 
it was referred to as blank-corrected NUE (NUE(bc)). 

32 

 
 
 
 
                                                 
 
 
Manure 
liquid 
fraction 
Manure solid 
fraction 

-48.6 

-41.3 

-33.0 

17 

Negative 

-38.8 

-31.4 

-23.4 

Compost 

-71.7 

-63.1 

-51.5 

-38.5 

-31.6 

-24.0 

17 

5 

65 

Negative 

Negative 

Negative 

Maize 
systems 

NUE (NR)  
Source: Wei et 
al. 2020 

Digestate 
slurry 
Overall 

Low  
(< 180 kg 
N/ha in EU) 
Optimal 
(180-250 kg 
N/ha in EU) 
High 
(> 250 kg 
N/ha in EU) 

-5.0 

0.6 

6.2 

185 

Non-significant 

Fertilisation rate 

-27.0 

-17.3 

-7.6 

30 

Negative 

-14.8 

-4.1 

6.6 

48 

Non-significant 

0.9 

8.8 

16.6 

107 

Positive 

Source: references in the second column. 

3.6.5  Effects on nutrient leaching and run-off 

Results in the literature quantify the decrease in leaching of nitrates (NO3-) and sometimes also in 
+), total dissolved inorganic nitrogen (DIN) and N runoff. We show results, 
leaching of ammonium (NH4
where available, for NO3- leaching and for DIN leaching (Table 20).  

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis papers (Schievano et al. 2023), the overall effect is positive. 

One synthesis paper (Wei et al. 2020) analyses the link between the substitution of mineral fertiliser 
with organic fertilisers and nitrogen losses by leaching and run off with positive results (i.e. decreased 
losses). Wei et al. 2020 performed a meta-analysis of 133 maize studies, conducted worldwide, to 
assess maize yield and environmental performance with substitution of mineral fertiliser with organic 
fertiliser  (Table  20).  This  substitution  is  performed  at  different  substitution  rates  (Rs),  defined  as 
organic nitrogen (N) input/total N applied (%)). At an equivalent nitrogen (N) rate, substituting mineral 
fertiliser with organic fertiliser reduced N leaching and runoff by 26.9%. The maximum decrease in 
N runoff and leaching (by 45.5%) was achieved at the optimal fertilisation rate. Results also changed 
with the substitution rate and the treatment duration. Partial substitution (0 < Rs < 100) of organic 
with mineral fertiliser did not affect N runoff and leaching except for substitution at a rate of 40
60%, at which N runoff and leaching significantly decreased by 41.9%. Full substitution of organic 
with mineral fertiliser significantly decreased runoff and leaching by 50.0%.  

Long-term organic fertiliser 
by 47%; this was significantly greater than the 11.2% reduction under short-term treatments (<3 
years).  

33 

 
 
 
Table  20  Effect  (%  change)  of  organic  fertilisation  on  N  leaching  and  runoff.  CI_LOW:  confidence  interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. Rs: mineral fertiliser substitution 
rate. 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

N leaching & runoff overall 

-39.6 

-26.9 

-14.1 

66 

Positive 

Low fertilisation rate 

-62.9 

-18.7 

26.1 

Optimal fertilisation rate 

-64.2 

-45.5 

-26.5 

N 
leaching 
& run-off 

High fertilisation rate 

-39.3 

-22.2 

60 < Rs < 100 

Rs = 100 

Long term 

-20.6 

-1.7 

-64.8 

-41.9 

-39.5 

-3.2 

-69.7 

-50.0 

-66.1 

-47 

Short term < 3 years 

-25.2 

-11.2 

Source: Wei et al. 2020. 

-5.1 

16.6 

-18.6 

33.0 

-29.5 

-27.5 

2.9 

10 

13 

43 

28 

11 

7 

20 

25 

41 

Non-significant 

Positive 

Positive 

Non-significant 

Positive 

Non-significant 

Positive 

Positive 

Non-significant 

3.7  Green manuring 

Fertilisation with green manure, or green manuring, refers to the use of herbaceous plants, often but 
not necessarily planted as cover crops, as source of nutrients for the subsequent cash crops. Plants 
are killed (either mechanically or chemically) and then usually incorporated into the soil, or in some 
cases  left  on  it.  Leguminous  plants  are  often  used  to  this  purpose  due  to  their  ability  of  fixing 
atmospheric nitrogen which is subsequently made available as nutrient for the following crops. From 
an environmental point of view, the purpose of the practice is to replace, partially or totally, mineral-
nitrogen (N) fertilisers. Therefore, examined synthesis papers included only studies where cover crops 
were explicitly used as green manure, assessing their effects by comparing their use to either mineral-
N fertilisation or to absence of mineral-N fertilisation.  

3.7.1  Effects on greenhouse gas emissions 

Results are provided for N2O and CH4 emissions in Table 21. The first selected synthesis paper for 
N2O emissions is Basche et al. 2014. Forty percent of the studies assessed in this analysis showed 
that cover crops not harvested decreased N2O emissions and 60% of the studies showed that cover 
crops  not  harvested  increased  N2O  emissions.  There  are  both  environmental  and  management 
factors that modified the impact of cover crops not harvested on N2O emissions, including fertiliser 
N rate, soil incorporation, and the period of measurement and rainfall. At low N rates, legume cover 
crops not harvested had higher relative N2O emissions. N2O emissions of non-legume cover crops 
not harvested were negative at low N rates but increased as N rate increased. In general, it seems 
that cover crops not harvested have a greater potential to reduce N2O emissions when non-legume 
species are utilized and cover crop residue is not incorporated into the soil. 

34 

 
 
 
 
 
 
 
 
With the same level of fertilisation both in treatment and control, Muhammad et al. 2019 found an 
overall decrease of N2O emissions, but also that the effect varies depending on several factors. In 
particular,  N2O  emission  increased  when  leguminous  crops  were  used  and  decreased  when  non-
leguminous crops were used. Incorporation of crop residues increased N2O emissions while leaving 
them on the soil decreased them.  

Results are also provided for paddy rice fields on the effect of legume incorporated to the soil both 
on N2O and CH4 emissions (Linquist et al. 2012). 

Table 21 Effect (% change) of green manuring on GHG emissions. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top. 

GHG 

Technique type or crop 
type 

CI_LOW 

Mean 

CI_HIGH 

Effect 

N2O  

Source: Basche et al. 
2014 

Overall 

Legume 

Non-legume 

Biculture (mixed legume 
and non-legume) 

NA 

469 

1.4 

-23.5 

NA 

490 

7.3 

8.8 

NA 

Not available 

511 

Negative 

13 

Negative 

54.7 

Non-significant 

Incorporation 

482 

494 

507 

Negative 

Surface 

Overall 

-48 

-17 

33.5 

Non-significant 

-26.8 

-16.3 

-5.6 

Positive 

N2O  

Source: Muhammad 
et al. 2019 

Leguminous cover-crop 

29.8 

60.8 

96.0 

Negative 

Non-leguminous cover-crop 

-46.2 

-36.5 

-25.8 

Positive 

Incorporated 

109.0 

133.3 

166.0 

Negative 

Surface 

-62.3 

-54.9 

-45.9 

Positive 

N2O Rice systems 
only 

Source: Linquist et al. 
2012 

CH4 from paddy rice 

Source: Linquist et al. 
2012 

Paddy rice with Sesbania 
(legume) incorporated to 
the soil 

Paddy rice with Sesbania 
(legume) incorporated to 
the soil 

Source: references in the first column. 

3.7.2  Effects on soil organic carbon 

-41 

-12 

12 

Non-significant 

71 

192 

396 

Negative 

Results are typically provided for soil organic carbon concentration, soil organic carbon stocks and 
soil carbon sequestration rates. SOC concentrations describe the organic carbon concentration on a 
weight by weight basis, SOC stocks on a weight by area basis, and C sequestration rates, on weight 
by area on an annual basis. Results on SOC stock change have been found for green manure. 

35 

 
 
 
 
Two synthesis papers have been selected: Muhammad et al. 2019 and Shackelford et al. 2019. The 
first one includes data from 41 experimental sites from various regions of the world with different 
soil and climatic conditions where cover crops were rotated with various cash crops. The second one 
focused on arable farmland in Mediterranean climate regions, in California and the Mediterranean, 
based on 326 experiments reported in 57 publications. They all refer to arable land, where the practice 
of cover crop is compared to the effect of bare soil. All or part of the N fertiliser rate was added as 
green manure (Table 22). 

Table 22 Effect (% change) of green manuring on soil organic carbon (stock per hectare). CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top. 

Biogeographic regions 

All regions 

Source: Muhammad et al. 2019 

Technique or crop 
type 

Overall (all types of 
green manure, all 
treatments) 

Removed from the soil 
(only aboveground 
biomass) 

Incorporated into the 
soil 

CI_LOW  Mean  CI_HIGH 

Effect 

9.5 

15.2 

22.4 

Positive 

-1.4 

1.2 

3.7 

Non-significant 

14.1 

22.1 

31.5 

Positive 

Legume 

Non-legume 

2.4 

0.4 

Mixed legume & not 

13.5 

10.7 

12.9 

22.1 

22.4 

28.9 

31.9 

Positive 

Positive 

Positive 

Mediterranean climate regions  

Source: Shackelford et al. 2019 

All types of green 
manure, all treatments  

4 

9 

15 

Positive 

Source: references in the first column. 

The results of non-harvested cover crops compared to no cover crop were positive for SOC. Overall, 
non-harvested cover crops increased SOC by 15% (95% CI: 9.5%, 22.4%) compared to no cover crop 
(Muhammad et al. 2019). The mixed cover crop resulted in a higher SOC than legume or non-legume 
cover crop alone. Incorporation of cover crop residue into the soil increased SOC compared to residue 
removal. 

3.7.3  Effects on the nutrient balance 

No synthesis paper explicitly reported results in terms of gross nutrient balance or nutrient surplus. 
Nevertheless, impacts have been found on other metrics related to the gross nutrient balance (Annex 
1): Partial Factor Productivity of Nitrogen (only for rice in China), that is a specific measure of the 
nitrogen use efficiency (yield divided by total N fertilisation including nitrogen from green manure) 

Ding et al. 2018 found that the fertilisation of rice fields in China using herbaceous cover crops as 
green manure (no distinction between legume and non-legume as experiments included Chinese milk 
vetch,  oilseed  rape  and  rye-grass),  compared  to  harvested  cover  crops,  increases  Partial  Factor 
Productivity of Nitrogen  by 4.9% (95% CI: 3.0%, 6.8%). Experimental and control treatments in each 
study received the same amount of inorganic NPK fertiliser (Table 23).  

36 

 
 
Table  23  Effect  (%  change)  of  green  manuring  on  N  use  efficiency.  CI_LOW:  confidence  interval  bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Population 

Cover crop / technique type  Metric  CI_LOW  Mean 

CI_HIG
H 

Nc 

Effect 

Rice in China 

Leguminous and non-legumi-
nous, non-harvested compared 
to harvested cover crop 

Source: Ding et al. 2018 

PFPN 

3.0 

4.9 

6.8 

84 

Positive 

3.7.4  Effects on nutrient leaching and run-off 

Results in the literature quantify the decrease of leaching of nitrates (NO3-), of ammonium (NH4+), 
of total dissolved inorganic nitrogen (DIN) and N runoff. We show results, where available, for NO3- 
leaching and for DIN leaching in Table 24.  

A synthesis paper (Tonitto et al. 2006) collected evidence on the effect of green manuring on nitrogen 
leaching. Tonitto et al. 2006 concluded that this practice has a statistically significant potential in 
reducing nitrogen leaching relative to conventional fertiliser-based systems due to increased organic 
nitrogen  retention.  Their  comprehensive  review  of  the  literature  (including  several  sites  in  North-
America and one in Brazil) revealed few studies where leaching or soil inorganic N was simultaneously 
measured in conventional fertiliser-driven and legume-based systems. For this reason, the synthesis 
paper  reported  comparisons  of  Haber-Bosch 13 N  fertilized  systems  in  which  the  cash  crop  was 
followed by either a non-legume cover crop or a bare fallow.  The meta-analysis compares nitrate 
leaching  from  non-legume  cover  crop  followed  by  an  inorganic  N  fertilized  cash  crop  and  nitrate 
leaching from legume cover crop followed by an unfertilized cash crop with the control, which was a 
winter bare fallow followed by a cash crop fertilized at recommended fertiliser rates . Nitrate leaching 
was  clearly  reduced  when  a  cover  crop  was  present.  The  meta-analysis  showed  a  70%  overall 
reduction in leaching under the non-legume cover crop relative to bare fallow systems, with a narrow 
95% CI (-77%, -62%; Nc=69). Though a limited sample size (Nc=8), the comparison between legume-
fertilized systems and conventional systems showed a significant 40% reduction in nitrate leaching 
(95% CI: -55%, -15%).  

Table 24 Effect (% change) of green manuring on NO3- leaching for major crop groups. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top. 

North-America and 
north-western Europe 

Overall 
Legume 
Non-legume 

NA 
-54.7 
-77.5 

NA 
-37.9 
-70.3 

NA 
-15.2 
-62.5 

Not available 
Positive 
Positive 

CI_LOW  Mean 

CI_HIGH 

Effect 

Source: Tonitto et al. 2006 

13 The Haber-Bosch process is a process that fixes nitrogen with hydrogen to produce ammonia. 

37 

 
 
 
 
 
                                                 
 
3.8  Soil amendment with Biochar 

Biochar  is  charcoal  that  is  produced  by  pyrolysis14 of  biomass;  different  biomass  can  be  used  to 
produce it, including manure, crop residues, sewage sludge, municipal wastes and compost. The use 
of Biochar has been proposed as a way to augment the long-term Carbon sink potential of soils and 
to ameliorate soil properties through the increase of soil organic matter. 

3.8.1  Effects on greenhouse gas emissions 

The effects of soil amendment with biochar on the emissions of different greenhouse gases from 
soils can vary. Contrasting results were found for CH4 and CO2 (soil respiration), with a prevalence 
of non-significant effects in both cases, while results for N2O are more consistently positive.  When 
considering the overall effect in terms of aggregated greenhouse gas potential, the resulting effect 
is also considered positive (Schievano et al. 2023). 

The selected paper for overall GHG emissions is Q. Zhang et al. 2020. They found that the weighted 
response ratio of soil GHG emissions exhibited a difference among management strategies, biochar 
characteristics,  and  soil  properties.  In  general,  global  warming  potential 15  and  the  intensity  of 
greenhouse gas emissions16 significantly decreased, and crop yield greatly increased, with an average 
change of -23%, -41%, and 21%, respectively. The impact of biochar application on GWP was mainly 
on soil N2O emission (Table 25).  

To  assess  biochar  characteristics,  biochar  was  divided  into  five  categories  according  to  the  raw 
material or feedstock for the meta-analysis: (1) shell residue (nutshell, oat hull, walnut shell, peanut 
hull, and bagasse); (2) straw waste (peanut straw, maize stalks, wheat straw, sorghum stalks, and 
rape  stalks);(3)  wood waste (bark, wood chips, pruning, trunk, and branches); (4) livestock manure 
(pig, cow and sheep manure); (5) municipal solid waste (household waste and excess sludge). Biochar 
derived from straw waste had a highly remarkable effect: it lowered GWP by 26% and increased crop 
yield by 21%, 3% more than biochar derived from crop residues. Biochar from straw waste and crop 
residue increased the crop yield by 18% and 22% and mitigated GHGI by 35% and 48%, respectively.  

Table  25  Effect  (%  change)  of  biochar  GHG  emissions.  CI_LOW:  confidence  interval  bottom,  CI_HIGH: 
confidence interval top. 

GHG 

Technique type or 
crop type 

CI_LOW  Mean  CI_HIGH 

Effect 

All GHG emissions 

Global Warming Potential 

Source: Q. Zhang et al. 2020. 

Overall 

Straw waste 

Crop residues 

-29 

-33 

-18 

-23 

-26 

-16 

-16 

-18 

-13 

Positive 

Positive 

Positive 

14 the thermal decomposition of materials at elevated temperatures in absence of oxygen 
15 GWP in CO2-C equivalents (kg ha-1) was estimated for the emission of different greenhouse gases using the relative 

radiation effect of forcing factors by following equation GWP = 25 x RCH4 + 298 x RN2O + RCO2 where RCH4, RN2O, 
and RCO2 are the soil CH4, N2O, and CO2 emissions (kg ha-1), respectively. 

16 Greenhouse gas emission intensity (GHGI) is the ratio of GWP to crop yield, which can be used to relate agricultural 

production to soil GHG emissions. GHGI was calculated as GHGI = GWP/crop yield. A smaller value of GHGI indicates 
that a lower GWP is produced to obtain the same crop yield. 

38 

 
 
                                                 
 
GHG 

Technique type or 
crop type 

CI_LOW  Mean  CI_HIGH 

Effect 

All GHG emissions 

Overall 

GHG emissions intensity 

Straw waste 

Source: Q. Zhang et al. 2020. 

Crop residues 

-50 

-46 

-59 

-41 

-35 

-48 

-31 

-24 

-37 

Positive 

Positive 

Positive 

N2O emissions 

Source: Z. Wu et al. 2019 

Overall 

Rice 

Wheat 

Maize 

Others 

Experimental 
duration 
(years) 

-23.4 

-18.7 

-13.9 

Positive 

-23.8 

-16.2 

-32.9 

-20.6 

-8.3 

-8.3 

-32.1 

-21.8 

-11.1 

-34.9 

-19.0 

 0.5 

-21.0 

-15.0 

-2.8 

-9.1 

0.5 - 1 

-36.8 

-25.0 

-13.1 

1 - 2 

-51.5 

-32.5 

-13.1 

> 2 

-42.0 

-21.4 

-0.8 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Source: references in the first column. 

For N2O emissions the selected paper is Z. Wu et al. 2019 (Table 25). According to the meta-analysis, 
biochar application significantly decreased N2O fluxes by 18.7% (95% CI: -23.4%, -13.9%). The soil 
and  biochar  characteristics  and  the  experimental  conditions  were  significantly  influencing  the 
response ratios of the soil GHG fluxes and the yield across all the studies. Soil texture, soil organic C 
and soil C/N were shown to be key factors influencing N2O emissions, while biochar with a higher 
pyrolysis temperature exerted a greater mitigation of N2O emissions in higher C/N soil with higher N 
application. Moreover, the long-term effects of biochar amendment on the GHG fluxes were confirmed 
by  the meta-analysis.  The  decrease  in  N2O  emissions  in  biochar-amended  soil  persisted  over the 
studied years after a single amendment in field experiments.  

3.8.2  Effects on soil organic carbon 

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis papers (Schievano et al. 2023), the effect of soil amendment with biochar on carbon storage 
in soil is positive, although some variables can lead to locally different results, including soil type and 
depth, climatic conditions, presence of crop rotation, N-fertilisation rate and biochar characteristics.  

The selected paper (Albert et al. 2021) is a meta-analysis showing that biochar significantly increased 
the soil total organic carbon by 54.3% (95% CI: 47.6%, 61.3%) compared to the control (Table 26). 
The  analysis  included  different  crops:  vegetables,  grass,  legume,  maize,  wheat,  rice,  and  bamboo. 
However, when they identified the effect of biochar application on soil property changes, they did not 
divide the data into subgroups.  

Due to the fact that results are very sensitive to a series of variables and that the selected synthesis 
paper  (Albert  et  al.  2021)  provide  just  a  single  result,  we  find  it  can  be  useful  to  provide  some 
additional results to illustrate the variability associated to soil characteristics and climatic conditions. 
We include data from a second selected synthesis paper (Bai et al. 2019), which found that depending 
on soil type, SOC increase due to biochar use can be between 32% (clay loam and clay soils) and 
63% (silty clay and silty clay loam soils) (Table 26). Depending on soil pH, biochar use increased SOC 
by 28%, 35% and 65% in acid, neutral and alkaline soils, respectively. Biochar markedly stimulated 

39 

 
 
 
SOC increases in irrigated croplands (49%), three times higher than those under rainfed condition 
(16%).  Biochar  increased  12%  (38%  vs.  26%)  more  SOC  in  arid  areas,  respectively,  compared  to 
humid areas. In warm areas, biochar applications only increased SOC by half of the enhancement 
observed in cool areas (17% and 32% respectively). Biochar amendments enhanced SOC by 52% in 
rotational cropping systems, much higher than that in the continuous cropping system (31%).  

 Table 26 Effect (% change) of biochar on soil organic carbon (stock per hectare). CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Biogeographic regions 

All regions 

Source: Albert et al. 2021 

Technique type or 
crop type 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Overall 

47.6 

54.3 

61.3 

166 

Positive 

All regions 

Source: Bai et al. 2019 

Overall 

In the field 

In incubation and pot 
experiments 

Irrigated fields 

Rainfed fields 

Cool regions 

Warm regions 

Arid regions 

Humid regions 

Acidic soils 

Neutral soils 

Alkaline soils 

0-10 cm depth 

0-20 cm depth 

0-30 cm depth 

33 

23 

44 

37 

1 

25 

8 

31 

21 

22 

21 

50 

27 

NA 

NA 

Short term (0-5 years) 

36.7 

39 

28 

57 

49 

16 

32 

17 

38 

26 

28 

35 

65 

40.7 

69.2 

14.2 

45.2 

45 

32 

71 

60 

31 

39 

26 

45 

31 

34 

48 

80 

55 

NA 

NA 

222 

Positive 

130 

Positive 

92 

Positive 

119 

Positive 

4 

99 

24 

23 

87 

Positive 

Positive 

Positive 

Positive 

Positive 

119 

Positive 

23 

67 

42 

NA 

NA 

Positive 

Positive 

Positive 

Positive 

Positive 

53.6 

146 

Positive 

Medium term (6-20 
years) 

18.8 

35.6 

52.4 

5 

Positive 

Source: references in the first column. 

3.8.3  Effects on ammonia emissions 

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis papers (Schievano et al. 2023), the link with ammonia emissions is contrasting. Authors 
found differences by type of biochar (biochar feedstock), soil pH, soil texture, fertilisation rate, etc.  

As  shown  on  Table  27  ,  the  literature  analysed  by  Sha  et  al.  2019  showed  that,  overall,  biochar 
application  had  non-significant  effect  on  ammonia  volatilization  (RR = 0.92%,  95%  CI:  12.45
13.38%). However, they also found differences by type of biochar. Wood-based biochar application 
significantly reduced ammonia volatilization (RR =  34.6%, 95% 

40 

 
 
did  not  find  a  significant  impact  for  other  feedstocks  such  as  lignocellulosic  waste  (including 
Macadamia nut shell, walnut shells, peanut shell and maize cobs) or herbaceous waste (including crop 
straw and green waste).  

Sha et al. 2019 report that biochar applied to acidic soils could increase ammonia volatilisation by 
38.4%. Increases in ammonia volatilisation were also caused by amendment with high pH biochar 
(30.8%),  or  combining  biochar  with  ammonium-based  fertilisers  (67.9%).  Reductions  in  ammonia 
volatilisation  were  observed  when  biochar  was  applied  to  fine  soils  (high 

15 t ha 1;  33.8%), was combined with urea or organic 
fertilisers  ( 18.6%  and  28.7%),  was  used  with  appropriate  N-fertiliser  rates  (<200 kg N ha 1; 

33.5%), and was applied as acidified bioch

Q. Liu et al. 2018 report that biochar significantly increases soil NH3 volatilization by 17% (P= 0.034) 
on average across different studies (Table 27). However, results depend on the type of biochar and 
soil pH. Manure biochar and straw biochar stimulate soil NH3 volatilization by an average of 43% and 
27%, respectively, whereas wood biochar tends to decrease soil NH3 volatilization by an average of 
30%. Biochar stimulates soil NH3 

NH3 
volatilization from clay textured soils are more prone to be increased by biochar than that from other 

soil NH3 volatilization, while a non-significant response to biochar is observed in soils with SOC> 10 

a significant increase in soil NH3 volatilization, however, biochar with pH lower than 9, or being applied 

non-significant effect. 

Table 27 Effect (% change) of biochar on ammonia volatilisation  by biochar feedstock. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Biochar feedstock 

Source 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Overall 

Wood 

Sha et al. 2019 

-12.4 

Q. Liu et al. 2018 

0 

0.9 

17 

38 

Sha et al. 2019 

-51.1 

-34.6 

-15.9 

Q. Liu et al. 2018 

13.4 

141  Non-significant 

-48 

-48 

-3.7 

12 

-55 

3 

-31 

-20 

11.4 

26 

40 

42 

1 

24 

29.7 

44 

370 

96 

99 

26 

13 

13 

92 

75 

10 

11 

Negative 

Positive 

Positive 

Non-significant 

Non-significant 

Negative 

Non-significant 

Negative 

Lignocellulosic waste 

Sha et al. 2019 

Herbaceous waste 

Sha et al. 2019 

Straw 

Manure 

Q. Liu et al. 2018 

Sha et al. 2019 

Q. Liu et al. 2018 

Source: references in second column. 

3.8.4  Effects on the nutrient balance 

The selected paper for data extraction is Q. Liu et al. 2018. They found that, on average, biochar leads 
to an increase of 11% (P < 0.001) in plant N uptake, which is derived from an increase of 12% in 
plant biomass (P<0.001) and a minor decrease of  2% (P = 0.014) in plant tissue N concentration 
(Table 28). 

There  are,  however, different  factors  that  can modify  this impact: soil  pH,  soil  texture,  soil  cation 
exchange capacity, biochar type and biochar application rate. Biochar generally increases N uptake in 

41 

 
 
 
 6.5), but it shows little effect in neutral or alkaline 
acidic soils (defined by the authors as soil with pH 
soils. The increasing impacts of biochar on N uptake are usually maximized in soils with poor structure 
(rich in either sand or clay) and in soils with low cation exchange capacity. Manure biochar induces a 
higher N uptake (27%) than wood biochar (10%) or straw biochar (9%). The relationship of biochar 
application rate with the response of N uptake follows a convex curve, and over application of biochar 

Table  28  Effect  (%  change)  of  biochar  on  N  plant  uptake.  CI_LOW:  confidence  interval  bottom,  CI_HIGH: 
confidence interval top. 

CI_LOW 

Mean 

CI_HIGH 

Overall- uptake 

Soil pH 3-5 

Soil pH 5- 6.5 

Biochar  

Soil pH 6.5- 7.5 

Soil pH > 7.5 

Wood biochar 

Straw biochar 

Manure biochar 

8 

26 

-6 

0 

-10 

5 

4 

18 

11 

35 

7 

3 

3 

10 

9 

27 

15 

44 

19 

6 

18 

15 

14 

36 

Effect 

Positive 

Positive 

Non-significant 

Non-significant 

Non-significant 

Positive 

Positive 

Positive 

Source: Q. Liu et al. 2018 

3.8.5  Effects of nutrient leaching and run-off 

Results in the literature quantify the decrease leaching of nitrates (NO3-), of ammonium (NH4+), of 
total dissolved inorganic nitrogen and sometimes also N runoff.  

The  selected  synthesis  paper  is  Borchard  et  al.  2019.  Overall,  nitrates  (NO3 )  leaching  was 
significantly reduced by 13% with biochar. Results were not significant for experiments of less than 
30 days, but for studies with an experimental time of more than 30 days (30-60, 60-120, >120), 
biochar reduced NO3 
 leaching by 26 to 32% (Table 29). Nitrates leaching was not affected when 
biochar was applied to grassland but was reduced by 36% for a majority of agricultural crops (except 
horticulture).  

Results  depended  on  the  feedstock  and  manufacturing  of  biochars.  Biochars  produced  from 
lignocellulosic biomass and biochars produced at temperatures of >500 °C reduced NO3  leaching. 
Low biochar application rates of <10 Mg ha 1 increased NO3  concentration in soils. Larger biochar 
application rates (e.g., >10 20 Mg ha 1) tended to reduce NO3  leaching.  

Regarding soil types, there was also a large variability. Only coarse textured soils (i.e. sand) showed 
a reduced leaching of NO3 . Leaching was exclusively reduced in Cambisols (i.e. soils of limited age), 
and semi-arid soils (e.g. Calcisol, Solonetz).  

The authors also looked at the effects of biochar with fertilisation: leaching was reduced by biochar 
in unfertilized soil and when the fertiliser application rate was below 150 kg N ha 1. However, nitrates 
leaching  progressively  increased  in  response  to  increased  N  application  rates  (i.e.  -26%  for 
<150 kg N ha 1,  7%  for  150 300 kg N ha 1,  +46%  for  >300 kg N ha 1).  Last,  nitrates  leaching 
remained unaffected when biochar was applied in combination with organic fertilisers. Hence, over 

42 

 
 
 
 
 
all  N  fertiliser  application  rates,  the  nitrates  leaching  reduction  was  not  significant  (i.e.  +3%  with 
mineral fertiliser

-mineral fertiliser). 

fertiliser

Table  29  Effect  (%  change)  of  biochar  on  NO3-  leaching.  CI_LOW:  confidence  interval  bottom,  CI_HIGH: 
confidence interval top. 

Overall 

-19 

-13 

-7 

Positive 

CI_LOW  Mean  CI_HIGH 

Effect 

Biochar 

Cereals, other crops and perennials 

Horticulture 

Grassland 

Forest 

Source: Borchard et al. 2019 

-42 

-68 

-17 

-46 

-36 

-53 

-6 

-6 

-31 

-29 

6 

+66 

Positive 

Positive 

Non-significant 

Non-significant 

3.9  Soil amendments with lime and gypsum 

Soil  amendments  are  practices  used  to  improve  the  soil  quality  in  terms  of  its  structure  and 
biochemical function. Most amendments use calcium-containing minerals, like lime and gypsum. Lime 
refers  to  a  material  that  can  come  in  different  forms,  especially  calcium  carbonate  (CaCO3)  and 
magnesium carbonate (MgCO3), and it is used to reduce soil acidity and to add calcium or magnesium 
to the soil. Gypsum is also a commonly used calcium containing mineral (calcium sulphate dihydrate, 
CaSO4 
subsoil problems due to its solubility.  

3.9.1  Effects on soil organic carbon  

From  the  systematic  review  of  synthesis  papers  (Schievano  et  al.  2023),  soil  liming  resulted  in  a 
marginal, non-significant increase in soil carbon stocks as found by a paper of global geographical 
extent (Eze et al. 2018), and one synthesis paper (Y. Wang et al. 2021) reported significant effects of 
the  application  of  gypsum  amendment  on  SOC  content  (Table  30).  They  analysed  the  impacts  of 
gypsum on saline-sodic soils in China. Saline-sodic soils cover around 10% of the global land surface 
and  deliver  various  ecosystem  services  to  human  society  in  the  arid/semiarid  regions.  Flue  gas 
desulfurization  gypsum  (FGDG),  a  byproduct  from  coal-fired  power  plants,  is  widely  used  to 
ameliorate saline-sodic soils. The FGDG application significantly reduced soil bulk density (by 7.3% ± 
3.6%)  and  increased  SOC  content  by  24.5%  ±  15.3%.  As  shown  on  Table  30  FGDG  application 
significantly increased SOC only in saline soil (not in different types of sodic soils), in the application 
season of spring, and in two soil depths (0-20 cm and 20-40 cm). The logarithmic response ratio 
values  of  SOC  were  positively  correlated  with  the  initial  values  of  soil  exchangeable  sodium 
percentage and negatively with the initial values of soil electrical conductivity, nitrate, and available 
potassium, and the logarithmic response ratio values of soil exchangeable sodium percentage. 

43 

 
 
 
 
 
 
Table  30  Effect  (%  change)  of  flue  gas  desulfurization  gypsum  on  soil  organic  carbon  in  China.  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

All techniques 
types 

CI_LOW  Mean  CI_HIGH 

Nc 

Effect 

Flue gas 
desulfurization 
gypsum on different 
types of saline-sodic 
soils in China 

Overall 

All 

Heavy sodic soil 

Strong sodic soil 

Moderate sodic soil 

Saline soil 

Incorporation 0-20 cm 

Incorporation >20 cm 

Application autumn 

Application spring 

Soil depth 0-20 cm 

Soil depth 20-40 cm 

9 

8 

-7 

-27 

-20 

47 

6 

-14 

-6 

10 

3 

21 

24.5 

25 

15 

9 

11 

94 

24 

35 

16 

33 

18 

39 

40 

43 

43 

63 

54 

158 

43 

113 

42 

61 

34 

60 

76 

76 

38 

9 

21 

8 

66 

10 

55 

21 

48 

28 

Positive 

Positive 

Non-significant 

Non-significant 

Non-significant 

Positive 

Positive 

Non-significant 

Non-significant 

Positive 

Positive 

Positive 

Source: Y. Wang et al. 2021 

3.10 Manure management: manure land application techniques 

Improved  manure  land  application  techniques  are  mainly  aimed  at  limiting  nutrients  losses  and 
emissions  of  ammonia  and/or  greenhouse  gases  during  manure  application  (either  solid  or  liquid 
fractions) to the soil. These are compared to the conventional broadcast spread application. Results 
have been found in literature on the effects of the following techniques: 

─ 

Injection of liquid manure (deep placement) or immediate incorporation of solid manure into the 
soil;  

─  Band application of manure by trailing hoses or other equivalent systems, by which the manure 

is applied beneath the crop canopy, but not incorporated into the soil; 

─ 

Irrigation coupled to manure application; 

─  Application  of  processed  manure  fractions  (e.g.  digestate,  composted,  solid  fraction,  liquid 

fraction, etc.); 

─  Separate application of solid or liquid fractions; 

─  Use  of  additives  to  manure,  like  nitrification  inhibitors,  lava  meal,  biochar,  superphosphate  or 

sawdust as emission mitigation options. 

44 

 
 
 
 
 
3.10.1 Effects on greenhouse gas emissions 

Manure land application techniques are found to have differing effects on GHG gases, depending on 
the  technique  and  the  gas.  Several  papers  have  been  selected  for  extraction  as  not  a  single  one 
provided information for all cases (Table 31).  

Different results are reported for deep placement and incorporation techniques. For CH4, two results 
are available from one synthesis study (Emmerling et al. 2020): a positive one for deep placement or 
injection  (-23%;  95%  CI:  -34%,  -13%)  and  non-significant  effect  for  incorporation  compared  to 
surface application. For N2O, non-significant effect was found for this practice.  

A negative effect on GHGs is reported for band application. A synthesis paper (Emmerling et al. 2020) 
found increased emissions of CH4 (+153%; 95% CI: 108%, 197%), while a non-significant change 
for N2O.   

Application of composted/digested manure showed a positive effect on N2O emissions (F. Xia et al. 
2020) while the effects of the application of manure digestates were found non-significant according 
to  Y.  Wang  et  al.  2017  and  Hou  et  al.  2015  as  compared  to  untreated  manure.  Hou  et  al.  2015 
examined the application of separated solid and liquid fractions on N2O emissions, on manure from 
dairy cows and swine. The overall effect of liquid fractions on N2O emissions did not differ from that 
of raw slurry. Field-applied solid fractions showed on average 46% (p < 0.01) lower N2O emissions 
than field-applied untreated manure. 

Similarly, results on the use of additives are provided by two papers with regard to N2O only. One 
synthesis paper (Y. Wang et al. 2017) reports a decrease in N2O emissions of -33 (±10.3)% with the 
use of nitrification inhibitors on  swine manure, and the other  (Y. Wang et al. 2018a)  reports  non-
significant effect of the application of additives (biochar and nitrification inhibitors) on cattle manure.  

Table 31 Effect (% change) of manure land application techniques on greenhouse gas emissions for major 
GHG types. SE: Standard Error, CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number 
of pairwise comparisons. 

GHG 

Technique and source 

CI_LOW  Mean  CI_HIGH  Nc  Effect 

CH4 

N2O  

Banding vs. surface application 
Source: Emmerling et al. 2020 
Deep placement or injection vs. 
surface incorporation 
Source: Emmerling et al. 2020 
Immediate incorporation vs. surface 
application 
Source: Emmerling et al. 2020 

Banding vs. surface application 
Source: Emmerling et al. 2020 

Deep placement and immediate 
incorporation 
Source: Emmerling et al. 2020 
Composted/digested 
manure (vs non 
composted or vs. 
raw slurry) 

Anaerobic 
digestate 
Source: Hou et 
al. 2015 

108 

153 

197 

-34 

-23 

-13 

2 

5 

Negative 

Positive  

- 167 

-37 

+117 

12  Non-significant 

-2 

25 

57 

6  Non-significant 

- 

- 

- 

-  Non-significant 

-46 

-25 

6 

19  Non-significant 

45 

 
 
 
Separated 
solid or 
liquid 
fractions  

Additives 

Solid fraction 
Source: Hou et al. 2015 

Liquid fraction 
Source: Hou et al. 2015 

Nitrification inhibitors   
swine  
Source: Y. Wang et al. 
2017 
Biochar & nitrification 
inhibitors   cattle 
Source: Y. Wang et al. 
2018a 

-66 

-46 

-15 

10 

Positive 

-25 

-2 

25 

40  Non-significant 

(-SE): -
43.6 
Min: -83  

Mean: -
33.3 
Median: 
-28 

(+SE): -
20.0 
Max: 34 

12 

Positive 

- 

- 

- 

-  Non-significant 

Source: references in second column. 

3.10.2 Effects on ammonia emissions 

Results  on  ammonia  emissions  differ  depending  on  the  technique  employed,  the  majority  being 
positive  for  deep  placement  or  injection,  immediate  incorporation  and  irrigation,  and  banding  and 
non-significant for the rest (Schievano et al. 2023). 

On  the  effect  of  deep  placement/incorporation  techniques  compared  to  conventional  broadcast 
spread application, the selected paper is Hou et al. 2015. Authors report that emissions of ammonia 
from manures following incorporation and injection were 70% (95% CI: 50%, 82%) and 80% (95% 
CI: 72%, 86%) lower than that from surface broadcasted manures, respectively. These differences 
were statistically significant (p < 0.01). 

For band application, Hou et al. 2015 found that emissions of ammonia from manures following band 
spreading were 55% (95% CI: 37%, 67%) lower than that from surface broadcasted manures. These 
differences were statistically significant (p < 0.01).  

For irrigation coupled to manure application, 1 synthesis paper with a global scale coverage including 
studies  from  Europe  is  available  (Ti  et  al.  2019),  reporting  a  significant  reduction  of  ammonia 
emissions (-73.6%) compared with the control.  

This same paper (Ti et al. 2019) provides a value for all of mitigation measures analysed together 
(deep  placement,  incorporation,  injection,  irrigation,  separation,  digestion,  crude  protein  content 
reduction  and band  application).  Overall,  the  average  reduction  of  land  application  strategies  was 
60.7% (95% CI: 53.5%, 66.6%).  

Table 32 Effect (% change) of the use of manure land application techniques on ammonia emissions. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. 

Technique 

CI_LOW  Mean 

CI_HIGH 

Effect 

Overall 
Source: Ti et al. 2019 
Land application with deep 
placement/incorporation 
Source: Hou et al. 2015 

Injection 

Incorporation 

-66.6 

-60.7 

-53.5 

-80 

-70 

-72 

-50 

-86 

-82 

46 

Positive 

Positive 

Positive 

 
 
 
Land application with banding or band 
application 
Source: Hou et al. 2015 
Irrigation coupled to manure application 
Source: Ti et al. 2019 

Source: references in the first column. 

-67 

-55 

-37 

Positive 

-92 

-73.6 

-25 

Positive 

3.11 Manure management: manure storage techniques 

This set of practices aims at limiting the loss of nutrient from manure in the form of ammonia and 
N2O during the storage and composting phase. Different specific techniques can be  used, among 
which the following ones have been examined:  

─  Use of physical, chemical or microbial additives that alters the bio physical processes responsible 

for the emissions of nutrients. 

─  Use of covers of either solid or liquid manure storage facilities. This includes plastic membranes, 

floating biomass or inert materials, and natural crusts. 

─ 

producing bacteria for the purpose of reducing NH3 emissions. 

-

─  Compaction of solid manure heap, to increase its density and decrease the air fraction within it.  

─  Storage with biofilters that intercept and treat air emissions from storage facilities. 

─  Manure cooling during storage. 

3.11.1 Effects on greenhouse gas emissions 

The effects of improved manure storage techniques on greenhouse gases are diverse across specific 
techniques and gases.  

Results on the effect of the use of additives are available for the main greenhouse gas separately 
and  for  the  total  global  warming  potential.  Specific  effects  may  vary  depending  on  the  type  of 
additives used, the type of manure and the considered greenhouse gas. Only one synthesis paper 
(Cao  et  al.  2019)  provides  results  on  the  aggregate  global  warming  potential,  reporting  positive 
results. This meta-analysis covers various types of additives, as well as different types of manure   
livestock, food/green waste and sewage sludge   with a global coverage including Europe. On average, 
during  composting,  additives  reduced  total  GHG  emissions  expressed  as  global  warming  potential 
(GWP)  by  54.2%  (95%  CI:  65%,  42%).  Physical  additives 17 (e.g.  biochar  and  zeolite)  were  more 

17 Physical additives are additives involved in adsorbing or changing the physical factors of the compost: Biochar; Zeolite; 
Bentonite; Medicine stone; Expandable perlite; Expandable vermiculite; pumice; Sand; Clay; soil; Raw attapulgite; 
Tannin; Activated charcoal; Biomass fly ash; 

47 

 
 
 
 
                                                 
 
effective  at  reducing  the  total  GHG  emissions  (67.2%)  compared  to  chemical  additives18 (29%). 
Regarding storage with microbial19 inocula (compared to no inoculation) the selected synthesis paper 
(Z.  Zhang  et  al.  2021)  reports  no  significant  effects  on  CH4  emissions  and  positive  ones  on  N2O 
emissions that decrease on -48% (95% CI: -72%, -23%; Nc=5). 

Results on the use of storage covers are available for  both CH4 and N2O. The  selected synthesis 
paper for data extraction (Hou et al. 2015) reports an overall no significant effect on emissions of 
these gases. Nevertheless, the authors report that emissions of N2O were enhanced by a factor of 
8.6, when stored slurry was covered by chopped straw (P < 0.01), and that slurry covered with artificial 
film decreased N2O emissions by 98% (p < 0.01).  

For acidification, a positive effect can be assumed both for CH4 and N2O.The results of the selected 
synthesis paper (Emmerling et al. 2020) showed that slurry acidification was effective in reducing 
CH4 
; 95% CI: -92%, -80%; Nc=33) and N2O emissions (-21%; 95% CI: -27%, -15%; 
Nc=15)  including  individual  studies  from  Europe.  Slurry  acidification  was  mainly  achieved  using 
sulphuric acid, with a pH-value between 5.1 and 6.5.  

The effect of manure compaction on greenhouse gases was examined by one synthesis paper with 
global coverage including Europe (Pardo et al. 2015), finding non-significant effects for N2O and CH4.  

One synthesis paper based on studies from different world locations including Europe (Y. Wang et al. 
2017)  investigated  the  effect  of  cooling  on  CH4,  finding  a  statistically  significant  reduction  of  -
76.3±5.5% (Nc=6).  

Table 33 Effect (% change) of the use of additives and acidification on greenhouse gas emissions by  major 
GHG  types.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of  pairwise 
comparisons. 

GHG 

Technique 

Source 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

GWP 

CH4 

  Overall  

s
e
v
i
t
i
d
d
A

s
e
v
i
t
i
d
d
A

Physical 

Chemical 

Overall 

Physical 

Cao et al. 
2019 
Cao et al. 
2019 
Cao et al. 
2019 

Cao et al. 
2019 

Cao et al. 
2019 

-65 

-54.2 

-42 

NA 

-78 

-67.2 

-52 

NA 

Positive 

-44 

-29 

-12 

NA 

-77 

-68 

-58 

68 

Positive 

-83 

-72 

-57 

40 

Positive 

18 Chemical additives are additives containing chemicals promote chemical reactions with the compost substrate relating 
to N turnover processes: Chemical additives PO43- and Mg2+ salts; Superphosphate; Gypsum; KH2PO4; H3PO4; 
CaSO4;Al2(SO4)3; CaCl2;H2SO4; FeCl3; MgCl2; Modified red mud; MgSO4; NaH2PO4 H2O and Na2HPO4 2H2O; S; 
KHSO4 and H2SO4; Ferrisulfas, sodium humate and superphosphate; Vinegar; NaAc; Apple pomace; Citric acid; 
Calcium magnesium phosphate. 

19 Microbial additives are additives containing microorganism that affect N turnover processes: NOB; NTB (ammonifiers, 
nitrobacteria, azotobacter) agent; TAT105 (thermophilic, ammonium-tolerant bacterium); Ammonia- oxidizing 
bacteria; commercial microbiological agent; T. thioparus 1904 inoculum. 

48 

 
 
 
 
                                                 
 
GHG 

Technique 

Source 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Chemical 

Microbial 

Covers   all types 

Acidification 

Compaction 

Cooling 

Overall 

Physical  

Chemical 

Microbial 

Overall 

s
e
v
i
t
i
d
d
A

s
r
e
v
o
C

N2O 

Cao et al. 
2019 
Z. Zhang et 
al. 2021 
Hou et al. 
2015 
Emmerling 
et al. 2020 

Pardo et al. 
2015 

Y. Wang et 
al. 2017 

Cao et al. 
2019 

Cao et al. 
2019 

Cao et al. 
2019 

Z. Zhang et 
al. 2021 

Hou et al. 
2015 

-74 

-62 

-46 

28 

Positive 

- 

- 

- 

- 

- 

- 

3 

- 

Non-significant 

Non-significant 

-92 

-86 

-80 

33 

Positive 

- 

- 

- 

-44.8 (-
SE) 
Min -61 

-39.3 
(Medi
an -
38) 

-33.8 
(+SE) 
Max -23 

- 

6 

Non-significant 

Positive 

-56 

-44 

-31 

99 

Positive 

-76 

-65 

-51 

51 

Positive 

-21 

-3 

14 

44 

Non-significant 

-72 

-48 

-23 

5 

Positive 

- 

- 

- 

Non-significant 

Artificial 
(plastic) film 

Hou et al. 
2015 

-100 

-98 

-69 

4 

Positive 

Straw cover 

Acidification 

Compaction 

Hou et al. 
2015 

Emmerling 
et al. 2020 

Pardo et al. 
2015 

43 

861 

6335 

11 

Negative 

-27 

-21 

-15 

15 

Positive 

- 

- 

- 

- 

Non-significant 

Source: references in the third column. 

3.11.2 Effects on ammonia emissions 

While  the  measured  effects  vary  depending  on  the  specific  additive  used,  overall  a  significant 
reduction is reported across a variety of different types of manure and additives, and from different 
geographic contexts, including Europe (Schievano et al. 2023).  

Ti et al. 2019 found that, overall, additives used by manure storage showed a significant reduction 
potential  of 29.3% (95% CI: 17%,  40%). Specifically regarding microbial inocula,  Cao et al. 2019, 
report a reduction of 43% (95% CI: -29%, -57%) in ammonia emissions thanks to this technique. 

The use of covers has an overall positive effect. Hou et al. 2015 do not provide an average value for 
all covers but specific values by type of techniques (crust, straw, granules, plastic films, oil), which 

49 

 
 
 
 
 
values range between -78% and -99%. Another synthesis paper (Ti et al. 2019) reports an average 
decrease in ammonia emissions from manure cover of -76% (95% CI: 72%, 78%).  

Regarding the effect of manure acidification versus non-acidified manure, the selected paper for data 
extraction (Hou et al. 2015) reports a reduction of 83% in ammonia emission (95% CI: -59%, -93%). 

The use of biofilters for emissions reduction during manure storage was investigated in two synthesis 
studies, both reporting a significant reduction of ammonia emissions for chicken and cattle manure 
in different geographic context, including Europe. The selected papers are Y. Wang et al. 2019a for 
chicken manure and Y. Wang et al. 2018a for cattle manure. For broilers and layers, the ammonia 
reduction  reported  by  Y.  Wang  et  al.  2019a  is  73%  (95%  CI:  ±11.7%),  while  for  beef  cattle,  the 
average decrease is -91% (Y. Wang et al. 2018a).  

Finally, only one synthesis paper (Pardo et al. 2015) investigated the effect of compaction of solid 
manure heaps, finding non-significant effects.  

Table  34  Effect  (%  change)  of  the  use  of  manure  storage  techniques  on  ammonia  emissions.  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. 

Technique 

CI_LOW 

Mean 

CI_HIGH 

Effect 

Additives 
Source: Ti et al. 2019 
Additive: microbial inocula 
Source: Cao et al. 2019 

Covers  

All 
Source: Ti et al. 2019 

Covers  
Source: Hou 
et al. 2015 

Crusting 

Straw cover 

Granules cover 

Artificial film 

Peat/kitchen oil 

Acidification 
Source: Hou et al. 2015 

Compost 
biofilters 

Broiler and layers 
Source: Y. Wang et al. 
2019a 
Beef cattle 
Source: Y. Wang et al. 
2018a 

Compaction 
Source: Pardo et al. 2015 

Source: references in the first column. 

-40 

-57 

-78 

-89 

-90 

-93 

-100 

-100 

-93 

-29.3 

-43 

-76 

-65 

-78 

-85 

-98 

-99 

-83 

-17 

-29 

-72 

14 

-53 

-69 

-93 

-97 

-59 

Positive 

Positive 

Positive 

Non-significant 

Positive 

Positive 

Positive 

Positive 

Positive 

-85 

-73 

-62 

Positive 

-100 

-91 

-80 

Positive 

-83 

-54 

+6 

Non-significant 

3.12 Manure management: manure processing techniques 

Manure  processing  techniques  can  be  used  to  change  manure  chemical/physical  properties  and 
composition  and  thus  increasing  manure  management  efficiency  and/or  limit  manure  emissions 

50 

 
 
 
compared  to  raw  manure.  Under  the  literature  review  of  synthesis  papers,  manure  processing 
techniques  included  the  following  techniques  (note  that  this  is  not  an  exhaustive  list  of  manure 
processing techniques, but of those found in the literature that meet the requirements to be included 
in the review):  

─  Composting of solid manure using improved techniques (e.g. turning, forced-air, bulking agents, 

C/N adjustment). 

─  Anaerobic digestion of slurries. Here, the environmental impacts of anaerobic digestion of manure 
alone  (i.e.  mono-digestion)  or  with  other  substrates  (i.e.  co-digestion)  are  reviewed  and  are 
considered either as result of the overall processing chain (pre-storage, digesters, post-storage, 
land distribution, energy generation using biogas) or from single steps. 

─  Liquid manure storage in anaerobic lagoons or in aerobic lagoons. 

─  Solid manure storage in piles. Improved techniques for manure storage in static stockpiles (e.g. 
physical, chemical or microbial additives, etc.) are excluded here, and included in a separate set 

─  Solid-liquid separation (e.g. decanter centrifuges, screw press, roller presses, decantation). The 
effects are considered by comparing either handling, storage or land application of the separated 
fractions, as compared to the raw manure.  

─  Drying of solid fractions  

─  Recovery  of  nutrients  through  physical  or  chemical  treatments  (e.g.  struvite  precipitation, 

ammonia stripping)  

─  Manure pasteurization  

3.12.1 Effects on greenhouse gas emissions 

The  effects  of  manure  processing  techniques  on  greenhouse  gases  are  diverse  across  specific 
techniques  and  gases  according  to  the  results  retrieved  from  the  synthesis  literature  review 
(Schievano et al. 2023).  

Regarding  composting  techniques,  for  both  CH4  and  N2O,  results  were  overall    positive  for  all 
composting  techniques20 for  pig  manure  according  to  Z.  Zhang  et  al.  2021.  They  report  a  mean 
decrease  of  -18  %  in  methane  (95%  CI:  -28%,  -9%)  and  -24%  in  N2O  (95%  CI:  -32%,  -15%). 
Nevertheless, the significance of the results vary depending on specific techniques. To illustrate this, 
in Table 35 we provide quantitative coefficients extracted from various selected synthesis papers (Ba 
et al. 2020; Pardo et al. 2015; Y. Wang et al. 2017; S. Zhao et al. 2020). 

Regarding anaerobic digestion, we provide results concerning the processing/storage GHG emissions 
as compared to raw slurry emissions and the saving of emissions from avoided fossil fuels for energy 
production. The selected synthesis paper (Miranda et al. 2015) reports a reduction of emissions from 
, and a reduction from field application of 
stored digested slurry of -82% (median
. Emissions from avoided fossil-derived energy 
digested slurry of -

20 The techniques included in the analysis are: optimal C/N ratios, optimal moisture, turning once weekly, intermittent 

ventilation or optimized aeration rates, covering and additives. 

51 

 
 
 
                                                 
 
are -115 % (median, P<0.01). The other selected synthesis paper (Emmerling et al. 2020) report a 
decrease in methane emissions of -46 % (95% CI: -65%,-27%) and a non-significant effect on N2O 
emissions. 

Solid-liquid separation, compared to no manure processing, showed a positive effect on CH4 reduction 
from 1 synthesis paper (Emmerling et al. 2020). They report a reduction of -27% (95% CI: -46%, -
8%). Regarding N2O emissions, the same paper reports non-significant effect. 

Table 35 Effect (% change) of the use of manure processing techniques on greenhouse gas emissions by major 
GHG  types.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of  pairwise 
comparisons. 

Technique 

GHG 

Source 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

g
n
i
t
s
o
p
m
o
C

All 

C/N adjust-
ment 

Bulking 
agents 

Vermi-
composting 

turning 

aeration 

chemical, 
physical or 
microbial 
additives 

CH4 

N2O 

CH4  

N2O 

CH4 

N2O  

CH4 

N2O 

CH4 

N2O 

CH4 

N2O 

CH420 

N2O20  

Z. Zhang et 
al. 2021 
Z. Zhang et 
al. 2021 

S. Zhao et 
al. 2020 
Pardo et al. 
2015 
Pardo et al. 
2015 

Ba et al. 
2020 

Ba et al. 
2020 

Pardo et al. 
2015 
S. Zhao et 
al. 2020 & 
Pardo et al. 
2015 
Pardo et al. 
2015 
Pardo et al. 
2015 

Y. Wang et 
al. 2017 

 Min 
-0.63 

Y. Wang et 
al. 2017 

Min 
-0.94 

-28 

-32 

NA 

-93 

-91 

-72 

NA 

NA 

-18.1 

-9 

77 

-23.8 

-15 

107 

Positive 

NA 

-30 

-71 

-53 

Median 
-0.78 
(P=0.32) 
Median -
0.53 
(P=0.32) 

NA 

Not available 

+30 

15 

Non-significant 

-31 

12 

Positive 

-21 

12 

Positive 

NA 

1 

Non-significant 

NA 

1 

Non-significant 

-90 

-71 

-18 

7 

Positive 

NA 
-75 

-95 

-35 

-55 
-50 

NA 
+2 

NA 
10 

Positive 

-63 

+24 

11 

Non-significant 

+45 

+161 

9 

Non-significant 

 Max 
+0.15 

Max 
-0.09 

 Mean 
-0.16 
p=0.465 
Mean 
-0.45 (p < 
0.01) 

Median  
-82 

P = 0.01 

Median  

4 

Non-significant 

11 

Positive 

24 

Positive 

35 

Positive 

All AD 
types 

c
i
b
o
r
e
a
n
A

n
o
i
t
s
e
g
d

i

GHG 
storage 

Miranda et 
al. 2015 

GHG field 
application 

Miranda et 
al. 2015 

52 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Technique 

GHG 

Source 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

GHG 
avoided 
fossil 
fuels 

CH4 

N2O 

CH4 

N2O 

Miranda et 
al. 2015 

Emmerling 
et al. 2020  

Emmerling 
et al. 2020 

Emmerling 
et al. 2020 

P = 
0.0026 

Median 
-11 
(p < 0.01) 

18 

Positive 

-65 

-10 

-46 

- 

-46 

-27 

NA 

Positive 

+10 

+30 

NA  Non-significant 

-27 

-8 

NA 

Positive 

- 

- 

- 

Non-significant 

All AD 
types 

Solid-liquid 
reparation 

Source: references in the third column. 

3.12.2 Effects on ammonia emissions 

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis  papers  (Schievano  et  al.  2023),  the  effect  of  manure  composting  techniques  can  be 
significant on reducing ammonia emissions, but depends on the analysed technique.  

Regarding  composting  techniques,  the  overall  effect  is  positive,  but  varies  depending  on  the 
considered composting technique (e.g. C/N adjustment, vermicomposting, addition of bulking agents, 
periodical turning, forced aeration, and/or the use of either chemical or physical or microbial additives 
to the composting piles). Z. Zhang et al. 2021, whose meta-analysis was based on 68 studies related 
to pig manure composting, report an average positive of -33% (95% CI: -38%, -28%) although they 
do not include all techniques. This overall value does not include vermicomposting nor bulking agents, 
but includes other physical, chemical and microbial additives, such as biochar and superphosphate. 
Separately, Z. Zhang et al. 2021 report an average positive effect of additives of -36% (95% CI: -
42%, -30%) with 128 observations. Similarly, Ti et al. 2019, report a positive effect with a reduction 
of -29% (95% CI:-385, -17%) with 49 observations and a -92.5% reduction for acidifiers alone (95% 
CI: -94%, -86%; Nc=17)  . In  Table 36, we provide data on additional  techniques such as adjusted 
moisture,  vermicomposting,  C/N  adjustment,  turning  frequency  and  bulking  agents  with  diverse 
effects on NH3 emissions.  

Table 36 Effect (% change) of the use of composting manure processing techniques on ammonia emissions. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Technique/Source 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Overall  
Source: Z. Zhang et al. 2021 

C/N adjustment 
Source: S. Zhao et al. 2020 

Adjusted moisture 
Source: Z. Zhang et al. 2021 

-38 

-32.7 

-28 

154 

Positive 

-27 

-9 

+7 

22 

Non-significant 

-33 

-19 

-10 

6 

Positive 

53 

 
 
 
 
 
Technique/Source 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Vermicomposting 

Source: Ba et al. 2020 

Additives 

Source: Z. Zhang et al. 2021 

Additives 

Source: Ti et al. 2019 

Acidifiers 

Source: Ti et al. 2019 

Turning 
Source: Pardo et al. 2015 

Turning 
Source: Z. Zhang et al. 2021 

Bulking agents 
Source: Pardo et al. 2015 

Source: references in the first column. 

median -
33.5 (p = 
0.002) 

6 

Positive 

-42 

-36 

-30 

128 

Positive 

-38 

-29.3 

-17 

49 

Positive 

-94 

-92.5 

-86 

17 

Positive 

+26 

+54 

+88 

13 

Negative 

+6 

+9 

+12 

2 

Negative 

+0.5 

+34.5 

+88.5 

14 

Negative 

3.13 Livestock housing techniques 

Livestock housing techniques include several improved strategies mainly used to reduce emissions to 
the environment and to improve animal welfare. Some of these techniques are floor type, in-house 
litter  amendment  with  conditioners,  additives  or  inhibitors,  frequency  of  litter/manure  removal, 
shading,  space  allowance,  light  management  and  ventilation.  However,  we  are  not  including  here 
manure management outside the house or on livestock feeding, nor the impact of livestock housing 
techniques on animal welfare, as these will be assessed separately. 

3.13.1 Effects on greenhouse gas emissions 

The effect of livestock housing techniques on greenhouse gas emissions depend on the technique 
applied and gas type (Table 37).  

Biofilters for exhaust air, compared to livestock houses without biofilters, have different effects on 
GHG emissions. Y. Wang et al. 2017 found that biofilters decrease CH4 emissions by -24% (p < 0.01) 
but have weak non-significant negative effect on N2O emissions. However, they also found that some 
studies  suggest  that  biofilters  may  increase  N2O  emissions  because  the  absorbed  NH3  from  the 
exhaust air may be nitrified and denitrified, generating N2O. The overall effect of biofilters on GHG 
emissions might be positive but cannot be determined without additional information.  

High frequency manure removal, compared to low frequency manure removal, has different effects 
on  GHG  emissions.  The  only  synthesis  paper  including  a  statistical  analysis (Hou  et  al.  2015)  has 
reported a positive effect on CH4 emissions (-55%; 95% CI: -70%, -33%) and not significant weak 
positive effect on N2O emissions. However, it is based in a very low number of observations (4 from 
one single study) therefore it cannot be considered a strong result.  

54 

 
 
 
 
 
Slurries underneath slatted floor showed higher CH4 emissions (+50%; 95% CI: 22%, 85%), but lower 
N2O emissions (-89%; 95% CI: -96%, -19%) than buildings with deep- litter, according to the only 
available synthesis paper (Hou et al. 2015). 

Table 37 Effect (% change) of livestock housing techniques on greenhouse gas emissions by major GHG types. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Technique 

GHG 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Biofilters for 
exhaust air 

High 
frequency 
manure 
removal 

Slurries 
underneath 
slatted floor 
as compared 
to deep-litter 

GHG 

CH4 

Source: Y. Wang et 
al. 2017 

N2O 

Source: Y. Wang et 
al. 2017 

GHG 

CH4 

Source: Hou et al. 
2015 

N2O 

Source: Hou et al. 
2015 

GHG 

CH4 

Source: Hou et al. 
2015 

N2O 

Source: Hou et al. 
2015 

NA 

Min 
-83 

Min 
-22 

NA 

NA 

-24 

p < 0.01 

+0.13 
P=0.195 

NA 

Max 

+10 

Max 
+81 

NA 

Not available 

14 

Positive 

13 

Non-significant 

NA 

NA 

NA 

Not available 

-70 

-55 

-33 

4 

Positive 

-100 

-42 

>+900 

4 

Non-significant 

NA 

NA 

NA 

NA 

Not available, 
mixed effect 

+22 

+50 

+85 

8 

Negative 

-96 

-89 

-19 

10 

Positive 

Source: references in second column. 

3.13.2 Effects on ammonia emissions 

Results on ammonia emissions differ depending on the technique applied. Several synthesis papers 
have been selected to cover the different techniques. 

According to Y. Wang et al. 2017 the use of biofilters on swine systems is one of the most effective 
mitigation measures for limiting NH3 emissions from animal houses (median -72%, p < 0.001). For 
comparison we also provide results by Y. Wang et al. 2019b, that reported a decrease of -85% in 
ammonia from the use of biotrickling filters on broilers systems, but it was not significant due to the 
low number of observations (Nc=3).  

Non-significant effects due to the low number of observations were reported for acid scrubbers (Y. 
Wang et al. 2019b). 

Mechanical  management  of  exhaust  air  and  manure,  compared  to  livestock  houses  without 
mechanical management, was addressed only by one  synthesis paper (Ti et al. 2019). The use of 

55 

 
 
 
mechanical management of air and manure was the most effective NH3 mitigation measure among 
those analysed by these authors, decreasing NH3 emissions by -58% (95% CI: -73.7%, -31.7%). This 
value is reflected in the results figure of the synthesis paper, while in the text the value is -73.6%, 
which in the figure exactly corresponds with the lower 95% CI. 

Results on the use of different floor types have been found in the literature review. A synthesis paper 
(Ti et al. 2019) found that several floor types and management reduced NH3 emissions by 48% (95% 
CI:  -61.2,  -29.6%;  Nc=25)  mainly in  pigs  but  also  cattle  barns.  These practices  include:  for  cattle, 
extra-straw addition compared to straw-bedded housing; for pigs: slatted floors compared with straw-
flow system, compared with concrete floors, compared with straw-based deep litter or sawdust-based 
deep litter; etc. The second best paper that can give more detailed information on specific floor types 
is Hou et al. 2015. They used data derived from 11 farm-scale studies to test the effects of animal 
houses with distinct floor constructions on emissions. Animal houses with alternative floors tended 
to have lower NH3 emissions compared to the reference floor construction. The difference between 
slatted-floor/deep litter stables compared with solid floor stables (-57%; 95% CI: -77.5%, -16.9%; 
Nc=4) was statistically significant (p < 0.01) while the difference between deep litter and slatted floor 
is not significant. Deep litter with extra straw addition has non-significant effect compared to deep 
litter without extra straw addition (Hou et al. 2015).  

High  frequency  litter/manure  removal,  compared  to  low  frequency  manure  removal,  has  non-
significant effect on NH3 emissions according to 2 synthesis papers (Hou et al. 2015; Y. Wang et al. 
2018a).  

In-house litter amendment with conditioners, additives or inhibitors, compared to untreated litter, has 
several results. Urease inhibitors showed a decrease on ammonia emissions of around 50% according 
to two synthesis papers (Ti et al. 2019; Y. Wang et al. 2018a). The selected synthesis paper is Ti et al. 
2019 with value -51.5%. A synthesis paper (de Toledo et al. 2020) finds an overall positive effect for 
litter treatment methods, but then they find a positive effect for acidifiers and gypsum, while  non-
significant  effects  for  alkalinizers,  adsorbents  and  superphosphate.  However,  we  cannot  use  their 
results as they provide them as the standardized mean difference,  which is calculated as the raw 
mean  difference  between  the  treatment  and  control  groups  divided  by  their  pooled  standard 
deviations. Y. Wang et al. 2019b found that acidifiers and zeolite (adsorbent) significantly reduced by 
44% ammonia emissions on poultry. Y. Wang et al. 2018a reported acidifiers decrease NH3 emissions 
(82%;  95%  CI:  -107%,  -58%;  Nc=4)  for  beef  cattle,  but  only  four  observations  were  available, 
resulting in an insignificant outcome (p = 0.068). Ti et al. 2019 reported positive effects for a type of 
adsorbents (XF & GY) in China different from the ones analysed by other authors (sepiolite, zeolite, 
bentonite, lignite and other types of coal, sawdust). Non-significant effects were reported for the rest 
of amendments, often due to the low number of observations.  

56 

 
 
 
 
 
Table 38 Effect (% change) of livestock housing techniques on ammonia emissions. SMD: Standardized mean 
difference  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of  pairwise 
comparisons. 

Technique 

Source 

Population  CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

t
s
u
a
h
x
e

r
o
f

s
r
e
t
l
i
f
o
B

i

r
i
a

Biofilters 

Biotrickling 
filters 

Acid scrubbers 

High frequency manure 
removal  

Slatted floor compared 
with several floors and 
extra straw compared with 
standard straw 

Deep litter compared with 
solid floors and slatted 
floor compared to solid 
floors 

Y. Wang 
et al. 
2017 

Y. Wang 
et al. 
2019b 

Y. Wang 
et al. 
2019b 

Hou et al. 
2015 / Y. 
Wang et 
al. 2018a 

Swine 

Broilers 

Poultry 

Livestock / 
Beef cattle 

Min: -86 

-63 

Mean-
SE: -71  

(median 
-72) 

Max: 
0.01 

Mean + 
SE: -56 

- 

- 

- 

-85 

-92.5 

- 

- 

- 

- 

14 

Positive 

3 

2 

- 

Non-
significant 

Non-
significant 

Non-
significant 

Ti et al. 
2019 

Pigs and 
cattle 

-61.2 

-48 

-29.6 

25 

Positive 

Hou et al. 
2015 

Livestock 

-77.5 

-56.7 

-16.9 

4 

Positive 

Deep litter compared to 
slatted floor 

Hou et al. 
2015 

Livestock 

-55.5 

-26.9 

19.9 

13 

Deep litter with extra 
straw compared to 
standard deep litter 

Mechanical management 
exhaust air and manure 

s
t
n
e
m
d
n
e
m
a

r
e
t
t
i
l

e
s
u
o
h
-
n
I

Urease 
inhibitors 

Overall: 
acidifiers, 
gypsum, 
alkalinizing, 
adsorbents, 
superphosphate) 

Gypsum 

Non-
significant 

Non-
significant 

Hou et al. 
2015 

Ti et al. 
2019 

Ti et al. 
2019 

Y. Wang 
et al. 
2018a 

Livestock 

-70.2 

-16.2 

138 

13 

Cattle, pig, 
poultry 

Cattle, pig, 
poultry 

-73.7 

-57.9 

-31.7 

16 

Positive 

-68.7 

-51.5 

-23.9 

9 

Positive 

Beef cattle 

-63.4 

-43.3 
(Median 
-59.5) 

-23.2 

20 

Positive 

de Toledo 
et al. 
2020 

Poultry 
(Broilers) 

NA 
(SMD -
1.722) 

NA 
(SMD -
1.014) 

NA (SMD 
-0.306) 

206 

Positive 

de Toledo 
et al. 
2020 

Poultry 
(Broilers) 

NA 
(SMD -
11.354) 

NA 
(SMD -
6.375) 

NA (SMD 
-1.396) 

32 

Positive 

57 

 
 
 
 
 
 
 
 
 
 
 
 
Technique 

Source 

Population  CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

Alkalizers21 

Acidifiers22 

Acidifiers23 

Litter additives 
(acidifiers + 
adsorbent)24 

Adsorbents (XF 
& GY)25 

Adsorbents26 

Adsorbent 
(Lignite) 

Adsorbent 
(Sawdust) 

Adsorbents: 
Yallourn brown 
coal and lignite 

Superphosphate 

de Toledo 
et al. 
2020 

de Toledo 
et al. 
2020 

Y. Wang 
et al. 
2018a 

Y. Wang 
et al. 
2019b 

Ti et al. 
2019 

de Toledo 
et al. 
2020 

Y. Wang 
et al. 
2018a 

Y. Wang 
et al. 
2018a 

Ti et al. 
2019 

Poultry 
(Broilers) 

Poultry 
(Broilers) 

Beef cattle 

NA 
(SMD -
0.803) 

NA 
(SMD -
1.898) 

-107 
(Max -
98) 

NA 
(SMD 
+0.184) 

NA 
(SMD -
1.075) 

-82.3 
(Median 
-85) 

NA (SMD 
+1.170) 

24 

Non-
significant 

NA (SMD 
-0.251) 

-58 
(Min -
61) 

122 

Positive  

4 

Non-
significant 

Poultry 

-75.1 

-44.2 

-13.3 

17 

Positive 

Cattle in 
China 

Poultry 
(Broilers) 

-50.9 

-30.5 

0 

26 

Positive 

NA 
(SMD -
3.410) 

NA 
(SMD -
0.231) 

NA (SMD 
+2.949) 

20 

Non-
significant 

Beef cattle  Max -66 

-41.3 

Min -28 

3 

Beef cattle  Max -45 

-20 

Min - 6 

3 

Cattle 

-91 

-37.1 

333.9 

3 

Non-
significant 

Non-
significant 

Non-
significant 

de Toledo 
et al. 
2020 

Poultry 
(Broilers) 

NA 
(SMD -
5.474) 

NA 
(SMD -
2.099) 

NA (SMD 
+1.276) 

8 

Non-
significant 

Source: references in second column. 

21 The alkalizing sub-group was formed by hydrated lime, quicklime, calcitic limestone and dolomitic limestone. 
22 Acidifiers include aluminum sulfate, sodium bisulfate, potassium permanganate, aluminum chloride, ferrous sul- fate, 

acidified clay, alum, hydrochloric-

23 Chemical amendments such as alum (Al2(SO4)3) and calcium chloride (CaCl2). They reduceNH3 emissions by 

decreasing the pH and increasing the function of H+ through cation exchange 

24 Litter additives were usually used in broiler litter house, including alum, sodium bisulfate (PLT), and zeolite, with the 

median NH3 mitigation for all the litter additives being 39.9% (p < 0.01). Alum and PLT were acidulants, and they 
could reduce NH3 emissions by decreasing the pH and increasing the function of H+ through cation exchange. Zeolite 
was an effective, non-corrosive and non-hazardous litter additive, which could reduce NH3 emissions by adsorption 

25 XF-4, GY1, GY2, GY3 and GY4 for cattle. 

26 Adsorbents include sepiolite, zeolite, bentonite and coal. 

58 

 
 
 
                                                 
 
 
3.14 Livestock feeding techniques 

Livestock  feeding  techniques  include  two  main  groups  of  interventions  used  to  improve  animal 
feeding management and to reduce emissions to the environment:  

─  Diet formulation. It includes these interventions: dietary legume; forage with higher digestibility; 
high concentrate level in diet; low crude protein diet; tannin rich forage; fermented feed; lipids or 
oils. 

─  Feed additives. They include these interventions: Coccidiostats and histomonostats (substances 
intended to kill or inhibit protozoa); enzymes (carbohydrase, phytase, not specified); nutritional 
additives (vitamins, amino acids, urea, etc.); sensory additives (any feed additive that improves or 
changes the organoleptic properties of the feed or the visual characteristics of the food derived 
from  animals,  e.g.  colorants,  flavourings);  technological  additives  (preservatives;  antioxidants, 
emulsifiers,  stabilisers,  thickeners,  acidity  regulators,  silage  additives);  zootechnical  additives 
(digestibility enhancers, gut flora stabilisers, etc.); and non-specified feed additives.  

This is not an exhaustive list of livestock feeding techniques but of those found in the literature that 
meet the requirements to be included in the meta-analysis review. The impact of livestock feeding 
techniques on animal welfare is not included here. Given the high number of substance under each 
category, we will only report the quantitative data when the impact is positive or negative. 

3.14.1 Effects on greenhouse gas emissions 

The effect on greenhouse gas emissions varies among livestock feeding techniques.  

Regarding diet formulation techniques, the analysis of synthesis papers found significant effects 
only for lipids.  

─  Lipids,  compared  to  no  lipid,  show  a  positive  or  non-significant  effect  on  CH4  emissions, 
depending mainly on the lipid type (e.g. medium chain fatty acids, long chain fatty acids; specific 
oil type). Given that lipids do not have N in their composition, a direct impact of lipids on N2O 
emissions is not expected. The selected paper for lipids is Y. Wang et al. 2018a. Their results for 
beef  cattle  showed  that  the  median  mitigation  efficiency  on  CH4  emissions  for  dietary  lipid 

-16.8% (95% CI: -28.6%, -5.0%; Nc=15). 

Table 39. Effect (% change) of the use of diet formulation techniques on GHG emissions by GHG type. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Technique 

Source 

GHG 

CI_LOW  Mean 

CI_HIGH  Nc  Effect 

Lipids overall 

Y. Wang et al. 
2018b 

CH4 

-28.6 

-16.8 

(Median -14.9) 

-5.0 

15  Positive 

Source: references in second column. 

The significant impacts of the different feed additives are synthesised in Table 40.  

59 

 
 
 
─  Coccidiostats  and  histomonostats:  results  are  not  reported  given  that  ionophores  are  not 

traditionally allowed for use in ruminants by the EU legislation (Y. Wang et al. 2018a)27.  

─  Sensory additives, compared to no feed additive, have mixed results depending on the additive 
used. There are no results regarding N2O emissions. Observed effects on CH4 emissions are either 
non-significant or positive. As it depends on the type of additive, we report results from several 
papers, ordered by the ranking performed based on the quality of the paper: 

o  The  highest  rank paper  (Torres  et  al.  2021)  compares  essential  oils (sensory additives) to 
monensin (ionophore), finding no significant differences on CH4 emissions of beef cattle.  

o  The  second  rank  paper  is  Belanche  et  al.  2020

supplementation with Agolin® Ruminant essential oils blend decreased the  CH4 production 
per day (-8.8%), per dry matter intake (-12.9%) and per fat and protein corrected milk yield 
(-9.9%). The meta-analysis addressing short-term exposure found non-significant effects. 

o  Darabighane  et  al.  2021  found  that  using  saponin-rich  sources  on  sheep  reduced  CH4 
production by 1.246 g CH4/day (but it is not significant) and CH4by 0.849 g CH4/kg dry matter 
intake (DMI) (significant).  

o  Y.  Wang  et  al.  2018b  found  non-significant  effect  for  sensory  additives  (saponins  and 

essential oils) on beef cattle with only 3 observations.  

o  A paper that provides a high number of results for specific additives is Lewis et al. 2015, but 
they  have  not  been  statistically  tested.  Nayak  et  al.  2015  found  that  tannins  and  (tea) 
saponins have proven effective to decrease CH4 emissions with a mean reduction of 15% ± 
4%.  

o  Yanza et al. 2020 analysed the effects of lauric and mystiric acids but only found positive 

impacts for in vitro experiments, not for in vivo ones.  

o  van  Gastelen  et  al.  2019  did  not  find  a  significant  effect  for  garlic  oil  and  tannins 

supplementation on ruminants. 

o  Dai &  Faciola  2019  looked  at  the effect  of  different  sensory  additives  on  ruminants  CH4 
emissions. The global impact of phytochemicals overall was -20%, mainly led by tannins (-
20%) and saponins (-15%) while for essential oils they did not find a significant effect. 

o  Harahap  et  al.  2020  found  a  positive  effect  in  in-vitro  experiments  for  Chitosan,  but  this 

product seems not to be among the approved feed additives in EU28. 

─  Zootechnical  additives,  have  a  majority  of  positive  results  on  CH4.  As  the  effect  on  CH4 
emissions depends on the type of additive, we collect results for the different types, starting from 
the highest ranking synthesis paper to the lowest: 

27 See https://food.ec.europa.eu/safety/animal-feed/feed-additives/eu-register_en for an up to date version of the 

European Union Register of Feed Additives 

28 https://food.ec.europa.eu/safety/animal-feed/feed-additives/eu-register_en 

60 

 
 
                                                 
 
o 

 X. Y. Feng et al. 2020 looked at the effects of nitrates supplementation on dairy and beef 
cattle. The overall effect size when slow release urea was included was -13.9±0.95% for CH4 
production (g/day) and -11.4±1.25% for yield (g/kg of DMI). If results with slow release urea 
were removed from the sample, the effect was a bit higher: -15±1.12% for CH4 production 
and  -13.2  ±1.46%  for  CH4  yield.  Results  depend  on  different  factors,  mainly  dry  matter 
intake, nitrate dose (g/kg of DMI) and type of cattle (beef or dairy). Nitrate effect sizes were 
significantly different between the 2 types of cattle for CH4 
CH4 

data  from  slow-release  nitrate  sources  were  removed  from  the  analysis,  there  was  no 
significant difference in type of cattle anymore for CH4 yield while values for CH4 production 
were -21.1±1.87% for dairy cattle and -8.54±2.21% for beef cattle. Nitrate dose enhanced 
the mitigating effect of nitrate on CH4 production and yield by 0.911 ± 0.1407% and 0.728 
±  0.2034%,  respectively,  for  every  1  g/kg  of DM  increase  from its  mean dietary  inclusion 
(16.7 g/kg of DM). An increase of 1 kg of DM/d in DM intake from its mean dietary intake 
(11.1 kg of DM/d) decreased the effect of nitrate on CH4 production by 0.691 ± 0.2944%. 

o  Dijkstra et al. 2018 look at results of 3-nitrooxipropanol (3NOP) on CH4 emissions of dairy 
and beef cattle. Using RVE random-effects models, an average dose of 123 mg of 3NOP/kg 
of dry matter in dairy and beef cattle reduced CH4 production (p < 0.001) by 32.5 ± 5.74% 
and CH4 yield (p < 0.001) by 29.3 ± 5.63%. When adjusted for 3NOP dose and dietary neutral 
detergent  fibre  (NDF)  content,  the  CH4-mitigating  effect  of  3NOP  was  less  in  beef  cattle 
3NOP 
dose from its mean (123 mg/kg of DM) enhanced the 3NOP effect on CH4 production decline 
by 2.56 ± 0.550%. However, a greater dietary NDF content impaired the 3NOP effect on CH4 
production by 1.64 ± 0.330% per 10 g/kg DM increase in NDF content from its mean (331 g 
of NDF/kg of DM). The factors included in the final mixed-effect model for CH4 yield were 

DM increase in 3NOP dose from its mean, and 1.52 ± 0.406% per 10 g/kg DM increase in 
NDF content from its mean.  

o  Lewis  et  al.  2015  analysed  fumaric  and  linoleic  acids  but  the  results  have  not  been 

statistically tested.  

o  Nayak et al. 2015 found positive effects for nitrates and chemical inhibitors, but the main 
chemical inhibitors analysed were ionophores and bromochloro-methane, banned in the EU. 
They also looked at methods for altering the microbial community: probiotic supplementation 
of  yeast  or  bacteria,  and  defaunation  (i.e.,  removal  of  ciliate  protozoa  from  the  rumen 
ecosystem), but they did not prove successful in reducing CH4 emissions (-1% ± 4% and 0% 
± 9% mean reduction respectively).  

o 

Instead,  Z.  Li  et  al.  2018  found  a  positive  result  for  the  elimination  of  rumen  protozoa 
(defaunation) from a high number of studies. However, the  effect size  used (standardized 
mean difference, SMD) does not allow us to extract useful quantitative coefficients. 

o  Ungerfeld  et  al.  2007  found  a  weak  positive  effect  for  fumarate.  They  found  a  linear 
relationship between CH4 emissions decrease and fumarate concentration, with a decrease 
in CH4 emissions of 0.037 µmol/µmol of added fumarate (P= 0.03). The observed decrease 
was more than 6-fold lower than the theoretical stoichiometry of -0.25 mol of CH4/mol of 
added  fumarate.  Fumarate  added  to  continuous  culture  appears  in  general  to  be  more 
effective in decreasing CH4 production than in batch cultures, which suggests that adaptation 
of ruminal microbiota to metabolize fumarate occurs.  

61 

 
 
Table 40. Effect (% change unless otherwise specified) of the use of feed additives on GHG emissions by major 
GHG  types.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of  pairwise 
comparisons. 

Technique 

GHG 

Source 

Popula
tion 

Factors  CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Sensory additives   plant extracts   phytochemicals overall 

Tea 
saponins 
and tannins 

Plant 
bioactive 
compounds 
(saponin and 
essential oil) 

CH4 

Nayak et al. 
2015 

Cattle 
& 
sheep 

CH4 

Y. Wang et 
al. 2018b 

Beef 
cattle 

Several 

CH4 

Lewis et al. 
2015 

Rumina
nts 

Tannins, 
saponins 
and 
essential oils 

CH4 g/Kg 
DMI29 

Dai & 
Faciola 
2019 

Rumina
nts 

-20 

-15 

-11 

29 

Positive 

- 

-- 

- 

- 

- 

- 

3 

Non-
significant 

- 

Not tested 

NA 

-20 

NA 

NA 

Positive 

Sensory additives   plant extracts - Tannins 

Tannins 
supplemen-
tation 

Tannins 
supplemen-
tation 

CH4 

van Gastelen 
et al. 2019 

Rumina
nts 

- 

- 

- 

- 

Non-
significant 

CH4 g/kg 
DMI 

Dai & 
Faciola 
2019 

Rumina
nts 

NA 

-20 

NA 

NA 

Positive 

Sensory additives   plant extracts - Saponins 

Saponins 
supplemen-
tation 

Saponins 
supplemen-
tation 

CH4 g/kg 
DMI 

CH4 
g/day 

CH4 g/kg 
DMI 

Darabighane 
et al. 2021 

Sheep 

Dai & 
Faciola 
2019 

Rumina
nts 

NA 

0.849 
g/kg 

NA 

15 

Positive 

- 

- 

- 

12 

Non-
significant 

NA 

-15 

NA 

NA 

Positive 

Sensory additives   Essential oils 

-13.2 

-8.8 

-4.2 

7 

Positive 

-7.9 

-4.6 

-1.3 

8 

Positive 

Agolin® 
Ruminant 
essential oils 
blend 
supplemen-
tation 

CH4 
productio
n (g/day) 

Belanche et 
al. 2020 

Dairy 
cattle 

29 DMI: Dry matter intake 

long term 
(>= 28 
days) 

overall 
(short + 
long 
term) 

62 

 
 
 
 
 
 
 
 
 
 
 
 
                                                 
 
Technique 

GHG 

Source 

Popula
tion 

Factors  CI_LOW  Mean  CI_HIGH  Nc 

Effect 

CH4 yield 
(g/kg 
DMI) 

Belanche et 
al. 2020 

CH4 
intensity 
(g/kg 
FPCM30) 

Belanche et 
al. 2020 

long term 
(>= 28 
days) 

overall 
(short + 
long 
term) 

long term 
(>= 28 
days) 

overall 
(short + 
long 
term) 

-19.8 

-12.9 

-5.5 

7 

Positive 

-8.2 

-1.8 

5 

8 

Non-
significant 

-19.3 

-9.9 

0 

5 

Positive 

-13.6 

-7.5 

-1.1 

8 

Positive 

Essential oils 
compared to 
monensin 

CH4 

Torres et al. 
2021 

Beef 
cattle 

Garlic oil 

CH4 

Essential oils 

CH4 g/kg 
DMI 

van Gastelen 
et al. 2019 

Rumina
nts 

Dai & 
Faciola 
2019 

Rumina
nts 

- 

- 

- 

Sensory additives   fatty acids 

- 

- 

- 

- 

- 

- 

- 

- 

Non-
significant 
compared 
to monensin 

Non-
significant 

Non-
significant 

Non-
significant 

- 

- 

- 

- 

Lauric acid 

CH4 

Yanza et al. 
2020 

Rumina
nts 

Mystiric acid 

CH4 

Yanza et al. 
2020 

Rumina
nts 

In vivo 

- 

In vitro 

NA 

-41% 

NA 

102 

Positive 

In vivo 

In vitro 

In vivo 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

Non-
significant 

Non-
significant 

Non-
significant 

Mixed 
depending 
on the 
metric 

Mixed lauric 
and mystiric 
acids 

CH4 

Yanza et al. 
2020 

Rumina
nts 

In vitro 

NA 

-51% 

NA 

102 

Sensory additives- Miscellaneous chemical and natural substances 

Chitosan 
(not 

CH4 

Harahap et 
al. 2020 

In vitro 
only 

- 

- 

- 

- 

Positive 

30 FPCM: Fat and protein corrected milk yield. 

63 

 
 
 
 
 
 
                                                 
 
Technique 

GHG 

Source 

Popula
tion 

Factors  CI_LOW  Mean  CI_HIGH  Nc 

Effect 

approved in 
EU) 

Zootech-
nical 
additives 

N2O 

Y. Wang et 
al. 2018b 

Zootechnical additives 

- 

- 

- 

- 

Not tested 

Overall 

-14.9 (SE) 

-13.9 

-12.9 (SE)  NA 

Positive 

CH4 
productio
n (g/day) 

X. Y. Feng et 
al. 2020 

Dairy 
cattle 

Beef 
cattle 

-22.3 (SE) 

-20.4 

-18.5 (SE)  NA 

Positive 

-11.6 (SE) 

-10.1 

-8.6 (SE)  NA 

Positive 

Overall 

-12.6 (SE) 

-11.4 

-10.2 (SE)  NA 

Positive 

CH4 yield 
(g/kg dry 
matter 
intake) 

X. Y. Feng et 
al. 2020 

Dairy 
cattle 

Beef 
cattle 

-16.6 (SE) 

-15.5 

-14.4 (SE)  NA 

Positive 

-10.7 (SE) 

-9.0 

-7.2 (SE)  NA 

Positive 

Overall 

-16.1 (SE) 

-15.0 

-13.9 (SE)  NA 

Positive 

CH4 
productio
n (g/day) 

X. Y. Feng et 
al. 2020 

Dairy 
cattle 

Beef 
cattle 

-23.0 (SE) 

-21.1 

-19.2 (SE)  NA 

Positive 

-10.7 (SE) 

-8.5 

-6.3 (SE)  NA 

Positive 

Overall 

-14.7 (SE) 

-13.2 

-11.7 (SE)  NA 

Positive 

CH4 yield 
(g/kg dry 
matter 
intake) 

X. Y. Feng et 
al. 2020 

Dairy 
cattle 

Beef 
cattle 

NA 

NA 

NA 

NA 

Positive 

NA 

NA 

NA 

NA 

Positive 

Overall 

-38.2 (SE) 

-32.5 

-26.8 (SE)  NA 

Positive 

CH4 
productio
n (g/day) 

Dijkstra et 
al. 2018 

Dairy 
cattle 

Beef 
cattle 

-40.4 (SE) 

-39.0 

-33.6 (SE)  NA 

Positive 

-25.5 (SE) 

-22.2 

-18.9 (SE)  NA 

Positive 

Overall 

-34.9 (SE) 

-29.3 

-23.7 (SE)  NA 

Positive 

Nitrates 
supplemen-
tation 
including 
slow release 
urea 

Nitrates 
supplemen-
tation 
excluding 
slow release 
urea 

3-
nitrooxpropa
nol (3NOP) 

CH4 yield 
(g/kg dry 
matter 
intake) 

Dijkstra et 
al. 2018 

Dairy 
cattle 

Beef 
cattle 

Cattle 
& 
buffalo 

Fumaric acid 

CH4 

Lewis et al. 
2015 

Linoleic acid 

CH4 

Lewis et al. 
2015 

Cattle, 
buffalo, 

64 

-44.3 (SE) 

-38.8 

-33.3 (SE)  NA 

Positive 

-21.3 (SE) 

-17.1 

-12.9 (SE)  NA 

Positive 

- 

- 

- 

- 

- 

- 

- 

Not tested 

- 

Not tested 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Technique 

GHG 

Source 

Chemical 
inhibitors 
(bromochlor
o  CH4  not 
allowed) 

Elimination 
of rumen 
protozoa 

CH4 

Nayak et al. 
2015 

CH4 

CH4 

Nayak et al. 
2015 

Z. Li et al. 
2018 

Fumarate 

CH4 
productio
n 

Ungerfeld et 
al. 2007 

Source: references in second column. 

Popula
tion 

sheep 
& goat 

Sheep 
and 
cattle 

Rumina
l batch 
culture
s 

Factors  CI_LOW  Mean  CI_HIGH  Nc 

Effect 

- 

- 

- 

- 

- 

- 

- 

- 

Positive but 
not allowed 

Non-
significant 

Metric: 
SMD 

NA 

NA 

NA 

49 

Positive 

NA 

0.037 
µmol/µm
ol of 
added 
fumarate 

NA 

7 

Positive 

3.14.2 Effects on ammonia emissions 

The effect on ammonia emissions varies among livestock feeding techniques. However, a synthesis 
paper (Ti et al. 2019) provides an average value for several aggregated feeding techniques, including 
crude protein content reduction, dietary yucca extracts, dietary bacteria, dietary acidifiers and other 
dietary additives. NH3 emissions decrease on average by -39.6% (95% CI: -44.5, -34.3%; Nc= 276). 

Table 41. Overall effect (% change) of analysed feeding techniques on ammonia emissions (effect varies with 
the  specific  technique  used,  see  following  tables).  CI_LOW: confidence  interval  bottom,  CI_HIGH:  confidence 
interval top, Nc: number of pairwise comparisons. 

Technique 

Source 

Population  Metric 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Feeding 
techniques 
overall 

Ti et al. 
2019  

Cattle, pigs 
and poultry 

NH3 
emissions 

-44.5 

-39.6 

-34.3 

276 

Positive 

Source: references in second column. 

The effects on diet formulation techniques are collected in Table 42.  

─  The systematic review of synthesis papers only found a significant effect on ammonia emissions 
for low crude protein diet. The selected synthesis paper is Ti et al. 2019. According to this paper, 
crude protein content reduction, compared to no reduction, abates NH3 emissions by 42.8% (95% 
CI: -51%, -33%; Nc= 56).  

65 

 
 
 
 
 
 
 
 
Table  42.  Effect  (%  change)  of  the  use  of  diet  formulation  techniques  on  ammonia  emissions.  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Technique 

Source 

Population  Metric 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Low crude 
protein diet31 

Ti et al. 
2019 

Cattle, pigs 
and hens 

NH3 
emissions 

-51.1 

-42.8 

-33.1 

56 

Positive 

Source: references in second column. 

The  rest  of  interventions  for  which  significant  effects  were  found  are  feed  additives.  Results  are 
shown in Table 43.  

─  Coccidiostats and histomonostats: results are not reported here as they apply to ruminants 
and these substances are allowed for use on some types of poultry but not on ruminants by the 
EU legislation (European Commission 2022). 

─  Sensory  additives,  compared  to  no  feed  additive,  have  different  effects  on  air  pollutant 
emissions.  Ti  et  al.  2019  showed  a  significant  reduction  of  33%  of  NH3  emissions  with  the 
supplementation of Yucca extracts32 on broilers. Harahap et al. 2020 showed a non-significant 
effect for Chitosan on ruminants. Last, Lewis et al. 2015 looked at a large number of additives 
(Allium arenarium oil; Anethum graveolens oil; Armoracia rusticana extract; Capsicum oleoresin; 
Cinnamaldehyde; Cinnamomum verum; Condensed tannins; Eucalyptus oil; Fenugreek; Juniperus 
communis berry oil; Mentha piperita oil; Origanum vulgare; Psidium guajava; Quillaja saponaria 
extract;  Syzygium aromaticum;  Tannic  acid;  Thymol;  Thymus vulgaris)  but  this  paper  does  not 
include the required statistical testing.  

─  Technological  additives,  compared  to  no  feed  additive,  have  mixed  effects  on  ammonia 
emissions,  depending  on  the  additive.  The  literature  found  non-significant  effect  on  ammonia 
emissions  and  electron  receptors  additives  (Y.  Wang  et  al.  2018b)  and  for  acidifiers 
supplementation  (Ti  et  al.  2019)  but  a  positive  effect  -37.8  %  (95%  CI:  -46.6%,  -27.5%)  for 
bacteria supplementation of pigs and poultry (Ti et al. 2019). 

─  Non-specified feed additives. The selected paper for livestock overall is Ti et al. 2019. They 
found that dietary additives can reduce NH3 emissions in pigs, poultry and cattle systems by -
45.5%  (95%  CI:  -54.2%,  -35.2%),  from  100  pairwise  comparisons.  For  the  types  of  additives 
included in this group see footnote of Table 43. Higher results are reported for poultry, as Y. Wang 
et al. 2019c found that the additives in feed reduced the in-house NH3 emissions by -64% (mean, 
-36.3% median) (p < 0.001). Instead, for pigs, H. Wang et al. 2020 report that mean emissions of 
NH3 decreased with the usage of other additives by -21.5% (p < 0.01).  

31 Low protein diet, all the low protein diets are supplemented with the first four crystalline amino acids (L-lysine, DL-

methionine, L-threonine and L-trypto-phan) to balance for ideal protein ratio;  

32 Under the 

s have included: 150 mg/kg Yucca extract; Yucca; Cinnamon 

0.625g/L; Cinnamon 1.25g/L; Cinnamon 2.5g/L; Cinnamon 5g/L; Cinnamon 10g/L; Garlic 0.0625g/L; Garlic 0.125g/L; 
Garlic 0.25g/L; Garlic 0.5g/L; Garlic 1g/L; Ydeca 0.025g/L; Ydeca 0.05g/L; Ydeca 0.1g/L; Ydeca 0.2g/L; Ydeca 0.4g/L. 

66 

 
 
 
                                                 
 
Table 43. Effect (% change) of the use of feed additives on ammonia emissions. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Technique 

Source 

Population  Metric 

CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

Yucca 
extracts33  

Chitosan 

Several 
sensory 
additives 

Electron 
receptors 
additives 

Acidifiers 

Bacteria 

Sensory additives 

Ti et al. 
2019 

Harahap 
et al. 
2020 

Lewis et 
al. 2015 

Broiler 

Ruminants 

Cattle, 
buffalo, 
sheep and 
goat 

NH3 
emissions 

NH3 
emissions 

NH3 
emissions 

-53.7 

-33.0 

-3.1 

19 

Positive 

- 

- 

- 

- 

- 

- 

- 

- 

Non-
significant 

Not 
tested 

Technological additives 

Y. Wang et 
al. 2018b 

Beef cattle 

NH3 
emissions 

- 

- 

- 

- 

Ti et al. 
2019 

Ti et al. 
2019 

Poultry and 
pigs 

NH3 
emissions 

Pigs and 
poultry 

NH3 
emissions 

-37.4 

-8.6 

33.5 

12 

-46.6 

-37.8 

-27.5 

89 

Positive 

Non-
significant 

Non-
significant 

Non specified additives 

Dietary 
additives34 

Other 
additives35 

Ti et al. 
2019 

H. Wang 
et al. 
2020 

Cattle, pigs 
and poultry 

NH3 
emissions 

Swine 

NH3 
emissions 

-54.2 

-45.5 

-35.2 

100 

Positive 

-26.4 

-21.5 

-11.0 

108 

Positive 

Diet 
additive36 

Y. Wang et 
al. 2019c 

Poultry 

NH3 
emissions 

-77.7 

-64.0 
(-36.3 
median) 

-50.3 

30 

Positive 

Source: references in second column 

33 

0.625g/L; Cinnamon 1.25g/L; Cinnamon 2.5g/L; Cinnamon 5g/L; Cinnamon 10g/L; Garlic 0.0625g/L; Garlic 0.125g/L; 
Garlic 0.25g/L; Garlic 0.5g/L; Garlic 1g/L; Ydeca 0.025g/L; Ydeca 0.05g/L; Ydeca 0.1g/L; Ydeca 0.2g/L; Ydeca 0.4g/L. 

34 Dietary additives included here: Cinnamon extract; Yucca schidigera extract; SS extract; OFL extract; Copper silicate 

nanoparticale; Aromex ME Plus at 100 mg/kg; Fresta F Plus at 150 mg/kg; Liquid Al+Clear1; Liquid Al+Clear2; Liquid 
Al+Clear4; Granular Al+Clear 0.5; Granular Al+Clear 1.0; Granular Al+Clear 1.5; GranularFerix-3 0.5; GranularFerix-3 
1.0; GranularFerix-3 1.5; PLT0.5; PLT1.0; PLT1.5; Zn1000; Zn2000; Zn3000; 0.45 % Tannin; 1.80 % Tannin; Dried 
distillers grains with solubles. 

35 Other additives: e.g. fermentable carbohydratesm acidifying agent/salts, probiotics and/or prebiotics. 
36 The feed additives used during chicken rearing include zeolite, probiotics, Yucca, etc. 

67 

 
 
                                                 
 
3.15 Landscape Features 

Landscape  features  in  general,  comprise  small  areas  of  non-productive  semi-natural  vegetation 
embedded in farmlands, as well as anthropogenic structures. They are usually defined as a group/list 
es  in  line,  in  group  or  isolated,  field 
of  elements 
margins,  terraces,  dry-stone  or  earth  walls,  vegetated  areas,  individual  monumental  trees,  water 
streams, springs or historic canal networks presented in agricultural land, either as historical remnants 
or newly established. 

Nevertheless, there is no standard definition and typology of landscape features, existing different 
interpretations  in  the  various  sectors  and  disciplines.  The  literature  review  feeding  this  document 
 the feature types addressed in the synthesis scientific 
literature (i.e., it is not an exhaustive list but comprises only the features found in the  papers that 
Thus,  the  quantitative  information  here  provided  is  related  to  the 

feature definition proposed by the authors of the selected papers.  

The impacts of landscape features include spatial and temporal comparisons between agricultural 
land (cropland or grassland) with and without landscape features embedded within the farm or with 
and without landscape features within the surrounding agricultural landscape. 

3.15.1 Effects on soil organic carbon 

Quantitative estimations on the effects of field margins/grass strips on SOC stocks are extracted from 
the MA by Van Vooren et al. 2017. The influence of this landscape feature was calculated considering 
field plots adjacent to a grass strips as intervention and without it as control. The width and age of 
the grass strip were considered too in the analysis as covariables. The overall effect is a significant 
increase in carbon stock (total carbon accumulated in the soil profile): +24.6% on average (95% CI: 
15.0%, 30.0%) within the grass strip itself. Authors warn that this result is reliable for the upper soil 
layer (up to 62 cm in this case), because the majority of observations (101 out of 108) were situated 
in the upper 30 cm. When only the 0-30 cm depth range is considered, the effect is even more marked, 
the average increase in SOC being +37% (CI not provided). 

Table  44  Overall  effect  (%)  of  field  margins  on  SOC  stock.  CI_LOW:  confidence  interval  bottom,  CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. 

Intervention 

Factor 

CI_LOW  Mean  CI_HIGH 

Field margins (grass 
strips) adjacent to field 
plot 

Full soil profile 
Upper layer (0-30 
cm) 

15.0 

24.6 

NA 

37 

35 

NA 

Nc 

108 

NA 

Effect 

Positive 

Positive 

Source: Van Vooren et al. 2017 

Drexler  et  al.  2021  provide  a  meta-analysis  on  the  effect  of  hedgerows  on  SOC  and  carbon 
sequestration. The review is based on 10 studies comprising 108 observations in total. Data from 
European  countries  were  included  in  this  global  study.  Two  metrics  calculated  by  the  authors  are 
reported here: change in SOC stock and average additional Carbon sequestration rate (compared to 
cropland alone) over a period of 20 years.  

Results show that the establishment of hedgerows on cropland leads to a significant increase in SOC 
stocks. The SOC stock under hedgerows was on average 32 % higher than in the adjacent cropland 
(95 % CI: 15 - 51 %). This translates into an absolute SOC stock increase of 17 ± 12 Mg C per hectare. 
In contrast, the difference between SOC under hedgerows and adjacent grasslands was close to zero 

68 

 
 
%, 19%). As for Carbon sequestration rate, hedgerows compared 
and not significant (9 %; 95% 
to cropland can sequester on average 0.9 (95% CI: 0.7, 1.7] more Mg C/ha/yr, calculated as the mean 
over a period of 20 years.  

Table 45: Effect of hedgerows on soil organic carbon (SOC) and carbon stock sequestration (C seq.) compared 
to cropland and grassland. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number 
of pairwise comparisons.  

Intervention 

Impact 

Metric 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

Hedgerows 
adjacent to 
cropland vs. 
cropland 

Hedgerows 
adjacent to 
grassland vs. 
grassland 

SOC stock 

C sequestration 
rate (mean over 
20 year period) 

% 
Mg C/ha 

15 
5 

Mg C/ha/yr  

0.7 

32 
17 

0.9 

51 
39 

1.7 

SOC stock 

% 

-30.1 

-8.6 

19.4 

45 

38 
38 

Positive 
Positive 

Positive 

Non-
significant 

 Source: Drexler et al. 2021 

Abera et al. 2020 found that terraces and bunds have a positive effect on soil carbon sequestration 
compared  to  cropland  and  grassland  without  terraces,  but  the  magnitude  and  significance  of  the 
effect depends on the type of terrace being considered (contour bund or stone terraces, vegetated or 
not). However, the study applies only to Ethiopia, and provides results for fanya juu37 terraces. 

Table 46: Effect (% change) of terraces and contour bunds on soil organic carbon (SOC). CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Practice 

Practice as 
named in the 
paper 

Region 

CI_LOW  Mean  CI_HIGH 

Nc 

Effect 

Terraces 

Fanya juu 

Ethiopia 

11 

>1038  Positive 

Contour bunds (stone or 
soil walls on slopes) with 
NO permanent green 
cover  
Contour bunds (stone or 
soil walls on slopes) 
WITH permanent green 
cover 

Source: Abera et al. 2020 

(Stone or soil) 
bunds 

Bunds + 
biological39 

Ethiopia 

-6 

4.9 

16 

>10 

Non-
signific
ant 

Ethiopia 

89 

139 

164 

NA 

Positive 

37 Fanya juu is a special kind of bund constructed by digging trenches along the contour of the slope and heaping the soil 

on the uphill side. 

38 

The statistics of effect size is calculated for agroecology and intervention 

combinations with 10 or more case- studies.  

39 Biological refers to options including agroforestry and tree/forage planting as part of restoration, intensification, and/or 

diversification options. 

69 

 
 
 
 
 
                                                 
 
3.15.2 Effects on nutrient leaching and runoff 

Quantitative estimations on the effects of buffer strips on nutrient leaching and runoff are extracted 
from Valkama et al. 2019, based on 46 primary studies carried out in different areas of the world. 
This synthesis paper estimates the retention of nitrogen in the form of nitrates (NO3) and total N in 
surface  runoff  (concentration  and  loads  pooled  together)  and  of  nitrates  (concentration  only)  in 
groundwater. Results are summarized in Table 47. 

The overall mean effect size is a reduction of 33% of NO3 in surface runoff (95% CI: -48%, -17%) 
and -57% (95% CI: -68%, -43%) when total N is considered. For N in groundwater, the mean effect 
size is  -70% (95% CI: -78%, -62%).  In all cases, results do not significantly  change whether N is 
measured as concentration or load, and for surface runoff, results do not depend on the type of runoff 
(natural vs artificial). 

More specific results are available for different types of pollution sources, i.e. whether buffer strips 
are located next to cereal fields, grasslands or feedlots. For N in groundwater, the above positive 
effects are not statistically affected by the source of pollution, while significant differences are found 
for surface runoff. A more marked positive effect was estimated for buffer strips next to cereal fields 
(mean effect size -53.3%; 95% CI: -62.9%, -42.2%) and for those next to feedlots (-51.2; 95% CI: -
72.8%,  -20.7%),  i.e.  where  the  initial  concentration  and  loads  of  N  are  higher.  Conversely,  non-
significant effect was estimated for buffer strips next to plots used for grass production, which is 
likely due to their initially low levels of pollution. Finally, the MA also considered effects in different 
continents and climatic zones, finding not significant differences based on studies from Europe only 
compared to the general results shown above. 

Table  47:  Effects  (%)  of  buffer  strips  on  N  retention  for  surface  runoff  (concentration  and  loads)  and  N 
groundwater (concentration). CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number 
of pairwise comparisons. 

Practice 

Impact 

Pollutio
n source 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

Buffer 
strips 

NO3-N in 
surface runoff 

Total N in 
surface runoff 

NO3-N and 
total N in 
surface runoff 
NO3-N and 
total N in 
surface runoff 
NO3-N and 
total N in 
surface runoff 

NO3-N in 
groundwater 

NO3-N in 
groundwater 

NO3-N in 
groundwater 

Overall 

-48 

-33 

Overall 

-68 

-57 

-17 

-43 

25 

16 

Positive 

Positive 

Close to 
cereals 

Close to 
grasslan
ds 

Close to 
feedlots 

-63 

-53.3 

-42.2 

24 

Positive 

-42.8 

-14.3 

9.5 

10 

Non-
significant 

-72.8 

-51.2 

-20.5 

7 

Positive 

Overall 

-78 

-70 

-62 

-78.7 

-70.3 

-60.2 

38 

28 

Positive 

Positive 

-83.7 

-72.5 

-52.3 

9 

Positive 

Close to 
cereals 

Close to 
grasslan
ds 

Source: Valkama et al. 2019 

70 

 
 
The  effects  of  hedgerows  and  field  margins  (grass  strips)  on  nutrient  leaching  and  runoff  are 
extracted from Van Vooren et al. 2017. They calculated the ratio of Nitrogen and Phosphorous inflow 
into the hedgerows or grass strips to the Nitrogen and Phosphorous outflow out of them. For Nitrogen, 
separate  results  on  interception  rates  for  surface  and  subsurface  flows  are  provided,  while  for 
Phosphorous only the effect on surface flows was calculated. In Table 48 below, we provide results 
expressed  in  terms  of  %  of  reduction  of  the  Nitrogen  and  Phosphorous  load.  Experiment  results 
expressed in total mass and concentration are pooled together.  

In all cases, results are significant and positive. Hedgerows can intercept on average the surface flow 
of N by 68.7% (95% CI: 60.5%, 77.7%) and the subsurface flow by 34.3% (95% CI: 9.5%, 52.8%). 
For P, the average reduction in the surface flow is 67.4% (95% CI: 46.7%, 80.2%). 

Similarly,  grass  strips  on  average  reduce  N  surface  flow  by  75.8%  (95%  CI:  58.5%,  86.1%),  N 
subsurface  flow  by  31.6%  (95%  CI:  6.8%,  50.3%)  and  P  surface  flow  by  72.7%  (95%  CI:  53.2%, 
84.1%).  

Table 48: Nutrient interception by hedgerows and grass strips: reduction of outflow compared to inflow (%). 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Practice 

Impact 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

N surface flow 

Hedgerows 

N subsurface flow 

Grass 
strips 

P surface flow 
N surface flow 
N subsurface flow 

P surface flow 

Source: Van Vooren et al. 2017 

60.5 

9.5 

46.7 
58.5 
6.8 

53.2 

68.7 

34.3 

67.4 
75.8 
31.6 

72.7 

77.7 

52.8 

80.2 
86.1 
50.3 

84.1 

49 

71 

36 
90 
22 

Positive 

Positive 

Positive 
Positive 
Positive 

116 

Positive 

The effects of small wetlands on N and P leaching and runoff are extracted from Carstensen et al. 
2020. Results refer to Free Water Surface constructed wetlands (FWS) in agricultural landscapes, and 
are  expressed  as  raw  nutrient  removal  efficiency.  This  removal  is  a  percentage  calculated  as  the 
loading to the system minus the loading from the system divided by the loading to the system. The 
calculation of the effect size was based 109 replicates from 33 different study sites including data 
from Europe.  

Table 49 below provides the extracted results. For both N and P results are significant and positive. 
Small wetlands have a N removal efficiency from system outflow of 41% (95% CI: 29%-51%) and a 
P removal efficiency of 33% (95% CI: 19%-47%). 

Table  49:  Nutrient  removal  efficiency  (%)  by  small  wetlands  (Free  Water  Surface  constructed  wetlands). 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Practice 

Impact 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

Small wetlands 
(free water surface 
constructed 
wetlands) 

Nitrate loading 
reduction 

Total P removal 

Source: Carstensen et al. 2020. 

29 

19 

71 

41 

33 

51 

47 

42 

41 

Positive 

Positive 

 
 
 
3.16 Fallowing 

Fallow land is defined by Eurostat40 
maintained in good agricultural and environmental condition (GAEC), whether worked or not, but which 
will not be harvested for the duration of a crop year. The essential characteristic of fallow land is that 
it is left  to recover, normally  for the whole  of a crop  year. On land lying fallow there shall be  no 
agricultural  production.  Land  lying  fallow  for  more  than  5  years  for  the  purpose  of  fulfilling  the 
ecological focus area shall remain arable land. In terms of land cover, fallow land may be: a) bare 
land with no crops at all; b) land with spontaneous natural growth which may be used as feed or 
ploughed in; c) land sown exclusively for the production of green manure (green fallow). The Eurostat 
definition includes arable land lying fallow for less than 5 years and arable land lying fallow for 5 

In the literature review that feeds this document, fallows have been consider as comprising: bare land 
bearing no crops at all, land with spontaneous natural growth that may be used as feed or ploughed, 
recently abandoned and set-

n of green 

manure (often with leguminous)

fallowing periods of annual crops.  

section  3.7).  The  review  does  not  include  short  and  seasonal 

3.16.1 Effects on soil organic carbon 

Only  one  synthesis paper was  retrieved  addressing  the  effect  of  fallowing  on  soil  organic  carbon 
(SOC)  (Kämpf  et  al.  2016a).  This  synthesis  paper  analyses  the  effect  of  the  abandonment  of 

when  the  abandonment  time  is  0-4  years,  and  t

for  the  purpose  of  fulfilling  the 
 for any time period. Extracted from this meta-analysis, in Table 50 we 
provide data in %, and in Table 51, we provide data on absolute values of soil Carbon change in t/ ha. 

On average, independently of the abandonment time, carbon stocks were 18% higher in soils of ex-
arable land (fallow land for EFA according to Eurostat definition) than in arable land. However, carbon 
sequestration was significantly affected by abandonment time, climate and the initial SOC stock.  

Time since abandonment was available for 54 estimates; the mean annual sequestration rate was 
fallowing periods up to 4 years, the lower limit of the 95% confidence interval 
barely touches 0, therefore it can be considered a marginally no significant positive effect (Table 50). 
Nevertheless,  the  absolute  soil  carbon  change  effect  is  significantly  positive  (see  Table  51). 
Considering that for the >4-8 years period the effects on SOC stock is significantly positive, and that 
for  absolute  SOC  change  in  fallows  of  less  than  5  years  the  effect  is  positive  and  statistically 
significant (2.8 ± 2.3 ton ha-1), a positive effect on fallow land with regard to SOC could be accepted. 

40 https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Fallow_land 

72 

 
 
 
 
                                                 
 
The mean effect size on SOC stock increases as duration of fallowing increases. However, after 16 
years, SOC increase stagnated at high level (+31%).  

The effects of climate and the initial  SOC stock are not differentiated by abandonment time. The 
proportional  gain  of  carbon  was  positively  correlated  with  the  aridity  index  of  the  study  site.  It 
increased from 12.5% in humid, to 31.7% in semiarid climate. Absolute values, however, did not differ 
significantly. The effect of the initial SOC stock on SOC change was negative for both proportional 
change as well as absolute values. SOC stocks were significantly higher in ex-arable land compared 
-use abandonment had non-
to arable land with initial SOC stocks of 0
significant  effect  on  S
sequestration rates were found in soils with high initial SOC stock, the mean annual sequestration 
rate did not correlate with the initial  SOC stock (linear regression; Nc= 54, p = 0.075, adjusted r2 
=0.042). 

Table 50 Effect (% of change) of land abandonment on soil organic carbon sequestration. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Nc 

69 

11 

15 

16 

10 

19 

18 

6 

14 

21 

6 

20 

22 

Effect 

Positive  

Non-
significant 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Non-
significant 

Non-
significant 

Positive 

Positive 

Practice 

Metric  CI_LOW  Mean 

CI_HIGH 

Overall (EFA fallow) 

Abandon
ment 
time 

Fallow as 
compared 
to no crops 

Global 
aridity 
index 

Initial 
SOC 
stock 

Source: Kämpf et al. 2016a. 

0-4 years 

>4-8 years 

>8-16 
years 

>16 years 

humid 

moderatel
y humid 

sub-humid 

semi-arid 

> 75 t/ha 

> 50-75 
t/ha 

> 25-50 
t/ha 

0-25 t/ha 

% 

% 

% 

% 

% 

% 

% 

% 

% 

% 

% 

% 

% 

18 

7.4 

16.1 

26.9 

31.1 

12.5 

19.5 

31.1 

31.7 

3.8 

0.3 

20.1 

32.9 

23 

15.2 

21.6 

34.7 

44.2 

20.5 

28.0 

43.6 

46.4 

9.0 

4.6 

30.1 

42.4 

12 

-0.2 

11.0 

20.1 

18.2 

5.1 

11 

18.2 

17.1 

-1.2 

-4.0 

10.4 

23.6 

73 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 51 Effect (absolute soil carbon change) of land abandonment on soil organic carbon. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Practice 

Metric  CI_LOW  Mean 

CI_HIGH 

Overall (EFA fallow) 

t/ ha 

Abandon
ment 
time 

Fallow as 
compared 
to no crops 

Global 
aridity 
index 

Initial 
SOC 
stock 

0-4 years 

>4-8 years 

>8-16 years 

>16 years 

humid 

moderately 
humid 

sub-humid 

semi-arid 

> 75 t/ha 

t/ha 

t/ha 

t/ha 

t/ha 

t/ha 

t/ha 

t/ha 

t/ha 

t/ha 

3.7 

0.5 

2.5 

7.0 

7.4 

2.3 

4.7 

11.8 

4.6 

-1.4 

> 50-75 t/ha 

t/ha 

-1.7 

> 25-50 t/ha 

0-25 t/ha 

t/ha 

t/ha 

5.0 

5.1 

5.4 

2.8 

5.0 

9.2 

11.4 

4.6 

7.5 

15.6 

7 

2.2 

1.0 

8.3 

6.9 

7.1 

5.1 

7.5 

11.4 

15.4 

6.9 

10.3 

19.4 

9.4 

5.8 

3.7 

11.6 

8.7 

N 

69 

10 

16 

15 

11 

19 

18 

6 

14 

21 

6 

20 

22 

Effect 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Positive 

Non-
significant 

Non-
significant 

Positive 

Positive 

Sources: Kämpf et al. 2016a. 

3.17 Intercropping 

Intercropping is a farming method that involves cultivating two or more crop species (i.e., crop mixture 
cropping) or genotypes (i.e., cultivar mixture cropping) in the same area and coexisting for a time so 
that they interact agronomically (Brooker et al. 2015; Vandermeer 1989). The results of the literature 
review here presented include different types of intercropping41, namely: 

Mixed intercropping: sowing multiple crop species or cultivars in the same field at the same time, in 
mixture with a given seeding ratio but random spatial arrangement.  

Row intercropping: sowing multiple crop species in the same field at the same time in alternate rows.  

Strip intercropping: sowing two (or more) crop species in the same field at the same time in multi-
row strips wide enough to allow independent cultivation.  

Relay (strip) intercropping: intercropping of two crop species in which the second species is under-
sown in the first at a later point in the growing season. 

However, most synthesis papers refer only to a mixed cropping, differentiating in some cases between 
crop mixture and cultivar mixture.  

41 Two practices are not included here: alley cropping, i.e., the cultivation of food, forage or specialty crops between rows 

of trees, that is included under Agroforestry; and dual-purpose cropping, i.e. the cultivation of two or more crops such 
as cereals (e.g. wheat, barley, oats and triticale) and brassicas (mainly canola) with the intended purpose of grazing 
during the vegetative stage and harvesting grain after the crop matures. 

74 

 
 
 
 
 
 
 
 
 
                                                 
 
3.17.1 Effects on soil organic carbon 

One synthesis paper (Daryanto et al. 2020) reports quantitative results on the effect of intercropping 
cereals and grain legumes on soil organic carbon content.  

The  synthesis  paper  is  based  on  individual  studies  in  several  African  countries.  It  is  nevertheless 
considered  relevant  for  the  purpose  of  the  present deliverable  as  the  cereals  considered  includes 
crops  largely  cultivated  in  Europe 
  maize,  wheat,  sorghum,  barley.  The  same  is  true  for  several 
intercropped grain leguminous crops covered by individual studies, such as fava bean, soybean and 
common bean. It is to be noted, however, that the synthesis paper includes also cereals and grain 
legumes not typical of the tropical and dry climates as millet, cowbean and pigeon pea.  

The mean  effect  size  expressed  as increase  of  soil organic  carbon  content is 14.9%,  C.I.  =  [6.9%, 
23.9%] (Table 52) 

Table 52 Effects of cereals and grain legume intercropping on soil organic carbon. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Cereals and grain legumes intercropping 

CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

Concentration of Soil organic carbon (% change) 

6.9 

14.9 

23.9 

NA 

Positive 

Source: Daryanto et al., 2020 

3.17.2 Effects on the nutrient balance 

Most impacts of this practice relate to nutrients excretion that could be linked to the nutrient surplus 
(assuming  there  is  not  manure  trade)  and  to  other  indicators  (e.g.  nitrates  leaching  and  runoff, 
ammonia emissions, etc.). 

First, we review diet formulation techniques (see  

Table 53): 

─  The only diet formulation technique with significant results is low crude protein diet, compared 
to no reduction of dietary crude protein. It has a positive effect on nitrogen excretion of swine (H. 
Wang  et  al.  2020).  On  average,  total  N  excretion  significantly  decreased  by  -28.5%  with  a 
lowering of dietary crude protein content. The effect of lowering dietary crude protein content on 
reducing  urinary  N  excretion  (39.6%)  was  stronger  than  that  on  reducing  faecal  N  excretion 
(10.4%). The ratios of total N excretion to average daily gain were reduced by lowering dietary 
crude protein content by 22%. Reduction of total N excretion varied with the level of lowering 
dietary crude protein content (see  

─  Table  53).  The  largest  decrease  was  derived  at  a  crude  protein  reduction  of  >  4%.  Faecal  N 
excretion and urinary N excretion were also decreased with lowering crude protein content (not 
shown in  

─  Table 53). 

75 

 
 
 
 
Table 53. Effect (% change) of the use of diet formulation techniques on nutrient excretion. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Technique 

Population 
and 
source 

Factor 

Metric 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

Low crude 
protein 
diet42 

Swine 
H. Wang et 
al. 2020 

Total N 
excretion 

Overall 

-27.6 

 -24.7 

-22.0 

175 

Positive 

Urinary N 

Overall 

-36.6 

-32.8 

-28.8 

113 

Positive 

Faecal N 

Overall 

-15.3 

-10.0 

-4.1 

115 

Positive 

Total N 
excretion to 
average daily 
gain 

Overall 

-23.6 

-19.7 

-15.6 

111 

Positive 

>6% 

-52 

>6% + 
AA43 

-49 

4-6% 

-42 

Total N 
excretion 

4-
6%+AA 

2-4% 

2-4% 
+ AA 

-40 

-21 

-24 

>2% 

-18 

-45 

-38 

-39 

-30 

-18 

-15 

-12 

-39 

10 

Positive 

-25 

3 

Positive 

-35 

33 

Positive 

-17 

5 

Positive 

-14 

71 

Positive 

-5 

-5 

16 

Positive 

35 

Positive 

Source: references in second column. 

Results on feed additives are addressed next and shown on Table 54. 

─  Enzymes  supplementation  compared  to  no  enzyme,  have  different effects  on  nutrient loss, 
with  one  synthesis  paper  (H.  Wang  et  al.  2020)  reporting  positive  effects  and  non-significant 
effects depending on the enzyme type (carbohydrase, phytase, enzymes and compound enzymes) 
and the metric (N or P exc
average, total N excretion significantly decreased by 8.9% with supplementation of exogenous 

Table 
54).  Supplementation  of  phytase  and  compound  enzymes  led  to  significant  reductions  while 
carbohydrase  enzymes  supplementation  was  not  effective.  There is  also  a paper  (Lewis  et  al. 
2015) with results for N and P excretion but lacking a proper statistical analysis.  

42 Low protein diet, all the low protein diets are supplemented with the first four crystalline amino acids (L-lysine, DL-

methionine, L-threonine and L-trypto-phan) to balance for ideal protein ratio. 

43 AA = supplementation of amino acids except for the first four crystalline amino acids (L-lysine, DL-methionine, L-

threonine and L-trypto-phan). 

76 

 
 
 
 
                                                 
 
─  Nutritional additives, compared to no feed additive, have 1 paper reporting positive effect on 
dairy  cattle  N  excretion  (Salami  et  al.  2021)  and  1  paper  does  not  have  a  proper  statistical 
analysis (Lewis et al. 2015). Feeding slow-release urea decreased nitrogen excretion by 2.7% to 
3.1%  (-12 to  -13  g/cow/d).  Salami  et  al.  2021  report  that  slow-release  urea  supplementation 
reduced the amount of nitrogen excreted per dairy cow by 2.7% to 3.1% (-12 to -13 g/cow/day) 
and per unit of milk by 3.6% to 4.0% (-0.50 to -0.53 g N/kg milk). In these results, N excretion 
has  been  calculated  by  Salami  et  al.  2021  from  result  variables  obtained  from  their  meta-
analysis: dry matter intake (kg/cow); nitrogen intake (g/day); milk yield (kg/day) and depend on 
the equation used for the calculation. 

─  Sensory additives, compared to no feed additive, have different effects on nutrient loss, with 1 
paper  (Herremans  et  al.  2020)  reporting  positive  effect  on  urinary  N  excretion  (-11%)  and 
negative effect on faecal N excretions (+10%) for tannins supplementation of dairy cows. The 
meta-analysis shows that tannins have no impact on nitrogen use efficiency (p>0.05) but there 
is a shift from urinary to faecal N that may be beneficial for environment preservation, as urinary 
N induces more harmful emissions than faecal N. Another paper (Lewis et al. 2015) that analyses 
the effects of condensed tannins and Quillaja saponaria extract on N excretion lacks a proper 
statistical analysis.  

─  Non specified feed additives, compared to no feed additive, have different effects on nutrient 

loss.  H.  Wang  et  al.  2020 
acidifying agent/salts, prebiotics etc., a positive effect for swine urinary N excretion, a negative 
effect for faecal N excretion and non-significant effect for total N excretion and for the ratio of 
total N excretion to average daily gain (%). 

Table 54. Effect (% change) of the use of feed additives on nutrient excretion. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Technique 

Population 
and source 

Metric 

CI_LOW  Mean  CI_HIGH 

Nc 

Effect 

Enzymes overall 

Swine 

Source: H. 
Wang et al. 
2020 

Carbohydrase44 

Swine  

Enzymes supplementation 

Total N 
excretion 

-12.9 

-8.6 

-4.4 

174 

Positive 

Urinary N 

-18.4 

-7.8 

3.9 

28 

Faecal N 

-13.1 

-1.9 

10.5 

31 

Non-
significant 

Non-
significant 

Ratio total N 
excretion to av. 
daily gain 

Total N 
excretion 

-16.5 

-12.4 

-8.163 

160 

Positive 

-14.1 

-7.2 

0.3 

72 

Non-
significant 

44 

-

-

-galactosidase, cellulose. 

77 

 
 
 
 
                                                 
 
Technique 

Population 
and source 

Metric 

CI_LOW  Mean  CI_HIGH 

Nc 

Effect 

Source: H. 
Wang et al. 
2020 

Swine 

Source: H. 
Wang et al. 
2020 

Swine 

Source: H. 
Wang et al. 
2020 

Swine 

Source: H. 
Wang et al. 
2020 

Total P 
excretion 

Ratio total P 
excretion to av. 
daily gain 

Total N 
excretion 

Total P 
excretion 

Ratio total P 
excretion to av. 
daily gain 

Total N 
excretion 

Total P 
excretion 

Total N 
excretion 

Total P 
excretion 

Ratio total P 
excretion to av. 
daily gain 

-14.8 

-4.0 

7.9 

31 

-13.0 

-5.0 

4.3 

63 

-13.7 

-5.3 

3.8 

35 

Non-
significant 

Non-
significant 

Non-
significant 

-32.5 

-26.9 

-21.0 

40 

Positive 

-24.5 

-20.2 

-15.5 

29 

Positive 

-25.6 

-16.7 

-6.7 

24 

Positive 

NA 

NA 

NA 

NA 

NA 

-17.3 

-8.1 

2.0 

43 

Non-
significant 

-28.2 

-20.6 

-12.2 

39 

Positive 

-21.1 

-14.6 

-6.9 

42 

Positive 

Nutritional additives 

Phytase 

Protease 

Compound 
enzyme45 

Slow-release 
urea 

Dairy cows 

N excretion / 
cow/day 

Source: 
Salami et al. 
2021 

N excretion / kg 
milk 

NA 

NA 

-2.7 to 
-3.1% 

-3.6% 
to -
4% 

NA 

44 

Positive 

NA 

44 

Positive 

Sensory additives 

Sensory 
additives: 
Tannins 

Dairy cows  

N use efficiency 

-2.3 

0.7 

3.7 

26 

Non-
significant 

Source: 
Herremans et 
al. 2020 

Urinary N 

-15.6 

-10.9 

-6.0 

34 

Positive 

Faecal N 

6.7 

10.3 

14.1 

42 

Negative 

Non specified feed additives 

45 Compound enzymes, containing at least one kind of phytase, protease and carbohydrase enzymes. 

78 

 
 
 
 
 
 
 
 
                                                 
 
Technique 

Population 
and source 

Metric 

CI_LOW  Mean  CI_HIGH 

Nc 

Effect 

Other additives 

Swine 

Source: H. 
Wang et al. 
2020 

Source: references in second column. 

Total N 
excretion 

-3.2 

1.7 

7.1 

123 

Non-
significant 

Urinary N 

-14.5 

-8.3 

-2.1 

105 

Positive 

Faecal N 

11.7 

18.9 

26.7 

105 

Negative 

Ratio total N 
excretion to av. 
daily gain 

-12.2 

-5.3 

1.9 

50 

Non-
significant 

3.17.3 Effects on the nutrient balance 

The  literature  provides  the  impacts  on  macronutrients  fertilisation,  which  is  a  component  of  the 
nutrient balance (see Annex 1).  

According  to  the  relative  majority  of  results  retrieved  from  the  systematic  literature  review  of 
synthesis  papers  (Schievano  et  al.  2023),  compared  to  monoculture,  intercropping  decreases 
fertilisation requirements as nutrient uptake is increased. The selected synthesis paper is C. Li et al. 
2020.  

The meta-analysis by C. Li et al. 2020 includes results from relay strip cropping, row intercropping 
and  mixed  intercropping.  They  found  maximum  yield  gains,  compared  with  monocultures,  for  two 
types of intercropping: (i) mixtures of maize with short-grain cereals or legumes that had substantial 
temporal niche differentiation from maize, when grown with high nutrient inputs, and using multirow 
strips of each species (highly fertilised relay strip intercropping); and (ii) growing mixtures of short-
stature crop species, often as full mixtures, with the same growing period and with low to moderate 
nutrient inputs. However, there were significant differences when maize was included. 

The N fertiliser equivalent ratio of intercrops (defined as the amount of N fertiliser used in sole crops 
to produce the same yields as obtained in intercropping) with and without maize were 1.33 ± 0.04 
and 1.19 ± 0.05, respectively. So, to achieve the same yield as intercrops, the sole crops used 19
33%  more  N  fertiliser  than  the  intercrops  (or  intercrops  used  -16%-  -24.8%  less  fertilisers  than 
monoculture),  indicating  increased  N  use  efficiency  in  intercropping  if  nutrient  use  efficiency  is 
expressed as fertiliser used per unit yield produced. The N fertilizer equivalent ratio of intercrops with 
maize was higher (P=0.01) than that of intercrops without maize, indicating that intercrops with maize 
save more N fertiliser compared with sole crops than intercrops without maize. 

The P fertiliser equivalent ratio of intercrops with maize (1.36 ± 0.03) was larger than the P fertilizer 
equivalent ratio of intercrops without maize (1.19 ± 0.04) (P<0.001), indicating that, while both types 
of intercrops save P fertiliser compared with sole crops, the savings are greater in intercrops with 
maize (26.5%) than in intercrops without maize (16%). 

79 

 
 
 
 
 
Table 55 Effects (% change) of intercropping on nutrients fertilisation. SE: Standard Error of the mean. Nc: 
number of pairwise comparisons. 

Metric 

Population 

Mean-SE  Mean  Mean+ SE 

Nc 

N 
fertiliser 

P 
fertiliser 

Crops include maize 

Crops do not include maize 

Crops include maize 

Crops do not include maize 

-27.0 

-19.4 

-28.1 

-18.7 

-24.8 

-16.0 

-26.5 

-16.0 

-22.5 

-12.3 

-24.8 

-13.0 

407 

231 

325 

119 

Effect 

Positive 

Positive 

Positive 

Positive 

Source: C. Li et al. 2020 

3.18 Crop rotation 

46, crop rotation on arable land is the practice of alternating crops 
grown on a specific field in a planned pattern or sequence in successive crop years, so that crops of 
the same species are not grown without interruption on the same field. In a rotation, the crops are 
normally changed annually, but they can also be multi-annual. If the same crop is grown continuously, 
the term monoculture can be used to describe the phenomenon.  

Results reported here concern rotation of 2 crops compared to monoculture, rotations of 3 or more 
crops compared to monocultures, and rotations of 3 or more crops compared to 2 crops rotations 
(also referred to as simplified rotation). Results do not include the effects of cover crops, break crops 
or fallowing, which are reported in specific sections. 

3.18.1 Effects on greenhouse gas emissions 

According  to  the  results  retrieved  from  the  systematic  literature  review  of  synthesis  papers 
(Schievano et al. 2023), significant results are found on the impact of crop rotation on Global Warming 
Potential (GWP), which includes CO2 equivalent emissions from aggregated N2O and CH4 emissions 

fertilisation.  

The selected paper, Sainju 2016 found differing effects on the GWP depending on the crop population: 
negative  effects  on  GWP  were  found  for  large  grain  crops  like  maize  and  soybean;  conversely,  a 
positive effect was found for small grain crops like barley and pea.  

Crop rotation increased GWP47 by 46% and Greenhouse gas intensity (GHGI)48 by 41% compared with 
monocropping (full dataset). This was especially true for large grain crops, such as maize (Zea mays 
L.) and soybean (Glycine max L.), where GWP and GHGI were 215 and 325%, respectively, greater 
under  maize-soybean  than  continuous  maize.  In  contrast,  for  small  grain  crops,  such  as  barley 
(Hordeum  vulgare  L.)  and  pea  (Pisum  sativum  L.),  GWP  was  22%  lower  under  barley-pea  than 
continuous barley. 

46 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Crop_rotation 
47 GWP and GHGI account for CO2 equivalents from N2O and CH4 emissions with or without equivalents from soil C 

48 GHGI is expressed as net GWP per unit crop yield 

fertilisation. 

80 

 
 
                                                 
 
reduced GWP and GHGI under continuous maize than maize-soybean rotation, although N fertilisation 
rate to produce sustainable yield was higher in continuous maize. In contrast, greater N2O emissions 
following  soybean  increased  GWP  and  GHGI  in  maize-soybean  rotation.  Under  small  grain  crops, 
however, several researchers found that including legumes, such as pea and lentil (Lens culinaris L.), 
in rotation with non-legumes, such as wheat (Triticum aestivum L.) and barley, reduced GWP and GHGI 
compared with continuous non-legumes. They observed this because (1) no N fertiliser was applied 
to legumes compared with non-legumes which required large amount of N fertilisers to sustain yields, 
as N fertiliser stimulates N2O emissions and (2) legumes supplied greater amount of N to succeeding 
crops due to higher N concentration when above- and belowground residues were returned to the soil 
legume-non-legume 
and  reduced  N  fertilisation  rate  than  non-legumes.

continuous non-legume. Greater number of experiments and magnitude of changes, however, resulted 
in higher GWP and GHGI with monocropping  than crop rotation under large than small grain crops 
when values were averaged across experiments during data analysis.  

Table 56. Effect (% change) of crop rotation on greenhouse gas emissions (together with soil organic carbon 
where  indicated).  SE:  Standard  Error  of  the  mean.  CI_LOW: confidence  interval  bottom,  CI_HIGH:  confidence 
interval top, Nc: number of pairwise comparisons. 

Interven
tion 

Source 

Impa
ct 

Crops 

Metric 

CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

Rotation 
2 crops 

Sainju 
2016 

N2O+
CH4+ 
SOC 
+ 
other 
GHG 

Rotation 
mostly 2 
crops 

Han et 
al. 2017 

N2O 

Rotation 
2 crops 

Decock 
2014 

N2O 

Full dataset 

Large grains 
(maize & 
soybeans vs. 
maize) 

Small grains 
(cereals like 
barley or 
wheat & 
legumes like 
pea or 
lentils vs. 
cereals) 
Maize
soybean or 
dry bean vs 
maize; 
lupin wheat 
vs wheat 
Maize-
soybean vs. 
maize 

Source: references in the second column. 

GWP 

33.7 (SE) 

46 

58.3 (SE)  11 

Negative 

GHGI 

25.3 (SE) 

41 

56.7 (SE)  11 

Negative 

GWP 

166.4 
(SE) 

215 

263.6 
(SE) 

GHGI 

0 (SE) 

325 

650 (SE) 

4 

4 

Negative 

Negative 

GWP 

-23 (SE) 

-22 

-21 (SE) 

3 

Positive 

GHGI 

NA 

NA 

NA 

3 

NA 

- 

- 

- 

- 

- 

31 

Non-
significant 

- 

17 

Non-
significant 

81 

 
 
 
 
3.18.2 Effects on soil organic carbon 

The review of synthesis papers showed that more positive/significant impacts of crop rotation on SOC 
were found for larger and more diverse crop rotations (i.e. with 3 or more crops and/or different crop 
families), while simplified successions of 2 crops like maize-soybean tend to show less significant or 
non-significant effects (Schievano et al. 2023).  

The  selected  synthesis  paper  is  McDaniel  et  al.  2014.  They  found  that  crop  rotation  overall,  as 
compared to monoculture, increased total carbon by 3.6% (95% CI: 2%, 5%; Nc=367). However, this 
impact is affected by several factors. The number of crops included in the rotation had a significant 
effect: increasing the number of crops in rotation from two to three increased total C from 1.9% to 
7.5%, but adding more than three appeared to have diminishing returns on total C (3.7% for four 
crops, and 7.7% for five or more crops). The presence of cover crops in the rotation had a significant 
impact: rotations without cover crops did not significantly influence SOC. The type  of monoculture 
crop under comparison to rotation also significantly influenced the rotation effect. Soybeans showed 
the greatest response to rotation, with an 11% increase, whereas introducing a rotation into maize 
monocultures did not increase total C. Last, mean annual temperature and precipitation correlated 
positively with rotation effects on total carbon. 

Table  57.  Effect  (%  change)  of  crop  rotation  on  soil  organic  carbon.  CI_LOW:  confidence  interval  bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Technique 

Number of 
crops 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

All 

2 

3 

4 

5 or more 

All 

2 

3 

All 

2 

3 

4 

5 

Crop rotation overall 

Crop rotation with no 
cover crops 

Crop rotation with cover 
crops 

Source: McDaniel et al. 2014. 

3.6 

1.9 

7.5 

3.7 

7.7 

1.5 

1.0 

5.8 

8.5 

7.7 

10.8 

3.5 

5.1 

3.7 

11.4 

8.7 

11.8 

3.5 

2.9 

12.6 

10.5 

12.2 

13.8 

6.7 

10.6 

14.8 

367 

239 

78 

25 

26 

251 

215 

44 

81 

17 

26 

17 

21 

Positive 

Positive 

Positive 

Non-
significant 

Positive 

Non-
significant 
Non-
significant 
Non-
significant 

Positive 

Positive 

Positive 

Non-
significant 

Positive 

2.0 

0.1 

2.8 

-0.8 

3.8 

-0.4 

-0.9 

-2.2 

6.8 

4.2 

7.9 

-0.3 

7.5 

82 

 
 
 
 
 
 
 
 
 
3.19 Cover and catch crops 

purpose during the fallow season, under-sown to winter/spring main crops, or crops that are sown 
between rows of  orchards or vineyards to function as living  cover to prevent erosion and thereby 
minimise the risk of surface runoff by improving infiltration. Cover crops may also function as catch 
crops,  which  scavenge  the  remaining  nitrogen  in  the  soil  after  the  main  crop  is  harvested  and 
effectively reduce nutrient losses.  

Cover crops are temporary crops and as such may either be cut and removed, or incorporated into 
the soil. The latter practice provides nutrients to the soil and is referred to as green manuring: in this 
document is analysed as a type of organic fertilisation in section 3.7. The terminology used in the 
selected meta-analyses also includes spontaneous vegetation that is left to grow to fulfil the same 
purpose as cover crops, which is thus considered as a form of cover crops in this exercise. In this work 
we distinguish between three farming practice interventions: cover crops in general,  legume cover 
crops and non-legume cover crops. 

3.19.1 Effects on greenhouse gas emissions 

CH4 emissions were significantly increased by cover crops according to  Jian et al. 2020, by 97.4% 
(95% CI: 61.1%, 141.3%) compared to bare soil. This result is obtained from data from North America, 
Europe,  Africa,  and  Asia,  specifically  eastern  China;  cash  crop  types included are:  maize,  soybean, 
wheat,  vegetable,  maize-soybean  rotation,  maize-soybean-wheat  rotation,  and  other.  Cover  crops 
include legume, grass and multi-species mixture. This effect is explained by the higher amounts of 
decomposable organic matter. 

Differing results are reported for N2O emissions and several factors influence these results, such as 
the  type  cover  crop  (legume  versus  non-legume),  the  cover  crop  residue  management  methods 
(removed, left on surface, or incorporated to the soil) and the mineral nitrogen fertilisation rates.  

We selected the paper from Muhammad et al. 2019 that has a complete range of results. According 
to them, if the cover crops are legumes, N2O emissions significantly increased by 60.6% (95% CI: 
29.8%,  95.7%)  as  compared  to  bare  soil  on  arable  cropland.  Instead,  non-legume  cover  crops 
decreased N2O emissions by -36.1% (95% CI: -45.8%, -25.5%).  

Effects are also influenced by the management of covers crops (e.g. left on the soil or incorporated 
into it). Muhammad et al. 2019 report that N2O emissions increased by 133.3% (95% CI: 108.6%, 
165.8%) if the residue was incorporated to the soil, but decreased by -54.7% (95% CI: -62%, -45.8%) 
if it was left on the surface. If cover crop residue was removed from the soil, there were not significant 
effects. Other factors affecting N2O emissions are cover crop biomass and the C/N ratio of the cover 
crop residue. 

83 

 
 
 
 
 
 
Table  58.  Effect  (%  change)  of  cover  and  catch  crops  on  greenhouse  gas  emissions.  CI_LOW:  confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Impact 

Intervention 

Source 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

CH4 

Cover crops 
overall 

Cover crops 
overall 

Jian et al. 
2020 

Jian et al. 
2020 
Muhammad 
et al. 2019 

Legume cover 
crops 
Non-legume 
cover crops 

Muhammad 
et al. 2019 
Muhammad 
et al. 2019 

N2O 

61.1 

97.4 

141.3 

25 

Negative 

12.7 

40.2 

75.1 

30 

-26.6 

-16.1 

-5.4 

480 

Cover crop type 

Contrasting 
effects 

29.8 

60.6 

95.7 

152 

Negative 

-45.8 

-36.1 

-25.5 

303 

Positive 

Cover crop residue treatment 

Left on surface 

Incorporated to 
the soil 

Removed 

Muhammad 
et al. 2019 
Muhammad 
et al. 2019 
Muhammad 
et al. 2019 

-62.0 

-54.7 

-45.8 

244 

Positive 

108.6 

133.3 

165.8 

201 

Negative 

-30.1 

-2.9 

26.5 

35 

Non-significant 

Source: references in the third column. 

3.19.2 Effects on soil organic carbon 

The majority of results retrieved from the systematic review of synthesis papers showed that cover 
and catch crops have an overall positive effect on carbon sequestration (Schievano et al. 2023).  

The selected synthesis paper is McClelland et al. 2021. It looks at the effects of cover crops on both 
annual and perennial cropping systems. Results show a strong, positive effect on SOC stock in the 
upper soil layer from 0 to 30 cm. The relative increase is 12% (95% CI: 7%, 16%), equal to 1.11 Mg 
C/ha (95% CI: 1.07, 1.16). Growing window (cover crop planting and termination date) and cover crop 
biomass production were strong predictors of SOC response to cover cropping. Continuous cover and 
autumn  planted  and  terminated  cover  crops  significantly  affect  SOC  response  (36%  and  27%, 
respectively)  relative  to  overwintering  (8%)  and  summer  cover  crops  (7%).  However,  there  were 
relatively  few  observations  for  all  growing  window  categories  except  for  overwinter,  thus  results 
should be interpreted with caution. Other factors C:N ratio, functional type, diversity, and termination 
method (incorporated or not incorporated) did not affect SOC response.. SOC response under high 
cover crop biomass production 
(12%) or moderate (11%) cover crop biomass production. Grasses and mixtures had strong positive 
effects on SOC when grown as a continuous cover rather than as an overwinter or summer cover 
crop, respectively. The effect of legume cover crops on SOC response was greater under fall, summer, 

crop was a poor predictor of SOC response even after controlling for outliers. Thus, authors of the MA 
were unable to make a robust estimate of the average annual SOC stock change from 0 to 30 cm 
under cover crops. A simple calculation dividing the overall mean (1.11 Mg C/ha) by the average time 
since introduction across the observations in the data set (5.2 yr) indicates an average annual SOC 

84 

 
 
general indication of the expected annual SOC stock change with cover crops. 

 0.22). While not robust, this estimate provides a 

Table  59  Effects  of  cover  crops  on  soil  organic  carbon.  CI_LOW:  confidence  interval  bottom,  CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. 

Population 

Source 

Factor and 
metric 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

0-30 cm 
soil on 
arable and 
permanent 
cropland 

Overall (%) 

7 

12 

16 

181 

Positive 

Overall (Mg 
C/ha) 

Overall (Mg C/ 
(ha yr)) 

1.07 

1.11 

1.16 

181 

Positive 

0.20 

0.21 

0.22 

181 

Positive 

McClelland et al. 2021. 

Growing window (period) (%) 

Overwinter 

Summer 

Autumn 

Continuous 

4 

0.9 

10.6 

18.8 

8.8 

6.9 

27.6 

36.6 

13.7 

120 

Positive 

13.3 

47.2 

57.1 

25 

11 

14 

Positive 

Positive 

Positive 

Source: references in the second column. 

3.19.3 Effects on the nutrient balance 

Only one synthesis paper (Quemada et al. 2013a) was found looking at the effects of cover crops on 
Nitrogen Use Efficiency (NUE) of the subsequent cash crop calculated as the yield per unit of N applied 
as fertiliser (Partial Factor Productivity of Fertiliser or PFPN). Their study focuses on irrigated cropping 
systems. The effect of cover/catch crops, as compared to bare soil, is positive for leguminous species 
(6%;  95%  CI:  -13%,  2%;  Nc=4),  while  non-significant  effect  resulted  for  non-legume  species. 
However, these results were supported by a limited number of primary studies.  

Table 60 Effects (% change) of cover crops on nitrogen use efficiency. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Population 

Metric 

Factor 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

Irrigated 
cropping 
systems 

PFPN = Yield/N 
fertiliser 

Source: Quemada et al. 2013a 

Legume 

1.2 

13.4 

29.6 

Non-legume 

-12.7 

-6.1 

1.6 

4 

9 

Positive 

Non-
significant 

3.19.4 Effects on nutrient leaching and runoff 

The majority of results retrieved from the systematic review of synthesis papers showed that the 
effect on nutrient loss of cover and catch crops overall, as compared to bare soil, is overall positive 
(Schievano et al. 2023).  

The selected papers for extraction of quantitative information are: R. Liu et al. 2021 that studies N 
and P losses with runoff in permanent cash crops (orchards) and  Thapa et al. 2018 that analyses 
nitrate leaching in arable land.  

85 

 
 
R. Liu et al. 2021 report that losses of inorganic N forms (NO3 -N and NH4+-N) were significantly 
reduced  by  cover  crops.  Due  to  limited  data,  only  nutrient  losses  with  runoff  were  compiled.  On 
average, the presence of ground cover (cover crops and mulch) reduced the losses of total N, NO3 -
N and NH4+-N by 60%, 48% and 53%, respectively. Non-legume cover crops reduced total N, NO3
-N  and  NH4+-N  losses  more  than  legume  cover  crops.  Cover  crop  significantly  reduced  total 
phosphorous and dissolved phosphorous loss by 62% and 61%, respectively. Nutrient losses were 
found  to  depend  on  several  factors:  higher  mean  annual  precipitation,  higher  mean  annual 
temperature and steeper slopes tended to increase total N loss reduction, and dissolved P reduction, 
but not total P reduction.  

Table 61 Effects of cover crops on nutrients run-off. CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top, Nc: number of pairwise comparisons. 

Population 

Source 

Metric 

Factor 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

Total N 
runoff 

NO3- 
runoff 

Cover crops 
overall 

-52.5 

-60.4 

-69.1 

57 

Positive 

Legume 

-32.0 

-47.7 

-63.7 

15 

Positive 

Non-legume 

-55.6 

-65.9 

-76.5 

38 

Positive 

Cover crops 
overall 

-39.9 

-48.4 

-57.4 

33 

Positive 

Legume 

-23.8 

-46.1 

-69 

8 

Positive 

Non-legume 

-39.3 

-49.0 

-59.5 

25 

Positive 

Permanent 
crops 
(orchards) 

R. Liu 
et al. 
2021 

NH4+ 
runoff 

Cover crops 
overall 

-44.3 

-52.9 

61.8 

31 

Positive 

Legume 

-29.5 

-49.6 

-70.4 

9 

Positive 

Non-legume 

-42.8 

-53.2 

-64.3 

21 

Positive 

Cover crops 
overall 

-54.8 

-62.4 

-70.4 

56 

Positive 

Total P 

Legume 

-43.8 

-58.8 

-74.0 

12 

Positive 

Non-legume 

-54.0 

-63.3 

-72.9 

43 

Positive 

Cover crops 
overall 

-50.0 

-60.8 

-72.2 

26 

Positive 

Dissolved 
P 

Legume 

-15.3 

-35.4 

-55.7 

5 

Positive 

Non-legume 

-51.4 

-64.8 

-78.7 

17 

Positive 

Source: references in the second column. 

Most of the studies included in  Thapa et al. 2018 evaluated the effectiveness of non-leguminous 
cover crops in reducing NO3  leaching (216 observations from 27 studies), whereas leguminous and 
non-legume legume  cover  crop  mixtures  were  evaluated  in  only  three  studies  with  9  and  13 
observations,  respectively.  Compared  with  no  cover  crop  controls,  non-leguminous  cover  crops 

86 

 
 
significantly  reduced  NO3  
combination with non-legumes had non-significant effect on NO3  leaching. 

%, 

Results by Thapa et al. 2018 depend on several factors: cover crop species; planting dates; cover crop 
biomass  production;  mean  annual  precipitation;  and  soil  texture.  Compared  with  no  cover  crop 
controls, grasses and broadleaf species reduced NO3  leach

-planted non-leguminous cover crops significantly 
reduced NO3  leaching compared with no cover crop controls (mean reduction of 64, 60, and 49% 
for August-, September-, and October-planted non-legume cover crops, respectively). When planting 
non-leguminous cover crops after November, there was no advantage of having a cover crop on NO3  

Table 62 Effects of cover crops on nitrates leaching. CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top, Nc: number of pairwise comparisons. 

Population  Source  Metric 

Factor 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

Arable 
crops 

Thapa 
et al. 
2018 

NO3
leaching 

Overall 

Non-
leguminous 

Leguminous 

Mixtures 

Grasses 

Broadleaf 

Planted 
August 
October 

NA 

-66 

-30 

-68 

-61 
(99% CI) 
-77 
(99% CI) 

NA 

-56 

-1 

-25 

-50 

-67 

NA 

238 

Positive 

-43 

216 

Positive 

40 

78 

9 

Non-significant 

13  Non-significant 

-37 
(99% CI) 
-54 
(99% CI) 

145 

Positive 

50 

Positive 

several 

several 

193 

Positive 

Planted after 
November 

-50 
(99% CI) 

-28 

3 
(99% CI) 

14  Non-significant 

Source: references in the second column. 

3.20 Leguminous (nitrogen-fixing) crops 

Leguminous crops  Fabaceae  develop symbiosis with bacteria within nodules in their root systems 
(rhizobia), producing reactive nitrogen compounds from atmospheric N2, which help the plant to grow. 
When the plant dies, the fixed nitrogen is released, making it available to other plants; this helps to 
fertilise the soil. The legume family include crops such as soybean, alfalfa, clover, lupin, kudzu, peanut 
and rooibos.  

In  the  exercise  of  systematic  review  of  synthesis  papers  (Schievano  et  al.  2023),  the  use  of 
Leguminous  (nitrogen-fixing)  crops  in  agriculture  is  considered  within  the  context  of  different 
agricultural practices, including: 1) crop rotations involving legumes, 2) leguminous cover crops, 3) 
intercropping, mixed cropping, practices involving two or more crops in proximity Including legumes, 
4)  agroforestry  systems  including  leguminous  perennials,  5)  green  fallows  including  legumes,  6) 
grassland for leguminous forage production and pastures enriched with legumes.  

Results were derived from two types of pairwise comparisons: 1) direct comparison, where a farming 
practice with leguminous crops was compared to the same farming practice, but without leguminous 

87 

 
 
 
 
crops; 2) indirect comparison, where a farming practice (either with or without leguminous crops) was 
compared to the absence of the practice. In this section we provide results from direct comparisons. 

3.20.1 Effects on soil organic carbon 

The use of leguminous crops in crop rotations, when compared to rotations without leguminous crops, 
show positive effects on carbon sequestration (higher soil organic carbon) according to the synthesis 
paper by Powlson et al. 2016.  

This  was  observed  in  tropical  agro-eco-systems,  concretely  for  the  Indo-Gangetic  Plains.  The 
individual  C  sequestration  rate  for  crop  diversification  (including  legumes  in  rotations)  was  0.47 
±0.046  Mg  C  ha-1  yr-1,  and  was  significantly  different  from  conventional  practice.  The  relatively 
large predicted rate of SOC increase from crop diversification was only based on 4 data comparisons 
from  two  studies,  showing  diverse  results.  In  one  study  crop  diversification  comprised  a  legume 
replacing rice in the rice wheat rotation and in the other a maize-wheat rotation was modified to 
include legumes. 

Table 63 Effects of leguminous or N-fixing crops on rates of increase of soil organic carbon. SE: Standard Error 
of the mean, Nc: number of pairwise comparisons. 

Population 

Factor and metric  Mean  SE  Mean  Mean + SE 

Nc 

Effect 

Arable crops in Indo-
Gangetic plains 

Source: Powlson et al. 2016 

Mg C ha-1 yr-1 

0.37  

0.47 

0.57  

4 

Positive 

3.21  Tillage practices: No tillage and reduced tillage 

In the sectoral literature and agronomic practice, a plethora of terms is used to refer to less intensive 
forms of tillage, compared to conventional tillage, which in turn is sometimes defined in different 
ways. The EUROSTAT definition49 of tillage management practices, is the following: 

Conventional tillage: refers to the arable land treated by tillage practices involving inversion of the 
soil,  normally  with  a  mouldboard  or  a  disc  plough  as  the  primary  tillage  operation,  followed  by 
secondary tillage with a disc harrow; 

 in the MA review): a system of practices 
that  leaves  plant  residues  (at  least  30  %)  on  the  soil  surface  for  erosion  control  and  moisture 
conservation, normally by not inverting the soil. It comprises more specific tillage practices, such as 
trip tillage, zonal tillage, tined tillage, vertical tillage, ridge tillage.  

Zero tillage (or No tillage in the MA review): a minimum tillage practice in which the crop is  sown 
directly into soil not tilled since the harvest of the previous crop. Weed control is achieved by the use 
of herbicides and/or appropriate mulching and stubble is retained for erosion control. 

49 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Tillage_practices 

88 

 
 
                                                 
 
Conservation  agriculture 50  encompasses  a  set  of  complementary  agricultural  practices  which 

crop rotations and 
fact that it also includes reduced tillage and not only no- tillage. 

-

MA review in the 

In the literature, additional terms are reported (e.g., low tillage, reduced tillage, minimum tillage) and 
specific  definitions  are  not  always  consistent.  For  the  purposes  of  this  document,  we  adopt  the 
Eurostat nomenclature50, including under the Conservation Tillage class all forms of tillage that are 
less intensive (i.e., determine less soil disturbance) than conventional tillage but more intensive than 
no tillage.  

3.21.1 Effects on greenhouse gas emissions 

No tillage (NT) compared to conventional tillage (CT):  

The study by Huang et al. 2018 analyses the effects of no-tillage on CH4, CO2, and N2O, as well as 
their  global  warming  potential  (GWP).  They  do  not  provide  values  for  CH4+N2O  but  only  GWP 
calculated as the sum of CH4+N2O+CO2 (only when fluxes for all three greenhouse gas emissions 
(GHG) species were reported in each single study).  

In  addition,  Huang  et  al.  2018  report  a  positive  effect:  no-tillage  decreased  soil  CH4  emission  by 
15.5%  and  on  average  had  non-significant  effect  on  soil  CH4  uptake 51 .  They  report  however 
significant  differences  with  crops,  climate,  and  soil  pH.  No-tillage  reduced  CH4  emission  in  rice 
production systems by 22.4% (95% CI: -32.4%, -11.3%; Nc= 48) but the effects on upland crops were 
not significant. It increased CH4 uptake in wheat by 31.1%. The overall results reported by Huang et 
al. 2018 have a high share of observations corresponding to rice paddies.  

The second selected synthesis paper, J. Feng et al. 2018, includes a lower number of individual studies 
for  rice  paddies.  This  study  reports  non-significant  effect  for  crops  overall.  No-tillage significantly 
increased N2O emission by 10.4% but these effects seem to diminish with long-term duration of NT. 
Changes in N2O emission between NT and conventional tillage were only significantly different with 
wheat production, where a 15.2% increase in emission under no-tillage was noted. The net effect of 
NT  (relative  to  CT)  was  influenced  by  several  environmental  and  agronomic  factors  (climatic 
conditions, tillage duration, soil texture, pH, and crop species)52. 

For results of N2O+CH4 together, we report both results by J. Feng et al. 2018 and Shang et al. 2021. 
Results  from  this  last  paper  are  not  provided  in  %  but  in  absolute  change  (Table  64).  For  those 
studies that measured fluxes of all three GHGs, no-tillage exhibited no significant difference in GWP 
compared to conventional tillage for all crops overall. However, Huang et al. 2018 report that soil pH 
and crop species significantly affected the difference in GWP. No-tillage decreased GWP by 31.2% in 
acidic soils and by 24.8% (95% CI: -39.7%, -6.1%; Nc=11) in rice fields. However, fluxes of all three 

50 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Agri-environmental_indicator_-_tillage_practices 
51 Soil absorbs CH4 from the atmosphere through the microbial process of CH4 oxidation by methanotrophs. 
52 Values by Huang et al. 2018 are supported by similar results from other synthesis papers. Values by J. Feng et al. 2018 

for no tillage are provided for comparison, as they also report results for reduced tillage + crop rotation, no tillage + 
crop rotation, reduced tillage + crop straw return and no tillage + crop straw return, both for uplands and paddies 
that are not reported here. 

89 

 
 
                                                 
 
GHGs and crop  yield were reported  in few studies comparing  no-tillage to  conventional  tillage, so 
these results should be interpreted cautiously. 

Reduced/conservation tillage (RT) compared to conventional tillage (CT):  

We used for quantitative data extraction the synthesis paper by J. Feng et al. 2018 which concludes 
that the effects of these practices depend on the interaction of no-tillage or reduced tillage with other 
agronomy practices and land use types.  

Overall, reduced tillage not combined with other techniques did not exhibit significant effects on CH4 
and  N2O  emissions  as  compared  with  conventional  tillage.  In  uplands  (non  rice/paddy  crops),  the 
effects of reduced tillage on CH4 uptake, N2O emission and CH4+N2O were not significant. In rice 
paddies, reduced tillage had non-significant effects on N2O emissions but significantly increased the 
CH4 emission by 17% (95% CI: 5%, 31%; Nc=22) and overall CH4+N2O by 13.6% (95% CI: 3%, 26%; 
Nc=22) compared with conventional tillage. The negative effect on CH4 emissions is based on 22 
CT  incorporated  crop  residue  into 
pairwise  comparisons  and  is  explaine
deeper soil than reduced tillage, reducing the decomposition of these residues through the protection 
of the soil matrix (Zhang et al. 2015). Lower soil disturbance and a shallower CH4 oxidation zone for 
no-tillage than reduced tillage were conducive to improving CH4 oxidation (Zhang et al. 2013; Koga 
et al. 2004)

J. Feng et al. 2018 found that the effectiveness of no-tillage and reduced tillage depended on tillage 
methods, land use type, and agricultural practices. In rice alternated with another crop  during the 
same year, reduced/conservation tillage led to a not significant increase of CH4 and GWP of N2O+CH4 
(Nc=14), while it was significant for double rice, with longer annual flooding time, but this result can 
be biased as the number of observations is very low (Nc=3). In upland, reduced tillage significantly 
decreased the overall GWP of CH4+N2O under single crop monoculture system (Nc=6) but the effect 
was not significant under the double crops rotation system (Nc=24). In uplands, when the crop straw 
was returned, the effect sizes of no-tillage and reduced tillage on CH4 uptake, N2O emission, and 
overall GWP were not significant. But in paddy fields, no-tillage did not affect the overall GWP when 
crop straw was returned while reduced tillage significantly increased the CH4 emission and overall 
GWP  when  crop  straw  was  returned.  The  effectiveness  of  NT/RT  on  CH4  and  N2O  emissions  was 
influenced by the percentage of basal N fertiliser (PBN), irrigation, and tillage duration.  

Table 64. Effect (% change unless otherwise specified) of tillage practices on greenhouse gas emissions by 
major GHG types. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. 

Technique  GHG  System 

Source 

CI_LO
W 

Mean 

CI_HIG
H 

Nc 

Effect 

h
t
i

w
d
e
r
a
p
m
o
c

e
g
a

l
l
i
t

o
N

e
g
a

l
l
i
t

l

a
n
o
i
t
n
e
v
n
o
c

2
O
C
+
)
O
2
N
+
4
H
C
(

P
W
G

all 

Huang et al. 2018 

-10.1 

-0.64 

10.1 

64 

Non-significant 

wheat 

Huang et al. 2018 

-3.0 

24.9 

59.7 

10 

Non-significant 

barley 

Huang et al. 2018 

-2.2 

14.5 

33.3 

31 

Non-significant 

maize 

Huang et al. 2018 

-23.5 

-10.2 

5.9 

12 

Non-significant 

rice 

Huang et al. 2018 

-39.7 

-24.8 

-6.1 

11 

Positive 

P
W
G

+
4
H
C
(

)

O
2
N

all 

Shang et al. 2021 

55 

Positive 

-1.1* 

-0.6* 

-0.2* 

*Mg CO2eq/ha/yr 

90 

 
 
 
 
 
 
 
 
 
 
 
 
 
Technique  GHG  System 

Source 

CI_LO
W 

Mean 

CI_HIG
H 

Nc 

Effect 

J. Feng et al. 2018 

-19.0 

-11.6 

-3.6 

93 

Positive 

uplands 

- 

-0.015* 

- 

44 

Non-significant 

Shang et al. 2021 

*Mg CO2eq/ha/yr 

J. Feng et al. 2018 

-15.1 

-5.4 

5.0 

48 

Non-significant 

paddies 

-4.8* 

-3.2* 

-1.5* 

Shang et al. 2021 

11 

Positive 

*Mg CO2eq/ha/yr 

Cereals 
(sigifica
nt  share 
of rice) 

all 
(negligib
le share 
of rice) 

Huang et al. 2018 

-26.1 

-15.5 

-3.4 

65 

Positive 

J. Feng et al. 2018 

-12.4 

-5.9 

0.9 

196  Non-significant 

wheat 

Huang et al. 2018 

-2.6 

40.2 

103.1 

10 

Non-significant 

maize 

Huang et al. 2018 

-35.1 

-2.0 

50.3 

7 

Non-significant 

rice 

Huang et al. 2018 

-32.6 

-23.0 

-11.4 

48 

Positive 

all 

wheat 

barley 

maize 

Huang et al. 2018 

Huang et al. 2018 

Huang et al. 2018 

Huang et al. 2018 

-14 

-1 

15 

56 

Non-significant 

3.9 

31.5 

65.8 

16 

Positive 

-28.2 

-13.6 

3.9 

29 

Non-significant 

-31.4 

-8.5 

22.5 

11 

Non-significant 

all 

Huang et al. 2018 

wheat 

Huang et al. 2018 

2.5 

1.0 

10.5 

18.9 

299 

Negative 

15.0 

31.1 

92 

Negative 

barley 

Huang et al. 2018 

-6.1 

11.5 

32.3 

58 

Non-significant 

maize 

Huang et al. 2018 

-6.7 

5.0 

19.0 

110  Non-significant 

rice 

Huang et al. 2018 

-9.5 

11.9 

39.1 

39 

Non-significant 

s
n
o
i
s
s
i
m
e
4
H
C

e
k
a
t
p
u
4
H
C

s
n
o
i
s
s
i
m
e
O
2
N

d
e
r
a
p
m
o
c

e
g
a

l
l
i
t

d
e
c
u
d
e
R

e
g
a

l
l
i
t

l

a
n
o
i
t
n
e
v
n
o
c

h
t
i

w

G
H
G

4
H
C

O
2
N

2
N
+
4
H
C
(

n
o
i
s
s
i
m
e

n
o
i
s
s
i
m
e

 upland 
O

)

J. Feng et al. 2018 

-28.3 

-4.6 

22.3 

33 

Non-significant 

paddies 

J. Feng et al. 2018 

2.8 

13.6 

25.8 

22 

Negative 

upland 

J. Feng et al. 2018 

-29.6 

-10.2 

11.2 

33 

Non-significant 

s

paddies 

J. Feng et al. 2018 

5.0 

16.9 

30.7 

22 

Negative 

upland 

J. Feng et al. 2018 

-8.1 

s

paddies 

J. Feng et al. 2018 

-10.7 

6.1 

3.4 

26.4 

33 

Non-significant 

20.1 

22 

Non-significant 

Source: references in the fourth column. 

91 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.21.2 Effects on soil organic carbon 

The synthesis paper selected for the extraction of quantitative data on the effects of reduced tillage 
and no tillage on soil organic carbon is the one from Haddaway et al. 2017. It covers arable soils in 
boreo-temperate regions and it is based on 351 primary studies, of which 142 from the US, 46 from 
Canada, 101 from Europe and the rest from other countries. This paper compares tillage management 
practices classifying them, for analytical purposes, in three intensity classes: no tillage, high intensive 
tillage  and  intermediate  intensity  tillage.  Other  factors  taken  into  consideration  are  the  depth  of 
tillage, the type of soil, the depth of the soil layer, the latitude and the climatic zone.  

The metrics for which effect sizes are reported are SOC concentrations (e.g. g/kg or %) and SOC stock 
(e.g. Tons C/ha). The difference between the two metrics depends on the bulk density of the soil, so 
that a similar concentration may not correspond to the same absolute SOC stocks if the density is 
not  the  same  and/or  is  not  uniform  along  the  soil  profile.  This  makes  the  assessment  not 
straightforward as bulk density is not always reported in primary studies and, moreover, it may be in 
turn affected by different tillage intensities. Accordingly, results expressed in SOC stocks tend to be 
more uncertain than SOC concentration. On the other hand, SOC stock is a more relevant measure 
when the focus is on quantitative SOC balances to determine the net effect on carbon sequestration 
and climate change mitigation potential. Here we report results for both SOC concentration ( 

Table 65) and SOC stocks ( 

Table  66),  for  the three  comparisons  of  interventions  and  for  different depths  of  the  studied  soil 
layer.  

Table  65  Differences  in  SOC  concentrations  (g/kg)  for  different  tillage  systems  and  different  soil  depths. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Pairwise 
comparison 

No tillage vs. 
Intensive tillage 

No tillage vs. 
Intermediate tillage 

Intermediate tillage 
vs. Intensive tillage 

Source: Haddaway et al. 2017 

Soil depth 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

0-15 cm 

1.41 

2.09 

15-30 cm 

-0.86 

-0.30 

>30 cm 

-0.88 

-0.37 

0-15 cm 

0.53 

15-30 cm 

-0.29 

1.18 

0.27 

>30 cm 

-0.52 

-0.16 

0-15 cm 

0.78 

1.23 

2.76 

0.26 

0.13 

1.83 

0.84 

0.21 

1.66 

15-30 cm 

-1.29 

-0.89 

-0.51 

>30 cm 

-0.67 

-0.26 

0.15 

102 

Positive 

49 

31 

95 

45 

20 

77 

42 

16 

Non-significant 

Non-significant 

Positive 

Non-significant 

Non-significant 

Positive 

Negative 

Non-significant 

For no tillage compared to intensive tillage, a significant mean increase in SOC concentration of 2.09 
g/kg (positive effect) was found in the upper layer of the soil (0-15 cm). Results in the lower layers 
are not significant. Importantly, the positive result for the 0-15 cm soil layer is not affected by factors 

92 

 
 
 
like latitude or climatic zone, so the result can be considered relevant for the entire Europe (though 
the type of soil is a relevant factor, see below). 

The same applies when no tillage is compared to intermediate tillage: a significant positive effect is 
found in the upper soil layer with an increase of 1.18 g/kg [0.53   1.83], while non-significant effects 
were detected in at deeper depths. Again, this result holds true regardless of latitude and climatic 
zones.  

When intermediate tillage is compared to intensive tillage, a significant positive result is still found 
for the upper soil layer (0 
 15 cm) with an average increase of 1.23 g/kg [0.78  1.66]. Instead, a 
statistically significant decrease in SOC concentration of -0.89 g/kg [-1.29   -0.51] was found in the 
15-30 cm layer, while at depth >30 cm non-significant effect was detected. Similarly, results do not 
change with latitude and climatic zones.  

Results  on  SOC  stocks  were  grouped  into  two  classes  of  soil  depth:  the  upper  layer,  comprising 

in the 0-150 cm profile or in the 30-150 cm profile.  

Table 66:  Differences in SOC stock (Mg/ha) for  different tillage systems and different soil depths.  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, NC: number of pairwise comparisons. 

Pairwise 
comparison 

No tillage   
Intensive tillage 
No tillage   
Intermediate tillage 
Intermediate tillage 
 Intensive tillage 
No tillage   
Intensive tillage 
No tillage   
Intermediate tillage 

Soil layer 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Upper Layer 
0-30 cm 
Full Profile 
0-150 cm 
Upper Layer 
0-30 cm 
Full Profile 
0-150 cm 
Upper Layer 
0-30 cm 
Full Profile 
0-150 cm 

0.78 

4.61 

8.44 

29 

Positive 

-5.04 

1.65 

8.33 

14 

Non-significant 

0.63 

3.85 

7.08 

32 

Positive 

-4.48 

0.83 

6.14 

13 

Non-significant 

-0.14 

1.72 

3.59 

29 

Non-significant 

-3.07 

1.88 

6.84 

10 

Non-significant 

Source: Haddaway et al. 2017 

The comparison of no tillage vs intensive tillage yields to significantly positive results, when the upper 
profile is considered, with a mean difference of 4.61 Mg/ha [0.78  - 8.44]. When the full  profile is 
considered, results are more uncertain, and not statistically different from 0 (mean 1.65; C.I. [-5.04 - 
8.33]). For the upper layer, results do not change with latitude and climatic zone, whereas where the 
full profile is considered, latitude was found to be positively correlated with SOC stock, i.e., higher 
differences were found at northern latitudes. 

No tillage shows significant higher SOC stock in the upper profile compared to intermediate tillage 
too, with a mean difference of 3.85 Mg/ha [0.63 - 7.08]. When the full profile is considered, results 
are more uncertain, and not statistically different from 0 (mean 0.83; C.I. [-4.48   6.14]). 

Intermediate tillage compared to intensive tillage has a mean positive but statistically not significant 
results for both the upper layer and the full profile. Latitude and climatic zones were not significant 
in both cases.  

93 

 
 
 
3.22 Crop residue management 

Crop residue management is the handling of stems, leaves, chaff and husks that remain in the fields 
after  crops  are  harvested for  grain,  seed  or  fibber. Main  strategies  for  crop  residue management 
involve residue retention at the surface, residue incorporation into the soil and rice residue burning. 
This section covers several crop residue management techniques, such as crop residue retention (crop 
residue or pruning residues are left in the field after harvest), crop residue incorporation into the soil 
(shallow and deep incorporation), and specific techniques for straw residue management, including 
additional straw application (beyond residue left by the crop), rice straw burned in paddy rice, and 
straw returning amended with straw decomposing microorganism inoculants. The impact of straw 
mulching is addressed as a separate practice in section 3.23 (Mulching). To assess the quantitative 
effect of the practice on the environment, the used comparator is crop residue removal, no residue 
return, or crop residue retention without additional straw application. 

3.22.1 Effects on GHG emissions 

The selected paper is Z. Li et al. 2021. However, given that this paper provides results only for N2O 
emissions but not for CH4 emissions, we will complement with the results by X. Zhao et al. 2020 for 
CH4. Results for N2O by X. Zhao et al. 2020 will be also provided for comparison purposes.  

According  to  Z.  Li  et  al.  2021,  on  average,  crop  residue  retention  significantly  stimulated  N2O 
emissions by 29.7% (95%CI: 17.7%, 42.7%). In upland soil, N2O emission increased by 46% but it 
decreased by -18% in paddy soil. Results depended on several factors: 

─  Climate:  Crop  residue  application  significantly  stimulated  N2O  emission  by  29.7%,  with  a 
significantly higher increase of 35.7% in the temperate zone. In contrast, no significant effect of 
crop residue application on N2O emission was observed for tropical zones. N2O emission was 
significantly  and  positively  correlated  with  latitude,  but  not  with  longitude,  mean  annual 
temperature and precipitation.  

─  Soil pH: Compared with the control, crop residue application significantly increased N2O emission 

by 54.0% when soil pH 5.5 6.5, and by 28.9% for soil pH > 7.5. 

─  Soil clay content: Crop residue return caused a particularly strong and significant increase in soil 
N2O  emissions  except  for  soil  with  clay  texture,  indicating  that  clay  content  is  an  important 
determinant of the soil N2O emission response to crop residue application. 

According  to  X.  Zhao  et  al.  2020,  under  crop  residues  retention  compared  with  residue  removed, 
significant  increases  in  GHGs  emissions  were  observed  in  China's  croplands.  Among  them,  CH4 
emission increased by 130.9% 95% CI: 85.2%, 186.1%), and N2O emissions by 12.2% (1.6%, 24.5%).  

This effect depended on several factors:  

─  Crop type: the most sensitive crop species for N2O was maize with a significant increase of 41.0%, 

while there was a non-significant effect for rice. 

─  Crop rotation: the increased emission of CH4 was enhanced for regions in which residue retention 
was adopted with crop rotation. Significant increase of 18.4% in N2O emission was observed in 
regions  where  residues  retention  was  adopted  with  crop  rotation  (p  <  .05),  but  it  was  non-
significant without crop rotation. 

94 

 
 
 
─  Tillage intensity: increases in CH4 and N2O emissions were observed under conventional tillage. 

─  Mineral N fertiliser amount applied  :  the  highest  emissions  of  CH4  and  N2O  occurred  under 

higher and medium nitrogen fertiliser input (150-250, and >250 kg N/ha).  

─  Duration  of  treatment:  over  the  short  term  (<5  years),  residues  retention  did not  significantly 
increase  N2O  emission.  Over  the  medium  term  (5-10  years),  residues  retention  significantly 
increased N2O emission. Over the long term (>10 years), residues retention sharply increased 
CH4 emissions. 

─  Proportion of residues retained: Compared with 100% in rate of retained residues, reducing the 
amount of retained parts of residues could lower the increase of CH4 emission. However, more 
N2O emission were observed under reduced amount of residue retention as compared with 100% 
amount of residues retained. 

Table 67. Effect (% change) of crop residues retention on greenhouse gas emissions. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

GHG  

Source 

system 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Z. Li et al. 
2021 

N2O 

Overall 

Upland 

Paddy 

Tropical 

Temperate 

cropland 

30 

-31 

-16 

22 

1.6 

X. Zhao et al. 
2020 

CH4 

X. Zhao et al. 
2020 

Rice 

 < -10 

Wheat 

Maize 

cropland 

0.9 

24.8 

85.2 

Source: references in the second column. 

3.22.2 Effects on soil organic carbon 

17.7 

29.7 

46 

-18 

2.8 

35.7 

12.2 

-1.6 

9.4 

41.0 

42.7 

64 

-3.7 

26 

52 

255 

204 

51 

67 

Negative 

Negative 

Positive 

Non-significant 

178 

Negative 

24.5 

117 

Negative 

17.3 

23.4 

64.7 

NA 

NA 

NA 

92 

Non-significant 

Negative 

Negative 

Negative 

130.9 

186.1 

Results are typically provided for soil organic carbon concentration, soil organic carbon stocks and 
soil  carbon  change  rates. SOC  content describes the  organic  carbon  concentration  on  a  weight  by 
weight basis, SOC stocks on a weight by area basis, and C sequestration rates on weight by area and 
over time. 

The selected synthesis paper is Xu et al. 2021. The study includes data from 248 experimental sites 
reported in 237 primary studies from various regions of the world. The study focuses on different 
ecosystems, included cropland and grasslands. It explores the impact of crop residue retention and 
additional straw application on SOC concentration measured at different soil depths, compared to 
crop residue removal and crop residues retention without additional straw application, respectively 
(Table 68). 

The effect of crop residue management on SOC depended on both the habitat and the portion of soil 
profile considered. Crop residue retention increased SOC content in grassland by 43% (95% CI: 14%, 

95 

 
 
79%) compared to crop residue removal, at 0-20 cm soil depth, but not at shallower sections of the 
soil  profile  (0-5  and  0-10  cm).  In  cropland,  crop  residue  retention increased  SOC  content  by  11% 
(95% CI: 7%, 14%) at 5-20 cm soil depth, by 14% (95% CI: 8%, 20%) at 0-10 cm, by 11% (95% CI: 
7%, 15%) at 0-20 cm, and by 11% (95% CI: 6%, 17%) at 20-100 cm.  

Additional straw incorporation, at 0-5 cm soil depth, increased SOC content in grassland by 18% (95% 
CI: 0%, 38%) compared to crop residue retention and no straw addition, but not at 0-20 cm soil depth. 
In cropland, additional straw application increased SOC content by 23% (95% CI: 13%, 37%) at 20-
100 cm, but had non-significant effect at 0-5 cm. 

Table 68. Effect (% change) of crop residues management on soil organic carbon (content). CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention  Comparator  Habitat 

Soil depth  CI_LOW  Mean  CI_HIGH  Nc  Effect 

0-5 cm 

-14 

-2 

12 

Grassland 

Cropland 

0-10 cm 

0_20 cm 

0-5 cm 

5-20 cm 

0-10 cm 

0-20 cm 

20-100 cm 

0-5 cm 

-4 

14 

-5 

7 

8 

7 

6 

0 

Grassland 

0-20 cm 

-3 

Cropland 

0-20 cm 

-14 

20-100 cm 

13 

11 

43 

10 

11 

14 

11 

11 

18 

25 

40 

23 

Crop residue 
retention  

Crop residue 
removal 

Additional 
straw 
application 

Crop 
residues 
retention 
without 
additional 
straw 
application 

Source: Xu et al. 2021. 

5 

9 

Non-
significant 

Non-
significant 

28 

79 

10  Positive 

27 

10 

Non-
significant 

14 

20 

15 

17 

38 

60 

127 

43  Positive 

35  Positive 

57  Positive 

44  Positive 

5 

Positive 

9 

5 

Non-
significant 

Non-
significant 

37 

4 

Positive 

3.22.3 Effects on the nutrient balance 

Results in the reviewed literature quantify the change in nitrogen use efficiency and nutrient uptake 
(Annex 1). The selected synthesis papers are the one by L. Xia et al. 2018 for the effect of crop residue 
retention (straw return with mineral fertilisation) and the one by X. Qin et al. 2021 for crop residue 
incorporation  (straw  incorporation).  Crop  residue  management  had  an  overall  positive  effect  on 
Nitrogen use efficiency: crop residue retention was found to increase nitrogen use efficiency by 14.9% 
(95%  CI:  11.3%,  19.5%)  in  rice  paddies  as  well  as  in  upland  crops  globally.  While  crop  residue 
incorporation was found to increase nitrogen use efficiency by 10% (95% CI: 8%, 12%) for maize in 
China. 

96 

 
 
Table  69.  Effect  (%  change)  of  crop  residue  management  on  nutrient  use  efficiency.  CI_LOW:  confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Source 

Intervention  Comparator  Factor 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

L. Xia 
et al. 
2018 

Crop residue 
retention 
(Straw return 
with mineral 
fertilisation) 

Crop residue 
removal (No 
straw return 
with mineral 
fertilisation) 

X. Qin 
et al. 
2021 

Crop residue 
incorpora-
tion (Straw 
incorpora-
tion) 

Crop residue 
removal (No 
straw return) 

Overall (rice 
paddies and 
upland crops 
globally) 

Warm 
temperate 
climate 

Subtropical 
climate 

Overall (maize in 
China) 

N application 
rate > 250 kg 
ha-1 

N application 
rate 150 - 250 
kg ha-1 

N application 
rate < 150 kg 
ha-1 

straw return 
amount > 9000 
kg ha-1 

straw return 
amount 6000 - 
9000 kg ha-1 

straw return 
amount < 6000 
kg ha-1 

11.3 

14.9 

19.5 

100  Positive 

5.2 

9.0 

13.0 

33 

Positive 

13.1 

18.2 

24.4 

66 

Positive 

8% 

10% 

12% 

48 

Positive 

2% 

6% 

11% 

89 

Positive 

7% 

11% 

14% 

20 

Positive 

8% 

15% 

23% 

27 

Positive 

3% 

10% 

16% 

73 

Positive 

3% 

7% 

10% 

32 

Positive 

11% 

17% 

23% 

48 

Positive 

Source: references in the first column. 

Effect of straw return on N use efficiency was higher in subtropical climate (18.2%; 95% CI: 13.1%, 
24.4%)  than in warm temperate climate (9.0%; 95% CI: 5.2%,  13.0%).  Crop  residue incorporation 
increased the most nitrogen use efficiency at low N application rates (< 150 kg ha-1; 15%; 95% CI: 
8%, 23%) and at the lowest straw returning amount (< 6000 kg ha-1; 17%; 95% CI: 11%, 23%). 

3.22.4 Effects on nutrient leaching and run-off 

Results in the reviewed literature quantify the change in leaching of nitrates (NO3-) or nitrogen, and 
the variation in nitrogen run-off. 

One selected synthesis paper, by Z. Li et al. 2021, includes data from 90 primary studies and explores 
the impact of crop residue return on NO3 leaching in different type of soils and in combination with 
different type and quantity of fertilisers, compared to no crop residue return (Table 70). 

97 

 
 
Table  70.  Effect  (%  change)  of  crop  residue  return  on  NO3  leaching.  CI_LOW:  confidence  interval  bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention  

CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

-21 

-7 

-12 

-45 

-2 

-13 

2 

3 

-32 

11 

-3 

12 

26 

-12 

28 

90 

19 

16 

23 

24 

Positive 
Non-
significant 
Non-
significant 
Positive 
Non-
significant 

-41 

-27 

-16 

13 

Positive 

-34 

-35 
-41 

-5 

-18 

-27 
-33 

3 

6 

-17 
-20 

12 

-30 

-20 

-7 

33 

4 
10 

12 

54 

Non-
significant 
Positive 
Positive 
Non-
significant 

Positive 

3 

22 

54 

15 

Negative 

-26 

-14 

1 

47 

Non-
significant 

-32 

-21 

-7 

18 

Positive 

-51 

-44 

-39 

4 

Positive 

-36 

-19 

-18 

-5 

6 

9 

60 

239 

22 

18 

4 

4 

8 

14 

52 

6 

-9 

9 

4 

54 

Non-
significant 

Non-
significant 

Non-
significant 
Non-
significant 
Non-
significant 
Non-
significant 
Non-
significant 
Positive 

Soil pH 

Soil type 

Fertiliser 
compo-
sition 

Fertiliser 
form 

Fertiliser 
applications 

overall 

pH < 5.5 

pH 5.5 - 6.5 

pH 6.5 - 7.5 

pH > 7.5 

Silty clay loam 
soils 

Loamy sand soils  

Sandy loam soils 
Silty loam soils 

Sandy soils 

Residues and N 
application 
Residues and 
NPK application 
Residues and 
Urea application 
Residues and 
NH4NO3 
application 
Residues and 1 
fertiliser 
application 
Residues and 2 
fertiliser 
application 
Residues and 3 
fertiliser 
application 

Crop residue 
return 
 Vs. no residue 
return 

Low C/N residues 

-19 

Residue 
type 

Sawdust 

Barley straw 

Maize straw 

Rye straw 

Wheat straw 

Source: Z. Li et al. 2021. 

-28 

-17 

-5 

-34 

-30 

98 

8 

12 

-5 

16 

-17 

-21 

 
 
 
 
Mean response ratio showed that crop residue return decreased NO3 leaching by 13% (95% CI: 3%, 
21%) compared to no residue return. Nevertheless, the effect of this farming practice depended on 
soil pH, type of soil, type and quantity of fertiliser applications, and type of residue. 

Crop residue return decreased NO3 leaching only for pH between 6.5 and 7.5 (-32%; 95% CI: -45%, 
-12%), but it had non-significant effects for other pH values. 

Crop residue return decreased NO3 leaching in silty clay loam, sandy loam and silty loam soils by -
27% (95% CI: -41%, -16%), -27% (95% CI: -35%, -17%), -33% (95% CI: -41%, -20%), respectively. 
But not in loamy sand and sandy soils. 

Nitrogen fertiliser composition significantly affected the effect  size  of crop  residue application on 
NO3  leaching. Application of NPK fertiliser increased the NO3  leaching by 22% (95% CI: 3 - 54), 
compared to control, whereas it was significantly decreased by 20% (95% CI: 7 - 30) with application 
of  single  N  fertiliser.  Among  the  different  forms  of  synthetic  N  fertilisers,  NH4NO3  significantly 
decreased NO3  leaching by 21% (95% CI: 7 - 32). In contrast, the effect of urea did not significantly 
change the effect of crop residue application on NO3  leaching. The study also shows that when the 
fertiliser was applied only once during the growing season, NO3  leaching was significantly reduced 
by 44.1% (95% CI: 39 -51). 

Only the application of wheat straw residues significantly reduced soil NO3  leaching (-21%; 95% CI: 
-30%, -9%), while the effect of return of other residues with different C:N ratios on NO3 
was not significant. 

3.23 Mulching 

Mulching consists on spreading various covering materials on the surface of soil mainly to minimize 
moisture  losses  and  weed  population  and  to  enhance  crop  yield.  The  most  common  mulching 
materials used in commercial agricultural systems are plastic film and straw. This practice includes 
several techniques, classified in two main groups: 1) flat planting with mulching; 2) ridge and furrow 
planting with mulching. 

Flat  planting  with  mulching  includes:  plastic  film  mulching  (black  and  transparent  film  mulching), 
straw  mulching  (straw  or  other  stubbles),  gravel  mulching,  mulching  (several  mulching  materials 
aggregated) and biodegradable mulching (rice bran, molecular film, vegetative film, starch-polyester 
mulch, paper-based mulch, and other degradable films). 

Ridge and furrow planting with mulching includes: plastic-covered ridge coupled with furrow planting 
zone with bare soil, plastic-covered ridge coupled with furrow planting zone with mulching (plastic or 
straw), and not covered ridge coupled with furrow planting zone with mulching (plastic or straw). 

The impact of mulching done with plants (also called living or biological mulching) is addressed as a 
separate practice in section 3.19 (Cover and catch crops). To assess the quantitative effect of the 
practice on the environment, the used comparator is no mulching or plastic film mulching. 

3.23.1 Effects on GHG emissions 

The selected paper is Yu et al. 2021. From this paper, results for CH4 emissions are available only for 
paddy fields while for CH4 uptake only for non-paddy fields or uplands. 

99 

 
 
 
In paddy fields, plastic film mulching significantly reduced CH4 emissi
and  by  -64.2  %  (95%  CI:  -73.4%,  -71,7%)  compared  to those  from  non-mulched  farmlands.  This 
result was mainly due to film mulching greatly increasing the activity of methane-oxidizing bacteria 
by maintaining aerobic conditions  under non-flooded water conditions. It is worth noting that film 
thickness significantly affected the response of CH4 emissions to mulching, and the use of thick film 
mm) and 
thick  film  (>0.01  mm)  significantly  (p  <  0.05)  decreased  CH4  emissions  by  60.9%  and  87.4%, 
respectively. 

Table 71. Effect (% change) of mulching on CH4 emissions. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. 

Intervention 
/comparator 

Plastic and 
biodegradable 
film vs. no 
mulching 

Plastic film vs. 
no mulching 

Biodegradable 
film vs. no 
mulching 

Factors 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

Overall 

Paddy fields only 

Paddy fields 

-73.4 

-73.4 

-64.2 

-64.2 

Crop type 

Rainfed uplands 

NA 

Irrigated uplands 

Na 

NA 

NA 

-51.7 

-51.7 

NA 

42 

42 

NA 

NA 

NA 

Positive 

Positive 

Not 
available 

Not 
available 

Film 
thickness 
(mm) 

<=0.01 

>0.01 

-70.8 

-60.9 

-47.1 

32 

Positive 

-87.3 

-87.4 

-97.3 

4 

Positive 

Film type 

Transparent PE 

-73.0 

-64.0 

-50.1 

33 

Positive 

Film type 

Biodegradable 

-76.3 

-66.6 

-52.5 

6 

Positive 

Source: Yu et al. 2021. 

In  rainfed  uplands,  the  soil  CH4  uptake  was  significantly  lower  in  mulched  uplands  than  in  non-
mulched uplands. However, the negative effect of film mulching on CH4 uptake in irrigated uplands 
was not significant.  

In uplands, the soil CH4 uptake was significantly (p < 0.05) reduced by -16.1% (95% CI: -27.8%, -
2.9%) following the application of plastic film. CH4 uptake was significantly (p < 0.05) reduced by -
25.5% in mulched rainfed up- lands compared to non-mulched fields. Moreover, fields covered with 
a low (<50%) coverage ratio or black PE film exhibited consistently lower CH4 uptake. The response 
of CH4 uptake to mulching differed significantly (p < 0.05) with film thickness. Covering the fields 
with thick films (>0.01 mm) significantly reduced CH4 uptake by -53.7%. Moreover, authors found no 
significant relationship between the RR of CH4 emissions/uptake to film mulching and other variables. 

100 

 
 
 
 
Table 72. Effect (% change) of mulching on CH4 uptake. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. 

Intervention 
/comparator 

Factors 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Overall 

Upland only  

-27.8 

-16.1 

-2.9 

70 

Negative 

Paddy fields 

NA 

Na 

NA 

Crop type 

Rainfed uplands 

-32.2 

-25.5 

-17.9 

Irrigated uplands 

-29.3 

-6.2 

<50% 
50-100% 

-32.2 
-28.5 

-20.1 
-13.1 

23.9 

-6.2 
5.9 

Transparent PE 

-27.8 

-13.5 

3.4 

Black PE 

Ridge-furrow 
mulching 
Flat mulching 

<=0.01 

>0.01 

-43.6 

-27.4 

-30.7 

-15.0 

-15.3 

1.2 

-50.5 

10.0 

143.0 

4 

-30.4 

-10.2 

14.7 

42 

-60.1 

-53.7 

-46.5 

2 

Plastic film vs. 
no mulching 

Coverage 
ratio 

Film type 

Mulching 
method 

Film 
thickness 
(mm) 

NA 

33 

37 

13 
53 

58 

11 

53 

Not 
available 
Negative 

Non-
significant 

Negative 
Non-
significant 

Non-
significant 

Negative 

Non-
significant 
Non-
significant 

Non-
significant 
Negative 

Source: Yu et al. 2021. 

The  application  of  film  significantly  (p  <  0.05)  increased  N2O  emissions  by  23.9%  compared  to 
those  from  non-mulched  fields,  in  paddy  fields,  rainfed  uplands  and  irrigated  uplands  analysed 
together.  However,  the  agricultural  system,  coverage  ratio,  film  type  and  mulching  method 
significantly  (p  <  0.05)  influenced  the  response  of  N2O  emissions  to  mulching.  Film  mulching 
significantly (p  <  0.05)  increased  N2O emissions  by 89.3%  in  paddy  fields  but  had  no  significant 
effects on rainfed or irrigated uplands. The increase in N2O emissions was consistent in fields with 
high  coverage  ratios  (50 100%),  transparent  and  black  PE  films,  and  flat  mulching.  In  addition, 
  mm)  significantly  increased  N2O 
compared  with  non-
emissions by 29.7%. 

Table 73. Effect (% change) of mulching on N2O emissions. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. 

Intervention 
/comparator 

Plastic and 
biodegradable 
film vs. no 
mulching 

Factors 

CI_LOW 

Mean 

CI_HIGH  Nc 

Effect 

Overall 

Paddy fields, 
rainfed uplands, 
irrigated 
uplands  

11.3 

23.9 

38.2 

136 

Negative 

Paddy fields 

42.8 

89.3 

Crop type 

Rainfed uplands 

-1.0 

Irrigated 
uplands 

-10.3 

6.7 

2.0 

152 

17.4 

39 

57 

17.8 

38 

Negative 

Non-
significant 

Non-
significant 

101 

 
 
<50% coverage 

-13.3 

-3.0 

9.7 

13 

Non-
significant 

5.1 

21.7 

40.9 

82 

Negative 

50-100% 
coverage 

Ridge-furrow 
mulching  

Flat mulching 

<=0.01 

>0.01 

Coverage 
ratio 

Mulching 
method 

Film 
thickness 
(mm) 

Film type 

-8.0 

25.5 

11.3 

-1.4 

1.7 

34.7 

29.7 

24.0 

22.8 

13.6 

45.1 

52.4 

57.4 

41.3 

Transparent PE 

7.4 

Black PE 

11.7 

22.4 

34.3 

52 

5 

82 

17 

89 

24 

Non-
significant 

Negative 

Negative 

Non-
significant 

Negative 

Negative 

Non-
significant 

Biodegradable 

-29.1 

-13.7 

5.5 

9 

Plastic and 
biodegradable 
film vs. no 
mulching 

Biodegradable 
film vs. no 
mulching 

Source: Yu et al. 2021. 

According to the results on this synthesis paper, overall, the effect of film mulching on the total GHG 
fluxes  (N2O+CH4  GWP)  differed  among  agricultural  systems.  In  rainfed  uplands,  film  mulching 
slightly  increased  GHG  fluxes  by  95.5  kg  CO2-
ds,  plastic  film 
mulching  slightly  reduced  GHG  fluxes  by  -37.3  kg  CO2-
application of plastic film greatly reduced the GWP of CH4 emissions by 3532.6 kg CO2-

1  with  a  results  on  GHG  fluxes  of 

3109.6 kg CO2-

3.23.2 Effects on ammonia emissions 

The selected paper for quantitative data extraction is Abdo et al. 2021. Authors found that mulching 
has a greater effect on reducing ammonia on maize crops (-29%; 95% CI: -32%,-25%) than on wheat 
(-26%; 95% CI: -30%, -23%) and rice (-20%; 95% CI: -23%, -18%). The efficiency of mulching on 
reducing ammonia volatilisation increased when increasing the N mineral fertilisation rate. 

Table 74. Effect (% change) of mulching on ammonia emissions. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention 

Crop 

Factor 

CI_LOW  Mean  CI_HIGH  Nc  Effect 

Mulching 

Maize 

Wheat 

Rice 

Urea N 
fertiliser level 
(kg N/ha) 

180-220 

-32.4 

-28.6 

-24.8 

NA  Positive 

90-120 

-5.2 

-4.9 

-4.6 

NA  Positive 

180-220 

-29.8 

-26.3 

-22.6 

NA  Positive 

180-220 

-22.8 

-20.4 

-18.2 

NA  Positive 

240-280 

-16.4 

-14.5 

-12.7 

NA  Positive 

Source: Abdo et al. 2021. 

102 

 
 
 
3.23.3 Effects on the nutrient balance 

Results in the reviewed literature quantify the change in nitrogen use efficiency and nutrient uptake 
(Annex 1). The selected synthesis paper is the one by W. Qin et al. 2015, that includes data from 74 
primary studies conducted in 19 countries and explores the impact of straw and plastic mulching on 
nitrogen use efficiency for different type of crops and in combination with different level of water 
and nitrogen inputs (Table 75). 

Table  75.  Effect  (%  change)  of  mulching  on  nutrient  use  efficiency.  CI_LOW:  confidence  interval  bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.  

Subpractice  Factors 

Crop 

CI_LOW 

Mean  CI_HIGH  Nc 

Effect 

Straw 
mulching 

Plastic 
mulching 
Straw 
mulching 
Plastic 
mulching 

overall 

overall 

overall 

overall 

Straw 
mulching 

water 
input 

Plastic 
mulching 

water 
input 

Straw 
mulching 

N 
input 

Plastic 
mulching 

N 
input 

Source: W. Qin et al. 2015. 

low water input < 
250 mm 
high water input 
> 250 mm 
low water input < 
370 mm 
high water input 
> 370 mm 
low water input < 
250 mm 
high water input 
> 250 mm 
low water input < 
370 mm 
high water input 
> 370 mm 
low N input < 
120 kg/ha 
high N input > 
120 kg/ha 
low N input < 
120 kg/ha 
high N input > 
120 kg/ha 
low N input < 
120 kg/ha 
high N input > 
120 kg/ha 
low N input < 
120 kg/ha 
high N input > 
120 kg/ha 

Wheat 

11.94 

17.91  24.88 

289  Positive 

Wheat 

12.44 

22.39  32.84 

72 

Positive 

Maize 

10.45 

21.89  35.32 

328  Positive 

Maize 

40.30 

58.71  79.10 

143  Positive 

Wheat 

11.84 

21.22  31.84 

NA 

Positive 

Wheat 

8.57 

15.31  22.65 

NA 

Positive 

Maize 

4.59 

24.49  48.06 

NA 

Positive 

Maize 

11.94 

20.20  29.39 

NA 

Positive 

Wheat 

2.45 

14.90  28.37 

NA 

Positive 

Wheat 

18.16 

30.20  43.47 

NA 

Positive 

Maize 

35.20 

60.00  92.76 

NA 

Positive 

Maize 

26.02 

40.00  53.88 

NA 

Positive 

Wheat 

17.32 

25.15  33.40 

NA 

Positive 

Wheat 

8.87 

16.08  23.71 

NA 

Positive 

Maize 

14.05 

22.78  33.04 

NA 

Positive 

Maize 

-12.53 

32.28  98.35 

NA 

Non-
significant 

Wheat 

5.15 

15.88  27.42 

NA 

Positive 

Wheat 

9.07 

20.82  33.61 

NA 

Positive 

Maize 

22.03 

35.00  56.96 

NA 

Positive 

Maize 

34.94 

78.00  126.08 

NA 

Positive 

103 

 
 
Mulching  had  an  overall  positive  effect  on  nitrogen  use  efficiency:  straw  mulching  was  found  to 
increase nitrogen use efficiency by 17.9% (95% CI: 11.9%, 24.9%) in wheat and by 21.9% (95% CI: 
10.5%, 35.3%) in maize; plastic mulching was found to increase nitrogen use efficiency by 22.4% 
(95% CI: 12.4%, 32.8%) in wheat and by 58.7% (95% CI: 40.3%, 79.1%) in maize. 

The mean effects of straw mulching on nitrogen use efficiency of wheat were slightly higher at low 
water input (+21.2%; 95% CI: 11.8%, 31.8%) than at high water input (+15.3%; 95% CI: 8.6%, 22.6%). 
On the contrary, mean effects of plastic mulching on NUE of wheat were lower at low water input 
(14.9%; 95%CI: 2.4%, 28.4%) than at high water input (+30.2; 95% CI: 18.2%, 43.5%). For maize, the 
mean effect of straw mulching on NUE was independent of water input level, whereas that of plastic 
mulching was higher at low water input level (+60.0; 95% CI: 35.2%, 92.8%) than at high water input 
level (+40.0%; 95% CI: 26.0%, 53.9%). 

The effects of mulching on nitrogen use efficiency were significantly positive at all N input levels. 
However, straw mulching did not significantly affect NUE in maize at high N input level. 

3.23.4 Effects on nutrient leaching and run-off 

Results in the literature quantify the change in soil total nitrogen loss, ammonium nitrogen loss, total 
phosphorus loss and dissolved phosphorus loss. The selected synthesis paper is the one by R. Liu et 
al. 2021, that includes data from 85 primary studies and explores the impact of mulching (several 
materials including plastic film, cereals straw and pruning residues) on nutrient loss in tree  crops, 
compared to no mulching (clean tillage management) (Table 76). Mulching had an overall positive 
effect on nutrient loss, reducing soil total nitrogen loss by 31.1% (95% CI: 13.9%, 49.0%), soil nitrate 
nitrogen loss by 86.0% (95% CI: 64.4%, 108.8%), ammonium nitrogen loss reduction by 83.9%, total 
phosphorus loss by 35.6% (95% CI: 18.9%, 52.5%), and dissolved phosphorus loss by 53.9% (95% 
CI: 29.6%, 78.3%). 

Table  76.  Effect  (%  change)  of  mulching  on  nutrient  loss.  CI_LOW:  confidence  interval  bottom,  CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. 

Metric 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Soil total nitrogen loss reduction 

Soil nitrate nitrogen loss reduction 

Ammonium nitrogen loss reduction 

Total phosphorus loss reduction 

Dissolved phosphorus loss reduction 

Source: R. Liu et al. 2021. 

13.94 

64.40 

NA 

18.95 

29.57 

31.08 

86.00 

83.92 

35.57 

53.91 

49.00 

19 

Positive 

108.80 

NA 

5 

1 

Positive 

Positive 

52.48 

15 

Positive 

78.26 

8 

Positive 

104 

 
 
 
 
 
 
3.24 Grassland conservation and restoration 

Grasslands are areas of land predominantly covered by communities of grass-like plants and forbs 
and may include sparsely occurring trees and shrubs. In the Eurostat classification, percentages of 
area covered by such canopies are limited to less than 10 % in the case of trees and to less than 20 
% in the case of shrubs (i.e., low woody plants capable of reaching heights of up to 5 metres) or 
shrubs and trees together53. However, in the scientific literature reviewed here, grasslands are usually 
more broadly defined and might not fully meet such canopy limits. 

The  literature  reviewed  includes  a  wide  variety  of  grassland  types:  savannah,  grassy  deserts, 
seasonally  flooded  grasslands,  prairies,  meadows,  pastures,  rangelands,  salt-marshes,  bioenergy 
perennial grasslands, calcareous grasslands and wooded grasslands. Fodder crops are not included 
as grasslands. In addition, for this exercise, grasslands may differ in terms of: management intensity 
(natural,  semi-natural,  improved  and  intensively  managed),  management  history  (permanent  and 
temporary),  successional  stage  (old  successional  and  secondary  grasslands),  biomes  and  climates 
(semi-arid, temperate, tropical, Mediterranean, tundra, alpine, subalpine, artic and subarctic).  

Grassland conservation refers to the preservation (i.e., no transformation to other land uses) of old 
successional  natural  grasslands.  Old  successional  natural  grasslands  are  grasses  and  forbs 
communities with no major human-induced structural or functional alterations so that their current 
management  closely  resembles  historical,  endogenous  disturbance  regimes (e.g.,  grazing intensity 
and fire frequency) (Nerlekar & Veldman 2020). In the scientific literature, these ecosystems are also 
referred as old-growth, ancient, remnant or native grasslands.  

Grassland restoration includes active and passive restoration methods usually aiming at enhancing 
plant community diversity, what is then expected to have cascading positive effects in higher trophic 
levels. This review includes several restoration methods: sowing of seed mixtures, addition of soil and 
hay,  reintroduction  of  grazing  and  burning  as  active  restoration  methods,  and  abandonment  of 
agricultural  use as passive restoration method. In this review, we only consider the restoration of 
former grassland areas degraded by agricultural use (i.e., degradation due to mining or other land 
uses are excluded). We define secondary grasslands as the herbaceous communities that assemble 
after  destruction  (here,  by  agricultural  use)  of  old  successional  natural  grasslands  where  the 
restoration efforts are conducted (Nerlekar & Veldman 2020). 

3.24.1 Effects on greenhouse gas emissions 

Evidence of impacts of grassland restoration on GHG was found from only one synthesis paper (J. Wu 
et al. 2020). They report that CH4 uptake rate was significantly increased when the cropland was 
converted to pasture, with Hedges' D effect size value of 1.38. However, the metric used does not 
allow to show the percentage nor the absolute impact. 

53 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Grassland 

105 

 
 
 
                                                 
 
Table 77. Effect (
by major GHG types. Nc: number of pairwise comparisons. 

of grassland conservation and restoration practices on greenhouse gas emissions 

Technique 

GHG 

Hedges' D effect size 
value 

Conversion to pasture 
vs. cropland 

Source: J. Wu et al. 2020. 

CH4 uptake 

1.38 

Nc 

6 

Effect 

Positive 

3.24.2 Effects on soil organic carbon 

The only synthesis paper that explores the effect of grassland conservation on soil organic carbon 
54 SOC stocks were 

-

55 soils (Kämpf et al. 2016b)

of ex-arable land did not reach the level of never ploughed 
However,  the  effect  was  different  with  time  of  abandonment.  In  soils  of  ex-arable  grasslands 

ancient grassland soils. Old ex-
(mean difference in abandonment time: 13 years).  

-arable land 

The effects of grassland restoration (land use changes from crop to pasture or perennial grasses) on 
soil carbon stocks are extracted from Harris et al. 2015 and Kämpf et al. 2016b.  

The data reported by Kämpf et al. 2016b regard the effects of the conversion to temperate grasslands 
as compared to arable land. Ex-arable grassland as compared to arable land significantly increased 
SOC by 17%, as average [95% CI: 12%, 22.7%]. However, these results depend on different factors: 
(i) abandonment time; (ii) Climate; (iii) initial SOC stock. In the first 16 years, SOC in ex-arable land 
increased evenly but subsequently stagnated at high level (+31%). The proportional gain of carbon 
was positively correlated with the aridity index of the study site: it increased from 12.5% in humid to 
31.7% in semi-arid climate. Absolute values, however, did not differ significantly. The effect of the 
initial SOC stock on SOC change was negative for both proportional change as well as absolute values. 
SOC stocks were significantly higher in ex-arable land compared to arable land with initial SOC stocks 
-use abandonment had non-significant effect on SOC 
of 0 25 and 25

The data reported by Harris et al. 2015 regard the effects of the conversion to perennial bioenergy 
grassland as compared to Cropland and Perennial Grasses. They report that conversion to perennial 
bioenergy grasslands as compared to cropland significantly increased SOC by 25.7% ± 6.7% (average 
time  since  transition  of  5.4  years,  with  2  and  16  years  as minimum  and maximum,  respectively). 
However,  when  comparing  with  grassland,  SOC  decreased  by  -10.9  ±  4.3%  (average  time  since 
transition 5.8 years, minimum and maximum 3 and 6), which we also report for comparison. 

54 Ex-arable grasslands is defined as abandonment of formerly ploughed fields.  
55 Ancient grassland was defined as land that is never ploughed. 

106 

 
 
 
                                                 
 
Table  78  Effect  (%  change)  of  grassland  conservation  on  soil  organic  carbon  (stock  per  hectare).  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Pairwise 
comparison 

Source 

Population 

Factor  CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

Grassland conservation 

Ex-arable 
grassland as 
compared to 
ancient grassland 

Kämpf et 
al. 
2016b 

Temperate 
grasslands 

-- 

-2.1 

-11.4 

-20.7 

36 

Positive 

Grassland restoration 

Ex-arable54 
grassland vs. 
arable land 
Perennial bioenergy 
grasslands vs. 
cropland 
Perennial bioenergy 
grasslands vs. 
grassland 

Kämpf et 
al. 
2016b 

Harris et 
al. 2015 

Harris et 
al. 2015 

Temperate 
grasslands 

Bioenergy 
perennial 
grasslands 
Bioenergy 
perennial 
grasslands 

- 

12.0 

17.1 

22.7 

69 

Positive 

17.9 

25.7 

33.9 

63 

Positive 

- 

-14.5 

-10.9 

-7.3 

43 

Negative 

Abandonment time 

18.1 

31.0 

44.1 

20.0 

26.8 

34.6 

10.9 

15.9 

21.4 

11 

15 

16 

-0.5 

7.2 

15.2 

10 

Global aridity index 

5.0 

12.4 

20.4 

11.0 

19.4 

27.9 

19 

18 

18.0 

31.1 

43.4 

6 

17.1 

31.6 

46.4 

14 

Initial SOC stock 

-1.1 

3.7 

9.0 

21 

-4.0 

0.2 

4.5 

6 

10.5 

20.1 

29.9 

23.5 

32.9 

42.4 

20 

22 

Positive 

Positive 

Positive 

Non-
significa
nt 

Positive 
Positive 

Positive 

Positive 

Non-
significa
nt 
Non-
significa
nt 
Positive 

Positive 

Ex-arable 
grassland vs. 
arable land 

Kämpf et 
al. 
2016b 

Temperate 
grasslands  

Ex-arable 
grassland vs. 
arable land 

Kämpf et 
al. 
2016b 

Temperate 
grasslands  

> 16 
years 
> 8-16 
years 
> 4-8 
years 

0-4 
years 

humid 
modera
tely 
humid 
sub-
humid 
semi-
arid 

> 75 
t/ha 

Ex-arable 
grassland vs. 
arable land 

Kämpf et 
al. 
2016b 

Temperate 
grasslands  

> 50-
75 t/ha 

Source: references in the second column. 

> 25-
50 t/ha 
0-25 
t/ha 

107 

 
 
 
3.24.3 Effect on nutrient leaching and runoff 

One synthesis paper (Y. Zhang et al. 2021), explores the effect of grassland restoration on the soil 
N/I ratio. Nitrate (NO3 ) is readily lost via leaching or transformation into gaseous nitrogen during 
nitrification  and  denitrification.  The  soil  ratio  of  nitrification  to  immobilization  (N/I)  reflects  the 
potential risk of nitrogen loss from soils and is therefore positively correlated with amounts of nutrient 
loss by leaching. Soils with a high N/I ratio have greater potential of nitrogen loss through leaching, 
runoff, or denitrification than those with a low N/I ratio. Thus, the N/I ratio has been used to evaluate 
the risk of nitrogen loss. The authors reported that converting cropland to grassland decreased the 
soil N/I ratio by 45% (Nc=5).  

Table 79 Effects (% change) grassland restoration practices on Soil nitrification to immobilization ratio.  SE: 
Standard Error of the mean, Nc: number of pairwise comparisons..  

Metric 

Population 

Mean-SE 

Mean 

Mean+ SE 

Nc 

Effect 

-57.3 

-45 

-23.3 

5 

Positive 

Soil nitrification to 
immobilization ratio 

Grasslands (vs. 
cropland) 

Source: Y. Zhang et al. 2021 

3.25 Grassland management 

Following the previous definition of grasslands as included in this literature review, the interventions 
here considered as grassland management with relevant effects on the climate and nutrients are:  

─  Use of soil organic amendments: Soil organic amendments are materials of plant or animal origin 
that can be added to soil, such as manures, biosolids, green wastes, and composts, to improve 
the soil quality in terms of its structure and biochemical function. This includes also biochar, which 
is  charcoal  produced  by  pyrolysis  of  biomass  in  the  absence  of  oxygen  (as  defined  in  the 
corresponding section 3.8 of this document). 

─  Use of Enhanced-efficiency fertilisers (EEF) : Enhanced-efficiency fertilisers (EEF) are different 
types  of  fertilisers  or  products  associated  to  fertilisers,  which  have  been  developed  to  better 
synchronize fertiliser nitrogen (N) release with crop uptake, offering the potential for enhanced 
N-use efficiency (NUE) in crops and reduce losses (as defined previously in 3.5). This section report 
results that are specific for the use of EEF on grassland, including the use of different types of 
EEF, such as nitrification inhibitors, urease inhibitors and polymer-coated fertilisers. 

─  Use of fertilisers  

─ 

Increasing grass/forb species richness 

3.25.1 Effects on greenhouse gas emissions 

The  effects  of  grassland  management  practices  on  greenhouse  gases  are  diverse  across  specific 
techniques and gases.  

Use of soil organic amendments:  

108 

 
 
 
 
One  synthesis  paper  explores  the  effect  of  the  application  of  biochar  to  grasslands  soils  on  GHG 
emissions, showing that soil amendments with biochar have non-significant effect in reducing N2O 
emissions as compared to not amending (Cai & Akiyama 2017). 

Table 80. Effect (% change) of soil organic amendments as compared to unmanaged grassland on soil GHG 
emissions.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of  pairwise 
comparisons. 

Pairwise 
comparison 

Biochar as compared 
to No amendments 

Source: Cai & Akiyama 2017. 

Metric 

CI_HIGH 

Mean 

CI_LOW 

Nc 

Effect 

Soil GHG 
emissions: soil 
N2O emission  

+19 

-15 

-39 

3 

Non-
significant 

Use of Enhanced-efficiency fertilisers (EEF):  

Regarding  the  effect  of  the  application  of  different  inhibitors  associated  to  fertilisers  on  N2O 
emissions in the framework of grassland management, overall the addition of these products resulted 
in  a  reduction  of  the  emissions  of  this  green-house  gas.  The  selected  paper  for  numerical  data 
extraction  in  this  impact  is  Cai  &  Akiyama  2017.  This  paper  explores  the  effects  of  grassland 
fertilisation using various nitrification inhibitors such as: Dicyandiamide, N-[n-butyl] thiophosphoric 
triamide or Pyrazole derivates. Different forms of these additives as well as combined applications 
of them are explored. Results are provided for soil N2O emissions and are valid for grasslands. 

Table 81. Effect (% change) of the use of Enhanced-efficiency fertilisers as compared to unmanaged grassland 
on soil GHG emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. 

Pairwise 
comparison 

Dicyandiamide in 
liquid form as 
compared to No 
inhibitor 
Dicyandiamide coated 
with zeolite as 
compared to No 
inhibitor 
N-[n-butyl] 
thiophosphoric 
triamide in liquid form 
as compared to No 
inhibitor 
N-[n-butyl] 
thiophosphoric 
triamide and 
Dicyandiamide in 
liquid form as 
compared to No 
inhibitor 
N-[n-butyl] 
thiophosphoric 
triamide and 

Metric 

CI_HIGH 

Mean 

CI_LOW 

Nc 

Effect 

Soil GHG 
emissions: 
soil N2O 
emission 
Soil GHG 
emissions: 
soil N2O 
emission 

Soil GHG 
emissions: 
soil N2O 
emission 

Soil GHG 
emissions: 
soil N2O 
emission 

Soil GHG 
emissions: 

-47 

-52 

-56 

105 

Positive 

-33 

-39 

-44 

6 

Positive 

+4 

-23 

-53 

4 

Non-
significant 

-41 

-47 

-53 

19 

Positive 

-46 

-52 

-56 

4 

Positive 

109 

 
 
Pairwise 
comparison 
Dicyandiamide coated 
with zeolite as 
compared to No 
inhibitor 
Pyrazole derivatives in 
liquid form [PD] as 
compared to No 
inhibitor 

Source: Cai & Akiyama 2017. 

Metric 

CI_HIGH 

Mean 

CI_LOW 

Nc 

Effect 

soil N2O 
emission 

Soil GHG 
emissions: 
soil N2O 
emission 

+34 

8 

-11 

10 

Non-
significant 

The  selected  article  also  explores  the  effect  of  using  Dicyandiamide  as  inhibitor  taking  into 
consideration different modifying factors as the type of excreta used for fertilisation or the deposition 
season. The numerical results considering these factors are shown in the table below.  

Table 82. Effect (% change) of use of enhanced-efficiency fertilisers as compared to unmanaged grassland 
on soil GHG emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. 

Pairwise 
comparison 

Metric 

Factor 

CI_HIGH 

Mean 

CI_LOW 

Nc 

Effect 

dairy 
cattle 
urine 
artificial 
urine 
beef cattle 
urine 

Summer 
Autumn 
Winter 
Spring 

Excreta type 

-47 

-52 

-56 

91 

Positive 

-44 

+261 

-15 
-48.3 
-1.1 
-42.7 

-53 

63 

-60 

17 

Positive 

-13 

2 

Non-
significant 

Deposition time 
-25.9 
-53.9 
-35.7 
-50.1 

-35.3 
-59.1 
-52.8 
-57.8 

5 
58 
11 
27 

Positive 
Positive 
Positive 
Positive 

Dicyandiamide 
as compared 
to No inhibitor 

Soil GHG 
emissions: 
soil N2O 
emission 

Dicyandiamide 
as compared 
to No inhibitor 

Soil GHG 
emissions: 
soil N2O 
emission 

Source: Cai & Akiyama 2017. 

Use of fertilisers:  

The effects of grassland fertilisation on GHG emissions are overall negative according to the retrieved 
synthesis papers examining CH4 uptake by soil and N2O emissions when Nitrogen, Phosphorous or 
the two combined are applied (Schievano et al. 2023).  

Among the available meta-analyses reporting negative results on GHG emissions, we selected these 
two for quantitative data extraction: L. Zhang et al. 2020 and J. Wang et al. 2018. 

Results are provided for soil GHG emissions measured as soil CH4 uptake and N2O emissions (Table 
83). The data reported by L. Zhang et al. 2020 regard the effects of grassland fertilisation on CH4 
uptake  as  compared  to  no  fertilisation,  covering  different  types  of  fertiliser  combination,  such  as 
phosphorous (P) fertilisation alone, nitrogen (N) fertilisation alone or N and P fertilisation combined 
(N+P). The data reported by J. Wang et al. 2018 cover the effect of N fertilisation on N2O emissions 
as compared with no fertilisation. 

110 

 
 
Table 83. Effect (% change) of the use of fertilisers as compared to unmanaged grassland on GHG emissions 
for the specified metrics. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. 

Reference 

L. Zhang et 
al. 2020 

L. Zhang et 
al. 2020 

L. Zhang et 
al. 2020 

J. Wang et 
al. 2018 

Pairwise 
comparison 

P fertilisation as 
compared to No 
fertilisation 
N+P fertilisation 
as compared to 
No fertilisation 
N fertilisation as 
compared to No 
fertilisation 
N fertilisation as 
compared to No 
fertilisation 

Source: references in the first column. 

Metric 

CI_HIGH  Mean 

CI_LOW 

Nc 

Effect 

Soil GHG 
emissions: Soil 
CH4 uptake 
Soil GHG 
emissions: Soil 
CH4 uptake 
Soil GHG 
emissions: Soil 
CH4 uptake 
Soil GHG 
emissions: soil 
N2O emission 

+16 

10 

+4 

7 

Positive 

-8 

-14 

-20 

7 

Negative 

-21 

-27 

-33 

7 

Negative 

+349 

271 

+207 

82 

Negative 

3.25.2 Effects on soil organic carbon 

Use of soil organic amendments:  

One revised meta-analysis found that organic amendment addition as compared to no amendment 
to rangeland has a positive effect on soil organic carbon (Gravuer et al. 2019). The dataset includes 
experiments and field measurements performed taking into account some particular factors, such as 
application rate (50 Mg/ha and 10 Mg/ha) and N content in amendment (3.6 % on dry weight basis 
and 1.8 % on dry weight basis). Results vary according to these particular factors. 

Soil  organic  amendments  significantly  increased  SOC.  The  overall  increase  was  33.4%  (95%  CI: 
21.4%, 45%) for rangelands in arid, semiarid, and Mediterranean climates, based on 244 data points 
from primary studies.  

Table 84 provides the main numerical results regarding the effect of this management practice on 
soil organic carbon SOC expressed in percentage of change as compared to the control. Population 
information such as geographical location, pedo-climatic conditions and other factors are provided. 

Table  84.  Effect  (%  change)  of  organic  amendments  as  compared  to  unmanaged  grassland  on  Carbon 
sequestration for the metric  SOC.  CI_LOW: confidence  interval  bottom, CI_HIGH: confidence interval top, Nc: 
number of pairwise comparisons. 

Pairwise 
comparison 

Soil organic 
amendment 
as compared 
to No 
amendment 

Population 

CI_HIGH 

Mean 

CI_LOW 

Nc 

Effect 

Rangelands in arid, 
semiarid, and 
Mediterranean climates. 
Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in 
amendment (1.8 %) and 
Application rate (10 
Mg/ha). 

45 

33.4 

21.4 

244 

Positive 

3 

-20.1 

9.9 

7 

Non-
significant 

111 

 
 
 
 
 
Pairwise 
comparison 

Population 

CI_HIGH 

Mean 

CI_LOW 

Nc 

Effect 

Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in 
amendment (1.8 %) and 
Application rate (50 
Mg/ha). 
Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in 
amendment (3.6 %) and 
Application rate (50 
Mg/ha). 

38.9 

9.4 

23.2 

7 

Positive 

45.2 

16.1 

30.8 

7 

Positive 

Source: Gravuer et al. 2019 

3.25.3 Effects on nutrient leaching and runoff 

Use of soil organic amendments:  

Amendment  addition  to  rangeland  soils  increases  N  and  P  runoff  (Gravuer  et al.  2019).  The  data 
reported  by  Gravuer  et  al.  2019  regard  the  effects  of  soil  amendments  as  compared  to  no 
amendment.  The  dataset  includes  experiments  and  field  measurements  performed  taking  into 
account some particular factors, such as application rate (50 Mg/ha and 10 Mg/ha) and N content in 
amendment  (3.6  %  on  dry  weight  basis  and  1.8  %  on  dry  weight  basis).  Results  are  consistently 
negative across all these particular factors. 

Table 85. Effect (% change) of amendment addition as compared to unmanaged grassland on nutrients runoff. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Metric 

Nutrients 
runoff: 
Nitrate 
runoff 

Nutrients 
runoff: 
Nitrate 
runoff 

Nutrients 
runoff: 
Nitrate 
runoff 

Nutrients 
runoff: 
Nitrate 
runoff 

Population 

CI_HIGH 

Mean 

CI_LO
W 

Nc 

Effect 

Rangelands in arid, 
semiarid, and 
Mediterranean climates. 

Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in amendment 
(1.8 %) and Application 
rate (10 Mg/ha). 
Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in amendment 
(1.8 %) and Application 
rate (50 Mg/ha). 
Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in amendment 
(3.6 %) and Application 
rate (50 Mg/ha). 

+307.1 

140.3 

+64.3 

57 

Negative 

+134.6 

58.2 

+25.2 

7 

Negative 

+292.1 

133.4 

+62.6 

7 

Negative 

+492.6 

227 

+104.6 

7 

Negative 

112 

 
 
Metric 

Population 

CI_HIGH 

Mean 

CI_LO
W 

Nc 

Effect 

+423.6 

223.5 

+117.4 

72 

Negative 

+276.8 

100 

+36.1 

7 

Negative 

+681.4 

274.3 

+111.4 

7 

Negative 

+1234.8 

488.2 

+194.8 

7 

Negative 

Nutrients 
runoff: 
Phosphorou
s runoff 

Nutrients 
runoff: 
Phosphorou
s runoff 

Nutrients 
runoff: 
Phosphorou
s runoff 

Nutrients 
runoff: 
Phosphorou
s runoff 

Rangelands in arid, 
semiarid, and 
Mediterranean climates. 

Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in amendment 
(1.8 %) and Application 
rate (10 Mg/ha). 
Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in amendment 
(1.8 %) and Application 
rate (50 Mg/ha). 
Rangelands in arid, 
semiarid, and 
Mediterranean climates: 
N content in amendment 
(3.6 %) and Application 
rate (50 Mg/ha). 

Source: Gravuer et al. 2019 

3.26 Grazing 

For the purpose of this exercise, grazing is defined as a method of animal husbandry in which livestock 
are allowed to feed outdoors consuming wild vegetation. The grazing livestock species included in the 
synthesis papers reviewed are mainly cows, but goats, sheep and domesticated horses and yaks are 
also included.  

Results from the literature review explore: 

─  grazing as compared to no grazing. No grazing refers to areas with grazing exclusion or where 

only wild herbivores graze in natural conditions. 

─  different grazing intensities Grazing intensities are classified, as reported by authors, in light (or 
extensive), moderate and heavy (or intensive). They are compared to no grazing or lower grazing 
intensity as control. It has been assumed that the authors of the original individual papers report 
grazing intensity based on their knowledge of the systems studied, meaning that, for instance, 
the  grazing  intensity  considered  light  in  one  study  system  can  be  high  in  another.  Therefore, 
grazing intensities across individual studies and synthesis papers cannot be compared. The review 
of the impacts of grazing intensities includes spatial and temporal comparisons. 

─  and rotational grazing. Rotational grazing comprises grazing regimes that incorporate periods of 
planned rest (McDonald et al. 2019). In the scientific literature it is also refereed as strategic‐rest 
grazing. For the exploration of the impacts of rotational grazing, nearby areas with no grazing or 
continuous grazing are used as control.  

113 

 
 
 
3.26.1 Effects on greenhouse gas emissions 

Only one synthesis paper (Tang et al. 2019) with global coverage was retrieved on the effects of 
different  grazing  regimes  on  GHG  emissions.  They  report  the  effects  of  light,  moderate  and 
intensive/heavy grazing compared to no grazing. Only heavy grazing was found to have a positive 
effect on N2O emissions and a negative effect on CH4 uptake by soil (decrease in the rate of uptake). 

─  Light/extensive grazing as compared to No grazing had non-significant effect on Soil CH4 uptake 

(6.5; 95% CI: -12.9%, 30.3%).  

─  Moderate grazing as compared to No grazing had non-significant effect on soil CH4 uptake (3.9; 

95% CI: -12.2%, 23.4%). 

─  Heavy/intensive grazing as compared to No grazing significantly decreased soil  CH4 uptake (-

30.3; 95% CI: -40.4%, -19.6%).  

─  Light/extensive  grazing  as  compared  to  No  grazing  had  non-significant  effect  on  soil  N2O 

emissions (-23.3; 95% CI: -60.7%, 50.9%).  

─  Moderate grazing as compared to No grazing had non-significant effect on soil N2O emissions (-

22.9; 95% CI: -57.4%, 40%).  

─  Heavy/intensive grazing as compared to No grazing significantly decreased soil N2O emissions (-

39.1; 95% CI: -62.7%, -3.3%). 

The main findings regarding the effects of Grazing as compared to No grazing on GHG emissions are: 

─  Grazing with a duration < 5 years as compared to No grazing had non-significant effect on soil 

CH4 uptake (-3.9%; 95% CI: 22.3%, 38.8%).  

─  Grazing with a duration of 5 - 10 years as compared to No grazing significantly decreased soil 

CH4 uptake (-23.6%; 95% CI: -40.9%, -1.3%).  

─  A grazing duration of 10 years or more, as compared to No grazing, significantly decreased soil 

CH4 uptake (-35.1%; 95% CI: -56.5%,-3.3%).  

─  Grazing (< 5 years) as compared to No grazing had non-significant effect on soil N2O emission 

(-18.6%; 95% CI: -54.8%, 49.1%).  

─  Grazing for 5 - 10 years, as compared to No grazing, significantly decreased soil N2O emission 

(-42.8; 95% CI: -54.8%, -27.6%.)  

─  Grazing  for  10  years  or  more,  as  compared  to  no  grazing,  significantly  decreased  soil  N2O 

emission (-70.9; 95% CI: -85.5%, -42.3%.)  

Findings also indicate that such results are significant in dry grassland (precipitation < 400 mm/year), 
while in areas with precipitation above 400 mm/year the effects on GHG fluxes are not significant. 
in inhibition of GHG emissions but at the cost of 
CH4  uptake  and  N2O  emission  were reduced  much  more 

under heavier grazing, longer grazing duration or less precipitation. 

114 

 
 
Table 86. Effect (% change) of grazing management practices on greenhouse gas emissions by major GHG 
types.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of  pairwise 
comparisons. 

Intervention 

GHG 

Factor 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Low intensity 
grazing vs. no 
grazing 

Medium 
intensity 
grazing vs. no 
grazing 

High intensity 
grazing vs. no 
grazing 

Grazing vs. 
no grazing 

Grazing vs. 
no grazing 

CH4 
uptake 

N2O 

CH4 
uptake 

N2O 

CH4 
uptake 

N2O 

CH4 
uptake 

- 

- 

- 

- 

- 

- 

< 5 years 

5-10 years 

< 5 years 

N2O 

5-10 years 

CH4 
uptake 

N2O 

< 400 mm 

< 400 mm 

-12.9 

6.5 

+30.3 

15 

Non-significant 

-60.7 

-23.3 

+50.9 

8 

Non-significant 

-12.2 

3.9 

+23.4 

20 

Non-significant 

-57.4 

-22.9 

+40 

10 

Non-significant 

-40.4 

-30.3 

-19.6 

-62.7 

-22.3 

-40.9 

-56.5 

-54.8 

-54.8 

-85.5 

-26.0 

-33.3 

-56.8 

-38.6 

-39.1 

-3.3 

3.9 

+38.8 

-23.6 

-35.1 

-18.6 

-42.8 

-70.9 

-15.4 

-14.0 

-41.5 

-7.9 

-1.3 

-3.3 

+49.1 

-27.6 

-42.3 

-5.4 

11.8 

-22.1 

39.3 

29 

16 

8 

10 

5 

4 

14 

5 

51 

13 

27 

13 

Negative 

Positive 

Non-significant 

Negative 

Negative 

Non-significant 

Positive 

Positive 

Negative 

Non-significant 

Positive 

Non-significant 

Source: Tang et al. 2019. 

3.26.2 Effects on soil organic carbon 

Light grazing compared to no grazing showed non-significant effect on SOC.  

Moderate grazing compared to no grazing showed a negative effect on SOC, but the effect depended 
on the soil layer. In the selected paper for data extraction (Lai & Kumar 2020), the authors report 
non-significant effect on SOC in the upper soil layer (0-10 cm) and a negative effect in the 0-30 cm.  

Heavy grazing compared to non-grazed or less intensively grazed areas has a negative effect on soil 
organic carbon. In the selected paper for data extraction (Lai & Kumar 2020), the authors found that 
heavy/intensive grazing as compared to no grazing significantly decreased SOC by -10.8% (95% CI: 
-17.7%, -3.8%) in the top soil layer (0-10 cm depth) and by -22.5% (95% CI: -33.9%, -10.2%) in the 
below layer (10-30 cm soil depth). Given that they give values classified by soil depth, we also provide 
the results from other synthesis papers for comparison purposes. Tang et al. 2019 report a decrease 
in SOC of -10.2% (95% CI: -17.6%, -3%), in line with the top soil layer value of Lai & Kumar 2020. 
Byrnes et al. 2018 report an average decrease of -13.9% (95% CI: -20.5%, -6.8%). Therefore, in case 
of further information on the soil layers, the value of the top soil layer seems appropriate.  

115 

 
 
 
 
 
 
Grazing in general (no intensity specified) showed a negative effect by most synthesis papers. In the 
selected synthesis paper (M. He et al. 2020), the authors report an average decrease of SOC of -4.1% 
(95%  CI:  -4.2%,  -4.0%).  Results  depended  on  several  factors  like  the  mean  annual  temperature, 
precipitation and altitude. 

Rotational grazing has a positive effect on SOC (+28.4%; 95% CI:+10.5%, 50.7%) when compared to 
continuous grazing and non-significant effect when compared to no grazing, according to the only 
available synthesis paper (Byrnes et al. 2018).  

Table  87.  Effect  (%  change)  of  grazing  management  practices on  soil  organic  carbon.  CI_LOW:  confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention  Source 

Soil 
depth 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Several 

- 

- 

- 

- 

- 

Non-significant 

Light grazing 
vs. no grazing 

Moderate 
grazing vs. no 
grazing 

Heavy grazing 

Lai & Kumar 
2020 

Lai & Kumar 
2020 

Tang et al. 2019 

Byrnes et al. 
2018 

Grazing vs. no 
grazing 

M. He et al. 
2020 

Rotational 
grazing vs. 
continuous 
grazing 

Rotational 
grazing vs. no 
grazing 

Byrnes et al. 
2018 

Byrnes et al. 
2018 

Source: references in the second column. 

3.27 Peatland management 

- 

- 

- 

- 

- 

0-10 cm 

-5.2 

1.9 

10-30 cm 

-28.7 

-16.4 

9.2 

-3 

0-10 cm 

-17.7 

-10.8 

-3.8 

10-30 cm 

-33.9 

-22.5 

-10.2 

-17.6 

-10.2 

-3 

NA 

NA 

NA 

NA 

18 

Non-significant 

Negative 

Negative 

Negative 

Negative 

-20.5 

-13.9 

-6.8 

59 

Negative 

-4.2 

-4.1 

-4.0 

380 

Negative 

10.5 

28.4 

50.7 

44 

Positive 

-5.8 

7.3 

20.9 

15 

Non-significant 

A peatland is a type  of wetland with a naturally accumulated  layer of peat at the surface due to 
organic matter production exceeding decomposition. Peatlands are generally classified as bogs and 
fens. Bogs are fed mainly by rain and snow, while fens develop in landscape depressions and are fed 
with surface and/or ground water. Other names referring to peatlands found in this review are swamp, 
and wet heath, though the search string included more synonyms. Carbon-rich soils are included in 
this definition. These soils have and organic layer usually with >15 % of organic matter. However, the 
synthesis papers included in this review do not clearly define what they consider carbon-rich soils and 

review focuses on peatlands under agricultural use and thus, includes mostly drained peatlands. 

This  section  includes  interventions  towards  an  improved  and  more  sustainable  management  of 
peatlands.  A  variety  of  interventions  have  been  proposed  such  as  low  tillage,  ban  of  burning, 

116 

 
 
 
paludiculture, etc. In this literature review we have found evidence for the following management 
interventions: 

─  No fertilisation. Fertilisation, and in particular N inputs in wetlands, might promote greenhouse 
gas  emissions  by  changing  element  cycling.  Here,  no  fertilised  peatlands  are  compared  to 
fertilised ones. 

─  Grazing: the direction and magnitude of grazing impacts on ecosystem properties depends on 
grazing management such as stocking density, grazing duration and grazer species. Here, grazed 
peatlands (comprising different management intensities and levels of drainage) are compared to 
mowed peatlands. 

─  No burning. Burning is a routinely vegetation management practice conducted in some in some 
areas with the objective of stimulating the growth of grasses, maintaining these areas in young 
productive  successional  stages  and  increasing  heterogeneity  at  the  landscape  scale  and  thus, 
supporting  biodiversity.  However,  peatland  burning  releases  carbon  to  the  atmosphere  and 
banning this practice has been suggested as a measure to reduce GHG emissions. 

─  Low intensity farming comprises a wide variety of farming practices analysed together that refer 
mainly to the maintenance of permanent and extensive grasslands and fallowing in peatlands. 
They include extensive livestock grazing, mowing and the lack of fertiliser inputs. 

3.27.1 Effects on greenhouse gas emissions 

The impacts of peatland management on GHG emissions found in this review differ depending on the 
management technique and on the GHG.  

N fertilisation, compared to no fertilisation, has non-significant effect on CH4 emissions but increases 
N2O emissions (Table 88). Nitrogen input significantly promoted N2O emission in peatlands compared 
to other wetlands. The external nitrogen enhanced microbial activity and triggered a priming effect 
that further facilitated the release of available nitrogen. Thus, nitrogen input has a greater effect on 
N2O emission from peatlands than other wetlands (M. Chen et al. 2020)
Mean Difference is 2.44 (95% CI: 1.86, 3.09). 

Low intensity farming (here defined as the maintenance of permanent and extensive grasslands and 
fallowing) has non-significant effect on CH4 and N2O emissions compared to high intensity farmed 
peatlands (Haddaway et al. 2014). 

Grazing has non-significant effect on CH4 emissions in peatlands compared to mowing (Haddaway 
et al. 2014). 

117 

 
 
 
 
 
 
of peatland management practices on greenhouse gas emissions 
Table 88. 
by  major  GHG  types.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of 
pairwise comparisons. 

Intervention 

Source 

GHG 

Units 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

N fertilisation 
vs. no 
fertilisation 

M. Chen et 
al. 2020 

CH4  

N2O 

 effect 

size 

 effect 

size 

-1.6 

1.9 

0.6 

2.4 

4.3 

11 

Non-
significant 

3.1 

67 

Negative 

Low intensity 
farming 

Grazing vs. 
mowing 

Haddaway 
et al. 
2014 

Haddaway 
et al. 
2014 

CH4   mg CH4 m-2 h-1 

-0.011 

-0.004 

0.004 

N2O  mg N2Om-2 h-1 

-0.05 

0.14 

0.34 

CH4   mg CH4 m-2 h-1 

-14.0 

-5.39 

3.23 

4 

5 

3 

Non-
significant 
Non-
significant 
Non-
significant 

N2O 

NA 

NA 

NA 

NA 

NA 

NA 

Source: references in the second column. 

3.28 Peatland conservation 

Conservation refers to the preservation (i.e., no transformation to other land uses) of natural non-
disturbed  peatlands  compared  to  either  degraded  or  restored  peatlands.  The  degraded  peatlands 
found in this review are mainly affected by alterations in their water regime due to drainage and/or 
conversion to cropland or pasture. The restored peatlands found in this review have been subject to 
different interventions including the re-establishment of peatland vegetation, water supplementation 
or passive restoration by abandonment of agricultural use. The estimation of peatland conservation 
impacts is based on the spatial comparison between preserved natural peatlands (intervention) and 
nearby degraded or restored peatlands (comparator). 

3.28.1 Effects on greenhouse gas emissions 

The conservation of natural peatlands compared to degraded peatlands has been reported to have 
different effects on GHG depending on the gas and the cause of degradation. 

When the cause of degradation is conversion to cropland and pasture, the selected synthesis paper, 
(Tan et al. 2020) reported not significant results for CH4 and N2O emissions. For net ecosystem CO2 
exchange, Tan et al. 2020 report non-significant effect when compared to cropland but a positive 
effect (CO2 sink) when compared to pastures (+130%; 95% CI: 8%, 392%; Nc=12). Given that Tan et 
al.  2020  report  %  values  for  degraded  peatlands  as  compared  to  values  of  natural  peatland 
(conservation of natural peatland), we have recalculated those values in order to express them as % 
values for conserved natural peatland as compared to degraded peatland. This conversion has been 
For example, a 
done using the formula (-
100% increase is converted to -50% decrease (-1.00/(1+1.00)=-0.5). 

When compared to drained peatland, Tan et al. 2020 reported non-significant effect on CH4 emissions 
but  the  synthesis  paper  selected  for  data  extraction  on  this  intervention  (Haddaway  et  al.  2014) 
reported negative effects in boreo-temperate lowland peatlands. According to Haddaway et al. 2014, 
drained peatlands may release 0.126 mg CH4 less than undrained peatlands per square metre per 

118 

 
 
 
hour; equating to an approximate annual  additional release of 11.0 kg  CH4 ha-1 (95% CI = 22.3, 
0.263) and a 100-year global warming potential difference of 276 kg CO2 equivalents ha-1, although 
this result is only marginally significant (p=0.055). Similarly, when degradation is due to drainage, 
conservation of natural peatlands has a positive effect (i.e., decrease) on N2O emissions, according 
to Haddaway et al. 2014, and non-significant effect according to another synthesis paper (Tan et al. 
2020). Drained peatlands release, on average, 0.008 mg N2Om-2 h-1 (95% CI=0.001, 0.016) relative 
to undrained peatlands (p=0.033). This corresponds to an approximate annual difference of 0.701 kg 
N2O ha-1 (95% CI=0.088, 1.4) and a 100-year global warming potential difference of 209 kg CO2 
equivalents ha-1 (Haddaway et al. 2014). 

Haddaway  et  al.  2014  found  a  non-significant  result in  net  ecosystem  exchange  CO2  for  drained 
versus undrained peatlands across the only three studies analysed (all of which were from Canada). 
Regarding ecosystem respiration CO2, drained peatlands released, on average, 125 mg CO2 m-2 h-
1 (95% CI=6.71, 243) more CO2 through respiration than undrained peatlands, approximating to an 
annual  difference  of  11,000  kg  CO2  ha-1  (95%  CI=0.588,  21.3,  Nc=10)  resulting  from  drainage 
(p=0.038). Studies varied in their findings.  

Table  89.  Estimated GHG changes of conservation of natural  peatlands as compared to different land use 
changes.  CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of  pairwise 
comparisons. 

Pairwise 
comparis
on 

Peatland 
conservati
on vs. 
cropland 
[cropland 
vs. natural 
peatland] 

Peatland 
conservati
on vs. 
pasture 
[pasture 
vs. natural 
peatland] 

Peatland 
conservati
on vs. 
drained 

GHG 

Source  Metric 

CI_LO
W 

Mean 

CI_HIG
H 

Nc 

Effect 

CH4 

N2O 

Net 
ecosystem 
exchange 
(CO2)56 

CH4 

N2O 

Net 
ecosystem 
exchange 
(CO2)57 

CH4 

Tan et al. 
2020 
Tan et al. 
2020 

Tan et al. 
2020 

Tan et al. 
2020 
Tan et al. 
2020 

Tan et al. 
2020 

Haddawa
y et al. 
2014 

% 

% 

-12* 

56* 

178* 

43 

Non-significant 

-53* 

-31* 

1* 

32 

Non-significant 

% 

-12* 

51* 

159* 

14 

Non-significant 

% 

% 

% 

mg CH4 
m-2 h-1 
kg CH4 
ha-1 yr-1 

-66* 

-3* 

170* 

19 

Non-significant 

-81* 

-50* 

35* 

12 

Non-significant 

8* 

130* 

392* 

12 

Positive 

-0.003 

0.126 

0.254 

0.26 

11 

22.3 

9 
studie
s 

Negative 

56 CO2 Net ecosystem exchange can be either positive or negative. The reported changes refer to the % change of 

absolute values. 

57 CO2 Net ecosystem exchange % change refers to absolute values, in this case, of the CO2 sink effect.  

119 

 
 
                                                 
 
Pairwise 
comparis
on 

[undrained 
vs drained] 

GHG 

Source  Metric 

CI_LO
W 

Mean 

CI_HIG
H 

Nc 

Effect 

kg CO2 
eq. ha-1 
(100yr) 

6.5 

276 

557.5 

Tan et al. 
2020 

Haddawa
y et al. 
2014 

Haddawa
y et al. 
2014 

Haddawa
y et al. 
2014 

N2O 

Ecosystem 
respiration 
CO2 

Net 
ecosystem 
exchange 
CO2 

% 

-71* 

-31* 

67* 

15 

Non-significant 

mg N2O 
m-2 h-1 
kg N2O 
ha-1 yr-1 
kg CO2 
eq. ha-1 
(100yr) 
mg CO2 
m-2 h-1 
kg CO2 
ha-1 yr-1 

mg CO2 
m-2 h-1 

-0.016  

-0.008  

-0.001 

-1.4 

-0.7 

-0.088 

-417 

-209 

-26 

-243 

-125 

-6.71 

-588 

-
10,950 

-
21,287 

-431 

-116 

200 

5 
studie
s 

10 
studie
s 

3 
studie
s 

Positive 

Positive 

Non-significant 

Source: references in the third column. (*) results marked with an asterisk have been converted as explained in the text. 

3.28.2 Effects on soil organic carbon 

The conservation of natural peatlands, compared to degraded peatlands converted to croplands, has 
a positive effect on SOC. Xu et al. 2019a found significant negative effects on SOC for the cultivation 
of wetlands with peat  soil, as compared to the conservation of natural  wetlands with peat soil  (-
42.4%, p < .001). This effect was not significant for other types of wetlands. We have converted the 
numbers to calculate the % change that implied the conservation of natural wetland as a % of the 
SOC of the degraded cultivated peatland, according to what explained in section 3.28.1 (e.g. from -
42.4% to 73.5% using the formula - 0.424/(1+0.424)=0.736). 

Table  90.  Effect  (%  change)  of  peatland  conservation  on  soil  organic  carbon.  CI_LOW:  confidence  interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Pairwise 
comparison 
Peatland 
conservation 
vs. 
cultivated 
peatlands 

Peatland 
conservation 
vs. restored 
peatlands 

Source 

Population 

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Xu et 
al. 
2019a 

Xu et 
al. 
2019b 

Wetlands 
with peat 
soil 

Wetlands 
with peat 
conditions in 
Northern 
hemisphere 

37* 

73.5* 

120.4* 

57 

Positive 

-13* 

15* 

52* 

19 

Non-
significant 

Source: references in the second column. (*) results marked with an asterisk have been converted as explained in the text. 

120 

 
 
 
 
Compared to restored peatlands, the conservation of natural peatlands has non-significant effect on 
soil organic carbon, according to 1 synthesis paper (Xu et al. 2019b). This synthesis paper indicates 
that  restoration  of  wetlands  with  peat  conditions,  as  compared  with  natural  wetlands  with  peat 
conditions (that we have identified with peatlands conservation) have no significant lower SOC (-13%; 
95%  CI:  -34,  14.5%).  In the  same  way  as  in  the  previous  section, percentage  changes  have  been 
recalculated to express peatland conservation as a % of cultivated or restored peatlands. 

3.29 Peatland restoration 

Restoration methods have been classified in: 

Management of water regime: Restoration by managing water regime refers to re-allocating a part 
of the natural water regime back to these soils/ecosystems, resulting in the full or partial recovery of 
water inputs and inundation by removing draining, ditching, precipitation exclusion, flooding, dams or 
groundwater extraction. 

Several  restoration  methods  pooled  together:  When  different  restoration  methods  are  analysed 
together  in  the  reviewed  synthesis  papers.  This  pool  includes  active  revegetation,  full  or  partial 
recovery of water inputs and passive restoration by cessation of human disturbance (i.e., agricultural 
abandonment and prohibited grazing). 

The estimation of peatland restoration impacts is based on the spatial and temporal comparisons 
between restored peatlands (intervention) and degraded peatlands (comparator). Spatial comparisons 
are  those  conducted  simultaneously  between  restored  peatlands  and  nearby  degraded  peatlands. 
Temporal comparisons are those conducted in the same peatland before and after restoration. 

3.29.1 Effects on greenhouse gas emissions 

Peatland  restoration  by  managing  water  regime  has  different  effects depending  on  the GHG.  The 
selected paper for CH4 (Huang et al. 2021) analyses the difference of higher and lower water tables. 
As the reference scenario is not indicated to be a peatland in natural status and the comparison is 
done in space (different sites) under the same time period, we assume that the effect of increasing 
the water table is just the same with opposite sign. According to this synthesis paper, the estimated 
mean value of changes in CH4 emissions is a significant increase of 26 mgCO2-eq m 2 h 1 (95% CI: 
35, 20). 

We  also  report  for  comparison  the  results  by  Haddaway  et  al.  2014,  that  focus  only  on  boreo-
temperate lowland peatland systems, while Huang et al. 2021 use a higher number of studies at the 
global scale, differentiating arctic, boreo-temperate, and tropical climatic zones. The increases in CH4 
)  as 
emissions  found  by Haddaway  et  al.  2014  are  considerably  lower  (i.e.  6 mgCO2-
compared  to  the  26  found  in  the  previous  paper (Huang  et  al.  2021).  This is probably  due  to  the 
difference in regions and climates considered. The selected paper (Haddaway et al. 2014) includes 
results from 3 studies from Finland with 2 intervention and control observations each, and 1 study 
from Canada with 11 observations. The results from Canada are low and determine the low value of 
the overall result, while the results of 2 of the 3 Finnish papers are much higher (around 20 and 37 
mgCO2-
nfluence, Huang et al. 2021 found that the 
average sensitivity of CH4 emissions change to unit water-table change was smaller (less change) in 
tropical  than  boreal  and  temperate  peatlands,  while  results  for  artic  zones  depended  on  the 
methodology for coefficient estimation. Another factor that can affect the results is the height of the 

121 

 
 
 
water table: Huang et al. 2021 also quantified the sensitivities of GHG fluxes to the magnitude of 
water-table increase and found that the overall average sensitivity to a 1 cm water-table increase 
was 2.9 (95% CI: 3.6, 2.2) mgCO2-

CH4.  

Regarding CO2 emissions, the restoration of drained peatlands by managing the water regime has 
been reported as positive for ecosystem respiration and net ecosystem exchange of CO2 (=ecosystem 
respiration  vegetation CO2 uptake). According to Huang et al. 2021, the water-table increase would 
induce  a  decrease  in  CO2  emissions  from  respiration  exceeding  the  decrease  of  vegetation  CO2 
uptake. The estimated mean value of net ecosystem exchange of CO2 is -62 mgCO2 
-47% to -77%), meaning a net decrease of CO2 emissions (or an increased sink) for a water table 
becoming higher. The decreases in terms of global warming potential (in CO2 equivalents), offsets 
the increase in CH4. Therefore, the measure would be overall positive for reducing GHG emissions. 
Individual values have a large variability, varying from +497 to -1,234 mgCO2 
The paper also quantified the sensitivities of GHG fluxes to the magnitude of water-table increase 
and found that the overall average sensitivity to a 1 cm water-table increase was -4.1 (95% CI: -3.3, 
CO2 net ecosystem exchange. The average sensitivity for total GHG (net 
-5.0) mgCO2 
ecosystem  exchange  CO2  +  net  methane  fluxes)  was  -1.6  (95%  CI:  -0.8,  -2.3)  mgCO2-

f experiments that measured both net ecosystem exchange and CH4. 

Haddaway et al. 2014 analysed several pairwise comparisons for restoration: drained and restored 
vs. undrained, restored vs. unrestored, and restored vs. natural. However, authors only found sufficient 
data allowing for meaningful meta-analysis for the comparison restored vss. unrestored and for 3 
outcome metrics: CH4 emissions, CO2 ecosystem respiration emission, and dissolved organic carbon 
concentration.  Ecosystem respiration, results significantly higher (125  mgCO2  m 2 h 1)  in  undrained 
compared to drained  peatlands in  the two selected synthesis papers. However, for net ecosystem 
exchange CO2 release, Haddaway et al. 2014 reported a non-statistically significant effect. 

Table  91.  Effects  of  peatland  restoration  on  greenhouse  gas  emissions  by  major  GHG  types.  CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Pairwise 
comparis
on 

High vs. 
low water 
table 

Restored 
vs 
unrestored 

CH4 

CH4 

Undrained 
vs drained 

CH4 

GHG 

Population 

Source 

Units 

CI_LO
W 

Mean 

CI_HIG
H 

Nc 

Effect 

Peatlands 
(mesocosmos 
and field 
experiments) 

Boreo-
temperate 
lowland 
peatlands 
Boreo-
temperate 
lowland 
peatlands 

Huang 
et al. 
2021 

Hadda
way et 
al. 
2014 
Hadda
way et 
al. 
2014 

kgCO2-
eq ha 1 
yr 1 
mgCO2-
eq58 m 2 
h 1 

mg CH4 
m-2 h-1 
(CO2-eq) 

1754 

2277.
6 

3066 

20 

26 

35 

532  Negative 

0.052 
(1.3) 

0.248 
(6.2) 

0.446 
(11.15) 

17   Negative 

mg CH4 
m-2 h-1 
(CO2-eq) 

-0.003 
(-
0.075) 

0.126 
(3.15) 

0.254 
(6.35) 

49  

Non-
significa
nt 

58 mg CO2-eq=mg CH4 x 25. 

122 

 
 
                                                 
 
Pairwise 
comparis
on 

High vs. 
low water 
table 

High vs. 
low water 
table 

Restored 
vs. 
unrestored 

Undrained 
vs drained 

Undrained 
vs drained 

GHG 

Population 

Source 

Units 

CI_LO
W 

Mean 

CI_HIG
H 

Nc 

Effect 

Net 
ecosyste
m 
exchange 
CO2 
Ecosyste
m 
respiratio
n CO2 
Ecosyste
m 
respiratio
n CO2 
Net 
ecosyste
m 
exchange 
CO2 
Ecosyste
m 
respiratio
n 

Peatlands 
(mesocosmos 
and field 
experiments) 

Peatlands 
(mesocosmos 
and field 
experiments) 
Boreo-
temperate 
lowland 
peatlands 
Boreo-
temperate 
lowland 
peatlands 

Boreo-
temperate 
lowland 
peatlands 

Huang 
et al. 
2021 

Huang 
et al. 
2021 

Hadda
way et 
al. 
2014 

Hadda
way et 
al. 
2014 

Hadda
way et 
al. 
2014 

mgCO2 
m 2 h 1 

mgCO2 
m 2 h 1 

mgCO2 
m 2 h 1 

mgCO2 
m 2 h 1 

mgCO2 
m 2 h 1 

-77 

-62 

-47 

376 

Positive 

-102 

-125 

-147.5  407 

Positive 

-155 

48.7 

253 

-431 

-116 

200 

-6.7 

-125 

-243 

3 
stu
dies 

Non-
significa
nt 

3 
stu
dies 

Non-
significa
nt 

10 
stu
dies 

Positive 

Source: references in the third column.  

3.29.2 Effects on soil organic carbon 

Peatland restoration (with several restoration methods pooled together in the analyses) has a positive 
effect  on  soil  organic  carbon,  compared  to  peatlands  converted  to  cropland,  according  to  one 
synthesis paper reviewed (Xu et al. 2019b).  

The paper looked at the effects of wetlands in general on SOC. Because many studies did not report 
whether a wetland was a peatland, they classified a wetland as peatland if the following conditions 
were met: the study clearly indicated that the wetland was a peatland, the soil had a high SOC content 
(>300 g/kg) and a low bulk density (<0.3 g/cm3), or the soil type was Histosol. All the studies were 
conducted  in  the  northern  hemisphere  with  a  latitude  range  from  1.6°  to  59.2°,  a  mean  annual 
precipitation range from 383 mm to 2570 mm, and a mean annual temperature range from  6.7 °C 
to  26.7 °C.  The  results  suggest  that the hydrological  conditions  of  cultivated wetlands,  soil  depth, 
vegetation type,  peat condition and restored age were important influential  factors affecting SOC 
after wetland restoration, with the first 2 being the most important influential factors.  

The SOC in restored wetlands was significantly higher (+34%; 95% CI: 6%, 68%) than in cultivated 
wetlands identified as peatlands. Their results also suggest that the SOC lost in peatlands was easier 
to recover than that in wetlands with no peat. 

123 

 
 
 
 
 
Table 92. Effect (% change) of peatland restoration on soil organic carbon. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Pairwise comparison 

Population 

CI_LOW  Mean 

CI_HIGH 

Nc 

Effect 

Peatland restoration vs. 
peatland converted to 
cropland 

Wetlands with 
peat conditions in 
Northern 
hemisphere 

Source: Xu et al. 2019b 

6 

34 

68 

16 

Positive 

Note: The comparison of the effect of SOC of peatland restoration with natural peatland (not significant) is 
not included here as it is included under peatland conservation practice. 

3.30 Wetland management 

Wetlands are areas where water is the primary factor controlling the environment and the associated 
plant and animal life. The Ramsar Convention on Wetlands adopts a broad definition of wetlands. It 
includes lakes and rivers, underground aquifers, swamps and marshes, wet grasslands, peatlands, 
oases, estuaries, deltas and tidal flats, mangroves and other coastal areas, coral reefs, and all human-
made sites such as fish ponds, rice paddies, reservoirs and salt pans. However, this review does not 
include all these wetland types either because they are not well represented in the scientific literature, 
because they are not relevant in a European context or because they are not under agricultural use. 
Besides, this review excludes peatlands and carbon rich soils, as their conservation, restoration and 
management are assessed in separate sections. This review focuses on wetlands under agricultural 
use and thus, includes mostly drained wetlands.  

The wetland management practice includes interventions towards an improved and more sustainable 
management  of  wetlands.  Specific  interventions  have  been  proposed  such  as  low  tillage,  ban  of 
burning,  paludiculture,  etc.  In  the  literature  review  we  have  found  evidence  for  the  following 
management interventions:  

─  No fertilisation. Fertilisation, and in particular N inputs in wetlands, might promote greenhouse 
gas emissions by changing element cycling. Here, no fertilised wetlands are compared to fertilised 
ones.  

─  Grazing. The direction and magnitude of grazing impacts on ecosystem properties depends on 
grazing management, such as stocking density, grazing duration and grazer species. Here, grazed 
wetlands are compared to wetlands with grazing exclusion or where only wild herbivores graze in 
natural conditions. 

3.30.1 Effects on greenhouse gas emissions 

Results by L. Liu & Greaver 2009 
significantly increased CH4 emission by an average of 95% when averaged across grassland, wetland 
and anaerobic agricultural systems. This response ratio did not differ among the three ecosystem 
types. Data were not sufficient to estimate an emission factor for non-
wetland). Overall, CH4 uptake was reduced, but not statistically significant for grasslands and drained 
wetlands. 

124 

 
 
 
According to the authors (L. Liu & Greaver 2009) 
/ha/yr, significantly increased N2O emi
wetlands by 207% (95% CI: 78%, 431%). 

93. Effect (% of change) of wetland fertilisation on greenhouse gas emissions by major GHG types. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

GHG 

Wetland type 

CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

Wetlands only 

NA 

NA 

NA 

NA 

Not available 

CH4 
emissions 

Wetlands (undrained) 
(Nc=11) + anaerobic 
agricultural systems (Nc=13) 
+ grassland (Nc=2) 

CH4 uptake 

Drained wetlands 

N2O 

Wetlands 

Source: L. Liu & Greaver 2009. 

3.31 Wetland conservation 

39.9 

97.2 

178.2 

26 

Negative 

-63 

78 

-37 

207 

9 

431 

6 

19 

Non-significant 

Negative 

Conservation refers to the preservation (i.e., no transformation to other land uses) of natural non-
disturbed wetlands compared to either degraded or restored wetlands.  

─  The  degraded  wetlands  found  in  this  review  are  mainly  affected  by  alterations  in  their  water 
regime  due  to  drainage  and/or  conversion  to  cropland  or  pasture,  or  by  their  conversion  into 
aquaculture ponds or other constructed wetlands.  

─  The restored wetlands found in this review have been subject to different interventions including 

water flow re-establishment, revegetation or addition of a sediment layer.  

The  estimation  of  wetland  conservation  impacts  is  based  on  the  spatial  comparison  between 
preserved natural wetlands (intervention) and nearby degraded or restored wetlands (comparator). 

3.31.1 Effects on greenhouse gas emissions 

Only one synthesis paper (Tan et al. 2020) was found on the impacts of the conservation of wetlands 
on (soil) GHG emissions. It compares natural coastal wetlands and riparian wetlands (wetlands on 
river banks) with different types of degraded wetlands: (1) constructed artificial wetland; (2) wetland 
converted to cropland; (3) drained wetland; (4) wetland converted to pasture; (5) wetland converted 
to aquaculture pond. Given that Tan et al. 2020 report values for degraded peatlands expressed as 
% of values of natural peatland (conservation of natural peatland), we have recalculated those values 
in  order  to  express  them  as  %  values  for  conserved  natural  peatland  as  compared  to  degraded 
peatland. This conversion has been done using the formula (-
divided by 100). For example, a 100% increase is converted to -50% decrease (-1.00/(1+1.00)=-0.5). 

The conservation of natural wetlands has different effects on GHG emissions, depending on the cause 
of degradation.  

125 

 
 
 
 
CH4 emission: A negative effect of the conservation of natural riparian wetlands compared to wetland 
degraded  due to  drainage  is  observed  (CH4  emissions  increase  by  317%;  95%  CI:  144%,  614%).  
Compared to wetland converted to cropland, we can assume a weak negative effect overall, with non-
significant effect for coastal  wetlands and a negative effect for natural  riparian wetlands (245%; 
95% CI: 117%, 400%). Lower CH4 emissions are observed only for the conservation of natural coastal 
wetland as compared to the conversion to aquaculture pond.  Non-significant effect was observed 
when degradation is due to conversion to pasture of riparian wetlands, or due to constructed artificial 
wetlands as compared to conserved coastal wetlands.  

N2O  emission:  Tan  et  al.  2020  report  a  weak  positive  effect  of  the  conservation  of  wetlands  as 
compared to their conversion to cropland (i.e. lower N2O emissions, by -78%; 95% CI: -19%, -94% 
for  natural  riparian  wetlands,  but  non-significant  effect  for  natural  coastal  wetlands).  A  negative 
effect was observed when natural riparian wetlands are converted to pasture (N2O decreases with 
the conversion by -80%; 95% CI:-4%, -96%, so that conservation would increase N2O by 400%; 95% 
CI: 4%, 2400%); non-significant effect was observed for the rest of the pairwise comparisons. 

Net ecosystem CO2 exchange: the conservation of natural wetlands has positive effects (i.e. higher 
CO2 sequestration capacity) for natural riparian wetlands as compared to drained wetland (that leads 
to an increase of net CO2 fluxes by 395%; 95% CI: 225%, 645%), and wetlands converted to pasture 
(398%; 95% CI: 95%, 1154%). As well, the conservation of natural coastal wetlands as compared to 
constructed wetlands has a positive effect (70%; 95% CI: 8%, 163%). Non-significant effects were 
observed for any type of natural wetland as compared to croplands.  

Table 94. Effect (% of change) of conservation of natural wetlands as compared to different land use changes 
on GHG emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. 

Comparison 

GHG 

Metric 

CI_LOW  Mean  CI_HIGH  Nc 

Effect of 
conservation 

Natural coastal 
wetland vs. 
cropland  

[cropland vs. 
natural coastal 
wetland] 

Natural riparian 
wetland vs. 
cropland 
[cropland vs. 
natural riparian 
wetland] 

Natural riparian 
wetland vs. 
pastures 
[pastures vs. 
natural riparian 
wetland] 

CH4 flux  % (g C m-2year-2) 

-40* 

122* 

733* 

N2O flux  % (g N m-2year-2) 

-92* 

-58* 

1133* 

27 

22 

Non-significant 

Non-significant 

Net 
ecosystem 
CO2 
exchange 

% (g C m-2year-2) 

-78* 

65* 

1128* 

4 

Non-significant 

CH4 flux  % (g C m-2year-2) 

117* 

245* 

400* 

N2O flux  % (g N m-2year-2) 

-19* 

-78* 

-94* 

24 

23 

Negative 

Positive 

Net 
ecosystem 
CO2 
exchange 

% (g C m-2year-2) 

-47* 

2* 

100* 

11 

Non-significant 

CH4 flux  % (g C m-2year-2) 

-71* 

-25* 

285* 

N2O flux  % (g N m-2year-2) 

4* 

400* 

2400* 

15 

11 

Non-significant 

Negative 

Net 
ecosystem 

% (g C m-2year-2) 

95* 

398* 

1154* 

4 

Positive 

126 

 
 
 
Comparison 

GHG 

Metric 

CI_LOW  Mean  CI_HIGH  Nc 

Effect of 
conservation 

CO2 
exchange59 

Natural riparian 
wetland vs. 
drained 
wetland 
[Drained 
wetland vs. 
natural riparian 
wetland] 

Natural coastal 
wetland vs. 
constructed 
wetlands 

[Constructed 
wetlands vs. 
natural coastal 
wetland] 

Natural coastal 
wetlands vs. 
aquaculture 
ponds  

[Aquaculture 
ponds vs. 
natural coastal 
wetlands] 

CH4 flux  % (g C m-2year-2) 

144* 

317* 

614* 

N2O flux  % (g N m-2year-2) 

-63* 

-29* 

37* 

10 

16 

Negative 

Non-significant 

Net 
ecosystem 
CO2 
exchange 

% (g C m-2year-2) 

225* 

395* 

645* 

6 

Positive 

CH4 flux  % (g C m-2year-2) 

-72* 

N2O flux  % (g N m-2year-2) 

-50* 

12* 

9* 

355* 

138* 

14 

15 

Non-significant 

Non-significant 

Net 
ecosystem 
CO2 
exchange 

% (g C m-2year-2) 

8* 

70* 

163* 

9 

Positive 

CH4 flux  % (g C m-2year-2) 

-99* 

N2O flux  % (g N m-2year-2) 

-33* 

-93* 

-7* 

-43* 

32* 

13 

10 

Positive 

Non-significant 

Net 
ecosystem 
CO2 
exchange 

% (g C m-2year-2) 

NA 

NA 

NA 

NA 

NA 

Source: Tan et al. 2020. (*) results marked with an asterisk have been converted as explained in the text. 

3.31.2 Effects on soil organic carbon 

One synthesis paper (Xu et al. 2019a) analyses the effects on SOC of the conservation of wetlands 
compared  with  cultivated  wetlands.  In  this  synthesis  paper,  wetlands  can  be  considered  peats 
(wetlands that are peatland, or the soil had a high SOC content (> 300 g/kg) and a low bulk density 
(< 0.3 g/cm3), or the soil type was histosol), While for wetlands in general, including peatlands, the 

non-significant  effect  was  reported  as  compared  to  cultivated  wetlands. 
However, as some of the primary studies also include peatlands and are analysed together, caution 
needs to be taken to interpret this result. 

Compared to restored (by adding a sediment layer) coastal wetlands,  the conservation of wetlands 
has a positive effect on carbon sequestration (i.e., increase of soil organic carbon), according to one 
synthesis paper (Ebbets et al. 2020). These authors compare restored wetlands with nearby reference 
natural wetlands. They differentiate an in

59 The change refers to absolute values of net ecosystem exchange, while the net ecosystem exchange for wetlands is 

negative (ln(-NEEi/-NEEc) where (NEEi/NEEc) is always positive). Therefore a positive change implies higher CO2 
sequestration. 

127 

 
 
                                                 
 
nd 

of the mean RRs de creased for all response groups, indicating a greater similarity between restored 

Therefore,  for  the estimation  of  the benefits  from wetland  conservation  as  compared  to  restored 
wetlands, we deduct that there is an increase of +211% of SOC as compared to restored wetlands in 
the first 15 years after restoration, while in the longer term, natural wetlands still have, on average, 
55% more SOC than restored wetlands 

Table 95. Effect (% change) of peatland conservation on soil organic carbon. SE: Standard Error of the mean. 
Nc: number of pairwise comparisons. 

Pairwise comparison 

Population 

Mean 
-SE 

Mean 

Mean 
+ SE 

Nc 

Effect 

Wetland 
conservation vs. 
restored wetlands 

Wetland 
conservation vs. 
restored wetlands 
(Original 
comparison: 
restored vs. natural 
wetlands) 
(Original 
comparison: 
restored vs. natural 
wetlands) 

Source: Ebbets et al. 2020 

Coastal wetlands 
in the northern 
Gulf of Mexico 

Coastal wetlands 
in the northern 
Gulf of Mexico 

Coastal wetlands 
in the northern 
Gulf of Mexico 

Coastal wetlands 
in the northern 
Gulf of Mexico 

15 
years 

>15 
years 

15 
years 

>15 
years 

182 

211 

243 

Positive 

111 

34 

55 

80 

Positive 

 -71 

-68 

 -64 

Negative 

111 

-45 

-36 

-25 

Negative 

3.32 Wetland restoration 

Restoration  methods  found  in  this  review  are  classified  into  three  main  classes:  management  of 
invasive plant species, management of water regime and a general  class with several restoration 
methods pooled together.  

─  Restoration  by managing  invasive plant  species  includes  the  use  of  chemical  (herbicides)  and 
mechanical (tilling, disking, mowing, crushing, cutting and uprooting) control methods, as well as 
burning, grazing and replacement of invasive plants with natives.  

─  Restoration by managing water regime includes the recovery of the duration, frequency and/or 
depth of inundation, waterlogging or environmental flow release. The creation of new wetlands 
is also considered here.  

128 

 
 
 
─  When different restoration methods are analysed together in the reviewed synthesis papers, we 
report  them  as  several  restoration  methods  pooled  together.  This  pool  includes  active 
revegetation, enhancement of in-wetland and structural heterogeneity, habitat creation, passive 
restoration,  restoration  of  hydrological  dynamics,  restoration  of  water  quality,  addition  of  soil 
amendments or wildlife management (i.e. reintroduction of native species and/or elimination of 
exotic ones). 

3.32.1 Effects on greenhouse gas emissions 

Only one synthesis paper (Wails et al. 2021) 
abiotic carbon fluxes (CO2 and CH4 emissions-sequestration from soil and water) of removal of two 
common  and  pervasive  invasive  plant  species  (Spartina  alterniflora  or  smooth  cordgrass  and 
Phragmites australis or common reed) in coastal wetlands (including swamps, mud flats, intertidal 
zones,  salt  marshes,  estuaries,  and  similar  marine,  inshore  habitats).  The  study  compared  non-
invaded  wetlands  in  original  status  pre-infestation  with  invaded  wetlands,  and  also  restored  or 
managed  wetlands  (post-infestation)  with  invaded  wetlands.  Results  correspond  to  P.  Australis 
invasions. It was found that abiotic C fluxes (emissions) and C pools (stocks) measured from soil (no 
data was available for C fluxes from water) were higher in invaded systems relative to managed 
areas, though overall results were only marginally 
C pools measured amongst  biotic sources (i.e. invertebrates, microbes, and plants), no differences 
were detected between managed-invaded comparisons.  

G, therefore the % change could not be calculated. Moreover, the synthesis paper includes under C 
fluxes the CO2 fluxes and CH4 fluxes, but no evidence was found of an aggregation of CO2 and CH4 
results from the same experiments with a conversion of units between both (e.g. a 1% change in net 
CO2 emissions is not comparable to a 1% change in net CH4 emissions in terms of Global Warming 
Potential). Results are also not provided differentiated in CO2 fluxes and CH4 fluxes.  

Table 96. 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

 on the differences in Carbon fluxes of wetland conservation. CI_LOW: confidence 

Comparison 

GHG 

Description 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Managed - 
Invaded 

C flux (CO2 
flux &CH4-
flux)  

Overall 
Species: P. australis 
Source: Soil 

-1.1 

-0.6 

-0.1 

26 

Positive 
(p 0.1) 

Source: Wails et al. 2021. 

3.32.2 Effects on nutrient leaching and runoff 

According to two synthesis papers retrieved, wetland restoration or wetland creation, compared to no 
wetlands or degraded wetlands, have a positive effect on nitrate and phosphorus leaching and run-
off (i.e., decrease of nutrient leaching and run-off).  

One synthesis paper (Carstensen et al. 2020) assesses the efficiency of the potential of free-water-
surface  constructed  wetlands  as  a  mitigation  measure  treating  drainage  water  before  it  enters 
streams  for  reducing  nitrogen  and phosphorus  losses  from  agricultural  areas. The  results  showed 
that free-water-surface wetland significantly reduced the nitrate and phosphorus loss from drainage 
systems, but variation in the reported removal efficiencies was large. Free water surface significantly 
(95% CI: 29%, 51%). The CI varied 

129 

 
 
 
 
from 29 to 51%. The average total phosphorus removal efficiency was 33%, ranging from 
to 68% (95% CI: 19%, 47%). 

 103% 

Similarly, another synthesis paper (Land et al. 2016) found that created and restored wetlands (when 
several  restoration  methods  pooled  together)  remain  appropriate  and  potentially  sustainable 
ecological engineering approaches for removing nutrients from treated wastewater and urban  and 
agricultural runoff. The overall average summary effect represents a median total N removal ratio 
(R) of about 0.63. This means that the median total N load reduction, or removal efficiency, is 37 % 
(95 % CI: 29 to 44 %). Removal efficiency of total nitrogen in wetlands was positively correlated with 
average  annual  air  temperature  and  negatively  correlated  with  hydraulic  loading  rate.  The  total 
nitrogen removal rate was positively correlated with the inflow concentration and was also found to 
be  positively  correlated  with  hydraulic  loading.  The  overall  average  summary  effect  represents  a 
median total P removal ratio (R) of about 0.54, which can be recalculated to a median TP removal 
efficiency of 46 % (95 % CI: 37 to 55 %). The total phosphorus removal rate was positively correlated 
with concentration at inlet and hydraulic loading rate. In contrast, the total phosphorus removal rate 
was negatively correlated with wetland area, especially for wetlands smaller than 2*10^4 m2.  

Table 97. Effect (% of change) in nutrients leaching and run-off of wetland creation or restoration as compared 
to no wetland. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. 

Pairwise 
comparison 

N in inflow 
vs. N in 
outflow 
(wetland vs. 
no wetland) 

Total P in 
inflow vs. P 
in outflow 
(wetland vs. 
no wetland) 

Source 

Population 

CI_HIGH  Mean 

CI_LOW 

Nc 

Effect 

Carstensen 
et al. 2020 

Land et al. 
2016 

Carstensen 
et al. 2020 

Land et al. 
2016 

Drainage systems 
with free water 
surface constructed 
wetlands 
Created and restored 
wetlands 
Drainage systems 
with free water 
surface constructed 
wetlands 
Created and restored 
wetlands 

-51 

-41 

-29 

42 

Positive 

-44 

-37 

-29 

112 

Positive 

-47 

-33 

-19 

41 

Positive 

-55 

-46 

-37 

146 

Positive 

Source: references in the second column 

3.33 No irrigation 

No-irrigation corresponds to rainfed (or non-irrigated) agriculture, where the soil water available to 
plants  comes  mainly  from  rainfall  (Molden  et  al.  2007).  Water-saving  irrigation  practices  aim  to 
satisfy  crop  water  requirements  while  improving  the  timing  and  reliability  of  water  deliveries  to 
minimize water use (Perry et al. 2017). Conversely, non-water-saving irrigation practices are standard 
techniques of irrigation that supply water to the crop in order to increasing crop productivity, with no 
intention  of  minimising  water  use.  They  rely  on  various  techniques  such  as  flooding  or  use  of 
sprinklers (European Parliamentary Research Service (EPRS) 2019).  

130 

 
 
 
3.33.1 Effects on greenhouse gas emissions  

The selected synthesis paper (X. Gu et al. 2022) includes data reported in 34 primary studies from 
various regions of the world. The study focuses on rice, and quantifies the effects of different water 
management  practices  on  CH4  emissions  from  paddy  fields,  compared  to  continuous  long-term 
flooding. As can be observed in Table 98, no irrigation reduced CH4 emissions by -151.45% (95% CI: 
-232.3%, 70.0%)60, compared to continuous long-term flooding. 

Table 98. Effect (% change) of no irrigation on CH4 emissions. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. 

Intervention   Comparator  

CI_LOW  Mean  CI_HIGH  Nc 

No irrigation  

Continuous long-term flooding  

-232.3 

-151.15 

-70 

4 

Effect 

Positive 

Source: X. Gu et al. 2022. 

3.33.2 Effects on nutrient leaching and run-off  

The selected synthesis paper (Sharma & Chaubey 2017) explores the effects of no irrigation on nitrate 
leaching in maize compared to non-water-saving irrigation practices. These results come from two 
peer  reviewed  journal  articles  discussing  irrigation  effect  on  nitrate  loss.  The  studies  related  with 
irrigation effect were conducted in two types of soil categories; sandy loam soil and silty loam soil. 
The data represents identical soil types that had a similar range of precipitation and fertilisation rate. 
Nevertheless, these experiments were not conducted over the same location. No irrigation was found 
to significantly decrease nitrate leaching compared to non-water-saving irrigation practices. A typical 
effect size was not calculated, but median and relative quartile values are reported (Table 99) for 
different treatments and local conditions.  

Table 99. Effect of no irrigation on nitrate leaching. Median and quartiles (Q1 and Q3) of difference in nitrate 
leaching  difference  between  irrigation  and  no  irrigation  practices  according  to  soil  texture  and  fertilisation 
management.  

Intervention  Comparator  Crop  

Factor  

Median 
(KgN/ha)  

Q1 
(KgN/ha)  

Q3 
(KgN/ha)  

Effect  

No irrigation  

Non water-
saving 
irrigation 
practices  

Maize  

Silty loam soil - irrigation  N 
fertiliser 248-296 kg/ha  
Silty loam soil - no irrigation   
N fertiliser 248-296 kg/ha  
Sandy soil - irrigation   N 
fertiliser 168-224 kg/ha  
Sandy soil - no irrigation   N 
fertiliser 150-280 kg/ha  

117.11   136.91   106.39  

47.01  

60.21  

39.59  

84.12  

143.51  

77.53  

Positive 

47.84  

59.38  

17.32  

Source: Sharma & Chaubey 2017.   

60 Dryland rice soils can be sinks of methane (Singh et al. 1998). 

131 

 
 
 
 
                                                 
 
3.33.3 Effects on the nutrient balance 

Results in the literature quantify the change in plant nitrogen use efficiency, the metric used being 
the  partial  factor  productivity  of  applied  nitrogen  (i.e.  the  grain  yield  production  per  kilogram  of 
nitrogen input in production).  

The selected synthesis papers are B. Y. Liu et al. 2020 and B.-Y. Liu et al. 2021 for the effect of no 
irrigation in winter wheat and maize, respectively, compared to non-water-saving irrigation practices.  

No irrigation had an overall negative effect on nitrogen use efficiency compared to non-water-saving 
irrigation practices, decreasing nitrogen partial  factor productivity by  -19.36% (95% CI:  20.8%,  -
17.4%) in winter wheat and by 20.0% (95% CI:  30.3%,  11.1%) in maize.  

In  winter  wheat,  the  decrease  in  nitrogen  partial  factor  productivity  depended  on  the  level  of 
precipitations (i.e., stronger negative effect at lower precipitation levels), nitrogen content in the soil 
(i.e., stronger negative effect at lower nitrogen values), nitrogen application rate (i.e., stronger negative 
effect at intermediate nitrogen values), and irrigation amount and frequency (i.e., stronger negative 
effect at intermediate amount and frequency values).  

Table  100.  Effect  (%  change)  of  no  irrigation  vs.  non  water-saving  irrigation  practices  on  partial  factor 
productivity of nitrogen. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. 

Crop and 
source 
Maize 
Source: B.-Y. Liu 
et al. 2021 

Winter wheat 

Source: B. Y. Liu 
et al. 2020  

Factor  

CI_LOW   Mean   CI_HIGH   Nc  

Effect  

Overall  

Overall  

mean annual 
precipitation (mm)  

seasonal precipitation 
(mm)  

annual precipitation 
(mm)  

Initial soil nitrogen 
content (g kg-1)  

Nitrogen application 
rate(kg N ha-1)  

Irrigation amount 
(mm)  

Irrigation times  

-11.14  

-20.00  

-30.29  

29  

Negative  

-17.39  

-19.36  

-20.84  

395  

Negative  

<=625  

-20.05  

-22.24  

-24.62  

202  

Negative  

>625  

<140  

>140  

-7.25  

-10.79  

-14.64  

60  

Negative  

-18.33  

-21.88  

-25.55  

139  

Negative  

-15.88  

-17.91  

-19.95  

231  

Negative  

<=550  

-24.03  

-29.92  

-36.02  

>550  

-9.41  

-13.07  

-16.81  

<=0.85  

-21.53  

-26.28  

-31.09  

23  

16  

23  

Negative  

Negative  

Negative  

>0.85  

-18.77  

-21.03  

-23.28  

189  

Negative  

<150  

-9.99  

-14.22  

-18.53  

150-180  

-22.12  

-24.20  

-26.34  

79  

31  

Negative  

Negative  

>180  

-18.00  

-20.04  

-22.16  

235  

Negative  

<80  

-13.25  

-15.28  

-17.23  

127  

Negative  

80-160  

-20.24  

-22.96  

-25.76  

162  

Negative  

>160  

-14.16  

-18.01  

-22.09  

106  

Negative  

1  

2  

-11.21  

-13.53  

-16.01  

116  

Negative  

-20.46  

-23.25  

-26.04  

157  

Negative  

>2  

-16.16  

-20.14  

-24.19  

110  

Negative  

Source: references in the first column. 

132 

 
 
 
  
 
  
 
3.34 Water-saving irrigation practice in flooded lands 

Water-saving  irrigation  practices  aim  to  reduce  water  use  in  irrigated  fields.  In  flooded  lands, 
specifically in rice paddy fields, the common feature of water-saving irrigation techniques is to keep 
the field in a water-free or thin water layer condition at a certain growth stage, by introducing short 
term  drainage  of  flooding  water  during  the  rice  cultivation  (Qiu  et  al.  2022;  Yagi  et  al.  2020). 
Conventional continuous flooding corresponds to flooding rice paddies throughout the growing season 
(Climate  and  Clean  Air  Coalition  2014).  In  this  review,  different  water-saving  irrigation  practices 
applied in flooded  lands are compared to continuous flooding. Water-saving irrigation practices in 
flooded lands in this review include: 

─  water-saving irrigation practices that vary according to the number of drainages applied during 
the rice growing season (single or multiple), irrigation water amount, and irrigation schedule (e.g., 
alternate wet and dry irrigation, intermittent irrigation, mid-season drainage, controlled irrigation, 
moist irrigation, shallow irrigation and deep storage, thin and wet irrigation); 

─  drip irrigation; 

─  dry cultivation (non-flooded rice, but might be irritated); 

─  and drill seeding methods (rice is continuously flooded from the 3-6 leaf stage to final drain for 

harvest). 

3.34.1 Effects on greenhouse gas emissions  

The selected synthesis paper (X. Zhao et al. 2019) includes data reported in 32 individual of primary 
studies from various regions of the world. It quantifies the effects of different water-saving irrigation 
practices on CH4 and N2O emissions from paddy fields, compared to continuous flooding (Table 101).  

Water-saving irrigation practices in rice paddies reduced area-scaled CH4 emissions by -51.5% (95% 
CI: -56.8%, -44.5%) compared to continuous flooding, and global warming potential (GWP, based on 
both CH4 and N2O emissions) by -39.1% (95% CI: -48.1%, -28.2%). However, water-saving irrigation 
practices in rice paddies increased area-scaled N2O emissions by 84.4% (95% CI: 53.2%, 128.7%) 
compared to continuous flooding. In addition, the changes in yield-scaled emissions are provided in 
the synthesis paper. 

Table 101. Effect (% change) of water-saving irrigation practices in flooded lands on GHG emissions. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention vs. 
comparator 

Water-saving 
irrigation practices in 
flooded land vs. 
continuous flooding 

Source: X. Zhao et al. 2019.  

CI_LOW  Mean 

CI_HIGH  Nc 

Effect 

CH4 emissions  

-56.82 

-51.50 

-44.55  172 

Positive 

N2O emissions  

53.17 

84.40 

128.57 

97 

Negative 

Global warming potential 
(N2O and CH4)  

-48.06 

-39.10 

-28.16 

97 

Positive 

3.34.2 Effects on soil organic carbon  

The selected synthesis paper (Livsey et al. 2019) includes data from 12 primary studies conducted in 
Asia. It explores the impact of water-saving irrigation practices (alternate wet and dry or mid-season 

133 

 
 
 
drainage) on soil organic carbon (SOC) concentration in rice paddies, compared to continuous flooding 
(Table  102).  Authors  found  a  consistently  negative  effect  of  alternate  wet,  dry,  and  mid-season 
, 

1.3%).  

 Table 102. Effect (% change) of water-saving irrigation practices in flooded lands on soil organic carbon (SOC) 
concentration. CI_LOW:  confidence interval  bottom, CI_HIGH: confidence interval top, Nc: number of  pairwise 
comparisons. 

Intervention  

Comparator  

CI_LOW 

Mean 

CI_HIGH 

Nc 

Effect 

Water-saving irrigation 
practices in flooded lands  

Continuous 
flooding  

Source: Livsey et al. 2019.  

3.34.3 Effects on ammonia emissions  

NA  

Negative 

The selected synthesis paper (Qiu et al. 2022) includes data from 74 primary studies and explores 
the impact of different water-saving irrigation practices in rice paddies on ammonia (NH3) emission 
compared to continuous flooding (Table 103).  

The effect of water-saving irrigation practices on NH3 emission was found to be either positive (i.e., 
decreased  emission)  or  non-significant,  depending  on  the  type  of  practice:  alternate  wet  and  dry 
9%,  14.7%), and controlled irrigation, instead, did not 
reduced NH3emission 
significantly influenced NH3 

3%, 9.2%).  

 Table 103. Effect (% change) of water-saving irrigation practices in flooded lands on NH3 emission. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention   Comparator  

Water-saving 
irrigation 
practices in 
flooded lands  

Continuous 
flooding  

Source: Qiu et al. 2022.  

Type of sub-
practice  
Alternate wet 
and dry  
Controlled 
irrigation  

3.34.4 Effects on the nutrient balance  

CI_LOW   Mean   CI_HIGH   Nc  

Effect  

-26.88  

-21.04  

-14.68  

17  

Positive  

-9.29  

-0.50  

9.15  

13   Non-significant  

The selected synthesis paper (Qiu et al. 2022) includes data from 74 primary studies and explores 
the impact of different water-saving irrigation practices in rice paddies on nitrogen use efficiency 
compared to continuous flooding (Table 104).  

The  effect  of  water-saving  irrigation  practices  on  nitrogen  use  efficiency  was  found  to  be  either 
positive (i.e., increased use efficiency) or non-significant, depending on the type of practice:  

─  Both  alternate  wet  and  dry  and  controlled  irrigation  had  a  positive  effect  on  nitrogen  use 

efficiency  by  0.47%  (95%  CI:  0.07%, 
respectively.  

, 

134 

 
 
  
  
  
 
─ 

0.05%). 

, 

 Table 104. Effect (% change) of water-saving irrigation practices in flooded lands on nitrogen use efficiency. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Type of sub-
practice  
Alternate wet and 
dry  

Intervention vs. 
comparator  

Water-saving irrigation 
practices in flooded 
lands vs. continuous 
flooding  

Source: Qiu et al. 2022.   

CI_LOW   Mean  

CI_HIGH  

Nc  

Effect  

0.07  

0.47  

0.87  

Controlled irrigation  

0.46  

1.06  

Dry cultivation  

-3.32  

-1.64  

1.66  

0.05  

27  

13  

Positive  

Positive  

4  

Non-significant  

3.34.5 Effects on nutrient leaching and run-off  

The selected synthesis paper (Qiu et al. 2022) includes data from 74 primary studies and explores 
the  impact  of  different  water-saving  irrigation  practices  in  rice  paddies  on  nitrogen  leaching  and 
runoff compared to continuous flooding (Table 105).  

The effect of water-saving irrigation practices on nitrogen leaching and runoff was found to be either 
positive (i.e., reduced leaching and runoff) or non-significant, depending on the type of practice and 
on the metric:  

─  Alternate wet and dry had a positive effect on both nitrogen leaching and runoff and the mean 
, 

, 

─  Controlled irrigation did not significantly 

, 

, 

─ 

, 

─  Shallow  irrigation  and  deep  storage  did  not  significantly  influence  both  nitrogen  leaching  and 

runoff.  

─ 

, 

Table 105. Effect (% change) of water-saving irrigation practices in flooded lands on N leaching and runoff. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention 
vs. 
comparator  

Type of sub-
practice  

Metric  

CI_LOW   Mean   CI_HIGH   Nc  

Effect  

Water-saving 
irrigation 
practices in 
flooded lands 
vs. continuous 
flooding  

Alternate wet and 
dry  

Controlled 
irrigation  

N runoff  

-42.1  

-33.2  

-22.6  

N leaching  

-22.7  

-14.3  

N runoff  

-45.3  

-19.5  

-4.9  

17.9  

21  

15  

Positive  

Positive  

6  

Non-significant  

N leaching  

-52.9  

-49.2  

-45.1  

24  

Positive  

Moist irrigation  

N runoff  

-84.7  

-66.3  

-25.3  

N runoff  

-31.6  

52.1  

238.9  

3  

3  

Positive  

Non-significant  

135 

 
 
 
 
 
  
Intervention 
vs. 
comparator  

Type of sub-
practice  

Shallow irrigation 
and deep storage 
Thin and wet 
irrigation  

Source: Qiu et al. 2022.   

Metric  

CI_LOW   Mean   CI_HIGH   Nc  

Effect  

N leaching  

-54.7  

-27.4  

16.4  

3  

Non-significant  

N leaching  

-48.8  

-35.2  

-17.8  

5  

Positive  

3.35 Water-saving irrigation practice in non-flooded lands 

Water-saving irrigation practices in non-flooded lands aim to satisfy crop water requirements while 
improving  the  timing  and reliability  of  water deliveries  to minimize  water  use  (Perry  et  al.  2017). 
Conventional non water-saving irrigation practices are standard techniques of irrigation that supply 
water to the crop in order to increase crop productivity, with no intention of minimizing water use 
(European Parliamentary Research Service (EPRS) 2019). 

The water-saving irrigation practices in non-flooded lands included in this review are: 

─  aerated irrigation: irrigation practice characterised by the delivery of aerated water directly to the 

root zone by subsurface drip irrigation (Du, Niu, Gu, Zhang, Cui, et al. 2018); 

─  deficit irrigation: an irrigation practice whereby water supply is reduced below maximum levels 

and mild stress is allowed with minimal effects on yield (Kijne et al. 2003); 

─  optimised irrigation period: irrigation during crucial stages of the growing season (e.g. G. He et al. 

2017; Quemada et al. 2013a), 

─  partial root-zone drying: a modified version of deficit irrigation where approximately half of the 
root system is in a dry state, while the remaining half is irrigated (e.g. Sonawane & Shrivastava 
2022); 

─  reduced  irrigation  amount:  reducing  the  amount  of  water  used  for  irrigation  (e.g.  Du,  Niu,  Gu, 

Zhang, Cui, et al. 2018; G. He et al. 2017); 

─  drip irrigation: water is dripped onto the soil at very low rates (2-20 litres/hour) from a system of 
small diameter plastic pipes fitted with outlets called emitters or drippers; Water can be applied 
at the surface (surface drip irrigation) or underground (subsurface drip irrigation) (Brouwer et al. 
1988); 

─ 

irrigation using water from other source: irrigation using reclaimed, brackish, saline, or treated 
waste water rather than fresh water(e.g. X. Liu et al. 2023). 

These practices are compared either to non-water saving irrigation practices in non-flooded lands, 
such as sprinkler irrigation, furrow irrigation, centre pivot irrigation, full or supplemental irrigation; or 
to an alternative water-saving irrigation practice in non-flooded lands. 

3.35.1 Effects on greenhouse gas emissions  

The selected synthesis paper (Kuang et al. 2021) includes data reported in 74 primary studies from 
various regions of the world. The study focused on croplands, and it quantifies  the effects of drip 

136 

 
 
  
irrigation  on  N2O  emissions,  compared  to  furrow  or  sprinkler  irrigation  (Table  106).  Water-saving 
irrigation practices (i.e., drip irrigation) reduced N2O emissions by 32% (95% CI: 20%, 42%) when 
compared to furrow irrigation and by 46% (95% CI: 30%, 58%) when compared to sprinkler irrigation.  

 Table  106.  Effect  (%  change)  of  water-saving  irrigation  practices in  non-flooded  lands  on  GHG  emissions. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention 

Comparator 

GHG 

CI_LOW  Mean   CI_HIGH  Nc   Effect  

Furrow irrigation 

N2O emissions 

20.00   32.00   42.00   68   Positive  

Drip irrigation 

Sprinkler 
irrigation 

Source: Kuang et al. 2021.   

N2O emissions 

30.00   46.00   58.00   26   Positive  

3.35.2 Effects on the nutrient balance  

The  two  selected  synthesis  papers  report  contrasting  results  depending  on  the  subpractice,  water 
reduction, and irrigation level used as comparator. According to Du, Niu, Gu, Zhang, & Cui 2018, sub-
optimal  water  input  has  a  negative  effect  (i.e. decrease  of  plant  nitrogen  uptake)  on  tomatoes.  It 
decreased  nitrogen  use  efficiency (NUE)  by  -18.7  %  (95%  CI:  -21.7%,-15.7%).  Sub-optimal  water 
input decreased yield by 35.3% and water use efficiency by only 5%. The optimal inputs of water and 
N are the minimum inputs of water and N that produced the highest yield. Sub-optimal water input 
would be defined as the water inputs that were lower than the optimal water input. NUE (kg kg 1) 
 ha 1) 

(Partial factor productivity of N). 

J. Gu et al. 2020 compared reduced water to full irrigation amount in greenhouse vegetables in China. 
They reported a non-significant overall effect on NUE. For small/moderate water reductions (lower 
than 20% of the full irrigation water) nitrogen use efficiency increases (ranging from 76% to 157%). 

non-significant when the proportion of reduced water was less than 40%. This finding suggested that 
full irrigation water amounts were greater than the vegetable requirements.  

Table  107.  Effect  (%  change)  of  water-saving  irrigation  practices  in  non-flooded  lands  on  nitrogen  use 
efficiency. CI_LOW:  confidence  interval  bottom,  CI_HIGH:  confidence  interval  top,  Nc:  number  of  pairwise 
comparisons. 

Intervention and   Sources  Population  

Water 
reduction 

CI_LOW  Mean  CI_HIGH  Nc 

Effect 

Sub-optimal water 
input vs. optimal 
water input 

Du, Niu, Gu, 
Zhang, & 
Cui 2018 

Tomatoes 
Global 
scale 

Reduced irrigation 
amount vs. full 
irrigation water 

J. Gu et al. 
2020 

Greenhouse 
vegetables 
China 

Source: references in the second column.  

Overall 

-21.7 

-18.7 

-15.7 

NA 

Negative 

Overall 

-12.3 

12.3 

45.4 

39 

20 40% 

-31.5 

-3.8 

33.1 

23 

Non-
significant 
Non-
significant 

<20% 

5.4 

76.2 

194.6 

12 

Positive 

137 

 
 
3.35.3 Effects on nutrient leaching and run-off  

The selected synthesis paper (Quemada et al. 2013b) includes data reported in 74 primary studies 
from various regions of the world. The study focused on cereals and vegetables, and it quantifies the 
effects of different non water-saving irrigation practices on nitrate leaching, compared to non-water-
saving irrigation practices (i.e., irrigation following crop needs).  

The effect of water-saving irrigation practices on nitrate leaching was found generally positive (i.e., 
decreased  nitrate leaching).  The  size  of  the  positive effect  depended  on the  type  of  water-saving 
irrigation practice (Table 108,, adjusting water application to crop needs reduced nitrate leaching by 
, 
,  30.0%); and improved irrigation technologies 

46

, 

,  1.6%).  

Table 108. Effect (%  change) of water-saving irrigation practices in non-flooded lands on  Nitrate leaching. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. 

Intervention  

Comparator  

CI_LOW   Mean   CI_HIGH   Nc   Effect  

Adjust water application to 
crop needs  

Improved irrigation schedule  

Excessive irrigation 

-87.86  

-77.29  

-61.02   24   Positive  

Suboptimal timing of 
application, same water 
amount 

-69.97  

-59.19  

-46.17   25   Positive  

Deficit irrigation  

Crop needs 

-70.98  

-51.25  

-29.90   16   Positive  

Improved irrigation 
technologies  

 Source: Quemada et al. 2013b.  

Less efficient system for 
water delivery, same water 
amount 

-42.10  

-23.19  

-1.63  

12   Positive  

138 

 
 
Conclusions  

This  document  presents  a  comprehensive  collection  of  numerical  coefficients  quantifying  the 
environmental  impacts  of  sustainable  farming  practices,  focusing  on  greenhouse  gas  emissions, 
carbon sequestration, and nutrient losses.  

Extracted  from  about  200  scholarly  synthesis  articles,  primarily  meta-analyses,  these  circa  1000 
coefficients offer valuable insights into the effects of various farming practices, with relevance to 
assess the likely impacts of CAP interventions on the environment and the climate. By synthesizing 
existing meta-analyses and applying a systematic review framework, the study ensures a rigorous 
and  transparent  approach  for  informing  policy.  The  collection  gathers  coefficients  quantifying  the 
effects of 35 agricultural management options, ranging from single farming practices to cropping 
systems  or  conservation  and  restoration  actions.  This  valuable  resource  enables  informed  policy 
decisions  at  various  levels,  being  targeted  intervention  design  or  assessing  the  agricultural  policy 
environmental performance. 

However,  it  is  important  to  acknowledge  some  limitations  of  this  approach.  Firstly,  as  with  every 
literature review, these analyses are performed at a specific moment in time and new papers can be 
published after our review. Secondly, the search strings used in the systematic review were tailored 
to specific farming practices, as defined within the document, but not targeted to particular impacts. 
In that sense, this document is not complete as it focuses on the impacts found at synthesis literature 
level for a selected set of practices. 

Finally, it is important to note that this work is based on synthesis literature, which does not exclude 
the  possibility  of  primary  studies  documenting  links  between  specific  farming  practices  and 
environmental  impacts  not  accounted  for  at  the  synthesis  level.  There  may  be  instances  where 
primary studies investigate the relationship between specific farming practices and environmental 
impacts, but these linkages have not been included in the synthesis literature due to a lack of meta-
analyses or systematic reviews addressing them. 

Despite  these  limitations,  this  work  offers  a  robust  foundation  assessing  sustainable  agricultural 
practices and contributing to the European Union's sustainability objectives; while further research 
and  primary  literature  analysis  is  encouraged  to  ensure  that  policymakers,  researchers,  and 
practitioners have access to the most current and robust information. 

139 

 
 
 
 
References 

Abdo, A. I., Shi, D., Li, J., Yang, T., Wang, X., Li, H., Abdel-Hamed, E. M. W., Merwad, A. R. M. A., & Wang, 

L. (2021). Ammonia emission from staple crops in China as response to mitigation strategies 
and agronomic conditions: Meta-analytic study. Journal of Cleaner Production, 279, 123835. 
https://doi.org/10.1016/j.jclepro.2020.123835 

Abera, W., Tamene, L., Tibebe, D., Adimassu, Z., Kassa, H., Hailu, H., Mekonnen, K., Desta, G., Sommer, 
R., & Verchot, L. (2020). Characterizing and evaluating the impacts of national land restoration 
initiatives on ecosystem services in Ethiopia. Land Degradation and Development, 31(1), 37
52. https://doi.org/10.1002/ldr.3424 

Aguilera, E., Lassaletta, L., Gattinger, A., & Gimeno, B. S. (2013). Managing soil carbon for climate 
change mitigation and adaptation in Mediterranean cropping systems: A meta-analysis. 
Agriculture, Ecosystems and Environment, 168, 25 36. 
https://doi.org/10.1016/j.agee.2013.02.003 

Albert, H. A., Li, X., Jeyakumar, P., Wei, L., Huang, L., Huang, Q., Kamran, M., Shaheen, S. M., Hou, D., 

Rinklebe, J., Liu, Z., & Wang, H. (2021). Influence of biochar and soil properties on soil and plant 
tissue concentrations of Cd and Pb: A meta-analysis. Science of the Total Environment, 755, 
142582. https://doi.org/10.1016/j.scitotenv.2020.142582 

Aznar-Sánchez, J. A., Piquer-Rodríguez, M., Velasco-Muñoz, J. F., & Manzano-Agugliaro, F. (2019). 
Worldwide research trends on sustainable land use in agriculture. Land Use Policy, 87, 
104069. https://doi.org/10.1016/j.landusepol.2019.104069 

Ba, S., Qu, Q., Zhang, K., & Groot, J. C. J. (2020). Meta-analysis of greenhouse gas and ammonia 

emissions from dairy manure composting. In Biosystems Engineering (Vol. 193, pp. 126 137). 
Academic Press. https://doi.org/10.1016/j.biosystemseng.2020.02.015 

Bai, X., Huang, Y., Ren, W., Coyne, M., Jacinthe, P., Tao, B., Hui, D., Yang, J., & Matocha, C. (2019). 

Responses of soil carbon sequestration to climate‐smart agriculture practices: A meta‐analysis. 
Global Change Biology, 25(8), 2591 2606. https://doi.org/10.1111/gcb.14658 

Basche, A. D., Miguez, F. E., Kaspar, T. C., & Castellano, M. J. (2014). Do cover crops increase or 

decrease nitrous oxide emissions? a meta-analysis. Journal of Soil and Water Conservation, 
69(6), 471 482. https://doi.org/10.2489/jswc.69.6.471 

Belanche, A., Newbold, C., Morgavi, D., Bach, A., Zweifel, B., & Yáñez-Ruiz, D. (2020). A Meta-analysis 
Describing the Effects of the Essential oils Blend Agolin Ruminant on Performance, Rumen 
Fermentation and Methane Emissions in Dairy Cows. Animals, 10(4), 620. 
https://doi.org/10.3390/ani10040620 

Borchard, N., Schirrmann, M., Cayuela, M. L., Kammann, C., Wrage-Mönnig, N., Estavillo, J. M., 

Fuertes-Mendizábal, T., Sigua, G., Spokas, K., Ippolito, J. A., & Novak, J. (2019). Biochar, soil and 
land-use interactions that reduce nitrate leaching and N2O emissions: A meta-analysis. In 
Science of the Total Environment (Vol. 651, pp. 2354 2364). Elsevier B.V. 
https://doi.org/10.1016/j.scitotenv.2018.10.060 

140 

 
 
 
Bosco, S., Bielza Diaz-Caneja, M., Schievano, A., Guerrero, I., Angileri, V., Assouline, M., Catarino, R., 

Chen, M., Lindner, S., Montero-Castano, A., Perez-Soba, M., Rega, C., Tamburini, G., Makowski, D., 
& Terres, J.-M. (2024). Carbon sequestration coefficients from meta-analyses on the effect of 
farming practices (Supplement to JRC137826). European Commission, Joint Research Centre 
(JRC). Dataset PID. http://data.europa.eu/89h/b097f5ed-7eba-4ee0-87a5-a582425eba3b 

Brooker, R. W., Bennett, A. E., Cong, W., Daniell, T. J., George, T. S., Hallett, P. D., Hawes, C., Iannetta, P. 
P. M., Jones, H. G., Karley, A. J., Li, L., McKenzie, B. M., Pakeman, R. J., Paterson, E., Schöb, C., 

synthesis of research in agronomy, plant physiology and ecology. New Phytologist, 206(1), 
107 117. https://doi.org/10.1111/nph.13132 

Brouwer, C. (International I. for L. R. and I., Prins, K. (Consultant), Kay, M. (Consultant), & Heibloem, M. 
(FAO L. and W. D. D. (1988). Irrigation water management training manual no. 5: Irrigation 
methods. https://www.fao.org/3/S8684E/s8684e00.htm 

Byrnes, R. C., Eastburn, D. J., Tate, K. W., & Roche, L. M. (2018). A Global Meta-Analysis of Grazing 
Impacts on Soil Health Indicators. Journal of Environmental Quality, 47(4), 758 765. 
https://doi.org/10.2134/jeq2017.08.0313 

Cai, Y., & Akiyama, H. (2017). Effects of inhibitors and biochar on nitrous oxide emissions, nitrate 
leaching, and plant nitrogen uptake from urine patches of grazing animals on grasslands: a 
meta-analysis. Soil Science and Plant Nutrition, 63(4), 405 414. 
https://doi.org/10.1080/00380768.2017.1367627 

Campbell, B. M., Beare, D. J., Bennett, E. M., Hall-Spencer, J. M., Ingram, J. S. I., Jaramillo, F., Ortiz, R., 
Ramankutty, N., Sayer, J. A., & Shindell, D. (2017). Agriculture production as a major driver of 
the Earth system exceeding planetary boundaries. Ecology and Society, 22(4), art8. 
https://doi.org/10.5751/ES-09595-220408 

Cao, Y., Wang, X., Bai, Z., Chadwick, D., Misselbrook, T., G. Sommer, S., Qin, W., & Ma, L. (2019). 

Mitigation of ammonia, nitrous oxide and methane emissions during solid waste composting 
with different additives: A meta-analysis. Journal of Cleaner Production, 235, 626 635. 
https://doi.org/10.1016/j.jclepro.2019.06.288 

Carstensen, M. V., Hashemi, F., Hoffmann, C. C., Zak, D., Audet, J., & Kronvang, B. (2020). Efficiency of 
mitigation measures targeting nutrient losses from agricultural drainage systems: A review. 
Ambio, 49(11), 1820 1837. https://doi.org/10.1007/s13280-020-01345-5 

Chalmers, I., Hedges, L. V., & Cooper, H. (2002). A Brief History of Research Synthesis. Evaluation & 
the Health Professions, 25(1), 12 37. https://doi.org/10.1177/0163278702025001003 

Chatterjee, N., Nair, P. K. R., Chakraborty, S., & Nair, V. D. (2018). Changes in soil carbon stocks 

across the Forest-Agroforest-Agriculture/Pasture continuum in various agroecological regions: 
A meta-analysis. Agriculture, Ecosystems and Environment, 266(August), 55 67. 
https://doi.org/10.1016/j.agee.2018.07.014 

Chen, M., Chang, L., Zhang, J., Guo, F., Vymazal, J., He, Q., & Chen, Y. (2020). Global nitrogen input on 

wetland ecosystem: The driving mechanism of soil labile carbon and nitrogen on greenhouse 
gas emissions. Environmental Science and Ecotechnology, 4, 100063. 

141 

 
 
https://doi.org/10.1016/j.ese.2020.100063 

Chen, Y., Camps-Arbestain, M., Shen, Q., Singh, B., & Cayuela, M. L. (2018). The long-term role of 
organic amendments in building soil nutrient fertility: a meta-analysis and review. Nutrient 
Cycling in Agroecosystems, 111(2 3), 103 125. https://doi.org/10.1007/s10705-017-9903-5 

Climate and Clean Air Coalition. (2014). Paddy rice production. 

https://www.ccacoalition.org/en/activity/paddy-rice-production 

Dai, X., & Faciola, A. P. (2019). Evaluating Strategies to Reduce Ruminal Protozoa and Their Impacts 

on Nutrient Utilization and Animal Performance in Ruminants   A Meta-Analysis. Frontiers in 
Microbiology, 10(November), 1 16. https://doi.org/10.3389/fmicb.2019.02648 

Darabighane, B., Mahdavi, A., Aghjehgheshlagh, F. M., Navidshad, B., Yousefi, M. H., & Lee, M. R. F. 
(2021). The Effects of Dietary Saponins on Ruminal Methane Production and Fermentation 
Parameters in Sheep: A Meta Analysis. Iranian Journal of Applied Animal Science, 11(1), 15
21. http://ijas.iaurasht.ac.ir/article_680280.html 

Daryanto, S., Fu, B., Zhao, W., Wang, S., Jacinthe, P. A., & Wang, L. (2020). Ecosystem service 
provision of grain legume and cereal intercropping in Africa. Agricultural Systems, 
178(November 2019), 102761. https://doi.org/10.1016/j.agsy.2019.102761 

de Toledo, T. dos S., Roll, A. A. P., Rutz, F., Dallmann, H. M., Prá, M. A. D., Leite, F. P. L., & Roll, V. F. B. 
(2020). An assessment of the impacts of litter treatments on the litter quality and broiler 
performance: A systematic review and metaanalysis. PLoS ONE, 15(5), 1 26. 
https://doi.org/10.1371/journal.pone.0232853 

Decock, C. (2014). Mitigating nitrous oxide emissions from corn cropping systems in the midwestern 
U.S.: Potential and data gaps. Environmental Science and Technology, 48(8), 4247 4256. 
https://doi.org/10.1021/es4055324 

Dijkstra, J., Bannink, A., France, J., Kebreab, E., & van Gastelen, S. (2018). Short communication: 
Antimethanogenic effects of 3-nitrooxypropanol depend on supplementation dose, dietary 
fiber content, and cattle type. Journal of Dairy Science, 101(10), 9041 9047. 
https://doi.org/10.3168/jds.2018-14456 

Ding, W., Xu, X., He, P., Ullah, S., Zhang, J., Cui, Z., & Zhou, W. (2018). Improving yield and nitrogen 

use efficiency through alternative fertilization options for rice in China: A meta-analysis. Field 
Crops Research, 227(January), 11 18. https://doi.org/10.1016/j.fcr.2018.08.001 

Dos Santos, M. J. P. L., & Ahmad, N. (2020). Sustainability of European agricultural holdings. Journal 

of the Saudi Society of Agricultural Sciences, 19(5), 358 364. 
https://doi.org/10.1016/j.jssas.2020.04.001 

Drexler, S., Gensior, A., & Don, A. (2021). Carbon sequestration in hedgerow biomass and soil in the 

temperate climate zone. Regional Environmental Change, 21(3). 
https://doi.org/10.1007/s10113-021-01798-8 

Du, Y.-D., Niu, W.-Q., Gu, X.-B., Zhang, Q., & Cui, B.-J. (2018). Water- and nitrogen-saving potentials in 

tomato production: A meta-analysis. Agricultural Water Management, 210, 296 303. 

142 

 
 
https://doi.org/10.1016/j.agwat.2018.08.035 

Du, Y.-D., Niu, W.-Q., Gu, X.-B., Zhang, Q., Cui, B.-J., & Zhao, Y. (2018). Crop yield and water use 

efficiency under aerated irrigation: A meta-analysis. Agricultural Water Management, 210, 
158 164. https://doi.org/10.1016/j.agwat.2018.07.038 

Ebbets, A. L., Lane, D. R., Dixon, P., Hollweg, T. A., Huisenga, M. T., & Gurevitch, J. (2020). Using Meta-
Analysis to Develop Evidence-Based Recovery Trajectories of Vegetation and Soils in Restored 
Wetlands in the Northern Gulf of Mexico. Estuaries and Coasts, 43(7), 1692 1710. 
https://doi.org/10.1007/s12237-019-00536-y 

EC JRC. (2023). 

-economic and resource Policy analysis 

- Tools to assess MS CAP strategic plans on environment and cli
 European 
Commission, Joint Research Centre. https://wikis.ec.europa.eu/display/IMAP/IMAP+Home+page 

Emmerling, C., Krein, A., & Junk, J. (2020). Meta-analysis of strategies to reduce NH3 emissions 
from slurries in European agriculture and consequences for greenhouse gas emissions. 
Agronomy, 10(11), 1633. https://doi.org/10.3390/agronomy10111633 

European_Commission. (2022). European Union Register of Feed Additives. 

https://food.ec.europa.eu/safety/animal-feed/feed-additives/eu-register_en 

European Parliamentary Research Service (EPRS). (2019). Irrigation in EU Agriculture. In Think Tank 

European Parliament (Issue December, pp. 1 12). European Union. 
https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/644216/EPRS_BRI(2019)644216_E
N.pdf 

Eze, S., Palmer, S. M., & Chapman, P. J. (2018). Soil organic carbon stock in grasslands: Effects of 

inorganic fertilizers, liming and grazing in different climate settings. Journal of Environmental 
Management, 223(June), 74 84. https://doi.org/10.1016/j.jenvman.2018.06.013 

Fan, X., Yin, C., Yan, G., Cui, P., Shen, Q., Wang, Q., Chen, H., Zhang, N., Ye, M., Zhao, Y., Li, T., & Liang, 
Y. (2018). The contrasting effects of N-(n-butyl) thiophosphoric triamide (NBPT) on N2O 
emissions in arable soils differing in pH are underlain by complex microbial mechanisms. 
Science of The Total Environment, 642, 155 167. 
https://doi.org/10.1016/j.scitotenv.2018.05.356 

FAO. (2009). Glossary on Organic Agriculture. Food and Agriculture Organization of the United 

Nations. http://www.fao.org/organicag/en/ 

Feng, J., Li, F., Zhou, X., Xu, C., Ji, L., Chen, Z., & Fang, F. (2018). Impact of agronomy practices on the 
effects of reduced tillage systems on CH4 and N2O emissions from agricultural fields: A global 
meta-analysis. PLOS ONE, 13(5), e0196703. https://doi.org/10.1371/journal.pone.0196703 

Feng, X. Y., Dijkstra, J., Bannink, A., van Gastelen, S., France, J., & Kebreab, E. (2020). 

Antimethanogenic effects of nitrate supplementation in cattle: A meta-analysis. Journal of 
Dairy Science, 103(12), 11375 11385. https://doi.org/10.3168/jds.2020-18541 

Gattinger, A., Muller, A., Haeni, M., Skinner, C., Fliessbach, A., Buchmann, N., Mäder, P., Stolze, M., 

Smith, P., Scialabba, N. E. H., & Niggli, U. (2012). Enhanced top soil carbon stocks under organic 

143 

 
 
farming. Proceedings of the National Academy of Sciences of the United States of America, 
109(44), 18226 18231. https://doi.org/10.1073/pnas.1209429109 

Gravuer, K., Gennet, S., & Throop, H. L. (2019). Organic amendment additions to rangelands: A meta-

analysis of multiple ecosystem outcomes. Global Change Biology, 25(3), 1152 1170. 
https://doi.org/10.1111/gcb.14535 

Gu, J., Wu, Y., Tian, Z., & Xu, H. (2020). Nitrogen use efficiency, crop water productivity and nitrous 
oxide emissions from Chinese greenhouse vegetables: A meta-analysis. Science of The Total 
Environment, 743, 140696. https://doi.org/10.1016/j.scitotenv.2020.140696 

Gu, X., Weng, S., Li, Y., & Zhou, X. (2022). Effects of Water and Fertilizer Management Practices on 
Methane Emissions from Paddy Soils: Synthesis and Perspective. International Journal of 
Environmental Research and Public Health, 19(12), 7324. 
https://doi.org/10.3390/ijerph19127324 

Guerrero, I. (2023). Knowledge synthesis on the effect of farming practices on environment and 

climate   iMAP assessment. In EUROPEAN COMMISSION   Directorate-General for Agriculture 
and Rural Development   Unit A.3 (2023): Addressing data gaps to evaluate CAP Strategic 
Plans. Report of the Good Practice Workshop 8-9 June 2023. 

Gurevitch, J., Koricheva, J., Nakagawa, S., & Stewart, G. (2018). Meta-analysis and the science of 
research synthesis. Nature, 555(7695), 175 182. https://doi.org/10.1038/nature25753 

Haddaway, N. R., Burden, A., Evans, C. D., Healey, J. R., Jones, D. L., Dalrymple, S. E., & Pullin, A. S. 
(2014). Evaluating effects of land management on greenhouse gas fluxes and carbon 
balances in boreo-temperate lowland peatland systems. Environmental Evidence, 3(1), 5. 
https://doi.org/10.1186/2047-2382-3-5 

Haddaway, N. R., Hedlund, K., Jackson, L. E., Kätterer, T., Lugato, E., Thomsen, I. K., Jørgensen, H. B., & 
Isberg, P. E. (2017). How does tillage intensity affect soil organic carbon? A systematic review. 
In Environmental Evidence (Vol. 6, Issue 1). BioMed Central. https://doi.org/10.1186/s13750-
017-0108-9 

Han, Z., Walter, M. T., & Drinkwater, L. E. (2017). N2O emissions from grain cropping systems: a 

meta-analysis of the impacts of fertilizer-based and ecologically-based nutrient management 
strategies. Nutrient Cycling in Agroecosystems, 107(3), 335 355. 
https://doi.org/10.1007/s10705-017-9836-z 

Harahap, R. P., Setiawana, D., Nahrowib, S., Suharti, S., Obitsud, T., & Jayanegara, A. (2020). Enteric 
Methane Emissions and Rumen Fermentation Profile Treated by Dietary Chitosan: A Meta-
Analysis of In Vitro Experiments. Tropical Animal Science Journal, 43(3), 233 239. 
https://doi.org/10.5398/tasj.2020.43.3.233 

Harris, Z. M., Spake, R., & Taylor, G. (2015). Land use change to bioenergy: A meta-analysis of soil 

carbon and GHG emissions. Biomass and Bioenergy, 82, 27 39. 
https://doi.org/10.1016/j.biombioe.2015.05.008 

He, G., Cui, Z., Ying, H., Zheng, H., Wang, Z., & Zhang, F. (2017). Managing the trade-offs among yield 
increase, water resources inputs and greenhouse gas emissions in irrigated wheat production 

144 

 
 
systems. Journal of Cleaner Production, 164, 567 574. 
https://doi.org/10.1016/j.jclepro.2017.06.085 

He, M., Zhou, G., Yuan, T., van Groenigen, K. J., Shao, J., & Zhou, X. (2020). Grazing intensity 

significantly changes the C : N : P stoichiometry in grassland ecosystems. Global Ecology and 
Biogeography, 29(2), 355 369. https://doi.org/10.1111/geb.13028 

Head, B. W. (2016). Toward 

‐Informed  Policy Making? Public Administration Review, 

76(3), 472 484. https://doi.org/10.1111/puar.12475 

Herremans, S., Vanwindekens, F., Decruyenaere, V., Beckers, Y., & Froidmont, E. (2020). Effect of 

dietary tannins on milk yield and composition, nitrogen partitioning and nitrogen use efficiency 
of lactating dairy cows: A meta-analysis. Journal of Animal Physiology and Animal Nutrition, 
104(5), 1209 1218. https://doi.org/10.1111/jpn.13341 

Hou, Y., Velthof, G. L., & Oenema, O. (2015). Mitigation of ammonia, nitrous oxide and methane 
emissions from manure management chains: A meta-analysis and integrated assessment. 
Global Change Biology, 21(3), 1293 1312. https://doi.org/10.1111/gcb.12767 

Huang, Y., Ciais, P., Luo, Y., Zhu, D., Wang, Y., Qiu, C., Goll, D. S., Guenet, B., Makowski, D., De Graaf, I., 
Leifeld, J., Kwon, M. J., Hu, J., & Qu, L. (2021). Tradeoff of CO2 and CH4 emissions from global 
peatlands under water-table drawdown. Nature Climate Change, 11(7), 618 622. 
https://doi.org/10.1038/s41558-021-01059-w 

Huang, Y., Ren, W., Wang, L., Hui, D., Grove, J. H., Yang, X., Tao, B., & Goff, B. (2018). Greenhouse gas 
emissions and crop yield in no-tillage systems: A meta-analysis. Agriculture, Ecosystems and 
Environment, 268(August), 144 153. https://doi.org/10.1016/j.agee.2018.09.002 

Huygens, D., Orveillon, G., Lugato, E., & Tavazzi, S. (2020). Technical proposals for the safe use of 
processed manure above the threshold established for Nitrate Vulnerable Zones by the 
Nitrates Directive ( 91 / 676 / EEC ). https://doi.org/10.2760/373351 

Jian, J., Lester, B. J., Du, X., Reiter, M. S., & Stewart, R. D. (2020). A calculator to quantify cover crop 

effects on soil health and productivity. Soil and Tillage Research, 199, 104575. 
https://doi.org/10.1016/j.still.2020.104575 

Kämpf, I., Hölzel, N., Störrle, M., Broll, G., & Kiehl, K. (2016a). Potential of temperate agricultural soils 

for carbon sequestration: A meta-analysis of land-use effects. Science of the Total 
Environment, 566 567, 428 435. https://doi.org/10.1016/j.scitotenv.2016.05.067 

Kämpf, I., Hölzel, N., Störrle, M., Broll, G., & Kiehl, K. (2016b). Potential of temperate agricultural soils 

for carbon sequestration: A meta-analysis of land-use effects. Science of the Total 
Environment, 566 567, 428 435. https://doi.org/10.1016/j.scitotenv.2016.05.067 

Kijne, J., Balaghi, R., Duffy, P., Jlibene, M., Joy, K. J., Klohn, W., Mollinga, P., Paranjape, S., Trava, J., & 

Tychon, B. (2003). Unlocking the Water Potential of Agriculture. FAO. 
https://www.fao.org/3/y4525e/y4525e00.htm 

Kuang, W., Gao, X., Tenuta, M., & Zeng, F. (2021). A global meta‐analysis of nitrous oxide emission 

from drip‐irrigated cropping system. Global Change Biology, 27(14), 3244 3256. 

145 

 
 
https://doi.org/10.1111/gcb.15636 

Lai, L., & Kumar, S. (2020). A global meta-analysis of livestock grazing impacts on soil properties. 

PloS One, 15(8), e0236638. https://doi.org/10.1371/journal.pone.0236638 

Land, M., Granéli, W., Grimvall, A., Hoffmann, C. C., Mitsch, W. J., Tonderski, K. S., & Verhoeven, J. T. A. 

(2016). How effective are created or restored freshwater wetlands for nitrogen and 
phosphorus removal? A systematic review. Environmental Evidence, 5(1), 1 26. 
https://doi.org/10.1186/s13750-016-0060-0 

Lau, J. (1997). Quantitative Synthesis in Systematic Reviews. Annals of Internal Medicine, 127(9), 

820. https://doi.org/10.7326/0003-4819-127-9-199711010-00008 

Lewis, K. A., Tzilivakis, J., Green, A., & Warner, D. J. (2015). Potential of feed additives to improve the 
environmental impact of European livestock farming: a multi-issue analysis. International 
Journal of Agricultural Sustainability, 13(1), 55 68. 
https://doi.org/10.1080/14735903.2014.936189 

Li, C., Hoffland, E., Kuyper, T. W., Yu, Y., Zhang, C., Li, H., Zhang, F., & van der Werf, W. (2020). 

Syndromes of production in intercropping impact yield gains. Nature Plants, 6(6), 653 660. 
https://doi.org/10.1038/s41477-020-0680-9 

Li, T., Zhang, W., Yin, J., Chadwick, D., Norse, D., Lu, Y., Liu, X., Chen, X., Zhang, F., Powlson, D., & Dou, 

Z. (2018). Enhanced-efficiency fertilizers are not a panacea for resolving the nitrogen problem. 
Global Change Biology, 24(2), e511 e521. https://doi.org/10.1111/gcb.13918 

Li, Z., Deng, Q., Liu, Y., Yan, T., Li, F., Cao, Y., & Yao, J. (2018). Dynamics of methanogenesis, ruminal 

fermentation and fiber digestibility in ruminants following elimination of protozoa: A meta-
analysis. Journal of Animal Science and Biotechnology, 9(1), 1 9. 
https://doi.org/10.1186/s40104-018-0305-6 

Li, Z., Reichel, R., Xu, Z., Vereecken, H., & Brüggemann, N. (2021). Return of crop residues to arable 
-analysis. Agronomy for 

Sustainable Development, 41(5), 66. https://doi.org/10.1007/s13593-021-00715-x 

Linquist, B. A., Adviento-Borbe, M. A., Pittelkow, C. M., van Kessel, C., & van Groenigen, K. J. (2012). 
Fertilizer management practices and greenhouse gas emissions from rice systems: A 
quantitative review and analysis. Field Crops Research, 135, 10 21. 
https://doi.org/10.1016/j.fcr.2012.06.007 

Liu, B.-Y., Lin, B.-J., Li, X.-

-L. (2021). 

Appropriate farming practices of summer maize in the North China Plain: Reducing nitrogen 
use to promote sustainable agricultural development. Resources, Conservation and Recycling, 
175, 105889. https://doi.org/10.1016/j.resconrec.2021.105889 

Liu, B. Y., Zhao, X., Li, S. S., Zhang, X. Z., Virk, A. L., Qi, J. Y., Kan, Z. R., Wang, X., Ma, S. T., & Zhang, H. L. 
(2020). Meta-analysis of management-induced changes in nitrogen use efficiency of winter 
wheat in the North China Plain. Journal of Cleaner Production, 251, 119632. 
https://doi.org/10.1016/j.jclepro.2019.119632 

146 

 
 
Liu, L., & Greaver, T. L. (2009). A review of nitrogen enrichment effects on three biogenic GHGs: The 
CO 2 sink may be largely offset by stimulated N2O and CH 4 emission. Ecology Letters, 
12(10), 1103 1117. https://doi.org/10.1111/j.1461-0248.2009.01351.x 

Liu, Q., Zhang, Y., Liu, B., Amonette, J. E., Lin, Z., Liu, G., Ambus, P., & Xie, Z. (2018). How does biochar 

influence soil N cycle? A meta-analysis. Plant and Soil, 426(1 2), 211 225. 
https://doi.org/10.1007/s11104-018-3619-4 

Liu, R., Thomas, B. W., Shi, X., Zhang, X., Wang, Z., & Zhang, Y. (2021). Effects of ground cover 

management on improving water and soil conservation in tree crop systems: A meta-analysis. 
CATENA, 199, 105085. https://doi.org/10.1016/J.CATENA.2020.105085 

Liu, X., Zhang, L., Yang, F., & Zhou, W. (2023). Determining reclaimed water quality thresholds and 
farming practices to improve food crop yield: A meta-analysis combined with random forest 
model. Science of The Total Environment, 862, 160774. 
https://doi.org/10.1016/j.scitotenv.2022.160774 

Livsey, J., Kätterer, T., Vico, G., Lyon, S. W., Lindborg, R., Scaini, A., Da, C. T., & Manzoni, S. (2019). Do 
alternative irrigation strategies for rice cultivation decrease water footprints at the cost of 
long-term soil health? Environmental Research Letters, 14(7), 074011. 
https://doi.org/10.1088/1748-9326/ab2108 

Makowski, D., Catarino, R., Chen, M., Bosco, S., Montero-Castaño, A., Pérez-Soba, M., Schievano, A., & 
Tamburini, G. (2023). Synthesising results of meta-analyses to inform policy: a comparison of 
fast-track methods. Environmental Evidence, 12(1), 16. https://doi.org/10.1186/s13750-023-
00309-y 

Makowski, D., Piraux, F., & Brun, F. (2019). From Experimental Network to Meta-analysis. Springer 

Netherlands. https://doi.org/10.1007/978-94-024-1696-1 

McClelland, S. C., Paustian, K., & Schipanski, M. E. (2021). Management of cover crops in temperate 
climates influences soil organic carbon stocks: a meta-analysis. Ecological Applications, 31(3), 
e02278. https://doi.org/10.1002/eap.2278 

McDaniel, M. D., Tiemann, L. K., & Grandy, A. S. (2014). Does agricultural crop diversity enhance soil 
microbial biomass and organic matter dynamics? A meta-analysis. Ecological Applications, 
24(3), 560 570. https://doi.org/10.1890/13-0616.1 

McDonald, S. E., Lawrence, R., Kendall, L., & Rader, R. (2019). Ecological, biophysical and production 
effects of incorporating rest into grazing regimes: A global meta-analysis. Journal of Applied 
Ecology, 56(12), 2723 2731. https://doi.org/10.1111/1365-2664.13496 

Miranda, N. D., Tuomisto, H. L., & McCulloch, M. D. (2015). Meta-analysis of greenhouse gas 

emissions from anaerobic digestion processes in dairy farms. Environmental Science and 
Technology, 49(8), 5211 5219. https://doi.org/10.1021/acs.est.5b00018 

Molden, D., Faurès, J.-M., Finlayson, C. M., Gitay, H., Muylwijk, J., Schipper, L., Vallée, D., & Coates, D. 

(2007). Chapter 1, Setting the scene. In L. and I. W. M. C. I. David Molden, Earthscan (Ed.), Water 
for Food, Water for Life, A Comprehensive Assessment of Water Management in Agriculture 
(pp. 41 53). Routledge, Taylor & Francis Group. https://www.iwmi.cgiar.org/assessment/Water 

147 

 
 
for Food Water for Life/Chapters/Chapter 1 Setting the Scene.pdf 

Mondelaers, K., Aertsens, J., & van Huylenbroeck, G. (2009). A meta-analysis of the differences in 
environmental impacts between organic and conventional farming. British Food Journal, 
111(10), 1098 1119. https://doi.org/10.1108/00070700910992925 

Muhammad, I., Sainju, U. M., Zhao, F., Khan, A., Ghimire, R., Fu, X., & Wang, J. (2019). Regulation of 
soil CO2 and N2O emissions by cover crops: A meta-analysis. Soil and Tillage Research, 
192(September 2019), 103 112. https://doi.org/10.1016/j.still.2019.04.020 

Nayak, D., Saetnan, E., Cheng, K., Wang, W., Koslowski, F., Cheng, Y. F., Zhu, W. Y., Wang, J. K., Liu, J. 

X., Moran, D., Yan, X., Cardenas, L., Newbold, J., Pan, G., Lu, Y., & Smith, P. (2015). Management 
opportunities to mitigate greenhouse gas emissions from Chinese agriculture. Agriculture, 
Ecosystems and Environment, 209, 108 124. https://doi.org/10.1016/j.agee.2015.04.035 

Nerlekar, A. N., & Veldman, J. W. (2020). High plant diversity and slow assembly of old-growth 

grasslands. Proceedings of the National Academy of Sciences of the United States of America, 
117(31), 18550 18556. https://doi.org/10.1073/pnas.1922266117 

Nkebiwe, P. M., Weinmann, M., Bar-Tal, A., & Müller, T. (2016). Fertilizer placement to improve crop 
nutrient acquisition and yield: A review and meta-analysis. In Field Crops Research (Vol. 196, 
pp. 389 401). Elsevier B.V. https://doi.org/10.1016/j.fcr.2016.07.018 

Pan, B., Lam, S. K., Mosier, A., Luo, Y., & Chen, D. (2016). Ammonia volatilization from synthetic 

fertilizers and its mitigation strategies: A global synthesis. Agriculture, Ecosystems and 
Environment, 232, 283 289. https://doi.org/10.1016/j.agee.2016.08.019 

Pardo, G., Moral, R., Aguilera, E., & del Prado, A. (2015). Gaseous emissions from management of 

solid waste: A systematic review. Global Change Biology, 21(3), 1313 1327. 
https://doi.org/10.1111/gcb.12806 

Perry, C., Steduto, P., & Karajeh, F. (2017). Does improved irrigation technology save water? A review 
of the evidence. In Regional Initiative on Water Scarcity for the Near East and North Africa (Vol. 
42, Issue 2). FAO. 
www.fao.org/publications%0Ahttps://www.researchgate.net/publication/317102271 

Philibert, A., Loyce, C., & Makowski, D. (2012). Assessment of the quality of meta-analysis in 

agronomy. Agriculture, Ecosystems & Environment, 148, 72 82. 
https://doi.org/10.1016/j.agee.2011.12.003 

Powlson, D. S., Stirling, C. M., Thierfelder, C., White, R. P., & Jat, M. L. (2016). Does conservation 
agriculture deliver climate change mitigation through soil carbon sequestration in tropical 
agro-ecosystems? Agriculture, Ecosystems & Environment, 220, 164 174. 
https://doi.org/10.1016/j.agee.2016.01.005 

Pretty, J. (2018). Intensification for redesigned and sustainable agricultural systems. Science, 

362(6417). https://doi.org/10.1126/science.aav0294 

Qiao, C., Liu, L., Hu, S., Compton, J. E., Greaver, T. L., & Li, Q. (2015). How inhibiting nitrification 

affects nitrogen cycle and reduces environmental impacts of anthropogenic nitrogen input. 

148 

 
 
Global Change Biology, 21(3), 1249 1257. https://doi.org/10.1111/gcb.12802 

Qin, W., Hu, C., & Oenema, O. (2015). Soil mulching significantly enhances yields and water and 

nitrogen use efficiencies of maize and wheat: a meta-analysis. Scientific Reports, 5(1), 16210. 
https://doi.org/10.1038/srep16210 

Qin, X., Huang, T., Lu, C., Dang, P., Zhang, M., Guan, X. kang, Wen, P. fei, Wang, T. C., Chen, Y., & 

Siddique, K. H. M. (2021). Benefits and limitations of straw mulching and incorporation on 
maize yield, water use efficiency, and nitrogen use efficiency. Agricultural Water Management, 
256(August 2020), 107128. https://doi.org/10.1016/j.agwat.2021.107128 

Qiu, H., Yang, S., Jiang, Z., Xu, Y., & Jiao, X. (2022). Effect of Irrigation and Fertilizer Management on 

Rice Yield and Nitrogen Loss: A Meta-Analysis. Plants, 11(13), 1690. 
https://doi.org/10.3390/plants11131690 

Quemada, M., Baranski, M., Nobel-de Lange, M. N. J., Vallejo, A., & Cooper, J. M. (2013a). Meta-

analysis of strategies to control nitrate leaching in irrigated agricultural systems and their 
effects on crop yield. Agriculture, Ecosystems & Environment, 174, 1 10. 
https://doi.org/10.1016/j.agee.2013.04.018 

Quemada, M., Baranski, M., Nobel-de Lange, M. N. J., Vallejo, A., & Cooper, J. M. (2013b). Meta-

analysis of strategies to control nitrate leaching in irrigated agricultural systems and their 
effects on crop yield. Agriculture, Ecosystems & Environment, 174, 1 10. 
https://doi.org/10.1016/j.agee.2013.04.018 

Regulation (EU) 2021/2115. (n.d.). Regulation (EU) 2021/2115 of the European Parliament and of 
the Council of 2 December 2021 establishing rules on support for strategic plans (...). 
https://eur-lex.europa.eu/eli/reg/2021/2115/oj 

Sainju, U. M. (2016). A Global Meta-Analysis on the Impact of Management Practices on Net Global 
Warming Potential and Greenhouse Gas Intensity from Cropland Soils. PLOS ONE, 11(2), 
e0148527. https://doi.org/10.1371/journal.pone.0148527 

Sala, S., Amadei, A. M., Beylot, A., & Ardente, F. (2021). The evolution of life cycle assessment in 
European policies over three decades. The International Journal of Life Cycle Assessment, 
26(12), 2295 2314. https://doi.org/10.1007/s11367-021-01893-2 

Salami, S. A., Moran, C. A., Warren, H. E., & Taylor-Pickard, J. (2021). Meta-analysis and sustainability 

of feeding slow-release urea in dairy production. PLoS ONE, 16(2 Febuary), 1 18. 
https://doi.org/10.1371/journal.pone.0246922 

Schievano, A., Perez-Soba Aguilar, M., Bosco, S., Montero Castaño, A., Catarino, R., Chen, M., 

Tamburini, G., Landoni, B., Mantegazza, O., Guerrero, I., Bielza Diaz-Caneja, M., Assouline, M., 
Koeble, R., Dentener, F., Van Der Velde, M., Rega, C., & Furlan, A. (2023). iMAP-FP dataset   An 
evidence library of the effects of Farming Practices on the environment and the climate. 
European Commission. https://doi.org/10.2905/4e3c371a-be72-4ea0-aa0b-45f8cdda2064 

Sha, Z., Li, Q., Lv, T., Misselbrook, T., & Liu, X. (2019). Response of ammonia volatilization to biochar 

addition: A meta-analysis. Science of the Total Environment, 655, 1387 1396. 
https://doi.org/10.1016/j.scitotenv.2018.11.316 

149 

 
 
Sha, Z., Ma, X., Wang, J., Lv, T., Li, Q., Misselbrook, T., & Liu, X. (2020). Effect of N stabilizers on 
fertilizer-N fate in the soil-crop system: A meta-analysis. Agriculture, Ecosystems and 
Environment, 290, 106763. https://doi.org/10.1016/j.agee.2019.106763 

Shackelford, G. E., Kelsey, R., & Dicks, L. V. (2019). Effects of cover crops on multiple ecosystem 

services: Ten meta-analyses of data from arable farmland in California and the 
Mediterranean. Land Use Policy, 88(August 2018), 104204. 
https://doi.org/10.1016/j.landusepol.2019.104204 

Shang, Z., Abdalla, M., Xia, L., Zhou, F., Sun, W., & Smith, P. (2021). Can cropland management 

practices lower net greenhouse emissions without compromising yield? Global Change Biology, 
27(19), 4657 4670. https://doi.org/10.1111/gcb.15796 

Sharma, S., & Chaubey, I. (2017). Surface and Subsurface Transport of Nitrate Loss from the 

Selected Bioenergy Crop Fields: Systematic Review, Analysis and Future Directions. Agriculture, 
7(3), 27. https://doi.org/10.3390/agriculture7030027 

Singh, J. S., Raghubanshi, A. S., Reddy, V. S., Singh, S., & Kashyap, A. K. (1998). Methane flux from 
irrigated paddy and dryland rice fields, and from seasonally dry tropical forest and Savanna 
soils of India. Soil Biology and Biochemistry, 30(2), 135 139. https://doi.org/10.1016/S0038-
0717(97)00112-0 

Skinner, C., Gattinger, A., Muller, A., Mäder, P., Fliebach, A., Stolze, M., Ruser, R., & Niggli, U. (2014). 

Greenhouse gas fluxes from agricultural soils under organic and non-organic management - A 
global meta-analysis. Science of the Total Environment, 468 469, 553 563. 
https://doi.org/10.1016/j.scitotenv.2013.08.098 

Smith, O. M., Cohen, A. L., Rieser, C. J., Davis, A. G., Taylor, J. M., Adesanya, A. W., Jones, M. S., Meier, 

A. R., Reganold, J. P., Orpet, R. J., Northfield, T. D., & Crowder, D. W. (2019). Organic farming 
provides reliable environmental benefits but increases variability in crop yields: A global meta-
analysis. Frontiers in Sustainable Food Systems, 3(September), 1 10. 
https://doi.org/10.3389/fsufs.2019.00082 

Sonawane, A. V., & Shrivastava, P. K. (2022). Partial root zone drying method of irrigation: A review. 

Irrigation and Drainage, 71(3), 574 588. https://doi.org/10.1002/ird.2686 

Stoate, C., Báldi, A., Beja, P., Boatman, N. D., Herzon, I., van Doorn, A., de Snoo, G. R., Rakosy, L., & 

Ramwell, C. (2009). Ecological impacts of early 21st century agricultural change in Europe   A 
review. Journal of Environmental Management, 91(1), 22 46. 
https://doi.org/10.1016/j.jenvman.2009.07.005 

Tan, L., Ge, Z., Zhou, X., Li, S., Li, X., & Tang, J. (2020). Conversion of coastal wetlands, riparian 

wetlands, and peatlands increases greenhouse gas emissions: A global meta-analysis. Global 
Change Biology, 26(3), 1638 1653. https://doi.org/10.1111/gcb.14933 

Tang, S., Wang, K., Xiang, Y., Tian, D., Wang, J., Liu, Y., Cao, B., Guo, D., & Niu, S. (2019). Heavy grazing 
reduces grassland soil greenhouse gas fluxes: A global meta-analysis. Science of the Total 
Environment, 654, 1218 1224. https://doi.org/10.1016/j.scitotenv.2018.11.082 

Thapa, R., Mirsky, S. B., & Tully, K. L. (2018). Cover Crops Reduce Nitrate Leaching in 

150 

 
 
Agroecosystems:A Global Meta-Analysis. Journal of Environmental Quality, 47(6), 1400 1411. 
https://doi.org/10.2134/JEQ2018.03.0107 

Ti, C., Xia, L., Chang, S. X., & Yan, X. (2019). Potential for mitigating global agricultural ammonia 

emission: A meta-analysis. Environmental Pollution, 245, 141 148. 
https://doi.org/10.1016/j.envpol.2018.10.124 

Tonitto, C., David, M. B., & Drinkwater, L. E. (2006). Replacing bare fallows with cover crops in 
fertilizer-intensive cropping systems: A meta-analysis of crop yield and N dynamics. 
Agriculture, Ecosystems and Environment, 112(1), 58 72. 
https://doi.org/10.1016/j.agee.2005.07.003 

Torres, R. N. S., Paschoaloto, J. R., Ezequiel, J. M. B., da Silva, D. A. V., & Almeida, M. T. C. (2021). 

Meta-analysis of the effects of essential oil as an alternative to monensin in diets for beef 
cattle. Veterinary Journal, 272, 105659. https://doi.org/10.1016/j.tvjl.2021.105659 

Ungerfeld, E. M., Kohn, R. A., Wallace, R. J., & Newbold, C. J. (2007). A meta-analysis of fumarate 

effects on methane production in ruminal batch cultures. Journal of Animal Science, 85(10), 
2556 2563. https://doi.org/10.2527/jas.2006-674 

Valkama, E., Usva, K., Saarinen, M., & Uusi-Kämppä, J. (2019). A Meta-Analysis on Nitrogen 
Retention by Buffer Zones. Journal of Environmental Quality, 48(2), 270 279. 
https://doi.org/10.2134/jeq2018.03.0120 

van Gastelen, S., Dijkstra, J., & Bannink, A. (2019). Are dietary strategies to mitigate enteric methane 
emission equally effective across dairy cattle, beef cattle, and sheep? Journal of Dairy Science, 
102(7), 6109 6130. https://doi.org/10.3168/jds.2018-15785 

Van Vooren, L., Reubens, B., Broekx, S., De Frenne, P., Nelissen, V., Pardon, P., & Verheyen, K. (2017). 
Ecosystem service delivery of agri-environment measures: A synthesis for hedgerows and 
grass strips on arable land. Agriculture, Ecosystems and Environment, 244(April), 32 51. 
https://doi.org/10.1016/j.agee.2017.04.015 

Vandermeer, J. H. (1989). The Ecology of Intercropping. Cambridge University Press. 

https://doi.org/10.1017/CBO9780511623523 

Wails, C. N., Baker, K., Blackburn, R., Del Vallé, A., Heise, J., Herakovich, H., Holthuijzen, W. A., 

Nissenbaum, M. P., Rankin, L., Savage, K., Vanek, J. P., & Jones, H. P. (2021). Assessing changes 
to ecosystem structure and function following invasion by Spartina alterniflora and Phragmites 
australis: a meta-analysis. Biological Invasions, 23(9), 2695 2709. 
https://doi.org/10.1007/s10530-021-02540-5 

Wang, H., Long, W., Chadwick, D., Velthof, G. L., Oenema, O., Ma, W., Wang, J., Qin, W., Hou, Y., & 

Zhang, F. (2020). Can dietary manipulations improve the productivity of pigs with lower 
environmental and economic cost? A global meta-analysis. Agriculture, Ecosystems and 
Environment, 289(November 2019), 106748. https://doi.org/10.1016/j.agee.2019.106748 

Wang, J., Chadwick, D. R., Cheng, Y., & Yan, X. (2018). Global analysis of agricultural soil 

denitrification in response to fertilizer nitrogen. Science of the Total Environment, 616 617, 
908 917. https://doi.org/10.1016/j.scitotenv.2017.10.229 

151 

 
 
Wang, Y., Dong, H., Zhu, Z., Gerber, P. J., Xin, H., Smith, P., Opio, C., Steinfeld, H., & Chadwick, D. 

(2017). Mitigating Greenhouse Gas and Ammonia Emissions from Swine Manure Management: 
A System Analysis. Environmental Science and Technology, 51(8), 4503 4511. 
https://doi.org/10.1021/acs.est.6b06430 

Wang, Y., Li, X., Yang, J., Tian, Z., Sun, Q., Xue, W., & Dong, H. (2018a). Mitigating Greenhouse Gas and 

Ammonia Emissions from Beef Cattle Feedlot Production: A System Meta-Analysis. 
Environmental Science and Technology, 52(19), 11232 11242. 
https://doi.org/10.1021/acs.est.8b02475 

Wang, Y., Li, X., Yang, J., Tian, Z., Sun, Q., Xue, W., & Dong, H. (2018b). Mitigating Greenhouse Gas and 

Ammonia Emissions from Beef Cattle Feedlot Production: A System Meta-Analysis. 
Environmental Science and Technology, 52(19), 11232 11242. 
https://doi.org/10.1021/acs.est.8b02475 

Wang, Y., Wang, Z., Liang, F., Jing, X., & Feng, W. (2021). Application of flue gas desulfurization 

gypsum improves multiple functions of saline-sodic soils across China. Chemosphere, 277, 
130345. https://doi.org/10.1016/j.chemosphere.2021.130345 

Wang, Y., Xue, W., Zhu, Z., Yang, J., Li, X., Tian, Z., Dong, H., & Zou, G. (2019a). Mitigating ammonia 

emissions from typical broiler and layer manure management   A system analysis. Waste 
Management, 93, 23 33. https://doi.org/10.1016/j.wasman.2019.05.019 

Wang, Y., Xue, W., Zhu, Z., Yang, J., Li, X., Tian, Z., Dong, H., & Zou, G. (2019b). Mitigating ammonia 

emissions from typical broiler and layer manure management   A system analysis. Waste 
Management, 93, 23 33. https://doi.org/10.1016/j.wasman.2019.05.019 

Wang, Y., Xue, W., Zhu, Z., Yang, J., Li, X., Tian, Z., Dong, H., & Zou, G. (2019c). Mitigating ammonia 
emissions from typical broiler and layer manure management   A system analysis. Waste 
Management, 93, 23 33. https://doi.org/10.1016/j.wasman.2019.05.019 

Wei, Z., Ying, H., Guo, X., Zhuang, M., Cui, Z., & Zhang, F. (2020). Substitution of mineral fertilizer with 
organic fertilizer in maize systems: A meta-analysis of reduced nitrogen and carbon emissions. 
Agronomy, 10(8). https://doi.org/10.3390/agronomy10081149 

Wu, J., Chen, Q., Jia, W., Long, C., Liu, W., Liu, G., & Cheng, X. (2020). Asymmetric response of soil 

methane uptake rate to land degradation and restoration: Data synthesis. Global Change 
Biology, 26(11), 6581 6593. https://doi.org/10.1111/gcb.15315 

Wu, Z., Zhang, X., Dong, Y., Li, B., & Xiong, Z. (2019). Biochar amendment reduced greenhouse gas 
intensities in the rice-wheat rotation system: six-year field observation and meta-analysis. 
Agricultural and Forest Meteorology, 278, 107625. 
https://doi.org/10.1016/j.agrformet.2019.107625 

Xia, F., Mei, K., Xu, Y., Zhang, C., Dahlgren, R. A., & Zhang, M. (2020). Response of N2O emission to 
manure application in field trials of agricultural soils across the globe. Science of the Total 
Environment, 733. https://doi.org/10.1016/j.scitotenv.2020.139390 

Xia, L., Lam, S. K., Chen, D., Wang, J., Tang, Q., & Yan, X. (2017). Can knowledge-based N 

management produce more staple grain with lower greenhouse gas emission and reactive 

152 

 
 
nitrogen pollution? A meta-analysis. Global Change Biology, 23(5), 1917 1925. 
https://doi.org/10.1111/gcb.13455 

Xia, L., Lam, S. K., Wolf, B., Kiese, R., Chen, D., & Butterbach-Bahl, K. (2018). Trade-offs between soil 

carbon sequestration and reactive nitrogen losses under straw return in global 
agroecosystems. Global Change Biology, 24(12), 5919 5932. 
https://doi.org/10.1111/gcb.14466 

Xu, S., Liu, X., Li, X., & Tian, C. (2019a). Soil organic carbon changes following wetland cultivation: A 

global meta-analysis. Geoderma, 347(March), 49 58. 
https://doi.org/10.1016/j.geoderma.2019.03.036 

Xu, S., Liu, X., Li, X., & Tian, C. (2019b). Soil organic carbon changes following wetland restoration: A 

global meta-analysis. Geoderma, 353(June), 89 96. 
https://doi.org/10.1016/j.geoderma.2019.06.027 

Xu, S., Sayer, E. J., Eisenhauer, N., Lu, X., Wang, J., & Liu, C. (2021). Aboveground litter inputs 

determine carbon storage across soil profiles: a meta-analysis. Plant and Soil, 462(1 2), 429
444. https://doi.org/10.1007/s11104-021-04881-5 

Yagi, K., Sriphirom, P., Cha-un, N., Fusuwankaya, K., Chidthaisong, A., Damen, B., & Towprayoon, S. 
(2020). Potential and promisingness of technical options for mitigating greenhouse gas 
emissions from rice cultivation in Southeast Asian countries. Soil Science and Plant Nutrition, 
66(1), 37 49. https://doi.org/10.1080/00380768.2019.1683890 

Yang, M., Fang, Y., Sun, D., & Shi, Y. (2016). Efficiency of two nitrification inhibitors (dicyandiamide 
and 3, 4-dimethypyrazole phosphate) on soil nitrogen transformations and plant productivity: 
A meta-analysis. Scientific Reports, 6(October 2015), 1 10. https://doi.org/10.1038/srep22075 

Yanza, Y. R., Szumacher-Strabel, M., Jayanegara, A., Kasenta, A. M., Gao, M., Huang, H., Patra, A. K., 
The effects of dietary medium-chain fatty acids on ruminal 

methanogenesis and fermentation in vitro and in vivo: A meta-analysis. Journal of Animal 
Physiology and Animal Nutrition, November 2019, 1 16. https://doi.org/10.1111/jpn.13367 

Yu, Y., Zhang, Y., Xiao, M., Zhao, C., & Yao, H. (2021). A meta-analysis of film mulching cultivation 
effects on soil organic carbon and soil greenhouse gas fluxes. Catena, 206(March), 105483. 
https://doi.org/10.1016/j.catena.2021.105483 

Zhang, L., Yuan, F., Bai, J., Duan, H., Gu, X., Hou, L., Huang, Y., Yang, M., He, J. S., Zhang, Z., Yu, L., Song, 

C., Lipson, D. A., Zona, D., Oechel, W., Janssens, I. A., & Xu, X. (2020). Phosphorus alleviation of 
nitrogen-suppressed methane sink in global grasslands. Ecology Letters, 23(5), 821 830. 
https://doi.org/10.1111/ele.13480 

Zhang, Q., Xiao, J., Xue, J., & Zhang, L. (2020). Quantifying the Effects of Biochar Application on 
Greenhouse Gas Emissions from Agricultural Soils: A Global Meta-Analysis. Sustainability, 
12(8), 3436. https://doi.org/10.3390/su12083436 

Zhang, Y., Pan, B., Lam, S. K., Bai, E., Hou, P., & Chen, D. (2021). Predicting the Ratio of Nitrification to 
Immobilization to Reflect the Potential Risk of Nitrogen Loss Worldwide. Environmental Science 
and Technology, 55(11), 7721 7730. https://doi.org/10.1021/acs.est.0c08514 

153 

 
 
Zhang, Z., Liu, D., Qiao, Y., Li, S., Chen, Y., & Hu, C. (2021). Mitigation of carbon and nitrogen losses 
during pig manure composting: A meta-analysis. Science of the Total Environment, 783, 
147103. https://doi.org/10.1016/j.scitotenv.2021.147103 

Zhao, S., Schmidt, S., Qin, W., Li, J., Li, G., & Zhang, W. (2020). Towards the circular nitrogen economy 

 A global meta-analysis of composting technologies reveals much potential for mitigating 
nitrogen losses. Science of The Total Environment, 704, 135401. 
https://doi.org/10.1016/j.scitotenv.2019.135401 

Zhao, X., Liu, B. Y., Liu, S. L., Qi, J. Y., Wang, X., Pu, C., Li, S. S., Zhang, X. Z., Yang, X. G., Lal, R., Chen, F., 

& Zhang, H. L. (2020). 
A meta-analysis. Land Degradation and Development, 31(6), 694 709. 
https://doi.org/10.1002/ldr.3492 

Zhao, X., Pu, C., Ma, S. T., Liu, S. L., Xue, J. F., Wang, X., Wang, Y. Q., Li, S. S., Lal, R., Chen, F., & Zhang, 
H. L. (2019). Management-induced greenhouse gases emission mitigation in global rice 
production. Science of the Total Environment, 649, 1299 1306. 
https://doi.org/10.1016/j.scitotenv.2018.08.392 

154 

 
 
 
List of abbreviations and definitions 

CAP 

CH4 

CI 

Common Agricultural Policy 

Methane (𝐶𝐻4) 

Confidence Interval 

CI_LOW 

Lower confidence interval 

CI_HIGH 

Upper confidence interval  

DCD 

DIN 

DMI 

Dcyandiamide (nitrification inhibitor) 

Total dissolved inorganic nitrogen 

Dry Matter Intake 

DMPP 

3,4-Dimethylpyrazole phosphate (nitrification inhibitor) 

FP 

g 

Farming practice(s) 

grams 

GAEC 

Good Agricultural and Environmental Conditions 

GNB 

HQ 

GPB 

JRC 

kg 

LCA 

MS 

N 

N2O 

Gross Nitrogen Balance 

Hydroquinone (urease inhibitor) 

Gross Phosphorus Balance 

Joint Research Centre 

kilograms 

Life Cycle Assessment 

Member Sates  

Nitrogen 

Nitrous oxide (𝑁2𝑂) 

155 

 
 
 
NA 

Nc 

NH4+- 

NBTP 

NO3- 

NR 

NU 

NUE 

PFPN 

PMEF 

PPD 

RR 

SE 

SMD 

SMR 

UAA 

yr 

Not available 

Number of samples or number of pairwise comparisons 

Ammonium (𝑁𝐻4

+) 

N-(n-butyl) thiophosphoric triamide (urease inhibitor) 

Nitrates (𝑁𝑂3

−) 

Nitrogen recovery (metric of NUE, see Annex 1) 

Nitrogen uptake 

Nitrogen use efficiency 

Partial factor productivity of nitrogen 

Performance and Monitoring Evaluation Framework 

Phenyl-phosphoryl-diamine 

Response ratio 

Standard Error 

Standardized mean difference 

Statutory Management Requirements 

Utilised Agricultural Area 

Year 

156 

 
 
 
List of figures  

Figure 1 Representation of the decision phases for the selection of meta-analyses for quantitative 
data extraction. ....................................................................................................................................................................................... 8 

157 

 
 
 
List of tables  

Table 1 Summary of results from the systematic literature review: Number of coefficients 
extracted for each combination of farming practice and environmental impact, based on statistically 
significant results and, in brackets, the corresponding section. Empty cells may indicate both 
situations of not statistically significant effects or of not retrieved results from literature review. .... 9 

Table 2 Effect of agroforestry on carbon sequestration (% change in SOC stock) in Mediterranean 
and temperate regions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: 
number of pairwise comparisons. ............................................................................................................................................. 11 

Table 3 Effect of organic systems on greenhouse gas emissions (change of emissions per hectare). 
All figures are in kg CO2eq/ha/yr. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval 
top, Nc: number of pairwise comparisons. ............................................................................................................................ 12 

Table 4 Effect of organic systems on carbon sequestration mostly for top soil and temperate 
zones. CI_LOW: confidence interval bottom. CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. .......................................................................................................................................................................................... 13 

Table 5 Effect (% change) of organic systems on carbon sequestration: change in SOC 
concentration or C stock and in C sequestration rate. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. ................................................................................ 15 

Table 6 Effect (% change) of organic systems on carbon sequestration by regions. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 ....................................................................................................................................................................................................................... 15 

Table 7 Effect of organic systems on nitrogen leaching (% change of kg N per hectare). CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. ................................................................................. 16 

Table 8 Effect (% change) of low-ammonia emission techniques on ammonia emissions. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. ................................................................................. 17 

Table 9 Effect (% change) of low-ammonia emission fertilisertechniques on nutrient uptake and 
nutrient recovery (NR). CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: 
number of pairwise comparisons. ............................................................................................................................................. 19 

Table 10 Effect (% change) of low-ammonia emission fertilisertechniques on NO3- leaching. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top. ............................................................. 21 

Table 11 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification 
inhibitors, and double inhibitors on N2O emissions for major crop groups. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top. .......................................................................................................... 23 

Table 12 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification 
inhibitors, and double inhibitors on NH3 emissions for major crop groups. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top. .......................................................................................................... 23 

Table 13 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification inhibitors 
and double inhibitors on N use efficiency (crop N fertiliser recovery) or N plant uptake, for major 
crop groups.CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top. .................................. 25 

Table 14 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification inhibitors 
and double inhibitors on N use efficiency or N plant uptake for different inhibitor types. ...................... 27 

158 

 
 
Table 15 Effect (% change) of controlled-release fertilisers, urease inhibitors, nitrification inhibitors 
and double inhibitors on NO3- leaching for major crop groups.CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top. ............................................................................................................................................... 28 

Table 16 Effect (% change) of organic fertilisation as compared to mineral fertilisation on GHG 
emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ...................................................................................................................................................................... 30 

Table 17 Effect (% change) of organic fertilisation on soil organic carbon. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ....................... 31 

Table 18 Effect (% change) of organic fertilisation on ammonia emissions as compared to urea. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top. ............................................................. 31 

Table 19 Effect (% change) of organic fertilisation on nitrogen use efficiency. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ....................... 32 

Table 20 Effect (% change) of organic fertilisation on N leaching and runoff. Rs: mineral fertiliser 
substitution rate. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number 
of pairwise comparisons. ................................................................................................................................................................ 34 

Table 21 Effect (% change) of green manuring on GHG emissions. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top. ............................................................................................................................ 35 

Table 22 Effect (% change) of green manuring on soil organic carbon (stock per hectare). CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. ................................................................................. 36 

Table 23 Effect (% change) of green manuring on N use efficiency. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ......................................... 37 

Table 24 Effect (% change) of green manuring on NO3- leaching for major crop groups. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top. ................................................................................. 37 

Table 25 Effect (% change) of biochar GHG emissions. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top. ............................................................................................................................................... 38 

Table 27 Effect (% change) of biochar on ammonia volatilisation by biochar feedstock. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 ....................................................................................................................................................................................................................... 41 

Table 28 Effect (% change) of biochar on N plant uptake. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top. ............................................................................................................................................... 42 

Table 29 Effect (% change) of biochar on NO3- leaching. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top. ............................................................................................................................................... 43 

Table 30 Effect (% change) of flue gas desulfurization gypsum on soil organic carbon in China. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. .......................................................................................................................................................................................... 44 

Table 31 Effect (% change) of manure land application techniques on greenhouse gas emissions 
for major GHG types. SE: Standard Error, CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top, Nc: number of pairwise comparisons........................................................................................................... 45 

159 

 
 
Table 32 Effect (% change) of the use of manure land application techniques on ammonia 
emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top. .................................... 46 

Table 33 Effect (% change) of the use of additives and acidification on greenhouse gas emissions 
by major GHG types. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: 
number of pairwise comparisons. ............................................................................................................................................. 48 

Table 34 Effect (% change) of the use of manure storage techniques on ammonia emissions. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top. ............................................................. 50 

Table 35 Effect (% change) of the use of manure processing techniques on greenhouse gas 
emissions by major GHG types. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval 
top, Nc: number of pairwise comparisons. ............................................................................................................................ 52 

Table 36 Effect (% change) of the use of composting manure processing techniques on ammonia 
emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ...................................................................................................................................................................... 53 

Table 37 Effect (% change) of livestock housing techniques on greenhouse gas emissions by major 
GHG types. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ...................................................................................................................................................................... 55 

Table 38 Effect (% change) of livestock housing techniques on ammonia emissions. SMD: 
Standardized mean difference CI_LOW: confidence interval bottom, CI_HIGH: confidence interval 
top, Nc: number of pairwise comparisons. ............................................................................................................................ 57 

Table 39. Effect (% change) of the use of diet formulation techniques on greenhouse gas 
emissions by GHG type. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: 
number of pairwise comparisons. ............................................................................................................................................. 59 

Table 40. Effect (% change) of the use of feed additives on greenhouse gas emissions by major 
GHG types. ............................................................................................................................................................................................... 62 

Table 41. Overall effect (% change) of analysed feeding techniques on ammonia emissions (effect 
varies with the specific technique used, see following tables). CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ............................................................ 65 

Table 42. Effect (% change) of the use of diet formulation techniques on ammonia emissions. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. .......................................................................................................................................................................................... 66 

Table 43. Effect (% change) of the use of feed additives on ammonia emissions. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 ....................................................................................................................................................................................................................... 67 

Table 44. Effect (% change) of the use of diet formulation techniques on nutrient excretion. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. .......................................................................................................................................................................................... 76 

Table 45. Effect (% change) of the use of feed additives on nutrient excretion. ........................................ 77 

Table 46 Overall effect (%) of field margins on SOC stock. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ............................................................ 68 

160 

 
 
Table 47: Effect of hedgerows on soil organic carbon (SOC) and carbon stock sequestration (C seq.) 
compared to cropland and grassland. CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top, Nc: number of pairwise comparisons........................................................................................................... 69 

Table 48: Effect (% change) of terraces and contour bunds on soil organic carbon (SOC). CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 ....................................................................................................................................................................................................................... 69 

Table 49: Effects (%) of buffer strips on N retention for surface runoff (concentration and loads) 
and N groundwater (concentration). CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top, Nc: number of pairwise comparisons........................................................................................................... 70 

Table 50: Nutrient interception by hedgerows and grass strips: reduction of outflow compared to 
inflow (%). CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ...................................................................................................................................................................... 71 

Table 51: Nutrient removal efficiency (%) by small wetlands (Free Water Surface constructed 
wetlands). CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ...................................................................................................................................................................... 71 

Table 52 Effect (% of change) of land abandonment on soil organic carbon sequestration. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 ....................................................................................................................................................................................................................... 73 

Table 53 Effect (absolute soil carbon change) of land abandonment on soil organic carbon. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. .......................................................................................................................................................................................... 74 

Table 54 Effects of cereals and grain legume intercropping on soil organic carbon. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 ....................................................................................................................................................................................................................... 75 

Table 55 Effects (% change) of intercropping on nutrients fertilisation. SE: Standard Error of the 
mean. Nc: number of pairwise comparisons. ...................................................................................................................... 80 

Table 56. Effect (% change) of crop rotation on greenhouse gas emissions (together with soil 
organic carbon where indicated). SE: Standard Error of the mean. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ......................................... 81 

Table 57. Effect (% change) of crop rotation on soil organic carbon. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ......................................... 82 

Table 58. Effect (% change) of cover and catch crops on greenhouse gas emissions. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 ....................................................................................................................................................................................................................... 84 

Table 59 Effects of cover crops on soil organic carbon. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ............................................................ 85 

Table 60 Effects (% change) of cover crops on nitrogen use efficiency. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ......................................... 85 

Table 61 Effects of cover crops on nutrients run-off. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. ................................................................................ 86 

161 

 
 
Table 62 Effects of cover crops on nitrates leaching. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. ................................................................................ 87 

Table 63 Effects of leguminous or N-fixing crops on rates of increase of soil organic carbon. SE: 
Standard Error of the mean, Nc: number of pairwise comparisons. ...................................................................... 88 

Table 64. Effect (% change unless otherwise specified) of tillage practices on greenhouse gas 
emissions by major GHG types. .................................................................................................................................................. 90 

Table 65 Differences in SOC concentrations (g/kg) for different tillage systems and different soil 
depths. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ...................................................................................................................................................................... 92 

Table 66: Differences in SOC stock (Mg/ha) for different tillage systems and different soil depths. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. .......................................................................................................................................................................................... 93 

Table 68. Effect (% change) of crop residues management on soil organic carbon (content). 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. .......................................................................................................................................................................................... 96 

Table 69. Effect (% change) of crop residue management on nutrient use efficiency. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons..
 ....................................................................................................................................................................................................................... 97 

Table 70. Effect (% change) of crop residue return on NO3 leaching. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ......................................... 98 

Table 75. Effect (% change) of mulching on nutrient use efficiency. CI_LOW: confidence interval 
bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ...................................... 103 

Table 76. Effect (% change) of mulching on nutrient loss. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ......................................................... 104 

Table 77. 
gas emissions by major GHG types. Nc: number of pairwise comparisons. ................................................... 106 

Table 78 Effect (% change) of grassland conservation on soil organic carbon (stock per hectare). 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. ....................................................................................................................................................................................... 107 

Table 79 Effects (% change) grassland restoration practices on Soil nitrification to immobilization 
ratio. SE: Standard Error of the mean. ................................................................................................................................. 108 

Table 80. Effect (% change) of soil organic amendments as compared to unmanaged grassland on 
soil GHG emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: 
number of pairwise comparisons. .......................................................................................................................................... 109 

Table 81. Effect (% change) of the use of Enhanced-efficency fertilisers as compared to 
unmanaged grassland on soil GHG emissions. CI_LOW: confidence interval bottom, CI_HIGH: 
confidence interval top, Nc: number of pairwise comparisons. ............................................................................. 109 

162 

 
 
Table 82. Effect (% change) of use of Enhanced-efficency fertilisers as compared to unmanaged 
grassland on soil GHG emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval 
top, Nc: number of pairwise comparisons. ......................................................................................................................... 110 

Table 83. Effect (% change) of the use of fertilisers as compared to unmanaged grassland on GHG 
emissions for the specified metrics. CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top, Nc: number of pairwise comparisons........................................................................................................ 111 

Table 84. Effect (% change) of organic amendments as compared to unmanaged grassland on 
Carbon sequestration for the metric SOC. CI_LOW: confidence interval bottom, CI_HIGH: confidence 
interval top, Nc: number of pairwise comparisons........................................................................................................ 111 

Table 85. Effect (% change) of amendment addition as compared to unmanaged grassland on 
nutrients runoff. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number 
of pairwise comparisons. ............................................................................................................................................................. 112 

Table 86. Effect (% change) of grazing management practices on greenhouse gas emissions by 
major GHG types. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number 
of pairwise comparisons. ............................................................................................................................................................. 115 

Table 87. Effect (% change) of grazing management practices on soil organic carbon. CI_LOW: 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 .................................................................................................................................................................................................................... 116 

Table 88. 
emissions by major GHG types. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval 
top, Nc: number of pairwise comparisons. ......................................................................................................................... 118 

Table 89. Estimated GHG changes of conservation of natural peatlands as compared to different 
land use changes ............................................................................................................................................................................. 119 

Table 90. Effect (% change) of peatland conservation on soil organic carbon. CI_LOW: confidence 
interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. .................... 120 

Table 91. Effects of peatland restoration on greenhouse gas emissions by major GHG types. 
CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. ....................................................................................................................................................................................... 122 

Table 92. Effect (% change) of peatland restoration on soil organic carbon. ............................................ 124 

Table 93. Effect (% of change) of wetland fertilisation on greenhouse gas emissions by major GHG 
types. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise 
comparisons. ....................................................................................................................................................................................... 125 

Table 94. Effect (% of change) of conservation of natural wetlands as compared to different land 
use changes on GHG emissions. .............................................................................................................................................. 126 

Table 95. Effect (% change) of peatland conservation on soil organic carbon. SE: Standard Error of 
the mean. Nc: number of pairwise comparisons. ........................................................................................................... 128 

 on the differences in Carbon fluxes of wetland conservation. CI_LOW: 
Table 96. 
confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of pairwise comparisons.
 .................................................................................................................................................................................................................... 129 

163 

 
 
Table 97. Effect (% of change) in nutrients leaching and run-off of wetland creation or restoration 
as compared to no wetland. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, 
Nc: number of pairwise comparisons. .................................................................................................................................. 130 

Table 98. Effect (% change) of no irrigation on CH4 emissions. CI_LOW: confidence interval bottom, 
CI_HIGH: confidence interval top, Nc: number of pairwise comparisons. ......................................................... 131 

Table 99. Effect of no irrigation on nitrate leaching. Median and quartiles (Q1 and Q3) of difference 
in nitrate leaching difference between irrigation and no irrigation practices according to soil texture 
and fertilisation management. ................................................................................................................................................. 131 

Table 100. Effect (% change) of no irrigation vs. non water-saving irrigation practices on partial 
factor productivity of nitrogen. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval 
top, Nc: number of pairwise comparisons. ......................................................................................................................... 132 

Table 101. Effect (% change) of water-saving irrigation practices in flooded lands on GHG 
emissions. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ................................................................................................................................................................... 133 

Table 105. Effect (% change) of water-saving irrigation practices in flooded lands on N leaching 
and runoff. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ................................................................................................................................................................... 135 

Table 107. Effect (% change) of water-saving irrigation practices in non-flooded lands on nitrogen 
use efficiency. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ................................................................................................................................................................... 137 

Table 108. Effect (% change) of water-saving irrigation practices in non-flooded lands on Nitrate 
leaching. CI_LOW: confidence interval bottom, CI_HIGH: confidence interval top, Nc: number of 
pairwise comparisons. ................................................................................................................................................................... 138 

164 

 
 
 
Annexes  

Annex 1. Metrics considered for the analysis of the effects of farm practices on 
the nutrient balance  

The Gross Nutrient Balance, including the Gross Nitrogen Balance and the Gross Phosphorus Balance, 
is a common indicator used in agriculture-related frameworks to estimate the surplus of nitrogen and 
phosphorus 61.  This  surplus  results  in  polluting  and  climate  changing  emissions,  mainly:  gaseous 
emissions of nitrogen in the form of NH3, NOx and N2O and losses of phosphorus and nitrogen to 
the water (leaching and runoff) that lead to eutrophication problems.  

Even though it is a relevant  policy relevant indicator62, synthesis papers do not provide results on 
nutrient balance or surplus but on other parameters related to it. Therefore, additional considerations 
are  needed  to  describe  which  results  from  available  synthesis  papers  can  be  used  to  derive 
meaningful quantitative coefficients applicable to it.  

Eurostat63 defines the gross nutrient balance simply as the sum of nutrient input minus the sum of 
nutrient outputs. 

The inputs include: 

  Consumption of Fertilisers, 

  Gross Input of Manure (nutrient excretion), and 

  Other Inputs. 

The outputs are: 

  Removal of nutrients with the harvest of crops, 

  Removal of nutrients through the harvest and grazing of fodder, and 

  Crop Residues removed from the field. 

While the retrieved synthesis papers do not explicitly report results in terms of gross nutrient balance, 
they report quantitative data on one or more of the components listed above and can therefore be 
informative.  The  main  impacts  retrieved  in  the  systematic  review  of  meta-analyses  that  are 
considered influencing the gross nutrient balances are: 

  Nutrient excretion (nitrogen and phosphorus)  

  Plant nutrient uptake (nitrogen use efficiency, nitrogen uptake, nitrogen partial factor productivity)  

A  net  nitrogen  balance is further  calculated  subtracting  the  nitrogenous  emissions  from  the  GNB. 
Several impacts from the meta-analysis review are linked to nitrogen losses in the environment: air 

61 Eurostat (2013). Nutrient Budgets   Methodology and Handbook. Version 1.02. Eurostat and OECD, Luxembourg. 
62 Communication from the Commission to the Council and the European Parliament - Development of agri-environmental 

indicators for monitoring the integration of environmental concerns into the common agricultural policy {SEC(2006) 
1136} COM/2006/0508 final https://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX%3A52006DC0508 

63 https://ec.europa.eu/eurostat/cache/metadata/en/aei_pr_gnb_esms.htm 

165 

 
 
                                                 
 
pollutants (NH3 and NO emissions), GHG emissions (N2O emissions), nutrients leaching and run-off 
(nitrogen leaching and run-off), soil nutrients (soil total nitrogen). 

A1.1 Nitrogen and phosphorus excretion 

The most simple and straightforward way to calculate the change in gross nutrient balance is when 
changes in all the inputs and the outputs are known. Some synthesis papers, however, only provide 
the change in some N input or output. This does not allow to calculate the change in gross nutrient 
balance, but if the quantitative amount of the elements of a nutrient balance are known, it can allow 
to calculate its change. 

In the case of nitrogen and phosphorus excretion, if a feeding technique results in lower (or higher) 
nutrient excretion from livestock, assuming that the other components of the gross nutrient balance 
do  not  change  and  that  the  change in manure  excretion  does  not  affect  the manure  trade in  the 
region, then the decreases (or increases) in the gross nutrient surplus can be calculated.  

A1.2 Plant nutrient uptake and nitrogen use efficiency 

Some synthesis papers provide information on how farming practices affect the plant nutrient uptake 
and the efficiency of fertilisation using different metrics. From a qualitative point of view, an increase 
of the plant nitrogen uptake or of the nitrogen use efficiency will allow to determine a decrease of 
the nitrogen surplus in Gross N balance (assuming N inputs remain constant). However, most often 
they do not allow to directly quantify the change of the N balance. Some of the metrics used for N 
use efficiency are: 

  Nitrogen uptake by the plants (NU): it is the amount of nitrogen in the crop biomass. It 
can be considered proportional to the N output with crop harvest in first approximation. This 
metric in absolute number (or in % if the output reference value is known) would be enough 
to quantify the change in gross nutrient balance, provided that the N inputs do not change.  

  Nitrogen  use  efficiency  (NUE):  it  is  defined  as  total  nitrogen  outputs  divided  by  total 
nitrogen inputs64. However, different definitions of NUE are used. It gives an indication of the 
relative utilization of nutrients applied to agricultural production system. The change of this 
metric affects directly the N uptake by the crop and indirectly the amount released in the 
environment.  

Other two metrics related to nitrogen use efficiency were retrieved in the review of meta-analyses: 

  Partial factor productivity of N (PFPN) or yield divided by N applied as fertiliser 

(used by Quemada et al. 2013a, Ding et al. 2018, B.-Y. Liu et al. 2021, Du, Niu, Gu, Zhang, & 
Cui 2018 and J. Gu et al. 2020): the changes in this metric can be assimilated to those of N 
uptake, assuming that the N fertilisation is constant and that the protein content of the crop 
yield is constant.  

𝑃𝐹𝑃𝑁 =

(𝑌𝑖𝑒𝑙𝑑𝑓 − 𝑌𝑖𝑒𝑙𝑑0)

⁄  
𝑁𝐹

64 Eurostat Statistics Explained. Archive:Agri-environmental indicator - gross nitrogen balance. 

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Agri-environmental_indicator_-
_gross_nitrogen_balance&oldid=328298#Nitrogen_use_efficiency  

166 

 
 
                                                 
 
where 𝑌𝑖𝑒𝑙𝑑𝑓 is the crop yield with fertiliser, 𝑌𝑖𝑒𝑙𝑑0 the non-fertilised crop yield and NF is 
the nitrogen fertiliser applied.  

  Nitrogen  fertiliser  recovery  (NR):  N  fertiliser  recovery  is  calculated  as  the  difference 
between N uptake in harvested crop with fertiliser minus N uptake without fertiliser divided 
amount:  
by 

fertiliser 

the 

𝑁𝑅 =

(𝑁𝑈𝑓 − 𝑁𝑈0)

⁄  
𝑁𝐹

where  NR  is  the  nitrogen  fertiliser  recovery, 𝑁𝑈𝑓  is  the  nitrogen  uptake  by  the  crop  with 
fertiliser, 𝑁𝑈0 the N uptake by the non-fertilised crop and NF is the nitrogen fertiliser applied.  

As the NUE was corrected based on the N uptake of a blank without fertiliser, it is also referred to as 
blank-corrected NUE (NUE(bc)).  

Comparing  the  %  changes  produced  by  the  farm  practice  with  the  two  metrics,  NU  and  NR,  the 
following equivalences apply: 

% 𝑐ℎ𝑎𝑛𝑔𝑒 𝑤𝑖𝑡ℎ 𝑁𝑈 =

(𝑁𝑈𝑓2 − 𝑁𝑈𝑓)

⁄  
𝑁𝑈𝑓

% 𝑐ℎ𝑎𝑛𝑔𝑒 𝑤𝑖𝑡ℎ 𝑁𝑅 =

(𝑁𝑈𝑓2 − 𝑁𝑈𝑓)

⁄

(𝑁𝑈𝑓 − 𝑁𝑈0)

Where 𝑁𝑈𝑓2 is the nitrogen uptake by the crop with fertiliser with the intervention or farm practice, 
𝑁𝑈𝑓 is  the  nitrogen  uptake  by  the  crop  with  fertiliser  used  as  comparator,  and 𝑁𝑈0 the  nitrogen 
uptake in the comparator without fertiliser (𝑁𝑈0). Thus, the percentage change with NR would be a 
higher  amount  (as  the  denominator  would  be  much  lower)  and  is  probably  less  useful  for  policy 
analysis and simulations as in most cases the nitrogen uptake without fertiliser (𝑁𝑈0) is not known. 
For this reason, we will give priority to synthesis papers providing values for the N uptake metric. 

A1.3 Nitrogen gaseous emissions, nitrogen leaching and soil accumulation 

Some synthesis papers provide effects on some or all of the N losses, as reported above. The most 
important N losses, as estimated by the CAPRI model65 at European scale for 2012 are: N leaching 
(32%); N accumulation in the soil (30%) and ammonia emissions (24%), then with a minor weight N 
runoff (8%), N2O emissions (4%) and other N gaseous emissions (2%).  

Ammonia  and  nitrates  are  among  the  most  important  components,  but  their  changes  often 
compensate each other when a farm practice particularly targets one of them. Therefore, if the FP 
reduces ammonia emissions with e.g. nitrification inhibitors, it may result in an increase of nitrates 
and there would be only a small net impact on the N balance. By contrast, if the FP reduces nitrogen 
input by e.g. precision farming, it will reduce both, and, therefore, may have a relatively higher impact 
on the N balance. Therefore, we will assume that, if the farm practice results in a decrease in ammonia 
and nitrates leaching in a similar amount, this indicates that the practice decreases the gross nitrogen 
surplus. 

65 Approximate results from the CAPRI model (https://www.capri-model.org/), 2012 output results from (2021 CAPRI MTO 

baseline). 

167 

 
 
 
 
                                                 
 
Getting in touch with the EU 

In person 

All over the European Union there are hundreds of Europe Direct centres. You can find the 
address of the centre nearest you online (european-union.europa.eu/contact-eu/meet-us_en). 

On the phone or in writing 

Europe Direct is a service that answers your questions about the European Union. You can 
contact this service: 

 by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

 at the following standard number: +32 22999696, 

 via the following form: european-union.europa.eu/contact-eu/write-us_en. 

Finding information about the EU 

Online 

Information about the European Union in all the official languages of the EU is available on the 
Europa website (european-union.europa.eu). 

EU publications 

You can view or order EU publications at op.europa.eu/en/publications. Multiple copies of free 
publications can be obtained by contacting Europe Direct or your local documentation centre 
(european-union.europa.eu/contact-eu/meet-us_en). 

EU law and related documents 

For access to legal information from the EU, including all EU law since 1951 in all the official 
language versions, go to EUR-Lex (eur-lex.europa.eu). 

EU open data 

The portal data.europa.eu provides access to open datasets from the EU institutions, bodies 
and agencies. These can be downloaded and reused for free, for both commercial and non-
commercial purposes. The portal also provides access to a wealth of datasets from European 
countries.