Guidelines for development of a classification system related to Farm Typology Guidelines for development of a classification system related to Farm Guidelines for development of a classification system related to Farm Typology Publication prepared in the framework of the Global Strategy to improve Agricultural and Rural Statistics Guidelines for development of a classification system related to Farm Typology December 2018 GUIDELINES FOR DEVELOPMENT OF A CLASSIFICATION SYSTEM RELATED TO FARM TYPOLOGY i ii GUIDELINES FOR DEVELOPMENT OF A CLASSIFICATION SYSTEM RELATED TO FARM TYPOLOGY Contents Tables and figures v Acronyms vii Acknowledgements viii Executive Summary ix CHAPTER 1 INTRODUCTION 1 1.1. Background on farm typology policy relevance 2 1.2. The farm diversity needs to be addressed in statistics 4 1.3. Purpose of the Farm Typology Guidelines 5 CHAPTER 2 CONCEPTS AND DEFINITIONS OF THE FARM TYPOLOGY 7 2.1. Overview 7 2.2. Unit of observation (Agricultural holding) 9 2.3. Scope of activity 10 2.4. Coverage and threshold 11 CHAPTER 3 DETERMINATION OF FARM TYPOLOGY DIMENSIONS AND THEIR CALCULATION 13 3.1. Farm profile 15 3.2. Farm size 20 3.3. Commodity specialization 48 3.4. Diversification 52 CHAPTER 4 BUILDING FARM TYPOLOGY FOR POLICY USE 57 4.1. Combination of the dimensions 58 4.2. Farm typology use 62 4.3. Farm typology for national purposes 64 4.4. Farm types in the SDG indicators 70 CHAPTER 5 SOURCES OF DATA AND DATA COLLECTION 71 5.1. Summary of classification variables identified for farm typology 71 5.2. Agricultural census with consistent questionnaire 73 5.3. The AGRIS Methodology 74 5.4. Other sources and databases 74 5.5. How to handle missing data and data quality issues 76 GUIDELINES FOR DEVELOPMENT OF A CLASSIFICATION SYSTEM RELATED TO FARM TYPOLOGY iii REFERENCES 85 ANNEXES 91 Annex I Use of farm specialization in the evaluation of sustainability and profitability of the agricultural holdings 91 Annex II Examples of regional farm typology classifications 95 Annex III Link to AGRIS methodology 99 Annex IV Case Studies/Examples from Countries 163 Testing the feasibility of farm typology using AGRIS data in Ghana 163 Desk study on farm typology in Zambia 179 Desk study on farm typology in Azerbaijan 194 iv GUIDELINES FOR DEVELOPMENT OF A CLASSIFICATION SYSTEM RELATED TO FARM TYPOLOGY Tables and figures TABLES Table 1. Classification of agricultural holdings by legal status. 16 Table 2. Classification of agricultural holdings by purpose of agricultural production (market integration). 18 Table 3. Classification of agricultural holdings by farm profile. 19 Table 4. Classification of holdings by AAU size (ha). 22 Table 5. Classification of holdings by economic size. 25 Table 6. Example of main products and residues for common crops. 26 Table 7. Example of calculation of weighted A1 per hectare of AAU. 32 Table 8. Main variables needed to compute average output and main data sources. 47 Table 9. Classification of holdings by commodity specialization 49 Table 10. Classification of holdings by level of diversification. 55 Table 11. Detailed classification of farm types. 59 Table 12. Example of tabulation for subsistence farms. 66 Table 13. Example of tabulation for units with marginal agricultural activity. 67 Table 14. Examples of tabulation of commercial farms at national level. 69 Table 15. Main sources of data required for the FT. 72 Table A1.1. Comparison of information collected, related to the activities conducted. 93 Table A3.1. Recommended AGRIS module flow. 101 Table A4.1. Legal status of the holder. 164 Table A4.2. Market integration. 165 Table A4.3. Farm size by AAU (aggregated classes). 166 Table A4.4. Economic farm size (aggregated classes). 167 Table A4.5. Production specialization. 168 Table A4.6. Presence of other economic activities on the farm. 168 Table A4.7. Share of agricultural output in total farm output. 169 Table A4.8. Detailed classification of farm types obtained from the test of the Ghana pilot AGRIS survey. 172 Table A4.9. Example of data presented per farm type. 173 Table A4.10. Ghana AGRIS variable correspondence for the purposes of FT. 175 Table A4.11. Definition of units. 181 Table A4.12. List of activities. 182 Table A4.13. List of classification variables, sources of existing data and missing data identified. 185 Table A4.14. Data sources quality assessment for the purposes of the FT. 187 Table A4.15. Distribution of agricultural holdings by legal status. 188 Table A4.16. Distribution of agricultural holdings by purpose of the agricultural production (market integration). 189 Table A4.17. Distribution of agricultural holdings by size of agricultural land. 190 Table A4.18. Distribution of agricultural holdings by commodity specialization. 191 Table A4.19. Number of agricultural holdings by farm type. 192 Table A4.20. Characteristics of the head of agricultural holdings by farm type. 192 Table A4.21. Definition of units. 195 Table A4.22. List of activities. 196 Table A4.23. List of classification variables, sources of existing data and missing data identified. 197 Table A4.24. Data sources quality assessment for the purposes of the FT. 199 Table A4.25. Distribution of agricultural holdings by farm profile. 200 Table A4.26. Distribution of agricultural holdings farm size. 201 GUIDELINES FOR DEVELOPMENT OF A CLASSIFICATION SYSTEM RELATED TO FARM TYPOLOGY v Table A4.27. Distribution of agricultural holdings per commodity specialization 202 Table A4.28. Distribution of agricultural holdings by diversification. 203 Table A4.29. Distribution of agricultural holdings per farm type. 204 FIGURES Figure 1. FT dimensions. 14 Figure 2. Classification by farm profile. 15 Figure 3. Example of classification scheme using three typology dimensions and grouped classes 61 Figure 4. Example of classification scheme and specific indicators per farm type. 63 Figure A1.1. Classification scheme for the FT of Mercosur countries. 96 Figure A4.1. AEZs of Zambia. 183 vi GUIDELINES FOR DEVELOPMENT OF A CLASSIFICATION SYSTEM RELATED TO FARM TYPOLOGY Acronyms AAU agricultural area utilized ARC Agricultural Research Centre (Ministry of Agriculture of Azerbaijan) EU European Union FADN Farm Accountancy Data Network FDMS Farm Data Monitoring System FT Farm Typology GSS Ghana Statistical Service GSARS Global Strategy to improve Agricultural and Rural Statistics LCMS Living Conditions Monitoring Survey LCU local currency unit OECD Organisation for Economic Co-operation and Development OGA other gainful activities RALS Rural Agricultural Livelihoods Survey PPP $ Purchasing Power Parity Dollars SDG Sustainable Development Goal SNA System of National Accounts SSC State Statistical Committee UN United Nations UNSD United Nations Statistical Division WAW World Agriculture Watch (FAO project) GUIDELINES FOR DEVELOPMENT OF A CLASSIFICATION SYSTEM RELATED TO FARM TYPOLOGY vii Acknowledgments The Guidelines were prepared and finalized by Mariana Toteva, International Consultant, Global Strategy to improve Agricultural and Rural Statistics (GSARS), with the support of Neli Georgieva (Global Office, GSARS). The Guidelines are based on a working paper and on the first draft of this publication prepared by Mary Clare Ahearn, International Consultant, GSARS, leading a team of international experts1, under the technical supervision and guidance of Flavio Bolliger and Neli Georgieva (Global Office, GSARS), with the support of Marie-Aude Even and Jean-François Giovannetti (FAO, WAW project). Valuable inputs were received from the SAC members, the participants at the expert meeting held at FAO headquarters in January 2018 and selected peer reviewers2. Arianna Martella coordinated the design and communication aspects. The publication was edited by Sarah Pasetto and formatted by Laura Monopoli. This publication was prepared with the support of the Trust Fund of the Global Strategy, funded by the UK’s Department for International Development (DFID), the Bill & Melinda Gates Foundation and the Italian Agency for Development Cooperation. 1 Silvia Saravia Matus, Ankouvi Nayo, Alvaro Ramos, Romeo Recide, Thierry Vard (consulted). 2 Amal Salibi ( Ministry of Agriculture, Lebanon), Eva Laczka (Hungary), Firdovsi Fikretzadeh (MA, Azebaijan), Gesa Wesseler (DG AGRI, European Commission), Redouane Arrach (MA, Morocco), Patrick Chuni (CSO, Zambia), Julio Berdegue (FAORLC – ADG), Piero Conforti (FAO), Pierre-Marie Bosc (FAO), Francesco Pierri (FAO), Sandra Corsi (FAO), Christophe Duhamel (Global Office, GSARS), Naman Keita (Global Office, GSARS), Arbab Asfandiyar Khan (Global Office, GSARS), Ankouvi Nayo (Global Office, GSARS). viii GUIDELINES FOR DEVELOPMENT OF A CLASSIFICATION SYSTEM RELATED TO FARM TYPOLOGY Executive Summary According to the most recent estimations1, there are at least 570 million farms worldwide, presenting great diversity in terms of size, land and livestock structures, production systems, etc. The large number of farms and contexts determine the vast range of agricultural policies and tools implemented at national and regional level. The growing interest in particular policy issues, such as food security and well-being of farms around the globe, leads to the need to identify and target vulnerable farms. There is clearly an interest in moving sector-level aggregate agricultural statistics into more disaggregated presentations to provide perspectives that are essential for sound policy design. Usually, agricultural data tabulation is made at national or lower (regional and local) level using a classification of farms