Generating cropping schemes from FADN data at the farm and territorial scale

Titolo Rivista Economia agro-alimentare
Autori/Curatori Guido M. Bazzani, Roberta Spadoni
Anno di pubblicazione 2022 Fascicolo 2021/3
Lingua Inglese Numero pagine 0 P. 1-32 Dimensione file 0 KB
DOI 10.3280/ecag2021oa12755
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più clicca qui

Vedi l'articolo Scarica l'articolo open access

FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

The paper presents an innovative approach to cropping scheme classification based on fad n data with two main goals. First, the identification at the regional level (NUTS 2) of land use patterns common to similar farms defined ‘group cropping scheme’. Second, the farm-level construction of farm cropping schemes, which expand the observed crop mix and identify suitable variation ranges considering the farm production context. The schemes are based on the observed behaviour of homogeneous farms and capture their common structural characteristics regarding land use.The schemes can be used at the territorial scale to analyse landuse trends and patterns over time. At the farm level, the method is designed to analyse short-term adaptations and is suitable to be used, together with other data, in mathematical programming models to run policy analysis exercises. At this latter scale, crop substitution within a scheme allows the set of eligible crops to be expanded while remaining linked to the observed behaviour on a spatial basis.The paper applies the methodology to identify and quantify the cropping schemes using FADN data on Italian farms specialising in annual field crops. An algorithm implemented in gams automates the process. Results confirm the validity of the method and open a field of research for future applications.

Keywords:FADN; Cropping schemes; Land use; Modelling

  1. Albertazzi, S. (2014). Using national FADN database to describe Italian farms and arable fields. A comparison of sustainability level between organic versus conventional regimes. [Doctoral Thesis, Alma Mater Studiorum - Università di Bologna], DOI: 10.6092/unibo/amsdottorato/6651
  2. Arfini, F. & Donati, M. (2013). Organic Production and the Capacity to Respond to Market Signals and Policies: An Empirical Analysis of a Sample of FADN Farms. Agroecology and Sustainable Food Systems, 37(2), 149-171, DOI: 10.1080/10440046.2012.695328
  3. Bazzani, G.M., Vitali, G., Cardillo, C. & Canavari, M. (2021). Using FADN Data to Estimate CO2 Abatement Costs from Italian Arable Crops. Sustainability, 13(9), 5148, DOI: 10.3390/su13095148
  4. Bezat-Jarzębowska, A., Rembisz, W. & Sielska, A. (2014). Developing of Modelling Tool for Policy and Economic Rent in Agriculture. In Zopounidis, C., Kalogeras, N., Mattas, K., van Dijk, G. & Baourakis, G. (a cura di). Agricultural Cooperative Management and Policy: New Robust, Reliable and Coherent Modelling Tools (pp. 87-107). Springer International Publishing, DOI: 10.1007/978-3-319-06635-6_5
  5. Bontinck, P.-A., Grant, T. F., Sevenster, M., Eady, S. & Crawford, D. (2020). Improving direct land use change calculations: An Australian case study. The International Journal of Life Cycle Assessment, 25(6), 998-1012, DOI: 10.1007/s11367-020-01751-7
  6. Bussieck, M.R., Meeraus, A. (2004) General Algebraic Modeling System (GAMS). In Modeling Languages in Mathematical Optimization (pp. 137-157). Springer: Boston, MA.
  7. Cagliero, R., Iacono, R., Licciardo, F., Prandi, T. & Rossi, N. (2018). La montagna e le zone svantaggiate nei Programmi di Sviluppo Rurale: Una valutazione delle indennità compensative attraverso la RICA. Economia Agro-Alimentare, 20(3), 479-501, DOI: 10.3280/ECAG2018-003011
  8. Cortignani, R. & Dono, G. (2020). Greening and legume-supported crop rotations: An impacts assessment on Italian arable farms. Science of The Total Environment, 734, 139464, DOI: 10.1016/j.scitotenv.2020.139464
  9. Cristiano, S., Carta, V., Proietti, P., Macaluso, D., Scardera, A., Alfonso Giampaolo & Varia, F. (2020). L’utilizzo della RICA per l’analisi delle performance aziendali delle imprese innovative: Uno studio pilota. Rete Rurale Nazionale (CREA). CREA.
  10. Dell’Aquila, C. & Cimino, O. (2012, giugno 27). Stabilization of farm income in the new risk management policy of the EU: a preliminary assessment for Italy through FADN data. New challenges for EU agricultural sector and rural areas. Which role for public policy? 126th EAAE Seminar, Capri (Italy).
  11. Di Bene, C., Marchetti, A., Francaviglia, R. & Farina, R. (2016). Soil organic carbon dynamics in typical durum wheat-based crop rotations of Southern Italy. Italian Journal of Agronomy, 11(4), 209-216, DOI: 10.4081/ija.763
  12. European Commission - EU fad n. (2018). EU Farm Economics Overviewbased on 2015 (and 2016) FADN data. European Commission.
  13. Finger, R. & El Benni, N. (2014). Alternative Specifications of Reference Income Levels in the Income Stabilization Tool. In Zopounidis, C., Kalogeras N., Mattas, K., van Dijk, G. & Baourakis, G. (a cura di), Agricultural Cooperative Management and Policy: New Robust, Reliable and Coherent Modelling Tools (pp. 65-85). Springer International Publishing, DOI: 10.1007/978-3-319-06635-6_4
  14. Forleo, M.B., Giaccio, V., Mastronardi, L. & Romagnoli, L. (2021). Analysing the efficiency of diversified farms: Evidences from Italian FADN data. Journal of Rural Studies, 82, 262-270, DOI: 10.1016/j.jrurstud.2021.01.009
  15. Gao, J., Sheshukov, A.Y., Yen, H., Kastens, J.H., & Peterson, D.L. (2017). Impacts of incorporating dominant crop rotation patterns as primary land use change on hydrologic model performance. Agriculture, Ecosystems & Environment, 247, 33-42, DOI: 10.1016/j.agee.2017.06.019
  16. Inan, H.I., Sagris, V., Devos, W., Milenov, P., van Oosterom, P. & Zevenbergen, J. (2010). Data model for the collaboration between land administration systems and agricultural land parcel identification systems. J. Environ. Manag., 91(12), 2440-2454, DOI: 10.1016/j.jenvman.2010.06.030
  17. Jensen, J.D. & Ørum, J.E. (2014). Economic Incentives and Alternative Nitrogen Regulation Schemes: A Spatial Sector Economic Modelling Approach. In Zopounidis, C., Kalogeras, N., Mattas, K., van Dijk, G. & Baourakis, G. (a cura di), Agricultural Cooperative Management and Policy: New Robust, Reliable and Coherent Modelling Tools (pp. 267-280). Springer International Publishing, DOI: 10.1007/978-3-319-06635-6_14
  18. Klepacka, A.M., Florkowski, W.J. & Revoredo-Giha, C. (2019). The expansion and changing cropping pattern of rapeseed production and biodiesel manufacturing in Poland. Renewable Energy, 133, 156-165, DOI: 10.1016/j.renene.2018.10.015
  19. Kollas, C., Kersebaum, K.C., Nendel, C., Manevski, K., Müller, C., Palosuo, T., Armas-Herrera, C.M., Beaudoin, N., Bindi, M., Charfeddine, M., Conradt, T., Constantin, J., Eitzinger, J., Ewert, F., Ferrise, R., Gaiser, T., Cortazar-Atauri, I. G. de, Giglio, L., Hlavinka, P., … Wu, L. (2015). Crop rotation modelling – A European model intercomparison. European Journal of Agronomy, 70, 98-111, DOI: 10.1016/j.eja.2015.06.007
  20. Kraatz, S., Libra, J.A., Drastig, K., Hunstock, U., Zare, M. & Jacobs, H. (2019). Water use indicators at farm level – An agro-hydrological software solution. Science of The Total Environment, 678, 133-145, DOI: 10.1016/j.scitotenv.2019.04.368
  21. Li, J., Rodriguez, D., Zhang, D. & Ma, K. (2015). Crop rotation model for contract farming with constraints on similar profits. Computers and Electronics in Agriculture, 119, 12-18, DOI: 10.1016/j.compag.2015.10.002
  22. Liu, X., Lehtonen, H., Purola, T., Pavlova, Y., Rötter, R. & Palosuo, T. (2016). Dynamic economic modelling of crop rotations with farm management practices under future pest pressure. Agricultural Systems, 144, 65-76, DOI: 10.1016/j.agsy.2015.12.003
  23. Mohring, A., Zimmermann, A., Mack, G., Mann, S., Ferjani, A. & Gennaio, M.-P. (2010). Modelling structural change in the agricultural sector – An Agent-based approach using fadn data from individual farms (N. 699-2016-47935). AgEcon Search, DOI: 10.22004/ag.econ.61094
  24. Offermann, F. & Margarian, A. (2014). Modelling Structural Change in Ex-Ante Policy Impact Analysis. In Zopounidis, C., Kalogeras, N., Mattas, K., van Dijk, G. & Baourakis, G. (a cura di), Agricultural Cooperative Management and Policy: New Robust, Reliable and Coherent Modelling Tools (pp. 151-162). Springer International Publishing, DOI: 10.1007/978-3-319-06635-6_8
  25. Pahmeyer, C., Kuhn, T. & Britz, W. (2021). ‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling. Computers and Electronics in Agriculture, 181, 105948, DOI: 10.1016/j.compag.2020.105948
  26. Pedrotti, F. (2013). Plant and Vegetation Mapping. Springer Berlin Heidelberg: Berlin, Heidelberg.
  27. Peltonen-Sainio, P., Jauhiainen, L. & Latukka, A. (2020). Interactive tool for farmers to diversify high-latitude cereal-dominated crop rotations. International Journal of Agricultural Sustainability, 18(4), 319-333, DOI: 10.1080/14735903.2020.1775931
  28. Poppe, K.J., Beers, G., Putter, I.D.D. & Ersoz, F. (1999). Models for Data and Data for Models PACIOLI6 Workshop Report, DOI: 10.13140/RG.2.2.22478.66885
  29. Purola, T. & Lehtonen, H. (2020). Evaluating profitability of soil-renovation investments under crop rotation constraints in Finland. Agricultural Systems, 180, 102762, DOI: 10.1016/j.agsy.2019.102762
  30. Purola, T., Lehtonen, H., Liu, X., Tao, F. & Palosuo, T. (2018). Production of cereals in northern marginal areas: An integrated assessment of climate change impacts at the farm level. Agricultural Systems, 162, 191-204, DOI: 10.1016/j.agsy.2018.01.018
  31. Schönhart, M., Schmid, E., & Schneider, U.A. (2011). CropRota – A crop rotation model to support integrated land use assessments. European Journal of Agronomy, 34(4), 263-277, DOI: 10.1016/j.eja.2011.02.004
  32. Špička, J., Tomáš Vintr, Renata Aulová & Jana Macháčková. (2020). Trade-off between the economic and environmental sustainability in Czech dual farm structure. Agricultural Economics – Czech, 66(6), 243-250, DOI: 10.17221/390/2019-AGRICECON
  33. Sulewski, P. & Kłoczko-Gajewska, A. (2018). Development of the Sustainability Index of Farms Based on Surveys and FADN Sample (SSRN Scholarly Paper ID 3256159). Social Science Research Network. https://papers.ssrn.com/abstract=3256159.
  34. Tomaselli, R., Balduzzi, A. & Filipello, S. (1973). Carta bioclimatica d’Italia, Ministero dell’agricoltura e delle foreste, Roma.
  35. Tomlinson, S.J., Dragosits, U., Levy, P.E., Thomson, A.M., & Moxley, J. (2018). Quantifying gross vs. Net agricultural land use change in Great Britain using the Integrated Administration and Control System. Science of The Total Environment, 628-629, 1234-1248, DOI: 10.1016/j.scitotenv.2018.02.067
  36. Vigneswaran, E.E. & Selvaganesh, M. (2020). Decision Support System for Crop Rotation Using Machine Learning. 2020 Fourth International Conference on Inventive Systems and Control (ICISC), 925-930, DOI: 10.1109/ICISC47916.2020.9171120
  37. Vitali, G., Cardillo, C., Albertazzi, S., Della Chiara, M., Baldoni, G., Signorotti, C., Trisorio, A. & Canavari, M. (2012). Classification of Italian Farms in the FADN Database Combining Climate and Structural Information. Cartographica: The International Journal for Geographic Information and Geovisualization, 47(4), 228-236, DOI: 10.3138/carto.47.4.1478
  38. Was, A. & Kobus, P. (2014). Measuring Biodiversity of Cropping Structure with the Use of FADN Data (C. Zopounidis, N. Kalogeras, K. Mattas, G. van Dijk & G. Baourakis, a cura di). Springer International Publishing, DOI: 10.1007/978-3-319-06635-6_21

Guido M. Bazzani, Roberta Spadoni, Generating cropping schemes from FADN data at the farm and territorial scale in "Economia agro-alimentare" 3/2021, pp 1-32, DOI: 10.3280/ecag2021oa12755