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1.Introduction
Agriculture in France is subject to numerous constraints, notably increasing meteorological risks in recent years due to climate change. Faced with natural disasters and pressure from bio-aggressors, farmers in both conventional and organic production are confronted with a multitude of risk factors leading to variability in their agricultural yields (Arora 2019; Malhi et al., 2021). Consequently, risk management is a key issue for the sustainability of agricultural activities. Crop insurance is considered an essential tool for safeguarding against these various risks and securing farmers' income (Folus et al. 2020; Frascarelli et al., 2021; Koenig et al., 2022). The objective of this study is to develop a method for estimating reference yields in large-scale crops (winter wheat, maize, spring barley) in organic agriculture, using statistical models, in order to determine the conditions and parameters to be considered for crop insurance pricing and the contractualization of Groupama Paris Val de Loire (GPVL) members.
2.Material and Methods
To determine the reference yield, the first step is to study the determinants of yield in large-scale crops (winter wheat, spring barley, maize grain) to identify the factors that impact yield (Ponisio et al. 2015; Ben Zekri et al., 2019). Then, statistical regression analysis was used to explain the variability of yields among members based on the factors determining yield. These factors primarily include climate, soil type, pressure from bio-aggressors, crop rotation, technical itinerary of the crop, as well as the farmer's expertise and landscape factors.
3.Résultats
The analysis of yield variability based on yield determinants has enabled the development of a method for estimating reference yields in organic agriculture using regression and prediction models such as Generalized Linear Regression (GLM), Partial Least Squares (PLS), Random Forest, and Gradient Boosting. The latter two models are the most effective and suitable for yield estimation, as determined through comparison using metrics and criteria such as R² and RMSE.
The yield estimation model was applied to predict the yields of insured members of Groupama Paris Val de Loire based on their location and the spatial intrinsic characteristics of the plots. The estimated average yield in organic farming for winter wheat ranges from 5.45 t/ha to 9.33 t/ha, spring barley ranges from 4.25 t/ha to 6.43 t/ha, while maize grain varies from 4.44 t/ha to 8.83 t/ha. This approach will facilitate a better assessment of risk factors and more precise crop insurance pricing, thereby helping to support farmers in the face of increasing uncertainty resulting from current climatic, agronomic, and environmental challenges.
4.Discussion
Each factor has its limitations and leads to a lack of precision regarding results. Several other factors have not been studied and may impact yields, including sowing date, bio-aggressor management, nitrogen management, soil cover, varieties used, and rotation duration.
5.References
Arora, N.K. (2019) Impact of climate change on agriculture production and its sustainable solutions. Environmental Sustainability 2, 95–96.
Ben Zekri, Y., Barkaoui, K., Marrou, H., Mekki, I., Belhouchette, H., & Wery, J. (2019). On farm analysis of the effect of the preceding crop on N uptake and grain yield of durum wheat (Triticum durum Desf.) in Mediterranean conditions. Archives of Agronomy and Soil Science, 65(5), 596-611.
Folus, D., Casal Ribeiro, P., Lepoivre, B. & Roumiguié, A. (2020). L’assurance et la protection financière de l’agriculture. Annales des Mines - Réalités industrielles, 2020, 30-38.
Frascarelli A., Del Sarto S., Mastandrea, G. (2021) A New Tool for Covering Risk in Agriculture: The Revenue Insurance Policy. Risks, 9, 131.
Koenig, R., Brunette, M., Delacote, P. & Tevenart, C. (2022). Assurance récolte en France : spécificité du régime et déterminants potentiels. Économie rurale, 380, 7-25.
Malhi G.S., Kaur M., Kaushik P. (2021) Impact of Climate Change on Agriculture and Its Mitigation Strategies: A Review. Sustainability, 13, 1318.
Ponisio, L.C., M’Gonigle, L.K., Mace, K.C., Palomino, J., de Valpine, P., et Kremen, C., 2015. Diversification practices reduce organic to conventional yield gap. Proceedings of the Royal Society B: Biological Sciences, volume 282, n° 1799. p. 20141396
| Keywords | Risk management; crop insurance; Yield determinants; Reference yield; Modeling |
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