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Farmers often grow only the few high-yielding crops in the area, and target maximum yields through high levels of inputs. These practices are based on the assumption of a positive relationship between productivity and profitability over a wide range of cropping systems (Woo et al., 2020; Erythrina et al., 2021). Part of the correlation between productivity and profitability could be related to differences in production situation and therefore yield potential, i.e. characteristics that do not depend on farmers’ decisions. For a given production situation, the systems with the highest profitability and the best environmental sustainability could be systems with moderate yields. The objective of our research is therefore to assess the relationship between productivity and profitability of cropping systems in order subsequently to help design of the most profitableand sustainable cropping systems. In this study, we use the French DEPHY network database collected on about 3,000 Frenchcommercial farms over ten years. Regression tree methods are conducted on the dataset to identify combinations of farm characteristics associated with the production situation to form a regression tree with a range of varied production situation groups. Crop yields areaggregated on the cropping system level rather than on the crop level to represent the overall cropping system productivity in each farm. Productivity is estimated by converting yields into the amount of energy produced by surface unit (GJ ha−1 yr−1), based on the energy contentof each given crop calculated by the higher heating value. Profitability (€ ha−1 yr−1) is computed by the monetary value generated by the output of the cropping system. Lasso (least absolute shrinkage and selection operator) regression method (Tibshirani, 1996) is used tocompute the marginal yielding effects on profitability. Treatment Frequency Index (TFI), energy use efficiency, and their interactions with productivity are also tested with their explanatory power at profitability in the Lasso regression model. Marginal yielding effects onprofitability computed by the Lasso regression method is used to explore the corresponded changes in profitability resulting from a one-unit increase in productivity within each production situation group to assess the relationship between productivity and profitabilityof cropping systems in a consistent context.381 Under the same production potential situation, we will test the relationship betweenproductivity and profitability notably to reveal if an intended reduction in targeted yields and an increase in the efficiency of inputs would lead to maximised overall profits compared to an increased use of inputs targeting high yields. The original oversimplified yield-profit relationship studied here under consistent production conditions from about 3,000 French commercial farms over ten years will question current advices targeting maximum yields instead of targeting maximum income and higher sustainability. Reference: Erythrina, E., Anshori, A., Bora, C. Y., Dewi, D. O., Lestari, M. S., Mustaha, M. A., ... & Syahbuddin, H. (2021). Assessing opportunities to increase yield and profit in rainfed lowland rice systems in Indonesia. Agronomy, 11(4), 777. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288. Woo, D. K., Riley, W. J., & Wu, Y. (2020). More fertilizer and impoverished roots required for improving wheat yields and profits under climate change. Field Crops Research, 249, 107756.

Overuse of pesticides is one of the main hanging issues which impede sustainable crop production. Despite a rapid growing body of research assessing the impact of agricultural diversification (practices/crop/ non-crop/ microorganism diversity) on the ecological perspective of the cropping system (e.g. ecosystem services and biodiversity), a synthesis on the agronomical impact is less documented. A few researches conducted on an experimental scale has proved that crop diversity can reduce agronomical input such as pesticides. However, those results are context-dependent, so it is hard to conclude on a broader view. Therefore, a quantitative analysis is missing about the relationship between crop diversity and pesticide use, on a large range of production situations.This study is focusing on depicting the relationship between two major components of sustainable cropping systems, i.e. crop diversity and pesticide use, based on the French DEPHY network dataset, including data from around 3,000 French commercial farms over ten years. Crop diversity is described using a combination of divisional methods, namely plant taxonomy (e.g. legume crop), ecological functional groups (e.g. nitrogen catching), agronomical functional groups (e.g. minor crop, cover crop, sowing season), and sale type (e.g. Farmers tend to use more pesticide on contracted crops: seed maize, etc.). Indicators from the domains of ecology and agronomy will be used to assess the crop diversity level. An analysis of the effect of preceding crop on pesticide use will also be conducted to test the crop diversity impact on a temporal scale, considering the variance caused by pedoclimate and cropping system characteristics.A reader-friendly summary of the Ph.D. target would be, an attempt to understand the complexity of “taking crop diversity as a tool to reduce pesticide”, during this period of time.