Temporal and inter-farm variability of economic and environmental farm performance: A resilience perspective on potato producing regions in the Netherlands

In the context of resilience and sustainability of farming systems it is important to study the trade-offs and synergies between economic and environmental variables. In this study, we selected food production, economic and environmental performance indicators of farms in three potato producing regions in the Netherlands: Flevoland, Zeeland and Veenkoloniën. We studied the period 2006 to 2019 using farm accountancy data. We used threshold regressions to determine gradual development and year-to-year variation of those indicators. Subsequently we applied a sparse Partial Least Square (sPLS) regression to study the response of performance, gradual development and year-to-year variation under different conditions regarding weather, market and farm structure. sPLS-model performance was at best moderate. Best model performance was attained for Veenkoloniën, a region with relatively little inter-farm variability and relatively stable economic prices. Model results were very sensitive to the selection of response variables. We found that food production, economic and environmental performance levels and gradual developments were primarily determined by input intensity levels. How these performance levels were determined by input intensity, i.e. positively or negatively, differed per case study. Year-to-year variability was determined by average yearly weather conditions and weather extremes. Overall, we conclude that the method applied to the data we had available mostly provided insights that confirm existing knowledge at case study level. sPLS can be seen as a filter and projector of high-dimensional data that accentuates patterns in the data. In the context of resilience of farms, while using a relatively small dataset, the applicability of our methodology seems limited to a rather homogeneous farm population in a stable economic environment. Researchers intending to apply this method to (arable) farming systems should be well aware of the influence they can have on the results through their selection of response variables.Author summary: The sustainability and resilience of farming systems is increasingly challenged by economic and environmental disturbance. It is, therefore, important to empirically assess farming system dynamics under these disturbances and to identify farm characteristics that improve sustainability and resilience. However, quantitative approaches to assess sustainability and resilience simultaneously are scarce. In this paper, we test a multi-variate statistical approach applied to three potato producing regions in the Netherlands under varying market and weather conditions over the period from 2006 till 2019. The performance of statistical models was at best moderate and model results were very sensitive to the selection of response variables. We found that sustainability levels are mainly influenced by input intensity levels. Year-to-year variability was determined by average yearly weather conditions and weather extremes. Farm characteristics that improve resilience could not be identified. Overall, we conclude that the method applied to the data we had available mostly provided insights that confirm existing knowledge at case study level. Researchers intending to apply this method to (arable) farming systems should be well aware of the influence they can have on the results through their selection of response variables.