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.