Coupling field monitoring with crop growth modelling provides detailed insights on yield gaps at field level: A case study on ware potato production in the Netherlands

Abstract

ContextYield gap analyses are useful to assess and benchmark the productivity of cropping systems. Often such analyses are performed at higher aggregation levels. As a result, these studies lack the detail to explain yield gaps at field level and hence make it difficult to translate findings into precise recommendations to farmers and extensionists. ObjectiveThis study provides a detailed approach for yield gap assessments at field level through coupling frequent field monitoring in farmers’ fields with crop growth modelling. We used ware potato production in the Netherlands as a case to study yield gaps at field level, as average productivity is high whilst yields are still highly variable among fields, and as ware potato is an important cash crop for farmers. MethodsOver two growing seasons, 96 ware potato fields were monitored throughout the growing season on a biweekly basis, taking measurements on soil, crop growth and yield. The crop growth model SWAP-WOFOST was used to simulate potential and water-limited potential yields. Various statistical methods were used to quantify yield gap explaining factors. ResultsThe average yield gap ranged from 20 to 31% depending on the year and soil type. Among fields, the yield gap ranged from 0 to 51%. On clayey soils, the yield gap was attributed mostly to oxygen stress. On sandy soils, the yield gap was determined mostly by drought stress in 2020, a relatively dry year, and by reducing factors (pests, diseases and poor agronomic practices) in 2021, an average year in terms of precipitation. The type of reducing factors differed per field. Furthermore, we found that earlier planting and later harvesting can increase yields, as Yp is radiation-limited. ConclusionsOverall, there is limited scope to narrow the yield gap as current ware potato production is already close to 80% of the potential yield, which is assumed to be approximately the maximum farmers can attain. However, yield and resource use efficiency gains are to be made for individual fields. Furthermore, we conclude that frequent field monitoring coupled with crop growth modelling is a powerful way to assess yield gap variability and to get detailed insight in the yield gap explaining factors at field level. SignificanceThis study showed that coupling frequent field monitoring with crop growth modelling allows to gain detailed insight in yield gap variability among fields. This method provides detailed information about yield gap explaining factors which can be used to improve yield and resource use efficiency at field level.