A Software Implementation of a Potato Yield Model to Effectively Support Crop Management and Monitoring

Forecasting of gross yield development and tuber size distribution in potato crops is a powerful tool in the effective management of commercial potato crops. As demonstrated by several studies there is a strong correlation between the absorption of solar radiation and the production of dry matter. In turn the absorption of solar radiation in growing potato crops can be modelled to be proportional to the fraction of canopy ground cover. The ability to readily record crop parameters such as canopy growth, emergence dates and environmental conditions such as rain and solar radiation has opened new opportunities to forecast yield production in potato crops. We will present a software implementation of a yield forecasting model developed at NIAB CUF that brings together these two elements, incidence of solar radiation during the growing season and canopy growth information, to effectively predict overall yield, tuber size and uniformity of potato crops. The software framework integrates varietal information and meteorological data with canopy development readings. The implementation uses an object-oriented approach, which can be extended to similar yield predictions model in other crops. Access to software architecture is presented via a RESTful application programming interface (API) that allows the yield models to be executed autonomously on a variety of inputs and climate conditions.