Reverse modelling to estimate yield losses caused by crop diseases

Abstract

Yield loss analysis is critical to inform tactical and strategic decisions in crop health management, and requires quantification of three elements: the levels of injury caused by disease or pest, the actual (injured) yield, and the attainable (uninjured) yield. Reverse modelling allows reconstruction of an object or a process from limited information combined with a mathematical model. This approach is applied to estimate yield losses caused by diseases in winter wheat using a process‐based simulation model (WHEATPEST), in combination with field data generated by a network of experiments across France, where multiple disease injuries and actual yields, but not attainable yields, were measured. The analysis covers 70 [year × region × variety × crop management] combinations encompassing five years (2004–2008), four French regions, two winter wheat varieties (one high‐yielding and one hardy variety), and two levels of crop management corresponding to two levels of chemical intensification. The analysis involved three main successive modelling steps where actual yield, attainable yield and yield losses associated with individual diseases were simulated. Overall, simulated yield losses to combined diseases ranged from 0 to 4.2 t ha−1, and averaged 0.80 t ha−1. Septoria tritici blotch caused the highest mean yield loss of 0.66 t ha−1. The results highlight the contribution of varietal improvement to agricultural sustainability and performances. Reverse modelling can be applied to other crops and diseases or pests, in order to estimate individual and combined disease yield losses.