No abstract

No abstract

No abstract

Farmers' use of fungicides and insecticides constitutes a major threat to biodiversity that is also endangering agriculture itself. Landscapes could be designed to take advantage of the dependencies of pests, pathogens and their natural enemies on elements of the landscape. Yet the complexity of the interactions makes it difficult to establish general rules. In our study, we sought to characterize the impact of the landscape on pest and pathogen prevalence, taking into account both crop and semi-natural areas. We drew on a nine-year national survey of 30 major pests and pathogens of arable crops, distributed throughout the latitudes of metropolitan France. We performed binomial LASSO generalized linear regressions on the pest and pathogen prevalence as a function of the landscape composition in a total of 39 880 field × year × pest observation series. We observed a strong disequilibrium between the number of pests or pathogens favored (15) and disadvantaged (2) by the area of their host crop in the landscape during the previous growing season. The impact of the host crop area during the ongoing growing season was different on pests than on pathogens: the density of most pathogens increased (11 of 17, and no decreases) while the density of a small majority of pests decreased (7 of 13, and four increases). We also found that woodlands, scrublands, hedgerows and grasslands did not have a consistent effect on the studied spectrum of pests and pathogens. Although overall the estimated effect of the landscape is small compared to the effect of the climate, a territorial coordination that generally favors crop diversity but excludes a crop at risk in a given year might prove useful in reducing pesticide use.

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 9 region 9 variety 9 crop management] combinations encompassing five years (2004)(2005)(2006)(2007)(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 À1 , and averaged 0.80 t ha À1 . Septoria tritici blotch caused the highest mean yield loss of 0.66 t ha À1 . 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.