The benefits of using quantile regression for analysing the effect of weeds on organic winter wheat

P>In organic farming, weeds are one of the threats that limit crop yield. An early prediction of weed effect on yield loss and the size of late weed populations could help farmers and advisors to improve weed management. Numerous studies predicting the effect of weeds on yield have already been conducted, but the level of uncertainty about weed effect is expected to be very high in organic crops. It is thus more appropriate to provide farmers and advisors with distributions of possible production levels, rather than with point values. The purpose of this study was to estimate the effect of early weed density at the end of the tillering stage of organic winter wheat on subsequent yield and on late weed density at flowering, by using quantile regression. Results showed that this method can be applied to a hyperbolic model and to an allometric density-dependent model, to describe the distribution of yield values and of late weed density respectively, as functions of early weed density measurements. Mechanical weed control showed no significant effect on the relationship between early weed density and grain yield, but it decreased late weed density. Yield and late weed density distributions derived by quantile regression provided sound information on the possible effect of weeds on organic winter wheat production.