Is low-input management system a good selection environment to screen winter wheat genotypes adapted to organic farming?

Abstract

The move toward resilient and productive agriculture requires, among other innovations, the design of new sustainable farming systems in which the variety plays a main role. Plant breeding strategies adapted to organic farming conditions have to deal with limiting factors. Whereas in north-west France, it is known that trials carried out under high-input management do not give a good prediction of genotype performance in organic conditions, less is known about the relative stability of wheat genotypes between low-input (LI) and organic cropping systems. A retrospective analysis of 34 winter wheat trials conducted from 2004 to 2011 was performed to determine whether data obtained on genotypes grown under LI conditions can be used to predict genotype performance in organic (ORG) target conditions. Every year, ORG and LI (no fungicide or growth regulators, N balance sheet-60 kgN/ha, weed control with herbicides) trials including 25–30 genotypes describing a large range of genetic diversity were sown in three different agro-climatic regions across north-west France. Genotype performance in ORG management system was reduced from 25 to 40 % for yield and from 10 to 22 % for grain protein content. Estimates of genotypic values appeared to be more precise under LI than ORG conditions. Because of high genetic correlations between LI and ORG conditions, the relative efficiency of indirect selection from LI to ORG conditions was approximately 1. Spearman’s rank correlations were high (Rs = 0.54–0.92) and genotype rank inversions generally had a minor extent. However, in 2005 and 2010, almost 50 % of the lines had to be retained in LI to keep 80 % of the top 20 % of genotypes in organic conditions. Compared with previous results from high-input conditions, LI management provided a better prediction of genotype performance under ORG conditions but crossover genotype × management interactions could be observed between both systems. Overall, combining information provided from both LI and ORG crop management systems appears to be a good process for building efficient and adapted breeding schemes for ORG farming conditions.