Predictive microbial-based modelling of wheat yields and grain baking quality across a 500 km transect in Québec

Abstract

Crops yield and quality are difficult to predict using soil physico-chemical parameters. Because of their key roles in nutrient cycles, we hypothesized that there is an untapped predictive potential in the soil microbial communities. To test our hypothesis, we sampled soils across 80 wheat fields of the province of Quebec at the beginning of the growing season in May-June. We used a wide array of methods to characterize the microbial communities, their functions and activities, including: (1) amplicon sequencing, (2) real-time PCR quantification and (3) community-level substrate utilization. We also measured grain yield and quality at the end of the growing season, and key soil parameters at sampling. The diversity of fungi, the abundance of nitrification genes and the use of specific organic carbon sources were often the best predictors for wheat yield and grain quality. Using 11 or less parameters, we were able to explain 64-90% of the variation in wheat yield and grain and flour quality across the province of Quebec. Microbial-based regression models outperformed basic soil-based models for predicting wheat quality indicators. Our results suggest that the measurement of microbial parameters early in the season could help predict accurately grain quality and quantity.