Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment

Kauê De Sousa,Jacob Van Etten,Dejene Kassahun Mengistu,Mario Enrico Pè,Jesse Poland,Yosef Gebrehawaryat Kidane,Matteo Dell’Acqua,Basazen Fantahun Lakew,Jean-Luc Jannink,Carlo Fadda,Svein Øivind Solberg

doi:10.1038/s42003-021-02463-w

Abstract

Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments. This need can be addressed by combining genomics, farmers’ knowledge, and environmental analysis into a data-driven decentralized approach (3D-breeding). We tested this idea as a proof-of-concept by comparing a durum wheat (Triticum durum Desf.) decentralized trial distributed as incomplete blocks in 1,165 farmer-managed fields across the Ethiopian highlands with a benchmark representing genomic prediction applied to conventional breeding. We found that 3D-breeding could double the prediction accuracy of the benchmark. 3D-breeding could identify genotypes with enhanced local adaptation providing superior productive performance across seasons. We propose this decentralized approach to leverage the diversity in farmer fields and complement conventional plant breeding to enhance local adaptation in challenging crop production environments.

Highlights

Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments
We focused on grain yield (GY) and farmers’ overall appreciation (OA) of wheat genotypes, which were both recorded in centralized and decentralized trials
We found that OASTATION was a better predictor than GYSTATION to capture both OASTATION and GYSTATION, including when disaggregated by gender (Supplementary Fig. 3)

Summary

Introduction

Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments This need can be addressed by combining genomics, farmers’ knowledge, and environmental analysis into a data-driven decentralized approach (3D-breeding). Genomic prediction may be used to predict the performance of untested new genotypes but is bound to the G E M interactions captured by the research stations that are used to train the selection models[5] This limitation of centralized breeding approaches may result in suboptimal development and deployment of crop varieties for use by farmers seeking local adaptation in challenging environments[6]. 3D-breeding can leverage local wheat diversity and knowledge and bring breeding closer to the target environments cutting through the complexity of G E M

Methods

Results

Conclusion