Association between the yield and the main agronomic traits of Tartary buckwheat evaluated using the random forest model

Abstract

AbstractThe popularity of Tartary buckwheat [Fagopyrum tataricum (L.) Gaertn], as a medicinal and food crop, has been increasing in recent years. However, its low yield seriously restricts its industrial development. Amongst the various studies conducted to enhance the productivity of Tartary buckwheat, the association between yield and main agronomic traits has formed the foundation for the breeding and cultivation of high‐yielding varieties, becoming the primary interest of breeders. The commonly used methods are often restricted by sample size, distribution assumptions and trait properties and confined to the linear relationship. In this paper, the random forest regression model was used to obtain a comprehensive and reliable evaluation. The phenotypic data of 200 Tartary buckwheat landraces with 15 quantitative and two qualitative agronomic traits for two consecutive years were used. Results were compared between planting seasons and with those from classical methods, such as the correlation analyses and the multiple linear regression model. The random forest model distinguished the number of grains per plant, plant height, and 1,000‐grain weight as the most influential agronomic traits in both seasons. The main and interactive effects were explored using the accumulated local effects plot and showed great conformity between the two seasons. The robustness and reliability of the random forest model make it a desirable methodology for breeding new varieties and germplasm innovation of Tartary buckwheat and other crops.