Time-to-maturity (TTM) is an important trait in soybean breeding programs. However, soybean is a relatively new crop in Africa. As such, TTM information is not yet well defined as in other major producing areas. Multi Environment trials (MET) allow breeders to analyze crop performance across diverse conditions but also pose statistical challenges (e.g. unbalanced data). Modern statistical methods, e.g.. Generalized Additive Models (GAM), can flexibly smooth a range of responses while retaining observations that could be lost under other approaches. We leveraged 5 years of data from a MET breeding program in Africa to identify the best geographical and seasonal variables to explain site and genotypic differences in soybean TTM. Using soybean-cycle features (minimum temperature, daylength) along with trial geolocation (longitude, latitude), a GAM model predicted soybean TTM within 10 days of the average observed TTM [x = 105 days post- planting]. Further, we found significant differences between cultivars (p<0.05) in TTM sensitivity to minimum temperature and daylength. Our results show promise to advance the design of maturity systems that enhance soybean planting and breeding decisions in Africa.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.