Implementation of a Generalized Additive Model (GAM) for Soybean Maturity Prediction in African Environments

Agronomy

Godfree Chigeza¹  NicolasF. Martin²  Michelle Da Fonseca Santos²  GuillermoS. Marcillo²  JosyH. Francischini²  EricaPontes Leles²  BrianW. Diers² 

[1] CGIAR-IITA International Institute of Tropical Agriculture, Ibadan 200001, Oyo State, Nigeria;Crop Sciences Department, University of Illinois, Urbana-Champaign, IL 61801, USA;

关键词: generalized additive model (GAM);    soybean;    Africa;    temperature;    photoperiod;   

DOI  :  10.3390/agronomy11061043

来源: DOAJ

【 摘 要 】

Time to maturity (TTM) is an important trait in soybean breeding programs. However, soybeans are a relatively new crop in Africa. As such, TTM information for soybeans is not yet as well defined as in other major producing areas. Multi-environment trials (METs) 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 (GAMs), can flexibly smooth a range of responses while retaining observations that could be lost under other approaches. We leveraged 5 years of data from an 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 (e.g., minimum temperature, daylength) along with trial geolocation (longitude, latitude), a GAM predicted soybean TTM within 10 days of the average observed TTM (RMSE = 10.3; x = 109 days post-planting). Furthermore, we found significant differences between cultivars (p < 0.05) in TTM sensitivity to minimum temperature and daylength. Our results show potential to advance the design of maturity systems that enhance soybean planting and breeding decisions in Africa.

【 授权许可】

Unknown