TY - JOUR
T1 - Implementation of a Generalized Additive Model (GAM) for Soybean Maturity Prediction in African Environments
AU - Marcillo, Guillermo S.
AU - Martin, Nicolas F.
AU - Diers, Brian W
AU - Da Fonseca Santos, Michelle
AU - Leles, Erica Pontes
AU - Chigeza, Godfree
AU - Francischini, Josy H.
N1 - Funding Information:
Funding: This research was funded by the USAID Feed the Future Innovation Lab for Soybean Value Chain Research (Soybean Innovation Lab, “SIL”) and partners from different private and public sectors in Africa, under USAID Award Number AID-OAA-L-14-00001.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
KW - Africa
KW - Generalized additive model (GAM)
KW - Photoperiod
KW - Soybean
KW - Temperature
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U2 - 10.3390/agronomy11061043
DO - 10.3390/agronomy11061043
M3 - Article
SN - 2073-4395
VL - 11
JO - Agronomy
JF - Agronomy
IS - 6
M1 - 1043
ER -