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

Guillermo S. Marcillo, Nicolas F. Martin, Brian W Diers, Michelle Da Fonseca Santos, Erica Pontes Leles, Godfree Chigeza, Josy H. Francischini

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number1043
Issue number6
StatePublished - May 2021


  • Africa
  • Generalized additive model (GAM)
  • Photoperiod
  • Soybean
  • Temperature

ASJC Scopus subject areas

  • Agronomy and Crop Science


Dive into the research topics of 'Implementation of a Generalized Additive Model (GAM) for Soybean Maturity Prediction in African Environments'. Together they form a unique fingerprint.

Cite this