A hierarchical Bayesian approach to assess the impact of environmental factors on soybean yield and yield components - Nur Habibi Luthfan; Matsui Tsutomu; Tanaka Takashi S.T. (BIO Web of Conferences)
Dividing soybean (Glycine max (L.) Merr) yield into several yield components, including the seeds per area and seed weight, offers better identification of the driver of yield variation, especially that is affected by environmental factors. The objectives of this study are to understand the relationship among yield, yield components, and environmental factors using a hierarchical Bayesian model, and to determine the potential limiting factors for soybean yield production. A hierarchical Bayesian approach offers a natural mechanism of the eco-biological system through a multi-level model. Precipitation data was used to represent the environmental factors during the key stages of soybean development. Yield in seven soybean environments, defined as the combination of location and year, was surveyed from 2018 to 2023. The results indicated that soybean yield varied between environments. Seed numbers per area was the main driver of the soybean yield. Moreover, precipitation during the early reproductive stages, where the seed was being developed, also significantly affected the final yield. Seed weights also contributed to the increase in soybean yield, even though the environmental factors during the seed-filling stage were not substantial. In summary, this study provides evidence of environmental conditions as a potential limiting factor of soybean yield.