A Bayesian approach for analyzing crop yield response data with limited treatments

This paper proposes a Bayesian multilevel modeling approach to incorporate response parameters from published studies into crop yield estimation procedures when nonlimiting or limiting treatment levels are omitted limited in agronomic experiments. Such circumstances may be encountered data farmer-led research, which use nonstandardized experimental designs. The paper's focus is on maize nitrogen fertilizer, but the procedure flexible enough accommodate other factors that could affect response. A proof-of-concept Monte Carlo (MC) exercise supplements an empirical application. MC simulation investigates small sample properties of proposed procedure. example uses field trial for planter experiment under different (N) fertilizer rates. compared mechanical planting methods used developing countries with access mechanized technology. Some experiments had no check plots and all lacked Linear quadratic functions plateaus results suggest estimates were closest true parameter values priors optimal N rates sources used.