The effects of supplemental nitrogen (N) on soybean [Glycine max (L.) Merr.] seed yield have been the focus of much research over the past four decades. However, most experiments were region-specific and focused on the effect of a single N-related management choice, thus resulting in a limited inference space. Here, we composited data from individual experiments conducted across the US that examined the effect of N fertilization on soybean yield. The combined database included 207 environments (experiment × year combinations) for a total of 5991 N-treated soybean yields. We used hierarchical modeling and conditional inference tree analysis on the combined dataset to establish the relationship and contribution of several N management choices on soybean yield. The N treatment variables were: N-application (single or split), N-method (soil incorporated, foliar, etc.), N-timing (pre-plant, at a reproductive stage, etc.), and N-rate (from a 0 N control to as much as 560 kg ha−1). Of the total yield variability, 68% was associated with the effect of environment, whereas only a small fraction of that variability (< 1%) was attributable to each N variable. Averaged over all experiments, a single N application and the split N application were 60 and 110 kg ha−1 greater yielding than the zero N control treatment, respectively. A split N application with more than one method (e.g., soil incorporated and foliar) resulted in 120 kg ha−1 greater yield than zero N plots. Split N application between planting and reproductive stages (Rn) resulted in greater yield than zero N and single application during a Rn; however, the effect was not significantly different than N application at other growth stages. Increasing the N rate increased the environment average soybean yield; however, 93% of the environment-specific N-rate responses were not significant which suggested a minimal effect of N across the examined region. A large yield variability was observed among environments within the same N rates, which was attributed to growing environment differences (e.g., in-season weather conditions, soil type etc.) and non-N related management (e.g., irrigation). Conditional inference tree analysis identified N-timing and N-rate to be conditional to irrigation, and to seeding rates >420,000 seeds ha−1, indicating that N management decisions should take into account major, non-N related management practices. Overall, the analysis revealed that N management decisions had a measurable, but small, effect on soybean yield. Given the growing pressure for increasing food production, it is imperative to further examine all soybean N decisions (application method, timing, and rate) in environment- and cropping system-specific randomized trials in important agricultural regions.
- Hierarchical model
- Regression tree