Water and nitrogen dynamics in soybeans: integration of crop modeling and field data - da Rocha Hintz, Gabriel (My University, 2024)
Soybeans [Glycine max (L.) Merr.] are a vital component of global agriculture, providing a major source of protein, oil, and animal fodder. Global production has increased significantly in recent decades, with Brazil, the United States, and Argentina collectively accounting for 80% of global soybean production. However, 60-70% of the soybean cropland area is grown under rainfed conditions, rendering yields particularly vulnerable to climate variability. Particularly, the variability in rainfall can account for up to 60% of yield variations in key agricultural regions. Moreover, soybean production requires substantial nitrogen (N) inputs, with approximately 80 kg of N needed per Mg of seed produced. Soybeans satisfy this N demand from both soil and symbiotic N fixation processes, making it essential to isolate those sources for investigating this nutrient dynamics. This study had two principal objectives. First, Chapter 2 aimed to characterize water stress (WS) patterns during El Nino Southern Oscillation (ENSO) events and assess the impact of climate-adaptive management strategies— particularly the timing of planting and the selection of maturity group (MG), on the mitigation of yield loss and the optimization of soybean seed yield in Southern Brazil. Second, Chapter 3 aimed to review current knowledge on N dilution curves for soybeans to establish a baseline model, define a critical N dilution curve using non-nodulating genotypes, and investigate the physiological mechanisms underlying the relationship between plant growth and N sources. For the first objective, Chapter 2, crop growth simulations were performed using Agricultural Production Systems Simulator (APSIM) Next-Generation to test three soybean MGs (5.0, 5.8, and 6.4) across eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in Rio Grande do Sul (RS), Brazil. The risk of crop failure was calculated as the percentage of simulations yielding less than the economic break-even yield threshold for soybean production. The simulated yields were grouped into four regions: Northeast (NE), North (N), Central (C), and Southwest (SW). The WS patterns were classified into four categories: no stress, early stress, late stress, and whole season stress. On average, the application of water stress (WS) resulted in a reduction in yield of up to 2 Mg ha⁻¹, which represents a decrease of approximately 50% relative to the yield obtained under conditions of no stress. The frequency and severity of WS patterns varied across regions, with the SW experiencing more frequent and severe stress, including up to 50% of whole season stress during La Nina events. ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events correlating with increased stress. The MG 5.0 resulted in the highest probability of failure in all regions, and early planting dates showed the greatest yield variability (up to 5 Mg ha⁻¹). For the second objective, Chapter 3, a literature review was conducted to compile data on N dilution curves for soybeans. Additionally, a field experiment was conducted in Kansas, United States, using four soybean genotypes (two nodulating and two non-nodulating) under four fertilizer N rates (0, 100, 200, and 300 kg N ha⁻¹). Non-nodulating soybeans exhibited a 46% greater N dilution compared to their nodulating counterparts. Nodulating genotypes reported greater specific leaf area (20%) and specific leaf nitrogen (45%), with a reduction in the specific leaf weight (17%). The intrinsic N capacity increased not only for the leaf organ but in a similar proportion in stem and pod tissues, all acting as N reservoirs to meet seed N demand. Soil-%Nc followed a typical dilution pattern with increasing biomass, while atmospheric-%Nc increased during growth. In conclusion, the implementation of climate-adaptive strategies, including the optimization of planting dates and MGs, has demonstrated a 15% reduction in the risk of crop failure, thereby providing invaluable insights for enhancing the stability of soybean yields and the resilience of agricultural systems to the uncertainties posed by climate change. Furthermore, a pivotal crop N dilution curve has been established for non-nodulating soybeans, exhibiting a greater degree of dilution than has been previously documented for nodulating varieties and presenting new evidence of N allocation within this crop.
Keywords: Soybean; Nitrogen; Crop modeling; Crop management; Water stress