Core Ideas The volatilization losses averaged 31.0% of applied N for urea and 14.8% for NBPT‐treated urea. NBPT‐treated urea showed a potential yield increase of 5.3% for major crops. The effect of NBPT in reducing volatilization losses were reduced under high N rates. NBPT had a limited effect on increasing yield in coarse‐textured soils and for NBPT rates >1060 mg kg−1. The urease inhibitor N‐(n‐butyl) thiophosphoric triamide (NBPT) slows urea hydrolysis, reduces NH3 volatilization loss, and enhances N availability to plants. Even though most studies have proved the potential of NBPT‐treated urea to reduce NH3 loss, the benefits to increase crop yield have been less consistent, mainly because N is not always the limiting factor. A meta‐analysis was carried out to evaluate the effect of soil properties (e.g., soil pH, soil texture, soil organic C [SOC]), N rate, and NBPT concentration on NH3 volatilization loss and crop yield when comparing urea with NBPT‐treated urea. Regression analysis indicated cumulative NH3 loss of 31.0 and 14.8% of applied N for urea and NBPT‐treated urea, respectively, a 52% reduction in NH3 loss by using the urease inhibitor. The use of NBPT delayed NH3 loss. It took 4.8 and 8.3 d for 50% of the total NH3 loss to occur for urea and NBPT‐treated urea, respectively. The meta‐analyses indicated that when compared with urea, NBPT‐treated urea reduced NH3 volatilization loss across all soil pH classes, soil texture classes, SOC contents, N rates, and NBPT concentrations. The meta‐analysis indicated an average crop yield increase of 5.3% for NBPT‐treated urea compared with urea. This trend was observed for all classes of soil pH, SOC content, and N rate, but yield increases were limited in coarse‐textured soils and NBPT rates >1060 mg kg−1.

Soil bulk density (ρ b ) is important because of its direct effect on soil properties (e.g., porosity, soil moisture availability) and crop yield. Additionally, ρ b measurements are needed to express soil organic carbon (SOC) and other nutrient stocks on an area basis (kg ha −1). However, ρ b measurements are commonly missing from databases for reasons that include omission due to sampling constraints and laboratory mishandling. The objective of this study was to investigate the performance of novel pedotransfer functions (PTFs) in predicting ρ b as a function of textural class and basic pedon description information extracted from the horizon of interest (the horizon for which ρ b is being predicted), and ρ b , textural class, and basic pedon description information extracted from horizons above or below and directly adjacent or not adjacent to the horizon of interest. A total of 2,680 pedons (20,045 horizons) were gathered from the USDA-NRCS National Soil Survey Center characterization database. Twelve ρ b PTFs were developed by combining PTF types, database configurations, and horizon limiting depths. Different PTF types were created considering the direction of prediction in the soil profile: upward and downward prediction models. Multiple database configurations were used to mimic different scenarios of horizons missing ρ b values: random missing (e.g., ρ b sample lost in transit) and patterned or systematic missing (e.g., no ρ b samples collected for horizons N 30 cm depth). For each database configuration scenario, upward and downward models were developed separately. Three limiting depths (20, 30, and 50 cm) were tested to identify any threshold depth between upward and downward models. For both PTF types, validation results indicated that models derived from the database configuration mimicking random horizons missing ρ b performed better than those derived from the configuration mimicking clear patterns of missing ρ b measurements. All 12 PTFs performed well (RMSPE: 0.10-0.15 g cm −3). The threshold depth of 50 cm most successfully split the database between upward and downward models. For all PTFs, the ρ b of other horizons in the soil profile was the most important variable in predicting ρ b . The proposed PTFs provide reasonably accurate ρ b predictions, and have the potential to help researchers and other users to fill gaps in their database without complicated data acquisition.

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