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Biological nitrogen fixation (BNF) in soybean [Glycine max (L.) Merr.] is an important process to supply nitrogen (N) for the crop. However, the N fixation capacity of soybean varies widely among soils and climatic conditions. The N fixation potential is purportedly minimum in soils that do not have any prior history of soybean cultivation, and the application of inoculum is reported as necessary to facilitate this process. Rhizobium inocula can be applied at planting to promote BNF; however, their efficacy remains highly variable. A field experiment was conducted at irrigated and dryland sites in 2017 and 2018, where soybean was not cultivated in the preceding 10+ years. Bradyrhizobium inoculum was applied in liquid, dry, and a combination of liquid and dry formulations. The results showed that inoculum addition, in any formulation or combination, did not significantly increase soybean yield compared with the untreated control at any site. This study showed that inoculum application did not consistently increase yield in fields without soybean in the previous crop rotation for years. Future research is needed to identify soils, varieties, management practices, and environmental conditions for soybean inoculation which can increase yield and net farm profits. Core Ideas: The effect of inoculum was tested on soybean yield in irrigated and dryland regimes.Study included seed‐applied dry inoculum alone, in‐furrow applied liquid inoculum alone, and seed‐applied dry inoculum followed by in‐furrow applied liquid inoculum.Overall, inoculum had no significant impact on soybean yield regardless of inoculum source and water regime. [ABSTRACT FROM AUTHOR]

Nitrogen (N) leaching could be a serious problem for coarse-textured soils. Various models are created to simulate N leaching in coarse-textured soils, but their testing is required to evaluate the accuracy of simulations. This study evaluated the Soil Water Atmosphere Plant (SWAP) and Agricultural NItrogen MOdel (ANIMO) models to predict soil water content and N leaching. Calibration was performed with soil water content data from wheat crop and validation was conducted with NO3-N and NH4-N concentrations data from rice crop of the same field. Hydraulic and various other N rate constants were calibrated using the PEST (Parameter EStimation Technique) model. The calibration of the SWAP model showed differences between measured and predicted soil water content at different soil depths were less than 2/5th of the measured absolute error (MAE). The calibration of the ANIMO model showed relatively large differences between measured and predicted NO3-N concentrations that could be attributed to erratic values of NO3-N concentrations found in the soil profiles. The validation of ANIMO model showed that differences between measured and predicted NH4-N concentrations at different soil depths were less than 2/5th of the MAE and R2was greater than 0.76. These results suggest that SWAP/ANIMO model has the potential to predict N leaching in rice and wheat crops in testing environments and their performance could be further improved with the availability of more field data. [ABSTRACT FROM PUBLISHER]

The nitrate (NO3−) leaching assessment from extensive fertilizer nitrogen (N) applications to croplands is crucial to optimize fertilizer-N recommendations that do not threaten the quality of drinking groundwater. SWAP (Soil Water Atmosphere Plant), a water balance model, was linked with ANIMO (Agricultural NItrogen MOdel), a nitrate leaching model and the Geographical Information System (GIS) to assess the spatial and temporal leaching of NO3−-N from fields under rice-wheat cropping system in the riparian wetlands in the Punjab in north-western India. The results revealed that NO3−-N concentration in the groundwater exceeded the 10 mg NO3−-N L−1 limit set by the World Health Organization (WHO) for drinking water only during December–January. The verification of these results using measured values indicated that the SWAP-ANIMO model satisfactorily predicted NO3−-N concentrations in the leachate in the vadose zone. A low value of the mean absolute error (0.5–1.4) and a root mean square error (0.6–1.5) was observed between the measured and the predicted NO3−-N concentration across the soil profile during the validation at five sampling sites. The NO3−-N predictions revealed that in the long-term, the ongoing fertilizer-N management practices in the riparian wetlands will not significantly change the average NO3−-N concentration in the groundwater. The modeling approach was satisfactory for an efficient quantitative assessment of NO3−-N pollution in groundwater while accounting for the spatial and temporal variability. [ABSTRACT FROM AUTHOR]