Publications

Assa, BG; Bhowmick, A; Cholo, BE (2023). Modeling Nitrogen Balance for Pre-Assessment of Surface and Groundwater Nitrate (NO3--N) Contamination from N-Fertilizer Application Loss: a Case of the Bilate Downstream Watershed Cropland. WATER AIR AND SOIL POLLUTION, 234(2), 105.

Abstract
Nitrogen is an essential plant nutrient, but in excess amounts in the soil can cause significant water quality problems. Since nitrate is very soluble and is not retained by soil, it easily leaches into groundwater and contaminates it. Therefore, modeling nitrate concentration derived from N-fertilizer in the area where the land dominating crop coverage helps to ensure the security of soil and water and also the environmental sustainability actions in the agricultural watershed. Adding N-fertilizer without understanding the concentration of nitrates in the soil and neglecting responsibilities and lacking concern of excess application of nitrogen N fertilizers on agriculture can cause further problems on groundwater resources. In order to evaluate nitrate contamination on surface water and groundwater, estimating partial nitrogen balance (PNB) in crop land is essential. The objective of this study was to model the crop land partial nitrogen balance (PNB) for pre-assessment of nitrate contamination in the downstream of Bilate watershed crop land based on agricultural field N-fertilizer application loss in a scenario within Bilate watershed. The loss of agricultural nitrogen fertilizer in the Bilate watershed is one of the major factors that may contribute to nitrate contamination in the downstream water bodies in Bilate watershed. Geographically weighted regression (GWR) model with (EO-MODIS 250 m-NDVI) and time series cropland from (MODIS-MCD12Q1-IGBP of crop land class) has been utilized. Field crop data using GPS is collected from the upper, middle, and lower parts of watershed confirming the IGBP crop land classification (0.92 kappas) has scored. Additionally, the (MODIS 250 m-NDVI) data observation has been calibrated by the Google Earth Engine using a machine learning approach. Based on the FAO-Agricultural Stress Index System (ASIS), crop growth phonology curves value interval has been indexed, and simulated crop growth index has been validated for 0.25 min and 0.75 max crop growth curves. The results were utilized to replicate the time series heterogeneous crop pattern on crop land NDVI mean zonal statics. For last 20 years, a partial nitrogen balance for observed nitrogen application (Nkg/ha/year(-1)) and crop N uptake (Nkg/ha/year(-1)) is predicted. For the simulated outcome, the model has been verified for its linear correlation value of (R-2 of 0.9986). The idea underlying this research is based on the scientific fact that nitrogen (NO3--N) contamination of surface and subsurface water is unattainable without knowledge of the nitrogen level above the root zone to the particular crop zonal area.

DOI:
10.1007/s11270-023-06114-0

ISSN:
1573-2932