Different approaches to assessing pollution load: the case of nitrogen-related grey water footprint of barley and soybean in Argentina
2021
Olivera Rodríguez, Paula | Holzman, Mauro Ezequiel | Mujica, Claudio Ramón | Rivas, Raúl Eduardo | Martínez Aldaya, Maite | Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Agriculture is among the main causes of water pollution. Currently, 75% of global anthropogenic nitrogen (N) loads come from leaching/runoff from cropland. The grey water footprint (GWF) is an indicator of water resource pollution, which allows for the evaluation and monitoring of pollutant loads (L) that can affect water. However, in the literature, there are different approaches to estimating L and thus contrasting GWF estimates: (A1) leaching/runoff fraction approach, (A2) surplus approach and (A3) soil nitrogen balance approach. This study compares these approaches for the first time to assess which one is best adapted to real crop production conditions and optimises GWF calculation. The three approaches are applied to assess N-related GWF in barley and soybean. For barley in 2019, A3 estimated a GWF value 285 to 196% higher than A1, while in 2020, the A3 estimate was 135 to 81% higher. Soybean did not produce a GWF due to the crop characteristics. A3 incorporated N partitioning within the agroecosystem and considered different N inputs beyond fertilization, improving the accuracy of L and GWF estimation. Providing robust GWF results to decision-makers may help to prevent or reduce the impacts of activities that threaten the world’s water ecosystems and supply.
Afficher plus [+] Moins [-]Funding: the participation of P. Olivera Rodriguez was possible through a grant from the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina. The project leading to these results received funding from Fundación La Caixa and Fundación Caja Navarra, under agreement LCF/PR/PR13/51080004, and Comisión de Investigaciones Científicas de la provincial de Buenos Aires Project (CIC) Project EX-2020-21965094-GDEBA-DSTYADCI.
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