4.6 Article

Different Approaches to Assessing Pollution Load: The Case of Nitrogen-Related Grey Water Footprint of Barley and Soybean in Argentina

Journal

WATER
Volume 13, Issue 24, Pages -

Publisher

MDPI
DOI: 10.3390/w13243558

Keywords

pollution load; nitrogen fertilization; mineralization; leaching; runoff; agricultural practices

Funding

  1. Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Argentina
  2. Fundacion La Caixa and Fundacion Caja Navarra [LCF/PR/PR13/51080004]
  3. Comision de Investigaciones Cientificas de la provincial de Buenos Aires Project (CIC) [EX-2020-21965094-GDEBA-DSTYADCI]

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This study compared three approaches for estimating the water resource pollution indicator GWF, with A3 method showing higher GWF values than A1 in barley production conditions. Soybean did not produce GWF due to crop characteristics. A3 method incorporated N partitioning and different N inputs, improving estimation accuracy.
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.

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