Applying grey water footprint assessment to achieve environmental sustainability within a nation under intensive agriculture: a high-resolution assessment for common agrochemicals and crops

Abstract

Achieving safer environment under intensive agriculture highly depends on input resource management. This research is the first grey water footprint (WF) assessment in which the field-scale heterogeneities in input data are considered to provide new insights for achieving sustainable environment under dense farming. For a 10-year period over 2005–2015, grey WF accounting and impact assessment was carried out, together with sensitivity and uncertainty analysis, for a wide range of agrochemicals commonly applied to the agricultural soils in Iran. Given results were then applied to provide new applicable solutions for reducing the overall pollutant loaded to freshwater bodies from diffuse sources. The 10-year average of the given results showed that with an overall load of 77.1 thousand t year−1, N was the most critical substance among various agrochemicals, especially within the irrigated lands, where are the origin of 83.1% of loaded N to freshwater bodies. Cereals, both irrigated and rainfed, always had the largest contribution in the overall pollutants loaded to water bodies. At the national sale, 48.2 billion m3 year−1 water is annually required to take up the N pollution loads, which is 26.8% less than actual runoff within the country. While the national-scale water pollutant level (WPL) was 0.7, the high-resolution assessment indicated the existence of local polluted area with WPL > 1 in 42.8% of the country, 96% of which located within the arid and semi-arid regions. In these areas, the river’s waste assimilation capacity has been overused by the factors of 1–32.8. Based on the quantitative impact assessment, the overall N-related grey WFs may be reduced by 47% if grey WFs are lowered down to the benchmark levels set at the 25th percentile of crop production, by 9.1% if crops’ yield is improved by 10%, and by 27.8% if N-application rates are reduced to the optimal rate. In addition, regional prioritizing of cropping pattern based on their grey WFs may result in an expressive reduction in WPL due to the large diversity in grey WFs among crops and regions. Based on the results, it could be concluded that, with a 10–16.1% uncertainties, a high-resolution local-scale grey WF assessment is a promising way to achieve safer agricultural environment in a dense-farming country.