Grey water footprint reduction in irrigated crop production: effect of nitrogen application rate, nitrogen form, tillage practice and irrigation strategy

Abstract

Abstract. Grey water footprint (WF) reduction is essential given the increasing water pollution associated with food production and the limited assimilation capacity of fresh water. Fertilizer application can contribute significantly to the grey WF as a result of nutrient leaching to groundwater and runoff to streams. The objective of this study is to explore the effect of the nitrogen application rate (from 25 to 300 kg N ha −1 ), nitrogen form (inorganic N or manure N), tillage practice (conventional or no-tillage) and irrigation strategy (full or deficit irrigation) on the nitrogen load to groundwater and surface water, crop yield and the N-related grey water footprint of crop production by a systematic model-based assessment. As a case study, we consider irrigated maize grown in Spain on loam soil in a semi-arid environment, whereby we simulate the 20-year period 1993-2012. The water and nitrogen balances of the soil and plant growth at the field scale were simulated with the Agricultural Policy Environmental eXtender (APEX) model. As a reference management package, we assume the use of inorganic N (nitrate), conventional tillage and full irrigation. For this reference, the grey WF at a usual N application rate of 300 kg N ha −1 (with crop yield of 11.1 t ha −1 ) is 1100 m 3 t −1 , which can be reduced by 91 % towards 95 m 3 t −1 when the N application rate is reduced to 50 kg N ha −1 (with a yield of 3.7 t ha −1 ). The grey WF can be further reduced to 75 m 3 t −1 by shifting the management package to manure N and deficit irrigation (with crop yield of 3.5 t ha −1 ). Although water pollution can thus be reduced dramatically, this comes together with a great yield reduction, and a much lower water productivity (larger green plus blue WF) as well. The overall (green, blue and grey) WF per tonne is found to be minimal at an N application rate of 150 kg N ha −1 , with manure, no-tillage and deficit irrigation (with crop yield of 9.3 t ha −1 ). The paper shows that there is a trade-off between grey WF and crop yield, as well as a trade-off between reducing water pollution (grey WF) and water consumption (green and blue WF). Applying manure instead of inorganic N and deficit instead of full irrigation are measures that reduce both water pollution and water consumption with a 16 % loss in yield.

Assessing water scarcity in Malaysia: a case study of rice production

“…Hence, nutrient reduction strategies and associated science assessment should be reviewed to help better guide implementation and tracking of water quality improvement practices [38][39][40]. In addition, nutrient management strategies for agricultural non-point source pollution control in irrigation area should be improved in order to protect water resources and to control environmental pollution in catchment area [41][42][43]. In this context, it is important to create and propose a good management plan in order to properly manage and distribute water supply to all sectors.…”

Agricultural Water Management Model Based on Grey Water Footprints under Uncertainty and its Application

“…The composition of non-point source pollutants is complex, and the same water can dilute component 1, component 2, and component 3, which is shown in Figure 1. Grey water footprint is determined by the pollutant that requires the most dilution [34]. There is a great deal of uncertainties in the grey water footprints from crop production.…”

Groundwater saving and quality improvement by reducing water footprints of crops to benchmarks levels

“…Formulating WF reduction targets for crop production, as a general national policy, and to downscale targets per climate zone to specific targets at farm level still await practical implementation. The field is still in its infancy, with only a few earlier studies available for total blue WF benchmarks ( Hoekstra, 2013;Mekonnen and Hoekstra, 2014;Chukalla et al, 2015Chukalla et al, , 2017Zhuo et al, 2016b ) or grey WF benchmarks Chukalla et al, 2018 ). Further studies, using different models and remote sensing, and validating findings based on field data, will be necessary to assess uncertainties in more detail, and test the feasibility of lowering the WFs of crops to benchmark levels at large scale.…”