Abstract

Abstract. Meeting growing food demands while simultaneously shrinking the water footprint (WF) of agricultural production is one of the greatest societal challenges. Benchmarks for the WF of crop production can serve as a reference and be helpful in setting WF reduction targets. The consumptive WF of crops, the consumption of rainwater stored in the soil (green WF), and the consumption of irrigation water (blue WF) over the crop growing period varies spatially and temporally depending on environmental factors like climate and soil. The study explores which environmental factors should be distinguished when determining benchmark levels for the consumptive WF of crops. Hereto we determine benchmark levels for the consumptive WF of winter wheat production in China for all separate years in the period 1961–2008, for rain-fed vs. irrigated croplands, for wet vs. dry years, for warm vs. cold years, for four different soil classes, and for two different climate zones. We simulate consumptive WFs of winter wheat production with the crop water productivity model AquaCrop at a 5 by 5 arcmin resolution, accounting for water stress only. The results show that (i) benchmark levels determined for individual years for the country as a whole remain within a range of ±20 % around long-term mean levels over 1961–2008, (ii) the WF benchmarks for irrigated winter wheat are 8–10 % larger than those for rain-fed winter wheat, (iii) WF benchmarks for wet years are 1–3 % smaller than for dry years, (iv) WF benchmarks for warm years are 7–8 % smaller than for cold years, (v) WF benchmarks differ by about 10–12 % across different soil texture classes, and (vi) WF benchmarks for the humid zone are 26–31 % smaller than for the arid zone, which has relatively higher reference evapotranspiration in general and lower yields in rain-fed fields. We conclude that when determining benchmark levels for the consumptive WF of a crop, it is useful to primarily distinguish between different climate zones. If actual consumptive WFs of winter wheat throughout China were reduced to the benchmark levels set by the best 25 % of Chinese winter wheat production (1224 m3 t−1 for arid areas and 841 m3 t−1 for humid areas), the water saving in an average year would be 53 % of the current water consumption at winter wheat fields in China. The majority of the yield increase and associated improvement in water productivity can be achieved in southern China.

Highlights

  • Half of the large river basins in the world face severe blue water scarcity for at least one month a year (Hoekstra et al, 2012)

  • We find that the best 10 % of winter wheat production in China has a maximum long-term average consumptive water footprint (WF) of 777 m3 t−1, which is larger than the maximum consumptive WF of the best 10 % of wheat production globally (592 m3 t−1) that was reported by Mekonnen and Hoekstra (2014)

  • WF benchmarks for warm years are 11 % smaller, on average, than for cold years

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Summary

Introduction

Half of the large river basins in the world face severe blue water scarcity for at least one month a year (Hoekstra et al, 2012). Meeting growing food demands and simultaneously reducing the water footprint (WF) of agricultural production is one of the greatest societal challenges of our time (Foley et al, 2011; Hoekstra and Wiedmann, 2014). When water is scarce, raising production per unit of water (i.e. increasing water productivity in terms of t m−3 or reducing the WF in m3 t−1) is a key challenge in order to save water and achieve sustainable water use at catchment level. In order to provide producers with an incentive to reduce the WF of their products to reasonable levels, Hoekstra (2013, 2014) has proposed to develop WF benchmarks, which can be used by governments, farmers and customers (crop traders and retailers) for setting WF reduction targets. WF benchmarks of crop production could be global, but would preferably be context-specific, given the fact that the WF of growing a crop varies as a function of environmental factors such as climate and soil (Mekonnen and Hoekstra, 2011; Siebert and Döll, 2010; Tuninetti et al, 2015)

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