Crop Water Footprint Research Articles

Water Footprint of Energy Crops under the Rain-Fed and Irrigated Cultivation in Eastern Thailand

Water Footprint of Energy Crops under the Rain-Fed and Irrigated Cultivation in Eastern Thailand

Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy

We studied the effect of prospective climate change upon crop yield, and related water footprint of maize (Zea mays L.) for a relevant case study area in the Po valley of Northern Italy. To simulate maize production we used a cropping system simulation model CropSyst, which we set up and validated by way of crop yield data during 2001–2010. We then calculated the present water footprint (green, blue) of maize in the area, defined as the absolute and specific (per kg yield) amount of water evapotranspired during growing season, under three irrigation scenarios, namely (i) no irrigation, (ii) manual irrigation at fixed dates, and (iii) automatic irrigation on demand. We then evaluated the effects of prospective climate change upon maize production until mid-century (2045–2054), and we quantified the water footprint therein. We considered climate variations with focus upon temperature, precipitation, and CO2. First, we assessed maize yield and water footprint sensitivity to potential changes of these weather variables. We then fed the maize yield model with properly downscaled climate projections (storyline A2, business as usual) from global circulation models (GCM), included within the board of the Intergovernmental Panel of Climate Change, IPCC, and with those from a local scenario LOC, obtained by projecting recently observed local climate trends (1975–2010). Under the worst, more likely future scenarios of increasing temperature and decreasing precipitation, crop yield decreased and water footprint, especially blue, increased, due to increased evapotranspiration, higher irrigation demand, and lower final yield. Increase of CO2, albeit possibly increasing water use efficiency, seemed not to affect the water footprint noticeably. A possible increase of precipitation as projected by some GCMs, may partly make up for the increase of temperature, especially under a no, or little irrigation scenario, further diminishing the blue water footprint. Uncertainty in future precipitation has the greatest impact in scenarios projecting maize yield and water footprint. Our study provides hints as to how one can (i) evaluate the amount of water required to cultivate maize or other crops, and virtually traded when such crops are sold or bought, (ii) evaluate the impact of climate change upon water footprint and virtual water trade, and (iii) benchmark objectively adaptation strategies for agricultural systems with an eye on least water consumption.

The impact of local crops consumption on the water resources in Beijing

Water stress is a worldwide issue and is being exacerbated by population increase, diets shifting, climate change and so on. Consumption of goods and services by humans often puts pressure on the water resources of production sites. The impact of people’s consumption can be indexed by the indicator of water footprint. Based on numerous on-farm data, this study determined the water footprint associated with the consumption of crop products produced locally in Beijing. The total water footprint related to the consumption of local crop products in Beijing during 2009 was 23.7 × 10 8 m 3. Grey water had the largest contribution (58%), followed by green water (23%) and blue water (19%). Vegetables, maize and wheat were the largest three contributors, accounting for 53, 33 and 9% of the total water footprint. Based on the production perspective, improving water and fertilizer management has great potential for reducing the blue and grey water footprints of crops in the farming stage. However, the challenge is how to inspire farmers to participate in the activities of reducing water consumption and pollution. Increasing farmers’, especially female farmers’ access to related technologies can have a positive impact on the sustainable use of water in Beijing.

The green, blue and grey water footprint of crops and derived crop products

Abstract. This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996–2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the Water Footprint Network. Considering the water footprints of primary crops, we see that the global average water footprint per ton of crop increases from sugar crops (roughly 200 m3 ton−1), vegetables (300 m3 ton−1), roots and tubers (400 m3 ton−1), fruits (1000 m3 ton−1), cereals (1600 m3 ton−1), oil crops (2400 m3 ton−1) to pulses (4000 m3 ton−1). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m3 GJ−1) than biodiesel, which supports earlier analyses. The crop used matters significantly as well: the global average water footprint of bio-ethanol based on sugar beet amounts to 51 m3 GJ−1, while this is 121 m3 GJ−1 for maize. The global water footprint related to crop production in the period 1996–2005 was 7404 billion cubic meters per year (78 % green, 12 % blue, 10 % grey). A large total water footprint was calculated for wheat (1087 Gm3 yr−1), rice (992 Gm3 yr−1) and maize (770 Gm3 yr−1). Wheat and rice have the largest blue water footprints, together accounting for 45 % of the global blue water footprint. At country level, the total water footprint was largest for India (1047 Gm3 yr−1), China (967 Gm3 yr−1) and the USA (826 Gm3 yr−1). A relatively large total blue water footprint as a result of crop production is observed in the Indus river basin (117 Gm3 yr−1) and the Ganges river basin (108 Gm3 yr−1). The two basins together account for 25 % of the blue water footprint related to global crop production. Globally, rain-fed agriculture has a water footprint of 5173 Gm3 yr−1 (91 % green, 9 % grey); irrigated agriculture has a water footprint of 2230 Gm3 yr−1 (48 % green, 40 % blue, 12 % grey).

Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use.

The water footprint of bioenergy from Jatropha curcas L.

The water footprint (WF) of energy crops should be calculated by relating the energy yield of a crop to its actual water use under actual climatic conditions during the growing season. Recently, average precipitation and additional irrigation to satisfy potential crop water requirements were used as input to the WF computation (1). We disagree with this approach because it relates optimized crop water use to actual yields, thereby introducing bias towards inefficient WF for low-yielding crops under suboptimal rain-fed conditions.