A global benchmark map of water productivity for rainfed and irrigated wheat

The growing pressure on fresh water resources demands that agriculture becomes more productive with its current water use. Increasing water productivity is an often cited solution, though the current levels of water productivity are not systematically mapped. A global map of water productivity helps to identify where water resources are productively used, and identifies places where improvements are possible. The WATPRO water productivity model for wheat, using remote sensing data products as input, was applied at a global scale with global data sets of the NDVI and surface albedo to benchmark water productivity of wheat for the beginning of this millennium. Time profiles of the NDVI were used to determine the time frame from crop establishment to harvest on a pixel basis, which was considered the modelling period. It was found that water productivity varies from approximately 0.2 to 1.8 kg of harvestable wheat per cubic metre of water consumed. From the 10 largest producers of wheat, France and Germany score the highest country average water productivity of 1.42 and 1.35 kg m⁻³, respectively. The results were compared with modelling information by Liu et al. (2007) who applied the GEPIC model at a global scale to map water productivity, and by Chapagain and Hoekstra (2004) who used FAO statistics to determine water productivity per country. A comparison with Liu et al. showed a good correlation for most countries, but the correlation with the results by Chapagain and Hoekstra was less obvious. The global patterns of the water productivity map were compared with global data sets of precipitation and reference evapotranspiration to determine the impact of climate and of water availability reflected by precipitation. It appears that the highest levels of water productivity are to be expected in temperate climates with high precipitation. Due to its non-linear relationship with precipitation, it is expected that large gains in water productivity can be made with in situ rain water harvesting or supplemental irrigation in dry areas with low seasonal precipitation. A full understanding of the spatial patterns by country or river basin will support decisions on where to invest and what measures to take to make agriculture more water productive.     

Integrating remote sensing, census and weather data for an assessment of rice yield, water consumption and water productivity in the Indo-Gangetic river basin

Crop consumptive water use and productivity are key elements to understand basin water management performance. This article presents a simplified approach to map rice (Oryza sativa L.) water consumption, yield, and water productivity (WP) in the Indo-Gangetic Basin (IGB) by combining remotely sensed imagery, national census and meteorological data. The statistical rice cropped area and production data were synthesized to calculate district-level land productivity, which is then further extrapolated to pixel-level values using MODIS NDVI product based on a crop dominance map. The water consumption by actual evapotranspiration is estimated with Simplified Surface Energy Balance (SSEB) model taking meteorological data and MODIS land surface temperature products as inputs. WP maps are then generated by dividing the rice productivity map with the seasonal actual evapotranspiration (ET) map. The average rice yields for Pakistan, India, Nepal and Bangladesh in the basin are 2.60, 2.53, 3.54 and 2.75 tons/ha, respectively. The average rice ET is 416 mm, accounting for only 68.2% of potential ET. The average WP of rice is 0.74 kg/m³. The WP generally varies with the trends of yield variation. A comparative analysis of ET, yield, rainfall and WP maps indicates greater scope for improvement of the downstream areas of the Ganges basin. The method proposed is simple, with satisfactory accuracy, and can be easily applied elsewhere.     

SEBAL for detecting spatial variation of water productivity and scope for improvement in eight irrigated wheat systems

A methodology has been developed to quantify spatial variation of crop yield, evapotranspiration (ET) and water productivity (WP_ET) using the SEBAL algorithm and high and low resolution satellite images. SEBAL-based ET estimates were validated over an irrigated, wheat dominated area in the Yaqui Valley, Mexico and proved to be accurate (8.8% difference for 110 days). Estimated average wheat yields in Yaqui Valley of 5.5 t ha⁻¹ were well within the range of measured yields reported in the literature. Measured wheat yields in 24 farmers’ fields in Sirsa district, India, were 0.4 t ha⁻¹ higher than SEBAL estimated wheat yields. Area average WP_ET in the Yaqui Valley was 1.37 kg m⁻³ and could be considered to be high as compared to other irrigated systems around the world where the same methodology was applied. A higher average WP_ET was found in Egypt's Nile Delta (1.52 kg m⁻³), Kings County (CA), USA (1.44 kg m⁻³) and in Oldambt, The Netherlands (1.39 kg m⁻³). The spatial variability of WP_ET within low productivity systems (CV = 0.33) is higher than in high productivity systems (CV = 0.05) because water supply in the former case is uncertain and farming conditions are sub-optimal. The high CV found in areas with low WP_ET indicates that there is considerable scope for improvement. The average scope for improvement in eight systems was 14%, indicating that 14% ET reduction can be achieved while maintaining the same yield. It is concluded that the proposed methodology is accurate and that better knowledge of the spatial variation of WP_ET provides valuable information for achieving local water conservation practices in irrigated wheat.     

Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops

Irrigation performance and water productivity can be benchmarked if estimates of spatially distributed yield and crop water use are available. A commonly used method to estimate crop evapotranspiration in irrigated areas is to multiply reference evapotranspiration values by appropriate crop coefficients. This study evaluated convenient ways to derive such coefficients using multispectral vegetation indices obtained by remote sensing. Detailed ground radiometric measurements were taken in small plots perpendicular to the crop rows to obtain canopy reflectance values. Ancillary measurements of green ground cover, plant height, leaf area index and biomass were taken in the cropped strip covered by the radiometer field-of-view. The results were up-scaled using 10 Landsat-5 and 1 Landsat-7 images. Crop measurements and ground radiometry were made at the time of Landsat overpass on two commercial fields, one grown with sugarbeet and the other with cotton. Crop height and ground cover were determined weekly in these two fields, three additional sugarbeet fields and one additional cotton field. The ground and satellite observations of canopy reflectance yielded similar results. Two vegetation indices, the normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI) were evaluated. Both indices described the crop growth well, but SAVI was used in further evaluations because it could be conveniently related to both ground cover and the basal crop coefficient using a simple model. Based on these findings, crop water use variability was analyzed in a large sample of sugarbeet and cotton fields, within a homogeneous irrigation scheme in Southern Spain. The yield versus evapotranspiration data points were highly scattered for both cotton and sugarbeet. The yield values obtained from the sugarbeet fields and cotton fields were substantially lower than values predicted by a linear yield function, and close to a curvilinear yield function, respectively. Evapotranspired water productivity varied in the cotton fields from 0.3 to 0.78 kg m⁻³, and in the sugarbeet fields from 7.15 to 14.8 kg m⁻³.     

Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a south Indian catchment

The combined use of remote sensing and a distributed hydrological model have demonstrated the improved understanding of the entire water balance in an area where data are scarcely available. Water use and crop water productivity were assessed in the Upper Bhima catchment in southern India using an innovative integration of remotely sensed evapotranspiration and a process-based hydrological model. The remote sensing based Surface Energy Balance Algorithm for Land (SEBAL) was used to derive an 8 month time series of observed actual evapotranspiration from October 2004 to May 2005. This dataset was then used in the calibration of the Soil and Water Assessment Tool (SWAT). This hydrological model was calibrated by changing 34 parameters to minimize the difference between simulated and observed actual evapotranspiration. The calibration efficiency was assessed with four different performance indicators. The calibrated model was used to derive a monthly basin water balance and to assess crop water productivity and crop water use for the irrigation year 2004-2005. It was found that evapotranspiration is the largest water loss in the catchment and total evaporative depletion was 38,172 Mm³ (835 mm). Of the total evaporative depletion 42% can be considered as non-beneficial and could be diverted to other beneficial utilization. Simulated crop water productivities for sugarcane, sorghum and winter wheat are relatively high at 2.9 kg/m³, 1.3 kg/m³ and 1.3 kg/m³, respectively. The frequency distributions of crop water productivity are characterised by low coefficient of variation, yielding limited scope for improvement in the agricultural areas under the current cropping systems. Further improvements in water productivity may however be achieved by shifting the crop base from sugarcane to a dual crop and introducing a fallow period from March to May or by converting non-productive rangelands to bio fuel production or other agricultural land uses.     

Irrigation reliability and the productivity of water: A proposed methodology using evapotranspiration mapping

Irrigation is the dominant user of water worldwide, but provision of potable water and water for industry are higher priorities and give higher social and economic returns. Irrigation will continue to lose water to competing sectors and the productivity of irrigation systems (since food demand continues to grow) remains a central issue in water management. Performance assessment of irrigation has traditionally been difficult when based on field measurements of flows, deliveries and depths over large areas. Furthermore, performance measures have shifted from narrow engineering indicators to broader productivity issues of production achieved per unit of water consumed. Remote sensing, applied to the estimation of evapotranspiration (ET) over large areas, provides analysts of irrigation systems with extraordinary new tools for the objective assessment of consumption and production – constituting a quantum leap in the assessment of irrigation system performance. Awareness and utilisation of these tools is spreading, but important areas remain to be “converted” from traditional approaches that rely on an array of estimated parameters. The next challenge for remote sensing will be to map the frontier between the reliability of the irrigation service and the productivity achieved. Reliability provides the inducement for farmers to invest in higher productivity – to the benefit of themselves and society – and understanding better how the individual maximises profits within an uncertain irrigation environment can provide important guidance to managers and system designers.     

Combining remote sensing and in situ soil moisture data for the application and validation of a distributed water balance model (HIDROMORE)

An application of the FAO56 approach to calculate actual evapotranspiration (AET) and soil moisture is reported, implemented by means of the HIDROMORE computerized tool, which performs spatially distributed calculations of hydrological parameters at watershed scale. The paper describes the application and validation of the model over 1 year in an area located in the central sector of the Duero Basin (Spain), where there is a network of 23 stations for continuous measurement of soil moisture (REMEDHUS; Soil Moisture Measurement Stations Network) distributed over an area of around 1300 km². The application integrated a series of Landsat 7 ETM+ images of 2002, from which the NDVI series (Normalized Difference Vegetation Index) and the map of land covers/uses were derived. Validation consisted of the use of the REMEDHUS soil moisture series and their comparison with the series resulting from the application. Two simulations were performed, with soil parameters values at the surface (0-5 cm depth) and at the mean of the profile scale (0-100 cm depth). The behaviour of the simulated soil moisture was described by means of its correlation with the measured soil moisture (determination coefficient, R² = 0.67 for the surface values and 0.81 for the mean profile values), and the Root Mean Square Error (RMSE), resulting in a range of it for the 23 stations between 0.010 and 0.061 cm³ cm⁻³. The application afforded an underestimation of the soil moisture content, which suggests the need for a redefinition of the limits of the plant available water used in the calculation. The results showed that HIDROMORE is an efficient tool for the characterization of hydrological parameters at global scale in the study zone. The combination of the FAO56 methodology and remote sensing techniques was efficient in the spatially distributed simulation of soil moisture.     

Combining an ecological model with remote sensing and GIS techniques to monitor soil water content of croplands with a monsoon climate

Soil water is an important factor affecting photosynthesis, transpiration, growth, and yield of crops. Accurate information on soil water content (SWC) is crucial for practical agricultural water management at various scales. In this study, remotely sensed parameters (leaf area index, land cover type, and albedo) and spatial data manipulated using the geographic information system (GIS) technique were assimilated into the boreal ecosystem productivity simulator (BEPS) model to monitor SWC dynamics of croplands in Jiangsu Province, China. The monsoon climate here is characterized by large interannual and seasonal variability of rainfall causing periods of high and low SWC. Model validation was conducted by comparing simulated SWC with measurements by a gravimetric method in the years 2005 and 2006 at nine agro-meteorological stations. The model-to-measurement R² values ranged from 0.40 to 0.82. Nash-Sutcliffe efficiency values were in the range from 0.10 to 0.80. Root mean square error (RMSE) values ranged from 0.028 to 0.056 m³ m⁻³. Simulated evapotranspiration (ET) was consistent with ET estimated from pan evaporation measurements. The BEPS model successfully tracked the dynamics and extent of the serious soil water deficit that occurred during September-November 2006. These results demonstrate the applicability of combining process-based models with remote sensing and GIS techniques in monitoring SWC of croplands and improving agricultural water management at regional scales in a monsoon climate.     

Estimation of water consumption and crop water productivity of winter wheat in North China Plain using remote sensing technology

The North China Plain (NCP) is one of the most water stressed areas in the world. The water consumption of winter wheat accounts for more than 50% of the total water consumption in this region. An accurate estimate of the evapotranspiration (ET) and crop water productivity (CWP) at regional scale is therefore key to the practice of water-saving agriculture in NCP. In this research, the ET and CWP of winter wheat in 83 counties during October 2003 to June 2004 in NCP were estimated using the remote sensing data. The daily ET was calculated using SEBAL model with NOAA remote sensing data in 17 non-cloud days whereas the reference daily crop ET was estimated using meteorological data based on Hargreaves approach. The daily ET and the total ET over the entire growing season of winter wheat were obtained using crop coefficient interpolation approach. The calculated average and maximum water consumption of winter wheat in these 83 counties were 424 and 475 mm, respectively. The calculated daily ET from SEBAL model showed good match with the observed data collected in a Lysimeter. The error of ET estimation over the entire growing stage of winter wheat was approximately 4.3%. The highest CWP across this region was 1.67 kg m⁻³, and the lowest was less than 0.5 kg m⁻³. We observed a close linear relationship between CWP and yield. We also observed that the continuing increase of ET leads to a peaking and subsequent decline of CWP, which suggests that the higher water consumption does not necessarily lead to a higher yield.     

Increasing water productivity of irrigated crops under limited water supply at field scale

Borkhar district is located in an arid to semi-arid region in Iran and regularly faces widespread drought. Given current water scarcity, the limited available water should be used as efficient and productive as possible. To explore on-farm strategies which result in higher economic gains and water productivity (WP), a physically based agrohydrological model, Soil Water Atmosphere Plant (SWAP), was calibrated and validated using intensive measured data at eight selected farmer fields (wheat, fodder maize, sunflower and sugar beet) in the Borkhar district, Iran during the agricultural year 2004-2005. The WP values for the main crops were computed using the SWAP simulated water balance components, i.e. transpiration T, evapotranspiration ET, irrigation I, and the marketable yield Y_M in terms in terms of Y_MT⁻¹, Y_M ET⁻¹ and Y_M I⁻¹.The average WP, expressed as $ T⁻¹ (US $ m⁻³) was 0.19 for wheat, 0.5 for fodder maize, 0.06 for sunflower and 0.38 for sugar beet. This indicated that fodder maize provides the highest economic benefit in the Borkhar irrigation district. Soil evaporation caused the average WP values, expressed as Y_M ET⁻¹ (kg m⁻³), to be significantly lower than the average WP, expressed as Y_MT⁻¹, i.e. about 27% for wheat, 11% for fodder maize, 12% for sunflower and 0.18 for sugar beet. Furthermore, due to percolation from root zone and stored moisture content in the root zone, the average WP values, expressed as Y_MI⁻¹ (kg m⁻³), had a 24-42% reduction as compared with WP, expressed as Y_M ET⁻¹.The results indicated that during the limited water supply period, on-farm strategies like deficit irrigation scheduling and reduction of the cultivated area can result in higher economic gains. Improved irrigation practices in terms of irrigation timing and amount, increased WP in terms of Y_MI⁻¹ (kg m⁻³) by a factor of 1.5 for wheat and maize, 1.3 for sunflower and 1.1 for sugar beet. Under water shortage conditions, reduction of the cultivated area yielded higher water productivity values as compared to deficit irrigation.     

Ground-based remote sensing for assessing water and nitrogen status of broccoli

Remote sensing (RS) can facilitate the management of water and nutrients in irrigated cropping systems. Our objective for this study was to evaluate the ability of several RS indices to discriminate between limited water and limited nitrogen induced stress for broccoli. The Agricultural Irrigation Imaging System (AgIIS) was used over a 1-ha broccoli field in central Arizona to measure green (550 nm), red (670 nm), far red (720 nm), and near infrared (NIR-790 nm) reflectances, and thermal infrared radiation. Measurements were taken at a 1 m × 1 m resolution, every several days during the season. The following indices were calculated: ratio vegetation index (RVI), normalized difference vegetation index (NDVI), normalized difference based on NIR and green reflectance (NDNG), canopy chlorophyll concentration index (CCCI), and the water deficit index (WDI). The experimental design was a two-factor, nitrogen × water, Latin square with four treatments (optimal and low water and optimal and low nitrogen) and four replicates. In addition to RS measurements, the following in-situ measurements were taken: SPAD chlorophyll (closely related to nitrogen status), plant petiole nitrate-nitrogen concentrations, soil water content, and plant height, width, and leaf area index (LAI). Fresh marketable broccoli yield was harvested from plots 130 days after planting.Seasonal water application (irrigation plus rainfall) was 14% greater for optimal than low water treatments, whereas total nitrogen application was 35% greater for optimal than low N treatments. Although both nitrogen and water treatments affected broccoli growth and yield, nitrogen effects were much more pronounced. Compared to the optimal water and nitrogen treatment, broccoli yield was 20% lower for low water but optimal nitrogen, whereas yield was 42% lower for optimal water but low nitrogen. The RVI, NDVI, and NDNG indices detected treatment induced growth retardation but were unable to distinguish between the water and nitrogen effects. The CCCI, which was developed as an index to infer differences in nitrogen status, was found to be highly sensitive to nitrogen, but insensitive to water stress. The WDI provided appropriate information on treatment water status regardless of canopy cover conditions and effectively detected differences in water status following several irrigation events when water was withheld from low but not optimal water treatments. Using a RS ground-based monitoring system to simultaneously measure vegetation, nitrogen, and water stress indices at high spatial and temporal resolution could provide a successful management tool for differentiating between the effects of nitrogen and water stress in broccoli.     

Diagnosing irrigation performance and water productivity through satellite remote sensing and secondary data in a large irrigation system of Pakistan

Irrigation policy makers and managers need information on the irrigation performance and productivity of water at various scales to devise appropriate water management strategies, in particular considering dwindling water availability, further threats from climate change, and continually rising population and food demand. In practice it is often difficult to access sufficient water supply and use data to determine crop water consumption and irrigation performance. Energy balance techniques using remote sensing data have been developed by various researchers over the last 20 years, and can be used as a tool to directly estimate actual evapotranspiration, i.e., water consumption. This study demonstrates how remote sensing-based estimates of water consumption and water stress combined with secondary agricultural production data can provide better estimates of irrigation performance, including water productivity, at a variety of scales than alternative options. A principle benefit of the described approach is that it allows identification of areas where agricultural performance is less than potential, thereby providing insights into where and how irrigation systems can be managed to improve overall performance and increase water productivity in a sustainable manner. To demonstrate the advantages, the approach was applied in Rechna Doab irrigation system of Pakistan’s Punjab Province. Remote sensing-based indicators reflecting equity, adequacy, reliability and water productivity were estimated. Inter- and intra-irrigation subdivision level variability in irrigation performance, associated factors and improvement possibilities are discussed.     

Assessment of vine development according to available water resources by using remote sensing in La Mancha, Spain

The relevance of growing vines under semiarid conditions is universally accepted because of its impacts on social, economic and environmental aspects. Improving the knowledge of the soil–plant–atmosphere system related to the expression of vine growth allows the study of vine cover in wide areas. Several aspects of vine growing under semiarid conditions, related to weather, soil, and plant cover are analysed in this paper. Once the ground truth is achieved, multitemporal studies by remote sensing are especially useful for vine growth monitoring. The purpose of this work is focussed on determining changes of vine cover development according to available water resources in relation to present remote sensing methods. The method is based on using multitemporal masking classification techniques based on the ground truth knowledge achieved during previous research.     

基于Cokriging插值修正冬小麦面积遥感监测

在研究区内冬小麦种植区选取149个地面样方,筛选样方内反映种植结构、地块破碎程度、地形因素的参数并利用差分GPS测量,对3个参数量化并确定插值的主辅变量。探讨和比较了利用普通克里格和协同克里格2种插值方法对研究区的冬小麦种植面积比例的插值结果。结果表明,相同采样数量下,协同克里格法相对于普通克里格法的均方根误差降低1.48%,预测值与实测值之间的相关系数提高了6.82%,利用COK插值获取研究区内冬小麦种植面积比例分布状况,可以分区域对大尺度冬小麦面积遥感提取结果进行修正。     

The effect of salinity on water productivity of wheat under deficit irrigation above shallow groundwater

Saline groundwater is often found at shallow depth in irrigated areas of arid and semi-arid regions and is associated with problems of soil salinisation and land degradation. The conventional solution is to maintain a deeper water-table through provision of engineered drainage disposal systems, but the sustainability of such systems is disputed. This shallow groundwater should, however, be seen as a valuable resource, which can be utilised via capillary rise (i.e. sub-irrigation). In this way, it is possible to meet part of the crop water requirement, even where the groundwater is saline, thus decreasing the need for irrigation water and simultaneously alleviating the problem of disposing of saline drainage effluent. Management of conditions within the root zone can be achieved by means of a controlled drainage system.A series of lysimeter experiments have permitted a detailed investigation of capillary upward flow from a water-table controlled at shallow depth (1.0 m) under conditions of moderately high (5 mm/day) evaporative demand and with different levels of salinity. Experiments were conducted on a wheat crop grown in a sandy loam soil. Groundwater salinity was held at values from 2 to 8 dS/m while supplementary (deficit) irrigation was applied at the surface with salinity in the range 1-4 dS/m.Our experiments show that increased salinity decreased total water uptake by the crop, but in most treatments wheat still extracted 40% of its requirement from the groundwater, similar to the proportion reported for non-saline conditions. Yield depression was limited to 30% of maximum when the irrigation water was of relatively good quality (1 and 2 dS/m) even with saline groundwater (up to 6 dS/m). Crop water productivity (grain yield basis) was around 0.35 kg/m³ over a wide range of salinity conditions when calculated conventionally on the basis of total water use, but was generally above 1.0 kg/m³ if calculated on the basis of irrigation input only.     

Water quality implications of raising crop water productivity

Because of a growing and more affluent population, demand for agricultural products will increase rapidly over the coming decades, with serious implications for agricultural water demand. Symptoms of water scarcity are increasingly apparent, threatening ecosystem services and the sustainability of food production. Improved water productivity will reduce the additional water requirements in agriculture. However, there is a tradeoff between the quantity of water used in agriculture and the quality of return flow. Where yields are low due to limited nitrogen (N) and water supply, water productivity can be enhanced through higher fertilizer applications and improved water management. This limits the amount of additional water needed for increased food demand, thus leaving more water for environmental requirements. But it also increases the amount of nitrate (NO₃–N) leaching, thus adversely affecting the water quality of return flows.This paper quantifies the tradeoff between enhanced water productivity and NO₃–N leaching and shows the importance of the right management of water and N applications. Using the Decision Support System for Agro-technology Transfer (DSSAT) crop model, several scenarios combining different water and N application regimes are examined for maize (Zea mays L.) in Gainesville, FL, USA. Without adequate water, nitrogen use efficiency (NUE) remains low, resulting in substantial NO₃–N leaching. Too much water leads to excessive NO₃–N leaching and lower water productivity. The lack of N is a cause of low water productivity but too much of it leads to lower NUE and higher losses. The paper concludes that increased NO₃–N leaching is an inevitable by-product of increased water productivity, but its adverse impacts can greatly be reduced by better management of water and N application. The paper briefly shows that leaching can be reduced and water productivity increased by split application of N-fertilizer. This implies that improved water and nutrient management at field- and scheme-level is a prerequisite to limit adverse impacts of agriculture on ecosystems, now and especially in the future.     

Managing water by managing land: Addressing land degradation to improve water productivity and rural livelihoods

The premise of this paper is that the key to effective water resources management is understanding that the water cycle and land management are inextricably linked: that every land use decision is a water use decision. Gains in agricultural water productivity, therefore, will only be obtained alongside improvements in land use management. Expected increases in food demands by 2050 insist that agricultural production - and agricultural water use - must increase. At the same time, competition for water between agricultural and urban sectors will also increase; and the problem is further compounded by land degradation. A global survey suggests that 40% of agricultural land is already degraded to the point that yields are greatly reduced, and a further 9% is degraded to the point that it cannot be reclaimed for productive use by farm level measures. Soil erosion, nutrient depletion and other forms of land degradation reduce water productivity and affect water availability, quality, and storage. Reversing these trends entails tackling the underlying social, economic, political and institutional drivers of unsustainable land use. This paper is based on a review of global experiences, and its recommendations for improving water management by addressing land degradation include focusing on small scale agriculture; investing in rehabilitating degraded land to increase water productivity; and enhancing the multifunctionality of agricultural landscapes. These options can improve water management and water productivity, while also improving the livelihoods of the rural poor.     

Modeling wheat yield and crop water productivity in Iran: Implications of agricultural water management for wheat production

In most parts of Iran, water scarcity has been intensifying and posing a threat to the sustainability of agricultural production. Wheat is the dominant crop and the largest irrigation water user in Iran; hence, understanding of the crop yield-water relations in wheat across the country is essential for a sustainable production. Based on a previously calibrated hydrologic model, we modeled irrigated and rainfed wheat yield (Y) and consumptive water use (ET) with uncertainty analysis at a subbasin level in Iran. Simulated Y and ET were used to calculate crop water productivity (CWP). The model was then used to analyze the impact of several stated policies to improve the agricultural system in Iran. These included: increasing the quantity of cereal production through more efficient use of land and water resources, improving activities related to soil moisture conservation and retention, and optimizing fertilizer application. Our analysis of the ratio of water use to internal renewable water resources revealed that 23 out of 30 provinces were using more than 40% of their water resources for agriculture. Twelve provinces reached a ratio of 100% and even greater, indicating severe water scarcity and groundwater resource depletion. An analysis of Y-CWP relationship showed that one unit increase in rainfed wheat yield resulted in a lesser additional water requirement than irrigated wheat, leading to a larger improvement in CWP. The inference is that a better water management in rainfed wheat, where yield is currently small, will lead to a larger marginal return in the consumed water. An assessment of improvement in soil available water capacity (AWC) showed that 18 out of 30 provinces are more certain to save water while increasing AWC through proper soil management practices. As wheat self-sufficiency is a desired national objective, we estimated the water requirement of the year 2020 (keeping all factors except population constant) to fulfill the wheat demand. The results showed that 88% of the additional wheat production would need to be produced in the water scarce provinces. Therefore, a strategic planning in the national agricultural production and food trade to ensure sustainable water use is needed. This study lays the basis for a systematic analysis of the potentials for improving regional and national water use efficiency. The methodology used in this research, could be applied to other water scarce countries for policy impact analysis and the adoption of a sustainable agricultural strategy.     

Increasing water productivity in crop production—A synthesis

Scarcity of water resources and growing competition for water in many sectors reduce its availability for irrigation. Effective management of water for crop production in water scarce areas requires efficient approaches. Increasing crop water productivity (WP) and drought tolerance by genetic improvement and physiological regulation may be the means to achieve efficient and effective use of water. But only high water productivity values carry little or no interest if they are not associated with high or acceptable yields. Such association of high (or moderate) productivity values with high (or moderate) yields has important implications on the effective use of water. In this paper we discussed the factors affecting water productivity, and the possible techniques to improve water productivity. A single approach would not be able to tackle the forthcoming challenge of producing more food and fiber with limited or even reduced available water. Combining biological water-saving measures with engineering solutions (water saving irrigation method, deficit irrigation, proper deficit sequencing, modernization of irrigation system, etc.), and agronomic and soil manipulation (seed priming, seedling age manipulation, direct- or wet-seeded rice, proper crop choice, integrating agriculture and aquaculture, increasing soil fertility, addition of organic matter, tillage and soil mulching, etc.) may solve the problem to a certain extent. New scientific information is needed to improve the economic gains of WP because the future improvements in WP seem to be limited by economic rather than a lack of technological means.     

Modernization and optimization of irrigation systems to increase water productivity

Population increase and the improvement of living standards brought about by development will result in a sharp increase in food demand during the next decades. Most of this increase will be met by the products of irrigated agriculture. At the same time, the water input per unit irrigated area will have to be reduced in response to water scarcity and environmental concerns. Water productivity is projected to increase through gains in crop yield and reductions in irrigation water. In order to meet these projections, irrigation systems will have to be modernized and optimised. Water productivity can be defined in a number of ways, although it always represents the output of a given activity (in economic terms, if possible) divided by some expression of water input. Five expressions for this indicator were identified, using different approaches to water input. A hydrological analysis of water productivity poses a number of questions on the choice of the water input expression. In fact, when adopting a basin-wide perspective, irrigation return flows often can not be considered as net water losses. A number of irrigation modernization and optimization measures are discussed in the paper. Particular attention was paid to the improvement of irrigation management, which shows much better economic return than the improvement of the irrigation structures. The hydrological effects of these improvements may be deceiving, since they will be accompanied by larger crop evapotranspiration and even increased cropping intensity. As a consequence, less water will be available for alternative uses.