Environmental Earth Sciences (2017)76:786 https://doi.org/10.1007/s12665-017-7121-8

ORIGINAL ARTICLE

Crop water footprints with special focus on response formulation: the case of Gomti river basin (India) S. S. Mali1 • D. K. Singh2 • A. Sarangi2 • S. S. Parihar2 Received: 28 April 2016 / Accepted: 13 November 2017 Ó Springer-Verlag GmbH Germany, part of Springer Nature 2017

Abstract Increasing water scarcity places considerable importance on precise quantification of water consumption. The concepts of virtual water content (VWC) and water footprint (WF) are increasingly being used to analyse the water consumption and to support optimal crop and water management practices at different spatial scales. In the present study, the blue, green and grey VWC of crops and WF of crop production within the Gomti river basin (GRB) in India were assessed for irrigated and rainfed conditions. Total WF is the sum of blue, green and grey WFs within the basin. Blue WF is the amount of surface or groundwater used in crop evapotranspiration (ETc), green WF refers to amount of rain water use in ETc, and the grey water use is the volume of freshwater that is required to assimilate the agricultural pollutant load to acceptable levels. On the basis of variability in ETc, the GRB was divided into four spatial resolution units (SRUs). A linear programming model was developed to optimize the area under each crop in different SRUs with the objective function of minimizing the blue WF within the GRB. The findings show that annual WF of crop production within the GRB was 12,196 Mm3, of which 89% was from irrigated agriculture. Wheat, paddy and sugarcane shared 94% of the total WF of crop production within the basin. Share of blue and green WFs in total WF of the basin was 48 and 46%, respectively. There was considerable variation in VWC of crops in different SRUs. The VWC-based optimal allocation of crops would result in savings of 196 Mm3 in blue WF per year. Considering the large WF of crop production, optimizing the crop planting pattern is the key to achieve more sustainable water use within the basin. The approach suggested in this study will be useful in devising informed policy decisions related to crop choices and their cultivation areas so as to ensure efficient use of water resources. Keywords Water footprint  Sustainability assessment  Optimization  Cropping pattern  River basin

Introduction Freshwater is a renewable but limited resource. Its availability and quality show enormous temporal and spatial variations, and it is sensitive to human influence and environmental degradation. With accelerated population and economic growth, many areas of the world are experiencing either current or potential shortages of water, as Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12665-017-7121-8) contains supplementary material, which is available to authorized users. & S. S. Mali [email protected] 1

ICAR-Research Complex for Eastern Region, Research Centre, Ranchi 834010, India

2

Water Technology Centre, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India

well as deterioration of water quality (Shiklomanov 2000). The water footprint (WF) is the relatively new approach used to promote efficient, equitable and sustainable use of water resources at different geographical scales such as state, country or river basin (Hoekstra and Chapagain 2007; Liu and Savenije 2008; Hoekstra et al. 2011; Dumont et al. 2013). The concept of virtual water was first introduced by Allan (1998) to describe the total volume of water embedded in agricultural products, suggesting that water poor regions should import water-intensive agricultural products. The WF concept is closely linked to the VWC and is designated as a consumption-based indicator of water use (Hoekstra and Hung 2004; Hoekstra and Chapagain 2007). The WF is total VWC of products consumed by an individual, business, town, city or country (Chapagain and Orr 2008). The water footprint as a consumptionbased environmental indicator was initially introduced by

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Hoekstra and Hung (2002) as an analogy to the ‘ecological footprint’. The WF can be divided into a blue, a green and a grey component. The WF is a spatially and temporally explicit indicator that looks at both direct and indirect water use by the consumer or producer. The WF analysis has been applied in many studies to devise river basin scale water governance and management policies. Blue and green water footprint analysis can be used to assess the current and future depletion of surface water in the basin (Dumont et al. 2013). Hydrologic and economic analysis of the WF can assist in the identification of the regions with ‘high virtual water-low economic value’ crops to facilitate more efficient water allocation, providing a robust framework for policy formulation (Aldaya and Llamas 2008). The concept of grey water footprint has been applied to river basins across the globe to assess the temporal variation in the pollution level of the river basins (Liu et al. 2009). Response formulation is an important step in WF analysis as it focuses on devising strategies and management options to reduce the WF and evaluates the impact of such strategies on WF of the river basin. Previous studies have demonstrated the applicability of WF analysis in devising basin-level water management and policy decisions. Improvement in water resource allocation, increasing water reuse, practising less aggressive agricultural production (Dumont et al. 2013), strict control on the extraction of water (Aldaya et al. 2010), changing cropping pattern or a drastic reduction in the irrigated surface (Aldaya et al. 2010; Aldaya and Llamas 2008) were identified as possible management alternatives to reduce the WF at the river basin level. Policy considerations could be paying the agricultural subsidies only in the case of proven responsible utilization of water (Sonnenberg et al. 2009), reducing the production of low value high virtual water content crops, and reforms in water pricing (Zeng et al. 2012) can also be possible alternatives. Assessment of the response of WF to proposed management alternatives is important to understand the impact of anthropogenic activities on water resource use within the basin. Previous studies suggested only the possible management alternatives (Dumont et al. 2013; Aldaya et al. 2010; Aldaya and Llams 2008; Sonnenberg et al. 2009; Zeng et al. 2012); none of these studies specifically focused on quantitative assessment of WF in response to management alternatives at the river basin scale. This study was planned with twin objectives of assessing the blue, green and grey WF of the GRB and to analyse the response of WFs to management alternatives, like changed cropping pattern. Considering the opportunities offered by the WF as a tool for a renewed view on agricultural water management, the present study aimed at optimizing cropping patterns to minimize the blue WF of the basin, which was a

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step ahead of quantification and mapping the WF of crop production.

Materials and methods Study area The Gomti river basin (GRB) is located in the IndoGangetic plains of India (Fig. 1). The Gomti river originates from a lake, ‘Fulhaar Jheel’, about 3 km east of Pilibhit town and about 50 km south of the Himalayan foothills. The river is an important tributary of the Ganga river, and it drains about 30,437 km2 area in the state of central Uttar Pradesh of India before it joins the main Ganga river at Kaithi in Varanasi (UP). The climate of the GRB is semi-arid to sub-humid tropical with average annual rainfall at different locations varying from 850 to 1100 mm. Gross cropped area in GRB is 2.67 Mha, out of which 2.27 Mha (87.3%) is irrigated (Table 1). There is substantial bias towards cultivation of certain crops with wheat, paddy and sugarcane being the most important irrigated crops of the GRB. Crops like black gram, maize and lentil are cultivated as major rainfed crops.

Spatial resolution of WF assessment Assessment of WF of agricultural crops requires spatially explicit estimates on crop water use. Apart from crop and

Fig. 1 Location of the GRB in India

Environmental Earth Sciences (2017)76:786 Table 1 Irrigated and rainfed areas in GRB during 2011

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Crops

Irrigated area (ha)

Wheat

1,127,555

5561

99.5

Paddy

721,896

18,117

97.6

Sugarcane

158,390

19,812

88.9

46,826

9623

83.0

0

54,227

0.0

Maize

2718

49,063

5.2

Potato

40,628

4142

90.7 0.0

Mustard Black gram

Lentil

Rainfed area (ha)

786

Per cent irrigated (%)

0

35,469

Pigeon pea

26

30,532

0.1

Sorghum

40

21,683

0.2

Pearl millet Chickpea Sesame Peas Groundnut Total

79

18,819

0.4

1143

14,667

7.2

35

15,717

0.2

15,109

0

100.0

124 2,114,569

9954 307,386

1.2

soil parameters, the crop water use also depends on temperature, wind velocity, solar radiation and relative humidity (Allen et al. 1998). Depending on the size of the study region, researchers have used different resolutions and approaches to estimate the crop evapotranspiration (ETc) in WF accounting. In the present study, the GRB was divided into smaller homogeneous units obtained by the intersection of the thematic maps pertaining to soil type, agro-ecological region and district boundaries. The smaller polygons resulting from such intersection were designated as agricultural production units (APUs) (Fig. 2). The ETc of selected crops was estimated for each APU using Food and Agricultural Organization of the United Nations (FAO) CROPWAT model (FAO 2012). APUs were considered as the basin unit in the assessment of the WF at the basin scale. This approach improves the accuracy of the WF assessments as it accounts for the regional variability of WFs within the river basin. In order to reduce the number of units to evaluate the impact of management alternatives, the APUs were clustered and merged on the basis of uniformity in crop evapotranspiration to form the spatial resolution units (SRU). K-means with n dimensions (ETc of each crop representing one dimension in clustering process) was used to cluster the APUs into four spatial resolution units (SRU) such that SRU boundary is different from initial district boundary. Delineated SRUs were considered as the basic unit for the development of management alternatives (optimal crop plan) at the river basin scale (Fig. 3).

Fig. 2 Delineated agricultural production units (APUs)

Virtual water content The VWC of a crop is the volume of freshwater used to produce the unit quantity of a crop. The virtual water content of a product is divided into blue, green and grey components. Assessment of WF requires precise and spatially explicit information on green, blue and grey VWC of crops within each APU. The VWC (m3/t) of primary crops is the ratio between the volume of water used during the entire period of crop growth (m3/ha) and the corresponding crop yield (t/ha). In this study, the crop water requirement

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irrigation and drainage paper No-56 (Allen et al. 1998). The crop planting and harvesting dates for the state of Uttar Pradesh were adopted from information published by Department of Economics and Statistics, Min of Agriculture, GOI (MoA 2011) and Crop Science Division of the ICAR. The climatic data for this study were derived from gridded daily time series data from the India Meteorological Department (IMD) obtained from the project on National Initiative on Climate Resilient Agriculture (NICRA-ICAR 2012). The volume of water required to dilute the nitrate leached to the groundwater to a desired level is used as an indicator of the grey water use of a crop (CWUgrey ½c, m3/ ha). Grey water use at the basin scale was estimated using crop-specific nitrogen application rates (Eq. 1). CWUgrey ½c ¼

Fig. 3 Derived spatial resolution units (SRUs)

was estimated using the CROPWAT model (Allen et al. 1998; FAO 2012). The ‘Irrigation schedule’ option in CROPWAT was used to estimate the green water use (CWUgreen , m3/ha) at a daily time step. Irrigation schedule option allows actual irrigation water application according to user-specified rules (e.g. fixed schedule and fixed quantity, or variable schedule depending on soil moisture depletion) and computes ten-daily soil water balances, crop ET and blue and green water consumption based on this schedule. CWR is calculated as the sum of total crop evapotranspiration (ETC, mm/day), accumulated over the entire growing period. Blue water use (i.e. irrigation water requirement) was estimated as the difference between seasonal crop evapotranspiration and effective rainfall. CROPWAT model calculates amount of effective rainfall based on USDA method. Blue water use is zero if the entire crop evapotranspiration requirement is met from effective rainfall. A linear relationship between yield and evapotranspiration as proposed by Doorenbos and Kassam (1979) was used to get crop yields under rainfed conditions. The CROPWAT model was run for all the crops and for each APU to estimate the blue and green components of crop water requirements at the APU level. The VWC was estimated at the APU level, and the results were further averaged to obtain the VWC at the SRU level. The crop evapotranspiration (ETc) was estimated from the reference evaporation (ET0) using the crop coefficients. Values of crop coefficient (Kc) for initial, middle and end stage and crop rooting depths were adopted from FAO

123

Napp ½c  lf ½c rl

ð1Þ

where Napp ½c is the amount of nitrogen fertilizer applied (kg/ha) to the crop c throughout growing season, lf ½c is the nitrogen leaching fraction (ratio between Nleach and Napp) for a crop c, and rl is the recommended level of nitrogen (kg/m3) in drinking water. It is worth mentioning here that, due to the lack of information on district-specific nitrogen leaching fractions, uniform lf is assumed for entire GRB. Due to very small amounts, the nitrogen leaching under rainfed conditions was not considered in this study. The green, blue and grey VWC (m3/t) of a crop in an APU was estimated as the ratio between respective water use and the crop yield (Eqs. 2–4). VWCgreen ½c; N  ¼

CWUgreen ½c; N  Y ½c; N 

ð2Þ

VWCblue ½c; N  ¼

CWUblue ½c; N  Y ½c; N 

ð3Þ

VWCgrey ½c; N  ¼

CWUgrey ½c; N  Y ½c; N 

ð4Þ

where VWCgreen ½c; N , VWCblue ½c; N , VWCgrey ½c; N  are the green, blue and grey VWC of the crop c in Nth APU. CWUgreen ½c; N , CWUblue ½c; N  and CWUgrey ½c; N  are the green, blue and grey water use by the crop c in the Nth SRU. Y ½c; N  is the yield (t/ha) of crop c in Nth APU. WF of crops under rainfed and irrigated conditions was estimated separately.

Water footprint of crops The agricultural WF of the GRB was estimated for the year 2011, a year with annual rainfall close to normal annual rainfall within the study basin. The WF assessments accounted for blue, green and grey water use by the major crops cultivated within the study basin. Blue, green and

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grey WF of each APU was obtained by multiplying the respective VWC with the crop production within the APU. The WFs of all APUs within the basin were summed to get the WF of the GRB. The present study calculates the water footprint of agricultural production within the GRB. The total water footprint of crop production was estimated using Eq. (5). WF½c; rb ¼

n X N X 2 X

fVWCtot ½c; N; iProd½c; N; ig

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account for the natural variability of river flow, the EFR of the study basin was estimated using the variable flow method (VFM) suggested by Pastor et al. (2013). Considering all the sources of water (run-off, groundwater and imported canal water) and accounting for environmental flow requirements, the annual water resource availability in the GRB during kharif (June–September) and rabi (October–November) seasons was estimated as 13.3 billion cubic metre (BCM) (Table 2).

c¼1 N¼1 i¼1

ð5Þ where WF½c; rb is the WF of crop production within the river basin rb, c is the crop index, n is the total number of crops selected, N is the number of APUs, and VWCtot ½c; N; i is the total virtual water content (blue ? green ? grey) of the crop c in Nth APU for the ith type of agriculture. i = 1 for irrigated agriculture and i = 2 for rainfed agriculture, and Prod½c; N; i is the total production of crop c in Nth APU for the ith type of agriculture.

Surface and ground water resources In the GRB, the surface water resource constituted surface run-off and canal water imported from other basins. Surface run-off in each SRU was assessed using the Soil Conservation Service Curve Number method (USDA 2004) at a daily time step, and results were combined to get monthly surface water availability. Monthly availability of canal water in the GRB was estimated from the canal rosters of the ‘Sharda canal project’ and ‘Sharda Sahayak Pariyojna’ obtained from the UP State Water Resources Agency (SWARA 2009). The groundwater resources for the part of the districts within the basin boundary were estimated by multiplying the district-level groundwater withdrawal (CGWB 2011) with geographical area factor, which is the ratio of geographical area of the district within the basin to total area of the district. The availability of blue water resources (WAblue Þ in time step t was estimated using Eq. (6). WAblue ½t ¼ SW½t þ GW½t  EFR½t

WF response formulation—optimization of cropping pattern Relocation of cultivation areas of crops to SRUs with low VWC can reduce the overall WF of the river basin. A linear optimization model was developed for optimal allocation of cultivated areas within SRUs to different crops. The objective function was a linear function subjected to a number of linear constraints. The objective of the model was to determine optimal cropping pattern for available water resources within the basin to achieve current levels of crop production but with minimized blue WF. The model constraints included (1) availability of irrigated and rainfed areas during kharif and rabi seasons, (2) blue water availability for irrigation and (3) the level of crop production that needs to be maintained by optimal cropping pattern. The optimization model makes allocations of cropped areas for both the kharif and rabi season and estimates the WF of river basin under optimal cropping pattern. The blue water footprint of a crop production within the basin was obtained as sum of the blue WF of all the crops in each SRU. The model makes the clear distinction between the SRUs (k), crops (c), growing seasons (s) and type of agriculture (i, irrigated or rainfed). The model was formulated as follows. Objective function: the objective function was formulated to minimize the blue WF in the basin (Eq. 7).

ð6Þ

where WAblue ½t is the available blue water resource (Mm3) at time step t; SW½t and GW½t are the surface and groundwater resources (Mm3), respectively, of the basin at time step t; and EFR½t is the environmental flow requirement (Mm3) at time step t. Water available for human consumption is the total water resource within the basin minus the environmental flow requirement (EFR). In previous WF studies (Hoekstra and Mekonnen 2011; WWF 2012; Zeng et al. 2012), the EFR was estimated using the ‘‘presumptive environmental flow standard’’ defined by Richter (2010). In order to

Table 2 Water resources availability (Mm3) in GRB Spatial resolution unit

Kharif season

Rabi season

SRU1 SRU2

1040.9 4428.4

713.5 2991.9

SRU3

763.2

518.7

SRU4

1584.5

1257.0

Basin total

7817.1

5481.0

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MinðWFblue Þ ¼

Environmental Earth Sciences (2017)76:786 4 X 2 X n X

VWCblue ½k; s; i; c

k¼1 s¼1 c¼1

3.

 Y ½k; s; i; c  A½k; s; i; c;

8ði ¼ 1Þ ð7Þ 3

where VWCblue ½k; s; i; c is blue virtual water content (m /t) of crop c for the cropping season s for the type of agriculture i in SRU k; Y ½k; s; i; c is yield (t/ha) of crop c for the cropping season s for the type of agriculture i in season s and in SRU k; A½k; s; i; c is area (ha) allocated to crop c for cropping season s in SRU k; k is the SRU index and has values from 1 to 4; s is season index (1 = kharif and 2 = rabi); i is the index for the type of agriculture (1 = irrigated and 2 = rainfed); and c is the crop index (n = 15). A½k; s; i; c is the decision variable, and the product VWCblue ½k; s; i; c  Y ½k; s; i; c forms the cost coefficient which indicates the quantity of water added to blue WF with per hectare increase in area under the crop. The objective function was subjected to the following constraints (Eqs. 8–14). 1.

4 X

Y ½k; s; i; c  A½k; s; i; c  P½c; rb;

8c

4 X

4. ð8Þ

2.

c¼1 n X

A½k; s; i; c  RFA[k; s;

8k; 8s; 8ði ¼ 2Þ

ð10Þ

c¼1

where A½k; s; i; c is area (ha) allocated to crop c for the cropping season s for the type of agriculture i in season s and in SRU k; IA½k; s is the available irrigated area (ha) in SRU k for cropping season s; and RFA½k; s is

123

ð11Þ

RFA[sc; k  ERFA[sc; rb]

ð12Þ

k¼1

k¼1 s¼1 i¼1

where P½c; rb is total production of crop c within the river basin (rb); Y ½k; s; i; c is yield (t/ha) of crop c for the cropping season s for the type of agriculture i in season s and in SRU k; A½k; s; i; c is area (ha) allocated to crop c for cropping season s in SRU k; k is the SRU index and has values from 1 to 4; s is season index (1 = for kharif and 2 = rabi); i is the index for the type of agriculture (1 = irrigated and 2 = rainfed); and c is the crop index (n = 15). Seasonal irrigated and rainfed area constraint Total irrigated and rainfed area allocation for a particular season is limited by the available irrigated and rainfed area in the SRU. n X A½k; s; i; c  IA[k; s; 8k; 8s; 8ði ¼ 1Þ ð9Þ

RFA½crabi ; k  ERFA½crabi ; rb

k¼1

Crop production constraint Production of individual crops obtained from both the irrigated and rainfed areas of all the SRUs should be greater than or equal to the prevailing level of production of a crop. 4 X 2 X 2 X

the available rainfed area (ha) in SRU k for cropping season s. Crop-specific rainfed area While making area allocations, the model will try to allocate maximum area of the rainfed regions to a crop with lowest blue WF so that total blue WF of the basin is reduced, leading to larger crop coverage under rainfed conditions and underutilizing the potential of irrigated areas. This type of allocation may not be practicable. Therefore, this constraint was added to limit the maximum rainfed area under sugarcane (sc) and other rabi crops (crabi) to their respective extent in the year 2011. Rainfed area within the SRUs was estimated using the approach of proportionality factors (see supplementary material II).

where RFA½crabi ; k is rainfed area (ha) allocated to rabi crop c in SRU k; RFA½sc; k is rainfed area allocated to sugarcane (sc) in SRU k; ERFA½crabi ; rb is the prevailing rainfed area (ha) of rabi crop c in the GRB; and ERFA½sc, rb is prevailing rainfed area (ha) under sugarcane in the GRB. Water availability This constraint restricts summation blue water use (crop irrigation water requirement) of rainfed and irrigated areas to total blue water availability within the SRU. 2 X n X

VWCblue ½k; s; i; c  Y ½k; s; i; c

s¼1 c¼1

 IA½k; s; i; c  WAblue ½k; s;

5.

8k; 8ði ¼ 1Þ

ð13Þ

where WAblue ½k; s is blue water availability (m3) in SRU k for the cropping season s; Y ½k; s; i; c is yield (t/ ha) of crop c for the cropping season s for the type of agriculture i in season s and in SRU k. Non-negativity constraints The irrigated and rainfed areas under any crop should not be less than zero. A½k; s; i; c  0

ð14Þ

where A½k; s; i; c is area (ha) allocated to crop c for the cropping season s for the type of agriculture i in season s and in SRU k. The solution to the linear programming model was obtained using the simplex method available with Open Solver 2.1Ò add-in in MS ExcelTM, which uses the COIN-OR CBC optimization engine (Mason and Dunning 2010). The model was run first with index variable i = 1 (irrigated) and then with i = 2 (rainfed). When i = 1, irrigated area within different SRUs was

Environmental Earth Sciences (2017)76:786

allocated to different cps with allocations starting for the crop with lowest blue VWC. When allocation of ‘irrigated’ area was completed, the index i became 2 (i = 2); then, the model allocated the rainfed area to different crops, again starting allocation of the crop with lowest blue VWC.

Results Virtual water content of crops (VWCc ) The VWC of the crops under irrigated and rainfed conditions was assessed separately for each SRU (Fig. 4). Due to considerable variability in the crop evapotranspiration and crop yields across the study basin (Mali et al. 2015), large differences were observed in VWC of the crops among the SRUs. Spatial variability in the soil, temperature and rainfall within the basin led to spatial variations in evapotranspiration and the amount of rainfall effectively used by the crops. For example, the VWCblue of chickpea varied from 3291 m3/t in SRU1 to 4765 m3/t in SRU4. This indicated that cultivating chickpea was beneficial in SRU1. During the rabi season for crops like chickpea, lentil, peas, potato and wheat, the VWCblue was consistently less in SRU1. Groundnut showed the highest VWCgreen (7281 m3/ t) in the SRU2, while the lowest (4209 m3/t) was in SRU3. Also, depending on the level of fertilizer use and leaching potential under particular soil types, the VWCgrey also varied across the SRUs. In a particular agro-climatic setup, the low values of VWC were due to higher crop productivity or lower crop water requirements. This fact highlighted the need to allocate the crops to different regions of the basin in accordance with the VWC of the crops such that the total WF of the basin can be reduced. Share of blue, green and grey VWC in the basin-level average VWC of crops under irrigated condition is presented in Fig. 5. Remarkable variation was observed in the proportion of green and blue VWC of crops under irrigated conditions. As expected, the blue VWC was higher in case of winter (rabi) season crops (chickpea, lentil, peas, potato, wheat and mustard) and was lower for the kharif (rainy) season crops like paddy, maize, groundnut, sorghum and sesame. The green VWC represented the opposite pattern. Among all the crops and considering basin-level average values, sesame had the highest VWC of 20,724 and 21,071 m3/t under irrigated and rainfed conditions (Fig. 4f). Extremely high VWC was mainly because of the low physical productivity of sesame compared to other crops. Black gram and pigeon pea also showed higher VWC. Other cereals (maize, wheat, sorghum and paddy) showed VWC in the range of 1586–4555 m3/t under

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irrigated condition, while under rainfed conditions values were slightly lesser. Among the studied crops sugarcane and potato exhibited the smallest virtual water content figures (298–277 m3/t, respectively), probably due to the higher yields. Blue water proportion (BWP), estimated as VWCblue divided by total VWC (Liu et al. 2009), was in the range of 2% (maize) to 65% (black gram) for kharif crops. The shorter growing season and sufficiency of rainfall to meet the water requirement resulted in lower BWP of the maize, paddy and other kharif crops. Black gram and pigeon pea had highest blue VWC in terms of m3/t. Due to intensive irrigation and low rainfall, the BWP for all the rabi crops was more than 96%. Higher dose of fertilizer application combined with accelerated leaching of fertilizers with percolating irrigation water resulted in higher proportions of grey VWC for wheat (31%), potato (28%), paddy (22%) and mustard (17%).

Water footprint of crops (WFc ) In order to improve the results of the analysis, the GRB was divided into four regions (SRUs) on the basis of uniformity in the VWC of the crops estimated at APU level. Estimated VWC and the crop production under irrigated and rainfed areas of the respective SRUs were used to assess the WF of the GRB for the year 2011, a year of average rainfall. Interesting patterns emerged from the water footprints of irrigated and rainfed farming production (Mm3/year) (Fig. 6). With the presence of agencies like Uttar Pradesh Water Sector Restructuring Project funded by World Bank, Uttar Pradesh Jal Nigam and Uttar Pradesh Irrigation Department, there was substantial advancement in irrigation infrastructure in the GRB. Larger proportion of the cultivated areas within the GRB was also provided with canal water from the Sharda Sahayak canal systems, providing year-round irrigation supplies to the farmers. Irrigated agriculture accounted for 88.9% (10,832.5 Mm3) of the WF of the crop production. Concerning rainfed and irrigated agriculture in the basin, total irrigated area was more than six times the rainfed area (Table 1). Water footprint of rainfed agriculture mainly comes from the consumption of rainfall in the crop evapotranspiration. Rainfed crop production consumed about 11.1% (1363.5 Mm3) of the total water consumed by the crop production (Table 3). Maize, black gram, sugarcane and pigeon pea were the major crops that together accounted for 64.3% (876.5 Mm3) of the WF of the rainfed areas. Comparatively lower share of rainfed agriculture in total crop WF of the basin indicated that crop production in GRB was more dependent on irrigation infrastructure (blue water).

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(a) Irrigated - blue VWC SRU1

SRU2

SRU3

25000

SRU4

VWC, m3/ton

VWC, m3/ton

6000

(b) Irrigated-green VWC

4000 2000

20000 15000

SRU2

SRU3

SRU4

10000 5000 0

Black gram Groundnut Maize Pearl Millet Pigeon pea Paddy Sesame Sorghum Sugarcane Chickpea Lenl Mustard Peas Potato Wheat

Black gram Groundnut Maize Pearl Millet Pigeon pea Paddy Sesame Sorghum Sugarcane Chickpea Lenl Mustard Peas Potato Wheat

0

(d) Irrigated- total VWC

(c) Irrigated - grey VWC SRU1

SRU2

SRU3

SRU4

800 600 400

VWC, m3/ton

SRU1 1000

VWC, m3/ton

SRU1

SRU2

SRU3

SRU4

30000 20000 10000

200 0 Black gram Groundnut Maize Pearl Millet Pigeon pea Paddy Sesame Sorghum Sugarcane Chickpea Lenl Mustard Peas Potato Wheat

Peas

Potato Wheat

Lenl Mustard

Sugarcane Chickpea

Sesame Sorghum

Pigeon pea Paddy

Pearl Millet

(e) Rainfed - green VWC

(f) Average total VWC within GRB

SRU1 20000

SRU3

25000

SRU2 SRU4

10000

Irrigated

Rainfed

20000 15000 10000 5000 Black gram Groundnut Maize Pearl Millet Pigeon pea Paddy Sesame Sorghum Sugarcane Chickpea Lenl Mustard Peas Potato Wheat

0 Black gram Groundnut Maize Pearl Millet Pigeon pea Paddy Sesame Sorghum Sugarcane Chickpea Lenl Mustard Peas Potato Wheat

0

VWC, m3/ton

Black gram

VWC, m3/ton

30000

Groundnut Maize

0

Fig. 4 a Blue, b green, c grey and d total VWC of crops under irrigated conditions, e green or total VWC of crops under rainfed conditions for each SRU and f basin-level average VWC of crops within GRB

VWC, m3/t

25000

Grey VWC

20000

Green VWC 15000

Blue VWC

10000

Chickpea Lentil Mustard Peas Potato Wheat

0

Black… Ground… Maize Pearl… Pigeonpea Paddy Sesame Sorghum Sugarcane

5000

Fig. 5 Basin-level average blue, green and grey virtual water content of crops (m3/ton) under irrigated conditions

The annual WFc for the irrigated areas within the GRB was 10,832.5 Mm3 in the year 2011. About 56.2% of the WFc was due to blue water use, 40.0% was from the

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consumption of rain water, and a smaller portion of 3.8% (718 Mm3) was the result of the pollution emanating from the activities of crop production (Fig. 6). At the national level, the share of blue and green WF in the total WF of the country was only 29 and 59%, respectively (Kampman et al. 2008). A large share of blue water in the WF of GRB can be attributed to the extensive irrigation development within the basin. Irrigated wheat, paddy and sugarcane production consumed 3705.1, 1358.9 and 515.7 Mm3, respectively, of the blue water resources together accounting for 96.6% of the blue WF for irrigated agriculture. Almost all green water use in the basin was through paddy, sugarcane and wheat. Cereal crops (paddy and wheat) accounted for 90.9% of the grey WF for crop production within the basin. Annual WF of crop production (irrigated ? rainfed) within the basin was 12,196 Mm3. The cereal crops paddy, wheat, maize and pearl millet accounted for 69.9% of the

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Fig. 6 Water footprint of the irrigated areas of the GRB, distinguishing green, blue and grey WFs. Crops with less than 0.1% share in WF are not shown in figure

Table 3 Cropwise share in the WF of irrigated and rainfed agriculture within the GRB Crop

Irrigated area WF (Mm3)

Rainfed area % share

WF (Mm3)

% share

161

Potato

161

11.19

Pigeon pea

153 104

Maize Pearl millet

18.3 1.5

0.17 0.01

258.7 74.7

Sorghum Sugarcane Chickpea Lentil Mustard Peas

3839.2

35.44

63.3

4.64

Sesame

0.1

0.00

103.9

7.62

Sorghum

88 76

0.2

0.00

88.1

6.46

Pearl millet

2373.3

21.91

231.8

17.00

Groundnut

68

1.27

Peas

44 39

3.5

0.03

17.3

0.0

0.00

39.0

2.86

Lentil

143.1

1.32

18.0

1.32

Chickpea Total

43.7

0.40

0.0

0.00

Potato

156.7

1.45

4.4

0.32

Wheat

4252.0

39.25

11.5

0.84

10,832.5

100.00

1363.5

100.00

Total

2605

Mustard

4.91

Sesame

3903

Sugarcane

18.98 5.48

17.11

66.9

Paddy

4263

Paddy

277

233.3

0.01

152.6

Wheat

233

0.00

0.8

0.00

Total WF

Black gram

0.0

Groundnut

0.1

Crop

Maize

Black gram

Pigeon pea

Table 4 Cropwise share in total annual water footprint of crop production within the GRB

WFc (Table 4). In particular, paddy, wheat and sugarcane comprised a large share of the cropped area, accounting for 88.3% of the WFc in the GRB. Being the staple food, paddy and wheat significantly influenced the WF. Sugarcane, being the remunerative cash crop, also accounted for 35% of the total WFc within the basin. On a national level,

21 12,196

wheat, paddy and sugarcane were the major determinants of the water footprints.

Response of WF to optimal cropping pattern The optimal allocation of irrigated and rainfed areas of crops is presented in Fig. 7. The allocations were based on the VWC of the crops in each SRU with a focus on reducing the blue WF of crop production within the basin.

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At the SRU level, restrictions on basin-level crop production, blue water use and extent of irrigated and rainfed area allocated to a particular crop were imposed. As shown in Fig. 7, the optimized cropping pattern assigned comparable weights to the crops in the SRU where it had low blue water use. Major shifts in the cultivation areas of paddy, wheat and sugarcane were observed. Irrigated area of wheat in SRU1 and SRU3 was reduced, while that in SRU2 and SRU4 was increased. Despite a higher blue VWC, the wheat area in SRU2 was further increased mainly because of higher productivity of wheat in SRU2. This resulted in higher opportunity to benefit in terms of blue water saving. Area under irrigated paddy was increased in SRU4 with the simultaneous reduction in the other SRUs. Under optimal allocation, the upper reach of the basin (SRU1) was found

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to be better for the cultivation of sugarcane. All the rainfed and irrigated area under sugarcane was allocated to SRU1. There was substantial increase in rainfed area of black gram in SRU3. Under rainfed conditions, the optimal cropping pattern reduced the number of rainfed crops to be cultivated in each SRU. For example, in SRU 3, six rainfed crops were cultivated under normal scenario, which reduced to only two crops (mustard and black gram) under optimal plan. Water footprint is a spatially explicit indicator of water use. Allocation of crops on the basis of variability in WF can reduce the water use within the basin. At the basin scale, this forms the part of the demand side management strategies. WF of crops in GRB also varied across the SRUs. In an optimal cropping pattern, the production of a

Fig. 7 Present and optimal areas under different crops for a irrigated and b rainfed areas within the GRB (UA_rabi: unallocated area during rabi season, UA_kharif: unallocated area during kharif season)

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Environmental Earth Sciences (2017)76:786 Table 5 Changes in WFs and gross cropped area under optimized cropping patterns

Particular

Gross cropped area (M ha)

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In 2011

Optimal

Irrigated

Rainfed

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Total savings

Irrigated

Rainfed

Irrigated

Rainfed 0.00

2.11

0.37

2.10

0.37

0.01

Blue WF (Mm3)

5908.20

0.00

5712.20

0.00

196.00

0.00

Green WF (Mm3)

4205.90

1363.70

4041.60

1310.80

164.30

52.90

particular crop was expected from the areas where it has comparatively less water consumption (low VWC). Optimization of cropping pattern resulted in saving of 0.01 Mha in irrigated area within the GRB. The optimized cropping pattern reduced the blue WF and irrigated area by 196.0 Mm3 and 0.03 Mha, respectively, maintaining the present levels of crop production (Table 5). This highlighted the potential of reorganizing cropped areas to reduce the pressure on the water resources of the basin without affecting the crop production pattern.

Discussion Agricultural water use statistics often report water withdrawal for irrigation. However, WF is a more suitable indicator for measuring water consumption because a large part of water withdrawal for irrigation will return to local water bodies like aquifers, ponds and regenerated stream flow which will be further used in the downstream. For example, on a global scale, about 40% of agricultural water withdrawals are not consumed, but go back to downstream water bodies as return flows (Perry 2007; Shiklomanov 2000). This study was focused on the assessment of VWC and actual water consumption (WF) of crop production within the GRB, distinguishing the blue, green and grey WFs within irrigated and rainfed areas. The VWC estimated in this study was slightly higher than those estimated for the state of Uttar Pradesh (Kampman et al. 2008), with paddy and sorghum being the two exceptions and having about 30% less VWC than the state average. This variation was mainly due to the different scales of assessments used in the study. Kampman et al. (2008) treated the entire state as a uniform unit and considered only one average value of VWC for a crop to represent the entire state, while the present study estimated VWC at higher spatial resolution (SRU). This fact suggests that VWC and WF of the river basin should be estimated at finer resolution units for better accuracy of the assessments. The annual WF of crop production in GRB was 12,196 Mm3 with irrigated and rainfed agriculture accounting for 89 and 11%, respectively. The higher percentage of blue water use can be attributed to the development of irrigation infrastructure in the GRB (Abeysingha et al. 2015). The irrigated area within the basin was 87.3%

which was much higher than the national average of 44.9%. The large area under paddy, wheat and cash crop like sugarcane accounted for a substantial portion of the WF of the basin. At the national level, paddy, wheat, coarse cereals and sugarcane are the major contributors to total WF (Kampman et al. 2008). The VWC and WF analysis showed the quantity of water needed to produce different crops and can be useful for suggesting the best use of the available water. In this sense, it is important to establish whether the water used in the production of a product is derived from rainwater (green water) or from surface and ground water (blue water) (Chapagain et al. 2006; Falkenmark 2003). National water policy for India (MOWR 2012) also emphasizes crop planning on the basis of WFs to favour water-efficient crops and to enhance water productivity of the region. Among several management practices for enhancing water utilization, adjustment of cropping pattern has been identified as a key to better utilization of available water and reduction in the WF within the basin (Dumont et al. 2013; Zeng et al. 2012; Aldaya et al. 2010; Aldaya and Llamas 2008). The optimization model developed in this study did not impose any greater restrictions on cropped areas but limited the values of certain parameters like crop production and rainfed area under certain crops to the levels of 2011. The cropped areas within the basin were organized in a way that the blue WF is minimized and cultivated area (irrigated and rainfed) was used in an optimal way without affecting the present levels of crop production within the basin. Relocation of certain crops in regions with low VWC resulted in considerable reduction in the blue water use within the basin. Optimal crop plans in conjunction with introduction of efficient crops can further ensure the sustainability of blue water resources (Kaur et al. 2010). The whole-farm agro-economic optimization models used for planning the agriculture sector cannot be used to devise appropriate management strategies at the land parcel level (Ghasemi et al. 2014). The optimization model developed in this study minimized the blue WF of the basin with due consideration to regional land and water resources within the SRUs, which were delineated on the basis of soil and agro-ecology. This was an advancement over allocations previously being done at the river basin scale to maximize the net returns or

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minimize the groundwater use (Kaur et al. 2010; Rejani et al. 2009; Sethi et al. 2006). The present study suggested that with the optimization of the cropping pattern on the basis of VWC, it is possible to reduce the blue WF of the basin without substantial alterations in existing cultivated area and total crop production within the basin. However, in the light of food security issues, future work should also look at the nutritional values of these crops (Zimmer and Renault 2003). Development of the optimal cropping plans should also consider the factors like labour and fertilizer availability (Sahoo et al. 2006). However, this has little significance in case of GRB where intensive agriculture is being practised after the green revolution in 1960s (Rena 2002). Also, the recently launched Pradhan Mantri Krishi Sinchai Yojana (Prime Minister Agriculture Irrigation Scheme) in India aims to provide water to every farm by improving on- and off-farm infrastructure, further widening the scope of reorganizing cropping patterns within the river basin.

Conclusions The study used district-level data of climate, soil and crop production to assess the VWC and WF of the GRB. The basin was divided into four region on the basis of soil and agro-ecological regions. Annual WF of crop production in GRB was 12,196 Mm3. Large portion of the total WF was from irrigated areas with blue WF accounting for 48% of total WF. In view of the larger share of green WF, improvement in green water management and green water productivity is also important for relieving the pressure on blue water resources. The share of the grey water footprint was relatively small (6%), but this was a conservative estimate, because we have analysed the required assimilation volume for leached nitrogen fertilizers only, leaving out other pollutants such as phosphorus and pesticides. Paddy, wheat and sugarcane emerged as major contributors to the total WF of the GRB. There is great potential for reducing blue WF (surface ? ground water consumption) through an optimized cropping pattern. Given the restrictions on water availability in irrigated and rainfed areas within the SRUs, the developed model was applied to determine the optimal land allocation that minimizes the blue WF within the basin. From the model output, it was possible to identify what to grow and where to grow without affecting present crop production levels but with substantial reduction in blue water consumption. To prevent pressure on available water resources, this new approach of reallocation on the basis of WFs offers better opportunities to other basins or regions of the world.

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Environmental Earth Sciences (2017)76:786 Acknowledgements Authors are thankful to Indian Council of Agricultural Research for providing required funds for the research. We are also grateful to Indian Agricultural Research Institute for providing the requisite facilities to undertake this research.

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