Evaluation of Nexus-Sustainability and Conventional Approaches for Optimal Water-Energy-Land-Crop Planning in an Irrigated Canal Command

Abstract

With the increasing threat to water, energy, and food resources world-wide, it is highly imperative to manage these resources sustainably. This study develops an optimal crop area allocation model based on a novel nexus-sustainability index (NSI), integrating the water use, energy use (environmental dimension); land use, labour use (social dimension); yield return, and per capita food production (economic dimension) indicators in agricultural food production. This NSI-based model is evaluated in a reservoir-canal command for optimal water and energy uses and, subsequently, compared with the conventional models of Net-Economic Return (NER), Water-Food (W-F) nexus, and Energy-Food (E-F) nexus based approaches using multi-criteria decision making (MCDM) analysis. The comparative results revealed that the NSI-based model is the best that could save water, energy and labour resources by 36.82(±1.91)%, 23.72(±2.47)%, and 2.29(±0.16)% during the Kharif season; and 17.5(±0.59)%, 19.82(±1.52)%, and 2.02(±0.42)% during the Rabi season as compared to the existing condition, respectively, enhancing the net economic return by 56.53(±3.28)% and 79.96(±2.97)% during the corresponding seasons, respectively. Finally, it is advocated that the NSI-based approach could manage the water and energy resources sustainably ensuring security in the local water-energy-land-food (WELF) nexus.

Introduction

With the ever increasing population, urbanization, industrialization, and changes in human lifestyle, challenges in water, energy, and food securities are amplifying at a rapid rate world-wide. By 2030, the global demands for water, energy, and food resources are estimated to be increasing by 40%, 50%, and 35%, respectively (United States National Intelligence Council US NIC (United States National Intelligence Council) (2012). Abundant use of water and energy resources in agricultural food production, along with maintaining the improved living standards and climate change issues, have severe ecological and environmental consequences (World Bank 1992; Mariolakos 2007). Under this context, the Bonn-2011 conference, held in Germany, introduced the concept of Water-Energy-Food (WEF) nexus as a solution for the green economy. The WEF nexus concept encourages the understanding of the inter-linkage among the water, energy, and food elements of the environment so that any management action on a particular element can ensure security to all the elements simultaneously.

The Sustainable Development Goals (SDGs) of the United Nations for the post-2015 agenda calls for to achieve the long-term sustainable development of the human society with a new set of actions towards achieving sustainable water use, energy use and agricultural practices, promoting more inclusive economic development (United Nations 2014). Thus, the nexus concept has drawn a world-wide attention (Kurian 2017). Studies on this nexus aspect mainly focus on understanding the connectivity among water, energy and food sectors (Howells et al. 2013; Giampietro et al. 2014; Rasul et al. 2014; Ringler et al. 2016; Cai et al. 2018), and building integrated analytical frameworks of the WEF nexus for various scenario generation (Giampietro et al. 2013; Daher and Mohtar 2015; Dargin et al. 2018; White et al. 2018). However, agriculture being the largest consumer of both the water and energy resources globally (Shiklomanov 2000; Khan and Hanjra 2009; Alluvione et al. 2011; Hoff 2011; Zhou et al. 2013; USDOE 2014), it is highly necessary to develop a framework for optimal use of water and energy resources for the agricultural production under specific consideration of the WEF nexus. This will ensure sustainable food production to feed the ever-increasing population with a limited supply of water and energy resources. Moreover, the land availability for agricultural production is also diminishing day-by-day with the rapid growth of urbanization and industrialization over time. Hence, with the growing population, declining availability of cultivable land, increasing stress on water and energy resources, and climate variability, an optimal allocation of the limited land area for various crops with the optimum use of the scarce water and energy resources could be a feasible resource conservation measure in agricultural food production ensuring future security to water, energy, and food sectors simultaneously.

Many studies in the literature prescribed the optimal cropping patterns for various regions (Kaushal et al. 1985; Mayya and Prasad 1989; Paudyal and Gupta 1990; Singh et al. 2001; Sethi et al. 2002; Sethi et al. 2006; Negm et al. 2006; Sahoo et al. 2006; Fawzy 2009; Kaur et al. 2010; Singh 2012; Garg and Dadhich 2014; Singh 2014; Nguyen et al. 2016) intending to maximize the net economic return. However, these studies ignore the sustainable nexus among the water, energy, land, and food resources in the cropping system. It is because maximizing the net economic return or crop yield would result in the consumption of higher water, energy, and land resources for agricultural food production. Conversely, the sustainability of the system entails reducing their consumption, which is conflicting and inconsistent at the same time. Moreover, social factors also play significant roles in agricultural production. These factors are silent in all the previous studies. Further, the planning of a comprehensive cropping pattern should account for the inherent interrelation among the environmental, economic, and social aspects of the farming systems. To address all the issues as mentioned earlier, there is a need to develop an optimal crop area allocation model with the help of a novel nexus-sustainability index (NSI) which integrates the environmental, economic, and social dimensions of sustainability in agricultural food production.

The NSI based model can guide food production towards sustainable agriculture, to meet the increasing food demands for the growing population, but not at the cost of water and energy security. Moreover, the developed model should be tested in a canal command area where the nexus among the water, energy, and food elements are quite prominent. The irrigated commands of the multi-purpose reservoir projects clearly depict the nexus among water (surface water and groundwater), energy (hydroelectricity), and food (crop production), which is a significant factor for optimal water allocation and reservoir operation. However, the mismanagement of both canal water and groundwater resources in the canal commands results in the water logging condition in some parts of the command. In contrast, the other parts could experience a depleting trend in groundwater level as a result of overexploitation. This scenario persists in many of the world canal commands due to improper crop planning, which affect agricultural productivity. To fulfill the requirement of the high water demanding crops subject to this improper cropping pattern, the upstream end-users of the canal command divert more water from the canals unethically resulting in excess seepage loss from the unlined upstream canal sections which raise the groundwater level in the region leading to waterlogging condition. Towards the downstream section of the canal command, lesser availability of canal water leads to more dependency on groundwater resources resulting in depleting groundwater level in that region. Hence, an optimal cropping pattern of varied water and energy demands in a canal command would account for the food sector of the region, ensuring sustainability in the water-energy-land-food (WELF) nexus with the optimal use of constituent resources simultaneously.

In light of the above discussion, the primary objective of this study is to minimize the uses of both water and energy resources in crop production with the optimal allocation of land area and labour resources without compromising the overall economic return significantly so as to ensure sustainability in the WELF nexus. Further, this novel approach is to be compared with the conventional approaches existing in the literature to establish its superiority over these approaches.

This paper is organized as follows. Section 2 presents a detailed methodology. Section 3 provides a brief description of the study area, Section 4 discusses the results, and section 5 concludes the study.

Methodology

For sustainable agricultural food production accounting for the WELF nexus, the six sustainability indicators, such as water-use indicator (WUI) and energy-use indicator (EUI) as the environmental dimension; yield-return indicator (YRI) and per capita food production indicator (PFPI) as the economic dimension; and labour-use indicator (LabUI) and land-use indicator (LUI) as the social dimension were developed in this study. All these indicators were suitably aggregated to develop the composite nexus sustainability index. The WUI and EUI account for the water-food nexus and energy-food nexus, respectively. Similarly, the LUI and LabUI account for the society-food nexus. The developed nexus-sustainability index was used to formulate an optimization model to best utilize the land water and energy resources with all the necessary system constraints for obtaining the optimal cropping pattern for a region.

For optimal allocation of the land area, ensuring sustainability in the water-energy-land-food nexus in the reservoir command, a linear programming-based optimization model was developed using the ‘nexus-sustainability index’ as illustrated in Fig. 1. The objective function of this optimal problem is comprised of the nexus-sustainability index and the area under various crops. The maximization of the nexus-sustainability index involves the simultaneous maximization of the indicators associated with it, providing an optimum solution for the land area under various crops.

Fig. 1
figure1

Modeling framework to ensure sustainability in the water-energy-land-food (WELF) nexus in canal command (NER = net economic return; W-F = water-food nexus; E-F = energy-food nexus)

The developed optimization model was tested in a typical canal command in eastern India, considering four major crop types, viz., paddy, maize, pulses, and oilseeds being grown in two seasons of Kharif (rainy/monsoon) and Rabi (non-monsoon). The performance of the NSI framework was evaluated against the existing cropping pattern and the traditional approaches (Fig. 1) using the performance indicators of maximization of net-economic return (MNER), minimization of water consumption (MWC), minimization of energy consumption (MEC), and minimization of labour consumption (MLC) in crop production. This evaluation was performed through multi-criteria decision making (MCDM) method (McDaniels 1995; Ruju and Kumar 1999; Levy et al. 2000; Store and Kangas 2001; Parnell et al. 2001; Gregory and Wellman 2001; Gregory and Failing 2002; Gomez-Limon et al. 2003). Finally, the best optimal cropping pattern was recommended ensuring sustainability in the local-scale WELF nexus of the command area under study.

Environmental Indicators

For optimal allocation of land resources, environmental sustainability is a primary concern along with the social and economic prosperity. Therefore, in the current study, two environmental indicators, namely, ‘water use indicator’ and ‘energy use indicator’, were incorporated in the advocated nexus sustainability index to account for the sustainable use of both water and energy resources in agricultural production while ensuring food security simultaneously. These indicators are discussed below.

Water Use Indicator (WUI)

The water use indicator of various crop types is the amount of yield produced per unit use of irrigation water in its production, expressed as:

$$ {WUI}_{si}=\frac{Y_{i,s}}{W_{i,s}} $$
(1)

where WUIs = water use indicator of the ith crop in the sth season (kg/m3); Yi,s = yield of the ith crop (kg/ha) in sth season; and Wi,s = water consumption per hectare of the ith crop (m3/ha) in the sth season.

Energy Use Indicator (EUI)

The energy use indicator of various crop types is the amount of cop yield produced per unit use of energy in its production, expressed as:

$$ {EUI}_{si}=\frac{Y_{i,s}}{E_{i,s}} $$
(2)

where EUIs = energy use indicator of the ith crop in the sth season (kg/MJ); and Ei.s = energy consumption per hectare of the ith crop (MJ/ha) in the sth season.

Social Indicators

Although the social dimension in agricultural production plays a major role in optimal land allocation, this factor is mostly ignored in the conventional optimization methods. Generally, with the rapid growth in industrialization and urbanization, the land availability for agricultural use is decreasing world-wide. Similarly, the labour availability for agricultural operations has become critical in the present context of seasonal nature of agricultural farm-jobs, higher wages in other locally available jobs, and a presumption of an agricultural job to be of low social esteem in many developing countries. Therefore, these two major social dimensions affecting the crop yield were incorporated in the current study to determine the composite nexus-sustainability index, as discussed below.

Land Use Indicator (LUI)

The land use indicator of various crop types represents the amount of crop yield produced per unit use of land, expressed as:

$$ {LUI}_{si}={Y}_{i,s} $$
(3)

where LUIs = land use indicator of the ith crop in the sth season (kg/ha).

Labour Use Indicator (LabUI)

The labour use indicator of various crop types represents the amount of crop yield produced per unit use of farm labour-hour (lab-h), expressed as:

$$ { LabU I}_{si}=\frac{Y_{i,s}}{LabU_{i,s}} $$
(4)

where LabUIs = labour use indicator of the ith crop in the sth season (kg/lab-hrs); LabUIi,t = labour consumption per hectare of the ith crop (lab-hrs/ha) in the sth season.

Economic Indicators

For a successful land allocation policy in the agricultural food production system, maximization of the economic return from the crop yield is an essential factor. To account for this factor, the yield return indicator (YRI) and per capita food production indicator (PFPI) were accounted for in the developed index.

Yield Return Indicator (YRI)

The yield return indicator of various crop types represents the amount of crop yield produced per unit cost of cultivation, expressed as:

$$ {YRI}_{si}=\frac{Y_{i,s}}{CoC_{i,s}} $$
(5)

where YRIs = yield return indicator of the ith crop in the sth season (kg/INR); CoCi,t = cost of cultivation per hectare of the ith crop (INR/ha) in the sth season; and INR = Indian national rupee (1 US$~75.97 INR).

Per Capita Food Production Indicator (PFPI)

The per capita food production indicator of various crop types is expressed as:

$$ {PF\mathrm{P}I}_{si}=\frac{Y_{i,s}}{POP} $$
(6)

where PFPIsi = per capita food production indicator of the ith crop in the sth season (kg per capita); and POP = population.

Nexus-Sustainability Index (NSI)

Considering the WELF nexus, a novel NSI has been introduced in this study as a composite measure of environmental, economic, and social indicators in agricultural food production. All the above-described indicators can be normalized in order to neglect the effect of multiple units while computing the NSI as:

$$ {X}_i=\frac{x_i-{x}_{i,\mathit{\min}}}{x_{i,\mathit{\max}}-{x}_{i,\mathit{\min}}} $$
(7)

where Xi = ith normalized nexus sustainability indicator; xi = actual value of the ith nexus sustainability indicator; xi,min = minimum value of xi; and xi,max = maximum value of xi.

The normalized indicators under each dimension can be aggregated by the arithmetic mean aggregation method as (Mazziotta and Pareto 2013):

$$ {DI}_d={\sum}_1^n\frac{w_i{X}_{id}}{w_i} $$
(8)

where wi = weight applied to the ith indicator; and DId = dth dimensional indicator; n = number of indicators under dth dimension; Xid = normalized indicator of dth dimension.

The nexus sustainability index can be estimated by aggregating the dimensional indicators as (Mazziotta and Pareto 2013):

$$ NSI=\left({\prod}_{i=1}^n{w}_i{DI}_i\right)\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right. $$
(9)

n = number of dimensional indicators.

Development of the Optimization Models

The NSI-based linear programming (LP) model was formulated to optimize the land area to be allocated to the major crops being cultivated in the study area during the Kharif and Rabi cropping seasons. Similarly, three more LP models were also formulated using the W-F and E-F based approaches, and the conventional net economic return (NER)-based approach. All the developed models consist of linear objective functions and a set of linear constraints, as described below. All the optimization models were solved using the LINGO 11.0 software package.

Objective Function for the Nexus-Sustainability Index-Based Approach

The objective function of the NSI-based optimization model is given by

$$ \mathit{\operatorname{Max}}\ Z={\sum}_{s=1}^m{\sum}_{i=1}^r{NSI}_{is}{A}_{is} $$
(10)

where NSIis = nexus sustainability index for the ith crop during the sth cropping season; Ais = area allocated to the ith crop during the sth season (ha); r = total number of crops considered for the study; and m = total number of cropping seasons.

Objective Function for the Conventional NER-Based Approach

The objective function for the NER-based model is given by

$$ \mathit{\operatorname{Max}}\ Z={\sum}_{s=1}^m{\sum}_{i=1}^r{NER}_{is}{A}_{is}-{C}_{cw}{\sum}_{s=1}^2{Vcw}_s-{C}_{gw}{\sum}_{s=1}^m{Vgw}_s $$
(11)

where NERis = net economic return from the ith crop during the sth cropping season (INR/ha); Csw = unit cost of canal water (INR/m3); Vcws = volume of canal water used for irrigation in the sth season; Cgw = unit cost of pumping groundwater (INR/m3); and Vgws = volume of groundwater used for irrigation in the sth season (m3).

The net economic return can be estimated as:

$$ {NER}_{is}=\left({P}_i\ {Y}_{is}\ \right)+\left({Pb}_i\ {Yb}_{is}\right)-{CoC}_{is} $$
(12)

where Pi = current market price of the ith crop (INR/kg); Yis = yield of the ith crop during the sth season (kg/ha); Pbi = current market price of the by-products of the ith crop (INR/kg); Ybis = yield of by-products of the ith crop in the sth season (kg/ha); and CoCis = cost of cultivation of the ith crop during the sth season excluding irrigation cost (INR/ha).

Objective Function for the W-F Based Approach

The water productivities of various crops considered in this study were maximized to obtain the optimum yield with the minimum water use. The objective function for the W-F based model can be given by:

$$ \mathit{\operatorname{Max}}\ Z={\sum}_{s=1}^m{\sum}_{i=1}^r{WUI}_{i,s}{A}_{is} $$
(13)

where WUIi,s = water use indicator of the ith crop during the sth season (kg/m3).

Objective Function for the E-F Based Approach

The energy productivity of various crops considered in the current study was maximized to obtain the optimum yield with the minimum uses of energy. The objective function for the E-F based model is given by:

$$ \mathit{\operatorname{Max}}\ Z={\sum}_{s=1}^m{\sum}_{i=1}^r{EUI}_{i,s}{A}_{is} $$
(14)

where EUIi,s = energy use indicator of the ith crop during the sth season (kg/MJ).

System Constraints

Land Area Constraint

The land area allocated to various crops during the Kharif and Rabi seasons should not be more than the total cultivable area in the canal command given by:

$$ {\sum}_{i=1}^r{A}_{is}\le {CCA}_s;\forall \mathrm{s} $$
(15)

where CCAs = total cultivable command area during the sth season (ha).

Water Availability Constraint

The total irrigation demand by all the crops during one season must be satisfied by the available water for irrigation from canal and groundwater sources during that season given by:

$$ {\sum}_{i=1}^r{GIR}_{is}{A}_{is}\le {ASW}_{irs}+{AGW}_{irs};\forall \mathrm{s} $$
(16)

where GIRis = gross irrigation requirement by the ith crop during the sth season (m); ASWirs = available volume of surface water for irrigation during the sth season (ha-m); and AGWirs = available volume of groundwater for irrigation during the sth season (ha-m).

The gross irrigation demand (GIR) by each crop can be estimated as:

$$ GIR=\frac{ET_{a_i}-{R}_{eff}}{\eta } $$
(17)

where ETai = actual seasonal crop evapotranspiration from the ith crop (m); Reff = seasonal effective rainfall (m); and η = irrigation project efficiency (−).

The actual evapotranspiration considering alternate wetting and drying phenomena in the crop fields can be estimated for the ith crop as (Allen et al. 1998; Sahoo et al. 2012; Swain and Sahoo 2015; Sahoo et al. 2018).

$$ {ET}_{a_i}=\left\{\begin{array}{c}{\sum}_{j=1}^{ns}{\left({K}_{c_i}\times {ET}_{o\kern0.75em }\right)}_j;{\uptheta}_j={\uptheta}_s\\ {}{\sum}_{j=1}^{ns}{\left(\frac{A_{wt}-{R}_d}{A_{wt}-{A}_{wr}}\right)}_j\times {K}_{c_i}\times {ET_o}_j;{\uptheta}_r<{\uptheta}_j<{\uptheta}_s\end{array}\right. $$
(18)

where Kci = crop coefficient of the ith crop (−); Awtj = available soil water in the crop root zone on the jth day (mm); Rd = root zone depletion on the jth day (mm), Awrj = readily available soil water in the crop root zone under unsaturated condition on the jth day, expressed as a fraction of available soil water (mm); ns = number of days in the sth cropping season; θj = variable soil moisture content in the root zone on the jth day (mm3/mm3); θs = saturated moisture content (mm3/mm3); θr= residual soil moisture content (mm3/mm3); EToj = reference crop evapotranspiration on the jth day (mm) estimated using the Hargreaves and Samani method as (Hargreaves and Samani 1985):

$$ {ET}_o=0.0023{R}_a\left[0.5\ \left({T}_{max}+{T}_{min}\right)+17.8\right]{\left({T}_{max}-{T}_{min}\right)}^{0.5} $$
(19)

where Ra = extraterrestrial solar radiation (mm/day); Tmax = daily maximum air temperature (°C); and Tmin = daily minimum air temperature (°C).

The effective seasonal rainfall (Re) can be determined by the Food and Agricultural Organization (FAO) method as (Dastane 1978):

$$ {R}_e=\kern0.5em \left\{\begin{array}{c}\ 0.7R,\mathrm{for}\ \mathrm{non}\ \mathrm{rice}\ \mathrm{crop}\kern0.5em \\ {}0.8R,\kern0.5em \mathrm{for}\ \mathrm{rice}\ \mathrm{crop}\mathrm{s}\ \end{array}\right. $$
(20)

where R = average actual rainfall received during the cropping season.

Groundwater Irrigation Constraint

The groundwater level should not deplete beyond the maximum permissible mining allowance of the aquifer which is represented by the following mass balance constraint as:

$$ {\sum}_{s=1}^m\left[{Vgw}_s-{R}_c\ {Vcw}_s-{R}_r\ {RF}_s\ {A}_T+{E}_s+{Vow}_s\ \right]\le PMA $$
(21)

where Rc = recharge fraction from canal water during conveyance = 0.35 (Das et al. 2015); Rr = recharge fraction for rainfall = 0.14 (Das et al. 2015); RFs = amount of rainfall during the sth season (m); AT = gross canal command area (ha); Es = seasonal evaporation loss from groundwater (m3) (Raul 2012); Vows = volume of groundwater used by sectors other than irrigation; and PMA = annual permissible groundwater mining allowance (m3).

The permissible groundwater mining allowance is calculated as:

$$ PMA=\Delta H\times {A}_T\times {S}_y $$
(22)

where ∆H = annual average groundwater table fluctuation in the region (m); and SY = specific yield of the aquifer = 0.04 for the selected study area (Raul et al. 2011).

Food Supply Constraints

To supplement the carbohydrate and plant protein availability to the local consumers in the study area, the food production from rice and pulses must satisfy the local nutritional demand as:

$$ {P}_{1s}{A}_{1s}\ge {F}_{1 ds} $$
(23a)
$$ {P}_{2s}{A}_{2s}\ge {F}_{2 ds} $$
(23b)

where P1s, P2s = productivity of rice and pulses during the sth season (kg/ha), respectively; and F1ds, F2ds = food demands for rice and pulses in the region during the sth season (kg), respectively.

Affinity Constraints

Keeping in view the local food requirement, socioeconomic issues, and affinity towards the prevailing cropping practices, a lower and upper limit of the area under different crops can be considered as:

$$ {f}_{is,\kern0.5em \mathit{\min}}{TA}_{is}\le {A}_{is}\le {f}_{is,\mathit{\max}}{TA}_{is} $$
(24)

where fis min = fraction associated with the lower limit of crop area constraint; fis, max = fraction associated with the higher limit of crop area constraint; and TAis = total crop area (ha).

Energy Use Constraints

The hydroelectricity energy requirement for pumping water from all sources for all the crops should not be greater than 30% of the total energy consumption by the farmers, given by:

$$ {\sum}_{i=1}^r{e}_i{A}_{is}\le 0.3{\sum}_{f=1}^{nf}{E}_{fs};\kern2.5em \forall $$
(25)

where ei = energy consumption for the ith crop (MJ/ha); Efs = total energy consumption of the fth farmer in sth season (MJ); and nf = number of farmers in the region.

Non-negativity Constraint

$$ {A}_{is}\ge 0 $$
(26a)
$$ {Vcw}_s\ge 0;{Vgw}_s\ge 0\kern2.5em \forall \mathrm{s} $$
(26b)

The NSI-based model uses the constraints given by Eqs. (15), (16), (23–25), and (26a). Similarly, the NER based model uses the constraints represented by Eqs. (15), (16), (21), (23–24), and (26). For the W-F based optimization model, the constraints are given by Eqs. (15), (16), (23–24), and (26a); whereas the E-F based optimization model uses Eqs. (15), (23–25), and (26a).

Evaluation of the Developed Approach

The nexus-sustainability based approach was evaluated against the traditional approaches, along with the baseline (existing) cropping practice followed in the study area. Four performance evaluation criteria, namely a) maximization of net-economic return (MNER), b) minimization of water consumption (MWC), c) minimization of energy consumption (MEC), and d) minimization of labour consumption (MLC) were used for inter-comparing the modeling frameworks. The evaluation was carried out using multi-criteria decision making (MCDM) approach. For each of the aforementioned evaluation criteria, weight was given as per their relative importance in the current decision-making process using a 10 point scale weighting, as stated by Nijkamp et al. (1990). The values of their relative weights were based on the relative importance of each criterion. As the current study focuses on the sustainability of the W-E-F nexus, minimizing the water and energy uses in agricultural production is the primary concern. Hence, both of these criteria were given the equal and the highest weights of 3/10. The net economic return was considered to be the second important criterion, which was given a weight of 2.5/10, whereas labour consumption is the third important criterion, which was given a weight of 1.5/10. All the criterion weights, while summing up, yields 10. Four model variants, along with the existing cropping practice, were treated as five alternatives, out of which the best has to be chosen. The alternatives were ranked under five-point ordinal scale: five as excellent, four as good, three as satisfactory, two as below average, and one as poor on the basis of their impacts (values) corresponding to the different criteria. The decision score for each alternative was calculated by summing the product of the alternative rank and their corresponding criterion weight. The alternative having the highest value of decision score was considered as the best among all. The seasonal criterion weights considered for each model under varied criterion and year were estimated as:

$$ {W_{ij}}^k\kern0.5em =\left\{\begin{array}{c}{W}_{ref}\times {V}_{ij}/{V_{ref}}_j, for\ j= MNER\\ {}{W}_{ref}\times {V_{ref}}_j/{V}_{ij}, for\ j= MWC, MEC, MLC\end{array}\right. $$
(27)

where Wijk = weight applied to the jth criterion in the kth model for the ith year; Wrefj = reference weight for jth criterion averaged over the years 2000–2015 (selected study period) for the existing practice; Vij = estimated value of the jth criterion for the selected season of the ith year; and Vrefj = reference seasonal value of the jth criterion (i.e., the maximum average for NER and the minimum average for WC, EC, LC).

The rank of each LP model under different criterion is provided on the basis of its value, satisfying the criterion objective as:

$$ {R_{ij}}^k=\left\{5= excellent;4= good;3= satisfactory;2= below\ average;1= poor\Big\}\right. $$
(28)

where Rijk = rank of the kth model under the jth criterion for the selected season of the ith year.

The score of each LP model under different criterion is calculated as

$$ {S_{ij}}^k=\kern0.5em {R_{ij}}^k\times {W_{ij}}^k $$
(29)

where Sijk = score of the kth model under the jth criterion for the selected season of the ith year.

To obtain the best model among all, the model decision score is calculated as:

$$ {D}_{ik}={\sum}_{j=1}^n{S_{ij}}^k $$
(30)

where Dik = decision score for the kth model for the selected season of the ith year.

Study Area and Data

Overview of the Study Area

The developed model frameworks were applied to the Hirakud reservoir command on the Mahanadi River in Eastern India. This command lies in between 20°53′ to 21°36′N latitudes and 83°25′ to 84°10′E longitudes (Fig. 2) with a cultivable command area (CCA) of 157,018 ha, being irrigated by the left bank Sason Main Canal (SMC) and right bank Bargarh Main Canal (BMC). The area is characterized by sub-humid tropical monsoon climate with extremely hot summer, cold winter, and uneven distribution of rainfall. The temperatures during the summer and winter seasons range from 35° - 45 °C and 10° - 20 °C, respectively. The average annual rainfall in the command is about 1245 mm/year, of which, about 80% is contributed from the tropical monsoon system, causing two principal cropping seasons, viz., Kharif season (June–October) and Rabi season (mid-November-mid April). Due to the food habit of the local people, rice is the major crop being cultivated during both the seasons, followed by pulses, oilseeds, and maize. The average area under these crops being cultivated during the monsoon and non-monsoon seasons over the years 2000–2015 are 150,737.3 ha (96.41% of CCA) and 120,315.9 ha (76.63% of CCA), respectively (see Fig. 3). The average area under Paddy crop is 91.09% of the CCA in Kharif, whereas it is 72.15% of the CCA in Rabi season. Similarly, the area under Maize, Pulses, and Oilseeds is 0.82%, 3.28%, and 1.22% of the CCA in Kharif; and 0.46%, 1.29%, and 2.73% of the CCA in Rabi season, respectively. The average seasonal water and energy consumption is 1106.55 Mm3and 1375.68 Mm3, and 31.82TJ, 33.96TJ in Kharif and Rabi seasons under the prevailing cropping practice, respectively.

Fig. 2
figure2

Index map of the Hirakud reservoir command

Fig. 3
figure3

Seasonal crop information used in the models over the years 2000–2015

Data Inputs

The existing cropping pattern, base period, irrigated area, seasonal crop yields, and cost of cultivation (CoC) for various crops were collected from the offices of the Deputy Directors, Agriculture located at the headquarters of Sambalpur, Bargarh, Bolangir, and Sonepur districts. The daily scale meteorological data, such as rainfall, maximum and minimum temperatures were collected from the India Meteorological Department (IMD), Pune. The base periods for Paddy, Maize, Pulses, and Oilseed are 155, 110, 120, and 120 days during the Kharif season, respectively; whereas the corresponding periods for Rabi season are 110, 100, 90, and 120 days, respectively. The seasonal canal releases from the SMC and BMC, and their respective operational timings were obtained from the Canal Division, Burla, Odisha state. The groundwater table information and volume of groundwater used by different sectors for the study period were collected from the Groundwater Survey and Investigation (GWSI), Bhubaneswar, Odisha. The minimum support prices (MSP) of various crops for different years obtained from the Directorate of Economics and Statistics, Government of India, were considered for estimating the net economic return (NER) from each crop. The seasonal input data over the period 2000–2015, as used in this study, is illustrated in Figs. 3, 4 and 5 (Table 1).

Fig. 4
figure4

Seasonal rainfall and actual crop evapotranspiration estimates (mm/season) during 2000–2015 for four major crops cultivated in the study area

Fig. 5
figure5

Seasonal variability of irrigation potentials from surface water and groundwater resources, water demands of various crops, and total labour consumption and total energy consumption for crop production in the study area during 2000-2015

Table 1 Estimation of various inputs for crop evapotranspiration model

Results and Discussion

There is an erratic in the rainfall pattern over the study period in this region, and it can be seen that the magnitude is decreasing gradually in the last four years (Fig. 4). This may affect the total water availability of the region. Moreover, the irrigation potential from both the surface as well as groundwater sources (Fig. 5) is decreasing in the region due to an increase in the multi-sectoral water demands. In this context, the high irrigation water demand (Fig. 5) led by faulty cropping pattern may threaten the entire agricultural system of the region and the production as a whole. The hydroelectricity, being one of the major green sources of energy, should be utilized judiciously by all the sectoral users. However, the energy demand for agricultural production in the region follows no trend (Fig. 5), which is attributed to the faulty cropping pattern followed in the region. Similarly, reduction in the availability of labour force for carrying out agricultural operation is becoming a social problem all around. On the contrary, the existing cropping pattern in the region follows no trend in labour-hours (Fig. 5) requirement for agricultural production. Therefore, the agricultural production would be severely affected if the existing cropping pattern continues for a long time in the region.

The basic statistics associated with all the indicators (prior to normalization) and the nexus sustainability indices over the years 2000–2015 as described in Section 2 are presented in Table 2 for both the Kharif and Rabi seasons, respectively. These estimates show that there are significant seasonal and annual variations among the indicators and indices for different crops, which need normalization for using in the proposed optimization models.

Table 2 Basic statistics of indicators and indices developed in the study over the years 2000–2015

The Box and Whisker plots (Fig. 6) illustrate the seasonal resource conservation (in %) by the NSI, NER, W-F, and E-F based optimized models with reference to the existing cropping pattern over the years 2000–2015. In the Kharif season, the average (±standard deviation, Stdev) area under paddy crop is 91.09 (±2.86)% of the CCA under the existing cropping pattern. However, it got reduced to 44.09 (±1.18)%, and 56.71 (±1.15)% of the CCA by the NSI and NER-based optimization approaches, respectively; which were 53.19 (±0.95)% and 48.87(±0.68)% by the W-F and E-F based approaches, respectively. Similarly, the optimized land areas under the maize, pulses, and oilseed crops were increased to 7.48 (±1.17)%, 28.05(±0.35)%, and 4.52(±0.28)% of the CCA by the NER-based method against 0.82(±0.18)%, 3.28(±0.19)%, and 1.22(±0.14)% of the CCA under the existing cropping pattern, respectively. The NSI-based optimization method suggested to increase the allocated area under maize, pulses, and oilseed crops to 13.42(±0.16)% and 25.55(±0.23)%, and 13.59(±0.21)% of the CCA respectively. Conversely, there was an increase in the allocated land area to 12.1(±0.52)%, 16.13(±0.58)%, and 19.67(±0.48)% under the maize, pulses, and oilseed crops by the EF approach, respectively; whereas the corresponding land areas for these crops as optimized by the W-F method were 23.07(±0.55)%, 0.91(±0.12)%, and 19.14(±0.46)% of the CCA, respectively. Similarly, during the Rabi season, the optimized land areas under paddy reduced to 56.35(±0.58)%, 55.82(±0.46)%, 49.31(±0.52)% and 53.22(±0.39)% of the CCA by the NER, NSI, WF, and EF-based approaches, respectively, against 72.15(±2.92)% of the CCA under the existing cropping pattern. Thus, the reduction in area under paddy during the Rabi seasons was compensated by an increase in the land area under the maize, pulses, and oilseed crops which were 19.64(±0.27)%, 20.17(± 0.29)%, and 1.18(±0.17)% of the CCA, respectively by the NSI-based approach; whereas the corresponding allocated areas by NER-based approach were 6.99(±1.11)%, 13.24(±0.38)%, and 20.1(±0.52)%; by the WF-based approach were 18.58(±0.59)%, 7.71(±0.51)%, and 20.66(±0.47)%, and by the EF-based approach were 11.76(±1.13)%, 12.79(±0.68)%, and 18.92(±0.37)%, respectively.

Fig. 6
figure6

Box and Whisker plots showing optimal crop areas under various crops (in %) with reference to the CCA and seasonal resource conservation (in %) with reference to the existing pattern, expressed in terms water, energy, and labour savings; and enhanced net economic return by different models over the years 2000–2015

One way ANOVA test was performed to verify the existence of any significant difference in the seasonal model behavior in terms of MNER, MWC, MEC, and MLC evaluation criteria. A comparative assessment of variance by means of Fisher’s ratio (F) indicated that, at 95% confidence level, there exists a significant difference between the NSI based model and the others with F > Fcritical and p value <0.05 for all the evaluating criteria in both the seasons.

It can be summarized from Fig. 6 that the overall seasonal performances of the models were in decreasing order: NER → NSI → WF → EF → Existing for maximization of the net economic return (MNER), NSI → WF → NER → EF → Existing for minimization of water consumption (MWC), NSI → EF → WF → NER → Existing for minimization of energy consumption (MWE). Since the water resources are limited, the NSI-based method could save water by 36.82(±1.95)% and 17.5(±0.61)% from the baseline (existing) water consumption in Kharif and Rabi seasons, respectively; which were 23.79(±2.31)%, 26.21(±2.12)%, and 30.47(±1.92)% during the Kharif season, and 2.03(±0.39)%, 4.51(±0.49)%, and 6.75(±0.36)% during the Rabi season by the EF, NER, and WF-based methods, respectively. Moreover, even with this amount of water savings, the net economic return was not significantly decreased by the NSI-based method than that of the NER-based method. The net economic return by the NSI based method was increased to 56.53(±3.28)% and 79.96(±2.97)% in the Kharif and the Rabi seasons from the baseline cropping practices, respectively. As regards the energy saving in food production, the NSI-based model also performed the best as compared to the rest of the models. The energy savings by the NSI, NER, WF, and EF-based models were 23.72(±2.47)%, 5.66(±2.87)%, 7.66(±2.02)%, and 13.42(±2.09)% during the Kharif season from the baseline farming practice, respectively; which were: 5.32(±1.78)%, 14.29(±1.62)%, and 15.69(±1.39)% for the NER, WF, and EF-based recommended cropping patterns from the baseline during the Rabi season, respectively. Conversely, the NSI-based recommended cropping pattern reduced the energy uses by 19.82(±1.52)% from the baseline during the Rabi season, with a reduced labour consumption by 2.29(±0.16)% and 2.02(±0.42)% during the Kharif and Rabi seasons, respectively. Moreover, the MCDM analysis (Table 3) also envisaged that the NSI-based model is the best with the highest decision score as compared to the rest of the modelling approaches for both the seasons. The average water savings of 407.95(±35) Mm3 and 240.61(±16) Mm3 during the Kharif and Rabi seasons by the NSI model would encourage for lesser canal releases from the reservoir storage ensuring increased supply for domestic, hydropower, and industrial water uses and to meet out the climatic adversaries of drought with a marginal penalty to the economic return. A lesser release of canal water would reduce the extent of waterlogging in the selected command due to excessive seepage loss, a major problem encountered in the study area. The NSI-based model that minimizes labour consumption could also solve the issue of labour shortage in an agricultural operation.

Table 3 Seasonal variation of MCDM-based decision score for different models over the period 2000–2015

Therefore, the results surmise that optimum security to water, food, land, and energy resources in the selected reservoir command can suitably be established with the recommended cropping pattern by the NSI-based model ensuring sustainability in the WELF nexus, aiding for a climate-resilient agriculture system with resource conservation feature.

Conclusions

An NSI-based model has been conceptualized in this study to ensure sustainability in the nexus of water, food, energy, and land resources, considering the environmental, economic, and social dimensions of agricultural food production. The six nexus sustainability indicators developed herein are the water use indicator, energy use indicator, land use indicator, labour use indicator, and economic indicator (yield return indicator, per capita food production indicator). Four linear programming (LP) models, along with all the necessary system constraints, were developed for obtaining the optimal cropping pattern in the typical Hirakud reservoir canal command. These LP models were formulated considering the traditional method of maximizing the net economic return (NER), maximizing water productivity (W-F model), maximizing energy productivity (E-F model), and a new approach of maximizing the nexus-sustainability index (NSI) comprising of the W-E-L-F nexus. The field evaluation of all the models revealed that the NSI-based model prescribes the optimal cropping pattern that uses the lowest amount of water followed by the WF, NER, and EF-based models with about 30.47%, 26.2%, and 23.8% saving of water from the baseline during the Kharif season, respectively; and the corresponding water savings were 6.8%, 4.5%, and 2.0% during the Rabi season, respectively. Similarly, there is about 23.7% saving of energy resources by the NSI model-based optimal cropping pattern against that of 13.4%, 5.7%, and 7.7% by the EF, NER, and WF model-based optimal cropping pattern during the Kharif season, respectively. Similarly, in the Rabi season, NSI model could save about 19.8% of the energy from the baseline, and these values are about 5.3%, 14.3%, and 15.7% by the NER, WF, and EF-based models respectively. Moreover, by the NSI-based model, the net economic return could also enhanced significantly by about 56.5% and 79.9% from the prevailing cropping pattern in the study area during the Kharif and Rabi seasons, respectively. This novel approach could also minimize the total labour uses by about 2.3% in the Kharif and 2.0% in the Rabi season from the baseline, respectively. Conclusively, the proposed NSI-based model could minimize the water, energy, and labour uses in agricultural food production with a maximized net economic return. The water and energy conserved in this cropping strategy could ensure their potential uses to meet the ever-increasing multi-sectoral water demands guaranteeing sustainability in the water, energy, land, and food (WELF) nexus existing in the study area. A similar study could be upscaled in other agricultural command areas of the world for optimum resource conservation to achieve the recent Sustainable Development Goals (SDG-12) for Sustainable Consumption and Production of the United Nations.

References

  1. Allen R. G, Pereira L. S, Raes D, and Smith M (1998) Guidelines for computing crop water requirements. Irrigation and drainage paper 56, food and agricultural Organization of the United Nations, Rome

  2. Alluvione F, Moretti B, Sacco D, Grignani C (2011) EUE (energy use efficiency) of cropping systems for a sustainable agriculture. Energy 36(7):4468–4481. https://doi.org/10.1016/j.energy.2011.03.075

    Article  Google Scholar 

  3. Cai X, Wallington K, Shafiee-Jood M, Marston L (2018) Understanding and managing the food-energy-water nexus–opportunities for water resources research. Advances Water Res 111:259–273. https://doi.org/10.1016/j.advwatres.2017.11.014

    Article  Google Scholar 

  4. Daher BT, Mohtar RH (2015) Water-energy-food (WEF) Nexus tool 2.0: guiding integrative resource planning and decision-making. Water Int 40(5–6):748–771. https://doi.org/10.1080/02508060.2015.1074148

    Article  Google Scholar 

  5. Dargin J, Daher B, Mohtar RH (2018) Complexity versus simplicity in water energy food nexus (WEF) assessment tools. Sci Total Environ 650:1566–1575

    Article  Google Scholar 

  6. Das B, Singh A, Panda SN, Yasuda H (2015) Optimal land and water resources allocation policies for sustainable irrigated agriculture. Land Use Policy 42:527–537. https://doi.org/10.1016/j.landusepol.2014.09.012

    Article  Google Scholar 

  7. Dastane N. G (1978) Effective rainfall in irrigated agriculture. Irrigation and drainage paper 25, food and agricultural Organization of the United Nations, Rome

  8. Fawzy G (2009) The impact of optimizing the irrigation water use on the economics of agriculture production and water saving. Journal of Water Science 15(2):65–68

    Google Scholar 

  9. Garg NK, Dadhich SM (2014) Integrated non-linear model for optimal cropping pattern and irrigation scheduling under deficit irrigation. Agric Water Manag 140:1–13. https://doi.org/10.1016/j.agwat.2014.03.008

    Article  Google Scholar 

  10. Giampietro M, Aspinall R, Bukkens S, Benalcazar J, Diaz-Maurin F, Flammin A, Gomiero T, Kovacic Z, Madrid C, Ramos-Martin J, Serrano-Tovar T (2013) An innovative accounting framework for the Food-Energy-Water Nexus—Application of the MuSIASEM approach to three case studies. Food and Agriculture Organization of the United Nations (FAO), Environment and Natural Resources Management Working Paper 56

  11. Giampietro M, Aspinall R. J, Ramos-Martin J, and Bukkens S. G (2014) Resource accounting for sustainability assessment: the nexus between energy, food, water and land use. Routledge, London, U.K

  12. Gomez-Limon JA, Arriaza M, Riesgo L (2003) An MCDM analysis of agricultural risk aversion. Eur J Oper Res 151:569–585. https://doi.org/10.1016/S0377-2217(02)00625-2

    Article  Google Scholar 

  13. Gregory R, Failing L (2002) Using decision analysis to encourage sound deliberation: water use planning in British Columbia, Canada. Journal of Policy Analysis and Management 21:492–499. https://doi.org/10.1002/pam.10059

    Article  Google Scholar 

  14. Gregory R, Wellman K (2001) Bringing stakeholder values into environmental policy choices. A community-based estuary case study. Ecol Econ 39:37–52. https://doi.org/10.1016/S0921-8009(01)00214-2

    Article  Google Scholar 

  15. Hargreaves GH, Samani ZA (1985) Reference crop evapo-transpiration from temperature. Appl Eng Agric 1(2):96–99. https://doi.org/10.13031/2013.26773

    Article  Google Scholar 

  16. Hoff H (2011) Understanding the nexus: background paper for the Bonn 2011 conference. The water, energy and food security Nexus, solutions for the green economy, Bonn Cof., environment Stockholm institute, Stockholm

  17. Howells M, Hermann S, Welsch M, Bazilian M, Segerström R, Alfstad T, Wiberg D (2013) Integrated analysis of climate change, land-use, energy and water strategies. Nat Clim Chang 3(7):621–626

    Article  Google Scholar 

  18. Kaur B, Sidhu RS, Vatta K (2010) Optimal crop plans for sustainable water use in Punjab. Agric Econ Res Rev 23(2):273–284

    Google Scholar 

  19. Kaushal MP, Khepar SD, Panda SN (1985) Saline groundwater management and optimal cropping pattern. Water Int 10(2):86–91. https://doi.org/10.1080/02508068508686316

    Article  Google Scholar 

  20. Khan S, Hanjra MA (2009) Footprints of water and energy inputs in food production – global perspectives. Food Policy 34:130–140. https://doi.org/10.1016/j.foodpol.2008.09.001

    Article  Google Scholar 

  21. Kurian M (2017) The water-energy-food nexus: trade-offs thresholds and transdisciplinary approaches to sustainable development. Environ Sci Pol 68:97–106. https://doi.org/10.1016/j.envsci.2016.11.006

    Article  Google Scholar 

  22. Levy J, Hipel K, Kilgour DM (2000) Using environmental indicators to quantify the robustness of policy alternatives to uncertainty Ecol Model 130:79–86. https://doi.org/10.1016/S0304-3800(00)00226-X

    Article  Google Scholar 

  23. Mariolakos I (2007) Water resources management in the framework of sustainable development. Desalination 213:147–151. https://doi.org/10.1016/j.desal.2006.05.062

    Article  Google Scholar 

  24. Mayya S. G, Prasad R (1989) System analysis of tank irrigation: crop staggering. J of Irri and Drain Eng. https://ascelibrary.org/doi/abs/10.1061

  25. Mazziotta M, Pareto A (2013) Methods for constructing composite indices: one for all or all for one. Rivista Italiana di Economia Demografia e Statistica 67(2):67–80

    Google Scholar 

  26. McDaniels TL (1995) Using judgment in resource management: a multiple objective analysis of a fisheries management decision. Oper Res 43:415–426. https://doi.org/10.1287/opre.43.3.415

    Article  Google Scholar 

  27. Negm A, El-eshmawiy K, Elfatah H, El-shiraif L (2006) The optimal Egyptian indicative cropping pattern using nonlinear fractional programming. Journal of Applied Science Res 2(2):91–99

    Google Scholar 

  28. Nguyen DCH, Dandy GC, Maier HR, Ascough JC (2016) Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge. J Water Resour Plan Manag 142:04016025. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000662

    Article  Google Scholar 

  29. Nijkamp P, Rietveld P, and Voogd H (1990). Multicriteria evaluation in physical planning. Amsterdam: North-Holland

  30. Parnell GS, Frimpon M, Barnes J, Kloeber JM, Deckro RF, Jackson JA (2001) Safety risk analysis of an innovative environmental technology. Risk Anal 21:143–155

    Article  Google Scholar 

  31. Paudyal G. N, Gupta A. D (1990) Irrigation planning by multilevel optimization. J of Irri and Drain Eng. https://ascelibrary.org/doi/10.1061, Irrigation Planning by Multilevel Optimization

  32. Rasul G (2014) Food, water, and energy security in South Asia: a nexus perspective from the Hindu Kush Himalayan region. Environ Sci Pol 39:35–48. https://doi.org/10.1016/j.envsci.2014.01.010

    Article  Google Scholar 

  33. Raul S. K (2012) Simulation-optimization modelling for integrated land and water resources management in the Hirakud canal command. Ph.D. Dissertation, Indian Institute of Technology Kharagpur (India)

  34. Raul SK, Panda SN, Hollander H, Billib M (2011) Integrated water resources management in a major canal command in eastern India. Hydrol Process 25:2551–2562. https://doi.org/10.1002/hyp.8028

    Article  Google Scholar 

  35. Ringler C, Willenbockel D, Perez N, Rosegrant M, Zhu T, Matthews N (2016) Global linkages among energy, food and water: an economic assessment. J Environ Stud Sci 6(1):161–171. https://doi.org/10.1007/s13412-016-0386-5

    Article  Google Scholar 

  36. Ruju KS, Kumar DN (1999) Multicriterion decision making in irrigation planning. Agric Syst 62(2):117–129. https://doi.org/10.1016/S0308-521X(99)00060-8

    Article  Google Scholar 

  37. Sahoo B, Lohani AK, Sahu RK (2006) Fuzzy multi-objective and linear programming based management models for optimal land-water-crop system panning. Water Resources Manage 20(6):931–948. https://doi.org/10.1007/s11269-005-9015-x

    Article  Google Scholar 

  38. Sahoo B, Walling I, Deka BC, Bhatt BP (2012) Standardization of reference evapotranspiration models for a sub-humid valley rangeland in the Eastern Himalayas. J Irrig Drain Eng 138(10):880–895. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000476

  39. Sahoo S, Sahoo B, Panda SN (2018) Hillslope-storage Boussinesq model for simulating subsurface water storage dynamics in scantily-gauged catchments. Adv Water Res 121:219–234. https://doi.org/10.1016/j.advwatres.2018.08.016

    Article  Google Scholar 

  40. Saxton K. E(2009) Soil-plant-atmosphere-water. https://hrsl.ba.ars.usda.gov/SPAW/Index.html accessed 25th April 2018

  41. Sethi LN, Kumar D, Panda SN, Mal B (2002) Optimal crop planning and conjunctive use of water resources in a coastal river basin. Water Resour Manag 16:145–169. https://doi.org/10.1023/A:1016137726131

    Article  Google Scholar 

  42. Sethi LN, Panda SN, Nayak AMK (2006) Optimal crop planning and water resources allocation in a coastal groundwater basin, Orissa, India. Agric Water Manag 83(3):209–220. https://doi.org/10.1016/j.agwat.2005.11.009

    Article  Google Scholar 

  43. Shiklomanov IA (2000) Appraisal and assessment of world water resources. Water Int 25:11–32. https://doi.org/10.1080/02508060008686794

    Article  Google Scholar 

  44. Singh A (2012) Optimal allocation of resources for the maximization of net agricultural return. J Irrig Drain Eng 138:830–836. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000474

    Article  Google Scholar 

  45. Singh A (2014) Optimizing the use of land and water resources for maximizing farm income by mitigating the hydrological imbalances. J Hydrol Eng 19:1447–1451. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000924

    Article  Google Scholar 

  46. Singh DK, Jaiswal CS, Reddy KS, Singh RM, Bhandarkar DM (2001) Optimal cropping pattern in a canal command area. Agric Water Manag 50(1):1–8. https://doi.org/10.1016/S0378-3774(01)00104-4

    Article  Google Scholar 

  47. SMOS-Barcelona Expert Centre (SMOS-BEC) (2007–2020) Land variables, Level 2 soil moisture user data product. http://bec.icm.csic.es/land-datasets (updated regularly). Accessed 10 Mar 2018

  48. Store R, Kangas J (2001) Integrating spatial multi-criteria evaluation and expert knowledge for GIS-based habitat suitability modeling. Landscape Urban Plann 55:79–93. https://doi.org/10.1016/S0169-2046(01)00120-7

    Article  Google Scholar 

  49. Swain R, Sahoo B (2015) Variable parameter McCarthy-Muskingum flow transport model for compound channels accounting for distributed nonuniform lateral flow. J Hydrol 530:698–715. https://doi.org/10.1016/j.jhydrol.2015.10.030

  50. United Nations (2014). United Nations, Introduction and proposed goals and targets on sustainable development for the post-2015 development agenda. https://sustainabledevelopment.un.org/content/documents/4528zerodraft12OWG.pdf Accessed 16th May 2018

  51. US NIC (United States National Intelligence Council) (2012). Global trends 2030: alternative worlds. US NIC, Washington DC, USA

  52. USDOE (2014). The water-energy Nexus: challenges and opportunities. U. S. Department of Energy, DOE/EPSA-0002

  53. Van Genuchten M. T, Leij F. J, Yates S. R (1991) The RETC code for quantifying the hydraulic functions of unsaturated soils. U. S. Environ. Prot. Agency, Ada, Oklahoma

  54. White DJ, Hubacek K, Feng K, Sun L, Meng B (2018) The water-energy-food Nexus in East Asia: a tele-connected value chain analysis using inter-regional input-output analysis. Appl Energy 210:550–567. https://doi.org/10.1016/j.apenergy.2017.05.159

    Article  Google Scholar 

  55. World Bank (1992). World development report 1992: development and the environment. Oxford University Press, U.K

  56. Zhou Y, Zhang B, Wang H, Bi J (2013) Drops of energy: conserving urban water to reduce greenhouse gas emissions. Environ Sci Technol 47:10753–10761. https://doi.org/10.1021/es304816h

    Article  Google Scholar 

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Acknowledgments

We thank the government organizations (duly cited in the text) for providing relevant data required for the study. We also thank the Ministry of Human Resources Development, Government of India, for providing the fellowship to the first author during the research tenure. We would like to extend our special thanks to the editors and anonymous reviewers for their valuable comments in greatly improving the quality of this paper.

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Das, A., Sahoo, B. & Panda, S.N. Evaluation of Nexus-Sustainability and Conventional Approaches for Optimal Water-Energy-Land-Crop Planning in an Irrigated Canal Command. Water Resour Manage 34, 2329–2351 (2020). https://doi.org/10.1007/s11269-020-02547-y

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Keywords

  • Canal-command
  • Nexus sustainability index
  • Multi-criteria decision making
  • Irrigated command
  • Water-energy-food nexus
  • Water management