Use of Water Footprint and Virtual Water to Determine the Optimal Cropping Pattern; Case Study: Ghaenat and Zirkoh Counties

Document Type : Research Article

Authors

1 Department of Agronomy, Faculty of Agriculture, University of Zabol, Zabol, Iran

2 Department of Agronomy, Faculty of Agriculture, University of Zabol, Zabol, Iran.

3 Research Institute of Advanced Science and Technology and Environmental Sciences, Kerman University of Industrial and Advanced Technology, Kerman, Iran

Abstract

Introduction
Management of water consumption in arid and semi-arid regions in recent years has been considered a key issue in the field of water resources management. Zirkoh and Ghaenat counties are the hot spots of water consumption in the region. Water footprint and virtual water are critical indicators for water resources management. Therefore, the development of the cropping pattern with high water requirements has led to a negative water balance in the Ghaenat and Zirkoh counties. To prevent the negative consequences of this crisis, it is necessary to consider the restriction of cropping pattern with high water requirements in the region, as well as the assessment of water footprint and virtual water of agricultural products. Also, one of the strategies for managing and optimizing the consumption of water resources in the region is to determine the appropriate cropping pattern with regard to the local conditions. Therefore, this study has been conducted with the aim of calculating the components of water footprint and virtual water in the cropping patterns of Zirkoh and Ghaenat counties and determining the optimal cropping pattern for planting crops from the perspective of water footprint.
Materials and Methods
In this study, the amount of virtual water and green, blue, gray, and white water footprint for wheat, barley, potato, sugar beet, cotton, alfalfa, and saffron were calculated in the current cropping pattern and at multi-objective nonlinear programming (MOP) approach with the objectives of maximizing net profit and minimizing virtual water, green, blue, gray and white water footprint as the optimal cropping pattern in Ghaenat and Zirkoh counties. The study area was irrigated areas of Zirkoh and Ghaenat counties in 2017.
Results and Discussion
The results showed that the indices of virtual water and water footprint components in Zirkoh county were higher than in Ghaenat county. Also, in two counties, the blue water footprint was more than the green water footprint, and the results showed that a large amount of water resources in this area is spent on the production of cotton. In all studied crops, blue water footprint was more than other components of water footprint, which shows the reliance on the surface and sub-surface water resources. The results also showed that the area under cultivation of potatoes, melons, and barley in the multi-objective pattern of Ghaenat and the area under cultivation of melons, sugar beets, potatoes, watermelons, alfalfa, and saffron increased compared to the current cropping pattern. Reduction of the area under cultivation of cotton, alfalfa, and watermelon in Ghaenat county and cotton, wheat, and barley in Zirkoh county by 50% in the multi-objective crop pattern were other important results. By determining the optimal cropping pattern, the environmental effects of the agricultural sector on water resources could be reduced so that the implementation of this pattern in the region reduced virtual water by 26%, blue water footprint by 4%, gray water footprint by 18% compared to the current cropping pattern.
Conclusion
According to the findings of this study, it is necessary to pay attention to the environmental objectives affecting water resources in optimizing cropping patterns. Using the proposed model, in addition to selecting the appropriate model and optimal use of water and land resources to increase profits and reduce water and virtual water footprint, about 13 million cubic meters of water could be saved compared to the current cropping pattern in the region. Therefore, by implementing the optimal cropping pattern, in addition to reducing the environmental effects, the net income could be maximized of water consumed per cubic meter in the region, and by reducing water consumption, we can help achieve sustainable consumption of limited water resources in the region.
Materials and Methods: In this study, the amount of virtual water and green, blue, gray, and white water footprint for wheat, barley, potato, sugar beet, cotton, alfalfa, and saffron were calculated in the current cropping pattern and at multi-objective nonlinear programming (MOP) approach with the objectives of maximizing net profit and minimizing virtual water, green, blue, gray and white water footprint as the optimal cropping pattern in Ghaenat and Zirkoh counties. The study area was irrigated areas of Zirkoh and Ghaenat counties during 2017.
Results and Discussion: The results showed that the indices of virtual water and water footprint components in Zirkoh county were higher than Ghaenat county. Also, in two counties, the blue water footprint was more than the green water footprint and the results showed that a large amount of water resources in this area is spent on the production of cotton. In all studied crops, blue water footprint was more than other components of water footprint, which shows the reliance on surface and sub-surface water resources. The results also showed that the area under cultivation of potatoes, melons, and barley in the multi-objective pattern of Ghaenat and the area under cultivation of melons, sugar beets, potatoes, watermelons, alfalfa, and saffron increased compared to the current cropping pattern. Reduction of area under cultivation of cotton, alfalfa, watermelon in Ghaenat county and cotton, wheat and barley in Zirkoh county by 50% in the multi-objective crop pattern were other important results. By determining the optimal cropping pattern, the environmental effects of the agricultural sector on water resources could be reduced, so that the implementation of this pattern in the region reduced virtual water by 26%, blue water footprint by 4%, gray water footprint by 18% compared to the current cropping pattern.
Conclusion: According to the findings of this study, it is necessary to pay attention to the environmental objectives affecting water resources in optimizing cropping pattern. Using the proposed model, in addition to selecting the appropriate model and optimal use of water and land resources, to increase profits and reduce water and virtual water footprint, about 13 million cubic meters of water could be saved compared to the current cropping pattern in the region. Therefore, by implementing the optimal cropping pattern, in addition to reducing the environmental effects, the net income could be maximized of water consumed per cubic meter in the region, and by reducing water consumption, we can help achieve sustainable consumption of limited water resources in the region.

Keywords

Main Subjects


Afshar, A., Tavakoli, M.A., and Khodagholi, A., 2020. Multi-objective hydro-economic modeling for sustainable groundwater management. Water Resources Management 1-15.
Agricultural organization of South Khorasan., 2016. https://kj-agrijahad.ir/. (In Persian)
Amiri, Z., Asgharipour, M.R., Campbell, D.E. and Armin, M., 2019. A sustainability analysis of two rapeseed farming ecosystems in Khorramabad, Iran, based on emergy and economic analyses. Journal of Cleaner Production 226: 1051-1066.
Antonelli, M., and Ruini, L.F., 2015. Business engagement with sustainable water resource management through water footprint accounting: the case of the Barilla Company. Sustainability 7(6): 6742-6758.
Bazrafshan, O., Etedali, H.R., Moshizi, Z.G.N., and Shamili, M., 2019. Virtual water trade and water footprint accounting of Saffron production in Iran. Agricultural Water Management 213:368-374.
Berenger V., and Verdier-Chouchane, A., 2007. Multidimensional measures of well-being: Standard of living quality of life across countries. World Development 35: 1259- 76.
Chouchane, H., Krol, M.S., and Hoekstra, A.Y., 2020. Changing global cropping patterns to minimize national blue water scarcity. Hydrology and Earth System Sciences 24(6): 3015-3031.
Delpasand, M., Bozorg-Haddad, O., and Loáiciga, H.A., 2020. Integrated virtual water trade management considering self-sufficient production of strategic agricultural and industrial products. Science of the Total Environment 743:140797.
Dury, J., Garcia, F., Reynaud, A., and Bergez, J.E., 2013. Cropping-plan decision-making on irrigated crop farms: A spatio- temporal analysis. European Journal of Agronomy 50: 1-10.
Fader, M., Gerten, D., Thammer, M., Heinke, J., Lotze-Campen, H., Lucht, W., and Cramer W., 2011. Internal and external green–blue agricultural water footprints of nations, and related water and land savings through trade. Hydrol. Hydrology and Earth System Sciences 15(5): 1641-1660.
Falahat, S.E., Hadi, H., Moghaddam, S.S., and Aryanfar, A., 2019. Exploring cultivation site of saffron (Crocus sativus L.) by utilizing GIS linked to AHP. Spatial Information Research 27(3): 285-293.
Feng, B., Zhuo, L., Xie, D., Mao, Y., Gao, J., Xie, P., and Wu, P., 2020. A quantitative review of water footprint accounting and simulation for crop production based on publications during 2002–2018. Ecological Indicators 120: 106962.
GAMS/CONOPT3, 2010. Bagsvaerdvej 246A, DK-2880 Bagsvaerd, Denmark: ARKI Consulting and Development.
Gao, J., Zhuo, L., Liu, Y., Xie, P., Wang, W., Li, M., Gao, X., and Wu, P., 2020. Efficiency and sustainability of inter-provincial crop-related virtual water transfers in China. Advances in Water Resources 103560.
Golabi, M., Hasili, M.A., and Nasab, S.B., 2020. Study and evaluation of irrigation and drainage networks using analytic hierarchy process in Khuzestan province: A virtual water approach. Agricultural Water Management 241: 106305.
Inas, E.G., Grigg, N., and Waskom, R., 2017. Water-food-energy: nexus and non-nexus approaches for optimal cropping pattern. Water Resources Management 31(15): 4971-4980.
Jones, D., and Barnes, E.M., 2000. Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management. Agricultural Systems 65(3): 137-158.
Jónsson, J.O.G., and Davíðsdóttir, B., 2016. Classification and valuation of soil ecosystem services. Agricultural Systems 145: 24–38.
Li, M., Fu, Q., Singh, V.P., Liu, D., Li, T., and Zhou, Y., 2020. Managing agricultural water and land resources with tradeoff between economic, environmental, and social considerations: A multi-objective non-linear optimization model under uncertainty. Agricultural Systems 178: 102685.
Mardani-Najafabadi, M.M., Ziaee, S., Nikouei, A., and Borazjani, M.A., 2019. Mathematical programming model (MMP) for optimization of regional cropping patterns decisions: A case study. Agricultural Systems 173:218-232.
Marzban, Z., Asgharipour, M.R., Ghanbari, A., Ramroudi, M., and Seyedabadi, E., 2020a. Evaluation of environmental consequences affecting human health in the current and optimal cropping patterns in the eastern Lorestan province, Iran. Environmental Science and Pollution Research 1-16.
Marzban, Z., Asgharipour, M.R., Ganbari, A., Nikouei, A., Ramroudi, M., and Seyedabadi, E., 2020b. Reducing environmental impacts through redesigning cropping pattern using LCA and MOP (Case study: East Lorestan province). Journal of Agricultural Science and Sustainable Production 30(3): pp.311-330.
Mekonnen, M.M., and Gerbens-Leenes, W., 2020. The water footprint of global food production. Water 12(10): 2696.
Mesgaran, M.B., Madani, K., Hashemi, H., and Azadi, P., 2017. Iran’s land suitability for agriculture. Scientific Reports 7(1):1-12.
Meteorology of South Khorasan province., 2020. http://skhmet.ir/index.php. (In Persian)
Niu, G., Li, Y.P., Huang, G.H., Liu, J., and Fan, Y.R., 2016. Crop planning and water resource allocation for sustainable development of an irrigation region in China under multiple uncertainties. Agricultural Water Management 166:53-69.
Nouri, H., Stokvis, B., Borujeni, S.C., Galindo, A., Brugnach, M., Blatchford, M.L., Alaghmand, S., and Hoekstra, A.Y., 2020. Reduce blue water scarcity and increase nutritional and economic water productivity through changing the cropping pattern in a catchment. Journal of Hydrology 125086.
Paramjita, D., Panigrahi, B., and Paul, J.C., 2018. STEP method of multi objective programming: An operational research tool for efficient resource planning for minor irrigation command. Journal of Krishi Vigyan 7(special): 144-150.
Qasemipour, E., and Abbasi, A., 2019. Virtual water flow and water footprint assessment of an arid region: A case study of South Khorasan province, Iran. Water 11(9): 1755-1768.
Ren, C., Guo, P., Tan, Q., and Zhang, L., 2017. A multi-objective fuzzy programming model for optimal use of irrigation water and land resources under uncertainty in Gansu Province, China. Journal of Cleaner Production 164: 85-94.
Ren, C., Li, Z., and Zhang, H., 2019. Integrated multi-objective stochastic fuzzy programming and AHP method for agricultural water and land optimization allocation under multiple uncertainties. Journal of Cleaner Production 210: 12-24.
Rodriguez, C.I., De Galarreta, V.R., Kruse, E.E., 2015 Analysis of water footprint of potato production in the Pampean region of Argentina. Journal of Cleaner Production 90:91-96.
Sun, H., Zhang, X., Liu, X., Liu, X., Shao, L., Chen, S., Wang, J., and Dong, X., 2019. Impact of different cropping systems and irrigation schedules on evapotranspiration, grain yield and groundwater level in the North China plain. Agricultural Water Management 211: 202-209.
Vallino, E., Ridolfi, L., and Laio, F., 2020. Measuring economic water scarcity in agriculture: a cross-country empirical investigation. Environmental Science and Policy 114: 73-85.
Woolson, R.F., Bean, J.A., and Rojas, P.B., 1986. Sample size for case-control studies using Cochran's statistic. Biometrics 927-932.
Xie, Y.L., Xia, D.X., Ji, L., and Huang, G.H., 2018. An inexact stochastic-fuzzy optimization model for agricultural water allocation and land resources utilization management under considering effective rainfall. Ecological Indicators 92: 301-311.
Zhang, T., Tan, Q., Zhang, S., Wang, S., and Gou, T., 2020. A robust multi-objective model for supporting agricultural water management with uncertain preferences. Journal of Cleaner Production 255: 120204.
Zhuo, L., Mekonnen, M.M., Hokestra, A.Y., and Wada, Y., 2016. Inter- and intra-annual variation of water footprint of crops and blue water scarcity in the Yellow River basin (1961-2009). Advances in Water Resources 87:29–41.
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  • Receive Date: 09 December 2020
  • Revise Date: 13 February 2021
  • Accept Date: 16 February 2021
  • First Publish Date: 16 February 2021