A fuzzy fractional programming model for optimizing water footprint of crop planting and trading in the Hai River Basin, China

2021 ◽  
Vol 278 ◽  
pp. 123196
Author(s):  
C. Dai ◽  
X.S. Qin ◽  
W.T. Lu
Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 414
Author(s):  
Huiqin Li ◽  
Cuimei Lv ◽  
Minhua Ling ◽  
Changkuan Gu ◽  
Yang Li ◽  
...  

As an effective means to coordinate cost–benefit allocation of ecological protection between upstream and downstream cities, ecological compensation is often used to improve collaborative basin-wide freshwater resources management. Yet, due to the complex relationships between upstream and downstream ecosystem services, calculating eco-compensation is not an easy task. We used ecological spillover (the amount of local ecosystem services not used in the region and thus flows to downstream areas) and emergy analysis to determine the amount of eco-compensation that the city of Xuchang should pay to the upstream city of Xinzheng (Qingyi River Basin, China) from 2010 to 2014. Eco-compensation was determined by deducting the emergy of the local, self-supplied ecosystem services of Xuchang City, calculated using an ecological-water-footprint-based analysis, from the emergy of the total ecosystem services used in Xuchang, and monetized accordingly. The results showed that the self-supplied ecosystem services decreased from 2010 to 2014 and, thus, Xuchang relied more on the ecological spillover services flowing from Xinzheng. As a result, eco-compensation increased from 990 million Chinese Yuan (¥) in 2010 to ¥509 billion in 2014, mostly due to increased demands for water purification and reduced precipitation around Xuchang. This method can be further enhanced by introducing larger datasets and can be replicated elsewhere to accurately determine ecological compensation, ensuring basin-wide collaboration towards the sustainable management of freshwater resources.


2020 ◽  
Vol 17 (11) ◽  
pp. 5046-5051
Author(s):  
Vandana Goyal ◽  
Namrata Rani ◽  
Deepak Gupta

The paper proposed an iterative parametric approach procedure for solving Bi-level Multiobjective Quadratic Fractional Programming model. The Model is divided into two levels-upper and lower. In the first stage of the approach, a set of pareto optimal solutions of upper Level is obtained by converting the problem into equivalent single non-fractional parametric objective optimization problem by using parametric vector and ε-constraint method. Then for the second stage, the solution of upper level is followed by the lower level decision maker while finding solution with the proposed algorithm to obtain the best preferred solution. A numerical example is solved in the last to validate the feasibility of the approach.


2011 ◽  
Vol 8 (1) ◽  
pp. 763-809 ◽  
Author(s):  
M. M. Mekonnen ◽  
A. Y. Hoekstra

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


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