Framework for computationally efficient optimal crop and water allocation using ant colony optimization

Optimal water utilization and allocation in industrial sectors based on water footprint accounting in Dalian City, China

Improper extraction of water from resources especially in arid and semi-arid regions leads to a decrease in the quality of water and soil resources. In such areas, management activities such as increasing water productivity in agricultural sector would be a key step towards sustainable development. Therefore, water resources management to improve the allocation of limited water supplies is essential. In this study, a non-linear programming optimization model have been combined with a AquaCrop model to determine the optimal water and land allocation considering the quality issues of both water and soil resources with focusing on enhancing agriculture water productivity. For this purpose, the spatial variations of chemical and physical properties of soil in the Qazvin plain were taken into account. The soil of study site was divided into three salinity classes, and three weather conditions were identified by Standardized Precipitation Index (SPI). Moreover, five irrigation strategies were modeled under each weather condition. To understand the response of major crops under cultivation to water and salinity, the AquaCrop model was calibrated and validated (2005-2020) and utilized in the objective function. Accordingly, the production functions of the different products were obtained, and the cultivation area as well as amount of water consumption of the crops were optimized by using the target functions of maximum net income and maximum water use efficiency. The results showed that the model is capable of simulating crop yield in salinity and water deficit conditions. The coefficient of determination (R2) for barley, wheat and maize was equal to 0.86, 0.92, and 0.96, respectively. Findings reveal that total irrigation water could be reduced by 20% on average without profit reduction when compared to the profit of the present situation. Total economic profit could be increased by 18% on average through the optimization of water allocation and cropping pattern with the same water supply amount as that of the current situation. Also, the water productivity increased between 12 to 30% under these conditions. Therefore, the proposed model can efficiently optimize the amount of irrigation water and cultivation area on a regional scale considering salinity conditions.

ABSTRACT- Inadequate water supply is the major problem for agriculture in arid and semi-arid regions. Thus, effective management should be considered for water resources planning. In this research, a model was provided which is able to estimate optimal land and water allocation in the Doroodzan irrigation network. Optimal water management model was used at farm level to evaluate different deficit irrigation (DI) strategies at various periods of crop growth. Genetic algorithm toolbox by MATLAB (Mathworks, 2009) software was used for benefit optimization considering practical constraints. Results showed that deficit irrigation technique significantly reduced water allocation and increased the crops cultivation area in the region. In addition, increase in water price and the occurrence of drought resulted in cropping pattern change and led to including crops with high economic values. Application of this model ensures the optimal use of available water resources in all conditions, especially under drought condition. The proposed model is capable of defining water management plan with regard to the amount of available water and price of water and product, for simultaneous optimal land and water allocation.

A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty

A dynamic irrigation scheduling method was developed for optimal water allocation from an irrigation reservoir and maximum net benefit from multiple crops under wet, average, and drought conditions. The model integrates two control volumes: (i) the plant root zone, which demands water, and (ii) the reservoir, which supplies water. The FAO Penman-Monteith Method was used to compute the reference crop evapotranspiration (ET0). Optimal allocation of water supply in the reservoir was determined by a dynamic programing method. The model allows for variable decision intervals for each crop. Since soil moisture exhibits a dynamic structure, various scenarios were developed for probable soil moisture deficit ratios. The objective function of the model developed in this study consists of the yield sensitive factor (Ky), the actual evapotranspiration (ETa), the potential evapotranspiration (ETm), the maximum crop yield (Ym), the benefit of unit yield in unit area (UB), the unit cost of unit yield in unit area (UC), is the unit cost of water of unit yield in unit area (UCW), and the crop area (AREA). The applicability of the method was demonstrated through a case study of Cavdarhisar Dam irrigation field (5000 ha) in Kutahya, Turkey. From the commonly grown crops in the area, wheat, maize, sunflower, potato, onion, sugar beet, beans, tomato, chickpea, and melonwatermelon were selected to be planted. Optimum water allocation scheduling of available water in the reservoir was determined based on maximum net benefit from multiple crops under wet average, and drought conditions.

Optimization of the irrigation water resources for Shijin irrigation district in north China

A linear programming model was formulated to represent the 10 interconnected hydrologic study areas of the state. The comprehensive model considers virtually all uses, areas, sources, transfers, and costs of water. The model has 204 constraints and 338 variables and was solved by the simplex method. Included in the results are the following: the optimal water allocation of the ground water, surface water, and water transfers which minimize the cost; the shadow prices of the resources; sensitivity analyses to identify the critical cost coefficients in the optimal solution; parametric analyses to test the effects of changing constraints; and manipulations of the model to test other factors such as operating rules, legal policies, political, and institutional limitations. Flow diagrams and graphs summarize the important information.

A distributed interval nonlinear multiobjective programming approach for optimal irrigation water management in an arid area

Due to the widespread uncertainties in agricultural water resources systems and climate change projections, the traditional optimization methods for agricultural water management may have difficulties in generating rational and effective optimal decisions. In order to get optimal future agricultural water allocation schemes for arid areas with consideration of climate change conditions, the model framework established in this paper integrates a statistical downscaling model, back propagation neural networks, and an evapotranspiration model (the Hargreaves model) with inexact irrigation water allocation optimization model under future climate change scenarios. The model framework, which integrates simulation models and optimization models, considers the interactions and uncertainties of parameters, thereby reflecting the realities more accurately. It is applied to the Yingke Irrigation Area in the midstream area of the Heihe River Basin in Zhangye city, Gansu Province, northwest China. Then, water allocation schemes in planning year (2047) under multiple future Representative Concentration Pathways (RCP) scenarios and the status quo (2016) are compared, in order to evaluate the practicability of generated water allocation schemes. The results show that the water shortages of economic crops are improved compared with the status quo under all RCP scenarios while those of the grain crops present opposite results. Meanwhile, the economic benefits decrease from the status quo to planning year under all future scenarios. This phenomenon is directly related to the amount of irrigation water allocation and is indirectly related to the changes of meteorological conditions. The model framework can reveal the regular pattern of hydro-meteorological elements with the impact of climate change. Meanwhile, it can generate irrigation water allocation schemes under various RCPs scenarios which could provide valuable decision support for water resources managers.

Optimal water allocation through a multi-objective compromise between environmental, social, and economic preferences

Irrigation water resources management under uncertainty: An interval nonlinear double-sided fuzzy chance-constrained programming approach

ABSTRACTThe South African government has put legislation in place to exercise better control over irrigation water usage. Thus, proper planning of irrigation areas and scheduling of irrigation events in order to stay within water quotas has become more important. Currently, the available methodologies to assist irrigation farmers overlook the complexities and interrelated relationships between crop-area planning and the multi-stage nature of irrigation-scheduling decisions within a stochastic dynamic environment. This paper contributes to knowledge through the development of a bio-economic model that uses evolutionary algorithms to optimise water use, taking cognisance of the complex interrelationships between crop-area planning, the multistage decision-making nature of irrigation-scheduling decisions, and the stochastic dynamic environment under conditions of limited water supply. The results show that gross margin variability is reduced and the expected outcomes are improved due to improved irrigation-scheduling decisions made sequentially in multiple stages. Multi-stage decisions tend to make the impact of risk aversion less profound because taking account of unfolding weather information is risk reducing. Ignoring the risk-reducing impact of sequential decision-making will over-estimate the cost of water restrictions. Caution is hence necessary when formulating agricultural water-allocation policies based on crop water optimisation models that overlook the complex nature of irrigation decisions.

This study deals with uncertainty-based optimal allocate of irrigation water and land resources in agricultural networks downstream of Zayandeh-Rud Dam, Isfahan Province, Iran. The proposed considers competition of the crops in a season, both for irrigation water and area of cultivation using a multilevel approach. The first level is a single-crop intraseasonal water allocation optimization model based on the stochastic dynamic programming (SDP), considering a limited total available water (TAW), and the uncertainty of evapotranspiration (ET), named SDP-ET. This model provides optimal 10-day water allocations over the growing season of each crop that maximizes the expected value of annual yield. Subsequently, the derived yield functions are used in the second level in which a multicrop deterministic dynamic programming (DP)-based model, named DP-WL, is employed in order to maximize the total net benefit across the entire irrigation network. The values of annual irrigation water for each crop resulted from DP-WL model, finally are inputted to a single-crop SDP-ET intraseasonal model to calculate the optimal 10-day irrigation allocations for each crop considering stress sensitivity factors of crops. Results indicate that the proposed methodology results in an increased net benefit compared to irrigation being used as business-as-usual in the region.

When designing cluster-randomized trials (CRTs), one important consideration is determining the proper sample sizes across levels and treatment conditions to cost-efficiently achieve adequate statistical power. This consideration is usually addressed in an optimal design framework by leveraging the cost structures of sampling and optimizing the sampling ratios across treatment conditions and levels of the hierarchy. Traditionally, optimization is done through the first-order derivative approach by setting the first-order derivatives equal to zero to solve for the optimal design parameters. However, the first-order derivative method is incapable of properly handling the optimization task when statistical power formulas are complex, such as those for CRTs detecting mediation effects under the joint significance test. The current study proposes using an ant colony optimization (ACO) algorithm to identify optimal allocations. We evaluate the algorithm’s performance for CRTs detecting main and mediation effects. The results show that the ACO algorithm can identify optimal sample allocations for CRTs investigating main effects with the same design efficiency as those identified through the first-order derivative method. Furthermore, it can efficiently identify optimal sample allocations for CRTs investigating mediation effects under the joint significance test. We have implemented the proposed methods in the R package odr.

Effectiveness of Weather Derivatives as a Cross-Hedging Instrument Against Climate Change: The Cases of Reservoir Water Allocation Management in Guanajuato, Mexico and Lambayeque, Peru