A practical planning software program for desalination in agriculture - SPARE:WATERopt

بهینه‌سازی تخصیص منابع یکی از راهکارهای مناسب برای دستیابی به توسعه پایدار و کاهش هدررفت منابع است. در دشت اسلام‌آباد غرب استان کرمانشاه به منظور دستیابی به توسعه پایدار و تخصیص بهینه اراضی به محصولات الگوی کشت، محدودیت‌های موجود لحاظ گردید و برای بیشینه سازی سود تولید، توابع هدف و محدودیت با برنامه‌ریزی خطی فرموله و با استفاده از روش سیمپلکس به کمک نرم افزار LINGO حل شد. سه سناریوی مدیریتی و 6 برنامه عملیاتی با لحاظ محدودیت‌های موجود شامل دسترسی به منابع، تناوب زراعی، اجتماعی- اقتصادی و غیر منفی بودن متغیرها تحلیل و آنالیز حساسیت شدند. نتایج حاصله بیانگر آن است که در کلیه سناریوهای مدیریتی کشت‌های آبی محصولاتی چون چغندر، ذرت، نخود، گوجه فرنگی و جالیز از الگوی کشت بهینه حذف شدند. گندم در 2 سناریو و 5 برنامه عملیاتی،افزایش سطح زیر کشت داشته است. میزان سود حاصل از بهینه‌سازی در کلیه سناریوهای مدیریتی مثبت بوده و این افزایش از 19 تا 55 درصد متغیر است نتایج بدست آمده از تجزیه و تحلیل حساسیت نیز نمایانگر تاثیر پذیری زیاد توابع هدف از میزان آب در دسترس، سطح کل اراضی و سطح کشت یونجه است.

Optimal cropping pattern decisions without consideration to water supply uncertainty would result in yield/benefit that is less than expected and probability of system failure in meeting a given irrigation demand. In this study, a chance constraint linear programming (CCLP) model was used for optimizing cropping pattern for major crops grown at Koga Irrigation scheme, Ethiopia. The model incorporated uncertainty of inflow at exceedance probability of 90%, 80%, 70%, 60% and 50%. The model objectives were yield and benefit maximizations subject to land and water availability constraints. Each objective function has four scenarios. The models were solved using LINGO14. The cropping patterns under yield and benefit maximization models were found to be identical under all scenarios. However, the cropping patterns of each model varied among scenarios. The study showed that the possibility of irrigating 5904.3 to 8051.0 hectares of land at 80% by optimizing cropping patterns at irrigation efficiency of 48%. This could increase the yield by 108 to 153%, benefit by 153 to 208% and physical water productivity by 132% to 186% and economic water productivity by 205% to 241% of the actual values. In conclusion, the irrigated land in 2012/13 was below the optimal value and the irrigation water was mismanaged. Therefore, with optimal crop planning and water management, the design command area of 7000 ha could be irrigated. Finally, a study should be made to determine optimal levels of crop water deficit that maximize water productivity.

Optimization techniques can be employed to determine the best cropping patterns to ensure optimum net benefits, food production, and labor employment. Therefore, the current investigation employed the linear programming module in LINGO 14.0 software to develop optimal crop area allocation plans in the Right Main Canal (RMC) of the Bhimsagar irrigation project. The optimal cropping patterns (OCPs) were generated for 2013–2014 under different canal run (CR) periods in a month, i.e., 30, 24, and 21 days using three objective functions, viz., net benefit, food production, and labor-days maximization. In addition, the OCP scenarios were generated for long-term agriculture sustainability scenarios, viz., 2015, 2020, and 2025. Results revealed that the net benefit was increased by 122.2, 93.4, and 95.5% for Ratanpura (RT), Chaplada (CP), and Maraita II (MT) minor, respectively, under OCP estimated for 30-day CR in 2013–2014, whereas food production was increased by 22.4, 33.8, and 25.9% for RT, CP, and MT, respectively, under OCP over existing cropping patterns. Similarly, under OCP, labor employment had increased by 40.9, 33.8, and 33.1% for RT, CP, and MT, respectively, for 30-day CR. These findings infer that higher net benefits, food production, and labor employment may be achieved by shifting to OCPs.

Assessing adaptation measures on agricultural water productivity under climate change: A case study of Huai River Basin, China

تدوین الگوی مناسب کشت محصولات به عنوان یکی از مهم‌ترین رسالت‌های برنامه‌ریزان، مستلزم تصمیم‌سازی دقیق و واقع‌بینانه بر اساس اهداف و معیارهای مختلف در راستای تأمین منافع کل مجموعه‌ی ذی نفع کشاورزی در بلندمدت است. مطالعه‌ی حاضر با هدف بازنگری در الگوی رایج بهره‌برداران شهرستان ساری و تدوین الگوی کشتی که معیارهای چندگانه‌ی منطقه‌ای و پایداری کشاورزی را در کنار ملاحظات اقتصادی مداخله داده، الگوی تلفیقی AHP و مدل برنامه‌ریزی خطی را به کار بست. بدین منظور، پس از طراحی مدل سه سطحی AHP، بردار وزن نهایی خروجی مدل AHP برای محصولات مختلف به عنوان ورودی ضرایب تابع هدف در الگوی برنامه‌ریزی خطی وارد شده و مدل مورد نظر در فضای محدودیت‌های حاکم حل گردید. نتایج حاصل از الگوی بهینه‌ی کشت در مدل تلفیقی، ضمن توصیه‌ی تغییراتی در شیوه‌ی توزیع سطح زیرکشت بین محصولات، میزان سود را نیز به میزان 63/3 درصد نسبت به الگوی رایج منطقه افزایش داد. مقایسه‌ی نتایج الگوی تلفیقی با سناریویی که تنها هدف بیشینه‌سازی منافع اقتصادی را دنبال می‌کند، نشان داد که چشم‌پوشی از مقدار مشخصی سود (به میزانی کم‌تر از 8 درصد) در الگوی بهینه، امکان توجه و دخالت دادن معیارهای مهم دیگری را از جمله سازگاری محصول با شرایط اقلیمی منطقه‌ای، میزان مصرف آب، تأثیرات زیست‌محیطی کشت محصول، اشتغال‌زایی، مهارت و تخصص مورد نیاز برای عمل‌آوری محصول و میزان ریسک کشت محصول که در وضعیت فعلی کشاورزی بسیار حیاتی بوده و دارای اثرات قابل توجه در بلندمدت است، فراهم می‌آورد. بر این اساس، پیشنهاد می‌شود الگوی بهینه‌ی کشت مورد نظر در سطح منطقه یا دست‌کم به‌صورت پایلوت در بخش‌هایی از شهرستان اجرا شود.

In arid and semi-arid areas, the profitability of irrigated agriculture mainly depends on the availability of water resources and optimal cropping patterns of irrigation districts. In this study, an integrated agricultural cropping pattern optimization model was developed with considering the uncertainty of water availability and water saving potential in the future, aiming to maximize agricultural net benefit per unit of irrigation water. The available water which was based on the uncertainty of runoff was divided into five scenarios. The irrigation water-saving potential in the future was quantified by assuming an increase in the rate irrigation water-saving of 10% and 20%. The model was applied to the middle reaches of Heihe River basin, in Gansu Province, China. Results showed that if the irrigation water-saving rate was assumed to increase by 10%, then the net water-saving quantity would increase by 21.5-22.5 million m3 and the gross water-saving quantity would increase by 275.7-303.0 million m3. Similarly, if the irrigation water-saving rate increased by 20%, then the net water-saving quantity would increase by 43.0-45.1 million m3 and the gross water-saving quantity would increase by 331.7-383.2 million m3. If the agricultural cropping pattern was optimized, the optimal water and cultivated area allocation for maize would be greater than those for other crops. Under the premise that similar volume of irrigation water quantity was available in different scenarios, results showed differences in system benefit and net benefit per unit of irrigation water, for the distribution of available irrigation water was diverse in different irrigation districts. Keywords: cropping pattern optimization, irrigation water-saving potential, different scenarios, water availability, water use efficiency, particle swarm optimization (PSO) DOI: 10.25165/j.ijabe.20181101.3658 Citation: Hao L N, Su X L, Singh V P. Cropping pattern optimization considering uncertainty of water availability and water saving potential. Int J Agric & Biol Eng, 2018; 11(1): 178–186.

Cropping pattern optimization considering water shadow price and virtual water flows: A case study of Yellow River Basin in China

Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty

Optimal allocation of water resources is an essential service in agriculture that must be considered by farmers. One of the most significant factors in optimal allocation of water resources in agriculture is to define optimal farm cropping pattern. In this study, in order to determine optimal cropping pattern and water resources allocation in central district of Mashhad city (Toos village), the two-stages multi-objective fuzzy linear programming was used. The required data was collected through interviews with farmers of the study area and filling in 116 questionnaire using simple random sampling during the years 2012-2013.The results indicated that, optimal values in the two-stage multi-objective fuzzy linear programming model for maximizing gross margin is 239420100 Rials, for utilizing organic fertilizers is 3867.19 Kg, and for minimizing the consumption of irrigation water is 53645.62 square meters, which were modified in the second phase. The objective amount of chemical fertilizer was 817.80 kg., having no change in the second phase. The cropping pattern will be optimized, if the most area under cultivation being allocated to potato, then to barley, wheat, t, onion and sugar beet, while tomato and corn cultivation being removed. Results illustrate that, two-stage multi-objective fuzzy linear programming model in comparison with multi-objective fuzzy linear model yield better results in defining optimal cropping pattern and allocation of irrigation water to the study area.

Determining the cropping pattern and schedule according to the availability and requirement of irrigation water is important in an irrigation command area. Supplying irrigation water in the Krueng Jreu Irrigation Area is still less effective in the dry season, so it is necessary to review the existing cropping pattern and schedule in the irrigation area by considering the K factor. To achieve optimal irrigation networks operation, simulation of cropping patterns and schedules based on the K factor was conducted. Optimal cropping patterns and schedules were determined by the highest frequency of the half-month K factor greater than 0.75. The best cropping pattern and schedule for Krueng Jreu Irrigation Area was rice-rice-soybean cropping pattern with first planting season started in mid-July, second planting season in mid-November and third planting season in mid-March. The results achieved the best frequency of K factor > 0.75 as many as 15 times, K factor in the range of 0.50 - 0.75 as many as 3 times, and K factor < 0.25 as many as 4 times. The application of the selected cropping pattern and schedule was done by allocating water into three groups in case of the K factor < 0.75, namely Group I (Menara and Krueng Aceh Extension secondary canal), Group II (Krueng Jreu Kiri secondary canal) and Group III (Kayee, Lamkrah, and Inong secondary canal).

This work proposes the GenSRT method for the Cropping Pattern Optimization (CPO) problem. GenSRT applies Genetic Algorithms, Simplex Method, and Regression Trees. The purpose is to maximize the net income of every cropping season. Simplex Method (SM) is the traditional approach for solving the problem; however, CPO is complicated, because the crop yield has a non-linear behavior which SM cannot consider directly. In GenSRT, regression trees are applied to non-linear regression models construction. The models are provided to GenSRT to evolve efficient cropping patterns and to maximize the benefits for sowing area distribution through a genetic search. Results show that GenSRT overcomes Simplex maximization by obtaining better resource distribution and a higher net profit.Keywordscropping pattern optimizationregression treesgenetic algorithmssimplex method

The water scarcity problem is globally getting worse especially in the light of increase in water demand among its competing uses. Thus, it is an important to optimize the water allocation to crops. In this paper, a linear programming model has been formulated to ensure the efficient allocation of scarce water resources among the competing crops. This model was constrained by land, water, labour, production costs, and organization constraints, determining the optimal plan for two possible future scenarios. The mathematical analysis was based on statistical data for the years (2009-2011) from the official statistical institutions in Egypt. The results of the two scenarios are as follows: Under the maximization of the net return per unit of land, there is an increase in total net returns by 3.56% more than the actual net returns. The optimized cropping pattern has been coupled with about 3.24% water saving and about 3.13% reduction in production costs compared to actual cropping pattern. However, under the minimization of irrigation water requirements, the total net returns decreased by 10.20% indicating losses below the actual situation. It has resulted in about 11.05% water saving and 11.24% reduction in the costs of production compared to the existing situation. These results can be used as a reference for indicative cropping pattern and irrigation water management in Egypt. Key words: Linear programming, efficient water allocation, optimal cropping pattern, water management.

This study improves the environmental water supply in a wetland using a novel framework in which the environmental impacts due to irrigation supply and the economic losses for agriculture are minimized through the proposal of an optimal cropping pattern that changes the total cropping area and cultivated area of each crop. The ecological degradation functions for rivers and wetlands were developed using a fuzzy approach and data-driven model. The net farming revenue was considered as the economic index to maximize benefits. The root mean square error (RMSE) and the Nash–Sutcliffe model efficiency coefficient (NSE) were applied to evaluate ecological models. According to the results, the optimal cropping pattern simultaneously minimizes environmental impacts due to irrigation supply and maximizes farmers’ benefits. The optimal cropping pattern provides more than 50% of the ideal net revenue on the catchment scale, which means that ecological degradations due to reductions in inflow in rivers and wetlands, as well as farmers’ revenue losses, are minimized simultaneously. Furthermore, the results indicate that cropping patterns should be dynamic, which means that changing the cropping pattern annually based on the available water is essential to mitigating ecological impacts. This study demonstrates that the linking of cropping pattern optimization and environmental flow simulation in freshwater bodies should be considered in land-use policies due to the impact of cropping patterns on environmental degradation in wetland catchments.

A linear programming model was developed to assess the impact of different water prices on cultivated areas, irrigation water demand, net income and optimal cropping pattern in the Northern Jordan Valley (NJV). The results reveal that the price for irrigation water does not reflect any elasticity in the range of water prices between 0.01 and 0.06 JD/M3 indicating constant real economic water price of 0.06 JD/M3. The change in cultivated areas as well as water demand (reduction) starts at water price 0.07 JD/M3. The expected reductions under optimal cropping patterns are 5%, 24%, and 60% for cultivated area and 4.7%, 18.9%, and 31% for water demand with water prices at 0.07, 0.1, and 0.16 JD/M3, respectively. Significant reductions in net incomes are resulted with increasing water prices over current average water price of 0.025 JD/M3. The expected reductions in net incomes are 33.6%, 53.8%, and 81.4% at water prices 0.07, 0.1, and 0.16 JD/M3, respectively. This result reflects the low land profitability as a result of low land productivity and/or low farm gate sale prices for most crops grown in NJV. The study also shows the inconsistency in quantity of water supplied and water demanded, leading to unbalanced water budget on monthly level and inconsequence, a noticeable waste in the quantity of available water during winter months, although there is a net surplus of water over the year. While the findings of this research reveal that a water price in the range of 0.07–0.1 JD/M3 does not significantly influence the farmers' socio-economic parameters in the NJV, it may help reach the stated goal of saving water especially when monthly distributions of irrigation water are based on real crops water demands and actual cropping patterns.

The present study investigates the prevailing cropping pattern, adopted by the farmers of the study area under consideration. It aims at improving the net benefits from the farming activities with present irrigation water allocation. To arrive at the optimal cropping pattern, various swarm intelligence techniques, genetic algorithm (GA), cuckoo search (CS) and particle swarm optimization (PSO) techniques are used to formulate an efficient cropping pattern for maximizing net return for the part of Hirakud command area, in India. Maximum available land area, water for irrigation and cropping area for different crops were considered as constraints. The results are compared with the output from linear programming (LP) to evaluate the efficiency of the models. The results reveal that the net economic return arrived at by adopting the optimal cropping pattern derived with the use of PSO works out to be 230.120 Billion Rupees, whereas it is 132.2 Billion Rupees from the prevailing crop pattern adopted by farmers; 199.271 Billion Rupees with the application of LP; 210.19 billion rupees with GA and 229.895 billion rupees with CS. Weightage is given to a given crop under consideration by allocating suitable land area as per the type of land and water availability.