Optimal Cropping Pattern Research Articles

Water-Food-Energy: Nexus and Non-Nexus Approaches for Optimal Cropping Pattern

Given the water-food-energy interconnections, integrated planning, policy and management using the nexus approach are required for the food production system. In this study the nexus and non-nexus approaches are compared to propose an optimal cropping patternthat considers water, energy and economic parameters. Linear optimization was applied to compare i) the nexus approach utilizing an objective function to maximize a water-food-energy nexus index and 2) the non-nexus approach utilizing three objective functions for water use, energy use, and agricultural net return. The study showed that the nexus approach is the best. Applying it through a water-food-energy nexus index provides a holistic method for identifying an optimal cropping pattern that reduces water and energy consumption and increases the agricultural net return.

Derivation of Optimal Cropping Pattern in Part of Hirakud Command using Cuckoo Search

The economicgrowth of a Nation depends on agriculture which relies on the obtainable water resources, available land and crops. The contribution of water in an appropriate quantity at appropriate time plays avitalrole to increase the agricultural production. Optimal utilization of available resources can be achieved by proper planning and management of water resources projects and adoption of appropriate technology. In the present work, the command area of Sambalpur distribrutary System is taken up for investigation. Further, adoption of a fixed cropping pattern causes the reduction of yield. The present study aims at developing different crop planning strategies to increase the net benefit from the command area with minimum investment. Optimization models are developed for Kharif season using LINDO and Cuckoo Search (CS) algorithm for maximization of the net benefits. In process of development of Optimization model the factors such as cultivable land, seeds, fertilizers, man power, water cost, etc. are taken as constraints. The irrigation water needs of major crops and the total available water through canals in the command of Sambalpur Distributary are estimated. LINDO and Cuckoo Search models are formulated and used to derive the optimal cropping pattern yielding maximum net benefits. The net benefits of Rs.585.0 lakhs in Kharif Season are obtained by adopting LINGO and 596.07 lakhs from Cuckoo Search, respectively, whereas the net benefits of 447.0 lakhs is received by the farmers of the locality with the adopting present cropping pattern.

Optimized cropping patterns under climate-change conditions

A mathematical model is developed in this study with the goal of maximizing agricultural benefits in the Aidoghmush river basin, Iran. The results show that the cultivated area of various crops and their agricultural benefits would be increased over the planning horizon with proper management and modification of cropping patterns despite the decline in streamflow and increasing water demand under climate-change conditions. Therefore, considering the optimal cropping pattern increases agricultural benefits by 14 and 17% under baseline climatic and climate-change conditions, respectively, compared to present conditions. This paper’s results indicate that areas cultivated with various crops would increase under climate-change conditions compared to baseline climatic conditions, except for alfalfa.

Water-food-energy nexus index to maximize the economic water and energy productivity in an optimal cropping pattern

ABSTRACTThis article illustrates a method for applying the water-food-energy nexus approach to propose an optimal cropping pattern. The proposed cropping pattern maximizes economic water and energy productivity and minimizes water and energy use. Through this method a water-food-energy nexus index is applied. A case study from Egypt is applied to illustrate the method.

Water\u2013food\u2013energy nexus index: analysis of water\u2013energy\u2013food nexus of crop\u2019s production system applying the indicators approach

Analysis the water–food–energy nexus is the first step to assess the decision maker in developing and evaluating national strategies that take into account the nexus. The main objective of the current research is providing a method for the decision makers to analysis the water–food–energy nexus of the crop production system at the national level and carrying out a quantitative assessment of it. Through the proposed method, indicators considering the water and energy consumption, mass productivity, and economic productivity were suggested. Based on these indicators a water–food–energy nexus index (WFENI) was performed. The study showed that the calculated WFENI of the Egyptian summer crops have scores that range from 0.21 to 0.79. Comparing to onion (the highest scoring WFENI,i.e., the best score), rice has the lowest WFENI among the summer food crops. Analysis of the water–food–energy nexus of forty-two Egyptian crops in year 2010 was caried out (energy consumed for irrigation represent 7.4% of the total energy footprint). WFENI can be applied to developed strategies for the optimal cropping pattern that minimizing the water and energy consumption and maximizing their productivity. It can be applied as a holistic tool to evaluate the progress in the water and agricultural national strategies. Moreover, WFENI could be applied yearly to evaluate the performance of the water-food-energy nexus managmant.

Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM

The crop pattern has a significant impact on the feasibility of sustainable agricultural practices. Selected crop pattern influences environmental and economic condition and affects sustainability profoundly in agricultural practices. Hence, a careful intervention is required in the selection of an optimal crop pattern for sustainable agricultural practices. Selection of a particular set of crop pattern depends on many criteria that may vary from place to place thus pose challenges in deciding an optimum crop pattern. The present research focuses on the crop selection pattern in Indian environment that considers comprehensive criteria related to sustainable farming practices. Based on the in-depth review of the literature and experts opinion, comprehensive criteria related to sustainable farming practices for Ravi season crop are identified. Total twelve criteria covering socioeconomic conditions, soil and water conditions, environmental and climatic conditions are earmarked and taken into account for eight most commonly grown crops in Ravi season and later on modeled to determine the crop pattern for most needed sustainability. A fuzzy-based multi-criteria decision-making model has been developed considering the Indian farming system. The scarce resources availability to Indian farmers poses many challenges to practice farming with most needed sustainability. The present research will be useful in the area of Indian farming practices in particular and global farming practices in general. It will also help stakeholders in their cost effective decision making for better crop productivity leading to sustainable farming practices. Additionally, the state policy makers will be able to formulate effective state driven sustainable farming policy to enhance its stake in gross domestic product to become self-reliance.

The simulation of cropping pattern to improve the performance of irrigation network in Cau irrigation area

Cau irrigation area located in Madiun district, East Java Province, irrigates 1.232 Ha of land which covers Cau primary channel irrigation network, Wungu Secondary channel irrigation network, and Grape secondary channel irrigation network. The problems in Cau irrigation area are limited availability of water especially during the dry season (planting season II and III) and non-compliance to cropping patterns. The evaluation of irrigation system performance of Cau irrigation area needs to be done in order to know how far the irrigation system performance is, especially based on planting productivity aspect. The improvement of irrigation network performance through cropping pattern optimization is based on the increase of water necessity fulfillment (k factor), the realization of planting area and rice productivity. The research method of irrigation system performance is by analyzing the secondary data based on the Regulation of Ministry of Public Work and State Minister for Public Housing Number: 12/PRT/M/2015. The analysis of water necessity fulfillment (k factor) uses Public Work Plan Criteria Method. The performance level of planting productivity aspect in existing condition is 87.10%, alternative 1 is 93.90% dan alternative 2 is 96.90%. It means that the performance of the irrigation network from productivity aspect increases 6.80% for alternative 1 and 9.80% for alternative 2.

Optimal irrigation water management for adaptation to climate change

In this paper, an optimisation model is developed to determine the optimal crop pattern and allocation of irrigation water under full and deficit irrigation conditions for adaptation to climate change. Climate change is considered as a global challenge with significant impacts on different aspects of social activity and on the natural environment. For this purpose, the optimisation model is applied for two study periods. The one period refer to historical data (1977–1997) and the other one refer to climate change during 2030–2050. Two climate change scenarios were used (SRES scenarios B1 and A2) – which were taken from the general circulation models (GCMs), CGCM3.1/T63. The optimisation approach is performed using a simulated annealing (SA) algorithm combined with a stochastic gradient descent technique. Given the continuous decrease of water resources availability due to climate change, the results of the optimisation model reinforce the necessity of adequate deficit irrigation practices.

Optimal Cropping Pattern for Water Resources Management in Sistan Region of Iran using Goal Programming Method

Optimal Cropping Pattern for Water Resources Management in Sistan Region of Iran using Goal Programming Method

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

Water shortage limits agricultural production worldwide, and irrigation leads to a rapid depletion of water resources. Using desalinated water for irrigation reduces pressure on local resources but it is expensive. An economically viable utilization for using desalinated water in agriculture is only possible through the optimal planning and management of cropland and irrigation scheduling management. We developed a software program aiming to maximize the gross margin (GM) by optimizing cropping patterns using a site-specific crop water requirement model. We used the simplex algorithm under the boundary conditions of limited water (temporal scale) and available land (spatial scale). In a proof of concept, we tested the software for a desalination plant in Saudi Arabia that produces 60,000m3day−1. A cropping pattern for 4278ha was derived that maximizes GM (92 Mio US$ yr−1) and uses most (97%) of the constant water supply to the plant. An optimal cropping pattern (4023ha) solely maximizing GM yields a higher outcome (125 Mio US$ yr−1) and requires even less water for irrigation. This surplus water can be used for leaching soils or other purposes. Both cropping patterns are dominated by cucumber and tomatoes, covering the largest fractions of cropland at 74% and 88%, respectively.

Discussion of “Multiobjective Management of Water Allocation to Sustainable Irrigation Planning and Optimal Cropping Pattern” by R. Lalehzari, S. Boroomand Nasab, H. Moazed, and A. Haghighi

Discussion of “Multiobjective Management of Water Allocation to Sustainable Irrigation Planning and Optimal Cropping Pattern” by R. Lalehzari, S. Boroomand Nasab, H. Moazed, and A. Haghighi

Closure to “Multiobjective Management of Water Allocation to Sustainable Irrigation Planning and Optimal Cropping Pattern” by R. Lalehzari, S. Boroomand Nasab, H. Moazed, and A. Haghighi

Closure to “Multiobjective Management of Water Allocation to Sustainable Irrigation Planning and Optimal Cropping Pattern” by R. Lalehzari, S. Boroomand Nasab, H. Moazed, and A. Haghighi

Discussion of “Multiobjective Management of Water Allocation to Sustainable Irrigation Planning and Optimal Cropping Pattern” by R. Lalehzari, S. Boroomand Nasab, H. Moazed, and A. Haghighi

Discussion of “Multiobjective Management of Water Allocation to Sustainable Irrigation Planning and Optimal Cropping Pattern” by R. Lalehzari, S. Boroomand Nasab, H. Moazed, and A. Haghighi

Engaging farmers on climate risk through targeted integration of bio-economic modelling and seasonal climate forecasts

Seasonal climate forecasts (SCFs) can be used to identify appropriate risk management strategies and to reduce the sensitivity of rural industries and communities to climate risk. However, these forecasts have low utility among farmers in agricultural decision making, unless translated into a more understood portfolio of farm management options. Towards achieving this translation, we developed a mathematical programming model that integrates seasonal climate forecasts to assess ‘what-if?’ crop choice scenarios for famers. We used the Rayapalli village in southern India as a case study. The model maximises expected profitability at village level subject to available resource constraints. The main outputs of the model are the optimal cropping patterns and corresponding agricultural management decisions such as fertiliser, biocide, labour and machinery use. The model is set up to run in two steps. In the first step the initial climate forecast is used to calculate the optimal farm plan and corresponding agricultural management decisions at a village scale. The second step uses a ‘revised forecast’ that is given six weeks later during the growing season. In scenarios where the forecast provides no clear expectation for a dry or wet season the model utilises the total agricultural land available. A significant area is allocated to redgram (pigeon pea) and the rest to maize and paddy rice. In a forecast where a dry season is more probable, cotton is the predominant crop selected. In scenarios where a ‘normal’ season is expected, the model chooses predominantly cotton and maize in addition to paddy rice and redgram. As part of the stakeholder engagement process, we operated the model in an iterative way with participating farmers. For ‘deficient’ rainfall season, farmers were in agreement with the model choice of leaving a large portion of the agriculture land as fallow with only 40ha (total area 136ha) of cotton and subsistence paddy rice area. While the model crop choice was redgram in ‘above normal and wet seasons, only a few farmers in the village favoured redgram mainly because of high labour requirements, and the farmers perceptions about risks related to pests and diseases. This highlighted the discrepancy between the optimal cropping pattern, calculated with the model and the farmer's actual decisions which provided useful insights into factors affecting farmer decision making that are not always captured by models. We found that planning for a ‘normal’ season alone is likely to result in losses and opportunity costs and an adaptive climate risk management approach is prudent. In an interactive feedback workshop, majority of participating farmers agreed that their knowledge on the utility and challenges of SCF have highly improved through the participation in this research and most agreed that exposure to the model improved their understanding of the role of SCF in crop choice decisions and that the modelling tool was useful to discuss climate risk in agriculture.

Conjunctive Use of Surface Water and Groundwater Resources under Deficit Irrigation

AbstractIn this study, a genetic algorithm optimization model is applied to two different scenarios of deficit irrigation in an attempt to develop an appropriate plan for the conjunctive use of surface and ground water resources in Nekouabad irrigation zone located in Zayandehrud Basin, Iran. The study aims to reduce water consumption, define an optimal cropping pattern, decrease the annual decline in the local water table, and to maximize the farmers’ net financial returns. Scenario I is based on normal conditions in terms of both water resources and weather parameters in the region while Scenario II is implemented to develop a 5-year irrigation plan for the study area. For the 5-year period in Scenario II, all possible weather (i.e., dry, normal, and wet) conditions are considered. Results show that water consumption would reduce by almost 24 million cubic meters and the net financial return would decline by 22% under Scenario I. Scenario II reveals that the decline in net revenues would be less under n...

Sustainable cropping pattern in North Iran: application of fuzzy goal programming

Due to the important role that the application of mathematical programming models have in determining optimal cropping patterns, this research presents a sustainable cropping pattern that considers selected economic, environmental, and social goals together. Using a random sampling method, a sample size of 168 farmers was selected in the Sari County, Iran. Our results showed that economic, self-sufficiency, environmental, and social goals have a distinctly different impact on cropping pattern performance. Compared to the current cropping pattern, the gross margins for economic and social goals increased by nearly 11 and 2 %, respectively, and the gross margins for self-sufficiency and environmental goals decreased by nearly 2 and 36 %. Interestingly, it has been found that the performance of the current cropping pattern has an average positive impact of 6 % if economic, self-sufficiency, environmental, and social (employment) goals are realized simultaneously.

A Modified Filtering Technique for Efficient Extreme Points and Application of MOLP in Agriculture Sector in Bangladesh

Multi-objective simplex algorithm computes all efficient extreme points of a multi-objective linear programming (MOLP) problem. For MOLP problem with large number of efficient extreme points, a filtering technique is necessary to present a small representative part of efficient extreme points. For this, we propose a filtering technique among efficient extreme points. In modern agriculture, we have to consider series of conflicting optimization criteria. For this reason, optimal cropping pattern, that is, allocation of land to various crops by making use of limited resources has become major challenges to get optimum return. To overcome this challenge, we illustrate the application of MOLP through a real life oriented problem of agricultural sector of Bangladesh. We also solve this problem by a self developed computer software which is based on multi-objective simplex algorithm and our proposed filtering technique.
 Dhaka Univ. J. Sci. 64(2): 147-152, 2016 (July)

Application of Wind Energy to Withdraw Groundwater for Irrigation Management

AbstractIncreases in greenhouse gases emissions have encouraged the replacement of fossil fuels with renewable energy sources. This paper investigates the potential of wind energy as a renewable resource for producing agricultural water in Eghlid city, Iran. The purpose of the optimization model herein considered is to maximize the net benefit from crop production by selecting an optimal cropping pattern. This paper’s results demonstrate that wind energy can be efficiently applied to provide irrigation water and optimize cropping patterns. Specifically, the application of wind energy to withdraw irrigation water increases agricultural production benefits in the amount of 1,254 million Rials (US$45,000).

Fuzzy Stochastic Genetic Algorithm for Obtaining Optimum Crops Pattern and Water Balance in a Farm

This paper is concerned with multi-objective fuzzy stochastic model for determination of optimum cropping patterns with water balance for the next crop season. The objective functions of the model is to study the effect of various cropping patterns on crop production subject to total water supply in a small farm. The decision variables are the cultivated area of different crops at the farm. The water requirement of the crops follows fuzzy uniform distribution and yields in the objective functions are taken as a fuzzy numbers. The model is solved by using fuzzy stochastic simulation based genetic algorithm without deriving the deterministic equivalents.

Development of a New Drought Index for Groundwater and Its Application in Sustainable Groundwater Extraction

AbstractThe main aim of this study was to develop an index to determine groundwater drought. Then, as a case study, for the sustainable use of groundwater, the optimum cropping pattern was determined in full compliance with the drought conditions in a plain. The newly presented index, known as the groundwater recharge drought index (GRDI), was determined based on the values of recharge in different zones in the plain. The GRDI for different zones within the region was calculated using the data covering 30 years (1980–2010). Based on this index, groundwater drought was found to occur frequently in these regions since 1981. According to the obtained results, the developed index, as compared with other indices, had several basic features including the ability to predict drought in groundwater, consideration of the possible effects of basin characteristics on groundwater drought, the capability of using different methods and models to calculate the GRDI, and separation of the human effects on the hydrological...