Optimal Crop Planning Research Articles

Optimization of Resources using Sen’s Multi Objective Programming (MOP) Model for Potato Growers of Nalanda District, Bihar

An appropriate resource use planning is required for the development of farming sector. Increasing income, employment with lesser use of irrigation, fertilizer may be the major objectives to be achieved. There are several mathematical models for achieving multiple objectives. Sen’s Multi Objective Programming (MOP) model is most popular for achieving several objectives simultaneously. In the present study, an optimal cropping plan was proposed for potato growers of Nalanda district of Bihar for increasing income, employment with lesser use of fertilizer.

Multi-objective particle swarm optimization for regional crop planning

Indian agriculture is heavily dependent on natural resources and climatic situations etc. Uncertain behaviour of climate and continued depletion of natural resources can cause food security issues due to low production. Optimal crop planning is one of the essential tasks for utilizing the minimum resources to acquire the maximum benefit. A novel crop planning model is proposed here for the optimal allocation of available resources under a water scarred region like Bundelkhand using data for the year 2017–18. This study used an evolutionary algorithm called Multi Objective Particle Swarm Optimization using Crowding Distance, to solve the constrained multi-objective crop planning problems. Maximize net returns and minimize the water requirement were the two objective functions used here with area constraints. The optimized results obtained from the multi-objective model were compared with the single objective PSO and linear programming approach. Overall, optimizing the water requirement instead of taking it as a constraint gives better crop planning strategies by allocating the area to suitable crops. Pareto-optimal solutions obtained from the MOPSOCD shows the linear relationship between net returns and water.

Optimal crop plan of cooperative farmers in Osun state, Nigeria: a linear programming approach

Optimal level of production requires better use of existing resources at the lowest possible cost. Despite the inherent advantage of cooperatives to the agricultural sector, the question of how farmers under cooperative umbrella use farm resource for optimal outcome remains unanswered. This study investigates optimal crop mix for cooperative farmers in rural communities in Southwest Nigeria. Primary data were collected for the study through structured questionnaire. The data were fitted to Linear Programming Model. Three different cropping patterns are identified among the cooperative farmers. Based on the results from linear programming model, only maize, cassava and yam are admitted in the final plan and this combination is to be produced at 2.23 hectares. The gross margin value associated with the plan is ₦156, 235.781 (1$ = N365). Input resources such as land, labour, fertilizer, and chemicals are not fully utilized. The slack values for these inputs are 0.31, 651.20, 1929.6 and 140.76 respectively. The sensitivity analysis shows that seed/seedling is the only binding resource in the final plan with a shadow price which suggests that proper allocation of seed and seedlings would improve returns to cooperative farmers. There is need for appropriate farm management strategies to ensure optimal return for farmers. More education and training is suggested to boost cooperative farmers understanding of optimum strategy that is needed to improve production and earnings.
 JEL code: Q10, Q13

Assessment of Meteorological Drought using Standard Precipitation Index (SPI) in Western Maharashtra, India

The crop production in rainy season in rainfed areas is dependent on vagaries of monsoon and is adversely influenced. The knowledge of different rainfall characteristics such as onset and withdrawal of monsoon, dry spell and drought would enable the optimal crop planning and implementation of protective irrigation strategies, thus mitigating the adverse impact of uncertainties of rainfall. The assessment of drought is one of the most important steps in risk management of drought analysis. The basis of drought indices is often based on measuring the deviation of precipitation values from long-term mean, during a specific period of time. The standard precipitation index (SPI) can be used for indicating the associated temporal and spatial variations. The aim of this research is the assessment of the characteristics such as intensity of meteorological drought using SPI with 12 months time scales in Western Maharashtra, India. The index has been computed in with 12 months time scales for 105 stations with 30 years record period in the study area and evaluated for the recent drought during 1983-2012. The results indicate that the rainfall pattern of Western Maharashtra exhibits non-uniform, irregular and erratic pattern of rainfall, as evidenced from SPI values. The meteorological drought shifted one step towards severity of drought condition for the time scales (month 1-12) i.e. from extremely wet to very wet; very wet to moderately wet; moderately wet to near normal; near normal to moderately dry; moderately dry to severely dry and severely dry to extremely dry condition.

‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling

Deciding on which crop to plant on a field and how to fertilize it has become increasingly complex as volatile markets, location factors as well as policy restrictions need to be considered simultaneously. To assist farmers in this process, we develop the web-based, open source decision support system ‘Fruchtfolge’ (German for ‘crop rotation’). It provides decision makers with a crop and coarse manure fertilization management recommendation for each field based on the solution of a single farm optimization model. The optimization model accounts for field specific location factors, labor endowments, field-to-farm distances and policy restrictions such as measures linked to the EU Nitrates Directives and the Greening of the EU Common Agricultural Policy. ‘Fruchtfolge’ is user-friendly by automatically including big data related to farm, location and management characteristics and providing instant feedback on alternative management choices. This way, creating a first optimal cropping plan generally requires less than five minutes. We apply the decision support system to a German case study farm which manages fields outside and inside a nitrate sensitive area. In the year 2021, revised fertilization regulations come in force in Germany, which amongst others lowers maximal allowed nitrogen applications relative to crop nutrient needs in nitrate sensitive areas. The regulations provoke profit losses of up to 15% for the former optimal crop rotation. The optimal adaptation strategy proposed by ‘Fruchfolge’ diminishes this loss to 10%. The reduction in profit loss clearly underlines the benefits of our support tool to take optimal cropping decisions in a complex environment. Future research should identify barriers of farmers to apply decision support systems and upon availability, integrate more detailed crop and field specific sensor data.

Climate adaptation strategies: Optimizing farm-level water use and profitability in Punjab

This study aims to develop optimal crop plans for farmers in Punjab who adopt climate-resilient technologies. In the macro perspective, the optimal crop plan envisages that 7.02 lakh hectares should be under paddy Punjab Rice Varieties (PR VAR) and 4.44 lakh hectares under paddy direct seeded rice. Raising the acreage of maize by 24% will save 3.42 billion cubic metre of water. Strengthening the linkages between research, extension, insurance, and institutional support would improve the capacity of farmers for adopting farm-level climate-smart practices and make the agriculture sector sustainable.

Micro irrigation based optimal crop plan for Surgi lift irrigation system

Micro irrigation based optimal crop plan for Surgi lift irrigation system

Identification of optimal crop plan using nature inspired metaheuristic algorithms

The present study deals with the identification of optimal crop plan to improve the net benefits from the farming activities for the study area under consideration.Three nature inspired metaheuristic techniques namely Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are investigated to identify the most efficient crop plan to maximize the net farm benefits. Different resource constraints considered for the study are maximum available land area, ground water availability and cropped area for different crops. The obtained results are compared with the solutions from LINGO, software for Linear Programming (LP). The results reveal the net benefit per ha derived using DE, PSO, GA and LINGO are 73841.69, 73439.08, 70555.99 and 73841.65 Indian Rupees (INR) respectively for kharif crops and 20184.71, 20172.78, 19860.80 and 20184.70 `Indian Rupees (INR) respectively for rabi crops.

Development of a linear programming model for the optimal allocation of nutritional resources in a dairy herd

A linear programming model that selects the optimal cropping plan and feeds allocation for diets to minimize the whole dairy farm feed costs was developed. The model was virtually applied on 29 high-yielding Holstein-Friesian herds, confined, total mixed ration dairy farms. The average herd size was 313.2 ± 144.1 lactating cows and the average land size was 152.2 ± 92.5 ha. Farm characteristics such as herd structure, nutritional grouping strategies, feed consumption, cropping plan, intrinsic farm limitations (e.g., silage and hay storage availability, water for irrigation, manure storage) and on farm produced forage costs of production were collected from each farm for the year 2017. Actual feeding strategies, land availability, herd structure, crop production costs and yields, and milk and feed market prices for the year 2017 were used as model inputs. Through optimization, the feeding system was kept equal to the actual farm practice. The linear program formulated diets for each animal group to respect actual herd dry matter intake and fulfill actual consumption of crude protein, rumen-degradable and rumen-undegradable fractions of crude protein, net energy for lactation, neutral detergent fiber, acid detergent fiber, forage neutral detergent fiber, and nonfiber carbohydrate. Production levels and herd composition were considered to remain constant as the nutritional requirement would remain unchanged. The objective function was set to minimize the whole-farm feed costs including cash crop sales as income, and crop production costs and purchased feed costs as expenses. Optimization improved income over feed costs by reducing herd feed costs by 7.8 ± 6.4%, from baseline to optimized scenario, the improved was explained by lower feed costs per kilogram of milk produced due to a higher feed self-sufficiency and higher income from cash crop. In particular, the model suggested to maximize, starting from baseline to optimized scenario, the net energy for lactation (+8.5 ± 6.3%) and crude protein (+3.6 ± 3.1%) produced on farm, whereas total feed cost (€/100 kg of milk) was greater in the baseline (20.4 ± 2.3) than the optimized scenario (19.0 ± 1.9), resulting in a 6.7% feed cost reduction with a range between 0.49% and 21.6%. This meant €109 ± 96.9 greater net return per cow per year. The implementation of the proposed linear programming model for the optimal allocation of the nutritional resources and crops in a dairy herd has the potential to reduce feed cost of diets and improve the farm feed self-sufficiency.

The future of intercropping under growing resource scarcity and declining grain prices - A model analysis based on a case study in Northwest China

Intercropping, i.e. mixed crop species cultivation on a field, can potentially reduce pressure on land and water resources by generating higher resource use efficiencies and crop yields through exploitation of complementarities between species. Intercropping systems in China and elsewhere have come under pressure through labor migration, growing water scarcity, changing crop prices and other factors. However, little hard evidence is available on how these socio-economic factors interplay and affect the prevalence of intercropping systems now and in the near future. The objective of this study is to explore the effect of growing scarcity of (water and labor) resources and declining (maize) grain prices on the share of intercropping in the optimal cropping plan and on associated agricultural income levels in an intercropping-dominated agricultural system in China. To undertake this analysis, we developed a mathematical programming model to simulate crop production for a model village in Gaotai county in the Hexi Corridor in northwest China, for given resources and economic conditions in 2013 and possible changes (scenarios) in the future. In the Hexi Corridor, conventional wheat/maize intercropping contributed greatly to rising food production while cash crops integrated with maize provided important cash income. With the introduction of seed crops and stricter water regulations, intercropping has become less prevalent in this area in recent years. In the absence of water constraints and at price levels and labor availability in 2013, our model results indicate that an optimal land use would entail that all land would be devoted to intercropping. Sole cumin and sole cotton enter the optimal cropping plan when water becomes scarce and the maize price declines substantially, while increases in hired labor wages have a strong negative impact on intercropping only when on-farm labor becomes scarce.

Optimal cropping pattern design for a major distributary of Hirakud canal command in India

Gravity flow on field to field flooding basis is prevalent in most of the canal command areas in India. Furthermore, the canals being mostly unlined and at many sections ill maintained, enormous conveyance loss of water occurs causing dwindling crop production. Hirakud canal command area located in India is no exception to this predicament. Thus, augmentation of irrigation infrastructures vis-a-vis prudent crop planning is of paramount importance for increasing the overall efficiency of the canal system. This paper describes development of optimal crop planning in the Hirakud command area by considering different objectives of planning (scenarios) so that improvement in irrigation efficiency, in terms of water productivity could be feasible. Among various scenarios, the cropping pattern obtained under Scenario—II, i.e. to utilize the maximum area for cultivation, under the constraint of limiting water availability for each outlet was found to be feasible for optimal land and water utilization and generation of requisite employment. However, keeping in view the affinity of the farmers towards paddy (a heavy duty crop), Scenario—III i.e. to utilize the maximum area for cultivation with the constraints of limited irrigation water availability, and providing irrigation to heavy duty crops for at least one-third of the culturable command area of each outlet, can be adopted.

Natural resource management for enhancing farmer's income: An optimal crop planning approach in Bihar

To maximize the income of farming community of the state and also to maintain the nutritional demand of an ever-increasing population, it is important to optimize the use of available land and water resources. An attempt has been made to revise the existing cropping pattern using linear programming considering land and groundwater as constraints. Data of Comprehensive Cost of Cultivation Scheme, Ministry of Agriculture and Farmers Welfare, GOI for the block year 2008-11 for Bihar was used to estimate the net returns at market price (MP), economic price (EP) and natural resource valuation (NRV) for obtaining an optimum crop plan. The optimal crop plan at existing groundwater use revealed the decline in gross cropped area (GCA) by -1.41% of existing GCA of the state. Few crops like paddy, wheat, ragi, sunflower and jute recorded declining trend in its area probably because of large water consuming crops. When groundwater use was increased by 25% of the existing GW use, the optimal crop plan indicated increase in GCA by 15.73% with respect to existing GCA. The level of profit would be earned more by 9% in optimum crop plan as compared to existing optimum crop plan. The optimum crop plan could be profitable and successful, when water development is further enhanced coupled with cheap source of irrigation facilities (electric and solar operated devices) made available to the farming community.

Biomass Production from Crops Residues: Ranking of Agro-Energy Regions

The aim of the paper is to rank the agro-energy regions according to their potentials of biomass production in the Region of Central Macedonia (RCM). For this reason, a model of Multi-Criteria Analysis (MCDA) is developed with the ELimination and Et Choix Traduisant la REalite (ELECTRE) ΙΙΙ method, with the construction of outranking relations. The aim is to compare in a comprehensive way each pair of action, in our case the agro-energy regions of the RCM, in order to satisfy the main goal which is to rank the seven regions as regards their biomass production. The final goal is to select the optimal crop plan as a pilot case for biomass production in the region. In the case of ELECTRE III multicriteria model, we used several conflicting criteria such as the farm income, the biomass production from crop residues, the variable costs, and the production of thermal energy and electrical energy. Alongside a technical and economic analysis of the study area is conducted for the existent crop plans of each agro-energy region. The results show that agro-energy regions with cereals and arable crops have better results than regions with fruit trees and other crops.

Optimal crop plans for a multi-reservoir system having intra-basin water transfer using multi-objective evolutionary algorithms coupled with chaos

Optimizing a multi-reservoir system is complicated, since the operation of one reservoir depends on other reservoir and may also have conflicting multiple objectives. The conflicting purposes of a multi-reservoir system requires a systematic multi-objective study. Recently, multi-objective evolutionary algorithms (MOEAs) have been widely used for the multi-objective analysis of the reservoir systems. However, the simple MOEAs result in premature convergence and local optimal solution for complex non-linear multi-objective optimization problems. To improve the performance and maintain the diversity in the population, chaos is being combined with the evolutionary algorithms for optimizing complex problems. In the present study, the chaos algorithm is coupled with MOEAs such as non-dominated genetic algorithm-II (CNSGA-II) and multi-objective differential evolution algorithm (CMODE) to derive an optimal crop planning for a multi-reservoir system having intra-basin water transfer. The model is developed with the objective of maximizing the net benefits and maximizing the crop production, subject to various physical, land and water availability constraints. The resulted optimal policy is further assessed using a simulation model and its performance is evaluated using various statistical indices. It is found that CMODE has resulted in slightly higher net benefits of Rs. 1921.77 Million and 1201.55 thousand tons of crop production with an irrigation intensity of 106.29% compared to other techniques used in this study. It has also resulted in an optimal spatial and temporal intra-basin water transfer from the upstream reservoirs to the downstream reservoir. The simulation of optimal results showed that the optimal policies obtained from CMODE performed well for longer period with less irrigation deficits. All the reservoirs in the system achieved more than 95% reliability in meeting the irrigation demands and intra-basin water transfer.

Potentials of biomass production in the region of Central Macedonia in Northern Greece

The aim of the study is to examine the potentials of biomass production in the region of Central Macedonia (RCM) in Northern Greece. For this reason the crop plans of the seven regional units of the RCM were analysed. The final goal is to select the optimal crop plan between the seven regional units as a pilot case for biomass production. The main indicators that were analysed are the farm income, the gross margin, the residual biomass, the variable costs, and the cogeneration of thermal and electrical energy. Alongside a technical and economic analysis of the study area was conducted for the existent crop plans of each regional unit. The main findings of the study are the biomass potential for the seven regional units of the region of Central Macedonia and the optimal crop plan for producing biomass from agricultural residues. The results show that crop plans with cereals and arable crops have better results than crop plans with fruit trees and other crops.

A multiobjective fuzzy linear programming model for sustainable integrated operation of a multireservoir system

AbstractIrrigated agriculture is the largest consumer of water, with high significance in India as the majority of its people depend on it. Rainfall uncertainties, and uneven distribution of utilizable water over space and time, have presented a serious challenge to irrigation water management and crop production. Surface water reservoirs play a major role in supplying irrigation water and also addressing uneven water distribution to some degree. Varying water requirements for different crops under multiple canals, however, requires that planners develop an optimal crop planning for the efficient operation of a multireservoir irrigation system. The spatial and temporal water transfer within a multireservoir irrigation system necessitates integrated operation of all the involved reservoirs. Thus, integrated operation of a multireservoir system is a fundamental requirement for efficient irrigation water management, and also become a key issue for sustainable agricultural development. This study derived an optimal crop plan for the integrated operation of a complex multireservoir system with intrabasin water transfer, using a multiobjective fuzzy linear programming (MOFLP) approach. The MOFLP model resulted in a level of satisfaction of 0.46, with an irrigation intensity of 102.18%, and a total crop area of 149 232.10 ha. The optimal policies were furthermore assessed with various statistical indicators with a simulation model, indicating they are performing well, with high reliability and resilience for longer periods.

Development of a direct-solution algorithm for determining the optimal crop planning of farms using deficit irrigation

The irrigation farms placed in areas of scare water demand methodologies that can increase their profitability via more efficient use of their resources. Determining the combination of factors that maximizes the profitability of any productive process requires the use of optimization methodologies. Traditionally, these types of problems were solved using heuristic methods. However, a direct-solution algorithm would produce faster and more accurate solutions. The aim of this work was to develop a direct-solution algorithm capable of determining the crop planning (area and volume of water per crop) that maximizes the profitability of an irrigation farm. The data required by the algorithm include the total cultivable area of the farm and the amount of available irrigation water as well as the “gross margin vs. irrigation depth” functions of the considered crops. Cultivating one or two crops is the way to reach higher profitability, but this strategy is not suitable from an agricultural point of view (i.e., crop rotation, diseases, weather risks, regulations of agricultural policies, etc.). Due to this algorithm must be compatible with the MOPECO model, a methodology has been developed to allow its implementation in this model. The objective of this software is to maximize the profitability of irrigation farms by incorporating a more efficient use of irrigation water using regulated deficit irrigation techniques. The current version of this model uses genetic algorithms for determining optimal crop planning, which are time consuming. For a hypothetical 100ha farm, considering 10 different crops and 11 scenarios of water availability, the developed algorithm adapted to MOPECO achieved gross margins around 0.5% lower than LINGO, and 1.1% higher than genetic algorithms, decreasing the calculation time requirements by between 50 and 100 and approximately 2000 times, respectively. Another relevant result is the fact that the algorithm may be used manually, by drawing the tangent lines between the gross margin curves, for reaching the optimal combinations of irrigation depth and, indirectly, the cultivable area of each crop. Moreover, the algorithm allows to understand the relationships among crops, which may advise users in the determination of the optimal solution under real conditions. This methodology also highlights the importance of using regulated deficit irrigation techniques when managing irrigation farms with a low supply of irrigation water. The developed algorithm may also be useful in the optimization of other production processes.

Probability analysis of rainfall and crop water requirement using CROPWAT model for crop planning in a canal command of upper Bhima Basin of Maharashtra

Rainfall is the most important climatic parameters influencing agriculture in Pune district of Maharashtra. Rainfall of this region is highly variable with respect to space and time and about 80–90% of precipitation falls in monsoon period from June to October resulting in drought and flood situation in the upper Bhima basin of Maharashtra. Therefore, for efficient water resources management, optimal crop planning and also for better understanding of rainfall behavior (i.e., distribution and minimum expected amount during crop growing period) probability analysis of rainfall was conducted. Probability analysis (at 50% and 80%) of monthly rainfall data of 13 raingauge stations of the left bank canal of upper Bhima basin viz., Urali, Loni Karbol, Kasurdi, Tajuproject, Yewat, Dahitane, Bhigwan, Madanwadi, Pondewadi, Kedgaon, Patas, Pimplegaon and Daund for the period from 1975 to 2002 was conducted. Reference evapotranspiration (ETo) has been calculated using climatic parameters like sun shine hour, wind speed, maximum & minimum temperature and rainfall humidity for the period from years 1993–2005 by CROPWAT model. It was found that ETo is maximum (7.72 mm/day) during April and low in December (3.10 mm/day). Effective rainfall of existing rain gauge stations falling in different sub-basins, BM48, BM49, BM50, BM51 and BM68 have been estimated using the CROPWAT model. Finally net irrigation requirement of crops Kharif Cotton, Summer Cotton, Sugarcane and Rabi Sorghum have been find out for all the sub-basin. From this study it has been concluded that, the crop planning in the area, represented by Pimplegoan and Urali stations should be done keeping in mind maximum deficit of 187 mm and 113 mm of water respectively during July. Similarly in other stations maximum deficit of water was observed during September which indicate that while selection of crops for the areas represented by these stations the crops requiring less water during September should be selected.

Determine optimal crop planning under conditions of water risk in Namakkal district of Tamil Nadu

Water is a critical input for success of agriculture and its ever widening technology creation. Water scarcity as a risk has always been a part of agriculture and farm business. Hence, management of available irrigation water assumes great importance in the field of agriculture. The present study was attempted in Namakkal district of Tamil Nadu, India, because of the availability of exclusive dry farms. In Namakkal district, blocks namely Namakkal, Mohanur, Pudhuchatram, Tiruchengode for each risky crop were purposively selected on the basis of area, production and productivity of those crops. Linear programming model was applied to derive optimal crop plan for the sample farms under water stress conditions. Optimization of crop portfolio for the selected farm is a type of risk management strategy. The decline in gross cropped area in Namakkal block would imply relatively less pressure on land that would again indicate the sign of bringing in sustainability of the productive capacity of the land. Even though the decline trend in net income, it showed that the resources which were used in the cultivation as efficiency factor in Pudhuchatram. The results indicated that water and other resources were used efficiently to get optimum pattern in the cultivation of different crops.

Multi-stage linear programming model for optimizing cropping plan decisions under the new Common Agricultural Policy

The new European Union’s Common Agricultural Policy will cover the period from 2015 to 2020. Significant novelties in the payment scheme have been introduced which may potentially encourage farmers to implement changes at the farm level by meeting certain environmental requirements in return for support payments. The mandatory requirements, commonly known as ‘greening rules’, consist of crop diversification, maintenance of permanent grassland and establishment of an ecological focus area.This paper presents a decision-support tool based on a multi-stage linear programming model that identifies optimal cropping plan decisions under the new Common Agricultural Policy. The capabilities of our tool are illustrated through its application to the Spanish agricultural regions. Our method identifies the optimal cropping plan (i.e., crops to be grown and their acreage each year during the reform horizon) that maximizes the farmer’s net return in each region. Furthermore, the model can also be used to calculate the minimum subsidy value that would make the implementation of greening rules economically appealing, thereby promoting the widespread adoption of more sustainable agricultural practises.