Crop Allocation Research Articles

A new bi-level TOPSIS based neutrosophic programming technique for land allocation to medium farm holders

Agriculture impacts a country's social and economic growth. Crop allocation, crop combinations, and crop production processes are all necessary to achieve optimal results during various growing seasons. To maximize farm earnings, proper farm planning and resource allocations are necessary. In agriculture, land allocation problems involve several uncertainties and unpredictable variables, includes water supply, labour demands, fertility use, and food requirements. The objective of this study is to propose novel bi-level programming approaches to overcome such issues and obtain optimal land allocation for medium-sized farmers. The current study examines a bi-level, TOPSIS-based neutrosophic programming approaches in two cases, including non-interactive and interactive approaches with linear, exponential, and hyperbolic membership functions to maximize net profit and minimize the expenditure. The proposed methods are compared to other approaches, such as the Torabi & Hassini approach, the Fuzzy Optimization Technique (FOT), and the Intuitionistic Fuzzy Optimization Technique (IFOT) and are found to be more effective than the existing ones.

Reallocating crops raises crop diversity without changes to field boundaries and farm-level crop composition

Higher crop diversity can enhance biodiversity and ecosystem services; however, it remains unclear to what extent and where crop diversity can be increased. We use spatially explicit multiscale optimization to determine potential and attainable crop diversity with field-level land use data for case studies in Brandenburg, Germany. Our model maximizes crop diversity at the landscape scale while reassigning crop types over multiple years to existing arable fields. The model implements field-level crop sequence rules and maintains the crop composition of each farm and for each year. We found that a 10% higher crop diversity can be attained on average compared to currently observed diversity; minor changes in crop composition would close this gap. Improved crop allocation can contribute to closing the gap between observed and attainable crop diversity, which in turn can increase biodiversity, improve pollination services, and support pest control.

GIS spatial optimization for agricultural crop allocation using NSGA-II

This study focuses on the shift from traditional farming methods, reliant on farmer intuition and manual processes, to modern, automated approaches crucial for Thailand’s agricultural sustainability. Despite its vital role in the country’s economy, outdated practices lead to supply imbalances and perpetuate poverty among smallholder farmers. Using geographic information systems (GIS) and mathematical optimization, the present study aims to determine optimal agricultural crop allocation. A multi-objective optimization crop spatial allocation model leverages geospatial data, including crop, soil and climate suitability, to enhance the accuracy of our model. Additionally, we incorporate agricultural economics data, such as market price, crop yield, production cost, distances to secondary producers, production budget limitations, and minimum crop production requirements. To speedup the convergence of the algorithm, we introduce more suitable crossover and mutation operators in NSGA-II, aiming to direct the search towards the Pareto optimal solutions. We demonstrate the effectiveness of our approach in a case study of the agricultural area in Chiang Mai province, Thailand, focusing on three major industrial crops: corn, cane, and rice. Our model suggests land allocation that adheres to both the budget constraint and the minimum production requirements, while retaining only a small surplus for each crop. The successful implementation of this approach in our case study marks a significant advancement in Thai agricultural research, paving the way for long-term economic and environmental sustainability.

Paroan Hasil Pertanian Perspektif Akad Muzara’ah

The aim of this study was to ascertain the agricultural product sharing system from the standpoint of a muzara'ah contract. In order to examine the empirical reality behind reality and facts in depth and in more detail, this research employs a descriptive qualitative approach. This type of research uses research methods that concentrate on actual problems as they existed at the time of the research and was thought to be particularly appropriate for the topic of the title. Qualitative research is research with data that does not use numbers but is descriptive. This study focuses on the role that muzara'ah contracts have in the agricultural product sharing system. The findings of this study suggest that the majority of people who utilize the paraon method in their contracts make them verbally without any written proof and simply rely on trust between the parties. East Dempo Village has two different models for the allocation of crops. Sacks are used as the primary distribution method in the first model. The simulation when the two divisors are allocated as cash.

Participatory modelling of scenarios to restore nitrogen cycles in a nutrient-saturated area

This paper aims to find socially acceptable solutions of circularity as measure to reduce nitrogen (N) losses and prevent environmental damage by combining participatory modelling and scenario Substance Flow Analyses (SFA). A local perspective was taken on the agro-food-waste system in the animal production-dominated German district Cleves. Three scenarios were programmed as Monte Carlo simulation of SFA with stakeholder input regarding crop allocation, livestock composition, livestock reduction, and manure allocation following the elimination of feed imports. The three scenarios either utilized the unaltered stakeholder input (PS), altered crop allocation to satisfy the demand for feed (CBS), or adjusted the livestock numbers to match the locally available feed (LBS). In the reference year (2020) agricultural losses amounted to 68 kg N year−1 ha−1 agricultural land and 116 kg N in feed was imported year−1 ha−1 agricultural land. In the PS feed import elimination led to deficits in feed availability. The LBS showed the biggest reduction of agricultural N losses and improved N use efficiency (+6 %), however agricultural losses were still high (50 kg N year−1 ha−1 agricultural land). The results show a limited effect of feed import elimination on N losses if no further measures are taken, such as reduced consumption of animal-based products. Further, the study shows that it is important to improve stakeholders' knowledge about approaches to circular agro-food-waste systems. The discrepancy between stakeholder visions and N circularity provide policy makers with the recommendation to improve stakeholders' visions of a circular agro-food-waste system.

Land Suitability and Capability Analysis for Sustainable Allocation of Agricultural Crops and Natural Plants, Northwest Jordan

Land Suitability and Capability Analysis for Sustainable Allocation of Agricultural Crops and Natural Plants, Northwest Jordan

Climate vs Energy Security: Quantifying the Trade-offs of BECCS Deployment and Overcoming Opportunity Costs on Set-Aside Land.

Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14-36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year-1 from the atmosphere, deliver up to 16 Tg CE year-1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.

Achieving grain security and carbon neutrality: Challenges from carbon allocation

Climate change and management practices influence crop allocation of carbon (C), and consequently can alter grain yield and the magnitude of C sequestration (or release) from agroecosystems. However, few in situ longitudinal studies are available to quantify these changes. Here, we combined the results from 13 years (from October 2007 to September 2020) of eddy covariance data and detailed crop production measurements to investigate changing climate and C allocation in a typical wheat (Triticum aestivum L.) and maize (Zea mays L.) double cropping agroecosystem in the North China Plain. We found that the agroecosystem on average acted as a slight C sink, i.e., net ecosystem carbon balance (NECB) is 36 g C m−2 yr−1) across the study period. Increased CO2 led to a rising trend of gross primary production (GPP, 72 g C m−2 yr−2), ∼35% of which led to increased NECB (the slope is 25 g C m−2 yr−2). However, concomitant increases in temperature and decreases in surface soil moisture caused higher partitioning of GPP to autotrophic respiration, leading to lower increases in net primary production and grain yield. Summer maize experienced a greater risk of C source increase, as well as greater grain yield reduction than winter wheat, most likely due to higher temperatures and drought in summer. Overall, our observational evidence suggests that current management and ongoing climate change increase the ability of the agroecosystem to increase NECB, but does not enhance crop production in this intensively managed high yield agroecosystems. However, C allocation strategies are unlikely to maintain constant in the future as multiple climate change factors act on the agroecosystem.

Complex aspects of climate change impacts on the cultivation of perennial energy crops in the Czech Republic

Perennial energy crops, called also second-generation energy crops, can make a significant contribution to the renewable energy balance in Central European, land locked regions, in the future. When estimating the biomass potential of energy crops, possible impacts of ongoing climate change must be considered. Climate change in Central European conditions will result in an increase in average temperatures and especially in the amount and distribution of precipitation. This will be reflected in a change in the type of climate region assigned to each land plot. Climate change leads to a change in growing conditions, a change in yields of conventional and energy crops and to a change in the allocation of energy crops to land in the region. Analysis of production data from long-term field experiments shows that the varieties of second-generation energy crops are and will be capable of adapting to the current effects of climate change over the next two decades. Modelling of biomass potential in a GIS environment shows that climate change will result in a change in land allocation between short rotation coppice and Miscanthus. The manner in which land is selected for energy crops has a significant impact on biomass potential from second generation energy crop. The paper presents a methodology for estimating the impact of climate change on the biomass potential of perennial energy crops using the Czech Republic as an example.

Solving crop planning and rotation problems in a sustainable agriculture perspective

In this paper, the problem of planning the allocation of crops to arable lands, taking into account crop rotations principles and diversification strategies promoted by sustainable agriculture is addressed. Optimization models for solving multi-period planning problems are proposed, able to decide how to allocate crops in each growing season in order to maximize the total expected profit. The allocation decisions are made considering the crop rotation benefits across seasons and the sustainable requirements stated by current regulations. A complexity analysis is performed, and polynomial special cases are presented. Integer Linear Programming models are proposed, for a case study related to structured professional Italian farms specialized in arable crops, following sustainability rules coming from public regulations and private initiatives, i.e., the Common Agricultural Policy by the European Union and “La Carta del Mulino” by the Barilla Group. Numerical experiments conducted on real data show the effectiveness of the proposed solution approaches.

An Integrated Risk Sensitive Crop Allocation Model with Pollination Intelligence Algorithm

A risk sensitive model for allocation of crops is considered in this work. The constructed model was designed to help farmers decision making process, thereby maximizing the use of agricultural land. Market price, cost of cultivation, yield of crops and climatic conditions were factors considered in the models. The theory of chance constraint programing was used to handle uncertainties that arise in crop planing. Data of known yield of crops were harvested and analyzed with the help of statistical tools. A class of Pollination Intelligence Algorithm was adopted to solve the model.

A Dynamic Stochastic Integrated Climate–Economic Spatiotemporal Model for Agricultural Insurance Products

We propose a new methodology for area yield distribution modeling. We explore a variety of new hybrid data emulators for spatialtemporal statistical modeling of agricultural crop yields. The regression models explored leverage from a combination of model-generated and observed features incorporating climate/weather variables relating to temperature and precipitation over space and time and agricultural variables for farms such as crop allocations, crop types, land use, and crop rotation. This provides the modeling framework to achieve a hierarchical decomposition of the yield distribution at multiple spatial scales over time, allowing us to study both county- and individual farm-level information with suitably selected weighting functions that take into account farm size and crop type. A core component of our model framework is the climate model component that involves a spatialtemporal local approximate seasonal autoregressive integrated moving average Gaussian process (La-SARIMA-GP) model that is suitable for accurately studying local monthly temperatures and rainfalls. Upon construction of the crop yield model, we demonstrate that for practitioners, there is a clear incentive to consider such a model because it accommodates apportioning of the county yield information to the farms level. This is of significance for both individual and index-based agricultural crop insurance product design and for farm risk management. We demonstrate an application of our model in the insurance context of crop insurance risk pooling and insurance policy rating where we investigate the impact of different temporal and spatial interpolation methods on insurance loss ratios using a rating game.

Ecosystem services and economic competitiveness of perennial energy crops in the modelling of biomass potential – A case study of the Czech Republic

The paper focuses on the design of a methodology that links the modelling of biomass potential from agricultural land using geographic information systems (derivation of biomass yields according to soil, topographical and climatic characteristics) with the growing need to provide non-productive functions contributing to sustainable land use and ensure the economic viability of biomass from energy crops. According to the proposed methodology, perennial energy crops are prioritized for allocation where the land risk index is highest. The land risk index is determined by assessing 5 basic characteristics - landscape connectivity, landscape heterogeneity, threat of drought, threat of water erosion and threat of soil erosion. In order to assess the economic competitiveness of biomass produced, a methodology for determining biomass production price is used, with the aim of matching the economic effect of growing conventional crops. Land best allocated to perennial energy crops is characterized by a high land risk index value and a competitive biomass production price. The application of the methodology is demonstrated using the case study of the Czech Republic. The results of the case study show that when energy crops are selected based on a combination of necessary non-productive functions and economic competitiveness, this significantly influences the allocation of perennial energy crops to individual land blocks and their contribution to biomass energy potential.

BLOEM: A spatially explicit model of bioenergy and carbon capture and storage, applied to Brazil

AbstractBioenergy could play a major role in decarbonizing energy systems in the context of the Paris Agreement. Large‐scale bioenergy deployment could be related to sustainability issues and requires major infrastructure investments. It, therefore, needs to be studied carefully. The Bioenergy and Land Optimization Spatially Explicit Model (BLOEM) presented here allows for assessing different bioenergy pathways while encompassing various dimensions that influence their optimal deployment. In this study, BLOEM was applied to the Brazilian context by coupling it with the Brazilian Land Use and Energy Systems (BLUES) model. This allowed investigating the most cost‐effective ways of attending future bioenergy supply projections and studying the role of recovered degraded pasture lands in improving land availability in a sustainable and competitive manner. The results show optimizing for limiting deforestation and minimizing logistics costs results in different outcomes. It also indicates that recovering degraded pasture lands is attractive from both logistics and climate perspectives. The systemic approach of BLOEM provides spatial results, highlighting the trade‐offs between crop allocation, land use and the logistics dynamics between production, conversion, and demand, providing valuable insights for regional and national climate policy design. This makes it a useful tool for mapping sustainable bioenergy value chain pathways.

A Machine Learning approach for solving land allocation problems

This paper describes a numerically efficient Machine Learning approach for solving the farm manager’s land allocation problem in a manner that requires neither a distributional assumption on crop returns nor an expected utility specification. The method is based on the idea that a farm manager seeks the allocation of crops that minimizes the chance of realizing a harvest return below some pre-determined target rate of return, a notion supported by results from financial portfolio theory, behavioral economics, and historical land allocation research. The method also includes a way to connect a farm manager’s choice of target rate to their risk attitudes. Corn-soybean return data from Illinois is used to demonstrate the approach for an array of shortfall values; the optimally fitted model matches well to observed planting behavior. The proposed approach is not inherently limited to land allocation decisions; future researchers may discover that it is useful in other types of shortfall and non-shortfall decision processes.

Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model

The aim of this paper is to assess the potential impacts of different fertilizer subsidy reform options on the performance of the Iranian crops production sector. This is achieved using a Regional Crop Programming (RCP) model, based on Positive Mathematical Programming, which includes in total 14 crop activities and encompasses 31 administrative regions. The RCP model is a collection of micro-economic models, working with exogenous prices, each representing the optimal crop allocation at the regional level. The model is calibrated against observed data on crop acreage, yield responses to nitrogen application, and exogenous supply elasticities. Simulation results show that a total removal of nitrogen fertilizer subsidies would affect the competitiveness of crops with the highest nitrogen application rates and lead to a slight reduction of national agricultural income, at approximately 1%. This effect, which is more pronounced at the regional level, is driven by area reallocation rather than land productivity. The reallocation of nitrogen fertilizer subsidy to only strategic crops boost their production and income but increase disparity among regions and affects negatively welfare compared to the current universal fertilizer program. The transfer efficiency analysis shows that both target and universal simulated options are inefficient with an efficiency score below one.

Spatial distribution and variability of soil salinity in film-mulched cotton fields under various drip irrigation regimes in southern Xinjiang of China

In addition to the shortage of water resources, the sustainable development of agriculture is also largely limited by soil salinization in arid and semi-arid regions of China. However, the spatial information of soil salinity at the farm scale in these regions is still poorly understood, which is the basis for the rational utilization of saline soils and allocation of crops. The main objective of this study was to explore the spatial distribution and variability of soil salinity in drip-irrigated cotton fields with film mulch under four irrigation levels (W0.6: 60% ETc, W0.8: 80% ETc, W1.0: 100% ETc and W1.2: 120% ETc, where ETc is the crop evapotranspiration) in northwest China. The results showed that the distribution of soil salinity was significantly affected by irrigation amount. The increase of irrigation amount reduced the total dissolved solids (TDS) in the soil, while it increased the area of the desalination zone around the dripper and the depth of salt accumulation below the soil surface. TDS in the 0–80 cm soil layer did not vary significantly after irrigation compared to that before irrigation, but the distribution of TDS differed from that before irrigation. The salt accumulation zone below the soil surface obviously moved downward after irrigation under sufficient irrigation conditions (W1.0 and W1.2). The variability of soil salinity was affected by irrigation amount, growth stage and soil depth. With the increase of irrigation amount and soil depth, the variability of soil salinity in the root zone decreased. The largest spatial variability of soil salinity occurred at the middle growth stage of cotton. The salt tolerance threshold of cotton (32 Mg ha−1 of TDS) and the optimal irrigation amount (W1.0) were obtained based on the relationships between irrigation amount, TDS and seed cotton yield.

Potential, attainable, and current levels of global crop diversity

High levels of crop species diversity are considered beneficial. However, increasing diversity might be difficult because of environmental constraints and the reliance on a few major crops for most food supply. Here we introduce a theoretical framework of hierarchical levels of crop diversity, in which the environmental requirements of crops limit potential diversity, and the demand for agricultural products further constrain attainable crop diversity. We estimated global potential, attainable, and current crop diversity for grid cells of 86 km2. To do so, we first estimated cropland suitability values for each of 171 crops, with spatial distribution models to get estimations of relative suitability and with a crop model to estimate absolute suitability. We then used a crop allocation algorithm to distribute the required crop area to suitable cropland. We show that the attainable crop diversity is lower in temperate and continental areas than in tropical and coastal regions. The diversity gap (the difference between attainable and current crop diversity) is particularly large in most of the Americas and relatively small in parts of Europe and East Asia. By filling these diversity gaps, crop diversity could double on 84% of the world’s agricultural land without changing the aggregate amount of global food produced. It follows that while there are important regional differences in attainable diversity, specialization of farms and regions is the main reason for low levels of local crop diversity across the globe, rather than our high reliance on a few crops.

Intuitionistic fuzzy optimization approach in optimal irrigation planning of Ukai-Kakrapar irrigation project, India

ABSTRACT This article addresses the formulation of the Intuitionistic Multiobjective fuzzy linear programming (IFO MOFLP) model for optimal allocation of crops in the command area of the Ukai-Kakrapar Irrigation Project, India. Initially, the crisp linear programming (LP) model is applied to acquire the optimal results of three conflicting objectives, namely, maximization of net irrigation benefits (NIBs), minimization of cost of cultivation (CC) and maximization of revenue generation from industrial and municipal supplies (MI). Subsequently, the LP solutions were used to develop the IFO MOFLP, IFO MOFLP with a hesitation index and two-phase IFO MOFLP (TPIFO MOFLP) models for the complete command area. The performance of the aforesaid models is assessed in terms of irrigation intensity, degree of acceptance (α), rejection (β) and hesitation index (π) for inflows at 75% probability of exceedance. The irrigation intensity from the proposed TPIFO MOFLP model has been found to be 101.28%, while NIB, CC and MI from the proposed model are 10,575.38, 5438.42 and 2638.20 million Rs, respectively, with α = 0.64, β = 0.18 and π = 0.18, respectively. The proposed TPIFO MOFLP model has been compared better with MOFLP as the former gives additional uncertainty controlling parameters like α, β and π, which would help the planner to take better decisions for real-world problems.

Estimating the economic value of green water as an approach to foster the virtual green-water trade

Green water – precipitation that is stored in the soil as moisture and consumed in the production of biomass – provides the main source of water for crop cultivation, pasturelands, forestry, and terrestrial ecosystems. At a local level, green water is land-bound and cannot be easily allocated between uses. However, at the global level, agricultural commodities and their embodied virtual water are traded between countries. This trade typically sees the cultivation of crops in water abundant rainfed locations exported to regions that would otherwise have employed local irrigation resources. The result is a global saving of irrigation water and the negative environmental externalities associated with irrigation. In addition, scarce blue water resources are freed up for other (often higher valued) uses.Here we assess whether there is an economic rationale for the virtual green-water trade and the increased and intentional allocation of crop cultivation to water abundant rainfed locations. We model a realistic case study of maize cultivation on representative farms in 16 major maize producing regions (across four continents) and provide the first spatially variable estimates of the economic value of the green water employed. These economic values are contrasted with the economic value of blue water used for irrigation. We find that the volume of green water employed in the cultivation of maize varies between 409 m3/tonne and 1547 m3/tonne; the estimated economic value of green water varies between $ −0.04 m3 and $0.12 m3. We demonstrate how these economic value estimates can inform crop allocation decisions in favour of green water-based cultivation and inform decisions regarding the intensification and horizontal expansion of rainfed agriculture. In so doing, we aim to provide a further rationale for the green water-based measures that have been identified in the literature as the principal means of providing the additional fresh water needed to address pressing global challenges beyond the case study.