Optimal Land Allocation Research Articles

The Correlation between Industrial Organization and Sustainable Growth of City Cluster Spatial: A Case Study of Harbin-Changchun City Cluster

The Harbin-Changchun City Cluster (HCCC) is an important growth engine for the revitalization of the Northeast, and it is also the main mode and spatial platform for the implementation of the new urbanization of the country. Industrial functional organization, structural upgrading and spatial transfer are important driving force for the growth of City Cluster. According to the statistical data of the employed population and industrial output value, we study the industrial spatial layout features and structural evolution of the HCCC and then explore the correlation between industrial organization and the City Cluster spatial based on the Industrial Agglomeration Indicators Analysis and the Locational Gini Coefficient. At the macro level, it is found that the industrial structure and specialized division of labor affect the spatial network and the axis pattern of the City Cluster; at the meso level, the industrial quantity and spatial distribution affect the layout of key cities and node towns in the City Cluster; at the micro level, the industrial level and transformation and upgrading affect the changes of urban spatial structure and the optimal allocation of relevant land.

A land-use and capital-investment allocation optimization model to develop a fair community opportunity framework for Columbus, Ohio

Land-use planning and capital investments have been increasingly recognized as important planning tools to mitigate social inequality. The idea is based on the concept of “neighborhood effects” that the built environment of a city has influence on the socioeconomic outcome of a residential community, which is defined as community opportunity in this study. The explicit local relationships between community opportunity and the physical setting of land uses, transportation infrastructure, and public facilities can be captured by a geographically weighted regression (GWR) model. The GWR results indicate the most effective locations for further developments and investments. This research is one of the few to incorporate such a GWR model into an optimization modeling framework in the contexts of Columbus, Ohio, to (1) maximize the total community opportunities over the region, and (2) minimize the total difference in community opportunities among the 284 census tracts, by an optimal allocation of future land uses and capital investments. Solving the optimization model for the two policy scenarios provides decision makers a new insight into the problem of social inequality. Comparison between the two allocation results has implications for the efforts to improve community opportunity without exacerbating social inequality, by allocating future activities to positive but less effective locations.

Optimal allocation of agricultural land for crop planning in Hirakud canal command area using swarm intelligence techniques

ABSTRACT 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.

Market-Level Implications of Regulating Forest Carbon Storage and Albedo for Climate Change Mitigation

We explore the optimal regulation of forest carbon and albedo for climate change mitigation. We develop a partial equilibrium market-level model with socially optimal carbon and albedo pricing and characterize optimal land allocation and harvests. We numerically assess the policy's market-level impacts on land allocation, harvests, and climate forcing, and evaluate how parameter choices (albedo strength, productivity of forest land, and carbon and albedo prices) affect the outcomes. Carbon pricing alone leads to an overprovision of climate benefits at the expense of food and timber production. Complementing the policy with albedo pricing reduces these welfare losses.

Compact cities or sprawling suburbs? Optimising the distribution of people in cities to maximise species diversity

Abstract Conservation of biodiversity in urban environments depends on species’ responses to the intensity of urban development. “Land sharing” and “land sparing” represent alternate ends of a gradient that conceptualises a trade‐off between the human population and biodiversity. We used a linear optimisation procedure to (a) identify the optimal allocation of land for people and nature, (b) assess whether the optimal allocation is closer to land sparing or land sharing and (c) examine how this might change under scenarios of human population growth. We surveyed birds in 28 landscapes, each 25 ha in size, along a gradient of human population density (zero to c. 1,600 persons/25 ha) in the Greater Melbourne region, Australia. Species’ responses to population density were estimated using generalised additive models (GAMs). These relationships were then used to determine the optimal allocation of land among different categories of population density based on maximising a community index, the geometric mean of relative abundance (G) of bird species. Human population density was an important driver of the reporting rate for 28 species. Response curves differed among “urban avoider,” “urban adapter” and “urban exploiter” species. For the current human population in the study area, the optimal allocation of land included elements of both land sharing and land sparing. However, for scenarios of increased population size, optimal allocation converged upon a land sparing design. Synthesis and applications. Urban areas represent a mosaic of land uses that offer habitats of differing quality. Land sharing, based on sustaining biota among residential areas, performed poorly under all scenarios due to its inability to support species that depend on natural or seminatural habitat. To sustain more than a homogenised avifauna in urban regions, large tracts of natural vegetation are needed within, or adjacent to, the urban environment. Protecting natural areas on urban fringes will be critical to the safeguarding of nature in the future as urban populations and land‐use inevitably expand.

Planning of agricultural inputs in Ur watershed to maximize net benefit under limited resources

Two models (CROPWAT and LINGO) were used to optimize the resource allocation and achieving higher efficiency in agricultural productivity. Quantitative evaluation of hydro-meteorology parameters was carried out for crop water and irrigation requirement planning using CROPWAT model. Whereas, LINGO model applied to determine the optimum land and water resources allocation to major crops of kharif and rabi season in the Ur watershed using agriculture data such as net income per ha which was calculated based on various sub factures, viz. cost of fertilizers and pesticides, cost of seeds, yield of crops, daily wages of labour and machine charges, selling base price of commodities. Results revealed that the crop water requirement in kharif season was 593 cm and in rabi season 421 cm, whereas irrigation requirement was 71.16 cm and 294.7 cm respectively. Because, the crop grown during kharif season require less irrigation water. Therefore only supplemental irrigation requirement have to be planned for kharif crops. But in rabi season, more irrigation water is required to bring whole area under cultivation. That’s why the scenarios were considered based on available resources of watershed to fulfill the demand. Output of the CROPWAT model was used as input of the LINGO model for formulation of the linear programming equations under different scenario. There were three scenarios considered in each cropping seasons, viz. in kharif season (i) Existing scenario; (ii) Some need based crop scenario; (iii) Limitation with cash crop scenario; and in rabi season (i) Existing scenario with conventional method of wheat growing; (ii) Scenario under System of Wheat Intensification; (iii) Scenario under increasing 10% irrigation water. It was observed that the combination of both the models is appropriate for finding the optimal land and water resources allocation to the major crops in kharif and rabi season for maximizing net income of the watershed.

The Multi-Objective Space Optimal Allocation of Urban Land Based on Spatial Genetic Algorithm

With the constant increasing scale of urban buildings, the contradiction between supply and demand of land use problems is more prominent. Therefore, the multi-objective space optimal allocation of urban land use based on spatial genetic algorithm was proposed in this paper. Firstly, the present situation of the urban land use resources was expounded; in view of the urban land use planning, a spatial genetic algorithm was proposed; then, the urban land was divided into different functional areas, and the land planning and design method was put forward; finally, taking a city's land space planning as an example, the optimal planning and design were carried out to the geological disasters, low hilly land and land overall utilization; by comparing the land use before and after the planning optimization, the advantages of land optimization design were confirmed.

Optimal Allocation of Land and Water Resources in a Canal Command Area in the Deterministic and Stochastic Regimes

Water logging is a universal problem of irrigated agriculture and it is a serious threat to the sustainability of irrigated agriculture in many arid and semiarid regions. Limbasi branch canal command area of Mahi Right Bank Canal (MRBC) project, Gujarat, India is also found to be affected by water logging conditions. Present study deals with the formulation of the Deterministic Linear Programming (DLP) and Chance Constrained Linear Programming (CCLP) models which maximizes net return from a canal command area while simultaneously mitigating water logging conditions. The developed models are applied to the Limbasi branch canal command area. The objective function is to maximize net annual return and decision variables are the seasonal cropping pattern and seasonal water supply. Analysis shows that under optimal conditions in the DLP model, there was a 40% deviation of crop area from existing cropping pattern and Net Irrigation Requirement (NIR) of crops was satisfied by conjunctive use of 41% of canal water and 59% of groundwater (annually). There was 91.1% increase in ground water exploitation which consecutively moderated rising water table issues. Net annual return was found to increase by 46.6%. In the CCLP model, NIR of crops was considered as a stochastic variable and normal distribution was found as the best fit. The CCLP model was run from 2 to 40% risk levels and cropping pattern under 10% risk level was considered as optimal at which NIR was satisfied by conjunctive use of 53.8% of canal water and 46.2% of ground water (annually). There was 86% increase in ground water exploitation. The outcome of the study can be used to assist the water resources planners and managers in taking appropriate decisions to develop a sustainable management plan of land and water resources for an overall balance of the system.

Conceptualization of Community-Based Integrated Farming System Model Design with Multi-Objective Optimization Management

Effective utilization of land and water resources is attempted in the present study through an integrated farming system and multi-objective optimization management framework model using goal programming algorithm in a coastal waterlogged paddy area in Odisha, India. A methodology is developed to identify the water harvesting structure locations in the study area using spatial science tool. Due to the uncertainty of parameters and control variables, development of management framework was considered with 85% and 75% probability of rainfall occurrence and runoff generation. To incorporate the uncertainties, a multi-objective linear goal programming optimization model is developed considering the objective of maximizing the net annual return and production subject to optimal allocation of land. While evaluating the model for different water resources scenarios, the net annual return is found to be Rs 4,343,474 and maximum production is 10,424 q from scenario I, whereas maximum production of 10,980 q is obtained in scenario II. Tomato and rice cultivation area increased from 11.47 to 21.43 ha and 8.82 to 10.48 ha respectively in scenario II. The developed methodology shows the potential applicability in similar farming situations in other areas.

Application of Goal Programming Approach on Finding an Optimal Land Allocation for Five Other Field Crops in Anuradhapura District

Application of Goal Programming Approach on Finding an Optimal Land Allocation for Five Other Field Crops in Anuradhapura District

Carbon taxation of the land use sector—the economics of soil carbon

AbstractGrowing vegetation removes carbon from the atmosphere. This carbon is then stored in biomass, soils and products for varying lengths of time. We outline a comprehensive tax policy for jointly regulating these carbon stocks for climate change mitigation. The central role of soil carbon storage is emphasized. We present an analytical partial equilibrium land use sector model, which we use to derive the optimal allocation of land and biomass over time, taking into account the raw‐material value of the produced biomass and the social value of carbon storage. The optimization yields a set of monetary shadow values for land and biomass carbon fluxes. These values can be interpreted as the fluxes’ socially optimal prices and can thus be used to outline a comprehensive land use sector carbon tax policy. The taxation can be organized in various ways. We present a solution that is easy to implement and monitor in practice. As the policy jointly regulates all carbon stocks, carbon impact leakage between land uses and regions is prevented.

Bridging the gap between multi-objective optimization and spatial planning: a new post-processing methodology capturing the optimum allocation of land uses against established transportation infrastructure

ABSTRACTThe optimal allocation of multiple land uses constitutes a complex multi-objective optimization problem with unknown feasible objective space and optimal planning alternatives. Despite the effectiveness of evolutionary algorithms to capture the underlying Pareto set of optimum maps, land use planners are bound to pursue the best possible spatial allocation of each use within an enormous population of non-dominated solutions. This article presents a novel post-processing methodology enhancing the comparative evaluation of alternative planning approaches without making any assumptions about the (relative) importance of each objective function. The proposed consolidated post-processing module is applied in a land use planning paradigm, revealing: (a) the existence of substantial planning guidelines whose validity is not affected by the relative significance of each criterion and (b) the variable planning component emerging from the (varying) relative importance of objective functions. Such planning feedback could not be extracted by the exhaustive review of non-dominated maps.

A novel understanding of land use characteristics caused by mining activities: A case study of Wu’an, China

An idea that which zones are the main disturbed areas in mining cities and what are the exact impacts in space concerns re-use and optimal allocation of land. Current research mostly concludes that mining activities impact land use greatly, but there is no definite spatial range of disturbance. To find out the exact range, this paper sets up 12 gradients composed of mineral locations and 11 surrounding gradients with an interval of 3km. And the “D-C-S” (Disturbance-Continuity- Sustainability) framework was then established to evaluate land use characteristics, including 9 indices in the three layers of disturbance, continuity and sustainability. Here, this paper adopted two approaches − transverse land use profiles and anomalous economic points − to test out the understanding of land use characteristics in mining cities. Our findings show that the areas surrounding mineral locations possibly play a more important role in land use structure and functions, designated to 0–9km, especially 3–6km away from mineral locations, where land needs more concerns about optimal allocation for future policy-making to improve the pattern, function and continuity.

Optimal allocation of water and land resources for maximizing the farm income and minimizing the irrigation-induced environmental problems

The continuous decrease in good quality water and land resources and concurrent increase in global population accentuates the need of optimal allocation of these resources to fulfilling the rising food requirements. This study presents the formulation and application a management model for the optimal allocation of available good quality water and land resources to maximize the farm revenue of a canal command area. A groundwater balance constraint was imposed on the model, which moderates the irrigation-induced environmental problems of waterlogging and salinization, while making the optimal allocation of resources. The model results show a reduction in mustard, rice, and gram crop areas against an increase in sorghum, millets, and wheat areas. The net annual revenue from the command area increased by about 18 % under the optimal allocation plans. The farmers and stakeholders concerned in the actual agricultural production process are suggested to use groundwater and canal water conjunctively to maximizing the farm income. This strategy would also mitigate the hydrological imbalances to the groundwater system without installing costly drainage systems which is not viable as the quality of groundwater is poor and drainage water may cause a serious disposal problem. The developed model can be used as a reliable decision tool for taking the farm and regional level decisions of optimal land and water resources allocation and is able to solve the irrigation-induced environmental problems of agricultural systems.

Determination of optimal and and water allocation under limited water resources using soil water balance in Ordibehesht canal of Doroodzan water district

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

Quantifying pest control services by birds and ants in Kenyan coffee farms

Ecosystem services, such as pest control and pollination, are critical benefits of biodiversity important for agricultural production. Predators, including insectivorous birds and ants, can provide important biological controls in agroecosystems, boosting crop yield and offsetting the need for expensive inputs such as pesticides. Local habitat and landscape characteristics can affect the delivery of ecosystem services, thereby influencing optimal land allocation for crop production and biodiversity. In order to better understand the relationship between ecosystem services and the surrounding habitat, we conducted a sentinel pest experiment to investigate predation levels in response to a novel pest on coffee farms in central Kenya. The frequency of predation decreased significantly with increasing distance from adjacent forest fragments and was correlated with bird species richness. Predation was also significantly higher on shade compared to sun coffee farms. We conclude that a land sharing approach, via both the integration of shade trees and the conservation of small forest fragments within or adjacent to a farm, can support increased levels of pest control services provided by birds and ants in Kenyan coffee farms.

Fuzzy linear mathematical programming in agriculture

In this paper we present a Fuzzy linear Mathematical programming approach for optimal allocation of land under cultivation. Fuzzy Mathematical programming approach is more realistic and flexible optimal solution for the agricultural land cultivation problem. In this study we have discussed how to deal with decision making problems that are described by Fuzzy linear programming (Flp) models and formulated with the elements of uncertainty. This form of approximation can be convenient and sufficient for making good decisions. BIBECHANA 13 (2016) 72-76

Land Allocation with the Introduction of Teak: A Case Study of Smallholder Farms in Southern Togo

Subsistence landholders in southern Togo are interested in planting teak on their land for income generation. The purpose of this study was to determine how smallholder farmers could allocate land among maize, cassava, and teak plantings in order to optimize financial returns. A linear programming model was developed to calculate the optimal land allocation for maize, cassava, and teak. The model was solved for five farmer types using 15- and 30-yr teak rotations, with timber priced at alternative market and government market prices, and with discount (real interest) rates of 8, 11, and 15%. The analyses indicate that growing teak is profitable for most smallholders if grown on a 15-yr rotation and sold on the alternative market. For Land Rich-Labor Poor farmers, teak is profitable under all regimes.

Optimal land and water resources allocation policies for sustainable irrigated agriculture

Conjunctive use of surface water and groundwater is being practiced in many regions of the world to bring more areas under irrigation, increase agricultural production and productivity, and also maintain overall system balance. Successful agricultural water management policies put the physical, hydro-geological, and socio-economic constraints on these integrated water supplies. To sustain these constraints, a linear programming (LP) model has been developed for optimal land and water resources allocation in various sectors of the Hirakud Canal Command, a multi-purpose irrigation project on the river Mahanadi in eastern India. To enhance the decision-taking ability of the Hirakud command area development authority, a menu-driven and user-friendly software has been developed by Visual-Basic that incorporates model-base, data-base, knowledge-base subsystems along with the user-interface. The model-base subsystem includes LP, groundwater balance, and evapotranspiration models. The data-base subsystem includes the meteorological, crop, and water resources data. The knowledge-base subsystem was developed from the knowledge derived from the results of the aforementioned models. Sensitivity analysis of the LP model parameters was performed by varying the parameters that affect the optimal cropping pattern and groundwater allocation. The result indicates that conjunctive use of 87% surface water and 13% groundwater is the sustainable water allocation policy of the command area. The model results also indicate that a 20% deviation in existing cropping pattern is the best alternative as it considered socio-economic need and also meets the entire food demand of the study area.

Land and water management planning for increasing farm income in irrigated dry areas

The global population is continuously increasing and good quality land and water resources are simultaneously declining. This condition emphasizes the need of optimal allocation of these resources to fulfill the rising food requirements. This study presents the development and application of two management models for the seasonal optimal allocation of resources to maximize the net annual return of a canal command area of India. The models results show a reduction in rice and mustard crop areas against an increase in wheat, millet, cotton, and sugarcane crops. Under the optimal land and water allocation, the groundwater utilization increased, which in turn mitigated the waterlogging problems of the command area. The net annual return from the command area has increased by about 22%. The different optimal resource allocation strategies show an increase in net annual return with the increase in ranges of deviation from the existing conditions. The developed models can be used as a reliable decision aid for taking the farm and regional level decisions of optimal resources allocation and are able to solve the waterlogging problems of irrigated agriculture. The management models presented here are simple and can be used in any part of the world for maximizing the net agricultural return by mitigating the waterlogging problems in irrigated agriculture. However, under different hydro-geologic conditions, the constraints of the model may differ depending on the groundwater quality/quantity conditions and surface water/groundwater availability.