A method for optimizing agricultural spatial layout to synergistically improve ecological and production benefits

Abstract

This invention discloses a method for optimizing agricultural spatial layout to synergistically enhance ecological and production benefits. The method includes: data collection and processing, assessment of ecosystem service and economic value, construction of a multi-objective quantitative optimization model, construction of spatial optimization rules, spatial pattern optimization based on cellular automata (CA), scenario simulation, and scheme evaluation. This invention combines quantitative and spatial optimization, overcoming the blind spots of traditional experience-based layouts and providing a systematic and scientific decision support method for industrial spatial layout. This invention fully considers the topographic heterogeneity and industrial compatibility of ecologically vulnerable areas such as karst regions, and the optimization rules are rationally designed with strong local adaptability. The spatial simulation results based on the CA model are intuitive and visual, facilitating planners' understanding and application. The optimized layout scheme can be directly used to guide actual production.

Description

Technical Field

[0001] This invention belongs to the field of agricultural ecological engineering and geographic information technology, specifically relating to an agricultural spatial optimization layout method suitable for ecologically fragile areas to synergistically improve ecological and production benefits. Background Technology

[0002] The karst region of southwestern my country has a fragile ecological foundation and exhibits strong spatial heterogeneity in water and soil resources. It features a diverse landscape with alternating peaks, depressions, tower peaks, and cone peaks, with vertical elevation differences ranging from 200 to 800 meters. Soil is distributed in patches within solution channels, troughs, and depressions between rock ridges, creating a vertically differentiated landscape characterized by "desertification on mountain peaks, dryland farming on hillsides, and paddy fields in depressions." While the region has abundant water and heat resources, surface water seepage is severe. Plant communities exhibit significant vertical differentiation based on altitude, slope, and lithology, ranging from drought-resistant grasses and shrubs on mountain peaks to a symbiotic relationship of trees, shrubs, and grasses in depressions. The ecosystem service value and agricultural production potential are highly heterogeneous spatially. For a long time, local farmers have developed adaptive practices such as "planting fruit trees on slopes and rice in depressions," but agricultural layout relies heavily on traditional experience and lacks quantitative spatial decision-making techniques, making it difficult to maximize economic benefits under ecological constraints.

[0003] Existing methods for optimizing agricultural spatial distribution have the following shortcomings: First, conventional agricultural zoning methods are based on the assumption of homogeneous surfaces, neglecting the strong spatial heterogeneity of topography, soil, and hydrology in karst regions, and failing to characterize the micro-habitat differences in peak-cluster depressions. Second, existing studies on the ecological-production trade-off mostly remain at the level of qualitative description or static evaluation, lacking technical means to spatially couple ecosystem service value with agricultural production benefits, making it difficult to achieve a precise layout that prioritizes ecology while taking production into account. Third, traditional layout optimization does not fully consider the vertical differentiation patterns of karst, failing to establish a vertical correlation model of topography-soil-vegetation-land use, leading to problems such as soil erosion caused by cultivation on steep slopes in the middle and upper sections and ecological degradation caused by over-development of depressions, making it difficult to synergistically improve ecological and economic benefits. Fourth, existing decision support systems lack adaptive algorithms for the special landforms of karst, and cannot provide spatial optimization solutions for composite agricultural models such as returning farmland to forest, specialty forestry and fruit, and ecological animal husbandry.

[0004] To address the shortcomings of existing technologies in adapting to the unique topography of karst peak-cluster depressions and effectively coordinating ecological and production benefits, there is an urgent need to establish a vertical differentiation evaluation method that couples ecosystem services and agricultural production functions. By quantitatively characterizing the spatial heterogeneity of topography, soil, hydrology, and vegetation, a spatial decision-making model for the coordinated optimization of ecological and production benefits can be constructed to achieve the scientific layout of agricultural industries along vertical gradients. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for optimizing the spatial layout of agricultural industries based on ecology-production coupling, in order to solve the problems of difficulty in balancing ecological and economic benefits and lack of scientific basis for spatial layout in existing technologies. To achieve the above objective, this invention provides the following technical solution:

[0006] To achieve the above-mentioned technical effects, the present invention employs the following technical means:

[0007] A method for optimizing agricultural spatial layout to synergistically improve ecological and production benefits includes the following steps:

[0008] (1) Data acquisition and processing

[0009] Collect basic geographic data for the target area, including digital elevation model (DEM), land use data, soil property data, and remote sensing imagery. Extract topographic factors such as slope and aspect based on the DEM data.

[0010] (2) Assessment of ecosystem service value and economic value

[0011] Establish a correlation between land use types and ecosystem service value, and calculate the equivalent ecosystem service value per unit area for each land use type. Obtain data on the output and market prices of major agricultural products in the target area, and calculate the economic returns per unit area for each agricultural industry type.

[0012] (3) Construct a multi-objective quantity optimization model

[0013] With the dual objectives of maximizing ecosystem service value and maximizing economic benefits, a multi-objective linear programming equation is constructed. Constraints are set based on the actual regional conditions, including: no reduction in basic farmland area, no reduction in water area, and conservation of the total regional area. By solving the multi-objective linear programming equation, the optimal combination of landscape types and their areas that satisfies both ecological and economic objectives is obtained.

[0014] (4) Constructing spatial optimization rules

[0015] Based on topographic suitability analysis, suitable distribution areas for different land use types (such as farmland, orchards, forest, shrubland, and mulberry gardens) are determined, including elevation and slope ranges.

[0016] Based on the ecological compatibility and production synergy among industries, a spatial compatibility judgment matrix is ​​constructed to define the compatibility level when different land use types are adjacent to each other.

[0017] (5) Spatial pattern optimization based on cellular automata (CA)

[0018] The target area is resampled into raster data at a preset resolution, and the cell size and neighborhood range are defined. Combining the optimized area obtained in step 3 and the optimization rules determined in step 4, iterative calculations are performed using a cellular automata model to simulate the spatial evolution of land use types until a stable state is reached, thus obtaining an optimized agricultural industrial spatial layout scheme.

[0019] (6) Scenario simulation and scheme evaluation

[0020] Different optimization scenario modes were set up, including an ecological priority mode, an economic priority mode, and an ecological-economic coordinated development mode. The CA model was run in each mode to obtain spatial layout schemes under different scenarios.

[0021] Calculate the expected total value of ecosystem services and total economic income of agricultural industry under different schemes, conduct a comprehensive benefit assessment, and select the optimal scheme.

[0022] As a preferred technical solution, the ecosystem service value assessment in step (2) is based on the China Ecosystem Service Value Equivalent Factor Table and revised in combination with the regional grain yield per unit area. As a preferred technical solution, the multi-objective linear programming equation in step (3) is transformed into a single-objective problem for solution by assigning different weights to ecological benefits and economic benefits.

[0023] As a preferred technical solution, the spatial compatibility judgment matrix in step (4) is used to ensure that industries sensitive to the ecological environment (such as mulberry gardens) and land with large pesticide application (such as orchards and cultivated land) are spatially isolated.

[0024] An agricultural spatial optimization layout system based on the synergistic improvement of ecological and production benefits includes:

[0025] The data acquisition module is used to acquire and process spatial and attribute data of the target area;

[0026] The assessment module is used to evaluate the ecosystem service value and economic value of various land use types;

[0027] The model building module is used to build multi-objective quantity optimization models and spatial optimization rules;

[0028] The simulation optimization module is used for dynamic simulation and optimization of spatial patterns based on cellular automata models.

[0029] The solution output and evaluation module is used to generate optimized solutions for different scenarios and evaluate their overall benefits.

[0030] The beneficial effects of this invention are as follows:

[0031] Scientific and systematic: This invention combines quantity optimization with spatial optimization, overcoming the blindness of traditional experience-based layout and providing a systematic and scientific decision support method for industrial spatial layout.

[0032] Ecological and economic synergy: By constructing a multi-objective optimization model, the contradiction between ecological protection and economic development is effectively balanced, achieving a win-win situation for both ecological and economic benefits.

[0033] Highly targeted: It fully considers the topographical heterogeneity and industrial compatibility of ecologically fragile areas such as karst areas, and the optimized rule design is reasonable and has strong local adaptability.

[0034] Highly visualized and operable: The spatial simulation results based on the CA model are intuitive and visual, making it easy for planners to understand and apply. The optimized layout scheme can be directly used to guide actual production. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the operation process of the optimized model of the present invention;

[0036] Figure 2 The diagram shows the spatial layout simulation results under different optimization scenarios, where: A represents the economic priority model, B represents the ecological priority model, and C represents the ecological-economic coordinated development model.

[0037] Figure 3 A land use status map of the study area;

[0038] Figure 4 A comparison chart of total ecosystem service value and total economic income under different optimization scenarios;

[0039] Figure 5 To show the changes in the ecological service value of different land types after optimization (compared with before optimization);

[0040] Figure 6 The changes in agricultural industry economic income and land ecological service value after optimization (compared with those before optimization). Detailed Implementation

[0041] The invention will be further described in detail below with reference to a specific embodiment of a typical village in Huanjiang Maonan Autonomous County, Guangxi Zhuang Autonomous Region (Tongjin Village).

[0042] Example 1

[0043] The study area is located in Mulian Village, Dacai Township, Huanjiang County, Hechi City, Guangxi Zhuang Autonomous Region. It lies in the transitional zone between karst peaks and depressions, exhibiting diverse landforms including flat alluvial plains, gentle red soil hills, and typical karst peaks and depressions. This area is agricultural, primarily cultivating mulberry trees for silkworm rearing, while also supporting fruit tree industries such as oranges and pomelos. The southern part of the area is the location of the Huanjiang Karst Ecosystem Observation and Research Station of the Chinese Academy of Sciences, which forms the main experimental area; therefore, the land use type in this area is not adjusted and is not included in the total area and proportion calculations.

[0044] Taking a typical karst peak-cluster depression village in Huanjiang County, Guangxi Zhuang Autonomous Region as the research object, this invention's method is applied to optimize the spatial layout of agricultural industries.

[0045] First, aerial imagery, 30m resolution DEM data, and soil data of the village were collected via drone. A current land use map was then obtained through visual interpretation (e.g., [image of land use]). Figure 3 (As shown).

[0046] Secondly, based on the revised China Ecosystem Service Value Equivalent Factor Table by Xie Gaodi et al., and combined with local grain yields, the ecosystem service value per unit area of ​​farmland, orchards, forests, shrubs, grasslands, and water bodies was calculated. Simultaneously, based on local market surveys, the yield per mu and unit price of major agricultural products such as corn and citrus were obtained to calculate the economic benefits per unit area.

[0047] Then, a multi-objective linear programming equation was constructed with the objectives of maximizing ecosystem service value and economic benefits. The optimized land use areas were obtained by solving the equation under the constraints of "no reduction in farmland area, no reduction in water area, and conservation of total area." The results show that orchards, due to their high dual value, significantly increased their area proportion after optimization.

[0048] Based on topographic analysis, suitable distribution ranges for each land use type were determined (e.g., mulberry orchards are best located on flat land with a slope of less than 5°; farmland is best located in areas with an altitude of <270m and a slope of <15°; gardens are best located in areas with an altitude of 270-300m and a slope of 12-25°; forests, shrubs, and grasslands are best located in areas with an altitude of >300m and a slope of >25°). Simultaneously, a spatial compatibility matrix was constructed, encompassing mulberry orchards, orchards, forests, shrubs, grasslands, and agricultural land, stipulating that mulberry orchards are incompatible with orchards and agricultural land.

[0049] Finally, programming was performed in the IDL environment, using 5m×5m cells and 3×3 neighborhoods. Combining the aforementioned area constraints, terrain suitability, and compatibility rules, the CA model was run for spatial simulation. The simulated spatial layout schemes under three scenarios—economic priority, ecological priority, and coordinated development—were analyzed (e.g., ...). Figure 2 As shown). By Figure 2As can be seen, after optimization using the CA model, the areas of each land use type under the three scenarios have changed significantly. In the economic priority model, a large amount of ecological forest land has been converted to economic forest land, and some traditional agricultural land and mulberry land have been converted to fruit tree planting. These converted areas are mostly located on relatively gentle slopes at the foot of mountains. The area of ​​mulberry planting has decreased significantly to less than 1%, while the area of ​​orchards has increased significantly by 18.12%. At the same time, the area of ​​forest, shrub, and grass has also decreased, with most of it being converted to orchards. In the ecological priority model, a large amount of ecological forest has been preserved, and the area of ​​forest, shrub, and grass has increased significantly. Most of the farmland planted with mulberry trees has been converted to other land use types, while the area of ​​orchards remains relatively stable. In the ecological-economic coordinated development model, the proportions of orchards and forest, shrub, and grass have increased to 39.15% and 41.04% respectively, both showing a slight decrease. Agricultural land planted with mulberry trees has decreased to 1.09% due to poor ecological and economic benefits. Compared to the land use type distribution map before optimization, the area of ​​fruit trees has increased significantly, and some traditional agricultural land and forest, shrub, and grass types have been converted to fruit planting land. Given the high output value of fruits such as tangerines and pomelos, expanding the production of specialty fruits will help increase farmers' income. In areas with steep slopes and mountaintops, the design preserves large areas of forest, shrubland, and grassland to maintain the ecological functions of the area, such as soil and water conservation. Mulberry fields are widely distributed and exhibit a concentrated, contiguous pattern, which aligns with the positioning and requirements of the mulberry industry. It is evident that the ecological-economic coordinated development model is relatively balanced. Because the requirements for ecological and environmental indicators are stricter than those of the economic-first model, it better protects ecological forests, increases the proportion of high-quality industries, and reduces the share of traditional, inefficient agricultural industries, thereby optimizing the land use pattern and improving industrial efficiency in the study area.

[0050] By comparing the changes in ecosystem service value and gross revenue across the three scenarios, we can determine the changes in ecological and economic benefits for each scenario model. Figure 4 It is evident that the expected total value of ecosystem service functions increased by 2.46%, 17.56%, and 12.63% respectively under the economic priority model, the ecology priority model, and the ecology-economy coordinated development model, while gross income increased by 28.06%, 8.30%, and 20.82% respectively. Relatively speaking, the ecology-economy coordinated development model can achieve a synergistic improvement in both ecological and economic benefits, demonstrating significant application value. Furthermore, the improved cellular automata model can simulate scenarios of land use type changes and ecological function enhancement, contributing to the optimization of regional agricultural industrial structure adjustment and cost-benefit analysis, thus providing technical support for improving regional ecosystem services.

[0051] The land use planning map generated after model optimization can effectively achieve a dual improvement in regional ecosystem service value and industrial economic benefits, thereby maximizing the comprehensive value of regional ecosystem services. According to the plan, the industrial structure of the study area is optimized, with a significant decrease in the previously large area of ​​traditional agriculture, while the area of ​​animal husbandry and forestry / fruit cultivation increases. The industrial structure of ecological farms is adjusted. Therefore, it is expected that the area of ​​the five types of land and the industrial regulation services, supply services, support services, and cultural services will all undergo significant changes. Specifically, the ecosystem service values ​​of ecological farms, rivers / ponds, ecological animal husbandry, and economic forestry / fruit cultivation areas will all increase significantly, while the ecosystem service value of traditional agriculture will decrease slightly due to its reduced proportion. Figure 5 The total ecosystem service value of the region increased from 572,300 yuan before optimization to 1,022,800 yuan, an increase of 1.79 times, while the economic income increased from 371,700 yuan to 1,275,200 yuan, an increase of 3.4 times. Figure 6 This indicates that the region has significant potential for improving its ecological and agricultural production benefits. In particular, developing suitable forest areas into fruit and fruit cultivation and harvesting industries can maintain regulatory services within the ecosystem, enhance support and supply services, and simultaneously boost economic benefits, thus promoting the region's sustainable and stable development. Therefore, the planning scheme obtained from the optimized model can ensure the stable maintenance of regional ecological value and the steady increase in industrial benefits, thereby improving the integrated mechanism for enhancing regional ecological functions and cultivating ecological industries.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing agricultural spatial layout to synergistically improve ecological and production benefits, comprising the following steps: (1) Collect basic geographic data of the target area, including: digital elevation model, land use status data, soil attribute data and remote sensing image data, and extract topographic factors; (2) Establish the correspondence between land use types and ecosystem service value, calculate the equivalent value of ecosystem service per unit area for each land use type, and calculate the economic benefits per unit area for each agricultural industry type. (3) With the dual objectives of maximizing ecosystem service value and maximizing economic benefits, a multi-objective linear programming model is constructed, and the optimal combination of landscape types is solved by combining constraints. The constraints include no reduction in the area of ​​basic farmland, no reduction in the area of ​​water, and conservation of the total area of ​​the region. (4) Based on topographic suitability analysis, determine the suitable distribution areas for different land use types, and construct a spatial compatibility judgment matrix to define the compatibility level when different land use types are adjacent to each other; (5) Resample the target area into raster data, combine the optimal landscape type area combination and spatial optimization rules, use the cellular automata model to perform iterative calculations, simulate the spatial evolution process of land use types, and obtain an optimized agricultural industry spatial layout scheme. (6) Set up different optimization scenario modes, run the cellular automata model respectively, calculate the expected total value of ecosystem services and total income of agricultural industry economy for different schemes, conduct comprehensive benefit evaluation, and screen the optimal scheme.

2. The method for optimizing agricultural spatial layout to synergistically enhance ecological and production benefits according to claim 1, wherein: The terrain factors mentioned in step (1) include: slope and aspect, which are extracted based on the digital elevation model.

3. The method for optimizing agricultural spatial layout to synergistically enhance ecological and production benefits according to claim 1, wherein: The ecosystem service value assessment described in step (3) is based on the ecosystem service value equivalent factor table and revised in combination with the regional grain yield per unit area; Step (3) transforms the multi-objective linear programming model into a single-objective problem by assigning different weights to ecological and economic benefits.

4. The method for optimizing agricultural spatial layout to synergistically enhance ecological and production benefits according to claim 1, wherein: The suitable distribution areas for different land use types determined by the terrain suitability analysis in step (4) include elevation range and slope range, and the land use types include farmland, orchard, forest, shrubland and grass, and mulberry garden; The spatial compatibility judgment matrix in step (4) is used to ensure that industries sensitive to the ecological environment and land use with large pesticide application are spatially isolated.

5. The method for optimizing agricultural spatial layout to synergistically enhance ecological and production benefits according to claim 4, wherein: The industries that are sensitive to the ecological environment include: mulberry orchards; The land areas with high pesticide application rates include: orchards and cultivated land.

6. The method for optimizing agricultural spatial layout to synergistically enhance ecological and production benefits according to claim 1, wherein: The different optimization scenario modes mentioned in step (6) include: ecological priority mode, economic priority mode and ecological-economic coordinated development mode; In the iterative operation of the cellular automata model described in step (6), the cell size and neighborhood range are determined according to the preset resolution, and the operation continues until a stable state is reached.

7. An agricultural spatial optimization layout system based on the synergistic improvement of ecological and production benefits, comprising: The data acquisition module is used to acquire and process spatial and attribute data of the target area; The assessment module is used to evaluate the ecosystem service value and economic value of various land use types; The model building module is used to build multi-objective quantity optimization models and spatial optimization rules; The simulation optimization module is used for dynamic simulation and optimization of spatial patterns based on cellular automata models. The solution output and evaluation module is used to generate optimized solutions for different scenarios and evaluate their overall benefits.

8. The agricultural spatial optimization layout system based on the synergistic improvement of ecological and production benefits as described in claim 7, wherein: The basic geographic data processed by the data acquisition module includes: digital elevation model, land use status data, soil attribute data, and remote sensing image data.

9. The agricultural spatial optimization layout system based on the synergistic improvement of ecological and production benefits as described in claim 7, wherein: The multi-objective quantitative optimization model constructed by the model building module takes the maximization of ecosystem service value and the maximization of economic benefits as its dual objectives, and sets constraints such as no reduction in basic farmland area, no reduction in water area, and conservation of total regional area.

10. The agricultural spatial optimization layout system based on the synergistic improvement of ecological and production benefits as described in claim 7, wherein: The simulation optimization module combines the land use type distribution area and spatial compatibility judgment matrix determined by terrain suitability analysis with iterative calculations using a cellular automata model.

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