A method for optimizing crop planting structure space

Abstract

This invention discloses a method for spatial optimization of crop planting structure, comprising: S1, collecting data on the spatial layout of crop planting structure, meteorological data, statistical data, soil data, etc., and identifying uncertainties related to crop planting decisions; S2, establishing a multi-objective interval optimization model, deriving the optimal interval solution using existing algorithms, wherein a carbon footprint objective function is established using life cycle assessment, and a water footprint objective function is established based on the Penman-Monteith formula; S3, constructing a Dyna-CLUE model based on the quantitative optimization results, using logistic regression analysis to analyze the relationship between crop spatial distribution and driving environmental factors, and simulating the optimization of the spatial layout of crop planting structure; this invention not only quantifies and balances the economic and ecological impacts of crop planting structure under multiple objectives, but also provides specific spatial locations and reveals the optimization results of crop carbon and water footprints; it extends the quantitative optimization of crop planting structure to spatial optimization, providing basic data and scientific support for guiding actual production and sustainable agricultural management.

Description

Technical Field

[0001] The invention belongs to the field of agricultural informatization, and specifically relates to a method for optimizing the space of crop planting structures. Background Art

[0002] Adjusting the spatial distribution of grain planting area and optimizing crop planting structure are important strategies to increase grain production. On the one hand, scientific and reasonable planting arrangements can increase the yield of major grain crops and ensure that food supply remains stable under the influence of uncertain factors such as climate change, natural disasters, and market fluctuations. On the other hand, optimized crop planting structure can change the material and energy flow of agricultural land systems, and have a positive impact on water resource utilization, greenhouse gas emissions, and ecosystem service functions.

[0003] Regarding the optimization of crop planting spatial layout, existing studies have mainly analyzed from the aspects of optimization objectives, constraints and ecological suitability. In terms of optimization objectives, early studies mostly pursued the potential for crop yield increase or economic benefits, and generally focused on the optimization of crop sowing area and area under a single economic goal (such as maximizing net income). With the deepening of the concept of sustainable agricultural development, people have begun to shift to a multi-objective production layout that emphasizes both economy and environment, and to construct a multi-objective planting structure optimization model with the goals of improving the net income of crop planting, increasing crop yields, and reducing the number of labor inputs. These methods provide valuable ideas for the spatial layout of crop planting, but in the construction of the optimization model, the uncertainties of parameters and actual crop planting are not fully considered, resulting in possible deviations in the results. In addition, existing studies rarely focus on the spatial optimization of crop planting structure, making it difficult to provide targeted crop planting adjustment suggestions. Therefore, it is urgent to develop a spatial optimization method for crop planting structure that considers uncertainties. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for optimizing the space of crop planting structure to solve the problems in the prior art. The technical solution adopted by the present invention is:

[0005] A method for optimizing crop planting structure space, comprising the following steps:

[0006] S1. Collect regional data on crop planting and the uncertainties associated with decision making;

[0007] S2. Establish a multi-objective interval optimization model and use the algorithm to derive the optimal interval solution; among them, use life cycle assessment to establish the carbon footprint objective function, and use the Penman-Monteith formula to establish the water footprint objective function;

[0008] S3. Based on the quantitative optimization results, the Dyna-CLUE model was constructed, and the relationship between crop spatial distribution and driving environmental factors was analyzed using logistic regression to simulate and optimize the spatial layout of crop planting structure.

[0009] The present invention has the following beneficial effects:

[0010] The present invention aims to clarify how to balance economic and ecological benefits, and to provide support to decision makers and farmers to make informed decisions and crop selection. The method allows for the effective communication of uncertain information (expressed as interval numbers) in the optimization process and the final solution, where the multi-objective interval optimization model provides a way to balance multiple objectives of different crop plantings, aiming to achieve the optimal benefit of the system. The optimization process mainly involves balancing farmers' income, carbon footprint, and water footprint, and making crop selections based on system value to achieve a trade-off between economic and environmental benefits. The model sets different target scenarios and simulates various crop planting structures;

[0011] In crop spatial optimization, managers need reliable spatial targets, while farmers need to make trade-offs. Therefore, an assessment is needed before crop selection to avoid overuse of resources and reduce ecological and environmental effects. We provide a multi-objective crop planting structure spatial optimization method that balances the economy and the ecosystem. The model uses different parameters as upper and lower limits, adopts life cycle assessment to estimate carbon footprint, uses the Penman-Monteith formula to evaluate water footprint, and considers the natural resource endowment and regional planning of the study area when setting constraints. These provide technical support for crop planting system optimization and regional agricultural sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the process of optimizing the space of the crop planting structure of the present invention;

[0013] Figure 2 It is a schematic diagram of system profit results. DETAILED DESCRIPTION

[0014] The following will be combined with the embodiments of the present invention Figure 1-Figure 2 , the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0015] like Figure 1As shown, the present invention couples multi-objective interval optimization, life cycle assessment, Penman-Monteith formula and Dyna-CLUE model method to develop a crop planting structure optimization method. This method can quantify and balance the economic and ecological impacts under multiple objectives, and also provides a spatial optimization path for crop planting structure. In addition, the model provides specific spatial locations and reveals the optimization results of crop carbon footprint and water footprint.

[0016] A method for optimizing crop planting structure space, comprising the following steps:

[0017] S1. Collect regional data such as spatial layout of crop planting structure, meteorological data, statistical data, soil data, etc. to identify uncertainties related to crop planting decisions;

[0018] S2. Establish a multi-objective interval optimization model and use the algorithm to derive the optimal interval solution. Among them, the carbon footprint objective function is established using life cycle assessment, and the water footprint objective function is established using the Penman-Monteith formula;

[0019] S3. Based on the quantitative optimization results, the Dyna-CLUE model was constructed, and the relationship between crop spatial distribution and driving environmental factors was analyzed using logistic regression to simulate and optimize the spatial layout of crop planting structure.

[0020] Step S1 further comprises the following steps:

[0021] S11. Collect regional relevant data, including (1) downloading bioclimatic data, including 19 parameters such as annual mean temperature, isothermality, temperature seasonality, maximum temperature of the hottest month, minimum temperature of the coldest month, annual temperature range, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality (coefficient of variation), precipitation of the wettest quarter, and precipitation of the driest quarter, from the Worldclim website (https: / / worldclim.org); (2) obtaining soil parameters from the National Tibetan Plateau Data Center (http: / / data.tpdc.ac.cn), including cation exchange capacity, soil organic matter, soil pH, total nitrogen, total phosphorus, exchangeable Ca 2+ etc.; (3) obtain terrain data, including altitude, terrain slope, slope aspect, and distance to water bodies; (4) collect data related to human activities, including distance to construction land, night light data, population, etc.; (5) obtain a map of the spatial distribution pattern of regional crop planting.

[0022] Preferably, in step S11, points (1)-(4) in the collected data are the main driving factors for crop planting space optimization. In addition, agricultural benefit-cost data, irrigation water prices, carbon emission trading prices, fertilizer usage, pesticide usage, etc. need to be collected from statistical data.

[0023] S12. Identify the uncertainties associated with crop planting decisions, including farmer income, government subsidies, ecological benefits, environmental capacity, and pollutant purification capacity.

[0024] In step S2, by setting different scenarios, multi-objective planting structure optimization is performed based on economic benefits, carbon emissions (ecological effects) and water resource consumption (water footprint). Step S2 further includes the following steps:.

[0025] S21. The preset model includes a first sub-model:

[0026]

[0027] S22, the preset model includes a second sub-model:

[0028]

[0029] In the formula is the upper and lower bounds of the carbon trading price (in yuan t -1 ), is the upper and lower bounds of the agricultural irrigation water price (in yuan t -1 ), are the upper and lower bounds of farmers’ income, are the upper and lower bounds of the crop carbon footprint; It is the upper and lower bounds of the crop water footprint. In crop planting, the economic benefit goal is the highest farmer income, while the carbon emission goal and water resource consumption goal are the lowest. There are conflicts between different goals. The multiple goals are integrated by deducting the value of carbon emissions and water resource consumption from the economic benefit value. The size of the target weight can reflect the importance of the objective function. Different weights are used to set different scenarios. Considering the uncertainty of parameter setting, the interval parameter method is used to set the target and constraint conditions.

[0030] S23. The constraints of the preset model are the total amount of cultivated land resources constraint, economic growth constraint, blue water and grey water footprint constraint, area constraint, carbon emission constraint, fertilizer use constraint, pesticide use constraint, crop planting diversity index constraint and the non-negative constraint of the system.

[0031] As the preferred choice, economic benefits mainly consider farmers' income It is necessary to integrate market price, output, production cost, etc. The calculation method is as follows:

[0032]

[0033] Where: represents output value; Represents labor cost; represents the cost of land; represents the seed cost; represents the fertilizer cost; represents the cost of pesticides; represents the cost of agricultural film; Represents the cost of leasing operations; represents the tool material cost; represents indirect costs; represents agricultural planting subsidies; ± represents the upper and lower limits.

[0034] Preferably, in step S2, the crop carbon footprint is calculated using the life cycle assessment method (LCA), which mainly quantifies greenhouse gas emissions related to crop cultivation, with carbon dioxide equivalent (CO2e) as the standard unit of measurement. The greenhouse gas emission sources during the life cycle of food crop production investigated in this study include the production and transportation of agricultural inputs such as fertilizers, pesticides, and diesel, as well as N2O and CH4 directly emitted from farmland. Carbon Footprint The calculation method is as follows:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] In the formula, is the carbon footprint of fertilizer (kgCO2eha -1 ); is the carbon footprint from other inputs (kgCO2eha -1 ); is the N2O emission from farmland (kgCO2eha -1 ); is CH4 emission from farmland (kgCO2eha -1 ), i is the crop type; is the consumption of N, P and K fertilizers (kgha -1 ); is the carbon emission factor of N, P and K fertilizers (kgCO2ekg -1 );FT is the carbon emission factor of fertilizer transportation, which is taken as a constant of 0.1; and The amount of seeds and mulch used (kgha -1 ); and Carbon emission factor representing seeds and mulch (kgCO2ekg-1 ); N2O emissions from nitrogen fertilizer use are an important source of greenhouse gases, Indicates nitrogen fertilizer consumption; unit is kgha -1 ; represents the N2O emission factor; 44 / 28 represents the coefficient for converting N into N2O; the constant 265 represents the global warming potential of N2O;

[0041] represents the CH4 emission rate from cropland; the constant 28 represents the global warming potential of CH4.

[0042] Preferably, in step S2, Calculation includes (WF green , Green Water Footprint, m 3 ha -1 ), (WF blue , Blue Water Footprint, m 3 ha -1 )and (WF gray , Greywater footprint, m 3 ha -1 ):

[0043]

[0044] Preferably, in step S2, in the crop water footprint calculation, the key parameter ET0 is calculated by the Penman-Monteith formula, wherein the effective rainfall is calculated according to the method of the Soil Conservation Service of the United States Department of Agriculture, and the crop water footprint is calculated using CROPWAT software.

[0045]

[0046] Where R n is the net radiation [MJm -2 ]; G is the average soil heat flux [MJm -2 ]; T is the daily temperature [℃]; U2 is the wind speed at 2 meters height [ms -1 ],(p a－ p d ) is the average saturated vapor pressure difference [kPa]; Δ is the slope of the vapor pressure curve [kPa℃ -1 ], γ represents the humidity constant [kPa℃ -1 ].

[0047] Preferably, in step S2, the constraints are mainly divided into the following categories: (1) total amount of cultivated land resources; (2) economic growth constraints; (3) ecological environment constraints and (4) non-negative constraints.

[0048] The constraints are as follows:

[0049] Preferably, in step S2, the total amount of cultivated land resources is constrained as follows:

[0050]

[0051] Preferably, in step S2, the economic growth constraint is:

[0052]

[0053] Preferably, in step S2, the blue water footprint constraint is:

[0054]

[0055] Preferably, in step S2, the grey water footprint constraint is:

[0056]

[0057] Preferably, in step S2, the crop planting area constraint is:

[0058]

[0059] Preferably, in step S2, the carbon emission constraint is:

[0060]

[0061] Preferably, in step S2, the fertilizer usage constraint is:

[0062]

[0063] Preferably, in step S2, the pesticide use constraints are:

[0064]

[0065] Preferably, in step S2, the crop planting diversity index constraint is:

[0066]

[0067] Preferably, in step S2, the non-negative constraint is:

[0068]

[0069] Decision variable description: is the optimized area of ​​crop type i (ha). Indicator Description: TS t is the total amount of cultivated land resources; t is the planning period; BWC t is the blue water footprint constraint (m 3 ); RWC tis the grey water footprint constraint (m 3 ); is the soybean planting area in the base year (ha); is the carbon emission constraint (t); Constraints for fertilizer use (t); is the pesticide environmental capacity constraint (t); SHDI is the Shannon diversity index of crop planting structure.

[0070] In step S3, the Dyna-CLUE model is used to spatially simulate the crop planting pattern, including spatial and non-spatial modules. The non-spatial module predicts the corresponding crop planting area under different scenarios, and the spatial module simulates the spatial distribution pattern of crops. The multi-objective interval optimization model constructed in S2 is used to predict the quantitative characteristics of crops under different scenarios, that is, the non-spatial module. In the Dyna-CLUE model, logistic regression is used to analyze the relationship between the spatial distribution of crops and driving environmental factors. Logistic regression uses data sampling and weighted calculation to determine the probability of crop changes, thereby generating regression coefficients for each driving factor. The specific formula is:

[0071]

[0072] In the formula, P represents the probability of crop type appearing in the grid, i represents the crop type, β i is the regression coefficient, X n,i represents the driving force of crop changes, and n represents the total number of land grids. The driving force mainly includes the main factors collected in step S11.

[0073] The optimized quantity change characteristics are obtained through the non-spatial module, that is, the optimal crop distribution ratio is obtained after balancing the economic value and ecological value. In the spatial module, the transfer elasticity coefficient of crop transformation is set through multiple iterations and corrections, and the spatial distribution data of bioclimate, soil parameters, topography, human activities, etc. are considered. Logistic regression is used to obtain the suitability distribution of crop spatial layout, and the Dyna-CLUE model is calibrated. Based on the principle of planting the most suitable crops in the most suitable areas, the optimal layout of crop planting is obtained. Through different scene settings, the traditional exploration of "how much to plant" is advanced to "where to plant", providing optional optimization solutions.

[0074] The proportion of rice and corn in a certain region is [27.5%, 30.6%] and [34.6%, 36.5%] respectively, and their combined proportion is the highest among the four scenarios, and the proportion of soybean is the smallest, which is [34.8%, 36%]. The rice area may increase or decrease, ranging from [-1071.0, 4474.7] km2. In terms of spatial layout optimization, rice is mainly distributed in the northeast, corn is mainly distributed in the west and south, and soybean is mainly distributed in the north and central regions.

[0075] Figure 2 The results show that by quantifying the benefits under different scenarios, there are some discrepancies compared with the system benefits before optimization. The economic value of carbon footprint and water footprint is deducted from the farmer's income, and the benefit is negative. However, after optimization, the environmental cost of crop planting has been reduced, and the farmer's income is the highest among the three optimization schemes, which is [160.8,514.6] billion yuan, which means that the probability of economic growth has increased, and economic growth has brought about an increase in systemic ecological and environmental risks, which means that in pursuit of higher system benefits, higher resource consumption and environmental sacrifice must be borne, thereby increasing systemic risks.

[0076] The embodiments described above are only descriptions of the preferred modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations, modifications, and substitutions made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for optimizing crop planting structure space, characterized in that: The following steps are involved: S1. Collect regional data on crop planting and the uncertainties associated with decision making; S2. Establish a multi-objective planting structure optimization model and derive the optimal interval solution; among them, use life cycle assessment to establish the crop carbon footprint objective function, and use the Penman-Monteith formula to establish the crop water footprint objective function; S3. Construct the Dyna-CLUE model, use logistic regression to analyze the relationship between crop spatial distribution and driving environmental factors, and simulate and optimize the spatial layout of crop planting structure; In step S2, multi-objective planting structure optimization is performed based on farmer income, carbon footprint objective function and water footprint objective function; Step S2 includes: S21, the optimization model includes a first sub-model: S22, the optimization model includes a second sub-model: In the formula are the upper and lower bounds of the carbon trading price; are the upper and lower bounds of the price of agricultural irrigation water; are the upper and lower bounds of farmers’ income; are the upper and lower bounds of the crop carbon footprint; are the upper and lower bounds of the crop water footprint; S23. The constraints of the optimization model are total cultivated land resource constraint, economic growth constraint, blue water footprint constraint, grey water footprint constraint, crop planting area constraint, carbon emission constraint, fertilizer use constraint, pesticide use constraint, crop planting diversity index constraint and non-negative constraint; Crop Carbon Footprint The calculation is as follows: In the formula, It’s the fertilizer carbon footprint; is the carbon footprint from other inputs; is the N2O emission from farmland; is the CH4 emission from farmland; i is the crop type; is the consumption of nitrogen fertilizer, phosphate fertilizer and potash fertilizer; are the emission factors of nitrogen fertilizer, phosphate fertilizer and potash fertilizer; FT is the carbon emission factor of fertilizer transportation; and for the amount of seed and mulch used; and represents the carbon emission factors for seeds and mulch; Indicates nitrogen fertilizer consumption; represents the N2O emission factor; 44 / 28 represents the coefficient for converting nitrogen into N2O; the constant 265 represents the global warming potential of N2O; the constant 28 represents the global warming potential of CH4; Crop Water Footprint Including Green Water Footprint Blue Water Footprint Grey water footprint The calculation formula is: in, is the optimized area for crop type i; Blue Water Footprint Constraints: Greywater footprint constraints: Crop planting area constraints: Carbon emission constraints: Fertilizer use restrictions: Pesticide use restrictions: Crop Planting Diversity Index Constraints: Non-negativity constraints: in, is the optimized area of ​​crop type i; TS t is the total amount of cultivated land resources; t is the planning period; for blue water footprint constraints; for grey water footprint constraints; The soybean planting area is the base year; To constrain carbon emissions; for fertilizer use restrictions; is the pesticide use constraint; SHDI is the crop planting diversity index constraint.

2. The method for optimizing crop planting structure space according to claim 1, characterized in that: Step S1 includes: S11. Collect regional data; S12. Identify the uncertainties associated with crop planting decisions, including farmer income, ecological benefits, environmental capacity, and pollutant purification capacity.

3. The method for optimizing crop planting structure space according to claim 2, characterized in that: In step S11, the region-related data includes bioclimatic data, soil parameters, topographic data, and data related to human activities; Bioclimatic data include annual mean temperature, isothermality, temperature seasonality, maximum temperature of the hottest month, minimum temperature of the coldest month, annual temperature range, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation in the wettest season, precipitation in the driest season; Soil parameters include cation exchange capacity, soil organic matter, soil pH, total nitrogen, total phosphorus, exchangeable Ca 2+ ; Topographic data include elevation, terrain slope, aspect, and distance to water bodies; Data related to human activities include distance to construction land, night light data, and population.

4. The method for optimizing crop planting structure space according to claim 1, characterized in that: Farmer income is calculated as follows: Where: represents output value; Represents labor cost; represents the cost of land; represents the seed cost; represents the fertilizer cost; represents the cost of pesticides; represents the cost of agricultural film; Represents the cost of leasing operations; represents the tool material cost; represents indirect costs; represents agricultural planting subsidies; ± represents the upper and lower limits.

5. The method for optimizing crop planting structure space according to claim 1, characterized in that: In step S3, the Dyna-CLUE model is used to simulate the spatial layout of crop planting structure, including a spatial module and a non-spatial module. The non-spatial module predicts the corresponding crop planting area under different scenarios, and the spatial module simulates the spatial distribution pattern of crops. In the Dyna-CLUE model, logistic regression is used to analyze the relationship between crop spatial distribution and driving environmental factors. Logistic regression analysis uses data sampling and weighted calculation to determine the probability of crop change, thereby generating regression coefficients for each driving factor. The formula is: In the formula, p represents the probability of crop type appearing in the land grid, i represents the crop type, β n is the regression coefficient, X n,i represents the driving force for crop changes, and n represents the total number of land grids.

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