A method for optimizing agricultural water and soil resources allocation considering planetary boundary disturbances

Abstract

The present invention discloses a method for optimizing the allocation of agricultural water and soil resources, taking into account planetary boundary perturbations, and relates to the field of smart agriculture technology. The method comprises: extracting interval uncertainty parameters as input parameters through the SSP-RCP path; using the regional farmland planting structure and the net irrigation water allocated to each crop during each growth period as decision variables; minimizing the impact of the entire food system, from raw materials to the table, on control indicators as the optimization objective; constructing an uncertain agricultural water and soil resource optimization model, taking into account the uncertainty of food production brought about by different climate scenarios; and obtaining an optimal allocation plan for agricultural water and soil resources. The present invention provides a calculation method using the life cycle approach and the nine processes included in the planetary boundary as optimization objectives, minimizing the perturbations of the food system, from agricultural production to the table, on the planetary boundary, thereby alleviating environmental and water and soil resource pressures under future uncertain scenarios.

Description

Technical Field

[0001] The present invention relates to the field of smart agricultural technology, and in particular to a method for optimizing the allocation of agricultural water and soil resources taking into account planetary boundary disturbances. Background Art

[0002] The impact of human activities on the Earth's environment continues to expand, and the Earth also has a certain "environmental carrying capacity". The stability and resilience of the Earth's environment are inseparable from human well-being. In 2009, a group of scientists proposed nine processes that regulate the stability and resilience of the Earth system and quantified the "planetary boundaries" for some of these processes. Planetary boundaries are defined as the boundary values ​​for Earth's environmental "safe operating space," which refers to the acceptable range or extent of human activities. Updated in 2015, this framework established global thresholds for nine key processes: climate change, changes in biosphere integrity, stratospheric ozone depletion, ocean acidification, biogeochemical cycles (P and N cycles), land use changes, freshwater use, atmospheric aerosol loading, and the introduction of new substances. The thresholds for these nine key processes are defined by critical values ​​of 16 control variables. For example, climate change is defined by atmospheric CO2 concentration and top-of-atmosphere energy imbalance as control variables, with uncertainty ranges for its planetary boundaries of (350-450) ppm CO2 and (1.0-1.5) W m -2 The global and regional phosphorus cycles are respectively based on the phosphorus flowing from freshwater systems into the ocean (uncertainty range 11~100 Tg·a -1 ) and phosphorus flowing from fertilizers into erodible soils (6.2 to 11.2 Tg·a -1 ) as a control variable. Planetary boundaries quantify, on a global scale, the thresholds that could disrupt Earth's long-term dynamic stability, thereby contributing to maintaining Earth's ecological security and sustainable development. The latest planetary boundaries research results from 2015 indicate that genetic diversity, nitrogen cycles, phosphorus cycles, and land use change have all entered high-risk zones in the current period. Furthermore, the atmospheric CO2 concentration, at approximately 398.5 ppm CO2, has already crossed the planetary boundary threshold, but remains within an acceptable range of uncertainty.

[0003] With economic development and population growth, the demand for food continues to expand. Greenhouse gas emissions from the global food system account for one-third of global anthropogenic greenhouse gas emissions. While the application of chemical substances such as fertilizers and pesticides plays an important role in increasing food production, pollutants generated by excessive application also damage terrestrial and aquatic ecosystems. At the same time, food production requires a large amount of water and energy input. As a key participant in greenhouse gas emissions, freshwater utilization, nitrogen and phosphorus emissions, and land use, research on optimizing the environmental impact of food production within the water-energy-food nexus continues to receive attention.

[0004] Existing optimization research focused on environmental benefits has focused on carbon and water footprints. For example, Xu et al. introduced a water and fertilizer production function to construct an optimization model, maximizing the net system benefit while minimizing both carbon and water footprints to achieve sustainable economic and environmental development. Yue et al. constructed an uncertainty optimization model based on the water-energy-food-environment relationship and proposed a water and soil resource management solution to balance economic and energy benefits with water and carbon footprint emissions. Anita D. Bayer evaluated spatial land use patterns on a global scale to maximize global benefits in terms of carbon storage, food production, and freshwater supply. From agricultural production to the table, food undergoes a complex process of production, distribution, consumption, and processing. Crippa et al. developed a global food emissions database that spatially accounts for greenhouse gas emissions from the global food system, from production to consumption, including processing, transportation, and packaging. However, current optimization research typically focuses solely on the food system at the farm gate, neglecting post-harvest processing, packaging, and cooking. For example, Han incorporated carbon footprint into the farmland planting structure optimization model and used the life cycle approach to calculate the carbon footprint generated by the food system from "raw material production" to "crop harvesting."

[0005] Currently, cropping structures and irrigation water volume plans in most regions are often based on empirical past cropping and irrigation systems, failing to consider the dynamic interactions and couplings between water and soil resource use and climate, energy, and the environment during food production. Climate change is a significant factor influencing food production. Numerous studies have assessed future climate change under various SSP-RCP pathways, but uncertainty remains in climate change predictions and simulations driven by diverse simulation models. Optimizing agricultural cropping structures and irrigation water volume, as crucial approaches to reducing negative environmental impacts and alleviating the imbalance between water and soil resource supply and demand, urgently requires consideration of uncertain future climate scenarios and adaptive research. A life cycle approach can effectively assess the environmental performance of products or processes, but many current studies focus solely on agricultural production, using the period from "raw material production" to "crop harvest" as the accounting boundary. These studies calculate environmental impacts at a single process and scale, such as greenhouse gas emissions, water footprints, and ecological indices, thereby ignoring the multiple processes encompassed by the food system from harvest to table. Furthermore, there is no comprehensive indicator that captures the overall impact of this system on the global environment. Therefore, as the boundary value for the safe operation of the earth's environment, the planetary boundary has certain research significance on how to effectively utilize water and soil resources to meet food needs on the basis of adapting to climate change, while minimizing the disturbance of the food system to the planetary boundary.

[0006] Therefore, it is an urgent problem for those skilled in the art to propose a method for optimizing the allocation of agricultural water and soil resources taking into account planetary boundary disturbances to solve the difficulties existing in the existing technology. Summary of the Invention

[0007] In view of this, the present invention provides a method for optimizing the allocation of agricultural water and soil resources by taking into account planetary boundary disturbances. Specifically, the method is based on the water-energy-food nexus. Under different future climate scenarios, the method uses the life cycle approach as the calculation method and the nine processes included in the planetary boundary as the optimization objectives. The method aims to minimize the disturbance of the food system to the planetary boundary from agricultural production to the dining table, thereby alleviating the pressure on the environment and water and soil resources under uncertain future scenarios.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for optimizing agricultural water and soil resources allocation considering planetary boundary disturbances includes the following steps:

[0010] S1. Extract interval uncertainty parameters through the SSP-RCP path and propose to use the obtained interval uncertainty parameters reflecting the data fluctuation range as input parameters;

[0011] S2. Using regional farmland planting structure and the net irrigation water allocated to each crop during each growth period as decision variables, and minimizing the impact of the entire food system process from raw materials to the table on control indicators as the optimization goal, we constructed an uncertain agricultural water and soil resource optimization allocation model, considering the uncertainty of different climate scenarios on food production.

[0012] S3. Solve the uncertain agricultural water and soil resource optimization configuration model and obtain the agricultural water and soil resource optimization configuration plan.

[0013] Optionally, the specific content of S1 is: based on the SSP-RCP pathway, the land use plans in different scenarios are used to extract the available farmland area value as a constraint on the total area of ​​all crops; based on the SSP-RCP pathway, meteorological data driven by different climate models are extracted in the same scenario; thereby obtaining interval uncertainty parameters reflecting the data fluctuation range as input parameters.

[0014] Optionally, the entire food system from raw materials to the table in S2 includes the front-end link, inside the farm gate, consumer use stage and the back-end link; among them,

[0015] The pre-processing stage is the production, processing and transportation of agricultural production materials, including seeds, fertilizers, pesticides and agricultural films;

[0016] Field emissions are located inside the farm gate, and include: fertilization, irrigation, and the use of agricultural machinery;

[0017] The consumer use stage includes processing food crops into food, packaging, and transporting them to sales locations;

[0018] The subsequent steps are food storage and cooking.

[0019] Optionally, in S2, the optimization goal is to minimize the impact of the entire process of the food system from raw materials to the table on the control indicators. The objective function is:

[0020]

[0021] X m =(x m -Min(x m )) / (Max(x m )-Min(x m ))

[0022] Among them, x m is the actual value of each indicator, Max(x m ) is the maximum value of each indicator, Min(x m ) is the minimum value of each indicator, X m is the normalized value of each indicator, w m is the weight coefficient assigned to each indicator.

[0023] Optionally, the specific content of constructing the uncertain agricultural water and soil resource optimization allocation model in S2 is: using the life cycle approach, determine the evaluation scope as the impact of all aspects of the food system on the eight processes of the planetary boundaries, and minimize the environmental impact by optimizing the planting type and water allocation of each 1° unit grid.

[0024] Optionally, crop planting types are divided into C3 cereals, C3 non-cereal crops, C4 crops, and rice, and representative crops of each crop planting type are selected for calculation.

[0025] Optionally, the eight planetary boundary processes include climate change, biosphere integrity, stratospheric ozone depletion, ocean acidification, biochemical cycles, land use change, freshwater use, and atmospheric aerosol loading.

[0026] Optionally, the constraints introduced in S2 include water supply constraint, crop water demand constraint, crop planting area constraint, crop yield constraint and non-negative constraint.

[0027] Optionally, in S3, the input parameters are input into the uncertainty agricultural water and soil resources optimization configuration model to solve the uncertainty agricultural water and soil resources optimization configuration model.

[0028] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a method for optimizing the allocation of agricultural water and soil resources taking into account planetary boundary disturbances, which has the following beneficial effects:

[0029] (1) This paper considers the optimization method of agricultural water and soil resources under the water-energy-food nexus. Under different future climate scenarios, the life cycle method is used as the calculation method, and the nine processes included in the planetary boundaries are used as the optimization objectives. The aim is to minimize the disturbance of the food system to the planetary boundaries from agricultural production to the table, thereby alleviating the environmental and water and soil resource pressures under uncertain future scenarios.

[0030] (2) The SSP-RCP pathway combines the socioeconomic pathway (SSP) with the greenhouse gas concentration pathway (RCP), taking into account the uncertainty of future scenarios, and generates a set of scenario plans that describe future land use and climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0032] Figure 1 This is a flow chart of a method for optimizing agricultural water and soil resources configuration taking into account planetary boundary disturbances provided by the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] Reference Figure 1 As shown, the present invention discloses a method for optimizing the configuration of agricultural water and soil resources considering planetary boundary disturbances, comprising the following steps:

[0035] S1. Extract interval uncertainty parameters through the SSP-RCP path and propose to use the obtained interval uncertainty parameters reflecting the data fluctuation range as input parameters;

[0036] S2. Using regional farmland planting structure and the net irrigation water allocated to each crop during each growth period as decision variables, and minimizing the impact of the entire food system process from raw materials to the table on control indicators as the optimization goal, we constructed an uncertain agricultural water and soil resource optimization allocation model, considering the uncertainty of different climate scenarios on food production.

[0037] S3. Solve the uncertain agricultural water and soil resource optimization configuration model and obtain the agricultural water and soil resource optimization configuration plan.

[0038] Furthermore, the specific content of S1 is as follows: based on the SSP-RCP pathway, the land use plans in different scenarios are used to extract the available farmland area value as a constraint on the total area of ​​all crops; based on the SSP-RCP pathway, meteorological data driven by different climate models are extracted in the same scenario; thereby, interval uncertainty parameters reflecting the data fluctuation range are obtained as input parameters.

[0039] Furthermore, the whole process of the food system from raw materials to the table in S2 includes the front-end link, inside the farm gate, consumer use stage and the back-end link; among them,

[0040] The pre-processing stage is the production, processing and transportation of agricultural production materials, including seeds, fertilizers, pesticides and agricultural films;

[0041] Field emissions are located inside the farm gate, and include: fertilization, irrigation, and the use of agricultural machinery;

[0042] The consumer use stage includes processing food crops into food, packaging, and transporting them to sales locations;

[0043] The subsequent steps are food storage and cooking.

[0044] Furthermore, in S2, the optimization goal is to minimize the impact of the entire process of the food system from raw materials to the table on the control indicators. The objective function is:

[0045]

[0046] X m =(x m -Min(x m )) / (Max(x m )-Min(x m ))

[0047] Among them, x m is the actual value of each indicator, Max(x m ) is the maximum value of each indicator, Min(x m ) is the minimum value of each indicator, X m is the normalized value of each indicator, wm is the weight coefficient assigned to each indicator.

[0048] Furthermore, the specific content of constructing the uncertain agricultural water and soil resource optimization allocation model in S2 is: using the life cycle approach, the evaluation scope is determined to be the impact of all aspects of the food system on the eight processes of the planetary boundary, and by optimizing the planting type and water allocation of each 1° unit grid, the purpose of minimizing environmental impact is achieved.

[0049] Furthermore, crop planting types were divided into C3 cereals, C3 non-cereal crops, C4 crops, and rice, and representative crops of each crop planting type were selected for calculation.

[0050] Furthermore, the eight planetary boundary processes include climate change, biosphere integrity, stratospheric ozone depletion, ocean acidification, biochemical cycles, land use change, freshwater use, and atmospheric aerosol loading.

[0051] Furthermore, the constraints introduced in S2 include water supply constraint, crop water demand constraint, crop planting area constraint, crop yield constraint and non-negative constraint.

[0052] Furthermore, in S3, the input parameters are input into the uncertainty agricultural water and soil resources optimization configuration model to solve the uncertainty agricultural water and soil resources optimization configuration model.

[0053] In a specific embodiment, the following are included

[0054] Step 1: Extract interval uncertainty parameters through the SSP-RCP path, and propose to use the obtained interval uncertainty parameters reflecting the data fluctuation range as input parameters.

[0055] Uncertainty parameters include meteorological data and farmland area data, among which,

[0056] Extraction of meteorological data

[0057] Based on five typical SSP-RCP scenarios (SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, SSP4-RCP6.0, and SSP5-RCP8.5), future land use types were categorized into farmland, urban, forest, bare land, and grassland. The study area was divided into 1°×1° cells, and the cropland portion of the land use type at this scale was extracted. Effective rainfall and reference crop evapotranspiration data were extracted for each decision cell at the same scale under the five typical SSP-RCP scenarios driven by three climate models (EC-Earth3, GFDL-ESM4, and MRI-ESM2-0). Due to differences in meteorological data under different model driving conditions, these data have interval uncertainties, expressed as interval numbers.

[0058] Extraction of farmland area data

[0059] Based on the land use options in different scenarios under the SSP-RCP pathway, the available farmland area is extracted as a constraint on the total area of ​​all crops.

[0060] Step 2: Using the regional farmland planting structure and the net irrigation water allocated to each crop in each growth period as decision variables, and minimizing the impact of the entire food system process from raw materials to the table on control indicators as the optimization goal, considering the uncertainty brought about by different climate scenarios on food production, and constructing an uncertain agricultural water and soil resource optimization allocation model.

[0061] Different crops require varying energy inputs and corresponding food outputs, resulting in varying degrees of environmental impact. The concept of planetary boundaries is introduced here to comprehensively quantify the impact of food systems on the global environment under different crop planting structures and irrigation water levels from the perspective of nine system processes. These nine system processes are characterized by 16 control variables, as detailed in Table 1. However, since no clear control variables exist to represent the introduction of new substances, these are not considered here. Therefore, this application uses 10 control variables to consider the impact of food systems on eight system processes.

[0062] Table 1 Control variables of nine system processes in planetary boundaries

[0063]

[0064] Consider the entire food production process, from raw material transportation, planting, and harvesting to post-farm transportation, processing, packaging, and ultimately sales, storage, and cooking. Each of these processes requires inputs of different resources (such as water, fertilizers, and fuel) and generates corresponding pollutants that impact the environment. A life cycle approach is introduced to comprehensively assess the impact of food on planetary boundaries from raw materials to the table.

[0065] ① Climate change (atmospheric CO2 concentration)

[0066] Greenhouse gases produced by food production that cause climate change mainly include CO2, N2O, and CH4. Therefore, it is necessary to calculate the greenhouse gases produced by four processes in the life cycle. Its sources are mainly fossil fuel emissions and N2O emissions directly generated by the application of chemical fertilizers in the field. The steps of using fossil fuels include: the processing, production, and transportation of agricultural materials in the early stage, electricity use and machinery use in the field, transportation, processing, packaging, storage in the consumption stage, and storage and cooking in the post-stage. In the field planting stage, soil carbon sequestration can alleviate the increase of greenhouse gases to a certain extent. The expression of this indicator is:

[0067] F1=E ff,1 +E fer -SCS

[0068]

[0069] E fer =U fer,j ·EF fer,1

[0070]

[0071] Where i is the decision unit number, j is the crop type, k is the step in each life cycle where fossil fuels are used, and U ff,k is the amount of fossil fuel used in step k, in kg, EF ff,n The impact coefficient of fossil fuels generated by the food system on the Earth boundary process n, E ff,n Impacts of fossil fuel production for food systems on planetary boundary processes n, E fer,n The impact of fertilizer application on Earth's boundary processes, UA k,j is the fossil fuel consumption per unit area of ​​crop j in step k, in kg / ha, EF fer,n is the impact coefficient of fertilizer application on the earth boundary process n, U fer,j is the fertilizer usage per unit area of ​​crop j, in kg, SCS is carbon sequestration, in kg, is the carbon sequestration coefficient of decision-making unit i, in kg / ha, U fer The total amount of fertilizer used, in kg, UA fer,j is the fertilizer usage per unit area of ​​crop j, in kg / ha, β is the proportion of nitrogen in fertilizer nutrients, A ij is the planting area of ​​crop j in cell i, in hm 2 .

[0072] ② Biosphere integrity

[0073] 1) Genetic diversity: The use of fossil energy in food production leads to the emission of greenhouse gases and pollutants, which in turn affects genetic diversity. Excessive use of chemical fertilizers leads to eutrophication of water bodies and damages aquatic habitats. Pesticide residues may lead to a decrease in non-target species.

[0074]

[0075] Among them, U per It is the total amount of pesticide used, in kg.

[0076] 2) Functional diversity: During food production, the use of fertilizers and pesticides affects soil and ecological functions.

[0077] F 2,F =E fer,G +E per,G

[0078] =U fer ·EF fer,F +U per ·EF per,F

[0079] ③Stratospheric ozone depletion

[0080] The steps of food production, processing, transportation, storage, etc. that use fossil energy will affect stratospheric ozone depletion.

[0081]

[0082] ④Ocean acidification

[0083] The rate at which the ocean absorbs carbon dioxide from the atmosphere is approximately proportional to the concentration of carbon dioxide in the atmosphere. Therefore, changes in ocean acidification can be expressed as:

[0084]

[0085] Here, α is the proportionality constant, the efficiency with which CO2 dissolves in the ocean and causes chemical changes, namely the formation of carbonic acid.

[0086] ⑤Biochemical cycle

[0087] 1) Nitrogen emissions

[0088] The impact of the food system on global N flows mainly includes N generated during the production and use of industrial fossil fuels, N entering the ecosystem through leaching and volatilization of fertilizers during field planting activities, and N fixed from the atmosphere into the soil by plants through biological nitrogen fixation.

[0089]

[0090] Among them, E crop,n is the impact of field agricultural input use on the Earth boundary process n, EF fer,leaching is the nitrogen emission factor caused by nitrogen leaching and deposition, FRAC leaching is the nitrogen leaching rate from farmland, FRAC volatilize is the nitrogen volatilization rate from farmland, EF fer,volatilize is the nitrogen emission factor caused by nitrogen volatilization and deposition, EF BNF,j is the nitrogen fixation rate of crop type j, in kgN / ha.

[0091] 2) P flow

[0092] The impact of phosphorus in the food system primarily comes from fertilizers. During application, not all fertilizer is absorbed by crops, and the remaining fertilizer is carried into the ocean through surface runoff and soil erosion.

[0093] F 5,P =E P,erosion +E P,runoff

[0094] =(R1·A ero ·C soil +R2·A runoff ·C runoff )·δ

[0095] Among them, δ is the ratio of phosphorus in freshwater system entering the ocean, E P,erosion The impact of fertilizers on the Earth's boundaries through soil erosion, E P,runoff is the impact of fertilizer on the earth's boundary through surface runoff, R1 is the soil erosion rate, in kg / ha, A ero For C soil is the concentration of phosphorus in the soil, the unit is kgP / kg soil, R2 is the surface runoff coefficient, the unit is mm, A runoff is the land area affected by runoff, in ha, C runoff It is the concentration of phosphorus in surface runoff, with the unit of kgP / mm.

[0096] ⑥Land use change

[0097] The evaluation indicator for land use change is the proportion of forest land to original forest land. Since this application only studies the impact of farmland on planetary boundaries under future climate, the proportion of forest land to original forest land will not change and is not considered here.

[0098] ⑦ Freshwater utilization

[0099] The impact of food systems on freshwater use is expressed in terms of blue water use. Blue water use during the food life cycle includes fossil fuel production and use, crop irrigation, and cooking.

[0100]

[0101] Among them, λ is the water coefficient used in the production and use of fossil fuels, t is the crop growth stage, E iw,n The impact of global boundary processes on irrigation water use, IW ijt is the decision variable, the irrigation water volume of crop j in cell i during period t, in mm, U cook,water,j is the amount of water used in the cooking process of crop j, in m 3 .

[0102] ⑧Atmospheric aerosol load

[0103] Atmospheric aerosols are primarily affected by fossil fuel emissions during the food system cycle, so only activities that include fossil fuel emissions are considered here.

[0104]

[0105] The optimization goal is to minimize the impact of the entire process of the food system from raw materials to the table on the control indicators. The objective function is:

[0106]

[0107] X m =(x m -Min(x m )) / (Max(x m )-Min(x m ))

[0108] Among them, x m is the actual value of each indicator, Max(x m ) is the maximum value of each indicator, Min(x m ) is the minimum value of each indicator, X m is the normalized value of each indicator, w m is the weight coefficient assigned to each indicator.

[0109] The constraints introduced are:

[0110] ① Water supply constraints

[0111]

[0112] in, Q The sum of available surface water and groundwater, in m 3 , η is the irrigation water utilization coefficient.

[0113] ② Constraints on crop water demand

[0114]

[0115] in, IW is the minimum evapotranspiration of crop j in cell i during period t, in mm. ijt is the decision variable, the irrigation water volume of crop j in cell i during period t, in mm. is the effective rainfall of cell i in period t, in mm. is the maximum evapotranspiration of crop j in cell i during period t, in mm.

[0116] ③ Constraints on crop planting area

[0117]

[0118] Among them, GA is the total area of ​​arable farmland, unit is hm 2 .

[0119] ④Crop yield constraints

[0120]

[0121] Among them, Y a,ij is the actual yield of crop j in cell i, in kg / hm 2 , Y max,j is the maximum potential yield of crop j, in kg / ha, k y,j is the yield factor of crop j, K j is the external adjustment coefficient of crop j, Z j It is the minimum social demand, unit is kg.

[0122] ⑤Non-negative constraints

[0123]

[0124] Step 3: Solve the uncertain agricultural water and soil resources optimization configuration model to obtain the agricultural water and soil resources optimization configuration plan.

[0125] From an environmental perspective, this paper constructs an optimal allocation model for agricultural water and soil resources under future uncertain climate scenarios to consider the differences in the food system's disturbance to the planetary boundary caused by different crop planting structures and irrigation water volumes. The eight processes in the planetary boundary are taken into account in terms of environmental effects, and the 10 indicators that need to be controlled are used as the optimization targets for minimization. The results achieved are (1) referring to future land use and climate change scenarios and considering the uncertainty of future scenarios; (2) taking food as the center within the optimization framework, considering the environmental impact of food throughout its life cycle, from raw material production to cooking; (3) introducing the concept of planetary boundaries to minimize the control indicators of the planetary boundary to achieve the purpose of minimizing environmental impact; (4) based on the life cycle method, the farmland planting structure and irrigation water allocation schemes under different future land use and climate scenarios are obtained, thereby implementing the effect of the concept of sustainable development.

[0126] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0127] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the allocation of agricultural water and soil resources considering planetary boundary disturbances, characterized in that: The following steps are involved: S1. Extract interval uncertainty parameters through the SSP-RCP path and propose to use the obtained interval uncertainty parameters reflecting the data fluctuation range as input parameters; S2. Using regional farmland planting structure and the net irrigation water allocated to each crop during each growth period as decision variables, and minimizing the impact of the entire food system process from raw materials to the table on control indicators as the optimization goal, we constructed an uncertain agricultural water and soil resource optimization allocation model, considering the uncertainty of different climate scenarios on food production. S3. Solve the uncertain agricultural water and soil resource optimization allocation model and obtain the optimal allocation plan for agricultural water and soil resources; The entire food system from raw materials to the table in S2 includes the front-end link, inside the farm gate, consumer use stage and the back-end link; The pre-processing stage is the production, processing and transportation of agricultural production materials, including seeds, fertilizers, pesticides and agricultural films; Field emissions are located inside the farm gate, and include: fertilization, irrigation, and the use of agricultural machinery; The consumer use stage includes processing food crops into food, packaging, and transporting them to sales locations; The post-processing stage is the storage and cooking of food; The specific content of constructing the uncertain agricultural water and soil resource optimization allocation model in S2 is as follows: using the life cycle approach, the evaluation scope is determined to be the impact of all aspects of the food system on the eight planetary boundary processes, and by optimizing the planting type and water allocation of each 1° unit grid to achieve the goal of minimizing environmental impact; The eight planetary boundary processes include climate change, biosphere integrity, stratospheric ozone depletion, ocean acidification, biochemical cycles, land use change, freshwater use, and atmospheric aerosol loading.

2. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbances according to claim 1, characterized in that: The specific content of S1 is: based on the SSP-RCP pathway, the land use options in different scenarios are used to extract the available farmland area value as a constraint on the total area of ​​all crops; Extract meteorological data driven by different climate models under the same scenario based on the SSP-RCP pathway; Thus, the interval uncertainty parameter reflecting the data fluctuation range is obtained as the input parameter.

3. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbances according to claim 1, characterized in that: In S2, the optimization goal is to minimize the impact of the entire process of the food system from raw materials to the table on the control indicators. The objective function is: X m =(x m -Min(x m )) / (Max(x m )-Min(x m )) Among them, x m is the actual value of each indicator, Max(x m ) is the maximum value of each indicator, Min(x m ) is the minimum value of each indicator, X m is the normalized value of each indicator, w m is the weight coefficient assigned to each indicator.

4. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbances according to claim 1, characterized in that: Crop planting types are divided into C3 cereals, C3 non-cereal crops, C4 crops and rice, and representative crops of each crop planting type are selected for calculation.

5. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbances according to claim 1, characterized in that: The constraints introduced in S2 include water supply constraint, crop water demand constraint, crop planting area constraint, crop yield constraint and non-negativity constraint.

6. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbances according to claim 1, characterized in that: In S3, the input parameters are input into the uncertainty agricultural water and soil resources optimization configuration model to solve the uncertainty agricultural water and soil resources optimization configuration model.

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