Agricultural water and soil resource optimal configuration method considering planet boundary disturbance

Abstract

The invention discloses an agricultural water and soil resource optimal configuration method considering planet boundary disturbance, and relates to the technical field of intelligent agriculture. Comprising the following steps: extracting interval uncertainty parameters as input parameters through an SSP-RCP path; a regional farmland planting structure and the net irrigation water amount distributed to each crop in each growth period are used as decision variables, the minimum influence of a grain system on control indexes in the whole process from raw materials to dining tables is used as an optimization target, and uncertainty of different climate scenes on grain production is considered. Constructing an uncertainty agricultural water and soil resource optimal configuration model; and obtaining an agricultural water and soil resource optimal allocation scheme. According to the method, the life cycle method is used as a calculation method, nine processes included in the planet boundary are used as optimization targets, disturbance of a grain system to the planet boundary in the whole process from agricultural production to dining tables is minimized, and therefore the environment and water and soil resource pressure under the future uncertain scene is relieved.

Description

Technical Field

[0001] The present invention relates to the field of smart agricultural technology, and in particular to a method for optimizing the configuration 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 number of scientists jointly proposed nine processes that regulate the stability and resilience of the Earth system, and quantified the "planetary boundaries" of some processes. "Planetary boundaries" are boundary values ​​used to define the "safe operating space" of the Earth's environment, while "safe operating space" refers to the reasonable scope or degree of human activities. After being updated in 2015, the framework set global thresholds for nine key processes: climate change, changes in biosphere integrity, stratospheric ozone depletion, ocean acidification, biogeochemical cycles (P and N cycles), changes in land system use, freshwater use, atmospheric aerosol loads, and the introduction of new substances. The thresholds of the nine key processes in the framework are defined by the critical values ​​of 16 control variables. For example, climate change is controlled by atmospheric CO2 concentration and energy imbalance in the top atmosphere, and the uncertainty intervals of its planetary boundaries are (350-450) ppm CO2 + (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 (6.2 to 11.2 Tg·a) flowing from fertilizers into erodible soils -1 ) as a control variable. Planetary boundaries quantify the boundary values ​​that may destroy the long-term dynamic stability of the Earth on a global scale, thus helping to maintain the ecological security and sustainable development of the Earth. The latest planetary boundary research results in 2015 show that in the current period, genetic diversity, nitrogen cycle, phosphorus cycle and land use change have all entered high-risk areas. In addition, the CO2 concentration in the atmosphere is about 398.5ppm CO2, which has crossed the planetary boundary threshold, but is still within an acceptable uncertainty range.

[0003] With economic development and population growth, food demand continues to expand. Greenhouse gas emissions generated by the global food system account for one-third of global anthropogenic greenhouse gas emissions. Although the application of chemical substances such as fertilizers and pesticides plays an important role in increasing food production, pollutants produced by excessive application also damage terrestrial and aquatic ecosystems. At the same time, food production requires a large amount of water resources and energy input. As an important participant in greenhouse gas emissions, freshwater utilization, nitrogen and phosphorus emissions, land use and other processes, the optimization research on the environmental impact of food production under the water-energy-food connection has been continuously valued.

[0004] In the existing technology, optimization research focusing on environmental benefits focuses on carbon footprint and water footprint. For example, Xu introduced water-fertilizer production function to construct an optimization model to maximize the net benefit of the system, minimize carbon footprint and water footprint to achieve sustainable economic and environmental development. Yue constructed an uncertainty optimization model based on the relationship between water-energy-food-environment and proposed a water and soil resource management plan to balance economic and energy benefits and water footprint and carbon footprint emissions. Anita D. Bayer weighed the spatial land use pattern on a global scale so that global regions can achieve maximum benefits in carbon storage, food production and fresh water supply. From agricultural production to the table, food needs to go through complex links such as production, distribution, consumption and processing. Crippa developed a global food emissions database, which calculated the spatial database of greenhouse gas emissions from the global food system from production to consumption, including processing, transportation and packaging. However, current optimization studies usually only focus on the links of the food system within the farm gate, while ignoring the processing, packaging, cooking and other links after the harvest of food. 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] At present, the planting structure and irrigation water volume plans in most regions are usually based on the empirical planting and irrigation systems in the region in the past, without considering the dynamic interaction and coupling relationship between the use of water and soil resources and climate, energy and environment in the process of food production. Climate change is an important factor affecting food production. Many studies have evaluated future climate change under various SSP-RCP pathways in the future, but driven by different simulation models, there is still a certain degree of uncertainty in climate change prediction and simulation. As an important way to reduce negative environmental impacts and alleviate the contradiction between supply and demand of water and soil resources, optimizing agricultural planting structure and irrigation water volume urgently needs to consider future uncertain climate scenarios and conduct adaptive research. The life cycle approach can better evaluate the environmental performance of products or processes, but currently most studies only start from the perspective of agricultural production, taking "raw material production" to "crop harvesting" as the accounting boundary, and calculating the environmental impact of single processes and scales such as greenhouse gas emissions, water footprint, and ecological index, thereby ignoring the multiple processes involved in the food system from harvest to table. At the same time, there is no comprehensive indicator to characterize the overall impact of the system on the earth's environment. Therefore, as the boundary value for the safe operation of the earth's environment, the planetary boundary has a 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 prior art. Summary of the invention

[0007] In view of this, the present invention provides an agricultural water and soil resource optimization configuration method taking into account planetary boundary disturbances, specifically an agricultural water and soil resource optimization method taking into account the water-energy-food nexus. Under different future climate scenarios, the life cycle method is used as a calculation method, and the nine processes included in the planetary boundary are used as optimization targets. The disturbance of the food system to the planetary boundary throughout the entire process from agricultural production to the dining table is minimized, thereby alleviating the environmental and water and soil resource pressures under future uncertain scenarios.

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

[0009] A method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbances comprises 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. Taking the regional farmland planting structure and the net irrigation water allocated to each crop in each growth period as decision variables, and taking the minimum impact of the food system from raw materials to the table on the control indicators as the optimization goal, considering the uncertainty of different climate scenarios on food production, a model for the optimal allocation of uncertain agricultural water and soil resources was constructed;

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

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

[0016] Inside the farm gate is field discharge, which includes: fertilization, irrigation, and 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 links are food storage and cooking.

[0019] Optionally, in S2, the optimization goal is to minimize the impact 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 index, Min(x m ) is the minimum value of each index, 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 links of the food system on the eight processes of the planetary boundary, 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 constraints, crop water demand constraints, crop planting area constraints, crop yield constraints and non-negative constraints.

[0027] Optionally, in S3, the input parameters are input into the uncertain agricultural water and soil resources optimization configuration model to solve the uncertain 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 invention 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 boundary are used as the optimization target to minimize the disturbance of the food system to the planetary boundary from agricultural production to the dining 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0032] Figure 1 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0034] Reference Figure 1 As shown, the present invention discloses a method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbance, 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. Taking the regional farmland planting structure and the net irrigation water allocated to each crop in each growth period as decision variables, and taking the minimum impact of the food system from raw materials to the table on the control indicators as the optimization goal, considering the uncertainty of different climate scenarios on food production, a model for the optimal allocation of uncertain agricultural water and soil resources was constructed;

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

[0038] Furthermore, the specific contents of S1 are as follows: based on the SSP-RCP pathway, the land use schemes 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 modes are extracted in the same scenario; thereby obtaining interval uncertainty parameters reflecting the data fluctuation range 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-stage link is the production, processing and transportation of agricultural production materials, which include seeds, fertilizers, pesticides and agricultural films;

[0041] Inside the farm gate is field discharge, which includes: fertilization, irrigation, and 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 links 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, and 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 index, Min(x m ) is the minimum value of each index, 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, determine the evaluation scope as the impact of all aspects of the food system on the eight processes of the planetary boundary, and minimize the environmental impact by optimizing the planting type and water allocation of each 1° unit grid.

[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 calculations.

[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 uncertain agricultural water and soil resources optimization configuration model to solve the uncertain 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, SSP5-RCP8.5), the future land use types are divided into farmland, urban, forest, bare land, grassland and other types. The study area is divided into cells of 1°×1° scale, and the farmland planting part of the land use type at this scale is extracted. The corresponding effective rainfall and reference crop evapotranspiration data are extracted when the decision cells are driven by three climate models (EC-Earth3, GFDL-ESM4, MRI-ESM2-0) in the five typical SSP-RCP scenarios at the same scale. Since the meteorological data will have certain differences under different model driving, the data has interval uncertainty, and the uncertainty is expressed in the form of interval number.

[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 values ​​are extracted as a constraint on the total area of ​​all crops.

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

[0061] Different crops require different energy inputs and corresponding food outputs, and their impact on the environment varies. The concept of planetary boundaries is introduced here to comprehensively quantify the impact of the food system on the earth's environment under different crop planting structures and irrigation water volumes from the perspective of 9 system processes. The 9 system processes are characterized by 16 control variables. See Table 1 for details. Among them, since there is no clear control variable to express the intervention of new substances, it is not considered here. Therefore, this application considers the impact of the food system on 8 system processes through 10 control variables.

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

[0063]

[0064] Consider the entire process of food production, from the transportation of raw materials, planting, harvesting, to transportation, processing, packaging, and finally sales, storage, and cooking. Each process requires the input of different resources (such as water, fertilizers, and fuel), and will also produce corresponding pollutants that affect the environment. Introduce a life cycle approach to comprehensively assess the disturbance of food to planetary boundaries from raw materials to the table.

[0065] ① Climate change (atmospheric CO2 concentration)

[0066] Greenhouse gases generated by food production that cause climate change mainly include CO2, N2O, and CH4. Therefore, it is necessary to calculate the greenhouse gases generated in four processes during the life cycle. Its sources are mainly fossil fuel emissions and N2O emissions directly generated by the application of field fertilizers. The steps of using fossil fuels include: processing, production, and transportation of agricultural materials in the early stage, electricity use in the field, use of machinery, transportation, processing, packaging, storage in the consumption stage, and storage and cooking in the post-stage. In the field planting stage, soil carbon sinks 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 Impact of fossil fuels produced for food systems on planetary boundary processes n, E fer,n The impact of fertilizer application on Earth 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 influence 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 unit i, in kg / ha, U fer is 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 will lead to the emission of greenhouse gases and pollutants, which will affect genetic diversity; at the same time, excessive use of fertilizers will lead to eutrophication of water bodies and damage aquatic habitats; pesticide residues may lead to a reduction in non-target biological populations.

[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] ③ Stratospheric ozone depletion

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

[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, the change 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) N emissions

[0088] The impact of the food system on global N flows mainly includes N produced during the production and use of industrial fossil fuels, N that enters the ecosystem through fertilizer leaching and volatilization during field planting activities, and N that is 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 food systems on phosphorus mainly comes from fertilizers. During the application process, not all fertilizers are absorbed by crops, and the unabsorbed fertilizers are 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 proportion 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 phosphorus concentration 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 of land use change is the proportion of forest land to original forest land. Since this application only studies the impact of the farmland portion 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 Fresh water 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 use coefficient in the process of fossil fuel production and use, t is the crop growth stage, E iw,n The impact of global boundary processes n on irrigation water use, IW ijt is the decision variable, the irrigation water volume of crop j in cell i in 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 mainly 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 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 index, Min(x m ) is the minimum value of each index, 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 It is 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 invention constructs an optimal allocation model for agricultural water and soil resources under future uncertain climate scenarios to consider the differences in food system disturbances to planetary boundaries 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 to be minimized. The following are achieved: (1) referring to scenario plans for future land use and climate change, and considering the uncertainty of future scenarios; (2) taking food as the center within the optimization framework, and considering the environmental impact of the entire life cycle of food from raw material production to cooking; (3) introducing the concept of planetary boundaries to minimize the control indicators of planetary boundaries to achieve the purpose of minimizing environmental impacts; (4) based on the life cycle method, the farmland planting structure and irrigation water allocation plan under different future land use and climate scenarios are obtained, thereby implementing the effect of the concept of sustainable development.

[0126] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0127] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but rather 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. Taking the regional farmland planting structure and the net irrigation water allocated to each crop in each growth period as decision variables, and taking the minimum impact of the food system from raw materials to the table on the control indicators as the optimization goal, considering the uncertainty of different climate scenarios on food production, a model for the optimal allocation of uncertain agricultural water and soil resources was constructed; S3. Solve the uncertain agricultural water and soil resources optimization configuration model and obtain the agricultural water and soil resources optimization configuration plan.

2. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbance according to claim 1, characterized in that: The specific content of S1 is as follows: based on the SSP-RCP pathway, the land use schemes 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 modes in 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 disturbance according to claim 1, characterized in that: In S2, the entire process of the food system from raw materials to the table includes the front-end link, inside the farm gate, the consumer use stage and the back-end link; among them, The pre-stage link is the production, processing and transportation of agricultural production materials, which include seeds, fertilizers, pesticides and agricultural films; Inside the farm gate is field discharge, which includes: fertilization, irrigation, and use of agricultural machinery; The consumer use stage includes processing food crops into food, packaging, and transporting them to sales locations; The subsequent links are food storage and cooking.

4. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbance according to claim 1, characterized in that: In S2, the optimization goal is to minimize the impact 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 index, Min(x m ) is the minimum value of each index, X m is the normalized value of each indicator, w m is the weight coefficient assigned to each indicator.

5. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbance according to claim 1, characterized in that: The specific contents of constructing the uncertain agricultural water and soil resource optimization allocation model in S2 are as follows: using the life cycle approach, the evaluation scope is determined to be the impact of all links of the food system on the eight processes of the planetary boundary, and the purpose of minimizing environmental impact is achieved by optimizing the planting type and water allocation of each 1° unit grid.

6. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbance according to claim 5, characterized in that: 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.

7. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbance according to claim 5, characterized in that: 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.

8. The method for optimizing agricultural water and soil resources configuration considering planetary boundary disturbance 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-negative constraint.

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

Images