Agricultural water resource optimization method based on crop water demand and multi-objective optimization

Abstract

The invention discloses an agricultural water resource optimization method based on crop water demand and multi-objective optimization, which comprises the following steps: S1, constructing a multi-objective planning model of crop yield, irrigation moisture productivity maximization and planting water cost minimization, and converting the multi-objective planning model into a deterministic single-objective model by adopting a Zimmerman method; s2, reading the monitoring data collected in the current monitoring period and the monitoring data corresponding to the non-collection time period of the current monitoring period in the previous monitoring period; the monitoring period comprises a complete planting period of corn and wheat; s3, inputting all the monitoring data into a deterministic single-target model, and solving the single-target model by adopting Lingo programming to obtain the irrigation volume of the corn and the wheat in each growth period in the current monitoring period; and S4, according to the current growth periods of the corn and the wheat, adopting the irrigation amount of the corresponding growth period obtained in the step S3 as the guidance irrigation amount of the current growth periods of the corn and the wheat.

Description

Technical Field

[0001] The present invention relates to irrigation water resource optimization technology, and in particular to an agricultural water resource optimization method based on crop water demand and multi-objective optimization. Background Art

[0002] As a core agricultural region, the North China Plain has become a key area for ensuring national food security. However, this high-yield model has long relied on groundwater overdraft, making the issue of groundwater overdraft and the optimal allocation of farmland water resources in the region a research hotspot in recent years. While the decline in groundwater levels has eased under the dual influences of climate change and human activities, water shortages continue to constrain regional food security and sustainable ecological development. Complementary crop water requirements indicate that winter wheat relies on groundwater to regulate moisture during its critical growth period, while summer corn requires supplemental irrigation to compensate for uneven rainfall distribution.

[0003] At the same time, precision agricultural water resource management, deeply coupled with the reproductive cycle, presents a complex decision-making environment characterized by multiple uncertainties. On the one hand, agricultural water resource management involves multiple conflicting objectives, such as maximizing yield, improving irrigation efficiency, and controlling costs. On the other hand, within farmland ecosystems, the dynamic circulation and transformation of atmospheric water (including rainfall and evapotranspiration from farmland), surface water, soil water, and groundwater, along with vertical and horizontal transport between different water bodies, jointly drive the dynamic allocation of water within farmland systems. Farmland water demand exhibits significant periodic fluctuations in response to climate and crop growth.

[0004] Currently, there is considerable research on agricultural water resource management in the winter wheat-summer maize rotation system in the North China Plain. However, previous studies on agricultural water resource management have primarily focused on static trade-offs between economic and ecological benefits, with insufficient attention paid to the integrated utilization of precipitation and irrigation, and the dynamics of crop water demand during the growing season. This inadequacy can lead to reduced water resource utilization efficiency and impact food security. Traditional approaches have limitations in weighting trade-offs and are unable to address the complex uncertainties associated with climate change, spatiotemporal mismatches in water demand, and groundwater overexploitation in the North China Plain. Summary of the Invention

[0005] In response to the above-mentioned deficiencies in the existing technology, the agricultural water resource optimization method based on crop water requirement and multi-objective optimization provided by the present invention solves the problem that the existing technology does not pay attention to the comprehensive utilization of precipitation and irrigation and the dynamic water demand of crops during the growth period, which may lead to a decrease in water resource utilization efficiency.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A method for optimizing agricultural water resources based on crop water demand and multi-objective optimization is provided, comprising the steps of: S1. Construct a multi-objective programming model to maximize crop yield and irrigation water productivity and minimize planting water cost, and use the Zimmerman method to transform the multi-objective programming model into a deterministic single-objective model; S2. Read the monitoring data collected during the current monitoring cycle and the monitoring data corresponding to the period not collected during the current monitoring cycle during the previous monitoring cycle; the monitoring cycle includes a complete planting cycle of corn and wheat; S3. Input all monitoring data into a deterministic single-objective model and solve the single-objective model using Lingo programming to obtain the irrigation amount of corn and wheat in each growth period during the current monitoring period; S4. Based on the current growth stage of corn and wheat, the irrigation amount for the corresponding growth stage obtained in step S3 is used as the guiding irrigation amount for the current growth stage of corn and wheat.

[0007] The beneficial effects of the present invention are as follows: This scheme uses winter wheat and summer corn as research objects, uses the monitoring data collected during the monitoring cycle and the monitoring data corresponding to the previous monitoring cycle and the period when the monitoring cycle did not occur to form a complete monitoring cycle data, and combines Lingo programming to solve the single-objective model to obtain the irrigation amount of the two crops in each growth period under the conditions of maximizing crop yield, irrigation water productivity, and minimizing planting water costs during the monitoring cycle, and uses the irrigation amount of the crop in the current growth period as the guiding irrigation amount. By determining the irrigation amount for each growth period in this way, this scheme deeply integrates the complementary law of winter wheat and summer corn water requirements, can reduce groundwater overexploitation and improve water resource utilization efficiency.

[0008] This solution can accurately determine the water resource allocation process in the winter wheat-summer maize rotation model, taking into account the water requirements of different growth stages. This enables the multi-objective planning model of this solution to provide a water resource allocation plan that conforms to the actual situation and provides a reliable basis for the optimized management of farmland water resources. Unlike traditional static irrigation allocation methods, this study quantifies the dynamic process and key parameters of the water cycle through the model, such as crop water demand, soil moisture changes, and evaporation, and outputs irrigation volume to help decision makers more scientifically understand and manage water resource allocation. Specifically, it accurately determines the water requirements of each growth stage.

[0009] Furthermore, the multi-objective programming model includes the objectives of maximizing crop yield, maximizing irrigation water productivity, and minimizing planting water cost; The expression of the crop yield maximization objective is: in, is the total crop yield, kg; is the yield per unit area of ​​crop c, kg / hm2 ; is the planting area of ​​crop c, hm 2 ; C is the total number of crops, and its value is 2; is the potential yield per unit area of ​​crop c, kg / hm 2 ; is the crop coefficient of crop c in growth period t; is the potential evapotranspiration of crop c during growth period t, mm; is the water sensitivity index of crop c at growth period t; is the soil moisture content of crop c at growth period t; is the field water capacity; is the wilting coefficient; is the reference crop evapotranspiration of crop c in growth period t; The expression of the irrigation water productivity maximization objective is: in, is the irrigation water productivity, kg / m 3 ; is the total yield of crop c, kg; is the irrigation amount of crop c during growth period t, mm; The expression of the objective of minimizing the cost of planting water is: in, is the total cost, CNY; is the planting cost of crop c, CNY / hm 2 ; is the water cost of crop c, CNY / m 3 .

[0010] The beneficial effects of the above technical solution are: the multi-objective programming model maximizes crop yield ( ), maximizing irrigation water productivity ( ) and minimizing the cost of water used for cultivation ( ) collaborative optimization mechanism, significantly improving the resource utilization efficiency and economic sustainability of the agricultural system. Objective To quantify the relationship between crop water availability and yield by incorporating the crop water production function (Jensen model); The goal is to drive the shift towards high-value crops through "unit irrigation water output" (numerator yield increase / denominator water saving); The target uses the water price parameter ( ) Automatically reduce irrigation for high-water-consuming crops.

[0011] Furthermore, the constraints of the multi-objective programming model include water balance constraint, water allocation constraint, soil moisture constraint and non-negative constraint; the expression of the water balance constraint is: in, is the soil moisture content of crop c at growth period t; and are the soil water contents of crop c at growth period t and growth period t+1, mm; is the effective rainfall of crop c during growth period t, mm; is the exchange volume between the root zone and the buffer zone of crop c during growth period t, mm; is the root zone depth of crop c at growth period t, 1 m; is the field water capacity; is the critical water storage capacity of the root zone, mm; a and b are dimensionless empirical parameters related to soil texture; The expression of the water allocation constraint is: in, is the irrigation water utilization coefficient; is the water requirement of crop c during growth period t, mm; is the target water allocation for crop c during growth period t, mm; 、 is the confidence level; It is a credibility measure used to quantify the likelihood of occurrence of fuzzy events; The expression of soil moisture constraint is: ; The expression of the non-negativity constraint is: .

[0012] The beneficial effect of this technical solution is that, through a dynamic synergistic mechanism combining water balance constraints, water demand constraints, soil moisture content constraints, and non-negativity constraints, it significantly improves the refinement and system adaptability of agricultural water cycle management. Its core innovation lies in upgrading traditional static constraints into a dynamic, closed-loop water cycle system with multivariable linkage. The water balance constraint couples soil moisture content, root zone depth, and groundwater exchange rate to form a "precipitation-irrigation-evaporation-groundwater recharge" feedback loop, accurately quantifying the impact of soil texture and deep seepage. The water demand constraint incorporates fuzzy credibility (Cr) to manage precipitation uncertainty, dynamically responding to drought risk through a dual-threshold elasticity interval (minimum water demand ensures survival, target water demand prevents waste). The soil moisture content constraint, through dual-boundary control of the wilting coefficient and field water holding capacity, synergistically ensures crop physiological water needs. The non-negativity constraint eliminates nonphysical solutions and ensures algorithm stability. This constraint system transcends the fragmented nature of traditional models, achieving a globally coordinated optimization of water, grain, and ecological risks.

[0013] Furthermore, the objective function of the deterministic single-objective model is: in, For satisfaction; The constraints of the objective function are: in, and They are the lower and upper limits of is an auxiliary parameter used to describe the nonlinearity of the membership function and is specified by the decision maker. ; and They are the lower and upper limits of and They are the lower and upper limits of and for quartile values ​​and median; and They are The fourth tertile and median values; is the conversion symbol; In the deterministic single-objective model As a variable, Lingo programming is used to solve the single-objective model and output .

[0014] The beneficial effects of the above technical solution are: this solution has passed the satisfaction test ( ) The innovative design of maximizing the target has achieved a systematic breakthrough in multi-objective collaborative optimization. Multi-objective membership integration mechanism: through nonlinear parameters Dynamically adjust the membership function of yield, water productivity and cost targets (e.g. yield target adopts structure , so that high-efficiency goals can obtain a gain tilt; fuzzy constraint deterministic transformation: the credibility measure (Cr) constraint is transformed into a quartile-based ( ) deterministic inequalities to quantify the stochasticity of precipitation to ensure water needs for survival while avoiding conservative irrigation.

[0015] Furthermore, the monitoring data include meteorological data, soil parameters, crop growth parameters and economic parameters. The meteorological data include daily average temperature, minimum temperature and maximum temperature, relative humidity, average wind speed, sunshine hours and precipitation; soil parameters include soil bulk density, soil moisture content, saturated moisture content, wilting coefficient and field holding capacity; crop growth parameters include duration of growth period, yield, plant height, biomass and leaf area; economic parameters include seed cost, fertilizer cost, labor cost, pesticide cost, agricultural machinery cost, water cost and crop unit price.

[0016] Furthermore, the confidence level 、 The value of is 0.5. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Flowchart of the agricultural water resource optimization method based on crop water requirement and multi-objective optimization.

[0018] Figure 2 Schematic diagram of changes in temperature, effective rainfall and crop water requirements during the growing period under the winter wheat-summer corn rotation model.

[0019] Figure 3 Schematic diagram of the trade-off results of the model objective function under six different credibility conditions, where (a) is the schematic diagram of the trade-off results in 2023 and (b) is the schematic diagram of the trade-off results in 2024.

[0020] Figure 4 Schematic diagram of the range of changes in different target values.

[0021] Figure 5 This is the optimization result diagram of various water elements in different growth periods under different scenarios in 2023 under the winter wheat-summer corn rotation model.

[0022] Figure 6 This is the optimization result diagram of various water elements in different growth periods under different scenarios in 2024 under the winter wheat-summer corn rotation model. DETAILED DESCRIPTION

[0023] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0024] refer to Figure 1 , Figure 1 The flowchart of the agricultural water resource optimization method based on crop water demand and multi-objective optimization is shown; Figure 1 As shown, the method includes steps S1 to S4.

[0025] In step S1, a multi-objective programming model of crop yield, irrigation water productivity and planting water cost is constructed, and the Zimmerman method is used to transform the multi-objective programming model into a deterministic single-objective model.

[0026] In one embodiment of the present invention, the multi-objective programming model includes a crop yield maximization objective, an irrigation water productivity maximization objective, and a planting water cost minimization objective; The expression of the crop yield maximization objective is: in, is the total crop yield, kg; is the yield per unit area of ​​crop c, kg / hm 2 ; is the planting area of ​​crop c, hm 2 ; C is the total number of crops, and its value is 2; is the potential yield per unit area of ​​crop c, kg / hm 2 ; is the crop coefficient of crop c in growth period t; is the potential evapotranspiration of crop c during growth period t, mm; is the water sensitivity index of crop c at growth period t; is the soil moisture content of crop c at growth period t; is the field water capacity; is the wilting coefficient; is the reference crop evapotranspiration of crop c during growth period t.

[0027] The expression of the irrigation water productivity maximization objective is: in, is the irrigation water productivity, kg / m 3 ; is the total yield of crop c, kg; is the irrigation amount of crop c during growth period t, mm.

[0028] In this scenario, irrigation water productivity Water productivity is a measure of agricultural output per unit of water resources, often expressed as water productivity. A higher water productivity indicates a more efficient use of irrigation water, effectively converting it into crop yields.

[0029] The expression of the objective of minimizing the cost of planting water is: in, is the total cost, CNY; is the planting cost of crop c, CNY / hm 2 ; is the water cost of crop c, CNY / m 3 .

[0030] The constraints of the multi-objective programming model include water balance constraint, water allocation constraint, soil moisture constraint and non-negative constraint; the expression of the water balance constraint is: in, is the soil moisture content of crop c at growth period t; and are the soil water contents of crop c at growth period t and growth period t+1, mm; is the effective rainfall of crop c during growth period t, mm; is the exchange volume between the root zone and the buffer zone of crop c during growth period t, mm; is the root zone depth of crop c at growth period t, 1 m; is the field water capacity; is the critical water storage capacity of the root zone, mm; a and b are dimensionless empirical parameters related to soil texture.

[0031] The expression of the water allocation constraint is: in, is the irrigation water utilization coefficient; is the water requirement of crop c during growth period t, mm; is the target water allocation for crop c during growth period t, mm; 、 is the confidence level; It is a credibility measure used to quantify the likelihood of occurrence of fuzzy events.

[0032] The expression of soil moisture constraint is: ; The expression of the non-negativity constraint is: .

[0033] In step S2, the monitoring data collected in the current monitoring cycle and the monitoring data in the previous monitoring cycle corresponding to the time period not collected in the current monitoring cycle are read; the monitoring cycle includes a complete planting cycle of corn and wheat.

[0034] To facilitate understanding, the following is an explanation with a small example. In North China, the growing period of winter wheat is from October to June of the following year, and the growing period of summer corn is from June to October. The monitoring period is from June of the current year to June of the following year. Assuming that six months have been executed in the current monitoring period, the monitoring data for these six months have been collected in real time, that is, the monitoring data from June to November are the data read in the current monitoring period. Since this scheme requires the monitoring data of the complete monitoring period in order to more accurately predict the irrigation amount of each growing period, this scheme uses the data of the previous monitoring period to supplement the period that did not occur in the current monitoring period, that is, the data from December to June of the following year in the previous monitoring period are supplemented into the current monitoring period, and then input into the deterministic single-objective model together and solved using Lingo programming.

[0035] In this plan, the monitoring data include meteorological data, soil parameters, crop growth parameters and economic parameters. The meteorological data include daily average temperature, minimum temperature and maximum temperature, relative humidity, average wind speed (2m), sunshine hours and precipitation; soil parameters include soil bulk density, soil moisture content, saturated moisture content, wilting coefficient and field holding capacity; crop growth parameters include growth period duration, yield, plant height, biomass and leaf area; economic parameters include seed cost, fertilizer cost, labor cost, pesticide cost, agricultural machinery cost, water cost and crop unit price.

[0036] In step S3, all monitoring data are input into the deterministic single-objective model, and the single-objective model is solved using Lingo programming to obtain the irrigation amount of corn and wheat in each growth period during the current monitoring period.

[0037] In one embodiment of the present invention, the objective function of the deterministic single-objective model is: in, For satisfaction; The constraints of the objective function are: in, and They are the lower and upper limits of is an auxiliary parameter used to describe the nonlinearity of the membership function and is specified by the decision maker. ; and They are the lower and upper limits of and They are the lower and upper limits of and for quartile values ​​and median; and They are The fourth tertile and median values; is the conversion symbol; In the deterministic single-objective model As a variable, Lingo programming is used to solve the single-objective model and output .

[0038] In step S4, based on the current growth stage of corn and wheat, the irrigation amount of the corresponding growth stage obtained in step S3 is used as the guiding irrigation amount for the current growth stage of corn and wheat.

[0039] The multi-objective planning model constructed in this scheme solves the problem of low adaptability of traditional models to spatiotemporal heterogeneous parameters. By deeply integrating the complementary laws of winter wheat and summer corn water requirements, combining the multi-objective planning model with the Zimmerman algorithm, it generates optimized irrigation strategies for sensitive stages such as the jointing and tasseling periods, improves irrigation efficiency and reduces groundwater over-exploitation, and simultaneously optimizes grain yield, groundwater extraction and replenishment balance, and ecological sustainability goals, providing scientific support for farmland water resources management.

[0040] The feasibility of the agricultural water resources optimization method of this scheme is described in detail below with reference to specific examples: 1. Determine the study area The North China region (32°-40°N, 114°-121°E), with its fertile soil and favorable climate, is a key agricultural production base in my country. The region has a warm temperate semi-humid continental monsoon climate, with an average temperature of 12.1°C and annual precipitation of 647 mm. Precipitation is primarily concentrated from June to September, exhibiting significant seasonal variation, particularly from July to August, which accounts for 60% of the annual precipitation. Annual actual evaporation is approximately 423 mm, potential evaporation is 1164.4 mm, annual sunshine duration is 2580 hours, and the frost-free period is approximately 190 days. Winter wheat and summer maize are the primary crops grown in the region, accounting for 51.4% and 35.1% of the total grain area, respectively. The winter wheat-summer maize rotation is a unique cropping pattern in the region, with the winter wheat growing period from October to June and the summer maize growing period from June to October. Winter wheat requires approximately 450-600 mm of water during its growing season, while the average annual rainfall (concentrated during the growing season) is only 139 mm, making it difficult to meet water demand. Summer corn, however, experiences abundant rainfall during its growing season, with approximately 334-458 mm of rainfall during the growing season, while it requires approximately 350-450 mm of water. This mismatch between water use for key crops and rainfall timing in North China has led to a conflicting system of overexploitation of groundwater and waste of rainfall resources, resulting in a decline in the region's overall water resource utilization efficiency.

[0041] 2. Monitoring data sources A. Field Experiment Data The basic experimental data for this example comes from the 2022-2024 winter wheat-summer corn rotation irrigation experiment at the Daxing Water-Saving Irrigation Experimental Station of the Beijing National Water-Saving Irrigation Technology Research Center (39°37′25″N, 116°25′51″E). The main data types include meteorological data (including daily average temperature, minimum and maximum temperatures, relative humidity, average wind speed (2 m), sunshine hours, and precipitation), soil parameters (including soil bulk density, soil moisture content, saturated moisture content, wilting coefficient, and field water holding capacity), and crop growth parameters (including growth period duration, yield, plant height, biomass, and leaf area). The growth process of winter wheat and summer corn can be divided into the following stages: the growth stages of winter wheat include sowing-overwintering period (S-OW), overwintering-greening period (OW-GU), greening-jointing period (GU-J), jointing-heading period (JB), heading-filling period (BF) and grain filling-harvest period (F-H1); the growth stages of summer corn include sowing-jointing period (SJ), jointing-tasting period (JT), tasting-filling period (TF) and grain filling-harvest period (F-H2).

[0042] B. Meteorological data Changes in temperature, effective rainfall, and crop water requirements during the growth period under the winter wheat-summer corn rotation model Figure 1As shown, the data are the climate data for 2022-2023 and 2023-2024, T mean represents the average temperature during the growing season of winter wheat and summer corn; the upper and lower limits of the shaded area represent the maximum and minimum temperatures, respectively; P represents effective rainfall; 2023 and 2024 represent 2022-2023 and 2023-2024, respectively, and the same below.

[0043] 3. Performance evaluation of multi-objective programming models Based on the above data, step S3 of this solution is executed. During the execution, six different credibility conditions are selected. The weighing results are referenced. Figure 3 . Figure 3 Different solutions in represent different confidence levels ( The area of ​​the triangle represents the degree of coordinated development; a larger area indicates better coordinated development in a particular scenario. Figure 3 The vertices of F1-AY, F2-IWUE and F3-TC correspond to the optimal values ​​of three different objectives respectively. The closer the indicator value is to the vertex, the better the indicator is.

[0044] In each scenario, the value of the objective function is limited to the corresponding maximum and minimum values. By setting the upper and lower limits of each target, the range of the target value is determined and normalized by applying the normalization method. The closer the indicator value is to 1, the more it meets the needs of the decision maker. For example, When the maximum output target is 1.708×10 4 kg, the maximum irrigation water productivity is 5.13 kg / m³, and the minimum cost target is 1.183 × 10 4 If these three goals are considered simultaneously, the yield can reach 1.655×10 4 kg, the irrigation water productivity is 4.87 kg / m³, and the cost is 1.193×10 4 CNY.

[0045] The above results indicate that there is a certain trade-off between multiple objectives, and the results obtained through multi-objective collaborative optimization are reasonable. Numerically, the cost indicator varies little across different scenarios, remaining stable and close to 1, demonstrating its relative stability. In contrast, the yield and irrigation water productivity indicators vary significantly, with significant variations. This suggests that while cost is relatively easy to control in the water resource optimization process, yield and water efficiency require appropriate trade-offs in multi-objective optimization.

[0046] The satisfaction of the model decreases as the confidence level increases. Specifically, the satisfaction levels in 2023 are 0.72, 0.68, 0.65, 0.61, 0.57, and 0.54 respectively; and in 2024 are 0.71, 0.67, 0.63, 0.60, 0.57, and 0.54 respectively. This shows that the model with lower confidence can provide higher satisfaction than the model with higher confidence. Both are set to 0.5, which can achieve more efficient water resource optimization, that is, improve the utilization rate of water resources. In addition, it is worth noting that the results of the low-confidence model cover a wider area than the high-confidence model, which indicates that the low-confidence scheme may provide a more flexible solution and contribute to the sustainable allocation of agricultural water and food resources.

[0047] 4. Objective Analysis of Multi-Objective Programming Model The multi-objective planning model of this scheme includes three objectives: yield, irrigation water productivity, and cost, and combines the uncertain constraint variables , also called confidence level, (variables will affect the allocation of agricultural water and soil resources) to generate multiple scenarios, which in turn affect the target value. The changes in each target value under different scenarios are statistically analyzed, and the results are as follows Figure 4 As shown. Under different combination scenarios, the output in 23 years ranges from 1.43 to 1.71×10 4 kg, the optimized value reaches 1.61×10 4 kg, an increase of 4.91% compared with the actual scenario; the range of irrigation water productivity in 23 years was 4.13~5.26kg / m 3 , the optimized value is 4.68 kg / m 3 , which is 5.84% higher than the actual scenario; the cost range is 1.18~1.21×10 4 CNY, the optimized value is 1.195×10 4 CNY, which is 5.94% lower than the actual scenario. The range of production in 24 years is 1.50~1.75 ×10 4 kg, the optimized value reaches 1.61×10 4 kg, which is 3.13% higher than the actual scenario; the irrigation water productivity range is 4.29~5.36kg / m 3 , the optimized value is 4.79 kg / m 3 , which is 5.74% higher than the actual scenario; the cost range is 1.175~1.198 ×10 4 CNY, the optimized value is 1.185×10 4 CNY, which is 8.40% lower than the actual scenario.

[0048] From the above data, we can see that the multi-objective planning model constructed in this scheme predicts the irrigation amount, and then guides crop irrigation based on this data. Crop yield and irrigation water productivity can be greatly improved, and costs will be reduced by a large proportion.

[0049] 5. Water allocation during the crop growing season Irrigation and precipitation are the main sources of water supply to crop fields. Figure 5 and Figure 6 The optimization results of various water elements at different growing stages under different scenarios in 2023 and 2024 are given for the winter wheat-summer maize rotation model. The optimization is based on the number of days in the growing season, crop water requirement, precipitation, water shortage sensitivity, and water allocation targets. The different scenarios in the figure represent different confidence levels ( From 0.5 to 1.0). Analysis shows that for winter wheat, the total water allocation was greatest during the jointing-to-heading and heading-to-grain filling periods, followed by the greening-to-jointing period. Water allocations during the sowing-overwintering, overwintering-greening, and grain filling-harvest periods were comparable. For summer corn, the total water allocation was greatest during the jointing-to-heading period, followed by the tasseling-to-grain filling and sowing-to-jointing periods, with the least during the grain filling-harvest period.

[0050] When the credibility ranges from 0.5 to 1, the proportion of crop water allocation in different growth stages does not change much, and the total water allocation increases with the increase of credibility. The proportion of water allocation in different growth stages of winter wheat in 23 years is [3.48%±0.17%], [6.57%±0.16%], [6.64%±0.31%], [8.83%±0.40%], [6.51%±0.32%], [3.92%±0.13%]; the proportion of water allocation in different growth stages of summer corn in 23 years is [10.75%±0.18%], [37.80%±0.14%], [12.79%±0.46%], [2.71%±0.14%]. The proportion of water allocation in different growth stages of winter wheat in 24 years was [2.78%±0.17%], [4.68%±0.20%], [5.53%±0.32%], [7.79%±0.38%], [6.55%±0.37%], [6.26%±0.16%]; the proportion of water allocation in different growth stages of summer maize in 24 years was [17.37%±0.45%], [39.65%±0.10%], [5.65%±0.31%], [3.75%±0.09%].

[0051] pass Figure 5 and Figure 6 The analysis of water allocation under different confidence levels shows that When the value is 0.5, water allocation efficiency is the highest. Compared with the actual treatment, the total average irrigation water consumption for winter wheat is reduced by 14.76%, and the total average irrigation water consumption for summer corn is reduced by 16.62%. This shows that the agricultural water resource optimization method using this scheme can significantly reduce irrigation water consumption, thereby achieving water conservation.

Claims

1. An agricultural water resources optimization method based on crop water demand and multi-objective optimization, characterized in that: Including steps: S1. Construct a multi-objective programming model for maximizing crop yield, irrigation water productivity, and minimizing planting water cost, and use the Zimmerman method to transform the multi-objective programming model into a deterministic single-objective model. S2. Read the monitoring data collected during the current monitoring cycle and the monitoring data corresponding to the period not collected during the current monitoring cycle during the previous monitoring cycle; the monitoring cycle includes a complete planting cycle of corn and wheat; S3. Input all monitoring data into a deterministic single-objective model and solve the single-objective model using Lingo programming to obtain the irrigation amount of corn and wheat in each growth period during the current monitoring period; S4. Based on the current growth stage of corn and wheat, the irrigation amount for the corresponding growth stage obtained in step S3 is used as the guiding irrigation amount for the current growth stage of corn and wheat.

2. The agricultural water resource optimization method based on crop water demand and multi-objective optimization according to claim 1, characterized in that: The multi-objective programming model includes the objectives of maximizing crop yield, maximizing irrigation water productivity and minimizing planting water cost; The expression of the crop yield maximization objective is: in, is the total crop yield, kg; is the yield per unit area of ​​crop c, kg / hm 2 ; is the planting area of ​​crop c, hm 2 ; C is the total number of crops, and its value is 2; is the potential yield per unit area of ​​crop c, kg / hm 2 ; is the crop coefficient of crop c in growth period t; is the potential evapotranspiration of crop c during growth period t, mm; is the water sensitivity index of crop c at growth period t; is the soil moisture content of crop c at growth period t; is the field water capacity; is the wilting coefficient; is the reference crop evapotranspiration of crop c in growth period t; The expression of the irrigation water productivity maximization objective is: in, is the irrigation water productivity, kg / m 3 ; is the total yield of crop c, kg; is the irrigation amount of crop c during growth period t, mm; The expression of the objective of minimizing the cost of planting water is: in, is the total cost, CNY; is the planting cost of crop c, CNY / hm 2 ; is the water cost of crop c, CNY / m 3 .

3. The agricultural water resource optimization method based on crop water demand and multi-objective optimization according to claim 2, characterized in that: The constraints of the multi-objective programming model include water balance constraint, water allocation constraint, soil moisture constraint and non-negative constraint; the expression of the water balance constraint is: in, is the soil moisture content of crop c at growth period t; and are the soil water contents of crop c at growth period t and growth period t+1, mm; is the effective rainfall of crop c during growth period t, mm; is the exchange volume between the root zone and the buffer zone of crop c during growth period t, mm; is the root zone depth of crop c at growth period t, 1 m; is the field water capacity; is the critical water storage capacity of the root zone, mm; a and b are dimensionless empirical parameters related to soil texture; The expression of the water allocation constraint is: in, is the irrigation water utilization coefficient; is the water requirement of crop c during growth period t, mm; is the target water allocation for crop c during growth period t, mm; 、 is the confidence level; It is a credibility measure used to quantify the likelihood of occurrence of fuzzy events; The expression of soil moisture constraint is: ； The expression of the non-negativity constraint is: 。 4. The agricultural water resource optimization method based on crop water demand and multi-objective optimization according to claim 3, characterized in that: The objective function of the deterministic single-objective model is: in, For satisfaction; The constraints of the objective function are: in, and They are the lower and upper limits of is an auxiliary parameter used to describe the nonlinearity of the membership function and is specified by the decision maker. ; and They are the lower and upper limits of and They are the lower and upper limits of and for quartile values ​​and median; and They are The fourth tertile and median values; is the conversion symbol; In the deterministic single-objective model As a variable, Lingo programming is used to solve the single-objective model and output .

5. The agricultural water resource optimization method based on crop water requirement and multi-objective optimization according to any one of claims 1 to 4, characterized in that: The monitoring data include meteorological data, soil parameters, crop growth parameters and economic parameters. The meteorological data include daily average temperature, minimum temperature and maximum temperature, relative humidity, average wind speed, sunshine hours and precipitation; soil parameters include soil bulk density, soil moisture content, saturated moisture content, wilting coefficient and field holding capacity; crop growth parameters include duration of the growth period, yield, plant height, biomass and leaf area; economic parameters include seed cost, fertilizer cost, labor cost, pesticide cost, agricultural machinery cost, water cost and crop unit price.

6. The agricultural water resource optimization method based on crop water demand and multi-objective optimization according to claim 4, characterized in that: The confidence level 、 The value of is 0.5.

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