A method to fit future crop yields into a safe climate space

Abstract

The present application proposes a method for incorporating future crop yields into a safe climate space, comprising calculating index data based on climate data of a reference period in a set area; constructing a first safe climate space by combining the index data with yield data of the crops in the reference period; moving the first safe climate space by adjusting the climate data, and combining the moving range with the first safe climate space to form a second safe climate space; selecting the optimal index data when the crop yield is maximum based on the climate data of the future period; constructing a third safe climate space for the crop under the optimal index data for the crop, and optimizing the distribution of the crop planting area to increase the crop yield in the third safe climate space.

Description

Technical Field

[0001] The present application relates to the field of climate change and agricultural production, and more particularly to a method for incorporating future crop yields into a safe climate space. Background Art

[0002] Currently, with the significant increase in surface temperature, the climate characteristics have changed significantly, among which the frequency and intensity of extreme heat and extreme precipitation events are on the rise; climate change is closely related to the food security component, and mitigating extreme climate is essential to meet the future food needs of the growing population. Climate change has already threatened crop production, especially corn, wheat, rice and oilseed soybeans.

[0003] Regarding the negative impact of climate change on crop yields, it is urgent to take certain measures to reduce yield losses and mitigate the impact of climate change on crop yields, which is of great significance to global food security. At present, relevant technologies have proposed a safe climate space (SCS), which is defined as the climate conditions to which the current food production system (here only refers to crop production) uses a combination of three climate parameters (annual precipitation (P), biological temperature (bioT) and dryness (R)) in a comprehensive manner. However, the research on the safe climate space (SCS) is limited to predicting the impact of future climate change on crop yields, and does not consider how to eliminate or mitigate the impact of future climate change. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the purpose of the present application is to propose a method for incorporating future crop yields into a safe climate space, so that more future crop yields are within the safe climate space, thereby reducing the impact of future climate change on crop yields and ensuring food security.

[0005] To achieve the above objectives, this application proposes a method to incorporate future crop yields into a safe climate space, including:

[0006] Calculate index data based on climate data of a base period in a set region; construct a first safe climate space by combining the index data with crop yield data of the base period;

[0007] The first safe climate space is moved by adjusting the climate data, and its moving range is combined with the first safe climate space to form a second safe climate space; based on the climate data of the future period, the optimal indicator data when the crop yield is the largest is selected;

[0008] A third safe climate space for crops is constructed under the optimal indicator data for the crops, and the distribution of crop planting areas is optimized to increase the yield of the crops in the third safe climate space.

[0009] In some embodiments, the climate data includes temperature and precipitation data; wherein the indicator data includes annual precipitation, biological temperature and dryness.

[0010] In some embodiments, the annual precipitation is calculated as follows:

[0011]

[0012] Where P is annual precipitation, mm; p is daily precipitation, mm; days is the number of days in a year, days.

[0013] In some embodiments, the biological temperature is calculated as follows:

[0014]

[0015] Where bioT is the biological temperature, °C; t is the average daily temperature less than 35 °C and greater than 0 °C, °C; days is the number of days in a year, days.

[0016] In some embodiments, the calculation formula of the dryness is as follows:

[0017]

[0018] Where R is dryness; EVP is potential evaporation, mm; P is annual precipitation, mm;

[0019] The calculation formula for potential evaporation is as follows:

[0020] EVP=58.93×bioT

[0021] Where bioT is the biological temperature, °C.

[0022] In some embodiments, the corresponding indicator data is calculated based on the climate data of the future period to determine whether future crop yields in a set area will be affected by climate change.

[0023] In some embodiments, optimizing the planting area distribution of crops is performed by a genetic algorithm, and the optimization program is edited using MATLAB, including operations of generating populations, selection, crossover, and mutation.

[0024] In some embodiments, in the process of optimizing the distribution of crop planting areas, parameter constraints of the genetic algorithm include the amount of change in irrigation and planting area.

[0025] Compared with the prior art, this application has the following advantages:

[0026] This application aims to improve the adaptability of crops to future climate and optimize the distribution of crop planting areas so that more future crop yields are within a safe climate space, thereby alleviating the impact of future climate change on crop yields and ensuring future food production.

[0027] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0029] Figure 1 This is a flow chart of a method for incorporating future crop yields into a safe climate space, as proposed in one embodiment of the present application.

[0030] Figure 2 This is a SCS diagram constructed in an embodiment of the present application; the blue part is the SCS, and the other colors represent the number of models that are not in the SCS, green - 1; yellow - 2; orange - 3; red - 4; black - 5;

[0031] Figure 3 is a process diagram of adaptive optimization in one embodiment of the present application;

[0032] Figure 4 It is a flow chart of a genetic algorithm involved in one embodiment of the present application. DETAILED DESCRIPTION

[0033] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be construed as limiting the present application. On the contrary, the embodiments of the present application include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0034] To achieve the above objectives, this application proposes a method to integrate future crop yields into a safe climate space, including

[0035] S1: Calculate index data based on the climate data of the base period in the set area; construct the first safe climate space by combining the index data with the yield data of the crops in the base period;

[0036] S2: The first safe climate space is moved by adjusting the climate data, and its moving range is combined with the first safe climate space to form the second safe climate space; based on the climate data of the future period, the optimal indicator data when the crop yield is the largest is selected;

[0037] S3: Construct the third safe climate space for crops under the optimal indicator data of crops, and optimize the distribution of crop planting areas to increase the yield of crops in the third safe climate space.

[0038] Among them, S1 calculates the index data according to the climate data of the base period in the set area, that is, a selected area is selected, and the index data of the first safe climate space is calculated based on the climate data of the base period in the area, such as temperature and precipitation data, wherein the index data include annual precipitation, biological temperature and dryness.

[0039] The calculation formula for annual precipitation in this embodiment is as follows:

[0040]

[0041] Where P is annual precipitation, mm; p is daily precipitation, mm; days is the number of days in a year, days.

[0042] The biological temperature is calculated as follows:

[0043]

[0044] Where bioT is the biological temperature, °C; t is the average daily temperature less than 35 °C and greater than 0 °C, °C; days is the number of days in a year, days.

[0045] The calculation formula of dryness is as follows:

[0046]

[0047] Where R is dryness; EVP is potential evaporation, mm; P is annual precipitation, mm;

[0048] The calculation formula for potential evaporation is as follows:

[0049] EVP=58.93×bioT

[0050] Where bioT is the biological temperature, °C.

[0051] After obtaining the annual precipitation, biological temperature and dryness in the selected area, the first safe climate space is constructed in combination with the base period crop yield data of the area.

[0052] In S2, it can be seen that when a certain range of temperature and precipitation data changes is set, the original first safe climate space is moved, and the moving range is combined with the original first safe climate space to generate a new SCS, that is, the second safe climate space; in other words, by changing the adaptability of crops in the region to temperature and precipitation, that is, changing the temperature and precipitation data of the baseline period, the first safe climate space will move, and it will be merged with the original first safe climate space to obtain a new, expanded second safe climate space. The temperature, precipitation and crop yield data of future periods simulated by different global crop models can be used to calculate the proportion of crops that exceed the first safe climate space.

[0053] According to the future climate data of the region, the optimal index data when the crop yield is the largest is selected; that is, according to the temperature and precipitation data of the future period, the crop yield in the second safe climate space is the largest under the future climate data. The index data when the crop yield is the largest is also the adaptability that the crop needs to improve. According to the future climate data, the corresponding index data is calculated to determine whether the future crop yield in the set area is affected by climate change.

[0054] In S3, a new SCS is established as the third safe climate space based on the improvement of crop adaptability in S2, and the distribution of crop planting area is optimized by genetic algorithm to increase the yield of crops in the third safe climate space. That is, the crop planting area in the third safe climate space is optimized by genetic algorithm; the optimization program is edited using matlab, including the operations of generating populations, selection, crossover and mutation, to further improve the yield of crops in the third safe climate space, thereby reducing the impact of future climate change on crop yields. In the optimization process of this step, the parameter restrictions of the genetic algorithm include the change in irrigation and planting area, which does not have a significant impact on the local planting structure and water use structure.

[0055] This application proposes to improve the adaptability of crops to changes in temperature and precipitation, thereby expanding the original safe climate space so that more future crop yields are located in the newly formed safe climate space. The distribution of crops is then optimized based on genetic algorithms, thereby increasing future crop yields in the safe climate space and reducing the impact of future climate change on crop yields, thereby ensuring future food production.

[0056] Example 1

[0057] like Figure 1As shown, this embodiment proposes a method for incorporating future crop yields into a safe climate space. In this embodiment, the base period uses the climate data of GSWP3 in Table 1, and the future period uses the climate data simulated by five climate models of CMIP6. The yield data is the crop yield data simulated by crop models (EPIC-IIASA, LPJmL, pDSSAT, and pepic), where crops include corn, soybeans, rice and wheat. In addition, the region is set as China in this embodiment, and the source of the climate data in this embodiment is shown in Table 1.

[0058] Table 1 Sources of climate data

[0059]

[0060] Based on the climate data of the reference period, the index data required for constructing the first safe climate space can be calculated. The index data include annual precipitation, biological temperature and dryness. The calculation formula of annual precipitation in this embodiment is as follows:

[0061]

[0062] Where P is annual precipitation, mm; p is daily precipitation, mm; days is the number of days in a year, days.

[0063] The biological temperature is calculated as follows:

[0064]

[0065] Where bioT is the biological temperature, °C; t is the average daily temperature less than 35 °C and greater than 0 °C, °C; days is the number of days in a year, days.

[0066] The calculation formula of dryness is as follows:

[0067]

[0068] Where R is dryness; EVP is potential evaporation, mm; P is annual precipitation, mm;

[0069] The calculation formula for potential evaporation is as follows:

[0070] EVP=58.93×bioT

[0071] Where bioT is the biological temperature, °C.

[0072] China's first safe climate space can be obtained by combining annual precipitation, biological temperature and dryness with yield data in the base period.

[0073] Based on the climate data and crop yield data of the future period, the proportion of crop yields that exceed the first safe climate space in the future period can be calculated. By changing the adaptability of crops to temperature and precipitation, the first safe climate space will move, and it will be merged with the original first safe climate space to obtain a new, expanded second safe climate space. In the optimization process, for example, the temperature change range is limited to 0-3℃, with a change of 0.1℃ each time; the precipitation change range is limited to -100-100mm, with a change of 10mm each time. There are 600 adaptation plans in total, and the plan with the largest crop yield in the second safe climate space is selected, such as Figure 2 As shown. Figure 2 The blue part in the middle is SCS, and other colors represent the number of models that are not in SCS. The number represents how many models there are at that point, i.e. green - 1, yellow - 2, orange - 3, red - 4, black - 5. For example, if it is green, it means that there is a model simulation result at that point. Different models will have their own range beyond the SCS, and this color represents the number of overlaps.

[0074] It should be noted that this embodiment only proposes the changes in temperature and precipitation that crops need to adapt to, and the final temperature and precipitation changes are shown in Table 2.

[0075] Table 2 Temperature and precipitation values ​​that crops need to adapt to

[0076]

[0077] On the basis of optimizing crop adaptability, the distribution of crop planting area in the future is optimized by genetic algorithm. Figure 3 In addition, Figure 4 As shown, the crop planting area in the third safe climate space is optimized by genetic algorithm; the optimization program is edited by matlab, including the operations of generating population, selection, crossover and mutation, which minimize the impact on the local planting structure and water use structure during the optimization process. For the total planting area, the total planting area before and after optimization is required to remain unchanged. The area change of each crop in each region cannot exceed 20% of its original planting area. If a certain crop has not been planted in the region, the increase in the area of ​​this unplanted crop cannot exceed 20% of the area of ​​all local crops. For the water demand of crops, it is required that the increase in the optimized water demand in each region cannot exceed 30% of the local runoff.

[0078] This embodiment optimizes the adaptability and distribution of crops so that 99% of future crop yields are within the third safe climate space, thereby mitigating the impact of future climate on China's crop yields.

[0079] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" is two or more.

[0080] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0081] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0082] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in the field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for integrating future crop yields into a safe climate space, characterized in that include Calculate index data based on climate data of a base period in a set region; construct a first safe climate space by combining the index data with crop yield data of the base period; The first safe climate space is moved by adjusting the climate data, and its moving range is combined with the first safe climate space to form a second safe climate space; based on the climate data of the future period, the optimal indicator data when the crop yield is the largest is selected; A third safe climate space for crops is constructed under the optimal index data for the crops, and the distribution of crop planting areas is optimized to increase the yield of the crops in the third safe climate space; wherein the optimization of crop planting area distribution is performed by a genetic algorithm, and the optimization program is edited using matlab, including operations of generating populations, selection, crossover and mutation; The parameter restrictions of the genetic algorithm include the change of irrigation and planting area.

2. The method according to claim 1, characterized in that The climate data include temperature and precipitation data; wherein the indicator data include annual precipitation, biological temperature and dryness.

3. The method according to claim 2, characterized in that The calculation formula for the annual precipitation is as follows: Where P is annual precipitation, mm; p is daily precipitation, mm; days is the number of days in a year, days.

4. The method according to claim 2, characterized in that: The calculation formula of the biological temperature is as follows: Where bioT is the biological temperature, °C; t is the average daily temperature less than 35 °C and greater than 0 °C, °C; days is the number of days in a year, days.

5. The method according to claim 2, characterized in that: The calculation formula of the dryness is as follows: Where R is dryness; EVP is potential evaporation, mm; P is annual precipitation, mm; The calculation formula for potential evaporation is as follows: EVP=58.93×bioT Where bioT is the biological temperature, °C.

6. The method according to claim 1, characterized in that The corresponding indicator data is calculated based on the climate data of the future period to determine whether the future crop yield in the set area is affected by climate change.

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