Characteristic meteorological index-based fruit tree ecological suitable region division method

Abstract

The invention discloses a fruit tree ecological suitable area zoning method based on characteristic meteorological indexes, and belongs to the technical field of agriculture, and the method comprises the steps: obtaining day-by-day meteorological index data of a target fruit tree planting area over the years; sampling a plurality of representative producing areas in the target fruit tree planting area in the fruit full ripening stage to obtain fruit samples; the method comprises the following steps: measuring quality indexes of fruit samples to obtain quality index data; performing statistical analysis on the quality index data and the climate factors of each region by applying a statistical method to obtain key climate factors influencing the fruit quality index; by constructing an ecological suitability index, a target fruit tree planting area is divided into different levels of ecological suitable areas, namely a suitable area, a secondary suitable area and an unsuitable area.

Description

Technical Field

[0001] The invention relates to the technical field of agriculture, and in particular to a method for zoning ecologically suitable areas for fruit trees based on characteristic meteorological indicators. Background Art

[0002] Fruit tree planting occupies an important position in agricultural production. The quality and yield of its fruits are closely related to the ecological environment, especially the climate conditions, of the planting area. With the development of agricultural modernization and the continuous improvement of the requirements for fruit quality, accurately determining the ecologically suitable areas for fruit trees has become the key to optimizing the layout and sustainable development of the fruit tree planting industry.

[0003] Traditional selection of fruit tree planting areas is mostly based on empirical judgment or rough geographical environment assessment, lacking detailed and quantitative analysis of climatic factors, which in turn affects the growth and development of fruit trees and the quality of fruit. In addition, existing regional zoning methods make it difficult to systematically integrate climatic factors with fruit quality indicators, and are unable to accurately quantify the suitability of different regions for fruit tree planting, which easily leads to waste of resources.

[0004] Therefore, there is an urgent need for a method of zoning ecological suitable areas for fruit trees based on characteristic meteorological indicators to provide a scientific basis for the rational planting of jujube trees. Summary of the invention

[0005] The purpose of the present invention is to provide a method for zoning ecological suitable areas for fruit trees based on characteristic meteorological indicators to overcome the shortcomings of the background technology.

[0006] The purpose of the present invention can be achieved by the following technical scheme: A method for zoning ecological suitable areas for fruit trees based on characteristic meteorological indicators, comprising:

[0007] Step 1: Obtain daily meteorological index data of the target fruit tree planting area over the years;

[0008] Step 2: During the fruit ripening period, sampling is conducted in multiple representative production areas within the target fruit tree planting area to obtain fruit samples;

[0009] Step 3, measuring the quality index of the fruit sample to obtain quality index data;

[0010] Step 4: Use statistical methods to conduct statistical analysis on the quality index data and climate factors in each region to obtain the key climate factors that affect the fruit quality index;

[0011] Step 5: Construct an ecological suitability index and divide the target fruit tree planting area into different levels of ecological suitability zones, namely suitable zones, sub-suitable zones, and unsuitable zones.

[0012] Preferably, in step 1, the daily meteorological index data over the years include climate factors of various regions.

[0013] Preferably, in step 2, a representative orchard is selected in each production area, and fruits are randomly picked from each fruit tree in five directions using the central five-point method; the collected jujube fruits are refrigerated and brought back to the laboratory, and placed in a -4°C refrigerator for standby use; wherein the five directions are east, west, south, north, and center.

[0014] Preferably, the quality indicators include phenotypic traits and intrinsic quality; the phenotypic traits include transverse diameter, longitudinal diameter and single fruit weight; the intrinsic quality includes protein content, soluble sugar content, vitamin C content, titratable acid content, flavonoids and total phenol content.

[0015] Preferably, in step 3, determining the quality index of the fruit sample specifically comprises: using a vernier caliper to measure the maximum width and maximum length of the fruit to obtain the horizontal diameter and the vertical diameter; using an electronic scale to weigh the mass of a single fruit to obtain the mass of a single fruit;

[0016] The protein content was determined by Coomassie brilliant blue colorimetry, and the soluble sugar content was determined by anthrone colorimetry.

[0017] The vitamin C content was determined by 2,4-dinitrophenylhydrazine colorimetry; the titratable acid content was determined by potentiometric titration;

[0018] The flavonoid and total phenolic contents were determined by hydrochloric acid methanol colorimetry.

[0019] Preferably, in step 4, based on the daily meteorological index data of previous years, Origin2018 software is used to draw a map, and the significance of the differences in climate factors in various regions is analyzed from four aspects: average temperature, sunshine duration, precipitation, and wind speed;

[0020] One-way ANOVA was performed using SPSSS tatistics 20.0 to obtain the differences in fruit phenotypic shape and intrinsic quality among regions and their coefficients of variation;

[0021] The key climatic factors affecting the quality of jujube fruit were screened out through principal component analysis; among them, the key climatic factors include annual average temperature, active accumulated temperature of not less than 10℃, average temperature of the coldest month, annual sunshine duration, average temperature during flowering period, average temperature during fruit hardening period, average temperature during maturity period, and average wind speed during flowering period.

[0022] Preferably, the correlation between the jujube fruit quality index and the key climatic factors is subjected to Spearman correlation analysis;

[0023] The specific calculation formula is as follows:

[0024] For two variables X and Y, sort the observed values ​​of the two variables from small to large to obtain the sorted variables X' and Y'; if there are multiple identical observed values, take their average value for sorting;

[0025] For each observation, calculate the difference d between its ranks after sorting X and Y i ;

[0026] By formula Calculate the Spearman correlation coefficient; where r s represents the Spearman correlation coefficient; d m represents the mth level difference after sorting; m = 1, 2, .., M, where M is the number of observations;

[0027] Select one of the jujube fruit quality indicators as a variable, and select one of the key climate factors as another variable, substitute them into the above formula for calculation, and obtain the Spearman correlation coefficient between each jujube fruit quality indicator and each key climate factor;

[0028] Analyze and judge the relationship between jujube fruit quality indicators and key climate factors; if |r s | is larger, the stronger the correlation between the two variables; if r s >0, indicating positive correlation; if r s <0, indicating negative correlation;

[0029] At the same time, the significance level P is used to determine whether the correlation is significant; among them, P<0.05 is a significant level, and P<0.01 is an extremely significant level.

[0030] Preferably, in step 5, the analytic hierarchy process AHP is used to determine the weight of each characteristic meteorological index, and the specific implementation content includes:

[0031] According to the results of principal component analysis and correlation analysis, the relative importance of any two characteristic meteorological indicators on fruit tree quality indicators was compared;

[0032] Construct judgment matrix A = (a ij ), where a ij Indicates the importance of characteristic meteorological index i relative to characteristic meteorological index j; i and j are both positive integers, with values ​​of 1, 2, ..., n, and n is the total number of characteristic meteorological indicators;

[0033] Use Python's NumPy library to calculate the maximum eigenvalue λ of the judgment matrix A max and its corresponding eigenvector w=(w 1 ,w 2 ,...,w n );

[0034] The eigenvector w=(w 1 ,w 2 ,...,w n) After normalization, the weight vector of each characteristic meteorological index is obtained;

[0035] The calculation formula is as follows:

[0036]

[0037] In the formula, is the i-th component of the eigenvector w; w i is the weight vector of the i-th characteristic meteorological index;

[0038] By formula Calculate the consistency index; where CI is the consistency index;

[0039] The average random consistency index RI is obtained by consulting the standard table related to the hierarchical analysis method. Calculate the consistency ratio and perform consistency test; where CR is the consistency ratio;

[0040] When CR<1, the weight vector of characteristic meteorological indicators is considered reasonable; otherwise, the judgment matrix needs to be readjusted until the consistency requirements are met.

[0041] Preferably, in step 5, for a certain fruit tree planting area, the observed values ​​of each characteristic meteorological index are obtained, and the ecological suitability index of the area is calculated;

[0042] The calculation formula is:

[0043]

[0044] In the formula, w i is the weight vector of each characteristic meteorological index in the region; D i is the observed value of each characteristic meteorological index in the region; SI is the ecological suitability index of the region;

[0045] Based on the calculated ecological suitability index of each region, the ecological suitable areas for fruit trees are divided;

[0046] Areas with an ecological suitability index of 0.7 < SI ≤ 1 were classified as suitable areas;

[0047] Areas with an ecological suitability index of 0.4 < SI ≤ 0.7 were classified as sub-suitable areas;

[0048] Areas with an ecological suitability index of SI ≤ 0.4 were classified as unsuitable areas.

[0049] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0050] The present invention obtains the daily meteorological index data of the target fruit tree planting area over the years, comprehensively and meticulously reflects the long-term climate changes in the target area, and lays a data foundation for the subsequent analysis of the relationship between fruit quality and climate;

[0051] The present invention collects samples from multiple representative production areas to ensure that the collected fruit samples can truly reflect the overall fruit situation in the production area, thereby ensuring the accuracy of subsequent quality measurement results; the collected fruit samples are tested and analyzed one by one, which is conducive to obtaining complete and accurate quality index data;

[0052] The present invention uses methods such as variance analysis, principal component analysis, and Spearman correlation analysis to explore the differences in jujube fruit quality in different regions and their relationship with climatic factors, and screens the key climatic factors that are most closely related to the formation of jujube fruit quality, which is conducive to providing a theoretical basis for the zoning of fruit ecological suitable areas;

[0053] The present invention divides the target fruit tree planting area into ecological suitability zones of different levels by constructing an ecological suitability index; through scientific zoning and management, the yield and quality of fruit trees are improved, and the healthy development of the jujube industry is promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described below in conjunction with the accompanying drawings.

[0055] Figure 1 The present invention provides a flowchart of the method for zoning ecologically suitable areas for fruit trees based on characteristic meteorological indicators. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0057] See also Figure 1 The method for zoning the ecological suitable area for fruit trees based on characteristic meteorological indicators includes the following steps:

[0058] Step 1: Obtain daily meteorological index data of the target fruit tree planting area over the years;

[0059] Among them, the daily meteorological index data of previous years include climate factors of various regions; the target fruit tree planting areas are different areas in southern Xinjiang;

[0060] Climate factors include but are not limited to annual average temperature, active accumulated temperature not less than 10℃, annual sunshine duration, average temperature of the coldest month, average temperature during flowering, average temperature during fruit hardening period, average temperature during maturity, average wind speed during flowering, average temperature during the dog days, and average temperature during the coldest days;

[0061] Step 2: During the fruit ripening period, sampling is conducted in multiple representative production areas within the target fruit tree planting area to obtain fruit samples;

[0062] Furthermore, representative orchards were selected in each production area, and fruit trees with basically consistent tree vigor and no pests and diseases were selected in the orchards; the central five-point method was used to randomly pick fruits from each fruit tree in five directions, requiring that the size and maturity were consistent and that there were no diseases or damage; the collected jujube fruits were refrigerated and brought back to the laboratory, and placed in a -4℃ refrigerator for standby use; the five directions were east, west, south, north, and center;

[0063] Step 3, measuring the quality index of the fruit sample to obtain quality index data;

[0064] Among them, quality indicators include phenotypic traits and intrinsic quality; phenotypic traits include transverse diameter, longitudinal diameter and single fruit weight; intrinsic quality includes protein content, soluble sugar content, vitamin C content, titratable acid content, flavonoids and total phenol content;

[0065] Furthermore, the maximum width and maximum length of the fruit were measured using a vernier caliper to obtain the horizontal and vertical diameters;

[0066] Use an electronic scale to weigh the mass of a single fruit to obtain the mass of a single fruit;

[0067] The protein content is determined by the Coomassie brilliant blue colorimetric method, wherein the protein content is quantified based on the color change after the protein binds to the Coomassie brilliant blue dye;

[0068] The soluble sugar content is determined by anthrone colorimetry, wherein sugar substances react with anthrone under acidic conditions to generate colored compounds, and the content of the compounds is determined by the colorimetric method;

[0069] The vitamin C content was determined by 2,4-dinitrophenylhydrazine colorimetry; wherein, based on the reaction of vitamin C with 2,4-dinitrophenylhydrazine to generate a red product, the content of the product was determined by colorimetry;

[0070] The titratable acid content is determined by potentiometric titration, wherein the acid content is determined by measuring the change in potential during the titration process;

[0071] The flavonoid content and total phenol content were determined by the hydrochloric acid methanol colorimetric method; wherein, based on the reaction of flavonoids and total phenols with methanol under acidic conditions to generate colored compounds, the content of the compounds was determined by the colorimetric method;

[0072] Step 4: Use statistical methods to conduct statistical analysis on the quality index data and climate factors in each region to obtain key climate factors that affect fruit quality indicators, namely characteristic meteorological indicators;

[0073] Furthermore, based on the daily meteorological index data of previous years, Origin 2018 software was used to draw maps, and the significance of the differences in climate factors in various regions was analyzed from four aspects: average temperature, sunshine duration, precipitation, and wind speed.

[0074] For example, the difference significance of climate factors in each region is analyzed from the average temperature: the average temperature in Moyu area is the highest, which is 13.53℃, which is significantly different from other regions;

[0075] The average temperature in Cele was 12.62℃, which was significantly different from that in Aksu, Awati, Korla, Ruoqiang, Qiemo, Maigaiti, Shache, and Moyu, but not significantly different from that in Shaya and Zepu.

[0076] The average temperatures in Shaya and Zepu were 12.37 and 12.31°C respectively, which were significantly different from those in Aksu, Awati, Qiemo and Moyu, but not significantly different from other regions.

[0077] The average temperatures in Ruoqiang, Shache, Korla and Maigaiti were 12.19, 12.15, 12.04 and 12.02℃ respectively, which were significantly different from those in Aksu, Awati, Qiemo, Moyu and Cele, but not significantly different from other regions.

[0078] The average temperatures in Awati, Aksu, and Qiemo were 10.93, 10.88, and 10.88°C, respectively, which were significantly different from those in other regions.

[0079] Furthermore, one-way ANOVA was performed using SPSSS tatistics 20.0 to obtain the differences in fruit phenotypic shape and intrinsic quality among regions and their coefficients of variation;

[0080] Exemplarily, as shown in Table 1 and Table 2, the means and standard deviations corresponding to the quality indicators of jujube fruits from different production areas in southern Xinjiang are obtained, and the coefficient of variation corresponding to each quality indicator is calculated;

[0081] By formula Calculate the coefficient of variation of the transverse diameter of the jujube fruit; where CV 1 represents the coefficient of variation of the transverse diameter, μ 1 represents the mean value of the horizontal diameter, s 1 represents the standard deviation of the horizontal diameter;

[0082] According to the above method, the coefficient of variation corresponding to each quality index was obtained and analyzed respectively. As shown in Table 1, there are differences in the phenotypic traits of jujube fruits from different origins in southern Xinjiang, and the magnitude of the differences in each index is different. Among the phenotypic traits, the coefficient of variation of single fruit weight is the largest, followed by the longitudinal diameter, and the coefficient of variation of the transverse diameter is the smallest, with the corresponding coefficients of variation being 21.23%, 7.20%, and 7.19%, respectively. The results show that the transverse and longitudinal diameters of jujube fruits have high genetic stability, that is, the jujube fruits from different origins in southern Xinjiang have good consistency in transverse and longitudinal diameters.

[0083] As shown in Table 2, there are differences in the intrinsic quality indicators of jujube fruits from different production areas in southern Xinjiang. Among the intrinsic quality indicators, the largest coefficient of variation is the titratable acid content, indicating that its dispersion is large and its genetic stability is poor. The order of variation coefficient from high to low is protein content, vitamin C content, total phenol content, and flavonoid content. The coefficient of variation of soluble sugar content is the smallest, indicating that the genetic stability of soluble sugar content in jujube fruits is good between different ecological regions.

[0084] Table 1 Phenotypic traits of jujube fruits from different origins in southern Xinjiang

[0085]

[0086] Table 2 Intrinsic quality of jujubes from different production areas in southern Xinjiang

[0087]

[0088]

[0089] Furthermore, the key climatic factors affecting the quality of jujube fruit were screened out through principal component analysis;

[0090] Each climate factor was standardized so that the mean of each variable was 0 and the standard deviation was 1;

[0091] The SPSSS software was used to calculate the correlation coefficient matrix of the standardized data and solve the eigenvalue and eigenvector of the matrix; the eigenvalue represents the variance of the principal component; the eigenvector represents the linear combination of the principal component on the original variable;

[0092] The number of principal components to be retained is determined according to the cumulative contribution rate, the eigenvalues ​​are arranged in descending order, and the first principal components whose cumulative contribution rates reach a preset ratio are selected; wherein, in this embodiment, the preset ratio is set to 80%, so the first 4 principal components are selected, as shown in Table 3;

[0093] Table 3 Results of principal component analysis of the effects of climate factors in southern Xinjiang on jujube fruit quality

[0094]

[0095]

[0096] Observe the eigenvalues ​​and cumulative contribution rates in Table 3, analyze the absolute values ​​of the eigenvectors of each climate factor in each principal component, select the climate factor that contributes most to the principal component, and record it as the key climate factor; the absolute value of the eigenvector of each climate factor can reflect its importance in the principal component;

[0097] The eigenvalue of the first principal component is 3.840, and the variance contribution rate is 38.48%; the eigenvalue of the second principal component is 2.076, and the variance contribution rate is 20.75%;

[0098] By analogy, the cumulative contribution rate reaches 81.79%, which is greater than the preset ratio, indicating that the selection of the first four principal components can reflect the information of climate factors;

[0099] In the first principal component, the absolute values ​​of the eigenvectors of the active accumulated temperature not less than 10℃ and the average temperature during the fruit hard core period are relatively large, which are 0.927 and 0.878 respectively, indicating that the first principal component can be classified as a heat factor;

[0100] In the second principal component, the absolute values ​​of the eigenvectors of the average temperature of the coldest month and the average temperature of the coldest days are larger, which means that the second principal component mainly reflects whether the jujube trees can safely survive the winter.

[0101] In the third principal component, the absolute value of the eigenvector of annual sunshine duration is relatively large, which mainly reflects the lighting conditions, indicating that the third principal component can be classified as a lighting factor;

[0102] In the fourth principal component, the absolute value of the eigenvector of the average wind speed during the flowering period is relatively large, which mainly reflects whether the fruit can be set normally, indicating that the fourth principal component can be classified as a wind speed factor;

[0103] Based on the analysis of the principal components, the key climate factors affecting the quality of jujube fruit are annual average temperature, active accumulated temperature not less than 10℃, average temperature of the coldest month, annual sunshine duration, average temperature during flowering, average temperature during the fruit hard core period, average temperature during the ripening period, and average wind speed during flowering.

[0104] Furthermore, Spearman correlation analysis was performed on the correlation between jujube fruit quality indices and key climatic factors;

[0105] Spearman correlation analysis is used to measure the monotonic relationship between two variables. The specific calculation formula is as follows:

[0106] For two variables X and Y, sort the observed values ​​of the two variables from small to large to obtain the sorted variables X' and Y'; if there are multiple identical observed values, take their average value for sorting;

[0107] For each observation, calculate the difference d between its ranks after sorting X and Y i ;

[0108] By formula Calculate the Spearman correlation coefficient; where r s represents the Spearman correlation coefficient; d m represents the mth level difference after sorting; m = 1, 2, .., M, where M is the number of observations;

[0109] Select one of the jujube fruit quality indicators as a variable, and select one of the key climate factors as another variable, substitute them into the above formula for calculation, and obtain the Spearman correlation coefficient between each jujube fruit quality indicator and each key climate factor;

[0110] Analyze and judge the relationship between jujube fruit quality indicators and key climate factors; if |r s | is larger, the stronger the correlation between the two variables; if r s >0, indicating positive correlation; if r s <0, indicating negative correlation;

[0111] At the same time, the significance level P given in Table 4 is used to determine whether the correlation is significant; P < 0.05 is a significant level, and P < 0.01 is an extremely significant level;

[0112] The phenotypic traits of jujube fruit were significantly positively correlated with the annual sunshine duration; the soluble sugar content was significantly positively correlated with the annual average temperature, the active accumulated temperature of not less than 10℃, the annual sunshine duration, the average temperature during the fruit hard core period, and the average temperature during the ripening period; among them, the soluble sugar content was extremely significantly positively correlated with the temperature during the fruit hard core period and the temperature during the ripening period;

[0113] The titratable acid content was significantly negatively correlated with the average temperature of the coldest month;

[0114] The protein content was significantly negatively correlated with the annual average temperature, and significantly positively correlated with the annual sunshine duration and the average temperature during the fruit hardening period.

[0115] Vitamin C content was significantly positively correlated with annual average temperature, active accumulated temperature not less than 10℃, annual sunshine duration, average temperature during flowering period and average temperature during fruit hardening period.

[0116] The flavonoid content was significantly positively correlated with the climate factor annual sunshine duration;

[0117] The total phenol content was significantly positively correlated with the active accumulated temperature of not less than 10℃ and the annual sunshine duration.

[0118] Table 4 Correlation between quality indexes of jujube fruit and climate factors in southern Xinjiang

[0119]

[0120] In Table 4, X1 represents the annual average temperature, X2 represents the active accumulated temperature of not less than 10°C, X3 represents the annual sunshine duration, X4 represents the average temperature of the coldest month, X5 represents the average temperature during the flowering period, X6 represents the average temperature during the fruit hard core period, X7 represents the average temperature during the ripening period, and X8 represents the average wind speed during the flowering period;

[0121] "*" indicates that the correlation reached a significant level (P < 0.05), and "**" indicates that the correlation reached an extremely significant level (P < 0.01);

[0122] It can be seen from Table 4 that the external quality of jujube fruit is mainly affected by light, while the internal quality is affected by the comprehensive influence of heat and light, including meteorological indicators such as annual average temperature, active accumulated temperature of not less than 10℃, flowering temperature, fruit hard core temperature, maturity temperature and sunshine duration.

[0123] Step 5: Construct an ecological suitability index and divide the target fruit tree planting area into different levels of ecological suitability zones, namely suitable zones, sub-suitable zones and unsuitable zones;

[0124] Furthermore, the analytic hierarchy process (AHP) is used to determine the weights of each characteristic meteorological index. The specific implementation contents include:

[0125] According to the results of principal component analysis and correlation analysis, the relative importance of any two characteristic meteorological indicators on fruit tree quality indicators was compared;

[0126] Construct judgment matrix A = (a ij ), where a ij It indicates the importance of characteristic meteorological index i relative to characteristic meteorological index j, and its specific value is determined by the 1-9 scaling method; i and j are both positive integers, with values ​​of 1, 2, ..., n, and n is the total number of characteristic meteorological indexes;

[0127] Use Python's NumPy library to calculate the maximum eigenvalue λ of the judgment matrix A max and its corresponding eigenvector w=(w 1 ,w 2 ,...,w n );

[0128] The eigenvector w=(w 1 ,w 2 ,...,w n ) After normalization, the weight vector of each characteristic meteorological index is obtained;

[0129] The calculation formula is as follows:

[0130]

[0131] In the formula, is the i-th component of the eigenvector w; w i is the weight vector of the i-th characteristic meteorological index;

[0132] By formula Calculate the consistency index; where CI is the consistency index;

[0133] The average random consistency index RI is obtained by consulting the standard table related to the hierarchical analysis method. Calculate the consistency ratio and perform consistency test; where CR is the consistency ratio;

[0134] When CR<1, the weight vector of characteristic meteorological indicators is considered reasonable; otherwise, the judgment matrix needs to be readjusted until the consistency requirement is met;

[0135] For a certain fruit tree planting area, obtain the observed values ​​of its characteristic meteorological indicators and calculate the ecological suitability index of the area;

[0136] The calculation formula is:

[0137]

[0138] In the formula, w i is the weight vector of each characteristic meteorological index in the region; D i is the observed value of each characteristic meteorological index in the region; SI is the ecological suitability index of the region;

[0139] Based on the calculated ecological suitability index of each region, the ecological suitable areas for fruit trees are divided;

[0140] Areas with an ecological suitability index of 0.7 < SI ≤ 1 were classified as suitable areas;

[0141] Areas with an ecological suitability index of 0.4 < SI ≤ 0.7 were classified as sub-suitable areas;

[0142] Areas with an ecological suitability index of SI ≤ 0.4 were classified as unsuitable areas.

[0143] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0144] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0146] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for zoning suitable areas for fruit trees based on characteristic meteorological indicators, characterized in that: include: Step 1: Obtain daily meteorological index data of the target fruit tree planting area over the years; Step 2: During the fruit ripening period, sampling is conducted in multiple representative production areas within the target fruit tree planting area to obtain fruit samples; Step 3, measuring the quality index of the fruit sample to obtain quality index data; Step 4: Use statistical methods to conduct statistical analysis on the quality index data and climate factors in each region to obtain the key climate factors that affect the fruit quality index; Step 5: Construct an ecological suitability index and divide the target fruit tree planting area into different levels of ecological suitability zones, namely suitable zones, sub-suitable zones, and unsuitable zones.

2. The method for zoning suitable fruit tree ecological areas based on characteristic meteorological indicators according to claim 1, characterized in that: In step 1, the daily meteorological index data of previous years include climate factors of each region.

3. The method for zoning suitable fruit tree ecological areas based on characteristic meteorological indicators according to claim 1, characterized in that: In step 2, representative orchards were selected in each production area, and fruits were randomly picked from five directions on each fruit tree using the central five-point method; the collected jujube fruits were refrigerated and brought back to the laboratory and placed in a -4°C refrigerator for standby use; the five directions were east, west, south, north, and center.

4. The method for zoning suitable fruit tree ecological areas based on characteristic meteorological indicators according to claim 3, characterized in that: Quality indicators include phenotypic traits and intrinsic quality; phenotypic traits include transverse diameter, longitudinal diameter and single fruit weight; intrinsic quality includes protein content, soluble sugar content, vitamin C content, titratable acid content, flavonoids and total phenolic content.

5. The method for zoning ecological suitable areas for fruit trees based on characteristic meteorological indicators according to claim 4, characterized in that: In step 3, the quality index determination of the fruit sample specifically includes: using a vernier caliper to measure the maximum width and maximum length of the fruit to obtain the horizontal diameter and the vertical diameter; using an electronic scale to weigh the mass of a single fruit to obtain the mass of a single fruit; The protein content was determined by Coomassie brilliant blue colorimetry, and the soluble sugar content was determined by anthrone colorimetry. The vitamin C content was determined by 2,4-dinitrophenylhydrazine colorimetry; the titratable acid content was determined by potentiometric titration; The flavonoid and total phenolic contents were determined by hydrochloric acid methanol colorimetry.

6. The method for zoning ecological suitable areas for fruit trees based on characteristic meteorological indicators according to claim 5, characterized in that: In step 4, based on the daily meteorological index data of previous years, Origin 2018 software was used to draw a map to analyze the significance of the differences in climate factors in each region from four aspects: average temperature, sunshine duration, precipitation, and wind speed; One-way ANOVA was performed using SPSSS tatistics 20.0 to obtain the differences in fruit phenotypic shape and intrinsic quality among regions and their coefficients of variation; The key climatic factors affecting the quality of jujube fruit were screened out through principal component analysis; among them, the key climatic factors include annual average temperature, active accumulated temperature of not less than 10℃, average temperature of the coldest month, annual sunshine duration, average temperature during flowering period, average temperature during fruit hardening period, average temperature during maturity period, and average wind speed during flowering period.

7. The method for zoning suitable areas for fruit trees based on characteristic meteorological indicators according to claim 6, characterized in that: Spearman correlation analysis was performed on the correlation between jujube fruit quality indices and key climatic factors; The specific calculation formula is as follows: For two variables X and Y, sort the observed values ​​of the two variables from small to large to obtain the sorted variables X' and Y'; if there are multiple identical observed values, take their average value for sorting; For each observation, calculate the difference d between its ranks after sorting X and Y i ; By formula Calculate the Spearman correlation coefficient; where r s represents the Spearman correlation coefficient; d m represents the mth level difference after sorting; m = 1, 2, .., M, where M is the number of observations; Select one of the jujube fruit quality indicators as a variable, and select one of the key climate factors as another variable, substitute them into the above formula for calculation, and obtain the Spearman correlation coefficient between each jujube fruit quality indicator and each key climate factor; Analyze and judge the relationship between jujube fruit quality indicators and key climate factors; if |r s | is larger, the stronger the correlation between the two variables; if r s >0, indicating positive correlation; if r s <0, indicating negative correlation; At the same time, the significance level P is used to determine whether the correlation is significant; among them, P<0.05 is a significant level, and P<0.01 is an extremely significant level.

8. The method for zoning ecological suitable areas for fruit trees based on characteristic meteorological indicators according to claim 7, characterized in that: In step 5, the analytic hierarchy process (AHP) is used to determine the weights of each characteristic meteorological index. The specific implementation contents include: According to the results of principal component analysis and correlation analysis, the relative importance of any two characteristic meteorological indicators on fruit tree quality indicators was compared; Construct judgment matrix A = (a ij ), where a ij Indicates the importance of characteristic meteorological index i relative to characteristic meteorological index j; i and j are both positive integers, with values ​​of 1, 2, ..., n, and n is the total number of characteristic meteorological indicators; Use Python's NumPy library to calculate the maximum eigenvalue λ of the judgment matrix A max and its corresponding eigenvector w=(w1,w2,...,w n ); The eigenvector w=(w1,w2,...,w n ) After normalization, the weight vector of each characteristic meteorological index is obtained; The calculation formula is as follows: In the formula, is the i-th component of the eigenvector w; w i is the weight vector of the i-th characteristic meteorological index; By formula Calculate the consistency index; where CI is the consistency index; The average random consistency index RI is obtained by consulting the standard table related to the hierarchical analysis method. Calculate the consistency ratio and perform consistency test; where CR is the consistency ratio; When CR<1, the weight vector of characteristic meteorological indicators is considered reasonable; otherwise, the judgment matrix needs to be readjusted until the consistency requirements are met.

9. The method for zoning ecological suitable areas for fruit trees based on characteristic meteorological indicators according to claim 8, characterized in that: In step 5, for a certain fruit tree planting area, the observed values ​​of each characteristic meteorological index are obtained, and the ecological suitability index of the area is calculated; The calculation formula is: In the formula, w i is the weight vector of each characteristic meteorological index in the region; D i is the observed value of each characteristic meteorological index in the region; SI is the ecological suitability index of the region; Based on the calculated ecological suitability index of each region, the ecological suitable areas for fruit trees are divided; Areas with an ecological suitability index of 0.7 < SI ≤ 1 were classified as suitable areas; Areas with an ecological suitability index of 0.4 < SI ≤ 0.7 were classified as sub-suitable areas; Areas with an ecological suitability index of SI ≤ 0.4 were classified as unsuitable areas.

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