Technical method for apple climate suitability planting regionalization in western loess plateau

Abstract

The application discloses a loess plateau western apple climate suitability planting zoning method and belongs to the technical field of agricultural climate zoning and GIS spatial analysis. The method selects seven climate factors such as annual average temperature and annual precipitation and three terrain factors such as altitude, slope and slope direction, and a total of ten core indexes; adopts a trapezoidal fuzzy linear membership function to perform normalization processing on each index; establishes a linear regression model based on latitude, longitude and altitude and combines inverse distance weighted interpolation (IDW) residual correction to realize high-resolution spatialization; uses a weighted comprehensive evaluation method to calculate a comprehensive evaluation value, and after superimposing a slope-slope direction binary mask, divides into four levels of most suitable, suitable, less suitable and unsuitable. The application solves the problems of low resolution, insufficient terrain correction and strong subjectivity of weight in traditional zoning, the coincidence rate of the zoning result and the actual growth of the existing orchard is high, the apple industry planning and layout can be directly guided, and the application has obvious scientificity and practicality.

Description

Technical Field

[0001] This invention relates to the field of agricultural climate zoning technology, specifically to the technology and method of apple climate suitability planting zoning in the western Loess Plateau. Background Technology

[0002] The western Loess Plateau is an important apple-producing area in my country, but existing apple climate zoning methods mostly use site interpolation or low-resolution grids, which are insufficient to reflect the complex terrain with its crisscrossing gullies. Furthermore, incomplete indicator systems, a lack of transparency and openness in membership functions and weights, and insufficient validation data result in low accuracy and poor repeatability of zoning results, failing to meet the needs of precise planning. Overall, existing technologies generally suffer from problems such as coarse resolution (kilometer-level), neglect of micro-topography (slope aspect, gradient), subjective weight determination, and lack of quantitative validation, severely hindering the scientific layout of the apple industry. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for climate-suitable apple planting zones in the western Loess Plateau.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: The technical method for climate suitability zoning of apple cultivation in the western Loess Plateau includes the following steps: S1. Screen 10 core indicators, including annual average temperature, annual precipitation, annual extreme minimum temperature, mid-January average temperature, June-August average temperature, June-August average minimum temperature, number of days with high temperature greater than or equal to 35℃, altitude, slope, and aspect, and determine the most suitable threshold. S2. A linear regression model is established based on latitude, longitude, and altitude, and combined with IDW residual correction to achieve spatialization of each indicator in 100m×100m. S3. Use trapezoidal fuzzy linear membership functions to normalize the raster data of each indicator; S4. Determine the weight of indicators by combining expert scoring with field verification; S5. Construct a weighted comprehensive evaluation model to calculate the comprehensive evaluation value p; S6. Use DEM to extract slope and aspect data, build a binary mask, and overlay it with the p-value; S7. Based on the p-value threshold after superimposing terrain factors, the four levels are divided into most suitable, suitable, second most suitable, and unsuitable.

[0005] Preferably, the trapezoidal fuzzy linear membership functions of each index in step S3 adopt the 10 specific formulas described in claim 1.

[0006] Preferably, in step S2, the linear regression model adopts the specific form Y=75.827604-0.770320×lat-0.300782×lon-0.004826×dem, and the residual correction adopts IDW with a search radius of 15km and a power of 2.

[0007] Preferably, the weights in step S4 are: annual precipitation 0.15, average temperature from June to August 0.15, and the remaining indicators 0.10 each. The weights are determined by three rounds of scoring using the Delphi method by 15 experts and verified in the field.

[0008] Preferably, in step S6, the suitable slope aspect range is 112.5° to 292.5° (east of southeast to west of northwest), and the most suitable slope is 5° to 10°. After generating a binary mask using a 30m DEM, the value is multiplied by the comprehensive evaluation value.

[0009] Preferably, the grading threshold in step S7 is p≥0.83 is most suitable, 0.75≤p<0.83 is suitable, 0.65≤p<0.75 is moderately suitable, and p<0.65 is unsuitable.

[0010] Preferably, the data base is daily observation data from 120 meteorological stations over a continuous 30-year period, combined with precipitation data from the China High Spatiotemporal Resolution Land Surface Model Meteorological Driven Dataset (CMFD), and the DEM is 1:250,000 data from the National Geomatics Center of China.

[0011] Preferably, the verification uses 30 representative sample points to calculate the Kappa coefficient. A Kappa > 0.75 is considered to indicate good consistency.

[0012] Preferably, it also includes Monte Carlo uncertainty analysis, with weights and thresholds perturbed by ±10% and ±5% respectively, and the output standard deviation raster is run 100 times.

[0013] Preferably, it is applicable to apple planting zoning in the western Loess Plateau region at an altitude of 800–2000m with no or limited irrigation.

[0014] The present invention has the following beneficial effects: 1. This invention significantly improves the accuracy and practicality of apple climate suitability zoning through high-resolution spatial simulation. Traditional methods are mostly limited to kilometer-level grids, making it difficult to capture the micro-topographical variations in the gully-ridden Loess Plateau, resulting in blurred zoning boundaries and significant guidance deviations. This method transforms the sparse meteorological station problem into a fine-grid distribution, which not only accurately reflects the gradient effects of altitude and slope on heat and water resources, but also provides pixel-level guidance for orchard site selection, avoiding the blind development of steep, erosion-prone or low-lying, waterlogged areas.

[0015] 2. This invention combines comprehensive climate assessment with topographical rigid masking, fully reflecting the ecological adaptability requirements for apple growth. Apples prefer cool temperatures and are susceptible to drought, but slope aspect affects sunlight and frost, while slope gradient restricts mechanized management. Traditional methods often overlook these factors, leading to inflated suitability zone assessments. This technology ensures that the final zoning is limited to areas with a slope gradient of 5°–10° from southeast to northwest, avoiding frost damage on north-facing slopes and poor drainage on flat land. This overlay mechanism conforms to the principles of apple cultivation, enhancing the biological accuracy of the zoning results.

[0016] 3. This invention can be directly applied to provincial / municipal / county-level apple industry planning, yielding significant economic and social benefits. The four-level zoning map generated by this method supports variety selection and project approval, avoiding inefficient investment. This technology is low-cost and simultaneously promotes ecological protection and reduces soil erosion. This method also extends to other fruit tree zoning, driving the modernization of agriculture and demonstrating the strategic value of technology empowering industry. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a threshold chart of the climate suitability zoning index for apple cultivation in the example; Figure 3 This is a diagram illustrating the specific form of the spatial small-grid extrapolation model for meteorological elements in apple cultivation in the western Loess Plateau, as shown in the example. Figure 4 This is the final weight chart of the climate suitability zoning factors for apple cultivation in this embodiment. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] This embodiment fully discloses the implementation process of the method for zoning apple planting areas based on climate suitability in the western Loess Plateau.

[0020] First, the research data was collected and organized. Daily meteorological data from 1981 to 2010 (30 years) was used, including daily average temperature, maximum temperature, minimum temperature, precipitation, and sunshine duration from approximately 120 meteorological stations. High-resolution precipitation data from the China High Spatiotemporal Resolution Land Surface Model Meteorological Driven Dataset (CMFD) was also incorporated for correction. The basic geographic data used was a 1:250,000 digital elevation model (DEM) provided by the National Geomatics Center of China, resampled from the original 30m resolution to 100m, and overlaid with vector layers of administrative boundaries and river systems. Industry data, including existing apple planting area and yield statistics, was collected for subsequent validation.

[0021] Next, 10 climate suitability zoning indicators for apple cultivation were selected and thresholds were determined. The optimal ranges for each indicator were: annual average temperature (optimal 8–12℃), annual precipitation (500–750 mm), annual extreme minimum temperature (>-21℃), average temperature in mid-January (>-8℃), average temperature from June to August (18–21℃), average minimum temperature from June to August (13–15℃), and number of days with temperatures greater than or equal to 35℃ (≤4 days). Topographic factors included altitude (1000–1700 m), slope (5°–10°), and slope aspect (east-southeast to west-northwest). The thresholds for the climate suitability zoning indicators for apple cultivation are as follows: Figure 2 As shown in the chart.

[0022] Then, spatialization of the indicators is performed. A linear regression model is established using latitude (lat), longitude (lon), and altitude (dem) as independent variables. The specific form of the spatial small-grid extrapolation model for meteorological elements for apple cultivation in the western Loess Plateau is as follows: Figure 3 As shown in the chart.

[0023] After calculating the residuals, the IDW method (search radius 15km, power 2, divided by altitude zone) is used for correction, and finally a raster layer of each index with a resolution of 100m×100m is generated.

[0024] Subsequently, the raster data is normalized. A trapezoidal fuzzy linear membership function is used, with 10 specific formulas as follows: Annual average temperature X1 (°C)

[0025] Annual precipitation x 2 (mm)

[0026] Annual extreme minimum temperature X3 (°C)

[0027] Average temperature from June to August x 4 (°C)

[0028] Average minimum temperature from June to August x 5 (°C)

[0029] Average temperature in mid-January x 6 (°C)

[0030] Number of days with temperatures above 35℃ x 7 (days)

[0031] Altitude x 8 (m)

[0032] Slope x9 (°)

[0033] Slope X 10

[0034] Next, the weights of the indicators were determined and an evaluation model was constructed. Fifteen experts (meteorology, fruit tree cultivation, GIS, zoning, and growers) were invited to conduct three rounds of expert scoring using the Delphi method. The Kendall's W coefficient was greater than 0.75. After passing the consistency test and undergoing fine-tuning based on field validation, the weights of the apple planting climate suitability zoning factors were finally determined. The results are as follows: Figure 3 As shown in the chart.

[0035] Calculate using a weighted comprehensive evaluation model Then multiply by the terrain masking.

[0036] The terrain mask was generated by extracting slope and aspect data from a 30m DEM. After processing with a linear membership function, a 0 / 1 raster was generated using the Reclassify tool. The suitable slope aspect range was 112.5°–292.5° (east of southeast to west of northwest), and the most suitable slope was 5°–10°. The two were merged into a mask named "Suitable for both slope and aspect.tif", which was then multiplied with the p-value to obtain the final comprehensive evaluation value raster.

[0037] Finally, grading, validation, and uncertainty analysis were performed. Based on the p-values ​​after overlaying topographic factors, the regions were divided into most suitable (p≥0.83), suitable (0.75≤p<0.83), moderately suitable (0.65≤p<0.75), and unsuitable (p<0.65). Thirty representative validation points covering Qingyang, Gansu and Guyuan, Ningxia were selected, and data on actual orchard growth, yield, and quality were recorded. The confusion matrix and Kappa coefficient (target >0.75) were calculated. Simultaneously, Monte Carlo uncertainty analysis was conducted, with weights and thresholds perturbed by ±10% and ±5% respectively, running 100 times to generate a standard deviation raster.

[0038] The entire process is implemented in ArcGIS 10.8 or ArcPy environment: First, linear regression and IDW residual correction are performed using Raster Calculator. Then, membership raster is calculated, weighted sum is applied, multiplied by a terrain mask, and reclassified. Finally, a Majority Filter (3×3) is used to denoise and remove areas smaller than 0.01km². 2This method is applicable to apple planting zoning in the western Loess Plateau region at an altitude of 800–2000m with no or limited irrigation.

[0039] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for zoning apple cultivation based on climate suitability in the western Loess Plateau, characterized by: Includes the following steps: S1. Screen 10 core indicators, including annual average temperature, annual precipitation, annual extreme minimum temperature, mid-January average temperature, June-August average temperature, June-August average minimum temperature, number of days with high temperature greater than or equal to 35℃, altitude, slope, and aspect, and determine the most suitable threshold. S2. A linear regression model is established based on latitude, longitude, and altitude, and combined with IDW residual correction to achieve spatialization of each indicator in 100m×100m. S3. Use trapezoidal fuzzy linear membership functions to normalize the raster data of each indicator; S4. Determine the weight of indicators by combining expert scoring with field verification; S5. Construct a weighted comprehensive evaluation model to calculate the comprehensive evaluation value p; S6. Use DEM to extract slope and aspect data, build a binary mask, and overlay it with the p-value; S7. Based on the p-value threshold after superimposing terrain factors, the four levels are divided into most suitable, suitable, second most suitable, and unsuitable.

2. The method according to claim 1, characterized in that, In step S3, the trapezoidal fuzzy linear membership functions of each index adopt the 10 specific formulas described in claim 1.

3. The method according to claim 1, characterized in that, In step S2, the linear regression model adopts the specific form Y=75.827604-0.770320×lat-0.300782×lon-0.004826×dem, and the residual correction adopts IDW with a search radius of 15km and a power of 2.

4. The method according to claim 1, characterized in that, In step S4, the weights are: annual precipitation 0.15, average temperature from June to August 0.15, and the remaining indicators 0.10 each. The weights were determined by three rounds of scoring using the Delphi method by 15 experts and verified in the field.

5. The method according to claim 1, characterized in that, Step S6. The suitable range of slope aspect is 112.5°~292.5° (east of southeast to west of northwest), and the most suitable slope is 5°~10°. After generating a binary mask using a 30m DEM, multiply it with the comprehensive evaluation value.

6. The method according to claim 1, characterized in that, In step S7, the grading thresholds are p≥0.83 as most suitable, 0.75≤p<0.83 as suitable, 0.65≤p<0.75 as moderately suitable, and p<0.65 as unsuitable.

7. The method according to claim 1, characterized in that, The data is based on daily observation data from 120 meteorological stations over the past 30 years, combined with precipitation data from the China High Spatiotemporal Resolution Land Surface Model Meteorological Driven Dataset (CMFD). The DEM is 1:250,000 data from the National Geomatics Center of China.

8. The method according to claim 1, characterized in that, The verification used 30 representative sample points to calculate the Kappa coefficient. Kappa > 0.75 was considered to be of good consistency.

9. The method according to claim 1, characterized in that, It also includes Monte Carlo uncertainty analysis, with weights and thresholds perturbed by ±10% and ±5% respectively, and runs 100 times to output standard deviation raster.

10. The method according to claim 1, characterized in that, This zoning is applicable to apple planting areas in the western Loess Plateau at altitudes of 800–2000m with no or limited irrigation.

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