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Article

A Study on the Suitable Areas for Growing Apricot Kernels in China Based on the MaxEnt Model

by
Runze Sun
1,†,
Guanjie Tong
2,†,
Qing Zhang
1,
Lingjie Xu
1,
Zihan Sang
1 and
Yanhui Li
1,3,*
1
College of Landscape Architecture and Tourism, Hebei Agricultural University, Baoding 071000, China
2
College of Economics and Management, Hebei Agricultural University, Baoding 071000, China
3
National Engineering and Technology Center for Northern Mountain Agriculture, Baoding 071000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2023, 15(12), 9635; https://doi.org/10.3390/su15129635
Submission received: 23 April 2023 / Revised: 24 May 2023 / Accepted: 12 June 2023 / Published: 15 June 2023
(This article belongs to the Special Issue Sustainable Management of Forest Genetic Resources in Response to Climate Change)
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Abstract

:
Research on the climatic adaptation of the apricot kernels (Prunus armeniaca L.) has significant meaning for optimizing their cultivation and utilizing climatic resources effectively. This research utilizes geographical distribution data, climatic environmental factors, soil data, and altitude data of the apricot kernel in China. By employing the maximum entropy model (MaxEnt) and geographic information system (ArcGIS), we identify the key factors influencing the distribution of apricot kernels in China and suitable areas for their cultivation. Our findings reveal that annual precipitation, frequency of frost days in April, altitude, soil pH, and effective soil water content are the primary environmental factors impacting the distribution of apricot kernels in China. We classify the planting suitability zones into four categories. The areas characterized by annual precipitation ranging from 330.54 mm to 616.42 mm, frost day frequency of 2.68 to 19.15 days in April, altitude between 84.22 m and 831.81 m, pH values ranging from 7.5 to 8.6, and effective soil water content of 1.16 to 3.88 are deemed most suitable for growing apricot kernels. The most suitable areas correspond to the main growing areas in reality. Given the limited existing research on suitable areas for apricot kernel cultivation, this study provides a scientific foundation for promoting the cultivation of apricot kernels.

1. Introduction

The apricot (Prunus armeniaca) holds a significant place among ancient fruit trees in China, being indigenous to the region. It is characterized by its multifunctionality, serving as a source of fresh fruit, almonds for consumption, ornamental value, and processing purposes. The apricot tree exhibits remarkable resilience to adversity, along with other advantages such as early fruiting and an extended bearing period [1]. Apricot kernels, derived from this tree, represent a dominant species in semi-arid climates and contribute significantly to the prosperity of mountainous regions and ecological conservation [2]. With their high nutritional and medicinal value, almonds are considered a premium product in great demand within both domestic and international markets. The apricot kernel of China encompasses sweet and bitter almonds varieties, exhibiting a wide distribution with distinct characteristics across different regions. At present, apricot kernel breeding primarily relies on seed selection and cross-breeding techniques. Notably, plants grafted with Prunus armeniaca as rootstock perform better and have high survival rates, making them valuable test subjects for further breeding endeavors [3]. Apricot kernels thrive within altitudes ranging from 700 m to 2000 m, with suitable environmental conditions characterized by annual precipitation of 380 mm to 700 mm, average annual temperatures of 3 °C to 15 °C, and slightly acidic or slightly alkaline sandy loamy soils. However, the degree of improved breeding in apricot kernel production in China remains relatively low, with substantial constraints imposed by natural climatic conditions, resulting in low yields. Current research on the growth habits of apricot kernels has primarily focused on local areas, leaving the factors influencing their cultivation on a national scale still requiring exploration. Blindly expanding apricot production in unsuitable environmental conditions can lead to decreased yields and compromised fruit quality, resulting in significant economic losses for fruit farmers and local governments. Therefore, the identification and implementation of a scientifically grounded approach to regionalize suitable planting areas for apricot kernels and optimize the production layouts is paramount. Such an approach enables the efficient utilization of agro-climatic resources, reduces vulnerability to meteorological disasters, and fosters the development of a highly productive and efficient apricot industry.
The Chinese apricot kernel industry exhibits a distributed pattern characterized by three relatively concentrated scale production areas, the east, center, and west. These regions encompass a total area of approximately 2.666 million hectares, with a combined annual almond production of about 20 tons [4]. Based on data from 2005, the eastern production area includes Liaoning, Jilin, Inner Mongolia, Hebei, Beijing, and Shandong, encompassing 1.709 million hectares dedicated to apricot kernel cultivation and yielding 69 million kg of almonds [5,6,7]. The central production area includes Shanxi and Henan provinces, with an area of 279,000 hectares under apricot kernel cultivation, and producing 3.08 million kilograms of almonds [8]. In the western production area, which consists of Shaanxi, Xinjiang, Gansu, and Ningxia provinces (autonomous regions), there are 138,000 hectares allocated to apricot kernel cultivation, yielding 100,000 kg of almonds [9]. Key production areas for apricot kernels in China include Hebei, Inner Mongolia, Xinjiang, Gansu, Shaanxi, Beijing, Shanxi, and Shandong, which are provinces, autonomous regions, and municipalities directly under the Central Government.
The MaxEnt model is a valuable tool for determining the optimal state of species distribution under specific ecological niche constraints. It utilizes known geographical distribution information of a species along with corresponding environmental variables to predict the potential distribution of the species within the predicted area, maximizing entropy based on the principle of climate similarity. The construction of the MaxEnt model relies solely on the species’ distribution information and the corresponding environmental variables, enabling a comprehensive analysis of potential habitats for the species. To evaluate the accuracy of the MaxEnt simulation results, the model employs the receiver operating characteristic (ROC) curve method. The area under the curve (AUC) serves as an indicator to assess the predictive accuracy of the model. A larger AUC value signifies better predictive performance, with values ranging from 0 to 1. The closer the value is to 1, the more ideal and accurate the model is [10]. The MaxEnt model also assesses the importance of variables by the Jackknife test [11]. This approach has gained widespread application in various studies. Wang et al. used the MaxEnt model to study the potential habitat of hawthorn in China [12], and Li et al. assessed the climate suitability of quinoa using the MaxEnt model [13]. Guo et al. utilized the MaxEnt model to analyze potentially suitable distribution areas of poplar [14].
Currently, there is a limited number of studies focusing on nationwide planting suitability zoning for apricot kernels in China. Cheng et al. used ArcGIS software to conduct a climate suitability zoning for apricot kernels in northern Shaanxi [15], and, similarly, Yin et al. used geographic information system (GIS) to perform a resource climate analysis and zoning analysis for apricot kernels in the Balin Zuo Banner region of Chifeng City, Inner Mongolia Autonomous Region [16]. In light of this research gap, the present study aims to address this knowledge deficiency by utilizing geographical distribution data and relevant environmental information pertaining to Chinese apricot kernels. By employing the MaxEnt model and spatial analysis technology within ArcGIS software, our study aims to identify the dominant environmental factors influencing the distribution of Chinese apricot kernels and delineate potential planting areas. Additionally, this study seeks to classify suitable growth levels, providing valuable theoretical guidance for the cultivation of Chinese apricot kernels.

2. Materials and Methods

2.1. Data Source and Processing

This article takes the apricot kernel as the research object and collects data on the planting and distribution of the apricot kernel nationwide. The data were sourced from the National Specimen Resource Sharing Platform of China (NSII, http://www.nsii.org.cn/ (accessed on 18 February 2023)), Chinese Fruit Tree Annals, Apricot Rolls [17]. To ensure data accuracy and eliminate redundancy, areas without detailed geographical location and duplicate specimen information were excluded. The Map Location tool (https://maplocation.sjfkai.com (accessed on 19 February 2023) was employed to determine the longitude and latitude of the population locations. A total of 188 distribution points were initially selected, and, using ArcGIS 10.4 software, distribution points with potential autocorrelation were further excluded, resulting in a final set of 168 distribution points (Figure 1). Additionally, one geographical indication product area for apricot kernel was collected using the National Geographic Indication Query System for Agricultural Products (http://www.anluyun.com/ (accessed on 19 February 2023)), indicated by the yellow triangle in Figure 1.
For the climate variables and elevation data, information was collected at a resolution of 10 m from the world climate database Worldclim (https://www.worldclim.org/data/worldclim21.html (accessed on 20 February 2023)) for 1971–2000. Additionally, climate variables at a resolution of 0.5° for the same time period were obtained from CRU TS (http://crudata.uea.ac.uk/cru/data/hrg/ (accessed on 21 February 2023)). Soil data were acquired from the Chinese Soil Dataset (v1.1) based on the World Soil Database (HWSD) of the National Tibetan Plateau Science Data Centre (2009).

2.2. Screening Potential Environmental Factors

According to the growth characteristics of the apricot kernel, 14 potential environmental factors affecting the distribution of the apricot kernel were selected (Table 1). Considering the specific requirements of fruit trees, several factors were chosen to reflect the heat, water, and terrain conditions necessary for the growth of apricot kernels. These factors include the range of annual average temperature (BIO1), annual temperature difference (BIO7), and diurnal temperature range (DTR) to capture the heat accumulation needed for the apricot kernel’s life cycle [18]. The annual precipitation (BIO12) was selected to reflect the water condition demands of apricot kernels, and the altitude (H) was used to reflect the demand for terrain conditions of the apricot kernel. The flowering and fruit setting rates of apricot kernels are vulnerable to frost during the flowering period [3]; therefore, the frequency of frost days in April (FRS) was included as a measure of the risk of spring cold and frost. The highest temperature in the hottest month (BIO5), the lowest temperature in the coldest month (BIO6), the average temperature in the hottest quarter (BIO10), and the average temperature in the coldest month (BIO11) were selected to reflect the degree of tolerance of apricot kernels to extreme weather [19]. The root system of apricot kernels has a significant demand for air, and heavy clay soil and low-lying, humid, poorly ventilated soil are not conducive to the growth of apricot kernel roots [20]. The available water content of soil (CLASS) is related to the soil texture, usually clay > loam > sandy soil [21]. To reflect soil properties, the available water content (CLASS) and soil bulk density (BULK) were used to indicate soil texture and permeability. Soil fertility was represented by the organic carbon content (OC), and soil pH was considered as an indicator of soil characteristics. The inverse distance weighting method was used in ArcGIS to process the April frost day frequency and diurnal temperature difference range. All data were converted into ASCII format using ArcGIS for subsequent analysis.

2.3. Model Construction

A maximum entropy model was constructed using geographic distribution data of Chinese apricot kernels, alongside filtered environmental factors. The dataset was divided into a training dataset, comprising 75% of the distribution data, and a test dataset, comprising the remaining 25% to evaluate the model’s performance. We set up 10 replicates using the cross-validation method, and the average was considered the final result. The remaining parameters, such as maximum iterations = 500, the maximum number of background points = 10,000, convergence threshold = 0.00001, and prevalence = 0.5, were set to the default values suggested in previous studies [22]. Based on the model runs, the dominant environmental factors affecting the distribution of apricot kernel were determined. This was achieved by analyzing the percentage contribution of each factor to the model, assessing the importance of substitution, and examining the univariate response curves obtained from the jackknife test results. The Pearson correlation coefficient was calculated using ArcGIS software. Factors exhibiting a high correlation, as determined by the Pearson correlation coefficient, were given particular consideration [15]. To classify the planting area of apricot kernel into distinct categories, the Jenks Natural Breaks Classification was employed, resulting in the division of the area into four categories [23].

3. Results and Analysis

3.1. MaxEnt Model Evaluation and Analysis

The evaluation of the initial MaxEnt model is presented in Figure 2, depicting the ROC curves. The corresponding AUC values for the training and test sets, representing the distribution area of Chinese apricot kernel cultivation based on potential environmental factors, are 0.891 and 0.864, respectively. These values indicate that the initial model achieved a prediction accuracy that meets the “good” standard. Therefore, the model can be utilized for the predicting the potential planting areas of apricot kernel.

3.2. Screening of Potential Environmental Factors

The contribution and replacement importance of each environmental factor to the model are presented in Table 2. Notably, BIO12, FRS, and H exhibited substantial contribution and replacement importance, surpassing 10%. Soil pH, BIO11, CLASS, BIO7, and OC ranked second, contributing over 2% to the model. The cumulative contribution rate of these aforementioned environmental factors reached 93.3% of the model. While the contribution percentage of BIO1 to the model was only 1.6%, its importance of substitution exceeded 10%. Conversely, BIO6, BULK, BIO5, BIO10, and DTR displayed contribution percentages and replacement importance below 2%, indicating a lesser reliance of the model on these five environmental factors. Consequently, these five factors were excluded from the subsequent suitability analysis to enhance the reliability of the results.
To address the issue of high correlation among various factors, which could potentially amplify the contribution of environmental factors and affect the simulation effect of the model [22], the Pearson correlation coefficient was calculated for factors with a contribution rate greater than 2%. For factor groups with |r| ≥ 0.8, only the factors with a more significant contribution rate and high replacement importance were retained. From Table 3, it can be observed that the correlation coefficient between BIO1 and BIO11 was 0.913. Additionally, the correlation coefficient between BIO1 and FRS was −0.945, while the correlation coefficient between BIO11 and FRS was −0.835. Based on these correlation coefficients, it is evident that FRS, which exhibited a significant contribution rate, should be retained, while the other two environmental variables should be eliminated.
From the perspective of the gain of various potential environmental factors on the geographical distribution model of apricot kernels (Figure 3), it can be observed that when removing a single factor, FRS demonstrated the smallest decrease in gain on the model. This suggests that FRS contains more informative content compared to the other factors, thus it should be retained. On the other hand, removing factors such as BIO5, BIO6, BIO7, and BIO10 did not substantially reduce the gain on the model, indicating that selecting any one of these factors as the dominant factor would not be appropriate. When considering the retention of only a single factor, BULK and OC exhibited the smallest gain on the model. Hence, they should not be chosen as the dominant factor and should be eliminated. In contrast, BIO12 displayed the most significant gain on the maximum entropy model of apricot kernel, implying that this factor carries the most valuable information. Therefore, BIO12 appears to be the most influential and informative factor within the model.
After conducting a comprehensive analysis considering the contribution percentage, replacement importance, correlation with other factors, and the results of the jackknife method, it is recommended to eliminate the following five environmental factors, DTR, BIO10, BIO5, BULK, and BIO6. The factors with a lower contribution rate and replacement importance to the model should be eliminated. Additionally, due to their significant correlation coefficient with FRS, it is advised to eliminate BIO11 and BIO1. Furthermore, OC, which showed the lowest gain in the model when only a single factor was retained, should also be eliminated. Lastly, BIO7 should be excluded from consideration because the model gain was not significantly reduced when it is removed.
By systematically eliminating these factors, the final selection of the influential environmental factors for the geographical distribution model of apricot kernel includes BIO12, FRS, H, soil pH, and CLASS. These factors have been carefully chosen based on their significant contributions and relevance to the model. The reconstructed maximum entropy model, incorporating these dominant environmental factors and utilizing the geographic distribution data of apricot kernels, demonstrated excellent performance. The AUC value of the ROC curve for the reconstructed model reached 0.845, indicating a high level of prediction accuracy that meets the “good” standard.

3.3. Threshold Value of Environmental Factors for Suitable Growth Area of Apricot Kernel

Following the identification of the five dominant environmental factors in the initial model, a reconstructed MaxEnt model was developed. The new model employed the Natural Breaks method to classify the planting areas of apricot kernel into four distinct classes, high-suitability area (0.65 ≤ p ≤ 1), medium-suitability area (0.4 ≤ p < 0.65), low-suitability area (0.15 ≤ p < 0.4), and unsuitable area (0 ≤ p < 0.15). To determine the threshold values for each of the dominant environmental factors, response curves were plotted to examine the relationship between suitability probabilities and these factors. By analyzing these curves, specific threshold values were identified. Table 4 presents the suitability class thresholds for the five dominant environmental factors pertaining to apricot kernels.
Although apricot kernels are intolerant to flooding, excessive drought can also have adverse effects on their photosynthesis [24]. The prolonged periods of drought effects can impede growth, fruiting, and nutrient accumulation, making the plants less resilient in subsequent years [25]. Therefore, the annual precipitation level plays a crucial role in determining the suitability of planting apricot kernels. In our study, it was found that annual precipitation below 126.35 mm or above 1482.20 mm is unsuitable for apricot kernel cultivation. Certain regions in southwestern China, including most of the Xinjiang Uygur Autonomous Region, the western part of the Tibet Autonomous Region, the western and northern parts of Inner Mongolia, and the western part of Qinghai Province, experience annual precipitation below 126.35 mm, making it challenging to meet the moisture requirements for the growth and development of apricot kernel. Conversely, areas such as Jiangxi Province, Guangxi Zhuang Autonomous Region, Guangdong Province, and most of Fujian Province, where annual precipitation exceeds 1482.20 mm, are also unsuitable due to the risk of waterlogging. In such waterlogged conditions, apricot kernels are unlikely to bear fruit or may yield poor-quality fruit. The frequency of frost days in April is closely associated with the average annual temperature of a region. In highly suitable areas for apricot kernel cultivation, the frost day frequency in April ranges from 2.68 to 19.15 days. Areas situated at elevations greater than 3704.91 m, such as the Tibetan Plateau region, are prone to low temperatures and frosts, making them unsuitable for apricot kernel cultivation. Weak alkaline soils provide favorable conditions for the growth of apricot kernels [26]. Therefore, areas with a pH range of 7.5 to 8.6 and available soil water content of 1.16 to 3.88 are considered highly suitable for apricot kernel cultivation.

3.4. Potential Distribution Area of Apricot Kernel

The prediction results indicate that the suitable area for apricot kernel cultivation is primarily concentrated in several regions of China. These regions include most of North China, Northeast China, the eastern and northwestern parts of Northwest China, the northern part of Central China, and the northern part of East China. Additionally, there is a smaller distribution of suitable areas in the eastern part of Southwest China. The total extent of the suitable area for growing apricot kernels amounts to 3,853,169 square kilometers, which accounts for approximately 40.14% of the total area of mainland China (Figure 4). These findings provide valuable insights into the potential distribution of apricot kernels and offer guidance for agricultural planning and cultivation strategies in the identified regions.
The high-suitability area for apricot kernel cultivation spans approximately 1,027,567 square kilometers, which accounts for 10.70% of China’s total land area. This area is primarily concentrated in the mountainous and hilly regions of North China, the northeastern part of Northwest China, and the southwestern part of Northeast China. Specifically, it includes Beijing, with the exception of the southeastern and northern parts of Chengde City, as well as most of Shanxi Province. In Shaanxi Province, it covers the majority of the Baoji, Xi’an, and Shangzhou cities. The northeast of Gansu Province, along with the Xifeng and Tianshui cities, is also part of the high suitability area. In Liaoning Province, the high suitability area excludes the southern Shenyang City, western Liaoyang City, northern Anshan City, and southeastern Jinzhou City. The eastern part of Jilin Province and southwestern Heilongjiang Province are also included. Other regions within the high-suitability area encompass the eastern part of Zibo City, Laiwu City, and Linyi City in Shandong Province. Additionally, it extends to the narrow northern section of Henan Province, the southeastern part of the Inner Mongolia Autonomous Region, and other provinces exhibiting high suitability for apricot kernel cultivation. Furthermore, the sporadic distribution of suitable areas for apricot kernels can be found in the northwestern Xinjiang Uyghur Autonomous Region and central Sichuan Province. These regions are influenced by warm and wet marine air masses from the Pacific Ocean during the summer, resulting in hot and rainy conditions that facilitate apricot kernel growth. These areas exhibit sufficient precipitation and abundant heat resources. The frequency of frost days in April is relatively low, and the soil’s available water content and pH levels are suitable for apricot kernel cultivation. As a result, these regions are considered dominant production areas for apricot kernels.
The medium-suitable area for apricot kernel planting encompasses a total area of 1,234,355 square kilometers, which accounts for 12.86% of mainland China’s total land area. It is primarily distributed in strips surrounding the high-suitability area. The concentrated distribution areas are found in the northern regions of the high-suitability areas, including Tianjin City, areas excluding the high-suitability regions in Heilongjiang Province, and the eastern part of Jilin Province. In Liaoning Province, the medium-suitable areas cover the remaining regions outside the high-suitability areas. The strip areas connecting with the high-suitability areas in the Inner Mongolia Autonomous Region, the central strip area of Hailar City, the northern area of Chengde City, and the narrow strip in the central area bordering the highly suitable zone of Hebei Province also fall within the medium-suitable category. Additionally, the northern part of Henan Province bordering the high-suitability zone, a narrow strip in central Shandong Province bordering a highly suitable area, Shangzhou City in Shanxi Province, the central area of Shaanxi Province, the southwest part of Ningxia Hui Autonomous Region, the central and southern parts of Gansu Province, and small portions of the northwest part of Xinjiang Uygur Autonomous Region and Sichuan Province exhibit patchy distributions of medium-suitability areas. These regions are primarily located within the temperate monsoon and temperate continental climate zones. The frequency of frost days in April ranges from 0.96 to 2.86 days or 19.15 to 25.88 days. Consequently, the apricot kernel is less likely to be affected by frost, resulting in reduced production. The soil pH in these regions ranges from 5.88 to 7.88, making them moderately suitable areas for planting apricot kernels.
The total area classified as having low suitability for apricot kernel planting spans 1,591,247 square kilometers. This area is distributed around the medium-suitability zone, including the remaining areas of Heilongjiang Province, Hebei Province, and Shandong Province. In the Inner Mongolia Autonomous Region, the low-suitability areas encompass all areas except Alashan Left Banner and the eastern part of Linhe City. The low-suitability areas also include the central, eastern, and northern parts of Jiangsu Province, dotted regions in Anhui Province, the northern, eastern, and central parts of Hubei Province, southern Shaanxi Province, specific areas in western Henan Province, central and northern Gansu Province, central Sichuan Province, southeastern Tibet Autonomous Region, a striped area in northwestern Yunnan Province, and northwest Xinjiang Uygur Autonomous Region. Additionally, there are scattered areas in the north-central and south of Chongqing, the east of Hunan Province, the west of Guizhou Province, the northwest of Jiangxi Province, the north of Zhejiang Province, and the north of Fujian Province. The low-suitability regions account for 16.58% of the total land area of mainland China. The unsuitability for apricot kernel cultivation is primarily concentrated in the northwest of the Inner Mongolia Autonomous Region, the northwest of Gansu Province, the central, eastern, and southern parts of the Xinjiang Uygur Autonomous Region, most of Qinghai Province, the Tibet Autonomous Region, Shanghai City, and most of Yunnan, Guizhou, Hunan, Jiangxi, Fujian, Guangdong, Guangxi Zhuang, and Hainan provinces. In these regions, precipitation is abundant, making the apricot kernels vulnerable to waterlogging and, therefore, they are unsuitable areas for cultivation.

4. Discussion

In this study, the MaxEnt model was employed to assess the climatic suitability of apricot kernel cultivation on a national scale. A dataset of 168 distribution points was utilized, ensuring the accuracy and stability of the model by addressing autocorrelation. Previous research on suitable planting area zoning for apricot kernels has focused on various factors. For instance, the probability of spring frost was quantified by the likelihood of the average daily temperature from March to April falling below −1 °C [19], while the average annual temperature was considered a determinant of the heat requirements of apricot kernels [27]. Annual precipitation has been used to gauge the water needs of apricot kernels [15]; however, in this study, after several attempts, it was discovered that using the frequency of frost days in April yielded more accurate results in capturing the impact of spring frost damage on apricot kernels. Furthermore, the findings of the model indicated that apricot kernels thrive in alkaline soils, corroborating the observations made by Shi et al. [26]. The study also revealed that apricot kernels exhibit greater suitability for growth in areas with lower soil effective water content, aligning with the conclusions drawn by Han et al. regarding the root system’s high demand for air [19]. In addition, considering the contribution percentage and replacement importance of apricot kernels to the model, it becomes apparent that they exhibit considerable tolerance to extreme weather conditions. This is evident from their relatively minor contribution to the model concerning the lowest temperature in the coldest month, the lowest temperature in the hottest month, and the average temperature in the hottest quarter. Moreover, the contribution rate of soil organic carbon content to the model is low, which corresponds to the drought tolerance characteristics of apricot kernels [1].
This study utilized the MaxEnt software and conducted 10 repetitions, resulting in an AUC value exceeding 0.8, indicating high reliability and accuracy. The analysis revealed that the primary suitable growing areas for apricot kernels are concentrated in North China, Northeast China, eastern Northwest China, northern Central China, northern East China, and parts of eastern Southwest China. These findings align with the established major apricot planting areas in China, including Hebei, Beijing, Liaoning, Gansu, Inner Mongolia, Shaanxi, Shanxi, and Xinjiang [3]. The predicted suitable growth areas from the model correspond with the climate suitability zones identified by Yin et al. in Balin Zuo Banner of Chifeng City [25], Li et al. in the “Three North Region” [28], and Cheng et al. in the Shaanxi Gansu Ningxia region [15]. However, there are significant differences between this study and Yue et al. concerning the climate adaptability in the area around the Tarim Basin [29]. The disparity can be attributed to Yue et al.’s focus solely on the influence of heat conditions on apricot kernel growth. The regionalization results of this study align well with the geographical indications of apricot kernels in the main producing areas and the designated geographical indication areas. These findings can provide theoretical support for apricot kernel cultivation worldwide, suggesting that suitable apricot kernel growth is achievable when the values of five environmental factors, namely annual precipitation (BIO12), April frost days (FRS), elevation (H), soil acidity (pH), and available soil moisture content (CLASS), fall within appropriate ranges in a given region.
In this study, two successive MaxEnt models were developed. Initially, 14 environmental factors were considered. Through the identification of factors with high contribution rates and replacement percentages in the model and the use of Pearson’s coefficient to eliminate highly correlated factors, five key environmental factors were selected. This approach ensured a more objective and comprehensive analysis compared to previous studies. In the second phase, these five dominant environmental factors were used to construct a more accurate model for regionalizing suitable planting areas for apricot kernels. It is important to note that this study focused only on certain climatic and environmental variables, altitude, and soil properties. Factors such as solar radiation, other soil characteristics, and human activities may also impact the distribution of apricot kernel but were not considered in this study. Additionally, while the study considered the blooming period of apricot kernels and the frequency of frost days in April, it did not account for the potential impact of frost on young fruits in May [30]. Furthermore, the collection of geographical distribution points of apricot kernels may have had some uncertainties and lack some information, which could result in slight deviations from the actual suitable areas for apricot kernels. Future studies should aim to collect more detailed data to continually improve the model’s simulation accuracy.

5. Conclusions

The study identified the five dominant environmental factors that significantly influence the planting suitability of apricot kernels, annual precipitation, frequency of frost days in April, altitude, soil pH, and available soil water content. These factors contributed to the model with percentages of 28.3%, 24.9%, 22.9%, 6.4%, and 2.4%, respectively. Using the Jenks Natural Breaks Classification in GIS, the suitability of apricot kernel planting was classified into four levels, high suitability, medium suitability, low suitability, and unsuitability. The thresholds for each suitability level were determined based on the ranges of the five environmental factors. The most suitable areas for planting apricot kernels were identified as having annual precipitation between 330.54 mm and 616.42 mm, frost days between 2.68 and 19.15 days in April, elevation between 84.22 m and 831.81 m, soil pH between 7.5 and 8.6, and available soil water content between 1.16 and 3.88. It should be noted that while most of the factors influencing the growth of apricot kernels were considered in this study, other factors such as human activities were not taken into account. Therefore, there may be slight biases in the experimental results, and further improvements can be made in future studies. The study revealed that apricot kernels have a widespread distribution in North China, Northeast China, eastern and northwestern Northwest China, northern Central China, and northern East China, with some presence in Southwest China. The high suitability areas were primarily concentrated in Hebei Province, Shanxi Province, Shaanxi Province, Liaoning Province, Jilin Province, Shandong Province, and Inner Mongolia Autonomous Region, and with sporadic distribution in the northeast of Xinjiang Uygur Autonomous Region. The medium-suitable areas were found in bands surrounding the high-suitability areas. These regionalization results align well with the concentrated distribution areas of apricot kernels and the geographical indication product areas of apricot kernels in China.

Author Contributions

Conceptualization, R.S. and Y.L.; methodology, R.S. and Y.L.; software, R.S., G.T. and L.X.; validation, R.S. and Q.Z.; formal analysis, R.S. and G.T.; investigation, R.S. and Z.S.; resources, R.S. and G.T., data curation, R.S. and Q.Z.; writing—original draft preparation, R.S., G.T. and Z.S.; writing—review and editing, R.S., L.X. and Y.L.; visualization, R.S. and G.T.; supervision Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “Hebei Science and Technology Support Project” (21326802D), the “National Key R&D Program of China” (2020YFD1000700), “Hebei Outstanding Returnees Funding Project” (CN201601).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in this article.

Conflicts of Interest

The authors declare no conflict of interest.

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Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author (s) and contributor (s) and not of MDPI and/or the editor (s). MDPI and/or the editor (s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
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Figure 1. Geographical distribution and geographical indication product location of apricot kernel.
Figure 1. Geographical distribution and geographical indication product location of apricot kernel.
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Figure 2. ROC curve of MaxEnt model constructed based on potential environmental factors affecting the planting distribution of apricot kernels.
Figure 2. ROC curve of MaxEnt model constructed based on potential environmental factors affecting the planting distribution of apricot kernels.
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Figure 3. Contribution of potential environmental factors to the planting distribution of apricot kernel based on the jackknife method.
Figure 3. Contribution of potential environmental factors to the planting distribution of apricot kernel based on the jackknife method.
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Figure 4. Planting suitability regionalization for apricot kernels in China.
Figure 4. Planting suitability regionalization for apricot kernels in China.
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Table
Table 1. Potential environmental factors affecting the planting and distribution of apricot kernel.
Table 1. Potential environmental factors affecting the planting and distribution of apricot kernel.
CodePotential Environmental Factors
BIO1 (°C) Annual average temperature
BIO5 (°C) The highest temperature in the hottest month
BIO6 (°C) The lowest temperature in the coldest month
BIO7 (°C) Annual temperature difference
BIO10 (°C) The average temperature in the hottest quarter
BIO11 (°C) The average temperature in the coldest quarter
BIO12 (mm) Annual precipitation
H (m) Altitude
CLASSAvailable soil water content
BULKSoil bulk density
OCSoil organic carbon content
pHSoil pH
DTR (°C) Range of temperature difference between day and night
FRS (d) Frost day frequency in April
Table
Table 2. Contribution percentage and replacement importance of potential environmental factors.
Table 2. Contribution percentage and replacement importance of potential environmental factors.
Environmental Factor Code Contribution Percentage (%) Replacement Importance (%)
BIO1228.316.4
FRS24.930.3
H22.921.9
pH6.42.3
BIO113.97.9
CLASS2.41.3
BIO72.30.7
OC2.21.8
BIO61.61.3
BIO11.613.7
BULK1.30.7
BIO510.8
BIO100.80.3
DTR0.60.8
Table
Table 3. Correlation detection of potential environmental factors.
Table 3. Correlation detection of potential environmental factors.
Environmental Factor CodeBIO1BIO7BIO11BIO12CLASSpHFRSOC
BIO7−0.428
BIO110.913 −0.751
BIO120.645 −0.700 0.742
CLASS−0.521 −0.166 −0.287 −0.231
pH−0.004 0.352 −0.157 −0.324 −0.241
FRS−0.945 0.350 −0.835 −0.548 0.523 −0.028
OC−0.127 −0.132 −0.043 0.112 0.166 −0.205 0.161
H−0.704 −0.193 −0.392 −0.383 0.722 −0.232 0.713 0.165
Table
Table 4. Range of environmental factors affecting the suitability of apricot kernel in the suitability area.
Table 4. Range of environmental factors affecting the suitability of apricot kernel in the suitability area.
High-Suitability AreaMedium-Suitability AreaLow-Suitability AreaUnsuitable Area
BIO12 (mm) 330.54~616.42240.70~330.54 or
616.42~1016.64		126.35~240.70 or
1016.64~1482.20		BIO12 < 126.35 or
BIO12 > 1482.20
FRS (d) 2.68~19.150.96~2.68 or
19.15~25.88		0~0.96 or 25.88~28.8328.83~30
H (m) 84.22~831.81−18.39~84.22 or
831.81~2517.56		−121.00~−18.39 or
2517.56~3704.91		H > 3704.91
pH7.5~8.65.9~7.5 or 8.6~8.95.5~5.94.3~5.5
CLASS1.16~3.881~1.56 or 3.88~4.244.24~4.68 or 5.88~64.68 < CLASS < 5.88
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Sun, R.; Tong, G.; Zhang, Q.; Xu, L.; Sang, Z.; Li, Y. A Study on the Suitable Areas for Growing Apricot Kernels in China Based on the MaxEnt Model. Sustainability 2023, 15, 9635. https://doi.org/10.3390/su15129635

AMA Style

Sun R, Tong G, Zhang Q, Xu L, Sang Z, Li Y. A Study on the Suitable Areas for Growing Apricot Kernels in China Based on the MaxEnt Model. Sustainability. 2023; 15(12):9635. https://doi.org/10.3390/su15129635

Chicago/Turabian Style

Sun, Runze, Guanjie Tong, Qing Zhang, Lingjie Xu, Zihan Sang, and Yanhui Li. 2023. "A Study on the Suitable Areas for Growing Apricot Kernels in China Based on the MaxEnt Model" Sustainability 15, no. 12: 9635. https://doi.org/10.3390/su15129635

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