Preamble

Spatiotemporal Evolution Characteristics of Crop Planting Structures in the Fenwei Plain

Miao Yingfeng, Yuan Ye, Zhou Zhengwei, Zhao Jiayu, Guo Yuxi
(School of Public Administration, Shanxi University of Finance and Economics, Taiyuan, Shanxi)

Food security serves as the fundamental cornerstone for national development and social stability. Investigating the spatiotemporal evolution of crop planting structures provides a theoretical basis for ensuring regional food security and promoting sustainable agricultural development. This study focuses on the Fenwei Plain, utilizing agricultural statistical data from 2000 to 2020 as the primary data source. By employing methods such as the Standard Deviational Ellipse (SDE) model and spatial autocorrelation analysis, we explore the spatiotemporal evolution patterns of major crop planting structures at the county level within the Fenwei Plain over the past two decades. The results indicate:

[FIGURE:1]

1. Introduction

The Fenwei Plain is one of the most important agricultural production bases in China. Understanding how planting structures shift over time is critical for optimizing resource allocation and responding to climate change. As urbanization accelerates, the competition for land between food crops and cash crops has intensified, leading to significant shifts in the spatial distribution of agricultural activities.

2. Research Methods and Data Sources

The study utilizes a comprehensive dataset of county-level agricultural statistics spanning the years 2000, 2010, and 2020. To analyze the spatial dynamics, we employ the following methodologies:

• Standard Deviational Ellipse (SDE): This method is used to identify the centrality, dispersion, and directional trends of various crop types. By calculating the mean center and the rotation angle of the ellipse, we can track the geographical shift of production centers.
• Spatial Autocorrelation (Moran's I): Global and local Moran's I indices are calculated to determine the degree of spatial clustering or dispersion in planting intensity across the region.

3. Results and Analysis

The analysis reveals that the planting structure in the Fenwei Plain has undergone significant transformations. There is a notable trend of "non-grain" conversion in certain areas, where traditional grain crops are being replaced by high-value horticultural crops.

[TABLE:1]

The spatial distribution of major crops, such as wheat and maize, shows a distinct clustering pattern. The SDE analysis indicates that the center of gravity for grain production has shifted slightly towards the northeast, reflecting changes in regional irrigation capabilities and land-use policies. Furthermore, spatial autocorrelation results demonstrate a high degree of spatial dependency, with "High-High

Analysis of Atmospheric Pollution in the Fenwei Plain

In recent years, the Fenwei Plain has emerged as a critical region for air pollution control in China. Characterized by its unique basin topography and intensive industrial activities, the region frequently experiences stagnant meteorological conditions that exacerbate the accumulation of particulate matter and gaseous pollutants.

Regional Characteristics and Pollution Trends

The Fenwei Plain encompasses portions of Shaanxi, Shanxi, and Henan provinces. Due to its geographical structure—surrounded by mountains—the dispersion of pollutants is often restricted, particularly during the winter heating season. Research indicates that the primary pollutants in this region include fine particulate matter ($PM_{2.5}$), sulfur dioxide ($SO_2$), and nitrogen oxides ($NO_x$).

[FIGURE:1]

As shown in [FIGURE:1], the spatial distribution of pollutant concentrations reveals significant hotspots near industrial clusters and densely populated urban centers. Over the past several years, while national air quality has improved, the Fenwei Plain has faced persistent challenges in meeting the secondary standards of the National Ambient Air Quality Standards (NAAQS).

Impact of Meteorological Factors

Meteorological conditions play a decisive role in the daily fluctuations of air quality. Parameters such as planetary boundary layer height (PBLH), relative humidity (RH), and wind speed directly influence the dilution and chemical transformation of pollutants. During periods of atmospheric inversion, the concentration of $PM_{2.5}$ can increase exponentially within a few hours.

[TABLE:1]

[TABLE:1] summarizes the correlation coefficients between major pollutants and key meteorological variables. The data suggests a strong positive correlation between relative humidity and secondary inorganic aerosol formation, indicating that aqueous-phase chemistry is a significant contributor to haze episodes in the Fenwei Plain.

Conclusion and Policy Implications

To address these environmental challenges, a multi-provincial coordinated control strategy is essential. This includes the transition from coal to cleaner energy sources, the optimization of industrial structures, and the implementation of advanced machine learning models for high-precision air quality forecasting. Future research should focus on the long-term health impacts of multi-pollutant exposure and the effectiveness of regional emission reduction protocols.

A total of 88 distinct crop planting structure types were identified. Among these, wheat, maize, and their various combination patterns constitute the primary crop planting systems within the Fenwei Plain.

Structural types. From the perspective of spatio-temporal distribution, the number of wheat-dominant counties has gradually decreased, while the number of maize-dominant counties has increased year by year. The wheat-maize mixed type has progressively expanded from the southwestern part of the Fenwei Plain toward the northeast. In contrast, the maize-wheat mixed type exhibits a scattered distribution pattern across various regions of the Fenwei Plain.

In terms of type richness, the crop planting structure was most diverse in 2010, while the richness index reached its minimum in 2020. Throughout the study period, the planting patterns for wheat, maize, and vegetables in the Fenwei Plain consistently exhibited a distribution aligned along a northeast-southwest axis. Specifically, the center of gravity for wheat remained relatively stable. In contrast, the center of gravity for maize shifted continuously toward the northeast, while the center of gravity for vegetables migrated southwest from Heyang County to Chengcheng County.

In summary, the spatial distribution of major crops in the Fenwei Plain has demonstrated a trend of differentiated development: the planting area for wheat has shown a contraction, whereas the areas for maize and vegetables have expanded. Consequently, future efforts to manage the crop planting structure in the Fenwei Plain should involve macro-level adjustments aimed at ensuring food security, grounded in a comprehensive analysis of these spatiotemporal development trends.

关键词

Crop planting structure; Spatiotemporal evolution; Spatial autocorrelation; Fenwei Plain. Article ID: [ID]

Crop planting structure refers to the planting proportions and spatial distribution of different crops within regional agricultural production. It plays a critical role in the economic status of rural households, regional sustainable agricultural development, and national food security. In recent years, alongside China's rapid urbanization and industrialization and the continuous improvement of living standards, the dietary structure of Chinese residents has undergone significant changes. Consequently, the demand for agricultural products is no longer limited to traditional grain crops but has become increasingly diversified and complex. Furthermore, factors such as climate change and water resource constraints have placed sustained pressure on the production of specific grain crops in certain regions. Meanwhile, the disparity in economic returns between grain crops and cash crops has further driven the evolution of regional crop planting structures. Therefore, accurately identifying the patterns of spatiotemporal evolution in regional crop planting structures provides an essential scientific basis for guiding structural adjustments in regional agriculture.

A review of relevant literature from databases such as China National Knowledge Infrastructure (CNKI) and Web of Science indicates that research on crop planting structures has shown an increasing trend year by year, gradually becoming a focal point of academic inquiry. Utilizing data sources such as satellite remote sensing and agricultural statistics, scholars have employed spatial...

分析

Methods such as time series analysis have been employed to explore crop planting structures at the national, provincial, and city/county levels. For instance, Wang Limin et al. \cite{1} utilized statistical data on wheat planting areas across various provinces (municipalities and autonomous regions) to systematically analyze the spatial distribution characteristics and dynamic changes of wheat cultivation. Their study further discussed the relationship between yield and the spatial distribution pattern of planting areas. The results indicated that maintaining the stability of wheat planting areas in central and eastern China is critical to ensuring the overall stability of the national wheat planting area. Conversely, the proportion of wheat planting in northern and southern regions remains relatively low, necessitating measures to curb the declining trend in these areas.

Utilizing gravity center models and location quotient indices, researchers have analyzed the spatio-temporal evolution of the planting patterns for various crops in Shaanxi Province. Findings revealed that the center of gravity for the planting industry in Shaanxi has generally shifted toward the northeast. Similarly, by applying mathematical statistics and spatial analysis, studies on the spatio-temporal changes in the planting structures of major crops across counties in Heilongjiang Province found that the total sown area increased annually during the research period, with rice and maize showing the most typical growth. While scholars have explored crop planting structures from multiple perspectives using diverse methodologies, providing a solid foundation for this study, research focusing on regional crop planting structures from a broad plains perspective remains scarce. In particular, studies targeting the planting structures of traditional agricultural regions in Central China have not yet been reported.

In 2019, during the Symposium on Ecological Protection and High-Quality Development of the Yellow River Basin, President Xi Jinping pointed out that major grain-producing areas, such as the Fenwei Plain, must develop modern agriculture and improve the quality of agricultural products to contribute to national grain security. The Fenwei Plain is one of China's seven major agricultural production zones. The region benefits from a favorable climate, with a cropping system typically consisting of two harvests per year or three harvests every two years. Furthermore, the fertile soil makes it the region with the best alignment of light, temperature, water, and soil conditions in the middle and upper reaches of the Yellow River, serving as a vital production base for wheat and maize in China. Exploring the evolution of crop planting structures in the Fenwei Plain can provide a theoretical basis for adjusting the regional agricultural industrial structure, promoting sustainable agricultural development, and ensuring regional food security. It also offers a reference for the formulation of relevant agricultural policies in other parts of Northern China. This paper employs spatial autocorrelation and other methods to reveal the spatio-temporal evolution patterns of major crop planting structures in this region from 2000 to 2020, aiming to provide theoretical support for optimizing the agricultural industrial structure and maintaining regional food security.

1 数据与方法

Overview of the Study Area

The Fenwei Plain is located in the North China region and is composed of the Fenhe Plain, the Weihe Plain, and their surrounding loess platforms and terraces. This region encompasses several cities, including Yuncheng in Shanxi Province, Xi'an and Tongchuan in Shaanxi Province, and Luoyang in Henan Province. It is not only the largest alluvial plain in the middle reaches of the Yellow River but also the fourth largest in China. Characterized by flat terrain, the region serves as a critical production base for wheat in China. According to the latest national administrative divisions, the Fenwei Plain includes multiple counties. The base map used in this study was produced based on the standard map (Review No. GS(2019)1822) from the Standard Map Service website of the Ministry of Natural Resources, with no modifications made to the boundaries. Considering that the urban districts of Xi'an—specifically Xincheng, Beilin, Lianhu, Weiyang, and Yanta—have limited agricultural land, this paper selects 50 county-level units (excluding these five districts) as the research subjects. Agricultural statistical data, such as the total sown area of crops and the sown area and yield of specific crops (e.g., wheat, corn, and vegetables) for each county in the Fenwei Plain, were sourced from the Shanxi Statistical Yearbook, Shaanxi Statistical Yearbook, Henan Statistical Yearbook, Henan Rural Statistical Yearbook, China County Statistical Yearbook, and various municipal-level statistical yearbooks from the three provinces. Geographic vector data were obtained from the base maps provided by the Map Technology Review Center of the Ministry of Natural Resources.

The classification and naming of crop planting structure types are defined based on the proportion of different crops relative to the total sown area. Specifically, crops with a proportion of the total sown area $\ge 30\%$ are defined as primary crops.

Crops accounting for $15\% \le \text{proportion} < 30\%$ of the total sown area are classified as auxiliary crops.

Crops with a proportion $< 15\%$ are not included in the classification of planting types.

In the naming convention for planting structure types, primary and auxiliary crops are connected using a hyphen ("-"). If a region has only one primary crop and no auxiliary crops, it is named after that single primary crop.

In this study on the spatio-temporal evolution of crop planting structures in the Fenwei Plain, if a region's wheat planting proportion exceeds $30\%$ and there are no auxiliary crops, the structure is named "Wheat type." If a region has both primary and auxiliary crops, it is named using both. For example, if the wheat proportion is $\ge 30\%$ and cotton and vegetables are auxiliary crops, it is named "Wheat-Cotton-Vegetable type." If a region has no primary crops (all crops $< 30\%$), the planting structure is determined by the top three auxiliary crops by proportion; for instance, if corn is the leading auxiliary crop, it might be named "Corn-Soybean-Other type." To analyze the spatial evolution of the main crop planting structures in the Fenwei Plain from 2000 to 2020, this paper extracts crops with a planting proportion $\ge 30\%$.

The spatial evolution patterns were visualized using ArcGIS software. The Crop Planting Structure Type Richness Index is used to measure the diversity of planting structures within the region.

This index is typically expressed as the ratio of the number of crop planting structure types appearing in a specific year to the total number of unique planting structure types observed across all years within the study period.

$$R_i = \frac{n_i}{N} \tag{1}$$

2. Methodology

2.1 Richness Index of Annual Planting Structure

The Richness Index of annual planting structure types is used to quantify the diversity of crops within a given year. A value closer to 1 indicates a richer variety of crop planting structure types for that year. The index is calculated based on the number of crop planting structure types appearing in a specific year relative to the total number of types observed across all years within the study period.

2.2 Standard Deviational Ellipse (SDE)

The Standard Deviational Ellipse (SDE) is a spatial statistical method used to describe the distribution characteristics of geographical features. It provides critical information regarding the central tendency, dispersion, and primary orientation of spatial elements. The SDE is primarily defined by three parameters: the rotation angle, the standard deviation along the long axis, and the standard deviation along the short axis. In this study, the SDE method is employed to analyze the concentrated planting areas, distribution directions, and shifts in the spatial range of crops in the Fenwei Plain. This approach reveals the spatio-temporal evolution patterns of the regional crop planting structure.

2.3 Spatial Autocorrelation Analysis

Spatial autocorrelation is a fundamental statistical method for analyzing spatial patterns, based on Tobler’s First Law of Geography: "Everything is related to everything else, but near things are more related than distant things." Spatial autocorrelation is categorized into global and local spatial autocorrelation.

Global spatial autocorrelation reflects whether the research object exhibits clustering effects or spatial dependency across the entire study area. The Global Moran’s $I$ index is commonly used for this purpose, with values ranging from $[-1, 1]$. Using ArcGIS software, we conducted a global spatial autocorrelation analysis on the planting area and yield of wheat, maize, and vegetables.

The interpretation of the Moran’s $I$ value is as follows:
- A value closer to 1 indicates a stronger positive spatial correlation, suggesting that similar values are clustered together.
- A value closer to -1 indicates a stronger negative spatial correlation, suggesting a dispersed or competitive distribution.
- A value of 0 indicates that the spatial distribution is random, with no significant autocorrelation.

The calculation formula for the Global Moran’s $I$ is as follows:

$$I = \frac{n \sum_{i=1}^n \sum_{j=1}^n w_{ij}(x_i - \bar{x})(x_j - \bar{x})}{\sum_{i=1}^n \sum_{j=1}^n w_{ij} \sum_{i=1}^n (x_i - \bar{x})^2} \tag{2}$$

Global spatial autocorrelation, represented by Moran's $I$, is used to characterize the spatial distribution patterns and modes of the research subjects. In this context, let $i$ represent a specific spatial unit and $j$ represent another spatial unit. Let $\bar{x}$ denote the mean planting area or total yield across all county-level units, while $x_i$ and $x_j$ represent the planting area or total yield of spatial units $i$ and $j$, respectively. The term $w_{ij}$ refers to the spatial weight value between these units.

Local spatial autocorrelation further categorizes these spatial patterns into four distinct types of clusters: High-High (H-H), Low-Low (L-L), High-Low (H-L), and Low-High (L-H). Among these, High-High and Low-Low clusters indicate positive spatial correlation, suggesting that similar values are geographically concentrated. Conversely, High-Low and Low-High clusters represent negative spatial correlation, indicating spatial outliers where a unit's value differs significantly from its neighbors.

$$I_i = \frac{(x_i - \bar{x})}{\sum_{i=1}^n (x_i - \bar{x})^2} \sum_{j=1}^n w_{ij}(x_j - \bar{x}) \tag{3}$$

To explore the specific spatial distribution patterns and correlations between crop planting areas and total yields, a local spatial autocorrelation analysis (Local Moran's $I$) was conducted using ArcGIS. This analysis focused on the planting areas and total yields of maize and vegetables.

The local spatial autocorrelation analysis was performed under a significance level of $P < 0.05$.

Based on these results, spatial autocorrelation distribution maps were generated for the planting areas and total yields of both crop types.

2 结果与分析

Interannual Changes in Crop Planting Structure Types in the Fenwei Plain

From 2000 to 2020, the richness index of crop planting structure types in the Fenwei Plain exhibited a trend of initial increase, followed by a decrease, and a subsequent rise. During the period from 2000 to 2010, the richness index of crop planting structures in the Fenwei Plain rose from 17 to 21. Conversely, from 2010 to 2015, the richness index decreased from 21 to 18.

By 2020, the richness index of crop planting structures in the Fenwei Plain increased again to 21, indicating a high diversity of crop species within the region. The evolution of this richness index is constrained by natural conditions such as precipitation and accumulated temperature. Simultaneously, socio-economic factors, including the economic benefits of crops and related agricultural policies, have driven changes in the planting structure richness of the area. Between 2000 and 2020, a total of 31 crop planting structure types appeared in the Fenwei Plain, primarily consisting of Wheat-Maize, Maize-Other, and Maize-Wheat types. The top-ranked planting structure types have consistently been combinations of wheat and maize. Notably, the proportion of counties where maize planting exceeds 50% has been increasing annually, while the proportion of counties where wheat planting exceeds 50% has been decreasing. This decline in wheat planting proportions may be attributed to a combination of lower economic returns, water resource constraints, shifts in market demand, and other agricultural factors. In contrast, maize has seen a continuous increase in its planting proportion due to its higher economic profitability. It is also noteworthy that from 2015 to 2020, the crop planting structure types ranked fifth and below in the Fenwei Plain began to include other crops such as vegetables, with an increasing number of counties adopting vegetable-oriented planting types.

ratios ≥ 15 % and ≥ 30 % on Fenwei Plain

This change reflects a shift in the crop planting structure of the Fenwei Plain from a single grain crop model toward a diversified, value-added model. The spatial evolution of the crop planting structure in the Fenwei Plain, particularly the spatial distribution of structure types, has undergone significant changes over the years.

The spatio-temporal evolution patterns of the major crop planting structure types (defined as crops or combinations with a planting proportion $\geq 30\%$) are as follows:

In 2000, more than half of the counties exhibited a wheat-based planting structure. These were primarily distributed in Linfen City (Shanxi Province), Sanmenxia and Luoyang Cities (Henan Province), and most areas of Weinan and Xianyang Cities (陕西 Province). By 2005, the number of wheat-type counties decreased, with distribution narrowing mainly to Sanmenxia and Luoyang. By 2010, the number of wheat-type counties dropped further, appearing only in small portions of Weinan and Baoji. As the number of wheat-type counties declined, the number of corn-type counties increased annually. In 2000, corn-type counties were concentrated in Jinzhong City and a few counties in Luliang City (such as Wenshui and Jiaocheng); by 2005, they were concentrated in Jinzhong and Luliang, with scattered distributions elsewhere. Wheat-corn type counties were primarily concentrated in Baoji and Xi'an. With the decrease in wheat-type and increase in corn-type areas, wheat-corn type counties gradually spread from the southwest to the northeast of the Fenwei Plain. Conversely, corn-wheat type counties exhibited a scattered distribution across the region. Although the number of vegetable-type and vegetable-wheat type counties remains small, it is steadily increasing. Feng County and Taibai County in Baoji, along with Yanliang District in Xi'an, are the primary vegetable-type regions, while Jingyang County and Huazhou District in Weinan are the primary vegetable-wheat type areas.

Crops such as millet, soybean, oilseeds, and cotton with planting proportions $\geq 30\%$ show high inter-annual variability in their spatial distribution.

The average number of counties covered by wheat-corn and corn-wheat types reached a significant level, indicating that wheat, corn, and their combinations are the dominant crop planting structure types in the Fenwei Plain. Regarding the spatial distribution of planting area and yield: in 2000, the wheat planting area in the Fenwei Plain decreased, and the number of counties with significant reductions was high. The average reduction in planting area was most pronounced in Xi'an, Weinan, and the western part of Yuncheng. Conversely, counties with lower reductions in planting area were scattered across the Fenwei Plain.

Research on the Spatio-Temporal Pattern Evolution of Crop Planting Structure in the Fenwei Plain

[TABLE: 2000, 2005, 2010, 2015, 2022. Types: Wheat-Corn, Corn-Other, Corn-Wheat. This table lists the top-ranked planting structure types and their combinations. Values in parentheses indicate the number of counties for each major crop planting structure type.]
Spatio-temporal changes of major crop planting structure types in the Fenwei Plain.

Wheat production is widely distributed across all regions, with total yield generally following a decreasing trend from southeast to northwest. In counties where total wheat yield decreased, the reduction was significant, while other counties saw significant increases. Most counties in Xi'an experienced an average annual decrease in total yield, whereas total wheat yields in Weinan and Luoyang increased significantly, with annual growth rates often exceeding a specific threshold in scattered areas. The spatial pattern of corn planting area in the plain shows a decrease in the west and an increase in the east. Counties with increasing corn planting areas account for a large proportion, with significantly increasing counties primarily located in the eastern belt of the Fenwei Plain, showing annual growth rates mostly within a specific range. Areas with significant decreases in corn planting area are mainly located in the western part of the Fenwei Plain.

In Xi'an, located in the western part of the plain, the average annual decrease in corn planting area exceeded $600\text{ hm}^2$.

In most counties of Xianyang, the average annual decrease remained within a certain range. The spatial distribution of total corn yield is similar to that of its planting area. Corn yield increased in many counties, with significant increases observed in a large portion of the region. The average annual growth rate of corn yield was positive in most areas, with increases primarily concentrated in Shanxi Province, Henan Province, and Weinan City in Shaanxi Province. In contrast, Xi'an and its neighboring cities, Baoji and Xianyang, showed a distribution of decreasing total corn yield. The spatial pattern of vegetable planting area in the Fenwei Plain shows an increase in the south and a decrease in the north. Vegetable planting areas increased in many counties, with significant increases in some and significant decreases in others. In most southern parts of the plain, the average annual growth rate of vegetable area was stable, while in some counties of Luoyang and Weinan, the growth rate reached higher levels. Regions with decreasing vegetable planting areas were mainly concentrated in contiguous blocks in Shanxi Province and scattered areas in Shaanxi. The spatial distribution of total vegetable yield mirrors that of its planting area. Total yield increased in many counties, with significant increases concentrated in Shaanxi, where the average annual growth rate was substantial. Decreasing vegetable yields were found in contiguous areas of Shanxi. These trends highlight the shifting dynamics of corn and vegetable planting areas and yields.

Research on the Spatio-Temporal Pattern Evolution of Crop Planting Structure in the Fenwei Plain

The spatial agglomeration patterns of major crop planting areas and yields in the Fenwei Plain were analyzed using spatial autocorrelation. Significance tests for the Moran's I of corn and vegetable planting areas and yields indicate the following:
[TABLE: Global Moran's I of wheat, corn, and vegetable planting area and total yield on Fenwei Plain (2000, 2005, 2010, 2015, 2022)]
There is a significant positive spatial correlation for the planting area and total yield of major crops, indicating clear spatial agglomeration characteristics. From 2000 to 2022, the Moran's I for wheat planting area and total yield gradually decreased (e.g., from a higher value to a lower one), suggesting a weakening of spatial correlation and a reduction in spatial agglomeration for wheat. The Moran's I for corn planting area fluctuated within a specific range, while the index for corn yield also showed variation. This indicates a general weakening of spatial correlation and agglomeration for corn. In contrast, the Moran's I for vegetable planting area and total yield showed an overall upward trend, indicating that the spatial correlation for vegetables has strengthened and spatial agglomeration has become more pronounced. The distribution patterns of spatial autocorrelation types for 2022 are shown in the figure. The distribution of autocorrelation types for wheat planting area and total yield is largely consistent. Low-Low (L-L) agglomeration areas for wheat are concentrated in the northern part of the plain.

These L-L areas are located in parts of Luliang and Linfen, as well as the urban area of Xi'an. High-High (H-H) agglomeration areas are primarily concentrated in the central-southern Fenwei Plain and Luoyang in the southeast. The distribution of autocorrelation types for corn yield and planting area is similar, though the spatial agglomeration is relatively lower. L-L areas for corn yield are mainly situated near Feng County and Jintai District in Baoji, the Xi'an urban area, and the border of Luliang, Jinzhong, and Linfen. H-H areas are predominantly in Weinan, with a few counties like Qi County also showing H-H patterns. Most counties exhibit non-significant spatial autocorrelation types. The spatial distribution of autocorrelation types for vegetable planting area and yield is also highly consistent. L-L agglomeration areas for vegetables are concentrated in most counties of Luliang and Linfen, while H-H areas are mainly distributed at the junction of Weinan, Xianyang, and Xi'an.

3 讨

Existing research indicates that plain regions serve as the primary production areas for regional grain and play a critical role in ensuring regional food security. However, studies examining the spatio-temporal changes of crop planting structures from a large-scale plain perspective remain scarce, particularly regarding traditional agricultural regions in central China. This paper focuses on the Fenwei Plain, utilizing crop planting area and yield data to analyze the spatio-temporal evolution of the region's planting structure, with a specific emphasis on the spatial distribution of wheat, maize, and vegetable crops. The evolution of the production centers of gravity for major crops in the Fenwei Plain from 2000 to 2022 is illustrated in [FIGURE:N]. The center of gravity for wheat cultivation in the Fenwei Plain is generally distributed along a northeast-southwest axis. During the study period, this center remained consistently within Dali County, indicating that the spatial pattern of wheat cultivation in the Fenwei Plain has reached a state of relative stability. In contrast, maize cultivation also follows a northeast-southwest distribution, but its center of gravity has shifted continuously toward the northeast. Between 2000 and 2005, the center was located in Heyang County, Weinan City, Shaanxi Province; from 2010 to 2015, it moved between Hancheng City, Weinan, and Wanrong County, Yuncheng City, Shanxi Province; and from 2020 onwards, it has been primarily situated in Hejin City, Yuncheng. Vegetable cultivation similarly exhibits a northeast-southwest distribution pattern. Its center of gravity remained in Heyang County from 2000 to 2005, shifted southwest to Chengcheng County in 2010, and moved southeast back to Dali County after 2015. Significant changes have occurred in the agricultural planting structure of the Fenwei Plain in recent years, characterized by an increasing proportion of maize and a decreasing proportion of wheat, which is consistent with the findings of Liu Dong et al. Furthermore, the rising prominence of cash crops such as vegetables in the planting structure aligns with the research of Zhang Rongtian et al. Previous studies have demonstrated that changes in crop planting structures are constrained by natural factors such as precipitation and accumulated temperature, as well as socio-economic factors and scientific technology. As agricultural production is the primary source of income for farmers, the planting structure is the result of a trade-off between spontaneous adjustments driven by revenue differentials and government macro-control measures, such as grain subsidies. As "rational economic agents," farmers inevitably consider the standard deviation ellipse distribution of wheat, maize, and vegetable planting areas in the Fenwei Plain when deciding which crops to plant.

Research on the Spatio-temporal Evolution Characteristics of Crop Planting Structures in the Fenwei Plain

Factors such as the economic benefits of different crops and the scale of government subsidies play a decisive role in planting choices. In recent years, the economic returns for maize and vegetables in the Fenwei Plain have been significantly higher than those for wheat. Additionally, comprehensive factors including agricultural technological innovation and subsidy policies have driven changes in the region's planting structure. Maintaining stable yields for primary grain crops remains the cornerstone of ensuring national food security. It is essential to utilize policy instruments to mitigate the adverse effects of market demand and price fluctuations on agricultural planting structures. Ensuring the stability of the primary grain structure and preventing its over-simplification are critical issues that must be addressed during regional agricultural structural adjustments.

4 结

From 2010 to 2020, the richness index of crop planting structure types in the Fenwei Plain exhibited a trend of initial increase, followed by a decrease, and a subsequent increase. Wheat, maize, and their combinations constitute the primary crop planting structures in this region. Specifically, the county-level average coverage for "Wheat-Maize" and "Maize-Wheat" types remained dominant. However, the number of counties characterized by the "Wheat-Maize" type gradually decreased over time, while the number of counties characterized by the "Maize-Wheat" type increased annually. Geographically, the "Wheat-Maize" type gradually spread from the southwestern part of the Fenwei Plain toward the northeast, whereas the "Maize-Wheat" type maintained a scattered distribution pattern throughout the region.

Between 2010 and 2020, the planting patterns for wheat, maize, and vegetables in the Fenwei Plain consistently followed a northeast-southwest distribution axis, which remained largely stable. During this period, the center of gravity for maize cultivation shifted continuously toward the northeast. Conversely, the center of gravity for vegetable cultivation shifted from Heyang County toward the southwest, moving into Chengcheng County.

The spatial differentiation of wheat, maize, and vegetable cultivation in the Fenwei Plain was significant during this decade. Based on these spatiotemporal development trends, it is recommended that policymakers strengthen macro-level regulation of the Fenwei Plain's crop planting structure from the perspective of ensuring food security.

References

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Crop Structure Extraction and Water Supply-Demand Analysis in the Yellow River Irrigation District Based on Sentinel-2 Imagery

Abstract

Accurate identification of crop types and their spatial distribution is fundamental for optimizing water resource allocation and ensuring food security in irrigation districts. This study utilizes multi-temporal Sentinel-2 satellite imagery to extract crop structure information and analyze the balance between water supply and demand in the Yellow River Irrigation District. By constructing a high-resolution classification dataset and employing machine learning algorithms, we achieved precise mapping of major crops, including wheat, maize, and sunflowers. Furthermore, we integrated meteorological data and irrigation records to quantify the spatial-temporal characteristics of crop water requirements and irrigation efficiency. The results demonstrate that the proposed method effectively captures the dynamic changes in crop patterns and provides a scientific basis for refined water management in large-scale irrigation systems.

1. Introduction

The Yellow River Irrigation District serves as a critical agricultural production base, where water scarcity remains a primary constraint on sustainable development. With the intensification of climate change and increasing competition for water across different sectors, traditional methods of crop monitoring and water management are no longer sufficient. Remote sensing technology, particularly the Sentinel-2 mission with its high spatial (10m) and temporal (5-day) resolution, offers unprecedented opportunities for detailed agricultural monitoring.

Accurate crop structure extraction is the prerequisite for calculating crop water requirements ($ET_c$). By combining remote sensing-based crop maps with the Penman-Monteith equation, it is possible to estimate the spatial distribution of water demand. Comparing these estimates with actual water diversion data allows for a comprehensive analysis of the water supply-demand balance, identifying areas of water stress or wastage.

2. Materials and Methods

2.1 Study Area and Data Sources

The study area is located within a typical irrigation block of the Yellow River basin. The region is characterized by a semi-arid climate where agriculture relies heavily on diverted river water. The primary crops include winter wheat, summer maize, and various economic crops.

We utilized Sentinel-2 Level-2A products, which provide Bottom-of-Atmosphere (BOA) reflectance. Pre-processing steps included cloud masking, atmospheric correction, and the calculation of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).

2.2 Crop Classification Methodology

To extract the crop structure, we employed a Random Forest (RF) classifier. The input features consisted of multi-temporal spectral

137 . [Sun Bin, Bi Chunning, Xue Jianchun, et al. Extraction of

crop structure and analysis of water supply and demand in irriga tion area of Yellow River diversion based on Sentinel- image[J].

The Impact of Rising Agricultural Labor Prices on Inter-provincial Variations in Crop Planting Structures

Abstract: As the opportunity cost of rural labor continues to rise, the structure of crop planting in China has undergone significant transformations. This study investigates the inter-provincial differences in how rising agricultural labor prices influence the allocation of land between grain and cash crops. By analyzing historical data and regional economic shifts, we examine the mechanisms through which labor scarcity and wage increases drive farmers to transition from labor-intensive to capital-intensive or mechanized agricultural practices.

1. Introduction

In recent decades, China's rapid urbanization and industrialization have led to a massive transfer of rural labor to non-agricultural sectors. This shift has resulted in a continuous increase in the price of agricultural labor. According to economic theory, changes in factor prices inevitably lead to adjustments in production structures. In the agricultural sector, this is primarily reflected in the reallocation of land across different crop types.

[FIGURE:1]

The impact of rising labor costs is not uniform across all regions. Due to differences in natural endowments, levels of mechanization, and regional economic development, provinces exhibit diverse responses in their crop planting structures. Understanding these inter-provincial differences is crucial for ensuring national food security and optimizing the spatial distribution of agricultural production.

2. Theoretical Framework and Methodology

The theoretical basis for this study rests on the induced innovation hypothesis and the theory of comparative advantage. When the price of labor rises relative to other factors (such as land or capital), farmers are incentivized to adopt labor-saving technologies or switch to crops that require less manual labor.

2.1 Data Sources and Variables

This study utilizes provincial-level panel data. The primary variables include:
- Crop Planting Structure: Represented by the ratio of the area sown with grain crops to the total sown area.
- Agricultural Labor Price: Measured by the daily wage rate of hired labor in the agricultural sector.
- Control Variables: Including irrigation infrastructure, mechanical power, and regional climate indicators.

We employ a fixed-effects model to account for unobserved provincial heterogeneity:

$$ Y_{it} = \alpha + \beta L_{it} + \gamma X_{it} + \mu_i + \epsilon_{it} $$

where $Y_{it}$ represents the planting structure of province $i$ in year $t$, $L_{it}$ is the labor price, $X_{it}$ is a vector

nitrogen and phosphorus losses in the Hai - River Basin since the

[J]. Journal of Cleaner Production, doi: [Zhang Li, Wu Wenbin, Yang Peng, et al. Temporal and spatial changes in crop patterns of Binxian County in Heilongjiang Province [J]. Scientia Agricultura Sinica].

Abstract

The spatial and temporal dynamics of crop distribution are critical indicators of agricultural intensification and land-use efficiency. This study investigates the evolution of crop patterns in Binxian County, Heilongjiang Province, a representative region of the Northeast China Plain. By integrating multi-temporal remote sensing data with spatial analysis techniques, we characterize the shifts in major crop types, including maize, soybeans, and rice, over the past decades. Our findings reveal significant spatial restructuring driven by both climatic shifts and socio-economic factors. The results provide essential baseline data for optimizing regional agricultural structures and ensuring food security under changing environmental conditions.

Introduction

Understanding the spatiotemporal variations in crop patterns is fundamental to sustainable agricultural management and the formulation of effective rural development policies. In recent years, the Northeast China Plain has undergone substantial transformations in its agricultural landscape, characterized by a marked shift in crop composition and distribution. Binxian County, located in the heart of Heilongjiang Province, serves as an ideal case study for examining these transitions due to its diverse topography and its role as a key grain-producing region.

Previous research has highlighted that changes in crop patterns are often the result of complex interactions between natural environmental factors—such as temperature and precipitation—and human-induced drivers, including market prices, agricultural subsidies, and technological advancements. However, high-resolution spatial analyses that capture the nuances of these changes at the county level remain limited. This study aims to fill this gap by analyzing the trajectory of crop pattern changes in Binxian County, providing insights into the mechanisms of agricultural land-use change in high-latitude regions.

Materials and Methods

3.1 Study Area

Binxian County is situated in the transition zone between the Songnen Plain and the Zhangguangcai Mountains. The region features a temperate continental monsoon climate, which is conducive to the cultivation of a variety of cold-temperate crops. The primary crops in this region include maize, soybeans, and paddy rice, which together constitute the backbone of the local agricultural economy.

3.2 Data Sources and Processing

The primary data used in this study include multi-spectral satellite imagery and historical agricultural statistics. We utilized 30m resolution imagery to classify land

tia Agricultura Sinica, 2013 , 46 ( 15 ): 3227 - 3237 . ]

Hu Yunfei, Liu Xu, Liang Junfen, et al. Spatiotemporal evolution of crop planting structure in the Guangdong-Hong Kong-Macao Greater Bay Area [J]. Guangdong Agricultural Sciences.

Ren Pinpin, Li Baoguo, Huang Feng. Spatiotemporal patterns of wheat and maize production under the evolution of crop planting structures in the Huang-Huai-Hai dry farmland, China [J]. Resources Science. Du Guoming, Zhang Yang, Li Quanfeng. The evolution path of crop structure in the Sanjiang Plain in the 21st century [J]. Research of Agricultural Modernization.

Modernization, 2019 , 40 ( 5 ): 736 - 744 . ]

Evolution Characteristics and Drivers of China's Spatial Grain Production Patterns

Abstract

Grain security is a fundamental cornerstone of national security. Analyzing the evolution of spatial grain production patterns and their driving mechanisms is of great significance for optimizing the layout of agricultural production and ensuring sustainable food security. This study examines the spatio-temporal dynamics of grain production in China, identifying key shifts in production centers and the underlying socio-economic and natural factors influencing these changes.

1. Introduction

As a major agricultural country with a massive population, China's grain production stability is directly linked to social stability and national development. In recent years, against the backdrop of rapid urbanization and industrialization, the spatial pattern of China's grain production has undergone profound changes. The traditional "grain transport from south to north" has transitioned into a "grain transport from north to south" pattern, reflecting a significant northward shift in the center of gravity for grain production. Understanding the characteristics of these changes and their driving forces is essential for formulating effective agricultural policies.

2. Data and Methodology

2.1 Data Sources

The data used in this study primarily include grain yield, sown area, and socio-economic indicators across various provinces and regions in China. These datasets are sourced from the China Statistical Yearbook, the China Agricultural Yearbook, and relevant provincial statistical bulletins.

2.2 Research Methods

To analyze the spatial evolution, we employ the center of gravity model and spatial autocorrelation analysis. The center of gravity for grain production is calculated as follows:

$$ \bar{X} = \frac{\sum_{i=1}^{n} P_i X_i}{\sum_{i=1}^{n} P_i}, \quad \bar{Y} = \frac{\sum_{i=1}^{n} P_i Y_i}{\sum_{i=1}^{n} P_i} $$

where $(\bar{X}, \bar{Y})$ represents the coordinates of the center of gravity.

Rong. The characteristics and driving mechanisms of China ’ s

Analysis of the Temporal and Spatial Pattern Evolution of Crop Planting Structure in Jiangsu Province

Abstract

The spatial and temporal evolution of crop planting patterns is a critical component of regional agricultural geography and food security research. This study examines the dynamic shifts in agricultural production within Jiangsu Province, focusing on the structural changes in crop distribution and their underlying drivers. By analyzing historical data and spatial trends, we identify the transition from traditional diversified planting to more specialized regional clusters.

1. Introduction

The evolution of grain spatial production patterns reflects the complex interplay between socioeconomic development, environmental constraints, and policy interventions. In the context of rapid urbanization and industrialization, Jiangsu Province has experienced significant transformations in its agricultural landscape. Understanding these changes is essential for optimizing land use and ensuring sustainable agricultural development.

[TABLE:1]

2. Methodology and Data Sources

This research utilizes long-term statistical data to track the changes in planting areas for major crops. Following the methodologies established in previous studies, such as the analysis of crop planting area characteristics in Liaoning Province \cite{WangShuai}, we apply spatial autocorrelation and shift-share analysis to quantify the intensity of structural changes.

The primary data sources include the Jiangsu Statistical Yearbook and regional agricultural reports. We focus on the spatial distribution of grain crops versus cash crops, examining how market demand and technological advancements have reshaped the agricultural map of the province.

3. Temporal Evolution of Planting Structures

Over the past several decades, the planting structure in Jiangsu has shifted from a grain-dominant system toward a more diversified model. While grain production remains a cornerstone of the provincial economy, there has been a notable increase in the acreage dedicated to high-value horticultural crops and oilseeds.

[FIGURE:1]

The temporal analysis reveals several distinct phases:
- Phase I: Stability. A period characterized by traditional double-cropping systems with minimal spatial variation.
- Phase II: Transition. Increasing market liberalization led to the initial expansion of non-grain crops in suburban areas.
- Phase III: Specialization. The emergence of distinct agricultural zones based on comparative advantages, such as the rice-wheat rotation in northern Jiangsu and high-efficiency facility agriculture in the south.

4. Spatial Pattern Dynamics

The spatial distribution of crop production exhibits significant regional heterogeneity. Using spatial analysis tools, we observe a "northward shift" in the center of gravity for grain production.

4.1 Regional Concentration

The northern region of Jiangsu (S

acteristics of crop planting area in Liaoning Province[J]. Agricul ⁃

Spatiotemporal Changes in Crop Planting Structures in China

1. Introduction

The planting structure of crops refers to the types, proportions, and spatial distribution of crops within a specific region. It is a core component of agricultural production systems and a direct reflection of the utilization of land resources. In recent years, driven by factors such as climate change, technological progress, market demand fluctuations, and national policy adjustments, China's crop planting structure has undergone significant and complex changes. Understanding these spatiotemporal dynamics is crucial for ensuring national food security, optimizing the allocation of agricultural resources, and promoting sustainable agricultural development.

2. Materials and Methods

2.1 Data Sources

The data used in this study primarily include agricultural statistical yearbooks, remote sensing interpretation data, and meteorological records spanning several decades. Specifically, crop yield and acreage data were sourced from the China Statistical Yearbook and relevant provincial statistical bulletins. Spatial distribution data were derived from high-resolution satellite imagery processed using machine learning algorithms to identify specific crop types.

2.2 Research Methodology

To analyze the spatiotemporal evolution of planting structures, we employed several quantitative indicators:
1. Information Entropy of Planting Structure: Used to measure the diversity and complexity of crop types in a given region.
2. Shift-Share Analysis: Applied to decompose the drivers of change in crop acreage into national growth effects, structural effects, and competitive effects.
3. Spatial Autocorrelation: Utilized to identify geographic clusters and hotspots of specific crop transitions.

The mathematical representation of the structural diversity index is given by:
$$H = -\sum_{i=1}^{n} P_i \ln P_i$$
where $P_i$ represents the proportion of the $i$-th crop's planting area relative to the total cultivated area.

3. Results and Analysis

3.1 Temporal Evolution of Planting Structures

Over the past few decades, China's planting structure has transitioned from a traditional focus on grain crops toward a more diversified system incorporating cash crops. While the total area dedicated to grain remains stable due to "red line" land protection policies, the internal composition has shifted. For instance, the area of corn ($\text{Zea mays}$) has seen a significant increase, often at the expense of soybean ($\text{Glycine max}$) or minor cereals, particularly in the Northeast and North China regions.

[TABLE:1]

3

分析

Liu Zhenhuan, Peng, Wu Wenbin, et al. Spatio-temporal changes in Chinese crop patterns over the past three decades [J]. Acta Geographica Sinica.

The shift of China's grain production center and its impacts. Research of Agricultural Modernization.

Yang Zonghui, Li Jinkai, Han Chenxue, et al. The evolution path of China's grain production base and the influencing factors [J]. Research of Agricultural Modernization.

Agricultural Modernization, 2019 , 40 ( 1 ): 36 - 43 . ]

Spatiotemporal Dynamic Changes in the Planting Structure of Wheat Acreage in China

Abstract

Wheat is one of the most important food crops in China, and understanding the spatiotemporal dynamics of its planting structure is crucial for ensuring national food security and optimizing agricultural resource allocation. This study analyzes the evolutionary characteristics of wheat planting areas across various regions of China, utilizing long-term statistical data and spatial analysis techniques. The results indicate significant shifts in the geographical distribution of wheat cultivation, characterized by a northward migration of production centers and increasing concentration in primary producing provinces. Factors such as climate change, technological advancements, and socio-economic shifts have played pivotal roles in shaping these patterns. This research provides a theoretical basis for policy formulation regarding regional agricultural development and food security strategies.

1. Introduction

As a staple food crop, wheat plays an indispensable role in China's agricultural economy. With the continuous growth of the population and the evolution of dietary structures, maintaining stable wheat production has become a core objective of national food security. In recent decades, influenced by the acceleration of urbanization, the adjustment of agricultural industrial structures, and global climate change, the spatial distribution and planting intensity of wheat in China have undergone profound transformations.

Previous studies have highlighted that the traditional "South Rice, North Wheat" pattern is undergoing subtle shifts. While the North China Plain remains the "granary" of the country, marginal producing areas have seen significant fluctuations in acreage. Understanding these dynamic changes is essential for predicting future production trends and implementing targeted agricultural interventions.

2. Materials and Methods

2.1 Data Sources

The primary data for this study were derived from the China Statistical Yearbook, the China Agricultural Statistical Report, and provincial-level statistical bulletins spanning the past thirty years. To ensure spatial accuracy, remote sensing products and land-use datasets were integrated to verify the reported acreage.

2.2 Research Methodology

To quantify the spatiotemporal dynamics, we employed several spatial econometric models and indices:
- Concentration Index: Used to measure the degree of spatial clustering of wheat production.
- Center of Gravity Model: Applied to track the geographical shift of wheat planting centers over time.
- Spatial Autocorrelation (Moran's I): Utilized to identify spatial dependencies and "hot spots" of planting intensity.

The mathematical representation of the center of gravity $(\bar{x}, \bar{y})$ is given by:
$$\begin{aligned} \bar{x} = \frac{\sum_{i=1}^n w_i x_i}{\sum_{i=1}^n w_i}, \quad \bar{y} = \frac{\sum_{i=1}^n w_i y_i}{\sum_{i=1}^n w_i} \end{aligned}$$

分析

References

Wang Limin, Liu Jia, Ji Fuhua, et al. Analysis of spatial temporal dynamic change of wheat planting structure of China [J]. Chinese Agricultural Science Bulletin.

Zou Jun, Zhu Yingxuan, Yang Yuhao, et al. Analysis of planting structure evolution and its driving mechanism in North China [J]. Journal of China Agricultural University.

from 1981 to 2015 [J]. Journal of China Agricultural University,

Research on the Evolution Characteristics and Mechanisms of Major Crop Production Patterns in Guangxi

Tu Shuangshuang, Jian Daifei, Long Hualou, et al.

1. Introduction

The spatial distribution and structural evolution of crop production are critical components of agricultural geography and regional sustainable development. As a significant agricultural province in Southern China, Guangxi Zhuang Autonomous Region possesses unique topographical features and climatic conditions that have shaped a diverse agricultural landscape. In recent years, driven by rapid urbanization, industrialization, and changes in agricultural policies, the production patterns of major crops in Guangxi have undergone profound transformations. Understanding these changes is essential for ensuring food security, optimizing agricultural resource allocation, and promoting rural revitalization.

2. Data and Methodology

2.1 Data Sources

The data utilized in this study primarily consist of agricultural statistics from the Guangxi Statistical Yearbook and the China Statistical Yearbook (Regional Economy) covering the period from 2000 to 2020. Specifically, data regarding the sown area and yield of major crops—including rice, sugarcane, maize, and various fruits—were collected at the municipal and county levels.

2.2 Research Methods

To analyze the spatial-temporal evolution of crop production, this study employs several quantitative methods:

• Production Concentration Index: Used to measure the degree of spatial concentration of specific crops across different regions.
• Shift-Share Analysis: Applied to decompose the changes in crop production into national growth effects, structural effects, and competitive effects.
• Spatial Autocorrelation (Moran's I): Utilized to identify spatial clustering patterns and hotspots of agricultural production.
• Econometric Modeling: A panel data model was constructed to analyze the driving mechanisms, incorporating variables such as labor force, mechanical power, irrigation facilities, and market prices.

3. Evolution Characteristics of Major Crop Production

3.1 Temporal Trends in Production Structure

Over the past two decades, the total sown area in Guangxi has remained relatively stable, but the internal structure has shifted significantly. While grain crops (particularly rice) have maintained a dominant position, their relative share has declined. Conversely, the production of cash crops, most notably sugarcane and tropical fruits, has seen substantial growth. This shift reflects a transition from traditional subsistence agriculture toward market-oriented, high-value agricultural production.

[FIGURE:1]

3.2 Spatial Distribution and Concentration

The spatial

Guangxi [J]. Acta Geographica Sinica, 2022 , 77 ( 9 ): 2322 - 2337 . ]

Introduction

The spatial and temporal evolution of crop planting structures is a critical area of research for ensuring food security and optimizing agricultural resource allocation. Recent studies have extensively documented these shifts across various regions of China. For instance, Yan et al. \cite{Yan2021} analyzed the planting structure of major grain crops in the three northeastern provinces, highlighting significant regional transitions. Similarly, Dang et al. \cite{Dang2021} investigated the evolution of agricultural planting structures in the northern Hebei Plateau, providing insights into how high-altitude regions adapt their agricultural practices over time.

In southern and western regions, research has focused on both patterns and underlying drivers. Huang et al. \cite{Huang2021} conducted a comprehensive analysis of the spatio-temporal patterns and driving factors of crop planting structures in Guangxi, emphasizing the role of socio-economic shifts. In the unique ecological context of the Tibetan Plateau, Wu et al. \cite{Wu2021} explored the spatial evolution and specialization of crop planting at the county level, offering a framework for regional agricultural zoning. Furthermore, Liu et al. \cite{Liu2021} examined the structural changes and spatio-temporal evolution of the planting industry in Shaanxi Province, contributing to the understanding of agricultural dynamics in the Loess Plateau region. Collectively, these studies underscore the necessity of monitoring planting structure transitions to inform sustainable agricultural policy and regional development.

Journal of Agricultural Resources and Regional Planning, 2021 , 42

Wang Hongyu, Fang Yangang, Liu Jianzhi. Spatio-temporal changes of crop structure in Heilongjiang Province from 2000 to 2015 [J]. Areal Research and Development, 2018, 37(6): 134-139. Zhao Qinyi, Wang Yuzhi. Evolution of rural settlement patterns in floodplain areas based on GIS: A case study of the Fenwei Plain in the middle reaches of the Yellow River [J]. Science Technology and Industry, 2021, 21(11): 1-7. Huang Xiaogang, Shao Tianjie, Zhao Jingbo, et al. Influence factors and spillover effect of $PM_{2.5}$ concentration on the Fenwei Plain [J]. China Environmental Science, 2019, 39(8): 3539-3548.

Jiang Lingxiao, An Yue, Tan Xuelan, et al. Temporal and spatial evolution and optimized countermeasures of crop planting structure in the Changsha-Zhuzhou-Xiangtan area in recent years [J]. Economic Geography, 2020, 40(10): 161-171.

Research on the Spatio-temporal Evolution Characteristics of Crop Planting Structure in the Fenwei Plain. Luo Qiancheng, Wang Yuetian, Chi Wenfeng, et al. Influencing factors of crop planting structure change in the black soil region of Inner Mongolia [J]. Resources Science, 2020, 42(3): 500-511. Xie Jia, et al. Spatio-temporal characteristics and evolution patterns of Chinese ski resorts from 1996 to 2019 [J]. Economic Regulatory Measures.

Wang Shijin, Dou Wenkang, et al. The spatio temporal characteris tics and evolution law of Chinese ski resorts from

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Tobler W. A computer movie simulating urban growth in the De troit region[J]. Economic Geography, umari M, Sarma K, Sharma R. Using Moran and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Mad hya Pradesh, India[J]. Remote Sensing Applications: Society and Environment, , doi: rsase.

Spatio-temporal pattern evolution characteristics of crop planting structure on Fenwei Plain MIAO Yingfeng, YUAN Ye, ZHOU Zhengwei, ZHAO Jiayu, GUO Yuxi (School of Public Administration, Shanxi University of Finance and Economics, Taiyuan , Shanxi, China) temporal evolution of crop planting structures provides a theoretical basis for ensuring regional food security and promoting the sustainable development of agriculture. This study examines the crop planting structure across counties (cities/districts) in the Fenwei Plain, Shanxi Province and Shaanxi Province, China based on agricultural statistical data for the period of . Employing methods such as the standard deviation ellipse model and spatial autocorrelation analysis, the spatial and temporal dynamics of major crop planting structures over this period are explored. The results indicate the following. ( ) Over the study period, distinct crop planting struc ture types were identified, with wheat, corn, and their combinations (wheat type, corn type, wheat-corn type, and corn-wheat type) being the dominant types. Moreover, spatially and temporally, the number of wheat-type coun ties gradually declined, while the number of corn-type counties steadily increased. Wheat-corn-type counties ex panded from the southwest to the northeast of the Fenwei Plain, while corn and wheat varieties were dispersed throughout the region. Crop planting structure diversity peaked in , whereas the richness index was lowest in ) For the period of , the planting patterns of wheat, corn, and vegetables exhibited a distribu tion trend along the northeast-southwest axis. The center of gravity for wheat remained stable, whereas that of corn shifted progressively northeastward. Meanwhile, the center of gravity for vegetables moved from Heyang County to Chengcheng County in a southwestward direction. Thus, the spatial distribution of major crops in the Fenwei Plain demonstrates a differentiated development trend, with a decline in wheat cultivation and expansion in corn and vegetable cultivation. To ensure food security, future adjustments to the crop planting structure of the Fenwei Plain should be made based on an analysis of these spatiotemporal trends and supported by macroeconom