# Exploring the Spatio-Temporal Dynamics of Development of Specialized Agricultural Villages in the Underdeveloped Region of China

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area and Data Processing

^{2}) with the SRTM DEM 30 m data. The primary river data of Henan province came from the same platform; it was used to calculate the spatial distance from each SAV to its main river by the near tool available in ArcGIS 10.7. The average annual rainfall data were from the China National Meteorological Data Center to get the average annual rainfall of SAVs. Soil quality data were derived from the land–air interaction research team of Sun Yat-sen University to get the soil quality grade of SAVs. Road network data of Henan province were obtained from the Land Resources Survey and Planning Institute of Henan Province. We used it to calculate the road network distance from each SAV to its county seat, prefecture-level city center, national road, and provincial road. The population, disposable income, and the number of county enterprises were derived from statistical yearbooks.

## 3. Method

#### 3.1. Measurement of DSAVs

#### 3.2. Quantification of the Potential Association Factors

#### 3.3. Global Moran’s I

_{i}and x

_{j}are the N∆GP, N∆ER, N∆FI, and SAVDI values of ith and jth SAV; ${\mathrm{x}}^{\prime}$ represents the average value of N∆GP, N∆ER, N∆FI, and SAVDI of all SAVs; W

_{ij}is the spatial weight matrix. The spatial weight matrix describes the degree of position association between every two SAVs. W

_{ij}= 1 means that ith and jth SAV are “neighbors”; otherwise, W

_{ij}= 0. The significant differences may appear in the autocorrelation analysis using different spatial weight matrices. I > 0 means positive correlation as a whole; I = 0 means the random distribution; I < 0 means the negative correlation as a whole. VAR(I) is the variance of the global Morin index; E(I) is the expected value of the global Morin index.

#### 3.4. Analyzing the Spatial Pattern of DSAVs

#### 3.5. Using Geographic Detectors to Identify the Significant Factors of DSAVs

_{h}is the number of SAVs in a given area; N is the number of SAVs in the region; ${\mathsf{\sigma}}_{\mathrm{h}}^{2}$ is the kernel density variance of SAVDI in an SAV; ${\mathsf{\sigma}}^{2}$ is the kernel density variance of SAVDI throughout regions 1, 2, and 3 in this study.

## 4. Results

#### 4.1. Spatial Pattern of DSAVs

^{2}in the western Nanyang (region 1), Luohe (region 2), and northwestern Shangqiu (region 3) from 2010 to 2014 (Figure 3B). The SAVs clustered in Luohe, Puyang, Jiaozuo, northwestern Shangqiu, and western Nanyang from 2015 to 2019, and the kernel density values were over 13 pcs/10,000 km

^{2}(Figure 3C). In these two time periods, region 1 (mountain–plain area), region 2 (hill–plain area), and region 3 (plain area) are areas where hot spots of SAVs persisted, but their development capabilities are different. Specifically, the accumulation of specialized shiitake villages was in region 1. The kernel density values were above 12.0 (in 2011–2014) and 15.0 (in 2015–2019). Specialized coarse cereals villages clustered in region 2 from 2010 to 2019. Specialized fruit and livestock villages were growing in region 3 from 2010 to 2019. Specialized Chinese herbal villages agglomerated in region 1, and the kernel density value was increasing from 2010 to 2019.

#### 4.2. Identifying the Key Influencing Factors of DSAVs

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**The results of the kernel density analysis of SAVDI in 2010 (

**A**), 2011–2014 (

**B**), and 2015–2019 (

**C**).

First-Order | Second-Order | Detailed Indicators |
---|---|---|

Terrain | Elevation | Elevation (T1) *, Mean coefficient of elevation (T2) *, Extreme coefficient of elevation (T3) * |

Slope | Slope (T4) *, Mean coefficient of slope (T5) *, Extreme coefficient of slope (T6) * | |

Resource | Water resource | Spatial distance from SAVs to river (R1) *, Mean coefficient of spatial distance from SAV to River (R2) *, Extreme coefficient of spatial distance to the river, Rainfall (R3) *, Mean coefficient of rainfall, Extreme value coefficient |

Soil resource | Soil quality grade (R4) *, Mean coefficient, Extreme value coefficient | |

Location | Distance to city | Spatial distance from SAVs to county (L1) *, Spatial distance from SAV to city |

Traffic accessibility | Network distance from SAVs to road network (L2) *, Mean coefficient of the network distance from SAVs to road network (L3) *, Extreme coefficient of the network distance from SAVs to road network (L4) * | |

Market | Market scale | County urbanization population (M1) *, Prefecture-level urban population, |

Degree of supply and demand | County urbanization rate (M2) *, Prefecture-level urbanization rate | |

Consumption level | Disposable income of urban residents in the county (M3) * | |

Economy | Total output value | Mean county GDP of former 5 years (E1) *, Mean municipal GDP of former 5 years |

Number of enterprises | The number of agricultural enterprises in the county (E2) * |

DSAV. | Global Moran’s I | Z-Value | P-Value |
---|---|---|---|

$\mathrm{N}\Delta \mathrm{GP}$ | 0.47 | 19.25 | 0.001 |

$\mathrm{N}\Delta \mathrm{ER}$ | 0.51 | 18.12 | 0.001 |

$\mathrm{N}\Delta \mathrm{FI}$ | 0.49 | 15.25 | 0.001 |

SAVDI | 0.45 | 17.56 | 0.001 |

Period of Time | Original Variables | Factors | ||||
---|---|---|---|---|---|---|

SSVDI | SGVDI | SFVDI | SLVDI | SCVDI | ||

2011–2014 | $\mathrm{N}\Delta \mathrm{GP}$ | 0.332 | 0.258 | 0.102 | 0.155 | 0.752 |

$\mathrm{N}\Delta \mathrm{ER}$ | 0.211 | 0.554 | 0.552 | 0.641 | 0.341 | |

$\mathrm{N}\Delta \mathrm{FI}$ | 0.635 | 0.285 | 0.311 | 0.166 | 0.156 | |

2015–2019 | $\mathrm{N}\Delta \mathrm{GP}$ | 0.212 | 0.125 | 0.158 | 0.265 | 0.711 |

$\mathrm{N}\Delta \mathrm{ER}$ | 0.601 | 0.561 | 0.441 | 0.421 | 0.256 | |

$\mathrm{N}\Delta \mathrm{FI}$ | 0.635 | 0.251 | 0.321 | 0.321 | 0.100 |

Indicator | SAVDI (2011–2014) | SAVDI (2015–2019) | ||
---|---|---|---|---|

q Statistic | p Value | q Statistic | p Value | |

T1 | 0.1311 | 0.0000 | 0.1012 | 0.0000 |

T4 | 0.3158 | 0.0000 | 0.1581 | 0.0000 |

R1 | 0.1521 | 0.0000 | 0.0325 | 0.0311 |

R3 | 0.1112 | 0.0000 | - | - |

R4 | - | - | 0.0125 | 0.0221 |

L1 | 0.4120 | 0.0000 | 0.1251 | 0.0000 |

L2 | 0.1985 | 0.0000 | - | - |

M1 | 0.1421 | 0.0000 | 0.3814 | 0.0000 |

M2 | 0.1025 | 0.0000 | 0.1528 | 0.0000 |

M3 | 0.0211 | 0.0325 | 0.1645 | 0.0000 |

E1 | - | - | 0.4021 | 0.0000 |

E2 | 0.0112 | 0.0412 | 0.1514 | 0.0000 |

**Table 5.**Geographical detector analysis results of the impact factors of the development of specialized shiitake villages in region 1.

Indicator | SSVDI (2011–2014) | SSVDI (2015–2019) | ||
---|---|---|---|---|

q Statistic | p Value | q Statistic | p Value | |

T1 | 0.1211 | 0.0000 | 0.1010 | 0.0000 |

T4 | 0.1158 | 0.0000 | 0.1147 | 0.0000 |

R1 | 0.1521 | 0.0000 | 0.1245 | 0.0000 |

R3 | 0.1011 | 0.0000 | - | - |

R4 | - | - | - | - |

L1 | 0.1623 | 0.0000 | 0.1058 | 0.0000 |

L2 | 0.4712 | 0.0000 | 0.0812 | 0.0301 |

M1 | 0.1371 | 0.0000 | 0.1821 | 0.0000 |

M2 | 0.1125 | 0.0000 | 0.1258 | 0.0000 |

M3 | - | - | 0.4513 | 0.0000 |

E1 | 0.1123 | 0.0000 | 0.1122 | 0.0000 |

E2 | 0.1128 | 0.0000 | 0.2115 | 0.0000 |

**Table 6.**Geographical detector analysis results of the impact factors of the development of specialized Chinese herbal medicine villages in region 1.

Indicator | SCVDI (2011–2014) | SCVDI (2015–2019) | ||
---|---|---|---|---|

q Statistic | p Value | q Statistic | p Value | |

T1 | 0.2211 | 0.0000 | 0.1561 | 0.0000 |

T4 | 0.1350 | 0.0000 | 0.1012 | 0.0000 |

R1 | 0.0121 | 0.0000 | 0.0000 | 0.0000 |

R3 | 0.0011 | 0.0000 | - | - |

R4 | - | - | 0.0320 | 0.0221 |

L1 | 0.1214 | 0.0000 | 0.0058 | 0.0311 |

L2 | 0.1104 | 0.0000 | 0.1012 | 0.0000 |

M1 | 0.0121 | 0.0111 | 0.0032 | 0.0124 |

M2 | 0.0352 | 0.0344 | 0.1058 | 0.0000 |

M3 | 0.0214 | 0.0221 | 0.0522 | 0.0000 |

E1 | 0.1251 | 0.0000 | 0.2136 | 0.0000 |

E2 | 0.3258 | 0.0000 | 0.4125 | 0.0000 |

**Table 7.**Geographical detector analysis results of the impact factors of the development of specialized coarse cereals villages in region 2.

Indicator | SCCVDI (2011–2014) | SCCVDI (2015–2019) | ||
---|---|---|---|---|

q Statistic | p Value | q Statistic | p Value | |

T1 | 0.1444 | 0.0000 | 0.1015 | 0.0000 |

T4 | 0.1026 | 0.0000 | 0.1145 | 0.0000 |

R1 | 0.2521 | 0.0365 | 0.0056 | 0.0311 |

R3 | 0.1147 | 0.0000 | 0.0651 | 0.0452 |

R4 | 0.1256 | 0.000 | - | - |

L1 | 0.3521 | 0.0000 | 0.4114 | 0.0000 |

L2 | 0.1099 | 0.0000 | 0.1789 | 0.0000 |

M1 | 0.0547 | 0.0211 | 0.3796 | 0.0000 |

M2 | 0.0158 | 0.0355 | 0.1485 | 0.0000 |

M3 | - | - | 0.1254 | 0.0000 |

E1 | - | - | 0.4388 | 0.0000 |

E2 | 0.1125 | 0.0000 | 0.2411 | 0.0000 |

**Table 8.**Geographical detector analysis results of the impact factors of the development of specialized fruit villages in region 3.

Indicator | SFVDI (2011–2014) | SFVDI (2015–2019) | ||
---|---|---|---|---|

q Statistic | p Value | q Statistic | p Value | |

T1 | 0.0325 | 0.0362 | 0.0025 | 0.0488 |

T4 | 0.0012 | 0.0500 | 0.0204 | 0.0362 |

R1 | 0.2111 | 0.0000 | 0.1145 | 0.0000 |

R3 | 0.1525 | 0.0000 | 0.1741 | 0.0000 |

R4 | 0.2855 | 0.0000 | 0.1401 | 0.0000 |

L1 | 0.1117 | 0.0000 | - | - |

L2 | 0.1109 | 0.0000 | 0.1789 | 0.0000 |

M1 | 0.0547 | 0.0311 | - | - |

M2 | 0.1425 | 0.0000 | 0.1811 | 0.0000 |

M3 | 0.1845 | 0.0000 | 0.3477 | 0.0000 |

E1 | - | - | 0.1201 | 0.0000 |

E2 | 0.1114 | 0.0000 | 0.1000 | 0.0000 |

**Table 9.**Geographical detector analysis results of the impact factors of specialized livestock villages’ development.

Indicator | SAVDI (2011–2014) | SAVDI (2015–2019) | ||
---|---|---|---|---|

q Statistic | p Value | q Statistic | p Value | |

T1 | - | - | - | - |

T4 | - | - | - | - |

R1 | 0.0045 | 0.0211 | 0.1156 | 0.0359 |

R3 | - | - | - | - |

R4 | - | - | - | - |

L1 | 0.1147 | 0.0000 | 0.1341 | 0.0000 |

L2 | 0.3250 | 0.0000 | 0.1658 | 0.0000 |

M1 | 0.1166 | 0.0000 | 0.1230 | 0.0000 |

M2 | 0.1014 | 0.0000 | 0.1552 | 0.0000 |

M3 | 0.1254 | 0.0000 | 0.3125 | 0.0000 |

E1 | 0.1030 | 0.0000 | 0.1311 | 0.0000 |

E2 | 0.1141 | 0.0000 | 0.1115 | 0.0000 |

Indicator | Shiitake | Coarse Cereals | Fruit | Livestock | Chinese Herbal Medicine |
---|---|---|---|---|---|

T2 | 0.84 | 0.85 | 0.8 | 0.94 | 0.83 |

T3 | 0.2 | 0.22 | 0.18 | 0.21 | 0.19 |

T5 | 0.75 | 0.73 | 0.83 | 0.82 | 0.81 |

T6 | 0.16 | 0.18 | 0.2 | 0.22 | 0.23 |

R2 | 0.85 | 0.91 | 0.88 | 1.03 | 0.93 |

L3 | 0.78 | 0.72 | 0.79 | 0.8 | 0.77 |

L4 | 0.19 | 0.21 | 0.21 | 0.20 | 0.23 |

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**MDPI and ACS Style**

Niu, N.; Li, X.; Li, L.
Exploring the Spatio-Temporal Dynamics of Development of Specialized Agricultural Villages in the Underdeveloped Region of China. *Land* **2021**, *10*, 698.
https://doi.org/10.3390/land10070698

**AMA Style**

Niu N, Li X, Li L.
Exploring the Spatio-Temporal Dynamics of Development of Specialized Agricultural Villages in the Underdeveloped Region of China. *Land*. 2021; 10(7):698.
https://doi.org/10.3390/land10070698

**Chicago/Turabian Style**

Niu, Ning, Xiaojian Li, and Li Li.
2021. "Exploring the Spatio-Temporal Dynamics of Development of Specialized Agricultural Villages in the Underdeveloped Region of China" *Land* 10, no. 7: 698.
https://doi.org/10.3390/land10070698