Study on the Crop Suitability and Planting Structure Optimization in Typical Grain Production Areas under the Influence of Human Activities and Climate Change: A Case Study of the Naoli River Basin in Northeast China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Framework
2.3. Data Source
2.3.1. Crops’ Spatial Information
2.3.2. Variables and Sources
- Environmental variables
- 2.
- Human activities
- 3.
- Future climate variables
- 4.
- Data processing
2.4. Simulation and Prediction by MaxEnt
2.4.1. Model Mechanism
2.4.2. Model Evaluation
2.4.3. Existence Probability and Crop Layout Optimization
3. Results
3.1. Model Evaluation
3.2. Potential Distribution and Suitability Levels
3.3. Optimized Cropping Structure
3.4. Identification of Dominant Factors and Threshold Characteristics
3.5. Characteristics of Dominant Factors
3.6. Distribution of Crop Suitability under Future Climate
4. Discussion
4.1. Factors Influencing Crop Optimization Layout
4.2. Impacts of Dominant Factors on Crop Planting
4.3. Impacts of Human Activities on Planting Structures
4.4. Potential Impacts of the Watershed’s Ecological and Water Security
4.5. Limitations and Prospects
5. Conclusions
- The MaxEnt model accurately simulated crop suitability when considering the comprehensive influence of human activities along with natural environmental factors compared to when only considering natural factors.
- The suitability distribution of different crops varied, with maize having the largest area of medium and suitable regions, followed by rice and soybeans.
- The highly suitable areas for major crops in the Naoli River Basin were primarily concentrated in the central plain area of the basin rather than in areas with higher population density, indirectly indicating highly mechanized and large-scale agricultural production in the basin.
- Population density (POP) and accessibility (DR) were the main human activity factors influencing the distribution of crop suitability, especially for soybeans.
- Climate change had varying degrees of impact on crop suitability, with maize being the most affected. Under low-emission scenario climate models, there was no significant change in maize’s suitability, while the suitability of rice and soybeans increased. Under high-emission scenario models, the suitable area for all crops decreased, posing challenges to regional food security due to climate change.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Factors | Abbreviation | Data Sources | |
---|---|---|---|---|
Climate Factors | Bioclimatic variables | Annual mean temperature | BIO1 | NMSDC |
Mean diurnal range | BIO2 | |||
Isothermality (BIO2/BIO7) (×100) (%) | BIO3 | |||
Temperature seasonality (standard deviation × 100) | BIO4 | |||
Max temperature of warmest month | BIO5 | |||
Min temperature of coldest month | BIO6 | |||
Temperature annual range (BIO5–BIO6) | BIO7 | |||
Mean temperature of wettest quarter | BIO8 | |||
Mean temperature of driest quarter | BIO9 | |||
Mean temperature of warmest quarter | BIO10 | |||
Mean temperature of coldest quarter | BIO11 | |||
Annual precipitation (mm) | BIO12 | |||
Precipitation of wettest month | BIO13 | |||
Precipitation of driest month | BIO14 | |||
Precipitation seasonality | BIO15 | |||
Precipitation of wettest quarter | BIO16 | |||
Precipitation of driest quarter | BIO17 | |||
Precipitation of warmest quarter | BIO18 | |||
Precipitation of coldest quarter | BIO19 | |||
Sunshine | Sunshine duration | SUN | ||
Nature Factors | Terrain | Digital elevation model | DEM | RESDC |
Slope | SLO | |||
Aspect | ASP | |||
Vegetation | Normalized difference vegetation index | NDVI | GEE | |
Water consumption | Evapotranspiration | ET | GEE | |
Soil | Organic content | OC | CARSDC | |
Total nitrogen | TN | |||
Total phosphorus | TP | |||
Total potassium | TK | |||
Topsoil calcium carbonate (CaCO3) | TC | |||
Ph | Ph | |||
Pore available water capacity | PAWC | |||
Soil type | ST | RESDC | ||
Soil erosion | ERO | |||
Human Activities | Socioeconomic factors | Distance from a water source | DW | GIS interpolation |
Distance from the settlement | DS | |||
Distance from the road | DR | |||
Population density | Pop | RESDC | ||
Gross domestic product | GDP | |||
Future Climate | Bioclimatic variables with strong importance under the climate scenarios of SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5 in BCC-CSM2-MR | BIO1–BIO19 | WorldClim |
Maize | Rice | Soybeans | ||
---|---|---|---|---|
Area (km2) | Unsuitable | 2807.14 | 10,731.47 | 11,445.82 |
Low suitability | 8821.37 | 5010.36 | 7967.24 | |
Medium suitability | 12,978.77 | 8813.32 | 4438.23 | |
High suitability | 1872.98 | 1430.01 | 1405.9 | |
Suitable | 23,673.11 | 15,253.69 | 13,811.37 | |
Percentage of Area | Unsuitable | 10.60% | 42.40% | 47.84% |
Low suitability | 33.31% | 18.92% | 30.09% | |
Medium suitability | 49.01% | 33.28% | 16.76% | |
High suitability | 7.07% | 5.40% | 5.31% | |
Suitable | 89.40% | 57.60% | 52.16% |
Crop Distribution | Suitability | Medium and High Suitability | High Suitability | |||
---|---|---|---|---|---|---|
Area (km2) | Percentage | Area (km2) | Percentage | Area (km2) | Percentage | |
Unsuitable | 2389.58 | 9.02% | 8203.82 | 30.98% | 22,811.33 | 86.14% |
Soybeans | 1372.63 | 5.18% | 1341.13 | 5.06% | 404.36 | 1.53% |
Rice | 3706.23 | 13.99% | 3449.38 | 13.02% | 1378.19 | 5.20% |
Rice and Soybeans | 461.17 | 1.74% | 461.21 | 1.74% | 17.41 | 0.07% |
Maize | 10,964.21 | 41.40% | 7104.26 | 26.83% | 1642.74 | 6.20% |
Maize and Rice | 3578.34 | 13.51% | 2825.78 | 10.67% | 35.83 | 0.14% |
Maize and Soybeans | 1420.76 | 5.36% | 958.44 | 3.62% | 194.18 | 0.73% |
Maize, Rice, and Soybeans | 2590.24 | 9.78% | 2138.95 | 8.08% |
Driving Force for Maize | Contribution | Driving Force for Rice | Contribution | Driving Force for Soybeans | Contribution | |||
---|---|---|---|---|---|---|---|---|
Percent | Cumulative | Percent | Cumulative | Percent | Cumulative | |||
BIO5 | 17.6 | 17.6 | DEM | 36.4 | 36.4 | DEM | 18.4 | 18.4 |
BIO3 | 17 | 34.6 | SLO | 23.2 | 59.6 | DR | 15.8 | 34.2 |
BIO10 | 16.6 | 51.2 | NDVI | 9.3 | 68.9 | ST | 14.9 | 49.1 |
DW | 5.8 | 57 | BIO16 | 3.7 | 72.6 | BIO12 | 5.1 | 54.2 |
BIO1 | 5.2 | 62.2 | BIO3 | 3.6 | 76.2 | BIO3 | 5.1 | 59.3 |
DEM | 4.8 | 67 | BIO15 | 2.6 | 78.8 | SLO | 4.9 | 64.2 |
BIO12 | 4.4 | 71.4 | BIO10 | 2.5 | 81.3 | BIO16 | 4.8 | 69 |
ST | 4 | 75.4 | BIO11 | 4.4 | 73.4 | |||
BIO13 | 3.7 | 79.1 | POP | 3.5 | 76.9 | |||
SLO | 3.5 | 82.6 | BIO4 | 3.4 | 80.3 |
Maize | |||||
SSP1-2.6 | SSP2-4.5 | SSP3-7.0 | SSP5-8.5 | Currently | |
Unsuitable | 10.68% | 11.46% | 11.65% | 11.01% | 10.60% |
Low Suitability | 37.00% | 41.11% | 41.20% | 40.29% | 33.31% |
Medium Suitability | 47.75% | 47.39% | 47.40% | 46.17% | 49.01% |
High Suitability | 4.57% | 2.04% | 1.74% | 2.53% | 7.07% |
Suitability | 89.32% | 88.54% | 88.35% | 88.99% | 89.40% |
Medium and High Suitability | 51.33% | 49.43% | 49.15% | 48.70% | 56.09% |
Rice | |||||
SSP1-2.6 | SSP2-4.5 | SSP3-7.0 | SSP5-8.5 | Currently | |
Unsuitable | 42.80% | 43.77% | 45.14% | 45.62% | 42.40% |
Low Suitability | 18.24% | 19.53% | 22.15% | 24.88% | 18.92% |
Medium Suitability | 33.16% | 32.71% | 28.56% | 25.58% | 33.28% |
High Suitability | 5.80% | 4.99% | 4.14% | 3.93% | 5.40% |
Suitability | 57.20% | 56.23% | 54.86% | 54.38% | 57.60% |
Medium and High Suitability | 38.96% | 36.70% | 32.71% | 29.50% | 38.68% |
Soybeans | |||||
SSP1-2.6 | SSP2-4.5 | SSP3-7.0 | SSP5-8.5 | Currently | |
Unsuitable | 47.98% | 54.01% | 52.45% | 53.76% | 47.84% |
Low Suitability | 29.21% | 28.22% | 29.12% | 29.11% | 30.09% |
Medium Suitability | 17.75% | 13.86% | 14.01% | 13.18% | 16.76% |
High Suitability | 5.06% | 3.91% | 4.41% | 3.94% | 5.31% |
Suitability | 52.02% | 45.99% | 47.55% | 46.24% | 52.16% |
Medium and High Suitability | 22.81% | 17.77% | 18.43% | 17.13% | 22.07% |
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Yin, J.; Wei, D. Study on the Crop Suitability and Planting Structure Optimization in Typical Grain Production Areas under the Influence of Human Activities and Climate Change: A Case Study of the Naoli River Basin in Northeast China. Sustainability 2023, 15, 16090. https://doi.org/10.3390/su152216090
Yin J, Wei D. Study on the Crop Suitability and Planting Structure Optimization in Typical Grain Production Areas under the Influence of Human Activities and Climate Change: A Case Study of the Naoli River Basin in Northeast China. Sustainability. 2023; 15(22):16090. https://doi.org/10.3390/su152216090
Chicago/Turabian StyleYin, Jian, and Danqi Wei. 2023. "Study on the Crop Suitability and Planting Structure Optimization in Typical Grain Production Areas under the Influence of Human Activities and Climate Change: A Case Study of the Naoli River Basin in Northeast China" Sustainability 15, no. 22: 16090. https://doi.org/10.3390/su152216090