BMP Optimization to Improve the Economic Viability of Farms in the Upper Watershed of Miyun Reservoir, Beijing, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas
2.1.1. Farm-VNWY
2.1.2. Farm-VLJF
2.2. Methods and Data Source
2.3. BMPs Scenarios
2.4. Modeling Validation
3. Results and Discussion
3.1. Baseline Simulations
3.2. Identification of Key Factors That Influence the Nutrient Reduction
3.3. Assessment of BMPs on Farm System Scenarios
4. Conclusions
- (1)
- The IFSM enabled a comprehensive evaluation of alternative farm planning strategies prior to their implementation. Two baseline farms were developed to simulate typical farms in the upper watershed of the Miyun reservoir; a crop farm (VNWY) and a dairy farm (VLJF). Type and timing of tillage practices is the key factor for the nutrient losses in VNWY; manure with higher nutrient content and the soil type are two key factors for nutrient losses in VLJF.
- (2)
- The sensitivity analysis demonstrated that the key factors that heavily influence BMPs’ effectiveness are physical and economic parameters. Changes in soil physical parameters, such as available water content and soil bulk density, had a greater impact on the return for the VNWY than for VLJF, while changes in interest rates were more influential at VLJF. However, actual farms could differ from the baseline model in many other ways. Differences in size, crop rotations, animal numbers and a variety of other farm characteristics could have considerable impacts on the simulated environmental and financial outputs; therefore, application of the baseline simulation results to actual farms should be exercised with caution.
- (3)
- Based on reduction of sediment and nutrient losses and impact on farm profitability by BMP scenarios simulation, the most cost-effective management strategies are often the simplest to implement. Nutrient management and strip cropping reduced environmental losses with very little or no cost to farmers. Some of the best practices, at baseline conditions, have already been adopted by many farms in the area (i.e., the conservation tillage at VNWY, or the buffer filter strip and storage of poultry on the VLJF).
- (4)
- The cost-effectiveness BMP combinations is a feasible policy option for China, because the Chinese government recently initiated the “Ten-Measures Action Plan for the Prevention of Water Pollution” strategy. This strategy is particularly relevant to this research study, with ongoing research and outreach efforts that promotes water environmental stewardship among the agricultural community. These results provide technical support for the development of nonpoint source pollution control programs and strategies in China.
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Information of Crop and Soil | ||
Crop Area (give typical values over the past ten years) | ||
Grass | acres | |
Pastureland | acres | |
Corn | acres | |
Wheat | acres | |
Soybean | acres | |
Predominant Soil Type(Check one which should represent the average over the farm) | ||
Deep loamy sand | ||
deep clay loam | deep loam | deep sandy loam |
Medium loamy sand | ||
medium clay loam | medium loam | medium sandy loam |
Shallow loamy sand | ||
shallow clay loam | shallow loam | shallow sandy loam |
Pastureland | ||
Life of stand | Years | |
Average annual yield | ton | |
Maximum expected yield | ton | |
Maximum annual irrigation | mm | |
Manure of total | % | |
Nitrogen | kg N/ac | |
Phosphorus | kg P2O5/ac | |
Potash | kg K2O/ac | |
Grass | ||
Life of stand | Years | |
Average annual yield | ton | |
Maximum expected yield | ton | |
Maximum annual irrigation | mm | |
Manure of total | % | |
Nitrogen | kg N/ac | |
Phosphorus | kg P2O5/ac | |
Potash | kg K2O/ac | |
Corn | ||
Plant population | plant/ac | |
Average annual grain yield | ton | |
Maximum expected yield | ton | |
Average annual silage yield | ton | |
Minimum expected yield | ton | |
Relative maturity index | days | |
Maximum annual irrigation | mm | |
Manure of total | % | |
Preplant nitrogen | kg N/ac | |
Anhydrous ammonia | kg N/ac | |
Nitrogen | kg N/ac | |
Phosphorus | kg P2O5/ac | |
Potash | kg K2O/ac | |
Wheat | ||
Average annual grain yield | ton | |
Maximum expected yield | ton | |
Average annual silage yield | ton | |
Minimum expected yield | ton | |
Maximum annual irrigation | mm | |
Manure of total | % | |
Nitrogen | kg N/ac | |
Phosphorus | kg P2O5/ac | |
Potash | kg K2O/ac | |
Soybean | ||
Plant population | plant/ac | |
Average annual grain yield | ton | |
Maximum expected yield | ton | |
Average annual silage yield | ton | |
Minimum expected yield | ton | |
Relative maturity index | days | |
Maximum annual irrigation | mm | |
Manure of total | % | |
Preplant nitrogen | kg N/ac | |
Anhydrous ammonia | kg N/ac | |
Nitrogen | kg N/ac | |
Phosphorus | kg P2O5/ac | |
Potash | kg K2O/ac |
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Scenario | Description |
---|---|
Baseline | Current farming system; conditions before management changes |
Conservation tillage | Reduce soil erosion, N mineralisation and P mobilisation |
Timing of chemical fertilisation | Reducing the risk of nutrient transport |
Contour Farming | Reducing surface runoff and erosion |
Filter strips | Delay runoff Trap sediments and nutrients |
Fertilizer reduction 30% | Reducing N and P inputs to soil |
Poultry numbers reduction 30% | Reducing N and P inputs to soil |
Storage of poultry manure | Reducing manure N content |
Manure spread during the dry season | Reducing the risk of transport |
Fence | Reducing the risk of poultry manure directly into streams |
Indicators | Average (AV) | Standard Deviation (SD) | Coefficient of Variation (CV) |
---|---|---|---|
Input-N (kg/ha) | 137 | 2.0 | 0.03 |
Output by agricultural products-N (kg/ha) | 15.7 | 3.7 | 0.24 |
Volatilization-N (kg/ha) | 14.5 | 0.6 | 0.04 |
Leaching-N (kg/ha) | 6.2 | 6.8 | 1.10 |
Denitrification-N (kg/ha) | 26.9 | 7.9 | 0.29 |
Surplus-N (kg/ha) | 73.7 | 2.8 | 0.06 |
Input-P (kg/ha) | 16.8 | 0.3 | 0.03 |
Output by agricultural products-P (kg/ha) | 2.7 | 0.5 | 0.18 |
Losses-DP (kg/ha) | 0.1 | 0.1 | 1 |
Losses-PP (kg/ha) | 5.8 | 0.7 | 0.12 |
Surplus-P (kg/ha) | 8.2 | 0.4 | 0.07 |
Economic benefits (¥) | 5.8 × 106 | 7971 | 0.01 |
Indicators | Average (AV) | Standard Deviation (SD) | Coefficient of Variation (CV) |
---|---|---|---|
Input-N (kg/ha) | 2761.8 | 185.9 | 0.13 |
Output by agricultural products-N (kg/ha) | 170.6 | 16.0 | 0.09 |
Volatilization-N (kg/ha) | 457.6 | 126.1 | 0.28 |
Leaching-N (kg/ha) | 85.1 | 169.4 | 1.99 |
Denitrification-N (kg/ha) | 656.9 | 155.3 | 0.24 |
Surplus-N (kg/ha) | 1391.6 | 100.95 | 0.08 |
Input-P (kg/ha) | 336 | 8.0 | 0.05 |
Output by agricultural products-P (kg/ha) | 31.4 | 2.9 | 0.09 |
Losses-DP (kg/ha) | 2.9 | 2.3 | 0.79 |
Losses-PP (kg/ha) | 141.7 | 6.8 | 0.05 |
Surplus-P (kg/ha) | 160 | 5.45 | 0.04 |
Economic benefits (¥) | 3.3 × 107 | 24,921 | 0.001 |
Validation Indicators | Mean Annual Yield | N in Feed (%) | P in Feed (%) | ||||||
---|---|---|---|---|---|---|---|---|---|
Simulated | Measured | Relative Error | Simulated | Measured | Relative Error | Simulated | Measured | Relative Error | |
VNWY | |||||||||
Corn (ton) | 1769.6 | 1896 | −6.7% | 9.6 | 12 | −20.0% | 46.9 | 40 | 17.2% |
Soybean (ton) | 65.4 | 67.5 | −3.1% | - | - | - | - | - | - |
Grass (ton) | 363.12 | 400.5 | −9.3% | 10.14 | - | - | 44.5 | - | - |
Milk (ton) | 2188.8 | 1825 | 19.9% | - | - | - | - | - | - |
Input-N (kg/ha) | 137 | 100.51 | 36.3% | - | - | - | - | - | - |
Output-N (kg/ha) | 63.3 | 44.31 | 42.9% | - | - | - | - | - | - |
Surplus-N (kg/ha) | 73.7 | 56.2 | 31.1% | - | - | - | - | - | - |
Input-P (kg/ha) | 16.8 | 14.3 | 17.5% | - | - | - | - | - | - |
Output-P (kg/ha) | 8.6 | 7.9 | 8.9% | - | - | - | - | - | - |
Surplus-P (kg/ha) | 8.2 | 6.4 | 28.1% | - | - | - | - | - | - |
Average Relative Errors | - | - | 20.1% | - | - | - | - | - | - |
VLJF | |||||||||
Corn (ton) | 2172.1 | 2250 | −3.5% | 9.7 | 12 | −19.2% | 43.9 | 40 | 9.7% |
Vegetable (ton) | 1257.4 | 1680.7 | −25.2% | - | - | - | - | - | - |
Grass (ton) | 280 | 300 | −6.7% | 9.67 | - | - | 39.8 | - | - |
Milk (ton) | 19,013.7 | 16,078.2 | 18.3% | - | - | - | - | - | - |
Input-N (kg/ha) | 2761.8 | 2658.9 | 3.9% | - | - | - | - | - | - |
Output-N (kg/ha) | 1370.2 | 1304.2 | 5.1% | - | - | - | - | - | - |
Surplus-N (kg/ha) | 1391.6 | 1354.7 | 2.7% | - | - | - | - | - | - |
Input-P (kg/ha) | 336.0 | 298.7 | 12.5% | - | - | - | - | - | - |
Output-P (kg/ha) | 176.0 | 157.2 | 12.0% | - | - | - | - | - | - |
Surplus-P (kg/ha) | 160.0 | 141.5 | 13.1% | - | - | - | - | - | - |
Average Relative Error | - | - | 11.0% | - | - | - | - | - | - |
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Geng, R.; Yin, P.; Gong, Q.; Wang, X.; Sharpley, A.N. BMP Optimization to Improve the Economic Viability of Farms in the Upper Watershed of Miyun Reservoir, Beijing, China. Water 2017, 9, 633. https://doi.org/10.3390/w9090633
Geng R, Yin P, Gong Q, Wang X, Sharpley AN. BMP Optimization to Improve the Economic Viability of Farms in the Upper Watershed of Miyun Reservoir, Beijing, China. Water. 2017; 9(9):633. https://doi.org/10.3390/w9090633
Chicago/Turabian StyleGeng, Runzhe, Peihong Yin, Qianru Gong, Xiaoyan Wang, and Andrew N. Sharpley. 2017. "BMP Optimization to Improve the Economic Viability of Farms in the Upper Watershed of Miyun Reservoir, Beijing, China" Water 9, no. 9: 633. https://doi.org/10.3390/w9090633