Agricultural nonpoint source pollution (ANPS) has caused widespread concern and attention due to the need for effective control of point source pollution [1
]. In agricultural activities, excessive pesticide and fertilizer application will lead to nutrient leaching and soil erosion, resulting in the loss of nonpoint source pollution increasing constantly. The United States Environmental Protection Agency (USEPA) has reported that 44% of water quality was deteriorated in United States in the 21st century [2
]. The main reason is that excessive nutrients enter the river, causing distinct degrees of eutrophication. In European countries, nonpoint source pollution has become the primary factor in water pollution [3
]. In China, the situation of agricultural nonpoint source pollution is also not optimistic; nitrogen and phosphorus pollution load accounts for more than 50% in the water bodies, and ANPS has become the main cause of water environment problems in the watershed [4
Nowadays, with the increasing prevalence of ANPS in China, the large-scale nonpoint source pollution that first occurred in the warm southern regions has been extended to the cold northern regions [5
]. China’s northeast regions are affected by the climatic conditions; the winter is cold and long, leading to the migration and transformation of nonpoint source pollution in particular. The winter freezing–thawing phenomenon promotes the mineralization of pollutants, and a large amount of nitrogen and phosphorus are accumulated in the soil [6
]. In early spring, the melt of snowfalls causes a severe snowmelt runoff scouring effect, and initiates concentrate outflows of nonpoint source pollutants during the snow melting period. Therefore, it’s an important challenge to research the control of ANPS, due to its accumulation and sudden appearance in the cold regions.
Among the measures to reduce ANSP, the best management practices (BMPs) have proven to be the most effective methods to control ANSP [7
]. The implementation of BMPs is an approach that aims to improve water quality based on the pollution control theory of “source reduction–interception–repair”. At present, since the primary purpose of BMPs is to reduce pollutants, the most important studies in China have focused on the environmental benefits of BMPs’ implementation, but few studies have assessed the cost-effectiveness of the measures, despite the cost of BMPs limiting their practical implementation [8
]. Stewart simulated the reduction of nonpoint source pollution by returning farmland to grassland, and found that TN (total nitrogen) and TP (total phosphorus) loads in the watersheds decreased by 31% and 36%, respectively [9
]. Liu investigated the effectiveness of seven BMP scenarios on ANSP reduction in the Three Gorges Reservoir; the results indicated that reducing the local fertilizer level should obtain a satisfactory environmental and economic efficiency [10
]. However, there are two important concerns about the environment and economy that need to be considered and managed at the same time. Marcelo selected alternative crop, buffer strips, and fertilization reduction as BMPs and estimated the pollutant reduction effect and costs benefit for the Treene catchment, Germany [11
]. Furthermore, some researchers discovered that a single BMP often fails to meet the needs of water quality control objectives in cold climatic conditions regions; however, the proposal of a reasonable set of combined BMPs implementation will greatly contribute to controlling ANSP [12
Assessment of the control efficiency of BMPs is a key step in determining whether a measure is applicable. There are many studies that have showed the effectiveness of ANSP based on site monitoring before and after different BMPs implementation [14
]. However, this approach takes a long time and requires extensive measurements; as a result, most research studies gradually transformed into hydrological model simulations. The Soil and Water Assessment Tool (SWAT) has been used widely to predict flow, sediment, and nonpoint source pollution loads at large-scale watershed [16
]. Meanwhile, multiple studies have applied SWAT by modifying the parameters inside the model to estimate the impact of BMPs on water quality [17
]. Panagopoulos applied the sensitivity analysis method to identify the SWAT model internal parameters corresponding to different no-tillage, irrigation, fertilization, contour planting, etc., in the Arachtos watersheds, and realized the simulation of different reduction measures [18
]. Haas used water quality models to predict the reduction rates of river discharge and nitrate loads, while developments in BMP optimization strategies have further improved the feasibility of implementing BMPs on large watersheds [19
]. In this way, these studies confirmed the suitability of the SWAT model for investigating the potential reduction of pollutants for different BMPs; the assessment can be considered both ecological and economic [20
The Liao River source area is located in the cold region of northeast China, where unique climate conditions and frequent agricultural activities lead to serious problems with ANPS. The changes of land use and fertilization management are major factors affecting runoff and nonpoint source pollution in the study area. This study attempts to use the SWAT model to analyze the spatial–temporal distribution of nitrogen and phosphorus pollution annually and during the snowmelt period in the source area of Liao River, and assess the efficiency of BMPs and their combination in reducing ANSP while considering cost expenditure during the precipitation and snowmelt periods, respectively. Ultimately, it will identify the cost-effectiveness of best management practices for the environment and the economy. Based on these purposes, the research can contribute to improving water quality regarding agricultural watershed and aid in making decisions for reducing pollution with the best management practices in the Liao River source area.
4. Results and Discussion
4.1. SWAT Model Calibration and Validation
The watershed was divided into 37 hydrologic response units (HRUs) in the SWAT. The LH-OAT (Latin hypercube one factor-at-a-time) sensitivity analysis was carried out 24 parameters that were identified as having an influence on flow, sediment, and nonpoint source pollution [42
], as shown in Table 2
. Among these parameters, the most sensitive parameters were CN2, SOL_K, CANMX, ALPHA_BF, and ESCO for flow, as well as USLE_P and SLSUBBSN for sediment. In addition, we compared simulation results with consideration of snowmelt parameters (SFTMP, SMTMP, SMFMX, SMFMN, TIMP) or not during the snow melting period (November to April). The detailed evaluation results can be found in our published papers [27
]. It shows that considerable snowmelt parameters can improve the accuracy for the snowmelt process.
The daily flows, sediment, TN data, and TP data from 2006 to 2010 that were obtained from Quantai station were selected for model calibration and validation with using the optimum value range of snowmelt parameters and other parameters. The calibration period was 2006 to 2008, and the validation period was 2009 to 2010. Graphs of observed and simulated values for monthly average daily flows, sediment, TN loads, and TP loads in the calibration and validation periods are shown in Figure 3
. It shows that the simulated results meet the accuracy requirements, of which R2
≥ 0.6 and NS ≥ 0.5; the SWAT model considering the snowmelt module was able to simulate flow, sediment, nitrogen, and phosphorus loads at the watershed outlet.
4.2. Spatial–Temporal Distribution of TN and TP Loads
Agricultural activities such as the excessive application of fertilizers and pesticides have significant effects on the nonpoint source pollution. Figure 4
is the spatial distribution of TN and TP loads for a general situation in 2010. It indicates the essential pollution zones that provide the contributions of nonpoint source pollutant exports, including HRU2, HRU5, HRU6, HRU7, HRU9, HRU12, HRU13, HRU25, HRU26, HRU27, and HRU28. These areas are mainly farmland with strongly agricultural activities, excess fertilization, livestock and poultry breeding, and an intensively rural population distribution, which leads to serious pollutant losses.
The temporal characteristics of TN and TP loads were also simulated for time series 2006 to 2010, as shown in Figure 5
. The outputs of TN and TP have almost the same change trend to precipitation in the study area, with two peaks focused on July to September and March to May (as seen in gray circled) every year. In order to analyze the high-risk period and driving factors of nonpoint source pollution, Figure 6
gives the average seasonal TN and TP loads during five years as well as the proportion in each season. It shows that nonpoint source pollution outputs are mainly concentrated in the summer, followed by spring. The average proportion of TN and TP loads accounted for 75.12% and 79.42% in the summer, as well as 17.28% and 14.74% in the spring, respectively. With the intensive agricultural activities, the higher precipitation in summer is the main reason for the large amount of pollutants leaching to the river. In addition, as the study area is located in cold regions, which has a long freezing period each year, both TN and TP loads will accumulate and become very high in the spring due to the snow melting accompanied by a severe snowmelt runoff flushing effect, resulting in pollutant losses concentrated in a short period of time. Therefore, snowmelt is the driving force for the occurrence of nonpoint source pollution in the spring. In summary, the temporal and spatial distribution of nonpoint source pollutants loads has certain seasonal particularity, especially during the precipitation runoff period (summer) and the snowmelt runoff period (spring) in the source area of the Liao River.
4.3. Simulation of BMPs
As described in the literature [43
], the effects of BMPs implementation are diverse. In generally, the implementation of BMPs confirmed reductions on TN and TP loads at the watershed outlet. In this study, as the particularity of spatial–temporal distribution on nonpoint source pollution in the study area, the BMPs projects were performed in one year, and the pollutant reductions efficiency was analyzed in a precipitation runoff period (summer) and snowmelt runoff period (spring) respectively as follows.
4.3.1. Buffer Strip Management
The BS simulation presented the highest effective reduction of TN and TP loads at the essential pollution zones. Figure 7
a showed the evaluation of loads reduction with different buffer widths. The BS1.5, BS3, BS5, BS10, BS15, and BS20 indicated 12.12–20.16%, 20.37–37.45%, 28.75–54.12%, 37.34–63.48%, 45.75–67.43%, and 47.44–70.54% TN loads reduction per year in precipitation runoff and snowmelt runoff periods, as well as 14.38–24.45%, 22.52–41.64%, 31.64–58.56%, 40.43–67.21%, 50.78–69.34%, 53.85–73.23% TP loads reduction. It illustrated that the reduction efficacy of TN and TP loads increase quickly with buffer width, while the rate of increase gradually decreases as the buffer becomes wider, until BS10 in the precipitation runoff period and BS15 in the snowmelt runoff period. Meanwhile, the reduction rate in the precipitation runoff period is higher than that in the snowmelt runoff period. It means that a greater effectiveness of BSs occurred in the summer, which had a larger runoff presence due to precipitation and higher load peaks, since the greater plant can effectively absorb and intercept pollutions to leach in this period. Moreover, there is a higher reductions efficiency of TP compared with TN under the same BS width. This is because soluble phosphorus is more easily lost with runoff and erosion than soluble nitrogen.
shows the relationship between the buffer strip width and pollutions reduction efficiency for TN and TP in four HRUs with different slopes. Among them, HRU9 and HRU28 are steeper than HRU12 and HRU25. All of these HRUs simulations illustrate that the increase in BS width will lead to higher pollutions reduction. However, for the same BS width, the units with relatively steeper slopes (HRU9 and HRU28) had lower pollutant reductions efficiency than the units with flatter slopes (HRU12 and HRU25).
4.3.2. Fertilizer Management
The simulations of TN and TP loads reduction with FR were significant at the watershed outlet. The FR results of loads reduction efficiency are showed in Figure 7
b. A fertilization rate reduction by 5% (FR5) led to a decrease TN and TP loads of 9.25–18.23% and 7.38–16.47% in the precipitation runoff and snowmelt runoff periods, the reduction by 10% (FR10) represented less 12.11–27.47% and 10.31–25.47%, as well as less 17.45–32.33% and 13.84–29.46% pollution loads reaching the river by 15% reduction (FR15), respectively. The percentage of loads reduction increases as fertilizer use decreases in all the phases, for both the precipitation runoff and snowmelt runoff periods. Summer presented the highest reductions, which was due to the excessive application of fertilizers during the previous farming period in the study area, once the large decrease in fertilizers results in a significant reduction in nonpoint source pollutants for being transported with precipitation runoff entering the river. For the spring snowmelt period, the accumulated nitrogen and phosphorus in the soil also decreased, leading to loads reduction for leaching with snowmelt runoff, but obviously lower than that in the summer. Furthermore, the TN reduction greatly improved compared with TP when fertilizer application was reduced. This is because TP is sedimentary, the utilization rate of phosphate fertilizer is low, and the annual application is not effective, leading to TP being deposited and accumulating in the soil year by year. Therefore, the reduction effect of TP takes a long time to appear. However, due to the negative charge of nitrate, it can’t be absorbed by soil particles and can be easily moved by surface runoff. Thus, the accumulation of TN is not as strong as TP, so the reduction of TN is more effective than that of TP.
In addition, FRs may also cause a reduction in crop yields. There is a direct correlation between fertilizer application and crop yields. Marcelo et al. (2017) concluded that crop production drops by 13% and 10% when the fertilization rate is reduced by 15% for corn and pasture, and drops by 26% and 20% when the fertilization rate is reduced by 30%. The above studies have indicated that crops will present an average reduction that is double or even stronger when fertilizers are reduced by different percentages. In this study, FR5, FR10, and FR15 would lead to reductions of 5%, 10%, and 15% on all the crop yields, respectively.
4.3.3. Land-Use Management
The simulation results of the SWAT model showed that the increase in forest and grassland areas has little impact on pollutants reduction in the watershed. Figure 7
c showed the results regarding the reduction efficiency of TN and TP loads with FLI and GLI. FLI led to a reduction of TN and TP loads of 18.56–38.24% and 22.43–41.35%, while the pollution loads of GLI only decreased 14.23–25.25% and 20.54–30.45% in the precipitation runoff and snowmelt runoff periods. Theoretically, land-use changes can lead to higher reductions because the soil is covered by forests and grasslands as well as the lower fertilization. However, changes to only 30% of the essential pollution zones—representing only 2.8% of the watershed—couldn’t significantly affect the TN and TP loads. Despite all this, the effect of reduction was highest in the precipitation runoff period. This result showed evidence that the existence of forests and grasses increase nitrogen and phosphorus uptake by lush plants and less surface runoff and soil erosion in this period. Moreover, when forest and grass areas are increased, the reduction of TP was much greater than that of TN. This is because soluble phosphorus attached to the sediment particles, which were easy to be transferred in runoff; decreased soil erosion can significantly reduce this part of phosphorus loss.
This study also found that the reduction efficiency of TN and TP loads by returning farmland to forests is greater than returning farmland to grasslands. This is because the canopy storage and interception capacity of forests is stronger, and the soil water storage capacity is larger.
Another factor is due to the strong and networked forest rooting systems, which will be firmly entangled with the soil, thus achieving the purpose of consolidating soil. Thereby, when precipitation runoff occurs, the forests have a better sand-fixing effect and stronger soil and water conservation capacity than grassland.
4.3.4. Combined BMPs
The comprehensive evaluation on TN and TP loads reduction of each BMP is shown in Figure 9
. It can be seen from the above research that the reduction effect of buffer strip is much better than that of returning farmland to forests or grasses and fertilizer reduction management. However, each BMP will influence different nitrogen and phosphorus transportation processes in the watershed, such as more storage in the soil, more vegetable absorption, as well as less infiltration and loss. However, due to the cumulative response of TN and TP, the effect of forest or grassland increase and fertilizer reduction management is slower, but it is a source control measure that is necessary to improve water quality in the long term. On the contrary, the buffer strip has the best effect, because it adopts the method of directly intercepting pollutants, and it is an important measure to quickly improve the water quality of the watershed.
Afterwards, the BMPs were combined to assess TN and TP loads reduction in the SWAT model that is caused by more than one BMP simultaneously. In the simulation, all the possible combinations were presented and experimented until all the BMP implementations were combined with each other; the results are shown in Figure 10
Through comprehensive management measures, the combined BMPs showed reductions of 18.75–88.62% of the TN loads and 20.45–85.33% of the TP loads. The lowest reduction was combined by “BS1.5” + “GLI”, and the highest reduction was “BS20” + “FR15” + “FLI”. It showed that the positive effect was obtained from different BMPs combinations complementing each other, which means that the more BMPs that were combined together, the more the TN and TP loads were reduced correspondingly. The significant trend of the combined BMPs simulation found that the appearance of BS20 causes the best results with respect to the reduction of TN and TP loads. However, BS width was important, since BS10 presented a relatively high reduction rate. Actually, there was a quick loads reduction rate before the implementation of BS10 (Figure 10
, the color shade of the first four columns in the small plots changes quickly); afterwards, the reduction rate is declined (Figure 10
, the color shade of the last three columns in the small plots changes slowly). The existence of FR15 also led to better reductions of TN and TP loads, so there is a 15% reduction of fertilizer application, which emerged in the case of relatively high load reductions (Figure 10
, first line in small plots). Then, a decaying order was presented in the reduction sequence when FR10 (Figure 10
, second line in small plots) and FR5 (Figure 10
, third line in small plots) were implemented. The lowest reductions of TN and TP loads in combination appeared without decreasing fertilization (Figure 10
, fourth line in the small plots). The application of FLI and GLI also contributed to TN and TP pollutant reductions. Especially, FLI contributions were greater than GLI when comparing the combinations with FLI (Figure 10
, top right big box) and the combinations with GLI (Figure 10
, top left big box), which was determined by the two important factors of forest canopy interception and forest root systems. Therefore, returning farmland to the forests and grasses had brought an important optimistic effect on nitrogen and phosphorus pollution reduction. Moreover, due to the reduction of pollutants mainly depending on interception activity, the reduction effect of TP is slightly better than TN when observing the combinations almost without FR (Figure 10
, third and fourth line in the small plots of top left big box and the bottom left big box). However, when comparing the combinations with FR, the reduction of TN loads is better than that of TP (Figure 10
, first and second line in the small plots of the top left big box and the bottom left big box).
Furthermore, it has been found that different combinations of BMPs may result in an analogous reduction effect. For example, the implementation of “BS20” + “FR10” + “GLI” caused similar results with “BS15” + “FR10” + “FLI”. It is necessary to determine different BMP combinations according to the characteristics of watershed; especially, the appropriate practice can be selected by considering the spatial difference of nonpoint source pollution loads. In fact, although some combinations have similar reductions, it should be emphasized that not all BMPs can be replaced by others. For example, FR can’t substitute for BSs, because their effect processes are completely different. However, attempting to shorten the BS width in a specific combination situation is possible.
In addition, the temporal differences of nonpoint source pollution loads are also a major factor to be considered in determining the BMPs combination, which is mainly affected by the two processes of plant absorption and pollutants migration with runoff. In this study, the unique climatic conditions in cold regions affected the simulation results of BMPs implementation—mainly the existence of precipitation runoff and snowmelt runoff—and the features caused a faster response to BS and the reduction efficiency of FR. According to the above analysis, the simulation of TN and TP reduction for all BMPs and their combinations was greater in the precipitation runoff periods, due to the larger runoff and greater plant growth during this period. Therefore, the different combination of BMPs implementation exhibits higher reductions of nitrogen and phosphorus pollutants, primarily in the most crucial periods.
4.4. Analysis of BMPs’ Cost-Effectiveness
The total costs for BMPs implementation are listed in Table 3
. The table shows the cost in 1000 € for the entire watersheds scale. BSs require relatively little costs of initial implementation and installation, and later maintenance costs are negligible. The buffer strips are arranged around the farmland, and the construction area is 0.5–10% of the farmland area. The implementation cost of the measure is about 267 €/ha, and the installation cost is approximately 7 €/ha each year. In contrast, FLI and GLI presented higher costs. Returning farmland to forests and grasses is the national promotion policy. The state provides appropriate grain subsidies according to the economic losses caused by the approved areas of cultivated land, as calculated at a convert of 0.2 €/kg. Simultaneously, the construction area of the project accounts for 30% of the farmland, the total costs of construction and maintenance are 2864 €/ha and 1492 €/ha, respectively. FR actually saves costs due to the reduction of the fertilization amount. According to relevant data, the rice yields in the study area is about 3701.2 kg/ha, the purchase market price is 0.35 €/kg, and the prices of urea (in terms of N) and superphosphate (P2
) are 0.61 €/kg and 0.94 €/kg. The costs of the fertilizer reduction measures are calculated by the income loss due to the decline in crop yield subtracted by the saving costs from reducing the purchase of fertilization.
Next, the efficiency of BMPs implementation associated with pollutants reduction and total costs (including implementation, installation, maintenance costs, and market revenue) suggests that this approach is very important for making decisions. Figure 11
illustrated the histogram of CE value with TN and TP; the higher CE value, the better the BMPs’ cost-effectiveness, which means a greater pollutant reduction and thus lower costs.
From the comparison of different scenarios, as the buffer strips width increases during the implementation of BSs, the CE value is lower, which is caused by the increase in cost when the buffer zone expands. In the FRs measure, the CE value of reducing fertilizer by 15% is slightly higher than that by 5%, which may be due to the high fertilization amount in the region. The further reduction of fertilization application has limited impact on crop yield; however, it also reduced the costs of purchasing fertilizer for farmers, so the total cost is lower. Among the land-use change measures, the environmental benefits of FLI are better than those of GLI, but the cost is much higher, resulting in a CE value that is lower than the measures to return farmland to grasslands.
Overall, the cost-effectiveness of interception measures (such as a buffer strip) is higher than that of source control measures (fertilizer reduction and land-use change). In other words, BS was generally the most efficient measure, since it has lower implementation costs and the highest pollution reduction efficiency. It is the best management practice that will be the most suitable for the area and the most acceptable for farmers so far. FR has higher effectiveness due to its nitrogen and phosphorus pollutants reduction and expenses saving, but it also significantly reduced the crop yields, which will lead to a significant loss of income, which may be not easily accepted by farmers. Therefore, the fertilization reduction should be within a certain extent. However, FLI and GLI demonstrated insignificant effectiveness because of the highest implementation costs and the lower pollutants reduction. Despite this, the measure still demands attention because the costs will decrease over the next few years to improve the efficacy; thus, it should be adopted by local governments and farmers in the long term. In any case, although the cost-effectiveness of interception measures is much higher than that of the source control measures, it does not mean that the entire basin only needs to arrange interception measures, the final decisions should be based on the analysis of actual land use, funds status, the social and economic development situation before practical measures are selected for arrangement.
4.5. Multi-Objective Optimization of BMPs
The BMPs discussed in this study are not mutually exclusive, and they can be applied simultaneously. In terms of combining several BMPs, the total costs were considered to be the sum of each cost in principle. This means that the more BMPs that were combined, the higher the costs that were spent (only FR is subtracted). In general, the introduction of FR was a strongly encouraged measure, which could be due to the saving of funds. The effect of combining with BSs will be more prominent, because BSs revealed the best pollutants reduction with reasonable costs for realizing the optimal cost-effectiveness. The efficiency of FR combined with FLI/GLI couldn’t be demonstrated as expected due to the huge implementation cost and income loss. Based on the above analysis, the suggested combination of BMPs for the study area were “BS1.5” + “FR15”. The cost-effectiveness values of the combined BMPs implementation within the watershed were 8.9 kg/€ and 7.3 kg/€ each year. Ultimately, the measures will be in accordance with the collaborative developmental goals for the economy and environment.
This study analyzed the cost-effectiveness of BMPs implementation and their impacts on reducing nonpoint source pollution in the source area of the Liao River. The consequence was assessed by using a SWAT model, and the results were simulated under the consideration of two aspects: the environment and the economy.
Initially, a SWAT model considering a snowmelt module could well simulate the spatial and temporal distribution of nonpoint source pollution process in a cold area such as the Liao River watershed. The simulated results indicated the essential pollution zones with different fertilization and land-use types, which meant that spatial differences affected the choice of measures and BMPs that needed to be applied to each area. Likewise, temporal differences were strongly particular and also required measures to meet the specific details of each area. The simulation showed that precipitation and snowmelt are the main driving forces of nonpoint source pollution; in relation, summer and spring are the high-risk seasons. Therefore, the implementation of BMPs in these two periods with the serious pollution issue in the watershed showed a large reduction of nitrogen and phosphorus pollutants.
Furthermore, the reduction effect of TN and TP was evaluated under the different BMPs scenarios, which included source control measures (FR, FLI and GLI) and process control measures (BS). The results of the single measure evaluation show that BSs on the HRU scale had higher efficiency in reducing pollutants than FR, FLI, and GLI, the average reduction rate was as high as 40% and 70% during the spring snowmelt period and the summer precipitation period, respectively, and BSs showed quite different reduction rates in different HRUs, reflecting the important influence of spatial–temporal characteristics on the effectiveness of the interception measures. In addition, a separated measure can’t be sufficient to reduce TN and TP loading significantly; instead, this study reinforced the necessity of combined measures. The results showed that the “BS20 + FR15 + FLI” scenario had the best pollutant reduction effect, and the “BS1.5 + GLI” scenario was limited, which shows the importance of the interception process in nonpoint source pollution control. Therefore, the choice of the best combined BMPs required considering suitable spatial and temporal objectives, so that the combination will have a higher potential for pollutants reduction.
However, it is necessary to consider costs as well as environmental benefit. FR was the most economical of all the BMPs, with the annual benefits saving up to 7.58 × 107 € per year. Followed by BSs; the benefits of BS1.5 were 7.7 × 104 € per year. As for the land-use change practice, FLI was more environmentally friendly, and GLI was more economically efficient. In the cost-effectiveness analysis of BMPs, the CE value of the interception process measures was higher than that of the source control measures. Among them, BS1.5 had the best cost-effectiveness, TN benefits were as high as 6.9 kg/€, and TP benefits were as high as 5.8 kg/€. Meanwhile, FLI had the worst cost-effectiveness; its TN and TP benefits were only 0.81 kg/€ and 0.61 kg/€, respectively. Above all, based on the best pollution loads reduction with reasonable costs, the simulation results suggested that combining BS1.5 and FR15 was the most effective method for the study area, and will satisfy both environmental and economic objectives.
In this way, as an effective approach to control nonpoint source pollution, BMPs play an essential role in pollutants source reduction, process interception, and ecosystem restoration. The choice of BMPs is not only according to the local situation, but also to consider the environmental benefits and economic costs; using water quality model simulation and an empirical cost algorithm to assess the impacts of BMPs can greatly reduce the waste of funds and resources. In addition, this paper highlighted that decision makers should integrate common advice from environmental agencies, administration offices, and farmers alongside BMPs alternatives and more effective agricultural measures to achieve the goals for different demands. In the future, a greater, wider, stronger, and clearer proposal through joint actions is needed to promote economic development with efficient and durable environmental protection programs.