Linking substance flow analysis and soil and water assessment tool for nutrient management
Introduction
Agriculture is a major source of livelihood in many developing countries. The world population growth and growing demand of food have accelerated the demand for crop production (FAO, 2011). However, unsustainable crop production can lead to significant adverse environmental impacts including deforestation, soil erosion, and nutrient and water pollution (FAO, 2011). In addition, improper and inefficient use of fertilizers causes significant loss of nutrients (Sutton et al., 2013). Around 80% of N and 25–75% of P are lost to the environment through run-off, leaching and off-gas emissions causing environmental impacts such as eutrophication and global warming and leaving insufficient nutrients in the soil for cropping (Sutton et al., 2013). It also triggers the demand for chemical fertilizers significantly; four times over the past forty years and the increasing trend still continues (Sutton et al., 2013). P fertilizer is made from phosphate-rock which is a limited non-renewable resource. N fertilizer can be produced by fixing N2 from the atmosphere; however, it requires large amount of energy to process, which in turn leads to large emissions of greenhouse gases (Gronman et al., 2016). Therefore, proper and efficient use of nutrients is urgently required for increasing soil fertility and yield, reducing environmental impacts, conserving non-renewable materials like phosphate rock and saving agricultural production cost. To do so, a tool is required to track the flow and balance of nutrients in order to reduce the loss and environmental impacts.
Substance flow analysis (SFA) has been applied to track nutrient flows and manage nutrients in several applications at the regional scale (Zhang et al., 2016, Wu et al., 2016, Cordell et al., 2009). Moreover, management of nutrient along the supply chain has been conducted and a new method of nutrient footprint has also been introduced in Gronman et al. (2016). A more comprehensive tool, life cycle assessment (LCA) has also been applied to assess the impacts of agricultural nutrient emissions like eutrophication, acidification and global warming (Ashworth et al., 2015, Mu et al., 2016). To do an LCA, the life cycle inventory (LCI) step is needed to account for nutrient inputs and outputs throughout the agricultural system in order to assess the related environmental impacts. Several SFA and LCI applications mentioned previously estimate nutrient outputs of the system based on average of several field data. However, monitoring of nutrient pollution in the field requires significant time and budget. Moreover, using the average data from several fields in different areas with varying environmental conditions such as soil type, physical geography, weather and differing agricultural practices that affect the release of nutrients to the environment may be less meaningful. Therefore, LCA may not be representative of the specific location being studied. Many LCIs estimate nutrient outputs of the system using the tier one equation in the methodology of Ecoinvent where many of the factors are based on European circumstances (Nemecek and Kagi, 2007, Nevison, 2000), and may therefore not represent the result of the local area of interest, especially in the Asian region.
The Soil and Water Assessment Tool (SWAT) is a process based model that has been adopted for in watershed modelling and management. The model's simulation schemes are based on the area-represented information (ie: soil type, slope, weather and agricultural practice) that results in spatial variability of nutrient balance and flow (Gassman et al., 2007). The model can account for spatial variability and uncertainty of flow arising from different climate and land conditions and agricultural management practices in different areas. Therefore, linking SFA and SWAT can overcome the limitation of SFA described in the previous section, and can assist in the management of the nutrients considering the local conditions. It can also facilitate the application of SFA and LCA to account for the local conditions more accurately, and save money and time. However, spatial and temporal issues and complexity of the SWAT model analysis and output results (associated with hydrological, soil, nutrient and pesticides modelling) can cause confusion. The spatial and temporal data output from the SWAT model has to be assigned, selected and extracted consistent with the objective of SFA model. Moreover, the ouput flows that are spatially allocated in the SWAT model have to be combined to obtain the total nutrient flow of the whole system boundary for the SFA model. Interlinking of input and output of specific spatially allocated substances between SWAT and SFA can be difficult to determine for the SFA practitioner who is not familiar with SWAT model but needs to use the results from the model.
This research introduces the framework to link SFA and SWAT to obtain the spatially variable local condition results more accurately. The framework can guide and facilitate SFA practitioners not familiar with SWAT model to use the results from the SWAT model. This research demonstrates the framework through SFA of Nitrogen (N) and Phosphorus (P) in the maize production system in Phayao, Thailand. The results can enable user and stakeholder to visualize and analyse the pathway of nutrient loss. The simulation of the improved nutrient management scenario was conducted for assessing the practice to reduce nutrient loss to the environment.
Section snippets
Substance flow analysis
Substance Flow Analysis (SFA) is a tool used to quantify and track useful substances or toxic species through anthropogenic or geogenic processes in order to manage substance recovery and reduce environmental impacts (Brunner and Rechberger, 2004). The SFA method is based on the mass balance principle that calculates stock or balance by subtracting the outputs from the inputs. Input flow and output data can be obtained from statistics data or field data collection. Output flow can also be
Selected SWAT data output
The selected SWAT data that was screened, exported and filtered resulted in 12 hydraulic response units (HRUs) related to maize production system in the system boundary. The characteristics and data in each HRU are shown in Table 5. The results shows that the 12 HRUs have different areas and characteristics especially, soil characteristics, even though they are in the same watershed and region. The HRUs that has dominated area are HRU 103 and 168 that have similar characteristics of 5% slope
Conclusion
This work demonstrates the framework to link SFA and SWAT to obtain the spatially variable local condition results more accurately. The framework facilitates the selection, screening, export and filtering of spatially and temporally complex SWAT data to the form that is related to the system definition in the SFA model and easy to be used in SFA. Application of the SWAT model assists in SFA modelling in that it can reveal the spatial variation of nutrient flows from different areas with
Acknowledgments
The authors would like to thank the University of Phayao for financial support number R020058223030 of this research project.
References (20)
- et al.
Environmental impact assessment of regional switchgrass feedstock production comparing nitrogen input scenarios and legume-intercropping systems
J. Clean. Prod.
(2015) - et al.
The story of phosphorus: global food security and food for thought
J. Glob. Environ. Change
(2009) - et al.
Modeling the effects of climate change on water, sediment, and nutrient yields from the Maumee River watershed
J. Hydrol. Reg. Stud.
(2015) - et al.
Nutrient footprint as a tool to evaluate the nutrient balance of a food chain
J. Clean. Prod.
(2016) - et al.
Nutrient balance at chain level: a valuable approach to benchmark nutrient losses of milk production systems
J. Clean. Prod.
(2016) Review of the IPCC methodology for estimating nitrous oxide emissions associated with agricultural leaching and runoff
Chemosphere-Global Change Sci.
(2000)- et al.
Assessing the impacts of sustainable agricultural practices for water quality improvements in the Vouga catchment (Portugal) using the SWAT model
Sci. Total Environ.
(2015) - et al.
Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large Basins
Sci. Total Environ.
(2015) - et al.
The effect of reforestation on stream flow in Upper Nan river basin using Soil and Water Assessment Tool (SWAT) model
Int. Soil Water Conserv. Res.
(2013) - et al.
Phosphorus flow management of cropping system in Huainan, China, 1990-2012
J. Clean. Prod.
(2016)
Cited by (18)
Response of soil nutrients to terracing and environmental factors in the Loess Plateau of China
2024, Geography and SustainabilityUnraveling spatial patterns and source attribution of nutrient transport: Towards optimal best management practices in complex river basin
2024, Science of the Total EnvironmentFertigation to recover nitrate-polluted aquifer and improve a long time eutrophicated lake, Spain
2023, Science of the Total EnvironmentNitrogen flow analysis in Spain: Perspectives to increase sustainability
2023, Science of the Total EnvironmentCitation Excerpt :In addition, inefficient use of fertilisers causes significant loss of nutrients (Sutton et al., 2013). Around 80 % of N and 25–75 % of phosphorus (P) are lost to the environment through run-off, leaching and off-gas emissions, causing environmental impacts such as eutrophication and global warming and leaving insufficient nutrients in the soil for crops (Jakrawatana et al., 2017; Sutton et al., 2013). In contrast to other essential nutrients such as P, N is abundant in the atmosphere in the form of gas (N2(g)).
Material flow analysis of the nitrogen loading to surface water of Miyun reservoir watershed under uncertainty
2022, Journal of Cleaner ProductionQuantification of land use/land cover impacts on stream water quality across Taiwan
2021, Journal of Cleaner Production