12:00 PM - 12:15 PM
[MIS11-12] Footprint analysis on nutrient runoff from farmland in regions of Japan
Keywords:RUSLE model, nitrogen footprint, phosphorus footprint, input-output analysis
Applied nutrients partly runoff into the hydrosphere along with soil due to soil erosion associated with rainfall, contributing to water pollution and eutrophication. Information on the quantitative relationship between nutrient runoff and consumption through the supply chains is needed to develop measures to prevent nutrient runoff throughout the food system. Nitrogen and phosphorus footprints quantify the loss of nitrogen and phosphorus to the environment due to resource consumption. This study aims to quantify the amounts of nitrogen and phosphorus runoff due to soil erosion caused by rainfall from croplands except for paddy fields in regions of Japan and link them to the consumption of agricultural crops excluding paddy rice in those regions based on a footprint analysis using an input-output table.
The amount of soil erosion from farmland except for paddy field in 2011 for each 1 km2 grid cell was calculated using the Revised Universal Soil Loss Equation (RUSLE), a model for estimating soil erosion. Soil erosion from the paddy field was considered to be negligible due to its flatness. The RUSLE model describes the annual soil erosion rate, Y [t ha-1 year-1] as the equation Y= R·K·LS·C·P (1)where R is the rainfall-runoff erosivity factor [MJ mm ha-1 hour-1 year-1], K is the soil erodibility factor [t hour MJ-1 mm-1], LS is the slope length and steepness factor [dimensionless], and C is the cover management factor [dimensionless], and P is the support practice factor [dimensionless]. R-values were obtained, following Udo (2019), by the number of annual rainfall events n, the kinetic energy per mm of rainfall given by the rainfall intensity Ii [mm hour-1] at any rainfall event i [MJ ha-1 mm-1], the rainfall amount ri [mm], the maximum rainfall for 60 minutes I60i [mm hour-1] using the equation R=∑ni=1(0.119+0.0873 log Ii) ri I60i (2). The Radar Automated Meteorological Data Acquisition System (1 km2 grid cell resolution, hourly rainfall data) was used as the rainfall data. Here, snowfall was considered as rainfall. K-values for 383 sites were calculated using the data of the plow layer in the 4th round of the Basic Survey on the Soil Environment (1994–1998) and the two equations as in Taniyama (2003). From the obtained K-values, the values for each soil series group in the Comprehensive Soil Classification System of Japan First Approximation were determined. LS-values were estimated using the statistical formula of Kohyama et al. (2012) for LS values and the gradient degrees. The gradient degree was calculated as the average gradient degrees between 50 m grid cells of the farmland within each 1 km grid cells. The digital soil map of cultivated soil in Japan by the National Agriculture and Food Research Organization (NARO) was used for farmland data (100m resolution), and the Digital Map 50m Grid (elevation) by the Japan Map Center was used for elevation data. The cover management factor C was taken from Taniyama (2003) and Imai and Ishiwatari (2007) and averaged in each grid cell based on the NARO data. The support practice factor P was uniformly set to 1, following Kohyama et al. (2012).
We estimated the nitrogen and phosphorus content of each soil category from the Basic Survey on Soil Environment and calculated the annual runoff of nitrogen and phosphorus due to soil erosion for each 1 km2 grid cell for the farmland except for the paddy field (Figure 1). On a country scale, the annual runoff was estimated at 656 Gg-N yr-1 and 96 Gg-P yr-1. On a prefectural scale, the annual runoff of nitrogen and phosphorus was high in Shizuoka Prefecture, where the target area was relatively large and the slope was relatively high due to heavy rainfall; Hokkaido Prefecture, where the target area was more than 40% of the national total but the slope was moderate due to little heavy rainfall; and Kagoshima Prefecture, where the target area was relatively large, and the slope was moderate due to heavy rainfall.
By the JpGU conference, we plan to estimate the quantitative relationship between the consumption of food and other resources and the amount of nitrogen and phosphorus runoff in each region of Japan based on the analysis using the inter-prefectural input-output table for 2011, and resultant nitrogen and phosphorus footprints for each prefecture. The results of this study will contribute to the optimization of nutrient management in the entire food system by showing the important supply chains in nutrient runoff control.
The amount of soil erosion from farmland except for paddy field in 2011 for each 1 km2 grid cell was calculated using the Revised Universal Soil Loss Equation (RUSLE), a model for estimating soil erosion. Soil erosion from the paddy field was considered to be negligible due to its flatness. The RUSLE model describes the annual soil erosion rate, Y [t ha-1 year-1] as the equation Y= R·K·LS·C·P (1)where R is the rainfall-runoff erosivity factor [MJ mm ha-1 hour-1 year-1], K is the soil erodibility factor [t hour MJ-1 mm-1], LS is the slope length and steepness factor [dimensionless], and C is the cover management factor [dimensionless], and P is the support practice factor [dimensionless]. R-values were obtained, following Udo (2019), by the number of annual rainfall events n, the kinetic energy per mm of rainfall given by the rainfall intensity Ii [mm hour-1] at any rainfall event i [MJ ha-1 mm-1], the rainfall amount ri [mm], the maximum rainfall for 60 minutes I60i [mm hour-1] using the equation R=∑ni=1(0.119+0.0873 log Ii) ri I60i (2). The Radar Automated Meteorological Data Acquisition System (1 km2 grid cell resolution, hourly rainfall data) was used as the rainfall data. Here, snowfall was considered as rainfall. K-values for 383 sites were calculated using the data of the plow layer in the 4th round of the Basic Survey on the Soil Environment (1994–1998) and the two equations as in Taniyama (2003). From the obtained K-values, the values for each soil series group in the Comprehensive Soil Classification System of Japan First Approximation were determined. LS-values were estimated using the statistical formula of Kohyama et al. (2012) for LS values and the gradient degrees. The gradient degree was calculated as the average gradient degrees between 50 m grid cells of the farmland within each 1 km grid cells. The digital soil map of cultivated soil in Japan by the National Agriculture and Food Research Organization (NARO) was used for farmland data (100m resolution), and the Digital Map 50m Grid (elevation) by the Japan Map Center was used for elevation data. The cover management factor C was taken from Taniyama (2003) and Imai and Ishiwatari (2007) and averaged in each grid cell based on the NARO data. The support practice factor P was uniformly set to 1, following Kohyama et al. (2012).
We estimated the nitrogen and phosphorus content of each soil category from the Basic Survey on Soil Environment and calculated the annual runoff of nitrogen and phosphorus due to soil erosion for each 1 km2 grid cell for the farmland except for the paddy field (Figure 1). On a country scale, the annual runoff was estimated at 656 Gg-N yr-1 and 96 Gg-P yr-1. On a prefectural scale, the annual runoff of nitrogen and phosphorus was high in Shizuoka Prefecture, where the target area was relatively large and the slope was relatively high due to heavy rainfall; Hokkaido Prefecture, where the target area was more than 40% of the national total but the slope was moderate due to little heavy rainfall; and Kagoshima Prefecture, where the target area was relatively large, and the slope was moderate due to heavy rainfall.
By the JpGU conference, we plan to estimate the quantitative relationship between the consumption of food and other resources and the amount of nitrogen and phosphorus runoff in each region of Japan based on the analysis using the inter-prefectural input-output table for 2011, and resultant nitrogen and phosphorus footprints for each prefecture. The results of this study will contribute to the optimization of nutrient management in the entire food system by showing the important supply chains in nutrient runoff control.