Estimation of soil erosion risk, its valuation and economic implications for agricultural production in western part of Rwanda

Abstract

Multifunctional soil conservation strategies have the capacity to control soil erosion as well as increase its quality, thus leading to sustained yields as long as planners have knowledge on the severity of soil loss. A comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques was adopted to determine the soil erosion vulnerability within Katabuvuga, Nyamyumba and Mukamira watersheds in western part of Rwanda, with the aim of supporting planning of land and water management interventions. The dominant slop class in all watershed was 16-40% covering 50% in Katabuvuga watershed, 43% in Mukamira watershed and 70.6% in Nyamyumba watershed. High erosion risk was recorded in Mukamira (72 %) and it was followed by Nyamyumba (46 %). The average soil loss in selected watersheds was 32t/ha/year. Among the various studied watershed, highest average loss was reported in Nyamyumba watershed (37t/ha/year) while the lowest average was in Mukamira watershed (28t/ha/year). Soil loss was higher in cropland and lower in settlement. The average loss of nutrients was 1705 kg/ha/year of carbon, 155 kg/ha/year of nitrogen, 3 kg/ha/year of phosphurus and 111 kg/ha/year of potassium, the highest nutrient loss occurred in cropland. Based on the cost of NPK the average value of N lost per ha per year is 167507 Rwandan Francs (Rwf) while the value of P and K loss per ha per year is 3309 Rwf and 120189 Rwf respectively.

Land Use Change Impacts on Water Erosion in Rwanda

“…In addition, our findings are relatively comparable with a recent study conducted over the entire East Africa region with a mean soil loss of 34.2 t ha −1 y −1 for Rwanda [83], and in line with mean soil erosion rates ranging between 35 t ha −1 y −1 and 75 t ha −1 y −1 predicted in Sub-Saharan Africa [84]. Comparing the results of Karamage et al [9,26] (Table 4) with the findings of this study and other studies carried out in Rwanda and in the region, suggests that their results may be overestimates, an issue previously highlighted by Kabirigi et al [36]. Table 5.…”

“…In addition, our findings are relatively comparable with a recent study conducted over the entire East Africa region with a mean soil loss of 34.2 t ha −1 y −1 for Rwanda [83], and in line with mean soil erosion rates ranging between 35 t ha −1 y −1 and 75 t ha −1 y −1 predicted in Sub-Saharan Africa [84]. Comparing the results of Karamage et al [9,26] (Table 4) with the findings of this study and other studies carried out in Rwanda and in the region, suggests that their results may be overestimates, an issue previously highlighted by Kabirigi et al [36]. [37] Lake Kivu Basin, D. R. Congo-Rwanda 30 USLE Karamage et al [9] Nyabarongo River Catchment, Rwanda 490 USLE Karamage et al [26] Rwanda 250 RUSLE Kagabo et al [29] Buberuka Highlands of Rwanda 41.5 Experimental design Lewis and Nyamulinda, [85] Rutoyi village in Northwestern Rwanda…”

“…The mean estimates of 39.2 t ha −1 y −1 of soil eroded in 2015 ( Figure 6b) and 41.4 t ha −1 y −1 from Buberuka highlands AEZ (Table A1) are in accordance with the work of Kagabo et al [29], Kabirigi et al [36] and Karamage et al [37], but far less from the results reported by Karamage et al [9,26] ( Table 5). In addition, our findings are relatively comparable with a recent study conducted over the entire East Africa region with a mean soil loss of 34.2 t ha −1 y −1 for Rwanda [83], and in line with mean soil erosion rates ranging between 35 t ha −1 y −1 and 75 t ha −1 y −1 predicted in Sub-Saharan Africa [84].…”

“…The RUSLE [34], derived from the USLE [33], is an empirically-based modeling approach developed for estimating long-term mean annual soil loss due to rill and interrill erosion (Equation (1)). The model was originally designed at the agricultural plot scale for farmland in the United States of America [36] but it has since been applied in many other countries, at many other scales, and in many other geo-climatic regions [31].…”

Calibration and validation of rainfall erosivity estimators for application in Rwanda

“…Since monitoring soil losses by water erosion is tedious and costly, soil erosion models have become crucial estimation tools at field, watershed, regional, continental and even global scales. The empirical Universal Soil Loss Equation (USLE) and its revised version (RUSLE) have been used most frequently to estimate long-term average soil losses by rill and interrill erosion, at field and landscape scales, in various climatic zones (Tamene and Le, 2015;Angima et al, 2003;Kabirigi et al, 2017;Roose, 1976;Yang et al, 2003;Sadeghi and Hazbavi, 2015). The (R)USLE estimates soil loss by multiplying six factors, including the rainfall erosivity (R-factor).…”