Landscape-based upstream-downstream prevalence of land-use/cover change drivers in southeastern rift escarpment of Ethiopia

Characterized by high population density on a rugged topography, the Gedeo-Abaya landscape dominantly contains a multi-strata traditional agroforests showing the insight of Gedeo farmers on natural resource management practices. Currently, this area has been losing its resilience and is becoming unable to sustain its inhabitants. Based on both RS-derived and GIS-computed land-use/cover changes (LUCC) as well as socioeconomic validations, this article explored the LUCC and agroecological-based driver patterns in Gedeo-Abaya landscape from 1986 to 2015. A combination of geo-spatial technology and cross-sectional survey design were employed to detect the drivers behind these changes. The article discussed that LUCC and the prevalence of drivers are highly diverse and vary throughout agroecological zones. Except for the population, most downstream top drivers are perceived as insignificant in the upstream region and vice versa. In the downstream, land-use/cover (LUC) classes are more dynamic, diverse, and challenged by nearly all anticipated drivers than are upstream ones. Agroforestry LUC has been increasing (by 25% of its initial cover) and is becoming the predominant cover type, although socioeconomic analysis and related findings show its rapid LUC modification. A rapid reduction of woodland/shrubland (63%) occurred in the downstream, while wetland/marshy land increased threefold (158%), from 1986 to 2015 with annual change rates of -?3.7 and +?6%, respectively. Land degradation induced by changes in land use is a serious problem in Africa, especially in the densely populated sub-Saharan regions such as Ethiopia (FAO 2015). Throughout the landscape, LUCC is prominently affecting land-use system of the study landscape due to population pressure in the upstream region and drought/rainfall variability, agribusiness investment, and charcoaling in the downstream that necessitate urgent action.     

Relationship between soil test phosphorus and phosphorus in runoff: effects of soil series variability

Phosphorus loss in runoff from agricultural fields has been identified as an important contributor to eutrophication. The objective of this research was to determine the relationship between phosphorus (P) in runoff from a benchmark soil (Cecil sandy loam; fine, kaolinitic, thermic Typic Kanhapludult) and Mehlich III-, deionized water-, and Fe(2)O(3)-extractable soil P, and degree of phosphorus saturation (DPS). Additionally, the value of including other soil properties in P loss prediction equations was evaluated. Simulated rainfall was applied (75 mm h(-1)) to 54 1-m(2) plots installed on six fields with different soil test phosphorus (STP) levels. Runoff was collected in its entirety for 30 min and analyzed for total P and dissolved reactive phosphorus (DRP). Soil samples were collected from 0- to 2-, 0- to 5-, and 0- to 10-cm depths. The strongest correlation for total P and DRP occurred with DPS (r(2) = 0.72). Normalizing DRP by runoff depth resulted in improved correlation with deionized water-extractable P for the 0- to 10-cm sampling depth (r(2) = 0.81). The STP levels were not different among sampling depths and analysis of the regression equations revealed that soil sampling depth had no effect on the relationship between STP and P in runoff. For all forms of P in runoff and STP measures, the relationship between STP and runoff P was much stronger when the data were split into groups based on the ratio of oxalate-extractable Fe to Al. For all forms of P in runoff and all STP methods, R(2) increased with the inclusion of oxalate-extractable Al and Fe in the regression equation. The results of this study indicate that inclusion of site-specific information about soil Al and Fe content can improve the relationship between STP and runoff P.