Assessment of Soil Loss in a Typical Ungauged Dam Catchment using RUSLE Model (Maruba Dam, Kenya)

Soil erosion is a serious land degradation problem which nations all over the world are struggling with. It has affected many river catchments most of which are very dynamic and have become quite vulnerable due to human influence. As such, the functionality of the ecosystem has been largely compromised. Soil erosion has been reported as an expensive problem to remedy and therefore numerous of efforts have shifted to its prevention. This has called for estimation of soil loss which has been adequately achieved by use erosion models over the past. One such model is the Revised Universal Soil Loss Equation (RUSLE) which has been applied at catchment level. Maruba dam catchment has become very unhealthy due to the unsustainable modifications of the terrain. This is evident at the rate at which the dam is losing its storage capacity due to sedimentation. The current situation in the dam formed the basis for this study. Information on soil loss within the catchment is missing and as such decision makers do not have a basis for initiating soil and water conservation plans. The methodological framework for this study was the use of RUSLE model integrated in a GIS framework. The parameters of the model were derived using GIS and RS tools. The study revealed that soil loss ranged between 0 and 29 t ha-1 yr-1 and this explains why the dam if silting up at a fast rate. With this set of information on soil loss, the health of the catchment would be adequately restored and this would save the dam from unwarranted sedimentation. Keywords: Soil erosion, catchment, RUSLE, sedimentation, GIS DOI: 10.7176/JEES/11-16-06 Publication date:June 30th 202

Microbial carbon use efficiency promotes global soil carbon storage

Soils store more carbon than other terrestrial ecosystems$^{1,2}$. How soil organic carbon (SOC) forms and persists remains uncertain$^{1,3}$, which makes it challenging to understand how it will respond to climatic change$^{3,4}$. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss$^{5–7}$. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways$^{4,6,8–11}$, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes$^{12,13}$. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved$^{7,14,15}$. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate

Microbial carbon use efficiency promotes global soil carbon storage

Funding Information: We thank H. Yang, M. Schrumpf, T. Wutzler, R. Zheng and H. Ma for their comments and suggestions on this study. This work was supported by the National Natural Science Foundation of China (42125503) and the National Key Research and Development Program of China (2020YFA0608000, 2020YFA0607900 and 2021YFC3101600). F.T. was financially supported by China Scholarship Council during his visit at Food and Agricultural Organization of the United Nations (201906210489) and the Max-Planck Institute for Biogeochemistry (202006210289). The contributions of Y.L. were supported through US National Science Foundation DEB 1655499 and 2242034, subcontract CW39470 from Oak Ridge National Laboratory (ORNL) to Cornell University, DOE De-SC0023514, and the USDA National Institute of Food and Agriculture. S.M. has received funding from the ERC under the European Union’s H2020 Research and Innovation Programme (101001608). The contributions of U.M. were supported through a US Department of Energy grant to the Sandia National Laboratories, which is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the US Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. We thank the WoSIS database ( https://www.isric.org/explore/wosis ) for providing the publicly available global-scale SOC database used in this study. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD

Measured soil indicators of salt-affected soils in northern Sudan

This data contains measured soil indicators of salt-affected soils in northern Sudan. The soil indicators are electrical conductivity (EC), pH, exchangeable sodium percent (ESP). The data is from 318 locations and were collected between January and August 2018. Each location was sampled at varying depths between 0 and 200 cm and the samples analysed in the laboratory for EC (dS/m), pH, and ESP. Laboratory analyses were done using the saturated soil paste extracts approach. pH was determined using glass electrode while EC was determined using digital EC-meter. ESP was calculated from measured exchangeable sodium ions and cation exchange capacit

Spatial prediction of soil organic carbon stocks in Ghana using legacy data

Soil organic carbon (SOC) is a major driver of the multiple functions of soils in the delivery of ecosystem services. While the spatial prediction and mapping of SOC stocks are of key importance for land managers to understand the synergies between SOC management and soil health, a spatially-explicit map of the distribution of SOC stocks in Ghana is non-existent. Therefore, we quantified the spatial distribution of SOC stocks and associated uncertainties to a target depth of 0–30 cm based on regression-kriging modelling to fill this knowledge gap. The mean error (ME) of the predictions is negligible. The mean absolute error (MAE) shows that the model has prediction errors of about 0.48%. The coefficient of determination (R2) shows that the model explains 34% of the variation in model predictions of SOC stocks. The RMSE is 0.63% of the prediction errors. The predicted SOC stocks show significant variation in their spatial distribution throughout the country. Generally, a trend of decreasing SOC stocks from the southwest to the northeast is clearly recognized. SOC stocks are highest in the Semi-Deciduous agro-ecological zone (43.5 Mg C ha−1) and lowest in the Guinea Savannah agro-ecological zone (0.05 Mg C ha−1). About 5.4 Tg of SOC stocks is stored in the top 0–30 cm of the soils in Ghana. This preliminary work at a spatial resolution of 30 arc-seconds (~1 km) has been accomplished within the framework and guidelines of the Global Soil Partnership (GSP). To our knowledge, this is the first time ever in Ghana that a soil property map has been produced along with its uncertainties. Thus, this study represents a significant first step towards revolutionizing future soil property mapping in Ghana. Even though there remains the need to improve on the quality and spatial resolution, the SOC stocks map presented herein is a satisfactory first step to guide future research on soil organic carbon management at both national and global scales.EEA MendozaFil: Owusu, Stephen. Council for Scientific and Industrial Research. Soil Research Institute; GhanaFil: Yigini, Yusuf. Naciones Unidas. Food and Agriculture Organization (FAO); ItaliaFil: Olmedo, Guillermo Federico. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; ArgentinaFil: Omuto, Christian Thine. University of Nairobi. Department of Environmental and Biosystems Engineering; Keni

Assessment of Land Cover and Land Use Change Dynamics in Kibwezi Watershed, Kenya

Land use and land cover (LULC) parameters influence the hydrological and ecological processes taking place in a watershed. Understanding the changes in LULC is essential in the planning and development of management strategies for water resources. The purpose of the study was to detect changes in LULC in the Kibwezi watershed in Kenya, using geospatial approaches. Supervised and unsupervised classification techniques using remote sensing (RS) and geographical information system (GIS) were used to process Landsat imagery for 1999, 2009, and 2019 while ERDAS IMAGINE™ 14 and MS Excel software were used to derive change detection, and the Soil and Water Assessment Tool (SWAT) model was used to delineate the watershed using an in-built watershed delineation tool. The watershed was classified into ten major LULC classes, namely cropland (rainfed), cropland (irrigated), cropland (perennial), crop and shrubs/trees, closed shrublands, open shrubland, shrub grasslands, wooded shrublands, riverine woodlands, and built-up land. The results showed that LULC under shrub grassland, urban areas, and crops and shrubs increased drastically by 552.5%, 366.2%, and 357.1% respectively between 1999 and 2019 with an annual increase of 35.55%, 35.38%, and 33.86% per annum. The area under open shrubland and closed shrubland declined by73.7%, and 30.4% annually. These LULC transformations pose a negative impact on the watershed resources. There is therefore a need for proper management of the watershed for sustainable socio-economic development of the Kibwezi area

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