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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 41 to 44 of 44 (0.07 seconds)Spelling suggestions: "subject:"currentunavailability"" "subject:"electricityavailability""
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Matching the availability of N mineralised from green-manure crops with the N-demand of field vegetables /Båth, Birgitta, January 1900 (has links) (PDF)
Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv. / Härtill 4 uppsatser.
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Urbanisation, Land Use and Soil Resource: Spatio-Temporal Analyses of Trends and Environmental Effects in Two Metropolitan Regions of Ghana (West Africa)Asabere, Stephen Boahen 19 June 2020 (has links)
No description available.
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Trace gas fluxes and belowground carbon allocation in tropical montane forest soils of Southern Ecuador / Spurengasflüsse und unterirdische Kohlenstoffspeicherung in den Böden tropischer Bergregenwälder SüdecuadorsWolf, Katrin 24 January 2011 (has links)
No description available.
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Determining the effects of elevated carbon dioxide on soil acidification, cation depletion, and soil inorganic carbon and mapping soil carbons using artificial intelligenceFerdush, Jannatul 09 August 2022 (has links) (PDF)
Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO2 affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO2 and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO2 effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils from the state of Mississippi, USA, representing acidic, neutral, and alkaline pH, were exposed to different CO2 enrichments (100%, 10%, and 1%) for 30 days. The leachates’ pH tended to attain an equilibrium state (neutral) with time under CO2 saturation. SIC increased under CO2 saturation, whereas cation exchange capacity (CEC) showed a decreasing pattern in all soils. In the third chapter, an eXplainable artificial intelligence (XAI) was performed to visualize the different forms of soil carbon variability across the Mississippi River Basin area. This model explains key insights and local discrepancies, suggesting a solution to the “Black-Box” issue. The best performing model, stack ensemble, showed improved RMSE (3 to 8%) and spatial variability for soil carbons than other ML models, especially after adding the residuals from regression analyses. Land cover type > soil pH > total nitrogen, > NDVI were identified as the top four crucial factors for predicting SOC when bulk density > precipitation, soil pH > mean annual temperature described SIC. The proposed automatic machine learning (AutoML) model with model agnostic interpolations might be a hallmark to mitigate the C loss under adverse climate change conditions and allow diverse knowledge groups to adopt a new interpretable ML algorithm more confidently. Findings from this study help predict the impact of elevated atmospheric CO2 on soil pH, acidification, and nutrient availability and develop strategies for sustainable land management practices under a changing climate.
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