Browsing by Author "Winowiecki, L."

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Assessing drivers of soil properties and classification in the West Usambara mountains, Tanzania
(Elsevier, 2017-10-16) Massawe, B. H. J.; Winowiecki, L.; Meliyo, J. L.; Mbogoni, J. D. J.; Msanya, B. M.; Kimaro, D.; Deckersf, J.; Gulinck, H.; Lyamchai, C.; Sayula, G.; Msokah, E.; Vagen, T.; Brush, G.; Jelinskii, N. A.
Improved soil information in tropical montane regions is critical for conservation, sustainable agricultural management, and land use planning, but is often challenged by topographic and land-use heterogeneity. The West Usambara mountains are a part of the Eastern Arc chain of mountains of Tanzania and Kenya, a globally important tropical montane ecoregion made up of isolated fault-block mountain complexes characterized by high biological endemism, population density, and agronomic productivity. We synthesized novel and legacy soil data from published and unpublished studies to better understand the drivers of soil property distributions and soil diversity in the West Usambaras, and to serve as a foundation for improved soil mapping efforts across the Eastern Arc. Analysis of the resulting dataset of 468 sites (ranging in elevation from 1040 to 2230 m.a.s.l.) revealed that soil properties varied more significantly by land use and topography than by soil type, suggesting that future mapping efforts in the region should focus primarily on soil property prediction and secondarily on soil classification. Sites under cultivated land uses had the lowest topsoil soil organic carbon (SOC) concentrations and highest pH values, and SOC generally increased with increasing elevation. Valley soils had significantly lower surface SOC concentrations but higher exchangeable bases and pH values than all other landscape positions. Soil pH decreased by an average of 3.5 units across the entire elevation gradient and decreased by 1 unit with elevation even after SOC, land use and landscape position were included in multiple regression models. The relationship of cation exchange capacity (CEC) to SOC and clay content varied by landscape position. Therefore, particularly in montane regions where soils can vary significantly over short distances, multiple functions may be necessary to produce improved estimates of parameters such as CEC. Soil classification was driven most strongly by topography, with Acrisols (WRB Reference Group) and Ultisols (U.S. Soil Taxonomy (ST)) as the dominant soil types, located primarily on mid slope, upper slope and crest landscape positions, making up 47% and 75% of observed profiles, respectively. However, five ST Orders and seven WRB Reference Groups were present in the dataset, with the highest soil diversity occurring at lower slope landscape positions. Conclusions drawn from this large dataset support previous work in the West Usambaras and provide a conceptual foundation from which to build improved soil maps across the Eastern Arc and in other tropical montane systems throughout the world.
Landscape-scale variability of soil health indicators: effects of cultivation on soil organic carbon in the Usambara Mountains of Tanzania
(Springer, 2015-11-02) Winowiecki, L.; Va°gen, T.; Massawe, B.; Jelinski, N. A.; Lyamchai, C.; Sayula, G.; Msoka, E.
Land-use change continues at an alarming rate in sub-Saharan Africa adversely affecting ecosystem services provided by soil. These impacts are greatly understudied, especially in biodiversity rich mountains in East Africa. The objectives of this study were to: conduct a biophysical baseline of soil and land health; assess the effects of cultivation on soil organic carbon (SOC); and develop a map of SOC at high resolution to enable farm-scale targeting of management interventions. Biophysical field surveys were conducted in a 100 km2 landscape near Lushoto, Tanzania, with composite soil samples collected from 160 sampling plots. Soil erosion prevalence was scored, trees were counted, and current and historic land use was recorded at each plot. The results of the study showed a decline in SOC as a result of cultivation, with cultivated plots (n = 105) having mean topsoil OC of 30.6 g kg-1, while semi-natural plots (n = 55) had 71 g OC kg-1 in topsoil. Cultivated areas were also less variable in SOC than seminatural systems. Prediction models were developed for the mapping of SOC based on RapidEye remote sensing data for January 2014, with good model performance (RMSEPcal = 8.0 g kg-1; RMSEPval = 10.5 g kg-1) and a SOC map was generated for the study. Interventions will need to focus on practices that increase SOC in order to enhance productivity and resilience of the farming system, in general. The highresolution maps can be used to spatially target interventions as well as for monitoring of changes in SOC.
Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning
(Elsevier, 2016-11-24) Massawe, B. H. J.; Subburayalu, S. K.; Kaaya, A. K.; Winowiecki, L.; Slater, B. K.
Inadequacy of spatial soil information is one of the limiting factors to making evidence-based decisions to improve food security and land management in the developing countries. Various digital soilmapping (DSM) techniques have been applied inmany parts of theworld to improve availability and usability of soil data, but less has been done in Africa, particularly in Tanzania and at the scale necessary tomake farmmanagement decisions. The Kilombero Valley has been identified for intensified rice production. However the valley lacks detailed and up-todate soil information for decision-making. The overall objective of this study was to develop a predictive soilmap of a portion of Kilombero Valley using DSM techniques. Two widely used decision tree algorithms and three sources of Digital ElevationModels (DEMs) were evaluated for their predictive ability. Firstly, a numerical classification was performed on the collected soil profile data to arrive at soil taxa. Secondly, the derived taxawere spatially predicted and mapped following SCORPAN framework using Random Forest (RF) and J48 machine learning algorithms. Datasets to train the model were derived from legacy soil map, RapidEye satellite image and three DEMs: 1 arc SRTM, 30 m ASTER, and 12 m WorldDEM. Separate predictive models were built using each DEM source. Mapping showed that RF was less sensitive to the training set sampling intensity. Results also showed that predictions of soil taxa using 1 arc SRTM and 12mWordDEMwere identical.We suggest the use of RF algorithmand the freely available SRTMDEMcombination formapping the soils for thewhole Kilombero Valley. This combination can be tested and applied in other areas which have relatively flat terrain like the Kilombero Valley
Multi-criteria land evaluation for rice production using GIS and analytic hierarchy process in Kilombero Valley, Tanzania
(Tanzania Journal of Agricultural Sciences, 2019) Massawe, B .H. J.; Kaaya, A. K.; Winowiecki, L.; Slater, B. K.
A GIS-based multi-criteria land evaluation (MCE) was performed in Kilombero Valley, Tanzania to avail decision makers and farmers with evidence based decision support tool for improved and sustainable rice production. Kilombero valley has been identified by the government and investors for rice production intensification. Five most important criteria for rice production in the area were identified through literature search and discussion with local agronomists and lead farmers. The identified criteria were 1) soil properties, 2) surface water resources, 3) accessibility, 4) distance to markets, and 5) topography. Surveys, on-screen digitizations, reclassifications and overlays in GIS software were used to create spatial layers of the identified criteria. Analytic hierarchy process (AHP) method was used to score the criteria using local extension staff and lead farmers as domain experts on a scale of 0.0 – 1.0. Surface water resource scored the highest weight (0.462) followed by soil chemical properties (0.234). Other criteria and their weight in paranthesis are soil physical properties (0.19), topography (0.052), accessibility (0.036), and distance to market (0.025). The MCE results showed that about 8% of the study area was classified as having low suitability for rice production while only 2% was highly suitable. The majority of the area (about 89%) was classified as having medium suitability for rice production. Since the suitability decision was dominated by the surface water resource criterion, the rice suitability in the study area can be greatly improved by improving the water resources management.