This report is the first of two survey reports. The survey was undertaken at Malinau Watershed area from 19 February -4 March 1999. Although this is not a full report, the information and its reviews are relevant for discussion and follow up. The objectives of the survey carried out by relevant institutions are to: (1) seek preliminary information on villages, their communities, history and adat forests along the Malinau watershed; (2) collect preliminary data on forest and natural forest management systems carried out by adat communities and their adat law; (3) have general picture of conflicts and potential conflicts in villages along the Malinau watershed and local institution readiness in solving the existing conflicts; (4) obtain preliminary information on communities’ and local institutions’ preparedness in mapping their adat forests; and (5) promote and encourage participatory mapping activities.
Tag: mapping
Mathematical models on diffusion of oxygen to and within plant roots, with special emphasis on effects of soil-root contact: I. Derivations of the models
A mathematical model is presented for diffusive transport of oxygen inside the root, for the case that oxygen can enter only through part of the root’s perimeter because the remainder is blocked by soil-root contact. Without soil-root contact, concentration profiles inside the root can be shown to converge rapidly to a steady-state solution. For the case of soil-root contact a steady-state solution is presented. Steady-state solutions have also been obtained for the presence of a water film, with and without rhizosphere respiration inside the water film. Results are presented in the form of isoconcentration lines.
Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning
Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning
Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms—random forest and gradient boosting, as implemented in R packages ranger and xgboost—and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40–85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatio-temporal statistical modeling framework.
Updating legacy soil maps for climate resilient agriculture: A case of kilombero valley, Tanzania
Since the first documented soil survey in Tanzania by Milne (J Ecol 35:192-265, 1936), a number of other soil inventory exercises at different scales have been made. The main challenge has been the fragmented nature of the often outdated detailed soil maps and small-scale less-informative country-wide soil maps. Recent advances in information and computational technology have created vast potential to collect, map, harness, communicate and update soil information. These advances present favorable conditions to support the already popular shift from qualitative (conventional) to quantitative (digital) soil mapping (DSM). In this study, two decision tree machine learning algorithms, J48 and Random Forest (RF), were applied to digitally predict k-means numerically classified soil clusters to update a soil map produced in 1959. Predictors were derived from 1 arc SRTM digital elevation data and a 5 m RapidEye satellite image. J48 and RF predicted the soil units of the legacy maps with greater detail. However, RF showed superiority for predicting clusters J48 could not predict and for showing higher pixel contiguity. No significant difference (P = 0.05) was observed between the soil properties of the predicted soil clusters and the actual field validation points. Young soils (Entisols and Inceptisols) were found to occupy about 56 % of the study site’s 30,000 ha followed by Alfisols, Mollisols and Vertisols at 31, 9 and 4 %, respectively. This study demonstrates the usefulness of DSM techniques to update conventionally prepared legacy maps to offer soil information at improved detail to agricultural land use planners and decision makers of Tanzania to make evidence-based decisions for climate-resilient agriculture and other land uses. © Springer International Publishing AG 2016.
Africa Tree Finder
This easy-to-use App shows you data on the distribution of indigenous tree species in different natural vegetation types, combined with information on the products and services that the tree species can provide. It arms you – local community members, government agencies, private sector owners, and other land managers – with the information you need to select the best tree species for your landscape restoration or agroforestry effort.
Tana-Kipini-Laga Badana Bush Bushle Land and Seascape
The Biodiversity Management Programme (BMP) is an initiative funded by European Commission (EC) through Intergovernmental Authority on Development (IGAD) aiming to contribute to poverty reduction by improving the social and economic wellbeing of the populations in IGAD region, through a better regional integration in the environmental sector.
Landscape Portal
The Landscape Portal is ICRAF’s interactive online spatial data storage and visualization platform. It comes with a rich set of features to store, document, search and retrieve, and visualize spatial data and maps.
The potential natural vegetation (PNV) map of eastern and southern Africa
Potential natural vegetation (PNV) is defined here as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions, including conditions created or altered by man.
Useful tree species for Eastern Africa: a species selection tool based on the VECEA map
To help farmers, foresters and others to ‘find the right tree for the right place’, a species selection tool was developed which provides information about tree and shrub species that have been documented to be useful to farming or pastoral communities. The tool provides an easy way to quickly find information about species that can potentially occur in any selected location, based on their documented occurrence in the different potential natural vegetation types. The tool is available for Google Earth, is part of the Vegetationmap4africa web-based map and is integrated in the mobile maps if used together with Locus map viewer