Evaluation of land and vegetation degradation indicators in Kiang'ombe Landscape, Mbeere North, Kenya

Land and vegetation degradation is mainly driven by inappropriate land use which mainly results from use of inadequate technologies and rapid increase of human population. Climate variability and change have also exacerbated the land and vegetation degradation problem. This study was undertaken to provide a valuable tool for assessing land and vegetation degradation risk and analyzing the effectiveness of various woodland rehabilitation practices. This was undertaken through integration of local and scientific techniques in Kiang’ombe landscape, Mbeere North in Kenya. The methods used included; Focus Group Discussions (FGD), key informant interviews with local community and vegetation data collection. Sample plots were laid to characterize and enumerate vegetation along degradation gradient. Major land degradation drivers prioritized by local people were overgrazing (70%), soil erosion (65%), unsustainable charcoal production (55%) wildfires (30%), and uncontrolled sand harvesting (20%). Major land and vegetation degradation indicators identified by local community were; reduced vegetation cover (75%), loss of soil fertility (70%) and low water discharge form springs (53%). Results on vegetation data revealed a significant difference in stocking density and tree diversity along degradation gradient (p<0.05). Protea gaugedi was identified as wildfire tolerant tree species due to its high occurrence in fire prone areas. In addition, Croton macrostachyus, Acacia hockii and Faurea saligna were prioritized tree species for rehabilitation of degraded areas. The study recommends integration of scientific and local knowledge during monitoring of woodland degradation and assessing the impact of rehabilitation interventions.Keywords: Local knowledge, woodland, land degradation gradient, rehabilitation, Kiang'ombe landscap

Co-limitation towards lower latitudes shapes global forest diversity gradients

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers

The DesertMargins Programme Approaches in Upscaling Best-Bet Technologies in Arid and Semi-arid Lands in Kenya

Kenya’s land surface is primarily arid and AQ1 semi-aridlands(ASALs)whichaccountfor84%ofthe totallandarea.TheDesertMarginsProgramme(DMP) inKenyahasmadesomecontributiontounderstanding which technology options have potential in reducing land degradation in marginal areas and conserving biodiversity through demonstrations, testing of the most promising natural resource management options, developing sustainable alternative livelihoods and policy guidelines, and replicating successful models. In extension of sustainable natural resource management, two types of strategies were used: (i) strategies for the promotion of readily available technologies and (ii) approaches for participatory learning and action research. Thus DMP-Kenya initiated upscaling of four ‘best-bet’ technologies. Under the rangeland/livestock management options, scaling-up activities include improvement of rangeland productivity, rangeland resource management through communitybasedrangeresourcesmonitoring/assessment,andfodder conservation for home-based herds. Restoration of degraded lands included rehabilitation of rangelands using the red paint approach in conservation of Acacia tortilis, control of Prosopis, planting of Acacia senegal trees in micro-catchments, and rehabilitation of degraded areas through community enclosures. Improved land, nutrient, and water management involved upscaling water harvesting and integrated nutrient management (INM) technologies. Activities A.O. Esilaba ( ) Desert Margins Programme, Kenya Agricultural Research Institute, Nairobi, Kenya e-mail: [email protected] under tree-crop/livestock interactions included upscaling of Melia volkensii and fruit trees (mangoes) and enhancing biodiversity conservation through support of beekeeping as a viable alternative livelihood. Participatory learning and action research (PLAR) was used for technology development and dissemination. Capacity building and training was a major component of upscaling of these best-bet technologies