Search

This near-infrared spectral dataset consists of 2,106 diverse mineral soil samples scanned, on average, on six different units of the same low-cost commercially available handheld spectrophotometer. Most soil samples were selected from the USDA NRCS National Soil Survey Center-Kellogg Soil Survey Laboratory (NSSC-KSSL) soil archives to represent the diversity of mineral soils (0–30 cm) found in the United States, while 90 samples were selected from Ghana, Kenya, and Nigeria to represent available African soils in the same archive. All scanning was performed on dried and sieved (

Sustainable land management is at the heart of some of the most intractable challenges facing humanity in the 21st century. It is critical for tackling biodiversity loss, land degradation, climate change and the decline of ecosystem services. It underpins food production, livelihoods, dietary health, social equity, climate change adaptation, and many other outcomes. However, interdependencies, trade-offs, time lags, and non-linear responses make it difficult to predict the combined effects of land management decisions. Policy decisions also have to be made in the context of conflicting interests, values and power dynamics of those living on the land and those affected by the consequences of land use decisions. This makes designing and coordinating effective land management policies and programmes highly challenging. The difficulty is exacerbated by the scarcity of reliable data on the impacts of land management on the environment and livelihoods. This poses a challenge for policymakers and practitioners in governments, development banks, non-governmental organisations, and other institutions. It also sets demands for researchers, who are under ever increasing pressure from funders to demonstrate uptake and impact of their work. Relatively few research methods exist that can address such questions in a holistic way. Decision makers and researchers need to work together to help untangle, contextualise and interpret fragmented evidence through systems approaches to make decisions in spite of uncertainty. Individuals and institutions acting as knowledge brokers can support these interactions by facilitating the co-creation and use of scientific and other knowledge. Given the patchy nature of data and evidence, particularly in developing countries, it is important to draw on the full range of available models, tools and evidence. In this paper we review the use of evidence to inform multiple-objective integrated landscape management policies and programmes, focusing on how to simultaneously achieve different sustainable development objectives in diverse landscapes. We set out key success factors for evidence-based decision-making, which are summarised into 10 key principles for integrated landscape management knowledge brokering in integrated landscape management and 12 key skills for knowledge brokers. We finally propose a decision-support framework to organise evidence that can be used to tackle different types of land management policy decision.

Abstract Background Mangoes are fruits of nutritional importance in Kenya. The fruits vary in their sugars depending on cultivar type and ripening stage. Current methods of sugar content analysis are based on HPLC methods, which are accurate but expensive and time-consuming. We evaluated the potential of diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy as a rapid tool for quantitative prediction of fructose and glucose sugars in the pulp of Kenyan mangoes. Methods Principal component analysis and partial least regression models were developed using the first derivative DRIFT spectra (400–4000 cm−1) to predict fructose and glucose sugars. A factorial analysis of variance compared effects of site (three locations), cultivar type (four cultivars), fruit position (inside/outside), and their interactions on fructose and glucose contents. Results The principal component analysis scores plot using components 1 and 2 explained 75 and 8% of the variance, respectively, with no clear grouping either by sites or cultivars. The PLS range for glucose was R 2 = 0.80, SECV = 0.55, and RPD = 11.52 and fructose R 2 = 0.70, SECV = 0.28, and RPD = 11.52. Site (F (2, 265) = 18.12, p 0.05. In general, fruits outside the canopy had higher fructose and glucose contents than those within the canopy. Conclusions DRIFTS coupled with chemometric techniques showed potential for prediction of fructose and glucose contents of mango fruits.

Wood density is an important variable for accurate quantification of woody biomass and carbon stocks. Conventional destructive methods for wood density estimation are resource intensive, prohibiting their use, limiting the application of approaches that would minimize uncertainties in tree biomass estimates. We tested an alternative method involving tree coring with a carpenter's auger to estimate wood density of seven tropical tree species in Western Kenya. We used conventional water immersion method to validate results from the auger core method. The mean densities (and 95% confidence intervals) ranged from 0.36 g cm−3 (0.25–0.47) to 0.67 g cm−3 (0.61–0.73) for the auger core method, and 0.46 g cm−3 (0.42–0.50) to 0.67 g cm−3 (0.61–0.73) for the water immersion method. The auger core and water immersion methods were not significantly different for four out of seven tree species namely; Acacia mearnsii, Mangifera indica, Eucalyptus grandis and Grevillea robusta. However, wood densities estimated from the auger core method were lower (t (61) = 7.992, P =

Designing and implementing biodiversity-based value chains can be a complex undertaking, especially in places where outcomes are uncertain and risks of project failure and cost overruns are high. We used the Stochastic Impact Evaluation (SIE) approach to guide the Intergovernmental Authority on Development (IGAD) on viable investment options in honey value chains, which the agency considered implementing as an economic incentive for communities along the Kenya-Somalia border to conserve biodiversity. The SIE approach allows for holistic analysis of project cost, benefit, and risk variables, including those with uncertain and missing information. It also identifies areas that pose critical uncertainties in the project. We started by conducting a baseline survey in Witu and Awer in Lamu County, Kenya. The aim of the survey was to establish the current farm income from beekeeping as a baseline, against which the prospective impacts of intervention options could be measured. We then developed an intervention decision model that was populated with all cost, benefit and risk variables relevant to beekeeping. After receiving training in making quantitative estimates, four subject-matter experts expressed their uncertainty about the proposed variables in the model by specifying probability distributions for them. We then used Monte Carlo simulation to project decision outcomes. We also identified variables that projected decision outcomes were most sensitive to, and we determined the value of information for each variable. The variable with the highest information value to the decision-maker in Witu was the honey price. In Awer, no additional information on any of the variables would change the recommendation to invest in honey value chains in the region. The analysis demonstrates a novel and comprehensive approach to decision-making for different stakeholders in a project where decision outcomes are uncertain.

Abstract  Sub-Saharan Africa faces huge food supply challenges due to increasing human population, limited opportunities to increase arable land, and declining yields associated with continuously declining soil fertility. To cater for their food requirements, smallholders use only modest levels of inorganic fertilizers and rely to a large extent on manure, which is generally of low quality. To explore factors influencing fertilizer and manure use at the farm level, 253 farm households in Vihiga district of western Kenya were sampled. A pair of Tobit models was used to relate amounts of manure and fertilizer used to household variables. The results indicate that the use of both manure and fertilizer reciprocally influence each other and are strongly influenced by household factors, and also imply that manure and fertilizer uses are endogenous. Policy changes are required to (1) reduce the burden on farming alone in rural areas; (2) promote the use of higher-cost, higher-value inputs such as fertilizers; (3) improve access to input and output markets; and (4) encourage farmer education so as to promote sustainable soil fertility management. Improved understanding of the biophysical and socioeconomic environment of smallholder systems can help target sustainable soil fertility interventions more appropriately. [ABSTRACT FROM AUTHOR]