Can commercialization of NTFPs alleviate poverty? A case study of Ricinodendron heudelotii (Baill.) Pierre ex Pax. kernel marketing in Cameroon

Ricinodendron heudelotii (Baill.) Pierre ex Pax. kernel (njansang) commercialization has been promoted by the World Agroforestry Centre (ICRAF) in project villages in Cameroon with the aim to alleviate poverty for small-scale farmers. We evaluated to what extent development interventions improved the financial situation of households by comparing project and control households. The financial importance of njansang to household livelihoods between 2005 and 2010 was investigated through semi-structured questionnaires with retrospective questions, focus group discussions, interviews and wealth-ranking exercises. The importance of njansang increased strongly in the entire study region and the increase was significantly larger in project households. Moreover, absolute numbers of income from njansang commercialization as well as relative importance of njansang in total cash income, increased significantly more in project households (p < 0.05). Although the lower wealth class households could increase their income through njansang trade, the upper wealth class households benefited more from the projects' interventions. Group sales as conducted in project villages did not lead to significantly higher prices and should be reconsidered. Hence, promotion of njansang had a positive effect on total cash income and can still be improved. The corporative actors for njansang commercialization are encouraged to adapt their strategies to ensure that also the lower wealth class households benefit from the conducted project interventions. In this respect, frequent project monitoring and impact analysis are important tools to accomplish this adaptation.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.