Increase in crop yield in many parts of Africa is largely the result of increase in cultivated land. This trend, if allowed to remain, will increase the already high levels of forest depletion. This study attempts to formulate a model useful in examining support systems that influence crop yield in Northern Ghana. Comparison of the Classical Generalized linear model to the Joint Generalized linear models and selection of the very best factors that influence crop yield based on the best of the two models are the points of interest for this study. Data from the regional Monitoring and evaluation office of the linking farmers to market (FtM) project in Tamale Ghana was analysed and discussed. Crop type, Financial Credit, Training, Study tour, Demonstrative Practical, Networking Event, Post-harvest Equipment, number of farmers in the FBO and Size of plot cultivated were our measured fixed effects variables with Total Crop Yield as our response. We settle on the Joint GLM for inference and selects access to credit facility, Crop type, Networking among farmer groups, access to equipment used in post-harvest, the number of farmers on site and size of plot as the most important physical support factors that influence crop yield in Northern Ghana. Stakeholders in the Food and Agricultural sector are advised to give these listed factors the needed attention in the midst of resource scarcity and our quest to increasing yield while minimizing the conversion of our forest lands into farm lands.

This study examines the patterns in the export of wood products in Ghana from 1997-2013. We also build a time series model to forecast the volume of wood products export over the same period. The study employs the Box-Jenkins methodology of building ARIMA (Autoregressive Integrated Moving Average) model. Monthly time series data on exports of wood products from 1997-2013 were extracted from monthly and annual reports on export of wood products published by the Timber Industry Development Division (TIDD) of the Forestry Commission of Ghana. Different selected models were tested to ensure the accuracy of obtained results and ARIMA (3, 1, 0) (0, 1, 1) 12 was adjudged the best model. This model was then used to forecast the volume of wood products export for 2014 and 2015. January and June represent the minimum and maximum export periods respectively. The model will guide TIDD in their annual timber export planning and also help avoid financial losses that could result from poor decision making and ultimately improve efficiency of their operations.

Knowledge of the dependence between random variables is necessary in the area of risk assessment and evaluation. Some of the existing Archimedean copulas, namely the Clayton and the Gumbel copulas, allow for higher correlations on the extreme left and right, respectively. In this study, we use the idea of convex combinations to build a hybrid Clayton–Gumbel–Frank copula that provides all dependence scenarios from existing Archimedean copulas. The corresponding density and conditional distribution functions of the derived models for two random variables, as well as an estimator for the proportion parameter associated with the proposed model, are also derived. The results show that the proposed model is able to show any case of dependence by providing coefficients for the upper tail and lower tail dependence.

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