A bivariate probit model was employed to jointly and separately estimate banana market participation decisions of buying and selling households in Rwanda and Burundi using household survey data. Selectivity bias was corrected for in estimating the transacted volumes using Heckman's procedure. The results showed that transaction cost related factors such as geographical location of households, market information sources, and travel time to the nearest urban centre influence market participation. Non price related factors such as security of land tenure, labor availability, off farm income, gender of the household head and years of farming experience had a significant influence on the transacted volumes. Output prices had a significant effect on sales volume, providing incentives for increased supply by sellers. Generally, the findings suggest that policies aimed at investments in rural road infrastructure, collective marketing and value addition of banana products may provide a potential avenue for mitigating transactions costs and enhancing market participation and production of marketed surplus by rural households.
JEL classification: D23, D01, D13, D71
Abstract. Modelling land surface water flow is of critical importance for simulating land surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL (TOPography-based hydrological MODEL), and the most important parameter of this model is the well-known topographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically conditioned HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) data using the GA2 algorithm (GRIDATB 2). At 15 arcsec resolution, these layers are 4 times finer than the resolution of the previously best-available topographic index layers, the compound topographic index of HYDRO1k (CTI). For the largest river catchments occurring on each continent we found that, in comparison with CTI our revised values were up to 20 % lower in, e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. For the majority of large catchments, however, the spread of our new GA2 index values is very similar to those of CTI, yet with more spatial variability apparent at fine scale. We believe these new index layers represent greatly improved global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.
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