Daniel Waiswa

Agrobiodiversity of homegardens in a commercial sugarcane cultivation land matrix in Uganda

ABSTRACT Understanding biodiversity in homegardens embedded in landscapes dominated by commercial monoculture agriculture is critical for sustainable management of agrobiodiversity and meeting rural households’ needs in the face of global changes. We assessed agrobiodiversity in the 120 homegardens and its contribution to rural household livelihood strategies within a commercial monoculture sugarcane cultivation land matrix in eastern Uganda. We recorded a total of 68 plant species from 46 genera representing 27 families. Species richness spanned 6 to 19 species, and α-diversity (H’) ranged from 0.6 to 2.3; with 86.67% of the homegardens having H’ >1. Species composition differed significantly (global RANOSIM = 0.153, p < 0.001) among the villages. The most important and commonly maintained plants were those that provided food, fuelwood and money income and included Zea mays L., Manihot esculenta, Phaesolus spp., Coffea sp., Musa spp., Ipomea batatus and Artocarpus heterophyllus. Most of the crops cited as useful by households were also frequent and visible in many of the homegardens. Although homegardens still hold some valuable plants, there is also loss of important plants from the agricultural system including cowpeas, soya beans, bambara groundnuts, finger millet, cotton, aerial yams and oysternut essential for sustaining household livelihoods. This loss, precipitated by increased land-use/cover change to commercial sugarcane plantations threatens agrobiodiversity conservation and the benefits households derive from homegardens. Our findings underline the importance of homegardens in the conservation of indigenous agrobiodiversity, and indicate that with the continued expansion of commercial sugarcane cultivation this opportunity may be lost.

Assessing the Performance of WRF Model in Simulating Rainfall over Western Uganda

Skillful rainfall prediction is important to sectors such as agriculture, health and water resources. The study assessed the ability of the Weather Research and Forecasting model to simulate rainfall over Western Uganda for the period 21st April to 10th May 2013 and tested six cumulus parameterization schemes. The root mean square error, mean error and the sign test method are used to assess the ability of the schemes to simulate rainfall along with an adapted contingency table. Results show that the Grell-Fretas scheme is better at simulating rainfall compared to other schemes over the study period while the Betts-Miller-Janji’c and the Kain-Fritsch schemes overestimated rainfall. However all the schemes under predicted heavy rainfall events but the Betts-Miller-Janjic and the Kain-Fritsch schemes over predicted the light rainfall. The variation of altitude presented a noticeable change in predicted rainfall where an increase of 25% in altitude increased the probability of prediction by 6.5% which shows a key role played by altitude in convection.

Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model

Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric the root mean square error RMSE, the mean absolute error MAE, mean error ME, skewness, and the bias easy estimate BES and nonparametric the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.