Prediction of yearly mid-growing season first and second critical dry spells using artificial neural networks (ANN) for enhanced maize yield in nine stations in Nigeria is performed. The ANN model uses nine meteorological parameters to predict onset dates and lengths of the critical dry spells. The daily dataset is from 1971 to 2013 of which about 70% is used for training while 30% is for testing. Seven ANN models are developed for each station with a view to measuring their predictive ability by comparing predicted values with the observed ones. Prediction lead times for the two critical dry spell onset dates generally range from about 2 weeks to 2 months for the nine stations. Error range during testing for the onset dates and lengths of first and second critical dry spells is generally ±4 days. The root-mean-square error (RMSE), coefficient of determination, Nash-Sutcliffe coefficient of efficiency, Wilmott\'s index of agreement, and RMSE observation standard deviation ratio range from 0.46 to 3.31, 0.58 to 0.93, 0.51 to 0.90, 0.82 to 0.95, and 0.30 to 0.69, respectively. These results show ANN capability of making the above reliable predictions for yearly supplementary irrigation planning, scheduling, and various other decision makings related to sustainable agricultural operations for improved rain-fed maize crop yield in Nigeria.
Part of the book: Maize Genetic Resources
This study uses observed and simulated data to analyze the dynamics LSB rotation along the Guinean Coast of West Africa. A non-hydrostatic fully compressible numerical model is used to simulate LSB circulation. To evaluate the model’s ability to capture the LSB kinematics, the study used a modified model code with ERA-Interim and CFS as forcing data. Comparison of observed and simulated LSB patterns shows that the model reliably captures the LSB circulation in the region. The simulated diurnal evolutions of hodographs and onshore/offshore winds also follow the observations. A dynamical analysis performed by extracting individual forcing terms from the horizontal momentum equations at selected regions within the study area showed that the direction of the wind rotation is a result of a complex interaction between surface and synoptic pressure gradients, advection, and horizontal and vertical diffusions forces. However, hourly analysis of the rotation term suggests that surface gradient seems to dominate over oceanic region, while diffusion terms are more important for land area. This may be attributed to the variation of surface roughness due the landscape and urbanization. Therefore, this reveals the link between urbanization and LSB circulation in coastal region of West Africa, where most important cities are located.
Part of the book: Numerical Simulation