No abstract

Saturated soil culture is one of the water saving techniques that can improve water productivity. However, it is either less implemented or adopted because it consumes more time and energy. Therefore, an experiment was conducted to determine the effective water depth that can keep soil moisture close to saturation for a commonly practiced irrigation interval, combined with a rainfall pattern for increasing water productivity. The design was a randomized complete block with three replications and four water treatments representing 120% (T120), 180% (T180), 240% (T240), and 300% (T300) of soil saturation or the application of 2, 3, 4 and 5 cm water depth. The results showed that the application of 3 cm was the effective depth. It decreased plant height, tiller number, chlorophyll content, and panicle number per hill by 12.37%, 20.84%, 7.59%, and 70.98%, respectively. The decrease of these parameters is followed by total recovery due to effective rainfall contribution, which led to low yield sacrifice (6% of reduction) and 40% of water saving. We argue that weekly application of a 3 cm water depth and matching crop needed-water period with the onset of rainfall may be implemented and recommended as suitable saturated soil culture practice for rice production in high water demand conditions.

The present study evaluates the predictive accuracy of the feed forward backpropagation artificial neural network (BP) in evapotranspiration forecasting from temperature data basis in Dédougou region located in western Burkina Faso, sub-Saharan Africa. BP accuracy is compared to the conventional Blaney-Criddle (BCR) and Reference Model developed for Burkina Faso (RMBF) by referring to the FAO56 Penman-Monteith (PM) as the standard method. Statistically, the models' accuracies were evaluated with the goodness-of-fit measures of root mean square error, mean absolute error and coefficient of determination between their estimated and PM observed values. From the statistical results, BP shows similar contour trends to PM, and performs better than the conventional methods in reference evapotranspiration (ET ref) forecasting in the region. In poor data situation, BP based only on temperature data is much more preferred than the other alternative methods for ET ref forecasting. Furthermore, it is noted that the BP network computing technique accuracy improves significantly with the addition of wind velocity into the network input set. Therefore, in the region, wind velocity is recommended to be incorporated into the BP model for high accuracy management purpose of irrigation water, which relies on accurate values of ET ref.