A mixture neural methodology for computing rice consumptive water requirements in Fada N’Gourma Region, Eastern Burkina Faso

Abstract

Crop consumptive water requirement (Crop-ET) is a key variable for developing management plans to optimize the efficiency of water use for crop production particularly in semiarid zone. In Burkina Faso, the unfavorable climatic conditions characterized by the low and unevenly distribution of rainfall have pushed water resources management to the forefront of the crop production issue. Crop-ET is extremely required in rainwater effective management for mitigating the impact of water deficit on the crops. Basically, Crop-ET determination involves reference evapotranspiration (ETo) and crop coefficient (Kc) which required complete climatic data and specific site crop information, respectively. ETo estimation with the recommended FAO56 Penman-Monteith (PM) equation is limited in Burkina Faso due to the numerous meteorological data required which are not always available in many production sites. In such circumstances, research to compute directly Crop-ET as an alternative to the two-step approach of calculating ETo and determining site specific Kc, seems desirable. Therefore, this study aims to evaluate the performance of a mixture principal component analysis neural network (PCANN) model for computing rice Crop-ET directly from temperatures data in Fada N'Gourma region located in Eastern Burkina Faso, Africa. From the statistical results, rice Crop-ET can be successfully computed by using PCANN methodology, when only temperatures data are available in this African semiarid environment. Thus, in poor data situation, Crop-ET direct computation can be rapidly addressed through PCANN model for agricultural water management in African semiarid regions.

Role of Canopy Coverage in Water Use Efficiency of Lowland Rice in Early Growth Period in Semi-Arid Region

“…Many countries in semi-arid regions are actually facing economic limitations (Traore et al, 2010). Furthermore, introduction of irrigation system has a risk to provoke salinity problem especially in semi-arid regions (Smedema and Shiati, 2002).…”

Predictive accuracy of backpropagation neural network methodology in evapotranspiration forecasting in Dédougou region, western Burkina Faso

“…ANN is capable of modelling any arbitrarily complex nonlinear process by drawing the estimate function directly from the training data (Kerh et al 2009;Wang and Traore 2009). ANN application in hydrology due to its high nonlinear functional characteristic has provided many advantages rapidly in hydrology including rainfall-runoff modelling (Firat 2008); suspended sediment forecasting (Wang et al 2009d), crop consumptive water requirements (Traore et al 2010b) and evapotranspiration estimation (Kizi 2006). ANN was potentially used to model ET ref as a function of climatic variables.…”