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

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.     

Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone

The major problem when dealing with modeling evapotranspiration process is its nonlinear dynamic high complexity. Researchers developed reference evapotranspiration (ET-ref) estimation models in rich and poor data situations. Thus, the well-known Penman-Monteith (PM) model always performs the highest accuracy results of ET-ref from a rich data situation. Its application in many areas particularly in developing countries such as Burkina Faso has been limited by the unavailability of the enormous climatic data required. In such circumstances, simple empirical Hargreaves (HARG) equation is often used despite of its non-universal suitability. The present study assesses the artificial neural network (ANN) performance in ET-ref modeling based on temperature data in Bobo-Dioulasso region, located in the Sudano-Sahelian zone of Burkina Faso. The models of feed forward backpropagation neural network (BPNN) algorithm type ANN and Hargreaves (HARG) were employed to study their performance by comparing with the true PM. From the statistical results, BPNN temperature-based models perform better than HARG. Beside, when wind speed is introduced into the neural network models, the coefficient of determination (r²) increases significantly up to 9.52%. While, sunshine duration and relative humidity might cause only 3.51 and 6.69% of difference, respectively. Wind is found to be the most effective variable extremely required for modeling with high accuracy the nonlinear complex process of ET-ref in the Sudano-Sahelian zone of Burkina Faso.     

Plasma glucose lowering mechanisms of catalpol, an active principle from roots of Rehmannia glutinosa, in streptozotocin-induced diabetic rats

Catalpol is one of the active principles from roots of Rehmannia glutinosa Steud (Scrophulariaceae) that is widely used to treat diabetic disorders in Chinese traditional medicine using the name of Di-Huang, which is used to investigate the mechanisms for lowering of plasma glucose in streptozotocin-induced diabetic rats (STZ-diabetic rats). Catalpol decreased plasma glucose in a dose-related manner, and this action was reduced by pretreatment with naloxone or naloxonazine. An increase of plasma β-endorphin by catalpol was also observed in parallel. The plasma glucose lowering action of catalpol was deleted in bilateral adrenalectomized rats. Moreover, catalpol enhanced β-endorphin release from the isolated adrenal medulla of STZ-diabetic rats. Otherwise, plasma glucose lowering action of catalpol failed to produce in opioid μ-receptor knockout mice. Also, repeated administration of catalpol for 3 days in STZ-diabetic rats resulted in a marked reduction of phosphoenolpyruvate carboxykinase (PEPCK) expression in liver and an increased expression of glucose transporter subtype 4 (GLUT 4) in skeletal muscle. These effects were also reversed by blockade of opioid μ-receptors. Our results suggested that catalpol increased glucose utilization through increase of β-endorphin secretion from adrenal gland in STZ-diabetic rats.