Trend Analysis in Rainfall, Reference Evapotranspiration and Aridity Index in Southern Senegal: Adaptation to the Vulnerability of Rainfed Rice Cultivation to Climate Change

Rainfall and evapotranspiration are two vital elements for food production under rainfed agriculture. This study aims at investigating the combined changes in these variables in the form of aridly index in the southern Senegal. The temporal trends in annual and monthly (from May to October) aridity index, rainfall and evapotranspiration are examined and adaptation strategies to the vulnerability of rainfed rice cultivation to the changes are developed. The results show a significant decreasing trend in annual rainfall at all study locations for the period 1922-2015. When analyzing the trends in sub-periods, there are two clear patterns in the annual rainfall series: a decreasing trend for the period 1922-1979 and a reversal increasing trend for the period 1980-2015. An increasing trend is also observed in annual reference evapotranspiration. The results reveal that the region will be drier with a significant increase in aridity at the annual and most monthly series. Appropriate adaptation strategies should be implemented to diminish the adverse influence of the increasing aridity on rice productivity for a sustainable agriculture.

Gap Filling of Net Ecosystem CO₂Exchange (NEE) above Rain-Fed Maize Using Artificial Neural Networks (ANNs)

The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [S&V method], and the ANN approach presented by Papale in 2003. The results showed that the RBF network was able to find better fits for missing values compared to the MLP (BP) network and S&V method. In addition, unlike the S&V method, which depends on different gap-filling procedures over the year;the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data;though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).

Performance of Twelve Mass Transfer Based Reference Evapotranspiration Models under Humid Climate

Reference evapotranspiration is very important parameter in the hydrological, agricultural and environmental studies and is accurately estimated by the FAO Penman-Monteith equation (FAO-PM) under different climatic conditions. However, due to data requirement of the FAO-PM equation, there is a need to investigate the applicability of alternative ETo equations under limited data. The objectives of this study were to evaluate twelve mass transfer based reference evapotranspiration equations and determine the impact of ETo equation on long term water management sustainability in Tanzania and Kenya. The results showed that the Albrecht, Brockamp-Wenner, Dalto, Meyer, Rohwer and Oudin ETo equations systematically overestimated the daily ETo at all weather stations with relative errors that varied from 34% to 94% relative to the FAO-PM ETo estimates. The Penman, Mahringer, Trabert, and the Romanenko equations performed best across Tanzania and the South Western Kenya with root mean squared errors ranging from 0.98 to 1.48 mm/day, which are relatively high and mean bias error (MBE) varying from -0.33 to 0.02 mm/day and the absolute mean error (AME) from 0.79 to 1.16 mm/day. For sustainable water management, the Trabert equation could be adopted at Songea, the Mahringer equation at Tabora, the Dalton and/or the Rohwer equations at Eldoret, the Romanenko equation at Dodoma, Songea and Eldoret. However, regional calibration of the most performing equation could improve water management at regional level.

Evaluation of Eleven Reference Evapotranspiration Models in Semiarid Conditions

The objectives of this study were to evaluate the performance of the FAO Penman Monteith reference evapotranspiration model under limited data and some mine temperature methods of reference evapotranspiration (ETo) under the semiarid and arid conditions in Mali. The results showed that under limited data conditions, the FAO-PM equation achieved accurate estimation of daily ETo when solar radiation, relative humidity, and wind speed are lacking individually with root mean squared errors (RMSE) averaging 0.52, 0.56 and 0.62 mm/day, respectively. Much more accurate ETo was estimated under relative humidity and wind speed missing data conditions with RMSE varying from 0.20 to 0.58 mm/day and average RE, MBE and MAE of 6.7%, -0.25 mm/day and 0.30 mm/day. The Jensen-Haise equation systematically overestimated ETo while the Hansen, Christiansen, and Irmak, and the two Tabari’s equations underestimated ETo at all weather stations. The Abtew equation showed the best performance among the selected ETo equations.