Management options for rainfed chickpea ( Cicer arietinum L.) in northeast Ethiopia under climate change condition

Chickpea (Cicer arietinum L.) is one of the important cool season food legumes in the semi-arid north-eastern Ethiopia. Climate change is projected to alter the growing conditions of chickpea in this region and there would be substantial reduction in grain yield of the crop due to drought. The overall objectives of the study were to identify crop management and genetic options that could increase rain-fed chickpea productivity. For this, a simulation study has been conducted using CROPGRO-model in two sites (Sirinka and Chefa) found in the semi-arid north-eastern Ethiopia. Change in planting date and cultivars having different maturity have been tested for their effectiveness to increase chickpea productivity. According to the prediction result, short duration cultivar is found to increase grain yield at Sirinka by about 11%, 10% and 11% in the baseline, 2030 s and 2050 s, respectively whereas long duration cultivar is found to decrease grain yield by about 6%, 9% and 11% as compared to the standard cultivar (control). On the other hand, short duration cultivar is found to decrease grain yield at Chefa by about 9%, 4% and 5% whereas long duration cultivar is found to increase grain yield by about 1%, 2% and 4% across the respective time periods. Early sowing (SSD − 20 days) is found to significantly increase grain yield of short duration cultivar at Sirinka by about 48%, 48% and 54% and that of long duration cultivar by 31%, 33% and 39% in the baseline, 2030 s and 2050 s, respectively. Early sowing (SSD − 20 days) is also found to increase grain yield of short duration cultivar at Chefa by about 26%, 27% and −1% and that of long duration cultivar by 37%, 32% and −2% across the respective time periods. However, the highest increase in chickpea grain yield can be achieved through combined application of early sowing and suitable cultivars. On the other hand, delayed sowing is found to significantly decrease chickpea grain yield in the semi-arid environments of north-eastern Ethiopia

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

Background: Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods: Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results: Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions: This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors