Evaluating satellite-based evapotranspiration estimates for hydrological applications in data-scarce regions: A case in Ethiopia.

Water resource development opens up opportunities for improving smallholder farmer livelihoods in sub-Saharan Africa; however, implementation of water resource interventions to ensure sustainability hinges on the availability of sufficient quantity and quality data for monitoring, analysis and planning. Such data is often acquired through instrumentation of water resources (e.g. stream flow monitoring) or the use of hydrological models. In sub-Saharan Africa, data scarcity has limited the ability to monitor and make appropriate decisions for water resource allocation and use. Data derived from remote sensing has been considered a viable option to fill this gap; however, there is limited research in the region that evaluate the quality of the remotely sensed based datasets. This study evaluated actual evapotranspiration (AET) estimates derived from Advanced Very High Resolution Radiometer (AVHRR AET) images and Moderate Resolution Imaging Spectrometer (MOD16 AET) images using estimates from a grid-based Soil and Water Assessment Tool (SWAT). The SWAT model was set up for the entire country of Ethiopia, and calibrated and validated using observed streamflow at several meso-scale watersheds in which satisfactory model performance was obtained. AET estimates from the calibrated and validated SWAT model were then used to evaluate remotely sensed based AET for three landscapes. The AVHRR AET better agreed with the SWAT-simulated AET than the MOD16 AET, although the AVHRR AET overestimated the SWAT-simulated AET in all of the landscapes. Both remotely sensed AET products showed better agreement with the SWAT-simulated AET over agriculture dominated landscapes compared to grassland and forest dominated landscapes. The findings of the study suggest that remotely sensed based AET may help to fine-tune hydrological models in agricultural landscapes in data-scarce regions to improve studies on the impacts of water management interventions aiming to ensure environmental sustainability while enhancing agricultural production, and household income and nutrition.

Evaluation of new farming technologies in Ethiopia using the Integrated Decision Support System (IDSS).

This study investigates multi-dimensional impacts of adopting new technology in agriculture at the farm/village and watershed scale in sub-Saharan Africa using the Integrated Decision Support System (IDSS). Application of IDSS as an integrated modeling tool helps solve complex issues in agricultural systems by simultaneously assessing production, environmental, economic, and nutritional consequences of adopting agricultural technologies for sustainable increases in food production and use of scarce natural resources. The IDSS approach was applied to the Amhara region of Ethiopia, where the scarcity of resources and agro-environmental consequences are critical to agricultural productivity of small farm, to analyze the impacts of alternative agricultural technology interventions. Results show significant improvements in family income and nutrition, achieved through the adoption of irrigation technologies, proper use of fertilizer, and improved seed varieties while preserving environmental indicators in terms of soil erosion and sediment loadings. These pilot studies demonstrate the usefulness of the IDSS approach as a tool that can be used to predict and evaluate the economic and environmental consequences of adopting new agricultural technologies that aim to improve the livelihoods of subsistence farmers.

Potential effects of Rift Valley fever in the United States.

Rift Valley fever virus (RVFV) has been the cause of disease outbreaks throughout Africa and the Arabian Peninsula, and the infection often results in heavy economic costs through loss of livestock. If RVFV, which is common to select agent lists of the US Department of Health and Human Services and the US Department of Agriculture, entered the United States, either by accidental or purposeful means, the effects could be substantial. A group of subject matter experts met in December 2009 to discuss potential implications of an introduction of RVF to the United States and review current modeling capabilities. This workshop followed a similar meeting held in April 2007. This report summarizes the 2 workshop proceedings. Discussions primarily highlighted gaps in current economic and epidemiologic RVF models as well as gaps in the overall epidemiology of the virus.

Diagnosis of foot-and mouth disease by real time reverse transcription polymerase chain reaction under field conditions in Brazil.

BACKGROUND: Foot-and-mouth disease (FMD) is an economically important and highly contagious viral disease that affects cloven-hoofed domestic and wild animals. Virus isolation and enzyme-linked immunosorbent assay (ELISA) are the gold standard tests for diagnosis of FMD. As these methods are time consuming, assays based on viral nucleic acid amplification have been developed. RESULTS: A previously described real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay with high sensitivity and specificity under laboratorial and experimental conditions was used in the current study. To verify the applicability of this assay under field conditions in Brazil, 460 oral swabs from cattle were collected in areas free of FMD (n = 200) and from areas with outbreaks of FMD (n = 260). Three samples from areas with outbreaks of FMD were positive by real-time RT-PCR, and 2 of those samples were positive by virus isolation and ELISA. Four other samples were considered inconclusive by real-time RT-PCR (threshold cycle [Ct] > 40); whereas all 200 samples from an area free of FMD were real-time RT-PCR negative. CONCLUSION: real-time RT-PCR is a powerful technique for reliable detection of FMDV in a fraction of the time required for virus isolation and ELISA. However, it is noteworthy that lack of infrastructure in certain areas with high risk of FMD may be a limiting factor for using real-time RT-PCR as a routine diagnostic tool.