TAMSAT-ALERT v1: A new framework for agricultural decision support

0Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.

Abstract

Early warning of weather related hazards enables farmers, policy makers and aid agencies to mitigate their exposure to risk. We present a new operational framework, Tropical Applications of Meteorology using SATellite data and ground based measurements-AgricuLtural EaRly warning sysTem (TAMSAT-ALERT), which provides early warning of meteorological risk to agriculture. TAMSAT-ALERT combines information on land surface properties, seasonal forecasts and historical weather to quantitatively assess the likelihood of adverse weather–related outcomes, such as low yield. This article describes the modular TAMSAT-ALERT framework and demonstrates its application to risk assessment for low maize yield in Northern Ghana. The modular design of TAMSAT-ALERT enables it to accommodate any impact/land surface model driven with meteorological data. The implementation described here uses the well-established General Large Area Model for annual crops (GLAM) to provide probabilistic assessments of the meteorological hazard to maize yield in northern Ghana throughout the growing season. The results show that climatic risk to yield is poorly constrained in the beginning of the season, but as the season progresses, the uncertainty rapidly reduces. The TAMSAT-ALERT methodology implicitly weights forecast and observational inputs according to their relevance to the metric being assessed. TAMSAT-ALERT can thus be used as a test-bed for the value of probabilistic seasonal forecast information. Here, we show that in northern Ghana, the tercile seasonal forecasts of cumulative rainfall and mean temperature, which are routinely issued to farmers, are of limited value for decision making.

Cite

CITATION STYLE

APA

Asfaw, D., Black, E., Brown, M., Jane Nicklin, K., Otu-Larbi, F., Pinnington, E., … Quaife, T. (2018). TAMSAT-ALERT v1: A new framework for agricultural decision support. Geoscientific Model Development, 11(6), 2353–2371. https://doi.org/10.5194/gmd-11-2353-2018

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free