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

• Published in: Geoscientific Model Development Vol. 11; no. 6; pp. 2353 - 2371
• Main Authors: Asfaw, Dagmawi, Black, Emily, Brown, Matthew, Nicklin, Kathryn Jane, Otu-Larbi, Frederick, Pinnington, Ewan, Challinor, Andrew, Maidment, Ross, Quaife, Tristan
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 19-06-2018; Copernicus Publications
• Subjects: Agricultural policy; Agricultural production; Agricultural productivity; Agriculture; Charities; Corn; Crop yield; Data processing; Decision support systems; Early warning systems; Emergency warning programs; Environmental aspects; Environmental risk; Evaluation; Frameworks; Growing season; Hazard mitigation; Impact assessment; Mathematical models; Mean temperatures; Meteorological data; Meteorology; Methods; Modular design; Rain; Rainfall; Rainfall forecasting; Risk assessment; Satellite data; Satellites; Seasonal forecasting; Seasons; Surface properties; Temperature; Tropical climate; Weather; Weather forecasting; Weather hazards; Ghana
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-11-2353-2018

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 aims to provide early warning for 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 (Tamale). The modular design
of TAMSAT-ALERT enables it to accommodate any impact or land-surface model
driven with meteorological data. The implementation described here uses the
well-established General Large Area Model (GLAM) for annual crops to provide
probabilistic assessments of the meteorological hazard for maize yield in
northern Ghana (Tamale) 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 is rapidly reduced. Based on the
assessment for the period 2002–2011, we show that TAMSAT-ALERT can
estimate the meteorological risk on maize yield 6 to 8 weeks in advance
of harvest. The TAMSAT-ALERT methodology implicitly weights forecast and
observational inputs according to their relevance to the metric being
assessed. A secondary application of TAMSAT-ALERT is thus an evaluation of the
usefulness of meteorological forecast products for impact assessment. Here,
we show that in northern Ghana (Tamale), the tercile seasonal forecasts of
seasonal cumulative rainfall and mean temperature, which are routinely issued
to farmers, are of limited value because regional and seasonal temperature and
rainfall are poorly correlated with yield. This finding speaks to the
pressing need for meteorological forecast products that are tailored for
individual user applications.