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

Abstract

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 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.

From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management

“…In many cases, growers do not need to be fluid on data management because the software can build maps or decision-making models with basic information introduced by growers. Furthermore, a critical feature of these applications is that they even help in the early warning of weather-related hazards that enables farmers, policy makers, and aid agencies to mitigate their exposure to risk [83]. However, it must be taken into consideration that the efficiency of a recommendation for a particular agent will depend on the factors included in the algorithms of the software (technical, economic, safety-wise.…”

Evaluation and validation of TAMSAT‐ALERT soil moisture and WRSI for use in drought anticipatory action

“…In the present study, the T-A forecasting method described by Asfaw et al (2018) is used to produce spatially variable probabilistic forecasts of soil moisture. This method is summarized as follows.…”

“…The historic data sets begin in 1983, when the TAMSAT rainfall archive began (Maidment et al, 2017). Alongside the historic data set, T-A importantly includes a forecasting system to predict soil moisture and the WRSI for a region and period of interest (Asfaw et al, 2018). In principle, T-A estimates of the WRSI and soil moisture can be updated throughout the season, and continually updated bulletins based on T-A forecasts are already operational in several regions of West and East Africa.…”

Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

“…As the spatial resolution of available meteorological information has become increasingly fine (Clark et al, 2016), it is necessary to ensure land surface models can utilise this information at its native resolution in order to provide outputs that are as accurate as possible for local populations. In this paper, our focus is on soil moisture, which plays an essential role in agriculture (Asfaw et al, 2018), weather and climate prediction (Hauser et al, 2017) and land surface energy partitioning (Beljaars et al, 1996;Bateni and Entekhabi, 2012).…”