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South Africa experiences a high interannual variability of rainfall which, in a region with abundant solar radiation, is the main determinant of year-to-year variations in crop yields. The coefficient of variation of annual rainfall ranges from less than 20% to about 40% across the country’s arable area (). As a result maize, which is the country’s staple food, exhibits a coefficient of variation in annual yields ranging from less than 15% to over 60% (). The variability in crop production has implications for food security in the country, particularly at household level amongst resource-poor farmers, whose livelihoods are heavily dependent on agriculture.

Ensuring household food security in a rainfed agricultural livelihood requires availability of climate information regarding onset of seasonal rains allowing for timely preparation for planting. Due to increased irregularity of the onset, amount and length of seasonal rains, the prediction of onset of seasonal rains at sufficient lead times is increasingly becoming a very critical issue for farmers. Currently climate scientists are able to use sea surface temperatures as scientific indicators, to forecast rainfall amounts of above normal, normal and below normal averaged over a period of three months. Whereas this type of information is important, the primary climate information need of the farmers is knowing in advance the expected onset of seasonal rains. As a coping mechanism, farmers attempt to use their traditional indicators, particularly local winds and temperatures, to forecast this important climate element. However, identification, validation and improvement of these indicators had not been done. As a synergy to the farmers practice, records of winds, temperature and rainfall from the existing synoptic weather stations can be used to study these relationships on scientific basis. Although analysis of pentad rainfall totals of records from some of the existing weather stations have been done indicating onset of seasonal rains on average basis, practically these seasonal rains set in at different periods of each year. Currently there is no availability of models to predict the different periods when the rains can set in.

Climate uncertainties, derived from annual climatic variability, often lead to conservative crop management strategies that sacrifice some productivity to reduce the risk of losses in bad years. The availability of ENSO-based climate forecasts has led many to believe that such forecasts may benefit decision-making in agriculture. The forecasting capability may allow the mitigation of negative effects of ENSO-related climate variability as well as taking advantage of favorable conditions ().

Global climate change will lead to shifts in climate behavior and could cause severe impacts on ecosystems in the next decades (). In particular, climate change will have significant effects on agricultural production. Negative climate-change impacts on agriculture could be avoided or reduced significantly by taking appropriate decisions, which can be based on the available crop-growth simulation models, as well as on forecasts and climate scenarios (; ).

Disease forecasting has become an established component of quantitative epidemiology. The mathematics of disease dynamics is the core of several disease forecast models that have been developed in the last four decades. However, many models have not lived up to the expectations that they would play a major role and lead to a better disease management. Amongst the reasons, the presumption of a disease forecast model is that it makes projections of major events in disease development and most present forecast models do not (). An exciting development in this area is the possibility to use weather forecasts as input into disease models and consequently output true disease forecasts. As weather forecasts improve together with more accurate estimations of micro environmental variables useful for plant disease models, as such precipitation and leaf wetness duration, it will be possible to provide seasonal estimates of disease likelihood and forecast outbreaks. This is especially interesting for field crops for the reason that unnecessary sprays has a significant impact on production costs, and no timely applications may result in inadequate control.

The Southeast Climate Consortium was initiated in 2001 as a regional expansion of the Florida Consortium. The Florida Consortium of Universities (FLC), consisting of the University of Miami, the University of Florida, and Florida State University was formed in 1996 and was funded by the U.S. National Oceanic and Atmospheric Administration-Office of Global Programs (NOAA-OGP) as a pilot Climate Applications Project. Following the establishment of the Regional Integrated Sciences and Assessment (RISA) program, the FLC became the first RISA east of the Mississippi. Initial research concentrated on the use of seasonal-to-interannual climate forecasts for the agricultural sector in Argentina. This focus was shifted to Florida in 1998. Following the success of the FLC in Florida, the University of Georgia was invited to join the consortium in 2001 and as a result the Southeast Climate Consortium (SECC) was formed. In 2002, Auburn University and the University of Alabama at Huntsville joined the SECC.

This opportunity to react to contemporary work on climate prediction in agriculture is a welcome one for someone who occasionally and mainly youthfully dabbled in the influence of climate in agriculture (e.g. , , , , ; ; ; ), who was excited at the prospects for informative predictions (e.g. , ; ) but who has long since been far too remote from the action. Accordingly, to jump across the decades of progress, the point of departure taken here is the opening keynote address by Sivakumar (2006), in which the state of the art is succinctly summarized, albeit in a way that emphasizes the possibilities in a guardedly positively manner. Intriguingly, and seemingly properly in the view of this observer, he uses cautious words such as “could help” when charting the situations where climate forecasting efforts are intended to assist farmers and other agricultural managers in their decisions in the face of climatic uncertainty.

Participants in the workshop concluded that: