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Chapter 1. Quick overview of FAO food security work, the raison d'être of the crop forecasting and presentation of the overall crop forecasting philosophy adopted by FAO

Background The incidence of drought-induced famine in many countries continues to be a global concern. Even in a good year, farmers in some pockets of a country may incur devastating crop losses. In times of civil strife or extensive floods, for example, some groups may experience a sharp reduction in their access to food supplies for reasons of physical exclusion from markets.

GIEWS: The FAO Early Warning SYSTEM Established in the wake of the world food crisis of the early 1970s, the Global Information and Early Warning System (GIEWS) is the leading source of information on food production and food security for every country in the world, whether or not it is an FAO member. Over the years, a large inventory on global, regional, national and subnational food security has been maintained, refined and continuously updated.

GIEWS country monitoring concentrates on a group of some 80 “Low-Income Food-Deficit Countries. These countries are often particularly vulnerable to supply fluctuations caused, for instance, by crop failure or high international cereal prices.

National and local food policymakers need to know what is happening to export prices, global production, trade, stocks and demand. Regular reporting on world food situation and outlook is part of the service offered by GIEWS.

The main focus of the analysis is on cereals as information on other types of food is often extremely weak. However, the system is expanding its coverage of non-cereal staple foods particularly in countries where they constitute a large part of the national diet.

Indicators and convergence of evidence. The System collects information on possible “indicators” of food crisis such as local market food supplies, retail price rises and evidence of individual and community responses to food insecurity.

GIEWS strives for objectivity and consistency but the extremely complex nature of food security and humanitarian analysis makes a strict application of single indicator thresholds both impractical and technically questionable in their application to a wide array of situations. GIEWS, rather, supports methodology based on convergence of evidence from multiple sources (not limited to single assessment findings) as evaluated by analysts. In this manner, the analysts use the reference outcomes as a guide, but ultimately make a classification statement based on the convergence of evidence from all available sources. This evidence-based approach is not only practical and accommodating to a wide array of situations, it also focuses the burden of proof on the analysts, who need to demonstrate to all stakeholders (as if in a court of law) the validity and relevance of evidence in support of a classification statement, even if that statement is based on considerable ‘own best judgment’. Such a process enables accountability and accessibility for critique

End-Users of food security information The end-users of Food security are government officials, policy makers, international bodies, aid agencies etc. Therefore rapid and effective communications are a key component of the System. Recent advances in computer technology and the Internet have enabled GIEWS to improve the timeliness of producing and disseminating reports. GIEWS’ core publications are “Food Outlook”, “Foodcrops and Shortages” and “Food Supply Situation and Crop Prospects in Sub-Saharan Africa”. Numerous Special Alerts and Special Reports are also produced. GIEWS’ publications are freely available to all institutions and individuals and are posted on the Internet at www.fao.org.

Unfortunately having an effective early warning system is no guarantee that interventions will follow. Famine, starvation and malnutrition continue to haunt many parts of the world. Food resources are not always mobilized in sufficient volume, or they arrive too late to save lives. War or civil strife often hamper logistic operations so much that relief programmes fail to reach the most needy. However, objective information and early warning continue to have a crucial role in ensuring that timely and appropriate action can be taken to avoid suffering.

CAPACITY BUILDING at a Regional an National level The main priority for effective response to food crises remains the strengthening of national institutions for food security monitoring. FAO provides technical assistance to develop the capacity of government statistical services and specialized units or “Early Warning and Food Information Systems. These systems act as a focal point within governments for collecting, processing and communicating information on all the key variables that influence food security.

Crop monitoring and crop forecasting One of the main fundaments of any early warning system is crop forecasting. During the agricultural season crops are monitored and increasingly accurate forecasts for crop-specific yield are produced.

There appears to be no standard classification of forecasting methods (Makridadis et al., 1998; Armstrong, 2001a). Roughly speaking, forecasting methods can be subdivided into various categories according to the relative share of judgement, statistics, models and data used in the process. Armstrong identifies 11 types of methods that can be grouped as • Judgemental, based on stakeholders’ intentions or on the forecaster’s or other experts’ opinions or Intentions. There exist some applications of this approach in agrometeorological forecasting, especially when other variables such as economic variables play a part (for instance the “Delphi expert forecasting method” for coffee, Moricochi et al., 1995); • Statistical, including univariate (or Extrapolation), Multivariate (statistical “models”) and Theory-based methods. This is the category where most agrometeorological forecasting belongs; • Intermediate types include Expert systems, basically a variant of Extrapolation with some admixture of Expert Opinion, and Analogies, which Armstrong places between Expert Opinions and Extrapolation models. This is also covered in the present chapter.