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Agricultural production is highly dependent on weather, climate and water availability, and is adversely affected by weather- and climate-related disasters. Failure of rains and occurrence of natural disasters such as floods and droughts could lead to crop failures, food insecurity, famine, loss of property and life, mass migration, and negative national economic growth. Hence agricultural communities around the world have always looked for ways and means to cope with the climate variability including the use of various traditional indicators to predict the seasonal climate behavior.

The science and practice of seasonal climate forecasts have progressed significantly in the last couple of decades (; ; ). It has been demonstrated that seasonal forecasts are skillful in many regions, particularly in the tropics (; ; ). General circulation models (GCMs) have been employed in seasonal climate forecasting at various centers (; ; ; ; ). Due to computational constraints, GCMs typically are run at relatively coarse spatial resolutions generally greater than 2.0° for both latitude and longitude. The direct result of the poor spatial resolution of GCMs is a serious mismatch of spatial scale between the available climate forecasts and the scale of interest to most climate forecast users. Some applications also require climate forecasts with higher temporal resolution. Most crop models, for example, require daily weather input. GCM outputs are available as the required daily values, but GCM daily precipitation shows very low daily variability and many high errors compared to observations ().

Seasonal and decadal prediction of crop productivity requires simulation of climate and its impact on crops ahead of time. Numerical models can provide such forecasts by using the output from a climate model as input to a crop simulation model. This modeling approach presents a number of challenges that will affect the skill of prediction of the crop forecast. Perhaps the most important of these is: at what scale (both spatial and temporal) should information pass between climate and crop models? This chapter examines this question and other issues concerned with the development of a combined crop and climate forecasting system.

Corn production is the principal source of family income for about 24 million Filipinos. Isabela Province, located in one of the most depressed regions in northern Philippines, is considered the top corn-producing province in the country contributing 17% or 536 353 tons of the total yellow corn production in the country. Corn is grown rainfed in Isabela. Monocropping of corn is predominantly practiced in Isabela and there are two cropping seasons per year — wet season cropping from May to August and dry season cropping from November to February. In 2003, a total of 146 965 hectares were planted to yellow corn in the province. In the same year, average yield of yellow corn was 3.65 tons per hectare (t ha) which was comparatively higher than the national yellow corn yield average of 3.03 t ha. Most of the corn type being produced in the province is yellow corn which comprised 95% of the total corn produced in the province (). Yellow corn is primarily used as animal feed ingredient especially for poultry and swine.

Although seasonal forecast applications are still in an early stage of development there is now enough collective experience from research efforts around the world to induce some meaningful considerations.

The potential value of seasonal climate forecasts is demonstrated by research studies, which are centered on individual projects (). Several climate application projects are discontinued by the scientists after signifying the practical value and potential applications. The apparent reasons for discontinuation are financial constraints, changing institutional mandates, personal motivation towards other areas of research and disabling institutional policies. Scientific community is deficient in accessibility to influence the policy to institutionalize the forecasting systems, although policy recognizes the importance of climate forecasts during extreme climate events. In this context, bridging the gap that exists between research, policy and users to facilitate generation and use of climate forecasts was recognized as a challenge (). The sustained operational use or institutionalization of climate forecasts is also constrained by distinct subcultures, institutional attributes of the key players like meteorologists, application scientists and extension personnel. As a result, the key players are very different actors bound by distinct sets of goals and mandates. Differences in disciplinary culture and perspective tend to reinforce the institutional separation (). Apart from these fundamental ‘cultures’, there are distinct, prominent and motivational factors that influence the sustained generation and use of seasonal climate forecasts. In this chapter, other justifiable factors responsible for institutionalization are discussed with example from Indonesia.

Climate variability creates risk in rainfed farming. Risk in turn discourages investment by farmers, governments and development agencies. For instance, in dry regions recurrent droughts debilitate and destabilize poor, agricultural-based societies, and contribute to land degradation by reducing vegetative cover and water supplies. Drought triggers the exploitation of diminishing resources in order to survive (). Climate change caused by global warming is likely to increase the frequency of climatic extremes in the future and result in changes in cropping practices and patterns over time and space.

Climate affects the lives of all living beings on earth. Every system is vulnerable to climate, and the impact of climate extremes can cause devastating damage to people and their economies and environment. This vulnerability, however, varies from region to region, and is a function of many factors, such as the geographic location, resiliency, capacities (economic, technical and financial), social capital, political environment, and livelihood security.

Water is a fundamental resource to ensure agricultural productivity. Access to hydrological resources to supplement rainfall during the growing season is seen as one of the key issues for food security. For this reason, the development of agricultural systems in arid and semi-arid regions has been closely linked to the scientific and technological advances in irrigation engineering.

Recent scientific advancements are improving the ability to predict some major elements of climate variability, in advance of the crop-growing season. In selected regions of the world, climate anomalies are linked to the onset and intensity of a warm or cold event of the El Niño-Southern Oscillation (ENSO) phenomenon. Southeast of South America is within the regions of influence of this phenomenon (). Hence seasonal weather and climate fluctuations have significant economical impacts on the agricultural production sector of this region.