Two main satellite-based variables used in the FAO crop forecasting approach are NDVI and cold cloud duration (CCD). In FAO food security programs, rainfall is often estimated from CCD. Low values of NDVI correspond to sparse or no vegetation (ochre-brown-green), and high values indicate dense vegetation (red-pink-purple). The CCD is an indicator based on high frequency (hourly) thermal infrared observations from geostationary meteorological satellites of the METEOSAT type. It is a measure of the duration, in hours, in which clouds become so cold (below -40° C) that the likelihood of their producing rain is very high. The CCD has been effectively used for agricultural drought monitoring because it serves as a proxy for rainfall. This gives a picture of whether the current season is better or worse than any previous season. However, its greatest potential is to provide and map the estimates of rainfall, which ultimately affects agricultural production in many parts of the world where agriculture is heavily dependent on rainfall, as is the case in the sub-Saharan Africa. The Early Warning System of the SADC has been using El Nino/Southern Oscillation Index values (SOI; chapter 3) to predict the likely outcome of the coming rainy season (FAO, 1996).
Satellite imagery has been used for agricultural drought monitoring and other activities for many years. However, less progress has been made in the quantitative use of remote-sensing imagery. For example, the NDVI-based analysis conducted by FAO, at continental and regional scales, has not changed much since 1988. Although data registration and calibration have become much better, the main type of analysis still is a qualitative assessment of the current vegetation situation during the growing season, as compared to previous years or the average, by comparing trends in NDVI.
The use of METEOSAT data for rainfall estimates has also only been partially successful. Although quantitative estimates of rainfall derived from METEOSAT are becoming more refined, the most popular METEOSAT-derived product for early warning and drought monitoring remains the CCD images. These images were originally intended to be an intermediate product only and not suitable for distribution. More recently, METEOSAT data have been merged with data from other sources to improve the quality of the estimates. At the national level, this is often done by interpolating between a large number of observations from meteorological stations using the CCD images as a weighted surface, guiding the interpolation patterns and the ground observations for quantification. At a continental scale, only ground-observed rainfall data through the Global Telecommunication System (GTS) is readily available in real time, which often is of uncertain quality in many food-insecure countries. The sparse density of the meteorological stations reporting to the GTS contributes to the uncertainty. Recent approaches now combine METEOSAT with GTS data and data from microwave images or sounding instruments, the most popular microwave data being Special Sensor Microwave Imager. Although improvements are still needed before remote sensing can provide the quantitative data required for crop yield models, the remote sensing data remain an essential, yet partial, component for monitoring food supply and demand to improve world food security.
Over the years, as the use of satellite-derived information became more and more integrated in the operations of the above programs, there was a growing demand for more and better data, which could only be partially met. The ARTEMIS area coverage was extended first to South and Central America with NOAA GAC-derived NDVI in cooperation with NASA's Goddard Space Flight Center and, later, through cooperation with the Japanese Meteorological Agency. Monsoon monitoring over Asia was also explored, based on Geostationary Meteorological Satellite data. Although this was certainly an improvement, many areas of special interest to GIEWS, such as the Commonwealth of Independent States and North Korea, were still not covered.
In southern Africa, FAO has assisted many SADC countries in rehabilitating the agricultural system after a devastating drought. In such circumstances, FAO makes arrangements for assessing the essential agricultural inputs needed to restore production in the affected countries. FAO also makes an appeal for financial assistance to implement emergency relief, short-term rehabilitation, and preparedness interventions to the international donor community.
The FAO crop water requirement satisfaction index (WRSI) has been used extensively, especially in Africa, for crop monitoring for food security. The model can detect the onset of agricultural drought, which is indicated by the crop stress. The model has been used to effectively monitor agricultural drought in many parts of the world.
The WRSI determines a cumulative water balance for each period of 10 days (1 dekad) from planting to maturity. The cycle of each crop is subdivided into successive dekads. For each dekad, using a water-balance approach involving rainfall, evaporation, crop water requirements, and soil water-holding capacity, the cumulative water available (either surplus or deficit) at the beginning of each dekad can be computed (FAO, 1996).
Basically, the water balance is the difference between the effective amounts of rainfall received by the crop and the amounts of water lost by the crop and soil due to evaporation, transpiration, and deep infiltration. The amount of water held by the soil and available to the crop is also taken into account. In practice, the water balance is computed using a bookkeeping approach. The computation is done dekad by dekad and begins before the planting to account for moisture stored in the soil. From the planting dekad, the crop water requirements are calculated as the potential evapotranspiration (PET) times the crop coefficient (KCR). Thus, the available water amount is the difference between the crop water requirements and the working rainfall. These amounts do not consider water stored by the soil. The working rainfall amount reflects the effective water received by the crop and is calculated through a ratio defined by the user on the basis of the type of soil, slope, and so on. Normal rainfall is used in case of missing values. Surplus or deficit result from the water balancing and ranges between the field capacity and the permanent wilting point, depending on the root depth and the soil water-holding capacity. Finally, the WRSI indicates the degree to which cumulative crop water requirements have been met at the growth stage. The WSI represents, at any time of the growing period, the ratio between the actual and the potential evapotranspiration (FAO, 1996).
This WRSI model could be considered a combination of dynamic (water balance) and statistical (calibration of yield function) approaches. In fact, at harvest time, the sum of dekadal water deficit (or stress) suffered by the crop can be used as a variable along with some other relevant variables to forecast crop yield by statistical regression. At present, it is difficult to incorporate, into yield models, the variables derived from soil fertility, technology (mechanization, fertilizer use), varietal differences, and farming practices. It is a characteristic of the FAO approach that these important parameters are considered, along with NDVI, at the next stage of development of yield ("agmet") models.
The yield function is valid for a crop and a group of stations in a homogeneous cropping area. The input data correspond to different geographical units, from weather stations, to pixels (NDVI, CCD: 50 km2), to administrative units. It is an important step in the forecasting method to convert the data to comparable units (area averaging)—usually administrative areas that are used by planners or decision-makers in the field of food security.
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