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7.0.2 Forecasting regional food/fiber production levels
Crop growth simulation relies on time and space discrete information. As a result, the outputs are only valid for one location and one period. For decision-making purposes this is often inadequate, since it is not of interest to planners to know what the current status of a particular land use systems is but to know the final outcome for multiple land use systems for a whole region. Preferably well in advance of harvesting so that for example in times of drought grain can still be purchased at reasonable (world market) price-levels. It is for this reason we examine the various techniques for forecasting food/fiber production levels in applications for food security.

Results from crop growth simulation reflect the compound effect of soil-weather conditions throughout the growing season on crop growth. However, these simulations cannot directly be considered as final yields as actual yields are in many cases considerably lower than the potential or water-limited yields due to sub-optimal cultivation practices and uncertainities of how weather conditions will progress beyond the date of assessment.

Theoretical illustration of factors influencing variability of regional agricultural productivity (arbitrary units).
Image 1: Theoretical illustration of factors influencing variability of regional agricultural productivity (arbitrary units).

One direction of reasoning has been to predict the annual variation of end-of-season crop yield per hectare by combining a linear time trend with the results of crop growth simulation (Vossen and Rijks, 1995). The Crop Growth Monitoring System (CGMS) used at the Joint Research Center of the European Union is based on this premise. Under this approach statistical analysis is used to select the most robust predictor of yield for different stages in the growing season. To accomplish this, four indicators of modeled yields are regressed against historical yields, and the most significant one is used for forecasting. To account for the influence of increasing farmers’ skill and increasing use of technology on yield, a fifth indicator, the so-called ‘time trend’, is tested as well. Literature suggests that a simple linear model to describe this trend is sufficient in most cases (Swanson and Nyankori, 1979 cited by Hooijer and van der Wal, 1994). A smooth trend of any type over a large number of years assumes a continuity that might be unrealistic. For that reason, Hooijer and van der Wal (1994) suggest to base this indicator only on data from the recent past. Its length should nevertheless be long enough to give a sufficient number of degrees of freedom in a regression analysis. In practice, the length of the time series used for the statistical model validation has been set to n = 9 years (if the total length l of the available series is smaller than n, then n=l). In the figures below the time trend is given based on yield statistics for two different regions.

Time trend for Zimbabwe based on yield statistics provided by two independent sources.
Image 2: Time trend for Zimbabwe based on yield statistics provided by two independent sources.
Time trend for Iran based on yield statistics for two land-use systems (rainfed and irrigated wheat).
Image 3: Time trend for Iran based on yield statistics for two land-use systems (rainfed and irrigated wheat).

Another direction primarily focused on preparing adequate surrogate weather data series for crop growth modeling (Hammer and Nicholls, 1996). Hodges et al. (1987) cited by de Jager et al. (1998) selected appropriate analogue historical weather data series, depending upon the 90-day weather outlook (below, above or normal). Randomized weather data series generation (e.g. the climate model, Weathergen) is a possibility, as is the use of the daily rainfall data series generator of Zucchini and Adamson (1984) as cited by de Jager et al. (1998). Lourens and de Jager (1997) forecast weather data within a growing season with historical data series that had delivered lower quartile, median and upper quartile seasonal rainfall (de Jager et al., 1998). Fouché (1992) cited by de Jager et al. (1998) constructed seasonal rainfall scenarios of composite monthly rainfall data from historical meteorological records, assuming that each month received median monthly rainfall. De Jager and Singels (1990) used combinations of daily sunshine, maximum, and minimum temperature and daily rainfall data selected randomly from historical data series (de Jager et al., 1998). McKeon (1996) sited by de Jager et al. (1998) simulated forage yields by completing the season with 5-10 analogue years of weather data from which he determined the mean and coefficients of variation. These methods vary in complexity but have in common that they forecast a season’s weather patterns with the implicit assumption that the variability of future climate will be similar to that of the past. At best such an analysis of historical climatic conditions can provide an envelope in which season forecasts can be fitted; the direction and extend of the variance cannot be predicted. Hence, modeled yields may prove inaccurate also. Scientists aware of this drawback have started utilizing so-called 'tele-connections' in their predictions in an attempt to improve the ‘robustness’ of seasonal weather forecasting.

7.0.2.0  Does vegetation growth in Africa follow El Niño?
A large-scale atmospheric process in modelling Africa's vegetation status

 


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U09-NRM-127: The role of Distributed Data Access Technologies in NRM - for ITC-IDV version 2.7 > Thematic Expert Models > Food security