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Crop monitoring and forecasting Agrometeorological Crop Forecasting
Crop Forecasting: Inputs

Introduction | Inputs | Model | Outputs

PRESENTED HERE is a list of the main factors for which data are required in order to forecast crops. The data come from different sources (ground data, satellites), and many of them have to be collected in real time. Consolidating this data is usually one of the main difficulties, as they are also typically provided by various sources (e.g. different ministries).

The importance of "reference data" should not be underestimated. Although most of them do not directly enter calculations, they are used as a yardstick against which to compare data characterizing the current season.

Weather data
Weather data are among the most important factors that condition the inter-annual variability of crop yields. Depending on the prevailing climatic conditions, limiting factors can be either rainfall (in semi-arid areas), sunshine (in many equatorial or temperate countries). In addition, weather affects crops indirectly, through pests and diseases.

  • Total rainfall map (1-10 August 1996, West Africa)
  •  GIF image: 19K / 665x403 pixels  

    In FAO food security programmes, rainfall is often estimated from cold cloud duration, a satellite-based parameter - this expresses the number of hours during which cloud top temperature drops below a certain threshold, below which clouds are likely to produce rainfall.

    Several techniques of weather monitoring are in use, for instance the comparison of actual rainfall with the normal value. Recently, the Early Warning System of the Southern African Development Community has begun using El Nino-Southern Oscillation Index values (ISO) to predict the likely outcome of the ongoing rainy season. In the graph below, the upper (green) curve corresponds to the average behaviour of ENSO (ISO) during wet years, while the red curve usually occurs during dry years. By plotting the current year (black: 1996/97 season) against the two previous years, it is possible to evaluate whether the current season is closer to the "dry" or to the "wet" pattern.

     GIF image: 6K / 600x293 pixels

    Crop calendar
    Detailed information about crop stages - also known as the "crop calendar" - plays an essential role in crop monitoring and forecasting. This is because the effect of environmental conditions on crops depends very much on crop growth stages. For instance, water requirements are normally low at the initial growth stages, while they reach a maximum just after flowering.

    Information about crop stages can be obtained from different sources, in isolation or in combination. An effective system involves field observers, usually agricultural extension staff, using a system of regular reporting, either by radio or by mail.

    National crop monitoring systems also rely more and more on satellite technology. Using a series of Normalized Difference Vegetation Images (NDVI), it is possible to monitor vegetation development. Assuming that crops follow a pattern similar to natural vegetation, planting dates - also known as the "start of the season" - can be estimated. The image below shows vegetation development in Ethiopia.

     GIF image: 12K / 433x335 pixels

    Crop reports
    Crop reports are essential for proper crop monitoring. They are collected from a number of different sources, such as agricultural extension staff, district agricultural officers and meteorological observers. The area covered, and the level of detail, varies widely. District officers usually try to summarize the situation in a large area, which necessarily leads to some loss of detail. On the other hand, meteorological observers might report only on the area immediate surrounding their station.

    Communicating the information is often a problem, particularly in developing countries. District officers and meteorological stations may have access to modern communication systems, but remote observers often have difficulties forwarding their data to the central cop forecasting or monitoring unit.

    Crop reports usually cover two essential aspects. The first is crop phenology, also known as crop "stage". The second is crop condition, a parameter that is more difficult to assess. Reporters can, for instance, rank crop condition on a scale from 1 to 5, where:

    1. indicates a very poor crop (almost no harvest expected)
    2. indicates a below average crop
    3. stands for an average crop
    4. above average crop condition
    5. a bumper harvest is expected

    Typically, observers also give indications of the negative factors that have affected crops, and of the extent of the damage they caused. Some negative factors can usually be modelled only in extreme cases - e.g. hail or insect attacks. On the other hand, drought and other factors that do not physically damage crops can be modelled.

    Two main satellite-based variables are used in the FAO crop forecasting approach. The first is known as Normalized Difference Vegetation Index, or NDVI; the second is Cold Cloud duration, or CCD.

    NDVI is provided by Earth resources satellites (of the NOAA series) and constitutes a relative and semi-quantitative measure of the living green plant biomass. It typically presents itself as "images" with a colour coding of the NDVI intensity, with low values corresponding to sparse or no vegetation (ochre-brown-green), and high values indicate dense vegetation (red-pink-purple). An example is given below for Ethiopia in mid-July 1995.

     GIF image: 19K / 512x339 pixels

    For different reasons, it is difficult to use NDVI directly to estimate crop yields. But the index is a crucial variable for monitoring, and it is used in the FAO methodology to interpolate yields.

    The second important indicator is Cold Cloud Duration (CCD). CCD is an indicator based on meteorological satellites of the METEOSAT type. It is a measure of the duration, in hours, in which clouds were actually so cold (below -40 degrees C) that the likelihood that they produce rain is very high. CCD can be used for monitoring, as it serves as a proxy for rain. However, its greatest potential is as an aid to estimate and map out rainfall.

    Farm inputs and other factors
    The factors which are listed below all play a part in crop yield and production forecasting. Farm inputs include mainly fertilizers and pesticides. In developing countries, it is sometimes difficult to obtain accurate data for inputs, particularly if they are required by administrative units. Among the "other factors", the following play an important role:

    • technology (e.g. mechanization, new varieties) which is the main factor in trends affecting crop yields
    • management - the decisions which are taken by the farmer, including the choice of varieties, planting dates, which crops to grow where, etc.
    • prices, which tend to affect the area cultivated, or the relative crop mix, more than yields
    • government policies, which are often implemented through pricing and taxing mechanisms, subsidies, etc.

    Reference data
    Reference data are all those that are not directly used in calculations but are nevertheless needed for meaningful crop forecasting. This includes, to start with, a good knowledge of actual farming practices in the areas being covered - for instance which crops are grown, and where, and how. It also covers, particularly for agrometeorological crop forecasting, climate and soil information

    Crop information
    Below are three sample images from the IGADD region (Horn of Africa), taken from FAO Agrometeorological Working Paper No. 10 (by van Velthuizen, Verelst and Santacroce, 1995).

  • Average length of growing season (days) in the IGADD region
  •  GIF image: 10K / 431x410 pixels  

    Length of growing season is important in crop monitoring. While the values given in the map are averages, during a give year the actual season may be longer or shorter, which leads to crops being unable to complete their cycles, and usually to below average yields. The map below shows average maize yields (only in areas where the crop is actually grown). This, again, indicates the areas with the highest potential, and those where unfavourable conditions are likely to cause most harm. Reference maize yields are also important for forecasting as one of the variables in yield function.

  • Average maize yields in the IGADD region
  •  GIF image: 10K / 445x408 pixels  

    Finally, while crops constitute the basis of the food consumed by most people, livestock is also a significant source of calories, particularly in semi-arid areas. In a food security context, it is thus useful to be well informed about the herd composition. Relevant information is given in the map below.

  • Percent of sheep in the herd composition
  •  GIF image: 10K / 430x411 pixels  

    Climate information
    Climatic databases must be assembled in all crop forecasting systems, since climate is the dominant factor in the variability of crop yields. (Of course, this may not be so for production, as the areas planted also depend on economic factors and incentives linked with policies and prices). In fact, the core of most crop forecasting models is a soil-water balance, which requires that crop water availability and consumption are known. Consumption depends on several variables, such as temperatures and wind speed. This is why agrometeorological data bases usually go beyond simply rainfall.

    Climate conditions sometimes vary significantly even over small distances. At a continental scale - as shown in the animated image of Africa below - the number, length and timing of rainy and cropping seasons is extremely diverse.

  • Rainy season in Africa - 10-daily rainfall normals, January-December
  •  GIF image: 510K! / 279x299 pixels  
    Prepared by the FAO Agrometeorology Group based on monthly rainfall maps by M.F. Hutchinson et. al., 1996 (CRED/ANU, Canberra)  

    FAO has assembled a major worldwide database of agroclimatic parameters, published as the FAOCLIM CD-ROM. The database covers about 25,000 stations and focuses on monthly averages and time series, which are essential tools for variability analyses and risk studies.

    Weather data are recorded at stations, but most analyses also cover the area between the station. Sophisticated techniques are needed to estimate the values on a regular grid covering the whole area under study. Gridding tools are thus needed, such as software (by Bogaert, Mahaut & Deckers) published in the FAO Agrometeorological Working paper series. Such techniques can then be applied for mapping the climate, as in the animation above, or the map below.

  • Sunshine fraction in Latin America
  •  GIF image: 6K / 265x292 pixels  
    July; sunshine fraction varies from 0 (permanent cloud cover) to 100 (full sunshine)  
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