Difference between revisions of "Objective of the yield forecast"
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'''''The objective of the Yield Forecasting module is to provide the most likely, precise, accurate, scientific, traceable and independent forecasts for the main crops’ yields at EU level as early as possible during the cropping campaign
'''''The objective of the Yield Forecasting module is to provide the most likely, precise, accurate, scientific, traceable and independent forecasts for the main crops’ yields at EU level as early as possible during the cropping campaign.'''''
Revision as of 12:30, 24 June 2011
The objective of the Yield Forecasting module is to provide the most likely, precise, accurate, scientific, traceable and independent forecasts for the main crops’ yields at EU level as early as possible during the entire cropping campaign.
An important aspect of the objective is to ensuring independence from all external sources of estimates, including the national statistical systems. To realise this objective crop yield forecast procedures are applied which combine all kinds of input such as official yield statistics, weather indicators from various sources, simulated crop indicators based on various sources, remote sensing based vegetation indices, additional information sources and expert knowledge.
Official versus actual yield
The time series of official yield statistics of EUROSTAT are an important data source in the procedure which is mainly based on regressions. In this context the MCYFS assumes that the official reported yield statistics are objective and reflect the actual yield. You could argue that by using the official yield as regressand the MCYFS is an instrument to forecast the official yield rather than the actual crop yield.
Vossen and Rijks (1995) stressed that due to unknown farming practices and uncertainty in the MCYFS input data quantitative forecasted yields can never be valid for a specific locality. They can only be reliable for large areas such as countries or large regions, provided the information and model outputs were first carefully weighted for the relative importance of soil types, groups of varieties, common farming practices etc. Vossen and Rijks (1995) also stated that in relation to these limitations forecasting methods have to be validated per country and/or large regions.
Provide the most likely, precise, accurate, scientific, traceable and independent forecast
To provide the most likely, precise accurate, scientific, traceable and independent forecast different statistical tools are used: trend analysis, regression analysis and scenario/similarity analyses.
The measurement error (cause of main bias) is constantly a concern for the MCYFS as this could affect the "precision" and "accuracy" of the results. This means that indicators (used as predictors) should be improved wherever possible. Think of improving the processing of remote sensing indicators or the re-calibration of crop parameters in CGMS. At the end of the process different possible forecasts are available and often statistically acceptable. The "most performant" result is then individuated according to statistical tests (De Koning et al ., 1993). The concept of "scientific" includes the individuation of different possible forecasts into a single forecast in such a way that repeated processing always leads to the same result. The system stores all variables, methods and models leading to the final forecast. Whenever the configuration of the system changes (for instance after re-calibration a parameter value) all relevant indicators are re-processed to maintain database consistency.
Inductive versus deductive
The crop simulation indicators are based on observed weather. This process could be called inductive since it is initially based on measurements collected at weather stations. Note that alternatively forecasted weather is used and the method is less clearly inductive. The remote sensing indicators give a measure of the general effect of the environmental conditions on the vegetation without specifying which part of the environmental conditions is really influencing the portion of the vegetation observed. The process is here deductive. The convergence of results from the two perspectives is considered to be sufficient to draw conclusions about crop behaviour and justify yield forecasts results with the smallest uncertainty.
In the MCYFS the error of measurement is often neglected and leads to underestimation of the uncertainty. Therefore improvement of the precision of the measures is a constant point of attention. Efforts are being made to estimate the uncertainty related to the gap between the moment of issuing the yield forecasts and the final harvests. One method is the carry-over of ensembles probabilistic weather indicators (see forecasted weather) into ensembles probabilistic crop indicators and finaly ensembles probabilistic crop yield forecasts.
The geographic dimension of the forecasts goes theoretically from thethrough grid cells (25x25km resolution) to the administrative regions. In theory the indicators used as predictors can be generated at any geographic level (e.g. catchments boundaries could also be used).
While indicators are systematically stored in grid resolution and for administrative regions at different levels, the final yield forecasts is given at a National level only and then aggregated at EU level weighting the results with the most recent crop area data available. The EU and National crop yield forecasts are made available to theand EUROSTAT and published in the MARS bulletins.
As early as possible
The main idea steering the MCYF System is that the weather conditions have a significant effect on crop yield, determining most of the interannual variability. The time lag of the forecast is normally one year ahead. It means that the system forecasts the crop yield at harvest during the current agriculture season. In the current conception of the System the 'realised' observed weather will determine the crop yield at harvest.
Here, three directions are developed:
- classic regression analyses
- Scenario/similarity analyses
- Ensembles probabilistic approach
With the classic regression approach the crop yield forecast on time “n” implies a “normal” effect of weather from time “n” to the harvest. With a scenario/similarity analyses the crop yield forecast at time “n” is determined according to the range given by the ensembles of climatically similar years. The latest development is the introduction of ensemble weather forecasts over different forecast depths: at time “n” the crop yield forecast at harvest will include the effect of the most probable weather from time “n” to harvest as given by the climatic forecasts. This approach uses output from ECMWF forecasted weather. It is expected that results from using ensemble weather forecast improves the timeliness of the crop yield forecasts and improves the uncertainty of the system.
The “predictors” are any variables which can be observed annually on a region resolution and related to crop yield at harvest time. The nature of the “predictor” can be meteorological (e.g. maximum temperature, minimum temperature, precipitation, global radiation), crop specific (e.g. relative soil moisture, above ground biomass, water limited storage organs) or derived from remote sensing (e.g., , ). The trend extrapolation is also considered as a predictor. The function of the trend and period on which to calculate it is object of discussion as can heavily influence the final model.
In the process of finalizing a bulletin report the MCYFS analysists goes through the following steps:
- Meteorological impact evaluation (module Weather Monitoring)
- Crop status assessment (modules Remote Sensing and Crop Simulation)
- Crop growth expectations (all modules)
- Yield forecasts (module Yield Forecasting)
The scheme is followed during the analysis and along the season. In steps from 1 to 3 the “predictors” are generated, confronted and analysed. In step 4 they are statistically evaluated. In the modelling process other medium and long-term non-biological or non-geophysical factors that could influence the crop yield, such as a technological trend, are taken into account, basically modelling the yield time trend, including the main factors of technological improvements. Hence, the classic approach in the field of prediction, suggesting that the past generates the future, is respected. The forecasts (or early estimates) are therefore always available, updated in near-real time and able to anticipating most other sources of information (Genovese G., 1998).