Objective of the yield forecast

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Objective

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 (and until harvest).


Independent=

The term independent refers to ensuring independence from all external sources of estimates, including the national statistical systems (Genovese, 1998). To realise this objective crop yield forecast procedures are applied which combine all kinds of input such as official yield statistics, weather indicators, simulated crop indicators, remote sensing based vegetation indices, additional information sources and expert knowledge.

Official versus actual yield

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.

Geographic scale

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.


Produce the most likely, precise accurate, scientific, traceable and independent forecast

To achieve this part of the objective different statistical tools are used:

  • trend analysis
  • regression analysis
  • scenario analyses
  • similarity analysis

At the end of the process different possible forecasts are available and often “statistically” acceptable. The “most performant result” is then individuated and selected according to statistical tests (De Koning et al ., 1993) on the models used and scenarios analysis results. The measurement error (cause of main bias) is constantly a concern for the MCYFS as this could affect the results of the whole analysis. This means that on the side of the predictors it is often a material of R&D to improve the processing of the remote sensing signals or the re-calibration of the CGMS on updated crop parameters in order to obtain “predictors” as much as possible correlated with the observed crop yield. The concept of “scientific” includes the individuation of a direct link within cause and effect in the models used and the repeatability of the experience. In fact the system is tailored in a way that the same model with the same data sets can be repeated in any time obtaining the same result. For that reason any time the configuration of the system changes (for instance a new parameter calibration is injected in the model) the all archive of output like the time series of “predictors” is re-generated. The source of the forecast can be in the system of different nature. For that reason the system keeps track systematically of the variables, methods and models which were at the base of the final forecast. The system is then tailored to measure the current climate (level 1) and transform the effects into crop biomass production (level 2). This part is “inductive” as the initial observations of the climate are measures given by samples on points (meteorological stations) in some day hours. The remote sensing indicators, as another piece of the system and part of the level 2, give a “measure” of the general effect of the environmental conditions on the vegetation without specifying which part of the climate is really influencing the portion of the vegetation observed. The process is here “deductive”. The convergence of “evidence” from the two sides (bottom-up “inductive” from model simulation/ top-down “deductive” from Remote Sensing measure) is often considered in the framework of crop forecasting as sufficient to conclude on current crop behaviour and justify resulting forecasts with the mostly reduced uncertainty. However, in this framework, the error of measurement is often neglected and let underestimate the uncertainty which should be associated to the forecast. The improvement of the “measures” precision do of the predictors is a constant effort of the MARS team in charge of the MCYFS. More efforts are being made in order to control the uncertainty related to unpredictable future climate impact remaining from the moment of issuing the crop yield forecasts and the final harvests. One is the use the “ensembles” probabilistic output from weather circulation models (See Vol. 1) as initial input in the model and define an ensembles-probabilistic final predictors on which base the crop yield forecasts. “EU level” and the geographic dimension The geographic dimension of the forecasts given by the MCYFS goes theoretically from the EMU (Elementary Mapping Unit) concept dimension (see Vol. 2 of this series) to the Continental dimension passing through the grid cells (50x50 km) (see Vol. 1 of this series), regions (NUTS2,1) and country levels. In theory the “predictors” production can be generated at whatever geographic level according to the layer available (catchments boundaries could also be used). For practical reasons linked to the management of the information and to the constraint of deriving the final forecast mainly re-calibrating the “predictors” on time series of observed yields, the system runs at Country level and then aggregates the results at EU level. In practice, while “predictors” are systematically produced and stored at grid, NUTS2, 1, 0 level, the quantitative final forecasts are given at National level 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 the DG-AGRI and EUROSTAT and published in the MARS bulletins. Taking into account the effect of climate during the season, as early as possible during the cropping campaign (until harvest). Concept of “predictors”. The main idea steering the MCYF System is that the climate can 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 climate so far observed will determine the crop yield at harvest. Here, two directions are developed; the first through the classic approach of regression modelling where at time “n” the forecast obtained by the model selected implies a “normal” effect of climate from time “n” to the harvest; the second one is developed through the scenarios where at time “n” the crop yield forecast is determined according to the range given by the ensembles of climatically similar years (developed in section 3.6). However, today one of the R&D directions to improve the system is the introduction of climate forecasts at different time span in the system. This approach uses output from CGM forecasts ensemble climate forecast approach (see vol.1 of this series) seems one of the most promising. The experience is investigating the use of forecasts at 10 days, monthly, seasonal and on climatic dimensions (dekade, century). The result expected could improve the timeliness of the forecasts, partially improve the uncertainty of the system treating its input/output flow in a probabilistic “ensemble” approach: 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. The “predictors” are any variables which can be observed annually and related to crop yield at harvest time. The nature of the “predictor” can be meteorological (any parameter as tmax, tmin, rain, radiation levels. PET) regionalised (CMETEO for instance see Vol. 1); crop specific simulated parameter (for instance CGMS crop-soil moisture see Vol. 2); derived from remote sensing (SPOT-Vgt NDVI and the regionalised version CNDVI see Vol. 3). 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

The scheme is followed during the analysis and along the season.

In steps from A to C the “predictors” are generated, confronted and analysed, then in step D 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).