Difference between revisions of "Yield Forecasting"
(→The forecasting approach) |
(→The forecasting approach) |
||
| Line 20: | Line 20: | ||
''Where:'' | ''Where:'' | ||
* ''f(t) = function of the trend of observed yields over the last 10 years'' | * ''f(t) = function of the trend of observed yields over the last 10 years'' | ||
| − | * ''f(i) = function of one ore more indicators that originate from: | + | * ''f(i) = optional function of one ore more indicators that originate from: [[Weather Monitoring]], [[Remote Sensing]], [[Crop Simulation]] or other data.'' |
| − | |||
| − | |||
| − | |||
| − | |||
| − | ' | ||
| − | + | ||
| + | This approach of using different combinations of indicators results in a large number of possible forecasts with only a few being statistically significant and even fewer also having a logical causal relation between indicator and observed yield. | ||
{{scientific_box_2|[[Objective of the quantitative forecast]]}} | {{scientific_box_2|[[Objective of the quantitative forecast]]}} | ||
Revision as of 15:25, 21 June 2011
The yield forecasting module is one of the five modules of the MCYFS and can be split in 3 topics.
Forecast objective and apporouch
Goal and assumptions
The objective of the quantitative yield forecast of the MCYF System is to provide the most likely, precise, accurate, scientific, traceable and independent forecasts for the main crops’ yields at EU level taking into account the effect of the climate during the season as early as possible during the cropping campaign (and until harvest).
The forecasting approach
The forecasted crop yield is calculated with a combination of methods and software tools. It is assumed to be a function of the trend of observed yields, possibly corrected with a function of one or more indicators from elsewhere out of the MCYFS and closed with a residual error:
Forecasted yield = f(t) + f(i) + residual error
Where:
- f(t) = function of the trend of observed yields over the last 10 years
- f(i) = optional function of one ore more indicators that originate from: Weather Monitoring, Remote Sensing, Crop Simulation or other data.
This approach of using different combinations of indicators results in a large number of possible forecasts with only a few being statistically significant and even fewer also having a logical causal relation between indicator and observed yield.
More information
Objective of the quantitative forecast
Forecast input data
Text in popular style
More information
Forecast methods
Text in popular style
Software tools
Text in popular style
