Submitted as: model description paper 15 Mar 2021

Submitted as: model description paper | 15 Mar 2021

Review status: this preprint is currently under review for the journal GMD.

The multiple linear regression modelling algorithm ABSOLUT v1.0 for weather-based crop yield prediction and its application to Germany at district level

Tobias Conradt Tobias Conradt
  • Potsdam Institute for Climate Impact Research, Potsdam, Germany

Abstract. ABSOLUT v1.0 is an adaptive algorithm that uses correlations between time-aggregated weather data and crop yields for yield prediction. At its core, locally (i.e. district-) specific multiple linear regressions are used to predict the annual crop yield based on four weather aggregates and a linear trend in time. In contrast to other statistical yield prediction methods, the input weather features are not predefined or based on a limited number of observed correlations but they are exhaustively tested for maximum explanatory power across all of their possible combinations in all districts of the modelling domain. Principal weather variables (such as temperature, precipitation, or sunshine duration) are aggregated over two to six consecutive months from the 12 months preceding the harvest. This gives 45 potential input features per original weather variable. In a first step, this zoo of possible input features is subset to those very probably holding explanatory power for observed yields. The second, computationally demanding step is making out-of-sample predictions for all districts with all possible combinations of the remaining features. Step three selects the seven combinations of four different weather features that have the highest explanatory power averaged over the districts. Finally, the district-specific best performing regression among these seven is used for district predictions, and the results can be spatially aggregated. To evaluate the new approach, ABSOLUT v1.0 is applied to predict the yields of ten major crops at the district level in Germany based on two decades of yield and weather data from about 300 districts. When aggregated to the national level, the predictions explain 70–90 % of the observed variance between years depending on crop type and time frame considered. District-level performance maps for winter wheat and silage maize show areas with > 40 % variance explanation covering about two thirds of the country.

Tobias Conradt

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-21', Anonymous Referee #1, 16 Jun 2021
    • AC1: 'Reply on RC1', Tobias Conradt, 17 Jun 2021
  • RC2: 'Comment on gmd-2021-21', Anonymous Referee #2, 17 Sep 2021
    • AC2: 'Reply on RC2', Tobias Conradt, 24 Sep 2021
  • AC3: 'Final Author Comment on gmd-2021-21', Tobias Conradt, 24 Sep 2021

Tobias Conradt

Data sets

ABSOLUT v.1.0 Input data for an example application on the districts of Germany Tobias Conradt

Model code and software

ABSOLUT v.1.0 R programs Tobias Conradt

Tobias Conradt


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Latest update: 16 Oct 2021
Short summary
Crop yields usually depend on weather and climate. It is possible to predict yields solely based on meteorological observations, and future yield scenarios may be calculated from climate scenarios. The ABSOLUT algorithm uses regionally distributed data to auto-adapt to the individual weather-yield relations of a certain crop in its application domain. It is presented with an example for Germany where more than 75 % of the national yield variations of major crops can be explained.