Abstract
The need for more comparisons among models is widely recognized. This study aimed to compare three different modelling approaches for their capability to simulate and predict trends and patterns of winter wheat yield in Western Germany. The three modelling approaches included an empirical model, a process-based model (LINTUL2), and a metamodel derived from the process-based model. The models outcomes were aggregated to general climate zones level of Western Germany to allow for a comparison with agricultural census data for validation purposes. The spatial patterns and temporal trends of winter wheat yield seemed to be better represented by the empirical model (R 2 = 70%, RMSE= 0.48 t ha -1 yr -1 , and CV-RMSE= 8%) than by the LINTUL2 model (R 2 = 65%, RMSE= 0.67 t ha -1 yr -1 , and CV-RMSE=11%) and the metamodel (R 2 = 57%, RMSE= 0.77 t ha -1 yr -1 , and CV-RMSE=13%). All models demonstrated a similar order of magnitude of yield prediction and associated uncertainties. The suitability of the three models is context dependent. Empirical modelling is most suitable to analyze and project past and current crop-yield patterns, while crop growth simulation models are more suited for future projections with climate scenarios. The derived metamodels are fast reliable alternatives for areas with well calibrated crop growth simulation models. A model comparison helps to reveal shortcomings and strengths of the models. In our case, a performance comparison between the three modelling approaches indicated that, for simulating winter wheat growth in Western Germany, higher sensitivity to soil depth and lower sensitivity to drought in the LINTUL2 model would probably lead to better predictions.
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Claims 6
Claim · model-written summary
The empirical model better represented spatial patterns and temporal trends of wheat yield than LINTUL2 and the metamodel.
- Taxon
- winter wheat
Triticum aestivume0 - Outcome
- spatial patterns and temporal trends of winter wheat yielde1
- Direction
- bettere1
- Value
- R 2 = 70%e1
- Comparator
- LINTUL2 modele1
- Setting
- Western Germanye0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“winter wheat yield in Western Germany”
e1“The spatial patterns and temporal trends of winter wheat yield seemed to be better represented by the empirical model (R 2 = 70%, RMSE= 0.48 t ha -1 yr -1 , and CV-RMSE= 8%) than by the LINTUL2 model (R 2 = 65%, RMSE= 0.67 t ha -1 yr -1 , and CV-RMSE=11%) and the metamodel (R 2 = 57%, RMSE= 0.77 t ha -1 yr -1 , and CV-RMSE=13%).”
Claim · model-written summary
All three models gave yield predictions and uncertainties of similar magnitude.
- Taxon
- winter wheatTriticum aestivume0
- Outcome
- yield prediction and associated uncertaintiese1
- Direction
- similare1
- Comparator
- All modelse1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“winter wheat yield in Western Germany”
e1“All models demonstrated a similar order of magnitude of yield prediction and associated uncertainties.”
Claim · model-written summary
Empirical modeling was judged most suitable for past and current crop yield patterns.
- Taxon
- winter wheatTriticum aestivume0
- Outcome
- analyze and project past and current crop-yield patternse1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“winter wheat yield in Western Germany”
e1“Empirical modelling is most suitable to analyze and project past and current crop-yield patterns”
Claim · model-written summary
Crop growth simulation models were judged more suited for future climate scenario projections.
- Taxon
- winter wheatTriticum aestivume0
- Outcome
- future projections with climate scenariose1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“winter wheat yield in Western Germany”
e1“crop growth simulation models are more suited for future projections with climate scenarios”
Claim · model-written summary
Metamodels were judged fast reliable alternatives where crop growth models are well calibrated.
- Taxon
- winter wheatTriticum aestivume0
- Outcome
- fast reliable alternativese1
- Qualifier
- areas with well calibrated crop growth simulation modelse1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“winter wheat yield in Western Germany”
e1“The derived metamodels are fast reliable alternatives for areas with well calibrated crop growth simulation models.”
Claim · model-written summary
Higher soil-depth sensitivity and lower drought sensitivity in LINTUL2 would probably improve predictions.
- Taxon
- winter wheatTriticum aestivume0
- Outcome
- better predictionse0
- Qualifier
- higher sensitivity to soil depth and lower sensitivity to droughte0
- Setting
- Western Germanye0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“for simulating winter wheat growth in Western Germany, higher sensitivity to soil depth and lower sensitivity to drought in the LINTUL2 model would probably lead to better predictions”