Solar Radiation Data Impact on Crop Models and Decision Support Systems

1. A comparison of methods for providing solar radiation data to crop models and Decision Support Systems Mike Rivington, Keith Matthews and Kevin Buchan Macaulay Institute, Aberdeen IEMSS 2002, Lugano, Switzerland.

2. Context of research • Use of models in spatial decision support systems • need spatially representative data • Input data determines model output quality • what constitutes a ‘representative’ data source? • making the most of what is available • Need to know how data source effects model performance • synchronisation of validation data • This study looks at the impacts of solar radiation data sources on crop models and decision support

3. Stations with records of solar radiation = 68 Stations with records of sunshine duration = 1261 Spatial distribution of solar radiation data

4. Identifying a suitable data source

5. Land Allocation Decision Support System (LADSS) A tool for investigating the financial, social and environmental consequences of alternative land-use patterns • A strategic planning tool • Spatially explicit • Multiple-objective - defining trade-offs • Applied at the farm / estate scale A framework integrating spatial data with land-use systems models and analysis methods

6. Expert Knowledge and Goals Oracle Database GIS Graphic User Interface Impact Assessments Land-use Planning Tools Land-use pattern allocation LADSS components Land-use Models

7. The CropSyst crop production model is used to represent arable based land-uses a robust, daily time step, multi-crop and multi-year model has output for a wide range of crop and soil biophysical properties Other land-uses modelled in LADSS Forestry (conifer and broadleaf species) Livestock production systems Land-use models within LADSS

8. Issues of using land-use models in DSS • Application of models in DSS when data is incomplete or not spatially or temporally representative • How to define spatially and temporally representative climate data • There is a fundamental need to identify representative climate data BEFORE model calibration and validation work can be undertaken • How does the climate data source impact on model performance? • Need to maintain parity between land-use models

9. Data source comparison method • Compare impact of four solar radiation data sources at three sites on CropSyst yield estimations • 4 solar radiation data sources tested: • conversion from sunshine duration • nearest meteorological station • second nearest meteorological station • Campbell-Donatelli model (solar radiation estimated from temperature)

10. The three test sites’ spatial separation: Rothamstead to Bracknell = 55 km Rothamstead to Wallingford = 57 km Bracknell to Wallingford = 33 km

11. Method • Run CropSyst with solar radiation from the four different data sources • using a standardised spring barley scenario (initial soil water and nitrogen not limiting factors) • only variables were input climate data • variables for the two nearest meteorological stations included precipitation and temperature

12. Example of simulation • Run CropSyst for each location with: • observed rain, temp and solar radiation data • observed rain, temp and solar radiation from nearest met station • observed rain, temp and solar radiation from second nearest met station • observed rain, temp plus solar radiation converted from sunshine duration • observed rain and temp plus solar radiation estimated by the Campbell-Donatelli model

13. Results - Conversion of Sunshine Duration Regression analysis of sunshine duration conversion method: observed versus estimated • Need to consider the time of year when correlation is poor • - During the growing season?

14. Growing season

15. Results: CropSyst estimated yield n = 13 years Yield in t/ha

16. Results: CropSyst estimated yield n = 13 years Yield in t/ha

17. Results: CropSyst estimated yield n = 13 years Yield in t/ha

18. Results: CropSyst estimated yield n = 13 years Yield in t/ha

19. Results: yield prediction errors n = 13 years

20. Results: yield prediction errors n = 13 years

21. Results: yield prediction errors n = 13 years

22. Results: yield prediction errors n = 13 years

23. Results: order of best fit

24. Conclusions • Greater similarity between Rothamstead and Wallingford simulations (57 km apart) than Wallingford and Bracknell (33 km apart) • Nearest Meteorological Station does not necessarily produce the best fitting results • Conversion from sunshine duration is a suitable substitute for observed solar radiation data • In the absence of both solar radiation and sunshine duration data, the Campbell-Donatelli model is a suitable substitute

25. Conclusions Order of solar radiation data source suitability • To optimise site representation: 1 Conversion from sunshine duration 2 Near by meteorological stations 3 Estimated from temperature • Depends on geographic location • the 3 test sites are in a geographically similar area • Depends on what data is available • Need to consider how spatially representative the rain and temperature data is...

26. Impacts on DSS • Choice of solar radiation data source will have an impact on crop model output • Impact on DSS performance depends on the model output assessment criteria / measure • Total yield / mean yield / magnitude of errors ? • Errors in crop model estimates arising from choice of data source will have impacts on overall quality of decision support • The significance of uncertainty of the data source affect and subsequent impact on model and DSS performance will need to be quantified

27. Thank you for your attention website: www.macaulay.ac.uk/LADSS