The High Plains Initiative for Integrated Phenology: Where we are today

1. The High Plains Initiative for Integrated Phenology: Where we are today Sherri Harms Jose Martinez University of Nebraska – Kearney June 16, 2005

2. Objectives for 2004/2005 • Gather information & communicate with each other • What do we already know? • What do we want to know? • Gather data – what is available? • Study the data – what does it tell us?

3. Issues We Would Like to Address The break-out sessions will address these issues & the related discussion questions.

4. What data is available? • Wheat flowering data for several sites since 1935 (Stephen Baenziger/Len Nelson) • Temperature & precipitation data by site for entire US (time lengths vary) (NOAA; NDMC; & see NADSS) • Horticultural flowering dates for 4 species, 1978-2004 (Lincoln, NE) (Richard Sutton) (many gaps) • Soybean yield data (2000-2004 from several NE sites) (L. Nelson) • Sorghum bloom data 1987-2004 (Lincoln, NE) (Jeff Perdersen) • Wheat stem rust data 1922-1992 several sites (USDA Cereal Disease Lab) • Oceanic climatic data (NOAA) • Other?

5. Preliminary Studies: What does our data tell us? • Trends • Temperature trends • Wheat flowering date trends • Horticultural date trends • Sorghum Blooming date trends • Associations/Correlations • Wheat flowering dates & temperature • Wheat flowering dates & horticultural flowering dates • Wheat flowering dates & sorghum flowering dates • Precipitation (Local) & Oceanic (Global) Conditions • Climatic change verification • Have wheat horticultural varieties, and sorghum dates been occurring earlier? • If so, can this be attributed to climatic change?

6. Trends

7. Temperature Trends High Plains Regional Climate Center (1905-2004)

8. Temperature Trends • Lincoln Data (from CALMIT) 1887-1998 • March 1- May 30 weekly data • Minimum temperatures showed a slight negative trend • For example:

9. Temperature Trends • Lincoln Data (from CALMIT) 1887-1998 • March 1- May 30 weekly data • Maximum temperatures showed a slight positive trend • For example:

10. Wheat Flowering Date Trends

11. Nationwide Kharkov Flowering Dates

12. Sorghum 50% Bloom Date Trends

13. Horticultural Flowering Date Trends

14. Associations/Correlations

15. Associations of wheat flowering dates to temperature • Initial Study • UNL Agronomy Farm (East Campus) & Mead NE Research Farm • Wheat flowering dates from 1935 - 1998 • Two wheat varieties: Kharkov & Scout • Lincoln daily minimum temperature 1935 – 1998 • Decision Trees & Neural Networks Data mining methods

16. Association of wheat flowering dates to temperature • Standardize all data into 7 categories (based on standard deviations) • Use minimum temperatures for a pre-defined number of weeks that precede the approximate flowering date • Experiment I: Ten weeks prior to the average flowering date. • Experiment II: Twenty four weeks prior to the average flowering date. • Build a model based on these temperatures • Performance measure: percentage of time the model arrived at the actual value on test data (using cross validation)

17. Experiment 1 Results

18. Partial Decision Tree for the Kharkov wheat variety 10-week dataset

19. Kharkov Flowering Dates Vs. Thermal Dates

20. Wheat vs. Sorghum Correlation Coefficent .33

21. Relationships between Horticultural, Wheat & Sorghum Flowering Dates Confidence = examples covered by the premise / covered by the consequence Lift =confidence/ the proportion of all examples covered by the consequence

22. Index type Brief meaning & calculation Sign of index value Other names/ Conditions Possible impacts on Nebraska SOI Standardized pressure difference between Tahiti and Darwin Positive La Niña Drier than normal Negative El Niño Wetter than normal MEI Calculated as the first unrotated principal component of six observed fields* (i.e., SLP, U, V, SST, A, and C) combined Positive El Niño Wetter than normal Negative La Niña Drier than normal NAO Normalized pressure difference between a station on the Azores and one on Iceland Positive Strong mid-latitude westerly flow Undetermined Negative Weak mid-latitude westerly flow Undetermined PDO Leading principal component of North Pacific monthly sea surface temperature variability (poleward of 20N) Positive Warm phase Wetter than normal Negative Cold phase Drier than normal PNA PNA = 0.25 * [ Z(20N,160W) - Z(45N,165W) + Z(55N,115W) - Z(30N,85W) ]where Z are standardized 500 hPa geopotential height values Positive Positive phase Undetermined Negative Negative phase Undetermined Climatic Associations to Precipitation in Nebraska T. Tadesse, 2002

23. Global Ocean Conditions Local Drought Conditions  Sample Relationships ed = extremely dry; sd = severely dry; md = moderately dry Tadesse & Harms

24. Predicting Phenological Development in Winter Wheat • Xue, Wiess, Baenziger, 2004 • Streck, Weiss, Xue, Baenziger, 2003 • Calculate daily development rate based on • Temperature • Vernalization response function • Photoperiod • Used to predict flowering dates with good accuracy (RMSE 5-6 days)

25. Climatic change verification

26. An interesting problem • Are there statistical or computer science analysis tools that can tie temporal data to singular events? • In other words, how do we tie climate data to phenological data?

27. Kharkov Scout Wheat Flowering Date Trends

28. Kharkov Scout Wheat Flowering Dates & Temperature

29. Summary of 2004/2005 Activities • Gathered information & communicated with each other • Gathered data – limited and with holes • Studied the data • Several data sets indicate trends toward earlier maturation in plants – why? • Worth further exploring – global warming?

30. Where do we go from here? • What other data do we want to tie together? • Repository for data (Tied to the National Phenology Network) • Decision Support System • Such as the National Agricultural Decision Support System (NADSS) for USDA Risk Management Association • Built-in analysis tools • Data access tools that can be used by researchers, policy makers, educators