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

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Presentation on theme: "The High Plains Initiative for Integrated Phenology: Where we are today Sherri Harms Jose Martinez University of Nebraska – Kearney June 16, 2005."— Presentation transcript:

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 Gather information & communicate with each other What do we already know? What do we already know? What do we want to know? What do we want to know? Gather data – what is available? Gather data – what is available? Study the data – what does it tell us? Study the data – what does it tell us?

3 Issues We Would Like to Address Data CollectionData AnalysisEcosystem Scope Education a. Existing dataData Mining/ Knowledge Discovery a.Managed Crops Pests a. Ourselves (researchers) b.Collection Network What How Visualizationb. Unmanaged Migrations Wildlife b. Policy Makers c.Data Formats Standardization GISc. Other faculty & students d. Ancillary Data Precision Accuracy (Web Tools)d. K-12 teachers & students 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) 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) 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) 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) Soybean yield data (2000-2004 from several NE sites) (L. Nelson) Sorghum bloom data 1987-2004 (Lincoln, NE) (Jeff Perdersen) Sorghum bloom data 1987-2004 (Lincoln, NE) (Jeff Perdersen) Wheat stem rust data 1922-1992 several sites (USDA Cereal Disease Lab) Wheat stem rust data 1922-1992 several sites (USDA Cereal Disease Lab) Oceanic climatic data (NOAA) Oceanic climatic data (NOAA) Other? Other?

5 Preliminary Studies: What does our data tell us? Trends Trends Temperature trends Temperature trends Wheat flowering date trends Wheat flowering date trends Horticultural date trends Horticultural date trends Sorghum Blooming date trends Sorghum Blooming date trends Associations/Correlations Associations/Correlations Wheat flowering dates & temperature Wheat flowering dates & temperature Wheat flowering dates & horticultural flowering dates Wheat flowering dates & horticultural flowering dates Wheat flowering dates & sorghum flowering dates Wheat flowering dates & sorghum flowering dates Precipitation (Local) & Oceanic (Global) Conditions Precipitation (Local) & Oceanic (Global) Conditions Climatic change verification Climatic change verification Have wheat horticultural varieties, and sorghum dates been occurring earlier? Have wheat horticultural varieties, and sorghum dates been occurring earlier? If so, can this be attributed to climatic change? 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 Lincoln Data (from CALMIT) 1887-1998 March 1- May 30 weekly data March 1- May 30 weekly data Minimum temperatures showed a slight negative trend Minimum temperatures showed a slight negative trend For example: For example:

9 Temperature Trends Lincoln Data (from CALMIT) 1887-1998 Lincoln Data (from CALMIT) 1887-1998 March 1- May 30 weekly data March 1- May 30 weekly data Maximum temperatures showed a slight positive trend Maximum temperatures showed a slight positive trend For example: 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 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 RuleConfidenceLift ChanomdiJaonica flowering early ==> SyrungaVulgans flowering is very early 100%9.00 Forsythis Xunbinedii flowering average ==> LincolnMead Wheat flowering average 33%0.82 Syrunga Vulgans flowering average ==> Hays Wheat flowering average 100%9.00 Stillwater Wheat flowering average and Chanomdi Jaonica flowering early ==> Syrunga Vulgans flowering very early 100%9.00 LincolnMead Wheat flowering very early ==> Sorghum blooming average 67%3.00 Sorghum blooming early ==> LincolnMead Wheat blooming early 50%1.50 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 PositiveLa 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 PositiveEl Niño Wetter than normal NegativeLa 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 Sample Relationships Sample parallel MOWCATL rules with 1 month windows and a max lag of 3 months. Location Location Rule Rule Confidence Confidence Clay Center Clay Center SOIsd, MEIed, PDOed => SPI9sd, SPI12sd, PDSIed SOIsd, MEIed, PDOed => SPI9sd, SPI12sd, PDSIed0.83 Ainsworth Ainsworth SOIsd, MEIed, PDOed => SPI9md, SPI12md SOIsd, MEIed, PDOed => SPI9md, SPI12md0.83 Alliance Alliance SOIed, MEIed, PDOmd => SPI6md, SPI12md SOIed, MEIed, PDOmd => SPI6md, SPI12md0.75 Hayes Center Hayes Center SOIsd, MEIed, PDOed => SPI12md, PDSIed SOIsd, MEIed, PDOed => SPI12md, PDSIed0.83 West Point West Point SOIsd, MEIed, PDOed => SPI6md, SPI9sd, PDSIed SOIsd, MEIed, PDOed => SPI6md, SPI9sd, PDSIed0.86 Local Drought Conditions Global Ocean Conditions  ed = extremely dry; sd = severely dry; md = moderately dry Tadesse & Harms

24 Predicting Phenological Development in Winter Wheat Xue, Wiess, Baenziger, 2004 Xue, Wiess, Baenziger, 2004 Streck, Weiss, Xue, Baenziger, 2003 Streck, Weiss, Xue, Baenziger, 2003 Calculate daily development rate based on Calculate daily development rate based on Temperature Temperature Vernalization response function Vernalization response function Photoperiod Photoperiod Used to predict flowering dates with good accuracy (RMSE 5-6 days) 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? 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? In other words, how do we tie climate data to phenological data?

27 Wheat Flowering Date Trends Kharkov Scout

28 Wheat Flowering Dates & Temperature Kharkov Scout

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

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


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