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Global Phenological Response to Climate in Crop Areas using Humidity and Temperature Models

Global Phenological Response to Climate in Crop Areas using Humidity and Temperature Models. Molly E . Brown, GSFC Code 618 Kirsten M . de Beurs , University of Oklahoma Anton Vrieling , ITC, Netherlands Michael Marshall, USGS Flagstaff AZ. Work of interest, outside of FEWS NET….

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Global Phenological Response to Climate in Crop Areas using Humidity and Temperature Models

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  1. Global Phenological Response to Climate in Crop Areas using Humidity and Temperature Models Molly E. Brown, GSFC Code 618 Kirsten M. de Beurs, University of Oklahoma Anton Vrieling, ITC, Netherlands Michael Marshall, USGS Flagstaff AZ

  2. Work of interest, outside of FEWS NET… • SMAP data – 9km global soil moisture data • Launch of mission in 2014 Oct. • UCSB is working to integrate SMAP data into WRSI and other applications • ICESat-2 – high resolution altimetry data • Launch of mission 2016 July • High resolution surface water elevation product • Integration of GeoSFM and Utah Energy Balance models into BASINS, a mapwindow-based program • New hyperspectral data project called FARMS • Diagnosing crop disease and mapping crop type

  3. Global Agriculture and Climate • What is the impact of combined changes in evapotranspiration and temperature on agriculture? • Changes in local food production will have a negative effect on food security in most of the developing world – particularly as commodity prices rise. • Objective: • To estimate changes of agriculturally-relevant growing season parameters in the primary agricultural regions globally over the past 26 years • To determine where temperature and precipitation variability affects agricultural production

  4. Approach • Analysis of interannual variability of phenology metrics: start of season, length of season and peak • Complex Quadratic model using humidity and temperature as inputs r2 = 0.8421 Brown and de Beurs (2008) RSE

  5. Data • GIMMS AVHRR NDVI dataset, 1981-2008 • Accumulated growing degree days (AGDD) and humidity data from the GLDAS dataset, 1981-2008 • Crop masks based on Monfreda harvested area and yields data for all major rainfed crop groups (a total of 175 crops) • Annual rainfed cereal production from 1982-2008 from the UN FAO database

  6. Results, 2010 paper Percent of region with significant trends during period North Atlantic Oscillation Pacific Decadal Oscillation Multiple ENSO Index Indian Ocean Dipole Brown, de Beurs, Vrieling, RSE 2010

  7. Climate Influences – 2010 RSE paper Impact of North Atlantic Oscillation patterns on Start of Season Impact of Multivariate ENSO Index (MEI) patterns on Start of Season Impact of Pacific Decadal Oscillation patterns on Cumulative NDVI Brown – negative corr. Green – positive corr. Brown, de Beurs, Vrieling, RSE 2010

  8. Results – 2012 RSE paper • 19% of all cropped pixels had a significantly longer growing period during the 24 years by 2.3 days/year. • 8% of all cropped pixels had a shorter growing period by 3.2 days/year, mostly in regions that were arid or semi-arid. • 23% (13% positive and 10% negative)of the land surface demonstrated a correlation between rainfed cereal production statistics by country with the length of the growing period • 13% (8% positive and 5% negative) of all cropped area had a statistically significant correlation between the length of the growing season, cereal production and fertilizer Brown, de Beurs, Marshall, RSE 2010

  9. Significant annual trends in phenology parameters Blue – positive trend (shorter) Red – negative trend (longer) Length Brown, de Beurs, Marshall, RSE 2010

  10. Significant annual trends in phenology parameters Blue – positive trend (earlier) Red – negative trend (later) Start of season Brown, de Beurs, Marshall, RSE 2010

  11. Phenology trends and Agricultural Production Overview map showing which model results in phenologicalmetrics that best correlate with production statistics. For each cropland pixel in each country/state or region the model type is shown that reveals the most correlated pixels with production. All countries shown have 25% of pixels correlated. AGDD = Temp. Arhum = Moisture

  12. Conclusions • Significant correlations between peak, length and start of the agricultural growing season with rainfed cereal production demonstrate the vulnerability of the agricultural system to local climate conditions • How to transform this research into guidance for policy? • Phenology models are not very flexible and do best when they have a complete year to derive seasonal metrics

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