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 OklahomaAnton Vrieling, ITC, NetherlandsMichael Marshall, USGS Flagstaff AZ

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

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

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

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

Results, 2010 paper

Percent of region with significant trends duringperiod

North Atlantic OscillationPacific Decadal OscillationMultiple ENSO IndexIndian Ocean Dipole

Brown, de Beurs,Vrieling, RSE 2010

Climate Influences – 2010 RSE paperImpact 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

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

Significant annual trends in phenology parameters

Blue – positive trend (shorter) Red – negative trend (longer) Length

Brown, de Beurs, Marshall, RSE 2010

Significant annual trends in phenology parameters

Blue – positive trend (earlier) Red – negative trend (later)

Start of season

Brown, de Beurs, Marshall, RSE 2010

Phenology trends and Agricultural Production

AGDD = Temp.

Arhum = Moisture

Overview map showing which model results in phenological metrics 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.

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