Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Recent trends in crops water productivity across the contiguous states

552 views

Published on

Recent trends in crops water productivity across the contiguous states

Published in: Science
  • Be the first to comment

  • Be the first to like this

Recent trends in crops water productivity across the contiguous states

  1. 1. January 26, 2017 Michael Marshall World Agroforestry Centre United Nations Ave, Gigiri P.O. Box 30677-00100 Nairobi, Kenya m.marshall@cgiar.org Recent trends in crop water productivity across the contiguous United States: a call for “more crop per drop”
  2. 2. USGS (2005)IPCC-AR5 Climate Change and Water Use in the U.S.
  3. 3. Williams et al. (2015) California Drought 2012-2016
  4. 4. North American Drought 2012-2013
  5. 5. Funk and Brown (2009) Jung et al. (2010) WARMER June et al. (2004) Globally
  6. 6. Blue-Green Revolution ▪ Crop type and variety ▪ Surface/groundwater coordination ▪ Precision agriculture ─ Deficit irrigation ─ Drip irrigation ─ Irrigation scheduling ─ Soil salinity ▪ Integrated assessments ▪ Water markets or tax
  7. 7. WP1 = Total Dry Matter (kg) Cumlative Transpiration (m3) WP2 = Grain or Seed Yield(kg) Cumlative Transpiration(m3) Water Productivity (WP) Ali and Talukder (2008) define crop WP in terms of total dry matter (net primary production-NPP) and yield: Rain-fed water productive crops assimilate more carbon, while losing less water to the atmosphere. Irrigated crops are typically on a deficit schedule, so crop yield is more appropriate.
  8. 8. Objectives Determine green, blue, and overall trends in WP for major crops in the U.S. (alfalfa, corn, cotton, rice, sorghum, and soy):  Parameterize models with high resolution Earth observation and climate geospatial data over the primary growing season  Quantitative assessment of WP1 (2001-2015)  Qualitative assessment of WP2 (2008-2015)
  9. 9. Crop Yield Model Development Light-Use or Production Efficiency Models (PEMs) are a compromise between simple empirical models and fully mechanistic models (e.g. APSIM) Process-based Transferable Climatic constraints (explicit) Non-climatic (implicit)
  10. 10. Crop Yield Model Development PEMs perform best for forests and more poorly for croplands/ecosystems. Based on Challinor et al. (2009) we optimized the approach for croplands:  Sensitivity Analysis  Rigorous model calibration (light- and water-use literature)  Multi-scale evaluation with MOD17 (Running et al., 2004)
  11. 11. Sensitivity Analysis One-parameter at a time approach on model inputs: PAR, NDVI, VPDX, and TX Parameter Description Equation Citation GPP MAX Maximum daily gross primary production ᵋMAX * FPAR * FM * FT * FA * PAR F PAR Fraction of photosynthetically active radiation 1.257 * NDVI - 0.161 Bastiaanssen et al., 2003 F M Short-term moisture stress min (1, 1 / √ VPDX) Zhou et al., 2014 F T Temperature stress 1.1814 / ((1 + e0.2 * (Topt - 10 - Tx) ) (1 + e0.3 * (Tx - 10 - Topt) )) Potter et al., 1993 F A Long-term (seasonal) moisture stress FPAR / FPAR, MAX Fisher et al., 2008 ᵋMAX Maximum quantum conversion efficiency C3 crops: 0.08 * (CA - Γ) / (CA + 2Γ) Collatz et al., 1991 C4 crops: 0.06 Collatz et al., 1992 Station Parameter B1 (gCO2 m -2 d -1 ) B0 (gCO2 m -2 d -1 ) US NE-1 TX (< 0) 16.65 35.73 TX (> 0) -17.96 36.94 (-0.65) VPDX -9.55 36.84 PAR 23.56 34.38 NDVI 17.43 25.66
  12. 12. Calibration The baseline model was most sensitive to PAR (FA), NDVI (FPAR), VPDX (FM), and TX (FT) Bastiaanssen et al. (2003), Running et al. (2004), and Potter et al. (2003) led to overall best performance for C3 and C4 crops
  13. 13. Optimized Water-Light use (OWL) model Marshall, M., Tu, K.P., Brown, J., 2017. Light- and water-use efficiency model optimization for large-area crop yield estimation. Remote Sens. Environ. (under review)
  14. 14. Eddy Covariance Flux Tower Data (OWL)
  15. 15. Eddy Covariance Flux Tower Data (MOD17)
  16. 16. GPP → NPP → Yield (Regional Assessment) GPP minus respiration costs were used to estimate net photosynthesis (Pn): Pn was summed over a fixed growing season (mid-May to late- October) to estimate NPP. NPP was converted to yield (Y) using the harvest index (HI), root- to-shoot ratio (RS), and seed moisture content (MC) 𝐘 = 𝐢=𝐒𝐎𝐒 𝐧 𝐏𝐧𝐢 × 𝐇𝐈 1 + 𝐑𝐒 × 1 1 − 𝐌𝐂
  17. 17. Climate Inputs  DOE interpolated climate fields from NCDC and NRCS stations  CONUS Mosaics  Daily 1km  Temperature and precipitation -- IWD weighted by DEM--(Thorton et al., 1997)  SWIN and VPD from DTR and dew point (Thorton et al., 2000)  2001-2015 Monthly Average MODIS Albedo  RN from albedo, SWIN, DEM, T, VPD, latitude (Allen et al., 1998)
  18. 18. eMODIS (January 2001) 1.00 0.00 Vegetation Input  USGS-EROS MODIS based expedited (eMODIS) NDVI  CONUS Mosaics  Near real-time  7-day 250m  MODIS Land Science Collection 5 Atmospherically Corrected Surface  Optimized Savitsky-Golay filter (Chen et al., 2004)  SAVI approximation (Los et al., 2000)
  19. 19. Cropscape 30m (2015)
  20. 20. OWL MOD17 OWL – MOD17 NPP (Fixed Season = mid-May to late-October)MeanStdev
  21. 21. D = 0.57 RMSE = 1.45 RMSEU = 0.97 RMSES = 1.08 D = 0.63 RMSE = 1.26 RMSEU = 0.66 RMSES = 1.07 D = 0.50 RMSE = 0.55 RMSEU = 0.42 RMSES = 0.36 D = 0.33 RMSE = 0.70 RMSEU = 0.53 RMSES = 0.45 CottonSoybeans OWL MOD17
  22. 22. Major Findings of Optimization Procedure  C3 and C4 partitioning was essential- particularly during green- up and brown-down  FA (soil moisture indicator) was an important improvement- particularly for the C3 pathway  The C4 pathway remains underestimated, but model bias is primarily systematic in nature  Model counters MOD17 bias in non-agroecosystems and should be further explored
  23. 23. Fisher, J.B., Tu, K.P., Baldocchi, D.D., 2008. Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ. 112, 901–919. NASA PT-JPL model
  24. 24. WP1 Trends (2001-2015) kg•m-3 32.0 -12.2
  25. 25. Yield ETC Alfalfa Irrigated Rainfed Combined
  26. 26. Irrigated Rainfed Combined Yield ETC Rice
  27. 27. Corn Irrigated Rainfed Combined Yield ETC
  28. 28. Improvements in Progress  eMODIS Remote Sensing Phenology  MODIS Irrigated Agriculture Dataset for the United States (MIrAD-US)  MODIS Global Food Security- support Analysis Data (GFSAD) crop type 2001-2015
  29. 29. Summary  WP (high to low): Alfalfa, Corn, Soybeans, Sorghum, Cotton, and Rice  WP increases –Mid-West: rain-fed corn and soybeans –Texas: irrigated/rain-fed cotton and sorghum  WP declines  Ogallala Aquifer: irrigated/rain-fed corn and wheat  Central Valley, California and Mississippi: irrigated rice  2012-13 North American Drought  Next step: 30+ year (1982-2012) global assessment
  30. 30. Thank You

×