Recent trends in crops water productivity across the contiguous states

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. USGS (2005)IPCC-AR5
Climate Change and Water Use in the U.S.

3. Williams et al. (2015)
California Drought 2012-2016

4. North American Drought 2012-2013

5. Funk and Brown (2009)
Jung et al. (2010)
WARMER
June et al. (2004)
Globally

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. 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. 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. 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. 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. 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. 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. 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. Eddy Covariance Flux Tower Data (OWL)

15. Eddy Covariance Flux Tower Data (MOD17)

18. 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

19. 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)

20. 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)

21. Cropscape 30m (2015)

22. OWL MOD17 OWL – MOD17
NPP (Fixed Season = mid-May to late-October)MeanStdev

23. 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

24. 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

25. 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

26. WP1 Trends (2001-2015)
kg•m-3
32.0
-12.2

27. Yield
ETC
Alfalfa
Irrigated
Rainfed
Combined

28. Irrigated
Rainfed
Combined
Yield
ETC
Rice

29. Corn
Irrigated
Rainfed
Combined
Yield
ETC

30. 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

31. 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

32. Thank You