The Value of ENSO Forecast Information To Dual Purpose Winter Wheat Production In the U.S. Southern...
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The Value of ENSO Forecast Information To Dual Purpose Winter Wheat Production In the U.S. Southern High Plains Steve Mauget USDA-ARS Plant Stress & Water Conservation Lab, Lubbock, TX John Zhang USDA-ARS Grazinglands Research Lab, El Reno, OK Jonghan Ko USDA-ARS Agricultural Systems Research Unit, Ft Collins, CO
The Value of ENSO Forecast Information To Dual Purpose Winter Wheat Production In the U.S. Southern High Plains Steve Mauget USDA-ARS Plant Stress & Water
The Value of ENSO Forecast Information To Dual Purpose Winter
Wheat Production In the U.S. Southern High Plains Steve Mauget
USDA-ARS Plant Stress & Water Conservation Lab, Lubbock, TX
John Zhang USDA-ARS Grazinglands Research Lab, El Reno, OK Jonghan
Ko USDA-ARS Agricultural Systems Research Unit, Ft Collins, CO The
Value of ENSO Forecast Information To Dual Purpose Winter Wheat
Production In the U.S. Southern High Plains Steve Mauget USDA-ARS
Plant Stress & Water Conservation Lab, Lubbock, TX John Zhang
USDA-ARS Grazinglands Research Lab, El Reno, OK Jonghan Ko USDA-ARS
Agricultural Systems Research Unit, Ft Collins, CO
Slide 2
Analog Years Method 1) Given set of analogous years in
historical record marked by a certain forecast condition over a
growing region 1) Given set of analogous years in historical record
marked by a certain forecast condition over a growing region 2) For
each analog year, conduct cropping simulations 3) Repeat
simulations for a range of management practices 4) Determine which
practice is optimally profitable for that forecast condition,
assuming certain price and cost conditions 4) Determine which
practice is optimally profitable for that forecast condition,
assuming certain price and cost conditions Net Profit Distribution
(Best Forecast Practice) Net Profit Distribution (Best Forecast
Practice) P($) $ $
Slide 3
Analog Years Method: Forecast Value Define a second set of
analog years, that include the entire historical record (e.g.
1915-1999) Repeat process 1-4 to define a best management practice
for climatological (i.e., No Forecast ) conditions Form a
distribution of profit outcomes for the forecast analog years,
using the best No-Forecast practice Define a second set of analog
years, that include the entire historical record (e.g. 1915-1999)
Repeat process 1-4 to define a best management practice for
climatological (i.e., No Forecast ) conditions Form a distribution
of profit outcomes for the forecast analog years, using the best
No-Forecast practice Profit Distribution (Best No-Forecast
Practice) Profit Distribution (Best No-Forecast Practice) P($) $
$
Slide 4
Average Forecast Profit Effect Where, = Average profit from
best management practice for the specified forecast condition. =
Average profit from best management practice when no forecast
information is available. Where, = Average profit from best
management practice for the specified forecast condition. = Average
profit from best management practice when no forecast information
is available. = - Profit Distribution (Best Forecast) Profit
Distribution (Best Forecast) Profit Distribution (Best No-Forecast)
Profit Distribution (Best No-Forecast)
Slide 5
NIN-3 ENSO Phase Forecast System Nio 3 Region Correlation of
December-January-Februrary (DJF) Panhandle Rainfall with DJF SSTA
Correlation of December-January-Februrary (DJF) Panhandle Rainfall
with DJF SSTA
Dual Purpose Winter Wheat Production Planting | | | | | | | | |
| | | Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun. Jul. |
| | | | | | | | | | | Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr.
May Jun. Jul. Dormant Period & Grazing Dormant Period &
Grazing Heading & Grain Filling Heading & Grain Filling
Harvesting
Slide 8
Tactical vs. Strategic Forecast Value = - Forecast Value
Distribution Forecast Value Distribution Min Max Median 33 rd % 66
th % Profit Distribution (Best No-Forecast) Profit Distribution
(Best No-Forecast) Profit Distribution (Best Forecast) Profit
Distribution (Best Forecast)
Slide 9
Q: Why Tactical Forecast Value ? Yakima River Valley (1977):
Glantz, M.H., 1982: Consequences and Responsibilities In Drought
Forecasting: The Case of Yakima, 1977, Water Resourc. Res., 18(1),
3-13 Yakima River Valley (1977): Glantz, M.H., 1982: Consequences
and Responsibilities In Drought Forecasting: The Case of Yakima,
1977, Water Resourc. Res., 18(1), 3-13 Zimbabwe (1997): Patt, A.G.
et al., 2007: Learning from 10 Years of Climate Outlook Forums in
Africa, Science, 318, 49-50. Zimbabwe (1997): Patt, A.G. et al.,
2007: Learning from 10 Years of Climate Outlook Forums in Africa,
Science, 318, 49-50. A: To provide a probabilistic Track Record of
the consequences of using forecast information in a single year. A:
To provide a probabilistic Track Record of the consequences of
using forecast information in a single year.
Slide 10
Q: Why Tactical Forecast Value ? A: Seasonal climate forecasts
are probabilistic. The profit effects of forecast information are
also probabilistic There is risk associated with forecast use A:
Seasonal climate forecasts are probabilistic. The profit effects of
forecast information are also probabilistic There is risk
associated with forecast use
Slide 11
Methods: Dual Purpose Simulations DSSAT winter wheat model +
grazing subroutine (J. Zhang) 85 years of simulation (1915-1999) at
3 farm sites using USHCN daily weather records. DSSAT winter wheat
model + grazing subroutine (J. Zhang) 85 years of simulation
(1915-1999) at 3 farm sites using USHCN daily weather records.
Slide 12
Methods: Management Options 5 planting dates: Aug. 24, Sep. 8,
Sep. 23, Oct. 8, Oct. 23. 5 nitrogen (N) application rates: 30, 60,
90, 120, or 150 kg ha-1 applied at planting. 5 stocking rates (SR):
0.5, 1, 1.5 or 2 head ha-1, or no grazing (SR=0.0 head ha-1). 5
planting dates: Aug. 24, Sep. 8, Sep. 23, Oct. 8, Oct. 23. 5
nitrogen (N) application rates: 30, 60, 90, 120, or 150 kg ha-1
applied at planting. 5 stocking rates (SR): 0.5, 1, 1.5 or 2 head
ha-1, or no grazing (SR=0.0 head ha-1).
Slide 13
| | | | | | | | | | | | Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar.
Apr. May Jun. Jul. | | | | | | | | | | | | Aug. Sep. Oct. Nov. Dec.
Jan. Feb. Mar. Apr. May Jun. Jul. 80 Dual Purpose: Grain + Grazing
Profits 25 Grain Only: Grain Profits Only 20 Grazing Only: Live
Weight Gain Profits Only 1 Fallowing Option: Net Profit = $0.0 /
ha
Slide 14
Slide 15
Analog Years: NIN-3 Phase Forecasts Forecast Dry, Normal, &
Wet Years 28 29 28 85 Observed NDJFM Panhandle Precipitation
Observed NDJFM Panhandle Precipitation Dry (< 66 mm) Dry (<
66 mm) Normal Wet (> 96 mm) Wet (> 96 mm) Forecast Wet
Forecast Normal 10 3 1 14 Analog Years Total 13 18 15 46 Analog
Years 5 8 12 25 Analog Years Forecast Dry Predicted NDJFM
Precipitation Via MJJ Nio-3 Predicted NDJFM Precipitation Via MJJ
Nio-3
Slide 16
Analog Years: Perfect Dry, Normal, & Wet Years Perfect Wet
Perfect Normal 28 29 28 85 Observed NDJFM Panhandle Precipitation
Observed NDJFM Panhandle Precipitation Dry (< 66 mm) Dry (<
66 mm) Normal Wet (> 96 mm) Wet (> 96 mm) 28 0 0 28 Analog
Years Total 0 29 0 29 Analog Years 0 0 28 28 Analog Years Perfect
Dry Predicted NDJFM Precipitation Predicted NDJFM
Precipitation
Slide 17
Price & Cost Conditions Wheat Prices $ 3.22 / bu Historical
(1978-2006) Mean $7.00 / bu Elevated Price (Sept. 2007) Wheat
Prices $ 3.22 / bu Historical (1978-2006) Mean $7.00 / bu Elevated
Price (Sept. 2007) Live Weight Gain (LWG) Value $0.75 / kg LWG -
Leased Pasture Rental Rate $2.42 / kg LWG Wheat Producer Owns
Cattle Live Weight Gain (LWG) Value $0.75 / kg LWG - Leased Pasture
Rental Rate $2.42 / kg LWG Wheat Producer Owns Cattle Production
Costs Texas Coop Extension 2007 dryland wheat and cow-calf budget.
Production Costs Texas Coop Extension 2007 dryland wheat and
cow-calf budget.
Slide 18
Four Production Scenarios 1.Historical Wheat Price Leased
Pasture 2.Historical Wheat Price Own Cattle 3.Elevated Wheat Price
Leased Pasture 4.Elevated Wheat Price Own Cattle 1.Historical Wheat
Price Leased Pasture 2.Historical Wheat Price Own Cattle 3.Elevated
Wheat Price Leased Pasture 4.Elevated Wheat Price Own Cattle
Slide 19
Historical Wheat Prices - Leased Pasture Conditions ($ 3.22 /bu
) ($0.75 / kg LWG) No-Forecast Profit ($/hectare) Planting Date
Applied N Stocking Rate Forecast Value ($/hectare) Best Management
Practice By Forecast Condition Best Management Practice By Forecast
Condition Perfect Wet Perfect Normal Perfect Dry Forecast Wet
Forecast Normal Forecast Dry Perfect Wet Perfect Normal Perfect Dry
Forecast Wet Forecast Normal Forecast Dry
Slide 20
Elevated Wheat Prices Leased Pasture Conditions ($ 7.00 bu )
($0.75 / kg LWG) No-Forecast Profit ($/hectare) Planting Date
Applied N Stocking Rate Forecast Value ($/hectare) Best Management
Practice By Forecast Condition Best Management Practice By Forecast
Condition Perfect Wet Perfect Normal Perfect Dry Forecast Wet
Forecast Normal Forecast Dry Perfect Wet Perfect Normal Perfect Dry
Forecast Wet Forecast Normal Forecast Dry
Slide 21
Q: Commodity Price Determines Forecast Value ? No-Forecast
Profit ($/hectare) Planting Date Applied N Stocking Rate Forecast
Value ($/hectare) Best Management Practice By Forecast Condition
Best Management Practice By Forecast Condition Perfect Wet Perfect
Normal Perfect Dry Forecast Wet Forecast Normal Forecast Dry
Perfect Wet Perfect Normal Perfect Dry Forecast Wet Forecast Normal
Forecast Dry No-Forecast Profit ($/hectare) Forecast Value
($/hectare) Perfect Wet Perfect Normal Perfect Dry Forecast Wet
Forecast Normal Forecast Dry Perfect Wet Perfect Normal Perfect Dry
Forecast Wet Forecast Normal Forecast Dry $ 3.22/ bu Wheat $0.75/
kg LWG $ 3.22/ bu Wheat $0.75/ kg LWG $ 7.00/ bu Wheat $0.75/ kg
LWG $ 7.00/ bu Wheat $0.75/ kg LWG
Slide 22
A: Profit Margin Determines Forecast Value $ 7.00/bu, $0.75 /
kg LWG & Production Costs * 2 No-Forecast Profit ($/hectare)
Planting Date Applied N Stocking Rate Forecast Value ($/hectare)
Best Management Practice By Forecast Condition Best Management
Practice By Forecast Condition Perfect Wet Perfect Normal Perfect
Dry Forecast Wet Forecast Normal Forecast Dry Perfect Wet Perfect
Normal Perfect Dry Forecast Wet Forecast Normal Forecast Dry
Slide 23
Value of Best No-Forecast Practices (No-F V) Best Management
Practice For No-Forecast Conditions Best Management Practice For
No-Forecast Conditions Reference Practice Value of Best No-Forecast
Practice Value of Best No-Forecast Practice No-F V = $(Best No-F
Practice) - $(Reference Practice)
Slide 24
General Conclusions Perfect Wet Perfect Normal Perfect Dry
Forecast Wet Forecast Normal Forecast Dry Perfect Wet Perfect
Normal Perfect Dry Forecast Wet Forecast Normal Forecast Dry Profit
Effects of Forecast Information are Probabilistic. Forecast
information may not Pay Off every year.
Slide 25
Summary Profit margins can influence forecast value effects
Value of best no-forecast practices Improved regional forecast
skill may not lead to increased tactical forecast value at the farm
level Profit margins can influence forecast value effects Value of
best no-forecast practices Improved regional forecast skill may not
lead to increased tactical forecast value at the farm level See:
Mauget, S.A., Zhang, J. and Ko, J., 2009: The value of ENSO
forecast information to dual purpose winter wheat production in the
U.S. Southern High Plains. Journal of Applied Meteorology and
Climatology, October 2009. See: Mauget, S.A., Zhang, J. and Ko, J.,
2009: The value of ENSO forecast information to dual purpose winter
wheat production in the U.S. Southern High Plains. Journal of
Applied Meteorology and Climatology, October 2009.
Slide 26
Summary Similar analyses could be done in any area sensitive to
climate-related risk But while seasonal forecasts may re-define
climate related risk they will never eliminate it To ease adoption,
provide a probabilistic track record of how forecast information
re-defines that risk. Similar analyses could be done in any area
sensitive to climate-related risk But while seasonal forecasts may
re-define climate related risk they will never eliminate it To ease
adoption, provide a probabilistic track record of how forecast
information re-defines that risk.
Slide 27
Conclusion (cont.) Mauget, S.A., Zhang, J. and Ko, J., 2009:
The value of ENSO forecast information to dual purpose winter wheat
production in the U.S. Southern High Plains. Journal of Applied
Meteorology and Climatology, October 2009. Mauget, S.A., Zhang, J.
and Ko, J., 2009: The value of ENSO forecast information to dual
purpose winter wheat production in the U.S. Southern High Plains.
Journal of Applied Meteorology and Climatology, October 2009.
Slide 28
Farm Level NDJFM Precipitation By Analog Years Perfect Wet
Years Forecast Wet Years Perfect Wet Years Forecast Wet Years
Perfect Normal Years Forecast Normal Years Perfect Normal Years
Forecast Normal Years Perfect Dry Years Forecast Dry years Perfect
Dry Years Forecast Dry years NDJFM Precipitation (mm)
Slide 29
Forecast Skill ~ Forecast Value? Perfect Wet Perfect Normal
Perfect Dry Forecast Wet Forecast Normal Forecast Dry Perfect Wet
Perfect Normal Perfect Dry Forecast Wet Forecast Normal Forecast
Dry Forecast Value ( $3.22/bu, $2.42/kg LWG)