View
216
Download
0
Category
Preview:
Citation preview
Questions
• Can models successfully predict variations in crop yield due to climate variations over time in a region in which soils, weather, and management vary?
• If so, how can they best be used?
• What are the requirements for this approach?
• Are there examples?
ObjectiveEvaluate approaches for predicting crop yield response to
climate variations, regional scale, using crop models
Hypotheses:
• 1. Use of local data is necessary for accurately predicting yield
• 2. Yield predictions are improved when local yield data are used to adjust estimates of soil properties relative to using general soil inputs
Irmak, Jagtap and Jones. 2002. (In Preparation)
Material and Methods• Region: Tift County, GA• Data:
– 24 years of county yield data. • 17 years for calibration• 7 years for independent validation (randomly selected)
– Yields were de-trended– 0.5 degree soils & weather data from VEMAP database
• Management: Rainfed practices based on NASS data, surveys• Simulations: Average of 9 planting dates, 3 varieties to account
for within county variations• Compare root mean square errors of prediction (independent
data)
Irmak, Jagtap and Jones. 2002. (In Preparation)
Case 1: Base Case, no parameter estimation
• RMSEP = root mean square error for county j• Where m = # of validation years• Yij = Observed yield (kg/ha) for county j, year i• yij = Simulated yield (kg/ha) for county j, year i• j = county• i = year
∑ =−=
m
i ijij yYm
RMSEP1
2)(1
Irmak, Jagtap and Jones. 2002. (In Preparation)
Case 2: Simulated deviations used to predict yield for year I, county j
1
1∑ ==
−=
+⋅=
n
i ijFj
Fj
Fjijij
FjFjijij
Yn
Y
y
yyd
YYdPv Predictions for each independent year
Compute deviations for years for which observed data are available, using simulated values only
Mean observed yield for years for which observed data are available
Irmak, Jagtap and Jones. 2002. (In Preparation)
Case 3: Regression model adjustment of simulated values to account for bias (systematic and non-
systematic)
jk
jkjkijjij bVcyaPv +⋅+= ∑)(
Fitted yield using F dataset
Simulated yield for validation dataset
)(ˆ jk
jkjkijjij bVcyay +⋅+= ∑
• Where aj, bj, cjk = regression coefficients
• Vjk = k variables such as rainfall at planting, and rainfall at harvest
Irmak, Jagtap and Jones. 2002. (In Preparation)
Case 4: Yield correction factor to account for average bias
)( ijij yYcfPv =
Yield Correction Factor = Observed/Simulated
Simulated yield for validation dataset
ij
ij
y
YYcf =
• Where yij = Simulated yield for grid j, year i
• Yij = Observed yield for grid j, year i
Irmak, Jagtap and Jones. 2002. (In Preparation)
Case 5: Combined model-regression approach; simultaneous estimation of soil parameters and
regression coefficients
)(ˆ jjijiiiijjij dRcRb,DULCNyayHP
+⋅+⋅+⋅=
• Where aj , bj and cj= Regression coefficient for grid j,
• yij = Simulated yield with estimated soil parameters for grid j, year i
• RjP and RjH = Cumulative (3 weeks) rainfall at planting and at harvest for grid j, respectively.
• Simulated annealing is used for parameter estimation
Irmak, Jagtap and Jones. 2002. (In Preparation)
Case 1, base case
0
500
1000
1500
2000
2500
3000
3500
4000
1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994
Year
Yie
ld (
kg
/ha
)
M S
Irmak, Jagtap and Jones. 2002. (In Preparation)
Case 1, base case
y = 1.9953x + 329.73
r2 = 0.5176
0
1000
2000
3000
4000
0 500 1000 1500 2000 2500 3000 3500 4000
Measured (kg/ha)
Sim
ula
ted
(kg
/ha)
RMSEP= 1588 kg/ha
Irmak, Jagtap and Jones. 2002. (In Preparation)
Calibration
0
200
400
600
800
1000
1200
1400
1600
1800
1973 1976 1979 1982 1984 1987 1990 1992 1995
Year
Yie
ld (
kg/h
a)
M S
Case 2, Simulate deviations
Validation
0
500
1000
1500
2000
1972 1974 1977 1981 1986 1989 1994
Year
Yie
ld (k
g/h
a)
M
SRMSEvalidation = 348 kg/ha
RMSEfitting = 164 kg/ha
Irmak, Jagtap and Jones. 2002. (In Preparation)
Calibrationy = 0.9432x + 64.251
r2 = 0.7062
Validationy = 0.6611x + 514.4
r2 = 0.2062
0
500
1000
1500
2000
2500
0 500 1000 1500 2000 2500
Measured (kg/ha)
Sim
ula
ted
(kg
/ha)
Calibration
Validation
Case 2:
Irmak, Jagtap and Jones. 2002. (In Preparation)
Calibration
0
500
1000
1500
2000
1973 1976 1979 1982 1984 1987 1990 1992 1995
Year
Yie
ld (
kg/h
a)
M S
Validation
0
500
1000
1500
2000
1972 1974 1977 1981 1986 1989 1994
Year
Yie
ld (
kg/h
a)
M S
RMSEfitting = 167 kg/ha
Case 5, Estimate crop model And regression parameters
RMSEvalidation = 269 kg/ha
Irmak, Jagtap and Jones. 2002. (In Preparation)
Validationy = 1.1034x + 34.366
r2 = 0.6186
Calibrationy = 1.0645x - 127.96
r2 = 0.7681
0
500
1000
1500
2000
2500
0 500 1000 1500 2000 2500
Measured (kg/ha)
Sim
ula
ted
(kg
/ha)
calibration
validation
1:1 line
Case 5:
Irmak, Jagtap and Jones. 2002. (In Preparation)
Preliminary Conclusions
• Variations in yield associated with climate variations over time can be estimated with accuracies on the order of 5-10%
• Use of simulated deviations and historical yields to predict yield variations is a good first approximation (errors on the order of 10-20%)
• Availability of historical yields for an area can improve predictions, reducing errors by half or more
Irmak, Jagtap and Jones. 2002. (In Preparation)
What is Required?
• Soil profile inputs, each grid or polygon
• Daily weather data, each grid or polygon
• Management inputs, each grid or polygon
• Crop model, evaluated using experiment station data for some location(s) in region
And, if available:
• Historical yield data for a number of years
Example for Georgia, USA
• Soybean study by Jagtap et al.
• 18 years of historical yield data (NASS database)
• Daily weather data provided at 50 km grid (VEMAP data base developed by NCAR)
• Dominant soil(s) for each 50x50 km grid cell (VEMAP)
• Management practices (NASS database & state Extension Specialists)
Georgia Soybean Yield Predictions
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
Experience in applying current crop growth models to predict regional productions and its variability is limited.
A number of challenges arise when applying dynamic crop models to regional scales.
The interest is now increasing to use models more and more for policy and decision making at regional scales that are relevant to farmers, grain traders and other decision makers
Georgia Soybean Yield Predictions
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
Known are…
üHistorical county yields
ü50-km digital soil and weather data base
ü<7% of area cultivated
Unknown are..
Spatial variability in
Inputs
outputs
Georgia Soybean Yield Predictions
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
@ Plot level•Soil•Plant Population•Variety•Inputs•Weather
@ Field/Regional•Input Variations•Crop Regions•Planting Calendar•Weather
Georgia Soybean Yield Predictions
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
@ Plot level•Soil•Plant Population•Variety•Inputs•Weather
@ Field/Regional•Input Variations•Crop Regions•Planting Calendar•Weather
Requires perfect aggregation ofPerfect modelsPerfect inputsacross range of variability
Aggregation errors distort spatialMean values of predictionYear-to year variability
To reduce errorsSampling input variability Calibration of inputsCalibration of outputs
Deal with Data Limitations
County Yields vs. Experimental Field Yields, Tifton, Georgia
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
0
1000
2000
3000
4000
5000
6000
86 87 88 89 90 91 92
Year
Yie
ld a
t g
rid
48
(kg
/ha)
Observed
Experimental
Crop Models Simulate Yield at Field Scale:Bias Exists
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
Why Does the Bias Exist?
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
0
1000
2000
3000
4000
5000
6000
86 87 88 89 90 91 92
Year
Yie
ld a
t g
rid
48
(kg
/ha)
Observed
SimulatedExperimental
Yield Gap between ..
ü1. Station and on-farm yields
ü2. Varies from year-to-year
ü3. Simulated yields more
variable due to imperfect
model and aggregation
errors (due to lack of
specific information,
accuracy of inputs,… )
To minimize the errors
•Simulated results and or
inputs must be calibrated
2.Model must be improved
3.Better sources of data
Unadjusted Crop Model Results
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
RMSE ranged from 0.24 to 0.65 t/ha or 16 to 47% of grid yields.RMSE during validation was 38-60% of the 1990-95 mean yields
0
1000
2000
3000
4000
5000
6000
70 75 80 85 90 95
Year
Yie
ld (
kg
/ha
)
ActualUn-Calibrated
Before Removing Bias, High RMSE for Validation Sites
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
Area lost in 1993<10%11 - 25 %>25%
N40 0 4 0 80 K ilo m eter s
RMSE 1993<20%21 - 40 % 41-60 %>60 %
N4 0 0 4 0 80 K ilo m ete rs
After Adjusting for Bias, – Yield Predictions at a Validation Site are Improved
1000
1500
2000
2500
3000
1000 1500 2000 2500 3000
Simulated Yield, kg/ha
Ob
serv
ed
Yie
ld, kg
/ha
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
Independent Validation Site, 10 Years
After Adjusting for Bias, – Yield Predictions at a 2nd Validation Site are Improved
1000
1500
2000
2500
3000
1000 1500 2000 2500 3000Simulated Yield, kg/ha
Ob
serv
ed Y
ield
, kg
/ha
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
Independent Validation Site, 10 Years
Yield Bias Variations
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
Yield correction0.35-0.400.41-0.500.51-0.60
N40 0 40 80 Kilometers
After Adjusting for Bias, RMSE for Independent Years Improved Greatly
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
RMSE<5 %6 - 10 %11 - 16 %
N40 0 40 80 Kilometers
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 0.5 1 1.5 2 2.5 3 3.5
Census Yield (Mg/ha)
Pre
dic
ted
Yie
ld (
Mg
/ha
)
Calibration
Validation
1:1 Line
After Adjusting for Bias, Predictions for Calibration Years and Validation Years
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
0.8
1.0
1.2
1.4
1.6
1.8
0.8 1 1.2 1.4 1.6 1.8
Actual Yield (Mg/ha)
Pre
dic
ted
Yie
ld (
Mg/
ha)
Aggregation Reduced Errors in This Study, Averaged over Fitting and Validation Years
Conclusions
• Yield bias must be removed when using crop models to predict regional yields
• RMSE averaged 14% when predicting yields for independent years, after correcting for bias
• Bias varied considerably across the state of Georgia, implying variations in management and other factors not accounted for in the model
• Other approaches may further reduce bias (see Irmak et al.)
Jagtap and Jones, 2002, Agr. Ecosystems & Env.
Yield Forecast Tool
• Conditioned on:– Climatology
– Climate Forecast
Time
TodayStart of Season
Future – Use climatology or forecastHistory – Use recorded weather
Predicted yield in 1994 for Cerro Gordo Co., IA
using 7 forecast dates
1000
1500
2000
2500
3000
3500
4000
160 180 200 220 240 260 280 300Yield Forecast Date
Yield,
kg/
Maximum Yield
Mean Yield
Minimum Yield
Measured Yield
Maturity Date Range
1994
From W. D. Batchelro et al.
F a m i n e E a r l y W a r n i n g i n B u r k i n a F a s o - - A C a s e S t u d y o f M i l l e t
P r o d u c t i o n• S c o p e f o r i m p r o v e m e n t i n y i e l d f o r e c a s t i n g m e t h o d s f o r mil le t a n d s o r g h u m i n t h e S a h e l
• P o t e n t i a l l y h i g h v a l u e o f i n f o r m a t i o n b e c a u s e o f t h e p o l i c y i m p l i c a t i o n s ( a l l e v i a t i o n m e a s u r e s )
• A p r o o f - o f- c o n c e p t c a s e s t u d y , i n v o l v i n g t h e a s s e m b l y of soi l s , sa te l l i t e ra in fa l l data , c rop m o d e l s , g o v e r n m e n t s t a t i s t i c s
T h o r n t o n e t a l . , 1 9 9 7 . A g r . & F o r . M e t e o r o l o g y 8 3 : 9 5 - 1 1 2 .
S A T E L L I T E - D E R I V E DC R O P U S E I N T E N S I T Y
Provincial AreaPlanted to Millet
P R O V I N C I A L S T A T I S T I C SProduction and Yield
Averages
M I L L E T
M O D E L
S O I L S
Profi le Data
W E A T H E RLong -term
Histor ical Data
P R O V I N C I A L Y I E L D SUnique Combinat ions of
Soi l and Weather
A N O M A L Y T I M E S E R I E S
Dekad
%
16 17 18 19 ...
100
M E T E O S A T I M A G EDekadal RainfallEst imate (RFE)
G E O G R A P H I C I N F O R M A T I O N
S Y S T E M
P R O V I N C I A L P R O D U C T I O N
Season Product ion AnomalyDekadal Estimate of Current
T h o r n t o n e t a l . , 1 9 9 7 . A g r . & F o r . M e t e o r o l o g y 8 3 : 9 5 - 1 1 2 .
How it was Done
R a i n f a l l E s t i m a t e : D e k a d 2 5 , 1 9 9 0
0
1 - 1 0
1 1 - 2 0
2 1 - 3 0
3 1 - 4 0
4 1 - 5 0
5 1 - 6 0
> 6 0
M i l l i m e t r e s
Met. station
T h o r n t o n e t a l . , 1 9 9 7 . A g r . & F o r . M e t e o r o l o g y 8 3 : 9 5 - 1 1 2 .
160 180 200 220 240 260
140
120
100
80
60
40
20
0
RE
LATI
VE
YIE
LD
DAY OF YEAR OF FORECAST
ST
AN
DA
RD
DE
VIA
TIO
N
60
40
20
0
1986 YIELD
1990 YIELD
MEANS
STD DEVS
DORI
19861990
T h o r n t o n e t a l . , 1 9 9 7 . A g r . & F o r . M e t e o r o l o g y 8 3 : 9 5 - 1 1 2 .
S i m u l a t e d M i l l e t Y i e l d A n o m a l i e s , 1 9 8 6
T h o r n t o n e t a l . , 1 9 9 7 . A g r . & F o r . M e t e o r o l o g y 8 3 : 9 5 - 1 1 2 .
Conclusions
• Satellite-derived rainfall, coupled with generated temperature and solar radiation values, have potential for early warning yield forecasts
• Yields forecast at mid season were within 15% of final yields
• Although more work is needed, this approach shows considerable promise
T h o r n t o n e t a l . , 1 9 9 7 . A g r . & F o r . M e t e o r o l o g y 8 3 : 9 5 - 1 1 2 .
Recommended