Uncertainty in Agricultural Responses using Climate Forecast
Climate variability associated with ENSO is now predictable.
This suggests a potential to tailor agricultural management to benefit from climate
variability.
Deterministic/stochastic crop model is recognized method to address climate
sensitivities and are used routinely in decision/management formulation.
Why Uncertainty in a crop model matters?
1. Most times, the only uncertainty considered is climate uncertainty
2. Quantification of uncertainty is required as input to any decision analysis
3. Uncertainty analysis should play a diagnostic role in an assessment to highlight
factors whose uncertainty is pivotal in determining actions
4. We have not put enough efforts to document impacts of uncertainty on decisions
Ho: Uncertainties add another level of complexity in decision making.
When uncertainty is likely to trigger change in a decision?
An evolving research theme in the SECC
Shrikant Jagtap (SECC Team)
Agricultural and Biological Engineering
University of Florida
Uncertainty in Agricultural Responses using Climate Forecast
How uncertainty is currently handled in Ag
Models?
Agricultural and Biological Engineering
University of Florida
Uncertainty in Agricultural Responses using Climate Forecast
How uncertainty is currently handled in Ag
Models?
3000
Nyankpala: 1999
2500
- Spatial / temporal probability inputs
2000
-1
Leaf dry weight (kg ha )
- Field calibration/validation
Fungicide
No Fungicide
Chinese
1500
1000
500
0
175
200
225
250
275
300
275
300
3000
2500
2000
F-mix
1500
1000
500
0
Agricultural and Biological Engineering
175
200
225
250
Day of the year
University of Florida
Uncertainty in Agricultural Responses using Climate Forecast
How uncertainty is currently handled in Ag
Models?
3000
Nyankpala: 1999
2500
- Spatial / temporal probability inputs
2000
9 Drought mitigation
9 Crop mix
9 Risk management/Insurance Products
9 Environmental protection
4-sources of uncertainties
1. Model
2. Downscaling climate forecast
3. Validity of assumptions
4. Scaling-up/ aggregation
Agricultural and Biological Engineering
-1
Examples within the SECC:
Leaf dry weight (kg ha )
- Field calibration/validation
Fungicide
No Fungicide
Chinese
1500
1000
500
0
175
200
225
250
275
300
275
300
3000
2500
2000
F-mix
1500
1000
500
0
175
200
225
250
Day of the year
University of Florida
Uncertainty in Agricultural Responses using Climate Forecast
Case Study: Sensitivity of peanut yields, a major cash crop, on a North Florida
peanut farm in Jackson County. Jackson county has been the
highest peanut growing county in the state.
65-years climate data suggests that higher rainfall and cooler
temperatures are observed during El Nino (14 years), with the
opposite being true in La Nina (16 years).
Virtual simulations used the
PNUT-GRO, a well
calibrated and tested
simulator, using current
management practices in the
region for varieties,
fertilization and sowing dates
(16 April to 12 June at
weekly interval).
Dearing-GA
(
Midville-GA
(
Dublin-GA
Camilla-GA
(
Escambia-FL
(
Vidalia-GA
(
(Tifton-GA
Dixie-GA
(
(
Jackson-FL
Quincy-FL
(
(
(
(
(
((
(
((((
Source : www.AgClimate.org
Agricultural and Biological Engineering
University of Florida
Uncertainty in Agricultural Responses using Climate Forecast
Virtual simulations used the PNUT-GRO model
Georgia-Green Simulated yield
Years
All yrs
65
Avg (kg/ha)
SD (kg/ha)
CV(%)
Nino
14
Avg (kg/ha)
SD (kg/ha)
CV(%)
Nina
16
Avg (kg/ha)
SD (kg/ha)
CV(%)
Neutral
35
Avg (kg/ha)
SD (kg/ha)
CV(%)
16-Apr
23-Apr
2870
1379
48
2792
1672
60
2839
1242
44
2915
1350
46
2937
1393
47
2935
1719
59
2928
1235
42
2942
1360
46
Recommended Dates
1-May
8-May
15-May
22-May
Uncorrected simulated yields
3003
3082
3097
3103
1376
1337
1258
1193
46
43
41
38
3027
3138
3146
3197
1689
1668
1554
1436
56
53
49
45
3072
3179
3128
3204
1210
1193
1163
1150
39
38
36
37
2962
3015
3029
3054
1350
1289
1203
1140
46
43
40
37
29-May
5-Jun
12-Jun
3107
1159
37
3216
1360
42
3055
1115
36
3086
1125
36
3060
1142
37
3219
1313
41
3033
1104
36
3009
1116
37
2742
1301
47
2856
1495
52
2712
1274
47
2709
1269
47
3300
Nino
3200
1. The case study shows potential for
enhancing peanut yields on average
about 4% by tailoring planting
dates to expected ENSO
conditions.
Nina
3100
Neutral
3000
2900
2800
2700
2600
2500
Agricultural and Biological Engineering
12-Jun
5-Jun
29-May
22-May
15-May
8-May
1-May
23-Apr
16-Apr
2400
2. Tailored management calls for
later planting during an El Nino,
and earlier planting in La Nina.
University of Florida
Sources of Uncertainty: 1. Model errors
Sources of uncertainty : The assumed form of model is likley to have substantial effect on the results of analysis .
Georgia-Green Simulated yield
Years
All yrs
65
Avg (kg/ha)
SD (kg/ha)
CV(%)
Nino
14
Avg (kg/ha)
SD (kg/ha)
CV(%)
Nina
16
Avg (kg/ha)
SD (kg/ha)
CV(%)
Neutral
35
Avg (kg/ha)
SD (kg/ha)
CV(%)
All yrs
65
Nino
14
Nina
16
Neutral
35
Avg (kg/ha)
SD (kg/ha)
CV(%)
Avg (kg/ha)
SD (kg/ha)
CV(%)
Avg (kg/ha)
SD (kg/ha)
CV(%)
Avg (kg/ha)
SD (kg/ha)
CV(%)
16-Apr
2870
1379
48
2792
1672
60
2839
1242
44
2915
1350
46
Recommended Dates
23-Apr 1-May
8-May
15-May
22-May
Uncorrected simulated yields
2937
3003
3082
3097
3103
1393
1376
1337
1258
1193
47
46
43
41
38
2935
3027
3138
3146
3197
1719
1689
1668
1554
1436
59
56
53
49
45
2928
3072
3179
3128
3204
1235
1210
1193
1163
1150
42
39
38
36
37
2942
2962
3015
3029
3054
1360
1350
1289
1203
1140
46
46
43
40
37
29-May
5-Jun
12-Jun
3107
1159
37
3216
1360
42
3055
1115
36
3086
1125
36
3060
1142
37
3219
1313
41
3033
1104
36
3009
1116
37
2742
1301
47
2856
1495
52
2712
1274
47
2709
1269
47
2907
1085
37
3058
1247
41
2881
1048
36
2858
1060
37
2330
1106
47
2427
1270
52
2306
1083
47
2303
1078
47
Simulated yields corrected for spotted w ilt virus of peanuts (Ref. Bulletin 1165, UGA, 2003)
2152
2203
2703
2774
3097
2951
3103
1034
1045
1238
1203
1258
1193
1101
48
47
46
43
41
38
37
2094
2201
2725
2824
3146
3056
3197
1254
1290
1520
1502
1554
1436
1292
60
59
56
53
49
45
42
2130
2196
2765
2861
3128
2903
3204
932
926
1089
1074
1163
1150
1059
44
42
39
38
36
37
36
2186
2206
2665
2714
3029
2932
3054
1013
1020
1215
1160
1203
1140
1069
46
46
46
43
40
37
36
Agricultural and Biological Engineering
University of Florida
Sources of Uncertainty: 1. Model errors
Methods for dealing with uncertainty on decision making
a. Hybrid model (s)
Calculate your risk
Risk Index
Peanut variety
10-50
Planting date
5-30
Plant population
5-25
Row pattern
5-15
Tillage
5-15
Insecticide used
5-15
Total Index Value
b. Agricultural Response Team - an inter-disciplinary or agency, effort to effectively
communicating forecast driven decisions and planning for agriculturally-related
activities
c. Participatory development and evaluation of forecast product (i.e. Australian
example)
Source of Uncertainty: Variability in ENSO signal
Agricultural and Biological Engineering
University of Florida
Sources of Uncertainty: 2. Downscaling forecast
10%
Tomato
Percent yield increase
5%
0%
-5%
-10%
-15%
-20%
La Niña
neutral
El Niño
Source of Uncertainty: Variability in ENSO signal
Agricultural and Biological Engineering
University of Florida
Sources of Uncertainty: 2. Strength of ENSO
Success in long-lead seasonal forecasts has led to, often unrealistic expectations. The
fact remains that climate predictions have modest skill, and in many instances no
or marginal skill, even with strong ENSO signal.
100
100
NEUTRAL
NIÑO
1983
1998
80
Probability of exceedance (%)
80
Probability of exceedance (%)
NEUTRAL
NIÑA
60
40
60
40
1992
20
1988 20
0
0
0
1000
2000
3000
4000
5000
Yield (kg/ha)
Agricultural and Biological Engineering
1999
0
6000
2000
4000
6000
Yield (kg/ha)
University of Florida
8000
Sources of Uncertainty: 2. Downscaling forecast
Methods for dealing with uncertainty on decision making
a. Enhance reliability of coupled ocean-atmospheric models
Agricultural and Biological Engineering
University of Florida
Raw forecast (95% CI r>0.065,n=906)
Forecast monthly rainfall (mm)
RMSE=195 mm
MBE =132 mm
d
=0.31
800
Raw forecast, r=0.009
600
400
200
Agricultural0and Biological Engineering
University of Florida
K-NN method corrected forecast (95% CI r>0.065,n=906)
RMSE= 66 mm (Raw=195 mm and MBC=87 mm)
MBE = -0.5 mm
d
= 0.74 (Raw=0.31 and MBC=0.52)
Forecast monthly rainfall (mm)
500
k-NN corrected forecast,
r=0.57
400
300
200
100
0
0
50
100
150
200
250
300
Measured monthly rainfall (mm)
Agricultural and Biological Engineering
350
University of Florida
400
Sources of Uncertainty: 2. Variability in ENSO strength
Methods for dealing with uncertainty on decision making
b. Integrated assessment with coupled ocean-atmospheric & crop models
Maize yield, T/ha
14
12
10
8
6
4
2
0
1988 1991 1993 1995 1997 1999
Climatology
Recorded
Agricultural and Biological Engineering
Forecast
University of Florida
Sources of Uncertainty: 3. Error of judgment (assumptions)
Value of climate forecast to users
Within in the SECC and elsewhere, it has been shown that climate forecasts have
tangible economic value for users.
Assumptions/hypothesis
1. the user must exercise the prescribed process over many years in a disciplined
and consistent manner.
2. The user’s decision-making environment remains static for entire period,
3. The user does not abandon use of the forecast, even it the forecast was wrong in
several successive years.
Let’s see what happens when a grower in Jackson county modifies planting date
according to ENSO phase forecast we developed earlier……
Agricultural and Biological Engineering
University of Florida
Sources of Uncertainty: 3. Validity of assumptions
May-29
-46
0
168
0
232
0
199
0
0
0
0
108
0
-333
0
0
167
451
-89
187
0
0
0
0
0
-74
37
57
0
-186
0
172
375
362
-130
-209
Recommendation: Ideal planting window: Mid-May
1500
Value=Forecat - NoForecast
May-08
146
-288
188
-490
11
685
596
831
251
256
-647
-93
174
680
-419
279
390
-408
218
-54
173
-238
-584
89
-257
-114
-214
698
-411
35
232
627
145
-197
725
282
May-08
1000
500
0
-500
-1000
-1500
600
Value=Forecast - NoForecast
Year Forecast
3335
1939
2406
1940
3837
1941
2761
1942
3817
1943
3047
1944
3626
1945
4880
1946
2519
1947
3268
1948
5607
1949
3993
1950
2508
1951
1789
1952
2683
1953
1309
1954
3970
1955
4632
1956
1846
1957
3373
1958
2148
1959
3328
1960
3465
1961
1828
1962
4498
1963
5258
1964
2373
1965
2588
1966
3407
1967
2529
1968
3570
1969
2887
1970
4938
1971
1006
1972
2132
1973
2097
1974
May-29
400
200
0
-200
-400
-600
1939 1944 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999
Agricultural and Biological Engineering
University of Florida
Sources of Uncertainty: 3. Error of judgment (measurement)
Methods for dealing with the psychology of users
a. How resilient is the user? – risk aversion/utility theory?
b. How many times, before the user abandons use of forecast?
c. Take into account these uncertainties to provide a sense of what skill levels are
necessary to increase chance that even relatively short sequence of decisions will
be of value.
Value=Forecat - NoForecast
1500
May-08
1000
500
0
-500
-1000
-1500
Agricultural and Biological Engineering
University of Florida
Sources of Uncertainty: 4. Scaling-up / Aggregation with plot-scale models
There are clear advantages to adopting plot-scale models to regional scale, as
recommendations and policies are generally implemented at this scale.
SECC – wants to produce forecasts of
agricultural responses at watershed and
state scales using climate forecasts, and
need to know the forecast uncertainty
In applying a field scale model over space
may require perfect aggregation of effects
of all possible input combinations over
geographic space and probability space.
What sort of uncertainties are introduced
by this mismatch of scales?
Agricultural and Biological Engineering
University of Florida
Sources of Uncertainty: 4. Scaling up plot-scale models
Ho: The use of probabilistic inputs will underestimate risks that producers face!
April-16
April-23
May -1
May-8
15-May
Ma y 22
May 29
June-05
June-12
Mean
SE
16-Apr
p-va lue
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
NIÑA
NEUTRAL
NIÑO
NEUTRAL
NIÑA
NEUTRAL
NIÑA
NEUTRAL
NEUTRAL
NEUTRAL
NEUTRAL
NIÑA
NEUTRAL
NIÑO
NEUTRAL
NEUTRAL
NIÑA
NIÑA
NIÑA
NIÑO
NEUTRAL
NEUTRAL
NEUTRAL
NEUTRAL
NEUTRAL
NIÑO
NIÑA
NIÑO
NEUTRAL
NIÑA
3755
3066
2431
4615
3150
1440
2008
3420
2694
1200
5429
2436
2115
475
2717
644
1801
4443
1965
4052
2713
3324
4102
2471
4973
4166
2878
1341
3806
3104
3457
3152
3018
4448
3333
1720
2312
3427
2537
1694
5989
2709
1928
553
2833
693
2230
4589
1811
3808
2477
3534
4151
2368
5007
4746
2934
1480
3862
2897
3235
2931
3373
3814
3508
2093
2601
3697
2347
2451
6230
3445
2110
770
3137
839
2832
4963
1683
3508
2270
3566
4019
2010
5107
5069
2863
1684
3937
2674
3189
2694
3649
3252
3806
2362
3030
4049
2269
3012
6254
4086
2335
1110
3102
1029
3580
5040
1629
3427
1975
3565
4049
1739
4755
5372
2588
1889
3818
2494
3335
2091
3744
2749
3817
2944
3626
4178
2591
3482
6080
3993
2484
1526
2688
1103
3970
4632
1846
3069
1982
3518
3845
1496
4060
5614
2373
2114
3570
2529
Agricultural and Biological Engineering
3186
1921
3697
2219
3740
3754
3968
4702
2905
3891
5876
4006
2604
2282
2521
1090
4244
4205
1687
2800
2230
3369
3509
1681
3144
5546
2323
2461
3368
2419
3585
1828
3372
1922
3382
4662
4111
4880
3133
4144
5607
3923
2761
2968
2508
969
4181
3885
1280
2530
2508
3268
3328
1679
3220
5331
2715
2715
3407
2336
4125
1488
3373
1788
3268
4821
4173
4812
3673
4029
5407
3522
2779
3179
2220
884
3955
3574
1154
2132
2757
3352
2919
1619
3282
5258
3140
2887
3455
2529
3979
1057
3174
1446
3074
4595
3704
4477
3816
3852
5292
3136
2586
0
1788
657
3561
3824
1179
1787
3037
3482
2280
1592
3046
5073
3098
2630
0
2685
3539
2247
3314
2917
3453
3154
3281
4183
2885
3084
5796
3473
2411
1429
2613
879
3373
4351
1581
3013
2439
3442
3578
1851
4066
5130
2768
2133
3247
2630
116
249
136
392
94
445
272
192
185
359
123
201
102
381
141
61
295
171
101
255
120
38
213
118
300
149
98
189
412
81
0.050
0.006
0.000
0.001
0.006
0.002
0.001
0.002
0.166
0.000
0.009
0.000
0.010
0.018
0.240
0.002
0.000
0.302
0.003
0.002
0.026
0.007
0.020
0.000
0.008
0.000
0.146
0.002
0.106
0.000
University of Florida
Sources of Uncertainty: 4. Scaling up plot-scale models
Probability that area averaged yield is the same as a site yield
90
Neutral
80
70
Nino
Nina
60
50
40
30
20
10
12-Jun
05-Jun
29-May
22-May
15-May
8-May
01-May
23-Apr
16-Apr
0
Deterministic models with probabilistic inputs will underestimate risks and should be a reason
for caution in using such deterministic analysis.
Agricultural and Biological Engineering
University of Florida
Summary
4-types of uncertainties have been identified
1. Model errors
2. Variability in ENSO
3. Measurement errors
4. Scaling-up/ aggregation
Likely Activities: Methodology development for routine applications to identify high impact
areas for uncertainty
1.Uncertainty can affect decisions.
2. Recognizing impacts require that uncertainty analysis should be an integral part of
forecast recommendation development process
3. It should be probabilistic, as actions may differ from those taken
when only the best estimates are provided to decision makers
4. Past SECC studies will be used to prioritize studies
Agricultural and Biological Engineering
University of Florida
Uncertainty in Agricultural Responses using Climate Forecast
Thanks!
Agricultural and Biological Engineering
University of Florida
350
30
(a)
(b)
25
Wetdays- days/month
Rainfall- mm/month
300
250
200
150
100
Sta tion
50
FSU-GCM
20
15
10
Sta tion
5
FSU-GCM
FSU-RCM
FSU-RCM
0
0
25
35
(d)
15
10
Sta tion
5
FSU-GCM
Maximum Temp- Deg C/day
20
2
Radiation- MJ/m /day
(c)
30
25
20
15
10
Sta tion
5
FSU-GCM
FSU-RCM
FSU-RCM
0
0
Ma r
Apr
Ma y
Jun
Jul
Aug
Agricultural and Biological Engineering
Ma r
Apr
Ma y
Jun
Jul
Aug
University of Florida
Sources of Uncertainty: 4. Scaling up plot-scale models
Methods for dealing with uncertainty
1. Use factorial analysis to simultaneously evaluate the contribution of each factor to the total
uncertainty.
2. Propagation of uncertainty using either addition or multiplication rule for n independent
events
Addition of the N independent variances: Var(f1+f2+…fn) = Var(f1)+…Var(fn)
Multiplication rule: p(f1+f2+…fn) = p(f1)+…p(fn)
Agricultural and Biological Engineering
University of Florida
Key References
1. SECC case studies
2. Morgan MG, and Henrion M, 1990. Uncertainty: a guide to dealing with
uncertainty in quantitative risk and policy analysis. Cambridge University Press,
Cambridge.
3. Katz RW, 2002.Techniques for estimating uncertainty in climate change scenarios
and impact assessment studies, Climate Research, 20:167-185
4. Hammitt JK, Shlyakhter AI, 1999. The expected value of information and the
probability of surprise. Risk Analysis, 19:135-152.
5. Mearns LO, Rosenzweig C, Goldberg R, 1997. Mean and variance change in climate
scenarios: methods, agricultural applications, and measures of uncertainty. Clim
Change 32:257-292
6. Webster MD, Sokolov AP, 2000. A methodology for quantifying uncertaionty in
climate projections. Clim Change 46:417-446.
7. Wilks DS, 1995. Statistical methods in the atmospheric sciences. Academic Press,
San Diego.
Agricultural and Biological Engineering
University of Florida
Sources of Uncertainty: 1. Model errors
Sources of uncertainty : The assumed form of model is likley to have substantial effect on the results of analysis .
Georgia-Green Simulated yield
Years
All yrs
65
Avg (kg/ha)
SD (kg/ha)
CV(%)
Nino
14
Avg (kg/ha)
SD (kg/ha)
CV(%)
Nina
16
Avg (kg/ha)
SD (kg/ha)
CV(%)
Neutral
35
Avg (kg/ha)
SD (kg/ha)
CV(%)
16-Apr
2870
1379
48
2792
1672
60
2839
1242
44
2915
1350
46
Recommended Dates
23-Apr 1-May
8-May
15-May
22-May
Uncorrected simulated yields
2937
3003
3082
3097
3103
1393
1376
1337
1258
1193
47
46
43
41
38
2935
3027
3138
3146
3197
1719
1689
1668
1554
1436
59
56
53
49
45
2928
3072
3179
3128
3204
1235
1210
1193
1163
1150
42
39
38
36
37
2942
2962
3015
3029
3054
1360
1350
1289
1203
1140
46
46
43
40
37
29-May
5-Jun
12-Jun
3107
1159
37
3216
1360
42
3055
1115
36
3086
1125
36
3060
1142
37
3219
1313
41
3033
1104
36
3009
1116
37
2742
1301
47
2856
1495
52
2712
1274
47
2709
1269
47
Tomato wilt virus has a profound effect on when growers plant peanuts. In past years, growers typically planed
peanuts in mid-April. Now, the ideal planting date for peanuts is between May 11-May 25. The risk increases at other
times. The planting date producing highest yield in each forecast category is marked
Ref: The Spotted Wilt Risk Index, Extension Bulletin No. 1165, 2003
The University of Georgia, Athens, Georgia
Agricultural and Biological Engineering
University of Florida