Transcript Application of seasonal climate forecasts to predict

Application of seasonal
climate forecasts to predict
regional scale crop yields in
South Africa
Trevor Lumsden and Roland Schulze
School of Bioresources Engineering and Environmental Hydrology
University of KwaZulu-Natal, South Africa
Acknowledgements
START, IRI and the Packard Foundation –
training institute and project funding
 Dr Emma Archer – project mentor
 University of KwaZulu-Natal – in kind
support

Objectives




to research methodologies required to produce
crop yield forecasts for small-scale/subsistence
agriculture in South Africa
to evaluate the quality (accuracy) of crop yield
forecasts produced
to assess the potential to apply the crop yield
forecasts to improve crop management
decisions
to make recommendations on future research
and operational needs
Desktop crop simulation study focussing
on assessing the potential to apply climate
forecasts in small-scale/subsistence
agriculture.
Chose to focus on maize, this being the
country’s staple crop.
Study Area
Mean Annual Precipitation
Methodolgy: Climate Forecast
Selection
Several sources of climate forecasts
considered for use in the study – local and
international
 Potentially limiting factors:




lead time (seasonal)
number of seasons archived (for testing)
availability of corresponding obseved data for
downscaling
Methodolgy: Climate Forecast
Selection
Most suitable forecasts:
A set of historical rainfall forecasts
(hindcasts) produced by the South African
Weather Service (SAWS) for validation of
their statistical seasonal rainfall forecast
model (Landman and Klopper, 1998)
Seasonal Rainfall Forecast Regions
Seasonal Rainfall Forecasts
Rainfall Forecast Skill
Rainfall Category Difference = Regional Forecast Category - Catchment Observed Category
Catchment
A91G
B81J
A62F
X24B
B32G
A31G
W22J
V40A
C81F
C52C
R10K
T34K
D41F
V13D
C52L
1981/82 1982/83 1983/84 1984/85 1985/86 1986/87 1987/88 1988/89 1989/90 1990/91 1991/92 1992/93
EN
LN
EN
LN
EN
1
0
1
1
1
0
-2
1
1
-1
0
-1
1
0
1
0
1
-1
-2
1
0
-1
0
-1
1
0
1
0
1
0
-2
1
1
-2
0
0
1
0
-1
-1
1
-1
-1
-1
0
-1
0
0
0
0
-1
0
1
-1
0
0
-1
-2
0
-1
0
0
1
1
1
-1
-1
1
0
-2
0
0
0
0
-1
2
1
-2
-2
-1
0
-1
0
0
1
0
-1
1
1
-1
-1
0
1
-2
0
-1
1
0
1
-1
-1
-1
-2
-1
0
-2
0
-1
-1
0
1
2
1
0
-1
0
1
-2
0
0
2
0
1
1
0
0
-1
1
0
0
0
0
1
0
1
1
0
0
-2
1
1
0
-1
0
2
0
1
2
2
0
-2
1
1
-2
0
0
2
0
1
0
1
-1
-1
0
1
-2
0
-2
2
0
1
2
1
0
-2
0
0
-2
0
0
Percentages
25
33
42
33
50
42
42
25
25
42
58
42
33
33
50
67
58
42
67
42
50
33
67
58
42
33
50
25
42
17
8
8
17
0
8
8
25
8
17
17
8
8
42
25
33
Key
Percentages
20
53
27
100
0
0
0
100
0
27
47
27
13
80
7
47
47
7
7
40
53
33
67
0
47
53
0
13
27
60
93
7
0
60
33
7
- Categories Identical
- Difference of 1 Category
- Difference of 2 Categories
EN - El Niño
LN - La Niña
Methodology: Downscaling Climate
Forecasts
Spatial domain: Forecast region →
Quaternary Catchment (QC)
 Temporal domain: Categorical 4 month
rainfall → daily values
 Different methods considered
 Selected use of historical analogues
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

simple and robust method
data required was available
Methodology: Downscaling Climate
Forecasts
Analogues drawn from 1950/51 – 1979/80
 Therefore 10 analogues per tercile
 Method results in multiple yield outcomes
for a season
 Date of forecast: 1 month prior to planting
 Seasons for forecast evaluation: 1981/82 –
1992/93
 Climate files for crop model prepared using
data from the relevant analogue seasons

Methodology: Crop Yield
Simulation
Applied Ceres-Maize simulation model
 Simulated 9 crop management strategies
representing all combinations of 3 different
planting dates and 3 different plant
populations
 Focussed on these variables as they are
important climate sensitive decisions
 Do forecasts improve strategy selection?

Crop Yield Simulation
Assumed manure fertilizers applied –
typical application rate and nitrogen
content
 Assumed a single crop cultivar that is
recommended throughout maize growing
region. Parameters obtained from the
Grain Crops Institute.

Crop Management Strategies
Simulated
Planting Dates Considered
Strategy Selection Methods
Strategy Selection Methods
Forecast selected strategy varied while the
long term strategy was fixed over the
seasons forecasted
 Benefit: Forecast selected strategy
performs better than long term strategy

Simulating the performance of the
forecast selected and long term strategies
Having identified strategies according to
the two methods, the performance of the
strategies during the forecast seasons
(1981/82 – 1992/93) was simulated using
the observed records from those seasons
Usefulness of Yield Forecasts
Assessed by comparing:
forecast selected strategy yields
vs
long term strategy yields
Frequency with which different crop management
strategies performed better (1981/82-1992/93)
Limpopo
Mpumalanga North West
100%
8
8
90%
Frequency (%)
80%
Kw aZulu-Natal
0
0
25
50
50
60%
83
58
42
50%
40%
10%
0%
58
50
67
67
100
17
30%
20%
50
17
58
Eastern Cape
25
50
70%
Free State
25
75
58
50
33
33
33
17
58
17
33
8
50
25
25
33
17
25
0
0
0
0
0
A91G B81J A62F X24B B32G A31G D41F W22J V40A V13D C81F C52C C52L R10K T34K
Catchments
Forecast selected strategy better
Long term strategy better
Equal performance
Differences in yields obtained from forecast selected and long
term strategies (cases where former outperformed latter)
3000
Limpopo
North
West
Mpumalanga
Kw aZulu-Natal
Eastern
Cape
Free State
Yield Difference (kg.ha -1)
2500
2000
1500
(76%)
1000
(84%)
n=3
n=4
(24%)
n=4
500
(104%)
n=4
(619%)
n=3
(505%)
n=9
(28%)
n=7
(50%)
n=7
0
A91G
X24B
B32G
D41F
W22J
V40A
V13D
n=3
(10%)
C52C
C52L
Catchments
Maximum difference
Mean difference
(70%)
n=2
Minimum difference
T34K
Cases where Forecast Selected Strategies
Outperformed Long Term Strategies on a
Seasonal Basis: KwaZulu-Natal
Conclusions and Recommendations
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Usefulness of the crop yield forecasts, varied across the
catchments with the greatest forecast usefulness being in KwaZuluNatal province.
The yield forecasting methodology should incorporate current
climate forecast formats (terciles with associated probabilities).
If there are an insufficient number of climate forecast seasons for
evaluation purposes, additional climate forecasts should be
generated retrospectively
Strategy selection should also consider risk minimization
practices such as applying a variety of strategies in case a
particular strategy fails. As confidence in yield forecasts grows,
forecast selected strategies could be applied more extensively.
The application of crop yield forecast information in crop
management decisions should be assessed in more detailed case
studies where:
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practical farming constraints can be taken into account
field data should be collected to ensure crop model inputs are realistic
and to verify forecasts
forecasts could be more tailored to the livelihoods of farmers.
Conclusions and Recommendations
Based on project experience (including a
review of forecast information currently
available) three potential applications of
crop yield forecasts to smallscale/subsistence agriculture were
identified for further research and
implementation in the country……
Potential Yield Forecast Applications Identified for
Small Scale Agriculture in South Africa
Potential Forecast Applications
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Production, dissemination and uptake of crop yield
forecasts is likely to be most successful for the first
category of forecast application
The crop yield forecasts produced in this research would
fit into the second category of forecast applications.
These forecasts could be used as a source of
quantitative information in current agricultural advisories
disseminated to farmers via extension services.
The detailed case studies proposed previously would fit
into the third category of forecast application. If the case
studies prove successful, the sites studied could become
demonstration sites showing the value of applying
forecast information in decision-making.
Possible Future Projects
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Detailed case studies: Apply medium to long
term climate forecasts at ± 5 sites to manage
crops. Compare with farmer managed fields.
Consider strategies available/specific to farmer.
Collect observed data.
Apply short to long term climate forecasts in 2-3
river basins, combined with near real time
observed data, to forecast water resources and
crop yields to aid in general water resources
management and agricultural water
management. Pilot study for national forecasting
system.