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MODELLING THE FINANCIAL VULNERABILITY OF FARMING SYSTEMS TO
CLIMATE CHANGE
Hamman OosthuizenHamman OosthuizenLouw, DB , Johnston, P , Backeberg, GR , Lombard, JP
Optimal Agricultural Business Systems&
University of Stellenbosch
Cape TownNovember 2012
Content
• Introduction
• Objective
• Methodology
• Modelling Results• Modelling Results
• Conclusions
Introduction
The Fourth Assessment Report of the IPCC (2007) states that “Agricultural
production and food security (including access to food) in many African access to food) in many African
countries and regions are likely to be severely compromised by climate change and climate variability”.
Objective of the study
The development of a conceptual framework to investigate the financial
vulnerability of different farming systems towards climate change towards climate change
(To develop a dynamic model that link climatology, hydrology, crop physiology and
economics at farm level)
Methodology
• Identify study areas (Summer, Winter, Irr, DL)
• Identification of selected case studies
• Primary & Secondary data collection
• Excel Modelling (Base case)
• DLP Whole Farm Modelling (Base)• DLP Whole Farm Modelling (Base)• Simulate case-study (20-year planning horizon)• DLP assume constant prices & technology
• Construct modelling inter-phases
Methodology
• Model verification – compare DLP results
(including interphases) with Excel results
• Impose CC on the farming systems without adaptation to test CC impact on financial vulnerability of the system
• Modeling scenarios• Modeling scenarios• Apsim Crop model scenarios
• Expert Opinion scenarios
Climate change modelling inter-phases
• The Apsim Crop model data - whole-farm
model inter-phase
• The crop temperature & rainfal threshold
whole-farm model inter-phase
• The crop yield & quality whole-farm model • The crop yield & quality whole-farm model
inter-phase
• An inter-phase to generate at random
variation coefficients (to be imposed on base scen & all crops where crop models are not
available)
Crop Climate Thresholds (Expert Opinions)
• C
Global circulation models (GCM’s)
•
GCM’s – Calculation of thresholds events
• C
Crop Climate Thresholds Yield Penalty Factor
• C
Yield scaling due to thresholds exceeded
• C
Allocation of Yield deviation per code
• C
Scaling of yield grading
• C
Random Variation Coefficients
• C
Modelling results: Scenarios
• Base run
• Present GCM’s Expert Opinions (PEO)
• Intermediate GCM’s Expert Opinion (IEO)
• Intermediate GCM’s Apsim Crop models (IACM)
Modelling results: Moorreesburg
1 000ha farm (445ha wheat, 445ha medics & 1300 sheep (ewes))
Modelling results: Moorreesburg
• C
9%140%
30%
8%
7%
135%
135%
33%
37%
Modellin
g re
sults
: Moorre
esburg
•C
6%
8%
10
%
12
%
IRR
(20
% S
tart-u
p D
:A R
atio
)
0%
2%
4%
6%
Base run
CCC Present …
CRM Present …
ECH Present …
GISS Present …
IPS Present (1971 …
CCC Intermediate …
CRM …
ECH Intermediate …
GISS Intermediate …
IPS Intermediate …
CM CCC …
CM CRM …
CM GISS …
CM IPS …
IRR
(20
% S
tart-u
p D
:A
Ra
tio)
Modelling results: Moorreesburg
• C
140%
145%
150%
Cash Flow Ratio (20% Start-up D:A
Ratio)
110%
115%
120%
125%
130%
135%
140%
Ba
se r
un
CC
C P
rese
nt …
CR
M P
rese
nt …
EC
H P
rese
nt …
GIS
S P
rese
nt …
IPS
Pre
sen
t …
CC
C …
CR
M …
EC
H …
GIS
S …
IPS
…
CM
CC
C …
CM
CR
M …
CM
GIS
S …
CM
IP
S …
Cash Flow Ratio (20%
Start-up D:A Ratio)
Modelling results: Moorreesburg
40%
60%
80%
100%
120%
140%
160%
180%
200%
Cash flow ratio norm
Base
CCCPres
CRMPres
ECHPres
GISSPres
IPSPres 140%
160%
180%
0%
20%
1 3 5 7 9 11 13 15 17 19
IPSPres
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200%
1 3 5 7 9 11 13 15 17 19
Cash flow ratio norm
CCCInt
CRMInt
ECHInt
GISSInt
IPSInt
0%
20%
40%
60%
80%
100%
120%
140%
1 3 5 7 9 11 13 15 17 19
Cash flow ratio norm
CMCCC
CMCRM
CMGISS
CMIPS
Modelling results: Moorreesburg
• C
35%40%45%
Highest Debt:Asset Ratio (20% Start-
up Debt Asset Ratio)
0%5%
10%15%20%25%30%35%
Ba
se r
un
CC
C P
rese
nt …
CR
M P
rese
nt …
EC
H P
rese
nt …
GIS
S P
rese
nt …
IPS
Pre
sen
t …
CC
C …
CR
M …
EC
H …
GIS
S …
IPS
…
CM
CC
C …
CM
CR
M …
CM
GIS
S …
CM
IP
S …
Highest Debt:Asset Ratio
(20% Start-up Debt Asset
Ratio)
Modelling results: Moorreesburg
• C
(6,000,000)
(5,000,000)
(4,000,000)
(3,000,000)
(2,000,000)
(1,000,000)
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Base
CCCPres
CRMPres
ECHPres
GISSPres
IPSPres
(2,000,000)
(1,000,000)
0
1 2 3 4 5 6 7 8 9 1011121314151617181920
CRMInt
(7,000,000)
(8,000,000)
(7,000,000)
(6,000,000)
(5,000,000)
(4,000,000)
(3,000,000)
(2,000,000)
(1,000,000)
0
1 2 3 4 5 6 7 8 9 1011121314151617181920
ECHPres
CCCInt
CRMInt
ECHInt
GISSInt
IPSInt
(8,000,000)
(7,000,000)
(6,000,000)
(5,000,000)
(4,000,000)
(3,000,000)
CRMInt
CMCCC
CMCRM
CMGISS
CMIPS
Modelling results: Moorreesburg
• C
50%
60%
70%
Highest Debt:Asset Ratio (40% Start-
up D:A Ratio)
0%
10%
20%
30%
40%
50%
Ba
se r
un
CC
C P
rese
nt …
CR
M P
rese
nt …
EC
H P
rese
nt …
GIS
S P
rese
nt …
IPS
Pre
sen
t …
CC
C …
CR
M …
EC
H …
GIS
S …
IPS
…
CM
CC
C …
CM
CR
M …
CM
GIS
S …
CM
IP
S …
Highest Debt:Asset Ratio
(40% Start-up D:A Ratio)
Modelling results: Moorreesburg
For the wheat growing area of Moorreesburg, the
results show that from a financial point of view a slight
decrease in profitability can be expected, although
farming operations will still be profitable. Farmers
with high debt levels ratios will be more financially with high debt levels ratios will be more financially
vulnerable than those with low debt levels.
Summary & Conclusions• The Model proof to be reliant in determining the
financial vulnerability of farming systems to
climate change.
• The results of the expert opinion models
correlate with the Apsim crop model results
(except grapes – prototype/no quality measurement)(except grapes – prototype/no quality measurement)
• In the absence of reliable crop models – expert
opinion panels can be used to determine crop
critical thresholds
• The quality of the expert panel inputs will
determine the accuracy of the results.
THE END
Thank you !
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