Climate Change Adaptation: Perspectives on Food Security in South Africa - Towards an Integrated Economic Analysis

 
Climate Change Adaptation: Perspectives on Food Security in South Africa - Towards an Integrated Economic Analysis
Climate Change Adaptation: Perspectives
    on Food Security in South Africa
         Towards an Integrated Economic Analysis

                      REPORT No. 5 FOR THE
LONG TERM ADAPTATION SCENARIOS FLAGSHIP RESEARCH PROGRAM (LTAS)

                               i
Climate Change Adaptation: Perspectives on Food Security in South Africa - Towards an Integrated Economic Analysis
Table of Contents

Executive summary ............................................................................................................................... vii
1      Introduction .................................................................................................................................... 1
2      Methodology................................................................................................................................... 4
    2.1        The sector model .................................................................................................................... 4
    2.2        Consumer Impact study .......................................................................................................... 5
    2.3        Climate Data............................................................................................................................ 7
3      Climate Change Impacts on Production and Prices ........................................................................ 9
    3.1        Developing the LTAS BASE 2030 projections .......................................................................... 9
    3.2        Comparing the LTAS scenarios to the base ........................................................................... 17
       3.2.1          Maize ............................................................................................................................. 17
       3.2.2          Wheat............................................................................................................................ 20
4      Consumer Impact Study ................................................................................................................ 23
    4.1        Approach 1: The ‘BFAP Poor person’s index ......................................................................... 23
    4.2        Approach 2: Staple food expenditure pattern based analysis .............................................. 25
       4.2.1          Annual household food expenditure implications........................................................ 25
       4.2.2          Energy intake implications of an constrained food budget .......................................... 25
    4.3        Conclusion: ............................................................................................................................ 26
5      Climate Change Impacts on Agricultural Employment ................................................................. 26
6      Food security and adaptation responses ...................................................................................... 29
    6.1        Small scale agriculture and food security ............................................................................. 29
    6.2        Continual adaptation and technology .................................................................................. 31
    6.3        Irrigation................................................................................................................................ 32
    6.4        Expansion of area planted .................................................................................................... 33
7      High-level messages and Policy recommendations ...................................................................... 34
8      Future research needs with links to future adaptation work and modelling capacity ................. 36
Annexes ................................................................................................................................................. 39

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Climate Change Adaptation: Perspectives on Food Security in South Africa - Towards an Integrated Economic Analysis
List of figures
Figure 1: The BFAP modelling system ..................................................................................................... 5
Figure 2: Rainfall for maize production: Historical and forecast .......................................................... 10
Figure 3: SA main field crop area .......................................................................................................... 11
Figure 4: Base - White maize production, consumption and yield ....................................................... 12
Figure 5: White maize price and trade space ....................................................................................... 13
Figure 6: Base - Yellow maize production, consumption and yield ...................................................... 13
Figure 7: Base Yellow maize price and trade space .............................................................................. 14
Figure 8: Base - Wheat production, consumption and yield ................................................................ 15
Figure 9: Base - Wheat price and trade space ...................................................................................... 15
Figure 10: South African Maize: Area Planted and Yield (5 year moving averages) ............................. 16
Figure 11: Precipitation influencing maize production– base versus alternative scenarios ................ 17
Figure 12: Base versus scenarios: white maize average yield .............................................................. 18
Figure 13: Base versus scenarios: white maize average SAFEX prices .................................................. 18
Figure 14: Stochastic precipitation – Base versus MPI 4.5 ................................................................... 19
Figure 15: Stochastic white maize price – Base versus MPI 4.5 ........................................................... 19
Figure 16: South African Wheat Production and Domestic Use ........................................................... 20
Figure 17: The BFAP Poor person’s index – Projected results .............................................................. 24
Figure 18: Cost share contributions of maize meal within the weighted five item food plate ............ 24
Figure 19: BFAP Employment matrix .................................................................................................... 27
Figure 20: Percentage Changes - Maize Employment .......................................................................... 28
Figure 21: Percentage Changes - Wheat Employment ......................................................................... 28
Figure 22: Reason for engaging in Agriculture ...................................................................................... 30

List of Tables
Table 1: Yield effects (% change from 2000 to 2050) by crop and management system ...................... 1
Table 2: Price outcomes of the overall scenarios ................................................................................... 2
Table 3: Composition of the BFAP Poor person’s index ......................................................................... 6
Table 4: LTAS Scenario Representative Models (2040-2050) ................................................................. 8
Table 5: Macro-economic baseline assumptions.................................................................................... 9
Table 6: Poor person food basket cost and composition ..................................................................... 23
Table 7: Compounded annual growth rates in employment ................................................................ 28
Table 8: Household sources of income, 2010 ....................................................................................... 29
Table 9: South African households’ access to agricultural land, 2010.................................................. 30
Table 10: Agricultural irrigation: Area and Method.............................................................................. 33

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Climate Change Adaptation: Perspectives on Food Security in South Africa - Towards an Integrated Economic Analysis
List of abbreviations
ACCESS        Australian Community Climate and Earth-System Simulator
BFAP          Bureau for Food and Agricultural Policy
CF            Carbon Dioxide Fertilisation
CSIRO         Commonwealth Scientific and Industrial Research Organization
DSSAT         Decision Support System for Agrotechnology Transfer
FAO           Food and Agricultural Organisation
FAPRI         Food and Agricultural Policy Research Institute
IES           Income and Expenditure Survey
IFPRI         International Food Policy Research Institute
GAM           Generalized Additive Models
IMPACT        Model for Policy Analysis of Agricultural Commodities and Trade
IPCC          Intergovernmental Panel on Climate Change
IPCC AR4      Intergovernmental Panel on Climate Change Fourth Assessment Report
IPCC AR5      Intergovernmental Panel on Climate Change Fifth Assessment Report
LTAS          Long Term Adaptation Scenario’s
MPI           Max Planck Institute for Meteorology
NAMC          National Agricultural Marketing Council
NCAR          National Centre for Atmospheric Research
NoCF          No Carbon Dioxide Fertilisation
OECD          Organisation for Economic Co‑operation and Development
RCP           Representative Concentration Pathways
SAWIS         South African Weather Information Service
StatsSA       Statistics South Africa
UCS           Union for Concerned Scientists
WAS           Water Management System

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Climate Change Adaptation: Perspectives on Food Security in South Africa - Towards an Integrated Economic Analysis
1   Acknowledgements
 2
 3   The Long-Term Adaptation Flagship Research Programme (LTAS) responds to the South African
 4   National Climate Change Response White Paper by undertaking climate change adaptation research
 5   and scenario planning for South Africa and the Southern African sub-region. The Department of
 6   Environmental Affairs (DEA) is leading the process in collaboration with technical research partner
 7   the South African National Biodiversity Institute (SANBI) as well as technical and financial assistance
 8   from the Gesellschaft für Internationale Zusammenarbeit (GIZ).
 9
10   DEA would like to acknowledge the LTAS Phase 1 and 2 Project Management Team who contributed
11   to the development of the LTAS research and policy products, namely Mr Shonisani Munzhedzi, Mr
12   Vhalinavho Khavhagali (DEA), Prof Guy Midgley (SANBI), Ms Petra de Abreu, Ms Sarshen Scorgie
13   (Conservation South Africa), Dr Michaela Braun, and Mr Zane Abdul (GIZ). DEA would also like to
14   thank the sector departments and other partners for their insights to this work, in particular the
15   Department of Water Affairs (DWA), Department of Agriculture, Forestry and Fisheries (DAFF),
16   National Disaster Management Centre (NDMC), Department of Rural Development and Land Reform
17   (DRDLR), South African Weather Services (SAWS).
18
19   Specifically, we would like to extend gratitude to the groups, organisations and individuals who
20   compiled the “Climate Change Adaptation: Perspectives on Food Security in South Africa” report,
21   namely the Bureau for Food and Agricultural Policy (http://www.bfap.co.za), including Prof Ferdi
22   Meyer (University of Pretoria), Jan Greyling (University of Stellenbosch), Gerhard van der Burgh
23   (Bureau for Food and Agricultural Policy [BFAP] analyst), Hester Vermeulen (BFAP analyst), Marion
24   Muhl (University of Pretoria), and Dr Lindsay Trapnell (University of Pretoria); and Dr James Cullis
25   (Aurecon) who provided the rainfall data for the future scenarios analysed in this report.
26   Furthermore, we thank the stakeholders who attended the LTAS workshops for their feedback and
27   inputs on proposed methodologies, content and results. Their contributions were instrumental to
28   this final report.
29
30   Over the past decade Bureau for Food and Agricultural Policy (BFAP) (www.bfap.co.za) has become a
31   valuable resource to government, agribusiness and farmers by providing analyses of future policy
32   and market scenarios and measuring their impact on farm and firm profitability. BFAP is a network
33   linking individuals with multi-disciplinary backgrounds to a coordinated research system that informs
34   decision making within the Food System. The core analytical team consists of independent analysts
35   and researchers who are affiliated with the Department of Agricultural
36   Economics, Extension and Rural Development at the University of
37   Pretoria, the Department of Agricultural Economics at the University
38   of Stellenbosch, or the Directorate of Agricultural Economics at the
39   Provincial Department of Agriculture, Western Cape. BFAP
40   acknowledges and appreciates the tremendous insight of numerous
41   industry specialists over the past decade.

                                                       v
1   Report overview
 2   This report develops preliminary high level messages on the socio-economic and food security
 3   impacts of climate change in South Africa, including consideration of impacts on South African
 4   consumers and employment in the agricultural sector, and implications for adaptation. Findings are
 5   preliminary because they are based on impacts only on the two main staple cereal crops only (maize
 6   and wheat) of a limited set of downscaled LTAS climate scenarios.
 7
 8   The first objective of this study is to translate the selected climate scenarios into maize and wheat
 9   production and price effects. In order to achieve this goal the results of four downscaled climate
10   models were incorporated into an econometric, recursive partial equilibrium model of the South
11   African agricultural sector. Within this model, rainfall, both in terms of the total during the
12   production season and the timing thereof, represent one of a host of variables that impact on the
13   eventual results on total area planted, total production and commodity price changes. These results
14   are compared to a baseline scenario that is generated from historic rainfall data. This study
15   therefore provides an economic perspective on the LTAS climate scenarios which bio-dynamic
16   modelling approaches are unable to do. The results also provide a simulated perspective on how
17   producers choose to respond to climate change in terms of area planted and the resulting impact on
18   the equilibrium of demand and supply in maize and wheat markets.
19
20   The second objective of this study is to incorporate these results into a consumer impact analysis in
21   order to evaluate the price effects of the respective climate scenarios on poor households. Within
22   this analysis three approaches where followed: The first is the ‘BFAP Poor person’s index’ is based on
23   the price of typical portion sizes of poor South African consumers’ typical portion sizes of the five
24   most widely consumed food items in South Africa. The second is the StatsSA Income and
25   Expenditure based analysis. The third is the balanced food plate approach that follows a balanced
26   daily food plate approach.
27
28   The third objective of this study is to evaluate the impact of these possible climate futures on
29   agricultural employment. This was achieved through incorporating the results into the BFAP
30   employment model. Collectively these results are interpreted in terms of their food security impacts,
31   both in terms of supply and access. This is translated into adaptation and mitigation
32   recommendations, policy recommendations and future research needed.
33   Chapter 1 (Introduction) provides an overview previous comparable research and will link with LTAS
34   phase 1, specifically the LTAS phase 1 report (no.3 of 6) on agriculture and forestry.
35   Chapter 2 & 3 (Methodology and Data) delivers an overview of the respective methodologies used in
36   the study and provide an overview of the data used within the relevant models.
37   Chapter 4 (Crop model results) provides the results of the partial equilibrium modelling in terms of
38   the changes in the area planted, total production, trade and price of maize and wheat due to the
39   respective climate scenarios.
40   Chapter 5 (Consumer Impact Study) presents the results of the consumer impact study.
41   Chapter 6 (Employment impacts) provides the results on the possible agricultural employment
42   impacts of the respective climate scenarios.
43   Chapter 7 (Food security and messages) this section concludes with a discussion of the food security
44   impacts, mitigation and adaptation responses, policy recommendations and future research needed.
45

                                                       vi
1   Executive summary
 2
 3   There have been numerous studies on the effects of potential climate change on crop suitability and
 4   productivity from a Southern- and South African perspective. These studies, however, do not provide
 5   an integrated perspective of how the agricultural and food system could be affected under various
 6   climate change scenarios from an economic and social perspective. Therefore a key need is to
 7   translate the respective climate scenarios into economic impacts, both in terms of the food access
 8   (price) perspective of food security but also in terms of the impact on the decision to produce, which
 9   impacts on agricultural employment. This study aims to fill this gap through a stochastic partial
10   equilibrium modelling approach in order to provide high level messages on the impact of climate
11   change on South African maize and wheat production towards 2030.
12
13   Within this study precipitation data generated from various climate models that best describe drying
14   LTAS climate scenarios were evaluated through the BFAP sector model. This sector model can be
15   best described as an econometric, recursive partial equilibrium model of the South African
16   agricultural sector, which presently covers 52 commodities. The results obtained were analysed
17   further from a consumer impact and employment perspective. The objective of this study is to
18   provide high level messages on the potential impact of climate change on food security and
19   employment under a set of assumptions in order to relate these into policy recommendations that
20   will enable adaptation and mitigation. This study has a strong element of foresighting and in order to
21   achieve these objectives, a combination of modelling tools is applied. When undertaking foresighting
22   analyses, it is useful to consider the future stochastic range of key fundamental variables and not
23   only be fixated on the deterministic projections. It is far more important to explore the possible
24   ranges of key variables and how climate change can potentially have an impact on the maize and
25   wheat industries.
26
27   For this study the actual precipitation data for the period 1950 to 2000 was modelled in order to
28   provide a “real world” baseline for 2000 to 2050 to which the results obtained from the inclusion of
29   the precipitation data from the various climate models could be compared. An analysis of the results
30   from the selected climate models only showed a significant divergence after 2030. In other words, it
31   is apparent that for most of the scenarios the effects on precipitation increase towards the end of
32   the simulation period only. Therefore, in order to analyse the potential economic impacts of climate
33   on the South African maize and wheat industries, the precipitation data projected for the period
34   2034 to 2050 is introduced into the BFAP sector model for each of the scenarios, which is then
35   compared to the base case. The BFAP sector model generates absolute and percentage shocks to
36   illustrate the relative deviations from the base. The implication thereof can be best described as the
37   effect of more than 20 years of climate change on the current context. It also became clear that only
38   one of the four climate models showed a significant deviation from the base after the data was
39   allocated according national maize and wheat production regions, both in terms of total
40   precipitation and the timing thereof.
41
42   An overview of the fundamental trends within the base precipitation scenario is essential for a
43   correct interpretation of the modelled results of the respective climate scenarios Under the base
44   scenario South Africa is anticipated to remain a net exporter of white and yellow maize (i.e.
45   domestic supply exceeds demand) and a net importer of wheat, with net imports growing beyond

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1   50% of consumption. Maize prices are therefore expected to remain at export parity levels whilst
 2   wheat prices are will remain at import parity levels. Within this scenario the total area under field
 3   crop production is anticipated to remain relatively stable towards 2030 around the current levels of
 4   around 4.7 million hectares. This does not reflect changes in the allocation of crops within this area,
 5   however, most notably the expected decline of the area planted under white maize from 1.5 million
 6   hectares to 1.1 million hectares and the doubling of the soybean area from around 500 000ha to
 7   more than 1 million hectares. This shift is the result of the expected continuation of the upward
 8   trend in maize yields whilst the demand for white maize is expected to remain flat due to continued
 9   shifts in consumer preferences in favour of bread. The area planted under yellow maize is
10   anticipated to show a small increase whilst the area under sunflower production is expected to
11   remain flat. The area planted under wheat is expected to decline slightly in favour of an increase in
12   canola production. Wheat imports are therefore expected to increase given the growing demand
13   and stable supply. Given this context one can evaluate the modelling results of the respective
14   climate models.
15
16   For the purpose of this study the data for each climate scenario (monthly precipitation per
17   quaternary catchment) was isolated according to the respective production regions in order to be
18   included in the model. One of the most interesting findings of this study is that the drying climate
19   model with the smallest decline in mean annual precipitation (MAP), the warmer/drier (MPI 4.5)
20   model, had the biggest adverse impact on maize and wheat production due to projected local
21   rainfall changes in the production areas. All the other scenarios showed a smaller deviation from the
22   base. This highlights the fact that region-specific agricultural impacts do not necessarily scale directly
23   with national rainfall changes, that probabilistic approaches of multiple scenarios are necessary for
24   comprehensive risk assessment, and that national scenarios require much more considered
25   assessment using aggregated region-specific assessments.
26
27   The modelling results of this warmer/drier (MPI 4.5) scenario shows that white maize yields are
28   anticipated to decline by 1.1 t/ha on average over the outlook period resulting in a drop in total
29   production of approximately 1.6 million tons per annum and an increase in the white maize prices of
30   16 percent. The white maize area harvested is expected to decline as explained above. Within the
31   MPI 4.5 scenario this decline is more than 200 000 hectares less than in the base case, due to
32   farmers opting to produce in response to the higher prices. Adverse climate scenarios therefore can
33   result in an increase in the area planted compared to the base due to price signals. Farmers as
34   rational economic decision makers are not confronted with a simple produce or not to produce
35   decision, but rather face a decision on expanding, contracting or shifting production to other crops
36   given prevailing prices and climate risk.
37
38   South Africa imported more than 40% of all wheat consumed in 2013 and thus the domestic price
39   moves in step with the world (import parity) price. Modelling results of the MPI 4.5 scenario show a
40   decline in domestic production that results in an increase in imports above the magnitude expected
41   in the base. As stated this does not impact on the domestic price but could have a greater negative
42   impact on the agricultural trade balance in comparison to the base scenario. The exports of other
43   agricultural product will therefore have to be increased in order to maintain a positive agricultural
44   trade balance.
45
46   This study evaluated the price impacts of the respective climate scenarios on (mostly poor)

                                                        viii
1   consumers through the use of three instruments – the BFAP poor person’s index, a staple food
 2   expenditure based analysis and a balanced daily food plate models. At present maize porridge and
 3   brown bread contribute 73.5% of costs of a five item low income weighted food plate. In terms of
 4   bread the analysis all the climate scenarios showed no deviation from the base due fact that each of
 5   the scenarios, like the base, tracks the world price throughout. In terms white maize meal the MPI
 6   4.5 scenario showed a small deviation from the base. The significance of this deviation decreases
 7   over time due to a declining trend in white maize production per household in favour of bread.
 8
 9   It has to be stated, however, that the anticipated increase in the price of white maize due to the MPI
10   4.5 scenario is on top of the food basket that is anticipated to increase significantly towards 2030:
11   The BFAP poor person’s index almost doubles in price towards 2025. From a food security
12   perspective this brings the affordability of a balanced food basket prominently to the fore. From a
13   supply perspective farmers have the ability to increase production in response to high prices. In
14   terms of wheat, the country has been and will continue to act as major importer. Food security
15   therefore is not a question of supply but rather of access both in terms of financial means or own
16   supplementary production.
17
18   The maize and wheat industry does not act as major employer and is also not regarded as an
19   industry with a significant growth potential, it estimated that these industries collectively employed
20   less than 7% of the agricultural labour force in 2013. For the period 2014 to 2025 employment in the
21   maize industry is expected decline by 2% within base scenario whereas the least favourable MPI 4.5
22   scenario delivers the smallest decline (-1.1%). This is in part due to the smaller contraction in the
23   area planted in response to the increase in the maize price relative to the base case. This reduction
24   does not necessary equate to a decline in absolute agricultural employment due to the transfer of
25   are to the production of other crops – within the base scenario the area under soybean production is
26   expected to increase from 500 000 to 1 000 000 hectares. Within thin in the wheat industry the
27   converse is true with the greatest expected decline (-2.2%) in the MPI 4.5 scenario, which is twice
28   that of the base scenario.
29
30   The challenges faced due to climate change do not act on producers in isolation but rather form part
31   of the bigger collective challenges. Any adaptation strategy should therefore be directed towards
32   bigger collective of challenges. These strategies also does have to be directed towards a specific
33   group of producers, i.e. small scale or commercial, options should be sought that are scale neutral
34   and that would be beneficial to the industry regardless which climate scenario materialises. Example
35   strategies that meet this criterion include improved transport infrastructure, improvements in
36   irrigation efficiency and water management, coordinated field trials in partnership between farmers,
37   private companies and the state as discussed below.
38
39   It also has to be emphasized that farmers are already adapting to climate change. The reduction in
40   the area under wheat production serves as a good example. Farmers have opted to decrease the
41   area planted by more than half, partly in response to price decreases but also in order to decrease
42   exposure to climate risk such as too low or rainfall during harvest. Farmers are also continually doing
43   formal and informal field trials in order to identify the best suited varieties for each locality. The role
44   foreseen for the public sector is not replace these initiatives but rather to support, expand and
45   integrate the results of these respective trials. Greater cooperation between the farmers, seed
46   companies and state will improve the focus of public research and improve quality of extension

                                                         ix
1   services provided, particularly for small scale farmers in close proximity of these trials.
 2
 3   This study showed that the area under irrigation can be expanded through new investments in
 4   storage capacity. Significant gains are possible, however, within existing systems through decreases
 5   in distribution loses, the adoption of more efficient irrigation systems and improvements in
 6   management of existing irrigation systems. This has the potential of adding a further area of 282 000
 7   hectares under production, simply by using available water. The maintenance of and improvements
 8   to existing irrigation systems is therefore an imperative. Incentives for the upgrading of existing
 9   systems to more efficient alternatives, for example flood irrigation to drip, would be beneficial
10   especially if incentivised by the state; alternatively the amount of water available to consumers
11   could be regulated in order to encourage investments in more efficient systems.
12
13   South Africa have been a net importer of wheat since the early 1990s but also experienced
14   significant increases in agricultural exports since. According to the MPI 4.5 climate scenario wheat
15   imports are expected to increase around 100 000tons due to a decline in production and increase in
16   consumer demand for wheat. Increasingly larger amounts of wheat will therefore have to be moved
17   between sources of supply or the respective ports, and sources of demand. A cost efficient transport
18   system is therefore an imperative in order to ensure the provision of this staple at the lowest
19   possible cost. An improvement in port, rail and road infrastructure is therefore of utmost
20   importance, especially railways. Inversely this infrastructure will also improve the competitiveness of
21   agricultural exports, particularly fruit and wine. These exports as significant earners of foreign
22   exchange currently and will continue to “afford” the substantial and growing primary food stuff
23   imports. The exports of these products will have to be expanded in future in order to maintain a
24   positive agricultural trade balance and avoid the negative effects of exchange deficits. This also
25   includes the improvement of SADC regional road and rail networks in order to enable more cost
26   efficient trade. Zimbabwe is currently one of South Africa’s biggest trade partners. Fruit and wine
27   exports to SADC is expect continue to grow whist Zambia could serve as important trade partner for
28   wheat for example.
29
30   Transport infrastructure is not limited to harbour, rail and main roads, however. A significant
31   number of (mostly rural) households have access to land and that a large percentage of them engage
32   in agricultural production as an additional source of food and to lesser extent as an additional source
33   of income. It is possible, however, that some of climate scenarios could decrease the ability of
34   households to engage in agricultural production as an additional source of food. This would result in
35   an increased need for food shipments to rural areas to address this deficit, which would put existing
36   (mostly poor) rural roads under further pressure. It is therefore essential that these rural roads
37   should be improved and properly maintained in order to increase the efficiency of transported food
38   items to these rural areas. Conversely this improved infrastructure could increase small scale
39   farmers’ ability to engage in agricultural production for income purpose through connecting them to
40   sources of demand and input supply. This is particularly important from the access perspective of
41   food security through ensuring the availability of food at the lowest possible cost or enabling small
42   scale farmers to earn an income in order to be able to purchase food items not produced.
43
44   This study has provided an essential first perspective but it is only a limited one. Maize and wheat
45   are components of a bigger production system wherein producers are continuously making
46   adjustments to the allocation of their resources in order to maximise the return to their labour and

                                                         x
1   capital. A study that follows an integrated approach to a much larger number of commodities is
 2   therefore needed. The expected trend in expanded soybean and canola production serves as a good
 3   example of shits not reflected in this study.
 4
 5   One of the major trends identified is that of a continuation of the increase in wheat imports, a trend
 6   that will be accelerated within some of the climate scenarios. The sector has been able maintain a
 7   positive agricultural trade balance despite the increase in wheat and other imports, mainly due to
 8   the strong growth in fruit and wine exports. A failure to maintain a positive trade balance will result
 9   in depreciation of the Rand that in turn will result in increases in the prices of the large and growing
10   amounts of imported wheat (and rice of which 100% is imported). Ultimately this could reduce
11   access to food due to reduced affordability.
12
13   The fruit and wine sectors are perceived to be under greater risk from climate change due to their
14   sensitivity to temperature increases. Future research on the climate impacts on these crops is
15   therefore very important, and should be linked to an understanding of the economic impacts,
16   including vulnerability to possible punitive tariffs due to the carbon intensity of international
17   transport. Various strategies directed towards the expansion of the competitiveness of these
18   industries are an essential adaptation strategy and a topic also in need of future research.
19
20   The narrow focus of current research on maize and wheat production may be a result of the
21   conflation between food security and food self-sufficiency. Given the current imports of wheat, the
22   inability to increase production and the expected increase in demand puts self-sufficiency clearly of
23   the table. An integrated approach to the availability and affordability aspects of food is therefore
24   much needed in future research.
25

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1   1 Introduction
 2   There have been many studies on the effects of potential climate change on agricultural yields
 3   particularly of maize in South and Southern Africa. However, what these studies lack is an integrated
 4   view of how the agricultural and food system would be affected under climate change due to the
 5   many complex social and economic feedbacks involved. Therefore a key need is to translate the
 6   respective climate scenarios into economic impacts, both in terms of the food access (price)
 7   perspective of food security but also in terms of the impact on the decision to produce, which
 8   impacts on agricultural employment. This study aims to fill this gap through a partial equilibrium
 9   modelling approach in order to provide high level messages on the impact of climate change on
10   South African maize and wheat production.
11
12   The International Food Policy Research Institute (IFPRI) has conducted some of the most
13   comprehensive regional and global studies on the possible impacts of climate change on agricultural
14   production and food security. A study by Nelson et al. (2009) developed possible food security
15   scenarios towards 2050 through the use their International Model for Policy Analysis of Agricultural
16   Commodities and Trade (IMPACT), which incorporates the Decision Support System for
17   Agrotechnology Transfer (DSSAT) biophysical crop simulation model. Within these models they
18   incorporated the results of two climate models in order to generate projections. These where the
19   National Centre for Atmospheric Research (NCAR) model from the USA and the Commonwealth
20   Scientific and Industrial Research Organization (CSIRO) model from Australia. Both assume the A2
21   scenario of the IPCC’s Fourth Assessment and predict a warmer climate (higher temperatures)
22   towards 2050 with an increase in both evaporation and total precipitation. The extent the expected
23   increases in precipitation show a wide disparity, however, with the NCAR model forecasting an
24   average increase of about 10%, compared to CSIRO’s 2%.
25
26   Table 1: Yield effects (% change from 2000 to 2050) by crop and management system
                                                                                   1)                                    2)
     Commodity                    Region                            CSIRO NoCF            NCAR NoCF          CSIRO CF          NCAR CF
                                  Sub-Saharan Africa                      0.3                  0.6                0.5            0.8
     Maize, irrigated
                                  Developing countries                     -2                  -2.8              -1,4           -2.1
                                  Sub-Saharan Africa                      -2.4                 -4.6              -0.8           -2.7
     Maize, rain fed
                                  Developing countries                    0.2                  -2.9               2.6           -0.8
                                  Sub-Saharan Africa                      0.7                  1.4                7.3            9.7
     Wheat, irrigated
                                  Developing countries                   -28.3                -34.3              -20.8          -27.2
                                  Sub-Saharan Africa                     -19.3                -21.9              -11.2          -15.9
     Wheat, rain fed
                                  Developing countries                    -1.4                 -1,1               9.3            8.5
27   Source:   Nelson et al, 2009
28   Notes:    1) Excluding higher atmospheric concentrations of CO2. 2) Including higher atmospheric concentrations of CO2.

29   The scenarios generated by this study are presented in Table 1 ‒ shown for both Sub-Saharan Africa
30   and developing countries, split according to whether the effects of higher atmospheric CO2
31   concentrations (carbon fertilisation, CF) or disregarded (no carbon fertilisation, NoCF) were included.
32   The results show that maize yields in sub-Saharan Africa are not affected to great extent, with
33   irrigated maize yields actually increasing. Yields for rain-fed wheat on the other hand show a decline
34   of between 11.2 to 21.9% in sub-Saharan Africa, however. Also note that the inclusion of carbon

                                                                     1
1   fertilisation results in an increase or lower decrease in production than what is the case with the
 2   without scenario. Ringler et al. (2010) concurs that wheat production will be most affected, their
 3   results show an expected decline of just over 20%.
 4
 5   A global study on food security, farming and climate change by Nelson et al. (2010), based on the
 6   same assumptions and modelling techniques. This study also included world price projects for
 7   staples, as presented Table 2. These results show that real grain prices are not expected to decline as
 8   they have done in most of the 20th century. The price increases vary from 31.2% for rice in the
 9   optimistic scenario to 106.3% for maize in the pessimistic scenario that assumes an increase in the
10   demand for staple food due to the combination of high population and low income growth rates.
11   Ringler et al. (2010) expect increases in the prices of maize, rice, and wheat prices of 4, 7, and 15 per
12   cent above the historic climate scenario.
13
14   Table 2: Price outcomes of the overall scenarios

     Scenarios           Maize               Rice                Wheat               Maize               Rice                 Wheat

                                                                % price change, 2010 mean to 2050 mean
                         % price change, 2010 mean to 2050 mean          with perfect mitigation
     Baseline            100.7         54.8          54.2               32.2          19.8         23.1
     Optimistic          87.3          31.2          43.5               33.1          18.4         23.4
     Pessimistic         106.3         78.1          58.8               34.1          19.5         24.4
15   Source:      Nelson et al, 2010
16   Notes: The percentage increase for the scenarios is the mean across the results for the four cli- mate scenarios, CSIRo and MIRoC GCMs
17   with the SRES A1B and B1 GHG forcings
18
19   All the studies cited above had a sub-Saharan or global focus, but studies on Southern Africa have
20   also been done. Hachigonta et al. (2013) applied the same modelling techniques as Nelson et al.
21   (2010) but assuming the IPCC’s A1B scenario towards 2050. In terms of South African maize
22   production their study projected a decrease in area planted but with an increase in average
23   production per hectare between 1.9 to 2.7 tons/hectare that results in an increase in total
24   production of 0.3 to 2.8 million tons. Three of the four models project significant productivity
25   increases in the North West province, currently one of the biggest maize producing areas but with a
26   low average yield. In the Free State province some areas currently growing maize are projected not
27   to be able to do so whilst others will see the opposite due to change in the current climate. The
28   same trend holds for the Eastern Cape. The models also forecast large areas of increased wheat
29   yields in the Free State and Mpumalanga whilst yields could come under treat in the Western Cape
30   due to decreases in rainfall. The results on the increased wheat yields, especially in Mpumalanga, is
31   somewhat suspicious given the fact that very little wheat is grown in this area, partly due to its
32   disease prone climate and rain during harvest.
33
34   Estes et al. (2013) applied both mechanistic (DSSAT, as utilised above) and empirical (Generalized
35   Additive Models, GAM) models to project the climate change impacts on the potential distribution
36   (suitability) and productivity of maize and spring wheat on a spatial level in South Africa under 18
37   downscaled climate scenarios (9 models run under 2 emissions scenarios) towards 2055. Both
38   models project that the current core maize production areas will remain suitable towards 2055 but
39   not equally so within both models. The DSSAT model projected a 9% gain, primarily along the south

                                                                         2
1   western boundary (north-western Free State) of the baseline, while the GAM model showed a 10%
 2   loss concentrated among the northern and western boundaries (see Map 1). Both models show an
 3   expansion in area suitable for wheat production with the GAM and DSSAT models projecting an
 4   increase of 48 and 20% respectively. The DSSAT model showed an expansion of the suitability into
 5   the interior from the north-west and southeast of the current growing region, whereas GAM showed
 6   gains into the interior along the entire length of its baseline suitability region, see Map 1. In terms of
 7   productivity the models projected of maize and wheat the GAM model projected a change of -3.6%
 8   and 6.2% respectively, compared to the 6.5% and 15.2% respectively by the DSSAT model.

 9
10   Map 1: The change in areas suitable for maize and spring wheat production in 2055

11   Source:      Estes et al. (2013)
12   Notes: Projected by both the Decision Support System for Agrotechnology Transfer (DSSAT) and Generalized Additive Model (GAM) models
13   under the median agreement criterion (at least 9/18 simulations agree regarding future suitability). Plots along the bottom row show the
14   percent change in suitable area (given in km2 9 1000) relative to each model’s simulated baseline suitability
15
16   The first phase of the LTAS project included a technical report (no. 3) that provided insights as to the
17   possible effects of climate change on the agricultural and forestry sectors. This was considered from

                                                                        3
1   biophysical perspective, specifically relating to yields and changes in the climatically optimum
 2   growth areas. These models illustrated a wide range of possible impacts on dry land maize yield ‒
 3   ranging from a decrease in 25% to an increase of 10% depending on the scenario.1 Assuming a CO2
 4   stabilisation at 450 ppm delivers results on yield impact of between -10% to +5%. The study finds
 5   similar results on wheat and sunflower. The study also evaluated the impact on other agricultural
 6   crops and grazing, as well as forestry production but all fall beyond the scope of this study. The study
 7   also provided possible adaptation responses, which will be returned to in the last section of this
 8   report, and highlighted future research needs (LTAS, 2013).
 9
10   Given the above it clear that little consensus exists between the various studies but some broad
11   trends can be identified, however.

12   2 Methodology
13   The BFAP sector model is used to evaluate the effect of climate scenario’s on the price, trade and
14   production effects on these key staple crops. These results will in turn be incorporated into the BFAP
15   consumer models in order to evaluate the effect of the possible price changes especially on poorer
16   households in South Africa. The data will also be incorporated into the BFAP employment model in
17   order explore the employment effects of the scenario’s on employment within the maize and wheat
18   industries.

19   2.1 The sector model
20   Section 3 presents the results obtained through the BFAP sector model. This model was first utilised
21   in 2003 and can be described as an econometric, recursive partial equilibrium model of the South
22   African agricultural sector, which presently covers 52 commodities. Within each sector, the
23   components of supply and demand are estimated and equilibrium is established based on balance
24   sheet principles, where demand equals supply at national level. The model is solved within a closed
25   system of equations, where grains are linked to livestock through feed, implying that a shock in the
26   livestock sector is transmitted to grains and oilseeds and vice versa. The model is linked to global
27   markets through the Food and Agricultural Policy Research Institute (FAPRI) in America, as well as
28   the Aglink-Cosimo modelling system used for the annual OECD-FAO agricultural outlook.
29   Figure 1 presents a diagram of the BFAP modelling system where weather is captured as an external
30   driver of the BFAP sector model.
31

     1
      Depending on the scenario, with unconstrained emissions (typified by IPCC SRES A2 and IPCC AR5 8.5 W.m-2
     RCP) and constrained emissions (typified by IPCC SRES B1 and IPCC AR5 4. 5 W. m-2 RCP).

                                                        4
1
 2   Figure 1: The BFAP modelling system

 3   The BFAP sector model does take biophysical elements, specifically rainfall, into account but does
 4   not provide for temperature and CO2 fertilisation effects. Within this study it is assumed that the
 5   negative effects of increased temperature are offset by the positive effects of CO2 fertilisation. Given
 6   the results of the study by Nelson et al. (2010) discussed in Section 1 it can be assumed that carbon
 7   fertilisation has a net positive effect on both wheat and maize yield. Within the model rainfall serves
 8   as one of the variables that impacts on the decision to plant and eventually also impacts on the yield
 9   realised by the respective crops. For this purpose precipitation in certain production areas for
10   certain months of the year is taken into account in the model.
11
12   For the purpose of this study the BFAP sector model can only be extended meaningfully to 2030 due
13   to the fact that model consists of a number of forecasted macro independent variables that are
14   currently only forecasted up to 2023 (typical 10-year outlook) and in some isolated cases up to 2030
15   by the international modelling institutions. From the long-run projections it also becomes evident
16   that beyond a certain period, a long-run equilibrium is established among markets and trends are
17   mostly unchanged until a new exogenous shock is introduced into the model.

18   2.2 Consumer Impact study
19   Section 4 presents the results of the consumer impact study. Within this study the retail prices for
20   maize meal and bread generated through the BFAP retail price transmission models will serve as the
21   main input for this analysis. The potential impact of maize and wheat price changes due to climate
22   change, especially on poorer households in South Africa will be investigated with multiple
23   approaches.
24

                                                        5
1   Approach 1: The ‘BFAP Poor person’s index’
 2
 3   The ‘BFAP Poor person’s index’ was developed based on poor South African consumers’ typical
 4   portion sizes of the five most widely consumed food items in South Africa: maize porridge, brown
 5   bread, sugar, tea and full cream milk (Nel and Steyn, 2002; Oldewage-Theron et al., 2005; Steyn et
 6   al., 2000). The term ‘most widely consumed’ means that these food items are consumed by the
 7   largest share of South African adults according to the National Food Consumption Survey and other
 8   similar studies among poor South African consumers. The BFAP Poor person’s index was calculated
 9   by weighing the food price data for these food items, based on the typical (cooked) daily portions of
10   very poor consumers (as obtained from the various nutritional studies listed above), in order to
11   calculate the cost of a ‘typical daily food plate’ for the poor. This index is usually calculated based on
12   the official food price database used by the NAMC for food price monitoring activities. For this
13   exercise the projected prices for brown bread and maize meal will be inserted into this model, to
14   investigate the potential impact on poor households’ basic food expenditure.
15
16   Table 3: Composition of the BFAP Poor person’s index

     Category                     Products
                                  Maize porridge (532g cooked portion)
     Bread & cereals
                                  Brown bread (150g portion)
     Dairy                        Full cream milk (56g portion)
     Sugary foods                 White sugar (22g portion)
     Hot beverages                Tea (2.5g dry tea portion)
17
18
19   Approach 2: Staple food expenditure based analysis
20
21   A model of the staple food consumption patterns of households from different socio-economic
22   groups in South Africa will be developed, departing from the average expenditure of the ten income
23   deciles in South Africa on main staple food commodities (StatsSA, 2012).
24   The projected prices for bread and maize meal will then be inserted into this model to estimate:
25    The potential additional expenditure on these staple foods if consumption quantities remain the
26       same at higher price levels:
27    The potentially reduced energy intake if staple food budgets remain unchanged but retail prices
28       of bread and maize meal increases.
29
30   Approach 3: A balanced daily food plate model
31
32   BFAP has been working with nutritionists to compile examples of balanced daily food plates
33   adhering to both the requirements of adequate energy intake and micronutrient composition.
34   The projected prices for bread and maize meal will then be inserted into this balanced daily food
35   plate model to estimate:
36    The potential additional expenditure on these staple foods if consumption quantities remain the
37       same at higher price levels;
38    The potentially reduced energy intake within this ‘daily food plate’ if food budgets remain
39       unchanged but retail prices of bread and maize meal increases.

                                                         6
1   Developing future scenarios with respect to a change in household characteristics such as food
 2   expenditure patterns, class mobility and others, falls beyond the scope of this study. Hence, the
 3   current characteristics will be applied in order to generate the impact of future maize meal and
 4   bread prices in the analyses described above. Thus, the assumption will have to be made that
 5   households will have the same characteristics 20 years into the future as is presently the case.

 6   2.3 Climate Data
 7   The LTAS Phase 1 developed a consensus view on the range of plausible climate scenarios for three
 8   time-periods for South Africa at national and sub-national scales under a range of global emissions
 9   scenarios. The time-periods considered were 2015 to 2035 (centred on ~2025, so-called short- term)
10   in addition to the previously followed approach of exploring climate change over several decades
11   into the future (centred on ~2050 (medium-term) and ~2090 (long-term).
12
13   These scenarios were developed through local and international climate modelling expertise using
14   both statistical and dynamical downscaling methodologies based on outputs from IPCC AR4 (A2 and
15   B1 emissions scenarios) and IPCC AR5 (RCP 8.5 and 4.5 Wm-2 pathways). These represent an
16   unmitigated future energy pathway (unconstrained, A2 and RCP8.5) and mitigated future energy
17   pathway (constrained, B1 and RCP4.5, or emissions scenarios equivalent to CO2 levels stabilising
18   between 450 and 500ppm).
19
20   South Africa’s climate future up to 2050 and beyond can be described by using four fundamental
21   climate scenarios at national scale, with different degrees of change and likelihood that capture the
22   impacts of global mitigation and the passing of time.
23
24       1. warmer (3°C above 1961–2000) and drier, with a substantial increase in the frequency of
31          drought events and greater frequency of extreme rainfall events.
32
33   The effect of strong international mitigation responses would be to reduce the likelihood of
34   scenarios 3 and 4. It is not possible to evaluate the effect of all the respective climate models
35   through the use of the BFAP model during the time frame of LTAs phase 2 due to the amount of
36   work required to prepare the data for analysis. It was therefore decided to conduct a preliminary
37   test of the effect of four climate models representing a range of rainfall changes at the national
38   level. A subset of scenarios was selected from those available to explore the vulnerability of wheat
39   and maize production and its effect on the food system.
40

                                                      7
1
 2   Table 4: LTAS Scenario Representative Models (2040-2050) with calculated percentage average
 3   rainfall changes

                                                              Avg. change in
            LTAS Scenario                   Model
                                                                MAP (%)
     Warmer/moderately drier        ACCESS RCP 4.5                - 3.3%
     Warmer/drier                   MPI RCP 4.5                   - 1.1%
     Hotter/moderately drier        ACCESS RCP 8.5                - 4.3%
     Hotter/drier                   MPI RCP 8.5                   - 14%
 4   Source: Aurecon (2014)
 5
 6   In light of the above, four CSIR CMIP5 climate models (see Table 4) were selected and the results
 7   incorporated into the BFAP sector model in order to evaluate the possible price and production
 8   impacts of each of these possible climate futures. The warmer models incorporate the assumptions
 9   of the RCP 4.5 climate futures whilst the hotter scenarios assume the RCP 8.5 futures. Both of the
10   moderately drier scenarios are the result of the ACCESS global climate model whilst the drier
11   scenarios are the result of the MPI global model.
12
13   These models delivered actual monthly precipitation, dynamically downscaled through the CCAM
14   (conformal-cubic atmospheric model) into quaternary catchments. These quaternary catchments in
15   turn where aggregated into secondary catchments by averaging the quaternaries that constitute
16   each of the secondary catchments. The resulting secondary catchments where then grouped in
17   according to production areas and months of interest specified by the BFAP sector model. With the
18   wheat model for example, both the winter (Western Cape) and summer (Free State) production
19   areas where compared with the calculated secondary catchments, the relevant catchments within
20   each of these production areas where then identified, the precipitation data on the relevant months
21   isolated, totalled per production area and then included in the model.
22
23   As stated above this model can only provide meaningful econometric results up to 2030 due the fact
24   that forecasted data on the determining factors (dependent variables) of the model, other than
25   precipitation in this case, is only available up to this date. The problem, however, is that an analysis
26   of the precipitation data of the respective models, grouped in accordance the BFAP model, show a
27   high correlation before 2030 and only show a significant divergence thereafter. In other words, it is
28   apparent that for some of the scenarios the effects on precipitation increase towards the end period
29   (2050). Therefore, in order to analyse the potential economic impacts of climate on the South
30   African maize and wheat industries, the precipitation data projected for the period 2034 to 2050 is
31   introduced in the BFAP sector model for each of the scenarios, which is then compared to a base
32   case. The BFAP sector model generates absolute and percentage shocks to illustrate the relative
33   deviations from the base. The implication thereof can be best described as the effect of more than
34   20 years of climate change on the current context.
35
36   It was decided to use actual historic precipitation data as a base to which the modelling results of
37   the respective scenarios could be compared. For this purpose the rainfall for the period 1950 to
38   2000 was used, this data was also aggregated to secondary catchments, and allocated to the
39   relevant production areas as explained above and included in the model. The modelling results of
40   this data hence serves as a “real world” stochastic base to which the other results can be compared.

                                                        8
1   3 Climate Change Impacts on Production and Prices

 2   3.1 Developing the LTAS BASE 2030 projections
 3   Various approaches and modelling techniques can be applied in the world of foresighting and
 4   developing future outcomes of agricultural markets. The methodology that BFAP has developed links
 5   scenario thinking techniques to a set of empirical models at global, national and farm level. The
 6   starting point for the empirical impact analyses is first to set a benchmark from where potential
 7   deviations can be measured. For BFAP, this benchmark is the most basic projections that are
 8   simulated in the BFAP sector model and are referred to as deterministic baseline projections. As
 9   discussed in section 2.1 of this report, the BFAP sector model is a recursive partial equilibrium
10   model. The model takes the interaction between various industries like livestock, grains and oilseeds
11   into consideration and projects the future equilibrium between demand and supply for a range of
12   agricultural markets subject to a set of assumptions.
13
14   Traditionally the BFAP baseline projections provide a 10-year outlook of commodity markets, yet for
15   the purpose of this study, this baseline is extended to 2030. In other words, a future scenario is
16   simulated for the next 17 years that is grounded in a series of assumptions about the general
17   economy, agricultural policies and technological change. The typical macro-economic assumptions
18   that are used to generate the baseline are presented in Table 5. The outlook for international maize
19   and wheat prices was generated by the Food and Agricultural Policy Research Institute’s (FAPRI at
20   the University of Missouri) in February 2014. These macro-economic projections were extended to
21   2030.
22
23   Table 5: Macro-economic baseline assumptions

                                            2014    2015    2016    2017    2018    2019    2020    2021    2022    2023

     Crude Oil Persian Gulf: (USD/barrel)
                                            101.0   95.5    100.9   105.4   109.8   114.3   117.7   121.0   124.4   127.8

     SA Population (Millions)               51.2    51.4    51.7    51.9    52.1    52.3    52.6    52.8    53.0    53.3

     Exchange Rate (R/USD)                  11.00   10.83   11.20   11.56   11.97   12.40   12.84   13.30   13.77   14.26

     Yellow maize, US nr 2 fob ($/ton)      231     215     216     218     221     224     223     222     221     218

     Wheat, US HRW nr 2 fob ($/ton)         287     251     242     244     249     256     258     257     256     257

24   Source: BFAP & FAPRI, 2014
25
26   Since the BFAP sector model also takes future rainfall into consideration when projecting the area
27   planted and the yield for a specific crop, a further critical driver that has to be incorporated is the
28   future expectations of rainfall. Traditionally in this type of modelling exercise, it is assumed that
29   “normal” rainfall conditions will prevail and in BFAP’s baseline analysis “normal rainfall” is taken as
30   the average rainfall received over the past thirty years from Weather SA’s database. One can,
31   therefore, anticipate that the deterministic outlook is far less volatile than what will actually be the
32   case. In order to simulate any alternative outcome and to illustrate the impact of volatile weather
33   conditions, the model is simulated stochastically where the variability of precipitation that occurred
34   in the past, is projected into the future.
35

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