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Agricultural Climate Change Impacts on Moroccan Agriculture and the Whole Economy Including an Analysis of the Impacts of the "Plan Maroc Vert (PMV)" in Morocco

Abstract and Figures

Climate change is one of the major risks facing developing countries in Africa for which agriculture is a predominant part in the economy. Alterations in rainfall patterns and increasing temperatures will most likely translate into yield reductions in many crops (Gommes et al. 2009). The early literature of economic impact assessment of climate change has provided some useful insights on the issue, but remained limited in scope and depth as it focused on highly aggregated units of analysis (e.g. at the continental or sub-continental levels) and relying on average trends, rather than capturing the underlying uncertainty characterizing the distribution of projected impacts. Policymakers in some countries feel the need to act upon the challenges of climate change, especially given that there is increased availability of climate projections at finer geographical scales that helps refine the analyses, and improve our ability to capture the intricate linkages that exist between climate change and the economy.
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Climate Change
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Policy, held in Helsinki on 28–29 September 2012. This is not a formal publication of UNU-WIDER and may reflect
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DRAFT
1
Agricultural Climate Change Impacts on Moroccan
Agriculture and the Whole Economy Including an Analysis
of the Impacts of the “Plan Maroc Vert (PMV)” in Morocco
Ismail Ouraich and Wallace E. Tyner
1
Abstract:
Climate change is one of the major risks facing developing countries in Africa for which
agriculture is a predominant part in the economy. Alterations in rainfall patterns and increasing
temperatures will most likely translate into yield reductions in many crops (Gommes et al. 2009).
The early literature of economic impact assessment of climate change has provided some useful
insights on the issue, but remained limited in scope and depth as it focused on highly aggregated
units of analysis (e.g. at the continental or sub-continental levels) and relying on average trends,
rather than capturing the underlying uncertainty characterizing the distribution of projected
impacts. Policymakers in some countries feel the need to act upon the challenges of climate
change, especially given that there is increased availability of climate projections at finer
geographical scales that helps refine the analyses, and improve our ability to capture the intricate
linkages that exist between climate change and the economy.
The study will focus on the Moroccan case using climate impacts on yields from the results of
the joint-study by the Ministry of Agriculture and Fisheries (MAPM) and the World Bank (WB),
in collaboration with the National Institute for Agricultural Research (INRA), the Food and
Agriculture Organization of the United Nations (FAO), and the Direction of National
Meteorology (DMN).
2
Building upon IFPRI’s CGE model structure, we use the updated version
by Dudu and Cakmak (2011) to analyze the potential welfare losses/gains from climate change
over the range of identified climate scenarios.
Historically, weather and rainfall variability have had major spillover effects on the rest of the
economy. There is high correlation observed between the growth rates of aggregate GDP and
agricultural GDP, with a correlation coefficient of 0.93 over the past 30 years. In other words,
when agriculture suffers due to drought, the whole economy suffers. In this study we evaluate
the A2 and B2 climate scenarios under differing assumption of CO
2
fertilization. Our preliminary
results show a similar tight linkage between agriculture and the rest of the economy. While the
1
Ismail Ouraich is a PhD Candidate at the Department of Agricultural Economics, Purdue University, and Wallace E.
Tyner is James and Lois Ackerman Professor at Purdue University.
2
We refer to the latter study as the WB/Morocco/FAO study.
2
largest impact is on the agricultural sector, national GDP falls by 2% under this climate change
scenario. In the paper we provide more detail on these summary results. In addition, we provide
a case in which the climate change shocks are accompanied by the changes in technology and
crop mix shocks envisioned by the Maroc Plan Vert (Morocco Green Plan).
The paper provides estimates of economic impact of climate change, compares these with
historic economy wide economic variability induced by agricultural shocks, and estimates the
extent to which the current Moroccan agricultural development strategy helps in agricultural
adaptation to climate change. All of this is done over two sets of climate shocks.
Key words: CGE models, agricultural policy, adaptation, climate change, SRES scenarios,
uncertainty
JEL Classification: O13, Q10, Q54.
3
1. Introduction
The trend of agricultural productivity growth in the last decades has been tremendous in many
ways, which helped to alleviate poverty and food insecurity in many areas (although there are
still substantial differentials across regions). This was primarily due to improved production
systems and investments in crop and livestock breeding programs. Nonetheless, climate change
threatens to exacerbate the existing challenges faced by agriculture. The global population is
estimated to reach 9 billion by 2050, with the bulk of the increase occurring mostly in Africa and
South Asia. Also, taking into account the accelerated demand for food and changes in dietary
habits, the FAO estimated that feeding world population will require a 70 percent increase in
total agricultural production (FAO, 2010).
3
Yet, the problem gets compounded as we take into
consideration the threat of climate change to the stability and productivity of the agricultural
sector. Numerous studies (Cline, 2007; Fisher et al., 2002; IPCC, 2007) have shown that the
specter of climate change is looming even bigger for regions already experiencing low and
erratic productivity levels (e.g. Africa and South Asia). For instance, it has been estimated that a
warming of 2˚C could result in a 4 to 5 percent permanent reduction in annual income per capita
in Africa and South Asia.
4
In its latest report, the Intergovernmental Panel on Climate Change (IPCC) stated that the
African continent is poised to be among the most vulnerable regions to climate change and
climate variability, a situation that is aggravated by existing developmental challenges such as
endemic poverty, complex governance and institutional dimensions, and limited access to
capital, infrastructure and technology (IPCC, 2007).
5
Chief among the concerns for the African
continent is the modernization of the agricultural systems (at the level of both commercial and
subsistence agriculture) deemed for many countries in the continent as a levy for economic
growth.
6
Reforming the agricultural sector in Africa is a necessity to tackle problems pertaining
to food security,
7
water scarcity, access and management, health and malnutrition, etc. Indeed,
many countries in the continent already experience challenging climatic conditions that impact
3
In terms of undernourished people in the world, the post economic crisis levels remain very high in comparison
with their levels 40 years ago, and even higher than the level that existed when the hunger-reduction target was
agreed at the World Food Summit in 1996 (FAO, 2010).
4
The World Bank, ‘World Development Report (WDR), 2010.
5
Overall, this finding has been robust for all of the SRES scenarios included in the analysis, although minor
differences in terms of projections exist among the different scenarios mainly driven by the different assumptions
underlining each scenario.
6
For example, the contribution of agricultural GDP varies from one country to the other, but is still significant
where the average in the continent is 21% (ranging from 10 to 70%) (Mendelson et al., 2000b)
7
In 2006, food prices escalated into a surge of food price inflation around the world, with Africa being particularly
hard hit which experienced food riots. In the wake of the Financial Crisis of 2007-08, the FAO food price index rose
by 27% in 2007, and this increase persisted and even accelerated during the first half of 2008 (FAO, 2009).
4
negatively the prospects for agriculture. For example, it has been projected in some countries that
yield reductions could reach as high as 50% by 2020, with small-scale farmers being the most
affected (IPCC, 2007).
8
In terms of socio-economic impacts in the continent, acute yield
reductions as mentioned above could have severe consequences in terms of economic growth
and poverty alleviation, given the fact that many African countries rely substantially (to varying
degrees) on the agricultural sector as a source of national income through exports of cash crops
and also as a major provider for job opportunities, especially in rural areas.
In recent years, there has been a great improvement in the science of climate change through
advances in our understanding of the biophysical processes of climate, which enhanced our
modeling capacity providing us with more robust climate projections at the global level.
Nonetheless, more analysis is needed on the economics of climate change. There are many
factors that explain this slower development of economic impact analysis, but chief among them
is the dependency of economic impact assessments upon reliable climate projections that could
be fed into economic models to measure impacts at the socio-economic level, and evaluate
policy mitigation and/or adaptation strategies. The early literature of economic impact
assessment of climate change has provided some useful insights on the issue, but remained
limited in scope and depth as it focused on highly aggregated unit of analysis (e.g. at the
continental or sub-continental levels). Nonetheless, the current trend of the empirical literature
on the issue of economic impact assessment of climate change display a shift towards engaging
in ‘case-by-case’ analyses at the country and/or sub-country level, especially given the fact that
consensus is growing among policymakers on the need to act upon the challenges of climate
change, and more importantly due to increased availability of climate projections at finer
geographical scales that helps refine the analyses, and improves our ability to capture the
intricate linkages that exist between climate change and the economy.
Therefore, and in recognition of this gap in the literature of climate change economic impact
analysis, we use a computable general equilibrium model to analyze the impacts of climate
change at a refined geographical scale, and focusing on Morocco as a case study. First, we
develop a set of yield projections under different climate scenarios using data from the study
conducted by the World Bank (WB) and the Moroccan Ministry of Agriculture, Rural
Development and Fisheries (MPAM), in collaboration with National Institute for Agricultural
Research (INRA), the Food and Agriculture Organization (FAO) and the National Meteorology
Authority (DMN). From this point forward, we will refer to the previous study as the
WB/Morocco/FAO study for ease of reference. Subsequently, these exogenous changes are
introduced in the regionally modified computable general equilibrium model, which is based off
IFPRI’s CGE templates (Logfren et al., 2002). This will allow us to map out region-specific
8
It should be noted that these projections are quite differentiated from one country to the other, driven by the
difference climatic scenarios and their underlining assumptions, as well as by the economic structures
characterizing each country in the African continent.
5
economic impacts of climate-driven yield alterations. Finally, we will investigate the potential
effects of adaptation policies in the agricultural sector being implemented at the regional level in
Morocco.
The paper will be organized as follows: Section 2 will briefly discuss some of the literature of
CGE analysis related to economic impact assessment of climate change. In Section 3, we will
present our methodological approach and data sources. Section 4 will summarize key findings
and results, and we will wrap up in Section 5 with concluding remarks.
2. Climate change impact assessment and CGE analysis
The recent literature using computable general equilibrium models to analyze climate change
impacts and adaptation linkages has taken two directions. The first one is based on country-based
CGE models that focus on domestic impacts, which allows for a more detailed analysis in terms
of mapping out the latter impacts to the domestic economy. The second is based upon a multi-
region structure at the global level (e.g. GTAP model), and where the focus is directed at
analyzing inter-regional impacts mainly driven through international trade linkages.
Horridge et al. (2005) use a bottom-up CGE model for Australia to analyze the impact of the
2002-2003 drought. The model was coined TERM (The Enormous Regional Model) which was
developed to deal with highly disaggregated regional data, and with the objective of analyzing
regional impacts of region-specific shocks. It uses data at a regional-sectorial disaggregation
based on national I-O tables, together with regional data on production (for agriculture) and
employment (in other sectors) for 45 regions and 38 sectors. Their findings suggest substantial
negative impacts on agricultural output and income, which decreased on average by 30% and
20% respectively. The most striking finding is that despite the small share of agriculture in
Australian GDP (3.6%), drought reduces GDP by 1.6% and worsens the balance of trade.
Diao et al. (2008), in an extension of an earlier CGE application of Diao et al. (2005), used a
country-based CGE model to analyze the impacts of conjunctive groundwater (GW) and surface
water (SW) management in Morocco. The objective of the study was to assess the direct and
indirect effects GW regulation on agriculture and nonagricultural sectors under different
scenarios such as (i) increased GW extraction costs, (ii) rural-urban transfers of SW, and (iii)
reduced availability of water supplies due to drought. For instance, they found that a reduction of
one standard deviation in SW supplies caused real output to fall by 11%. Additionally,
agricultural exports (mainly of irrigated crops) with the European Union (EU) experienced a
decline of 13.6%.
6
Berrittella et al. (2007) used a multi-region world CGE model, GTAP-W;
9
to analyze the effects
of restricted water supply as it pertains to international trade linkages for agricultural products.
Water resources usage in commodity production is captured through water intensity
coefficients,
10
which describe the amount of water necessary for sector j to produce one unit of
output. They contrasted a market solution to the scarcity problem, where water owners have the
ability to capitalize on their water rent, to a non-market solution, where supply restrictions imply
productivity losses. They conclude that improvement to allocative efficiency can be achieved
through supply constraints imposed on the resource, especially in the context of heavily distorted
agricultural markets. They argue that welfare gains from curbing inefficient production may
outweigh the welfare losses due to resource constraints.
Berrittella et al. (2008) used the same model, GTAP-W, to analyze the impacts of trade
liberalization on water use at the global level. They particularly focused their analysis on the
Doha Development Agenda launched in 2001, and which sets forth a set of trade liberalization
scenarios in both developed and developing countries. They found that trade liberalization
induces reduction in water usage for regions with scarce supply, and increases it for water
abundant regions.
Calzadilla et al. (2008) used a CGE model to analyze the impacts of improved irrigation
management under water scarcity. They used an updated version of GTAP-W (Berrittella et al.,
2007), where a new production structure is introduced which separates rainfed and irrigated crop
production. Their findings suggest that improved irrigation efficiency in water-stressed regions
produces positive effects on welfare and demand for water, whereas results are more mixed
(mostly negative) for non-water scarce regions.
Laborde (2011) analyzes the impacts of climate-induced yield changes on agriculture in South
Asia, and investigates the potential for trade policy options to mitigate the latter. A modified
version of the MIRAGE CGE model was used, where yield estimates were first obtained via the
IMPACT model for 13 SRES scenarios. The latter are introduced as exogenous shocks in the
modified MIRAGE CGE model, where baseline results are contrasted with the results from 8
different trade policy landscapes for the region.
9
GTAP-W is a refined version of the GTAP model that accounts for water resources, and which is based off the
extension work by Burniaux and Truong (2002).
10
Calculated based on water requirement in terms of blue water (surface and ground water) and green water
(moisture stored in soil strata). The data is taken from Chapagain and Hoekstra (2004) for agricultural production
and from AQUASTAT database for the water distribution services (i.e. household and industrial consumption). A
major limitation with respect to the water intensity coefficients data for agriculture is that it does not differentiate
between rainfed and irrigated agriculture.
7
Kuik et al. (2011) used the newly developed MOSAICC model
11
by the Food and Agriculture
Organization (FAO), in partnership with European research institutes. The model allows for
country-based climate change impact analysis via its modular platform. The latter include a
climate data module, which aims at statistical downscaling of climate data to be used in
subsequent modules. Crop and hydrological modules are used to simulate crop growth and river
basins hydrology under different climate change scenarios, using data from the previous module.
An economic module, which is a country-based Dynamic CGE model,
12
was employed for the
economic analysis of climate change impacts through yield variations. The authors tested the
model using Morocco where data projections were used for the period 2001-2030.
3. Background on Moroccan agriculture and methodological approach
3.1. Moroccan agriculture and climate change
Morocco enjoys a very interesting geostrategic location with its 3,500 kilometers of seashores,
spanning the Atlantic Ocean and the Mediterranean. And equally important is its diversity in
terms of landscapes and ecosystems: the Mountain chains of the North, and the Northeast to the
Southwest, the Plateaus of the East, the Plains in the West and the Centre, and the Desert in the
South. In terms of climatology, the country enjoys a typical temperate Mediterranean climate,
but with dry conditions in much of the country.
13
The country suffers from a cruel paradox in the
form of advantageous precipitation patterns in the northern regions, but with very poor soil
quality, and vice-versa in the southern regions (Akesbi, 2006).
The agricultural sector in Morocco is still highly dependable on climatic conditions as depicted
by the high correlation observed between precipitations levels and agricultural value-added
(Figure 1, Appendix B). This is due in part to the general structure of production activities in the
sector, which is highly skewed toward crop varieties with very low value-added; e.g., cereals,
which are highly sensitive to climatic conditions and represent 55% of total value-added of crop
production and occupy 65% of agricultural area. Export crops, mainly citrus and vegetables,
represent 15% of value added and respectively occupy 0.85 and 3% of total agricultural area.
14
Although in terms of vegetative cover of agricultural land, citrus and vegetables occupy a very
small share, yet their share in agricultural-added is substantially high given the fact that those
niches are usually more labor, chemical, and water intensive compared with cereals. Post-
independence agricultural reforms that Morocco has engaged in helps explain the present
11
MOSAICC - Modelling System for Agricultural Impacts of Climate Change
(
http://www.fao.org/climatechange/mosaicc/en/)
12
The Dynamic CGE model was developed in partnership with the Free University of Amsterdam, and is inspired by
the IFPRI DCGE model (Logfren et al., 2002; Thurlow, 2004).
13
Half of the country’s area is desert, whereas the rest is split among: cultivable agricultural area (9 million Ha),
forests (6 million Ha), grassland (3 million Ha), and rangeland (21 million Ha).
14
Conseil General du Développement Agricole (CGDA), 2004.
8
situation, where upon investigating the long term trend in the sector’s performance; we can
identify three phases representing distinct growth patterns (Figure 2, Appendix B): Phase I
(1965-1970s until 1985) characterized by rather a weak performance of agricultural production,
and even a slight decline of the per capita levels. The performance recorded during this period
was contingent upon the performance of policies targeting the agricultural sector adopted in the
early post-Independence years. The first set of policies was oriented towards a reform of the
status of property rights of land ownership through the nationalization of official and private
colonial lands, and their redistribution by the State.
15
Moreover, and in parallel to the land
reform efforts, a charter of agricultural investments was adopted in 1969
16
with the objective of
mobilizing the hydrologic potential of the country and providing incentives for the development
of irrigated perimeters. This effort has been accompanied by a set of incentives to farmers to
encourage investments in new technologies (e.g. machinery, fertilizers, seeds, etc.). Nonetheless,
the State has intervened heavily and selectively to regulate markets and control prices for so-
called “strategic” commodities, which translated technically into controlling the flow of imports
and exports.
17
Hence, the combined effect of these policies has led to an implicit taxation of the
sector, especially when accompanied with the overvalued exchange rate at the time (Doukkali,
2006). Phase II (1985-1991), displays a substantial increase in value of agricultural production,
on average by 9.4%/year; whereas the per capita levels increased by 6.7%/year. The boost in
agricultural productivity during this period came as result of favorable climatic conditions, but
also due to the combined effect of the King’s plan in 1985 to double the area cultivated in wheat,
and the sustained liberalization effort in the agricultural sector and the exoneration of agricultural
revenues from income tax. The result was an expansion of agricultural area and a reduction of
small scale farms, which came about due to increased investment and consolidation in the
sector.
18
Phase III (1991-2009) displayed a slowdown of growth in agricultural output at the
aggregate and per capita levels. For instance, agricultural per capita output decreased by 14.39%
for the period 1991-2002. Nonetheless, the trend is reversed from 2002 onward when there was a
significant improvement. In terms of the policy, this period is characterized by continued effort
of liberalization in the agricultural sector. Overall, the level of production compared to pre-1991
levels was clearly higher. Nonetheless, agricultural growth still subjected to important
15
Akesbi, N., Benatya, D., and El Aoufi, N., “L’agriculture marocaine a l’épreuve de la libéralisation”, Ed. Economie
Critique, 2008.
16
The charter of agricultural investments, of its French name ‘Code d’Investissements Agricoles (CIA)’, was a set of
laws passed in 1969 to primarily manage the public irrigation schemes at the time. It is presented as a contract
between farmers and the State, defining rights and duties in public Large Scale Irrigation schemes. Historically, this
policy has been coined as “Politique des Barrages” which consisted of huge investments by the State in public
irrigation infrastructure (i.e. building of grand dams) with the objective of reaching the milestone of 1 million Ha of
irrigated agricultural land by 2000 (Doukkali, 2005).
17
Basically, in the post-independence era, the economic strategy adopted by Morocco was ambitious since it
involved the combination of an “import-substitution” led growth strategy coupled with promotion of exports, and
in which the agricultural sector was the main engine (Akesbi, 2006; RDH50, 2006).
18
This was depicted in the results of the General Agricultural Census in 1996, and which demonstrated an increase
in the arable agricultural area by 21%, whereas the number of small farms without land and with less than a
hectare of land decreased by 85.6% and 28.3% respectively (Doukkali, 2006; RDH50, 2006).
9
fluctuations driven by the successive drought episodes that characterized the period, and which
were particularly severe for crop production.
In conclusion, it appears that the agricultural sector in Morocco has been, and is still at the core
of the State’s economic strategy given its strategic importance with respect to issues pertaining to
employment, food security, poverty alleviation, etc. Despite the progress that has been achieved,
there remain important challenges in the face of fully taking advantage of the potential of the
agricultural sector. The value-added problem is particularly acute with respect to the valuation of
water usage in the sector. There is a strong consensus among policymakers that the growing
hydrologic constraints in the country, owing among other things to climate change and its
impacts on precipitation patterns, will be one of the major challenges in subsequent decades due
to increased scarcity of water resources and demand driven by demographic pressure.
3.2. Modeling, methodology and materials
The Morocco Country-based CGE model
The economic model to be used in this study is inspired by the IFPRI CGE model (Logfren et al.,
2002). The model was developed to include a number of features critical to analyses focusing on
developing countries such as including household consumption of non-marketed (or
“subsistence”) commodities, and multi input-output production structure that allows for any
activity to produce multiple commodities and any commodity to be produced by multiple
activities. The IFPRI CGE modeling infrastructure allows for a regionalized disaggregation that
would support the regional structure chosen for this analysis, and which is based on the regional
disaggregation of Moroccan territory at the administrative regional scale (Table 1, Appendix A).
The data to be used in the model is taken from the compilation of a national social accounting
matrix for 2003 based on the work of Dr. Rachid Doukkali of IAV/Hassan II in Rabat, Morocco,
and which identifies 60 activities and 68 commodities. The institutional block in the data is
represented by 10 household categories,
19
the government and the rest of the world accounts.
Tables 2 and 3 (Appendices) summarize the list of activities and commodities adopted in the
analysis, and which, after some data manipulations, collapse the dimensions of the model to 31
production activities producing 31 commodities.
Production is modeled under the assumption of profit maximization subject to a production
technology (Figure 1, Appendix B). The model specification allows for flexibility in terms of
production technology to be used. At the top level, the technology is specified as constant
elasticity of substitution (CES) function or, alternatively, a Leontief function of the quantities of
value-added and aggregate intermediate input. For the purpose of our analysis, we use the default
19
Urban households and rural households identified at the level of five income deciles.
10
specification of a Leontief technology since we assumed that each activity at the aggregate level
uses bundles of value-added and aggregated intermediate inputs to produce one or more
commodities according to fixed yield coefficients. The profit-maximizing decision process
assumed for each activity implies that factors are used up to the point where marginal revenue
product of each factor is equal to its wage (or factor price). In the model, an economywide wage
variable is free to vary to assure that the sum of demands from all activities equals the quantity of
factor endowments, which is assumed to be fixed at the observed level.
Household consumption is modeled via a Linear Expenditure System (LES), which results from
the household’s utility maximization problem using a “Stone-Geary” utility function subject to a
consumption expenditure constraint. Household consumption covers marketed commodities,
purchased at market prices, and home commodities, which are valued at activity specific-specific
producer prices. Government collects taxes (fixed at ad valorem rates) and receives transfers
from other institutions, which constitute its revenue. Government consumption expenditures are
assumed to be fixed in real terms, transfers to domestic institutions are CPI-indexed.
At the level of commodity markets (Figure 2, Appendix B), total domestic supply comes from
total aggregate output across activities, which is obtained via a CES function that accounts for
the imperfect substitutability of different outputs due to, for instance, differences in quality, and
distance between locations of activities. In order for market clearance to occur, an activity-
specific price serves to clear the implicit market for each disaggregated commodity. In the next
stage, aggregated domestic supply is allocated between exports and domestic sales via a constant
elasticity of transformation (CET) function.
Domestic demand is made up of the sum of demands from households, government, investments
and intermediate inputs. The latter demands are, to the extent that a commodity is imported, for a
composite commodity made up of imports and domestic output.
Regionalization assumptions and data
As previously mentioned, the model regional disaggregation is based on the administrative and
economic regional disaggregation of Morocco (Table 1, Appendix A).
In order to regionalize the data in the social accounting matrix (SAM), regional statistics on
production were used as a basis. For example, for agricultural crop production (11 activities), we
used statistics from the Agricultural Survey of Major Crop Production for the agricultural
campaign 2002-2003, which corresponds to the base year of our SAM. Regionalization shares
are based on production levels in each region (Table 5, Appendix A), and which are provided for
all production activities retained in the model. For the livestock sectors (4 activities), regional
statistics on livestock headcount for 2004 of cattle and sheep were used to regionalize production
activities for bovine and ovine meat production (HCP, 2005); whereas for poultry meat
11
production, regional statistics pertaining to 2005
20
were used as a basis for regionalization. Table
6 summarizes the regionalization procedure and data used for all production activities and
institutions as represented in the SAM.
One of the main features of the model is the interregional trade structure adopted in order to
account for the flows of commodity accounts to activity accounts at the regional level. The latter
interregional trade flows are computed as a residual, assuming no transaction costs. This is
achieved in the model by calculating the difference between a region’s production and
consumption. The resulting residual, if a surplus, is then distributed to other regions based on
their demands. We assume that for regions with surpluses, the latter consume only their own
products, and export the rest to regions with commodity deficits. As for the importing regions,
imports are subtracted from the region’s production to keep the balance between consumption
and production (Dudu and Cakmak, 2011).
Data sources for yield projections and selected scenarios analysis for Morocco
Yield estimates were obtained from the study conducted by the World Bank (WB) and the
Moroccan Ministry of Agriculture, Rural Development and Fisheries (MPAM), in collaboration
with National Institute for Agricultural Research (INRA), the Food and Agriculture Organization
(FAO) and the National Meteorology Authority (DMN). From this point forward, we will refer
to the previous study as the WB/Morocco/FAO study for ease of reference.
Yield projections were developed through a multi-step process. First, using a statistical
downscaling procedure, projected change in temperatures and precipitation were obtained for
Morocco at the agro-ecological zone (AEZ) level (i.e. a downscaling from the global 250 km x
250 km grid-boxes to a finer resolution of 10 km x 10 km grid-cell compatible with the AEZ
identified by the MPAM) (Figure 5, Appendices). Second, the results of the downscaling
procedure in terms of projected change in temperatures and precipitation were used to infer
impacts on variables pertaining to crop growth such as evapotranspiration and water stress
indicators. The study used for the determination of climate impacts yield functions calibrated
using the Crop Specific Soil Water Balance (CSSWB) model (Allen, R.G. et al., 1998).
Estimated impacts on crop yields were provided for four time horizons, which include: “current
period” (or baseline) covering the years 1979-2006, “2030” (from 2011 to 2040), “2050” (from
2041-2070) and “2080” (2071-2099). For the purpose of the study, we used the projected yield
data pertaining to the period “2050”. The decision was based on the fact that most studies
suggest impacts of climate change are likely to be exacerbated in the long-run, but with increased
uncertainty in terms of magnitude. Therefore, we chose the “2050” period as a middle-road
20
Data was provided by Dr. Abdellah MDAFRI,
Head of the Central Zone Division, Project Management Department, Agricultural Development Agency (ADA),
Morocco.
12
solution since it allows us to capture the long term impacts, but with a reduced uncertainty as to
the projected impacts.
In what pertains to the scenarios identified for the analysis, we have identified 8 scenarios as
described in Table 6 (Appendix B). These scenarios are defined based on the climate-driven
yield shocks to be introduced for selected crops, and refer to each SRES used in the analysis (A2
and B2), as well as the adaptation policies to be investigated in our analysis.
With respect to the climate-induced yield impacts, the objective is: a) to capture the uncertainty
underlying the projections in yield responses to climate change across SRES scenarios, and b) to
capture the underlying uncertainty within each climate scenario, which is achieved through the
percentile distribution based on 10
th
(low), 50
th
(medium) and 90
th
(high) percentiles of the
distribution of projected yields as estimated for each SRES scenario. In this case, “low
represents the worst case scenario in terms of impacts on yield, and “high” represents the best
case scenario, whereas “medium” represents a median between the two.
With respect to adaptation policies to be investigated, the objective is to shed light on the
relevancy of policy reforms in the “Plan Maroc Vert” (PMV) (Morocco Green Plan) as an
adaptive force in the face of climate change. From this point forward, we will refer to the “Plan
Maroc Vert” by its adopted abbreviation. The PMV is the new agricultural strategy adopted in
Morocco in 2008, and which lays down a vision of transforming the agricultural sector by 2020
to ensure a sustainable path of productivity growth, consolidate integration with local and
international markets, job creation and mitigate poverty impacts (especially in the rural areas).
The PMV includes well-defined productivity targets at the subnational level, i.e. at the level of
the administrative and economic regions identified in Table 1, achievable through a series of
policy reforms centered on public-private partnerships for investment programs divided into
“Pilier I” and “Pilier II” programs.
21
The PMV is mainly an investment program.
For the purpose of the analysis, we capture the adaptive potential of the PMV by converting the
region-specific projected productivity gains into potential yield changes for selected crop
commodities. The latter will be introduced in the model as Hicks-Neutral output-increasing
technical change that compensates for the projected climate-induced yield changes.
4. Productivity shocks, results and discussions
21
For detailed discussions of the “Plan Maroc Vert” (PMV), see Ministère de l’Agriculture et de la Pêche Maritime
(MAPM) and Agence pour le Développement Agricole (ADA), 2011, « Projet d’Intégration du Changement
Climatique dans la Mise en œuvre du Plan Maroc Vert (PICCPMV) : Etude Cadre de l’Impact Environnemental et
Social » ; Ministère de l’Agriculture et de la Pêche Maritime (MAPM) and Fonds de Développement Agricole (FDA),
2011, « Les Aides Financiers de l’Etat pour l’encouragement des investissements agricoles » ; Agence pour le
Developpement Agricole (
http://www.ada.gov.ma/en/Plan_Maroc_Vert/plan-maroc-vert.php) [08/25/2012].
13
In this section, first we will discuss the projected yield estimates of the WB/Morocco/FAO study,
under the different SRES scenarios (A2 and B2) and crop categories included in the analysis.
Second, we will contrast projected yield impacts with the historical trend in yields for selected
commodities. And last but not least, we will present a detailed discussion of our findings from
the simulation results.
4.1. Historical yield trends for selected crops and projected climate-driven productivity
shocks
Figures 6, 7 and 8 represent the historical record of yields for common and durum wheat, and
barley. For both wheat varieties, there is no evidence of any trend. But for barley, the historical
record suggests falling yields. Although for common and durum wheat, the situation improved
compared to the post-independence performance in the early 1960s until mid-1980s, the latter
came about partly due to an expansionist public policy that encouraged conversion of favourable
agricultural land into wheat cultivation; while at the same time driving out barley production to
be reallocated into marginal agricultural lands (Serghini and Tyner, 2005). Nonetheless, a
common feature that we observe for all three crops is the high level of volatility in the yields,
which is primarily explained by the high correlation between rainfall and agricultural
productivity for the latter crops.
Figures 9 (Appendix B) summarize the nation-wide percentile distribution of average yield
impacts across SRES scenarios as projected by 2050 for all selected crops. It is worth mentioning
that for all selected crop categories, the WB/Morocco/FAO projected impacts on yield are
estimated without and with CO
2
fertilization effects.
First, we notice that at the national level, projected yields depict variation across crops and
climate scenarios. Based on the yield projections, we can divide the crops into three categories:
negative yield impacts, mixed yield impacts, and positive yield impacts. For instance, wheat
(durum and common), barley and olives are the most negatively affected crops, where
respectively, yields are projected to decline on average by -7% to -26% for wheat (durum and
soft), -6% to -17% for barley, and -8% to -20% for olives. Whereas vegetables (i.e. tomatoes,
other vegetables and industrial vegetables) benefit from climate change, with impacts ranging
from a minimum of +2% to +7% for tomatoes, +0.5% to +6% for other vegetables and industrial
vegetables. For forage crops, citrus, other fruits and other crop, the projected impacts of climate
change on yield are mixed where they range respectively from -7% to +3% for forages crops, -
3% to +7% for citrus, -7% to +0.6% for other fruits, and -6% to +5% for other crops.
Nonetheless, there exist significant differentials in projected yield from one SRES scenario to the
other. Additionally, there exist a substantial differential in projected yield impacts within each
SRES scenario when comparing with and without CO
2
fertilization cases. Figure 10 (Appendix
14
B) summarizes the percentile distribution of projected yield impacts for all crop categories for
SRES A2 and B2, with and without CO
2
fertilization effects at the national level. The latter plays
a significant role in mitigating the negative impacts of climate change, but its impact is uncertain
and varies widely depending on the growth conditions of crops. For instance, the projected
impact on yield for tomatoes under the no CO
2
fertilization case is mixed, where it ranges from -
2% to +2% for SRES A2 and -3% to 3% for SRES B2; whereas including CO
2
fertilization
effects boosts significantly projected yield gains with the latter ranging from +8% to +16% for
SRES A2 and +6% to +12% for SRES B2. These results are verified for other vegetables, other
industrial vegetables, citrus and other fruits crop categories. This is in line with findings from
numerous impact studies in the literature that suggest that irrigated crops will experience positive
impacts due to climate-induced CO
2
fertilization effects owing to higher concentrations of CO
2
in the atmosphere. Indeed, vegetable and fruit production in Morocco is mostly irrigated, which
explains the observed results. As for cereals and olives, we notice that the positive impact of CO
2
fertilization effect, the latter is not large enough to induce a sign reversal in the projected yield
impacts which remains largely negative. This is explained by the fact that cereals in Morocco are
grown under rainfed conditions.
Nevertheless, using national averages does not inform us on the regional distribution of projected
yield impacts, which matter significantly in determining the overall impact. Table 6 summarizes
for each crop category production statistics at the regional level in Morocco,
22
and which were
used as a selection criteria in discussing the regional distribution of yield impacts. For instance,
projected yields for durum wheat are expected to fall for all regions and all SRES scenarios.
Among the hardest hit are Chaouia-Ouardigha (TR4), Doukkala-Abda (TR9), Meknes-Tafilalet
(TR11), and Taza-Taounate-Al Hoceima (TR13), which respectively account for 19%, 17%, 9%
and 16% of total durum wheat production, will experience yield declines ranging from -21% to -
40% in TR4, -28% to +14% in TR9, -12% to -22% in TR11, and -11% to -31% in TR13 across
all scenarios without accounting for the CO
2
fertilization effects. The impacts are somewhat
dampened when including the CO
2
fertilization effects, but the overall impact remains largely
negative. The same patterns hold for common wheat where the overall impact is largely negative
to slightly positive in the main producing regions. For tomatoes, the yield projections in most
regions depict slightly positive impacts for all SRES under the no CO
2
fertilization case, and
showcase substantial yield gains when we include the latter. In the main producing regions, i.e.
Souss-Massa-Draa (TR2), Gharb-Cherarda (TR3) and Doukkala-Abda (TR9) regions (which
respectively account for 46%, 22% and 10% of total production), yield impacts range from -3%
to +20% in TR2, -5% to +21% in TR3, and -2% to +9% in TR9 across all SRES scenarios, with
and without CO
2
fertilization effects. The same pattern is checked for other vegetables.
In conclusion, the results depict the wide range of variability that exists in terms of projected
yield impacts at the national level, across and within SRES, but also across regions. Capturing
22
The statistics are for the 2003 agricultural campaign obtained via the
15
this variability, and accounting for its economic impacts is key to understand the potential inter-
regional linkages in terms of climate change, and how they translate into welfare impacts.
4.2. Findings and results
As previously mentioned, the present analysis identifies 8 scenarios as defined in Table 5
(Appendix A). The model closure rules follow conventional neoclassical assumptions, where
supply and demand adjust in all markets to satisfy the market clearing conditions thought relative
price changes.
For imports and exports, we assume that Morocco is a small-country facing infinitely elastic
supplies and demands at world prices. Full employment of factors is assumed, where capital and
land are activity-specific; whereas labor is mobile across regions and sectors. The numeraire is
assumed to be the Consumer Price Index (CPI).
At the macro level, government savings, i.e. the difference between government’s revenues and
expenditures, is a fixed share of GDP. In order to reach the targeted level of government savings,
the tax rate is allowed to adjust uniformly across all sectors. We assume fixed foreign savings,
and allow for a flexible exchange rate in order to clear the balance of the rest of the world.
As we mentioned earlier, and in conjunction to analyzing the economy-wide impacts of climate
change in Morocco, we aim to investigate the adaption potential of the PMV in Morocco. Table
6 (Appendix A) summarizes the key projected impacts of the PMV strategy by region and by
crop. The latter are expressed in terms of percent change of projected yield improvements
achievable through the various policy reforms and investments described in the PMV. In other
words, they represent assumptions on productivity enhancements which we use in our analysis as
a dampening effect on the impacts of climate change. With this in mind, and given the
uncertainty in the range of climate impacts as predicted for Morocco and discussed earlier, we
are asking the question of whether the PMV strategy and its policy reforms agenda will be of any
help as an adaptation to climate change.
Simulation results suggest that effects of a climate change, under the worst case scenarios, can
be significant given their overall impact that act as a drag on the economic performance in all
sectors, which in turn affects the performance of aggregate indicators. Indeed, the results suggest
that under a severe climate shock, the overall aggregate indicators deteriorate, which indicate a
contraction in the level of economic activity driven by the climate shock (Table 8, Appendix).
For instance, under the worst case scenarios (i.e. no CO2 fertilization effects and assuming no
adaptation), we notice that the impact of GDP are quite substantial where the latter range from -
1% to -3% under SRES A2 and -0.5% to -2.3% under the SRES B2. Furthermore, and upon
investigating the different GDP components, we observe that consumption, investment,
16
government spending, imports and exports all decrease with varying degrees. Whereas a fall of
imports might suggest an improvement in the balance of payments, nonetheless, we notice that
the fall in exports is larger than the one observed in imports. Most of the impact on GPD can be
traced back to the impact of climate change on private (household and intermediate
23
)
consumption. The latter experience a fall ranging from -0.6% to -3.4% across both SRES
scenarios. The decline observed in investment is closely linked to one observed in private
consumption, especially the one related to intermediate consumption as it relates to demand for
intermediate consumption from the productive sectors in the economy which can be thought of
as a proxy to the level of economic activity. Indeed, a fall in intermediate consumption suggests
a contraction in the level of economic activity of the productive sectors, which suggests less
demand for investment. This contraction in the level of economic activity is clearly depicted in
our results (Table 9, Appendix A), where we notice that the impact on GDP by sector is falling
for all sectors. As one might expect, the agricultural sector (especially the crop production
sectors) is the most hard hit due to the direct impact on yield where the impacts of sectorial GDP
range from -3% to -15%, followed by the food processing sectors with a decrease ranging from -
1% to -6%. The impact on sectorial GDP in other sectors follows a similar trend, but with
varying degrees depending on the strength of their link to the agricultural sector. Historically,
these results were observed in the wake of severe droughts, e.g. in the wake of the 1981 and
2000 severe droughts (Table 10, Appendix A).
Private consumption falls substantially following the increase in prices and the negative impact
on household income. Indeed, household income decreases substantially, with effects more or
less equally distributed across regions (Table 11, Appendix). This is driven by the decline in
returns on factor payments to households due to decreased productivity captured by declining
factor wages, which in turn is caused by the climate-induced contraction in factor demand from
all sectors (Tables 13-16, Appendix A). Hence, the observed decline in GDP is mainly driven by
the negative impacts of climate change on agricultural crop production and sectors closely
related to agriculture, such as the food processing sector. For instance, aggregate agricultural
crop production declines by -15% for the worst case scenario under SRES, and assuming no
adaptation. Aggregated output from food processing sectors, due to their direct linkages with the
agricultural sector follows a similar trend, albeit with a lesser magnitude in terms of impacts. The
effects are somewhat lessened under the SRES B2, but remains mostly negative.
Overall, under both SRES scenarios (A2 and B2) and assuming no-adaptation, impacts on
agriculture range from a serious fall in production under the “low productivity scenario
assuming no CO
2
fertilization effects to a moderate increase driven by the positive dampening
23
By intermediate consumption we refer to the demand of the production sectors for intermediate inputs.
17
effect of CO
2
fertilization effects
24
on crop yields under the “high productivity” scenario (Table
17, Appendix A). Factor employment drastically increases to compensate the loss in production
due to declining yields. Agricultural trade deficit deteriorates tremendously under the worst case
scenarios. For instance, assuming no-adaptation under the A2_noCO
2
_low, imports increase by
more than 30 percent while exports decrease by 11 percent. The impacts are lessened under the
“medium” and “high” productivity scenarios, and the impacts get reversed under the “high
productivity” scenario if we include CO
2
fertilization effect’s on yield.
Food processing is the second most affected sector from the climate change. Most indicators for
the sector closely follow the observed trend in agricultural production sector. Factors of
employment respond to the change in wages in all scenarios. The trade figures move in the same
direction compared to the agricultural sector. The other sectors follow the food production sector
with less significant changes in production and employment.
However, and given the uncertainty underlying the climate projections and their impacts on
yields as discussed earlier, it seems that Morocco will have time to adjust to adverse effects of
climate change for a limited period of time if policies are adopted in a timely manner. This is
depicted by our results (Table 8, Appendix A), where we observe a substantial positive impact
when we include the PMV adaptation targets and also the CO
2
fertilization effects. Indeed,
including the PMV targets boosts agricultural production significantly, and especially under the
scenario including the CO2 fertilization effects (Table 17, Appendix). The increase reaches up to
20 percent under the best case scenario of “A2_wCO
2
_High”, which is mainly due to increased
yield. Wages faced by agricultural sector increased since marginal productivity of factors
increase when yields increase. Consequently factor employment decreases. However, note that
the decline in factor employment is quite low compared to the improvement in yield.
Agricultural trade deficit significantly improves as a result of increasing exports and declining
imports. These conclusions rest on the assumption that the capital investments programmed in
the PMV will, in fact, achieve the targeted productivity increases. There are reasons to believe
that achieving these targets will be a real challenge.
5. Conclusion
The agricultural sector in Morocco consists of a heterogeneous distribution of production activity
across regions. This diversity in the regional structure of agriculture brings about complicated
linkages in terms of projected impacts of climate change across regions, which in turn trickles
down to affect the rest of the economy.
24
The latter vary quite significantly from one SRES scenario to other and across crops. From our computations
using the data from the WB/Morocco/FAO study, we find that the latter fall within the range of +2% (Olives) to
+15% (Tomatoes).
18
In this paper, we attempt to shed light on interregional linkages under different climate-driven
agricultural productivity shocks using a regional adaptation of a CGE model. In Morocco,
climate change intervenes by substantially changing the regional production patterns and hence,
introduces changes in prices of commodities. As showcased in the results, agriculture, and to a
certain degree, food processing sectors production levels are substantially affected by climate
change. Depending on the strength of the linkages that exist between the primary sector and the
rest of the economy, the former tend to act as a drag on the rest of the sectors under the worst
case scenarios where substantial climate-induced yield shocks (mostly negative) tend to trickle
down and affect productivity in other sectors through many channels, such as increased prices
for primary inputs and reduced demand for private and intermediate consumption.
Like any research, there are areas for improvement in the future. Currently interregional linkages
are modeled simplistically through redistribution of excess supply regions to excess demand
regions based on their shares. Alternative approaches could be explored. Additionally, the
projected impacts of the PMV investments strategy are captured using the simplistic assumption
of compensating climate-induced yield losses by the projected yield improvements entailed
under the PMV. A better strategy would be to directly model the provisional impacts of the PMV
investments into our modeling framework which is beyond the aim of this paper.
Finally, there is need to update the data used in the analysis. The current model assumes all
regional households have the same preferences, but differ only based on their level of
expenditure from one region to the other. Access to better household surveys could provide us
with better understanding the dynamics of consumption patterns and a better evaluation of
welfare impacts.
19
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22
APPENDIX A. Data Tables
Table 1: Administrative regions in Morocco
TR1*
Guelmim-Es Semara, Laayoune-Boujdour-Sakia
El Hamra and Oued Eddahab-Lagouira
TR8
Rabat-Sale-Zemmour-Zaer
Souss-Massa-Draa
TR9
Doukkala-Abda
Gharb-Cherarda-Bni Hsan
TR10
Tadla-Azilal
Chaouia-Ouardigha
TR11
Meknes-Tafilalet
Marrakech-Tensift-El Haouz
TR12
Fes-Boulemane
L'Oriental
TR13
Taza-Taounate-Al Hoceima
Grand Casablanca
TR14
Tanger-Tetouan
N.B:
*TR1 represents the aggregation of three regions due to data limitations.
Source: Authors’ adaptation
Table 2: List of Activities
Source: Authors’ adaptation
hdwht-a Hard wheat
forst-a Forestry
sfwh t-a Soft wheat
fshry-a Fishery
barly-a Barley
dairy-a Dairy
sgrcr-a Sugar crops (incl. sugarbeet and sugarcane)
sugar -a Sugar processing
tomat-a Tomatoes
milhw-a Hard wheat mill
xvegts-a Other vegetables (incl. potatoes and onions)
milsw-a Soft wheat mill
xvgin-a Other industrial vegetables
oilpr-a Processed oil
forags-a Forage crops (incl. Alfalfa)
olvwh-a Whole olives
olive-a Olives
olvol-a Olive oil
agrms-a Citrus
xfdpr-a Other food processing
xfruts-a Other fruit (incl. grapes, dates, and almonds)
chmcl-a Chemical industries
xcrops-a Other crops nested (incl. other grains, grain legumes, other ind. Crops)
refol-a Refined petroleum
bovin-a Bovine meat wtr el-a Water and electricity utilities
ovine-a Sheep and other red meats xinds-a Other industries
avine-a Poultry srvpr-a Private services
xmeat-a Other meat production srvpb-a Public services
Agriculture (incl. crop production and livestock), forestry and fishery
Manufacturing and industry (incl. food processing)
Services
23
Table 3: List of commodities
Source: Authors’ adaptation
Table 4: Sample data of regional crop production statistics for durum wheat
HARD WHEAT (in million Qx)
DPA
ORMVA
REGION 1: Guelmim-Es Semara, Laayoune-Boujdour-
Sakia El Hamra and Oued Eddahab-Lagouira
1.7 1.3
REGION 2: Souss-Massa-Draa
0.0
102.9
REGION 3: Gharb-Cherarda-Bni Hsan
343.8
535.9
REGION 4: Chaouia-Ouardigha
3,375.5
0.0
REGION 5: Marrakech-Tensift-El Haouz
738.3
601.3
REGION 6: L'Oriental
471.7
108.1
REGION 7: Grand Casablanca
34.6
0.0
REGION 8: Rabat-Sale-Zemmour-Zaer
874.3
0.0
REGION 9: Doukkala-Abda
995.6
2,011.6
REGION 10: Tadla-Azilal
657.4
432.6
REGION 11: Meknes-Tafilalet
1,351.8
321.6
REGION 12: Fes-Boulemane
852.7
0.0
REGION 13: Taza-Taounate-Al Hoceima
2,827.4
0.0
REGION 14: Tanger-Tetouan
846.2
175.9
TOTAL
13,371.0
4,291.2
Source: author’s adaptation
hdwht-c Hard wheat
forst-c Forestry
sfwht-c Soft wheat
fshry-c Fishery
barly-c Barley
dairy-c Dairy
sgrcr-c Sugar crops (incl. sugarbeet and sugarcane)
sugar-c Sugar processing
tomat-c Tomatoes
milhw-c Hard wheat mill
xvegts-c Other vegetables (incl. potatoes and onions)
milsw-c Soft wheat mill
xvgin-c Other industrial vegetables
oilpr-c Processed oil
forags-c Forage crops (incl. Alfalfa)
olvwh-c Whole olives
olive-c Olives
olvol-c Olive oil
agrms-c Citrus
xfdpr-c Other food processing
xfruts-c Other fruit (incl. grapes, dates, and almonds)
chmcl-c Chemical industries
xcrops-c Other crops nested (incl. other grains, grain legumes, other ind. Crops)
refol-c Refined petroleum
meatrbov-c Bovine meat wtrel-c Water and electricity utilities
meatrov-c Sheep and other red meats xinds-c Other industries
meatw-c White meats srvpr-c Private services
xmeat-c Other meat production srvpb-c Public services
Agriculture (incl. crop production and livestock), forestry and fishery
Manufacturing and industry (incl. food processing)
Services
24
Table 5: Description of scenarios analysis
Scenario
Description
A2_noCO2
Projected yield impacts by 2050 under SRES A2, with no CO2
fertilization effect and no adaptation
B2_noCO2
Projected yield impacts by 2050 under SRES B2, with no CO2
fertilization effect and no adaptation
A2_wCO2
Projected yield impacts by 2050 under SRES A2, with CO2 fertilization
effect and no adaptation
B2_wCO2
Projected yield impacts by 2050 under SRES B2, with CO2 fertilization
effect and no adaptation
A2_noCO2_PMV
Projected yield impacts by 2050 under SRES A2, with no CO2
fertilization effect and with PMV adaptation
B2_noCO2_PMV
Projected yield impacts by 2050 under SRES B2, with no CO2
fertilization effect and with PMV adaptation
A2_wCO2_PMV
Projected yield impacts by 2050 under SRES A2, with CO2 fertilization
effect and with PMV adaptation
B2_wCO2_PMV
Projected yield impacts by 2050 under SRES B2, with CO2 fertilization
effect and with PMV adaptation
Source: Authors’ adaptation
Table 6: Projected yield impacts of the Plan Maroc Vert (PMV) for strategic crops by region
Code Regions
PMV - Crop sectors targeted
Cereals
Vegetables
Olives
Citrus
TR2
Souss-Massa-Draa
n.a.
47%
59%
33%
TR3
Gharb-Cherarda-Bni Hsan
73%
n.a.
63%
67%
TR4
Chaouia-Ouardigha
69%
68%
91%
n.a.
TR5
Marrakech-Tensift-El Haouz
52%
n.a.
80%
30%
TR6
L'Oriental
n.a.
n.a.
26%
96%
TR7
Grand Casablanca
86%
80%
n.a.
n.a.
TR8
Rabat-Sale-Zemmour-Zaer
87%
68%
83%
n.a.
TR9
Doukkala-Abda
93%
62%
n.a.
n.a.
TR10
Tadla-Azilal
38%
n.a.
79%
50%
TR11
Meknes-Tafilalet
80%
n.a.
78%
n.a.
TR12
Fes-Boulemane
93%
57%
92%
n.a.
TR13
Taza-Taounate-Al Hoceima
85%
n.a.
44%
77%
TR14
Tanger-Tetouan
n.a.
n.a.
53%
79%
Source: Authors’ adaptation (Data source: Agence pour le Développement Agricole
http://www.ada.gov.ma/en/Plan_Maroc_Vert/plan-maroc-vert.php)
25
Table 7: Regional production statistics (in Qx) and percent shares for all crops in Morocco for the 2003 agricultural campaign
Durum
wheat
Common
wheat
Barley Tomatoes
Other
vegetables
Oth.
Industrial
vegetables
Forages
crops
Olives Citrus Oth. fruits Oth. crops
Production (in Qx)
TR1
3
7
10
266
373
373
1
7
0
61
0
TR2
103
558
1,046
4,764
4,847
4,847
11
206
6,552
1,721
20
TR3
880
5,178
538
2,284
6,825
6,825
15
344
2,163
973
13,803
TR4
3,376
4,043
4,449
136
6,460
6,460
12
39
0
282
388
TR5
1,340
2,619
4,273
200
3,603
3,603
28
2,792
659
1,797
56
TR6
580
1,511
3,134
123
2,365
2,365
11
336
1,993
537
3,192
TR7
35
217
317
145
1,058
1,058
12
0
0
0
42
TR8
874
2,904
1,112
283
2,648
2,648
15
132
70
692
253
TR9
3,007
2,607
3,886
1,079
5,593
5,593
63
46
10
853
14,715
TR10
1,090
3,867
1,894
43
2,652
2,652
25
726
1,276
307
9,831
TR11
1,673
4,522
1,273
245
6,752
6,752
26
515
30
2,886
355
TR12
853
2,243
879
66
1,469
1,469
3
811
23
802
252
TR13
2,827
2,471
2,758
191
1,048
1,048
11
2,001
178
491
711
TR14
1,022
1,060
635
544
5,785
5,785
26
346
191
509
4,816
Shares (in %)
TR1
0.02%
0.02%
0.04%
2.56%
0.73%
0.73%
0.43%
0.09%
0.00%
0.52%
0.00%
TR2
0.58%
1.65%
3.99%
45.95%
9.42%
9.42%
4.26%
2.48%
49.84%
14.45%
0.04%
TR3
4.98%
15.32%
2.05%
22.03%
13.26%
13.26%
5.93%
4.14%
16.45%
8.17%
28.50%
TR4
19.11%
11.96%
16.98%
1.31%
12.55%
12.55%
4.49%
0.47%
0.00%
2.37%
0.80%
TR5
7.58%
7.75%
16.31%
1.93%
7.00%
7.00%
10.77%
33.63%
5.01%
15.09%
0.12%
TR6
3.28%
4.47%
11.96%
1.19%
4.59%
4.59%
4.34%
4.05%
15.16%
4.51%
6.59%
TR7
0.20%
0.64%
1.21%
1.40%
2.05%
2.05%
4.46%
0.00%
0.00%
0.00%
0.09%
TR8
4.95%
8.59%
4.24%
2.73%
5.14%
5.14%
5.81%
1.59%
0.53%
5.81%
0.52%
TR9
17.03%
7.71%
14.83%
10.41%
10.87%
10.87%
24.37%
0.56%
0.08%
7.16%
30.38%
TR10
6.17%
11.44%
7.23%
0.41%
5.15%
5.15%
9.69%
8.74%
9.71%
2.58%
20.30%
TR11
9.47%
13.38%
4.86%
2.36%
13.12%
13.12%
10.19%
6.20%
0.23%
24.23%
0.73%
TR12
4.83%
6.63%
3.35%
0.63%
2.85%
2.85%
1.08%
9.77%
0.17%
6.74%
0.52%
TR13
16.01%
7.31%
10.53%
1.84%
2.04%
2.04%
4.26%
24.11%
1.35%
4.12%
1.47%
TR14
5.79%
3.14%
2.42%
5.25%
11.24%
11.24%
9.92%
4.17%
1.45%
4.27%
9.94%
Source: Authors’ adaptation (data source: PV No. 29 of the 2002-2003 agricultural campaign)
26
Table 8: Effects on macro accounts and gross domestic product (GDP) – without and with adaptation (Base value at billion Dhs)
BASE
Climate Change, No Adaptation (%Change from base)
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Absorption - A = C + I + G
492,093
-2.98%
-1.62%
-0.86%
-2.27%
-1.18%
-0.49%
-1.32%
-0.21%
0.55%
-1.35%
-0.24%
0.39%
Consumption - C
272,986
-3.37%
-1.84%
-0.97%
-2.61%
-1.36%
-0.56%
-1.50%
-0.18%
0.71%
-1.55%
-0.24%
0.51%
Investment - I
133,622
-2.88%
-1.54%
-0.84%
-2.09%
-1.10%
-0.48%
-1.29%
-0.31%
0.35%
-1.27%
-0.29%
0.26%
Government - G
85,485
-1.91%
-1.01%
-0.53%
-1.45%
-0.74%
-0.30%
-0.83%
-0.13%
0.35%
-0.85%
-0.15%
0.25%
Exports - X
139,736
-0.85%
-0.44%
-0.22%
-0.71%
-0.27%
0.00%
-0.03%
0.45%
0.69%
-0.14%
0.32%
0.55%
Imports - M
-153,254
-0.78%
-0.40%
-0.20%
-0.65%
-0.25%
0.00%
-0.02%
0.41%
0.63%
-0.13%
0.30%
0.50%
Gross Domestic Product (GDP)
478,574
-3.07%
-1.66%
-0.88%
-2.33%
-1.21%
-0.51%
-1.36%
-0.21%
0.57%
-1.39%
-0.24%
0.40%
BASE
Climate Change, with Adaptation-PMV (%Change from base)
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Absorption - A = C + I + G
492,093
-0.26%
1.00%
1.69%
0.39%
1.39%
2.02%
1.30%
2.31%
3.00%
1.26%
2.27%
2.83%
Consumption - C
272,986
0.05%
1.47%
2.28%
0.75%
1.91%
2.64%
1.82%
3.01%
3.83%
1.76%
2.95%
3.62%
Investment - I
133,622
-0.90%
0.34%
0.98%
-0.17%
0.74%
1.31%
0.63%
1.51%
2.12%
0.62%
1.51%
2.00%
Government - G
85,485
-0.28%
0.52%
0.95%
0.14%
0.76%
1.14%
0.71%
1.30%
1.72%
0.68%
1.27%
1.62%
Exports - X
139,736
1.47%
1.89%
2.14%
1.56%
2.07%
2.36%
2.32%
2.84%
3.12%
2.18%
2.70%
2.94%
Imports - M
-153,254
1.34%
1.73%
1.95%
1.43%
1.89%
2.15%
2.12%
2.59%
2.85%
1.99%
2.46%
2.69%
Gross Domestic Product (GDP)
478,574
-0.27%
1.03%
1.74%
0.40%
1.43%
2.08%
1.34%
2.37%
3.08%
1.30%
2.33%
2.91%
Source: Simulations results
Table 9: Effects gross domestic product (GDP) disaggregated by sectors – without and with adaptation (Base value at billion Dhs)
Climate Change, No Adaptation (%Change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Agriculture (crop production)
52,047
-14.50%
-8.08%
-4.12%
-11.46%
-6.12%
-2.56%
-6.73%
-0.88%
3.40%
-7.15%
-1.25%
2.20%
Livestock
11,260
-4.32%
-2.51%
-1.38%
-3.53%
-1.84%
-0.89%
-2.33%
-0.63%
0.55%
-2.30%
-0.56%
0.40%
Forestry
1,072
1.14%
0.60%
0.30%
0.97%
0.50%
0.22%
0.61%
0.00%
-0.36%
0.69%
0.07%
-0.24%
Fisheries
5,065
-2.47%
-1.51%
-0.90%
-1.95%
-1.09%
-0.57%
-1.42%
-0.67%
-0.08%
-1.30%
-0.53%
-0.07%
Dairy
618
-1.96%
-1.17%
-0.55%
-1.91%
-0.82%
-0.30%
-1.04%
-0.08%
0.52%
-1.10%
-0.07%
0.39%
Food processing
22,133
-5.89%
-3.34%
-1.87%
-4.53%
-2.51%
-1.24%
-3.17%
-1.01%
0.52%
-3.09%
-0.92%
0.35%
Industry and Manufacture
99,567
-0.60%
-0.31%
-0.21%
-0.38%
-0.19%
-0.09%
-0.18%
-0.04%
0.02%
-0.17%
-0.02%
0.05%
Services
232,890
-1.56%
-0.86%
-0.47%
-1.18%
-0.62%
-0.26%
-0.67%
-0.13%
0.24%
-0.66%
-0.14%
0.18%
Climate Change, with Adaptation-PMV (%Change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
27
Agriculture (crop production)
52,047
2.35%
8.48%
12.21%
5.17%
10.30%
13.65%
9.95%
15.51%
19.59%
9.44%
15.05%
18.29%
Livestock
11,260
-2.72%
-0.99%
0.05%
-1.94%
-0.36%
0.52%
-0.83%
0.76%
1.83%
-0.77%
0.83%
1.71%
Forestry
1,072
-0.07%
-0.56%
-0.83%
-0.21%
-0.64%
-0.90%
-0.56%
-1.15%
-1.50%
-0.47%
-1.06%
-1.36%
Fisheries
5,065
-1.44%
-0.55%
0.01%
-0.93%
-0.15%
0.31%
-0.46%
0.19%
0.70%
-0.33%
0.33%
0.73%
Dairy
618
-1.24%
-0.53%
0.05%
-1.23%
-0.20%
0.27%
-0.38%
0.50%
1.04%
-0.44%
0.50%
0.90%
Food processing
22,133
-2.05%
0.29%
1.63%
-0.77%
1.05%
2.19%
0.47%
2.36%
3.73%
0.56%
2.45%
3.58%
Industry and Manufacture
99,567
-0.04%
0.22%
0.30%
0.17%
0.32%
0.41%
0.35%
0.45%
0.49%
0.36%
0.47%
0.51%
Services
232,890
-0.21%
0.42%
0.78%
0.14%
0.64%
0.97%
0.61%
1.08%
1.40%
0.63%
1.07%
1.35%
Source: Simulations results
Table 10: Macroeconomic impacts of historical droughts in Morocco
Year GDP (current US$)
Value added (current US$)
Agriculture
Industry
Manufacturing
Services
1980
18,820,809,836
3,468,322,918
5,823,121,475
3,166,717,472
9,474,290,346
1981 (Drought)
15,280,300,833
1,973,145,409
5,205,421,186
2,764,862,827
8,102,995,075
%Change
-18.8%
-43.1%
-10.6%
-12.7%
-14.5%
1998
40,021,694,631
7,175,553,492
9,831,727,514
6,136,031,660
18,474,314,520
2000 (Drought)
37,020,609,825
4,916,337,286
9,575,098,814
5,744,965,180
18,406,644,081
%Change
-7.5%
-31.5%
-2.6%
-6.4%
-0.4%
Source: Authors adaptation (data source: World Bank, 2012)
Table 11: Percent change in aggregate domestic output by sector – without and with adaptation (base values in billion Dhs)
Climate Change, No Adaptation (%Change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Agriculture (crop production)
75,813
-4.26%
-2.07%
-0.74%
-3.57%
-1.64%
-0.45%
-1.59%
0.68%
2.18%
-2.08%
0.23%
1.38%
Livestock
34,380
-1.74%
-0.47%
0.06%
-1.13%
-0.28%
0.25%
0.04%
0.79%
1.30%
-0.24%
0.54%
0.94%
Forestry&Fishery
10,162
-4.54%
-2.58%
-1.48%
-3.47%
-1.87%
-0.88%
-2.25%
-0.73%
0.34%
-2.16%
-0.63%
0.25%
Dairy
5,856
-3.78%
-2.08%
-1.10%
-3.02%
-1.50%
-0.65%
-1.80%
-0.37%
0.58%
-1.82%
-0.35%
0.42%
Food processing
166,078
-4.74%
-2.54%
-1.38%
-3.56%
-1.85%
-0.79%
-2.16%
-0.44%
0.72%
-2.16%
-0.44%
0.55%
Industry and Manufacture
544,386
-3.06%
-1.67%
-0.99%
-2.25%
-1.18%
-0.55%
-1.37%
-0.39%
0.22%
-1.32%
-0.34%
0.19%
Services
592,030
-3.83%
-2.09%
-1.14%
-2.91%
-1.52%
-0.66%
-1.70%
-0.30%
0.62%
-1.71%
-0.32%
0.45%
Climate Change, No Adaptation (%Change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Agriculture (crop production)
75,813
3.73%
5.62%
6.74%
4.18%
5.89%
6.91%
6.17%
8.21%
9.58%
5.57%
7.65%
8.68%
Livestock
34,380
0.67%
1.81%
2.28%
1.23%
1.96%
2.42%
2.31%
2.88%
3.31%
2.03%
2.64%
2.96%
28
Forestry&Fishery
10,162
-1.93%
-0.15%
0.83%
-0.91%
0.49%
1.35%
0.17%
1.44%
2.34%
0.29%
1.55%
2.29%
Dairy
5,856
-1.20%
0.32%
1.19%
-0.50%
0.83%
1.57%
0.62%
1.82%
2.62%
0.60%
1.82%
2.47%
Food processing
166,078
-1.85%
0.16%
1.21%
-0.74%
0.78%
1.72%
0.52%
1.99%
2.99%
0.56%
2.00%
2.85%
Industry and Manufacture
544,386
-0.82%
0.39%
0.96%
-0.07%
0.80%
1.33%
0.69%
1.45%
1.92%
0.76%
1.50%
1.90%
Services
592,030
-0.63%
0.94%
1.78%
0.22%
1.44%
2.20%
1.33%
2.50%
3.29%
1.33%
2.48%
3.14%
Table 12: Effects on household income at the regional and national level – without and with adaptation (Base value at billion Dhs)
Climate Change, No Adaptation (%change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
TR1
10,365
-3.77%
-2.10%
-1.21%
-2.85%
-1.53%
-0.73%
-1.86%
-0.58%
0.31%
-1.81%
-0.52%
0.21%
TR2
33,508
-3.30%
-1.66%
-0.64%
-2.51%
-1.19%
-0.25%
-0.98%
0.73%
1.82%
-1.26%
0.45%
1.30%
TR3
23,649
-1.64%
-0.77%
-0.18%
-1.96%
-0.46%
0.30%
0.00%
1.85%
2.47%
-0.09%
1.27%
1.89%
TR4
24,596
-4.98%
-2.94%
-1.00%
-3.76%
-2.22%
-0.95%
-3.15%
-1.66%
0.13%
-2.75%
-1.35%
-0.22%
TR5
31,369
-3.19%
-1.84%
-1.08%
-2.38%
-1.38%
-0.73%
-1.55%
-0.49%
0.25%
-1.60%
-0.47%
0.23%
TR6
29,339
-3.24%
-1.76%
-1.08%
-2.71%
-1.44%
-0.67%
-1.62%
-0.24%
0.55%
-1.69%
-0.43%
0.40%
TR7
107,311
-3.81%
-2.08%
-1.17%
-2.85%
-1.52%
-0.70%
-1.81%
-0.52%
0.36%
-1.77%
-0.47%
0.27%
TR8
40,695
-4.01%
-2.31%
-1.44%
-3.07%
-1.68%
-0.91%
-2.15%
-0.83%
0.04%
-2.05%
-0.69%
0.02%
TR9
24,742
-4.29%
-1.63%
-0.61%
-2.57%
-1.31%
-0.39%
-0.91%
0.18%
1.14%
-1.45%
-0.14%
0.82%
TR10
14,973
-2.09%
-0.60%
-0.09%
-1.26%
-0.28%
0.28%
0.39%
0.99%
1.41%
-0.26%
0.73%
1.03%
TR11
27,666
-2.99%
-1.86%
-1.18%
-2.47%
-1.41%
-0.83%
-2.12%
-0.84%
-0.03%
-2.01%
-0.67%
-0.04%
TR12
22,580
-4.23%
-2.49%
-1.54%
-3.32%
-1.80%
-0.96%
-2.35%
-0.95%
0.00%
-2.27%
-0.76%
-0.02%
TR13
14,867
-4.92%
-2.86%
-1.95%
-3.82%
-2.00%
-1.24%
-2.95%
-1.38%
-0.53%
-2.75%
-1.00%
-0.36%
TR14
34,661
-3.48%
-1.69%
-0.92%
-2.57%
-1.20%
-0.45%
-1.28%
0.08%
0.84%
-1.29%
-0.02%
0.65%
National_Urban
344,485
-3.58%
-1.92%
-1.03%
-2.73%
-1.41%
-0.61%
-1.63%
-0.30%
0.59%
-1.66%
-0.32%
0.42%
National_Rural
95,838
-3.69%
-1.98%
-1.06%
-2.81%
-1.46%
-0.63%
-1.68%
-0.31%
0.61%
-1.71%
-0.33%
0.43%
National
440,323
-3.60%
-1.94%
-1.04%
-2.75%
-1.42%
-0.62%
-1.64%
-0.30%
0.59%
-1.67%
-0.32%
0.42%
Climate Change, with Adaptation-PMV (%change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
TR1
10,365
-1.22%
0.28%
1.06%
-0.35%
0.78%
1.48%
0.53%
1.60%
2.35%
0.60%
1.65%
2.26%
TR2
33,508
0.26%
1.78%
2.74%
0.98%
2.20%
3.09%
2.46%
4.01%
5.06%
2.15%
3.73%
4.53%
TR3
23,649
3.18%
3.87%
4.35%
2.62%
4.10%
4.77%
4.62%
6.41%
6.93%
4.51%
5.76%
6.30%
TR4
24,596
-1.47%
0.29%
2.10%
-0.41%
0.90%
2.03%
0.13%
1.29%
2.91%
0.45%
1.56%
2.56%
TR5
31,369
-0.76%
0.49%
1.16%
0.03%
0.88%
1.45%
0.75%
1.68%
2.32%
0.74%
1.68%
2.27%
TR6
29,339
0.06%
1.42%
2.04%
0.56%
1.72%
2.42%
1.58%
2.82%
3.55%
1.53%
2.65%
3.41%
TR7
107,311
-1.25%
0.29%
1.08%
-0.35%
0.78%
1.49%
0.57%
1.63%
2.36%
0.63%
1.67%
2.28%
TR8
40,695
-1.11%
0.37%
1.10%
-0.24%
0.91%
1.56%
0.56%
1.62%
2.32%
0.66%
1.74%
2.31%
TR9
24,742
-0.54%
2.01%
2.93%
1.17%
2.25%
3.05%
2.71%
3.58%
4.41%
2.20%
3.26%
4.06%
29
TR10
14,973
0.66%
2.16%
2.63%
1.46%
2.45%
2.98%
3.17%
3.74%
4.13%
2.44%
3.45%
3.72%
TR11
27,666
-0.10%
0.64%
1.11%
0.23%
0.95%
1.37%
0.37%
1.33%
1.94%
0.49%
1.47%
1.95%
TR12
22,580
-1.01%
0.47%
1.26%
-0.19%
1.06%
1.76%
0.66%
1.75%
2.52%
0.74%
1.91%
2.50%
TR13
14,867
-2.33%
-0.54%
0.22%
-1.33%
0.23%
0.86%
-0.54%
0.75%
1.43%
-0.35%
1.08%
1.60%
TR14
34,661
-1.51%
0.10%
0.75%
-0.67%
0.52%
1.17%
0.52%
1.65%
2.28%
0.52%
1.55%
2.10%
National_Urban
344,485
-0.64%
0.83%
1.61%
0.14%
1.27%
1.97%
1.14%
2.25%
3.01%
1.11%
2.22%
2.84%
National_Rural
95,838
-0.65%
0.86%
1.67%
0.14%
1.31%
2.03%
1.17%
2.33%
3.11%
1.15%
2.29%
2.93%
National
440,323
-0.64%
0.84%
1.62%
0.14%
1.28%
1.98%
1.14%
2.27%
3.03%
1.12%
2.23%
2.86%
Source: Simulations results
Table 13: Percent change in demand for labor in all sectors across scenarios analysis without and with adaptation
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Climate Change, No Adaptation (%change from base)
Agriculture (crop production)
6,151
-0.09%
0.26%
0.48%
-0.11%
0.10%
0.25%
0.21%
0.30%
0.52%
-0.09%
0.09%
0.12%
Livestock
3,093
-7.70%
-4.43%
-2.43%
-6.24%
-3.26%
-1.53%
-3.99%
-0.92%
1.19%
-3.99%
-0.87%
0.87%
Forestry
130
-1.78%
-0.98%
-0.61%
-1.28%
-0.67%
-0.33%
-0.79%
-0.30%
0.00%
-0.71%
-0.22%
0.03%
Fishery
2,129
-4.41%
-2.54%
-1.47%
-3.40%
-1.85%
-0.90%
-2.27%
-0.80%
0.26%
-2.17%
-0.69%
0.18%
Dairy
115
-4.47%
-2.43%
-1.19%
-3.74%
-1.74%
-0.65%
-1.98%
-0.06%
1.16%
-2.14%
-0.13%
0.86%
Food Processing
5,029
-8.37%
-4.65%
-2.61%
-6.38%
-3.48%
-1.67%
-4.26%
-1.17%
0.98%
-4.23%
-1.11%
0.71%
Industry and Manufacture
33,568
-2.86%
-1.55%
-0.90%
-2.09%
-1.10%
-0.50%
-1.27%
-0.32%
0.28%
-1.24%
-0.29%
0.23%
Services
105,555
-3.05%
-1.67%
-0.91%
-2.31%
-1.21%
-0.53%
-1.37%
-0.27%
0.46%
-1.37%
-0.28%
0.34%
Climate Change, With Adaptation-PMV (%change from base)
Agriculture (crop production)
6,151
0.02%
0.41%
0.55%
0.07%
0.23%
0.30%
0.30%
0.38%
0.56%
0.01%
0.19%
0.20%
Livestock
3,093
-3.96%
-0.81%
1.08%
-2.51%
0.31%
1.93%
-0.38%
2.48%
4.43%
-0.33%
2.54%
4.15%
Forestry
130
-0.86%
-0.21%
0.07%
-0.40%
0.05%
0.31%
-0.03%
0.28%
0.46%
0.07%
0.36%
0.52%
Fishery
2,129
-1.98%
-0.26%
0.71%
-1.00%
0.37%
1.22%
0.00%
1.26%
2.17%
0.13%
1.38%
2.12%
Dairy
115
-1.71%
0.15%
1.26%
-1.05%
0.76%
1.73%
0.61%
2.29%
3.36%
0.46%
2.21%
3.06%
Food Processing
5,029
-2.08%
1.33%
3.20%
-0.21%
2.38%
4.02%
1.75%
4.45%
6.35%
1.79%
4.48%
6.09%
Industry and Manufacture
33,568
-0.78%
0.38%
0.94%
-0.07%
0.77%
1.28%
0.67%
1.43%
1.91%
0.71%
1.45%
1.87%
Services
105,555
-0.57%
0.67%
1.34%
0.11%
1.07%
1.68%
0.97%
1.90%
2.53%
0.98%
1.89%
2.42%
Sources: Simulations results
30
Table 14: Percent change in demand for capital in all sectors across scenarios analysis without and with adaptation
Climate Change, No Adaptation (%change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Agriculture (crop production)
26,771
-0.33%
0.15%
0.32%
-0.28%
0.03%
0.10%
-0.17%
0.30%
0.45%
-0.32%
0.15%
0.25%
Livestock
6,139
-8.24%
-4.67%
-2.47%
-6.50%
-3.46%
-1.60%
-4.22%
-1.07%
1.21%
-4.18%
-1.00%
0.85%
Forestry&Fishery
4,007
-4.90%
-2.77%
-1.59%
-3.76%
-2.01%
-0.95%
-2.41%
-0.75%
0.41%
-2.34%
-0.66%
0.30%
Dairy
1,291
-9.17%
-5.32%
-3.25%
-6.88%
-4.08%
-2.36%
-5.07%
-2.00%
0.17%
-4.79%
-1.79%
-0.13%
Food Processing
17,104
-9.31%
-5.21%
-2.86%
-7.15%
-3.89%
-1.83%
-4.71%
-1.26%
1.17%
-4.67%
-1.21%
0.83%
Industry&Manufacture
160,811
-4.53%
-2.49%
-1.40%
-3.39%
-1.81%
-0.83%
-2.08%
-0.52%
0.53%
-2.06%
-0.49%
0.40%
Services
35,077
-3.66%
-1.83%
-0.99%
-2.76%
-1.28%
-0.43%
-1.32%
0.17%
0.96%
-1.36%
0.05%
0.79%
Climate Change, With Adaptation-PMV (%change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Agriculture (crop production)
26,771
-0.01%
0.01%
-0.09%
-0.17%
-0.30%
-0.47%
-0.33%
-0.26%
-0.34%
-0.50%
-0.46%
-0.56%
Livestock
6,139
-3.88%
-0.49%
1.59%
-2.18%
0.66%
2.39%
-0.03%
2.86%
4.95%
0.04%
2.93%
4.61%
Forestry&Fishery
4,007
-2.05%
-0.11%
0.95%
-0.96%
0.58%
1.51%
0.24%
1.65%
2.62%
0.34%
1.74%
2.55%
Dairy
1,291
-5.23%
-1.51%
0.48%
-2.98%
-0.32%
1.31%
-1.24%
1.63%
3.69%
-0.94%
1.84%
3.38%
Food Processing
17,104
-3.02%
0.82%
2.99%
-0.92%
2.03%
3.91%
1.33%
4.38%
6.56%
1.41%
4.43%
6.24%
Industry and Manufacture
160,811
-1.07%
0.77%
1.73%
0.00%
1.38%
2.24%
1.19%
2.50%
3.39%
1.23%
2.52%
3.27%
Services
35,077
-0.63%
0.99%
1.69%
0.16%
1.45%
2.16%
1.47%
2.71%
3.32%
1.46%
2.58%
3.17%
Sources: Simulations results
Table 15: Percent change in demand for irrigated land in agricultural sectors across scenarios analysis – without and with adaptation
Climate Change, No Adaptation (%change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Agriculture (crop production)
7,972
-1.20%
-0.20%
0.44%
-1.55%
-0.26%
0.44%
0.21%
1.94%
3.42%
-0.94%
1.14%
2.19%
HDWHT-A
290
2.10%
3.15%
1.61%
3.05%
3.01%
2.24%
3.91%
3.69%
1.68%
3.76%
3.13%
2.15%
SFWHT-A
772
-1.24%
0.43%
0.58%
-0.41%
0.57%
0.59%
0.58%
1.26%
0.82%
0.48%
1.01%
0.77%
BARLY-A
344
15.47%
12.28%
9.84%
11.54%
8.89%
6.52%
13.19%
9.30%
6.57%
10.17%
6.72%
3.34%
TOMAT-A
887
-4.12%
-2.25%
-1.06%
-3.36%
-1.67%
-0.52%
-1.03%
1.13%
2.10%
-1.16%
0.68%
1.50%
XVEGTS-A
1,169
-3.42%
-1.95%
-1.26%
-2.48%
-1.45%
-1.04%
-2.73%
-1.78%
-1.00%
-2.46%
-1.43%
-0.98%
XVGIN-A
33
-3.28%
-2.61%
-2.72%
-2.03%
-2.10%
-2.96%
-6.09%
-7.32%
-7.42%
-4.81%
-5.48%
-6.17%
FORAGS-A
698
6.63%
3.72%
1.30%
8.70%
2.44%
0.47%
1.70%
-4.24%
-6.78%
2.81%
-3.27%
-4.86%
OLIVE-A
203
12.49%
6.63%
5.70%
6.93%
4.29%
4.03%
9.48%
5.35%
4.78%
7.06%
3.18%
2.10%
31
AGRMS-A
1,634
-8.27%
-3.65%
-0.06%
-10.15%
-2.71%
1.12%
-0.44%
8.39%
15.85%
-5.32%
5.28%
10.55%
XFRUTS-A
1,155
3.43%
0.55%
0.38%
0.62%
-0.32%
-0.72%
-1.39%
-0.67%
0.37%
-1.65%
-0.73%
0.07%
XCROPS-A
787
-5.53%
-1.67%
-0.85%
-3.25%
-1.22%
-0.29%
-1.18%
0.57%
1.14%
-1.51%
0.26%
0.95%
Climate Change, With Adaptation-PMV (%change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Agriculture (crop production)
7,972
14.71%
15.90%
16.74%
13.82%
15.78%
16.73%
16.70%
19.15%
21.20%
14.91%
17.94%
19.40%
HDWHT-A
290
5.97%
6.93%
5.29%
6.85%
6.70%
5.85%
7.69%
7.32%
5.20%
7.51%
6.70%
5.63%
SFWHT-A
772
-11.15%
-10.97%
-11.50%
-11.16%
-11.47%
-11.93%
-10.75%
-11.13%
-11.94%
-11.09%
-11.65%
-12.19%
BARLY-A
344
23.26%
19.46%
16.60%
18.91%
15.73%
12.91%
20.50%
15.93%
12.75%
17.26%
13.18%
9.35%
TOMAT-A
887
-0.60%
0.95%
1.85%
0.11%
1.42%
2.24%
1.65%
3.09%
3.89%
1.57%
2.86%
3.53%
XVEGTS-A
1,169
-3.50%
-2.39%
-1.97%
-2.79%
-2.01%
-1.78%
-3.02%
-2.36%
-1.82%
-2.82%
-2.06%
-1.81%
XVGIN-A
33
3.88%
4.16%
3.77%
4.98%
4.54%
3.38%
0.43%
-1.30%
-1.62%
1.72%
0.63%
-0.29%
FORAGS-A
698
14.53%
11.00%
8.22%
16.50%
9.41%
7.18%
8.92%
2.24%
-0.62%
9.98%
3.16%
1.31%
OLIVE-A
203
-15.66%
-18.61%
-18.88%
-18.57%
-19.85%
-19.84%
-17.00%
-19.09%
-19.12%
-18.42%
-20.33%
-20.66%
AGRMS-A
1,634
62.46%
69.52%
75.11%
58.59%
70.88%
76.83%
74.90%
88.82%
99.78%
67.14%
83.78%
91.67%
XFRUTS-A
1,155
8.78%
5.41%
5.06%
5.61%
4.38%
3.83%
3.46%
3.98%
4.92%
3.10%
3.84%
4.56%
XCROPS-A
787
1.93%
5.82%
6.52%
4.19%
6.16%
7.01%
6.42%
8.03%
8.48%
5.96%
7.61%
8.23%
Sources: Simulations results
Table 16: Percent change in demand for rainfed land in agricultural sectors across scenarios analysis – without and with adaptation
Climate Change, No Adaptation (%change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
Agriculture (crop production)
11,153
-0.18%
0.39%
0.77%
-0.46%
0.20%
0.49%
0.25%
1.21%
2.12%
-0.46%
0.69%
1.29%
HDWHT-A
627
0.47%
1.84%
0.70%
1.72%
1.99%
1.53%
2.41%
2.71%
1.39%
2.33%
2.33%
1.95%
SFWHT-A
2,156
-2.47%
-0.25%
0.27%
-1.67%
0.07%
0.48%
0.03%
1.85%
2.13%
-0.37%
1.32%
1.72%
BARLY-A
544
12.39%
10.15%
8.49%
9.13%
7.35%
5.64%
10.94%
8.20%
6.82%
7.82%
5.87%
3.65%
TOMAT-A
1,595
-7.87%
-4.60%
-2.16%
-6.94%
-3.44%
-1.13%
-3.13%
0.88%
4.16%
-3.89%
0.28%
2.62%
XVEGTS-A
2,209
-5.06%
-3.07%
-1.89%
-4.03%
-2.31%
-1.45%
-3.84%
-1.94%
-0.20%
-3.70%
-1.66%
-0.44%
XVGIN-A
1,739
-4.96%
-3.73%
-3.35%
-3.61%
-2.95%
-3.37%
-7.20%
-7.53%
-6.76%
-6.04%
-5.73%
-5.70%
FORAGS-A
3,374
5.06%
2.64%
0.71%
7.42%
1.61%
0.03%
0.58%
-4.74%
-6.41%
1.49%
-3.72%
-4.61%
OLIVE-A
2,812
9.85%
5.00%
4.78%
4.90%
3.11%
3.48%
7.78%
4.83%
5.59%
5.05%
2.74%
2.70%
AGRMS-A
1,934
-12.49%
-6.22%
-1.18%
-14.24%
-4.60%
0.79%
-2.32%
9.44%
20.22%
-8.31%
5.74%
13.35%
XFRUTS-A
2,241
1.53%
-0.74%
-0.30%
-1.05%
-1.31%
-1.16%
-2.74%
-1.16%
1.11%
-3.21%
-1.19%
0.54%
XCROPS-A
13,726
-11.08%
-5.78%
-2.99%
-8.29%
-4.19%
-2.12%
-6.23%
-1.95%
0.50%
-5.55%
-1.65%
0.19%
Climate Change, With Adaptation-PMV (%change from base)
BASE
A2_noCO2
B2_noCO2
A2_wCO2
B2_wCO2
Low
Med
High
Low
Med
High
Low
Med
High
Low
Med
High
32
Agriculture (crop production)
11,153
11.76%
12.31%
12.74%
10.96%
11.99%
12.41%
12.52%
13.92%
15.19%
11.26%
13.03%
13.90%
HDWHT-A
627
10.60%
12.19%
11.12%
11.60%
12.28%
11.98%
13.00%
13.71%
12.59%
12.54%
13.03%
12.90%
SFWHT-A
2,156
-3.31%
-2.24%
-2.25%
-3.53%
-2.62%
-2.49%
-1.75%
-0.54%
-0.39%
-2.72%
-1.57%
-1.23%
BARLY-A
544
32.24%
29.50%
27.54%
28.21%
26.37%
24.18%
30.92%
27.73%
26.13%
27.06%
25.01%
22.38%
TOMAT-A
1,595
7.32%
10.90%
13.44%
8.03%
12.04%
14.45%
12.35%
16.54%
20.17%
11.13%
15.80%
18.40%
XVEGTS-A
2,209
4.09%
6.14%
7.33%
4.78%
6.91%
7.87%
5.68%
7.95%
9.93%
5.45%
8.02%
9.38%
XVGIN-A
1,739
12.39%
13.56%
13.86%
13.56%
14.38%
13.81%
9.61%
9.15%
10.10%
10.73%
11.16%
11.21%
FORAGS-A
3,374
21.85%
18.98%
17.05%
24.25%
17.58%
16.30%
17.02%
11.14%
9.56%
17.51%
11.94%
11.16%
OLIVE-A
2,812
-10.12%
-12.26%
-11.65% <