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World Institute for Development Economics Research
wider.unu.edu
WIDER Working Paper 2014/083
Climate change impacts on Moroccan
agriculture and the whole economy
An analysis of the impacts of the Plan Maroc Vert in Morocco
Ismail Ouraich and Wallace E. Tyner*
April 2014
*Purdue University, corresponding author: iouraich@purdue.edu
This study has been prepared within the UNU-WIDER project on The Middle East, North Africa, and Climate Change, directed
by Imed Drine and Wallace E. Tyner.
Copyright © UNU-WIDER 2014
ISSN 1798-7237 ISBN 978-92-9230-804-9
Typescript prepared by Minna Tokkari at UNU-WIDER.
UNU-WIDER gratefully acknowledges the financial contributions to the research programme from the governments of
Denmark, Finland, Sweden, and the United Kingdom.
The World Institute for Development Economics Research (WIDER) was established by the United Nations University (UNU)
as its first research and training centre and started work in Helsinki, Finland in 1985. The Institute undertakes applied research
and policy analysis on structural changes affecting the developing and transitional economies, provides a forum for the advocacy
of policies leading to robust, equitable and environmentally sustainable growth, and promotes capacity strengthening and training
in the field of economic and social policy-making. Work is carried out by staff researchers and visiting scholars in Helsinki and
through networks of collaborating scholars and institutions around the world.
UNU-WIDER, Katajanokanlaituri 6 B, 00160 Helsinki, Finland, wider.unu.edu
The views expressed in this publication are those of the author(s). Publication does not imply endorsement by the Institute or the
United Nations University, nor by the programme/project sponsors, of any of the views expressed.
Abstract: The paper provides estimates of economic impacts of climate change, compares these
with historical impacts of drought spells, and estimates the extent to which the current Moroccan
agricultural development and investment strategy, the Plan Maroc Vert, helps in agricultural
adaptation to climate change and uncertainty. We develop a regionalized Morocco Computable
General Equilibrium model to analyse the linkages of climate-induced productivity losses (gains)
at the level of administrative and economic regions in Morocco. Yield projections are obtained
from the joint-study by the Moroccan Ministry of Agriculture and Fisheries and the World Bank,
in collaboration with the National Institute for Agricultural Research, the Food and Agriculture
Organization of the United Nations, and the Direction of National Meteorology. We model the
climate change impacts as productivity (or yield) shocks in the agricultural sector, and which are
region- and crop-specific. The yield projections are for 2050, and introduced with respect to a
2003 baseline. With no adaptation, GDP impacts range from -3.1 per cent (worst-case scenario)
to +0.4 per cent (best case scenario). The decline in GDP under the worst-case scenario results
from a general contraction in economic aggregates. Accounting for the adaptation measures in
the Plan Maroc Vert, the GDP impacts from climate change are reduced and range from -0.3 per
cent to +3 per cent. Nonetheless, the adaptation potential of the Plan Maroc Vert is based upon
the assumption of achieving the identified productivity-enhancement targets, and which remains
questionable.
Keywords: CGE models, agricultural policy, adaptation, climate change, SRES scenarios,
uncertainty
JEL classification: O13, Q10, Q54
Acknowledgements: This paper is one of a series of studies on assessing the climate change
impact on economic development opportunities in North Africa. The authors gratefully
acknowledge the financial contribution of the World Institute for Development Economics
Research of the United Nations University (UNU-WIDER). Also, we express our gratitude for
Rachid Doukkali who generously provided us with the Social Accounting Matrix (SAM) of
Morocco for 2003 and which serves as the baseline for the CGE model. We extend our gratitude
to Hasan Dudu for his valuable feedback and insights.
1
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 differences across regions). This was primarily due to improved production systems and
investments in crop and livestock breeding programmes. Nonetheless, climate change threatens to
exacerbate the existing challenges faced by agriculture. The global population is estimated to reach
nine 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 Food and
Agriculture Organization (FAO) estimated that feeding the world population will require a 70 per
cent increase in total agricultural production (FAO 2010).1 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. 2005; IPCC 2007) have shown that
the spectre of climate change is looming even bigger for regions already experiencing low and erratic
productivity levels. For instance, it has been estimated that a warming of 2 ˚C could result in a 4 to 5
per cent permanent reduction in annual income per capita in Africa and South Asia (World Bank
2010).
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, as well as limited access to capital,
infrastructure, and technology (IPCC 2007).2 Reforms and modernization of the agricultural sector
is paramount in order to adapt and/or mitigate the impacts of climate change and their linkages with
current policy issues such as sustainable economic growth,3 food security,4 health, and malnutrition.
Indeed, many countries in the continent already experience challenging climatic conditions that
impact negatively to the prospects of agriculture. For example, it has been projected in some
countries that yield reductions could reach as high as 50 per cent by 2020 (IPCC 2007).5
In recent years, the science of climate change achieved great strides in advancing our understanding
of the bio-physical linkages of climate change. Enhancements of modelling capabilities provide more
robust climate projections at the global level. In addition, recent modelling efforts in the area of crop
model simulations provide better integration between the climate change science and the biophysical
1 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).
2 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.
3 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 per cent (ranging from 10 to 70 per cent) (Mendelsohn et al. 2007)
4 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
per cent in 2007, and this increase persisted and even accelerated during the first half of 2008 (FAO 2009).
5 It should be noted that these projections are quite differentiated from one country to the other, driven by the
uncertainty in climate-induced productivity impacts on agriculture and their underlining assumptions, as well as by the
economic structures characterizing each country in the African continent.
2
science of plant growth dynamics. Nonetheless, more analysis is needed on the economics of climate
change. There are many factors that explain this slower development of economic impact analysis.
Chief among them is the dependency 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 on climate change
provided 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 displays a shift towards engaging in ‘case-by-case’ analyses at
the country and/or sub-country level.
In this context, we use a computable general equilibrium (CGE) model to analyse the impacts of
climate change at a refined geographical scale, with a focus on Morocco. First, we develop a set of
yield projections 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 the
National Institute for Agricultural Research (INRA); the Food and Agriculture Organization (FAO);
and the National Meteorology Authority (DMN) (Gommes et al. 2009). From this point forward, we
will refer to the previous study as the WB/Morocco/FAO study for ease of reference. We assume
the yield projections to represent productivity (or efficiency) shocks introduced exogenously to the
model. In other words, they are modelled as shifts in the total factor productivity (TFP). The model
is based on the International Food Policy Research Institute’s (IFPRI) CGE templates (Lofgren et
al. 2002) and the updated version by Dudu and Cakmak (2011). This will allow us to map out
region-specific economic impacts of climate-driven productivity impacts on crop yields. Finally, we
investigate the potential for adaptation in the Plan Maroc Vert (PMV) strategy being implemented at
the regional level in Morocco.
The paper will be organized as follows: Section 2 discusses the literature of CGE analysis related to
economic impact assessment of climate change. In Section 3, we present our methodological
approach and data sources. Section 4 summarizes key findings and results, and Section 5 provides a
summary conclusions.
2 Climate change impact assessment and CGE analysis
The recent literature using CGE models to analyse 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. Global Trade Analysis Project model), and where the focus is directed at analysing inter-
regional impacts mainly driven through international trade linkages.
Horridge et al. (2005) use a bottom-up CGE model for Australia to analyse the impact of the 2002-
03 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 analysing 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 per cent and 20 per cent, respectively. The
most striking finding is that despite the small share of agriculture in Australian gross domestic
3
product (GDP) (3.6 per cent), drought reduces GDP by 1.6 per cent, 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 analyse 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 non-agricultural 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. Although the study did not tackle climate change directly, the
scenarios identified capture features of potential linkages with the latter. For instance, they found
that a reduction of one standard deviation in SW irrigation supplies caused real agricultural output
from all regional perimetres to fall by 11 per cent. Additionally, agricultural exports (mainly of
irrigated crops) with the European Union (EU) experienced a decline of 13.6 per cent. As a result of
resources’ shift from irrigated to rainfed crop sectors, small increases in output occur in the latter. In
addition, non-farm sectors experience a decline in real GDP and total consumption given the
linkages between the irrigated crop production and the rest of the economy.
Berrittella et al. (2007) used a multi-region world CGE model, GTAP-W;6 to analyse 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,7 which
describe the amount of water necessary for a sector to produce one unit of output. They analyse five
scenarios to capture the effects of water scarcity due to reduced groundwater availability. Four
scenarios describe ‘market-based’ solutions, and are contrasted with outcomes from a fifth ‘non-
market’ scenario. Under higher constraints on water use, the world is worse off. Production and
exports expand for water-intensive products in unconstrained regions, driven by shifts in trade
patterns globally. Welfare gains and losses at the regional level increase with higher water-use
constraints; with gains responding less proportionally and losses more than proportionally.
Berrittella et al. (2008) in an extension of a previous analysis (Berritella et al. 2007) uses the same
model, GTAP-W, to analyse the impacts of trade liberalization on water use at the global level. They
particularly focus 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 find
that trade liberalization induces reduction in water usage for regions with scarce supply, and
increases it for water abundant regions.
Calzadilla et al. (2008) uses a CGE model to analyse the impacts of improved irrigation management
under-water scarcity. They use 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
6 GTAP-W is a refined version of the GTAP model that accounts for water resources, and which is based on the
extension work by Burniaux and Truong (2002).
7 The water intensity coefficients are 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 (FAO 2013) 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.
4
effects on welfare and demand for water, whereas results are more mixed (mostly negative) for non-
water scarce regions.
Thurlow et al. (2009) analyse the impacts of climate variability on economic growth and poverty
reduction in Zambia. The modelling framework combines a dynamic computable general
equilibrium (DGCE) model and a hydro-crop model. Projected yield impacts are simulated via the
hydro-crop model for five agro-ecological zones in Zambia, and complemented with a drought
index analysis to identify zonal level extreme weather events. Yield estimates from the hydro-crop
modelling simulations are subsequently used to design simulation scenarios for the DGCE model to
assess the climate variability impacts at the agro-ecological zones and nationally. Their findings
suggest substantial negative impacts associated with climate variability. Their estimates suggest a
total loss of US$4.3 billion over a ten-year period, and reach as high as US$7.1 billion under the
worst rainfall scenario.
Arndt et al. (2011) develop a stochastic economy-wide framework to analyse climate change-induced
economic impacts and evaluate potential adaptation policies in Ethiopia. They extend a recursive-
dynamic CGE model to allow for stochastic analysis of climate change impacts. Based on statistical
regression analysis, they develop a historical distribution of climate variability for Ethiopia and
which serves as the baseline forward-looking scenario; i.e. historical climate variability remains
unchanged in the future. Climate change impacts are accounted through parametric modifications
altering the nature of the historical distribution of climate variability. Their main findings suggest
that the burden of adjustment to increased climate variability in Ethiopia falls on consumers, which
is expected given that productivity shocks occur in agriculture. In turn, climate-induced variability in
agricultural output affects the vulnerability of poor households who spend a disproportionate share
of income on food and who are disproportionately represented in agricultural labour.
Laborde (2011) analyses 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 12 climate scenarios representing alternative pathways for future agricultural productivity.
The latter are constructed from the IPCC Special Report on Emission Scenarios (SRES) and general
circulation models (GCM) for the climate. The SRES emission scenarios correspond to assumption
about the evolution in the emission of greenhouse gases (GHG) based on the dynamics of projected
economic growth, technological progress, and demographic pressures. The GCM models are
numerical representations of the climate system based on the physical, chemical, and biological
properties of their components (IPCC 2007). The study makes use of 3xSRES scenarios (A1B, B1,
and A2) in combination with 4xGCMS (CNR, CSIRO, ECH, and MIROC) to obtain the 12 climate
scenarios used in the analysis. The latter are introduced as exogenous shocks in the modified
MIRAGE CGE model, where baseline results are contrasted with the results from eight different
trade policy landscapes for the region. The findings suggest that pinpointing optimal trade policy is
difficult in the light of uncertainty in potential climate-induced impacts on yields.
Dudu and Cakmak (2011) develop a regionalized CGE model for Turkey to analyse projected
impact of climate change on agriculture and its wide economy linkages. The model is regionalized to
Turkey’s 12 regions at the NUTS 1 level, and corresponds to an extension and enhancement of
earlier analyses (Dudu et al. 2010). Climate change impacts are modelled directly through
parameterization of the production function for agricultural activities. This is achieved by explicitly
modelling production as a function of yearly rainfall. Climate shocks are introduced exogenously as
5
projected impacts on rainfall, and are based on the IPCC emission scenario SRES A2 (IPCC 2007)
and the GCM ECHAM5 (Roeckner et al. 2003). Their findings suggest significant negative impacts
on agricultural and food-processing sectors. Households at the lowest quintile of income
distribution are worse off compared to other household groups given their propensity to spend
most of their income on food products. The trade balance is significantly worsened due to increased
import demand for agricultural and food products, which in turns affect the prospects of food
security negatively.
Kuik et al. (2011) used the newly developed MOSAICC model (FAO 2011), 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 DCGE model,8 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 2001-30.
3 Background on Moroccan agriculture and methodological approach
3.1 Moroccan agriculture and climate
Morocco enjoys a very interesting geo-strategic location with its 3,500 kilometres of coast line,
spanning the Atlantic Ocean and the Mediterranean. 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.9 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).
Upon investigating the trend in annual per cent change of agricultural value added and all others, we
notice that the sector suffers from a significant volatility. This is driven by the general structure of
the production, dominated by cereal crops. The latter mainly include common wheat, durum wheat,
barley, and maize, which on aggregate account for 55 per cent of total value-added of crop
production and occupy 65 per cent of the agricultural area. The productivity of the four main cereals
experiences significant annual variations, hence the observed fluctuations characterizing the
evolution of agricultural value-added (Figure 1, Appendix B). The main driver in the productivity
performance of cereal production is precipitation. This is so due to the fact that most of the
production is located in non-irrigated perimetrs (Figure 2, Appendix B). Export crops, mainly citrus
and vegetables, represent 15 per cent of value-added and respectively occupy 0.85 and 3 per cent of
the total agricultural area. Although in terms of vegetative cover of agricultural land, citrus and
vegetables occupy a very small share, yet their share in agricultural value-added is substantially high
given the fact that those niches are usually more labour-, chemical-, and water-intensive compared
8 The Dynamic CGE model was developed in partnership with the Free University of Amsterdam, and is inspired by the
IFPRI DCGE model (Lofgren et al. 2002; Thurlow 2004).
9 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).
6
with cereals. Post-independence agricultural reforms that Morocco was engaged in helps explain the
present situation, where upon investigating the long-term trend in the sector’s performance we can
identify three phases representing distinct growth patterns: Phase I (1965-1985), Phase II (1985-
1991), and Phase III (1991-2012) (Figure 3, Appendix B).
The first phase was characterized by rather a weak performance of agricultural production, and even
a slight decline in 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 (Akesbi et al. 2008). Moreover, and in parallel to the land reform efforts, a
charter of agricultural investments was adopted in 196910 with the objective of mobilizing the
hydrologic potential of the country and providing incentives for the development of irrigated
perimetres. This effort has been accompanied by a set of incentives to farmers to encourage
investments in new technologies (e.g. machinery, fertilizers, and seeds). 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. 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). Nonetheless, the combined effect of these
policies has led to an implicit taxation of the sector, especially when accompanied with the over-
valued exchange rate at the time (Doukkali 2006). Hence, agricultural productivity during the period
was modest. This is captured by the evolution of agricultural value-added and agricultural value-
added per capita, which grew respectively by 3.7 per cent/year and 1.1 per cent/year.
During the second phase, agricultural productivity registered significant gains, with value-added
rising on average by 11 per cent/year; whereas per capita levels increased by 9 per cent/year. The
boost in agricultural productivity during this period came as result of favourable 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. This was depicted in the results of the General Agricultural Census in 1996, and which
registered an increase in the arable agricultural area by 21 per cent. The number of small farms
without land and with less than a hectare of land decreased by 85.6 and 28.3 per cent, respectively.
(Doukkali 2006).
The third phase displayed a slowdown of growth in agricultural productivity at the aggregate and per
capita levels. During the first half of the period, aggregate (per capita) agricultural value decreased by
32 per cent (41 per cent), from US$8.2 billion (US$332) in 1991 to US$5.6 billion (US$197) in 2000.
The observed results are not surprising given the significant dependence of agricultural productivity
10 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 one million hectare of irrigated agricultural land
by 2000 (Doukkali 2005).
7
on precipitation. For instance, during the agricultural campaign of 1990-91 precipitation increased by
128 per cent during the critical months of January through March, which correspond to the
sowing/planting season. In turn, yield of the four main cereals registered a 41 per cent increase
compared to the previous season. In the 1999-2000 campaign, we observe a decrease in precipitation
by 78 per cent from the previous year. As a result, cereals yield decreased by 51 per cent.
Nonetheless, the trend is reversed during the second half from 2001 onward. Aggregate (per capita)
agriculture value-added reached US$11 billion (US$336) by 2012, which corresponds to an increase
of 64 per cent (45 per cent) compared to 2001 levels. Annual growth for the period averaged 6 per
cent/year (7 per cent/year). 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 is still subjected to important 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. There is a strong consensus among policy makers 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 Modelling, methodology, and materials
3.2.1 Morocco’s country-based CGE model
The analysis uses a regionalized general CGE model for Morocco. The model development follows
IFPRI CGE modelling framework (Lofgren et al. 2002) and the Turkish regional CGE model
developed by Dudu and Cakmak (2011). The model includes a number of features critical to
analyses focusing on developing countries such as modules on 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.
Production is modelled 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 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 economy-wide 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.
8
Household consumption is modelled 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 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, 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.
3.2.2 Regionalization assumptions and data
The data used in the model is based on a national social accounting matrix (SAM) for 2003
developed by Rachid Doukkali of IAV/Hassan II in Rabat, Morocco, and the modified version by
Dominique Van der Mensbrugghe.11 The initial SAM identifies 67 activities and 68 commodities
(Tables 1 and 2, Appendix A). The institutional block in the data is represented by household, value-
added, taxes, government, investment-savings, and rest of the world accounts (Table 3, Appendix
A).
Given the regional dimension adopted in the model (Figure 4, Appendix B), it was necessary to
reduce the dimension of the SAM accounts in order to facilitate the modelling enterprise and
analysis, and handling of the results. Tables 4, 5, and 6 (Appendix A) summarize the new structure
of the SAM accounts. Crop production is captured through 11 activity accounts each producing a
corresponding commodity. Livestock, forestry, and fishery accounts remain unchanged. The food
processing sectors are represented by eight activity accounts; whereas the industry and
manufacturing and services accounts remain unchanged represented by four and two activity
accounts, respectively.
In order to regionalize the data in SAM, official statistics on regional value-added by sector were
used to compute regionalization shares. The data was obtained from a study (DSFF 2010)
conducted by the Department of Studies and Financial Forecasts within the Moroccan Ministry of
Economy and Finance.
3.2.3 Data sources for yield projections and selected scenarios analysis for Morocco
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
11 Dominique Van der Mensbrugghe is a senior economist at the FAO.
9
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, Appendix B). 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 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 (2011-40), 2050
(2041-70), and 2080 (2071-99). 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 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 eight scenarios as
described in Table 7 (Appendix A). 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 range of uncertainty within each climate scenario, which is achieved through the
percentile distribution based on 10th (low), 50th (medium), and 90th (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 PMV as an adaptive force in the face of climate change. 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 sub-national
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
programmes divided into ‘Pilier I’ and ‘Pilier II’ programmes.12 The PMV is mainly an investment
programme. 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. Table 8
(Appendix A) summarizes the productivity targets of the PMV strategy by crop sector.
12 For detailed discussions of the PMV, see Ministère de l’Agriculture et de la Pêche Maritime (MAPM) and Agence
pour le Développement Agricole (ADA) (2011).
10
4 Productivity shocks, results, and discussions
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
Figure 7 represents the historical record of yields for common and durum wheat, barley, and maize
from 1960 to 2006. For both wheat varieties, the trend displays a significant increase in productivity.
But for barley, the historical record suggests a stagnant performance; whereas maize depicts a
moderately decreasing trend. As argued in Section 3, the enhanced productivity achieved from the
mid-1980s for both wheat varieties came about partly given the expansionist public policy that
encouraged conversion of favourable agricultural land into wheat cultivation; while at the same time
driving out barley and maize production 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 cereal crops.
Figure 8 (Appendix B) summarizes the distribution of average yield impacts for SRES A2 and B2 as
projected by 2050 for all crops at the national level. 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
per cent for wheat (durum and soft), -6 to -17 per cent for barley, and -8 to -20 per cent for olives.
Whereas vegetables (i.e. tomatoes, other vegetables, and industrially produced vegetables) benefit
from climate change, with impacts ranging from a minimum of +2 to +7 per cent for tomatoes,
+0.5 to +6 per cent for other vegetables and industrially produced 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 per cent for forages crops, -3 to +7 per cent for citrus, -
7 to +0.6 per cent for other fruits, and -6 to +5 per cent for other crops.
Nonetheless, significant differentials in projected yield from one SRES scenario to the other exist.
Additionally, there is a substantial differential in projected yield impacts within each SRES scenario
when comparing with and without CO2 fertilization cases. Figure 9 (Appendix B) summarizes the
percentile distribution of projected yield impacts for all crop categories for SRES A2 and B2, with
and without CO2 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 CO2 fertilization case is mixed, where it ranges from -2 to +2 per cent for
SRES A2 and -3 to 3 per cent for SRES B2; whereas including CO2 fertilization effects boosts
significantly projected yield gains with the latter ranging from +8 to +16 per cent for SRES A2 and
+6 to +12 per cent 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
11
impact studies in the literature that suggest that irrigated crops will experience positive impacts due
to climate-induced CO2 fertilization effects owing to higher concentrations of CO2 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 CO2 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. 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 per cent of
total durum wheat production, will experience yield declines ranging from -21 to -40 per cent in
TR4, -28 to +14 per cent in TR9, -12 to -22 per cent in TR11, and -11 to -31 per cent in TR13
across all scenarios without accounting for the CO2 fertilization effects. The impacts are somewhat
dampened when including the CO2 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 CO2 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 per cent of total production), yield impacts range from -3 to +20 per cent in TR2,
-5 to +21 per cent in TR3, and -2 to +9 per cent in TR9 across all SRES scenarios, with and without
CO2 fertilization effects. The same pattern is checked for other vegetables (Figures 10a,b and 11a,b,
Appendix B).
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 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 eight scenarios as defined in Table 7
(Appendix A). The model closure rules follow conventional neo-classical assumptions, where supply
and demand adjust in all markets to satisfy the market clearing conditions through 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 labour 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.
12
As we mentioned earlier, and in conjunction to analysing the economy-wide impacts of climate
change in Morocco, we aim to investigate the adaption potential of the PMV in Morocco. Table 8
(Appendix A) summarizes the key projected impacts of the PMV strategy by region and by crop.
The latter are expressed in terms of per cent 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 climate change, under the worst case scenario, 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 9, 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 is quite substantial where the latter range from -1 to -3 per cent under SRES A2
and -0.5 to -2.3 per cent under the SRES B2. Furthermore, and upon investigating the different
GDP components, we observe that consumption, investment, 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 GDP can be traced back to the impact of climate
change on private (household and intermediate)13 consumption. The latter experience a fall ranging
from -0.6 to -3.4 per cent 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 10, 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 per cent, followed by the food processing sectors with a
decrease ranging from -1 to -6 per cent. 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 11, 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 13, 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
14-17, Appendix A). Hence, the observed decline in GDP is mainly driven by the negative impacts
13 By intermediate consumption we refer to the demand of the production sectors for intermediate inputs.
13
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 per
cent 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
CO2 fertilization effects to a moderate increase driven by the positive dampening effect of CO2
fertilization effects14 on crop yields under the ‘high productivity’ scenario (Table 18, 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_noCO2_low, imports increase by more than 30 per
cent while exports decrease by 11 per cent. The impacts are lessened under the ‘medium’ and ‘high’
productivity scenarios, and the impacts get reversed under the ‘high productivity’ scenario if we
include CO2 fertilization effects 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 (Table 19, Appendix A).
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 9, Appendix A), where we observe a substantial positive impact when we include the PMV
adaptation targets and also the CO2 fertilization effects. Indeed, including the PMV targets boosts
agricultural production significantly, and especially under the scenario including the CO2 fertilization
effects (Table 18, Appendix A). The increase reaches up to 20 per cent under the best case scenario
of ‘A2_wCO2_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
14 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 per cent (olives) to +15
per cent (tomatoes).
14
linkages in terms of projected impacts of climate change across regions, which in turn trickles down
to affect the rest of the economy.
In this paper, we attempt to shed light on inter-regional 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 inter-regional linkages are
modelled 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
modelling 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.
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17
APPENDIX A: Tables
Table 1: Nomenclature of activity accounts in the Morocco’s social accounting matrix
Sectors/Activities Description Sectors/Activities Description
Agriculture,
crops (x26) xmeat-a Other meat production
hdwht-a Hard wheat Pasture (x2)
sfwht-a Soft wheat fl-a Fallow land
barly-a Barley ps-a Pasture land
xgrns-a Other grains Byproducts (x2)
gnleg-a Grain legumes sp-a Agricultural byproducts
sgrbt-a Sugar beets spfdp-a Byproducts of agro-food
indu strie s
sgrcn-a Sugar cane Forestry (x1)
xcshc-a Other industrial crops incl oil
seeds forst-a Forestry
tomat-a Tomatoes Fishery (x1)
potat-a Potatoes fshry-a Fishery
onion-a Onions Food processing (x10)
melon-a Melons dairy-a Dairy
wtmln-a Watermelons sgrrw-a Raw sugar
xvegt-a Other vegetables sgrrf-a Refined sugar
xvgin-a Other industrial vegetables milhw-a Hard wheat mill
alfaf-a Alfafa milsw-a Soft wheat mill
forag-a Forage crops oilrw-a Raw oil
olive-a Olives oilrf-a Refined oil
agrms-a Clementines and small citrus olvwh-a Whole olives
xagrm-a Other citrus olvol-a Olive oil
grape-a Grapes xfdpr-a Other food processing
almnd-a Almonds Industry and manufacturing
(x4)
apple-a Apples chmcl-a Chemical industries
dates-a Dates refol-a Refined petroleum
xfrut-a Other fruit wtrel-a Water and electricity utilities
xcrop-a Other crops nes xinds-a Other industries
Livestocks (x4) Services (x2)
bovin-a Bovine meat srvpr-a Private services
ovine-a Sheep and other red meats srvpb-a Public services
avine-a Poultry
Source: Authors’ adaptation based on data from Doukkali’s SAM (2003).
18
Table 2: Nomenclature of commodity accounts in the Morocco’s social accounting matrix
Commodities Description Commodities Description
Agriculture, crops (x26) eggrw-c Eggs
hdwht-c Hard wheat xmeat-c Other animal products
sfwht-c Soft wheat Pasture (x2)
barly-c Barley fl-c Fallow land
xgrns-c Other grains ps-c Pasture land
gnleg-c Grain legumes Byproducts (x2)
sgrbt-c Sugar beets sp-c Agricultural byproducts
sgrcn-c Sugar cane spfdp-c Byproducts of agro-food
industries
xcshc-c Other industrial crops incl oil seeds Forestry (x1)
tomat-c Tomatoes forst-c Forestry
potat-c Potatoes Fishery (x1)
onion-c Onions fshry-c Fishery
melon-c Melons Food processing (x10)
wtmln-c Watermelons dairy-c Dairy
xvegt-c Other vegetables sgrrw-c Raw sugar
xvgin-c Other industrial vegetables sgrrf-c Refined sugar
alfa-c Alfafa milhw-c Hard wheat mill
forag-c Forage crops milsw-c Soft wheat mill
olive-c Olives oilrw-c Raw oil
agrms-c Clementines and small citrus oilrf-c Refined oil
xagrm-c Other citrus olvwh-c Whole olives
grape-c Grapes olvol-c Olive oil
almnd-c Almonds xfdpr-c Other food processing
apple-c Apples Industry and manufacturing (x4)
dates-c Dates chmcl-c Chemical industries
xfrut-c Other fruit refol-c Refined petroleum
xcrop-c Other crops nes wtrel-c Water and electricity
utilities
Livestocks (x5) xinds-c Other industries
meatr-c Red meats Services (x2)
meatw-c White meats srvpr-c Private services
mlkrw-c Raw milk srvpb-c Public services
Source: Authors’ adaptation based on data from Doukkali’s SAM (2003).
19
Table 3: Nomenclature of institutional accounts in the Morocco’s social accounting matrix
Institution Description Institution Description
vaadd Value added rhdc1 Rural households first decile
txsub Taxes and subsidies rhdc2 Rural households second decile
govnt Government rhdc3 Rural households third decile
uhdc1 Urban households first decile rhdc4 Rural households fourth decile
uhdc2 Urban households second decile rhdc5 Rural households fifth decile
uhdc3 Urban households third decile invst Investment savings account
uhdc4 Urban households fourth decile rowld Rest of the world
uhdc5 Urban households fifth decile
Source: Authors’ adaptation based on data from Doukkali’s SAM (2003).
Table 4: Updated nomenclature of activity accounts for the 2003 Morocco’s social accounting matrix
Sectors/Activities Description Sectors/Activities Description
Agriculture, crops (x11) Fishery (x1)
hdwht-a Hard wheat fshry-a Fishery
sfwht-a Soft wheat Food processing (x8)
barly-a Barley dairy-a Dairy
tomat-a Tomatoes sugar-a Sugar processing
xvegts-a Other vegetables milhw-a Hard wheat mill
xvgin-a Other industrial vegetables milsw-a Soft wheat mill
forags-a Forage crops oilpr-a Processed oil
olive-a Olives olvwh-a Whole olives
agrms-a Citrus olvol-a Olive oil
xfruts-a Other fruit xfdpr-a Other food processing
xcrops-a Other crops nes Industry and manufacturing (x4)
Livestocks (x4) chmcl-a Chemical industries
bovin-a Cattle etc. refol-a Refined petroleum
ovine-a Sheep wtrel-a Water and electricity
utilities
avine-a Poultry xinds-a Other industries
xmeat-a Other animal products Services (x2)
Forestry (x1) srvpr-a Private services
forst-a Forestry srvpb-a Public services
Source: Authors’ adaptation based on data from Doukkali’s SAM (2003).
20
Table 5: Updated nomenclature of commodity accounts for the 2003 Morocco’s social accounting matrix
Commodity account Description Commodity account Description
Agriculture, crops (x11) Fishery (x1)
hdwht-c Hard wheat fshry-c Fishery
sfwht-c Soft wheat Food processing (x8)
barly-c Barley dairy-c Dairy
tomat-c Tomatoes sugar-c Sugar processing
xvegts-c Other vegetables milhw-c Hard wheat mill
xvgin-c Other industrial vegetables milsw-c Soft wheat mill
forags-c Forage crops oilpr-c Processed oil
olive-c Olives olvwh-c Whole olives
agrms-c Citrus olvol-c Olive oil
xfruts-c Other fruit xfdpr-c Other food processing
xcrops-c Other crops nes Industry and manufacturing (x4)
Livestocks (x4) chmcl-c Chemical industries
meatrbov-c Cattle etc refol-c Refined petroleum
meatrov-c Sheep wtrel-c Water and electricity utilities
meatw-c Poultry xinds-c Other industries
xmeat-c Other animal products Services (x2)
Forestry (x1) srvpr-c Private services
forst-c Forestry srvpb-c Public services
Source: Authors’ adaptation based on data from Doukkali’s SAM (2003).
Table 6: Updated nomenclature of institutional accounts for the 2003 Morocco’s social accounting matrix
Institution Description Institution Description
Value-added accounts comtax Commodity tax
flab Labour imptax Tariff
fcap Capital instax Institutional tax
flandfl Fallow land factax Factor Taxes
flandps Pasture land Government account
flandir Irriagted land gov Government
flandrf Rainfed land Savings-Investment
Households account s-i saving-investment
uh Urban household Rest of the world
rh Rural household row rest of the world
Tax accounts
actax Activity tax
Source: Authors’ adaptation based on data from Doukkali’s SAM (2003).
21
Table 7: 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 based on the study of WB/Morocco/FAO (Gommes et al. 2009).
Table 8: Projected yield impacts of the Plan Maroc Vert 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’ calculations based on ADA (2014).
22
Table 9: Effects on macro accounts and gross domestic product (GDP)―without and with adaptation (base values in million 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.
23
Table 10: Effects gross domestic product (GDP) disaggregated by sectors―without and with adaptation (base values in million 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
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.
24
Table 11: 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 based on World Bank (2014).
Table 12: Per cent change in aggregate domestic output by sector―without and with adaptation (base values in million 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 chan
g
e, no ada
p
tation
(
%chan
g
e 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%
Forestr
y
&Fisher
y
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%
Source: Simulations results.
25
Table 13: Effects on household income at the regional and national level–without and with adaptation (base values in million Dhs)
Source: Simulations results.
Climate change, no adaptation (% change from base)
BASE A2_noCO2 B2_noCO2 A2_wCO2 B2_wCO2
Low Med Hi
g
hLowMedHi
g
h Low Med Hi
g
hLow Med Hi
g
h
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
A
2
_
noCO2 B2
_
noCO2
A
2
_
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%
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%
26
Table 14: Per cent change in demand for labour in all sectors across scenarios analysis without and with adaptation (base values in million Dhs)
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%
Source: Simulations results.
27
Table 15: Per cent change in demand for capital in all sectors across scenarios analysis without and with adaptation (base values in million 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) 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 & 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%
Source: Simulations results.
28
Table 16: Per cent change in demand for irrigated land in agricultural sectors across scenarios analysis―without and with adaptation (base values in million 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) 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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%
agrms-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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%
Source: Simulations results.
29
Table 17: Per cent change in demand for rainfed land in agricultural sectors across scenarios analysis―without and with adaptation (base values in million 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) 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 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
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-c 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-c 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-c 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-c 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-c 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-c 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-c 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-c 2,812 -10.12% -12.26% -11.65% -13.09% -13.18% -12.30% -10.17% -10.89% -9.32% -12.43% -12.40% -11.54%
agrms-c 1,934 97.79% 110.27% 119.98% 92.54% 113.31% 123.64% 118.77% 143.62% 163.58% 106.97% 135.44% 150.05%
xfruts-c 2,241 15.47% 13.01% 13.61% 12.26% 12.40% 12.77% 11.37% 13.53% 16.33% 10.36% 13.18% 15.30%
xcrops-c 13,726 6.42% 11.89% 14.84% 8.59% 13.47% 15.81% 11.88% 16.88% 19.62% 12.11% 16.79% 18.92%
Source: Simulations results.
30
Table 18: Selected indicators for the agricultural sector―without and with adaptation (base values in million Dhs)
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
(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%
Employment Capital 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%
Labour 6,151 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
LandIr 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%
LandRf 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%
Trade Exports 13,257 -8.95% -4.43% -1.32% -8.15% -3.15% 0.03% -1.12% 4.75% 8.47% -2.89% 3.10% 6.09%
Imports 13,739 47.82% 26.50% 14.44% 35.55% 20.42% 11.12% 29.56% 13.51% 1.59% 27.89% 11.68% 2.43%
Exports/Imports 0.96 -38.40% -24.45% -13.77% -32.24% -19.57% -9.98% -23.68% -7.71% 6.77% -24.07% -7.68% 3.58%
Climate change, with adaptation-PMV (% change from base)
Agriculture
(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%
Employment Capital 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%
Labour 6,151 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
LandIr 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%
LandRf 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%
Trade Exports 13,257 13.21% 18.02% 21.29% 13.67% 19.37% 22.73% 21.54% 28.04% 32.19% 19.40% 26.16% 29.44%
Imports 13,739 33.04% 13.79% 3.49% 21.15% 8.98% 1.18% 16.41% 3.08% -7.06% 14.93% 1.80% -6.10%
Exports/Imports 0.96 -14.91% 3.71% 17.21% -6.18% 9.53% 21.30% 4.41% 24.22% 42.23% 3.89% 23.93% 37.84%
Source: Simulations results.
31
Table 19: Selected indicators for the Food processing sector―without and with adaptation (base values in million Dhs)
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)
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%
Employment Capital 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%
Trade Exports 5,733 -10.20% -5.46% -3.51% -6.93% -4.00% -2.40% -5.98% -2.56% -0.65% -5.23% -2.00% -0.22%
Imports 10,643 1.27% 0.56% 0.33% 0.64% 0.40% 0.30% 0.98% 0.64% 0.38% 0.77% 0.45% 0.23%
Exports/Imports 0.54 -11.33% -5.98% -3.83% -7.52% -4.39% -2.70% -6.90% -3.18% -1.03% -5.95% -2.43% -0.45%
Climate change, with adaptation-PMV (% change from base)
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%
Employment Capital 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%
Trade Exports 5,733 -2.74% 1.17% 2.82% 0.04% 2.33% 3.68% 0.84% 3.41% 4.91% 1.52% 3.85% 5.26%
Imports 10,643 3.19% 2.75% 2.51% 2.79% 2.64% 2.52% 3.10% 2.88% 2.62% 2.94% 2.72% 2.50%
Exports/Imports 0.54 -5.74% -1.53% 0.30% -2.68% -0.30% 1.13% -2.19% 0.51% 2.23% -1.38% 1.10% 2.70%
Source: Simulations results.
32
APPENDIX B: Figures
Figure 1: Evolution of agricultural value-added and the four main cereals production (in annual % change) and correlations across cereal production (1960-2006)
Source: Authors’ adaptation based on World Bank (2014).
33
Figure 2: Evolution of cereals yields and average precipitation (in annual % change) and seasonal correlation (1960-2006)
Source: Authors’ adaptation based on World Bank (2014).
34
Figure 3: Evolution of annual per cent changes in agriculture value-added and agriculture value-added per capita (in constant 2005 US$)
Source: Authors’ adaptation based on World Bank (2014).
35
Figure 4: Administrative and economic regions in Morocco
Source: Authors’ construction.
36
Figure 5: Production technology
Source: Authors’ adaptation based on Lofgren et al. (2002).
Output
(fixed yield coefficients)
Activity level
(CES/Leontief fucntion)
Value-added
(CES function)
Primary factors
Labor Capital Land
Intermediate inputs
(Leontief function)
Composite commodities
Imported Domestic
37
Figure 6: Flows of marketed commodities
CET
Source: Authors’ adaptation based on Lofgren et al. (2002).
CES
CES CET
Commodity
output from
activity 1
(QXAC/PXAC)
Commodity
output from
activity n
(QXAC/PXAC)
Aggregate
output
(QX/PX)
Aggregate
exports
(QE/PE)
Domestic
sales
(QD/PDS-
PDD)
Aggregate
imports
(QM/PM)
Composite
commodity
(QQ/PQ)
Household
consumption
(QH/PQ)
+
Government
consumption
(QG/PQ)
+
Intermediate use
(QINT/PQ)
.
.
.
38
Figure 7: Evolution of common wheat, durum wheat, barley, and maize yields for the period 1960-2006
Source: Authors’ adaptation based on World Bank (2014).
39
Figure 8: Projected yield impacts at the national level for SRES A2 and B2
Source: Authors’ adaptation based on the study of WB/Morocco/FAO (Gommes et al. 2009).
40
Figure 9: Projected yield impacts at the national level for SRES A2 and B2 with and without CO2 fertilization effects
Source: Authors’ adaptation based on the study of WB/Morocco/FAO (Gommes et al. 2009).
41
Figure 10a: Regional distribution of average projected yield impacts across crops under the SRES A2 by region
Source: Authors’ adaptation based on the study of WB/Morocco/FAO (Gommes et al. 2009).
4
2
Figure 10b: Regional distribution of average projected yield impacts across crops under the SRES B2 by region
Source: Authors’ adaptation based on the study of WB/Morocco/FAO (Gommes et al. 2009).
43
Figure 11a: Distribution of projected yield impacts in Morocco by crop and by region under SRES A2 with and without CO2 fertilization effects
Source: Authors’ adaptation based on the study of WB/Morocco/FAO (Gommes et al. 2009).
4
4
Figure 11b: Distribution of projected yield impacts in Morocco by crop and by region under SRES B2 with and without CO2 fertilization effects
Source: Authors’ adaptation based on the study of WB/Morocco/FAO (Gommes et al. 2009).
