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Agrometeorological
Cereal Yield Forecasting
in Morocco
Riad BALAGHI
Mohammed JLIBENE
Bernard TYCHON
Herman EERENS
National Institute for Agronomic Research
Morocco
2013
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Morocco. Short extracts from INRA publications may be reproduced without authorization, provided that the
complete source is clearly indicated. Editorial correspondence and requests to publish, reproduce or translate
this publication in part or in whole should be addressed to:
INRA Avenue Ennasr Rabat, Morocco
BP 415 RP Rabat, Morocco
www.inra.org.ma
Tel : +212 537 77 09 55
Fax : +212 537 77 00 49
National Institute for Agronomic Research (INRA)
Division of Information and Communication
2013 edition
ISBN: 978 - 9954 - 0 - 6683 - 6
A word from the Director
Food security, in Morocco as in many parts of the world, relies heavily on cereal production which
fluctuates depending on weather conditions. Cereal production in Morocco does not meet
consumption needs of the ever growing population, leading to massive import of grain to fill the
gap. The cost of imported grain is high and is expected to increase due to several factors: (1) the
continuous rising of prices in conjunction with increasing world population and inputs prices, (2) the
use of cereal grains for bio-fuel production, and (3) the negative impacts of climate change. In
Morocco cereals are highly exposed to climatic risks, since they are mainly produced in arid and
semi-arid lands, characterized by limited soil and water resources to satisfy crop growth
requirements.
The Green Morocco Plan which is the strategy of the Government of Morocco for the agricultural
sector, aims at insuring food security through a sustainable improvement of productivity while
saving water and soil resources. It is an ambitious but achievable objective, given the range of
available agricultural technologies and know-how developed at National Institute for
Agronomic Research (INRA), in the field of adaptation to drought and land valorization. These
include weather risk management tools for decision-making at both farm and policy levels, in
addition to improved cultivars, agronomic and plant protection practices. Weather risk
management refers to the drought issue in particular, for which INRA developed operational
approaches and tools to monitor the season and forecast cereal yields, so as to timely undertake
appropriate mitigation measures, and deal with the international cereal market. These
achievements are the result of INRA’s investment in the field of agrometeorology oriented toward
operational cereal yield forecasting systems.
The present work is the result of a long term research programme started in the 1990s, sustained
by our institution and involving committed human resources which are aware of the critical issues
challenging the agricultural sector in Morocco. The research was supported by international
cooperation with key European institutions (Ulg, JRC, VITO, Alterra, UNIMI). This research was
fruitful, since a national cereal yield forecasting system called “CGMS-MAROC” was developed and
implemented according to international quality standards. The system is currently operational, and
a dedicated Web viewer was developed (www.cgms-maroc.ma). It is managed autonomously by a
consortium composed of three national institutions (INRA, DMN, DSS) bound by a mutual strategic
agreement. Cereal yields can presently be forecasted three months ahead of harvests, offering a
large flexibility for decision making.
The document I have the pleasure to present, relates the history and findings of the operational
agrometeorology research carried out at INRA.
Pr. Mohamed BADRAOUI
Director of INRA
Preface
The science of agrometeorological crop forecasting is also an art, because different experts come
up with different solutions, where efficiency, accuracy and elegance all play a part. It is usually
practiced in collaboration by several institutions, under the pressure of fixed deadlines, for mostly
critical and demanding customers. Beyond the limitations that affect data, methods and models,
the practitioners of this art have learned to provide crop yield estimates on time for decision
making, and with acceptable errors.
Over the last fifteen or twenty years, due to significant advances in computer science and
hardware, remote sensing, geo-statistics, and Geographic Information Systems, there has been a
tendency to develop “industrial” or “brute-force” methods to monitoring crops and predicting
yields. Some people are satisfied with the approach; they can easily be recognized: they produce
semi-automatic graphs and maps of the current season based on satellite indices and compare
them with a “historical average” assumed to represent “normal crop conditions.”
The authors of the present publication are of a different calibre. While using modern knowledge
and techniques, they also know by experience that yield forecasting remains in essence an
agronomic application. Beyond describing crop conditions, they emphasize the need to understand
climate and its variability at all temporal and spatial scales relevant to agriculture, and the
variability brought about by farming activities and practices, which are often controlled by
economic or policy factors. This is what characterizes this little book: it explains the reasoning and
the methods, ranging from traditional ones like frequency analysis of rainfall, to more advanced
features like the use of satellite data and indices, and makes us understand why and how they can
be used to quantify the effect of factors that affect crop yields.
I have known the authors for a long time. Their art of predicting crop harvests stems from
experience in the real world. They do not neglect traditional techniques which are often ignored, or
unknown to the practitioners of recent “industrial” approaches. One can be convinced by
consulting the bibliography where classics like “De Martonne, Célérier and Charton (1924)” get
along well with recent ones like “Bénichou and Le Breton (1987)” and “de Wit, Duveiller and
Defourny (2012)”.
By their meticulous way of trying to understand, Riad Balaghi and his colleagues somehow remain
traditional natural scientists. This can be seen in some of their empirical rules, like the one
providing the level of accumulated rainfall above which the relation between yields and rainfall
fades out. It can also be seen in numerous original figures that literally dissect data; they are unique
and found nowhere else in the literature.
The work of Balaghi, Jlibene, Tychon and Eerens deserves ample diffusion, beyond Moroccan
boundaries. First, in schools that teach crop yield forecasting, at various levels, under one name or
another. Students will learn how to thoroughly observe data and extract all relevant information
they contain. Second, and more importantly, professionals and practitioners, even experienced
ones, will be surprised to find “know-how” and original analyzes that are overlooked in most texts.
As to me, I am delighted that such a publication of “advanced popular science” on crop yield
forecasting is now made widely available. I have no doubt that it will meet the success that it
deserves.
Dr. René GOMMES
Doctorate in bio-geochemistry (1977).
Agroclimatologist at FAO since 1980.
Coordinator of the Agrometeorology Group of FAO from 1994 to 2007,
and Leader of the “Climate impact team” of FAO, from 2008 to 2010.
Senior scientist in the FoodSec Unit at the EC Joint Research Center at
Ispra, from 2010 to 2012.
Since 2012, visiting professor at the Chinese Academy of Sciences.
Member of the European Society of Agronomy (ESA) and the
International Society of Biometeorology (ISB).
Acknowledgement
Research on agro-meteorology was initiated at the Regional Agronomic Research Center of Meknes
(INRA-Meknes), in 1992, as part of the research actions of the “High Rainfall Research Programme”
(Programme Bour Favorable) coordinated then by Dr Mohammed Jlibene, until 2002. It has been
later pursued under supervision of Dr Riad Balaghi, as head of the research unit “Agronomy and
Plant Physiology” at INRA-Meknes, from 2002 to 2008, and as head of “Department of Environment
and Natural Resources”, at INRA-Rabat, from 2008 to date.
Since 2008, research in agrometeorology has received moral support from the Director of INRA, Pr
Mohamed BADRAOUI, not just as a manager but also as a scientist convinced by the interest of
supporting this research project for the benefit of food security in Morocco.
This research project needed considerable amount of agro-meteorological and agronomical
databases, as well as scientific and technical help from national and international cooperation.
Institutions involved in the project were: “Direction de la Stratégie et des Statistiques" (DSS,
Morocco), “Direction de la Météorologie Nationale (DMN, Morocco), “University of Liege (Arlon
Campus Environnement, former Fondation Universitaire Luxembourgeoise, ULg - Belgium)", "Joint
Research Centre of the European Commission " (JRC, Italy), "Flemish institute for technological
research" (VITO, Belgium) and "Food and Agriculture Organization of the United Nations" (FAO,
Italy).
The project was partly financed by the Belgium Agency of Development” (CTB, Belgium) as well as
the European Union thanks to the Seventh Framework Programme (FP7 - Project « Crop Monitoring
as an E‐Agricultural Tool for Developing Countries », E‐AGRI).
The idea of this research project has matured with the help and perseverance of INRA researchers
who can identify themselves and find the expression of their common output. Researchers of great
scientific and technical quality have contributed to this achievement with varying degrees:
Mr. Moha MARGHI, former Director of “Direction Provinciale de l’Agriculture de Meknes” and later
former general secretary of the Ministry of Agriculture and Marine Fishery (MAPM), was the first
official to have requested INRA-Meknes to present scenarios of drought consequences on cereal
production in the Meknes region during the dry season of 1994-1995. By approaching us, he
inspired us with the idea of investing in research for forecasting cereal yields.
From the Joint Research Centre of the European Commission (JRC): Dr. Giampiero GENOVESE, Dr.
Bettina BARUTH, Dr. Mohamed EL AYDAM, Dr Giovanni NARCISO, have supported the research
agreement between JRC and INRA in the field of harvest forecasting as well as the E-AGRI research
project.
From the “Direction de la Stratégie et des Statistiques” (DSS, Rabat): Mr. Redouane ARRACH and
Mr. Mustafa TAHRI, who were committed to provide agricultural statistical data and helped with
the research collaboration agreement between DSS, INRA and DMN, as part of the E-AGRI project.
From the “Direction de la Météorologie Nationale” (DMN, Casablanca): Mr. Tarik EL HAIRECH and
Mr. Rachid SEBBARI have concretized the research agreement between DMN and INRA and were
formally committed to the E-AGRI project.
From the E-AGRI: Dr. Qinghan DONG, Dr. Allard DE WITT, Mr. Steven HOEX, helped in the
adaptation of “CGMS” to Moroccan context. Dr Mouanis LAHLOU, from Institute of Agronomy and
Veterinary Hassan II, developed the Web viewer of the “CGMS-MAROC” forecasting system.
From INRA: Dr. Rachid MRABET, Mr. Hassan BENAOUDA, Dr. Hamid MAHYOU, Dr. Sliman ELHANI,
Dr. Rachid DAHAN, Dr. Hassan OUABBOU and Mr. Mohamed BOUGHLALA have exchanged data and
information with us and participated in different research projects some of which were not directly
related to yield forecasting per se.
The authors would also like to thank all colleagues who directly or indirectly contributed in our
research work, either by providing agro-climatic data or contributing to the overall thinking process,
particularly: Mr. Abdelaziz EL OUALI (former Head Agrometeorology Service at DMN), for his advice
on climatology in Morocco; Mr. Wolfgang GÖBEL (former researcher at INRA) for exchange
information on agro-meteorology and for common publications on the “Atlas Agroclimatique du
Maroc”; Dr. Mohamed EL MOURID (former researcher and research manager at INRA) who initiated
the first research studies on crop modelling at INRA; Mr. Hamid FELLOUN and Ms. Fatiha SELOUANI
(MAPM) who provided us with climatic data of the Ministry of Agriculture and Marine Fishery.
Least but not last, thanks go also to Mr. Chafik KRADI, head of the “Division de l’Information et de la
Communication” at INRA and his staff, particularly Mr. Reddad TIRAZI who helped in the diffusion
of this document.
Our special thank goes to Dr. René GOMMES, ex senior scientist at FAO with whom we were
honored to work on several occasions on operational agro-meteorology and climate change and
who was kind to preface this document.
The authors
SUMMARY
The present document provides a summary of research work carried out, at National Institute for
Agronomic Research of Morocco (in French, Institut National de la Recherche Agronomique - INRA),
since early 1990s, in the area of operational agrometeorology oriented toward forecasting crop
harvests. Forecasting the production of crops early before harvest allows decision makers to be
prepared in advance for eventual consequences of abnormal deviations of the climate, particularly
for strategic commodity crops to food security like cereals. To our knowledge, to date there is no
official method to forecast cereal production in Morocco on the basis of agrometeorological data.
However, cereal productions are estimated based on a sampling method some weeks before
harvest, every year by the Ministry of Agriculture and Marine Fishery (MAPM) through the
Direction of Strategy and Statistics (in French, Direction de la Stratégie et des Statistiques - DSS). It
is a direct method, precise, and applied directly before harvest, but requires consequent human
and financial resources. The need to elaborate an indirect method to early forecast yields that is
fast and economical, has been understood at INRA as early as in 1995, triggered by the severe
drought of that particular season, described as the worst dry season of the 20th century in Morocco.
Neither the classical frequency analyses of the climate used to identify seasons of close similarity to
1994-1995 season, nor the available mechanistic models for crop forecasting used in developed
countries, have been able to monitor crop development during that season and a fortiori predict
the catastrophic harvest of 1995. Therefore, it became necessary to come up with a new approach
for forecasting cereal yields using an innovative methodology which combines empirical and
statistical approaches with agronomic and meteorological expertise. First we had to study the
interaction between the climate and the cereal crops behaviors, particularly climatic and crop
cycles were analyzed together in a series of long term data, initially for Meknes region where the
first two authors were posted, extended later to other regions of Morocco. Preliminary results
indicated for the first time in Morocco that inter-annual variation of cereals yields could be
explained by variation in the amount of rainfall cumulated during the crop cycle, with a relatively
high accuracy. The relationship could be enhanced by partitioning the season into three or more
phases. In collaboration with the University of Liège (ULg, Belgium) and later with the Joint
Research Centre of the European Commission (JRC), a new indicator was identified as highly
correlated to cereal yields, which is the Normalized Difference Vegetation Index (NDVI) derived
from satellite images. Unlike many European countries, this index was highly correlated to cereal
yields in Morocco, mainly due to the aridity of Moroccan climate and the predominating coverage
of cereals of agricultural areas. NDVI is correlated with cereal yields as long as cropping season
rainfall did not exceed 550 mm, which explains the irrelevance of NDVI to forecast crop yields in
Northern Europe. The combination of both rainfall and NDVI allowed forecasting of cereal yields as
early as March, three months before harvest, and at a low cost, with a level of accuracy similar to
the one of the direct sampling method used at crop maturity by DSS. These astonishing results have
led INRA to publish for the first time in Morocco three crop forecasting bulletins between 2009 and
2011, in collaboration with JRC. In these bulletins, an approach combining four individual
approaches was used: (1) similarity approach using rainfall and/or NDVI as criteria of comparison,
(2) regression models using rainfall and NDVI as predictors of cereal yields, and (3) the JRC
approach which is based on a simulation model of crop growth called WOFOST. The deterministic
model WOFOST is now being adapted to the Moroccan agro-climatic context and incorporated in
an operational forecasting system. To ensure durability of the system, a strategic partnership
between INRA, DSS and DMN was formalized in addition to that bounding INRA and JRC. This new
collaboration has allowed establishment of the first national cereal yields forecasting system
named “CGMS-MAROC”, based on the combined approach developed in the present document.
The system is carried out by the three national institutions (INRA, DSS and DMN), leading to the
edition of a fourth bulletin of cereal yields forecasts issued for the 2012 season. The combined
approach can be extended to forecast yields for other crops in morocco as well as in countries of
similar climatic pattern, provided some adjustments. In parallel to yield forecasting, a new field of
research can be explored, dealing with estimating cropped areas, using low resolution and
inexpensive satellite images.
Key words: Agrometeorology, yield forecasting, similarity analysis, NDVI, rainfall, temperature,
cereals, soft wheat, durum wheat, barley, drought, Morocco, INRA.
TABLE OF CONTENTS
I. INTRODUCTION .............................................................................................................................................. 1
II. APPROCHES OF ANALYSIS .............................................................................................................................. 9
1. Data base sets used ............................................................................................................................................... 9
1.1. Climatic data ............................................................................................................................................... 10
1.2. North Atlantic Oscillation Index .................................................................................................................. 11
1.3. Vegetation index from satellite imagery ..................................................................................................... 12
1.4. Administrative boundaries of Morocco in GIS format ................................................................................ 13
1.5. Land cover maps ......................................................................................................................................... 13
1.6. Agricultural statistics ................................................................................................................................... 14
2. Methods of Analyses ........................................................................................................................................... 15
2.1. Exploratory treatment of rainfall data ........................................................................................................ 15
2.2. Frequency analysis of rainfall ...................................................................................................................... 17
2.3. Ombrothermic index of bagnouls and gaussen .......................................................................................... 17
2.4. Length of the growing period ..................................................................................................................... 18
2.5. Analysis of breaking point in chronological series of rainfall ...................................................................... 19
2.6. Productivity of rain water ........................................................................................................................... 19
2.7. processing of satellite images ..................................................................................................................... 20
2.8. Cereal yield forecasting approach .............................................................................................................. 21
2.8.1. Technological trend .................................................................................................................................... 23
2.8.2. Similarity analysis ........................................................................................................................................ 24
2.8.3. Linear regression analysis ........................................................................................................................... 25
III. ANALYSES OF MOROCCAN CLIMATE ............................................................................................................ 27
1. Influence of North Atlantic Oscillation................................................................................................................ 28
2. Climate change..................................................................................................................................................... 29
3. Temperature ........................................................................................................................................................ 31
3.1. Spatial variation of temperature................................................................................................................. 31
3.2. Inter-annual variation of temperature ....................................................................................................... 33
3.3. Seasonal variation of temperature ............................................................................................................. 35
4. Rainfall ................................................................................................................................................................. 36
4.1. Spatial Variation of rainfall ......................................................................................................................... 36
4.2. Interannual variation of rainfall .................................................................................................................. 38
4.3. Seasonal variation of rainfall....................................................................................................................... 40
4.3.1. Frequency analysis of dekadal rainfall ........................................................................................................ 41
4.3.2. Linear approximation of dekadal rainfall .................................................................................................... 43
4.4. Seasonal variation of rainfall....................................................................................................................... 45
5. Growing season from Ombrothermic diagram ................................................................................................... 48
6. Length of the growing Period .............................................................................................................................. 52
IV. ANALYSYS OF CEREAL PRODUCTION ............................................................................................................ 54
1. End uses of cereals ............................................................................................................................................... 54
2. Geographical distribution of cereals ................................................................................................................... 55
3. Technological trend ............................................................................................................................................. 58
4. Cereal crop development .................................................................................................................................... 63
V. AGROMETEOROLOGICAL ANALYZES ............................................................................................................ 65
1. Relationship between transpiration and growth ............................................................................................... 65
1.1. Relationship between rainfall and cereal yields ......................................................................................... 67
1.2. Relationship between rainfall and cereal area ........................................................................................... 68
1.3. Relationship between rainfall and rainwater productivity ......................................................................... 71
1.3.1. At national scale .......................................................................................................................................... 71
1.3.2. At agro-ecological zone scale ...................................................................................................................... 74
2. Agricultural drought ............................................................................................................................................ 74
3. Relationship between growth and rainfall ......................................................................................................... 76
4. Relationship between NDVI and rainfall ............................................................................................................. 77
5. NDVI profile.......................................................................................................................................................... 79
5.1. Intra and inter-annual Variation of NDVI .................................................................................................... 79
5.2. Regional variation of NDVI .......................................................................................................................... 80
VI. CEREAL YIELD FORECASTING APPROACHES ................................................................................................. 82
1. Non-parametric approach ................................................................................................................................... 83
2. Similarity approach .............................................................................................................................................. 84
2.1. Similarity analysis using rainfall .................................................................................................................. 84
2.2. Similarity analysis using NDVI ..................................................................................................................... 90
2.3. Similarity analysis using NDVI and rainfall combined ................................................................................. 94
3. Regression models approach............................................................................................................................... 97
3.1. Forecasting using rainfall as predictor ........................................................................................................ 98
3.2. Forecasting using agro-climatic indices .................................................................................................... 104
3.3. Forecasting using remote sensing............................................................................................................. 105
3.4. Forecasting using weather data and NDVI ................................................................................................ 111
4. Combined approach ........................................................................................................................................... 113
VII. INSTITUTIONALIZATION AND OPERATIONALIZATION OF THE FORECASTING SYSTEM .............................. 115
VIII. CONCLUSIONS AND OUTLOOK ................................................................................................................... 118
IX. REFERENCES ............................................................................................................................................... 121
LIST OF TABLES
Table 1: Classes of rainfall and corresponding aridity levels with administrative provinces
included in each class. Rainfall data are averages of 1988-2005. ....................................... 4
Table 2: Change point analysis in chronological series of cumulated rainfall between
September and May, at the provincial level (Source: Balaghi, 2006). ............................... 30
Table 3: Linear regression models applied to cumulated dekadal rainfall over the growing
season (from September to May) at the country level. ................................................... 44
Table 4 : Ombrothermic Index, as a ratio between precipitation (P) and temperature (T), by
synoptic station. Unit of P/T is in mm/°C. ...................................................................... 50
Table 5 : Main uses of cereal grains in Morocco (Source: ONICL, 2012). ......................... 55
Table 6: Partitioning of cereal production, area and yield, by agro-ecological zones
(Average of 1990 to 2011). .......................................................................................... 56
Table 7: Yield trend over years (Quintal/ha.year), due to technology improvement of the
three main cereals, by agro-ecological zone, and coefficients of determination (R2) of the
regression lines. (Data series of 1979 till 2006; Source: Balaghi and Jlibene, 2009). ......... 59
Table 8 : Average and Potential Rain Water Productivity (RWP) of cereals, at national level.
RWP is equal to the slope of the relationship between yield and rainfall. ......................... 73
Table 9: Rain Water Productivity (gram/liter), for the three winter cereals, at the national
and agro-ecological zones levels (average from 1988 to 2011). ...................................... 74
Table 10 : Non-parametric relationship between wheat grain yield (Q/ha) and drought
indicators, during critical season’s phases (beginning, middle and end of cycle). (0: No
drought; 1:Drought). .................................................................................................... 83
Table 11 : Coefficient of determination (R2) of the linear regression models between cereals
yields (soft wheat, durum wheat and barley) and cumulated rainfall over the cropping
season (from October till March), at the level of the agro-ecological zones (Data from 1988
to 2008). ................................................................................................................... 102
Table 12: Coefficient de determination (R2) of the regression models between grain yields
of the three winter cereals (soft wheat, durum wheat and barley) and average NDVI
(SPOT-VEGETATION) from February till March and from February till April, at the level of
the agro-ecological zones of Morocco (Data of 1999 to 2011). ...................................... 109
Table 13: Schematic table of the combined approach and predictors developed for cereal
yield forecasting in Morocco. Shaded areas indicate the predictors used for each method.
................................................................................................................................. 114
LIST OF FIGURES
Figure 1: Positive phase (left) and negative one (right) of the North Atlantic Oscillation. At
the positive phase, drought reigns over the Mediterranean region, while storms are
frequent over Europe. On the contrary, at negative phase, the Mediterranean region enjoys
a humid weather while Europe is less humid.
(Source:
http://www.ldeo.columbia.edu/res/pi/NAO/).
................................................................. 12
Figure 2: Country average dekadal (10-days) rainfall, of a series of crop seasons from 1988
till 2011. ...................................................................................................................... 16
Figure 3 : Crop Growth Monitoring System (CGMS) for crop harvests forecasting. ............ 22
Figure 4: Hypothetical example showing how crop yields (red curve) can be influenced by
weather, technology innovation, policy, extreme factors and general trend (Source:
Gommes
et al
., 2010). .................................................................................................. 24
Figure 5: Correlation between the North Atlantic Oscillation (NAO) index, averaged from
September to February and cumulated precipitation from September to May in Morocco
(data of 1979 to 2011) ................................................................................................. 29
Figure 6: Maximum temperature of the warmest month (A), minimal temperature of the
coldest month (B), annual mean temperature (C) and temperature annual range (D) in
Morocco (these maps were created from data taken from www.worldclim.org at the spatial
resolution of 1 km, Series from 1950 to 2000, Hijmans
et al
., 2005) ............................... 32
Figure 7 : Sum of daily average temperatures, in growing degree days, along cereal
cropping season, at the country level. Growing cycles from 1999 to 2009 (year of harvest)
are sorted out in descending order of cumulated temperature. ....................................... 34
Figure 8 : Evolution of coefficient of variation of average inter-annual dekadal temperatures
at the country and Meknes levels (data of 1999 to 2009). .............................................. 35
Figure 9 : Average maximal (red curve), mean (yellow curve) and minimal dekadal
temperature (blue curve), along the cereal growing season. Red dots correspond to
dekadal values of maximal temperature and blue dots to minimal temperature, from 1999
until 2009. ................................................................................................................... 36
Figure 10 : Country average rainfall cumulated between September and April. The map
created from data of www.worldclim.org (Hijmans
et al
., 2005). Red dots refer to main
cities of Morocco. ......................................................................................................... 37
Figure 11 : Inter-annual variation of cumulated rainfall between September and May, at the
country level, for 1988 to2011. ..................................................................................... 38
Figure 12 : Geographic localization of the 35 synoptic weather stations used to generate
weather data of Morocco, on a background map of elevation provided from radar data of
«
Shuttle Radar Topography Mission
» (http://www2.jpl.nasa.gov/srtm/). ....................... 39
Figure 13 : Examples of dekadal rainfall distribution along the cropping cycle, for specified
contrasting synoptic weathers stations (averages of 1988 to 2010). ................................ 40
Figure 14 : Distribution of dekadal rainfall, at the country level, for 5 frequency levels (0.1;
0.3; 0.5; 0.7 and 0.9). Data series from 1988 to 2011. ................................................... 41
Figure 15 : Annual rainfall distribution (cumulated from September to June) at Meknes
(1932-2004), Khemisset (1986-2004), Settat (1910-2004) and Safi (1901-2004). The red
curve represents adjusted theoretical distribution curve to observed one (Balaghi
et al
.,
2005). ......................................................................................................................... 42
Figure 16 : Dekadal cumulated rainfall from September till May, at the country level.
Cropping cycles of 1988 to 2011 are arranged in ascending order of cumulated rainfall.
... 43
Figure 17 : Average increment rate of rainfall per dekad (x-axis, in mm/dekad), for 4
classes of cumulated rainfall (200 to 300, 300 to 400, 400 to 500 and > 500 mm). Data are
country averages of 1988 – 2011 series. ....................................................................... 45
Figure 18 : Monthly rainfall distribution, as influenced by the overall volume of rainfall in
Morocco (data of 1988 to 2005). ................................................................................... 46
Figure 19 : Monthly rainfall distribution for 15 contrasting synoptic weather stations
scattered along the Atlantic coast (data from 1988 till 2005). Stations are arranged in
ascending order of latitude. Red lines represent points of equal monthly rainfall, of 20 mm,
40 mm, 60 mm and 80 mm. ......................................................................................... 47
Figure 20 : Ombrothermic diagram, at the country level.
(Average rainfall and temperature,
from a series of 1999 to 2009).
..................................................................................... 48
Figure 21: Average Ombrothermic index (from September to May) as influenced by latitude.
Monthly data series of 1999-2009 for 25 different synoptic stations were used. ............... 49
Figure 22 : Geographic areas where cereal growing seasons extend from September until
March (areas in blue, green and brown colors), or until April (areas in green and brown
colors) or until May (areas in brown color), according to Ombrothermic index of Bagnouls
and Gaussen (1953). The map was generated from data of www.worldclim.org (Hijmans
et
al
., 2005). ................................................................................................................... 52
Figure 23 : Lengths and time limits of different periods within the cereal crop cycle: (1) soil
preparation (W in purple), (2) period of growth (green) which includes sowing period (S),
(3) humid period (light green), flowering (F) and grain filling (G); and (4) grain maturity
and ripening (M in pink). The humid period includes times of emergence (L), tillering (T),
and heading (H) of cereal crops in the north-western Morocco (Jlibene and Chafai, 2002).
Weather data are a series of 70 years from Arbaoua Meteorological station (Data source:
ORMVAL). .................................................................................................................... 53
Figure 24 : Map of average production (in Mega tons) of autumn cereals (soft wheat,
durum wheat and barley confounded) by province (data series from 1990 to 2010). ........ 57
Figure 25 : Country soft wheat yield variation and trend in Morocco (in green) (Source of
data: FAOSTAT and DSS). ............................................................................................ 60
Figure 26 : Curves of cumulative probability of soft wheat yields at farm level (real
situation) and at potential situations at Meknes (Source: Boughlala
et al
., 1994). ............ 61
Figure 27 : Technology trend or improvement achieved in 28 years for soft wheat in
Morocco. ..................................................................................................................... 62
Figure 28 : Technological trend or improvement achieved in 28 years for durum wheat in
Morocco. ..................................................................................................................... 62
Figure 29: Evolution of daily actual evapotranspiration of soft wheat, simulated on the basis
of temperature alone (Eta_T°) and of temperature in association with relative humidity
(RH), wind speed and global radiation, at Meknes (Balaghi, 2000). ................................. 66
Figure 30: Schematic figure illustrating the relationship between transpiration and biomass
production (Jlibene and Balaghi, 2009). ......................................................................... 67
Figure 31: Deviation (%) from mean values of cumulated rainfall over September to May
period, and main cereal yield (soft wheat, durum wheat and barley), at the country level
(data from 1988 to 2011); indicating the co-variation of rainfall and cereal yields. ........... 68
Figure 32: Deviation (%) from mean value of cumulated rainfall over September to May
period, and of main cereal area (soft wheat, durum wheat and barley), at the country level
(data from 1988 to 2011). ............................................................................................ 69
Figure 33: Coefficient of determination (R2) of the relationship between main winter cereal
area (soft wheat, durum wheat and barley) and cumulated rainfall during the cropping
season (starting in October in average), at the country level (Data series from 1988 to
2011). Average dekadal rainfall is plotted in second axis (blue bars), for illustration. ........ 70
Figure 34: Relationship between soft wheat country yield (Kg/ha) and cumulated rainfall
during the cropping season (mm) (data from 1988 to 2011). .......................................... 71
Figure 35 : Relationship between durum wheat country yield (Kg/ha) and rainfall during the
cropping season (mm) (data from 1988 to 2011). .......................................................... 72
Figure 36: Relationship between barley country yield (Kg/ha) and rainfall during the
cropping season (mm) (data from 1988 to 2011). .......................................................... 72
Figure 37 : Dekadal cumulated rainfall between September and May, of dry cropping
seasons. Periods of drought are recognized as flat segments of the curves (in bold).
Drought can occur at diverse periods of the crop cycle. .................................................. 75
Figure 38: Spatial distribution of cumulated dekadal rainfall (from September till May) for
the dry season of 1996-1997. Locations used cover 23 provinces of Morocco, scattered all
over the country (Source: Jlibene, 2011). ...................................................................... 76
Figure 39: Typical weather conditions, during wheat growing season. Long term average of
dekadal rainfall (series from 1988 to 2011) and dekadal temperature (series from 1998 to
2009) were used. Major development phases of wheat crop are also displayed (green
arrow). ........................................................................................................................ 77
Figure 40: Relationship between NDVI (NOAA-AVHRR), averaged over the period from
February till April and over agricultural zones, and cumulated rainfall over the cropping
season from September till April, in Morocco. The 345 dots in this figure are data from 23
weather stations, over the period from 1990 to 2004. .................................................... 78
Figure 41: Comparison of the vegetation status in Morocco, between a wet season (2005-
2006) and a dry season (1999-2000), using NDVI (SPOT-VEGETATION) of the 3rd dekad of
March. Dark green colored pixels indicate high NDVI values. .......................................... 79
Figure 42: NDVI profile, during the cropping season for all croplands of the country (SPOT-
VEGETATION) and for 12 cropping seasons (1999-2000 to 2010-2011), arranged in
descending order of observed cereal yields. ................................................................... 80
Figure 43: Average NDVI profile (SPOT-VEGETATION), during the cropping season and
over all agricultural lands, for the six agro-ecological zones of Morocco (Data from 1999-
2000 to 2010-2011). .................................................................................................... 81
Figure 44 : Cropping seasons, from 1987-1988 to 2011-2012, arranged in ascending order
of cumulated rainfall between 1st of September and 10th of April of the following calendar
year (Source: Balaghi
et al
., 2012). Numbers in x-axis correspond to years of harvest.
Green line indicates average cumulated rainfall over the 25 years. The red bar points to
cumulated rainfall of the latest season i.e. 2011-2012. ................................................... 85
Figure 45: Dendrogram of cluster analysis, discriminating cropping seasons from 1960-1961
to 1999-2000, with regard to monthly rainfall of 8 months, from September to April, for
Meknes region (Source: Balaghi, 2000). ........................................................................ 86
Figure 46: Cumulated rainfall over the cropping season (September till May) for the 1994-
1995 cropping season, at Meknes, compared to historical data from 1960-1961 to 1993-
1994. Seasons are arranged in ascending order of cumulated rainfall. ............................. 87
Figure 47: Similarity analysis of 2007-2008 cropping season, using cumulated dekadal
rainfall, at the country level. ......................................................................................... 88
Figure 48: Forecasted country yields of soft wheat, for cropping seasons of 2007-2008,
2008-2009, 2009-2010 and 2010-2011, based on similarity analysis of cumulated rainfall
since October. ............................................................................................................. 89
Figure 49: "MARSOP3" Web viewer, for agro-climatic analysis of the cropping season.
Deviation of NDVI (SPOT-VEGETATION), at the first dekad of April for agricultural lands,
from long term average NDVI is provided by this application. Comparison with other
seasons can also be performed. Spatial resolution of grids is 25x25 km (Source: Balaghi
et
al
., 2012). ................................................................................................................... 91
Figure 50: Similar seasons to the 2011-2012 cropping season, based on dekadal NDVI
(SPOT- VEGETATION) over agricultural lands, at the country level. The 2011-2012 cropping
season (in orange) is compared to historical seasons. The 2000-2001 cropping season is
the most similar to 2011-2012 with regard to NDVI profile from 1st of February to 1st of
April, followed by 2007-2008 and 2004-2005 (Source: Balaghi
et al
., 2012). .................... 92
Figure 51: Forecasted soft wheat grain yields at the country level, for cropping seasons of
2007-2008, 2008-2009, 2009-2010 and 2010-2011, based on similarity analysis and using
dekadal NDVI (NOAA-AVHRR) since 1st of February. ....................................................... 93
Figure 52: The "CGMS Statistical Toolbox" software, for cereal yield forecasting, adapted to
Morocco. ..................................................................................................................... 94
Figure 53: Similarity analysis of 2010-2011 cropping season, based on Principal Component
Analysis, using in "CGMS Statistical Toolbox" software CST (http://e-
agri.wikispaces.com/CGMSStatTool). ............................................................................. 95
Figure 54: Observed and forecasted durum wheat grain yield at the country level, based on
Principal Component Analysis, for the 2008-2009, 2009-2010 and 2010-2011 cropping
seasons, using NDVI (NOAA-AVHRR) and cumulated rainfall (Data from 1988 to 2011), at
dekadal time step. ....................................................................................................... 96
Figure 55: Coefficient of determination (R2), by step of one dekad, of the regression line
between grain yields of the three main cereals (soft wheat, durum wheat and barley) in
Morocco and cumulated rainfall since September till May, at the country level. Average
(1988-2011) cumulated rainfall is displayed in blue bars, for illustrating the steady increase
of R2 with rainfall during the season, from the 2nd dekad of October to the 2nd dekad of
March. ......................................................................................................................... 99
Figure 56 : Relationship between country yields of soft wheat, durum wheat and barley,
and cumulated rainfall over the cropping season (from September to March), for data series
of 1988 to 2008. ........................................................................................................ 101
Figure 57: Coefficient of determination (R2) of linear regression models between grain
yields of the three main cereals (soft wheat, durum wheat and barley) in Morocco and
rainfall cumulated over significant periods during the cropping season, at the country level
(data from 1988 to 2011).
R
2 is higher when using cumulated rainfall over 6 months, and in
partitioning the cropping season’s rainfall into two groups of three months and three
groups of 2 months each (in these two latter cases, lengths of colored bars correspond to
partial R
2 of multiple regressions).
.............................................................................. 103
Figure 58: Coefficient of determination (R2) of regression models between soft wheat grain
yields and agro-meteorological indices derived from AgroMetShell program of the FAO, in
Meknes province (Source: Balaghi, 2006). ................................................................... 104
Figure 59: Coefficient of determination (R²) of regression models between grain yields of
the three main cereals (soft wheat, durum wheat and barley) in Morocco, and dekadal
NDVI (SPOT-VEGETATION), at the country level (data from 1999 to 2011), by step of one
dekad, since 1st dekad of January. .............................................................................. 105
Figure 60: Coefficient of determination (R2) of regression models between grain yields of
the three main cereals (soft wheat, durum wheat and barley) in Morocco, and average
NDVI (SPOT-VEGETATION), at the country level (data from 1999 to 2011), by step of one
dekad, since the 1st dekad of February. ....................................................................... 107
Figure 61: Linear regression between country grain yields of the three main cereals (soft
wheat, durum wheat and barley) and average dekadal NDVI (SPOT-VEGETATION) between
1st of February and end of March, and between 1st of February and end of April (data of
1999 to 2011). ........................................................................................................... 108
Figure 62 : Mapping of four classes of coefficient of determination (R2) of the linear
regression models between yields of soft wheat, durum wheat and barley, and average
dekadal NDVI (SPOT-VEGETATION) from February till March and from February till April.
................................................................................................................................. 110
Figure 63 : Relationship between observed and forecasted yields of soft wheat at Meknes
and Settat, based on multiple linear regression models, involving NDVI, rainfall and
temperature (Source: Balaghi
et al.
, 2008). ................................................................. 112
Figure 64 : Increase of precision and accuracy of the country soft wheat yield forecast,
based linear regression models, using NDVI (SPOT-VEGETATION), as the cropping seasons
reach completion. Examples of the 2008-2009, 2009-2010 and 2010-2011 cropping seasons
are shown. ................................................................................................................ 113
Figure 65: The Web viewer of CGMS-MAROC (www.cgms-maroc.ma) for bio-climatic
monitoring of cereals, and preliminary statistical analysis of the cropping season. On the
left side window is displayed the cumulated rainfall over the season on agricultural lands
(10x10 km grid). On the right window is shown, the similarity analysis using cumulated
rainfall, in the district of Ben Ahmed (province of Settat). ............................................. 117
ACRONYMES
°C
Degree Celsius
AFI
Area Fraction Image
AGRIMA
Agriculture Maroc
AgroMetShell
FAO tool for monitoring crops ftp://ext‐
ftp.fao.org/sd/reserved/agromet/AgroMetShell/
Alterra
Research Institute at the University of Wageningen, Netherlands
http://www.alterra.wur.nl
AURELHY
Analysis Using RELief for Hydro‐meteorology
AVHRR
Advanced Very High Resolution Radiometer
CGMS
Crop Growth Monitoring System
www.marsop.info/marsopdoc/cgms92/1_en.htm
CRTS
Centre Royal de Télédétection Spatiale www.crts.gov.ma
CST
CGMS Statistical Toolbox http://e-agri.wikispaces.com/CGMSStatTool
CV
Coefficient of Variation (%)
DMN
Direction de la Météorologie Nationale www.marocmeteo.ma
DMP
Dry Matter Productivity www.geoland2.eu
DPA
Direction Provinciale de l’Agriculture
DPAE
Direction de la Programmation et des Affaires Économiques, currently
DSS
DSS
Direction de la Stratégie et des Statistiques www.agriculture.gov.ma
E‐AGRI
A research Project on Crop Monitoring as an E‐agriculture tool in
Developing Countries www.e‐agri.info
ENSO
El Niño–Southern Oscillation or El Niño/La Niña–Southern Oscillation
ENVISAT
Environment Satellite, European satellite launched in 2002 to observe
Earth
ETa
Actual Evapotranspiration
ETP
Potential Evapotranspiration
EU
European Union
EVI
Enhanced Vegetation Index
FAO
Food and Agriculture Organization of the United Nations:
http://www.fao.org
FAOCLIM
FAO database of monthly agro-climatic observed and calculated
parameters http://www.fao.org/nr/climpag/pub/EN1102_en.asp
fAPAR
Fraction of solar radiation absorbed by plants
g
Gram, unit of weight
GHCN
Global Historical Climatology Network
GIS
Geographic Information System
GLC2000
Global Land Cover for the year 2000
GlCropV2
Land cover map made of in collaboration with VITO
ha
Hectare (1 hectare = 10.000 m²)
INRA
National Institute for Agronomic Research: www.inra.org.ma
JRC
Joint Research Center: http://ec.europa.eu/dgs/jrc/index.cfm
Kg
Kilogram
Km
Kilometer = 1000 meters
l
Liter (1 l = 1 dm3)
LAI
Leaf Area Index
MAPM
Ministry of Agriculture and Marine Fishery www.agriculture.gov.ma
MARS
Monitoring Agricultural ResourceS mars.jrc.ec.europa.eu
MERIS
Medium Resolution Imaging Spectrometer
MIAC
Mid America International Agricultural Consortium
mm
Millimeter rainfall (1mm=1 liter per square meter)
MODIS
Moderate Resolution Imaging Spectro-radiometer
http://modis.gsfc.nasa.gov/
NAO
North Atlantic Oscillation
NDVI
Normalized Difference Vegetation Index
NOAA‐AVHRR
The Advanced Very High Resolution Radiometer sensor carried by the
National Oceanic and Atmospheric Administration satellite
ONICL
Office National Interprofessionnel des Céréales et
Légumineuses www.onicl.org.ma
ONSSA
Office National de la Sécurité Sanitaire des produits Alimentaires
www.onssa.gov.ma
ORMVA
Office Régional de Mise en Valeur Agricole
PCA
Principal Component Analysis
PROBA‐V
Project for On‐Board Autonomy – VEGETATION sensor to be launched
in 2013 http://www.vgt.vito.be/
PSDA
Projet de Soutien au Développement Agricole dans les ORMVA
Q
Quintal (1 quintal = 100 Kg)
R²
Coefficient of determination
RWP
Rain Water Productivity
RWUE
Rain Water Use Efficiency
SEEE
Secrétariat d’État chargé de l’Eau et de l’Environnement
http://www.minenv.gov.ma
SPOT-
VEGETATION
Programme conceived to allow daily monitoring of terrestrial
vegetation cover through remote sensing, at regional to global scales
http://www.vgt.vito.be/pages/mission.htm
SRTM
Shuttle Radar Topography Mission: www2.jpl.nasa.gov/srtm
ULg
University of Liege (Arlon Campus Environnement)
http://www.facsc.ulg.ac.be/cms/c_636656/en/arlon-campus-
environnement-home
UNDP
United Nations Development Programme
USAID
United States Agency for International Development
VITO
Flemish institute for technological research, Belgium www.vito.be
WMO
World Meteorological Organization http://www.wmo.int
WOFOST
WOrld FOod STudies is a simulation model for the quantitative analysis
of the growth and production of annual field crops
http://www.wageningenur.nl/en/Expertise-Services/Research-
Institutes/alterra/Facilities-Products/Software/WOFOST.htm
Agrometeorological Cereal Yield Forecasting in Morocco
Introduction
Balaghi R., Jlibene M., Tychon B., Eerens H.
1
I. INTRODUCTION
The main authors’ interest, in studying the climate and its impact on cereal production with the aim
of yield forecasting was sparked by the major drought that occurred during the 1994-1995 growing
season in Meknes region. Scenarios of the prospective consequences of drought on cereal yields
were analyzed by identifying, from the historical climatic and crop database, similar seasons to
1994-1995 in terms of cumulative rainfall over the period of September-January. While similar
cropping seasons could be identified from early rainfall distribution, it turned out later as the season
evolved, that the season of 1994-1995 was exceptional, far away from recorded past seasons. The
severe drought during this unexpected season was about to cause an economic disaster in Morocco,
narrowly averted by the bumper harvests of the following season (1995-1996). Analysis of historical
data since 1960 indicated that the climate in Morocco was relatively stable until 1980 from where a
change had occurred, expressed in lower rainfall and higher temperatures during the next three
decades. Also, higher intra and inter annual variations tended to increase. Frequency and severity of
climatic hazards were amplified with negative consequences on water resources and agriculture,
and ultimately on the whole economy of the country. It then became necessary to invest in
agrometeorological research science in order to develop effective methods and rapid tools capable
of forecasting cereal yields early in the season. The present document reports the thinking process
and research work done at National Institute for Agronomic Research that led to the development
of a global approach of cereal yields forecasting in Morocco. The document provides also some
ideas worth exploring to improve precision and accuracy of the forecasts.
Crops evolve under the direct influence of agrometeorological factors such as temperature,
moisture, sunlight and radiation, or hygrometry. The development of methods for crop yield
forecasting, requires a thorough understanding of the interaction between these factors and the
crops. The study of these interactions is the result of a maturing process of an emerging science
termed “Agrometeorology” stemming from “Biogeography” and later from “Bioclimatology”. The
term agrometeorology
1
, meaning meteorology applied to agriculture, emerged during the 1920s
(WMO, 2006), and developed as a recognized and established science in the 1950s (Seemann et al.,
1979). The objective of operational agrometeorology is to predict the response of crops to external
conditions of natural (climate, soil, diseases, parasites, etc.) or human origins (cultural practices,
prices, etc.).
Research on operational agrometeorology at National Institute for Agronomic Research of Morocco
(INRA), precisely at its Regional Agronomic Research Centre of Meknes, was triggered by the
1
Agricultural meteorology can be defined in large terms as « a scientific discipline that is concerned with the study on
heat, air and biomass inside and above ground, in areas devoted to agricultural production, in addition to the incidence
of parasites and diseases on crops and animals which also depend on these factors for their expression” (WMO, 2006).
Agrometeorological Cereal Yield Forecasting in Morocco
Introduction
Balaghi R., Jlibene M., Tychon B., Eerens H.
2
extreme drought that happened during the cropping season of 1994-1995. This drought sparked
the awareness of the need to forecast crop yields. Cumulated rainfall over the cereal cropping cycle
(September till May) in Meknes region, as example, was less than half (43%) the amount of an
average season (530 mm). The season started with a 33% deficit in rainfall (September till
November) as compared to the long term average (126.5 mm) and stayed dry until February,
receiving practically no rainfall during three months (7.6 mm). Between the months of February and
May, rainfall was low and insufficient (134.8 mm) to help crop rebound.
Alarmed by the persistent drought during December 1994 and January 1995, the representative of
the Department of Agriculture in the province of Meknes (Direction Provinciale de l’Agriculture de
Meknes) requested the Regional Research Centre of INRA in Meknes to provide scenarios of
prospective developments of the season. Due to lack of established methodologies and forecasting
tools for cereal forecasting at that time, distribution of rainfall of the season of 1994-1995 was
compared to historical weather data available at the National Meteorological Service since 1960,
using frequency analysis method
2
. Based on this type of analysis, it appeared that season of 1974-
1975 was displaying a rainfall pattern similar to that of 1994-1995. During season of 1974-1975,
despite early drought the total amount of rainfall was close to average in the region. Hence,
frequency analysis of rainfall indicated that expected total season rainfall would be greater than
445 mm, with 90% probability, and greater than long term average with 50% probability. On the
basis of these two scenarios (50% and 90% probabilities), two figures of expected yields were
proposed. Considering the scenario of 90% probability, expected yields of soft wheat would be 11
Quintals per hectare (Q/ha) at the national level and 22 Q/ha at Meknes. Based on the scenario of
50%, expected yields would be 14 Q/ha at the national level and 27 Q/ha at Meknes. Finally at the
end of the season, none of the two scenarios has helped to provide yield forecasts for the season of
1994-1995. The reason is that this season was particular, with no similarity in both rainfall amount
and distribution with past recorded seasons since 1960. The drought period lasted long, covering
two thirds of the cereal cropping cycle. Final yields of soft wheat (official statistics of the MAPM),
were 4.79 and 5.70 Q/ha at the national and regional levels, respectively, which was an
unprecedented situation.
From the unexpected results, it was understood that the hypothesis of a stationary climate in
Morocco was no longer holding, that is historical rainfall frequencies have changed as a result of a
change in the climate since 1980. Other methods and tools for cereal yield forecasting had to be
invented based on an agro-meteorological approach resulting from a fine study of the climate and
its interaction with cereals development.
2
Frequency analysis is a statistical methodology that quantifies probabilities of a hazardous event like climate or
hydrology, based on past recorded events. It relies on the hypothesis of stationary or homogeneous distribution of the
time series.
Agrometeorological Cereal Yield Forecasting in Morocco
Introduction
Balaghi R., Jlibene M., Tychon B., Eerens H.
3
Preliminary analysis indicates that, in general characteristics, Moroccan climate is of
Mediterranean
3
type with major influences of the ocean, the desert and the mountains,
determined basically by its extended latitude (between 21°N and 36°N), sea shore, Sahara desert
and Atlas mountains. The latitude determines temperature due to the curving of the globe and the
inclination of solar rays, as compared to the tropics, resulting in a decrease of temperature from
south to north. The Mediterranean Sea in the North, with 512 Km of coast from Saïdia East to Cap
Spartel West, and the Atlantic Ocean to the west, with a longer coast of 2934 Km, from Cap Spartel
north to Lagouira South, attenuate temperature variation and temper the seasons. During summer,
temperatures are mild, similar to those on the Mountain, and moderate in the winter, creating an
environment of low temperature amplitude. The climate becomes continental from the Ocean
toward East, and temperature becomes cooler with altitude. The Sahara desert south of the
country, which has an arid climate (< 150 mm) influences inland climate through the movement of
tropical dry and hot air masses moving from south to north and from East to West. In the Atlas and
Rif mountains temperature is cooler due to elevation. Due to low temperature in mountains,
moisture precipitates as snow during winter and sometimes during spring.
The Azores anticyclone located in the Atlantic Ocean near Portugal, and the Saharan depression in
the south, exert antagonistic actions on the Moroccan climate. Humid and cold air masses reaching
Morocco from the Atlantic Ocean are accompanied with rainfall and snow in high elevation, When
the Azores anticyclone moves to the west or south west. During spring and summer, the Azores
moves up to high latitudes, pushing perturbations to the 45th parallel North. At the same time,
tropical dry and hot air moves up from south, leading to a net decrease in precipitation. Other
anticyclones of less importance influence Moroccan climate as well, particularly the Atlantic one
and the Mediterranean one.
Total annual precipitations in Morocco increase along latitude from south to north and along
longitude from east to west. Water supply in Morocco is entirely dependent on precipitations,
unlike countries of the Middle East, Eastern and central Africa. There are no supplied water sources
outside the boundaries of the country, unlike some other countries such as Egypt whose water
supply is dependent on the Nile River or Syria or Iraq who depend on the Euphrates River, or the
central African counties who are sharing the great lakes. Therefore, because of the total
dependence of Moroccan agriculture on precipitation, any sudden deficit has immediate negative
impact on agriculture and water resources, and consequently on the economy of the country as a
whole. This great dependence of Moroccan economy on rainfall was understood early by Marchal
Louis Hubert Lyautey whose famous statement “Governing Morocco is raining” is still at date.
3
The Mediterranean climate is characterized by a rainy season in autumn and winter seasons, hot and dry summers,
and cool winter temperatures.
Agrometeorological Cereal Yield Forecasting in Morocco
Introduction
Balaghi R., Jlibene M., Tychon B., Eerens H.
4
The gradient of moisture from south to north is so large that six relatively homogeneous agro-
ecological zones could be distinguished (Table 1):
Saharan desert zone, with less than 150 mm of annual rainfall ;
Pre- Saharan zone, with annual rainfall in the range of 151-250 mm ;
Arid zone, where annual rainfall in the range of 251 and 350 mm ;
Semi-arid zone, with annual rainfall in the range of 351 and 450 mm ;
Sub-humid zone, with annual rainfall in the range of 451-550 mm ;
Humid zone, with annual rainfall above 550 mm.
Table 1: Classes of rainfall and corresponding aridity levels with administrative provinces included
in each class. Rainfall data are averages of 1988-2005.
The Atlas Mountains lanyard the country diagonally, along the axis north-east to south-west. They
act as a natural barrier against desert influence from its Southern side and against moisture
originating from the Ocean from its Western side. The blocked oceanic moisture by the Atlas
Mountain chains precipitates as snow or rainfall depending on the harshness of winter
temperatures (Figure 10). Because of its natural geography, the Atlas Mountains are assimilated to
the national water reservoir. Elevation peaks at 4,165 meters above sea level at Jbel Toubkal
Mountain. Beyond this natural barrier, climate is therefore arid and pre-Saharan. Water masses,
coming from runoff of rain water and melting of the snow, in the mountainous chains, feed the
major rivers of the country, which flow to the ocean for most of them (Loukkos, Bouregreg, Sebou,
Oum Er-Rbia, Souss), to the Mediterranean Sea (Moulouya) or to the Sahara (Ziz and Draâ). The
Middle Atlas, a mountainous range stretching from south-east to north-west along 350 km, is the
North African Mountain most covered with moist areas, mainly natural lakes, rivers and fresh
springs.
Due to its geographic configuration, agriculture in Morocco, is confined within the borders of the
Mountains and the Seas, and is highly influenced by climatic factors, mainly rainfall. Availability of
Annual rainfall (mm)
Aridity level
Province
<150
Sahara
Dakhla, Lâayoune, Tantan, Errachidia, Ouarzazate, Bouarfa,
Tiznit, Sidi Ifni
151-250
Pre Sahara
Midelt, Taroudante, Marrakech, Oujda, Agadir
251-350
Arid
Settat, Nador, Al Hoceima, Essaouira, Beni Mellal,
Nouasser, Khouribga, Kasba Tadla
351-450
Semi-arid
El Jadida, Safi, Casablanca, Sidi Slimane
451-550
Sub-humid
Rabat-Sale, Kenitra, Tounate, Meknes, Fes, Taza
> 551
Humid
Larache, Tetouan, Tangier, Chefchaouen, Ifrane
Agrometeorological Cereal Yield Forecasting in Morocco
Introduction
Balaghi R., Jlibene M., Tychon B., Eerens H.
5
water, which is the most vital factor to agriculture, is shrinking due to the growing pressure of
demand for water for urban, industrial and touristic development, and to the negative impact of
climate change on rainfall. Major expected expressions of climate change in Morocco include
reduced precipitations, increased temperatures, and intensified extreme hazards like drought, heat
waves, frost, flooding, etc. (SEEE, 2010).
The economic weight of agriculture on the Moroccan economy (15 to 20% of GDP and 40%
employment) is so high that any temporal or seasonal variation of the climate will immediately
affect agricultural production, particularly that involving crops used as the basis of food security like
cereals. Rainfall variation between the successive cropping seasons of 1994-1995 and 1995-1996, in
a ratio of 1 to 3, affected cereal yields in a ratio of 1 to 3.61 and production in 1 to 5.74.
Comparison between the seasons of 1994-1995 and 1995-1996, indicates a cumulated rainfall over
the crop cycle (September till May) of 198 mm vs. 591 mm, and yield a of 4.79 Q/ha vs. 17.27 Q/ha,
and a production of 17 vs. 98 million quintals. The unpredicted harvest of 1994-1995 could have led
to an economic disaster if the next season didn’t happen to be rainy and productive.
Cereal imports were consistent since 1980, representing nearly half (48.7%) of the cereal
production and most of imported food products and import cost. Annual cereal imports amounts to
2.6 million tons on average for the period of 1980-1981 till 2010-2011 (ONICL, 2012), most of it
composed of soft wheat which accounts for 77%, followed by durum wheat (12%) and barley (11%).
Cereal imports are in constant progression since early 1990s, fluctuating over time and ranging
from 10% of average cereal production (during season of 1994-1995) following the good harvest of
1993-1994 to 244% during 2000-2001 following the dry season of 1999-2000. However, cereals are
imported even during record productive seasons like during 2008-2009 (10.2 million tons of
production), where significant quantity was imported during the next season (2.56 million tons),
that is 25% of the 2008-2009 total cereal production. Total cereal supply, as the sum of production
and import, without accounting for stocks, which may represents total needs for food and feed,
increases over years with a rate of 0.16 million ton per year since the 1990s. This high dependence
on imported cereals is associated with risks of short supplies and high prices in the international
market which may result from the variation in global production, embargos on imports and
speculation.
The 5.3 million hectares of cereal land have been, by commodity, subdivided by the MAPM into six
agro-ecological zones: Favorable, Intermediate, Unfavorable South, Unfavorable East, Mountainous
and Saharan. The favorable zone is located in the northern parts of Morocco, starting at its
southern limit at the Kenitra-Taza line. The Intermediate zone is located in the center of the country
north of the Casa-Benslimane line. The Unfavorable South zone is located between the Casablanca-
Benslimane line and Agadir province, while the Unfavorable East zone is located in the eastern
parts of the country. The Saharan zone is located South of Agadir province. The Mountainous zone
is mainly located in the Atlas Mountains. The Favorable, Intermediate and Unfavorable South
account for 75% of total cereal production and 75% of total area. The Mountainous zone
contributes to total cereal production and total cereal area by 12% and 10%, respectively. Yields are
Agrometeorological Cereal Yield Forecasting in Morocco
Introduction
Balaghi R., Jlibene M., Tychon B., Eerens H.
6
higher in the Mountain zone with an average of 1.41 ton per hectare for the period 1991-2011
(DSS), followed by the Favorable zone (1.39 tons per hectare), Intermediate zone (1.19 t/ha) and
Unfavorable South (0.76 t/ha).
Yields at the country level are low, half of those obtained in research experiments at the INRA
experiment stations (Jlibene, 2009), indicating that there is still important room for cereal yields
improvement in Morocco. National yields have slightly improved over the years. For example, soft
wheat yields evolved from 0.7 t/ha in the 1940s, to 0.9 t/ha in the 1950s and remained unchanged
during the 1960s and the 1970s, despite large scale state development programs: “plowing”,
“fertilizers”, and “seed” (Jlibene, 2009). During the next three decennials (1980s, 1990s, 2000s)
country average yields improved to 1.4 t/ha despite a decrease in rainfall and frequent drought
episodes. However, this improvement was yet insufficient to cover the fast growing population
needs. Imports rocketed from 0.9 million ton in the 1970s to more than 2.0 in the 2010s, with peaks
of 5.0 million tons in extremely dry year.
Episodes of drought can occur early in the cropping season, in the middle or at the end of the
season, or in combination of two out of three periods, in association with variation of temperature
and biotic stresses (insects and fungi). Rainfall variation, in amount and distribution within the
season, in addition to temperature variation and biotic stresses, creates a multitude of agro-
climatic situations which appear difficult to model.
Studies on the behavior of cereals in interaction with the climate in Morocco are scattered and lack
precise data and consistent monitoring of both the climate and plant parameters along the cycle.
Grain yield is the most often studied variable in relation to rainfall which is considered as by far the
most important climatic variable. The influence of climate on cereal growth and production is a
reality traditionally known to Moroccan farmers and students, becoming almost a faith not worth
investigating. Farmers usually wait for the first rain storms of September and October to prepare
the soil for planting before the rainy season in November and December, and entertain the crop
during spring before harvest in the summer. This is an ancestral practice that was perpetuated over
centuries. As a consequence, research studies have focused mainly on means to improve yields
instead of understanding their formation in relation to climate. Without understanding the
interaction between crops and the climate, yields forecasting would be unrealistic.
Crop harvest forecasting
4
provides the opportunity to become prepared for consequences of any
shortage in production through actions to reduce vulnerability to climatic risks. It is hence a
valuable tool for decision making in agriculture, allowing for planning in advance actions like aids to
farmers, or cereal imports. It also allows quantifying drought impacts needed by institutions like
agricultural insurance company. Harvest forecasting evolved from applied research status to an
operational one, due to INRA research efforts in collaboration with partner institutions national and
international.
4
Crop forecasting is a science that permits to foresee crop yields using mathematical models.
Agrometeorological Cereal Yield Forecasting in Morocco
Introduction
Balaghi R., Jlibene M., Tychon B., Eerens H.
7
From a practical point of view, harvest forecasting can be realized at different spatial scales, ranging
from farmer’s level to country level. Approaches to use for crop forecasting depend on the scale,
available basic data, and the required precision. From a scientific point of view, approaches can be
grouped into three categories depending on the level of conceptualization (Gommes et al., 2010):
Expert approach is based either on experience or opinions of the investigator or farmers
when economic factors are at stake. This is the case of Delphi approach for predicting
harvests (Moricochi et al., 1994) ;
Extrapolation approach is based on diverse statistical analyses (simple or multiple
regression, principal component analysis, neuronal net, etc.) of agricultural production and
environmental factors (climate, fertilizers, prices, etc.) or semi-empirical models and
simulation
5
models. This approach is often used in operational agrometeorology ;
Intermediary approach, which is a combination of expertise and extrapolation approaches.
Empirical approaches are often based on statistical relations between the climate and vegetation,
whether it is natural or grown. These relations can also be studied using directly measured climatic
parameters by the parametric approach, or by subjective appreciations of the cropping seasons by
an expert agricultural scientist. Relations that use empirical approaches are valid only for contexts
similar to the one that led to their development. Statistical approaches are quite important because
they are practical; they can rapidly recognize relevant climatic element that effect crops. However,
they are valid only for stationary phenomena in time, that is, when climatic phenomena stay
unchanged over the period under consideration. In Morocco, we have observed since early 1980s a
change in climate leading to more frequent droughts and increased aridity. Statistical approaches
can be used only for series of climatic data recorded after the change point.
Agrometeorological forecasting of crop harvests, at the country or the region levels, which is a
branch of operational agrometeorology, refers to two main schools of thought: the school of
modelling approach of interactions between crops and their environment (water balance,
physiology processes, energy absorption, etc.) and the school that can be qualified as “pragmatic”
which relies on methods using statistical models linking crop production to agronomic factors,
climatic, environmental, or economical indices
6
.
The pragmatic approach has been adopted by FAO for forecasting crop harvests worldwide due to
its effectiveness and relative simplicity to implement (Gommes, 2001). Agro-meteorological
forecasting approach of crop harvests adopted by the World Meteorology Organization is a
combination of simulation models and statistical models (WMO, 2010). Likewise, the approach
5
Agro-meteorological simulations models of growth and crop development allow understand the response of plants to
variations of the environment.
6
Indices are useful for modelling because they constitute an integrative or a combination of a range of environmental
parameters (rainfall, temperature, moisture, hygrometry of soil, etc.) which explain the behavior of plants. An index is a
practical way of simplifying the plant environment.
Agrometeorological Cereal Yield Forecasting in Morocco
Introduction
Balaghi R., Jlibene M., Tychon B., Eerens H.
8
used by FAO to estimate crop yields, relies on linear regressions between official statistics and
outputs of a model named AgroMetShell
7
which calculates water balance of cultivated soils.
Outputs of this model are regressed linearly to agricultural statistics, which accounts for technology
improvement, real field conditions of farmers, and resolves the problem of spatial resolution
8
, and
the bias of simulation models. The pragmatic approach has also been used to propose indicial
systems to manage agricultural insurance for cereals (Skees et al., 2001; Stoppa and Hess, 2003)
and sugar beet (Koch, 2011) in Morocco.
Operational forecasting of cereal yields has been attempted for the first time in Morocco in 1994
(Bazza and Tayaa, 1998), for the province of Settat, as a part of AGRIMA (Agriculture Maroc)
project, launched jointly by the MAPM, the Royal Centre of Spatial Remote Sensing (CRTS) with
assistance from the United Nation Development Program (UNDP). In this project, proposed
statistical models for forecasting cereal yields used actual evapotranspiration (ETa) as the
predicting variable for Settat province. To generalize this model to other regions, a simulation
model of evapotranspiration was tested. However, this experience was carried out for only one
year, and could not be conclusive.
At the research level, numerous studies have tested simulation models developed in other
countries in varietal selection to identify criteria of selection for wheat (Confalonieri et al., 2012), in
agronomy research to simulate grain yields of cereals at research experiment plots (El Mourid,
1991; Bennani et al., 1993), in risk analysis of climatic risk in relation with choice of barley cultivar
and planting date (Hanchane, 1998 and 2009), in improving wheat productivity through
management of genotype and irrigation (Debaeke and Aboudrare, 2004), in irrigation management
of wheat (Hadria et al., 2006) or sugar beet productions (Taky, 2008), etc.
Now a day, the only public institutions that realize operational agrometeorological cereal yield
forecasting at the country level are from one side the consortium composed of INRA, DSS and DMN,
and the other the central bank of Morocco (Bank Al Maghrib http://www.bkam.ma/). Both
institutions are using pragmatic approach with different methods and tools.
The objective of the present document is to summarize research works on agrometeorological
cereal yield forecasting in Morocco, including both the thinking process and the results elaborated
by the authors.
7
AgroMetShell is a tool for monitoring and forecasting crops, developed by FAO (ftp://ext-
ftp.fao.org/sd/reserved/agromet/AgroMetShell/)
8
Major problem concerning agrometeorological simulation models is the scaling up from the experiment station field
to the administrative province or region. Most variables used in modelling are obtained in small plots controlled
experiments, and are difficult to find or measure on larger scales.
Agrometeorological Cereal Yield Forecasting in Morocco
Approaches of analysis
Balaghi R., Jlibene M., Tychon B., Eerens H.
9
II. APPROCHES OF ANALYSIS
To develop a cereal yields forecasting approach, two things were needed: (1) assembling and
managing a data base on meteorological, biological, agricultural, geographical, satellite and
administrative variables, and (2) identifying agro-climatic indicators that correlate to yields. Satellite
data were provided by our European partners, while other types of data were available in Morocco,
but raw and often fragmented and discontinuous, requiring preliminary treatments. Search of yield
correlating agro-meteorological indicators was first limited to indicators derived from temperature
and rainfall data, independently from the cereal crops, particularly statistics of means and variances
of inter region, inter and intra season variations, or else probability of occurrence of a fixed rainfall
amount. Similar to climatic analyses, indicators of cereal crop production were also studied
independent from climate variation. Likewise, means and variances across regions and years were
computed. These analyses remained descriptive, suggesting search and validation of other
indicators which integrate both the climate and the crop. New indicators of cereal yields forecasting
highly correlated to yields, were explored, mainly water balance indices and vegetation indices
derived from satellite imagery. Normalized Difference Vegetation index (NDVI) is delivered as an
image in a “raster” format (pixel), with no distinction among the numerous land covers. Therefore, a
specific land cover map was developed for Morocco, by compiling various maps available at the
global level, and which were improved by adding extra information reflecting the importance of
agricultural land in each pixel. Recent development of computer science tools, particularly the
geographic information system, and the availability of satellite images of high resolution at low
price, have opened new direction toward analyzing interactions of crops with the climate, in general
and toward forecasting cereal yields in particular.
1. DATA BASE SETS USED
Agrometeorology requires compilation of data sets of different types: meteorology, soil, hydrology,
biology, agronomy, satellite, geography, etc. These data sets have to be satisfactory in quality and
quantity, so as to detect interrelations among agricultural environment and crop behavior. The
different data sets must inter cross, spatially and temporally, so as to be able to be cross analyzed.
Compilation of data, its storage, data updating including quality control, are all part of required
upstream work
9
.
Data sets used in this document include:
9
Gathering and managing agro-meteorological data bases (climatic, satellite imagery, geographical, soil, etc.) need an
inter-institutional effort to develop operational agro-meteorology.
Agrometeorological Cereal Yield Forecasting in Morocco
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Balaghi R., Jlibene M., Tychon B., Eerens H.
10
Meteorological data, mainly rainfall, solar radiation, air temperature, air moisture, wind
speed and direction ;
Satellite imagery data provided by Flemish Institute of Technology Research (VITO,
Belgium) ;
Historical data on North Atlantic Oscillation Index
10
(Hurrell, 1995) ;
Administrative boundaries in GIS vector format of the country, the regions, the agro-
ecological zones and provinces ;
Agricultural land cover map “GICropV2” developed in collaboration with VITO in 2012 ;
Agricultural statistical data, involving historical series of yields and areas of cereals by
province.
All these data sets were compiled in a new data set entertained at the Environment and Natural
Resources Department of INRA.
1.1. CLIMATIC DATA
Data sets on climate used in this document, are of different sources:
Historical set of 35 synoptic stations, available at the Ministry of Agriculture and Marine
Fishery (MAPM). These stations are part of the 44 synoptic stations of the “Direction de la
Meteorologie Nationale” (DMN). Data include rainfall data from 1987 till 2011, and
decadal temperature data (minimum and maximum) for the period of 1999-2009. The 35
stations are localized each in one of the provinces, the reason why their names are the
same as those of the provinces. The synoptic stations of DMN operate day long for most
of them (24h/24h) and produce hourly observations on main meteorological variables:
atmospheric pressure, temperature, relative humidity, strength and direction of the wind,
cloudiness, quantity and intensity of precipitations, insulation time, and radiation.
However, these stations cover mostly costal zones, and less mountainous, Eastern and
Saharan regions ;
Long term daily historical data on rainfall and temperature from some synoptic stations,
provided by DMN. Some of these data date back to the start of last century ;
Data of Global Historical Climatology Network (GHCN,
http://www.ncdc.noaa.gov/ghcnm/) (Peterson and Vose, 1997). This network provides
daily temperature, precipitation and air pressure. It is managed by the National Climatic
Data Center at the University of Arizona (USA). Data are collected continuously from a
10
North Atlantic Oscillation index is generally calculated as the difference in air pressure at sea level, between two
meteorological stations localized respectively at the Island depression and Azores anticyclone.
Agrometeorological Cereal Yield Forecasting in Morocco
Approaches of analysis
Balaghi R., Jlibene M., Tychon B., Eerens H.
11
great number of fixed stations on the surface of the globe (approximately 6,000 stations
for temperature, 7,500 stations for rainfall, and 2,000 stations for air pressure) ;
Daily climatic data on rainfall, temperature max and min, sped of wind, and relative
humidity, provided by the web site www.tutiempo.net which reuse data of the GHCN
network ;
FAO climatic data “FAOCLIM 2.0” (http://www.fao.org/nr/climpag/pub/en1102_en.asp;
FAO, 2001). This website contain daily climatic data worldwide, including Morocco, over
the period of 1960-1990 ;
Dekadal meteorological data over a 70 years period for the rural district of Arbaoua,
kindly supplied by the “Office Regional de Mise en Valeur Agricole de Loukkos” (ORMVA-L)
for the PSDA project (Jlibene and Chafai, 2002). These data were used to determine
periods of cultural interventions for the Loukkos region using the FAO method (FAO,
1978) ;
World climatic data (www.worldclim.org/; Hijmans et al., 2005), containing among others,
monthly precipitation and temperature (mean, max and min) for the 1950-2000 period,
interpolated at a spatial resolution of 1x1 km ;
Meteorological data provided by INRA automated stations, particularly the one of Meknes
regional research center, for the period of 1995-2000. Hourly data were recorded in this
station on rainfall, temperature, relative humidity, wind speed and direction, dew point
and solar radiation.
1.2. NORTH ATLANT IC OSCILLATION INDEX
History of the North Atlantic Oscillation Index (NAO) is available for download at the website of the
University of East Anglia, England (http://www.cru.uea.ac.uk/ ~timo/datapages/naoi.htm). Data on
this index are available for all months of the year without interruption since 1821, which allows for
reconstitution of past climatic events. It was used to demonstrate the influence of NAO index on
precipitation pattern in Morocco. NAO index links the intensity of Island depression to that of the
Azores anticyclone. NAO fluctuations have direct consequences on Moroccan climate (Figure 1).
Agrometeorological Cereal Yield Forecasting in Morocco
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12
Figure 1: Positive phase (left) and negative one (right) of the North Atlantic Oscillation. At the
positive phase, drought reigns over the Mediterranean region, while storms are frequent over
Europe. On the contrary, at negative phase, the Mediterranean region enjoys a humid weather
while Europe is less humid. (Source: http://www.ldeo.columbia.edu/res/pi/NAO/).
1.3. VEGETATION I NDEX FROM SATELLITE IMAGERY
With the development of satellite imagery, it was possible to develop agro-meteorological indices
from the spectral reflectance of the vegetation. These indices can be used to forecast crop harvests,
either directly as predicting factors in regression equations (Kogan, 2000; Maselli et al., 2000;
Balaghi et al., 2008), or indirectly to estimate biophysical variables like LAI
11
or fAPAR
12
used as
input variables to simulate growth of crops (Duchemin et al., 2006; De Wit et al., 2012). However,
one of the major obstacles of using such indices in simulation models lies in the discrepancy
between the spatial resolution of the geographic information and that of the physiological
processes of photosynthesis. Physiological parameters are obtained in small experiment plots,
while satellite imagery derived indices are obtained for large areas and in high frequency (dekadal)
to allow agro-meteorological monitoring (Balaghi et al., 2010).
Normalized Difference Vegetation Index or NDVI, as derived from NOAA-AVHRR sensor since 1980,
SPOT-VEGETATION since 1988 or MODIS
13
since 2001, is one of the indices most used to measure
the vitality of vegetation. NDVI has been largely used to monitor vegetation and forecast crop yields
all over the world. It is computed as follows:
11
Leaf Area Index (LAI) is the projected area of leaves in a unit area of soil surface (Watson, 1947).
12
fAPAR is the fraction of solar radiation absorbed by plants, in the spectral range of photosynthesis.
13
Moderate Resolution Imaging Spectroradiometry (MODIS) sensor is installed on board of satellite Terra and Aqua of
the National Aeronautics and Space Administration (NASA), used for the follow up of vegetation (NDVI and EVI) at 250
meters spatial resolution.
Agrometeorological Cereal Yield Forecasting in Morocco
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13
REDNIR REDNIR
NDVI
Where, NIR and RED are respectively measures of reflected radiation in the near infra-red, the red.
NDVI increases progressively with increased vegetal density; from a value of 0.15 (average value)
for bare soils, to a value of 0.75 (average value) for dense plant covers. One of the main benefits of
using NDVI is the integration of environmental factors, in a sense that it reflects the state of global
environmental stress of the vegetation, more than separate climatic variables or simulation models
can do (Balaghi et al., 2010). For example, water stress resulting from a prolonged deficient water
balance is reflected by low NDVIs. Decreases of NDVI values can also be caused by abiotic stresses
like mineral deficiency or toxicity or biotic stresses like epidemics of diseases and insects.
NDVI values are delivered on a 10-days basis for NOAA-AVHRR images since 1982 and SPOT-
VEGETATION since 1999, and a 15-days basis for MODIS images since 2001. These images are
pretreated by VITO before delivery, correcting for radiometric, geometric and atmospheric
variations.
1.4. ADMINISTRAT IVE BOUNDARIES OF MOROCCO IN GIS FORMAT
Official agricultural statistics are delivered on administrative province basis; which makes the
province, the smallest territorial area for forecasting. Polygons delimiting provinces are available in
a GIS format. The rural districts boundaries are also available in GIS format and can be used to
forecast yields at this level when statistical yield data will made available.
1.5. LAND COVER MAPS
Different land cover maps issued from spatial remote sensing, are available for free use at the
global level, with varied quality and precision. Global Land Cover-2000 (GLC2000 version 5.0,
Mayaux et al., 2004), CORINE-2000 and GlobCover (Tchuente et al., 2010; Neumann et al., 2011),
were of particular interest to us. The European program CORINE Land Cover (CORINE-2000) is an
inventory of 29 European states land cover from satellite images. It covers part of Morocco as well.
The CORINE-2000 land cover map has a geometric precision greater than 100 m which allows for
elaborating maps of less than 30 meters spatial resolution, has been updated in 2006 by the Global
Monitoring for Environment and Security initiative (GMES) (http://sia.eionet.europa.eu/CLC2000).
GlobCover initiative of the European Spatial Agency aims at producing a global map of land cover,
using data of 300 meters of spatial resolution of MERIS sensor embarked on board of ENVISAT
satellite platform (http://postel.mediasfrance.org/fr/PROJETS/Pre-operationnels-
MES/GLOBCOVER/). Digital GlobCover land cover map elaborated in 2008 is the unique reference of
Agrometeorological Cereal Yield Forecasting in Morocco
Approaches of analysis
Balaghi R., Jlibene M., Tychon B., Eerens H.
14
intermediate resolution which covers Morocco. A land cover map for Africa is also made available
for free use by the « Southern African Development Community (SADC) » (http://www.sadc.int/).
To develop a specific land cover map for Morocco, GlobCover V2.2, CORINE-2000, AfriCover
14
, and
SADC maps were all grouped in one map covering Moroccan agricultural territory and improved by
the superposition of another map developed by USGS which provides data on the proportion of
agricultural land for each pixel, reducing there after intra pixel variation. Agricultural zones of this
map were extracted to serve as mask for NDVI images. Therefore, only values of NDVI of
agricultural zones are saved for possible use in establishing relationship between NDVI and cereal
yields.
1.6. AGRICULTURAL STATISTICS
Area and yield data of the three main cereal crops
15
, soft wheat, durum wheat and barley, were
graciously provided by “la Direction de la Stratégie et des Statistiques
16
” (DSS). They are available
for 40 provinces of the country for the period of 1978-2011. Production at a province level is
obtained by multiplying the yield value with the area estimated by DSS.
Area estimation for cereal crops in Morocco is made every year by DSS between February 10th and
March 30th, using a sampling method of 3,000 unit areas representing 19 million hectares. Starting
since 2008, DSS has renewed the sampling procedure to integrate modern techniques of satellite
remote sensing and GIS which improved precision of estimators. A GIS application has been
specifically developed for this purpose with capability of automated steps of the sampling
procedure.
Within the sampled areas, sub-sampled plots are harvested and their yields directly measured.
Production of a sampled area is the product of measured yield on sub-sampled plots and area
represented by the sample. Data on production and area is then aggregated by province.
Monitoring of vegetation, area and yield estimations are carried out by DSS along the cereal crop
cycle in three phases:
Phase 1: A survey on the evolution of harvest, is done in February, to evaluate crop
growth stages and vegetative stand of crops ;
14
The objective of Africover project is to establish a numerized data base geo-referenced for global vegetation cover
and geographic referential for all Africa, including: Geodesic referential, toponymic, roads, hydrographic. The polyvalent
data base Africover for environmental resources is produced at a scale of 1:200.000 (1:100.000 for small countries and
specific zones). www.africover.org/.
15
These cereals are sown in the fall (autumn) and are sometimes called autumnal cereals as compared to spring cereals
like corn or sorghum.
16
Previously called « Direction de la Programmation et des Affaires Économiques » (DPAE).
Agrometeorological Cereal Yield Forecasting in Morocco
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Balaghi R., Jlibene M., Tychon B., Eerens H.
15
Phase 2: A survey on land cover, done between February and June, to estimate cereal
areas ;
Phase 3: A survey on expected production, done in April (1 to 2 months before harvest) to
estimate production of the three main cereals: soft wheat, durum wheat and barley.
2. METHODS OF ANALYSE S
2.1. EXPLORATORY TREATMENT OF RAINFALL DATA
In operational agrometeorology, the first modelling steps consist in computing basic statistics of
agro-climatic variables (rainfall, temperature, yield, etc.). Simple statistics like averages, minima and
maxima, or deviations, are calculated from daily or 10-days raw data of a series of crop cycles or
meteorological stations. For temperature, daily average is obtained as the mean of minimum and
maximum values. Monthly average temperatures are obtained as the mean of daily averages over a
period of the month. The mean of 12 monthly averages represents year average temperatures. The
mean temperature of a series of year’s averages represents long term average. Difference between
maximum and minimum of each average temperature represents thermal amplitude.
For rainfall, daily total is the sum of hourly rainfall records over a day. Likewise, 10-days, monthly or
yearly averages are cumulated rainfall over each respective period. The sum of daily or 10-days
rainfall data is used for better graphic visualization and interpretation, since punctual daily or 10-
days
17
data graph representations for a number of years or stations are overlapping and hence
difficult to interpret. An example of 10-days rainfall graphics of a series of years is presented in
Figure 2.
17
Only 10-days data on rainfall at the province level and satellite imagery were available and therefore used in all
computations. 10-days rainfall data has generally proved to be sufficient for crop yield forecasting.
Agrometeorological Cereal Yield Forecasting in Morocco
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Balaghi R., Jlibene M., Tychon B., Eerens H.
16
Figure 2: Country average dekadal (10-days) rainfall, of a series of crop seasons from 1988 till
2011.
The first rainfall data treatment consists simply in analyzing a graphic representation of average
climatic data in the form of climograms, which indicate, for each dekad or month of the cropping
cycle, the heights of average precipitations (or medians).
The next rainfall data treatment consists in cumulating rainfall over the cropping cycle for 10-days
data points; reproducing clear graphs easy to interpret and even model statistically. Cumulus of
rainfall has many advantages, it allows:
Representation in a same curve, of the annual amount of rainfall as well as its distribution
along the cropping season ;
Modelling the obtained curve of rainfall by using linear, logarithmic or sigmoid equations;
Identify and determine periods of droughts; they will appear as flat horizontal segment in
the cumulative rainfall curve ;
Comparison of different crop cycles and regions, with regard to amount and distribution
of rainfall.
Agrometeorological Cereal Yield Forecasting in Morocco
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Balaghi R., Jlibene M., Tychon B., Eerens H.
17
2.2. FREQUENCY ANALYSIS OF RAINFALL
Frequency analysis is justified for variables displaying Gaussian distribution (Nicholson, 1986),
which is the case for 10-days or monthly rainfall in semi-arid regions. Frequency analysis method
(or probability analysis) of rainfall has been used by Gibbs and Maher (1967) to study drought in
Australia. In this analysis, values in series of rainfall data are arranged in ascending order, then
limits of deciles of the distribution are calculated from a frequency curve or a table. The 1st decile
represents the rainfall value that is not exceeded by one tenth of the values of the data series. Two
tenths of the rainfall values are less than the 2nd decile; three tenths are less than the 3rd decile and
so forth. The 5th decile or median is the rainfall value which could be received once in two seasons,
dividing the data series into two groups of equal number.
Frequency distribution is one way of showing the general form of intra-annual rainfall distribution.
A given season can be compared with median rainfall, which could be considered as reference
value. For example, rainfall values smaller than the 1st decile indicate situations of severe drought,
and those greater than the 9th decile indicate situations of high moisture. A major disadvantage of
this method is that the deciles are calculated for a given dekad or a month of the season
independently from other dekads or months.
2.3. OMBROTHERMIC INDEX OF BAGNOULS AND GAUSSEN
The Ombrothermic
18
index developed by Bagnouls and Gaussen (1953) is a climatic index which is
used to identify dry and humid months during the season. It is simply calculated as the ratio of
monthly rainfall (mm) over average monthly temperature (°C). This index was developed by these
authors, for the purpose of comparing different stations over the world where periods of drought
occur. Whenever the ratio is less or equal to 2, the month is considered as dry for plants,
considering that evaporative
19
demand of the air is greater than rainfall supply. On the reverse,
periods of the season where the index is greater than 2, are considered as wet. Dry and wet months
can be identified graphically from an Ombrothermic diagram where variations of temperatures and
precipitations are plotted in standardized grading: precipitation grade is “mm” and temperature
grade is “°C”. Using this diagram, climates of different stations all over the world can be compared
(Gaussen, 1956; Bagnouls and Gaussen, 1957). This diagram is however best adapted to the
Mediterranean climate where variations of rainfall and temperature result in periods of drought
and moisture. The Ombrothermic diagram was used in this document for the period of 1999-2009,
since the 10-days data of both rainfall and temperature were available for only this same time
period.
18
Ombrothermic diagram includes both rainfall (Ombros in Greek) and temperature.
19
Capacity of the atmospheric air to extract water vapor from the soil-plant system.
Agrometeorological Cereal Yield Forecasting in Morocco
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18
2.4. LENGTH OF THE G ROWING PERIOD
The concept of Length of Growing Period
20
(LGP) was developed by FAO (1978) for the purpose of
identifying periods of the year where moisture and temperature conditions are adequate for crop
growth and development. It was later used by FAO for classifying agro-ecological zones to evaluate
potential and resources of global agriculture (FAO, 1996; IIASA/FAO, 2012). It is a renewed version
of Bagnouls and Gaussen (1953) ombrothermic diagram, taking into account water holding capacity
of the soil and evaporative capacity of the atmospheric air.
In Morocco, the same concept was used (Jlibene and Chafai, 2002; Jlibene and Balaghi, 2009) to
optimize cultural practices and placement of the crop cycle within the season, in order to minimize
risks of shortage of water (drought) or excess and improve agricultural productivity in.
LGP is determined by producing classes of rainfall on the basis of potential evapotranspiration (ET0,
i.e. atmospheric demand), under the following assumptions:
An amount of rainfall above one tenth and half of ET0 is sufficient for land
preparation for sowings ;
Conditions of rainfall between half and 100% of ET0 would be favorable to plant growth
and development ;
Conditions of rainfall greater than ET0 correspond to moist periods ;
The soil can hold a moisture equivalent to 100 mm of rainfall, which may extend the
growing period longer depending on ET0.