1 Developing Sustainable Livelihoods of Agropastoral Communities of West Asia and North Africa Project (M&M III) Authors by alphabetic order AHMED M. A., BOUAYAD A., EL MOURID M., MAHYOU H.; MIMOUNI J.,SNAIBI W. October 2008 Centre Régional de la Recherche Agronomique d'Oujda Bd. Mohammed VI, B.P 428, Oujda, Tel : (212) 036500210/30, Fax : (212) 036500211 ICARDA
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
1
Developing Sustainable Livelihoods of Agropastoral Communities
of West Asia and North Africa Project (M&M III)
Authors by alphabetic order AHMED M. A., BOUAYAD A., EL MOURID M., MAHYOU H.; MIMOUNI J.,SNAIBI W.
October 2008
Centre Régional de la Recherche Agronomique d'Oujda
Bd. Mohammed VI, B.P 428, Oujda, Tel : (212) 036500210/30, Fax : (212) 036500211
ICARDA
2
Contents
Chapter I. Characterization of Sekouma-Irzaine . ..................................................................... 6 Geographical situation ............................................................................................................ 6 Climate characteristics. .......................................................................................................... 6 Soil characteristics .................................................................................................................. 7 Vegetation .............................................................................................................................. 7
Livestock ................................................................................................................................ 7 Human .................................................................................................................................... 7 Land use in Irzain Area .......................................................................................................... 7
Chapter II. Monitoring drought dynamics using climatic and remote sensing data in the agro-
pastoral zones. ............................................................................................................................ 9
Monitoring drought using climatic data in Sekouma-Irzaine. .................................................... 9 Rainfall variability in Sekouma-Irzaine. ................................................................................ 9 Annual variability of rainfall ................................................................................................ 10
Variability of seasonal rainfall ............................................................................................. 11 Variability of monthly rainfall ............................................................................................. 11 Probabilities of drought occurrence in Sekouma-Irzaine ..................................................... 13 Frequency of drought ........................................................................................................... 14
Intensity of drought ....................................................................................................................... 15
Persistence of drought .................................................................................................................. 16
Typology of drought in Sekouma-Irzaine ...................................................................................... 16
Contribution to drought forecasting ..................................................................................... 17 Relationship between seasonal drought and annual drought ...................................................... 17
Monitoring drought using Remote Sensing and GIS in Sekouma-Irzaine. .............................. 21
Introduction .............................................................................................................................. 21 Materials and methods ............................................................................................................. 21
The study area ...................................................................................................................... 21 Meteorological data ...................................................................................................................... 22
Satellite images ............................................................................................................................. 22
Image processing ........................................................................................................................... 22
Satellite based drought indices for drought characterization ....................................................... 23
Results and discussion .............................................................................................................. 23 Influence of land cover types on NDVI data. ....................................................................... 23
NDVI and rainfall variation. ................................................................................................ 24 Annual rainfall and NDVI variation ................................................................................................ 24
Seasonal rainfall and NDVI variation. ............................................................................................ 25
CVI and rainfall variation. ................................................................................................... 27 Annual rainfall and CVI variation. .................................................................................................. 27
Seasonal rainfall and VCI variation in rangeland area ................................................................... 28
NDVI-Rainfall and CVI-Rainfall relationship as indicator of drought in rangeland area. . 29 Correlation analysis. ............................................................................................................. 30
References ................................................................................................................................ 32 Chapter III. Livelihood and local knowledge to mitigate drought in Sekouma-Irzaine .......... 34 Introduction .............................................................................................................................. 34
3
Objectives ................................................................................................................................. 34
Methodological frame .............................................................................................................. 35 Methodology ............................................................................................................................ 35 Results and discussion .............................................................................................................. 37
Recent history of drought ..................................................................................................... 37 Strengths Weakness Opportunities and Threats of the community ..................................... 38 Comunuty Livelihood Assets ............................................................................................... 39
Social capital ................................................................................................................................. 39
Physical Capital ............................................................................................................................. 39
Natural Capital .............................................................................................................................. 40
Human Capital ............................................................................................................................... 40
Financial capital ............................................................................................................................ 40
Sekouma-Irzaine socioeconomic differentiation (Annex 1 and 2) ................................................ 40
Market prices analysis .......................................................................................................... 43 Variation of small ruminants number ............................................................................................ 43
Variation of animal feeds prices .................................................................................................... 44
Variation of animals selling prices ................................................................................................ 46
Perception, impacts and strategies to mitigate drought at Sekouma-Irzaine ............................ 46
Local drought Perception ..................................................................................................... 46 Droughts episodes in Sekouma-Irzaine ................................................................................ 47 Impacts of drought ............................................................................................................... 48
Drought Episode 2005-2006 (Annex 4) ........................................................................................ 48
Drought Episode 2003-2004.......................................................................................................... 49
Drought Episode 1988-1989.......................................................................................................... 49
Drought Episode 1945-1952.......................................................................................................... 49
Relative importance of the impacts of past droughts .................................................................... 50
Trends of the main impacts of droughts ............................................................................... 51
Impacts of a possible future drought .................................................................................... 51 Strategies and solutions developed by the population to mitigate drought effects .............. 53
Drought episode 1945. .................................................................................................................. 53
Drought episode 1988-1989 .......................................................................................................... 54
Drought episode 2005-06 .............................................................................................................. 55
Strategies tendency ............................................................................................................... 56
Projection in the future ......................................................................................................... 57 Conclusion ................................................................................................................................ 58
References ................................................................................................................................ 59 Annex 1: Characteristics of the four farm types (number and percentage).............................. 60 Annex 2: quantitative characteristics of the four farm types ................................................... 62 Annex 3: Factor Analysis ......................................................................................................... 63
Annex 4: Drought Impact Matrix ............................................................................................. 64
4
Introduction
Droughts have no universal definition. As drought definitions are region specific, reflecting
differences in climatic characteristics as well as incorporating different physical, biological
and socioeconomic variables, it is usually difficult to transfer definitions derived for one
region to another. However some of the common definitions of drought can be noted as
under:
• “Drought is an interval of time, generally of the order of months of years in duration, during
which the actual moisture supply at a given place rather consistently falls short of the
climatically expected or climatically appropriate moisture supply (Palmer, 1965).
• Another definition given by Flag is worth mentioning “Drought is a period of rainfall
deficiency, extending over months or year of such a nature that crops and pasturage for stock
are seriously affected, if not completely burnt up and destroyed, water supplies are seriously
depleted or dried up and sheep and cattle perish”.
• “Drought is considered by many to be the most complex but least understood of all natural
hazards affecting more people than any other hazard.” Hangman (1984).
Drought is considered by many to be the most complex but least understood of all natural
hazards, affecting more people than any other hazard (G.Hagman 1984). However, there
remains much confusion within the scientific and policy communities about its characteristics.
It is precisely this confusion that explains, to some extent, the lack of progress in drought
preparedness in most pails of the world. Drought is a slow-onset, creeping natural hazard that
is a normal part of climate for virtually all regions of the world; it results in serious economic,
social, and environmental impacts. Drought onset and end are often difficult to determine, as
is its severity. The impacts of drought are largely non-structural and spread over a larger
geographical area than are damages from other natural hazards. The non-structural
characteristic of drought impacts has certainly hindered the development of accurate, reliable,
and timely estimates of severity and, ultimately, the formulation of drought preparedness
plans by most governments. The impacts of drought, like those of other hazards, can be
reduced through mitigation and preparedness.
The impacts of a drought can be economic, environmental or social. Drought produces a
complex web of impacts that spans many sectors of the economy and reaches well beyond the
area experiencing physical drought. This complexity exists because water is integral to
society’s ability to produce goods and provide services. Impacts are commonly referred to as
direct and indirect. Direct impacts include reduced crop, rangeland, and forest productivity,
increased fire hazard, reduced water levels, increased livestock and wildlife mortality rates,
and damage to wildlife and fish habitat. The consequences of these direct impacts illustrate
indirect impacts. For example, a reduction in crop, rangeland, and forest productivity may
result in reduced income for farmers and agribusiness, increased prices for food. In Morocco, the management of water became a vital strategy because its climate context is
difficult. The water resources are limited and depend entirely on rainfall which is characterized
by a significant irregularity and variability. The annual rainfall vary strongly from north
towards south (800 mm in north and less than 25 mm in the south) and from west towards east
(600 mm to 100 mm) with an amplification on the mountains of Rif and Atlas (until more
than 1200 mm) (Agoumi and Debbarh, 2006).
Climate change in Morocco, characterized by reduced rainfall and increased frequency of
droughts in recent decades has worsened the precarious situation of water resources.
In drought years, the reduction in rainfall is very important, to less than 60 to 75% of normal
rainfall (Bzioui, 2004). Moroccan agriculture is depending of rain at 85%, consequently cultivable lands in Morocco are
subjected strongly to the climatic risk. Under such conditions, the economy of morocco is
considerably affected, owing to the fact that the national economy is very influenced by
5
agriculture. Indeed the agriculture contributes at 17% to gross domestic product (GDP) and the
value of the vegetable productions accounts for 68% of the agricultural GDP (Aghrab, 2005). In the agro-pastorals areas, such as Eastern Morocco, the precipitation constitutes the major
factor limiting water availability and rangeland cover.
These regions are characterized by a low rainfall, a high temperatures, a strong winds and a
weak soil storage capacity.
For several decades, this regions has known a serious periods of drought like those of 1982 to
1984 and 1998 to 2000. These deficits negatively affected the natural resources of the area
and the socio-economic activities of the populations of the area which rest primarily on the
exploitation of these resources.
The drought causes a decreasing of agricultural and rangeland productivity; consequently the
incomes of population are strongly reduced. So the drought is certainly one of the most
important threats of the livelihood of population.
The objective of the current project is to develop drought evaluation methods in socio
territorial unit of Eastern Morocco (STU Sekouma-Irzaine). These methods will allow
defining options: institutional, technical and policies to mitigate drought effects. Thus, the
specific objectives of this study consist to Monitoring drought dynamics using climatic and
remote sensing data; evaluate the population perception of drought and its impacts; examine
the current and last answers of adaptation and the strategies to mitigate drought and finally
assess the national drought mitigation strategy.
6
Chapter I. Characterization of Sekouma-Irzaine .
Geographical situation The site of Sekouma-Irzaine with an area of 5800 ha is located in the rural commune of
Tancherfi, Oujda province, Morocco. The coordinate of the projection in the Conical
Lambert system are : From X = 428000 to X = 436000 and from Y= 744000 to Y= 758000
(figure 1)
Figure 1: Map of localization of Irzaïn Area
Climate characteristics. The Sekouma-Irzaine area receives an average rainfall less than 250 mm/year. The rainfall
regime is characterized by a high inter-annual variation with a tendency toward a rainfall
reduction these last years. The average number of rainy days is between 39 and 44 days. The
first significant rains (25 mm) fall in the last week of October.
The thermal regime in the study area is stable. The average minimal temperatures are around
10°C with a minimum of 5°C and a maximum of 16°C. The coldest months are December and
January. The average maximal temperatures are around 33°C, with a minimum of 23°C and a
maximum of 48°C recorded during the months of July and August (fig 3).
The analysis of daily temperature data reveals that 32 days have presented temperatures
inferior to zero. These negative temperatures are often recorded during the months of
December and January. The coldest temperature is (-4°C) that has been recorded in January 2,
1985. Since 1974, the total days with a temperature above 40°C are 41 days and were
recorded in July and August. The hottest temperature is (46°C), that has been recorded in
July 2, 1999. One often attends frequent frosts in winter and in the spring and to strong and
dry winds in summer
7
Soil characteristics The main soil types that dominate this area are shallow with low fertility and water retention
capacity. They varies from clay sandy loamy soils to rocky calcareous ones. Many small
waterways cross the area zone of Irzain.
The erosion poses a serious problem in this zone, in particular where the watertable is very
below the sill of the rivers, especially close to the source, what makes that these rivers are
infiltrating rather than draining. Consequently, water flows in the fairways that at the time of
big storms and only for short periods. These fairways are therefore most of the time to dry.
When there is out-flow of storm rains in these generally dry and often very permeable
fairways, the debit is reduced by the infiltrations in the bed of the river, the strands and
possibly the plain of flooding.
Vegetation The most dominant species are:
The steppe of Stipa tenacissima with vestiges of Artemisia herba-alba. Stipa
tenacissima spreads over an area of approximately 2097 ha (37% of the total area).
These species are characteristic of semi-arid climate inferior to (280 mm). Stipa
tenacissima is moderately grazed. This specie is cleared in approximately 43% of the
cases.
Steppes of Anabasis aphyllum, Peganum harmala, Artemisia herba-alba, Asphodelus
microcarpus and Thymaelea sp. These species of arid to semi-arid climate (240-
280mm). They cover an area around (412 ha). These area covered by these species is
subject to clearing in most cases.
Clear forests cover 1% of the total area.
The most degraded steppes represent 865 ha (15%). Noaea mucronata, accompanied
with others species (Launea sp., Asphodelus microcarpus, Urginea maritima,
Atractylis humilis and Ziziphus lotus), typical species of arid climate with leaves
generally transformed into thorns that is a form of adaptation to drought.
The remaining area is covered by crops. It represents 40 % the total area of the
perimeter, with an area of 2198 ha. The most frequent crops are cereals (soft wheat,
durum wheat and barley) and alley cropping with Atriplex /barley or Atriplex /oats.
(figure 6)
Livestock The community has a herd of small ruminants with size of 6880 heads composed respectively
of 74% of sheep and 24% goats. It also has a cattle herd of 35 cows. In addition, the
community posses about 2692 heads of poultry and 131 heads of rabbits.
Human The total population of Sekouma-Irzaine is about 1253 people in 177 households, with an
average of 7 persons per household. Enrolment and literacy is around 22 and 29%. These rates
are very low compared to the national average which is 60%. Despite this low literacy rate,
the population has a remarkable expertise in agriculture and livestock.
Land use in Irzain Area The population of Irzain is use to cereal crops, especially in the Northen part of the
Commune. The southern mountains are used for the grazing. The whole area is subject to
intensive degradation. The thuya and the green oak dominate the forest of the region. The
8
rosemary is associated to Stipa tenacissima. The Cyclic herd movements are increasingly less
frequent due mountains’ degradation and drought.
9
1961-1985 1986-2005
Chapter II. Monitoring drought dynamics using climatic and remote sensing data in the agro-pastoral zones.
Monitoring drought using climatic data in Sekouma-Irzaine.
Rainfall variability in Sekouma-Irzaine. In Morocco, the study of the temporal variability of rainfall since 1930 until 2005 in several
stations, show tendencies to a light fall of rainfall in several regions of Morocco. In a recent
study carried out by Direction of National Meteorology (figure 2), shows that the climatic
tendencies of last years go to the progression of the arid climate from Southern towards
Northern and the extension of the semi-arid climate. These climatic changes occur at the
expense of humid climate localised in the North-western of Morocco which narrowed
spatially.
Figure 2: Evolution of the climate during the last decades in Morocco
The Sekouma-Irzaine area knew several climatic changes. Hence, the temporal variability
study of rainfall is very important to define the current climatic conditions in this zone.
To illustrate this variability, we have collected climatic data from El Aioun station located at
12 km of study area. The geographical coordinates are as follows: Latitude 34°35N,
Longitude 2°30 W with an altitude of 610 m.
The various parameters measured in this station are the rainfall, the minimum and maximum
temperatures, the wind speed and the insolation (Table 1).
Table 1: Periods of climatic data
Parameters daily data Period of observation
Rainfall 1982-2007 1932-2007
Temperature 2002-2007 2002-2007
Speed of the wind 2002-2007 2002-2007
Insolation 2002-2007 2002-2007
10
Annual variability of rainfall The annual rainfall series covering the period 1932 to 2007 presented in figure 3, shows an
annual average of 271.6 mm/year. We also note, like in the dry areas, a very significant
variability of annual rainfall (Bernati and Habaibi., 2001). The coefficient of variation is
about 35%.
The minimum of 112 mm was recorded during the agricultural year (September to August)
1965/1966 and the maximum of 620 mm in 1962/1963 year.
During the last decades, the frequency of the dry years increased considerably. Consequently,
a tendency toward a decrease in rainfall is observed.
Variability of annual rainfall in Sékouma-Irzain (1932-2007)
0
100
200
300
400
500
600
700
19
31
/32
19
36
/37
19
41
/42
19
46
/47
19
51
/52
19
56
/57
19
61
/62
19
66
/67
19
71
/72
19
76
/77
19
81
/82
19
86
/87
19
91
/92
19
96
/97
20
01
/02
20
06
/07
Rainfall (mm)
Mean= 271 mm/year
CV= 35%
Figure 3: Variability of annual precipitations in Sékouma-Irzain
Given the decline in rainfall, we submitted our series to statistical analysis to test the
homogeneity of series and detect any change or " break point " of the average annual rainfall.
The break point corresponds to a change in probability of the time series at a given time
(Lubes-Niel and al., 1994).
The use of four statistical tests, test of Pettit (1995), test of Lee and Heghinian (1977), test of
Buishand (1984) and test of Hubert and al (1989), shows a very significant break point in
1977.
This serial heterogeneity, or break point, observed may be related at climate change (often
gradual decreasing) or artificial caused by a change of measuring operator or instrument.
The information available at El Aioun station does not indicate any correlation with a possible
change of station or measuring instrument. This suggests the break point is of a climatic
nature. Similar results were observed in areas adjacent as Ain Beni Mathar and Oujda
(Mimouni, J. and Mahyou H).
So, our results suggest that the time series representative of Sekouma-Irzaine consists of two
climatic series: the first one, starts since 1932 until 1976 with 291.7 mm/year as mean and the
second since 1977 to 2007, presenting an annual rainfall average of 242.6 mm/year (figure 4).
These results indicate that Sekouma-Irzaine had knew a climatic change occurring a
reduction about 17% of the rainfall annual average. These results are in agreement with other
national climatic studies (Driouech , 2008).
11
Variability of annual rainfall according to climatic series
0
100
200
300
400
500
600
7001
93
1/3
2
19
36
/37
19
41
/42
19
46
/47
19
51
/52
19
56
/57
19
61
/62
19
66
/67
19
71
/72
19
76
/77
19
81
/82
19
86
/87
19
91
/92
19
96
/97
20
01
/02
20
06
/07
Series 1932-1976
mean = 291.6 mm/year
CV= 32.4%
Series 1977-2007
mean = 240.9 mm/year
CV= 32.2%
Rainfall (mm)
Figure 4: Variability of annual rainfall according to climatic series: 1932-1976 and 1977-2007
Variability of seasonal rainfall The seasonal rainfall analysis of these two climatic series, shows that rainfall mode is Winter-
Spring type. The comparison between the two climatic series, indicate that the reduction of
rainfall observed during the three last decades, is significantly related to spring season
(reduction of 31 %) (Figure 5). Variability of seasonal rainfall according to climatic series
0
20
40
60
80
100
120
Autumn Winter Spring Summer
Series 1932-1976
Series 1977-2007
Rainfall (mm)
*
Figure 5: Variability of seasonal rainfall according to climatic series1932-1976 and 1977-2007.
Variability of monthly rainfall The reduction on rainfall, localized particularly in December, April and May, is estimated at
30%(figure 5).
Although the average distribution of rainfall preserves its bimodal character, it seems there
was a shift in the peaks of rainfall during the last decades. Indeed, March and November are
the rainiest months. while, April and December were the rainiest before 1977 (figure 6). In
addition, the date of significant rains necessary to sow cereals was changed. This period is
localized in the medium of October before 1977 and that of November during the last decades
(results coming from daily data).
These results suggest that climate change were caused a short cycle of rainfall . consequently,
the cycle of vegetation will be short and characterized by a delay in sowing date and an
advance in rainiest months.
12
Distribution of monthly rainfall according to climatic series
0
5
10
15
20
25
30
35
40
45
Sept Oct Nov Dec Jan Feb Mar April May June July Aug
Series 1932-1976
Series 1977-2007
Rainfall (mm)
Figure 6: Monthly rainfall distribution of climatic series (1932-1976 and 1977-2007)
13
Probabilities of drought occurrence in Sekouma-Irzaine The analysis of the drought years in Morocco during the XX century, revealed a higher
frequency and a larger spatial extension of drought. The drought frequency has increased
since the last forty years. It was 5 years during 40 years between 1940 and 1979, 6 on 16
years between 1980 and 1995 then 4 on 7 years recently between 1996 and 2002 (Barkat and
Handouf, 1998, Driouech et al. , 2006).
According to White and O' Meagher (1995), there are four types of drought: meteorological
drought, hydrological drought, agricultural drought and socioeconomic drought.
We are particularly interested at meteorological drought for two principal reasons. Firstly, the
meteorological drought is most important because it is the beginning of all droughts.
Secondly, the database available in the study area allow to determinate only indicators related
to weather drought.
To characterize drought in Sekouma-Irzaine, we will focus at its frequency, severity and its
recurrence through Standardized Precipitation Index (SPI). This index is used to assess the
severity and frequency of drought. it's based on the probability of rainfall for various time
scale (3, 6, 9, 12 and 24 months). Moreover, the SPI is a easy index to calculate where:
SPI = (Pi – Pm) / σ
Pi: rainfall of year
Pm : average rainfall
σ : Standard deviation
To compare the drought frequency determined from SPI, other indices of drought were used:
- The number of standard deviation Index: this index makes it possible to compare
current rainfall with the mean rainfall adjusted with one or two values of the standard
deviation. The years are classified as follows:
- Drought year: Pi < Pm - σ/2
- Normal year: Pm - σ/2 < Pi < Pm + σ/2
- Wet year: Pm + σ/2 < Pi < Pm + σ
Pi: rainfall of year (mm) Pm: mean rainfall (mm) σ: standard deviation
- Frequencies Analysis: Pc = R / n+1
P: cumulated probability R: row n: a number of years of the climatic series. In
this case the rainfall is classified in the order ascending according to their probability of
appearance. This index is independent of the mean and distinguishes five classes of severity.
14
Frequency of drought According to McKee (McKee et al. (1993), the severity of drought is arbitrarily defined by 7
classes, depending of the value of the SPI. In Morocco, Aghrab (2005) used several drought
indices. He has established a new classification of the SPI which it named corrected SPI
(SPIc) (Table 2). In our study we used the SPI adapted to the Moroccan conditions (SPIc)
after having to check it with other indicators of the drought.
Table 2: Classification of drought severity.
Class Classification of McKee Classification Of Aghrab
Extremely humid > 2 > 2
Very humid 1,5 to 1,99 1 to 1,99
Moderately humid 1,0 to 1,49 0,31 to 0,99
Near normal -0,99 to 0.99 -0,30 to 0,30
Moderately dry -1,0 to -1,45 -0,31 to -0,99
Severely dry -1,5 to -1,99 -1 to -1,99
Extremely dry <-2 <-2
The calculation of SPI in Sekouma-Irzaine area for the two climatic series indicates an
increasing in drought frequency during the last decades. This frequency became 4 on 10 years
(or 2/5) instead of 3 on 10 years (figure 7).
SPI of climatic series 1932-1976 in Sékouma-Irzain
-3
-2
-1
0
1
2
3
19
31
/32
19
34
/35
19
37
/38
19
40
/41
19
43
/44
19
46
/47
19
49
/50
19
52
/53
19
55
/56
19
58
/59
19
61
/62
19
64
/65
19
67
/68
19
70
/71
19
73
/74
Drought year
Value of SPI
Frquency of drought 33%
SPI of the climatic series 1977-2007 in Sékouma-Irzain
-3
-2
-1
0
1
2
3
19
76
/77
19
79
/80
19
82
/83
19
85
/86
19
88
/89
19
91
/92
19
94
/95
19
97
/98
20
00
/01
20
03
/04
20
06
/07
Value of SPI
Drought year
Frquency of drought 40%
Figure 7: SPI of the climatic series 1932-1976 and 1976-2007
The calculation of drought frequency with other drought indices such as index of the number
of standard deviation or the frequencies analysis, shows some comparable results with the SPI
index’s (Table 3). Thus for all indices used, the frequency of drought increased significantly
during the three last decades. Nevertheless, the threshold of detection of drought differs from
an index to another: it signify that certain indices are more severe than others to characterize
drought.
For characterization of drought , the SPI produces values that indicate the severity of drought.
For this reason the SPI will be used for our work.
15
Table 3: Characterization of drought with several indices
Index Drought in 1932-1976 (%) Drought in 1977-2007 (%)
SPI 33 40
Frequencies analysis 31 43
Number of standard
deviation
29 43
According to the classification of Aghrab (2005), the comparison of the frequency of different
classes in both periods, shows the disappearance of two types of classes. These classes
represent the extremes: class extremely wet and class extremely dry.
This suggests that Sekouma-Irzaine does not knew a severe drought, during these last
decades, comparable at 1945. We also see a decrease in frequency classes of wet and normal,
during the three last decades. It seems that the climatic changes occurred in this zone, have
partially transformed the years classified as moderately wet to normal years and normal to dry
years (Table 4).
Table 4: Classification of the years in Sekouma-Irzaine
Classes Classification
Aghrab
Frequency in 1932-1976
(%)
Frequency in 1977-2007
(%)
Extremely humid > 2 2,2 0
Very humid 1 à 1,99 8,9 13,3
Moderately
humid
0,31 à 0,99 31,1 26,7
Near normal -0,30 à 0,30 24,4 20
Moderately dry -0,31 à -0,99 15,6 20
Severely dry -1 à -1,99 13,3 20
Extremely dry <-2 4,4 0
Intensity of drought
The intensity of the drought is a very important parameter that must be quantified. It is
calculated by the average of SPI value of drought years. The results show that the mean value
of this index increased before 1977(table 5). This suggests that droughts during the last
decades are less severe than before these decades.
Even though the frequency of drought was increased during the last decades, its intensity is
less than the period before 1977. The absence of extreme drought during last decades
decreased the average value of SPI in this period.
Table 5: Intensity of drought in climatic series at Sekouma-Irzaine:
Intensity Climatic series
1932-1976
Climatic series
1977-2007
average of SPI value of
the drought years -1,12 -1,02
16
Persistence of drought
The persistence of drought was evaluated by the process of Markov, it consists to determinate
the probability of recurrence of drought for a climatic series (Benarti et Habaibi, 2001).
According to Markov process the probabilities are as follows in the all periods (Table 6):
If year is dry, the probability to will be followed by a dry year is lower than to have a
not dry year (43% instead 57% or 45% instead 55%);
If year is not dry, the probability of having a dry year is very lower than to have a not
dry year, during the next year (23 % instead 77% or 35% instead 65%).
These results suggest that the repetition of the drought did not change during time; this
recurrence of drought is one or two years at maximum in Sekouma-Irzaine.
Table 6: Persistence of the drought according to climatic series in Sekouma-Irzaine
Current year ( year n) following year (year n+1)
Drought (%) No drought (%)
1932-1976 1977-2007 1932-1976 1977-2007
drought 43 45 57 55
No drought
(normal or wet) 23 35 77 65
Typology of drought in Sekouma-Irzaine
During drought years, the study shows a significant decrease in rainfall since the beginning of
agricultural year. Indeed, a fall of about 10 to 50% of the monthly rainfall was recorded since
the first months of the agricultural year. It seems that October, November and March are the
most affected by this decrease (figure 8).
We also note that the distribution of rainfall during drought is bimodal, similar than in normal
years. So the indicators defining drought in Sekouma-Irzaine, characterize only the deficit of
rainfall without indicting the bad distribution of precipitation causing also drought sometimes.
Typology of drought year in Sékouma-Irzain
0
5
10
15
20
25
30
35
Sep Oct Nov Dec Jan Fev Mar Apr Mai June July Aug
Mean rainfall of climatic series
Mean rainfall in drought year
Rainfall (mm)
Figure 8: Typology of the drought year in Sekouma-Irzaine
In the end, we can say that climate of Sekouma-Irzaine has changed. These changes have
affected the variability of precipitations and the occurrence of drought.
The study of the variability of rainfall permitted to define the current weather conditions. The
changes observed had certainly a negative effect on the natural resources and on the
livelihoods. The reduction of rainfall, particularly during spring, caused a limited production
of seeds of perennial plants, annual plants and cereals and reduced the growth of these plants.
17
Indeed, spring is the stage of seeds filling and growth of plants. This fact the yield of these
plants is very affected during the last decades in this zone.
In addition, the reduction of length of rainy period (November-March), observed in our
climate series, has decreased the period of growth (LGP). Consequently, the use of plants
species in this area, is limited by their agronomic requirements. Thus, the zone of study
known by its potential of production of common wheat and durum wheat were converted into
production of barley requiring less water with a short period of growth than other cereals.
In the rangelands, the degradation caused by climate change and overgrazing constrained the
population and development agencies to use rational management in this areas by planting
fodder shrubs and by the creation of setting in rest, more used during the drought periods.
The increase of droughts frequency in recent decades has worsened the precarious situation of
rangelands and seriously affected the local population who has become more vulnerable to
weather conditions.
Contribution to drought forecasting The effects of drought are severe on livelihoods, so, it is very important to be able to predict
drought to mitigate its impacts. This forecast can lead to installation of a drought early
warning system (DEWS) which is a tool based on indicators contributing to the decision-
making to manage the risk of drought at an early time.
Morocco has made important progress in this field through the creation of a National Drought
Observatory (NDO), dependent of the Ministry of Agriculture, or by financing some projects
aiming the realization of DEWS. Thus, Al Massifa and Al Mubarak projects instituted by
National Direction of Meteorology, aim the establishment a DEWS at national level. These
projects concern respectively knowledge the influence of abnormal surface temperatures of
sea on regional rainfall and use of North Atlantic Oscillation for seasonal forecasting.
However the forecast of drought is often difficult because, to be interesting (i.e to allow the
establishment of effective management measures), it must be made several weeks or several
months in advance. But, in the current state of knowledge on the variability of the climate, it
is impossible to achieve deterministic reliable weather forecasts after 10 days.
DePauw (2000) explained that the current practice in this field is to forecast the remainder of
an ongoing season on the basis of statistical or empirical relationships between meteorological
events, such as precipitation, in the beginning and at the end of the season.
We will use this approach that consists to make probabilistic forecast and establishment of
correlations existing in annual rainfall for estimating that the year will be dry or not.
Relationship between seasonal drought and annual drought
SPI of various scales 3 6 9 and 12 months
Figure 9 shows that the evolution of various SPI is comparable. The SPI3, SPI6, SPI9 and SPI
12 correspond to rainfall recorded during the first 3 months (September to December), the
first 6 months (September to February), the 9 first month (September to March) and the 12
months (September to August) respectively.
The correlations analysis (Pearson correlation) between these four SPI indicates significant
correlations. Thus, SPI 12 is correlated with different levels of significance to SPI 3, SPI 6
and SPI 9 (Table 7). The correlation is more significant with the SPI 6 and 9 and less with SPI
3. This suggests that annual drought may be caused by drought during different periods of
agricultural year.
We can conclude that annual drought can be forecasted, reliably and very significant, during
the 9 or 6 months of agricultural year. However, this forecast, although it is significant, it
remains unreliable during the first three months of the agricultural year.
18
SPI of various periods 3, 6, 9 and 12 months in Sekouma-Irzain
-3
-2
-1
0
1
2
3
19
76
/77
19
79
/80
19
82
/83
19
85
/86
19
88
/89
19
91
/92
19
94
/95
19
97
/98
20
00
/01
20
03
/04
20
06
/07
SPI 12 SPI 3
SPI 6 SPI 9
Value of SPI
Figure 9: SPI of various periods 3,6,9 and 12 months in Sekouma-Irzaine
Table 7: Correlations between the various SPI
SPI 3 SPI 6 SPI 9 SPI 12
SPI3 1 0,57 ** 0,40 * 0,319 *
SPI 6 1 0,77 ** 0,69 **
SPI 9 1 0,96 **
SPI 12 1 ** Significant correlation at 1%.
* Significant correlation at 5%.
Index of the number of standard deviation
The normal variability of intra-annual rainfall is very important. The statistical analysis of this
variability does not show any correlation of rainfall between seasons. However in drought,
this variability decreases and relationships between seasonal rainfalls can appear.
We have observed that drought it often starts with a decrease in rainfall since the first months
of agricultural year (Figure 8). We tried to use this characteristic to forecast annual drought.
However, drought can be reached at the beginning, middle or end of agricultural year.
The frequency of these various periods of drought is interpreted like the occurrence
probability of these periods.
Using the drought index, number of standard deviation, we can determine these different
periods of drought in relationship to annual drought (Table 8).
Table 8: Relationship between annual drought and seasonal drought
If drought is in: Annual drought (%)
Autumn 25
Winter 8
Spring 8
Autumn and in winter 17
Autumn and in spring 17
Winter and spring 17
Autumn, winter and spring 8
The results show that annual drought may be due to various droughts during the agricultural
year. Thus, annual drought is due to one seasonal drought in 25%, 8% and 8% , respectively
19
in autumn, winter or spring. Other droughts are caused by a combination of those three
seasons. It also seems whenever the autumn is dry (autumn drought only or accompanied by a
drought during other seasons), the probability to have one annual drought increases. So when
the autumn is dry, the year is dry in 77% of cases, normal in 8% and wet in 15%.
We can conclude that in comparison with others seasons, it seems that the probability of
having an annual drought caused by autumn drought is very high.
As in the case of SPI, it seems that the first three months of the agricultural year are very
critical in annual drought, consequently the drought early warning system from the first three
months of the agricultural year seems very plausible.
However it should be noted that this study was conducted on a series of 30 years of climate
data, the calculated probability can be improved if we have a large homogeneous climate
series.
Table 9: Probability of occurrence drought annual starting from the seasonal drought.
Drought in : Drought year (%) Normal year (%) Wet year (%)
Autumn 77 8 15
Winter 50 25 25
Spring 55 36 8
Autumn and in winter 75 25 0
Autumn and in spring 100 0 0
Winter and spring 60 40 0
Autumn, winter and spring 100 0 0
Relationship between monthly drought and annual drought
In the objective to know the various months of the autumn season which are crucial in annual
drought, we calculated different probabilities of occurrence of monthly drought in autumn
season (Table 10). The results showed that autumn drought may be due to droughts in one or
several months of this season.
The observations of the climate series show that we have never autumn drought starting from
September. Also it appears rather rare to have an autumnal drought stating from drought in all
month of this season. Otherwise said, the probability of having consecutive monthly droughts
during three months is very weak in Sekouma-Irzaine.
But we can note whenever October or November, taken jointly or individually, are dry the
probability of having a drought in autumn is high compared to other classes. Actually, it
seems that the drought during these two months affects the autumnal drought significantly.
Tableau 10 : Monthly and seasonal droughts in Sekouma-Irzaine
If drought in: Drought in autumn (%)
Sep 0
Oct 8
Nov 31
Sep+Oct 31
Sep+Nov 0
Oct+Nov 23
Sep+Oct+Nov 8
In relation with the annual drought, (only for indication) we calculated the probability of
occurrence of annual drought starting from autumn month’s droughts. It is quite obvious that
the exact calculation of this probability requires the probability of occurrence of monthly,
20
seasonal and annual droughts. Nevertheless, we can see relationship between drought that
occurs during the first months of year and annual drought (Table 11).
This relationship appears more important when October is dry, alone or jointly with other
months of the autumn. When October and November are dry the probability of having annual
drought is very high. Otherwise said when autumn drought is due to droughts, consecutive
and continues for two months, the annual drought is very likely.
These results are in agreement with the local knowledge of the population concerning
forecasting of drought. Indeed, the local population consider that rainfall in October is crucial
for annual conditions.
Table 11: Relationship between monthly drought and annual drought
If drought in Drought year (%) Normal year (%) Wet year (%)
Sep 50 8 42
Oct 67 8 25
Nov 50 25 25
Sep+Oct 75 0 25
Sep+Nov 0 100 0
Oct+Nov 100 0 0
Sep+Oct+Nov 100 0 0
Finally, we can say, although we have a small 30 years climatic series of data, the climatic
forecasts can be approached by the existing relationships in intra-annual rainfall. The use of
various indices showed that the forecast of annual drought, in a probabilistic way, starting
from the seasonal drought is possible. This forecast is very likely when two seasonal
droughts, consecutive and continued, (Autumn and Winter, or Autumn and Spring) occur. In
the same way, it seems that the first months of the agricultural year are determining in annual
drought forecast. Thus the data of Sekouma-Irzaine show that when the drought comes for
two successive months (October and November), the agricultural year is automatically dry. It
obvious that these findings are specific for the climate series studied and the area of Sekouma-
Irzaine. The similarities or differences can be observed in other regions.
The forecast of the drought is thus possible with a certain degree of reliability; however its
impact on the natural resources must be determined for assess really, its effects . Thus the
utility of the remote sensing in the drought early warning system proves to be paramount;
indeed this tool must inform us about the initial state of the vegetation and the impact of the
possible drought on this vegetation.
21
Monitoring drought using Remote Sensing and GIS in Sekouma-Irzaine.
Introduction Evaluation of drought is one important items for the mitigation if its effects. The use of
remote sensing and GIS is useful for drought evaluation to obtain up to date information that
is difficult to collect by traditional methods such as field survey and sampling questionnaires
in the agro-pastorals area.
Remote sensing is the acquisition of digital data in the reflective, thermal or microwave
portions of the electromagnetic spectrum. There are many sensors on board numerous
satellites, which can be used to assist prediction of drought decision-making process. One of
the best methods to distinguish of drought conditions is the vegetation monitoring with
reflective remote sensing. The reflective of the electromagnetic spectrum ranges normally
from 0.4 to 3.7μm.
Drought indicators assimilate information on rainfall, stored soil moisture or water supply but
do not express much local spatial detail. Also, drought indices calculated at one location is
only valid for single location. Satellite derived drought indicators calculated from satellite-
derived surface parameters have been widely to study droughts. Normalized Difference
Vegetation Index (NDVI), Vegetation Condition (VCI), and are some of the extensively used
vegetation indices.
Also, the Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for climate
and environmental changes including drought monitoring and climate impact assessment at
regional and global scales (Wang et al. 2004; Gao al. 2008)
The objectives of this study were to establish a statistically significant relationship between
Vegetation Indices and precipitation, and to evaluate the time lag between the occurrence of
precipitation and vegetation response in Tancherfi area. If such relationships could be
established, satellite data could be used for drought monitoring.
Materials and methods
The study area The study area is the rural commune of Tancherfi located in eastern Morocco. It is located
between 2 ° 15'-2 ° 42 'N and 34 ° 10'-34 ° 40' E. It covers an area of approximately 649 Km2
(Figure 10).
It is characterized by mountains in the south and plains in the north. The climate is arid, dry in
Summer and cold in winter. The annual average rain is lower than 250 mm. The annual
average temperature is 17°C and ranges between -4°C to 45°C.
The total agricultural area is 61.200 ha, with 37% (22.665 ha) private (melk) and 63% (38.535
ha) collective (domains). The rangeland area is about 18.000 ha. The main vegetation species
of these rangelands are Stipa tenacissima, Artemisia herba-alba, Anabasis aphylla and Noaea
mucronata. Vegetation is in an advanced degradation stage. Forest occupies 30.200 ha.
Agricultural area counts 7.394 ha, with 536 ha irrigated, and the uncultivated soils are about
5.518 ha. Non irrigated soils are cultivated mainly by cereals (81%). similarly, the fallow
takes an important place in the rotation (14%).
22
Figure 10: Land cover of Tancherfi Commune
Meteorological data
Precipitation data were collected at ground station located in the Laayoun city placed at a
distance of 12 km from the study area over a nine-year period from 2000 to 2008.
Satellite images
The MODIS 16-day product (the MOD13Q1 MODIS Terra Vegetation Indices 16-Day L3
Global 250m SIN Grid; Version 5, (Huete et al., 2002) was used in this study. The MODIS
Vegetation Indices (VI) is robust spectral measures of the amount of vegetation present on the
ground. They involve transformations of the red (620-670 nm), near infrared (841-876 nm),
and blue (459-479nm) bands designed to enhance the "vegetation signal" and allow for
precise inter-comparisons of spatial and temporal variations in terrestrial photosynthetic
activity. The VI products contain the Normalized Difference Vegetation Index (NDVI). Each
MOD13Q1 product includes VI quality information in addition to composited surface
reflectance bands 1-3 and 7 (red, NIR, blue, and MIR (2105-2155nm)).
The data (187 files of 16-day periods) used in this study were downloaded from the Land
Processes Distributed Active Archive Center (LPDAAC) using the Earth Observing System
Data Gateway (EOS). The time series starts on 18 February 2000 and ends on 5 April 2008,
representing one image in each 16 days.
Details documenting the MODIS NDVI compositing process and Quality
Assessment Science Data Sets (QASDS) can be found at NASA’s MODIS web site.
Image processing
The native format of the files was .hdf, and these were imported into ERDAS IMAGINE,
converted to .img files.
The MODIS data were reprojected from the Sinusoidal projection to the UTM projection
(WGS 84, zone 30N).
23
The data are a subset of the Tancherfi region with a spatial resolution of 250m.
For the separation between the "rangelands", the "crops" and the "forest" area, we used the
mask of each of these categories. The mask have been created within a GIS using land cover
map of the study area available (Acherkouk and al 2002). In this study we extracted only the
"rangeland" and "crop" area to monitoring the drought in this homogeneous area. So the
dataset contains 187 Vegetation indices observations per pixel per categories.
Satellite based drought indices for drought characterization
Some of indices are defined for vegetation monitoring such as NDVI (Normalized Difference
Vegetation Index) and Vegetation condition index (VCI). Vegetation Indices, measures of
greenness, have been used for vegetation health condition and drought monitoring.
Normalized Difference Vegetation Index (NDVI)
The Normalized Difference Vegetation Index (NDVI) is the most common Vegetation Index
that is sufficiently stable to permit meaningful comparisons of seasonal, inter-annual, and
long-term variations of vegetation structure, phenology, and biophysical parameters (Tucker
and Sellers, 1986).
These NDVI is based on the channel near infra-red (NIR), where the vegetation has an
important reflectance, the red channel (R), where the vegetation have a low reflectance. The
formula of NDVI: NDVI = (NIR - R)/ (NIR + R)
The vegetation indices values range from -1 to +1. Because of high reflectance in the NIR
portion of the electromagnetic spectrum, healthy vegetation is represented by high NDVI
values. Conversely, no vegetated surfaces such as water bodies yield negative values of
NDVI. Bare soil areas represent NDVI values which are closest to 0 due to high reflectance in
both the visible and NIR portions of the electromagnetic spectrum (Rouse and al. 1974;
Tucker 1979).
Vegetation condition index (VCI)
The NDVI has been used successfully to identify stressed and damaged crops and pastures but
interpretive problems may arise when these results are extrapolated over non-homogeneous
areas. In these areas, differences between levels of vegetation can be related to differences in
environmental resources (i.e. climate, soil, vegetation, relief). For example, under similar
vegetation conditions, a region with abundant resources shows NDVI values twice as large as
compared to adjacent regions with insufficient resources (Kogan, 1987).
The VCI, given by Kogan (1995), has been used to estimate the weather impact on vegetation.
The weather-related NDVI envelope is linearly scaled to 0 for minimum NDVI and 1 for the
maximum for each grid cell and period. It is defined as: VCI = (NDVI - NDVImin)/
(NDVImax - NDVImin) where NDVI, NDVImax, and NDVImin are the smoothed two
weekly NDVI, multi-year maximum NDVI and multi-year minimum NDVI, respectively, for
each grid cell. VCI changes from 0 to 1, corresponding to changes in vegetation condition
from extremely unfavorable to optimal.
Several studies suggest that VCI captures rainfall dynamics better than the NDVI particularly
in geographically non-homogeneous areas. Also, VCI values indicate how much the
vegetation has advanced or deteriorated in response to weather (Wang and al. 2004; Vogt and
al. 2000).
Results and discussion
Influence of land cover types on NDVI data. Figure 11 shows the multitemporal NDVI profiles of Tancherfi Rangeland and crop area from
18 February 2000 to 5 April 2008. Rangeland NDVI values are ranging from 0.14 to 0.34
24
(Std. Deviation = 0.45). However, the profile of NDVI maximum have values ranging from
0.31 to 0.67 (Std. Deviation = 0.8).
Also, it should be noted that Rangeland and crop NDVI values are low. the highest NDVI
values are observed mainly between April and May (Spring). This means that we have
significant vegetation compared to the other seasons.
The comparison of NDVI curves of rangeland and crop during the periods of the satellite
images shows the same tendency during the various seasons. The NDVI values of the two
categories are predominantly similar.
In term of average annual NDVI value, we note that Rangeland and crop NDVI curves are
more separable. The NDVI of the rangeland area is highest than crop NDVI from 2000 to
2008. This would be explained mainly by the importance of fallow in crop area and low
rainfall (less than 250 mm) does not allow important cereals vegetation cover.
Thus, the practice of cereals without irrigation is not recommended because the water needs
of cereals are superior to annual precipitation in this area. in addition, the storms are frequent
and can cause considerable hydrous erosions in agriculture area.
The following analysis on drought monitoring will focus on the rangeland area.
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
18-F
eb-00
18-M
ay-00
18-A
ug-00
18-N
ov-00
18-F
eb-01
18-M
ay-01
18-A
ug-01
18-N
ov-01
18-F
eb-02
18-M
ay-02
18-A
ug-02
18-N
ov-02
18-F
eb-03
18-M
ay-03
18-A
ug-03
18-N
ov-03
18-F
eb-04
18-M
ay-04
18-A
ug-04
18-N
ov-04
18-F
eb-05
18-M
ay-05
18-A
ug-05
18-N
ov-05
18-F
eb-06
18-M
ay-06
18-A
ug-06
18-N
ov-06
18-F
eb-07
18-M
ay-07
18-A
ug-07
18-N
ov-07
18-F
eb-08
Period
ND
VI
NDVI_Rangeland NDVI_Crop
2000 2001 2002 2003 2004 2005 2006 2007 2008
(a)
0
0.05
0.1
0.15
0.2
0.25
0.313-S
ep
28-S
ep
14-O
ct
30-O
ct
15-N
ov
1-D
ec
17-D
ec
2-Jan
16-Jan
1-F
eb
17-F
eb
4-M
ar
20-M
ar
5-A
pr
21-A
pr
7-M
ay
23-M
ay
8-Jun
24-Jun
10-Jul
26-Jul
11-A
ug
27-A
ug
12-S
ep
Period
ND
VI
0
5
10
15
20
25
30
Precip
itati
on
(m
m)
P(mm)
NDVI_crop
NDVI_Rangeland
(b)
Figure 11: Temporal profiles of MODIS NDVI data from 2000 to 2008 (a) and average
variation of NDVI and precipitation in Tancherfi Rangeland and crop area.
NDVI and rainfall variation.
Annual rainfall and NDVI variation
Generally, NDVI values observed are relatively low as compared with other regions
indicating weak vegetation cover in this zone. A good agreement is observed between the
peak NDVI and precipitation. Figure 12 exhibits the annual variations of NDVI along of
Tancherfi Rangeland area from 18 February 2000 to 5 April 2008 with precipitation schemes
in Laayoun station with 16 day precipitations values that ranging from 0 to 97 mm (Mean=
11mm, Std.Deviation = 17).
The comparison of NDVI response to precipitation between years shows that NDVI values
are generally higher during 2002-03, 2003-04 and 2007-08 indicating better vegetation cover
in these years as compared with other years. This is also confirmed by the important quantity
of precipitation during the cycle of vegetation. Also, the rainfall recorded during 2001-02 is
superior to all other except 2002-03. However, the NDVI value of this year is less or equal
than to the values of other years for the reason that in 2001-02 a significant quantity of
precipitation is occurred late.
25
The lowest precipitation value is seen in 2004-05 from which only 128 mm occurred. This
year is considered as drought years. But, NDVI value was equal to that of normal years
because the rainfall distribution was good.
Hence it is expected that the vegetation condition is also controlled by precipitation
distribution.
23.40
317.50334.40
127.60
204.10
293.00
192.50
308.20
238.40
0.17
0.19
0.200.20
0.19
0.21
0.240.24
0.24
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
0-J
an-0
0
00-0
1
01_02
02_03
03_04
04_05
05_06
06_07
07_08
Period
P (
mm
)
0.15
0.16
0.17
0.18
0.19
0.20
0.21
0.22
0.23
0.24
0.25
ND
VI
(mm
)
P(mm)
NDVI
NDVI_mean
Figure 12: The average NDVI and annual precipitation profiles of Tancherfi Rangeland
area from 18 February 2000 to 5 April 2008.
Seasonal rainfall and NDVI variation.
Figure 13 shows the multitemporal NDVI and precipitation profiles from 18 February 2000 to
5 April 2008.
The highest and lowest NDVI values are generally observed in spring and winter,
respectively. The highest NDVI values are found where the highest precipitations have
occurred. The period of low precipitation also corresponds to low NDVI value. Moreover in
end Spring (from May), a significant precipitation corresponds a low NDVI.
An interesting point for the precipitation distribution is that the precipitation after April has no
effect on vegetation. For example, in 2000, 2002, 2003, 2004, 2006, a tendency to lower
NDVI values is observed after May although the high precipitation recorded in this month.
Also, there was a remarkable difference in the temporal precipitation distribution between the
nine years. The precipitation in 2002-03, 2003-04 was better distributed throughout the year
and, in fact, there was not a well-defined break between Autumn, Winter and spring rainy
seasons. The highest NDVI values are seen during theses years. In addition, a better
agreement is observed between the previous precipitation season and NDVI values.
The precipitation in 2004-05 was a good distribution but a little quantity that corresponds to a
low NDVI value. It's considered as a drought year.
The year 2001-02, had significant precipitation during winter (October to December) and
spring (March to April) producing NDVI mean. The peak NDVI appeared in March.
The year 2000-01 had an important precipitation during autumn (October to December), a
little during winter (January) and a dry spring. The NDVI is high mainly in February and
March.
The rainfall has a bad distribution during 2005-06 and 2006-07. Indeed, the period 2005-06
had a few a precipitation during winter and spring, but a wet summer (May to July). A small
amount during autumn (September to November) and wet spring. The NDVI increased during
26
the period of March to April 2006, reaching a maximum in March. During 2006-07, NDVI
had high values during the period of January to February then during May to June.
0
0,05
0,1
0,15
0,2
0,25
18-f
évr-
00
04-m
ars
-00
20-m
ars
-00
05-a
vr-
00
21-a
vr-
00
07-m
ai-00
23-m
ai-00
08-juin
-00
24-juin
-00
10-juil-
00
26-juil-
00
11-a
oût-
00
27-a
oût-
00
0
2
4
6
8
10
12
14
16
18
20
P(mm)
NDVI
Mean
2000
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
12-s
ept-
00
28-s
ept-
00
14-o
ct-
00
30-o
ct-
00
15-n
ov-0
0
01-d
éc-0
0
17-d
éc-0
0
02-janv-0
1
16-janv-0
1
01-f
évr-
01
17-f
évr-
01
05-m
ars
-01
21-m
ars
-01
06-a
vr-
01
22-a
vr-
01
08-m
ai-01
24-m
ai-01
09-juin
-01
25-juin
-01
11-juil-
01
27-juil-
01
12-a
oût-
01
28-a
oût-
01
020
40
60
80
P(mm)
NDVI
Mean
2001-2002
0,00
0,05
0,10
0,15
0,20
0,25
0,30
13-s
ept-
01
29-s
ept-
01
15-o
ct-
01
31-o
ct-
01
16-n
ov-0
1
02-d
éc-0
1
18-d
éc-0
1
03-janv-0
2
16-janv-0
2
01-f
évr-
02
17-f
évr-
02
05-m
ars
-02
21-m
ars
-02
06-a
vr-
02
22-a
vr-
02
08-m
ai-02
24-m
ai-02
09-juin
-02
25-juin
-02
11-juil-
02
27-juil-
02
12-a
oût-
02
28-a
oût-
02
0
5
10
15
20
25
30
35
40
45
50
P(mm)
NDVI
Mean
2002-2003
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
13-s
ept-
02
15-o
ct-
02
16-n
ov-0
2
18-d
éc-0
2
16-janv-0
3
17-f
évr-
03
21-m
ars
-03
22-a
vr-
03
24-m
ai-03
25-juin
-03
27-juil-
03
28-a
oût-
03
Period
ND
VI
0
5
10
15
20
25
30
35
40
45
50
P(m
m)
P(mm)
NDVI
Mean
2002-2003
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
13-s
ept-
03
29-s
ept-
03
15-o
ct-
03
31-o
ct-
03
16-n
ov-0
3
02-d
éc-0
3
18-d
éc-0
3
03-janv-0
4
16-janv-0
4
01-f
évr-
04
17-f
évr-
04
04-m
ars
-04
20-m
ars
-04
05-a
vr-
04
21-a
vr-
04
07-m
ai-04
23-m
ai-04
08-juin
-04
24-juin
-04
10-juil-
04
26-juil-
04
11-a
oût-
04
27-a
oût-
04
Period
ND
VI
0
10
20
30
40
50
60
70
80
90
100
P(m
m)
P(mm)
NDVI
Mean
2003-2004
0,00
0,05
0,10
0,15
0,20
0,25
0,30
12-s
ept-
04
28-s
ept-
04
14-o
ct-
04
30-o
ct-
04
15-n
ov-0
4
01-d
éc-0
4
17-d
éc-0
4
02-janv-0
5
16-janv-0
5
01-f
évr-
05
17-f
évr-
05
05-m
ars
-05
21-m
ars
-05
06-a
vr-
05
22-a
vr-
05
08-m
ai-05
24-m
ai-05
09-juin
-05
25-juin
-05
11-juil-
05
27-juil-
05
12-a
oût-
05
28-a
oût-
05
Period
ND
VI
0
5
10
15
20
25
30
35
P(m
m)
P(mm)
NDVI
Mean
2004-2005
27
0,00
0,05
0,10
0,15
0,20
0,25
0,30
13-s
ept-
05
29-s
ept-
05
15-o
ct-
05
31-o
ct-
05
16-n
ov-0
5
02-d
éc-0
5
18-d
éc-0
5
03-janv-0
6
16-janv-0
6
01-f
évr-
06
17-f
évr-
06
05-m
ars
-06
21-m
ars
-06
06-a
vr-
06
22-a
vr-
06
08-m
ai-06
24-m
ai-06
09-juin
-06
25-juin
-06
11-juil-
06
27-juil-
06
12-a
oût-
06
28-a
oût-
06
Period
ND
VI
0
5
10
15
20
25
30
35
40
45
50
P(m
m)
P(mm)
NDVI
Mean
2005-2006
0,00
0,05
0,10
0,15
0,20
0,25
0,30
13-s
ept-
06
29-s
ept-
06
15-o
ct-
06
31-o
ct-
06
16-n
ov-0
6
02-d
éc-0
6
18-d
éc-0
6
03-janv-0
7
16-janv-0
7
01-f
évr-
07
17-f
évr-
07
05-m
ars
-07
21-m
ars
-07
06-a
vr-
07
22-a
vr-
07
08-m
ai-07
24-m
ai-07
09-juin
-07
25-juin
-07
11-juil-
07
27-juil-
07
12-a
oût-
07
28-a
oût-
07
Period
ND
VI
0
20
40
60
80
100
120
P(m
m)
P(mm)
NDVI
Mean
2006-2007
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
13-s
ept-
07
29-s
ept-
07
15-o
ct-
07
31-o
ct-
07
16-n
ov-0
7
02-d
éc-0
7
18-d
éc-0
7
03-janv-0
8
16-janv-0
8
01-f
évr-
08
17-f
évr-
08
04-m
ars
-08
20-m
ars
-08
05-a
vr-
08
Period
ND
VI
0
10
20
30
40
50
60
P(m
m)
P(mm)
NDVI
Mean
2007-2008
Figure 13: Multitemporal NDVI and precipitation profiles
from 18 February 2000 to 5 April 2008
CVI and rainfall variation.
Annual rainfall and CVI variation.
Variations of VCI values and precipitation are presented in Figure 14 where the highest and
lowest VCI values are found to correspond to 2002-03 and 2006-07. As seen in the graph, the
lowest precipitations coincide with the lowest VCI values indicating a relatively good
agreement between minimum VCI and minimum precipitation. However, The bad distribution
of high quantity of precipitation in 2006-07 caused a low VCI.
23.40
240.40
315.50308.20
334.90
130.60
233.40
260.60
192.50
0.410.42
0.43
0.47
0.48
0.44
0.42
0.42
0.44
0.38
0.40
0.42
0.44
0.46
0.48
0.50
2000
00_01
01_02
02_03
03_04
04_05
05_06
06_07
07_08
Period
VC
I
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
P(m
m)
P(mm) VCI VCI_mean
Figure 14: The average VCI and annual precipitation profiles of Tancherfi Rangeland
area from 18 February 2000 to 5 April 2008
28
Seasonal rainfall and VCI variation in rangeland area
Figure 15 shows the multitemporal CVI and precipitation profiles from November 2000 to
January 2003. The highest VCI values are observed for February, March, June and July 2001,
whereas the highest value is seen for July and august 2000.
Although, 2001-02 had significant precipitation during winter and spring. The peak VCI
appeared after April. VCI values are more or less in agreement with previous precipitation.
Also, the lowest VCI values are observed from September to Marsh, but the highest value is
seen for April during 2002-03, 2003-04.
The general lack of agreement between VCI values and precipitation data clearly shows that
the maximum and minimum NDVI values used to determine the VCI at local scale have not
been influenced by the weather condition rather they have been affected by other factors.
0.6 0.4
1.7
18.5
2.1
0.1
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
4-M
ar-
00
20-M
ar-
00
5-A
pr-
00
21-A
pr-
00
7-M
ay-0
0
23-M
ay-0
0
8-J
un-0
0
24-J
un-0
0
10-J
ul-00
26-J
ul-00
11-A
ug-0
0
27-A
ug-0
0
12-S
ep-0
0
Period
VC
I
0
2
4
6
8
10
12
14
16
18
20
P(m
m)
P(mm)
VCI
VCI_mean
2000
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
12-S
ep-0
0
28-S
ep-0
0
14-O
ct-
00
30-O
ct-
00
15-N
ov-0
0
1-D
ec-0
0
17-D
ec-0
0
2-J
an-0
1
16-J
an-0
1
1-F
eb-0
1
17-F
eb-0
1
5-M
ar-
01
21-M
ar-
01
6-A
pr-
01
22-A
pr-
01
8-M
ay-0
1
24-M
ay-0
1
9-J
un-0
1
25-J
un-0
1
11-J
ul-01
27-J
ul-01
12-A
ug-0
1
28-A
ug-0
1
Period
VC
I
0
10
20
30
40
50
60
70
80
P(m
m)
P(mm) VCI VCI_mean
2001-2002
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
13-S
ep-0
1
29-S
ep-0
1
15-O
ct-
01
31-O
ct-
01
16-N
ov-0
1
2-D
ec-0
1
18-D
ec-0
1
3-J
an-0
2
16-J
an-0
2
1-F
eb-0
2
17-F
eb-0
2
5-M
ar-
02
21-M
ar-
02
6-A
pr-
02
22-A
pr-
02
8-M
ay-0
2
24-M
ay-0
2
9-J
un-0
2
25-J
un-0
2
11-J
ul-02
27-J
ul-02
12-A
ug-0
2
28-A
ug-0
2
Period
VC
I
0
5
10
15
20
25
30
35
40
45
50
P(m
m)
P(mm) VCI VCI_mean
2002-2003
0.30
0.35
0.40
0.45
0.50
0.55
0.60
13-S
ep-0
2
29-S
ep-0
2
15-O
ct-
02
31-O
ct-
02
16-N
ov-0
2
2-D
ec-0
2
18-D
ec-0
2
3-J
an-0
3
16-J
an-0
3
1-F
eb-0
3
17-F
eb-0
3
5-M
ar-
03
21-M
ar-
03
6-A
pr-
03
22-A
pr-
03
8-M
ay-0
3
24-M
ay-0
3
9-J
un-0
3
25-J
un-0
3
11-J
ul-03
27-J
ul-03
12-A
ug-0
3
28-A
ug-0
3
Period
VC
I
0
5
10
15
20
25
30
35
40
45
50
ND
VI(
mm
)
P(mm) VCI VCI_mean
2002-2003
0.30
0.35
0.40
0.45
0.50
0.55
0.60
13-S
ep-0
3
29-S
ep-0
3
15-O
ct-
03
31-O
ct-
03
16-N
ov-0
3
2-D
ec-0
3
18-D
ec-0
3
3-J
an-0
4
16-J
an-0
4
1-F
eb-0
4
17-F
eb-0
4
4-M
ar-
04
20-M
ar-
04
5-A
pr-
04
21-A
pr-
04
7-M
ay-0
4
23-M
ay-0
4
8-J
un-0
4
24-J
un-0
4
10-J
ul-04
26-J
ul-04
11-A
ug-0
4
27-A
ug-0
4
12-S
ep-0
4
Period
VC
I
0
10
20
30
40
50
60
70
80
90
100
P(m
m)
P(mm) VCI VCI_mean
2003-2004
0.30
0.35
0.40
0.45
0.50
0.55
28-S
ep-0
4
14-O
ct-
04
30-O
ct-
04
15-N
ov-0
4
1-D
ec-0
4
17-D
ec-0
4
2-J
an-0
5
16-J
an-0
5
1-F
eb-0
5
17-F
eb-0
5
5-M
ar-
05
21-M
ar-
05
6-A
pr-
05
22-A
pr-
05
8-M
ay-0
5
24-M
ay-0
5
9-J
un-0
5
25-J
un-0
5
11-J
ul-05
27-J
ul-05
12-A
ug-0
5
28-A
ug-0
5
13-S
ep-0
5
Period
VC
I
0
5
10
15
20
25
30
35
P(m
m)
P(mm) VCI VCI_mean
2004-2005
29
0.30
0.32
0.34
0.36
0.38
0.40
0.42
0.44
0.46
0.48
29-S
ep-0
5
15-O
ct-
05
31-O
ct-
05
16-N
ov-0
5
2-D
ec-0
5
18-D
ec-0
5
3-J
an-0
6
16-J
an-0
6
1-F
eb-0
6
17-F
eb-0
6
5-M
ar-
06
21-M
ar-
06
6-A
pr-
06
22-A
pr-
06
8-M
ay-0
6
24-M
ay-0
6
9-J
un-0
6
25-J
un-0
6
11-J
ul-06
27-J
ul-06
12-A
ug-0
6
28-A
ug-0
6
13-S
ep-0
6
Period
VC
I
0
5
10
15
20
25
30
35
40
45
50
ND
VI
P(mm) VCI VCI_mean
2005-2006
0.30
0.35
0.40
0.45
0.50
0.55
29-S
ep-0
6
15-O
ct-
06
31-O
ct-
06
16-N
ov-0
6
2-D
ec-0
6
18-D
ec-0
6
3-J
an-0
7
16-J
an-0
7
1-F
eb-0
7
17-F
eb-0
7
5-M
ar-
07
21-M
ar-
07
6-A
pr-
07
22-A
pr-
07
8-M
ay-0
7
24-M
ay-0
7
9-J
un-0
7
25-J
un-0
7
11-J
ul-07
27-J
ul-07
12-A
ug-0
7
28-A
ug-0
7
Period
VC
I
0
20
40
60
80
100
120
P(m
m)
P(mm) VCI VCI_mean
2006-2007
0.30
0.35
0.40
0.45
0.50
0.55
0.60
13-S
ep-0
7
29-S
ep-0
7
15-O
ct-
07
31-O
ct-
07
16-N
ov-0
7
2-D
ec-0
7
18-D
ec-0
7
3-J
an-0
8
16-J
an-0
8
1-F
eb-0
8
17-F
eb-0
8
4-M
ar-
08
20-M
ar-
08
5-A
pr-
08
Period
VC
I
0
10
20
30
40
50
60
P(m
m)
P(mm) VCI VCI_mean
2007-2008
Figure 15: Multitemporal NDVI and precipitation profiles
from 18 February 2000 to 5 April 2008
NDVI-Rainfall and CVI-Rainfall relationship as indicator of drought in rangeland area. The relation between the vegetation indices and precipitation was used by several authors to
explain the vegetation monitoring (Evans and Geerken, 2004). Indeed, Davenport and
al.(1993), Wong et al. (2003) showed a high correlation between NDVI and precipitation.
Liu and Kogan (1996) found that the NDVI was highly correlated with water deficit and
rainfall for Cerrado (Savanna grassland) and Caatinga (woodland and open woodland) which
both grow in areas with district wet-dry seasons.
As presented above, NDVI presents a positive response to previous precipitation except after
April when the vegetation is no responsive to a significant precipitation. In fact, the
determining factor of the biomass production in Morocco is precipitation occurring before
May. Figure 15 presents average season NDVI and Sum season precipitation (Winter,
Autumn and Spring). This clearly shows that vegetation is responsive to previous season
precipitation. The highest and lowest average NDVI values correspond to the highest and
lowest previous season precipitation respectively.
So, it was concluded from several studies that VCI has provided an assessment of spatial
characteristics of drought, as well as its duration and severity and were in good agreement
with precipitation patterns (Wang and al.2004; Vogt and et al.2000; Thenkabail and al. 2004).
However, in our study, average season VCI presents more or less a positive response to
previous season precipitation (Figure 16).
30
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
24-J
un-0
0
2-J
an-0
1
6-A
pr-
01
25-J
un-0
1
3-J
an-0
2
21-M
ar-
02
25-J
un-0
2
3-J
an-0
3
21-M
ar-
03
25-J
un-0
3
3-J
an-0
4
20-M
ar-
04
24-J
un-0
4
2-J
an-0
5
21-M
ar-
05
25-J
un-0
5
3-J
an-0
6
21-M
ar-
06
25-J
un-0
6
3-J
an-0
7
21-M
ar-
07
25-J
un-0
7
3-J
an-0
8
20-M
ar-
08
6-
Apr-
00
29-
Sep-
00
1-
Jan-
01
7-
Apr-
01
30-
Sep-
01
1-
Jan-
02
22-
Mar-
02
30-
Sep-
02
1-
Jan-
03
22-
Mar-
03
30-
Sep-
03
1-
Jan-
04
21-
Mar-
04
29-
Sep-
04
1-
Jan-
05
22-
Mar-
05
30-
Sep-
05
1-
Jan-
06
22-
Mar-
06
30-
Sep-
06
1-
Jan-
07
22-
Mar-
07
30-
Sep-
07
1-
Jan-
08
Period
P(m
m)
0.15
0.17
0.19
0.21
0.23
0.25
0.27
0.29
0.31
ND
VI
P(mm) NDVI
Figure 15: Average NDVI and Sum season precipitation (Winter, Autumn and spring)
in Tancherfi Rangeland area from 2000 to 2008.
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
24-J
un-0
0
2-J
an-0
1
6-A
pr-
01
25-J
un-0
1
3-J
an-0
2
21-M
ar-
02
25-J
un-0
2
3-J
an-0
3
21-M
ar-
03
25-J
un-0
3
3-J
an-0
4
20-M
ar-
04
24-J
un-0
4
2-J
an-0
5
21-M
ar-
05
25-J
un-0
5
3-J
an-0
6
21-M
ar-
06
25-J
un-0
6
3-J
an-0
7
21-M
ar-
07
25-J
un-0
7
3-J
an-0
8
20-M
ar-
08
6-
Apr-
00
29-
Sep-
00
1-
Jan-
01
7-
Apr-
01
30-
Sep-
01
1-
Jan-
02
22-
Mar-
02
30-
Sep-
02
1-
Jan-
03
22-
Mar-
03
30-
Sep-
03
1-
Jan-
04
21-
Mar-
04
29-
Sep-
04
1-
Jan-
05
22-
Mar-
05
30-
Sep-
05
1-
Jan-
06
22-
Mar-
06
30-
Sep-
06
1-
Jan-
07
22-
Mar-
07
30-
Sep-
07
1-
Jan-
08
Period
P(m
m)
0.30
0.35
0.40
0.45
0.50
0.55
VC
I
P(mm) VCI
Figure 16: Average VCI and Sum season precipitation (Winter, Autumn and spring) in
Tancherfi Rangeland area from 2000 to 2008.
Correlation analysis. In order to study the statistical relationships between various time lag periods and NDVI or
VCI, Pearson correlation analysis was performed and correlation coefficients (r values)
between the values of vegetation indices and precipitation data were determined.
Therefore, the correlation between average NDVI and VCI with sum of precipitation of
previous season is performed. The seasons used in this correlation are Winter, Autumn and
Spring.
The results of such correlations are presented in Figure 17: The correlation coefficient (r) for
this relationship was determined to be 0.83 (significant at 99% confidence level), which
indicates a strong positive linear relationship between previous season precipitation and
NDVI.
31
The correlation was more significant with NDVI than VCI (r = 0.58**). Consequently, NDVI
variations can be a good indicator of vegetation changes and in turn, the drought conditions in
the study area.
R2 = 0.6948
0.14
0.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00 200.00
P(mm)
ND
VI
Pearson Correlation = 0.832**
NDVI = 0.001P + 0.19
R2 = 0.354
0.30
0.35
0.40
0.45
0.50
0.55
0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00 180.00 200.00
P(mm)
ND
VI
Pearson Correlation = 0.583**
NDVI = 0.001P + 0.41
Figure 17: Correlation between average NDVI/VCI and Sum season precipitation (Winter,
Autumn and spring) in Tancherfi Rangeland area from 2000 to 2008
Conclusion
It is evident that precipitation is the dominant attribute that controls the growth of plant in arid
rangeland. However, the distribution of precipitation during the growth cycle of vegetation is
more significant that the quantity. In Tancherfi region plant growth is dependent on amount
and three precipitation arrival time: the precipitation of October-November, December-
January and Mars-April. Consequently, a wet year corresponds to a distribution of
precipitation in these three times. A normal year is characterized by two times of precipitation
and dry year had one period of precipitation. In this area we can define drought in two ways.
These are NDVI lowers than 0.20 or VCI lowers than 44%.
Significant correlations have been found between NDVI values and precipitation data in
Laayoun station.
Therefore, NDVI values can be used for monitoring and mapping of drought conditions in the
study area. VCI values from the existing data in this area proved to be unreliable.
The present study has also confirmed that both NDVI and VCI indices could be used to
monitor drought in the study area. This is a promising finding that could eventually lead to the
preparation of drought risk maps if the study is extended over a larger region of the country.
However, in order to obtain more reliable results there is a strong need for wider time span of
satellite data.
32
References Acherkouk, M.; Bouayad, A.; El Koudrim, M.; Maâtougui, A. ; Mahyou, H. et Rahmi
M. 2002. Etude phyto-écologique. Projet de Développement Rural Taourirt-Taforalt.
Convention de Recherche & Développement- Etudes de Base
Aghrab A. 2003. Caractérisation de la sécheresse et élaboration des indicateurs climatiques
pour son alerte précoce dans la région de Saiss. Mémoire de fin d’étude. Ecole National
d’Agriculture de Meknes. Maroc.
Aghrab A. 2005. Etude de la sécheresse au Maroc. Editions le Manuscrit pp 45
Agoumi A et Debbarh A. Ressources en eau et bassins versants du Maroc. 50 ans de
développement (1955-2005).
Agoumi A., Naji A., Rahib H. 1991. Changements climatiques : Évolution régionale Maroc.
Revue Marocaine de Génie Civil, no 33.
Barakat F, Handoufe A. 1998. Approche agroclimatique de la sécheresse agricole au Maroc.
Sécheresse; 9 : 201-8.
Benarti Z., Habaibi H. 2001. Etude de la persistance de la sécheresse en Tunisie par
utilisation des chaines de Markov (1909-1996). Sécheresse, 12, 215-225.
Buishand T.A. 1984. Tests for detecting a shift in the mean of hydrological time series. J.
Hydrol., 73, 51-69.
Bzioui M. 2000. Politique et stratégies de gestion des ressources en eau au Maroc ».
Académie du Royaume du Maroc Session de novembre.
Bzioui M. Rapport national 2004 sur les ressources en eau au Maroc.
Davenport, M. L., and Nicholson, S. E. 1993. On the relation between rainfall and the
Normalized Difference vegetation Index for diverse vegetation types in East Africa. Int. J. of
Remote Sensing. 14. 2369-2389.
DePauw, E. 2000. Drought early warning systems in West Asia and North Africa. In
“Improving drought early warning systems in the context of drought preparedness and
mitigation. P. 65-85. Editions: Wilhite, D.A. Sivakumar, M.V.K. and Wood, D.A. Lisboa,
Portugal, 5-7 Sept. 2000”.
Driouech F. 2008. Drought in Morocco: Observation and evolution. Conference on African
Drought: Observations, Modeling, Predictability, Impacts.- Italy, 02 - 06.
Evans, J., Geerken, R. 2004. Discrimination between climate and human-induced dryland
degradation. Journal of arid environment. 57, 535-554.
Hubert P., Carbonnel J.P., Chaouche A. 1989. Segementation des séries hydrométriques.
Application à des séries de précipitations et des débits de l’Afrique de l’Ouest. J. Hydrol.,
110, 349-369.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. 2002.
Overview of the radiometric and biophysical performance of the MODIS vegetation indices.
Remote sensing of Environment, 83, 195–213.
Kogan, F.N., 1987. Vegetation index for a real analysis of crop conditions. In: Proceedings of
the 18th Conference on Agricultural and Forest Meteorology, AMS, W. Lafayette, Indiana,
15–18 September 1987, Indiana, USA, pp. 103–106.
Kogan, F.N., 1995. Application of vegetation index and brightness temperature for drought
detection. Advances in Space Research 15, 91–100.
Lee A.F.S., Heghinian S.M. 1977. A shift of the mean level in a sequence on independent
normal random variables. A Bayesian approach, Technometrics 19, 503-506.
Lubès-Niel H., Masson J.M., Servat E., Paturel J.E., Kouamé B., Boyer J.F. 1994.
Caractérisation de fluctuations dans une série chronologique par application de tests
statistiques. Etude bibiolographique. Programme ICARE, Rapport N°3, ORSOM, Montpelier.
33
M. Gao , Z. Qin , H. Zhang, L.Lu, X. Zhou and X. Yang. 2008. Remote Sensing of Agro-
droughts in Guangdong Province of China Using MODIS Satellite Data. Sensors 2008, 8,
4687-4708.
Marché n° 54 sur 97-98. INRA/DPA Oujda.
Mckee T.B., Doesken N.J., Kleist J. 1993. The relationship of drought frequency and
duration times scales. AmePreprints, 8th Conference on applied Climatology, 17-22. January,
Anaheim, CA, 179-184.
Mimouni, J and Mahyou H. 2007. Risk management and drought mitigation in agro pastoral
arid areas of Morocco. Workshop: Risk management and drought mitigation in agro pastoral
arid areas of West Asia and North Africa. Damascus (Syria), July 15-18.
Pettit A.N.1979. A non paramétric approch ti the change-point prpbleml. Appl. Stat., 28,
126-135.
Rouse, J.W., Haas, R. H., Shell, J. A., and Deering, D. W. 1973. Monitoring vegetation
system in the Great Plains with ERTS. Third ERTS symposium , NASA SP-351, vol. 1, 309-
317.
Tansfer F. C., Palmer A., R., 2000. Vegetation mapping of the great Fish River Basin,
South Africa: Integrating spatial and multi6spectral remote sensing techniques. Applied
vegetation science 3: 197-204.
Thenkabail, P. S., Gamage, M. S. D. N. et al. 2004. The Use of Remote Sensing data for
drought assessment and monitoring in south west Asia. Colombo,Srilanka, International
Water Management Institute: pp.1-23.
Tucker, C. J., and Sellers, P. J. 1986. Satellite remote sensing of primary production. Int. J.
Remote Sens., 7, 1395–1416.
Tucker, C.J. 1979. Red and photographic Infrared linear combination for monitoring
vegetation, vol. 8, 127-150.
V.Vogt, J., A. A.Viau, et al. 1998. Drought Monitoring from Space using Empirical Indices
and Physical Indicators. International Symposium on Satellite Based Observation: A toll For
the Study of Mediterranean Basin, Tunis,Tunisia.
Wang Z., Wang P., and al. 2004. “Using MODIS Land Surface Temperature and
Normalized Difference Vegetation Index products for Monitoring Drought in the Southern
Great Plains,USA.” International Journal of Remote Sensing Vol.25 No.1: pp.61-72.
White D.H., O’Meagher B. 1995. Coping with exceptional drought in Australia. Drought
Network News 13-7.
Wong, J., Rich, P.M. and Price, K.P. 2003. Temporal responses of NDVI to precipitation
and temperature in the central Great Plains, USA. Int. J. of Remote Sensing. 24. 2345-2364.
34
Chapter III. Livelihood and local knowledge to mitigate drought in
Sekouma-Irzaine
Introduction "Drought is a climatic phenomenon that affects specific geographical regions of the globe.
Morocco is considered among the most vulnerable to the adverse effects of climate change
and it became a semi-arid or arid country. The climate vulnerability combined with
desertification and deforestation increasingly pronounced, was confirmed over the years of
drought that hit the country and severely affected the economy, heavily dependent on
agriculture. This drought becomes structural. It is a heavy burden on the state budget and
represent an obstacle to development." (M. Sihaddou, 2005)
"In their routine speeches, Moroccan politicians and policymakers often insist on the climatic
and geological conditions unfavorable for the Moroccan economy. Drought and energy act as
an alibi for independence for the socio-economic problems of the country. If this speech was
acceptable 50 years ago, It cannot still be an outlet for the failures of development programs.
The knowledge now becomes the real wealth of a nation and the best reactor to begin the
development of a society" (Sihaddou, 2005).
This knowledge should take as its starting point the knowledge of rural communities who are
the first victims of the adverse effects of the phenomenon of drought. How they perceive
drought? What are the episodes and the impacts of drought they have known in the past? And
what kind of solution they have practiced to mitigate these impacts?
Objectives As to the Damascus workshop, the objective of this study was set as the analysis of the local
knowledge and the existing community livelihood strategies to mitigate the effects of drought.
Specific Terms of Reference are as follows:
1. Extract from the Community Development Plans (CDP) the components concerning
drought effect mitigation as defined by the communities.
2. Assess the drought-related vulnerability context for the community.
a. Summarize the major trends occurring in the communities that exasperate and
alleviate the effects of drought.
b. Identify the potentials and the constraints of the communities using the
Sustainable Livelihood Framework.
2. Assess the livelihood strategies used by the communities to cope with and/or adapt to
the aforementioned trends.
3. Recommendations should be developed so that: Based on the potentials/constraints
and the ensuing livelihood strategies, the prioritized drought component interventions
in the CDPs should be characterized to determine the interventions’ that have the most
potential impact.
35
Methodological frame “The component developed utilizing a number of data and information that has been
previously gathered by the NAR (INRA) through a consistent database of the M&M III
involved communities and the information provided through the community-based plan,
which had been developed during 2007.
These information and data helped a lot in framing the physical/material and the
social/organizational dimensions, which are critical to our analysis.
In order to organize these information in a comprehensive and participatory way, with due
relevance provided to the motivational/ attitudinal aspects of the community members, a
Sustainable Livelihoods approach (SLA) was undertaken.
This component specifically targeted community perceptions about issues related to drought,
which had not specifically been tackled before (i.e. the CBP does not focus drought and its
specific implications).
More specifically a participatory tool has been tailored to gather and discuss the following
information with the community focus groups:
identify major drought events and related time period,
get the definition of ‘drought’ according to local stakeholders,
understand which elements are locally utilized to forecast drought events,
participatory analysis of local vulnerability to drought and related coping mechanisms
within existing livelihood strategies.
The applied PRA methodology consisted in the ‘Historical Livelihood Matrix’, which the
consultant has been applying in a variety of different contexts with fruitful results.
Further information will be gathered and existing data examined in specific fields of interest,
so that the analysis currently developed can be reinforced and become more comprehensive.
In particular, further analysis will address the domains of climatology, environmental change,
market analysis, community socio-economic differentiation.
The team was composed by experts in different disciplines, so to endorse an integrated and
holistic approach. In particular:
Agro-economy
Climatology
GIS
Sociology
The work of this group specifically targeted community perceptions about issues related to
drought, which had not specifically been tackled before (i.e. the CBP does not focus drought
and its specific implications).
It is believed that this overall analysis and its synergies with the ongoing works on Policy
Options and Institutional Capacities will provide a powerful framework to understand local
livelihoods and their related vulnerability to drought, so that appropriate TIPOs can develop
accordingly.” (M. Nori, 2008).
Methodology In order to assess the community livelihood asset effort has consisted in extracting the
components related to droughts from the CDP as a Strengths Weakness Opportunities and
36
Threats Matrix (SWOTM). Then livelihoods approach has been used to address and assess the
diverse categories of livelihood capitals.
To identify the community socio-economic differentiation, a typology of households has been
carried out using the Principal Component Analysis (PCA) which has covered almost all
farms (175 farms). The data used come from the database collected during the elaboration of
community development plan.
Further market analysis has been realized in order to evaluate the impacts of drought on the
variation of prices of small ruminants and animals feeds. The data used come from the
monitoring of livestock conducted monthly by the agricultural extension service of El Aïoun.
All these elements provided important reference guidelines for the participatory activities with
the community focus groups.
The discussion with the focus group was organized according to a PRA methodology named
‘Historical Livelihood Matrix’. The HL Matrix represents an interesting instrument to
stimulate debate within a heterogeneous group of community. It allows an analysis of current
livelihood system as the product of historical dynamics, and provides chance to critic and opts
between different options at individual as well as collective levels.
First of all the participants identified the episodes of drought which they lived. Then, they
have defines for each episode the impacts which they classified according to their relevance
specific to the episode and also for a possible drought events in the future (scoring).
In the same way, concerning the answers of the community to face the drought a discussion
was started. A ranking by drought episode (lived and future) of the various strategies of
adaptation was realized. Then a comparative analysis in the time of the various ranks obtained
was done (horizontal dynamics).
Two specific Matrices have been developed (Figure 1):
1. Drought Impacts Matrix describing the drought impacts on local livelihoods,
2. Solutions Matrix presenting the strategies adopted by the community to cope with
these impacts.
3. Drought episodes
The Impact Matrix is a double entry. The impacts of drought perceived by the community
included in the entrance lines, while episodes of drought are the entries of the columns. The
cases of intersection of rows and columns are reserved for weighting each impact (scores of 0
to 10) for a given episode.
37
Impact Matrix
Strategies Matrix
Figure 1: Drought Historical Livelihood Matrix (Impacts and strategies matrices)
The trend curves of the importance given to each impact is drawn using the scores assigned to
each impact for individual episodes of drought. Afterwards, we add a new column assigned to
a future drought in order to identify the perception of the community to the relative
importance of possible impacts.
In the same way the strategies matrix has been used. The unique difference between the two
matrices is the use of scoring method for the first one and the raking for the second.
Results and discussion
Recent history of drought
Historically, Sekouma-Irzaine has experienced several periods of drought. Years of drought
are presented according to different decades (Table 12). It is observed that since the 30s the
region has experienced 24 periods of drought. Drought occurred during all the past decades
but with different frequencies. The highest one was in the decades 40, followed by the 80’s
and the 90’s.
These droughts, for the most part, did not concern only the study area but they touched all the
country.
The impact of drought on natural resources and the livelihood of the population depend on the
intensity of drought, its duration and the area affected by drought. Thus in farming areas
drought leads to a general decline in the yield of cereals and fruit trees, while in the agro-
pastoral areas, agriculture and food pathways are strongly affected by the water deficit. In
those areas where the population is already vulnerable to drought, the occurrence or
recurrence of this phenomenon causes serious consequences witch threatens people's lives.
Table 12: Droughts History in Sekouma-Irzaine
30 40 50 60 70 80 90 2000
1940-41 1950-51 1960-61 1970-71 1980-81 1990-91 2000-01
1931-32 1941-42 1951-52 1961-62 1971-72 1981-82 1991-92 2001-02
1932-33 1942-43 1952-53 1962-63 1972-73 1982-83 1992-93 2002-03
38
1933-34 1943-44 1953-54 1963-64 1973-74 1983-84 1993-94 2003-04
1934-35 1944-45 1954-55 1964-65 1974-75 1984-85 1994-95 2004-05
1935-36 1945-46 1955-56 1965-66 1975-76 1985-86 1995-96 2005-06
1936-37 1946-47 1956-57 1966-67 1976-77 1986-87 1996-97 2006-07
1937-38 1947-48 1957-58 1967-68 1977-78 1987-88 1997-98
1938-39 1948-49 1958-59 1968-69 1978-79 1988-89 1998-99
1939-40 1949-50 1959-60 1969-70 1979-80 1989-90 1999-00
3/9 6/10 2/10 2/10 2/10 5/10 4/10 1/7
Strengths Weakness Opportunities and Threats of the community
The components related to the drought have been extracted from Sekouma-Irzaine CDP and
organized in Strenth Weakness Opportunities and Threats Matrix (Table 13):
Table 13 : Sekouma-Irzaine SWOT matrix
Weakness Strengths
Poor and stony soil Private land status (Melk) constitutes 90%
Low rainfall important number of NGO (12)
Low equipment in tanks and pumps Practice of some rangeland management techniques
(resetting, Alley cropping ...)
Lack of public drinking network Important knowledge in agriculture (fruit trees and
breeding)
Groundwater very deep, few wells Young people represent 48% of the community.
Rangeland Degraded Large family manpower
Dominance of cereals crops without
irrigation
Presence of a religious leadership
Low diversification of revenue sources
(64%) are content with their main
activity (agriculture and small ruminants
breeding)
Open locality
Irrigated area is limited and fragmented important production of olive and olive oil
High demographic growth (6 to 7 per
household)
important production of honey
important Rate of illiteracy Strong relationship with the agents of development
Low dynamism of the basic organization
(low communication with the population)
important adoption of the new suitable technologies
Low community adhesion to the basic
organizations
presence of a local market
High dependency vis-à-vis the market to
satisfy the animal feeding (46%)
Limited skills of the community in
activities other than agriculture
Low use of water harvesting techniques
Rural Exodus
Threats Opportunities
High frequency of drought Strong demand for olive products at regional,
national and international markets
Trend towards a public policy
disengagement
Presence of microfinance institution in the region
39
Presence of development project (Taourirt-Taforalt
Rural Development Project, National Initiative for
Human Development)
Availability of public subsidies related to
agricultural investment
The PDC has not addressed the following components which are linked to drought:
Evaluation of Household income
Level of poverty
Negative effects of drought (rangeland degradation, loss of profits, Conflicts)
Comunuty Livelihood Assets
The composition of the five categories of capital of the community Sekouma-Irzaine is as
follow:
Social capital
The Sekouma-Irzaine community has developed a very important social capital (SC). It
concerns:
Six organizations in honey production. They produce two kind of honey specific to
their territory. The first one is based on zizyphus lotus and the second one is based on
Rosmarinus officinalis;
An association of Stone removal that works in narrow collaboration with the Rural
Development Taourirt Tafoghalt Project (PDRTT1);
An association dealing with the extraction of olive oil. It possesses a modern machines
and produces an olive oil of quality;
Two associations witch deal with the rural and social development.
In addition, the community belongs to the area where Sheep and Goat National Association
(ANOC2) operates.
Physical Capital
The physical capital of the community consists of:
A park of modern transport which is composed of 18 pickups and three trucks plus
more than 31 carts as traditional transport;
A capacity of significant water storage composed of 17 fixed tanks and 8 mobiles;
Sources of irrigation and drinking water constituted by 124 private wells of which
56% are equipped with a motor. Note also that the community Sekouma-Irzaine is part
of Tancherfi rural district which is equipped by 15 collective water points;
131 private stables that are used as a shelter for animals and storage of animal feed.
16 tractors, used both in farming operations (labor ...) and the transport of animals and
people.
In addition, the community has 53 traditional plows.
1 PDRTT: Rural Development Taourirt Tafoghalt Project.
2 ANOC: sheep and goat National association
40
Natural Capital
Sekouma-Irzaine locality is a plain that stretches over a length of 8 km with an area of 8382
hectares. The agricultural land is 2,607 ha. It consists of 2462 ha as rainfed and 146 ha as
irrigated area. In this area, there are wide variety of fruit trees: 15822 olive trees, 14358
almonds, 436 figs and 1551 others fruit trees.
Human Capital
The total population of Sekouma-Irzaine is about 1253 people in 177 households, with an
average of 7 persons per household.
Enrollment and literacy is around 22 and 29%. These rates are very low compared to the
national average which is 60%.
Despite this low literacy rate, the population has a remarkable expertise in agriculture and
livestock.
Financial capital
The community has a herd of small ruminants with size of 6880 heads composed respectively
of 74% of sheep and 24% of goats. It also has a cattle herd of 35 cows. In addition, the
community possesses about 2692 heads of poultry and 131 heads of rabbits.
In spite of the quality and the quantity of these categories of capitals, the results of the
analysis related to the community differentiation shows that these capitals present an unequal
repartition according to the socioeconomic groups of the community. The characteristics of
these different groups are presented in the next part.
Sekouma-Irzaine socioeconomic differentiation (Annex 1 and 2)
In this community there is a parfait integration of the productive and domestic activities in all
farm. Actually, the word farm or household means the same thing.
The implementation of the Principal Component Analysis (ACP ) reveals that the following
four variables: The class of small ruminants (cl_prum), the number of olive trees (nb_oliv),
the irrigated area (sup_irig) and the rainfed area (sup_bour); arrive to explain 78% of the
variance between existing households of the community (Annex 3).
The two main components produced by the ACP (Figure 18) are determined, respectively, by
the variable cl_prum (45.460% of the variance) and the variable nb_oliv (32.915% of the
variance).
41
Figure 18: Main components
The projection of all households on the factorial plan permits to identify four different groups
(Figure 19).
Figure 19: Farms on the factorial plan
The type 4 is the most present in the community Sekouma-Irzaine (Table 14), while the type 1
constitutes the less frequent type.
42
Table 14: Distribution of the different types of farm Type 1 Type 2 Type 3 Type 4 Total
Farm number 13 27 42 93 175
Pourcent 7 15 24 53 100
Type 1
This includes farms with a herd of small ruminants relatively large. For 87% of these farms
the size of the herd exceeds 50 heads. The farms of this type are limited in number and
represent only 7% of all the farms in the community Sekouma-Irzaine. The largest
concentration of such family farms is found in douar Zrakha (23%).
All these farms have at least one well with a motor. In the average, the irrigated area is equal
to 4 ha, while the rainfed area is approximately 47 hectares per farm. The arboricultural fruit
is very practiced, it consists of an average of 388 olives trees and 244 almond trees per farm.
Type 2
This includes farms without flock of small ruminants (44%) and farms with the herd size of
small ruminants under 50 heads. Some of these farms (56%) have a means of transport (pick
up in the case of 26% of farms and a cart for 30% of the others). Similarly some farms have a
tractor (23%). The majority of these farms have a well (56%). But only 23% of these wells are
equipped with a motor.
The farms belonging to such type constitute 15% of all the farms in the community Sekouma-
Irzaine. They are frequent in douar Zrakha (33%) and douar Irkayine (19%).
The family of these farms lives in houses built in hard (26%) or hard and earth (41%).
The average irrigated area is 3 hectares, while the rainfed area is about 13ha per farm. The
arboricultural tree is very present in this type of farms. There is an average of 343 olives trees
and 160 almond trees per farm.
Type 3
For this type of farms, the herd of small ruminants is small, in 70% of cases, this size is less
than 50 heads. The majority of these farms (81%) do not have any head of bovine animals.
In general, these farms do not have modern means of transport (truck or pick up), but 30% of
them have a cart as a traditional mean of transport.
The majority of this group of farms (65%) have a well, but only 19% of them are equipped
with a motor. Therefore, the irrigated area is virtually absent. The rainfed area is about 22 ha
per farm. The arboricultural fruit is less practiced, it consists of an average of 17 olives trees
and 74 almond per farm.
The majority of such households live in houses built in hard and earth (52%). The light source
is traditional for 67% of cases, but 29% of households use a plate solar energy.
This group of farms represents 24% of all the farms in the community Sekouma-Irzaine. It is
encountered mainly in douars: Ouled Ahmed (24%), Elkhatayine (17%) and Irkayine (14%).
Type 4
The farms witch belong to this type are small farms with no flock of small ruminants (13%) or
the size of their herds do not exceed 50 heads. These farms are without any cattle heads. Such
type of farms is the most common in the community Sekouma-Irzaine and represents 53% of
all farms. They are concentrated in two douars: Ouled Mustapha (21%) and Ouled Ahmed
(34%). These farms are characterized by lack of means of transportation (truck or pick up).
43
Similarly these farms do not have a tractor. The dry area does not exceed 6 ha, while the
irrigated area is virtually absent.
They are also characterized by the absence of wells (62% of them). Even in the case of the
presence of wells in some of these farms, these wells are not equipped with motor.
Households of these farms usually live in houses built using earth. The light source is
traditional (candle, lamp oil or gas).
Market prices analysis
The luck of a complete long set of data, has limited the market price analysis to the variation related to
small ruminants number and prices of animal feeds and animals.
Variation of small ruminants number
The figure 20 shows that the annual average number of the sheep during the period 2000-2007
knew a light fall passing from 77330 to 74000 heads.
In addition, the greatest fall was recorded at the time of the dryness which prevailed in the
zone of study and even on a national scale between 2004-2005 reducing annual average
number to 73962 heads at the end of the this period.
In fact, the breeders facing to the dryness choose the reduction of the size of their herds to
provide for the needs for the remainders.
Trend of annual average number of the small ruminants
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
2000
-01
2001
-02
2002
-03
2003
-04
2004
-05
2005
-06
2006
-07
Years
Nu
mb
er
0
50
100
150
200
250
300
350
400
Rai
nfa
ll (
mm
)
Sheep
Caprine
Rainfall
Figure 20: Trend of annual average number of the small ruminants
But overall, one can say that there is a stability of number of the sheep during this period; this
is due to the effects of the programme of safeguard of the livestock. Which program
undertakes several interventions food and watering of the livestock. It is about the distribution
of the barley and concentrated feed, the assumption of responsibility of the transport charges
of animal feeds in particular the subsidized barley and the acquisition of the tracks.
However, as for caprine, their annual average number knew an upward trend passing from
46591 heads in 2001 to 64700 heads in 2007.
This increase in caprine number during this period can be due to specificities of this animal
species which profits more than the sheep of the least fodder possibilities and adapts more to
the dryness.
44
Also, the caprine ones benefit from a small forest in the zone of study and received a great
profit-sharing on behalf of the local agencies of development at the time of the last years
(Taourirt Tafoughalt Rural Development Project).
Variation of animal feeds prices
Special importance is given to barley witch constitutes the principal feed used by the breeders
in Sekouma –Irzaine. The other feeds are also treated in this analysis.
Barley price
The figure 21 shows a tendency towards the rise in the average price of the barley bought in
the souk during the period 1983-2007.
It is also during the period of January until April that the evolution of this price knew peaks
which correspond at ends of the years of local dryness. They are in particular the agricultural
years 1984-85, 1989-90, 1999-00 and even 2004-05 with respectively 174, 160, 254 and 212
dirhams/quintal.
Evolution of annual average barley price and rainfall
100
150
200
250
300
1983/8
4
1985/8
6
1987/8
8
1989/9
0
1991/9
2
1993/9
4
1995/9
6
1997/9
8
1999/0
0
2001/0
2
2003/0
4
2005/0
6
Years
Pri
ce (
dh
/ql)
100
150
200
250
300
350
400
Rain
fall (
mm
)Rainfall
Price
Figure 21: Evolution of annual average barley price and rainfall
However, generally during the rainy years, a fall of the annual average of barley price is
noticed. For example at the time of the agricultural years 1986-87, 1991-92, 1994-95 and
2002-03 with respectively 115, 173, 186 and 163.
On another side, we notice a raising of average barley price during the period 1983-2007,
particularly at the time of January until April (wet months).
In fact, during this season the fodder offer of the rangeland is weak and the food needs for the
during the period of January until April are high (phase of fine gestation- lactation beginning).
45
Months evolution of average barley price
100
120
140
160
180
200
220
Sept Oct Nov Déc Jan Fév Mars Avril Mai Juin Juil Août
Months
Pri
ce (d
h/ql
)
Price
Figure22: Evolution of annual average barley price and rainfall
Others feeds prices
The table 15 shows that the annual average price of animal feeds in particular of dry pulp of
beet, the bran and concentrated feed, knew an increase at the time of the period of dryness
which prevailed in the zone of study even in all the kingdom during the current decade. This
rise of the prices of this animal feeds was accentuated by the noticed enrichment of the prices
of the straw and the hay of alfalfa which recorded a variation respectively of more than 50 and
24%.
Table 15: Feeds prices variation over years (Dh/ql)3
Years DPB C. feed Straw F. alfalfa Bran
2001 197,9 208,8 138,7 185,6 -
2002 207,5 219,0 109,5 167,7 -
2003 187,0 - - 168,1 -
2004 194,6 - 86,0 177,2 -
2005 220,4 218,5 129,5 220,0 241,3
2006 208,8 215,2 123,6 223,7 211,0
2007 267,1 227,9 115,4 208,8 251,7
In front of the dearness of animal feeds which increases considerably the cost price for the
ovine breeding on the one hand and the fall of the selling prices of these animals at the time of
the dryness of 2004-2005, the breeders were constrained to give up part of their ovine herds
what explains the reduction of the annual average number of this animal species during this
period. In fact, the breeders of the area fear more the national drynesses that local because in
the first case, the rises in price of the animal feeds are certain according to them.
In addition, it is noted that there is an upward trend in the prices of animal feeds of industrial
origin since 2007 due mainly to the increase in the world costs of the raw materials necessary
for the manufacture of these products.
In normal situation, the satisfaction of part of the food needs for the small ruminants for the
zone are ensured by the rangeland, the pasturages, the thatches, the fallow, the shrubs
plantations and the forest rangeland like by the forage stored by the breeders.
3 DPB : Dry pulp of beet, C.feed : Concentrated feed, F.Luzerne : forage of alfalfa, Dh/ql : Dirham/quintal
46
Variation of animals selling prices
Since 2001 until the 2007, overall, there was an upward trend of the selling prices of animals
for slaughter (ram and teg) with a notable variation of 90% for the price of the rams whereas
the price of teg, it more than doubled during the same period (Figure 23). This report can be
due to the increase in the red meat domestic demand.
Trend of annual average price of sale of the sheep
500
1000
1500
2000
2500
2000
-01
2001
-02
2002
-03
2003
-04
2004
-05
2005
-06
2006
-07
Years
Pri
ce (
dh
/hea
d)
100
150
200
250
300
350
Rai
nfa
ll (m
m)
Ram
Teg
Ewe
Rainfall
Figure 23: Trend of annual average of sale price of sheep
As for the livestock (ewe), their annual average price and after a significant progression of
741dh in 2001 up to 1039 dh/head in 2004, was penalized by the dryness which knew the
zone during the current decade reaching one of low the levels (811dh/head).
Perception, impacts and strategies to mitigate drought at Sekouma-Irzaine In order to elucidate the perception of drought in the Sekouma-Irzaine, workshop has been
organized with the participation of the diverse socioeconomic groups of the community. Thus
the workshop has been attended by: large, medium and small farmers, farmers with no
livestock and technicians from the local institution for development (CT Aioun). In addition,
the participants were of different ages and are mostly members of cooperatives or associations
of the study area. The diversity of participants has made the discussion about drought richer
and deeper.
As women cannot attend the workshop together with men of the community, the project team
has organized another workshop attended and animated only by women4.
The purpose of the workshops was to answer four main questions:
1. What is the perception of drought?
2. What are the impacts of drought at Sekouma-Irzaine?
3. What are the strategies people adopted during the drought?
4. What are the future strategies of the population?
Local drought Perception
Before developing the analysis of the results relatives to the impact of drought, it is very
significant to understand how the community perceives the drought?
4 Thanks to Dr. F. Nassif who animated the women workshop
47
According to the workshop attended by men, the drought is:
Reduction of rainfall and the dryness of wells
Reduction of rangeland forage supply
Bad distribution of rainfall during the year
Increase of price of animal feed
Reduction of working day (unemployment)
Reduction of income
Decrease of agricultural activity and productivity
According to women population of Sekouma-Irzaine, drought is:
Reduction or lack of precipitation
Lack of means to extract water from groundwater
The definition of drought differs according to its impact on the individual. If for women the
drought is only a lack of water and rainfall, for men drought is closely related to all factors of
life, especially, agriculture and livestock activities. So among this group of people drought
has many faces. Scientists also give give diferents definitions to drought: The meteorological
drought, agricultural drought, hydrological drought and socio-economic drought.
But result of the workshop organized with the men shows that the socio-economic impacts
dominate their perception of drought. This is du to climate change (decrease in rainfall over
time and increase of the frequency of drought) which led to a deterioration of natural
resources and have induced more dependency of the population vis-à-vis the market.
Drought is perceived by the community under four main angles: Water resources, feeding
livestock, household incomes and agricultural production.
For the water resources, drought has resulted in poor distribution of rainfall and dryness of
wells. In the case of animal feed, the drought manifested by the significant reduction of
rangeland forage supply, shortage of feed in the market and feed prices rising.
In addition, the drought is felt in terms of a fall in agricultural production, increased
unemployment and households’ income decrease.
Thus, the perception of drought by the community is more focused on the impact thereof on
the description of the phenomenon itself.
In addition, the drought is considered as a divine will that community cannot predict. This
does not means that the community do not try to predict drought as the following tow adages
show:
1. "The year of the freeze, you have to labor and persevere"
2. "The Year of dew, plow or resigns"
Droughts episodes in Sekouma-Irzaine
Given that the community lives in a situation of frequent drought, it is sometimes difficult to
identify episodes of drought with the exception of that of 1945-1952, because of the
seriousness of its adverse effects.
In fact, during the last two decades drought in the region has become a structural
characteristic. According to the community, the rainfall year, currently, represent the
exception.
The debate around the main years of drought experienced by the community has identified the
following drought episodes: 2005-06, 2003-04, 1988-1989 and 1945-1952.
48
The community does not name these local droughts except for that of 1945-1952 which gives
it the nomenclature "Year of boune”.
Note that the episode of drought known in Morocco between 1982 and 1985 is not considered
drought by participants at the workshop. They explain the omission by the fact that the
locality Sekouma-Irzaine received normal rainfall during the said drought period.
The identified episodes (Table 16) are different from those shown in table 12 (official data
series of rainfall). To overcome this problem, in the workshop conducted with women, we
have tried to link the drought years with family events (marriage in the family, childbirth ....).
The results presented in Table 6 show that the use of this technique permits a better
identification of drought episodes witch are close to that reported by official climate data. It
also seems that women have identified the most severe droughts compared to men. Thus
during the years 1980 and the years 1995-2000 Morocco and the study region have
experienced the most intense droughts of the past three decades.
Table 16: Drought episodes
Drought identified 1945 1980’s 1988-89 1995-97 2003-04 2004-05 2005-06
By men Drought - Drought - Drought - Drought
By women Drought Drought - Drought Drought -
By Statistical analysis Drought 5 dry
years
Normal Drought Humid Drought Normal
The difficulty for the community to identify the same episodes of drought as those resulting
from the analysis of the official climate data is probably due to several reasons:
The high rate of illiteracy of the population do not permits the identification of the
exact years of drought;
Droughts during the last decade are relatively negligible to that of 1945 witch caused a
strong shock that people remember perfectly;
Drought in the region has become structural hence the years are similar;
Climate change that occurred in the area and caused a decrease in the average normal
rainfall cause confusion between normal years and years of drought;
Strategies developed by the population make the recent moderate droughts less felt by
the population;
Impacts of drought
Drought episode 2005-2006 (Annex 4)
The most recent episode of local drought (2005-2006) resulted in adverse effects, mainly on
water resources, agricultural production and income (Table 17). The decline of the latter is
attributed to the fall of sale prices of animals.
Furthermore, the significant decrease of water level of wells and / or their dryness indicates
that the groundwater is poor and dependent on local rainfall. Therefore, excessive exploitation
of the water even in normal years would lead to its collapse.
Table 17: Impacts of the 2005-2006 drought
Impacts Scores
Reduction of rainfall 9
Decrease in the level of water wells 9
49
Decrease in cereals and fruit trees production 8
Fall of sale prices of animals 7
Reduction of rangeland forage supply 6
Sheep Abortions 5
Exodus 2
Drought episode 2003-2004
As in previous episode, water resources and household income were the most affected by the
drought of 2003-2004 (Table 18). The rangeland forage supply and agricultural production,
too, have suffered a significant reduction.
Table 18: Impact of the 2003-2004 drought
Impacts Scores
Reduction of rainfall 10
Decrease in the level of water wells 10
Reduction of rangeland forage supply 8
Fall of sale prices of animals 8
Decrease in cereals and fruit trees production 6
Difficulties to repay Farm Credit 5
sheep Abortions 3
Exodus 2
Increase of the intensity of transhumance 2
Drought episode 1988-1989
For this episode of drought, new impacts have been identified (Table 19). They concern, first,
the difficulty in repayment of agricultural credit, and secondly, the increase in mobility of
livestock in search of better grazing sites in response to the fall in rangeland forage supply.
As, during that time, breeders had enormous difficulties in the repayment of their debt
towards the Regional Agricultural Credit Fund, they were forced to increase sales of their
animals despite the low prices on the market.
Table 19: Impacts of the 1988-1989 drought
Impacts Scores
Reduction of rangeland forage supply 10
Difficulty in repayment of agricultural credit 8
Increase of the intensity of transhumance 8
Increase of the livestock sale 8
Reduction of rainfall 8
Fall of prices for the sale of animals 7
Decrease in the level of water wells 5
Decrease in cereals and fruit trees production 4
Drought episode 1945-1952
This is the episode of an extreme drought that had indelibly marked the collective memory of
Moroccans including the population of Sekouma-Irzaine. The famine was the most dramatic
impact of this period. In fact, the majority of herds were decimated. The famine was coupled
50
to a mass exodus (Table 20). The severity of this drought had forced many heads of household
to abandon their family group.
Table 20: Impacts of the 1945-1952 drought
Impacts Scores
Famine 10
Reduction of rainfall 10
Decrease in cereals production 10
Fall of sale prices of animals 10
Increase of mortality of livestock 9
Reduction of rangeland forage supply 9
Exodus 8
Decrease in the level of water wells 2
Relative importance of the impacts of past droughts
To assess the relative importance of different impacts of drought experienced in the region,
the sum of the scores of each impact has been used (Table 21). The data shows that the
community attaches special importance in descending order to the following impacts:
1. The decrease of rainfall
2. The reduction of rangeland forage supply
3. The fall of sales prices of animals
4. The decrease of cereals and fruit trees production
5. The decrease of the water in wells.
In fact, the reduction of rainfall is somewhat the drought itself, while the other reported
impacts are only the logical consequence of the latter since Sekouma-Irzaine is a part of a
semiarid region. This shows that any proposed TIPOS must take into account the main
problem which is the continual reduction of water resources in the area.
Table 21: Relative importance impact of drought
Impacts 2005-06 2003-04 1988-89 1945-52 Total
Reduction of rainfall 9 10 8 10 37
Reduction of rangeland forage supply 6 8 10 9 33
Fall of sale prices of animals 7 8 7 10 32
Decrease in cereals and fruit trees production 8 6 4 10 28
Decrease in the level of water wells 9 10 5 2 26
Difficulty in repayment of agricultural credit 0 5 8 0 13
Exodus 2 2 0 8 12
Increase of the intensity of transhumance 0 2 8 0 10
Famine 0 0 0 10 10
Increase of the mortality of livestock 0 0 0 9 9
Increase of sales of livestock 0 0 8 0 8
sheep Abortions 5 3 0 0 8
51
Trends of the main impacts of droughts
The analysis of the trends of the main impacts lets one to be more pessimist about the future
of the livelihood of Sekouma-Irzaine community unless the local authority and the non-
governmental organizations get seriously involved to change the trends.
As a result of the growth and expansion of irrigated area, combined with successive episodes
of drought , the water levels in wells tends to worsen (Figure 24).
The role of rangeland as source of forage tends to collapse. Indeed, the supplementation has
become a widespread practice in the region. In a study conducted by the CRRA of Oujda, it
finds that "the burden caused by food are very important and represent over 60% of the total
cost of the small ruminants production .... This shows that the use of supplementation has
become a general behavior of breeders in the region. It is mainly due to drought and
secondarily to the phenomenon of settlement that the region has known in recent years... Once
the effect of drought and the use of supplements impose standardization of the behavior of
breeders in terms of sheep production. "(Bouayad, & al., 2001). The same phenomenon was
noted by Bourbouze (2000) which states that "the decline of mobility and settlement lead to a
new kind of animal husbandry. The contributions of cereal after clearing combine now
widespread complementation on the route should be seen as part of an anti-risk strategy
different from the past ".. Also, note that the community has expressed clearly the correlation
between the trend of the declining rainfall with the fall in sales prices of animals.
Figure 24: Trends of the main impacts of droughts
Impacts of a possible future drought
The inquiry of the community about the impacts of a future drought showed that Sekouma-
Irzaine fear the same impacts experienced in the past, namely the decrease in rainfall and
lowering the water level of wells. The reduction of rangeland forage supply and the fall down
of the sale price of animals (Table 22).
The main fear in this community, in the event of a possible drought, is the complete
liquidation of the flock and the abandon of small ruminants breeding activity. In fact, breeders
52
believe that young people in the community do not like this activity because of its harshness
and its insignificant pay.
Table 22: Impacts of a possible future drought
Impacts Scores
Decrease in the level of water in wells 10
Increase of sales of livestock 9
Reduction of rainfall 9
Reduction of rangeland forage supply 8
Fall of sale prices of animals 8
Decrease in cereals and fruit trees production 6
Increase of mortality of livestock 5
Exodus 3
Increase of sheep abortion 2
Increase of the intensity of transhumance 1
53
Strategies and solutions developed by the population to mitigate drought
effects
The community Sekouma-Irzaine developed various types of strategies to mitigate drought
effects. As mentioned before, participants in the women workshop have identified the drought
episodes and the strategies used to cope with drought impacts (Annex 5), but they were
unable to use the scoring or ranking methods to arrange them by priority as men did (Table
23).
Table 23: Solution matrix of the population in Sekouma-Irzaine Solution 1945 1988/89 2003/04 2005/06 Tendency Future
Use aromatic and medicinal
plants for food 2
Use stocks (stock of food for
people and livestock ) 1 1 3 3
4
Purchase of animal fodder 4 4
Use of the shrubs planted by
government 1 1
Reducing the animal load 5 5 2
Credits 4 6 6 6
Purchase of water 7 7
Immigration 3 10 10 8
Veterinary interventions :
(Analyze diseases and purchase
of vaccines and drugs)
9 9
Animal sale to purchase food 2 2 5 2
Well digging 8 8
Asking of the government
subsidies (individually or with
associations). 2 2 1
Use subsidy of the transport of
barley. 5 2 2
Diversification of incomes
(occasional works…) 3 4 4
Drought episode 1945.
The agricultural year 1944-1945 is the most important drought which marked the population
memory because this drought was severe and general in all the country.
In Sekouma-Irzaine, according to old community people, the population was not ready to face
this drought. Moreover the drought persistence during five years (1942 to 1947) worsened the
situation.
In front of the fall of agricultural and rangelands production, the population used, initially, its
stock to feed itself and feed its livestock.
54
In second time and in front of gravity of the shortage and loss of the livestock caused by
absence of foods, the population had recourse to the spontaneous aromatic plants such as
"Bkouka" (Arisaraum vulgar) which caused serious food poisonings.
The drought of 1945 was general in all the country and almost in all Africa, of this fact the
transhumance which was a strategy and traditional practice to mitigate drought effects was not
possible during this period. To face this food crisis, the rural migration was the ultimate
solution for the population which invaded the cities to search food.
The cities knew long queues of people in front of the food distribution stores which were
controlled by the French colonial administration.
According to some survivors of this period (present in the workshop), the presence of the
colonialism worsened the drought effects: population profited little of the available richness at
this time because they were monopolized by the colonialists.
This strong drought has affected firstly natural resources (agro-pastoral productions),
secondly the livestock (disease and died of livestock) then the life of population (food
shortage) which caused a massive exodus to search food.
During drought of 1945, we observe, in addition to the similar strategies identified by men
(transhumance, the consumption of the spontaneous plants aromatic.), others strategies have
been announced by women (annex 5). It concerns the fact that women during this drought
episode participated to generate additional income (weaving carpet for example) and the sale
of lands.
Drought episode 1988-1989
The drought in this year marked modestly the population. Only few participants, in the
workshop, remember this year like drought.
We can note that the first population strategies remain the same as in 1945: use of food stock.
However, given the experiment lived in 1945, the food stocks were more important so
drought did not cause any famine (table 23). These stocks helped the population to ensure
their needs and needs of their livestock during several days or several months.
The population strategies developed, seem effective during this period to attenuate drought
effects. It should be noted however that these strategies are effective only for a short period
like few months. These strategies cannot certainly satisfy the population if drought was
prolonged like drought of 1942 to 1947.
According to the population, the availability of transport, the roads and the proximity of
markets and their frequency are the factors which allowed constitution and consolidation of a
food stock enabling them to face a brutal shock or a sudden food shortage.
The climatic changes occurred in the zone; do not allow an advantageous agriculture. So the
livestock became the principal activity of income sources. Indeed, in this area, the agro-
pastoral system which was dominated by agricultural activities changed to a system with
livestock dominance.
The livestock is considered as the personal capital; it is not regarded as an additive activity
but became a professional job. In spite of its high capital cost, the livestock generates a very
appreciable benefit allow to fill the loss caused by agricultural activities, in particular during
difficult periods, for the population survival. So the new strategies are based primarily on the
keeping of livestock activity.
55
To remain in this activity and to face drought, the population is forced to reduce the size of
the herd, undertake the occasional works or they take credit. Money, generated by these
actions, allows ensuring needs for the livestock until the sale during festival of sheep (Aid El
Adha).
However we noted that during this drought year, certain strategies developed by the
community to face drought became ineffective. Thus, the sale of the livestock, which aims to
ensure the food of the remaining herd, is rather used to pay the contracted credit to invest in
agricultural and livestock fields.
This crisis started at the beginning of the 1980's when several farmers/breeders took credits to
finance their activities. In this period, the strategy government consists at granting credits
easily to promote the agriculture in Morocco. However for mitigate droughts of 1982 to 1984
and in the objective of safeguard of livestock, the government intervened by limiting sacrifice
of sheep individually during the festival of sheep. Thus in 1984, only one sacrifice was
tolerated by one household. In front of a decrease of demand and an increase of the offer of
the livestock, the prices fell what induced not only reduction in herder incomes but also
induced the accumulation of the credits and the interest payments.
This government strategy, which became a burden for the stockholders during several years,
causes a disaster at several stockholders. For refunding the credit agricultural, some of
stockholders are obliged to sell their livestock and others to leave the activity of livestock.
Thus during drought of 1988/89, we can say, that is not the drought in itself which marked the
memory of the community (shock) but the fact of not being able to refund the credit with the
risk of financial ruin.
Given the bad experiment in this case, the recourse to the credit is not a more the priority
solution for the stockholders who prefer to sell a part of the livestock or to realize occasional
works to provide the needs of livestock.
When the strategies developed by the community are ineffective or unsatisfactory to
compensate the loss caused by drought, the population asks for the subsidies of government,
like barley, necessary to maintaining livestock.
During the 1980's droughts, it seems that the first solutions against drought are individual, the
population used firstly their strategies to face drought and secondly they look for the
government subsidies to escape at this crisis.
According to women of the community, the drought of 1980’s were long, the effective
strategy to attenuate its impacts consist to find new water resources. The population thus
started to dig wells or to deepen the existing ones. This strategy had been very useful, a few
years later, to plant fruit trees or vegetables crops. The incomes generated with these activities
(sale of olive oil) contributed to large diversification of households’ income.
Drought episode 2005-06
As the drought is more recent, the majority of participants in the workshop remember well
that the date of the drought is not correct (2004/05 according to our Analyses).
Concerning this drought, the population has several solutions to mitigate drought (Figure 1:
more than 10 solutions in 2005/06 against 5 in 1988/89).
In the other hand, we observe a change in the behaviour of the population. During the last
drought (1988-89), the population uses its strategies firstly to mitigate the drought effects
whereas during this drought (2005-06), the population prefers the use firstly the government
56
actions (fodder shrubs, subsidies of food of cattle…) before consuming their stocks or selling
a part of the livestock to feed the remainder of the herd.
The actions undertaken by the government were carried out by PDRTT (Project of Rural
Development Taourirt-Taforalt) project financed by the Morocco government and IFAD
(International Fund of Development of Agriculture). This project is initiated in the beginning
of 1990’s; it aims the improvement of livelihoods of the rural population through several
actions allowing the increase in the production.
These actions of sustainable management of the argo-pastoral resources allowed an
attenuation of the drought effects. Thus, according to the population, the plantation of the
fodder shrubs by the project allowed significantly to face drought during 2004/05.
During the last decade, we observed the appearance of strategies based on the actions
undertaken by the government and on its subsidies.
To achieve this goal and to benefit at maximum of these advantages, the population is
organized in associations and co-operatives. Thus the requests for subsidies which have done
individually with little success are currently made by a framework legislative, legal and good
organized. When the limits of the government grants are reached, the population returns to its
strategies developed for a long time as, the use of stock, the sale of a part of the livestock and
occasional works
Currently, given the difficulty to refund credits and access to it, the community considers
rarely the use of credit to invest in the livestock activity.
In general during the period of drought 2005/06, the local knowledge of the population
combined to the efforts made by government allowed to mitigate drought effects and to
maintain the population in this locality.
According to women, during the last decade (drought in 2005), to counter the prices fall of
animals which involved a strong reduction of profit of the breeders, households tried to
diversify their income sources by the practice a new activities. The beekeeping, practice of
farmyard (rabbit and poultry), trade of the olive oil and sale of carpet were the strategies
which used to mitigate drought.
Strategies tendency
Chronologically it is observed that the importance of strategies developed individually to
mitigate drought, tend to decrease in the course of time (sale of the animals, stocks,
diversification of the incomes…), whereas the strategies based on the actions of government
increase.
We also observed that the population has developed strategies that can be described as
strategies of vulnerability. Thus, these new strategies such as the reduction of animal,
deepening wells, giving more consideration to animal health and income diversification ...
have allowed people to be less vulnerable to drought.
Overall, we can say that the trends of strategies for drought mitigation moving towards a
population increasingly less vulnerable to drought thanks to individual strategies but also to a
population increasingly dependent of government interventions.
Indeed, it is a criticism that we can do for the project (PDRTT) which aimed the improving of
the capacity of the rural population to manage its development while the results show the
creation of a population dependent of the government assistance.
57
Projection in the future
According to the population, drought in the study area and the region in general has become
structural, so any development program in this region must include mitigation of drought that
would allow the population to cope.
In addition, the community is fairly confident about the role of government in drought case, it
is estimated that the government must take urgent action by every drought as it did in recent
years. As a result, some dependency of the government and its actions in drought case settled
in the study area.
Thus, in the future, according to the population, their strategies allow only survival during a
few days or a few months during the drought periods, whereas government strategies (actions
of development, subsidies…) can constitute tools against drought effects at long term.
It should however be noted that the disadvantage of these strategies based on the government
grants can cause a disaster when the day the government cannot come to assistance of the
population in the future drought. In spite of this dependence, the population developed
strategies which according to it, combined with those of the government can attenuate
efficiently the consequences of the future drought.
The strategies usually developed by the population (sale of animals, storage of food…) are
added to others which can be described as strategies for long-term.
Indeed thanks to the framing of the population by the technicians of the offices (CT), the
receptivity and use of new techniques of water harvesting and the deepening of the wells, the
community seems more armed against the drought effects. These strategies at long term
would make it possible to reduce the vulnerability of the population to drought.
To face preceding or future drought, we observe that rural migration is the ultimate solution
for the population. However, it seems that the rate of exodus during the drought is the same as
in a normal year. It is thus a usual phenomenon which touches the whole of area and
kingdom.
It is probable that government actions as equipment of the basic infrastructures in the rural
field (program of electrification of the rural zone, construction of road and of dispensary…)
allowed the establishment and the keeping of rural population in its natural environment.
To mitigate future drought, the women think that the community must have preventive
strategies which consisted on:
The storage of cereals in the pits (matmora)
Culture of a local leguminous plant known as «tabelhsente» (Anthyllis cytisoides)
Multiplication of sinking
Reducing the animal load by sale of animals
Increase the saving in the form of cash
Emigration
58
Conclusion The perception of drought differs according to gender. Indeed women perceive drought as a
shortage or lack of water and rainfall, but men observe drought as negative effects on all
aspects of their live, manly, agriculture and livestock activities.
The study shows that the community perception of drought is more focused on the impacts
than on the description of the phenomenon itself. Thus the socio-economic impacts dominate
this perception.
Drought is perceived according to four main scopes: Water resources (Bad distribution of
rainfall and dryness of wells), feeding livestock (significant reduction of rangeland forage
supply, shortage of feed in the market and feed prices rising), unemployment and decrease of
household incomes and agricultural production.
The analysis of the relative importance of different impacts of drought experienced in the
region shows that the community attaches special importance in descending order to the
following impacts:
1. The decrease of rainfall
2. The reduction of rangeland forage supply
3. The fall of sales prices of animals
4. The decrease of cereals and fruit trees production
5. The decrease of the water in wells.
Concerning the strategies developed by the local households to mitigate drought impacts are
manly: The reduction of heard size, deepen wells, more consideration to animal health and
income diversification.
As the participants to the workshop belong to different socioeconomic groups, it was
impossible to identify neither how the drought impacts affect each group nor how this one
reacts to attenuate drought effects. In fact, the community is composed of four main
socioeconomic types with a large livelihood asset differentiation. Indeed the analysis of the
socioeconomic differentiation results reveal that the type 4, which represents 53% of
Sekouma-Irzaine households, is the group with the least livelihood asset, consequently it is
the most vulnerable to drought effects. A deeper study is needed to evaluate this vulnerability
and to identify the appropriate development actions according to the specificity of each
socioeconomic category.
The methodology adopted presents a high efficiency at different levels, particularly, in saving
time of data gathering, stimulating discussion by the use of participatory tools (Historical
Livelihood Matrix), and allowing a rich exchange of experiences among participants.
All dough, the project team felt a kind of weakness of the methodology. As mentioned above
the heterogeneity of the workshop participants do not allow the identification of drought
impacts and strategies for each socioeconomic group. Thus, for similar studies, it is
recommended to organize workshop for each category of the community.
59
References
Bouayad A., Acherkouk M. and Maatougui, A. Animal Production Systems:
Characteristics and cost of production in the zone of action of Pastoral Development Project
and Livestock of the Oriental, Oujda, Morocco, 2001.
Bourbouze A. Pastoralists in the Maghreb: The Silent Revolution, fodder, 2000
Developing sustainable livelihoods of agropastoral communities of West Asia & North
Africa- Mashreq & Maghreb Project (Phase III), Minutes of the Workshop on risk
management of drought in pastoral arid areas of West Asia and North Africa, Damascus, 2007
Nori M., Report from the M&M III component: TIPOs for Risk Management and Drought
Mitigation - Morocco experience, 2008
Sihaddou M. Drought and hydro-agricultural policy in Morocco, Le Matin, 2005
60
Annex 1: Characteristics of the four farm types (number and percentage) Type1 Type2 Type3 Type4
Class of sheep Nb % Nb % Nb % Nb %
0 heads 12 44 1 2 18 19
> 0 & <= 50 heads 9 69 15 56 36 86 76 81
> 50 & <= 100 heads 4 31 4 10
> 150 heads 1 2
Total 13 100 27 100 42 100 94 100
Class of goats Nb % Nb % Nb % Nb %
0 heads 1 8 18 67 5 12 38 40
> 0 & <= 50 head 12 92 9 33 36 86 56 60
> 50 & <= 100 head 1 2
Total 13 100 27 100 42 100 94 100
Class of cows Nb % Nb % Nb % Nb %
No cow 10 77 21 78 34 81 91 97
at least one cow 3 23 6 22 8 19 3 3
Total 13 100 27 100 42 100 94 100
Class of small ruminants Nb % Nb % Nb % Nb %
0 heads 12 44 12 13
> 0 & <= 50 heads 3 23 15 56 30 71 82 87
> 50 & <= 100 heads 8 62 10 24
> 100 & <= 150 heads 2 15 1 2
> 150 heads 1 2
Total 13 100 27 100 42 100 94 100
Douar Name Nb % Nb % Nb % Nb %
Amer Ben Hamou 2 15 2 7 1 2 1 1
Ifkirine 1 8 2 5 7 7
Irkayine 2 15 5 19 6 14 4 4
Khatyine 2 15 2 7 7 17 7 7
Lamgader 4 15 1 2 3 3
None 1 8 1 4 7 17 11 12
Ahmed od 2 15 3 11 10 24 32 34
Od Kadi 2 5 1 1
Mustapha od 2 5 8 9
Zrak'ha 3 23 9 33 4 10 20 21
Matlili 1 4
Total 13 100 27 100 42 100 94 100
Age class of the household head Nb % Nb % Nb % Nb %
<= 30 1 8 1 4 2 5 7 8
> 30 & <= 60 5 39 13 48 24 57 58 62
> 60 7 54 13 48 16 38 28 30
Total 13 100 27 100 42 100 93 100
NB of Pickups Nb % Nb % Nb % Nb %
0 8 62 20 74 38 91 92 98
1 5 39 7 26 4 10 2 2
Total 13 100 27 100 42 100 94 100
NB of Trucks Nb % Nb % Nb % Nb %
0 12 92 25 93 42 100 94 100
1 1 8 2 7
Total 13 100 27 100
NB of Tractors Nb % Nb % Nb % Nb %
0 8 62 18 67 40 95 94 100
1 5 39 9 33 2 5
Total 13 100 27 100 42 100
NB of Wells Nb % Nb % Nb % Nb %
61
Type1 Type2 Type3 Type4
NB of Wells Nb % Nb % Nb % Nb %
0 2 7 15 36 58 62
1 9 69 15 56 23 55 33 35
2 3 23 9 33 4 10 3 3
3 1 8 1 4
Total 13 100 27 100 42 100 94 100
NB of motors Nb % Nb % Nb % Nb %
0 7 26 34 81 89 95
1 11 85 15 56 8 19 4 4
2 4 15 1 1
3 2 15 1 4
Total 13 100 27 100 42 100 94 100
NB of Charette Nb % Nb % Nb % Nb %
0 9 69 19 70 32 76 85 90
1 4 31 8 30 10 24 9 10
Total 13 100 27 100 42 100 94 100
Electricity Type Nb % Nb % Nb % Nb %
No 4 31 18 67 28 67 79 84
ONE5 9 69 1 4 1 2 2 2
Solar 8 30 12 29 12 13
Total 13 100 27 100 42 100 94 100
Nature of Construction Nb % Nb % Nb % Nb %
Hard 4 31 7 26 8 19 15 16
Mixed 5 39 11 41 14 33 24 26
Tent 1 2 5 5
earth 4 31 9 33 19 45 50 53
Total 13 100 27 100 42 100 94 100
5 ONE: National office of electricity
62
Annex 2: quantitative characteristics of the four farm types Min Max Som Moy E.T Min Max Som Moy E.T Min Max Som Moy E.T Min Max Som Moy E.T
Farm Type T1 T1 T1 T1 T1 T2 T2 T2 T2 T2 T3 T3 T3 T3 T3 T4 T4 T4 T4 T4
Nb.6 of households 13 27 42 93
Nb of small ruminants 4 110 885 68 29 0 50 402 15 17 8 220 1936 46 39 0 50 1478 16 12
Nb of Sheep 8 120 856 66 32 0 70 557 21 24 0 250 1982 47 40 0 140 1697 18 19
Nb of Sheep 4 100 588 45 22 0 40 342 13 14 0 180 1345 32 29 0 40 1044 11 9
Nb of goats 0 80 387 30 25 0 15 94 3 6 0 125 754 18 22 0 37 553 6 8
Nb of Goat 0 50 297 23 17 0 14 60 2 4 0 80 591 14 14 0 30 434 5 6
Nb of Cows 0 4 7 1 1 0 2 10 0 1 0 4 13 0 1 0 2 5 0 0
Nb of Berge employee 0 1 4 0 0 0 1 2 0 0 0 2 8 0 0 0 1 5 0 0
Nb of permanent manpower 0 1 1 0 0 0 1 7 0 0 0 0 0 0 0 0 0 0 0 0
Nb casual workers 0 40 152 12 15 0 240 585 22 51 0 60 463 11 17 0 40 407 4 8
Age of the Head of housholds 30 74 751 58 14 30 80 1608 60 15 27 76 2389 57 12 21 90 4769 51 16
Male 6 to18 years 0 6 22 2 2 0 3 23 1 1 0 5 44 1 1 0 4 84 1 1
Women 6 to18 years 0 3 12 1 1 0 3 30 1 1 0 5 49 1 1 0 5 89 1 1
Men 19 to 25 years 0 4 17 1 1 0 3 18 1 1 0 5 43 1 1 0 3 57 1 1
Women 19 to 25 years 0 2 12 1 1 0 4 14 1 1 0 3 36 1 1 0 4 53 1 1
Men 26 to 60 years 0 4 25 2 1 0 4 32 1 1 0 4 47 1 1 0 3 83 1 1
Women 26 to 60 years 0 5 23 2 1 0 2 28 1 1 0 5 53 1 1 0 6 104 1 1
Total Men 2 11 80 6 3 1 6 95 4 2 0 8 162 4 2 0 9 289 3 2
Total Women 1 10 62 5 2 0 10 93 3 2 1 9 166 4 2 0 8 306 3 2
Total population 3 18 142 11 4 1 15 188 7 3 1 17 328 8 3 0 15 595 6 3
Men Alphabitise 0 5 27 2 2 0 5 35 1 2 0 5 54 1 2 0 8 88 1 2
Women Alphabitise 0 3 18 1 1 0 3 19 1 1 0 4 32 1 1 0 11 86 1 2
Nb of Pickup 0 1 5 0 1 0 1 7 0 0 0 1 4 0 0 0 1 2 0 0
Nb of Rabbits 0 10 16 1 3 0 20 58 2 5 0 15 39 1 3 0 5 18 0 1
NB of Poultry 0 50 185 14 18 0 2010 2106 78 386 0 15 194 5 5 0 15 207 2 3
Irrigated Area (ha) 1 8 47 4 2 0 8 87 3 2 0 3 7 0 1 0 2 4 0 0
Rainfed Area (ha) 7 261 608 47 66 1 48 340 13 10 6 83 920 22 16 0 20 594 6 4
Total (ha) 15 264 652 50 66 2 54 430 16 11 6 83 927 22 16 0 20 598 6 4
Nb of olive trees 100 940 5040 388 247 70 1200 9249 343 291 0 120 706 17 31 0 100 827 9 21
Number almond 0 1500 3170 244 406 0 1500 4311 160 300 0 810 3125 74 168 0 500 3752 40 96
6 Nb. : Number
63
Annex 3: Factor Analysis Correlation Matrix
Classe de petits
ruminants
Nombre
oliviers
Superficie
irriguée (ha)
Superficie
bour (ha)
Correlation Classe de petits ruminants 1,000 ,012 ,036 ,324
Nombre oliviers ,012 1,000 ,733 ,386
Superficie irriguée (ha) ,036 ,733 1,000 ,201
Superficie bour (ha) ,324 ,386 ,201 1,000
Communalities
Initial Extraction
Classe de petits ruminants 1,000 ,772
Nombre oliviers 1,000 ,879
Superficie irriguée (ha) 1,000 ,819
Superficie bour (ha) 1,000 ,665
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 1,953 48,814 48,814 1,953 48,814 48,814 1,818 45,460 45,460
2 1,182 29,561 78,375 1,182 29,561 78,375 1,317 32,915 78,375
3 ,634 15,861 94,236
4 ,231 5,764 100,000
Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1 2
Classe de petits ruminants ,255 ,841
Nombre oliviers ,897 -,273
Superficie irriguée (ha) ,832 -,357
Superficie bour (ha) ,626 ,523
Extraction Method: Principal Component Analysis.
a. 2 components extracted.
Rotated Component Matrixa
Component
1 2
Classe de petits ruminants -,119 ,871
Nombre oliviers ,929 ,126
Superficie irriguée (ha) ,905 ,023
Superficie bour (ha) ,350 ,736
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
Component Transformation Matrix
Component 1 2
1 ,909 ,417
2 -,417 ,909
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
64
Annex 4: Drought Impact Matrix Impacts Future 2005-06 2003-04 1988-89 1945-52
Impacts 8 6 8 10 9
Reduction of rangeland forage supply 10 9 10 5 2
Decrease in the level of water in wells 3 2 2 8
Exodus 10
Famine 2 5 3
Increase of sheep abortion 8 7 8 7 10
Fall of sale prices of animals 6 8 6 4 10
Decrease in cereals and fruit trees production 5 8
Difficulty in repayment of agricultural credit 1 2 8
Increase of the intensity of transhumance 5 9
Increase of mortality of livestock 9 8
Increase of sales of livestock 9 9 10 8 10
Annex 5: Solutions of women community of Sekouma-Irzaine
1945 1980 95-97 2005 Futur Rang
solutions
Weaving carpet
with esparto to
market it
digging wells Plantation
of fruit
trees
Consumption of
spontaneous
plants
«Tabalahssent »
(Anthyllis
cytisoides)
Stocks of cereals
(Matmora)
1
Consumption of
spontaneous
plants
Practical of
market
gardening
Occasional works
in agriculture
Culture of
spontaneous
plants
(Tabelhssente)
2
Consumption of
small ruminants
Wool weaving to
be marketed
Increase in the
operations of
sinking
3
tranhsumance Marketing of the
olive oil
Sale of animals to
reduce the size of
the herd
4
Sale of lands for
purchase the cattle
foods
(beekeeping) Storage of money 5
Wool weaving to
be marketed
Farmyard (rabbit
and poultry)
Immigration of
men abroad
6
Consumption of
the flour mixed
with water
65
Chapter IV. National Strategy to mitigate drought
AHMED MOHAMED ABDELWAHAB
66
Chapter V. Technical, Institutional and politic options to mitigate drought. The study of risk management and drought mitigation in Sekouma-Irzaine indicates that
drought is become a greater threat of livelihoods. The impacts of this phenomenon can be
economic, environmental or social.
Hence, drought mitigation efforts will be increasingly important. in addition, the measures of
drought mitigation already implemented have not realized the expected results.
Thus, drought mitigation options suggested for this zone are as follow:
Technical Options.
Soil and Water Conservation Soil and water conservation can be approached through Rainwater Harvesting and agronomic
measures.
Rainwater Harvesting is a way to capture the rain water when it rains, store that water above
ground or charge the underground and use it later. Several methods to capture the rain water
are available.
Currently some Rain Water Harvesting techniques exist Sekouma-Irzaine including Dry stone
cords, Dry stone walls, impounded water (or Magen), Thresholds of Gabion, Soil banked-up
(see water harvesting report). These techniques, elaborated individually or with technicians,
must be evaluated and generalized in the all study area.
Conservation practices minimize the disruption of the soil's structure, composition and natural
biodiversity, thereby reducing erosion and soil degradation and surface runoff. The following
are established practices of soil conservation:
Crop rotation
Contoured rowcrops
Terracing
Tillage practices
Erosion-control structures
Windbreaks
Litter management
Options in agriculture The Irregular rainfall patterns enhanced environment vulnerability and reduced farming
sustainability. Indeed, climate changes are characterized by significant decreasing in rainfall
quantity; shortening of the growth cycles, increased within and among year’s rainfall
variability and high drought frequency.
These changes in the climate in addition to rangeland degradation (rangeland cultivation,..)
influenced negatively on farm feed production, that became more and more scarce and not
sufficient to respond to the population needs.
Populations needs became clear and urgent for alternatives that would alleviate drought
effects, enhance on farm feed production, reduce feed costs and that technically sound, easy to
adopt with low risk on farm management.
A technology that will respond to these needs are:
67
Alley-cropping. The alley cropping system is a type of intercropping where fodder shrubby species (Atriplex
numularia) are planted in rows in-between which one or more annual crops such barley
(ACSAD 60, Naima varieties); oats (Swalem varietie), triticale etc.
This technique is recommended for agriculture and rangeland areas because the water needs
of cereals are superior to annual precipitation. This system reduces the degradation of soils
and improves the cover.
Fodder shrubs Fodder shrubs have remarkable abilities of drought resistance. These skills make these plants
a material of choice for the enrichment of flora and reduce water and wind erosion. They
create microclimates that allow the restoration of indigenous species. Shrubs also have a
nutritional value adequate to maintain at acceptable levels even in advanced stages of
development.
Fodder shrubs can be food reserves used up during periods of extended drought.
The shrub species most used in the regeneration of rangeland, include: Atriplex nummularia,
Acacia cyanophylla and Medicago arborea. These species are adapted to a wide range of soils
and can survive in areas of less than 300 mm of annual precipitation.
Research conducted in the region have shown that the optimum density that ensures adequate
production of biomass shrub without affecting the strength of shrubs is 1000 shrubs / ha.
Arboriculture The practice of arboriculture is an important alternative to improve the population incomes.
Indeed, currently the olive-tree and almond tree plantation is very promising since the
national and international demand of olive and olive oil is in full expansion.
The plantation of these trees must be adapted to the climatic conditions of Sekouma-Irzaine.
The Picholine variety of olive-tree and its clones Haouzia and Menara appear as the most
adapted in Sekouma-Irzaine. These varieties have a high productivity (60 kg/tree/year), a
important content of oil (24%), an early entry of production (starting at third year) and a
resistant to diseases.
The Marcona variety of almond tree is the principal variety cultivated in Morocco. the clone
Marcona-AT, seems to have a high potentiality in Sekouma-Irzaine.
The spacing between trees for both species must be over than 7 meters.
Drought early warning system. Drought early warning and monitoring are crucial components of drought preparedness and
mitigation plans. Recent advances in operational space technology and climatology have
improved our ability to address many issues of early drought warning and efficient
monitoring. With help from environmental satellites, climatic data and socio-economic
indicators, drought can be detected three months earlier. Its impact on agriculture can be
diagnosed far in advance for establish measures of drought mitigation.
Our study in Sekouma-Irzaine showed that elaboration of this system type can be possible.
The development of the climatic indicators and remote sensing as well as the forecast of the
drought showed promising results. To be operational, this system needs several socio-
economic and vulnerability indicator which must be identified.
68
Institutional options. In Morocco, the measures against the drought effects are based on management of crisis.
When drought is declared at national level, a national program is established and the means
necessary are allocated. However, its means are distributed to local and national institutions
without any coordination between them. Thus in drought year several institutions such as
ministry for agriculture, ministry of interior, direction of National Forestry and the offices of
agricultural development…, profit from its programs.
At the local level, in absence of coordination between the services and of criteria of merit, the
distribution is done in an equitable and arbitrary manner. Thus, zones very touched by
drought perceive the same subsidies as zones less touched. In addition, the subsidies are
distributed individually without utilizing associations and co-operatives which, theoretically,
represent the population.
Therefore, it is very important to define firstly the main interlocutor of drought mitigation
which must be the coordinator at national level.
At the regional level, a multidisciplinary team, grouping all local institutions, must be created.
It's should be responsible of drought management during the crisis. also, this team should
conduct long-term actions to reduce vulnerability to drought .
The local organizations are the responsible for the implementation of a local sustainable
development. These organizations have a high experience in local Knowledge to execute and
monitor the programs of development .
The government must support these organizations by integrating them into policy decisions
and their achievement.
Policy options
AHMED MOHAMED ABDELWAHAB
Related Documents
