Incorporation of Farmer-Based Climate and Risk Indicators into Research Design
and Farmer Typologies in Southern Mali for Decision Support
John S. Caldwell*, Abou Berthé+, Mamadou Doumbia+, Hiromitsu Kanno”, Kiyoshi
Ozawa*, Abdouramane Yorote+, Kaori Sasaki”, Takeshi Sakurai*
* Japan International Research Center for Agricultural Sciences (JIRCAS),
Tsukuba, Ibaragi, Japan
+ Institut d’Economie Rurale (IER), Sotuba, Bamako, Mali
“ National Agricultural Research Center for the Tohoku Region, Morioka, Iwate, Japan
ABSTRACT
To target research aimed at development of a decision aid system for climatic risk
management, a reconnaissance was conducted of nine villages, followed by focus group
assessment and household surveys in two villages. One village each in the semi-arid 800
mm rainfall (Niessoumana) and the semi-humid 1200 mm rainfall (Diou) zones was
selected by team ranking of 10 criteria. The extension typology based on animal traction
was used for focus groups:
fully-equipped (A), sub-equipped (B), and
non-equipped/manual (C/D). A second typology was generated from key informant
(farmer group leaders) classification of farmers as more risk robust (A1, B1) or more risk
vulnerable (A2, B2). A third typology was based on farm household’s self-assessment of
risk robustness indicators from the focus groups: food self-sufficiency, cattle herd size,
and exterior remittances. The majority of farmers were sub-equipped (Diou, 75%;
Niessoumana, 67%). Key informants classified more sub-equipped farmers as less risk
robust (B2) (Diou, 70%; Niessoumana, 68%). Self-assessment revealed greater risk
robustness in the semi-humid than the semi-arid zone. Only 4% of A farmers and no B
farmers had food deficits in Diou, while 37% of A and 63% of B farmers had food deficits
of 2-3 months in Niessoumana.
2
INTRODUCTION
Uncertain rainfall is a key risk for farmers in Mali. In a 1988 survey of 374 farm
households in eight villages in the Opération Haute Vallée du Niger (OHVN, Upper River
Valley of the Niger) region of central Mali, poor distribution of rainfall was identified as
one of four key constraints. In a 1990 survey of 263 farm households, for sorghum and
millet, 73% and 52%, respectively, of farmers fell into a cluster in which the two principal
factors were water and weed problems (Yeboah et al., 1991).
Precipitation isohyets have moved southward by 0.5-1.0° latitude (Ministère IHE, 1990)
since the 1980s. Locations that formerly had 1000 mm and fell in the sub-humid zone
(900-1100 mm annual rainfall, Berthé et al., 1990) have 800 mm of rainfall and now fall
into the semi-arid zone (500-900 mm). Rainfed rice can be produced in small inland
valleys (bas-fonds) in sub-humid areas, but sorghum and millet-centered systems are
predominant in semi-arid areas. The proportion of semi-arid area in the two main
agricultural regions of Mali, the OHVN and the southeastern cotton producing CMDT
(Compagnie Malienne du Développement du Coton, Malian Cotton Development
Company) regions, has increased from 10% to over 30% as result.
These changes mean that farmers face greater risk in production of upland crops and
lowland rainfed rice. It is difficult for farmers to justify intensification of inputs and
management. However, little is known about farmer risk indicators that could contribute
to development of a decision support system for improved risk management.
In Mali, farmers have been classified based on possession and use of animal traction. The
CMDT classifies farmers into 4 groups (Berthe et al., 1991):
A: fully-equipped: 2 pairs of draft cattle, plow, cultivator, seeder, and cart
B: sub-equipped: 1 pair of draft cattle, either plow or cultivator
C: non-equipped: lack draft cattle, incomplete equipment, some experience
D: manual: use only manual methods, lack animal traction experience
New techniques for understanding farming systems have been developed in the past 10
years. Participatory techniques can reveal farmer perceptions of weather variability and
indicators of risk robustness. New, inexpensive weather monitoring equipment makes it
3
possible to obtain field-level weather data to compare with farmer practices.
To develop a decision aid system for farmer responses to climatic variability, the Japan
International Research Center for Agricultural Sciences (JIRCAS) and the Institut
d’Economie Rurale (IER), Mali, initiated a collaborative research program in 2001. This
began with a reconnaissance of villages between 1200 mm and 680 mm annual rainfall,
followed by focus group participatory assessment and household surveys in two villages.
The objectives of this initial work were to involve farmers in the identification of
indicators of climatic variability and risk robustness and develop typologies of farmers
based on resource levels and risk criteria, as the basis for monitoring of crop production
practices in response to spatial and temporal climatic variability. Results of the
monitoring will be used to develop risk management scenarios in response to climatic
variability for different farmer types in the decision aid system.
MATERIALS AND METHODS
Village selection
The reconnaissance was conducted in the OHVN and CMDT zones in February 2001 by a
team of two climatologists, two soil scientists, one agronomist, one range ecologist, and
one economist. Preliminary selection of villages was made from topographical maps
showing the location of larger bas-fonds, and other secondary information. Final
selection of nine villages to visit was made in consultation with local extension offices.
In each village, a meeting was held in which farmers drew a land use map and explained
local soil taxonomic categories. Farmers described indicators that they had found
empirically useful to anticipate the coming rainy season and use of meteorological
forecasts provided by radio. Following the meeting, the research team and farmers visited
the principal bas-fonds identified on the map.
Villages were ranked by the team for 10 criteria: type of bas-fonds, rice and vegetable
cultivation, upland cultivation with cotton, farmer climatic indicators, isohyet location,
market access, experience with extension, willingness to collaborate, accessibility, and
social complexity.
4
Identification of risk factors, robustness indicators, and typology development
Risk factors and robustness indicators were identified in each village in May 2001. The
survey sequence was: day 1, meeting with village leaders to explain objectives and plan
work; day 2, general village meeting; day 3, focus groups and household survey; day 4,
key informant ranking and household survey tabulation; day 5, general village meeting
for collaborator selection; day 6, transect survey and complete installation of monitoring
stations.
Focus groups were organized based on the CMDT typology. In each village, farmer
group leaders had lists indicating each farmer’s CMDT type. In Diou, due to small
numbers of C and D farmers, focus groups were formed only for types A and B. In
Niessoumana, types C and D were combined, and information gathered separately for
types C/D, B, and A. Focus groups had 7-15 farmers, who participated voluntarily, and
each session lasted 2-3 hours. Focus group sessions used oral and visual techniques. Risk
was explained as difficulties (gelea) whose chance of occurrence is not known in advance.
Farmers give indicators of risk robustness, the household’s ability to withstand risk
events.
Risk factors and robustness indicators were used in two ways. First, key informants
(leaders of local farmer groups) classified A and B farmers as more risk robust (A1, B1) ,
nye fe (“in front,” in terms of the indicators), or more risk vulnerable (A2, B2), ko fe
(“behind”), based on overall knowledge of the farm households. The key informants
were asked to make their assessment based on the key indicators from the focus groups.
This classification generated the second typology. Farmer collaborators were chosen in
the second village meeting the following day based on this typology and farmer-defined
land use types.
Second, a census was carried out by farmer group leaders to obtain each farm household’s
self-assessment of four risk robustness indicators from the focus groups. Numbers of
census farmers were 118 in Diou and 143 in Niessoumana. The self-assessment was used
to develop a third typology consisting of 23 combinations of risk-robust or risk-vulnerable
levels of three factors and shown as subtype labels after the CMDT type label A or B:
food self-sufficiency as number of months surplus (As, >12 months), adequate (no label,
5
12 months), or deficit (d, <12 months): cattle herd size 10 or more (T) or less (no label);
remittances received (E) or not (no label). Farmers were also reclassified between A and
B based on the number of pairs of draft animal numbers reported.
RESULTS AND DISCUSSION
Village selection
Table 1 shows the results of village scoring. Niessoumana (N, semi-arid, 800 mm
rainfall) and Diou (Di, semi-humid, 1200 mm) were chosen for monitoring.
Table 1. Team ranking of villages for agro-climatological monitoring
Criteria
OHVN
CMDT Est / Nord CMDT Sud- CMDT SudLoulouni
Kadiola
Fs
San
Fk
N
Da
Ba
Ka
Di
DM
1.Bas-fonds
++
+
++
++
+
+
++
++
－
2.rice+
++
+
++
++
++
++
++
++
－
vegetables
3.upland +
+
+
+
+
+
+
+
+
+
cotton
4. climatic
+
+
+
+++
+
++
+
+++
+++
indicators
5.isohyets
1000 1000 800
800
800 1200 1200 1200 1200
6.market
access
7.extension
experience
8. willingness
to collaborate
9.accessibility
10.social
complexity
TOTAL
++
++
+
++
++
++
+
++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
－
+
+
+
+
－
6–2
=4－
11-0
=11－
8–0
=8+
13-0
=13+
10-2 11-0
=8+ =11+
－
9-0
=9+
13-0
=13+
13-1
=12+
Results highlighted the importance of climatic variability. In the 800 mm rainfall zone,
variation ranged from complete absence of rice production (village excluded from
reconnaissance), to good production in four out of five years. Even in the 1200 mm zone,
rice production was variable, with successful production in only about half the years in
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two of the four southern villages.
Farmers used both secondary (non-climatic) and primary (climatic) indicators in the dry
season to anticipate the coming rainy season. In seven villages, farmers used secondary
indicators based on flowering or fruiting of specific trees or plants. Four villages used
apparition of specific insects or birds. In two villages in the south, farmers observe a
change in wind direction in February, and in one village, farmers found this indicator to
be a good predictor of relative yield over 20 years.
Risk factors, robustness indicators, and typologies
In both villages, rainfall was ranked first or second among risk factors. For risk
robustness, Diou fully-equipped farmers gave food stocks, herd size, draft animal number,
remittances from outside the village, and ownership of a small store Niessoumana
fully-equipped farmers indicated food stocks, herd size, draft animal number, and
non-agricultural activities. Niessoumana sub-equipped and manual farmers ranked food
stock and herd size highest. Food stock, herd size, outside remittances, and draft animal
number were retained as robustness indicators.
The CMDT typology placed most farmers in type B (75% in Diou, 67% in Niessoumana).
Niessoumana had over twice as many farmers in types C and D (19%) as Diou (8%). Key
informants classifed a larger proportion of sub-equipped farmers (B) as less risk robust
(B2) (70% in Diou, 68% in Niessoumana) (Fig.1). Key informant classification was
combined with soil type and toposequence position to select farmers for climate and crop
production monitoring.
Classification based on household self-assessment showed greater risk robustness in the
semi-humid than the semi-arid zone, and indicted more risk robustness than the key
informant assessment (Fig. 1). The proportion of sub-equipped farmers (B types)
decreased to 51%, while fully-equipped farmers (A types) increased to 41%. The
percentage of A types with food surpluses (A-AsTE, A-As) increased to 40%. No B
farmers had food deficits. In Niessoumana, the proportion in B types decreased to 55%,
but most reclassified farmers had food deficits (B-dT, B-d).
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S elfassessm ent
D
C
B -d
B -dT
A -T E
A -A s
B -A s
A -d
K ey
inform ants
D
CM DT
D
C
B2
B1
A1
A2
Niessoumna
C
0
20
A -A sT E
A -dT
B
40
60
A
80
100
120
140
160
no. of farm ers
D
DA
1
As
T
fully equipped
risk robust
surplus food stock
cattle herd size >= 10
S elf-
D C
D assessm ent
B
B sub-equipped
2 risk vulnerable
d deficit food stock
E exterior rem ittances
BE
A -E A -T E
B -A s
K ey
inform ants
D C
C non-equipped
A -d
B2
D m anual
A -A sT E
A -A s
B1
A2
A1
Diou
D C
CM DT
0
B
20
40
A
60
80
100
120
140
160
Fig.1. Typologies generated by three methods (CMDT extension, farmer group
leader key informants, farm household self-assessment of risk robustness
indictors) in two villages, Mali, 2001
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Compared to Diou, farmers in Niessoumana had smaller herds, more food deficits, and
lacked remittances (Fig. 2). Herd size and outside remittances generally paralleled food
self-sufficiency, but three subtypes presented contrasting scenarios. A-dT farmers (more
prevalent in semi-arid Niessoumana) have large herds (approximately 20 animals) but
food stocks are in deficit. Animals are a risk buffer for this subtype. B-As farmers had
approximately 3 months food surplus despite small herd size and little or no remittances.
Their strategy is cereal self-sufficiency. B-E farmers depend on outside remittances to
compensate for lack of food stock surplus.
9
D iou
40
N iessoum ana
6
40
5
5
30
4
30
4
3
3
20
20
2
10
2
10
1
0
0
-1
-2
-3
1
0
AAs
TE
ATE
A- BE
As
BE
B
C /D
-10
-1
0
AA sT
AAs
AdT
Ad
BAs
Bd
C
no. of cattle
Months of grain, persons
6
D
-10
-2
-3
-20
-20
Fig. 2. Risk robustness based on farmer self-assessment in two villages, Mali 2001.
A, fully-equipped; B, sub-equipped: C, non-equipped; D, manual;
As, food surplus; d, food deficit; T, herd size > 10; E, remittances.
upper line, cattle herd number (right axis); lower line, draft animal number (right axis)
left bars, months of food stocks greater or less than 12 months (left axis),
right bars, number of persons sending outside remittances (left axis)
CONCLUSIONS AND FUTURE DIRECTIONS
The original CMDT typology was based on possession of animal traction resources.
However, in Burkina Faso, livelihood diversification was equally as important in
adaptation and reduced vulnerability of farm households (Roncali et al., 2001). The focus
groups added herd size, a second resource measure; exterior support, a livelihood
diversification indicator; and food stocks, a livelihood status indicator.
The method of using key informants to generate the second typology had advantages of
rapidity of integration of risk indicators and good farmer ownership. Results were
generated in one afternoon with a number of villagers observing and used the following
day in a village meeting for collaborator selection. However, key informant integration of
10
the three resource and livelihood factors identified in the focus groups to classify farmers
as nye fe or ko fe was not quantifed. Figure 1 shows good corrrespondence between key
informants and self-assessment in Niessoumana, but less correspondence in Diou. This
suggests that there may be variability in key informants’ assessment.
The method of the third typology complements the key informant typology by providing
quantified separation of farmer-derived risk robustness indicators. As each farmer
provides his or her own self-assessment, there is wider ownership of the results. However,
ownership of analysis was reduced, as results were generated in the evening by the senior
author. We did not have a color printer to make copies or a battery system, projector, and
large screen to share graphical results with farmers in the collaborator selection meeting.
In a later meeting, we did print and use copies of key graphs. In the future, software
might be developed for real-time analysis with farmers.
In both villages, the separation of indicators indicated considerable variation in risk
robustness of A and B farmers. The key question for monitoring is to determine how
farmer choices of planting dates, varieties, and weeding practices change as food stocks,
herd size, and remittances decrease, and farmers lack these buffers to cover crop losses
due to climatic variability. Alternative risk management scenarios in the decision aid
system will be based on the presence or absence of these risk robustness indicators.
REFERENCES
Berthe A.L., Blockland A., Boure S., Diallo B., Diallo M.M., Geerling C., Mariko F.,
N’Djim H and Sanogo B. (1991) Profil d’environnement Mali-Sud. Institut d’Economie
Rurale, Bamako, Mali, and Institut Royal des Tropiques, Amsterdam.
Ministere de l’Industrie, de l’Hydraulique et de l’Energie et Programme des Nations
Unies pour le Devloppement (1990) Synthese hydrogeologique du Mali. Departement de
la Cooperation Technique pour le Developpement (DCTD), Bamako, Mali.
Roncali, C., Ingram, K., and Kirshen, P. (2001) The costs and risks of coping with
drought: livelihood impacts and farmers’ responses in Burkina Faso. Climate Research:
CR 19:119-132. http://www.int-res.com/abstracts/cr/v19/n2/pl119-132.html.
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Yeboah A.K., Caldwell J.S., Fofana M., Maiga T. (1990) Use of multiple classification
criteria for identification of recommendation domains and research domains through
cluster analysis in Central Mali. Institut d’Economie Rurale, Bamako, Mali.
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Corresponding author information:
John. S. Caldwell
Japan International Center for Agricultural Sciences (JIRCAS)
1-1 Ohwashi
Tsukuba, Ibaragi 305-8686
Japan
tel. 81-298-38-6384
fax 81-298-38-6342
caldwelj@jircas.affrc.go.jp
oral presentation for Theme 3, Farming Systems Knowledge and Information Systems