Using small-scale on-farm weather monitoring equipment as a tool for understanding
farmer rationales and risk management in response to climatic risk.
Kaori Sasaki”, John S. Caldwell*, Abou Berthé+, Mamadou Doumbia+, Hiromitsu
Kanno”, Kiyoshi Ozawa*, Abdouramane Yorote+, Takeshi Sakurai*
”National Agricultural Research Center for the Tohoku Region, Morioka, Iwate, Japan
*Japan International Research Center for Agricultural Sciences (JIRCAS), Tsukuba,
Ibaragi, Japan
+Institut d’Economie Rurale (IER), Sotuba, Bamako, Mali
ABSTRACT
Farmer decision making and management are highly influenced by climate, especially
uncertain rainfall, in rainfed agriculture in areas such as Mali, West Africa. We have
used small-scale, automated weather monitoring equipment on-farm as a tool for
understanding farmer rationales in response to climatic risk. Precipitation and air and
soil temperature recording stations equipped with a data logger (Onset, HOBO) that can
record over a long time period without maintenance were placed on 15 collaborating
farmers’ fields in each of two villages chosen in a reconnaissance. Using farmer-defined
land units (terroirs), farmers indicated areas on maps with early rainfall, and this
empirical knowledge was supported by the distribution of early rainfall measured by the
stations. We will show the equipment and results of analysis of temporal and spatial
rainfall variability compared with farmer management monitored weekly, particularly
dry seeding.
INTRODUCTION
The climate of Mali, West Africa, is not uniform, but varies depending primarily on
latitude. In the north is the Sahara desert, while the south is a transition region from the
Sahel to the tropical rain forest. Even in the south, the total amount of rainfall varies
from 200 mm at the limit the arid zone, through 500-900 mm annual rainfall in the
semi-arid zone, and 900-1100 mm in sub-humid zone, reaching up to 1400 mm at the
southernmost limit. Overall, West Africa is extremely vulnerable to changes in water
availability, as precipitation shows high interannual, seasonal and spatial variability.
As a result, farm management and decision making are greatly influenced by climate.
The risk of uncertain rainfall has increased since the 1980s. Precipitation isohyets
have moved southward by 0.5-1.0° latitude, or approximately 100-200 km (Ministère
IHE, 1990). Locations that formerly had 1000 mm and fell in the sub-humid zone
(900-1100 mm annual rainfall, Berthé et al., 1991) now have 800 mm of rainfall and fall
into the semi-arid zone (500-900 mm annual rainfall).
In the February 2001 reconnaissance of nine villages, farmers were highly aware of
spatial variability. Farmers had indicators which they found empirically useful to
anticipate the coming rainy season, while they also made use of meteorological
forecasts over the radio. However, current meteorological forecasts are made at the
regional level, which can span a range of 700 mm to 1200 mm annual rainfall.
Farmers said that they could not use radio forecasts for their specific conditions, and
they needed fine-tuned forecasting at the district or sub-district level.
On the other hand, meteorological observatories are limited in number and location.
Previous studies of rainfall variability in West Africa have used rainfall data from the
regional level of about 100 km2, which includes a large amount spatial variability.
Mechanisms of local rainfall variability have not been identified completely. In addition,
the degree of spatial variability occurring at the field revel is not known. To address
these issues, we have undertaken assessment of interannual, seasonal and spatial
variability at the farmer’s level. This will be also useful for downscaling of strategic
rainfall prediction.
In recent years, meteorological observation technology has developed considerably.
New, inexpensive weather monitoring equipment now makes it possible to obtain
field-level weather data to compare with farmer practices.
Our ultimate goal is the development of a decision aid system, based on analysis of
risks associated with climatic variability in alternative farmer production choices.
In this paper, we report on:
 The on-farm weather monitoring equipment used

Results of analysis of weather data from these stations, especially temporal and
spatial variability of precipitation, and comparisons with empirical knowledge of
farmers.
 Some results of comparisons of farmer behavior with rainfall events.
We will show these results in several examples that are derived from collaboration
between farmers and researchers.
MATERIALS AND METHODS
A reconnaissance survey of nine villages was conducted in February 2001. In the
southeastern area of Mali, preliminary selection of villages was done using topography
and the location of larger lowlands that are called bas-fonds. After surveying nine
villages retained, we chose two villages as research sites based on team ranking of 10
criteria (Caldwell et al., 2002a, 2002b). One is Niessoumana (10 x 14 km area) located
at the frontier of the semi-arid zone with 800 mm of average annual rainfall, and other is
Diou (23 x 19 km) located in sub-humid area with 1200 mm average annual rainfall (Fig.
1). The topography is essentially flat in Niessoumana (although microvariation in
topography was documented in our research), and more undulating in Diou.
Fig. 1 Location of the two research
sites
Annual rainfall is averaged from 1969 to
1992.
Semi-arid Village
Niessoumana
(mm)
Semi-humid Village
Diou
One larger main meteorological observation station was placed in the center of each
village. These stations record precipitation, air temperature, relative humidity, wind
speed, wind direction, solar radiation and air pressure. In addition, 15 farmer
collaborators were selected based on risk robustness and land use criteria (terroirs, as
defined by farmers using a farmer-drawn land use map in May 2001) for the weather
monitoring equipment, which we call ‘HOBO station’ (Fig. 2). These measure
precipitation, air and soil temperature. Each HOBO station is equipped with a compact,
inexpensive and high performance data logger (Onset, HOBO). It is thus able to record
over a long time period without maintenance. A total of 29 HOBO stations were set, 14
in Niessoumana and 15 in Diou, respectively. The locations of the HOBO stations in
Niessoumana are indicated in Figure 3. All of the HOBO station equipment was set on a
wooden stand. Installation conditions are shown in Figure 2. HOBO stations were set in
the different types of crop fields, including cotton, rice, millet, maize, and groundnut, of
farmer collaborators. The collaborators were chosen based on farmer-defined risk
indicators and land use through a participatory process (Caldwell et al., 2002b).
Meteorological observation began in late May 2001 and is still continuing. We report
here on the results of the 2001 rainy season 2001 from 26 May to 19 September in the
semi-arid village, Niessoumana, in this paper.
In each village, we presented our activities and results, providing feedback back to
farmers and checking with their perceptions and knowledge through farmer’s meetings.
On the fourth visit in July 2002, we showed a rainfall map based on HOBO observations
and compared it with farmer’s understanding and recognition of rainfall variability. We
also conducted transect surveys with farmers, recording local topographical variability
as well as microchanges in flora and the location of plants that served as indicators of
the rainy season. In addition, we obtained aerial photograph images of the topography
and compared these with transect and field observations of topography.
Air temp. and
Rain gauge
Logger(HOBO)
in a Shelter
Logger
Soil temp.
(HOBO)
Fig. 2 HOBO station for
recording
precipitation and
temperature
(installed May 2001)
RESULTS AND DISCUSSION
Performance of weather monitoring equipment
Routine maintenance and data collection from HOBO stations is conveniently done
using a notebook personal computer through the serial port. Functioning of the data
logger is easily checked based on a flashing light. Detailed precipitation data were
collected by the HOBO for each event with high resolution. Air and soil temperature
data were collected every an hour. A HOBO is able to store 8000 data, which is
sufficient to record an entire rainy season.
However, during the year, many kinds of unexpected problems occurred. Some of the
soil temperature cables were eaten by cattle, and wooden stands were attacked by
termites during the dry season. At certain times, the HOBO logger stopped unexpectedly.
We attributed the cause to conveyed lightning. All of the problems were resolved, and
from these experiences, field assistants (one per village) learned what to check for to
assure functioning of the equipment. These problems caused us to understand the
difficulty of obtaining a complete set meteorological observations, and what is needed
to assure this. It is very important to consider the environment in which stations are
placed in advance and establish a program for frequent maintenance.
Spatial variability of rainfall: focus on the onset of rainy season
The onset of the rainy season is critical for crop seeding and germination. Weekly
precipitation in the first month of the rainy season was mapped from HOBO station data
in the semi-arid village, Niessoumana, and is shown in Figure 3. The northeast, east and
southeast extremes had highest rainfall, more than 100 mm in this period. Rainfall then
decreased gradually as one moved westward. However, there were no observation
sites at the far western part of the village, and this trend could not be confirmed all the
way to the western limit of the village... The maximum spatial difference is about 30
mm in this village, within an area of 10 x 14 km. This value indicates that during the
first stage of the rainy season, rainfall varied by 30% between fields only few km away
from each other. At the end of rainy season, the total rainfall of Niessoumana averaged
about 1000 mm, a value 100 mm greater than average rainfall at the N’Tarla agronomic
research station which is close to Niessoumana. The spatial variability of total rainfall
had same trend as shown in Figure 3 for the first month of the rainy season. This leads
us to conclude that rainfall spatial variability is a characteristic of the rainy season as a
whole. We are currently assessing topographical variation revealed from the transect
survey and aerial photograph imagery as a factor affecting spatial variability.
Comparison between rainfall map generated from HOBO observations and farmer
understanding of rainfall variability
We held a meeting with farmers in Niessoumana in July 2002 to compare farmer
knowledge and the results of HOBO station observations. We printed key maps and
graphs and shared these as graphical results with the farmers. At first, however, in the
meeting we did not show these, but instead asked the farmers in which areas they
observed rainfall to start and in which areas rainfall was highest. Farmers answered that
rainfall starts from the east side, followed by the northeast and southeast areas, and that
these areas also have highest rainfall, based on their observations. Then we showed our
rainfall map, and the farmers agreed that it matched their observations. This shows that
farmer empirical knowledge of rainfall is quite accurate. If this can be confirmed in
other villages in the future, then farmer knowledge may be used to help assess and
improve agronomic practices on a spatial basis.
Fig. 3 Spatial Variability of rainfall, June 22-28 in semi-arid village, Niessoumana.
■ : main meteorological station, ● : HOBO station. Isohyets show total rainfall during June
22-28, 2001. Hatching indicates areas with greater than 100mm rainfall.
Farmer behavior at the onset of rainfall
Farmers often divided plantings of the same crop between two fields. One field was
seeded earlier, prior to the first rains, and the other after the first rains. This is a farmer
strategy for reducing risk. Early seeding is called semis-á-sec, or “dry seeding”. If it
succeeds, the crop is in the field for a longer period of time and can benefit from all the
rainfall of that year. However, considerable variability was seen in the success of
semis-á-sec, depending on the number of days prior to the first rain. When semis-á-sec
failed, due to late rains or poor distribution of rainfall in the young seedling stage,
farmers must reseed. Reseeding increases seed and labor requirements and may result in
lower yield if maturity comes after rains end. There was high variability in seeding
dates for each crop. For some crops, the standard deviation in initial seeding was greater
10 days. The pattern of seeding matched the pattern of rainfall spatial variability (Fig. 3).
This again validates the value of farmer knowledge.
35
P recipitation
Tem perature
30
20-Sep
13-Sep
6-Sep
30-Aug
23-Aug
0
16-Aug
0
9-Aug
5
2-Aug
20
26-Jul
10
19-Jul
40
12-Jul
15
5-Jul
60
28-Jun
20
21-Jun
80
14-Jun
25
7-Jun
100
o
120
Mean temperature(C)
N iessoum ana
31-May
Weekly total precipitation (mm)
140
D ate
Fig. 4 Weekly rainfall and temperature, May-September, 2001, in semi-arid
village, Niessoumana.
Weekly precipitation and temperature are calculated from main station data. The dashed line
shows the division of the rainy season into early and middle stages apparent in rainfall event
frequencies. On June 22-28, data was lost with rain gauge trouble, and interpolated the average
of nearest 3 HOBO stations.
100
N iessoum ana
Precipitation (mm)
90
80
70
60
50
40
30
20
10
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Localtim e
Fig. 5 Hourly total rainfall averaged over the period May 26-Sept. 19 in Niessoumana.
Values are averages of 13 HOBO stations.
Intraseasonal temporal variability of rainfall
At the beginning of rainy season, rainfall events are not constant, and the amount of
rainfall per week is unstable (Fig. 4). There were breaks in rainy season, with no rainfall
recorded. The earlier breaks increase the risk of seedling failure. Conversely, one
rainfall event exceeded 100 mm in one night (July 5).
As the rainy season progresses, air temperature slowly decreases. After the middle of
July, rainfall events happen more frequently and amount of rainfall of each event is less
more constant while temperature decrease stops and temperature stays at 26oC.
Figure 5 shows hourly rainfall variability in Niessoumana. We can see a clear diurnal
variation, in which the maximum rainfall is observed late in the night and / or early in
the morning, while on the other hand, minimum rainfall tends to appear in midday.
Also, daytime spatial variability is higher (data not shown). These characteristics of
diurnal variation are in good agreement with the nature of convective activity over the
land, caused by the movement of the Inter Tropical Convergence Zone (ITCZ).
CONCLUSIONS AND FUTURE DIRECTIONS
The extent of local spatial Fig.6
variability of rainfall was determined from on-farm
meteorological observations. Hour
Also, farmer behavior, particularly for seeding,
corresponded with rainfall events
ly and indicated a strategy of risk dispersion. Similar
analysis of the spatial variability
of rainfall and seeding dates is being undertaken now
total
for the semi-humid village.
rainf
The mechanisms of spatial and
all temporal rainfall variability have not been established,
but observation of localizedavera
topography through transect surveys with farmers,
together with aerial photographs,
gedin comparison with rainfall observation conducted in
this study, may provide a keyforto understanding this variability. We plan to assess
topographical relationships among
May the sample fields, to determine if topographical
location is related to the spatial 26-se
variability in arrival of the first rains.
The key question for monitoring
is to determine how rainfall variability interacts with
p. 19
farmer choices of planting dates,
in varieties, and weeding practices. We are gradually
developing an understanding ofNiess
farmer responses and their decision making process in
response to climatic variability.ouma
In the semi-arid village, the 2001 season represented a
wet year, and indicated farmer responses
to wetter-than-normal conditions. Conversely,
na.
the semi-humid village had drier-than-normal conditions in 2001. As we observe
farmer choices and rationales under
different sets of rainfall conditions over the four
The
years anticipated in this research,
we hope to be able to provide in a future report a
values
complete picture of farmer strategies
in response to micro-level rainfall spatial and
are
temporal variation.
averag
e of 13
HOBO
stations
.
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.
Caldwell, J.S., H. Kanno, A. Berthe, A. Yorote, K. Sasaki, M. Doumbia, K. Ozawa, T.
Sakurai (2002a) Climatic Variability in Cereal based Cropping Systems in Mali, West
Africa. Farming Japan 36-4, 35-41
Caldwell, J.S, A. Berthé, M. Doumbia, H, Kanno, K. Ozawa, A. Yorote, K.Sasaki, T.
Sakurai (2002b). Incorporation of Farmer-Based Climate and Risk Indicators into
Research Design and Farmer Typologies in Southern Mali. Journal for Farming
Systems Research-Extension 17th International Symposium issue (in press).
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.
Corresponding author information:
Kaori SASAKI
National Agricultural Research Center for Tohoku Region
4, Akahira, Simokuriyagawa Morioka, Iwate, 020-0198, Japan
Tel. 81-19-643-3461
Fax. 81-19-643-3573
E-mail kaoris@affrc.go.jp
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