Vol.4, No.8A, 9-15 (2013) Agricultural Sciences
http://dx.doi.org/10.4236/as.2013.48A002
A framework for the use of decision-support tools at
various spatial scales for the management of
irrigated agriculture in West-Africa
Joost Wellens1,2*, Farid Traoré3, Mamadou Diallo4, Bernard Tychon2
1Association pour la Promotion de l’Education et de la Formation à l’Etranger (APEFE), Brussels, Belgium;
*Corresponding Author: [e-mail]
2Département Sciences et Gestion de l’Environnement, Université de Liège, Arlon, Belgium
3Institut de l’Environnement et de Recherche Agricole (INERA), Ouagadougou, Burkina Faso
4Observatoire de l’Eau, Bobo-Dioulasso, Burkina Faso
Received 26 May 2013; revised 27 June 2013; accepted 16 July 2013
Copyright © 2013 Joost Wellens et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
The Kou watershed, situated in the Southwest-
ern part of Burkina Faso, has succumbed since
a couple of decades in a typical theater play of
anarchistic water management. With its 1800
km², this small watershed holds the second larg-
est city of Burkina Faso (Bobo-Dioulasso), a for-
mer State ran irrigated rice scheme and several
informal agricultural zones. Despite the abun-
dance on water resourc es, most water users find
themselves regularly facing to water shortages
due to an increase in population and low irriga-
tion efficiencies. Local stakeholders are hence
in need of easy-to-use and low-cost decision
support tool s for the mo nitoring and exploi t ation
of the water resources at different spatial and
user levels. A top-to-bottom string of adapted
water management tools has been successfully
installed to tackle the problems: from w atershed
(top) to field level (bottom), passing by the 1200
ha irrigation scheme. Land use maps have been
derived from time-series of free satellite images.
Combined with data from a network of hydro-
logic gauging stations, regional water use maps
were established. SIMIS was put in place for the
public-private management of the regions irri-
gated rice scheme. Day to day water use on ir-
rigated plots was followed by soil humidity and
crop canopy measurements. A simple field-crop-
water balance model Aqua Crop was used by
extension workers to draft optimal irrigation
charts. Each tool was applied independently, re-
quiring only limited data; but their combined
results contributed to an improved integrated
water management.
Keyw ords: Irrigation; Remote Sensing; Multi-Level;
Decision-Support Tool
1. INTRODUCTION
By 2030 irrigated land is predicted to increase by 28%
[1]. In sub-Saharan Africa rapid population growth, cou-
pled with recurring droughts, has led to a renewed call
for irrigation development [2]. In Burkina Faso alone,
the State has introduced several water and agriculture
related programs. Water is from now on to be managed at
a watershed level, and big State funded irrigation sch-
emes are to raise their productivity and efficiency, and
where water is available smallholder irrigation initiatives
are being promoted. In such a situation water manage-
ment becomes complex because it involves various spa-
tial scales, multiple stakeholders and varying go als. Stud-
ies exist on multi-user and multi-level water management
using integrated computer models [3,4], but they require
most often highly skilled operators and vast amounts of
data; both are not easily available everywhere.
Local stakeholders in Burkina Faso needed easy-to-
use and low-cost decision support tools for the monitor-
ing and exploitation of their water resources at different
user and spatial levels: 1) for watershed agencies at re-
gional level, 2) for water user associations at irrigation
scheme level and 3) for extension services at field level.
This study shows how different tools have been devel-
oped or adapted to tackle water management problems at
different scales or levels, and how th eir combined results
contribute to an integrated water management approach.

J. Wellens et al. / Agricultural Sciences 4 (2013) 9-15
10
2. STUDY AREA
The Kou watershed, situated in the Southwestern part
of Burkina Faso, is relative rich on water resources due
to several sources and a perennial water course. Unfor-
tunately these resources are increasingly being solicited
by a rapidly growing irrigation demand, due to an in-
crease in population and low irrigation efficiencies.
The main irrigation water users are presented in Fig-
ure 1. In the upstream regions, river banks and low lands
are occupied by informal irrigated agriculture. The occu-
pied surface has been estimated at 864 ha [5]. Water is
directly diverted, siphoned or pumped for basin or fur-
row irrigation to a vast patchwork of smallholder plots.
No water regulation exists in these parts, the most up-
stream user is first and best served. More downstream is
the 1200 ha irrigation scheme named the “Kou Valley”
established in 1973. Since 1993 structural adjustment
programs (SAPs) forced the management of the scheme
by State officials to be hastily transferred to a new and
inexperienced Water Users Association (WUA). Mainte-
nance works declined, yields started falling and an in-
crease in upstream water use made it harder to meet the
overall water needs, resulting in almost a quarter of the
farmers abandoning their plots.
3. MATERIALS AND METHODS
3.1. Regional Level
In order to monitor regional irrigation, available water
resources and agricultural water use need to be observed
and compared. The available water resources are since
Figure 1. Scheme of the watershed’s water users; with the
available water resources, demands and extractions. (a) 286 ha
[5, 31 ,3 4]; ( b) 578 h a and 53% irrigated by river diversion [ 5, 31 ,
34].
longtime being followed by a national network of hy-
drometric gauging stations (Figure 1). As for agricultural
water use, a map of the irrigated areas was needed.
Several mapping studies exists using remote sensing,
however few studies have focused on the complex sub-
Saharan landscape. Irrigated agriculture in tropical sub-
Saharan Africa is characterized by highly fragmented
smallholder plots with average sizes of 0.5 ha, seldom
exceeding 1 ha. In a such a context high-resolution im-
ages (Quickbird, Ikonos) are most appropriate, since the
size of the fields are easily 3 - 4 times greater than the
pixel size of the satellite image [6]. But such data are
costly and not always available [7]. Hence a low-cost
remote sensing method based on Landsat images was
developed.
Although freely available, the 30 m resolution Landsat
images bring with them the problem of mixels. These are
pixels that contain information about more than on type
of ground cover [8] and are usually important sources of
inaccuracy in image classifications [9]. However, when
several images of the study area taken at different times
are available, it is possible to use the information pro-
vided at each time to improve the classification of each
image using multi-te mporal analysis meth ods. The multi-
temporal analysis of classified images has great potential
for identifying irrigated areas [10,11].
Change detection is the process of identifying differ-
ences in the state of an object or phenomenon by ob-
serving it at different times. The change detection analy-
sis technique for this study focused on a pixel-by-pixel
post-classification comparison. This method generates a
complete matrix of pixel trajectories for a series of clas-
sified images. Expert knowledge-based classification (or
post-classification) was used to allow anomalies in a
given pixel trajectory to be studied for the entire series of
images [12].
All images were spatially and radiometrically cor-
rected, in the first stage a maximum-likelihood classifi-
cation (MLC) was applied. Classification assessment was
determined by the overall accuracy (percentage) and
Kappa coefficients of the error matrix [13,14]. Whatever
the results of MLCs, they are still prone to error because
of noises due to similarities of the spectral responses of
certain land cover categories [15]. The MCL process was
followed by change detection analysis. Change detection
analysis enabled to detect trajectories unlikely to be ob-
served in reality in the study region. For example, be-
cause of the marked intensification of agriculture, it was
unusual to see a transition of irrigated cropland towards
rangeland or natural vegetation. Simple rules for pixel
trajectory correction were defined:
1) A pixel of natural vegetation can evolve towards
rangeland or crops, or it can retain its original state.
2) A pixel of rangeland can evolve towards crops or

J. Wellens et al. / Agricultural Sciences 4 (2013) 9-15 11
retain its original state; evolution towards natural
vegetation is fairly unlik ely.
3) A pixel of crops can retain only its original status.
Evolution towards natural vegetation is unlikely be-
cause of the pressure on agricultural land.
Once the pixel trajectories are corrected, the post-
classification images are assessed again based on the
overall accuracy and Kappa coefficients.
3.2. Irrigation Scheme Level
To tackle the water management problems of the Kou
Valley’s WUA, a public-private partnership (PPP), based
on the “outsour cing through service or manag ement con-
tracts” model [16], was established. State officials, a pri-
vate operator specialized in water management (Obser-
vatoire de l’Eau) and a technical assistance, funded by
the Association pour la Promotion de l’Educationet d e la
Formation à l’Etranger (APEFE) and Wallonie-Bruxelles
International (WBI), joined forces. Technical studies
were conducted to assess the water problems, including:
mapping the scheme and creating a database, monitoring
land use and evaluating the water distribution through a
set of efficiency parameters [17].
The analysis of the scheme’s land occupations and
performance indicators clearly demonstrated the need for
a decision-support too l to achieve a more equitable water
distribution [18]. Lozano and Mateos [19] show amongst
a list of decision-support tools, that SIMIS is a useful
tool for irrigation scheme management, especially when
fixed rotation is used to achieve equity during peak pe-
riods. SIMIS is the FAO decision support-tool for irriga-
tion scheme management [20,21].
Throughout the study, public awareness and participa-
tory work sessions were organized. Based on the varied
soil characteristics (filtering sandy or heavy clayish) and
the scheme’s two most cultivated crops, paddy rice and
maize, different management scenarios were presented.
At all times, farmers were encouraged to express their
opinions and the various scenarios were fine-tuned ac-
cordingly. The WUA finally adopted a deficit irrigation
[22] based scenario: land use was optimized by taking
account of the different soil types (maize on sandy soils
and rice on clayish soils) with paddy rice under deficit
irrigation.
At the end of each jointly managed irrigation season,
an evaluation was carried out. Proposed vs actual land
use and proposed vs observed discharges at the head of
the different secondary canals were presented to the
WUA. Farmers talked about encountered difficulties and
recommendations were made for the following irrigated
season.
3.3. Field Level
FAO has developed AquaCrop, a field-crop-water-
productivity simulatio n for use as a decision support-tool
in planning and analysis [23,24]. It uses a relatively
small number of parameters to be adjusted according to
case and crop. Often intuitive default input-parameters
that can be determined using simple methods [25] are
sufficient.
Elaborating efficient irrigation schedules merely on
the basis of individual field research is rather difficult
and time consuming. Crop water productivity models,
such as AquaCrop , offer a more than convenien t solution
[26]. Once calibrated and validated, adapted irrigation
schedules can be elaborated.
AquaCrop has been assessed on several cabbage fields
in the region [27]. Few field data were required. Weather
and soil data were provided by the respective competent
State agencies. Irrigation calendars were registered. The
gravimetric soil water content was measured weekly in
layers of each 0.2 m thick up to a depth of 0.6 m. These
measurements were repeated in three replications per
treatment and served to evaluate the soil water balance
simulation. All supplementary needed crop data were
derived on each field by taking weekly tens of overhead
photos at 2 m above the canopy cover [28].
Irrigation calendars were developed to give farmers
simple guidelines on how to adjust their irrigation during
the growing season. For the design of irrigation calen-
dars, the irrigation application depth is often considered
as fixed. Fixed application depths in combination with a
variable irrigation interval result in an efficient use of the
irrigation water [29]. For simplicity and to promote
adoption by farmers, the number of irrigation scheduling
calendars should be kept to a minimum. This requires
some generalization. Irrigation calendars for each crop
are normally determined for two planting dates, for the
major soils and perhaps for two different initial soil wa-
ter contents at the beginning of the irrigation season [30].
4. RESULTS AND DISCUSSION
4.1. Regional Results
A Landsat-4 TM (taken on 5 May 1988), a Landsat-7
ETM (20 April 2000) and a Landsat-5 TM (24 June 20 0 9)
were used. Vegetation was divided into three main class-
es: farmland (irrigated crops class); rangeland (pasture
class); and trees and shrubs (natural vegetation class).
The variety of crops, the small size of plots and the im-
agery resolution (30 m) did not allow more detailed sub-
classes, but this was quite sufficient for the purpose of
the study. Thanks to post-processing, classification accu-
racies grew from 85% - 92% to 95% - 98% [31].
The study showed that irrigated areas have increased
by almost 70% in 20 years, particularly during the past
10 years. As reported by Ouédraogo [32], most of range-
lands converted to irrigated farmlands because of an

J. Wellens et al. / Agricultural Sciences 4 (2013) 9-15
12
influx of migrants from regions with poorer access to
water. The falling costs of farm equipment (pumps) and
the popularity of some high-yielding crops such a banana
also explained the increase in cultivated land.
Figure 1 links available water resources, estimated
water use (difference between 2 gauging stations) and
estimated water need [33]. A standard gross water need
of 1 l/s/ha was assumed [34]. It is evident that in a gen-
eral way all upstream irrigation is highly inefficient, in
some regions water use is twice the water need. A situa-
tion that weighs heavily on the downstream irrigation
scheme who faces chronic water shortages. Additional
hydrometric stations are being installed to guarantee a
complete coverage of the region.
This method of retrieving information is particularly
useful in developing countries where lack of information
is a major constraint to the efficient management of
natural resources. It is also best suited to these countries,
because it applies a low-cost and easy-to-understand
process for monitoring expansion and water use of irri-
gated crops.
4.2. Irrigation Scheme Results
Detailed irrigation calendars were elaborated using
SIMIS, a water distribution was proposed from head-
work to parcel level (Figure 2). Important was the in-
stallation of “discharge legends” (Figure 3) next to the
hydrometric scales at the head of each secondary canal,
giving the farmers an idea of the water being consumed
by the different secondary canals and hence stimulating
control based on peer pressure [35].
An evaluation of the PPP was carried out. Land use
was surveyed. Discharges at the head of each secondary
were monitored daily and a survey was conducted to
assess farmers’ satisfaction with the PPP. Some impro-
vement was noted, but none of the changes was signifi-
cantly different from the ex-ante situation [18]. More
farmers had access to water, though not all needs were
met. In general, the more equitable water distribution
was respected, but much work remains to be done. How-
ever in a place where water is becoming scarcer, a further
decline in water management was avoided.
A substantial proportion of the scheme’s farming
population (198 out of 1291 plot owners) was probed
upon their satisfaction. With 90%, the overall response
was very positive and most are willing to continue to
participate in this PPP-based management scheme. Al-
most a quarter said they’ve seen an improvement in their
livelihood because of better water distribution, but none
said with how much.
4.3. Field Results
Figure 4 shows, as an example, simulated and ob-
Figure 2. Proposed discharge file card; with equitable dis-
charge in green.
Figure 3. Example of an irrigation distribution calendar
(www.ge-eau.org/simis.html).

J. Wellens et al. / Agricultural Sciences 4 (2013) 9-15 13
Figure 4. Observed (dots) vs simulated (line) soil water content
for a cabbage plot. Each dot represents an average of three data.
served soil water contents on a cabbage plot. Field moni-
toring started three weeks after planting. Soil water con-
tent exceeds field capacity during most of the growing
season, leading to water losses due to percolation .
The above mentioned weather, soil and crop data were
used to optimize the irrigation schedule using AquaCrop.
After applying a single irrigation event to prepare the
field for transplanting, initial water content was assumed
to be at field capacity. A gross application depth of 35
mm is a common irrigation dose in the region if applied
on basins using a standard motor pump. The resulting
soil water content is given in Figure 5. Soil content wa-
ter remains well below field capacity and above the read-
ily available amount of water (RAW). The irrigation
chart is presented in Figure 6 and can with help of ex-
tension workers be transferred to farmers.
5. CONCLUSIONS
Agricultural water management quickly becomes com-
plex when interventions are needed at different spatial
and user levels, especially when interactions exist be-
tween these different levels. Stakeholders in a watershed
in Burkina Faso faced this defeat. The watershed agency
needed a tool at regional level for monitoring the water
use of its different regions, and to guide them in water
allocations and to arbitratein eventual conflicts. A Water
Users Association, facing chronic water shortages, sear-
ched a more equitable water distribution for its irrigatio n
scheme. And at field level, extension workers wanted a
way to advice the vast patchwork of informal and inde-
pendent smallholders. Yet all these services operate in
the same watershed using the same water resources, but
at different interacting levels.
At watershed level, a change detection technique was
developed to improve classical maximum likelihood
classification of land use for freely available Landsat
images. Combined with an existing network of hydro-
logical stations, the efficiency in water use could be
Figure 5. Soil water content when the proposed irrigation sche-
dule is followed.
estimated for different regions. The result is an objective
map confirming the inefficient irrigation practices of the
informal upstream water users and its devastating effect
on the downstream formal irrigation scheme. Based on
these findings, it was concluded that amongst the stake-
holders intervention also existed at the two lower levels:
irrigation scheme level and field level.
At irrigation scheme level, the WUA has no choice but
to accept the declining available water resources. With
the installation of a PPP, water management gained a
new impetus. More equitable, but deficient, irrigation
calendars were designed using SIMIS. Throughout the
process all users were heard and contributed actively to
its elaboration. After merely three years no significant
changes could yet be noted. However a further decline
was halted. Farmers do appear satisfied with the ap-
proach and results, and are willing for the PPP-based
management to continue. Thanks to objective indicators
and several information sessions, the farmers developed
a good awareness of the scheme’s functioning problems
and no longer related all their water problems to the up-
stream users.
Hardest to advice are the thousands of upstream small-
holders scattered all over the watershed. The regional
approach highlighted their irrigation inefficiencies, but
an individual monitoring of these fields is impossible. No
water users associations exit at this level, all farmers op-
erate individually. Simple and indicative irrigation charts
were developed using AquaCrop and will be transferred
by extension workers in order to raise irrigation efficien-
cies and hence economize water for downstream users.
Irrigation charts for other crops are still to be elaborated.
In a complex and heterogeneous agricultural landscape,
where limited data and human capacities are available,
easy to use and adapt water management approaches
were introduced; from scale-to-scale and tool-to-tool.
Each tool is applied independently, but the combined
esults contribute to an integrated water management. r

J. Wellens et al. / Agricultural Sciences 4 (2013) 9-15
14
Figure 6. Example of an irrigation chart for cabbage cultivated in the region of Bobo-Dioulasso on a clayish soil.
In response to a request from the Ministry of Agricul-
ture of Burkina Faso, this approach is also being intro-
duced in other watersheds in the regions of Banfora and
Mogtedo.
6. ACKNOWLEDGEMENTS
OPEN A CCESS
This study is part of a larger project entitled “Structural strengthen-
ing of the management capacity of water resources for agriculture by
means of decision support tools (Burkina Faso)” (see www.ge-eau.org).
The authors gratefully acknowledge the funding for this project pro-
vided by “Association pour la Promotion de l’Education et de la For-
mation à l’Etranger” (APEFE) and “Wallonie-Bruxelles International”
(WBI).
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