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8/2/2019 Africa-Latin America Article
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Abstract
Since the 1960s, IMT/PIM has formed the central tenant of institutional reform in the
irrigation sector worldwide. Despite the widespread application of IMT as a remedy for
poor systems performance, the actual effectiveness of transfer programs in terms of
improving irrigation, agricultural and management efficiency is still a subject of much
debate. Through the systematic review of 65 case studies of IMT/PIM in Africa and
Latin America, this study identifies important gaps in the literature on management
transfer, in particular the need for higher quality and longer-term impact assessments
of transfer programs to better establish a causal link between IMT intervention and
impact. It also provides insights into the conditions that shape the effectiveness of
institutions for irrigation management . In particular we find that in the African context,
three factors were consistently correlated with successful IMT/PIM, these are credit
and market access, property rights and NGO-donor assisted implementation. In Latin
America, governance issues such as WUA organizational structure and water rights
appear to increase the likelihood of successful management transfer. Thus, our research
suggests that what matters in Africa is not always relevant in Latin America and vice
versa, Even within these regions performance is mixed and impact across various
technical, social, economic and agricultural indicators is highly variable. Moreover, our
analysis suggests that some factors may be so critical to successful transfer, for instance,
the ability to absorb the costs of management that they impact the significance of
others, such as the type and size of scheme. Put simply, no clear, consistent pattern
emerges that offers a recipe for the successful replication of IMT/PIM interventions
elsewhere. Rather our work suggests that irrigation management reform must move
beyond single-policy solutions if it is to address farmers’ development needs.
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Introduction
Since the 1960s, the debate on how to best improve the management of the public
irrigation sector has stagnated. The policy which has ruled irrigation reform discourse
for the past 50 years claims the answer lies in devolving some or all of the management
authority for irrigation systems to organised user groups or Water User Associations
(WUA). This phenomenon is termed Irrigation Management Transfer (IMT) or
Participatory Irrigation Management (PIM) and has been adopted in more than 60
countries around the world (FAO, 2007).
The literature on public sector irrigation reform is replete with case studies of PIM/IMT.
Although individual case studies are an important source of data and analysis on
performance trends at a scheme or country-level it is also useful to situate them in alarger context and differentiate between localised trends and regional/global
developments (Giordano et al. 2010?). This is especially pertinent when single policy
solutions are taken up based on their ‘success’ in particular schemes and transplanted
to a wide range of locations and problems, as has been the case with IMT/PIM
interventions.
In spite of this, very few papers systematically combine and analyse the findings of
IMT/PIM case studies. The value of such systematic review is established in the works
of Petrosino et al. (2001); Farrington (1998, 2003); Welsh and Farrington (2006) and
Van der Knapp et al. (2008) who argue that when done well and with full integrity, they
provide the most reliable and comprehensive statement about what policy
interventions work, under which conditions and why.
This review attempts to partially fill that gap. It draws on the most comprehensive
database of IMT/PIM cases in Africa and Latin America to date and updates the very
limited number of existing reviews on the policy (Vermillion, 1997; FAO, 2007) with
new evidence and methods of analysis. While this paper builds on previous research it
also differs from it in several ways. In particular, with 65 case studies from 20 different
countries it taps into a much larger database and geographical scope than Vermillion or
FAO and is thus amenable to regression analysis.1 Moreover, unlike FAO we analyse
only localised case studies of IMT/PIM and hence are able to capture in-country
variation in terms of IMT/PIM impacts and outcomes.
The results of this review provide insights into the quality of evidence and research
methods used in IMT/PIM impact evaluations. Through the systematic analysis of case
study design we establish that available impact assessments largely fail to causally link
improvements in scheme performance and transfer programs. For this reason, the
actual effectiveness of IMT in terms of improving irrigation, agricultural and
management efficiency remains questionable.
1 FAO’s review (2007) covered only 8 cases from Africa and 7 from Latin America and Vermillion’s
(1997), analyzed only 4 from Africa and 4 from Latin America.
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We also develop and apply a methodology for classifying each of these IMT/PIM case
studies as successful or failed. Performance is measured from several perspectives,
drawing on ten financial, technical, social and agricultural outcome and impact
indicators. Each intervention is assigned a unique score and is ranked in terms of
success or failure. Finally, drawing on these success scores we investigate patterns of
success/failure through a combination of simple correlation, binary logistic regressions
and qualitative comparative analysis. From this we provide empirical support to some
prevalent hypotheses regarding the pre-conditions for successful management transfer.
Without doubt, PIM policy has generated some success stories, particularly in the
dynamic commercial farming sectors of South Africa and Northern Mexico. However,
overall our results show that the outcomes of IMT/PIM in the African and Latin
American regions are mixed and the positive impacts of the devolution program,
ranging from livelihood security to improved technical efficiency, show great variability
across and within locations. This variation, we argue, reflects the ‘difficulty of
transplanting institutions from one context to another’ when local situations vary
significantly in terms of technology, water availability, cropping patterns, market
development, social capital and government policies (Meinzen-Dick, 2007: 15200).
Nowhere is this institutional mismatch and disconnect more pronounced than in the
small-holder irrigation context of Africa. In this context of moderate dynamism and
poverty, interventions in the irrigation sector must move beyond single-policy solutions
and encompass, land reform, market access, and other support services which will help
farmers increase their livelihoods and consequently their ability to absorb the costs and
responsibilities of irrigation management.
Research Questions
Our review considers evidence from 65 case studies of IMT/PIM in Latin America and
Africa to answer the following questions:
1. How well have the impacts and outcomes of IMT/PIM in Africa and LatinAmerica been documented and what have we learnt from those studies?
2. How can we evaluate the impact and outcomes of IMT/PIM and differentiate thesuccessful cases from ‘not so successful’ ones?
3. What are the conditions under which successful or at least promising WUAs inpublic irrigation systems are found? Are those conditions replicable?
The substantive sections of this paper each address one of these research questions.
Methodology
Given the objectives of this paper, a five-stage review process was undertaken. First we
attempted to locate all existing case studies of IMT/PIM by searching academic
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databases and Google and Google Scholar search engines.2 Sources were also identified
from the bibliographies of previous meta-reviews (Vermillion, 1997: Shah 2002; FAO
2007). These searches returned over 2,500 records. However only studies which met
the following criteria were included in our review:
a) Case studies were limited in geographic scope to Africa and Latin America.
b) Records were localized case studies of one or more Water User Association andnot country level studies.
c) Case studies were of publicly owned irrigation systems that were later handedover to the farmers for management either as a decision of the government,
donors or due to the interest of the farmers. This meant we did not include casestudies of farmer managed irrigation systems (FMIS), which, unlike the turnedover or transferred systems of our interest, were built and managed by thefarmers themselves.
d) Case studies were published after 1994. The year 1994 marked a watershed yearin the study of IMT/PIM initiatives. It was in this year that a major conference onPIM/IMT was held at Wuhan, China. Here, more than 100 case studies werepresented and later documented in 2 volumes of conference proceedings. Basedon the assumption that IMT/PIM initiatives in the pre-1994 era were adequatelystudied and documented, we wanted to review only the post-1994 case studiesso as to reduce duplication in results and make our review more up to date andfocused.
e) Case studies were documented in the English language. We include thereferences for papers excluded on this basis so that they may be incorporatedinto future reviews.
After reviewing titles and abstracts, all but 137 case studies were excluded on the basis
of relevance. A further 57 studies were eliminated because they did not contain a
localised intervention. Another 8 sources were eliminated as they did not provide
enough information on the impact and performance of IMT and 7 sources were
eliminated because they were published in languages other than English (French,
Spanish and Japanese). In total, 65 distinct case studies of IMT/PIM were included in
our review.
We made a concerted effort to include as many case studies from as many countries as
possible. To this end we undertook country and schemes specific searches in an attempt
to maximize sample size and country representation; however despite our best efforts
potentially relevant sources were either inaccessible or failed to meet our inclusion
2 The following databases were searched: Water Resources Abstract, CAB Direct, Econ Lit, SociologicalAbstracts, Web of Science, Scopus, World Bank Publications, FAO’s AGRIS database, EnvironmentalSciences and Pollution Management, ProQuest Central, EVA Environmental Abstracts, Ingenta Connect,Ovid Databases, JSTOR, Sage Journals, Science Direct and IWMI’s catalogue.
Comment [SN(1]: In his comments earlier this
year, Mark thought this was not a good reason-
perhaps some more justification that there would
be overlap?
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criteria. As a result, there are regional biases in the data collected, with some countries
such as South Africa, Mexico and Columbia over-represented and others, such as
Burkina Faso, Chad and Ecuador not accounted for at all.
Second, we coded studies on a range of methodological, descriptive and
outcome/impact related attributes. The purpose of the coding was to make comparable
a number of seemingly diverse and inconsistent results across IMT/PIM case studies.
Coding was based on the Institutional Analysis and Development (IAD) framework
which draws on various disciplines such as political science, economics, anthropology,
game theory and law, to study common property resources (CPR). In short, this
framework analyses the physical attributes of the resource, the community attributes of
the people managing them and the attributes of the institutions that have been formed
to manage the resource (Oakerson 1986). Coding was done to gain insights into the
three core research questions which frame this paper. It enabled us to:
a) Assess the quality and strength of evidence used in case studies. This was done by
identifying and coding the study design and data collection methods used in case
studies. We collated information on case study authors’ methods of analysis;
whether or not they undertook independent verification of outcomes and
impacts and the number of years that elapsed between the IMT/PIM
intervention and the impact assessment. There were 21 methodological
parameters assessed in this study and these were then used to evaluate the
quality of research into IMT/PIM in general. Our list of indicators conforms to
and builds on Vermilion’s (1997) analysis of the evidence on IMT/PIM.
b) Measure IMT/PIM case studies in terms of their outcomes and impacts . We chose
10 indicators of success which closely conform to the definition of successful
IMT/PIM initiatives endorsed by both the International Network on
Participatory Irrigation Management (INPIM, see http://www.inpim.org/ ) and FAO
(2007). We measure the following outcomes: irrigation service fee collection
rates; financial viability of WUA; functional condition of infrastructure (good
maintenance); enhanced equity; reliability and adequate water supply; reduction
in government expenditure after transfer, popular participation and reduction in
disputes (or increase in rule conformance). In addition we measure the following
impacts: crop related impacts (increase in production, yield, cropping intensity)
and livelihoods related impacts (increase in income, employment, wage rates,
reduction in forced out-migration etc.). Table X presents the list of outcome andimpact indicators we used to evaluate success.
c) Identify factors that that influence the success of IMT interventions. To do this we
coded cases on 25 descriptive attributes including their technical specifications
(type of scheme, size, complexity, whether or not it had been rehabilitated);
socio-economic and agricultural characteristics (population served, major crops,
per capita GDP, access to credit, markets, importance of agriculture, social
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cohesion, land rights, water rights); and their IMT/PIM experience (level of
transfer, training, political will, legislation, election to WUAs).
Third, to allow for further commentary on the methodological quality of each selected
IMT/PIM case study we developed and applied a modified version the Maryland
Scientific Methods Scale (SMS) which is recommended for systematic reviews by Welsh
and Farrington (2006) and Van der Knapp et al. (2008). This is a five-point scale that
rates the strength of scientific evidence used in impact evaluations, with 1 being the
weakest and 5 the strongest scientific evidence in terms of inferring cause and effect. 3
Given that our sample does not contain a single case study with a traditional SMS score
of 4 or above (i.e. none used an experimental design with statistical control of
extraneous influences on the transfer process) we adapted the scale to better reflect the
actual analytical techniques and evidence used in our source documents. This is because
the nature of PIM/IMT interventions rules out impact assessments by experimental
design (such evaluation research measures change before and after an intervention in
units that are randomly assigned to experimental and control conditions).4 It does not,
however rule out scope for rigor by combining a “before-after” with a “with-without”
comparison. We assigned a rating of one to four based on design robustness, as
described in detail below.
Level 1. Studies which measure the correlation between an intervention and an
indicator of performance at a certain point in time, after the introduction of the
intervention. This design fails to rule out any threat to internal validity and also
fails to establish causal order.
Level 2. Studies which measure the performance before and after the
intervention, without a (comparable) control condition or statistical analysis.
Level 3. Studies which measure the performance before and after the
intervention, without a (comparable) control condition but with statistical
analysis.
Level 4. Studies which measure the performance before and after the
intervention, and also have a comparable control group (quasi-experimental
design) with statistical analysis.
By grouping case studies according to SMS levels we can distinguish between high and
low quality impact assessments. Given that the primary objective of a systematic review
is to combine the findings of a large number of studies with the aim of drawing credible,
generalizable conclusions about what policy interventions work, where and why,
3 The SMS was designed by Sherman et al, as a tool to evaluate the methodologies for their review of over500 crime prevention interventions for the U.S congress. The scale has since been considered a keyresource for appraising quantitative studies. The five levels are described by Welsh and Farrington(2006).
4 This type of impact assessment is common in the field of public health.
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attribution challenges arise from the inclusion of studies with non-experimental
evaluation designs. Through the SMS we can assign labels of success or failure with
caveats and soften many problems that arise from the presence of low quality studies
(SMS of 2 and below) in a review of this kind. In the end, systematic reviews are only as
robust as the existing evidence on a given intervention enables them to be.
Fourth, we constructed a composite success score (CSS) from the coding of outcome and
impact indicators and use this to differentiate successful cases of IMT from not so
successful ones. This scoring system (CSS) is adapted from Mukherji et al. (2009).5
The CSS draws on the dichotomous coding of outcome and impact indicators (1= if
performance improved; 0= no change in performance or deteriorated) outlined in Table
X and simply involves adding up all positive scores and dividing this sum by themaximum possible score. Here, the challenge was to deal with missing values in each of
the case studies (as the data availability among case studies varied between 4-10
outcome/impact indicators). We decided that whenever data was not available, we
would leave it out from our calculation. For example, if outcome and impact indicators
were available for 8 out of 10 (maximum) possible indicators, then we calculated the
CSS assuming a maximum possible score of 8 (and not 10). If out of these 8 indicators,
four were positive (therefore getting a cumulative score of 4) and 4 were negative or
neutral (getting a cumulative score of 0), our CSS would be 4 out of 8 or 0.5 which can
be converted to 5 on a 10 point scale (see Appendix 7).
Table X : List of outcome and impact indicators used for construction of success sc ores
Sr.No.
Name of the indicator Scoring system Number of cases wherethis indicator was present
(Max=65)
A. Outcome indicators
1. Irrigation service fee collection rate 1= if it has gone up
0= no change or declined
n/a = not available
56
2. Financial viability of WUA 1= if it has improved
0= no change or deteriorated
n/a = not available
59
3. Functional condition of infrastructure 1= if it has improved
0= no change or deterioration
n/a = not available
64
4. Equitable distribution of water 1= if it has gone up
0= no change or declined
n/a = not available
46
5. Reliability and adequacy in water
distribution
1= if it has gone up
0= no change or declined49
5 For a discussion on the strengths and limitations of this scoring method please refer to Mukherji et al(2009); pp. 13-14.
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Sr.
No.
Name of the indicator Scoring system Number of cases where
this indicator was present
(Max=65)
n/a = not available
6. Reduction in government
expenditure
1= Yes
0= Otherwise
n/a = not available
30
7. Popular awareness and participation
in WUA activities
1= if it has gone up
0= no change or declined
n/a = not available
60
8. Reduction in frequency of disputes 1= Yes
0= No or got worsen/a = not available
33
B. Impact indicators
1. Crop related impacts (production,
yields, cropping pattern, cropped
area)
1= If any one of these registered
an increased after transfer
0= Otherwise
n/a= not available
50
2. Livelihoods and household
parameters (income, wage,
employment, poverty reduction,
reduction in forced migration)
1 = if any of these have gone up
after transfer
0=Otherwise
n/a = Not available
39
All cases that got a value of 5 and above in our CSS scale were categorized as effective
and those with scores of 5 and less were categorized as ineffective cases of IMT. While it
may be argued that using a score of 5 as the cut off point is rather arbitrary, we justifyour cut off point on the grounds that varying the cut off point between 5 and 6 (when
5.5 is the mean value on a scale of 0 to 10) does not change the overall number of
successful and failed cases.
In short, every case study was scored and ranked on a uniform scale of 0 to 10. In cases
which scored 0, the transfer process produced no positive changes in scheme
performance. In cases that scored 10, the transfer process produced only positive
changes in scheme performance
There are obvious limitations with some of the indicators used it our system of
classification. This is particularly true for the impact indicators. Very often, increase in
crop yields, incomes or area under acreage, even, if it happens after the intervention,
may not be directly attributable to greater farmer involvement in O&M per se. Rather,many factors exogenous to IMT interventions such as, changes in fertilizer application
rates, higher crop prices, rainfall patterns and more favourable market conditions can
affect the performance of irrigated agriculture. Furthermore, it should be pointed out
that investments in farmer management are often part of a wider set of economic and
agricultural interventions, including promoting marketing and micro-credit, high-
yielding seed technology and better on farm management. However, because case
studies do not explicitly examine casual links between IMT interventions and systems
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performance our CSS is forced to draw on these problematic but very well documented
indicators (over 70% of case studies measure success or failure of IMT interventions
based on crop and livelihood related impacts). This may mean that our evaluation of
IMT may appear to be more positive than might actually be the case.
An additional source of bias with our database stems from the fact that these studies are
snapshots of cases at one point in time ranging from 1995 to 2010. In most cases, we do
not have any information on current status of these schemes. It is possible that schemes
classified as success/potential success stories in our analysis might as well have failed
over the years or our failed cases might have turned around and become successful
later on. We do not have the time or the resources needed for verifying the current
status of our case study schemes. In short, this type of review gives expression to static
perspectives: performance is measured at one point in time. However, we recognize
that WUAs may be at different stages of maturity and that IMT interventions are
impacted by spatial and temporal dimensions.
Finally, we drew on simple correlations and regression analysis to identify and
investigate which socio-economic and technical mechanisms appear to be active in
successful management transfer. Given the limited number of case studies from Latin
America (18 in total) our regressions only include data from African case studies. The
dependent and independent variables that we consider for our Logit model are detailed
in Table X.
The dependent variable in our case is dichotomous and takes the value “1” if an IMT
intervention was successful or “0” if failed. On the other hand, we considered severalindependent variables that might influence the probability of successful management
transfer. All are drawn from the list of 25 descriptive indicators coded in step 2. Two
variables were dropped from the regression as they were problematic these are,
‘importance of agriculture in people's livelihood’ and ‘amount of O&M authority
transferred’.
Table X : Description of dependant and independent variables used in the logit model.
Variables Domain
Dependent Variable
SUCCESS Binary dummy variable, where 1 =
successful/potentially successful case of IMT/PIM and 0= failed/potentially failed
cases Independent Variables:
Type of scheme Dummy variable: “scheme_type” where Lift &
pump =1, others =0
Size of scheme Dummy variable: “scheme_size” where
Small=0, Large & Medium=1
Value of crops grown Dummy variable: “crop_value” where low ormixed=0, high=1
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Type of crops grown Dummy variable: “crop_type where Paddy=0,non paddy and mixed=1
Access to credit/markets/support services Dummy variable: “acess_credit” where low ormixed=0, high=1
Land rights Dummy variable: “landrights” where low ormixed=0, high=1
Implementing Agency Dummy variable: “Implementer” where Donor& NGO assisted or farmers =1, Government=0
By correlating success and failure with individual scheme characteristics, we were not
only able to identify patterns of success but also, test some of the hypotheses put
forward in the literature on the determinants of successful management transfer. Our
regressions do not attribute causality rather we can only comment only on theassociation of variables and rates of success.
1. Review of the Evidence: How well have the impacts and outcomes of IMT/PIM
in Africa and Latin America been documented and what have we learnt from
those studies?
Over a decade ago, Vermillion characterised the literature on the impacts and outcomes
of IMT as a ‘disparate collection of definitions and methodologies from which it is
difficult to deduce general conclusions or policy implications’ (1997: 5). At the heart of
his criticisms were problems of causal attribution which arise as a result of post-hocfallacies, fallacies of induction and short-term impact assessments. Our review,
fourteen years on, is frustrated by these very shortcomings and as such the
generalizations that can be derived from these case studies are very limited. We are
however, able to systematically assess the evidence about the impacts of management
transfer with reference to performance measures and the quality of research methods
used in our case studies.
By grouping case studies according to the Maryland Scientific Methods Scale (SMS) we
show the overwhelming majority of evidence on IMT/PIM in Africa and Latin America,
is of low scientific quality. As Figure X indicates most IMT impact assessments rely
solely on post-impact observations and data (SMS 1) to infer cause and effect.
Assessments which claim interventions produced changes in performance without
providing a reference point for those comparisons are inherently problematic.
Studies that use only the “before and after” or “with or without” methodological design
(SMS 2 and 3) also often fail to establish a causal relationship between IMT intervention
and impacts. Many factors exogenous to IMT such as better farm management, changes
in fertiliser application rates, rainfall patterns, changes in technology or cultivation
practices and more favourable market conditions can affect the performance of
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irrigated agriculture. Moreover, in the majority of available case studies IMT followed or
was part of a wider set of neoliberal economic and agricultural reforms all of which
impact systems performance.
Figure X : IMT case studies according to SMS
We found that only 4 of our case studies (out of 65) used a quasi-experimental design to
assess impact by combining “before after” and “with-without” analysis (SMS level 4).Qualitative review of these high qualitycase studies suggest that it is only possible to
trace certain changes to IMT/PIM policies such as, improvements in management
efficiency, staff accountability and the significant shift in the burden of cost from the
government to farmers. From these high quality studies it is clear that ‘transfer has not
had substantial impacts on the performance of operations and maintenance, or on the
agricultural and economic productivity of irrigated land or water – neither improving
negative performance nor causing detriment where performance is positive’
(Vermillion and Garcés-Restrepo, 1998: 33).
For example, in the rigorous evaluations of Samacá and RUT irrigation systems
(Columbia), performance cross almost all documented impact/outcome indicators,
remained constant or could not be linked to management transfer. In Samacá and RUT
farmers shifted to higher value crops just before transfer and thus, positive post-transfer crop impacts may not be attributable to farmer management. These high
quality impact assessments do not shed light on the factors that increase the probability
of IMT failure, as much as they reinforce arguments about the difficulty of attributing
changes in scheme performance to IMT alone. Indeed, they draw attention to the link
between the quality of design and empirical findings, by reinforcing Rossi’s (1987)
37
1113
4
0
5
10
15
20
25
30
35
40
1 2 3 4
N u m b e r o f C a s e S t u d i e s
Adapted SMS Score
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‘stainless steel’ law of evaluation which states that the better designed impact
evaluations are, the less effective the interventions or programs seem to be.
It is also worth noting that many of these attribution problems are manifest in case
studies which evaluated rehabilitated schemes. In 31 studies, transfer was accompanied
by physical improvement of the system. In these cases, it is difficult to determine the
extent or degree to which initial positive results, in terms of water delivery and crop
related impacts, are attributable to either farmer management or to systems
rehabilitation. Of the 31 studies which were rehabilitated before or during transfer only
4 were evaluated via with and without comparisons. As a result, case studies authors
made little attempt to disaggregate the effects of rehabilitation and transfer by
comparing systems that were transferred with and without rehabilitation. These
problematic causal links between IMT and changes in scheme performance make it
difficult to draw robust conclusions about the impacts of transfer.
We also assessed case studies according to their use of time series and statistical
analysis. Of the 24 studies which compared performance before and after transfer, only
14 examined data on 5 or more consecutive time periods. Without sufficient time series
analysis it is impossible to firmly establish the timing of impacts, to control for
alternative causes of change and to identify the extent transfer has influenced the
outcome (Vermillion, 1997).
The low rates of quantitative analysis also exacerbate problems of casual attribution.
Only 30 cases used statistical tests compared to 52 studies which quoted descriptive
statistics. Statistical and econometric analyses may help to overcome some of themethodological shortcomings identified in this review. For example, combining with
and without and before and after analyses with difference in difference analysis will
help establish a causal link between intervention and impact. Using other quasi-
experimental methods such as an instrumental variable approach will similarly help in
identifying casual attribution and therefore resolve the most pertinent methodological
weakness in IMT/PIM studies.
Further attribution problems arise from the types and rigour of data collection methods
used by case study authors.
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Figure X : Data collection methods used in IMT case studies
In particular, Figure X suggests several common tendencies. First, 94% of case studiesrely on secondary sources, including data collected by irrigation agencies, government
departments and/or other scholars. Second, less than half (41%) of the studies which
conducted farmer interviews selected participants through systematic, random
sampling methods. As a result, the conclusions drawn and perspectives expressed on
the basis of farmer interviews may not represent the general perception or actual
performance of the transferred unit. Finally, out of 65 cases we found that only 13
evaluations undertook independent measurements of water distribution operations and
only 17 involved the direct inspection of scheme infrastructure. Taken together, these
flaws suggest that in many cases, authors may have either overestimated or
underestimated performance levels in their studies. For example, before and after
analyses which depend on data from government sources may be biased in the
reporting of pre-transfer performance levels leading to an underestimation of post-transfer impacts.6 Similarly, interviews or surveys which only report the perspectives of
upstream farmers or WUA leaders may risk overestimating positive changes in terms of
water distribution and equity. In short, our analysis indicates that most studies failed to
adequately verify their conclusions about the outcomes and impacts of IMT.
6 For a detailed discussion of this see: Vermillion (1997).
51
40
46
19
31
1317
19
32
0
10
20
30
40
50
60
S e c o n d a r y d a t a f r o m
a g e n c y / g o v e r n m e n t
d e p a r t m e n t s
L i t e r a t u r e
R e v i e w / S y n t h e s i s o f
d a t a f r o m o t h e r s t u d i e s
F a r m e r I n t e r v i e w s
S y s t e m a t i c S a m p l i n g
S t a k e h o l d e r
p e r s p e c t i v e s
D i r e c t m e a s u r m e n t o f
o p e r a t i o n s
D i r e c t i n s p e c t i o n o f
s t r u c t u r e s
A n e c t o d a l e v i d e n c e /
f i e l d o b s e r v a t i o n s
I n d e p e n d e n t
m e a s u r e m e n t o f
o u t c o m e s &
i m p a c t s ?
N
u m b e r o f C a s e S t u d i e s
Indicators
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Our case studies also varied in terms of the number of years that had elapsed between
the transfer process and the impact assessment/documentation carried out by authors
(we refer to this as the period of evaluation). For the purpose of our review we
categorized case studies as either, short term (3 or less years) medium term (4-10
years) or long term (over 10 years) studies.
Although our adapted SMS ranking does not explicitly address the period of evaluation
as a scoring criteria, our data reveals a clear relationship between short-term impact
assessments and low overall methodological quality. All short-term studies scored a
SMS of 1, suggesting that 3 years is not enough time to fully understand the outcomes
and impacts of IMT. Problems of casual attribution, discussed above, are also more
pronounced in short-term impact assessments, especially when the transfer process is
accompanied by the rehabilitation of physical infrastructure, which often produces
immediate improvements to the technical efficiency of the system.
The methodological quality of medium and long-term studies varies significantly. 31
medium and long-term studies were categorised as SMS 1 or 2 due to their low overall
methodological quality. This is hardly surprising given that only 22 (out of 46) long and
medium-term studies used time series analysis and actual drew upon long term data.
Thus, period of evaluation is not a clear cut measure of methodological quality. Long
and medium term studies do however, have the potential to contribute new and useful
insights into our understanding of IMT/PIM impacts and if integrated with more
systematic research methods, to permit conclusions about the conditions under which
transfer programs could be expected to succeed or not.
Taken together, our review and analysis of IMT case studies reveals that data on the
impact and outcomes of management transfer is weak in terms of rigor and method.
Having said that, case studies do include a range of perspectives on the impacts of
management transfer and some results are independently verified. Ultimately, these
studies are the only available sources of information on the impact of IMT/PIM and we
rely solely on the information contained therein to construct our CSS and to evaluate
and classify each of the projects as successful or failed interventions.
2. Definition of success: How can we evaluate the impact and outcomes of
IMT/PIM and differentiate the successful cases from ‘not so successful’ ones?
One of the main objectives of this review is to evaluate the overall impact and
effectiveness of IMT/PIM interventions in Africa and Latin America. To do this, we first
needed to define what constitutes a successful IMT/PIM intervention. In the literature
(for example, FAO, 2007; Tang, 1992; INPIM website) IMT success is defined as multi-
dimensional. We found that except for Tang (1992), who defined success in terms of
only two indicators namely, rule conformance and good maintenance, most others,
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including our case study authors, defined success based on a number of outcome and
impact related indicators.
On the whole, the performance criteria used by case study authors to evaluate success
are quite diverse. Studies in Latin America emphasised financial viability and improved
water supply while the most common performance indicators in African case studies
were functional condition of infrastructure and popular awareness and participation.
On average, CSS was calculated on the basis of 6 criteria and none of the case studies
contained data on all 10 impact and outcome indicators. This stems from a lack of
consensus on what constitutes successful transfer in the literature. As Mukherji et al .
(2009) argue, definitions of success are often informed by authors’ disciplinary
background. Social scientists for instance, ‘define success in terms of popular,
community participation, while water resource scientists would define success in terms
of better water control of delivery schedules’ (Mukherji et al: 18).
A single indicator, such as increased reliability of water supply or crop productivity may
highlight particular aspects of an IMT intervention but taken alone does not allow us to
comment on the overall impact of management transfer or to examine trade-offs
between key performance measures. For example, changes in short-term productivity
versus long-term reliability and adequacy of supply (Vermillion, 1997).
For this reason our system of classification (CSS) measures success from several
perspectives, drawing on ten financial, technical, social and agricultural outcome and
impact indicators. Theoretically, successfully transferred systems should show a
marked improvement in systems performance because users receive adequate and reliable supply of water at reasonable and affordable costs over a sufficiently long period
of time enabling them to increase their crop production, productivity and incomes. This
definition of success integrates technical, financial and agricultural outcomes over the
long-term.
Figure X: Distribution of successful and failed IMT interventions in Africa and Latin
America (one map)
What are the chances that our method and success scores may misclassify the actual
outcome of an IMT intervention? To ensure the robustness of our system of
classification we compare our success scores with our evidence base. For this purpose
we documented authors’ classifications of success or failure; when authors did not give
an explicit opinion on the success or failure of the initiative we coded it is inconclusive.
Table 2 compares our success ratings based on CSS, with those of the authors. We find
that none of the cases where authors have declared a case to be a failure, turned out to
be successful according to our success scores, showing, there is not a single example of a
failed case, mistakenly coded as a successful one by us. Similarly, we find that only one
case study which the author declared to be successful turned out to be a failed case in
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our scoring system. There were 23 cases where the authors had not given any firm
verdict on the outcome of IMT and which we classified as inconclusive. Of these, 66%
turned out to be failed according to our CSS, which is not surprising given our way of
calculating CSS involves multiple indicators, while case study authors often declare an
intervention to be successful based on one or few indicators.
Table 2: Comparison of author’s evaluation of success with our Composite Success
Score
Evaluation according to CSS
E v a l u a t i o n a
c c o r d i n g
t o c
a s e s
t u d y
a u t h o r s
Categories Successful Failed Total
Successful cases 15 1 16
Failed cases 0 26 26
Inconclusive cases 7 16 23
Total 22
(34%)
43
(66%)
65
(100%)
It is important to note however, that the high level of consistency between our
classifications of success and failure does not confirm the robustness of the evidence
itself. Given that we did not have the resources to verify the accuracy of the data and
conclusions drawn by case study authors via field research, our system of classification
instead distinguishes between ‘high’ and ‘low’ quality studies. In doing so we
acknowledge that many changes in scheme performance may have been misclassified
(by case study authors) due to methodological shortcomings.
3. Review of IMT/PIM interventions: What are the conditions under which
successful or at least promising WUAs in public irrigation systems are found?
Are those conditions replicable?
A primary aim of this review is to gain insights into how effective IMT interventions
work (or why they fail). To this end, we examined the relationship between a range of
descriptive attributes and rates of successful management transfer. By correlating
success and failure with individual scheme characteristics, we were not only able to
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identify patterns of success but also, test some of the hypotheses put forward in the
literature on the determinants of successful management transfer. However, due to the
poor methodological quality of the majority of studies in the field, it is not possible to
make definitive conclusions about what matters and why in successful IMT
interventions. As a result some of the key arguments of the paper remain hypotheses
rather than conclusions and must be verified with extensive and robust field research
and analysis. However, a central argument of this paper – that imposing ‘blue-print,’
one-size fits all institutional models will not fix the complex and diverse management
problems of irrigation systems – remains a robust conclusion.
Degree of commercialisation: Success as a function of access to support services and
crop value
A core objective of IMT was to make irrigation schemes financially self-sufficient
however as argued by Veldwisch (2006), this objective has an implicit bias towards
commercialisation. In order for farmers to absorb O&M expenses the costs of self-
management must be a small proportion of their income (Vermillion, 1997; Shah et al
2002). Put simply, irrigated agriculture must produce enough economic benefits to
cover the economic and financial expense of farmer management. Often this requires
commercialisation.
To investigate the relationship (if any) between commercialisation and successful
management transfer we systematically documented farmers’ access to credit/markets
and crop value. Our results suggest that when output marketing systems are strong and
well developed and farmers have access to credit the chances of successful IMT increasesignificantly in African schemes. According to our system of classification, of those
African schemes with no or low access to input and output markets 92 % are failures. In
contrast, high access to credit and support services did not appear to have a strong
relationship with successful management transfer in Latin America. Rather, the highest
rates of success in the region were found in schemes where the degree of
commercialisation is varied (i.e. schemes served both commercial farms and emerging
or subsistence farmers). Indeed, our sample suggests that intra-scheme inequity in
terms of access to credit does not impede successful transfer in Latin America.
The divergent result in Africa and Latin America on this front may be the result of biases
in the literature. Of the African case studies which house both commercial and
historically disadvantaged farmers (HDF’s), 8 centred upon the perspectives of HDF’s
and as such there was greater emphasis on the legacies of historical inequity and their
continued influence in scheme performance. In Latin American case studies there is less
stress placed on the bifurcated structure of irrigated agriculture. Indeed, even though,
there are clear class divisions in the agricultural sectors of Mexico and Colombia and
these are often perpetuated in the structure and composition of WUAs, these
perspectives are rarely taken up by case study authors in their assessments of IMT
impacts.
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The evidence from both Latin America and Africa also suggests that in most cases,
cultivating high value crops does not greatly increase the probability of success. Rather
in both regions, it appears that diversifying cropping systems (which include both high
and low value crops) are most positively correlated with successful management
transfer. Having said that, rates of failure in schemes which cultivate low value crops
were consistently high: at 91 % in Africa and 75% in Latin America.
Taken together, our evidence suggests that the relationship between commercialisation
and successful management transfer is mixed. It is important to consider these results
in context; in many small-holder African schemes the enabling environment for
commercialisation is often not in place, this is in part due to poor support services,
inaccessible and unaffordable credit, unreliable markets and poor marketing skills. In
the White Nile Pump Schemes (Sudan), for example, repressive state regulation of
cotton and wheat production (the dominant crops on the schemes) and poorly
developed private markets are a critical factor underpinning the sub-optimal
performance of these schemes (Samad et al. 1995). Similarly, in the Sepeteri and Itoikin
schemes of Nigeria, farmers associated the high risks of irrigated agriculture with low
credit availability and market access (Olubode-Awosola et al., 2005).
Moreover, prior to transfer small-holder irrigated farming in Africa was a highly capital-
intensive activity. Indeed, many small-holder schemes were only made financially viable
as the result of large government subsidies. As a result, the expectation that farmers are
capable of absorbing the costs of self-management (when these costs were a source of
budget deficits for many governments) is paradoxical unless it is accompanied by a
significant improvement in farmers’ livelihoods.
Capacity: Success as a function of pre-existing Farmers Organisations
In both Africa and Latin America, schemes that had farmer’s organizations prior to
transfer appear to succeed more often that those without. However, in the literature the
relationship between pre-existing organizations and successful management transfer is
not clear cut; sometimes WUAs were built on existing organizations (Mexico, white
commercial sector in South Africa, Dominican Republic), and sometimes they neglected
them all together (Mali). Moreover, even when pre-existing organizations become the
basis of WUAs, they sometimes reinforce pre-transfer power hierarchies and social
inequality. In Mexico for example, the IMT program was developed on an alreadyexisting, strong organizational base: the ejidos and the organizations of private growers.
However, more than ten years after the transfer process, ejido farmers continue to
complain that they lack the financial and political muscle to check the influence of large
landowners over the decision-making process (Wilder & Romero Lankao, 2006).
In spite of the post-transfer politicization of WUAs, the results of our study suggests that
pre-existing organizational strength, which we regard as a proxy f or farmers’
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management capacity, impacts the likelihood of successful transfer. Moreover, we
argue that the high rate of failure among studies which did not have any pre-existing
organisations, 89%, reflects enduring capacity deficits especially in African small-holder
schemes. Indeed, in many cases, farmers were entirely excluded from management
decisions and often were dependant upon the central agency for farm management and
support services. For example, in the Sepeteri scheme (Nigeria) and Hindustan
Irrigation System (South Africa), the agency applied a strict system of management
under which the farmers and very little freedom of choice or control in crop
management, marketing, land tenure and other economically significant aspects of
irrigated agriculture (Olubode-Awosola et al, 2005; Tren and Schur, 2000). Ultimately,
farmers were responsible for little more than weeding and applying fertilizers to their
plots.
More broadly, the ‘capacity deficit’ or ‘dependency syndrome’ which is characteristic of
some African case studies highlights important contradictions in IMT policy. The way in
which the farmer participation concept has been applied in many locations often
implies that WUAs are designed to replace the government agency in scheme
management, at least at the tertiary level. However, in cases where farmers were
previously excluded from management decisions and where local leadership and
organisation have been crippled for decades, it is unrealistic to expect a culture of
collective management to take hold (Mukherji et al. 2009; Shah, 2002).
IMT interventions where there has been a long history of donor involvement are
similarly paradoxical. In the Musengezi schemes in Zimbabwe for example, farmers
viewed the turnover experiment as a game of finding the right savior to bail them out onoperation and maintenance costs. Horticultural companies, international donors and
politicians took turns in picking up the tab.7 As Bolding et al. (2003) suggest, farmers
internalized the idea that both the state and donor are responsible for bankrolling
cultivation and maintenance.
Stakes: success as a function of property rights and the importance of irrigated
agriculture
IMT/PIM policy was built upon the assumption that as farmers have the largest stake in
irrigation schemes, they have more incentive than a government agency to manage
these systems efficiently and sustainably. While this line of reasoning is suggestive, it presupposes that farmers are a homogenous group with harmonious interests. The
evidence from our review indicates that the stakes farmers have in scheme
management, which we measured by the importance of irrigated agriculture to
livelihoods and property rights, varies considerably across and within locations.
7 Farmers were not passive recipients of aid but rather exploited political competition in the area to settletheir debts and keep the scheme operational. See: Bolding et al. (2003): 197.
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Our results indicate that in Africa the greater the importance of irrigated agriculture in
people’s livelihoods the greater the likelihood of success. According to our system of
classification 63% of schemes which were cultivated and managed by full-time farmers
succeded. This suggests that when farmers derive a substantial proportion of their
livelihoods from irrigated agriculture it builds their stake in self management and
increases the likelihood that they will commit time and resources to it .
The 100 % rate of failure among schemes where irrigated agriculture is not central to
livelihoods, also raises questions about the compatibility of IMT and part-time farming.
In many smallholder schemes, farmers depend on a variety of sources to earn a
livelihood, such as pensions, livestock and jobs in urban centres. In Strydkraal,
Krokodilheuvel, Wonderboom and Veeplaats (South Africa), for example, it is common
for men to work in mines, as farm labour for commercial farmers and in cities while
women cultivate the plots. For a majority of households in these schemes, off farm
income (pensions, labour, wages etc.) exceeds farm income.8 Farmers’ who derive their
incomes from diverse sources have a reduced stake in managing the system. The high
degree of failure among this group suggests this has important flow on effects in terms
of the time and resources that are committed to cultivation and scheme management.
Latin American case studies only offer fragmentary data on the importance of
agriculture in the livelihoods and farmers (only 33% of studies had data on this
indicator) and hence we could not use this information for analysis.
Property rights (both land and water) also provide important security and incentives
for farmers to invest in irrigation management. Insecure tenure, lack of crop choice,
rigid rental markets and the inability to offer land as collateral for obtaining credit,limits the time, materials and cash farmers are willing to commit to long term
development investments on their land (let alone in the scheme as a whole). Moreover,
insecure land rights restrict the incentive/ability for uninterested farmers to sell out,
and interested and capable ones to expand their holdings (Shah et al., 2002). For
example, in the small-holder pump schemes of Saga, Kourani-Baria I and II (Niger) the
precise ownership rights of irrigated lands remains unclear as scheme members cannot
sell their land (even if they wish to) but may be evicted from their land (against their
will) by the Water User Association (Abernethy et al, 2000). The issue of uncertain land
tenure arrangements also assumes a gendered dimension in Veeplaats irrigation
scheme (South Africa). Kamara et al (2002) note that well over 50% of farmers
currently cultivating plots (mostly women) do not have the Permission to Occupy (PTO)
registered in their name. Rather, titles are mostly in the names of men, creatingdisincentives for the actual farmers (women) on the scheme.
In Latin America land tenure is not a major institutional issue, indeed all of the schemes
in our sample had either high or mixed degrees of land rights and neither had a
significant relationship with success rates in the region. As a result many of the
8 For detailed analysis see: A.B. Kamara et al. (2002): 820.
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challenges posed by insecure land rights in Africa (documented above) do not appear to
be relevant in the Latin American context. However, our results indicate that water
rights do appear to impact the extent to which farmers are willing to invest in collective
action for irrigation management (see Figure 17). Of the case studies which had clearly
defined water rights, almost 60% were from Mexico, and all successful cases (in Latin
America) with comprehensive rights were Mexican (thus our results on this front are
skewed by a regional bias and may also reflect other factors unique to the Mexican
case).
Indeed, our results on a number of indicators are biased by the strong representation of
Mexican schemes. As a result, it is difficult to tease out which factors matter most and
why. We can only assume that several factors may have influenced these positive
results. First, Mexico’s IMT program was initially concentrated in the large, commercial
areas of the North and Northwest where the concept of turnover had strong local
support. Five (out of a possible six) cases in our study were drawn from these areas and
thus our results from the country have a strong regional bias . Second, the IMT program
had high level political commitment (from the Office of the President), and as a result,
turnover was preceded by legislation (the national water law) which established water
rights and the legal status of WUAs. Finally, the speed and success of IMT in Mexico can
partly be explained by the high degree of social organization within the irrigation sector
prior to transfer. As mentioned earlier, users were mainly organized through the ejido,
production cooperatives, and farmer unions. WUAs were built upon these traditional
organizational lines and thus harnessed existing social capital or patterns of
cooperation. Most of these factors were highly correlated with successful transfer in
Latin American schemes (see Table 3). Only 45% of African case studies had data on
this indicator and thus comparative analysis on this front is not possible.
Taken together our results suggest that when farmers are heavily invested in scheme
management, either through property rights or because they derive their main source of
livelihoods from the scheme, the likelihood of successful transfer is high.
Process: Success as a function of implementing agency and level of transfer
As argued by Shah et al. (2002), the overwhelming majority of the literature on
IMT/PIM assumes that successful management transfer to user organizations is in
principle viable provided the ‘process’ is right (Vermillion 1996; Fredericksen and Vissia1998; FAO, 2007). Drawing on our rich database we were able to test this hypothesis by
analysing the relationship between the method and extent of devolution and rates of
successful management transfer.
Our results indicate that chances of success are significantly higher when NGOs or
donors assist the implementation of IMT/PIM policies. This finding was consistent
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across Africa and Latin America (and Asia).9 For instance, in the case of a pilot WUA
within the Gezira Scheme (Sudan), farmers benefited from extensive donor and
government funds and intensive capacity building by national consultants and
international organisations such as, FAO and the World Bank . It is important to note
that the areas chosen as pilot sites for IMT are by no means representative of irrigation
farming communities in Africa as a whole. For example, farmers identified for the pilot
project in Gezira had well defined land rights; were located in proximity to trainers and
consultants (Sudan Gezira Board headquarters), and had above average levels of
education. In addition the pilot site was identified based on high average production
levels (prior to transfer, the average yield of the pilot site was 16% above the scheme
average), adequate water supply, and absence of socio-economic constraints (Hussein,
2006).
As part of the project, farmers were trained for 3 hours a week throughout the season
on maintenance of minor canals, water management and to some extent in matters such
as cropping systems, animal husbandry, marketing and accounts keeping. Training also
stressed equity in distribution along minor canals. At the field level, over 900 training
sessions were held through 12 Farmers Field Schools (FFS). FAO also established a
Revolving Fund in the first season ($15,000) which was used to procure fertilizers and
seeds. Abdelhadi et al. (2004), further attributes the success of the Gezira pilot project
to farmers’ accumulated ‘experience in technology transfer.’ Put simply, farmers in pilot
areas had prior training through the auspices of another project (Integrated Pest
Management Research and Training Centre (IPMRTC) and FAO had been training
farmers in the project area through field schools since 1993).
In contexts such as these, it is very likely that NGO assisted implementation produced
favourable outcomes, especially vis-a-vis comparable schemes who did not benefit from
similar long-term investments in capacity building. However, many factors that shaped
the success of this particular case have their roots in pre-transfer processes and trends.
Thus, similar results cannot be expected in other blocks of the same scheme (the
majority of which do not share these traits) let alone in entirely different systems which
are costly to run, suffer from technical problems, dilapidated infrastructure and
poverty, have lower productivity levels and are unable to collect adequate irrigation
fees from farmers to cover O&M.
Moreover, because the pilot experience was so process intensive it difficult and costly to
replicate on a mass scale. More broadly, NGO involvement in IMT/PIM implementation
has been highly localized and rarely produces reform beyond a very local scale
(Shashidharan 2000). Finally, in our sample the majority of the NGO assisted case
studies were short-term impact assessments and therefore may not capture the actual
management capacities of farmers or the sustainability of NGO-donor investments in
capacity building etc.
9 See Mukherji et al, (2009).
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In Latin America, there was also a 100 per cent success rate amongst IMT programs
initiated by farmers. However, the four studies in question (Coello Irrigation District,
Saldana Irrigation District, A.U.P.A Culiacancito WUA and Productores Agrícolas
Asociados, A.C WUA) are exceptional cases in which existing farmer cooperatives were
well organized and were motivated by a strong belief that they could administer
schemes more efficiently than the government. Indeed in Coello and Saldana (Colombia)
high de facto farmer participation in irrigation management existed prior to transfer
and the schemes were already highly profitable. Both cases are from a relatively
prosperous farming area, where agriculture and agro-business are the mainstream of
the local economy. In short, these case studies share the ‘double coincidence of need
and capacity’.
The extent of devolution varies significantly across and within locations as do the roles
of farmers and irrigation agencies in O&M. Our sample contains a mix of IMT models, in
which either total or partial management authority is devolved at one of the following
hydraulic levels: headworks, subsystem, main/branch or distributary. Our results show
that the hydraulic level transferred does not have any bearing on success rates but
partial O&M transfer is more likely to succeed that total O&M transfer. This is consistent
with earlier findings that suggest continued agency involvement in O&M is sometimes
necessary, especially in contexts where there is a significant capacity shortfall. On the
whole however, it is difficult to make clear distinctions between farmer and agency
management in many post-transfer systems. Rather, in cases of total O&M transfer, the
agency is still involved in systems management at higher hydraulic levels (our sample,
includes only one case in which management of headworks were transferred to
farmers). According to Aw and Diemer (2005), many of the inefficiencies of farmer
management at the distributary level can be traced to main system managers, who fail
to deliver reliable water supplies in predictable schedules at the outlets. In such cases,
power continues to be vested outside WUAs, technically in the main system and
institutionally with the Irrigation Agency (Narain, 2008).
So while one to one correlation indicates that the ‘right process’ is an important factor
determining success, it may not be sufficient to explain performance outcomes,
especially in the African small-holder context. Our results suggest that the relationship
between ‘process’ and successful transfer is mediated by other factors. In the small-
holder schemes of Africa, for example, irrigated farming faces an ‘entire complex of
constraints’ which prevents effective farmer management (many discussed above, such
as market access, property rights and low incomes and productivity).10 Indeed, in manycase studies, income per household remains too low to meet all subsistence
requirements let alone generate the surplus needed for development (Shah et al. 2002).
In situations such as these the method and extent of devolution do not address the true
10 Shah et al. refer to these constraints as downward ratchets or multiple constraints which disable poorpeople “like movements down past a cog which are difficult or impossible to reverse, making poor people
permanently poorer.” (Chambers 1983: 115).
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causes of sub-optimal performance. This requires support systems that extend beyond
irrigation and which significantly enhance farmers’ livelihoods.
Technical Specifications: Success as a function of the size of system and type of
scheme
A common assumption underpinning the literature on common pool resources (CPR) is
that small sized systems with well defined boundaries are more amenable to
cooperative management.11 This idea draws strength from the endurance of many
traditional self-governed irrigation systems (FMIS) which are well bounded in terms of
both physical infrastructure and scheme membership. However, our results indicate
that irrigation systems which were constructed and formerly managed by the state do
not conform to the hypothesis put forward in CPR literature. On the contrary, in our
sample the likelihood of success does not vary much according to size, if anything
smaller systems are slightly more likely to fail. According to our system of classification,
small and medium-large systems in Africa had failure rates of 85% and 58%
respectively.
In particular, cooperative farmer management has failed to make a mark in small‐holder
farming contexts of Africa. This inconsistency stems from fundamental differences in
the socio-technical conditions and organisational characteristics of WUAs vis-à-vis
farmer organisation under FMIS. As Mukherji et al (2009) argue the ‘farmerparticipation concept’ and ‘culture of irrigation’ common to FMIS is not easily
reproduced in government management systems. This is because there are significant
differences of design and socio-technical complexity between government systems and
FMIS. Indeed, in Africa many irrigation sites were designed based on politico-social
rather than technical rationales (i.e. African irrigation schemes provided the means to
realize food self sufficiency and/or a policy of segregated development, with much less
attention paid to their commercial viability). In addition, small-holder farming in Africa
under agency-management was highly mechanized. The ARDC in South Africa for
example, used heavy equipment for ploughing, land preparation, spraying and
harvesting (Shah et al. 2002). The transfer to farmer management has significantly
increased the cost of hiring farm equipment which has reduced the profitability and
increased the risks of small-holder farming. Thus, the poor design of the majority of smallholder schemes exposes a central contradiction in IMT policy: farmers are now
being called upon to manage expensive and complex outfits that were never meant to be
run by farmers.
11 Refer to: Agrawal (2001); Wade (1988); Ostrom (1990) and Baland and Platteau (1996).
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The mismatch between IMT models and farmers’ managerial and financial capacities
appears to have serious implications for rates and success and failure, especially in the
African context. For instance, the high rate of failure among African pump schemes,
82%, in our sample is largely due to the high cost of energy (following the removal of
government subsides) and pump maintenance or repair. In an analysis of four
smallholder pump schemes in Niger, Shah et al. (2002) note that turned over pump
schemes almost always leave farmers in the red as net income is 20-25 per cent of gross
income and irrigation fees are as high as 15-20 per cent of gross income. Similarly, in
Senegal Valley’s small-scale schemes, rundown pumps coupled with high energy and
maintenance costs have resulted in the suboptimal performance of pump schemes
(Wester et al, 1995).12 In particular, higher input prices and the greater complexity of
dry season irrigation after management transfer in the Doue Region, lead farmers toalter their cultivation patterns and focus on wet season crops. In short, low farm
productivity and income prevents many African smallholders from taking advantage of
the managerial advantages and better water control offered by high cost pump schemes.
In Latin American pump schemes, high energy and maintenance costs have also led to
sub-optimal results across key performance indicators. In the case of Roldanillo-Union-
Toro (RUT), for instance, the total cost of irrigation is relatively high due to two-stage
pumping. The financial viability of the scheme, prior to transfer, was assured by large
government subsidies which covered 60-80% of total costs. When the government
eliminated subsidies to the agriculture sector and halted its expenditures for O&M in
the scheme, farmers struggled to absorb the high pumping costs involved in irrigated
farming. As Vermillion and Garcés-Restrepo note, ‘over 6 years after transfer, total
farmer payments declined in real terms by 22 per cent, from US$83 to US$65 per
hectare by 1995. This was due to both a decline in fee-collection rate, from above 90 per
cent before transfer to less than 70 per cent by 1995 and a reduction in the amount of
water delivered per hectare’ (1998: 13). The decline of RUT’s financial solvency appears
to be attributable to its high cost and the sudden loss of substantial subsidies from the
government.
Samacá irrigation district (Columbia) followed a similar trajectory post-transfer. It was
also a high-cost system previously sustained by generous government subsidies, which
witnessed substantial cuts in O&M expenditure and declines in irrigation fee collection
rates following IMT. In contrast, low cost Rio Recio was able to sustain financial
solvency for the first 5 years after transfer and dramatically increase fee collection
rates. The O&M budget remained at virtually the same pre-transfer level for severalyears after transfer.
Qualitative review of failed cases suggests that if a scheme is financially viable prior to
transfer, it is likely to financially viable following transfer. If however, pre-transfer
12 Farmers had to travel 350km to Saint -Louis or 650km to Dakar to obtain spare parts needed for pumprepair.
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viability depends of significant government inputs, then farmers must be able to absorb
the costs of irrigated farming by cross-subsidising irrigation costs.13
Table X: Frequency distribution of descriptive indicators
DESCRIPTIVE INDICATOR
AFRICA LATIN AMERICA
% of data available % of data available
Type of scheme 98 100
Size of scheme 97 100
Scheme complexity 85 94
Age of the scheme 70 67
Crop systems (high/low value) 88 89
Crop Systems (Paddy/non Paddy) 97 94
Population served by the scheme 65 44
Physical water scarcity 80 100
GDP/capita (PPP US$) 100 100
Access to support services 83 72 Importance of agriculture in people'slivelihood 87 33
Social cohesion 55 44
Well defined water rights 45 94
Land rights 83 89
Average size of land holding 68 33 Year of transfer/formation of WUA/initiation of PIM 82 89
Highest hydraulic level transferred 70 72
Amount of O&M authority transferred 98 100
Implementer 100 100
Rehabilitation before or during transfer 68 72
Training and capacity building 58 61 Farmer's organization created beforethe transfer 73 100
Strong political will 52 83 Supporting legislation before thetransfer 47 83
WUA Chairman are elected 75 94
13 This was done in Coello, Colombia by diversifying revenue sources after transfer, including equipment rental, interest from small loans, provision of fertilizers and agro-chemicals etc.). See: Garcés-Restrepoand Johnson III (1997).
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Table X: Contextual factors correlated with successful IMT/PIM
AFRICA
TYPE OF SCHEME SIZE OF SCHEME SCHEME
COMPLEXITY CROP SYSTEMS CROP VALUE
Diversion Pump
and Lift Storage Mixed Small Medium
and Large Simple Complex Paddy Non-
Paddy High Low Mixed
Percentagesuccessful
Percentage FailedTotal number of cases
PHYSICAL WATER
SCARCITY
ACCESS TO SUPPORT
SERVICES
IMPORTANCE OF
AGRICULTURE IN PEOPLE'S
LIVELIHOOD
WELL DEFINED
WATER RIGHTS LAND RIGHTS
High Low Mixed High Low Mixed High Low Mixed High Low High Low Mixed
Percentagesuccessful
Percentage FailedTotal number of cases
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AFRICA
HIGHEST HYDRAULIC LEVEL TRANSFERRED
AMOUNT OF
O&M
AUTHORITY
TRANSFERRED
IMPLEMENTER REHAB TRAINING
Headworks Main/Branch Subsystems Distributary
Partial Total Donor/NGOAssisted
Gov Farmers Yes No Yes No
Percentagesuccessful
Percentage Failed
Total number of cases
FARMER'S ORGANIZATION
CREATED BEFORE THE
TRANSFER
STRONG POLITICAL WILL
SUPPORTING LEGISLATION
BEFORE THE TRANSFER WUA CHAIRMAN ARE ELECTED
YesNo Yes No Yes No Yes Low
Percentagesuccessful
Percentage FailedTotal number of cases
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Table 5: Logit estimates (40 observations from Africa); Dependant v ariable: SUCCESS
Logit estimates Number of obs = 40LR chi2(7) = 36.09Prob > chi2 = 0.0000
Log likelihood = -9.2293616 Pseudo R2 = 0.6616
------------------------------------------------------------------------------success_sc~s | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------scheme_type | .816976 1.934049 0.42 0.673 -2.97369 4.607642scheme_size | -.4322562 1.705611 -0.25 0.800 -3.775192 2.910679crop_value | .2825835 1.659349 0.17 0.865 -2.96968 3.534847crop_type | 1.388716 1.904762 0.73 0.466 -2.344549 5.121981
acess_credit | 3.29745 1.813107 1.82* 0.069 -.256174 6.851074landrights | 3.437275 1.529967 2.25* 0.025 .4385957 6.435954Implementer | 4.084172 2.312326 1.77* 0.077 -.4479038 8.616248
_cons | -7.047084 3.61534 -1.95* 0.051 -14.13302 .038852------------------------------------------------------------------------------
From this model, we comment only on the variables that are significant. In this case,
high access to credit/markets, well defined land rights and NGO-assisted
implementation are significant at 10% levels. The model correctly predicts in 92% of
cases.
Following this, the marginal effects model was also estimated. This model shows how
much the probability of success changes when various independent variables change
from 0 to 1
Table 6: Marginal effects model
Marginal effects after logity = Pr(success_scores) (predict)
= .21811936------------------------------------------------------------------------------variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X---------+--------------------------------------------------------------------schem~pe*| .1423305 .34043 0.42 0.676 -.524898 .809559 .45schem~ze*| -.0701373 .26589 -0.26 0.792 -.591271 .450996 .3crop_v~e*| .0512344 .3079 0.17 0.868 -.552248 .654717 .1crop_t~e*| .1767934 .20553 0.86 0.390 -.226038 .579625 .85acess_~t*| .6516267 .24434 2.67* 0.008 .172736 1.13052 .275landri~s*| .6210161 .22622 2.75* 0.006 .177633 1.0644 .4implemen*| .647597 .22287 2.91* 0.004 .210779 1.08442 .5------------------------------------------------------------------------------
(*) dy/dx is for discrete change of dummy variable from 0 to 1
The marginal effects model shows that for a discrete change in type of scheme from 0 to
1, the probability of success increases by 0.14 units. For discrete changes in access to
credit, landrights and implementing agency from 0 to 1 (i.e. from low to high; or from
government to donor-NGO assisted) the probability of success increases by more than
0.62 units and this is significant at 1%.
We also predicted the logs odds ratio, our results are given below.
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Table 7: Logs odds ratio
Logit estimates Number of obs = 40LR chi2(7) = 36.09Prob > chi2 = 0.0000
Log likelihood = -9.2293616 Pseudo R2 = 0.6616
------------------------------------------------------------------------------success_sc~s | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------scheme_type | 2.263644 4.377999 0.42 0.673 .0511143 100.2475scheme_size | .6490431 1.107015 -0.25 0.800 .0229327 18.36927crop_value | 1.326553 2.201213 0.17 0.865 .0513197 34.28978crop_type | 4.009698 7.63752 0.73 0.466 .0958905 167.6671
acess_credit | 27.04359 49.03291 1.82 0.069 .7740073 944.8949landrights | 31.10209 47.58516 2.25 0.025 1.550528 623.8777implementer | 59.39275 137.3354 1.77 0.077 .6389662 5520.634
------------------------------------------------------------------------------
This model shows that for one unit increase (or in this case, a change from 0 to 1) in
type of scheme, the odds of success increase by a factor of 2.26. For a change in
implementer (from 0 to 1) that is from government to NGO assisted, the odds of success
increase by a factor of 59.39.
Lastly, we ran a simple test to see if the model is specified correctly. Through this test
we can determine if any other significant variable has been left out of our analysis.
Table 8: Link test
Logit estimates Number of obs = 40LR chi2(2) = 36.10Prob > chi2 = 0.0000
Log likelihood = -9.2238174 Pseudo R2 = 0.6618
------------------------------------------------------------------------------success_sc~s | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------
_hat | .9941093 .352405 2.82 0.005 .3034082 1.68481_hatsq | -.0130492 .1252164 -0.10 0.917 -.2584688 .2323703_cons | .0449714 .7504288 0.06 0.952 -1.425842 1.515785
------------------------------------------------------------------------------
The results show that the model is correctly specified in the sense that no variable
which can significantly influence the probabilities has been left out. This is because the
“_hat ” is significant but ‘_hatsq’ is not significant. This is an indication of correct model
specification.
From this analysis three factors consistently appear to influence the probability of successful management transfer in Africa, these are high access to credit/markets, well
defined land rights and NGO assisted implementation. The other variables do not
appear to be significant.
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Taken together, our analysis suggests that the results of management transfer, across
and within Latin America and Africa, are mixed. In the African context, schemes which
have high access to credit/markets, large stakes in irrigated agriculture, and which
benefited from a NGO-Donor assisted implementation process are most amenable to
successful management transfer. In Latin America, water rights, farmers’ organisational
capacity and political commitment at the highest levels of government were all
positively correlated with success. Thus, our analysis makes plain that what matters in
Africa is not always relevant in Latin America and vice versa. It also suggests that some
factors may be so critical to successful transfer (i.e. the ability to absorb the costs of
management) that they impact the significance of others (i.e. type and size of scheme).
Moreover, our results indicate that there is great variation in terms of the maturity and
functionality of schemes earmarked for IMT/PIM interventions. Different phases of
political, administrative and institutional development shape the maturity and
functionality of WUAs, which in turn determines the modes and outcomes of PIM that
are realistic at the present moment. It follows then that there is not a sole IMT/PIM
model that can be replicated across locations and that context shapes the scope for
change and as a result institutions that are effective in some places (Northern Mexico)
cannot be transplanted or socially engineered in another environment (smallholder
irrigation sectors of Africa) with the same outcome. In the case of the latter it may be
neither necessary nor possible to create and sustain formal local organisations focused
on irrigation management, such as WUAs. Rather as Srivastava and Brewer argue ‘in
societies characterised by diversified livelihoods and marginally profitable irrigated
agriculture, it may not be worthwhile for farmers to invest heavily in management
associations’ (1994:15). In such contexts, adapting existing multipurpose local
organisations, like village based administrative units, Farmers Organisations or
traditional water operators to manage irrigation may be the more sustainable and
effective option.
Conclusions
The lessons that emerge from our review are multi-fold. First, although the literature on
IMT/PIM is extensive, the evidence linking improvements in scheme performance to
management transfer is far from compelling. Our study highlights important limitations
of existing impact assessments, particularly their heavy reliance on post-intervention
data to infer cause and effect. It also highlights the value of high quality studies (with
both a strong comparison group and pre-and post-intervention data) and long-termimpact assessments for making credible, generalizable conclusions about what works,
where and why.
We also contribute new insights into which socio-economic and technical mechanisms
appear to be active in successful management transfer. In both qualitative and
quantitative review of African case studies three factors were consistently correlated
with successful IMT/PIM, these are credit and market access, property rights and NGO-
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donor assisted implementation. In Latin America, governance issues such as
organizational structure and water rights increased the likelihood of successful
management transfer. Our research suggests that at least in the African context, the IMT
process needs to be accompanied by greater interventions, relating to land reform,
market access, and other support services which will help farmers increase their
livelihoods and consequently their ability to absorb the costs and responsibilities of
management.
Our analysis also indicates that management transfer, in itself, rarely produces
significant changes in the quality of financial management or technical efficiency. In the
dynamic agrarian sectors of Africa and Latin America the transition to famer
management was smooth, effortless and in many cases merely formalised the status
quo.14 In the overwhelming majority of systems however, transfer takes place in a
context of moderate dynamism and poverty.15 Here, the causes of sub-optimal scheme
performance rarely lie in the degree or intensity of farmer participation but rather,
reflect design flaws, problems of market access and property rights, land
fragmentation, the low profitability and productivity of irrigated agriculture, unstable
water supply and unequal water distribution. Unless, the actual causes of poor
performance are understood and addressed reform of the public irrigation sector will
remain only a piecemeal affair, as it has been so far
14 Examples include Coello and Saldana (Colombia), A.U.P.A Culiacancito WUA and Productores AgrícolasAsociados, A.C WUA (Mexico) and the white, commercial farms of South Africa and Zambia)15 Examples include most African small holder schemes , Maria La Baja (Columbia) Yaque del NorteProject (Dominican Republic)