The perceived impact of agricultural advice in Ethiopia
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The perceived impact of agricultural advice in
Ethiopia
Alexander Hamilton & John Hudson
To cite this article: Alexander Hamilton & John Hudson (2016): The perceived impact of
agricultural advice in Ethiopia, The Journal of Agricultural Education and Extension, DOI:
10.1080/1389224X.2016.1245151
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3. There is evidence of an impact on agricultural efficiency. Dercon et al. (2009) showed
that one visit from a DA raised production growth by 7% and reduced poverty by 10%.
The posting of agricultural extension agents in local communities has improved their
attentiveness to farmers’ needs and constraints, and enhanced the working relationship
between them (Cohen and Lemma 2011). However, others have tended to be more scep-
tical about the impact of extension agents (Lefort 2012; Pender, Place, and Ehui 2006).
Davis et al. (2010) also note that agricultural productivity remains low, inputs are
scarce and expensive, and market and credit access are extremely limited and according
to Buchy and Basaznew (2005), women-focused extension is also limited.
In general, Ethiopia has tended to be viewed unfavorably in terms of agricultural pro-
ductivity (Dercon and Christiaensen 2011; Spielman et al. 2010). However in recent years,
certainly since 2008, there has been a substantial, even dramatic, improvement in cereal
yields. This has been accompanied by a 12.5% expansion in land under cereal production
between 2003 and 2012; hence it is not the case that more marginal land has disappeared
from the picture, raising the average productivity of what is left. Apart from the extensive
changes to the extension system, other changes impacting on Ethiopian agriculture
include substantial transport improvements which between 2000 and 2013 tripled the
length of all-weather surface roads, and a rapid increase in the urban population of
some 3.7 million (Bachewe et al. 2015). Together these have increased the market for com-
mercial crops and partly as a consequence output–input price ratios have increased sub-
stantially. There has also been increased access to credit, which saw the number of active
borrowers rise from about half a million in 2003 to approximately 3.5 million in 2014.
In this paper, we will be focusing on the potential role of the extension system in explain-
ing this recent success. Specifically, we analyze perceptions of the difference that the advice
made to the recipients of that advice with respect to (i) crop yields and (ii) income. Self-
perceptions data have the advantage that the focus is on the difference made by the exten-
sion service advice, whereas actual output and income can change for numerous reasons
unconnected with that advice. To include all the factors that might affect yields and
income is difficult and such data are also likely to be subject to measurement errors. We
assume that the farmers in evaluating the impact of the advice take account of all these
other factors. In addition, because the dependent variable relates to the impact of advice,
we can examine how that impact varies with variables such as the farmer’s age, education
and water resources. Other studies have tended to focus on the impact of variables such
as education on productivity, but not on the effectiveness of advice.
The main research question is whether extension advice benefits farmers both in terms
of their crop yields and income. We expect this to be the case, but the literature is ambig-
uous on the issue. A secondary question is what types of advice work best and in what
context. There is relatively little literature on this. However, there is a literature which
suggests that different types of technology, for example, fertilizers, work better in some
conditions rather than others (Dercon and Christiaensen 2011; Kassie et al. 2010). This
is of relevance, but advice on implementing a technology is different to the implemen-
tation of that technology. In addition part of this research relates to more than a
decade ago and, as already noted, in this time the extension system has substantially
changed, technology has changed and Ethiopia has changed. Hence, there is a need for
new research in this area.
2 A. HAMILTON AND J. HUDSON
4. We will be analyzing the impact of advice on (i) agricultural practices, (ii) land manage-
ment, (iii) fertilizers, (iv) marketing, (v) access to credit facilities and (vi) animal husban-
dry practices. We find that extension agent advice does lead to a perceived positive impact
on both crop yields and income. However, the effectiveness of the advice depends upon
the level of education of the farmer. It also differs between drought-affected and non-
drought-affected lands. The paper proceeds as follows. In the next section we discuss
the relevant literature. We then discuss, from a theoretical perspective, how advice
might impact on farmers and other methodological issues, and we also present the
data. The penultimate section presents the results and finally we conclude the paper.
Background
The literature on the extension program has been somewhat ambivalent in terms of its
impact. Extension services generally have positive impacts on nutrition and poverty
reduction (Dercon et al. 2009). However, their success has been said to be constrained
by weaknesses elsewhere in the system. Hence EEA/EEPRI (2006) argue that distribution
channels and institutions are flawed, the formal seed system has weaknesses, and there is a
lack of markets, both for inputs and outputs. Agents transfer knowledge to farmers, with
relatively little knowledge flow in the reverse direction (Buchy and Basaznew 2005), which
can lead to the knowledge not being tailored to the farmer’s needs. The literature argues
that the extension system has focused on the distribution of standard packages to farmers,
including seeds and commercial fertilizer, credit, soil and water conservation, livestock
and training. Efforts to promote other aspects of sustainable land management have con-
centrated on soil erosion without consideration of the underlying socioeconomic reasons
for low soil productivity (Kassie et al. 2010). As a consequence, advice has been given
which has been unprofitable, risky or irrelevant given the farmer’s resource constraints
(Pender, Place, and Ehui 2006). Lefort (2012) cites research which concludes the extension
program was not much use to farmers. However, Spielman et al. (2010) also note that a
series of reforms have been made to redress these weaknesses.
Berhanu and Poulton (2014) argue that the extension system is used to promote gov-
ernment control, with extension workers facilitating scrutiny and control of activities.
Extension workers engage in non-extension activities, such as administration, credit
repayment and tax collection (Kelemework and Kassa 2006). Apart from diverting their
attention from extension services, this can also strain relationships with local farmers.
In addition, it is claimed that in their allocation of seeds, fertilizers and credit, extension
workers prioritize farmers loyal to the governing coalition. It is also important to stress
that extension agents are not always transmitting knowledge and advice which can
immediately add to the farmer’s productivity. They may also be concerned with societal
impact. For example, in some cases the advice relates to environmental factors (Abegaz
and Wims 2015) for which the farmer may perceive little personal benefit, although
this is not to say that such benefit is absent.
More recent evidence is a little more positive, although still emphasizing that practices
could be better. Elias et al. (2013) found that participation in the extension program
increased productivity by about 20%. Other factors which influenced productivity
included age, male head of household and plot characteristics. Despite this, crop yields
were below the targets set by the extension program. Khan et al. (2014) conclude that
THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 3
5. woreda-level spending on agricultural extension workers is associated with higher yields
for major crops and increases the probability that farmers will improve their farming
techniques.
There is relatively little evidence on the relative impact of different forms of advice, but
some related work has been done on the different constraints facing, and the different
technologies used by, farmers. One of the reasons Dercon and Christiaensen (2011)
gave for the poor performance of Ethiopian agriculture was lack of fertilizer use. Lack
of knowledge and skills in adopting modern inputs was only a very minor constraint in
1999, which would suggest only a limited role for extension agent advice in this respect.
Nor was lack of credit deemed a major factor. Fertilized plots were characterized by
greater yields than non-fertilized plots, although not in periods of extreme droughts
and floods. Thus, Dercon and Christiaensen argued that fertilizer use is a high return,
but high risk technology. Kassie et al. (2010) find evidence of a strong impact of land man-
agement practices on agricultural productivity in the low agricultural potential areas. In
the high agricultural potential region, however, fertilizers have a very significant and posi-
tive impact on crop productivity, whereas land management practices have no significant
impact. Fertilizers may be less profitable in such areas due to a lack of soil moisture.
Hence, their analysis raises the important point that the impact of different forms of
advice and increasing knowledge may not be the same in all areas, but vary according
to local conditions.
Methods
We will be analyzing the impact of advice on both crop yields and inccome. For the
moment, we focus on the impact on crop yields (Y), although the analysis for income
follows a similar path. We assume Y to be a function of resources, which are in turn a func-
tion of knowledge:
Yit = Aig(Sit, Lit,Kit, Fit, Wit), (1)
where i denotes the ith farmer and t the time period. Ai denotes overall efficiency with
which the different factors of production are used, that is, it is total factor productivity
(TFP) defined at the level of the individual farmer. In our analysis, we assume that this
is the vehicle by which extension agent advice impacts on output. g(.) can be regarded
as the basic output of the farm independent of the characteristics and expertise of the
farmer. The production function is composed of land (S), labor (L), capital (K), fertilizer
(F) and water (W). The impact of extension agent advice to farmer i (Ei) on output is then
given by
∂Yi
∂Ai
∂Ai
∂Ei
Ei =
6
j=1
∂Yi
∂Ai
∂Ai
∂Eij
Eij = g(Sit, Lit,Kit, Fit, Wit)
6
j=1
∂A
∂Ej
Eij. (2)
This is the combined impact of the six specific types of advice in our analysis.2
Eij is a con-
tinuous measure of the advice given to individual i on advice type j, although we only have
a discrete measure of this, which takes a value of one if advice was given. It has a lower
bound of zero, which is operative when advice of this type is not given. The marginal
impact of any one piece of advice is composed of ∂Ai/∂Eij, the impact of this advice on
4 A. HAMILTON AND J. HUDSON
6. TFP, and secondly, the impact of TFP on output, which is from (1) just g(.). ∂Ai/∂Eij will
depend upon the characteristics of the farm (T). It may also depend upon the character-
istics of the farmer, with more knowledgeable farmers more able to implement the advice
efficiently, although at the same time more knowledgeable farmers may be less likely to
seek advice. In this case, we link knowledge to education (Ed) and age, the latter
through learning by doing. Thus,
∂Ai
∂Eij
= fj(Edi, Agei, Ti, Wi). (3)
The dependent variable is coded 1 if the extension agent advice was perceived as making
no difference, 2 if it made some difference and 3 if it made a lot of difference. The response
lies in the kth category if:
ak−1 , g(.)
6
j=1
fj(.)Eij , ak; k = 1, . . . , m. (4)
In our analysis m = 3. Note that α0 = −∞ and αm = +∞, and hence we estimate just a1 and
a2. Define Zi,k = 1 if g(.) 6
j=1 fj(.)Eij is in the kth category, and otherwise Zi,k = 0. Lin-
earizing g(.) 6
j=1 fj(.)) we can estimate both the coefficients and the dividing points (αk)
between the different categories by ordered probit. The independent variables will include
farm characteristics, individual characteristics and dummy variables operative if a particu-
lar type of advice was given. This suggests a number of hypotheses which we will be
testing:
H1: Extension agent advice impacts positively on both crop yields and income.
H2: The degree of the impact for different types of advice will depend upon the characteristics
of the farmer and the characteristics of their farm.
H3: The degree of impact of this advice will be greater for more educated individuals and also
for older individuals, although we can expect these individuals to be less likely to receive
advice.
H1 reflects our first research question, on whether extension advice benefits farmers in
terms of both income and crop yields. The other two hypotheses relate to our second
research question as to what types of advice work best and under which conditions.
Our theoretical analysis has helped inform both of our research questions. The advice
will make a difference if it is relevant advice and if the farmer, given their own and
their farm’s characteristics, will implement it efficiently. The analysis also emphasizes
that different types of advice will have different impacts, which are linked to differences
in ∂Ai/∂Eij.
Data
The data were obtained from the Woreda and City Benchmarking Survey (WCBS) col-
lected in 2014 using a multi-stage stratified sampling approach based on the remoteness
and food security levels of households. Within each region the sub-sample size was deter-
mined by population (based on census data). Data were collected on 326 kebele in 48
THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 5
7. woreda covering the whole of the country.3
In total 7429 individuals were interviewed.
This survey is focused on rural areas. The variables we use are defined in Table 1. Infor-
mation on the characteristics of the plot relates to whether the individual grew crops and
had animals. Most people sampled (60.8%) raised both animals and grew crops, a substan-
tial proportion just grew crops (31.3%) and an even smaller proportion just had animals
(7.9%).
The proportions receiving one, two, three, four, five and six types of advice as listed in
the appendix were 27%, 16%, 17%, 14%, 5% and 13%, respectively. Other advice is feasible,
which is why 8% received none of the types of advice specified. Table 2 shows the
summary data as it varies across individual characteristics. It is noticeable that the
young tend to be more likely recipients of advice, possibly reflecting that they will have
learnt less ‘by doing’. It is also noticeable how the highly educated are more likely to
have received advice on marketing and credit. The final two columns relate to the
impact this advice, in general, has had on crop yields and the individual’s income.4
The
responses ranged from 1 (none) to 3 (a lot). Thus, we assume that output and income
cannot fall as a result of the advice received. The responses to both questions were
fairly enthusiastic, although very slightly more so for crop yields than income. The
biggest gainers in both respects tend to be the better educated. An important question
relates to the role of the advice apparently not included for example, mechanization;
crop protection measures etc.? To an extent they may be subsumed within one of the
other categories, particularly agricultural practices, which as we note from Table 2 was
the most frequently cited form of advice. Any advice which still falls outside these cat-
egories would be picked up by the constant term in the regressions.
Regression results
In Table 3, we present the results relating to the impact on yields.5
Column 1 shows that
advice received on animal husbandry, marketing and land management were all signifi-
cant at the 1% level of significance and fertilizers at the 5% level. Advice on agricultural
Table 1. Data definitions.
Socioeconomic, demographic variables
Age Age in years
Education Coded from 1 (no schooling) to 24 (degree) and 25 above degree
Male Coded 1 ifa male
Family size Number of people currently living in the individual’s household
Plot characteristics
Grows crops Coded 1 if the individual grows crops, otherwise 0
Rears animals Coded 1 if the individual rears animals, otherwise 0
Drought Coded 1 if the individual suffers from regular periods of drought in the sense of a shortage of
drinking water, otherwise 0
Received advice on (coded 1 for yes and 0 no)
Agricultural practices; land management; fertilizer; marketing; credit facilities; animal husbandry
Impact
Crops improve/Income
improves
The difference the above advice has made to the crop yield/income
ranging from 1 (none) to 3 (a lot)
Kebele based variables (average of responses of others in the individual’s locality). Regional dummy variables relate to Tigray,
Amhara, Oromiya, SNNP, Binshangul Gumuz, Afar, Somali and Gambela. Because of the small numbers of people in our
sample who are in Gambela we join this region together with SNNP, the two being adjacent in the south west part of the
country. We also join the second smallest region, Afar, with Tigray, who are adjacent on the northern edge of Ethiopia
6 A. HAMILTON AND J. HUDSON
8. practices was not significant. However being in receipt of advice on credit was significantly
negative. This does not imply that output was lowered as a result of the advice. Rather, it
implies that being in receipt of advice on credit increases the probability of extension agent
advice having no impact. However, this does alert us to a potential problem of endogene-
ity. To the extent that the farmer is the one seeking this advice, rather than being proffered
it by the extension agent or some other person, then it could signal that the individual is in
financial problems. At the very least it reflects an interest by the farmer in gaining access to
credit. The negative sign in the regression may be picking this up. Another aspect of endo-
geneity is that it is possible that the individual selected for the advice is in some way more
able to use it.
Because of this possibility, in the second regression we instrument the advice variables
in a two-stage process. Firstly, we regress the advice variables on all the exogenous vari-
ables present in column 1 plus a series of variables relating to the extent others in the indi-
vidual’s kebele received different forms of advice. That is, taking credit as an example, for
each individual we calculate the average number of people in their kebele, excluding them-
selves, who received advice on credit. These effectively act as instruments. The second
stage of the estimation takes the predicted values from these regressions and re-estimates
the relationships shown in column 1. The results are shown in column 3.2. The main
change is the insignificance of advice on marketing and also on credit. A Hausman test
indicated that the two sets of coefficients were significantly different and hence the
need for an instrumental variable (IV) approach. This two-stage estimation technique is
similar to that employed by Adams, Almeida, and Ferreira (2009). The literature tends
to bootstrap the standard errors (Clarke and Windmeijer 2012) and this has been done.
On each of the 100 iterations of the bootstrap, both stages of the two-stage estimation tech-
nique were estimated. The t statistics reported are based on these standard errors.
In addition to the above, we also estimated the equations using IVs in the context of a
conditional mixed process (cmp) model (Roodman 2011), that is estimating a system
where the different equations can have different kinds of dependent variables. We
assume joint normality of the error terms of the different equations. It is a full system tech-
nique, which takes account of potential correlation between the different error terms in the
equations. There are potentially eight equations in the system, one for each of the advice
variables and two more for the impact variables, that is, the impact on crops and income.
However, this makes considerable demands on the data and we had to simplify the
equations in order to obtain estimates. Firstly, we estimated each of the policy impact
Table 2. Summary data relating to individual characteristics.
Animal
husbandry Credit Marketing Fertilizer
Land
management
Agricultural
practices
Crops
improve
Income
improves
All 0.62 0.291 0.329 0.679 0.524 0.817 2.36 2.3
Young <30 0.631 0.321 0.329 0.731 0.561 0.847 2.37 2.29
Older ≥30 0.612 0.264 0.326 0.634 0.488 0.787 2.35 2.29
Male 0.621 0.278 0.317 0.665 0.525 0.815 2.36 2.3
Highly
educated
0.585 0.342 0.447 0.707 0.523 0.845 2.51 2.41
No education 0.622 0.292 0.313 0.678 0.501 0.804 2.32
2.28
Notes: The final two columns relate to the average response which varied from 1 (none) to 3 (a lot) to the difference the
support has made. All other columns relate to the proportion receiving advice in the different headings.
THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 7
9. Table 3. Regression results: impact on crop yields.
Sample Probit: Full 3.1 IV 2 stage Full 3.2 Probit: Full 3.3 Drought 3.4 No drought 3.5 CMP: Full 3.6 Drought 3.7 No drought 3.8
Extension agent advice
Animal husbandry 0.255**
(5.35)
0.4696*
(2.41)
0.4695*
(2.41)
0.9789**
(4.07)
0.1662
(0.52)
0.6085**
(5.27)
0.6273**
(4.44)
0.648**
(3.22)
Credit −0.2344**
(4.77)
0.0069
(0.03)
−0.0284
(0.12)
−0.2431
(0.95)
0.9285*
(2.11)
−0.3592*
(2.42)
−0.1748
(1.03)
−0.2966*
(2.18)
Marketing 0.4172**
(8.76)
0.3156
(1.72)
0.4122*
(2.38)
−0.0737
(0.35)
0.9992**
(2.91)
0.5683**
(4.45)
−0.1576
(0.93)
1.452**
(13.77)
Fertilizers 0.0884*
(1.98)
0.8863**
(3.68)
1.006**
(4.24)
0.8349*
(2.46)
0.7405*
(2.01)
0.8513**
(7.69)
0.9966**
(8.28)
0.7314**
(4.29)
LM – land management 0.2874**
(6.47)
0.6833**
(3.09)
AG – agricultural practices 0.0756
(1.30)
0.1408
(0.61)
LM + AG 0.8374**
(2.69)
0.3536
(0.73)
2.378**
(4.74)
1.134**
(5.82)
0.3289
(0.74)
1.222**
(4.67)
Individual characteristics
Log age 0.2891**
(4.52)
0.1094
(1.62)
0.102
(1.51)
−0.0723
(0.83)
0.1991
(1.79)
0.0518
(0.78)
−0.0664
(0.75)
0.1341
(1.33)
Education 0.0264**
(5.26)
0.0244**
(4.05)
0.0249**
(4.16)
0.0192*
(2.34)
0.0236**
(2.91)
0.0276**
(5.61)
0.0262**
(3.86)
0.0122
(1.67)
Male −0.0538
(1.52)
Log family size −0.0785
(1.78)
Farm characteristics
Crops 1.316**
(12.81)
0.6006**
(3.43)
0.4652**
(3.04)
0.058
(0.26)
0.3388
(1.39)
−0.2539
(1.60)
−0.3369
(1.18)
0.279
(1.14)
Animals −0.0251
(0.51)
−0.3715**
(2.91)
−0.3685**
(2.83)
−0.3783*
(2.35)
−0.4832*
(2.50)
−0.1909*
(2.16)
−0.2073
(1.94)
−0.1815
(1.22)
Constant −1.562**
(5.20)
−1.232**
(2.97)
−2.250**
(4.99)
Estimated cutoff points
Cutoff point 1 ˆa1 0.2798
(1.06)
−0.7927**
(2.66)
−0.7646*
(2.56)
−0.9719*
(2.26)
−0.3325
(0.69)
Cutoff point 2 ˆa2 2.573**
(9.65)
1.477**
(4.91)
1.503**
(5.00)
1.570**
(3.65)
1.801**
(3.71)
Observations 5192 5185 5185 2928 2257 5192 2932 2260
Log likelihood −3733 −3768 −3770 −2010 −1534 −12516 −7489 −4612
X2
1228 1089 1089 380.9 850.8 355571 159143 6384
Notes: Equation 3.1 estimated by ordered probit, 3.2–3.5 by a two stage instrumental variable ordered probit with bootstrapped standard errors, 3.6–3.8 by a conditional mixed processor estimator
with a binary dependent variable. (.) denotes t statistics and **/* denotes significance at the 1% and 5% levels. Regional dummy variables included in all regressions.
8A.HAMILTONANDJ.HUDSON
10. variables separately. Secondly, we used binomial probit for the impact equations rather
than ordered probit, with the variable differentiated between a lot of impact (coded 1)
and some or no impact. Finally, we combined two of the advice variables together, that
is land management and agricultural practices. These potentially relate to all farmers
and also to farming per se rather than other aspects of the business and have a reasonably
high correlation. The cmp estimator is complex, but the interpretation of the coefficients
and t statistics is as with techniques such as OLS.
The results are shown in column 3.6 and are similar to previously. The main difference
is the significance, at the 1% level, of the advice variable relating to both agricultural prac-
tices and land management and also marketing advice. In column 3.3, we replicate these
using the two stage approach used in 3.2. Comparing 3.3 and 3.6, the only significant
difference relates to the negative coefficient on the credit advice variable. Using both tech-
niques provides a robustness check on the findings and the cmp results largely confirm
those of the two stage IV approach.6
Taking the equations as a whole, family size is
never significant, nor gender and were dropped after the first equation.7
More educated
people and older people tend to have benefited more from the advice than others, although
the latter only in 3.1. However, in the cmp regression the variable relating to crop growers
is no longer positively significant.
The literature has suggested that the impact of advice may vary according to the con-
ditions facing the individual, for example soil moisture. We do not have in the database a
measure of this nor rainfall in the kebele, but we do have a variable which asked the indi-
vidual whether they were usually subject to water shortages for drinking at some time in
the year. Slightly over 51% responded that they were subject to such shortages. We now
split the sample into those who were and were not subject to such water shortages.
Columns 3.4 and 3.5 relate to those who were and were not subject to water shortages.
The positive impact of marketing advice is limited to the latter as is that of land management
and agricultural practices. However, the negative impact of credit advice is evident only for
those in non-drought areas. Finally, we note that animal husbandry advice is only significant
for those in areas of drought and advice on fertilizers is significant in both areas. These
equations were estimated using the two stage approach. Columns 3.7 and 3.8 show the
results of using the cmp estimator. The main difference is the positive significance of
animal husbandry advice in both areas. In Table 4, we look at the results pertaining to
income. The results are very similar. This was to be expected, but it was always possible
that because of expenses incurred in implementing the advice, a positive impact on yield
would not translate into a positive impact on income. The main difference to Table 3 is
the significantly negative coefficient on credit advice in both areas, implying that receivers
of such advice are significantly less likely to have perceived a beneficial impact on income.
In addition, those with either crops or animals were also less likely to perceive benefits.
Because the regression is limited to those who received advice, the possibility exists for
sample selection bias. When we tested for this in the two-stage IV regressions the evidence
was mixed. The inverse Mills ratio is insignificant in the income regression, but significant
in the crop yield regression, although not in drought-affected areas. The significance of the
coefficients reported in Table 3 for the full sample for crop yields did not change and nor
did those in the non-drought regression. The main difference in both these regressions was
an increase in the size of the coefficient on animal husbandry advice. The sample selection
equation included a kebele based variable reflecting the number in the kebele, other than
THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 9
12. the respondent, receiving advice. Apart from this, older and more educated people were
less likely to receive advice. Men were more likely to receive advice, as were those in
large families and those with animals or crops. This is potentially consistent with those
who report a bias in extension advice towards men (Buchy and Basaznew 2005). A variable
differentiating drought from non-drought areas was insignificant.
In Table 5, we show the probabilities of the respondent finding that the advice made ‘a
lot of difference’ under different scenarios. These are obtained from the two stage instru-
mental ordered probit regressions. They are based on a 45-year-old individual with crops,
but no animals. The regional variables are averaged according to sample population. The
first element in the first column shows the probability for someone with the above charac-
teristics who receives none of the specific types of advice listed and has no schooling. The
second column relates to someone with an education corresponding to ‘grade 8’, and this
raises the probability to 0.0627. In the second row, we have the probabilities for someone
of the above characteristics who received advice on animal husbandry. These are consider-
able higher, reflecting the effective nature of this advice. However, the most effective forms
of advice are on fertilizers and the combined advice on land management and agricultural
practices. The next two columns relate solely to farmers in drought areas. The most impor-
tant forms of advice are on animal husbandry and fertilizers, with land management and
agricultural practices being third. This is in sharp contrast to the non-drought areas, where
the most effective advice is on agricultural planning and land management. The results for
income are also shown. The main difference is the increased impact of marketing advice.
Some of these impacts look quite small, although it should be borne in mind that they do
relate to the probability of having made ‘a lot of difference’ and also that multiple forms of
advice are often given which substantially increases this probability.
Conclusions and policy implications
With respect to the main research questions, we find that extension agent advice impacts
positively on both crop yields and income as reflected by the farmers’ own perceptions.
Table 5. Probabilities of advice have a ‘lot of difference’ under different scenarios.
Full sample Drought areas Non-drought areas
Education None Grade 8 None Grade 8 None Grade 8
Crop yield
None 0.040 0.063 0.059 0.082 0.0036 0.0067
Animal husbandry 0.099 0.144 0.280 0.341 0.0059 0.0105
Marketing 0.089 0.131 0.051 0.072 0.0458 0.0702
Credit 0.037 0.059 0.036 0.051 0.0394 0.0612
Fertilizer 0.226 0.299 0.233 0.289 0.0259 0.0415
Planning and land man. 0.179 0.243 0.113 0.150 0.3790 0.4620
Income
None 0.059 0.080 0.087 0.110 0.0146 0.0186
Animal husbandry 0.214 0.264 0.508 0.561 0.0445 0.0545
Marketing 0.129 0.166 0.088 0.110 0.1520 0.1760
Credit 0.050 0.069 0.031 0.041 0.0431 0.0529
Fertilizer 0.163 0.206 0.211 0.251 0.0325 0.0403
Planning and land man. 0.204 0.253 0.082 0.104 0.4430 0.4820
Notes: These probabilities are based on regressions 3.3–3.5 and 4.3–4.5. They relate to a 45-year-old individual with crops,
but no animals in a typical region. They show the probability of a single type of advice making ‘a lot of difference’ to crop
yield and income.
THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 11
13. This confirms the first hypothesis and confirms the more favorable findings of Elias et al.
(2013) and Khan et al. (2014), rather than, frequently older, research such as Pender, Place,
and Ehui (2006) and Lefort (2012). We also find such advice to have a varying impact
according to both the farmer’s characteristics, such as education, and the characteristics
of the farm. The pattern of significance suggests that agricultural advice yields its best
results when targeted at those with the ability to use it, that is, the better educated.
However, it is also suggestive perhaps that the advice given to less well educated people
needs to be different, made simpler or given in more detail. This helps answer the
second of our research questions and partially supports the third hypothesis, although
we find only limited evidence of impact being related to age.
There are also significant differences in the impact of advice between different areas.
This again relates to the second research question and provides confirmation of our
second hypothesis. In drought-affected areas, advice on animal husbandry and fertilizers
is most effective. In non-drought areas, advice on marketing and land management and
agricultural practices is best. The impact of marketing on crop yields is plausibly an indir-
ect one whereby farmers respond to increased prices and a greater ability to sell output by
increased effort. Our findings are consistent with our theoretical analysis which suggested
that advice had the greatest potential in areas and conditions most conducive to applying
the advice. This differential impact of advice in different areas is consistent with the results
of Kassie et al. (2010). However, they find that advice on land management practices
works best in low agricultural potential areas which is slightly at odds with our findings.
In addition, they found advice on fertilizers to have a very significant and positive impact
on crop productivity in high potential areas, whereas we found this advice worked well in
both types of areas. Why the differences with Kassie et al.? Firstly, there is the time period.
Their data relate to 1998 and 2001. Ours is more recent. Technology moves on and what
might have been the case over a decade ago may no longer be the case today. Secondly,
their analysis relates to usage of fertilizer, minimum tillage, etc. Our data relate to exten-
sion agent advice on these technologies. The advice may feasibly be to use less fertilizer, or
to use it in a different manner. Thirdly, some technologies may take several years before
they have an impact. Hence Schmidt and Tadesse (2014) conclude that that sustainable
land and water management infrastructure in the Ethiopian highlands has a positive
impact on the value of production only seven years after construction. This might be
too long a time frame for our results to be picking up. Finally, there may be a difference
between their split of high and low productivity areas and ours of drought and non-
drought areas.
In addition, more educated individuals and older people were significantly less likely to
receive advice. The kind of advice received also differed between drought and non-drought
areas and also varied with the size of the family, education and gender, suggesting that
advice is targeted rather than given randomly. Thus, this does not support the observation
made by Kassie et al. (2010) that the advice showed little variation across different
environments nor responded to household-specific factors. This again confirms part of
the third hypothesis. Thus in terms of the three hypotheses we set out to test, all three
have been largely substantiated.
Expanding further on our second research question, some advice has been found to
be more effective than others. In virtually, all the regressions, advice on credit appears
to have the least positive impact on both yields and income, especially the latter. This
12 A. HAMILTON AND J. HUDSON
14. may be linked to credit being heavily under the control of the government and possibly
being used for political purposes (Berhanu and Poulton 2014). Advice on credit is also
almost always given in tandem with other advice and the combined effect, as can be
seen from the coefficients in Tables 3 and 4, is generally positive, at least for yields.
Nonetheless, it does raise questions about the recent emphasis on credit (Bachewe
et al. 2015).
We did not have enough data to investigate whether different types of advice work
better in tandem. But a variable equal to the number of types of advice given, although
negative, gave only weak evidence for declining returns with respect to the amount of
advice given. Hence, advice appears best if given on several dimensions with the impact
being largely cumulative. This is consistent with Teklewold et al.’s (2013) conclusion
that different types of sustainable land practices work best when adopted in combination
rather than isolation.
There is an important policy issue in that extension system advice can be unsuccessful
either because the problem lies with the ‘extension message’ or in the way the message is
transmitted. Too often the latter tends to get the blame for lack of impact while the
problem may be with the ‘advice’ or message. In a sense that is the more serious
problem in indicating that the advice is flawed to begin with, whereas retraining can alle-
viate problems with the messenger. Our analysis may be the start of a process of determin-
ing where the problem lies, if indeed there is a problem. In this context, our results
tentatively show that with much of the advice being effective in at least some contexts
the messenger is at least partially exonerated. This also links in to the current debate on
the relative roles of R&D and technology transfer (Anandajayasekeram 2011). Our
results point to the importance of the latter, in the specific context of extension agents.
But often new knowledge comes from R&D and the roles of research centers remain of
potential importance and here perhaps the linkages between agricultural research
centers and extension agents could be improved (Abegaz and Wims 2015; Teshome, de
Graaff, and Kassie 2015). Similarly, the use of regression analysis to quantify the impact
of extension agent advice has provided results which may be potentially useful to improv-
ing and informing extension agents’ advice in part by internalizing the results of this type
of analysis within the formal education system.
Notes
1. http://www.farmingfirst.org/wordpress/wp-content/uploads/2012/06/Global-Forum-for-
Rural-Advisory-Services_Fact-Sheet-on-Extension-Services.pdf.
2. There are types of advice given in addition to the six specified, but for the purpose of the
theory we focus on the impact of these six types of advice.
3. The kebele is the lowest administrative tier in Ethiopia’s federal structure, below the woreda.
4. If the question had been on what had happened to yields and income, then just focusing on
those who received advice would be problematic. But, we cannot ask a similar question of
those who did not receive advice as the question we are analysing pertains to the impact
the advice had on yields and income. Obviously, this question cannot be asked of those
who did not receive advice.
5. The constant term has been absorbed into the cut-off points for the ordered probit
regression.
6. The coefficients cannot be readily compared as the one relates to ordered probit and the other
to probit.
THE JOURNAL OF AGRICULTURAL EDUCATION AND EXTENSION 13
15. 7. They were however significant at times in the equations relating to policy advice, rather than
impact and have been retained in those. For example, land management advice tended to
increase with family size, which may reflect the size of the holding, and credit advice declines
with family size. There was weaker evidence of credit and marketing advice declining for
men.
Acknowledgements
We acknowledge the valuable comments of referees and the editor.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by the Department for International Development (DFID).
Notes on contributors
Dr Alexander Hamilton is a political economist, and evaluation specialist with significant
academic and field experience in fragile states. He has numerous publications and research
experience in the fields of corruption, impact evaluations, public financial management,
economic policy, and econometrics. He also has field experience from work conducted
in Ethiopia, Senegal, Sudan, and Yemen. He has an MPA in Public and Economic
Policy form the London School of Economics and a DPhil (PhD) from the University
of Oxford.
Professor John Hudson studied at the Universities of London and Warwick. He is a pro-
fessor of economics at the University of Bath. He has published more than 100 journal
papers in leading journals in all areas of economics and the wider social sciences, but in
particular development economics.
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The perceived impact of agricultural advice in
Ethiopia
Alexander Hamilton & John Hudson
To cite this article: Alexander Hamilton & John Hudson (2016): The perceived impact of
agricultural advice in Ethiopia, The Journal of Agricultural Education and Extension, DOI:
10.1080/1389224X.2016.1245151
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