OSIPP Discussion Paper : DP-2010-E-001

Estimating Agroforestry’s Effect on Productivity in Kenya: An
Application of a Treatment Effects Model
March 11, 2010
Tsunehiro Otsuki
Associate Professor, Osaka School of International Public Policy (OSIPP)【 Keywords 】 soil conservation technology; sustainability and agricultural
productivity; self-selected participation; treatment effects model【JEL Codes】O13, H43, Q57【Abstract】
This study investigates the effects of adopting agroforestry and other soil conservation
technologies (SCTs) on agricultural productivity in Kenya, using plot-level data on
agricultural production. Using a treatment effects model, it is found that adopting
agroforestry methods, as well as manure, chemical fertilizer, and terracing/trenching,increases total factor productivity (TFP) and land productivity. The TFP gain is
estimated to be 40.7 percent from agroforestry.
The average treatment effect for the adopters, however, turns slightly negative due to thenegative self-selection effect, possibly because the agroforestry adopters tend toperceive adverse conditions on their land, which motivates them to adopt SCTs. In thissense, agroforestry and the other SCTs are preventive actions predominantly taken byfarmers facing adverse conditions. The analysis demonstrates that both the simple mean
comparison and the least squares estimation, due to their failure to reflect thosecomplexities, could obscure the real benefits of SCTs.

Correspondence: Tsunehiro Otsuki, The Osaka School of International Public Policy,
Osaka University, 1-31 Machikaneyama-cho, Toyonaka, Osaka 560-0043 Japan.
E-mail address: otsuki@osipp.osaka-u.ac.jp
Acknowledgement: I am grateful for financial support from the Japan Society for the
Promotion of Science, Grant-in-Aid for Scientific Research (C), 2007-2009.

1

1.

Introduction
In Africa, sustainable use of agricultural land is becoming increasingly important for

maintaining capacity for the food supply and livelihood of the agricultural sector. The
increased food demand due to the rapidly growing population has increased theimportance of improving productivity of land; however, degradation of existing
cultivated land due to soil erosion and decline in soil fertility has constrained thesustainable use of existing cultivated land which frequently leads to land abandonmentand conversion of natural forests into agricultural land. Recently, many studies in social
science as well as conservation science have demonstrated the agroforestry’s effect ofsoil conservation. Benefits of agroforestry are manifold, for example, mitigation of soil
erosion, preservation of soil moisture, replenishment of soil fertility by providingorganic fertilizers and alleviation of crop failure risks (Young, 1989; Okoji and Moses,
1998; Mathuva et al., 1998). The advantage of agroforestry is that it relies mainly on
locally available natural resources, and thus is suitable to local agro-systems.
Studies to assess the economic benefits of agroforestry are rather scarce, although theextent to which this technology should be promoted critically depends on the size andnature of the benefits. The diverse and complex effects of agroforestry and its long wait
period before realization of the effects seem to make it difficult to quantify the effects.
In addition, the self-selective nature of adoption of agroforestry complicates
econometric estimation.
This study analyzes the effect of the adoption of agroforestry on agricultural
productivity in Kenya, using plot-level data obtained from the International Food Policy
Research Institute (IFPRI) in two districts in western Kenya in 2001. We measure theimpacts of adoption by estimating in terms of the total factor productivity (TFP) ofmajor crops because sustained high TFP ensures stable long-run economic developmentin the agricultural sector. A positive effect of agroforestry on TFP would imply that
more output can be produced for a given amount of inputs, or that land and other inputs
can be conserved for given output levels. Empirical studies on soil conservation2

technologies (SCTs) have focused on their effect on income or agricultural revenue.
However, since those unnormalized outcome variables are correlated with land size or
the amount of other inputs, the estimated effect of adoption is likely to be biased if
adoption is not independent of input size. Land productivity is a normalized outcome
variable that is often used in the literature of agricultural intensification (Lee et al.,2006), but this partial productivity measure fails to reflect substitution with other inputs,
like labor and capital; thus, an increase in land productivity does not guarantee anincrease in agricultural revenue. If a positive effect on TFP alone is found, the existenceof other benefits of agroforestry makes it even more profitable.
Furthermore, we take the self-selective nature of adoption into account in ourempirical method by employing a treatment effects model that is widely used in theliterature of program participation (for example, Pitt and Khandker, 1998). A leastsquares estimation that ignores self-selection is expected to yield biased estimates of the
impact, as is common in program participation studies of development projects. For
example, an adopter of agroforestry may have earned higher agricultural income even
without adoption, if there are common determinants of his ability to earn higher income
and likelihood of adoption. We also examine the effect of the adoption of several SCTs
such as the application of manure and chemical fertilizer, trenching, terracing, andridging.
This paper is organized as follows. Section 2 reviews the situation of Kenya andAfrica regarding agricultural productivity and the circumstances of the adoption ofagroforestry and other SCTs. Section 3 presents the empirical methodologies. Section 4
describes the data and study sites used for this study. Section 5 discusses the empiricalresults, and Section 6 concludes.

2.

Agricultural productivity and soil conservation technologies in Kenya
The growth of agricultural productivity in Africa has been remarkably low in the past

decades. As Figure 1 shows, the growth rate of agricultural productivity in Africa in3

terms of the yield of cereals in hectogram per hectare in 1960–2007 was 63 percent,whereas the average growth rat for the world during that period was 150 percent. In
1960 Kenya’s productivity was about 50 percent above Africa’s average, and was
almost equal to Asia’s average, but in 2007 Africa’s productivity was only half that of
Asia. This fact demonstrates the poor performance of Kenya’s agriculture and calls forfurther investigation of the factors that constrain growth in agricultural productivity.
In Kenya, soil quality is known to be relatively good among African countries. The
region generally has fertile Alfisols, and aggressive soil conservation measures have notbeen widely employed. Fallows are commonly practiced to maintain soil fertility in
Kenya, but they have become untenable as the fallow period became shorter due rapid
population growth (Amadalo et al., 2003). Kenyan farmers have gradually come toperceive the severity of soil erosion and resulting productivity decline (Amadalo et al.,2003). In these circumstances, SCTs have drawn significant attention from farmers andgovernment in the country.
The function of agroforestry in conserving soil seems to make it a promisingalternative to the traditional fallow technique. With the help of the agroforestry
extension program by the Kenya Forest Department and dissemination by international
non-governmental organizations, agroforestry has been implemented in several regionsin Kenya (Scherr, 1995). Agroforestry contributes to soil conservation in several ways.
Pattanayak and Mercer (2002) report that intercropped trees (contour hedgerows)
successfully mitigate soil erosion by forming natural terraces in a sloping land andreplenish soil fertility with prunings from the trees. Many scientific studies point out
agroforestry’s role of maintaining or improving soil fertility by preserving soil organic
matter and physical properties of soil (for example, Okoji and Moses, 1998).

4Figure 1: Yield of cereals (hectogram per hectare (Hg/Ha))
40000
35000
30000
25000
World
20000

Asia
Africa

15000

Kenya

10000
5000
0
1961

1966

1971

1976

1981

1986

1991

1996

2001

2006

Source: Author’s calculation based on FAOSTAT of the Food and Agricultural Organization.

Despite various advantages of agroforestry, agroforestry-based development projects
in the 1980s and 1990s were frequently unsuccessful due to inadequate attention tosocioeconomic issues affecting its adoption and continuation (Mercer and Pattanayak,
2003). It is generally found in the empirical literature that adverse conditions of the land,such as slope, tend to promote adoption (e.g., Obunde et al., 2004; Place et al., 2005;
Otsuki and Ogo, 2009). It is also generally found that only relatively richer farmers tend
to adopt agroforestry (Marenya and Barrett, 2007; Otsuki and Ogo, 2009). That may be
because they can bear the setup and running costs of agroforestry and because they canbear the time cost of waiting for the effects to be observed due to their relatively lower
time preference. It is also generally supported that formal education and access to
information on appropriate agricultural technologies promote adoption (Caviglia-Harris,
5

2003). Obunde et al. (2004) and Otsuki and Ogo (2009) found that having formal land
titles promotes the adoption of agroforestry in Kenya.
The dominance of the economic benefit and cost of agroforestry is not self-evident,
due both to its multifaceted effects and to the potential of hindering efficient production.
It is possible that the space needed for planting trees and the inflexibility of the choice
and plantation patterns of crops may reduce productivity per unit of land. It is also
possible that the increased labor requirement for maintenance may reduce productivity
in the total factor measure. Agricultural experiments in Kenya also demonstrated adrawback of the intercropping of tree species that compete with crops for water(Mathuva et al., 1998). Place et al. (2005) found that the average productivity of theagroforestry adopters in terms of per hectare yield of maize (Zea mays) is more than twotimes that of the non-adopters in western Kenya although the self-selection problem is
not taken into account. 1 Pattanayak and Mercer (2002) found that the land user’s
perception of soil quality tends to be higher for those who adopt agroforestry.
A farmer that adopts one type of SCT may be also active in adopting other SCTs.
Agroforestry is frequently combined with other SCTs like chemical fertilizer andterracing. Some techniques are more complementary, rather than substitutive. Table 1
presents a tabulation of the incidence of agroforestry and other SCTs in both counts andprobability. Most of the technologies are adopted jointly with agroforestry. The
conditional probability of the adoption of manure and chemical fertilizers, is muchhigher for agroforestry adopters than for non-adopters. Organic fertilizers generated
from intercropped trees or fallow plants do not contain sufficient phosphorus, andtherefore, it is effective to combine agroforestry with chemical fertilizers (Amadalo etal., 2003). The conditional probability for agroforestry adopters to also adopt trenching,terracing and ridging is lower than that of non-adopters.

1

Two types of agroforestry technologies are examined in Place et al. (2005) – (1) improved fallow thattree species are planted in the fallow period and (2) biomass transfer that organic nutrients from tree
species are brought to crops as fertilizer.

6

Table 1: Combination patterns between agroforestry and other soil conservation
technologies by the number of sample observations
ManureAgroNo

forestry
Yes

Total

Chemical fertilizer

Trenching/Terracing

Ridging

No

Yes

Total

No

Yes

Total

No

Yes

Total

No

Yes

Total

56

5

61

56

5

61

70

49

119

64

55

119

(32.4%) (2.9% )(35.3%)(32.4%)(2.9%)(35.3%)

112

61

51

112

(35.3%) (29.5%) (64. 7 % )(35.3%)(29.5%)(64.7%)

61

117

51

56

173

117

56

173

(67.6%) (32.4%)(100%)(67.6 %)

(32.4%)(100% )

(23.7%) (16.6%) (40.2%)
110

177

154

(37.2%) (22.6%) (59.8%)(52.2%)

180

67(21.7%) (18.6%) (40.3%)

116

296

(60.8%) (39.2%) (100%)

218

22

(7.5%) (59.7%)
77

Empirical methodologies

3.1 Literature on treatment effects models
Our objective is to statistically examine the effect of the adoption of agroforestry on
the agricultural productivity in the studied area of Kenya. Since the adoption of SCT is
self-selective, both a simple comparison of the means of the outcomes between adoptersand non-adopters and a least squares estimation with an adoption dummy variable areinappropriate. Both approaches assume that adoption is random event, and so theoutcomes would be comparable. However, unobserved factors such as the management
and production skills of the farmers can increase both the likelihood of adoption andproductivity. In this sense, the adoption and non-adoption outcomes for the same person
should be compared in order for the effect of adoption to be evaluated. Since thecounterfactual (the outcome for the case which is not chosen) is not actually observable,
the above approaches fail to make a proper comparison, resulting in an inconsistentestimate of the treatment effect.

7295(73.9%) (26.1%) (100%)

Source: Author’s estimation based on the IFPRI plot-level data for Suba and Laikipia.
Note: The counts are based on non-missing observations in each category of conservation technology.
Inside the parentheses are fractions in percentage.

3.

176

The treatment effects model that is widely used in the program evaluation literature(e.g., Pitt and Khandker, 1998) allows us to compare the real outcome with thecounterfactual, thus incorporating the self-selective nature of agroforestry. Pattanayak
and Mercer (2002) estimated the effect of agroforestry on perceived soil quality using atreatment effects model where the selection bias is corrected by including the inverse
Mill’s ratio as a regressor following Heckman (1978). Caviglia-Harris (2003) also used
the two-stage sample selection model to estimate the agroforestry’s deterrent effect on
deforestation in the Brazilian Amazon. Kassie et al. (2008) estimated the effect of the
adoption of stone bunds as a kind of SCT on the value of crop outputs using matching
method introduced first by Rosenbaum and Rubin (1983).
There are several studies that apply the treatment effects model to evaluate the effectof SCTs other than agroforestry. Warning and Key (2002) evaluated the effect ofparticipation in contract-farming program in Senegal, using a two-step estimation of theequations for participation (the first stage) and for determining the impact ofparticipation (the second stage). Here, the inverse Mill’s ratio estimated from the firststage probit model is included in the right hand side of the second stage model in order
to correct for the sample selection bias caused by the self-selection of participation.
Bolwig et al. (2009) examined the effect of participation in the organic farming contract
on agricultural revenue in Uganda using a similar sample selection model but withmaximum likelihood estimation rather than the two-stage method.

3.2 Treatment effects modelA standard treatment effects model is as follows:
Yi = X i′β + δ Ii +ν i ,(1)where Yi , i = 1, 2,..., N is a dependent variable that represents an outcome, X i avector of exogenous variables, β a vector of coefficient parameters for X i , Ii
adoption status which is a binary treatment variable, δ a coefficient estimator for Ii
8that is interpreted as a treatment effect, and vi an error term that follows normal
distribution with mean zero and variance σν 2 . The adoption of individuals based on aset of determinants Z i is specified as:=
Ii* Zi′γ + υi ,(2)

where Ii* is a latent variable, γ a vector of coefficient parameters, and υi an error
term. The latent variable is unobservable, and its relationship with Ii is specified by:Ii =1 if Ii* > 0, otherwise Ii =0.

(3)

If unobserved factors in (2) are correlated with ν i , then the correlation coefficient
between υi and ν i (denoted as ρ ) is non-zero, and thus the OLS estimator isinconsistent (Greene, 2008). Following Greene (2008), the expected outcome forparticipants assuming normal distribution for I becomes
E[Yi | I=i 1, X i , Zi=] X i′β + δ + E[ν i | I=i 1, X i , Zi ]= X i′β + δ + ρσν [φ (Zi′γ ) / Φ(Zi′γ )] ,(4)

where ρσν equals the covariance between ν i and υi , φ (Zi′γ ) the marginal
probability density of standard normal at Zi′γ , and Φ(Zi′γ ) the cumulative probabilityof standard normal at Zi′γ . The third term includes the inverse Mill’s ratio to control
for a possible sample selection bias, λi ≡ φ (Zi′γ ) / Φ(Zi′γ ) , and βλ ≡ ρσν will be thecoefficient parameter for λi . The expected outcome for non-participants becomes| Ii 0, X i , =E[Yi =Zi ] X i′β + ρσν  −φ (Zi′γ ) /1 − Φ(Zi′γ )  .




(5)

The inverse Mill’s ratio for (5) is λi ≡ −φ (Zi′γ ) /1 − Φ(Zi′γ ) . The difference in the
expected outcome between participants and non-participants then becomes
E[Yi | Ii =1, X i , Zi ] − E[Yi | Ii =0, X i , Zi ] =δ + selection term . (6)The positive (negative) sign of the selection term implies that the OLS overestimates(underestimates) δ , and the sign of the selection term depends on that of ρ . A
maximum-likelihood estimation is proposed by Maddala (1983) and Greene (2008), as
9

it produces consistent estimators. Maddala (1983) also proposed a two-step estimationthat also produces consistent estimators. It estimates (3) by a probit estimation in thefirst stage and then estimates (1) by including the predicted value of selectivity
correction as an additional regressor. While many studies that evaluate the effect of
SCTs use the two-stage estimation for the analytical tractability, we use themaximum-likelihood estimation. We chose that method because it estimates the
adoption equation and productivity equation jointly and because it allows us to test thesignificance of the cross-equation correlation ρ . If we cannot achieve convergence inthe maximum-likelihood estimation, we use the two-stage estimation.

3.3 Productivity estimation
We focus on total factor productivity (TFP) in measuring a measure of individual
farmer’s performance in agricultural production as well as land productivity whereas
most studies focus on land productivity such as per acre yield in the impact analysis ofsoil conservation. Our analysis mainly focuses on TFP because an increase in TFP
unambiguously leads to an increase in on-farm income from the crops of interest given
the input factor and product prices being unchanged. In contract, an increase in land
productivity does not ensure an increase in on-farm income because the amount of laborand other inputs may change simultaneously. If a SCT requires a substantial amount ofadditional labor and other inputs, on-farm income may decline on net.
In the standard definition, TFP is defined as TFPi = yi / F ( X i ) , where F (⋅) is a
production function, yi the actual output, and X i a vector of inputs. In our analysis,
the output is measured in terms of the total value of maize and beans produced. Maize is
the preferred staple and main crop and is often planted jointly with beans (Amadalo etal., 2003). As inputs, we include labor ( L ) and land ( H ), as they are the major inputs
in the agricultural system in Kenya. 2 We construct labor from the total number of2

It is ideal to include a measure of capital stock as an input in the production function estimation, but it
is difficult to construct such a variable from the dataset since not many farmers in the sample use

10man-days for production activities, including land preparation, plantation, weeding, andharvesting. We use area under cultivation for the land variable.
We use a stochastic frontier model because it has the advantage of isolating random
noise from the productivity component (see Kumbhakar and Lovell, 2000, for example).
The stochastic frontier model for the Cobb-Doublas production function is specified asfollows:
ln yi = α 0 + α L ln Li + α H ln H i + ε i − ui ,(6)

where α ’s are intercept and coefficient parameters, and ε i is an error term that
represents a random noise which is associated with with a zero mean and a constant
standard error and represents a random component of output associated with random
events such as weather. The term ui is the inefficiency component that will be
converted to technical efficiency scores under a simple transformation (see Kumbhakarand Lovell, 2000, for example). The technical efficiency is interpreted as TFP in the
cross-section framework in a single time period. This model is estimated using amaximum-likelihood method assuming half-normal distribution on the inefficiency
term.

4.

The study area and data
Our empirical analysis relies on a survey dataset of household attributes, agricultural

production, environmental conservation measures, and other factors that affectagricultural production in Kenya in 2001. The dataset is titled “The Land Tenure,
Agricultural Productivity and the Environment: Suba and Laikipia Districts, Kenya,
2001” and is publicly available from the IFPRI. The details of the survey project and
key findings are reported in Obunde et al. (2004).

machinery or other capital goods. Also manure and chemical fertilizer are often considered as inputs, but
we deal with them as soil conservation technologies for the comparison with agroforestry.

11

Two districts in Kenya were selected for the survey project. Suba is a district locatedat Kenya’s western border with Tanzania, where the land is largely characterized as
hilly with thorny bushes. Laikipia is a district located slightly west of the center of
Kenya, in an area dominated by an elevated plateau covered by volcanic ash (Obunde etal., 2004). Since a major part of the areas of those districts are highland, their climate ismild, despite its equatorial location. In addition, these areas receive moderate rainfall,
and therefore, the climate is favorable for crop production. However, the land in the two
districts is generally hilly; as approximately 75 percent of the plots in the sample
reported that the land is sloping, thus making soil conservation important.
Approximately a quarter of the sample area experiences erosion of surface soil.
According to the Obunde et al. (2004), four sub-locations were chosen from a clusterof 10 sub-locations in each of the two districts, with attention to (1) the similarity ofboth clusters in terms of agro-ecological conditions and (2) the wide variety of land
tenure systems. In each sub-location, 40 respondents were randomly selected. The
empirical analysis in this study uses the observations from 245 plots that are held by
209 farmers after eliminating observations with missing values. The number of plots isgreater than that of farmers because some farmers hold multiple plots.
Table 2 shows the list of variables used in our empirical analysis. The variables aregrouped into SCTs, plot-specific attributes, and farmer-specific attributes. The variablesof SCTs reflect the status of the adoption of those technologies. For example, theagroforestry variable, which is a binary variable, takes value one when agroforestry isadopted and zero otherwise. 3 The plot-specific and farmer-specific attributes are used as
explanatory variables either in the first-stage adoption regression or the second-stage
productivity regression.
The factors that seem to affect both adoption and productivity, such as soil fertility,
slope, land title, farm size, access to credit and years of education, enter into both the3

Improved fallow may be considered as agroforestry, because fast growing tree species are sometimes
planted in the fallow period, but it is not distinguished. As long as crops are produced, it is consideredthat the land is not left fallow. So we rule out the possibility of improved fallow from our data.

12

adoption and productivity equations. The adoption equation also includes the perceived
severity of environmental degradation, income, the number of owned cattle, the dummyof residence of more than 10 years, and the district dummy. Those variables are thought
to affect the adoption decision more significantly than productivity, and they are also
useful for identification. Distance to input and output market is included only in the
productivity equation because market access presumably affects the costs of input
acquisition and sales of outputs directly.

Table 2: The variables used for the empirical analysis
Soil conservation technologies
Agroforestry

Dummy of adoption of SCTs; 1 = adopted, 0 = not adopted

ManureChemical fertilizer
Trench/Terrace
RidgesPlot- specific attributes
Soil fertility

Fertility of soil; 1 = poor, 2 = fair, 3 = good, 4 = very good

SlopeLand title

Gradient of land; 1 = flat, 2 = gently sloping, 3 = steep
Perceived environmental degradation; 5 = none, 4 = negligible, 3 =moderate, 2 = serious, 1 = very serious
Holding formal land title; 1 = yes, 0 = no

Farm size

Total area of land in hectare

Distance to the input market

Distance to the input market in kilometer

Distance to the output market

Distance to the product market in kilometer

District dummy

Dummy of district; 1 = Suba, 0 = Laikipia

Degradation

Farmer- specific attributes
Log income

Log of total income

CreditApplication to loans; 1 = applying, 2 = not applying

Cattle head

The number of cattle head owned

Family size

The number of family members

Sex of head

The sex of the household headYear of education

The number of years of formal education

Residence more than 10 years

Dummy of residency for more than 10 years

13

5.

Empirical results

5.1 Total factor productivity
The coefficients of the production function, i.e., α L and α H in Equation (6), areestimated to be 0.366 and 1.084, respectively, using a maximum-likelihood method.
They are significant at the 1 percent level as well as the intercept. 4 Technical efficiency
scores are calculated for each sample once the inefficiency term ui is adjusted suchthat technical efficiency scores will not take values beyond the range [0, 1]. 5 The
descriptive statistics for the technical efficiency measure are the mean being 0.458, thestandard error 0.206, the minimum 0.009, and the maximum 0.851.
5.2 The effect of adoption of agroforestry and other soil conservation technologies
Our econometric model to examine the impact of the adoption of agroforestry on
productivity, which we denote as the productivity equation, is specified by setting a
productivity index as Yi , exogenous factors to influence Yi as X i , and the adoption
dummy of agroforestry as Ii in Equation (1). The adoption equation is specified by
setting the exogenous determinants of the adoption of agroforestry as Zi in Equation (2).
For the productivity equation, we also consider alternative SCTs, namely, manure
application, chemical fertilizer application, trenching and/or terracing, and ridging. A
total of 153 to 245 observations are used, depending on data availability.
Table 3 presents the results of both the productivity and the adoption equations where
the technical efficiency scores are used as the productivity measure. The table shows thecoefficient estimate and the standard error of each variable and the inverse Mill’s ratio

λ . The table also shows the estimate of the coefficient parameter ρ for the
productivity and the adoption equations, and the chi-squared statistics for the Wald test
4

We do not assume constant returns to scale (CRS) in the underlying production technology which isoften done in this kind of analysis because this assumption is sometimes too restrictive and unrealistic.
We obtain a very similar result for the estimated coefficients and the TFP scores, however, when we
assume the CRS.
5
The exponential transformation should be made to the estimated inefficiency because the variables inEquation (6) are in the logarithm.

14

for model predictability. The p-values for the Wald test suggest joint significance of thecoefficient parameters at the significance of more than one percent in all the five models,
implying good model predictability.
The results of the adoption equation indicate that slope, income, and land titles areimportant for the adoption of agroforestry, confirming the results in the previous studies.
Significant coefficient parameters are largely different across the treatment types, but it
can be generally said that adverse conditions of the land tend to push farmers to adopt
SCTs. Using a survey from farmers in Zambia, Ajayi (2007) showed that the potential
to adopt soil conservation technologies is higher for farmers who are more concerned
about the condition of their such as soil fertility. Having the land title seems to stimulate
the adoption of agroforestry, fertilizer and ridges, confirming the previous studies (e.g.,
Place et al., 2005).
The results of the productivity equation indicate that adoption increases TFP while
only ridging decreases TFP. 6 Agroforestry adopters as well as adopters of manure,fertilizer, and trenching/terracing, on average, enjoy higher productivity and therefore
higher on-farm income. Combining this with the first stage finding that farmers withsloping land tend to adopt agroforestry, mitigation of possible productivity loss due tosoil erosion seems to be the motivation behind adoption of agroforestry. The negative
coefficient estimate for ridges may result from their typical use only to make space forcrop roots to spread out and to promote drainage of water. Ridges are supposed toenhance outputs, but the labor needed to create and maintain ridges and the land spaceneeded for ridges may result in a lower TFP.

6

Given the fact that those technologies are often jointly adopted, it is perhaps more appropriate toestimate their effects jointly in the productivity regression. In this study, however, we focus on the modelwith a single treatment because it is a common approach in the literature and because models withmultiple treatments involve further methodological complexities.

15

Table 3: Regression results on productivity (dependent variable=total factor
productivity)
Soil conservation technology
Productivity equation
Soil conservation technology
Soil fertility
SlopeLand title
Farm size
CreditYears of education
Distance to input market
Distance to output market

λAdoption equation
Soil fertility
Slope
Environmental degradation
Land title
Farm size
Log income
CreditCattleFamily size
Sex of household headYears of formal education
Residence more than 10 years
District dummy
Estimation methodρ ( P-value)
Model χ 2 ( P-value)N

Model1Model2Model3Model4Model5

Agroforestry

ManureFertilizer

Trench/terrace

Ridgesb0.148 **
a(0.070)0.000(0.024)0.012(0.024)0.143 ***
(0.034)－0.006 **(0.003)0.031(0.054)0.008 ***
(0.003)0.000(0.001 )0.012 *
(0.006)－0.101 **(0.043)0.275 ***
(0.039)0.076 ***
(0.029)0.070 ***
(0.026)0.150 ***
(0.031)－0.012 ***(0.004)－0.047(0.061)0.007 **
(0.003)－0.001(0.001 )0.008(0.007)－0.142 ***(0.011)0.223 ***
(0.047)0.034(0.028)0.037(0.023)0.113 ***
(0.034)－0.009 ***(0.004)－0.019(0.05)
0.004(0.004)－0.001(0.003)0.01(0.009)－0.115 ***(0.035)0.361 ***
(0.033)－0.003(0.030)－0.010(0.029)0.256 ***
(0.034)－0.010 ***(0.003)－0.012(0.086)0.004(0.004)0.002 **
(0.001)0.012 *
(0.006)－0.217 ***(0.018)－0.165 **(0.084)－0.020(0.022)－0.006(0.028)0.178 ***
(0.026)－0.006 **(0.003)－0.001(0.065)0.009 ***
(0.003)0.000(0.001)0.012 *
(0.007)0.116 **
(0.048)0.118(0.219)0.480 ***
(0.175)－0.174 *(0.096)
1.007 ***
(0.339)－0.017(0.030)0.305 **
(0.121)－0.577(0.368)0.034(0.030)－0.047(0.042)－0.159(0.236)－0.030(0.025)0.561(0.351)1.476 ***
(0.271)－0.560 **(0.253)－0.172(0.213)0.132(0.141)0.074(0.291)－0.030(0.035)0.240 *
(0.132)0.274(0.473)0.260 ***
(0.073)－0.053(0.040)－0.027(0.278)－0.013(0.036)1.614 **
(0.680)4.186 ***
(1.281)0.449(0.352)0.367(0.326)－0.207(0.251)0.677 *
(0.388)0.036(0.059)0.392 *
(0.222)－0.825(0.761)
0.195(0.149)－0.193 ***(0.074)0.187(0.477)0.064(0.047)0.287(0.984)10.765 ***(2.779)0.090(0.155)0.393 **
(0.153)－0.087(0.060)－0.691 ***(0.196)0.010(0.020)0.028(0.062)0.148(0.370)0.063 ***
(0.015)－0.011(0.020)0.143(0.130)0.005(0.020)－0.191(0.152)0.844 ***
(0.157)－0.803 ***(0.259)－0.742 ***(0.204)0.093(0.097)0.643 **
(0.285)0.016(0.022)0.118(0.113)－6.917(0.000)0.005(0.036)0.000(0.055)－0.103(0.255)0.038(0.029)－0.277(0.313)－2.009 ***(0.386)ML－0.545 (0.040) **155.4 (0) ***245

ML
Two-step
MLML－0.816(0) ***－0.691－0.914 (0) ***0.619 (0.039) ***155.3 (0) ***91.0 (0) ***258.1 (0) ***138.2 (0) ***
153
154
245
244

Source: Author’s estimation based on the IFPRI plot-level data for Suba and Laikipia.
Note: a Inside parentheses are robust standard errors. b The symbols “*,” “**” and “***” mean a 10, 5,
and 1 percent significance levels, respectively. A command “treatreg” of Stata version 11 is used for theestimation.

16

The difference of TFP between adopters and non-adopters is given by the coefficientestimate for the adoption dummy, and is greatest for trenching/terracing, indicating theeffectiveness of this type of SCT. Combining the results of the adoption regression thattrenching and terracing are likely used by farmers who have land on a hillside, adopters
are not only successful in preventing soil erosion, but also in increasing productivity. A
simulation using the predicted values demonstrates that TFP would increase by 116.2
percent as a result of adoption compared to the predicted values under a non-adoption
scenario. The same logic seems to hold for agroforestry, manure, and fertilizer. The
simulated TFP increase for those technologies is estimated to be 40.7, 71.9 and 56.6
percent, respectively.
Soil fertility and slope have a positive impact on TFP in Model 2, but their effect is
not significant in the other models. With the full application of SCTs, low soil fertility
and slope may no longer have a devastating effect. Having land title is found to increase
TFP in all models. This result is consistent with findings of previous studies that having
secure land title promotes a farmer’s investment in land improvement (e.g., Feder andOnchan, 1985). Land size has a negative effect, possibly because a small-scale
operation is more efficient in traditional agricultural production, which does not rely
heavily on machinery. This result is consistent with Obunde et al. (2004) that used thesame dataset, and they claim that this results from the intensification of production withdecreases in land size. This negative correlation between land size and productivity isreported in various studies. Formal education has a positive effect in three models,confirming the fact that formal education contributes to acquisition of skills andknowledge in improved agricultural production.
The chi-squared test for the significance of the cross-equation correlation coefficientρ indicates independence of the two equations, and the results suggest thatindependence is rejected at the 1 or 5 percent levels of significance. This implies thatthe sample selection bias is present in terms of adoption of SCTs. The negative selection
bias implies a negative correlation between the unobserved determinants of adoption
and those of productivity. This result deviates from our original expectation that a more
active adopter of SCTs is likely to have higher returns to the adoption of SCTs. A
possible explanation is that the farmers who encounter unobserved adverse factors of
their land tend to adopt SCTs. There seem to be other adverse factors than low soil
fertility and sloping, such as limited access to irrigation and susceptibility to soil erosion.
These negative factors may have dominated the positive factors such as production andmanagement skills.
In addition, using the same estimation method as for TFP, we examine the impact of
SCTs on land and labor productivity. The outcome variable is replaced by the value of17

output per hectare as a land productivity index, or the value of output per worker as alabor productivity index. Those indices are normalized by the maximum value of
productivity in the sample. The results are presented in Table 4. The results of land
productivity are quite comparable with those of TFP, confirming that those SCTs
mainly increase land productivity. The impact of SCTs on labor productivity issignificantly negative in the case of agroforestry, while most of other measures are
insignificant. Agroforestry might be a technique that requires more labor than its
alternatives, resulting in the negative correlation with labor productivity. When the factthat its effect on TFP is positive is considered, it can be said that its effect of increasing
land productivity outweighs its effect of lowering labor productivity. This result alsoemphasizes the necessity to consider labor opportunity cost. If the opportunity cost is
sufficiently low, as is likely in low-income countries, techniques to increase land
productivity are justified (Lee et al., 2006).

Table 4: Regression results on land and labor productivity
Dependent variable = land productivity
Treatment type

Agroforestry

0.250 ***b(0.029)a(The rest of the regression result is suppressed)
Treatment

ManureFertilizer

Trench/terrace

Ridges0.280 ***
(0.049)0.335 ***
(0.046)

0.327 ***
(0.033)－0.243 ***(0.058)0.008(0.013)－0.013(0.025)－0.037(0.040)0.052 *
(0.031)

Dependent variable = labor productivity－0.040 ***(0.016)(The rest of the regression result is suppressed)
Treatment

Source: Author’s estimation based on the IFPRI plot-level data for Suba and Laikipia.
Note: a Inside parenthesis are robust standard errors. b The symbols “*,” “**” and “***” mean a 10, 5, and1 percent significance levels, respectively.

For comparison, we demonstrate the difference between the treatment effects modeland a simple comparison of the means and a least squares estimation with a focus on
TFP. A simple comparison of the means of TFP between adopters and non-adopters ofagroforestry indicates that the TFP of the adopters is higher than that of the
non-adopters as was found in Place et al. (2005). The mean TFP of the adopters is 0.495,
and that of the non-adopters is 0.395. The difference is 0.100 and 25 percent higher than18

the non-adopters’ average, but smaller than the estimates from the treatment effects
model. In addition, the ordinary least squares estimation for agroforestry is found toyield a downwardly biased estimator for the treatment effect (0.013) due to ignoring theselectivity correction. Thus, the use of those alternative methods could generally lead toerroneous estimates for the effect of SCTs.
Those results generally suggest that most the SCTs of interest can contribute tosustainable land use by maintaining or improving productivity of crops. This
sustainability implication is not fully confirmed, however, since the productivity ismeasured at a particular time period in our analysis. The temporal change of
productivity is not investigated. Still, it is fair to presume that the adopters of SCTs arelikely to maintain high productivity in a long term because most SCTs, particularly
agroforestry, involve sunk cost and wait period which can be only compensated by along-term stream of benefits.
A common caveat associated with the use of a cross-section dataset need attention,
however. Some of the regressors in the productivity equation may be endogenous, andthus are simultaneously determined with the productivity. The endogeneity of the
adoption decision is most likely, but has been dealt with by the stage-wise estimation.
Among the rest, land titles are likely to be a source of endogeneity. If one tends toacquire private land titles for highly productive land, an upward bias on the coefficientis expected.
5.3 Average treatment effect on adopters
The average treatment effect (ATE) on adopters of SCTs can be investigated bytaking the difference between the conditional mean outcomes of the adoption ( I = 1 )and the counterfactual ( I = 0 ). The treatment effects model allows us to estimate theoutcome under both scenarios (“adopt” and “do not adopt”) for each sample. As Table 5shows, the conditional mean of the case of adoption is 0.486, which is lower than that of
non-adopters (0.539), and the difference is statistically significant although small. This
result is accounted for by the negativity of the selection term in Equation (6) thatoutweighs the positive effect of soil conservation δ . The negative selection term
follows the negative cross-equation correlation ρ . According to the standard
interpretation of the average treatment effect (ATE), a particular farmer who adopts
agroforestry would have had lower productivity, had he not adopted agroforestry. This
does not imply that adoption actually makes a farmer worse off than in the case of
non-adoption, but implies that farmers with potentially low productivity tend to adopt
SCTs. Adopters are aware of their land’s proneness to soil erosion due to greater
19

disadvantages than those of average producers, driving them to adopt countervailingmeasures. In other words, without soil conservation efforts, they would suffer from poor
productivity.
We obtain negative ATEs for chemical fertilizer and trenching/terracing, and positive
ATEs for manure and drainage as shown in the table. The same explanation applies to
the SCTs with negative ATE. For manure, the ATE is still positive after discounting thepositive selection effect. For drainage, the adoption effect ( δ ) is negative, but thepositive selection effect outweighs the adoption effect, resulting in a positive averagetreatment effect.

Table 5: The mean predicted total factor productivity of adopting farmers
I=1 (adoption) I=0 (counterfactual)
Difference
t-valueP-value
Agroforestry
0.486
0.539
－0.053－18.460.000
Manure
0.552
0.529
0.023
2.58
0.013
Fertilizer
0.590
0.619
－0.029－2.77
0.000
Trench/terrace
0.451
0.453
－0.002－0.84
0.403
Ridges
0.419
0.378
0.042
5.76
0.000
Source: Author’s estimation based on the IFPRI plot-level data for Suba and Laikipia.

6.

Conclusions

Soil conservation technologies (SCTs) will be a key factor to ensure sustainable foodproduction and to alleviate the pressure for deforestation to expand cultivated land. This
study investigates the effect of adopting agroforestry and other SCTs on agricultural
productivity in Kenya, using plot-level data on agricultural production in two selecteddistricts in Kenya in 2001. A treatment effects model is used to accommodate theself-selective nature of technology adoption, and it is found that the adoption ofagroforestry, as well as manure, chemical fertilizer, and terracing/trenching,significantly increase both land and total factor productivity (TFP). The TFP gain is
estimated to be 40.7 percent on average for agroforestry, demonstrating a considerable
contribution of agroforestry to the sustainability of agricultural production. The TFPgain mainly stems from the soil conservation effect of agroforestry, and its otherbenefits such as the harvest of tree crops would make it even more attractive.
Agroforestry may be labor-intensive technique, thus lowering labor productivity, but it
remains profitable if the labor opportunity cost is low, as is likely in low-income20African countries. Thus, the dissemination programs to support adoption of agroforestry
and other SCTs are expected to have significant economic impacts on the agricultural
sector in Kenya.
The average treatment effect for the adopters, however, turns slightly negative due tothe negative self-selection effect, possibly because the adopters of agroforestry tend toperceive adverse conditions of their land, which drives them to adopt SCTs. In thissense, agroforestry and the other SCTs can be better characterized as preventive actions
predominantly taken by farmers facing adverse conditions. This makes it difficult for
the evaluators to observe the benefits of the SCTs as they often rely on the simple mean
comparison and the least squares estimation which could obscure the real benefits of
SCTs due to their failure to reflect those complexities. The dissemination programs
should consequently aim to demonstrate appropriate economic benefits of the adoption.
Such programs also would benefit farmers who do not necessarily face adverse
conditions in production.

21

References
Ajayi, O. C. 2007. User Acceptability of Sustainable Soil Fertility Technologies:
Lessons from Farmer’s Knowledge, Attitude and Practice in Southern Africa.
Journal of Sustainable Agriculture 30(3): 21-40.
Amadalo, B., Jama, B., Niang, A., Noordin., Nyasimi, M., Place, F., Franzel, S., &Beniest, J. 2003. Improved Fallows for Western Kenya: An Extension Guideline.
Nairobi: World Agroforestry Center.
Bolwig, S., Gibbon, P., & Jones, S. 2009. The economics of smallholder organic
contract farming in Tropical Africa. World Development 37(6): 1094－1104.
Caviglia-Harris., & Jill, L. 2003. Sustainable agricultural practices in Rondônia, Brazil:
do local farmer organizations affect adoption rates? Economic Development andCultural Change 52: 23–49.
Feder, G., & Onchan, T. 1987. Land ownership and farm investment in Thailand.
American Journal of Agricultural Economics 69(1): 311–320.
Greene, W. H. 2008. Econometric Analysis. 6th Edition. New Jersey: Prentice–Hall,
Upper Saddle River.
Heckman, J. J. 1978. Dummy endogenous variables in a simultaneous equation system.
Econometrica 46(6): 931–959.
Kassie, M., Pender, J., Yesuf, M., Kohlin, G., Bluffstone, R., & Mulugeta, E. 2008.
Estimating returns to soil conservation adoption in the Northern Ethiopian
highlands. Agricultural Economics 38: 213–232.
Kumbhakar, S. C., & Lovell, C. A. K. 2000. Stochastic Frontier Analysis. New York:
Cambridge University Press.
Lee, D. R., Barrett, C. B., & McPeak, J. G. 2006. Policy, technology, and management
strategies for achieving sustainable agricultural intensification. Agricultural
Economics 34: 123–127.
Maddala, G. S. 1983. Limited-Dependent and Qualitative Variables in Econometrics.
New York: Cambridge University Press.
Marenya, P. P., & Barrett, C. B. 2007. Household-level determinants of adoption of
improved natural resources management practices among smallholder farmers inWestern Kenya. Food Policy 32(4): 515–536.

22

Mathuva, M. N., Rao, M. R., Smithson, P. C., & Coe, R. 1998. Improving Maize (Zeamays) yields in semiarid highlands of Kenya: agroforestry or inorganic
fertilizers? Field Crops Research 55(1-2): 57–72.
Mercer, D. E., & Pattanayak, S. K. 2003. Agroforestry adoption by smallholders. In
Forests in a Market Economy, Sills EO and Abt KL (eds). The Netherlands:
Kluwer Academic Publishers: 283–299.
Obunde, P. O., Mbogo, C. M., Oluoch-Kosura, W., & Kamoni, A. W. 2004. Effects ofland tenure on agricultural productivity, and the environment: a case study ofSuba and Laikipia districts. IFPRI Eastern Africa Food Policy Network Report 7.
Washington, D.C.: International Food Policy Research Institute.
Okoji, M. A., & Moses, J. 1998. Adoption of Agroforestry for Soil Conservation in
Akwa Ibom State, Nigeria. Journal of Sustainable Agriculture 13(1): 5 – 13.
Otsuki, T., & Ogo, T. 2009. Determinants of adoption of agroforestry in Kenya. Osaka
University. mimeographed.
Pattanayak, S. K., & Mercer, D. E. 2002. Indexing soil conservation: farmer perceptionsof agroforestry Benefits. Journal of Sustainable Forestry 15(2): 63–85.
Pitt, M. M., & Khandker, S. R. 1998. The impact of group-based credit programs onpoor households in Bangladesh: does the gender of participants matter? Journalof Political Economy 106(5): 958–996.
Place, F., Adato, M., Hebinck, P., & Omosa, M. 2005. The impact of agroforestry-based
soil fertility replenishment practices on the poor in Western Kenya. IFPRI
Research Report 142. Washington, D.C.: International Food Policy ResearchInstitute.
Rosenbaum, P. R., & Rubin, D. B. 1983. The central role of the propensity score in
observational studies for causal effects. Biometrika 70(1): 41–55.
Scherr, S. J. 1995. Economic factors in farmer adoption of agroforestry: patterns
observed in Western Kenya. World Development 23(5): 787–804.
Warning, M., & Key, N. 2002. The social performance and distributional consequencesof contract farming: an equilibrium analysis of the Arachide de Bouche programin Senegal. World Development 30(2): 255–263.
Young, A. 1989. Agroforestry for Soil Conservation. Walingford and Nairobi: CAB
International and International Council for Research in Agroforestry.

23