World Development Vol. 39, No. 1, pp. 77–86, 2011
Crown Copyright Ó 2010 Published by Elsevier Ltd. All rights reserved
0305-750X/$ - see front matter
www.elsevier.com/locate/worlddev
doi:10.1016/j.worlddev.2010.06.010
An Inquiry into Constraints on a Green Revolution in
Sub-Saharan Africa: The Case of NERICA Rice in Uganda
YOKO KIJIMA
University of Tsukuba, Ibaraki, Japan
KEIJIRO OTSUKA
Foundation for Advanced Studies on International Development (FASID), Tokyo, Japan
National Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan
and
DICK SSERUNKUUMA *
Makerere University, Kampala, Uganda
Summary. — In Uganda, New Rice for Africa (NERICA), a high-yielding upland rice variety suitable for the African environment, was
introduced to increase food security and reduce poverty in rural areas in 2002. However, more than 50% of the NERICA adopters in
2004 had abandoned it in 2006. The regression results indicate that the low profitability of NERICA relative to alternative crops in variable rainfall areas explains the massive dropout. It is also found that the profitability of NERICA production was low when farmerproduced seeds were used in 2006, suggesting the weak dissemination of appropriate information on seed production to rice farmers.
Crown Copyright Ó 2010 Published by Elsevier Ltd. All rights reserved.
Key words — Green Revolution, technology adoption, NERICA, Uganda, Africa, panel data
1. INTRODUCTION
the average yield of NERICA per hectare is found to be 2.5
tons on farm in Uganda (Kijima, Sserunkuuma, & Otsuka,
2006), which is significantly higher than the average upland
rice yield of one ton per hectare in SSA (Balasubramanian,
Sie, Hijmans, & Otsuka, 2007). Moreover, there is evidence
that the adoption of NERICA increases the income of NERICA adopters significantly (Adekambi, Diagne, Simtowe, &
Biaou, 2008; Kijima, Otsuka, & Sserunkuuma, 2008). In
addition to these characteristics, another advantage of NERICA is that the seeds can be self-produced by farmers as with
other rice varieties. Hence, there is no need to purchase new
seeds for several years, which enables the wide adoption of
NERICA even in countries in which rice seed markets are
underdeveloped. 1
Even though NERICA varieties seem to have great potential for realizing rice Green Revolution in SSA, the adoption
rate is still low except in a few countries (Rodenburg et al.,
2006). One reason for this low adoption rate is the farmers’
low exposure to NERICA varieties (Diagne, 2006; Diagne &
Demont, 2007; Spencer, Dorward, Abalu, Philip, & Ogungbile, 2006). The low exposure in West African countries was
The recent sudden and sharp increase in food prices in international markets has significantly and adversely threatened
food security in developing countries, especially in Sub-Saharan Africa (SSA), which is a net importer of food grains
(Benson, Mugarura, & Wanda, 2008; Ivanic & Martin, 2008).
There is, thus, an urgent need to increase food production to alleviate the widespread poverty and food insecurity in this region.
Given the increasingly limited room for the further expansion of
cultivated area in many countries in SSA, there is no substitute
for a Green Revolution, which enhances crop yield per unit of
land, in an effort to boost the food production in SSA (Diao,
Headey, & Johnson, 2008; Otsuka & Kijima, in press).
Since most of the increased consumption of rice associated
with urbanization and change in diet is currently filled by imports ( Africa Rice Center, 2008), the scarce foreign reserves
that could have been used for the import of capital goods
and materials necessary for economic development are actually used for rice imports. Thus, a Green Revolution in rice
production is urgently required in this region. One of the ways
of realizing a rice Green Revolution is to expand the adoption
of New Rice for Africa (NERICA) varieties, high-yielding upland rice varieties suitable for the African environment developed by the Africa Rice Center (or WARDA). NERICA
varieties have traits of traditional rice varieties in SSA (Oryza
glaberrima) characterized by good weed competitiveness and
resilience against major African biotic and abiotic stresses
and Asian rice varieties (Oryza sativa) characterized by good
yields, and absence of lodging and grain shattering, and high
fertilizer returns (Jones, Dingkuhn, Aluko, & Semon, 1997;
Wopereis, Diagne, Rodenburg, Sié, & Somado, 2008). Indeed,
* We would like to thank Peter Hazell, Derek Barley, Takashi Yamano,
Don Larson, Tomoya Matsumoto, Kei Kajisa, and four anonymous referees for very helpful comments. We have benefitted from discussions with
participants of the Coalition for African Rice Development (CARD)
meeting in Nairobi, the Center for the Study of African Economics
(CSAE) meeting in Oxford, and the Theoretical Economics and Agriculture (TEA) meeting in Tsukuba, seminar at Nagoya University. We are
grateful to Paul Kandasamy for editorial assistance. Final revision accepted: June 23, 2010.
77
78
WORLD DEVELOPMENT
due to the lack of effective extension and seed delivery systems
(Dalton, 2004; Spencer et al., 2006). In consequence, the diffusion of the NERICA varieties was mostly through participatory varietal selection (PVS) systems and community-based
seed system (CBSS) training sessions conducted by WARDA
and NARS, both systems of which are time consuming.
While paying attention to the role of extension, this paper
highlights the dynamic behavior of the NERICA adoption
by undertaking a case study of Uganda which is considered
as a model country of rapid NERICA adoption due to successful public–private partnerships among the national program, NGOs, seed companies, and farmers (Africa Rice
Center (WARDA), 2006). We found from the panel data of
374 rural households in Uganda collected in both 2004 and
2006 that more than 50% of the NERICA adopters in 2004
had abandoned it in 2006. Unless the problem of this high
dropout rate is resolved, a NERICA-led Green Revolution
will not be realized.
The purpose of this paper is to examine why the dropout
rate of the seemingly profitable rice variety was so high. We
identify that the major constraining factors are problematic
program design and targeting at variable rainfall areas where
the profitability of NERICA is low, and underdeveloped markets for seeds and rice milling services. Although we take the
specific case of NERICA varieties in Uganda, the results will
be useful for other countries which attempt to introduce NERICA varieties. We find that while the seed distribution program contributed to the early adoption of NERICA, it
targeted not only suitable but also unsuitable areas for NERICA production where the profitability of NERICA relative to
other crops is low, thereby resulting in massive dropouts in the
unsuitable areas. We also find that while favorable access to
input and output markets is an important determinant of
NERICA adoption, the use of farmer-produced seeds reduces
the profitability of NERICA most likely due to the deterioration of the quality of the seeds.
The rest of this paper is structured as follows. Section 2 provides an overview of the NERICA program in Uganda. Section 3 presents the conceptual framework. Section 4
describes the survey data used in this study and their major
characteristics. Section 5 explains the empirical models and results for the adoption decision. Section 6, analyzes the NERICA profit function, and Section 7 discusses the conclusions
and policy implications.
2. NERICA PROGRAM IN UGANDA
Rice is now widely grown in many parts of the country,
especially in the eastern and northern regions. Domestic rice
production has not been able to keep up with the demand,
which is growing fast because of rapid urbanization and
changing food habits. As a result, Uganda resorts to importing
about $90 million of rice (the third largest import item in the
country) every year to meet the demand (Africa Rice Center
(WARDA), 2006). The government is, therefore, keen to increase local rice production in order to reduce its import.
In 2002, a NERICA variety was released in Uganda by two
independent sources, the National Agricultural Research
Organization (NARO) and the NASECO seed company, under two different names—NARIC 3 (NARO) and SUPARICA 2 (NASECO) (Africa Rice Center (WARDA), 2006). At
that time, the diffusion of NERICA was limited around these
sources. Since the Ugandan Vice President was convinced that
the poor could escape the poverty trap by growing NERICA
varieties, he initiated a NERICA-based rice initiative as part
of Uganda’s poverty eradication campaign in January 2004.
Subsequently, the President officially launched the initiative
in March 2004, in the Vice President’s farm in Wakiso district.
NERICA seeds were distributed to representatives of farmers’
groups from 11 districts.
According to the Vice President’s office, the targeted areas
were selected based on agro-ecological suitability for upland
rice cultivation in poorer areas. After selecting the areas, local
NGO staff provided seeds to farmers who were interested in
growing NERICA varieties often in the form of in-kind credit
to offer the opportunity to try the new technology even to poor
farmers. Since the purpose of the government’s NERICA program is to expand the adoption to wider areas, the seeds are
not distributed to the same farmers more than once and the
program areas are shifted to uncovered areas.
A seed shortage proved to be a bottleneck to the wide dissemination of NERICA and an attempt was made to alleviate
this by partnering with NGOs and private seed companies.
The two NGOs—Sasakawa-Global 2000 and the USAIDfunded Investment in Developing Export Agriculture (IDEA)
project of the Agribusiness Development Centre (ADC)—purchased breeder seed from the NASECO seed company for
formal seed multiplication during the initial promotion of
NERICA varieties in the country. These activities have played
a pivotal role in the NERICA dissemination in Uganda
(Africa Rice Center, 2006).
3. CONCEPTUAL FRAMEWORK
Our conceptual framework is based on the unique characteristics of the crop under study, i.e., upland rice in the context of
Central and Western Uganda in which the high dropout took
place within a short period of time. We focus on the possible
effects of three factors, namely program targeting, market
development, and relative profitability.
(a) Program design and targeting
It must be emphasized at the outset that the method of diffusing NERICA varieties was changed after the government
program started. Especially in the first year, newspapers and
radio and TV programs presented the dissemination of NERICA varieties as a way of getting out of poverty (New Vision,
2004). Since there were only a small number of the extension
workers who knew about rice cultivation, there is no doubt
that the commitment of the government to NERICA dissemination contributed to the enhancement of the farmers’ exposure to NERICA varieties. It is likely, however, that only
positive information about NERICA varieties as an income
generating crop was highlighted and thus NERICA’s impact
could have been overvalued by the public. The production
of upland NERICA is greatly affected by the vagaries of rainfall, even though it is drought-tolerant. It also requires intensive labor use for planting, weeding, harvesting, and bird
scaring, compared with other subsistence crops such as maize
(Kijima, Otsuka, & Sserunkuuma, 2008). It is possible that
some of the early adopters might not have been aware of these
characteristics before they adopted NERICA. This lack of
accurate knowledge about this new crop may have resulted
in disappointment among the early adopters of NERICA in
subsequent years.
According to the Vice President’s office, the targeting criterion was the suitability of rice cultivation in poor areas. However, there was neither a clear definition of “suitability” nor a
specific threshold to divide suitable and unsuitable areas.
AN INQUIRY INTO CONSTRAINTS ON A GREEN REVOLUTION IN SUB-SAHARAN AFRICA
Because of this ambiguity, it is possible that some of the areas
under the government’s seed distribution program were not
suitable for rice production but were politically favored. Given
that NERICA in Uganda is grown without irrigation, the initial dissemination of NERICA in unsuitable areas for rice cultivation characterized by low and variable rainfall might have
resulted in the high dropout rates.
(b) Market access
Upland rice is a recently introduced crop to most of the
sample farmers. When a new crop is introduced, the related
markets are often missing. This was the case in the sample
areas. Our informal interviews with rice farmers in 2004 revealed serious problems in marketing the harvested paddy rice
due to the absence of rice millers in nearby towns. Since in
Uganda, rice is considered a commercial crop rather than a
subsistence food crop in contrast to West Africa, accessibility
to markets is expected to affect the adoption decision (Ashraf,
Gine, & Karlan, 2009; Carletto, de Janvry, & Sadoulet, 1999).
Limited NERICA seed availability was also a serious constraint in 2004 when a small amount of rice seed was produced
by seed companies and distributed mainly by the seed programs. In the survey period, there were four possible ways
through which farmers could obtain NERICA seed: (1) participation in the seed distribution program, (2) direct purchase of
seed from seeds companies, (3) by use of own seeds saved from
the previous harvests, or (4) purchase of seed from other farmers. Note that the seed distribution programs procured certified seed from seed companies and distributed the seed to
farmers via NGOs and extension workers. However, the direct
purchase of seed from seed companies by individual farmers
was observed mainly in areas close to such companies, where
the farmers engage in contract farming for the seed companies
which require the farmers to use the treated seeds from the
company to maintain the quality of the seeds produced by
these farmers. Because NERICA rice seeds can be produced
by farmers, it is also reasonable to expect that the effect of seed
distribution programs is moderated over time as farmers produce their own seeds.
It is found in existing studies that lack of access to credit
tends to limit the adoption of new technology since such adoption of new technology needs to be accompanied by the increased use of complementary inputs such as labor, fertilizer,
herbicide, and pesticide (Feder, Just, & Zilberman, 1985;
Sunding & Zilberman, 2001). Since the seed program did not
provide credit, the high dropout rate of NERICA may be
explained by such constraints. In the sample areas, on one
hand, NERICA rice varieties were grown without fertilizer
and the credit to purchase chemical fertilizer would thus not
be a serious constraint to growing NERICA varieties. In terms
of labor, on the other hand, rice requires more labor use than
other subsistence crops such as maize and banana, which
make the households who are not endowed with abundant
family labor or are unable to hire labor not to adopt NERICA
varieties.
(c) Relative profitability
The farmers will choose to keep using the new technology as
long as the expected profit of the new agricultural technology
exceeds that of the traditional technology (Cameron, 1999).
Given that traditional upland rice varieties were not grown
in the sample areas, we cannot compare the profitability of
NERICA varieties with that of traditional upland rice varieties. According to our field interviews, those who decided to
79
discontinue the adoption of NERICA varieties were disappointed with the low returns to NERICA production in the
past. For this reason, the profitability of NERICA compared
with that of alternative crops may have played an important
role. Note that since the production of NERICA requires
more water than alternative upland crops, such relative profitability may be significantly affected by the rainfall pattern. For
those who adopt a new technology for the first time, favorable
access to information and intense social interactions among
community members are expected to increase the adoption
(Bandiera & Rasul, 2006; Conley & Udry, 2005).
It is found that the households who have other job opportunities such as employment in urban areas and the production
of other cash crops tend to discontinue the adoption of laborintensive cropping systems in developing countries since their
opportunity costs of adopting such systems are high (Barrett,
Moser, McHugh, & Barison, 2004; Moser & Barrett, 2003;
Moser & Barrett, 2006; Neill & Lee, 2001). Given that the production of NERICA requires more labor inputs than the alternative crop, households who reside closer to towns and are
more educated may stop growing NERICA since their higher
labor costs decrease the relative profitability of NERICA to
the activities.
4. DATA
The data used in this paper were collected in 2004 and 2006.
Since the dissemination of NERICA started in 2002 as explained in Section 2, farm households growing NERICA rice
were found mainly in areas targeted by the government’s
NERICA seed dissemination programs and in areas near seed
companies. Thus, we intentionally selected 10 NERICA-growing areas covering the Central and Western regions (Kijima et
al., 2006; Kijima et al., 2008). 2 In each sample area, we drew a
random sample of 25 households that grew NERICA rice in
the second cropping season of 2004, and 15 households that
did not (the total sample size is 40 10 = 400). In order to
control for the differences in the population of NERICA
growers in each area, sampling weights are used for all analyses. In the second survey (2006), we had to decrease the number of sample districts up to nine due to the budget constraints
(40 households were dropped). In addition, we could not collect data of 13 households in the remaining nine districts due
to out-migration from the sample areas, the dissolution of
households, and the absence of household members during
the data collection period. In total, we used a panel sample
of 347 households for this analysis. 3
Based on differences in the NERICA adoption behavior, we
stratified the sample into four groups. The first group, nonadopters, has never adopted NERICA; the second group,
dropouts, includes households that grew NERICA in 2004
but not in 2006; the third group, continuous adopters, consists
of households that grew NERICA in both 2004 and 2006; and
the last group, late adopters, refers to households that adopted
NERICA in 2006 but not in 2004. We also refer to the dropouts
and the continuous adopters as the early adopters. Table 1 presents the data on the household and community characteristics
of the four adoption categories.
The table shows that there are significant differences in
household characteristics such as educational attainment and
asset holdings between the early adopters and the other
groups. 4 It is important to recognize that the dropouts and
continuous adopters are almost equally educated and also
equally endowed with family labor and land, indicating that
the reason for abandoning NERICA rice during 2004–06
80
WORLD DEVELOPMENT
Table 1. Household and community characteristics by type of adoptersa
Early adopters
Dropouts
Number of Households
129
Household characteristics in 2004
Rice cultivation experience (years)
Household head’s age
Household head’s years of schooling
Number of adult males aged 15–59
Number of adult females aged 15–59
% of female-headed households
Land area per capita (ha)b
Land area per household (ha)b
Land cultivated in 2nd season (ha)
Household asset (USD)c
Value of livestock (USD)
Yield in 2004 (ton per ha)
Yield in 2006 (ton per ha)
Profit from rice production in 2004 (USD/ha)
Profit from alternative crop in 2004 (USD/ha)g
1.49
48.2
7.0
1.80
1.96
10.2
0.38
4.52
1.19
149
371
2.01
na
40.54
86.83
Community characteristics
Availability of seed program in 2004 (%)
Availability of seed program in 2006 (%)
Distance to rice miller in 2004 (km)
Distance to rice miller in 2006 (km)
Traveling time to town (hours)
Relative price of maize to rice in 2004
Relative price of maize to rice in 2006
Three years av. rainfall 2001–03, 2004–6, 90 days (mm)
Three years C.V. rainfall 2001–03, 2004–6, 90 days
37.4
20.4
15.4
11.1
0.62
0.512
0.606
440
0.151
Other groups
Continuous adopters
d
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Late adopters
Non-adopters
99
e
25
94
f
1.88
43.9
7.7
1.81
1.68
8.0
0.47
4.23
1.30
172
390
2.97
2.54
202.21
72.54
*
0.50
49.4
4.5
0.99
1.34
29.7
0.38
2.53
0.78
54
80
na
1.49
na
na
0.07
48.7
4.9
1.43
1.50
32.8
0.24
2.56
0.92
160
307
na
na
na
na
*
17.3
23.9
28.9
5.5
0.66
0.448
0.467
465
0.145
18.6
10.7
19.0
14.1
0.42
0.385
0.452
417
0.192
33.7
28.6
26.9
6.2
0.77
0.465
0.563
470
0.115
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
a
The data pertain to 2004 unless stated otherwise.
Land area refers to owned land and tenanted land under the mailo regime.
c
Household assets include furniture, bicycles, and electrical appliances.
d
* Indicates significant difference in means between dropouts and continuous adopters by the t-tests on the equality of means at the 5% level.
e
* Indicates significant differences in means of late adopters and continuous adopters by the t-tests on the equality of means at the 5% level.
f
* Indicates significant difference in means between early adopters (continuous or dropouts) and the others by the t-tests on the equality of means at the 5%
level.
g
Profit from alternative crop is only available in 2004. Alternative crops to rice are mainly maize and beans (Kijima et al., 2008).
b
cannot be attributed to the lack of human capital, family labor, and land. The average yield of the continuous adopters
in 2004 is 3.0 tons per hectare, which attests to the high yield
potential of NERICA. It is significantly higher than that of the
dropouts in 2004. This result is expected because the poor
performance of NERICA production likely discourages
NERICA adoption in subsequent periods. 5 Moreover, the
dropouts obtained significantly lower profit from rice production in 2004 than that of the continuous adopters. In contrast,
the profit from alternative crops is much higher among the
dropouts than the continuous adopters. 6
The community characteristics seem to be different between
the continuous adopters and the dropouts. Though the distance to the nearest rice miller was shorter for the dropouts
than for the continuous adopters in 2004, it has shortened considerably in the past 2 years, particularly for the continuous
adopters. Table 1 clearly indicates that the early adopters
had better access to the seed distribution programs in 2004.
However, the availability of such programs decreased in
2006 for all NERICA adoption categories, except in areas
where the late adoption took place.
The average rainfall and the coefficient of variation in
the previous 3 years, i.e., 2001–03 for 2004 and 2003–05
for 2006, which are intended to capture the longer-term
rainfall patterns during the main cropping season, show
that the non-adopters reside in areas with low and variable
rainfall. The comparison between the dropouts and the
continuous adopters indicates that areas where the dropouts reside are less favorable in terms of the reliability
of rainfall than in the areas where the continuous adopters
reside.
5. DETERMINANTS OF DROPOUT AND ADOPTION
In order to rigorously identify the critical factors affecting
the adoption of NERICA rice varieties, we conduct regression
analyses in this section. The adoption decision is modeled as
the decision between planting a plot of land to NERICA
and to other alternative crops such as maize and beans. It is
assumed that if the expected profitability of rice is greater than
that of the other alternative crop, rice is adopted. The expectation is updated by using past experience, while considering
the effects of household and plot specific characteristics. The
adopters decide whether they remain adopters or become
non-adopters of NERICA varieties based on their actual relative profitability in the previous year, while the non-adopters
decide whether to start growing NERICA varieties or to remain non-adopters without information about relative profitability.
Since we have only two-period data, the past information
utilized in the regression analyses pertain to the information
AN INQUIRY INTO CONSTRAINTS ON A GREEN REVOLUTION IN SUB-SAHARAN AFRICA
only 2 years ago, which is denoted by t 1. Households who
did not cultivate rice at t 1 do not update their expectations
on the relative profitability of NERICA cultivation. Thus, we
specify the empirical model for the early adopters and nonearly adopters as:
y ijt ¼ 1½ai þ bðpNijt1 pAijt1 Þ þ dX ijt þ cZ jt þ eijt
> 0 if y ijt1 ¼ 1
ð1Þ
y ijt ¼ 1½ai þ dX ijt þ cZ jt þ eijt > 0 if y ijt1 ¼ 0
ð2Þ
where 1[] is an indicator function if household i in village j
grew NERICA rice at time t and is zero otherwise, pijt1 is
the profit per hectare from NERICA (N) or the alternative
crop (A) plot at t 1, and X and Z are household and community level characteristics at the time of the adoption
decision, respectively. We model the adoption decision differently for those who have adopted NERICA at t 1 shown
in Eqn. (1) and for those who did not adopt it at t 1 shown
in Eqn. (2). Since we use only early adopters to estimate Eqn.
(1) for the adoption decision in 2006, selection bias is likely to
arise. Thus, we estimate Eqns. (1) and (2) by the Probit model
with selection. 7
The possible determinants of NERICA adoption include (1)
a set of variables indicating the suitability of rice production
and the transaction costs of acquiring seeds and selling paddy
rice, such as the average rainfall, rainfall variation, availability
of seed programs, and distance to rice millers; and (2) household characteristics such as the household head’s education,
the number of adult male and female household members aged
between 15 and 59 years old, and asset holdings. As a measure
of the relative profitability, we use the profit differential of rice
and the alternative crop in the previous period.
Column (1) in Table 2 shows the first stage regression results
of the determinants of NERICA adoption in 2004. Column (2)
is the second stage regression results of NERICA adoption for
the early adopters. Column (3) shows the second stage result
for the non-early adopters indicated. In the second stage
Table 2. NERICA adoption in 2004 and 2006 (Probit model with selection)
Adoption in 2004a Stay adopting in 2006 (early adopters)b Adoption in 2006 (non-early adopters)c
Average rainfall (mm)
C.V. rainfall
Distance to rice miller (km)
Seed program dummy
Traveling time to town (hours)
Profit from rice plot – Profit from
alternative crop plot in 2004 (100 USD)d
Number of adult males aged 15–59
Number of adult females aged 15–59
% of female-headed households
Household head’s age
Household head’s years of schooling
Land area per capita (ha)
Household asset (1000 USD)
Value of livestock (1000 USD)
Number of groups household belonged to
Head’s education number of groups
household belonged to
Rice experience (years)
Wald chi-squared
(p-Value)
[1st stage]
(1)
[2nd stage]
(2)
[2nd stage]
(3)
0.001
(4.33)**
0.436
(2.01)*
0.002
(3.35)**
0.092
(4.66)**
0.092
(4.40)**
0.001
(3.27)**
2.353
(4.41)**
0.004
(1.61)
0.056
(0.58)
0.442
(4.15)**
0.066
(1.93)*
0.048
(1.85)+
0.025
(0.90)
0.308
(3.09)**
0.002
(0.84)
0.003
(1.50)
0.018
(0.44)
0.254
(1.95)*
0.141
(2.53)**
0.029
(0.63)
0.001
(9.10)**
0.899
(9.14)**
0.097
(4.68)**
0.021
(1.58)
0.002
(3.87)**
0.033
(4.59)**
0.027
(4.75)**
0.047
(2.68)**
0.004
(7.92)**
0.000
(0.45)
0.068
(5.70)**
0.055
(1.96)*
0.095
(5.35)**
0.069
(6.53)**
46.71
(0.000)
289.8
(0.000)
0.051
(6.86)**
0.032
(4.17)**
0.018
(0.97)
0.001
(1.93)*
0.009
(2.54)*
0.004
(0.25)
0.004
(0.51)
0.016
(1.31)
0.089
(3.29)
0.011
(2.88)**
0.036
(9.05)**
81
**, *, and + indicate significance at 1%, 5%, and 10%, respectively. The figures are marginal effects.
a
Estimated by the Probit model with selection, assuming that the error terms of the first and second stage regression models are bivariate normal.
b
The decision of dropout or continuous adoption in 2006 is only observed among those who adopted NERICA in 2004.
c
Similarly, the decision of new adoption or non-adoption at all in 2006 is only observed among those who did not adopt NERICA in 2004.
d
Endogenous variable: instrumental variable – rice cultivation experience before 2004 (year).
82
WORLD DEVELOPMENT
regressions, self-selection bias is controlled for as explained in
the previous section. The figures shown in this table are the
marginal effects.
As may be expected, higher average rainfall and lower rainfall variations in the previous 3 years, which are favorable for
NERICA production, significantly raised the probability of
remaining NERICA adopters and newly adopting NERICA
varieties in 2006 (see columns 2 and 3). An increase in average
rainfall in the previous 3 years by 10 mm enhances the
adoption rate by 1% point in 2006. It is important to note that
higher rainfall decreases and higher rainfall variation increases
the probability of adoption in 2004 (see column 1). This suggests that the new adoption in 2006 took place in areas that
are more suitable for NERICA production, while in 2004,
NERICA tended to be adopted in less suitable areas for
NERICA production characterized by high rainfall variation
and lower average rainfall. In other words, some of the areas
that received NERICA dissemination programs were likely to
have been mis-targeted.
This interpretation is supported by the positive coefficient of
the relative profitability of NERICA to the alternative crop in
column (2): it significantly increased the probability of continuing NERICA adoption in 2006. This means that the observed
high dropout rate in 2006 is partly due to the low profitability
of NERICA compared to that of the alternative crop. According to the estimated coefficients, an increase in the profit differential by USD 10 decreases the dropout rate of NERICA by
0.7% points.
The effects of the distance to rice millers on the adoption
decision are significant in columns 1 and 3. This indicates that
NERICA is less likely to be adopted in areas where the cost of
marketing paddy rice is higher. The availability of seed programs increased the probability of adoption in 2004 but not
in 2006. This may suggest that the role of such programs as
the seed source became less important in 2006 due to the development of seed markets or the use of self-produced seeds.
There are positive relationships between NERICA adoption
and the household head’s education for early adoption in
2004. It appears that the ability to decode new information
and rice production knowledge may matter in NERICA adoption. Among the early adopters, however, the higher education
does not increase the probability of continuing NERICA production in 2006. Rather, there is a weak evidence that the educated farmers quickly abandoned NERICA production in
2006 (insignificant but negative coefficient of household head’s
education in column 2) since their opportunity costs of adopting labor-intensive cropping systems are higher than those for
the less educated farmers.
Asset ownership variables have mixed effects on adoption
decisions. Household asset and livestock ownership do not
have an impact on NERICA adoption in 2004 while they have
positive effects on continuous adoption in 2006 and negative
effects on new adoption in 2006. As shown in column (3), larger land per capita increases the probability of adopting NERICA in 2006. Since rice is considered as a cash crop and
farmers tend to grow their own food crops as well, small farm
size can be a limitation for NERICA adoption. As shown in
column (1), the endowment of family labor, measured by the
number of adult household members, positively affected NERICA adoption in 2004. However, the positive effects of family
labor endowment are not observed in 2006.
In conclusion, we would like to point out that the low relative
profitability of NERICA compared with that of the alternative
crops and unfavorable access to markets are critical factors
explaining the high dropout rate during 2004–06. Our analysis
also suggests that the inappropriate targeting of NERICA seed
distribution programs toward areas with low and variable rainfall is partly responsible for the massive dropouts.
Table 3. NERICA plot characteristics by type of adopters
2004
Dropouts
Number of plots
Size of NERICA plot (ha)
129
0.423
Plot location and cultivation practice
% of plots with straight-row planting
% of late planting
% of plots with zero yield
% of plots with steep slope
% of plots with rice in the previous two seasons
% of plots which are rented in
Walking time from homestead to plot (minutes)
94.4
8.5
4.4
12.9
0.8
16.1
6.3
Seed source
% of self-produced seeds
% of purchased seed from neighbors
% of program seeds (NGO, VP)
% of other seeds (purchased from traders, contract farming)
Input use (per hectare)
Chemical fertilizer (kg)
Seeds (kg)
% of pesticide/herbicide used
Male labor (hours)
Female labor (hours)
Child labor (hours)
a
5.2
3.8
53.8
37.2
7.97
87.97
3.64
666.3
788.0
547.4
a
2006
Continuous adopters
b
107
0.377
*
*
*
*
*
*
93.1
2.8
0.4
8.6
0.7
12.6
6.0
7.7
11.7
42.9
37.7
2.43
147.32
6.22
930.3
1020.8
607.6
Continuous adopters
c
100
0.471
*
*
*
*
93.2
11.1
6.9
1.7
2.6
6.9
10.5
41.5
12.0
15.0
31.5
5.40
97.0
10.6
1038.7
1166.5
412.4
Late adopters
23
0.241
*
*
*
86.6
6.1
24.9
0
0
2.0
10.2
5.8
46.5
10.2
37.5
0
95.0
2.0
1257.5
1589.2
434.7
* Indicates significant difference in means between dropouts and continuous adopters in 2004 by the t-tests on the equality of means at the 5% level.
* Indicates significant difference in means of continuous adopters during 2004–06 by the t-tests on the equality of means at the 5% level.
c
* Indicates significant difference in means between late adopters and continuous adopters in 2006 by the t-tests on the equality of means at the 5% level.
b
AN INQUIRY INTO CONSTRAINTS ON A GREEN REVOLUTION IN SUB-SAHARAN AFRICA
6. DETERMINANTS OF PROFIT
In the previous section, we found that relative profitability is
one of the key determinants of the dropout from NERICA
production in 2006. To achieve sustainable adoption, we need
83
to understand what determines the profitability of NERICA.
Thus, in this section, we estimate the profit functions.
Table 3 shows the plot-level characteristics such as plot location and cultivation practices related with soil fertility, seed
source, and input use by the different adoption categories.
Table 4. Determinants of NERICA profita
2004
2006
(1)
(2)
(3)
(4)
4.470
(1.64)
13.837
(1.33)
0.056
(0.00)
22.812
(0.66)
99.669
(0.63)
55.281
(0.73)
43.834
(0.45)
0.794
(0.01)
56.486
(0.71)
1.544
(0.68)
19.866
(0.15)
0.030
(0.07)
165.562
(1.29)
8.905
(3.36)**
0.625
(0.49)
78.972
(1.96)*
5.141
(0.13)
147.845
(1.25)
106.302
(1.23)
341.542
(2.20)*
73.287
(0.81)
11.280
(0.15)
9.109
(1.10)
132.797
(1.29)
0.585
(1.64)
41.182
(0.47)
99.743
(0.48)
7.866
(2.73)**
1.106
(0.88)
61.118
(1.66)+
10.959
(0.28)
302.307
(2.30)*
66.099
(0.81)
197.439
(1.33)
89.083
(0.99)
134.288
(1.60)
4.085
(0.57)
174.094
(1.25)
0.551
(1.42)
18.225
(0.20)
250.942
(1.77)*
102.772
(1.19)
57.730
(0.18)
335.885
(2.44)*
148.517
(2.02)*
0.194
(0.07)
0.412
(0.43)
1.269
(1.79)*
298.552
(2.54)*
0.164
(5.08)**
0.028
(1.51)
0.165
(3.10)**
160.244
(0.55)
0.38
0.63
Constant
111.221
(0.29)
3.410
(1.23)
3.340
(0.32)
1.417
(0.05)
43.001
(1.16)
112.052
(0.79)
17.987
(0.24)
38.610
(0.39)
82.270
(0.97)
6.004
(0.07)
0.559
(0.22)
109.919
(0.76)
0.139
(0.32)
0.000
(0.11)
102.271
(0.77)
249.546
(1.90)+
10.712
(0.11)
96.043
(0.86)
126.270
(1.34)
0.146
(0.07)
1.307
(1.58)
0.448
(2.06)*
75.723
(0.51)
0.124
(2.59)*
0.108
(2.17)*
0.014
(0.39)
379.106
(0.94)
R-squared
0.07
0.27
Household head’s age
Household head’s years of schooling
Number of adult males aged 15–59
Number of adult females aged 15–59
% of female-headed households
Land area per capita (ha)
Household asset (1000 USD)
Value of livestock (1000 USD)
Seed program dummy
Distance to rice miller (km)
Traveling time to town (hours)
Rainfall for 90 days in the second cropping season
(mm)
Maize–rice price ratio
Straight-row planting dummy
Late planting dummy
Steep slope plot dummy
Rent-in plot dummy
Farmer-produced seed dummy
Walking time from homestead to plot (min)
Chemical fertilizer application per ha (kg)
Seed used per ha (kg)
Pesticide/herbicide use dummy
Male labor hours spent per ha
Female labor hours spent per ha
Child labor hours spent per ha
**, *, and + indicate significance at 1%, 5%, and 10%, respectively.
a
The numbers in parentheses are t-statistics. The second stage regression results are shown. In the first stage regression, the interaction term of household
head’s education with the number of farmers’ groups is used for identification to be adopters in each specification. The result for the specification (1) is
shown in Table 2 column (1).
84
WORLD DEVELOPMENT
The continuous adopters are less likely to plant late than the
dropouts. 8 Partly due to this difference, the proportion of
plots with zero yields is higher among the dropouts than the
continuous adopters in 2004, which is directly responsible
for lowering the average yield. In 2006, compared with the
continuous adopters, the late adopters are less likely to practice straight-row planting, which makes weeding more difficult, thereby resulting in lower yield.
The seed source has changed drastically during 2004–06. In
2004, the proportion of sample plots planted to seed obtained
either from the seed companies or from the seed distribution
programs was 80–90%, and the use of farmer-produced seeds,
i.e., self-produced seeds and seeds purchased from neighbors,
was rare. There is not much difference in seed source between
the continuous adopters and the dropouts in this year. However, in 2006, the continuous adopters used mainly their own
self-produced seeds, while the late adopters used mainly purchased seeds from neighbors. Although NERICA seeds can
be produced by farmers themselves as with other rice varieties,
farmers have to remove undesirable plants to obtain highquality rice seed. Once farmers learn how to produce highquality seed, the farmer-produced seeds can be as good as that
sold by seed companies. 9 Given that most of the sample farmers had only recently started rice production, there is a possibility that the quality of the farmer-produced seed was not as
good as that of the purchased seeds, unless the extension services provided appropriate information on seed production.
Thus, we also test whether using self-produced seed lowers
the profit or not.
To rigorously analyze what increases the NERICA profit,
we estimate the profit function by using the plot-level data.
Since the profit information is only available among adopters,
we apply Heckman’s two-step model separately for 2004 and
2006 so as to control for self-selection bias. We estimate two
specifications with and without the variables of cultivation
practice and plot location which are likely to be endogenous.
The explanatory variables used for this analysis are plot-level
variables such as land tenancy, the walking time from the
homestead to the plot, the slope, the location, and the cropping pattern shown in Table 3 as well as the household and
community level data indicated in Table 1. Note that we use
the rainfall for 3 months after NERICA was planted in the
NERICA profit functions, while we used the average rainfall
over the 3-year period in the estimation of the adoption function as a proxy for the expected rainfall pattern.
Table 4 reports the estimated results of the NERICA profit
function. In 2004, the NERICA profits were not significantly
affected by household and community characteristics. The
profits tended to be lower when planting was late, when fewer
seeds were used, and when more male and female labor inputs
were used. The signs of the labor inputs are contrary to the
expectation, but this is probably because the unfamiliarity of
the farmers with certain activities such as planting and harvesting in rice cultivation resulted in longer working hours
without much labor productivity gains.
In 2006, household characteristics such as the household
head’s age and the number of male adult members affected
the NERICA profit. In terms of plot characteristics, recycled
seed, straight row planting, rent-in plots, the amount of seeds,
pesticide/herbicide use, and male and child labor had significant impacts on the profits The coefficient of the farmer-produced seed dummy is negative and significant, suggesting the
possibility of the deterioration of seed quality over time. If
so, there is an urgent need for training on the production of
high quality seeds. Whether the plot is rented or owned affected the profit only in 2006. In terms of the location of the
plot, the slope and distance from the homestead do not have
significant impacts on the profit. 10
7. CONCLUSION AND POLICY IMPLICATIONS
Using panel data of 347 households collected in 2004 and
2006 in rural Uganda, we identified four types of NERICA
adoption behaviors: continuous adoption in the 2 years, dropouts, late adoption, and non-adoption. The NERICA yield
was found to be much higher among the continuous adopters
than among the dropouts. Furthermore, for the dropouts, the
profit from the NERICA plot is much lower than that for the
alternative crop. The regression analysis confirmed that this
relatively low profitability of NERICA partly explains the
high dropout rate of NERICA. The relative profitability seems
to be determined largely by the rainfall pattern. Indeed, NERICA adoption is positively affected by sufficient rainfall and
lower rainfall variation in 2006. However, farmers in areas
unsuitable for its cultivation, such as those in areas with high
rainfall variation, have been targeted to receive NERICA seed
program in 2004. These findings strongly suggest the mis-targeting of the NERICA dissemination programs.
We also found that the availability of seed distribution
programs was a critical determinant of NERICA adoption
in 2004 but not in 2006, most likely because the use of
farmer-produced seed was widespread in 2006. Shortened
distance to rice millers, whose number has increased rapidly
in the past 2 years in response to the increasing demand for
milling services, significantly increased the NERICA adoption. These findings suggest that improved access to rice
millers and the increased exchange of farmer-produced seeds
among farmers stimulated the new adoption of NERICA
rice, which, in turn, point to the possibility of a rice Green
Revolution.
The first policy implication of this study is that in order to
achieve the wider dissemination of NERICA and to realize a
rice Green Revolution, the extension system must be strengthened with regard to teaching farmers how to grow rice. The
appropriate timing of planting is critical to avoid crop failure.
The failure to disseminate appropriate methods for producing
high-quality farmer-produced seed is another important factor
which will likely reduce not only the yield of NERICA but
also the adoption of NERICA. 11 Also important is the appropriate targeting of NERICA dissemination areas. Capacity
building for extension workers and the allocation of resources
to extension activities are the keys to the realization of a
“NERICA Revolution” in this country.
The second implication is that rice development policies
should be designed to support the development of markets
for rice seed and milling services, as they promote sustainable NERICA adoption. Although we do not have concrete
evidence, in all likelihood, the development of the rice milling market was a response to the increased demand for milling services. The fact that the distance to rice millers
declined more significantly in areas where the continuous
adopters are located than in those where the dropouts and
the non-adopters are located is consistent with our conjecture. The burgeoning trade of seed from experienced rice
farmers to new farmers is indicative of the emergence of
an informal seed market. Thus, there seems to be a virtuous
circle of increased seed production, the increased production
of rice, and the improvement of rice milling markets. To the
extent that these markets still “fail” due to credit constraints
and imperfect information about the methods of seed production, the quality of seeds, and the rice milling business,
AN INQUIRY INTO CONSTRAINTS ON A GREEN REVOLUTION IN SUB-SAHARAN AFRICA
there is room for the government to support the further
development of these markets in order to improve the
85
efficiency of rice production and reduce poverty among
smallholders in Uganda.
NOTES
1. In the case of our study country, Uganda, it can be said that the higher
price of rice is another potential benefit of rice production. Although it is
difficult to obtain solid statistical evidence, it is widely believed that the
prices of imported goods, including rice, are generally high in Uganda due
importantly to poor infrastructure.
2. Two to three local councils (the lowest administrative unit in Uganda
called LCI) constitute each NERICA growing area, and our sample covers
29 LC1s.
3. In addition, we use the rainfall data provided by the department of
meteorology, Government of Uganda.
4. The early adopters tend to be more educated according to the
literature on the adoption of new agricultural technologies (Sunding &
Zilberman, 2001; Feder et al., 1985).
5. A recent study conducted in Uganda (Sserunkuuma, 2008) shows that
the occurrence of severe drought conditions during the cropping season
significantly reduced the rice yield.
6. We have tried the multinomial approach to analyze the transitions
between states of adoption and non-use of NERICA, but the qualitative
results are similar to the current results.
7. Since the dependent variable at the second stage regression is an
indicator variable, we apply the probit model with sample selection
(heckprob for STATA command) where the error terms in the first and
second stage regression models are assumed to be bivariate normal. The
profit is computed by subtracting both the actual and the imputed costs of
non-land inputs from the gross value of production.
8. We constructed a dummy variable for whether farmers planted rice
too late according to their planting date. We consider a case as late
planting when farmers plant upland rice later than August 15 for the
second cropping season in 2004 and 2006 based on the upland cultivation
manual provided by APEP (The Uganda Agricultural Productivity
Enhancement Program, a 5-year USAID-funded activity).
9. This is why rice seed suppliers cannot make large profits in Asia, where
farmers are adept at producing high-quality seeds. In Uganda, certified
seeds are actually produced by farmers under contract with seed
companies which provide detailed instructions on seed production.
10. Since we have soil data only for 2004, we included the soil carbon
content, one of the soil fertility measures, as extra explanatory variables.
The soil fertility measures do not have a significant impact on NERICA
profit after controlling for cultivation practices.
11. See Feder, Jock, Birner, and Deininger (2010) and Benin et al. (2007)
for the recent reform of agricultural extension systems in Uganda.
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