HIAS-E-54

Contract Farming, Farm Mechanization, andAgricultural intensification:
The Case of Rice Farming in Cote d’Ivoire
Yukichi Y. Mano
Hitotsubashi University, Graduate School of Economics, Tokyo 186-8601, Japan

Kazushi Takahashi
Sophia University, Facullty of Economics, Tokyo 102-8554, Japan

Keijiro Otsuka
Kobe University, Graduate School of Economics, Hyogo 657-8501, Japan

September 2017

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8604
http://hias.ad.hit-u.ac.jp/
HIAS discussion papers can be downloaded without charge from:http://hdl.handle.net/10086/27202
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Contract Farming, Farm Mechanization, and Agricultural Intensification: The Case of
Rice Farming in Cote d’Ivoire

Yukichi Y. Mano, Hitotsubashi University, Graduate School of Economics, Tokyo, 186-8601, Japan
Corresponding author: +81-42-580-8813; yukichi.mano@gmail.com

Kazushi Takahashi, Sophia University, Faculty of Economics, Tokyo, 102-8554, Japanand
Keijiro Otsuka, Kobe University, Graduate School of Economics, Hyogo, 657-8501, Japan

September 30, 2017

Acknowledgements
The authors with to thank Kei Kajisa, Yuko Nakano, Takeshi Sakurai, and other seminar
participants at JICA Research Institute for their many useful comments, which improved thispaper substantially. We are grateful for the financial support of the Institute of Developing
Economies-Japan External Trade Organization (IDE-JETRO), Sophia University, andGrant-in-Aid for Scientific Research on Innovative Areas-25101001, Japan.

1

Abstract
It is critically important to intensify farming systems by disseminating proper agronomic
practices and promoting the increased application of inputs to raise agricultural productivity
in sub-Saharan Africa. However, the region’s public agricultural extension systems are weak,
and their input and output markets often fail to function properly. Under these circumstances,contract farming (CF) is expected to be a promising way to overcome market imperfections
by providing inputs, production training, and marketing services. We examine this possibility
by analysing the case of rice production CF in Cote d’Ivoire. We find that CF did not lead tofarming intensification, due mainly to the inadequate and uncertain provision of tractor
services. Further analysis reveals a complementarity between tractor use and labour inputs,
whereby tractor use in land preparation enhanced the adoption of input- and labour-intensive
practices in subsequent farming activities, thereby increasing labour use and improving land
productivity. The diffusion of tractors is thus likely to be key to the intensification of rice
farming systems in sub-Saharan Africa. (165 words)

Keywords: contract farming, rice production, tractor, farm mechanization, agriculturalintensification, Green Revolution, sub-Saharan Africa, Cote d’Ivoire
JEL classification: N57, O12, O13, Q12, Q16, Q18

2

1. Introduction
The intensification of land use is indispensable for food security and poverty alleviation in
many countries in sub-Saharan Africa (SSA), where arable land per capita has been
decreasing rapidly due to rapid population growth (Holden and Otsuka, 2014; World Bank,
2007). Among major cereal crops, rice has the most promising prospects of boosting crop
yields in SSA because improved seed-fertilizer technologies and agronomic practices – suchas levelling, bund construction, and transplanting in rows – are well-established andtransferable from Asia (Otsuka and Larson, 2013, 2016).
The adoption of the recommended technology has been disappointingly low across
many countries in SSA. The literature reveals failures in input markets, credit markets, andproduct markets as well as a lack of institutions able to disseminate technology informationas major constraints on the adoption of improved practices in SSA in general (e.g. Barrett etal., 2017; Croppenstedt et al., 2003; Duflo et al., 2011; Ndjeunga and Bantilan, 2005) and on
the intensification of rice farming in particular (e.g., Kijima et al., 2012; Saliu et al., 2016;
Njeru et al., 2016; Otsuka and Larson, 2016). Given the underdevelopment of public
extension services, which were weakened by structural adjustment policies in the 1980s and1990s in SSA, private companies are expected to fill that gap through such means as contract
farming (CF). However, CF is generally adopted for such high-quality products as fresh
vegetables and fruits and livestock products (Barrett et al., 2012; Otsuka et al., 2016; Oya,2012), and we know little about whether CF is effective in enhancing the productivity of3

staple crops such as rice.
To fill this research gap, this study (1) examines whether CF helps enhance riceproductivity and profits through the adoption of improved agronomic practices and (2)investigates the detailed mechanisms by which CF achieves or fails to achieve highprofitability. We draw on a case in Cote d’Ivoire, where CF for rice farming was recently
introduced to increase domestic rice production. In Cote d’Ivoire, unlike other SSA countries,
improved seed-fertilizer technologies have been widely diffused due to training provided by
local governmental and international organizations such as AfricaRice (formerly known as“WARDA” [West Africa Rice Development Association]). In fact, virtually all the
rice-growing farmers we study apply modern varieties of rice and significant amounts ofchemical fertilizer.
On the other hand, Cote d’Ivoire’s difficulties are similar to those of other SSA
regions, in that proper land preparation is difficult due to the absence of draft animals, and thetractor rental market is underdeveloped (Binswanger 1978, 1986; Binswanger and Pingali
1988; Pingali 2007; Takeshima et al., 2013; Takeshima, 2015; Takeshima et al., 2015).1
Manual land preparation is possible but laborious without sufficient irrigation facilities,
which could provide a soft soil mass. Because of constraints on ploughing by animals andtractors, expanding the cultivation area is impeded, as is the intensification of land use
through improved agronomic practices such as levelling, bund construction, and1

Note that ‘tractor’ refers to a power tiller for our study site.
4

transplanting.2 Contract farming is expected to provide tractors in a timely manner.
Our main findings are as follows. Contrary to our expectation, CF was not effectivein enhancing rice farming productivity at our study site, partly because its rice yield was
already reasonably high and partly because CF failed to provide tractors in a timely mannersince the contractor, a private agricultural company, lacked experience in rice production andwas unable to coordinate tractor use across villages. This failure induced contracted farmers
to adopt fewer of the recommended agronomic practices, such as seed incubation andtransplanting, leading to decreased labour input per hectare per season. In other words, CF
farmers reduced labour input, not because of better access to tractors, but because of anunavailability or uncertain availability of tractors. Our further investigation reveals that theuse of tractor led to the adoption of input- and labour-intensive farming practices and to
improved productivity and profitability of rice farming overall.
Our study contributes to two strands of the literature. First, we address the roles of CF
in rice production in SSA, which is underexplored in the literature. To the best of ourknowledge, only two studies show that CF has improved the yield and incomes of
rice-growing farmers in SSA, including in Tanzania (Nakano et al., 2014) and Benin(Maertens and Velde, 2017). However, unlike ours, these studies do not examine theunderlying mechanism in detail, including the potentially complementary roles of machinery
2

Farming intensification may be constrained by different factors in different environments. Emerick et al.
(2016) recently documented that improved technology in rice variety (i.e. flood tolerance) reduced
production risk, promoted the intensive application of input, increased the adoption of agronomic practices,
and improved agricultural productivity in eastern India.
5and the adoption of improved agronomic practices. To obtain deeper insights, we not only
compare CF farmers with non-CF farmers in their use of tractors and other inputs, but wealso compare non-CF farmers who use tractors with non-CF farmers who prepare their land
manually in their adoption of input- and labour-intensive farming practices.3
Second, this study sheds new light on the question of whether tractor use islabour-saving or labour-using. As exemplified by the induced innovation theory, thedominant view is that a new technology is employed to save a factor of production, therelative price of which increases. For example, Boserup (1965) argues that the populationpressure on land induces the adoption of labour-intensive farming systems to serve as a
substitute for increasingly scarce factors of production, such as land. Similarly, Hayami and
Ruttan (1985) argue that population pressure induces the development and adoption ofland-saving and yield-enhancing biological and chemical technology, whereas themechanization and adoption of labour-saving technologies are induced when wages increase.
The substitution of capital for labour and draft animals has been widely observed in regions
where wages are rising in the US since the 1940s, in Europe and Japan since the 1950s, andin tropical Asia since the 1960s. As the conventional view suggests, tractor use should
substitute for labour in land preparation in the SSA. However, a unique feature of SSA is thatthoroughly ploughed land increases the profitability of input- and labour-intensive agronomic
practices, so that the total application of labour and fertilizer per unit of land increases withIn the latter comparison, we focus on non-CF famers to identify the effect of tractor use
alone.
63

the use of tractors. This finding is consistent with the emerging literature that suggests thepotential role of mechanization in enabling input-intensive production in SSA (Takeshima etal., 2013) and implies that promoting agricultural mechanization does not necessarily lead toincreased rural unemployment (Pingali, 2007).
The rest of this paper is organized as follows. Section 2 explains the study setting
and postulates empirical hypotheses on the effectiveness of CF in the provision of tractors
and other inputs. Section 3 explains the study’s empirical strategy, and Section 4 discusses
the estimation results on the impacts of CF. After hypotheses are proposed regarding theeffects of tractor use in Section 5, Section 6 examines the descriptive statistics of tractor use
in our study site. Section 7 then discusses the estimation results on the effect of
mechanization on the intensification of rice farming. Finally, Section 8 concludes the paper.

2. Study Setting
2.1 CF in Cote d’Ivoire
Our study site is the Yamoussoukro district, where the capital city is located. It is connectedto the highway stretching from the country’s business centre, Abidjan (about two hours away),thus providing good market access. The local government and a foreign firm jointly
established an agricultural company to conduct CF in rice farming so as to enhance rice
farming productivity in the region.4 According to our interviews with local farmers, however,4

The government of Cote d’Ivoire divided the country into eight regions based on vegetation and gave7

this agricultural company was neither familiar with the local production environment nor
well-organized in CF management, although it employed several agronomists.
This agricultural company started CF by contracting with three farmers in March 2013as a pilot phase to produce certified seeds. This was followed by another pilot phase,
involving seed and paddy production with 55 farmers in October 2013 in four villages. The
scheme gradually expanded and involved eight villages by July 2014. Adjao (2016) explainsthat this scheme was intended (at least initially) to be developed over five years with fivemain objectives: (i) the creation of a model farm for promoting appropriate mechanizationand incorporating a modern seed centre; (ii) the rehabilitation of existing rice schemes; (iii)the development of additional arable irrigated land; (iv) the establishment of a complete riceprocessing unit; and (v) the creation of storage capacity.
The contract determined the responsibility of each party involved. According to theoriginal contract signed by typical CF farmers, the major terms covered the provision of
inputs, technical services, and marketing services by the agricultural company. Specifically,(1) the company was to secure tractors for land preparation and provide improved seeds,
fertilizers, and other chemical inputs on credit; (2) it was also to provide training inagronomic practices. (3) The farmer was to undertake production under the supervision of thecompany. (4) The agricultural calendar and the choices and prices of inputs were to be
subject to negotiation between the company and the farmers’ cooperative. (5) The company
permission to a large foreign or joint agricultural trading company to operate exclusively in each region.
8

was to harvest rice crops; and (6) the paddy was to be purchased at a predetermined price forthe 2014/2015 season.
Thus, the contractor would provide inputs (including tractors) on credit as well as
technical services, and the farmers would adopt appropriate agronomic practices and deliver
the output to the contractor. This is a typical form of CF, with the possible exception of theprovision of tractor services.

2.2 Hypotheses
Given that CF is designed to overcome the failures in the input and output markets and in theprovision of technical services (Otsuka et al., 2016; Maertens and Velde, 2017; Key andRunstein, 1999; Grosh, 1994), we postulate the following:

Hypothesis CF1: Contract farming increases the use of inputs, including the use of tractor
services.
Hypothesis CF2: Contract farming increases the adoption of improved agronomic practices.

By increasing the application of improved inputs and the adoption of improved technologies,
CF is expected to help farmers improve the output quality, production efficiency, andprofitability in rice farming. Thus, we propose the following:9

Hypothesis CF3: Contract farming leads to the production of higher-quality rice, which will
be reflected in higher rice prices.
Hypothesis CF4: Contract farming leads to improved rice farming performance, which may
be reflected in higher rice yield, rice income, and rice production profits
per hectare of land.

Note that we assume that variations in paddy price in the same cropping season reflect
differences in rice quality. We test our hypotheses by analysing the unique case of CF rice
farming in Cote d’Ivoire using our primary household data.

2.3 Data
We conducted a household survey on the sample rice farmers in 10 villages near
Yamoussoukro in early 2015. The data cover the rice plots of farmers who cultivated in the2014 and 2015 crop seasons. At our study sites, CF started full operation in July 2014, andour sample distinguishes between the pre- and post-July 2014 periods. For convenience, wecall the period before July 2014 the ‘pre-treatment period’ and the period after the‘post-treatment period’. We interviewed the CF farmers using the list provided by the
agricultural company. The sample farmers cultivated rice at least once during the year.5 Our
sample CF farmers cultivated 68 plots in the pre-treatment period, whereas they cultivated5

We have 220 sample farmers, 147 of whom cultivated one plot in one cycle only.
10

only 33 plots in the post-treatment period. According to our field interviews with thesefarmers, the agricultural company failed to provide the tractors in a timely manner. As aresult, some farmers could not fully implement the improved management practices, whileothers gave up rice cultivation during that season. The non-CF farmers were randomly
selected from the same villages. These farmers cultivated 104 plots in both the pre-treatment
and post-treatment periods.

2.4 Descriptive analysis
Table 1 presents the basic characteristics of the sample farmers who cultivated rice in the
pre-treatment and post-treatment periods by CF status. We conducted a t-test of the equalityof means between each column and the third column (i.e. the non-CF farmers who cultivatedrice in the pre-treatment period are the reference group). The average household head was amale in his mid-40s with barely any education. The average household size was nine
members, and the households had learned six improved agronomic practices via agriculturaltraining programs.6 They cultivated 0.75 hectare or more in the pre-treatment period, but the
average size of the cultivated plots slightly decreased in the post-treatment period. The
reduction of cultivated land is especially apparent among the CF farmers in the
post-treatment period.
6

The institutions that provided this training were either governmental organizations, such as L'Office
National de Développement de la Riziculture (ONDR; National Office of Rice Agriculture Development)
and Agence National D’appui au Development Rural (ANADER; National Agency to Support Rural
Development), or international organizations such as AfricaRice (formerly known as ‘WARDA’ [WestAfrica Rice Development Association]), the World Bank, and the governments of Japan and EU nations.
11According to the sample farmers and key informants in the region, the CF farmers werehighly dependent on the agricultural company to secure tractors as promised in the contract,
but, to their surprise, the company often failed to supply tractors in a timely manner due totheir lack of experience in rice production and the failure to coordinate tractor use acrossvillages. Furthermore, the supply of tractors was limited and was dominated by Chinese
products. These tractors broke down easily and lacked spare parts or a maintenance system
for repairs. Since rice is typically more widely cultivated in the latter half of the year, thedemand for tractors surged against their limited supply in the post-treatment period, making it
difficult to secure tractors during this period. This increasing cost of securing tractors may
explain the smaller size of rice cultivation and the substantially reduced number of cultivatedplots in the post-treatment period.
Table 2 illustrates the adoption of agronomic practices and the amount of appliedchemical fertilizer on the rice plots cultivated by the CF and non-CF farmers. We pay special
attention to the agronomic practices commonly adopted in rice production in Asia, which led
to success during the rice Green Revolution (David and Otsuka, 1994; Otsuka and Larson,2016). These agronomic practices are levelling, bund construction, canal construction, seed
selection, seed incubation using paper or straw, transplanting, and transplanting in rows.
These practices are known to be highly complementary with each other. For example, proper
land preparation and bund construction are prerequisites for better water and weed control
and more effective transplanting, while seed incubation is associated with the use of nurseries
12and transplanting, which in turn improves weed control. From the pre- to the post-treatment
period, the number of adopted agronomic practices declined from 4.7 to 3.7 on the CF plots
and from 4.7 to 4.4 on the non-CF plots. The last column provides an unconditional
difference-in-differences (DID) regression estimate of the CF effect (i.e. the difference in thetime trend of the number of agronomic practices adopted on the CF and non-CF plots). We
find that the CF is negatively and significantly associated with the number of improved
practices adopted. As mentioned, it was difficult for CF farmers to secure tractors in a timely
manner in the post-treatment period, and they seem to have been discouraged from adopting
improved agronomic practices, particularly seed incubation and transplanting in rows, despite
their initial plan to disseminate them.
On average, the sample farmers apply about 250 kilograms of chemical fertilizer per
hectare.7 This highly intensive application of chemical fertilizer in Cote d’Ivoire is likely tobe related to the technical training and campaigns provided by the government through
ONDR and ANADER and by AfricaRice, as well as the well-functioning chemical fertilizer
market. However, when tractors were unavailable and land preparation was poor, the farmers
seem to have reduced their application of chemical fertilizer, though the effect was notstatistically significant.
We next examine the cultivated land size and the application of labour and machinery,7

Njeru, Mano, and Otsuka (2016) summarize the FAO data that farmers in Indonesia and Kenya apply
almost 150 kilograms of chemical fertilizer per hectare, and much lower amounts in other countries insoutheast and south Asia and in Sub-Saharan Africa.
13

which consisted mainly of tractors for land preparation (see Table 3). For both groups, thecultivated land size and the application of labour and machinery were smaller during thepost-treatment period, which may be related to the reduced availability of tractors. The
DID-estimates of the CF effects on the application of family labour and machine costs weresignificantly negative. This is also consistent with the fact that the agricultural company
failed to secure tractors. In fact, the CF effect on the application of family labour is negative
for all the farming tasks; in particular, the estimated effect is significantly negative for land
preparation (see Table 4). The cost of hired labour also declined in the post-treatment period
for various faming tasks, but the unconditional DID-estimate of the CF effect was notstatistically significant (see Table 5). We also observe reductions in the cost of machinery use
in land preparation and other tasks, and the effect of CF is also negative and significant (seeTable 6).
We now turn to the performance of rice farming (see Table 7). We observe that the
average paddy price received by the non-CF farmers declined from 186 FCFA in the
pre-treatment period to 173 FCFA in the post-treatment period, which coincided with themain rice farming season. Thus, the market supply of paddy was generally high. By contrast,
the paddy price remained at almost the same level among the CF farmers (i.e. 182 FCFA in
the pre-treatment period and 184 FCFA in the post-treatment period). The DID estimate of
the CF effect on the paddy price was 14.33 FCFA, but it was not statistically significant.
The reduced application of labour and machinery in agronomic practices was not14necessarily associated with a reduction in rice yield. Somewhat surprisingly, it was associatedwith increased income and profits from rice farming, where income is sales revenue minus
the paid-out cost and profit is income minus the imputed cost of family labour.8 Thisoccurred presumably because the climatic conditions are more favourable to rice farming in
the main rice season – the latter half of the year, or the post-treatment period in our study.
Thus, farming performance was better in the post-treatment period except for the paddy price,whereas we observed little systematic difference in rice production performance between the
CF famers and non-CF farmers. In fact, none of the unconditional DID-estimates of the CF
effects on agricultural performance was statistically significant (see Column 5 of Table 7).

3. Empirical Strategy
The descriptive analysis shows that the average areas of the rice plots cultivated by the CF
and non-CF farmers were similar. Both reduced the application of labour and capital in the
post-treatment period, when the availability of tractors declined. Although the descriptive
analysis is informative, we attempt to control for the covariates affecting participation in CF,
the application of inputs, the adoption of agronomic practices, and rice farming performancein order to accurately evaluate the effect of CF.

8

Note that we compute profit without deducting the labor cost of bird-scaring because this activity isoften carried out by children at play, whose market wage is not available.
153.1 Doubly robust estimator
We apply a doubly robust (DR) estimator to evaluate the effectiveness of CF in rice farming.
The DR method is more effective than the propensity score matching estimator and theinverse probability weighting (IPW) estimator because it can provide a consistent estimate of
the average treatment effect (ATE) so long as either the propensity score ! ! (i.e. theprobability model of participating in CF as a function of the farmers’ characteristics) or the
regression function of outcomes ! in terms of covariates ! is correctly specified(Wooldridge, 2010). Moreover, the asymptotic variance in the DR method is smaller than thatin the IPW estimator. Specifically, the expected outcome under the treatment E !! by the
DR method is expressed as!

! !! = !

!!
!
!!! ! !
!

!

!!! + 1 − ! !!

!

!!! ,

where variable !! indicates the treatment status of plot !, and !!! is the predicted value
from the regression function of the outcome variable in terms of covariates. If the propensity
score is correct, E!
! !

=1, and if the regression specification is correct, E !! ! =E !! ! . Thus, if either one is true, we can estimate E !! without bias. Similarly, we canalso estimate the expected outcome under the control E !! by calculating!

! !! = !

!!!!
!
!!! !!! !
!

!!!

!!! + 1 − !!! !!

!

!!! .

Taking the difference between the two estimators above, ! !! − !! = ! !! − ! !! , we
can obtain an unbiased estimate of ATE.9 We explain each component of the DR method, the9

We used the STATA command teffects ipwra to implement the DR method.
16

propensity score, and the regression function of the outcome below.

3.2 Participation in contract farming
We apply the DR method to the case of multiple treatment statuses. As part of the DR method,
we run the multinomial probit model of CF to estimate the propensity score:
Prob C! = ! !!

,where C! is a variable taking value k if plot i is under treatment k. Specifically, (1) k = 1
presents the plot cultivated by the CF farmer in the pre-treatment period; (2) k = 2 presents
the plot cultivated by the CF farmer in the post-treatment period; and (3) k = 3 presents theplot cultivated by the non-CF farmer in the post-treatment period. The reference group is theplot cultivated by the non-CF farmer in the pre-treatment period. Covariate !! is the vectorof the basic characteristics of plot i and the farmer who cultivates it. The basic characteristics!! consist of (1) the number of improved agronomic practices that the farmer has learned informal agricultural training programs; (2) the age of the household head and its square term;(3) a dummy variable for the household head who completed primary school or above; (4) the
number of household members; and (5) the logarithmic value of agricultural assets.

3.2 Agronomic practices and farming performance
The remaining part of our DR estimation assesses the effectiveness of CF on outcomes ofinterest, including the adoption of agronomic practices, the application of inputs, and rice17

farming productivity and profitability, by estimating the following regression function:

Y! = ! + !!! + !! ! + !! ,where Y! is the outcome variable related to the adoption of improved agronomic practices,
the application of inputs, paddy prices, and productivity and profitability in rice farming; !!
is the vector of the basic characteristics of plot i and the farmer who cultivated it; !, !, and! are the regression parameters to be estimated; and !! is the random error term.
Outcome variable Y! is represented by (A) the adoption of agronomic practices,consisting of (A1) the number of adopted improved agronomic practices; (A2) a dummy
variable for levelling; (A3) a dummy variable for bund construction; (A4) a dummy variablefor canal construction; (A5) a dummy variable for seed selection; (A6) a dummy variable forseed incubation using paper or straw; (A7) a dummy variable for transplanting; (A8) adummy variable for transplanting in rows; (A9) the applied amount of chemical fertilizer inkilograms per hectare; (B) the application of major production factors, consisting of (B1) thecultivated land size; (B2) the application of family labour measured in person-days per
hectare; (B3) the cost of hired labour per hectare; (B4) the cost of machinery per hectare; and(C) rice farming performance, consisting of (C1) the price of paddy; (C2) rice yield; (C3)income from rice farming; and (C4) profit from rice farming.

4. Estimation Results on the Effects of CF
Table 8 presents the DR estimation results regarding the adoption of improved agronomic
18

practices with the plots of non-CF farmers in the pre-treatment period used as a control group.
The last three rows in the table present the estimated CF effect, its chi-square statistic forsignificance test, and the associated p-value. We find that both the CF and non-CF farmers
adopted fewer improved agronomic practices in the post-treatment period and that theestimated CF effect was not statistically significant. Examining the adoption of each practicein turn, we find that the CF effect was significantly positive on the adoption of levelling,whereas the CF effect was significantly negative on the adoption of seed incubation using
paper or straw, transplanting, and transplanting in rows. The CF effect on the amount ofapplied chemical fertilizer was negative but not statistically significant. Overall, there is no
indication that the CF systematically increased the adoption of improved agronomic practices
or the application of chemical fertilizer, which contradicts hypotheses CF1 and CF2. As
discussed above, the farmers in our study site had many opportunities to obtain formal
training in improved agronomic practices and had good access to the major input and output
market, Abidjan, which is only two hours away. These favourable conditions may explain
why the non-CF farmers adopted the improved agronomic practices and applied chemicalfertilizer to almost the same extent as the CF farmers did.
Table 9 presents the estimated effects of CF on cultivated land size, the application offamily labour measured in person-days per hectare, the cost of hired labour, and the cost ofmachinery. Both the CF and non-CF farmers in the post-treatment period cultivated a smaller
land size and applied significantly less family and hired labour and machinery than the19non-CF farmers did in the pre-treatment period. However, the reduction in the cultivated landsize, the application of family labour, and the costs of hired labour and machinery were much
more pronounced for the CF farmers. As a result, the estimated CF effect on the applicationof labour and machinery was significantly negative, indicating again that hypothesis CF1 isinvalid.
Table 10 shows the estimated effects of CF on paddy price, yield, and income andprofit from rice farming. Income increased by 20% to 30% and profit by 40% to 50% among
both the CF and non-CF farmers in the post-treatment period. This may be related to the factthat the post-treatment period coincided with the favourable rice cultivation season. Overall,the estimated CF effect was insignificant for the paddy price, income, and profit, while theCF effect was significantly negative for the rice yield. These results are inconsistent withhypotheses CF3 and CF4.
Overall, CF did not systematically improve input application, including the use of
tractors, the adoption of improved agronomic practices, and rice farming performance. The
failure to improve rice profitability through CF is in sharp contrast to the findings of studies
conducted in Tanzania (Nakano et al., 2014) and Benin (Maertens and Velde, 2017) and isthus worth examining in more detail. We suspect that the unreliable availability of tractors is
one of the key reasons for this result.

20

5. Impact of Mechanization
The conventional view of agricultural development assumes that capital substitutes for labour
as wages increase due to, for example, the development of the non-farm sector (e.g. Hayami
and Ruttan, 1985). However, our analysis of CF in Cote d’Ivoire suggests that the unreliableavailability of tractors reduced the application of total labour use and the adoption of
improved agronomic practices, suggesting the potentially complementary roles of capital andlabour, which is discussed in Takeshima et al. (2013) and Pingali (2007). To identify themechanisms behind the failure of CF observed in our case, we explore in this section whether
significant complementarity exists between capital and labour in the intensification of rice
farming. In particular, we explore the importance of tractor use in land preparation, which
provides a basis for the application of labour-intensive agronomic practices. Based on these
considerations, we postulate the following:

Hypothesis M1: Tractor use leads to the intensive use of paddy land for rice cultivation.
Hypothesis M2: Tractor use leads to the adoption of improved agronomic practices (e.g. theimplementation of levelling and bund construction, a greater application offertilizer, a more frequent adoption of transplanting and hand weeding, anda greater application of labour inputs).
Hypothesis M3: Tractor use leads to improved rice farming performance, which may bereflected in higher rice prices and in higher rice yields, rice incomes, and21

rice production profits per hectare of land.

6. Descriptive Statistics on Mechanization
We analyse the effects of mechanization by using the sample of non-CF farmers, in order toremove the impact of CF. Table 11 compares the basic characteristics of the farmers and riceplots between the plots the farmers cultivated using tractors (the ‘tractor plots’) and the plots
the farmers cultivated manually (the ‘manual plots’). The two groups had similar basic
characteristics, except that the farmers who used tractors had larger families, cultivated more
often in the pre-treatment period, and had better water access.
Table 12 compares the adoption of improved agronomic practices and the applicationof chemical fertilizer by the use of tractors. The number of adopted agronomic practices was
significantly greater on the tractor plots. In particular, the use of tractor led to a higher
adoption of levelling, bund construction, canal construction, and seed incubation but a lower
adoption of seed selection. The farmers also applied significantly more chemical fertilizer on
the tractor plots, which are more thoroughly ploughed.
Table 13 presents the cultivated land size, the application of family labour, and thecosts of hired labour and machinery by the use of tractors. While there was no significant
difference in the application of family labour, the tractor plots were larger, and the farmers
applied significantly more hired labour and machinery. To analyse the application of labour
in more detail, Tables 14 and 15 present the application of family and hired labour,
22respectively, across different tasks. Tractor use does not seem to affect the application offamily labour across different tasks, whereas the farmers applied significantly more hiredlabour for crop establishment, crop care, and harvesting on the tractor plots. There is a weakindication that tractor use saved labour for land preparation, but, more importantly, it also
induced an intensification of the farming system and increased the application of labour,which more than offset the reduction in labour use for land preparation.
Table 16 presents the paddy price, rice yield, and income and profits from rice farming
through the use of tractors. Paddy price, rice yield, rice income per plot, and rice profit perplot were significantly higher on the tractor plots, while income and profit per hectare werealso higher but not significantly so.

7. Estimated Effects of Mechanization
We use the DR method to estimate the effects of the use of tractors on the adoption ofagronomic practices, the application of inputs, and agricultural performance. We control forthe basic characteristics as well as for the season of cultivation.
Table 17 presents the estimation results for the effects of tractor use on the adoption ofagronomic practices. As expected, tractor use significantly increased the number of adopted
agronomic practices. In particular, it led to a substantially higher adoption of bund and canalconstruction but, unexpectedly, a slightly lower adoption of seed selection.
Table 18 presents the estimated effects of tractor use on the size of cultivated land and23the application of labour and machinery. While tractor use was associated with greater
cultivated land size and greater application of hired labour and machinery, the application offamily labour did not change significantly.
To understand these results more thoroughly, Tables 19 and 20 present the applicationof family labour and hired labour, respectively, across different tasks. Although the estimates
were not statistically significant, the coefficients suggest that tractor use tended to increase
the application of family labour in crop care but reduce the application of family labour inland preparation, crop establishment, and crop harvest. Notice that crop care includes theapplication of fertilizer and chemicals and water control, for which family labour’s care andjudgement are important (Hayami and Otsuka, 1993). As for hired labour, tractor use led to a
substantial increase in the application of hired labour in crop establishment and harvesting,which are simpler and easier to monitor than is crop care. As may be expected, tractor use isassociated with an increase in hired labour in crop care but to a much lesser extent.
Finally, Table 21 presents the estimated effects of tractor use on paddy prices, yields,
and incomes and profits from rice farming. Tractor use significantly increased paddy prices,
rice yields, rice income per plot, and rice profit per plot, consistent with the greater adoptionof improved agronomic practices. Tractor use also increased income and profit per hectare,but the estimated effects are not statistically significant.
Overall, tractor use increased the cultivated land size, induced the adoption ofagronomic practices by applying more labour and capital, and increased paddy prices,24

productivity, and profitability. These results support hypotheses M1 to M3 and are also inline with Takeshima et al. (2013), who found that households showing greater tractor use incrop production in Nigeria applied other inputs – such as fertilizer, seed/chemicals, and hired
harvesting labour – more intensively. As is argued by Pingali (2007), our results also provide
supporting evidence that the use of agricultural machinery does not necessarily reduce thedemand for total labour in crop production. Taken together, these results shed light on thepotentially complementary roles between agricultural machinery and labour in crop
production in SSA, which have not been extensively investigated in conventional studies.

8. Conclusion
In sub-Saharan Africa, where the population is rapidly growing and uncultivated land isdeclining, farming intensification is important for addressing food security. This study
analysed the effects of CF in the promotion of intensive farming by overcoming potential
failure in input markets and by providing technical services for rice farming. Using primarydata drawn from Cote d’Ivoire, we compared the adoption of improved agronomic practices,
input applications, and farming performance between CF and non-CF farmers. We found that
CF failed due to an inability to secure tractors in a timely manner, which led to a lower
application of labour and capital, a reduced adoption of improved agronomic practices, andlower rice yields. We next examined the complementarity between the mechanization in land
preparation and the intensification of farming by comparing the adoption of agronomic
25

practices between farmers who used tractors and farmers who prepared their land manually.
Tractor use may have saved labour in land preparation, but, more importantly, it also induced
an intensification of the farming system and increased labour application, which more thanoffset the reduction in labour use in land preparation. In other words, a complementarity was
revealed between mechanization and input- and labour-intensive farming.
These findings provide solid support for the emerging literature that is suggesting a
positive association between mechanization and the adoption of intensive farming systems in
sub-Saharan Africa (Takeshima et al., 2013, 2015; Pingali, 2007). Based on our findings andthose offered in the recent literature, we recommend that policies be developed forintensifying the farming system in sub-Saharan Africa by increasing the stable supply ofdecent tractor services. To do this, the government should (1) build an extension system that
promotes not only the adoption of improved agronomic practices but also the use of tractors;(2) help develop a tractor service market by providing information on tractor quality throughinspections; and (3) train mechanics in tractor repair.

26

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30

Wooldridge, J. (2010) Econometric Analysis of Cross Section and Panel Data, Cambridge,
Massachusetts: MIT Press.
World Bank. (2007) World Development Report 2008: Agriculture for Development,
Washington D.C.: World Bank.
Table 1. Basic characteristics of farmers by CF status and timing of cultivation.
CF

Non-CF

Pre-treat

Post-treat

Pre-treat

Post-treat

0.10

0

0.06

0.02

(0.30)(0)(0.25)(0.16)

46.7**

45.3

42.1

45.3**(12.0)(12.7)(11.8)(10.6)

0.51**

0.60

0.68

0.56*

(0.50)(0.49)(0.46)(0.49)

10.39*

9.93

8.84

9.67

(5.74)(5.67)(5.80)(6.21)

No. practices trained in the past 7.27**

6.96

5.76

6.66

(3.67)(3.64)(4.29)(4.31)

2.98***

3.41

3.74

3.86

(1.03)(1.43)(1.64)(1.61)

68

33

104

104

HH head female (=1)
HH head age
HH head any schooling (=1)
Family sizeln asset (000 FCFA)
Obs.

Notes: The unit of observation is plot. Standard deviations are in the parentheses. We
conducted the t-test of equality of means between each column against the third column,
which presents the control plots in the pre-treatment period. ***, ** and * indicate statisticalsignificance level of 1%, 5%, and 10%, respectively.

31

Table 2. Adoption of agronomic practices and fertilizer application by CF status and timingof cultivation.
CF
Post-treat(2)

Pre-treat(3)

Post-treat(4)Effect of CF{(2)-(1)}-{(4)-(3)}4.70

3.69***

4.73

4.36*

-0.64*(1.77)(1.26)(1.69)(1.43)[p=0.087]

0.70

0.72

0.77

0.66*

0.13

(0.45)(0.45)(0.40)(0.47)[p=0.231]

0.69

0.54*

0.71

0.56**

0.00(0.46)(0.50)(0.45)(0.49)[p=0.990]

0.75

0.63

0.74

0.77

-0.15

(0.43)(0.48)(0.44)(0.41)[p=0.190]

0.82

0.84

0.83

0.84

0.01

(0.38)(0.36)(0.37)(0.36)[p=0.869]

0.55

0.27***

0.60

0.55

-0.23**(0.50)(0.45)(0.49)(0.49)[p=0.048]

0.66

0.42**

0.67

0.63

-0.20

(0.47)(0.50)(0.47)(0.48)[p=0.107]

0.51*

0.24

0.38

0.31

-0.20*(0.50)(0.43)(0.48)(0.46)[p=0.081]

fertilizer 297.1*

225.5

265.6

230.6*-36.6(106.2)(183.7)(104.4)(184.12)[p=0.361]

68

33

104

104

Leveling (=1)
Bund construction (=1)
Canal construction (=1)
Seed selection (=1)
Seed incubation (=1)
Transplanting (=1)
Transplanting in row (=1)

Obs.

DID

Pre-treat(1)
No. adopted practices

Chemical(kg/ha)Non-CF

Note: Standard deviations are in the parentheses, and p-values are in the brackets. We
conducted the t-test of equality of means between each column against the third column,
which presents the control plots in the pre-treatment period. ***, ** and * indicate statisticalsignificance level of level of 1%, 5%, and 10%, respectively.

32

33

(41.19)(124.28)68

20.09**(29.00)(69.45)111.26***13.13***(109.92)(140.94)68.40***

63.02*(0.35)(0.43)
153.35***

0.64*

0.75

(2)

(1)

104

(69.67)68.21(100.8)115.70(97.92)102.27(0.46)

0.80

104(46.48)29.74***(87.12)69.97***(83.07)64.94***(0.52)

0.76(4)

Post-treat[p=0.004]-52.7***[p=0.781]-9.54[p=0.063]-53.0*[p=0.530]-0.07{(2)-(1)}-{(4)-(3)}Effect of CF

DID

33

p-values in the brackets. ***, ** and * indicate statistical significance level of level of 1%, 5%, and 10%, respectively.

Note: Standard deviations are in the parentheses. From columns (1) to (4), we conducted the t-test of equality of means between each column against the
Column (3), which presents thHe control plots in the pre-treatment period. We also report the unconditional DID effect of CF in the last column, and theObs.

Machine cost (000 FCFA/ha)

Hired labor cost (000 FCFA/ha)

Family labor (person-days/ha)

Cultivated land size (Ha)Pre-treat

Post-treat

Pre-treat(3)

Non-CFCF

Table 3. Cultivated land, family labor use, cost of hired labor, and machinery cost by CF status and timing of cultivation.

Table 4. The application of family labor across different farming tasks by CF status andtiming of cultivation (in person-days/ha).
CF

Land preparation
Crop establishment
Crop care
Harvesting
Obs.

Control

DID

Pre-treat(1)

Post-treat(2)

Pre-treat(3)

Post-treat(4)Effect of CF{(2)-(1)}-{(4)-(3)}27.80***

4.22***

13.35

8.56*

-18.79***(47.62)(4.35)(16.30)(19.42)[p=0.003]

19.74

10.04

16.44

11.91

-5.17

(28.11)(12.35)(22.22)(28.77)[p=0.337]

56.68***

31.51

33.29

20.06***-11.94(53.94)(79.87)(35.36)(25.51)[p=0.451]

46.00

16.79**

36.12

22.88**-15.97(63.44)(25.39)(45.02)(32.51)[p=0.125]

68

33

104

104

Notes: Standard deviations are in the parentheses. From columns (1) to (4), we conducted thet-test of equality of means between each column against the Column (3), which presents thecontrol plots in the pre-treatment period. We also report the unconditional DID effect of CF
in the last column, and the p-values in the brackets. ***, ** and * indicate statisticalsignificance level of level of 1%, 5%, and 10%, respectively.
Crop establishment consists of seeding and transplanting. Crop care includes weeding,fertilizer application, pesticide and herbicide application, and water control. Harvesting
consists of not only harvesting but also threshing and drying.

34

Table 5. Cost of hired labor across different farming tasks by CF status and timing ofcultivation (in 1000 FCFA/ha).
CF

Land preparation
Crop establishment
Crop care
Harvesting
Obs.

Non-CF

DID

Pre-treat

Post-treat

Pre-treat

Post-treat

Effect of CF

(1)

(2)

(3)

(4)

{(2)-(1)}-{(4)-(3)}10.78*

0.48**

22.89

7.67***

4.92(20.48)(2.78)(49.59)(23.02)[p=0.676]

28.35*

3.33***

36.48

13.68***-2.22(30.06)(13.38)(32.37)(25.30)[p=0.707]

11.17

0.57**

12.96

8.95

-6.59

(26.47)(3.28)(27.15)(20.33)[p=0.157]

16.74***

7.68***

38.77

31.33

-1.62

(28.94)(16.49)(49.87)(56.44)[p=0.852]

68

33

104

104

Notes: Standard deviations are in the parentheses. From columns (1) to (4), we conducted thet-test of equality of means between each column against the Column (3), which presents thecontrol plots in the pre-treatment period. We also report the unconditional DID effect of CF
in the last column, and the p-values in the brackets. ***, ** and * indicate statisticalsignificance level of level of 1%, 5%, and 10%, respectively.
Crop establishment consists of seeding and transplanting. Crop care includes weeding,fertilizer application, pesticide and herbicide application, and water control. Harvesting
consists of not only harvesting but also threshing and drying.

35

Table 6. Cost of machinery use across different farming tasks by CF status and timing ofcultivation (in 1000 FCFA/ha).
CF

Land preparation
Harvesting
Obs.

Non-CF

DID

Pre-treat

Post-treat

Pre-treat

Post-treat

Effect of CF

(1)

(2)

(3)

(4)

{(2)-(1)}-{(4)-(3)}54.22*

13.52**

40.88

22.43***-22.25**(44.71)(27.69)(42.59)(36.03)[p=0.014]

57.03*

6.56*

27.32

7.31***-30.46**(115.05)(16.18)(52.14)(27.98)[p=0.048]

68

33

104

104

Note: Standard deviations are in the parentheses. From columns (1) to (4), we conducted thet-test of equality of means between each column against the Column (3), which presents thecontrol plots in the pre-treatment period. We also report the unconditional DID effect of CF
in the last column, and the p-values in the brackets. ***, ** and * indicate statisticalsignificance level of level of 1%, 5%, and 10%, respectively.

36

Table 7. Paddy price, yield, income, and profit by CF status and timing of cultivation (inFCFA for rice price & in 1000 FCFA/ha for the rest).
CF

Paddy price
Rice yield
Rice income
Rice profit
Obs.

Non-CF

DID

Pre-treat

Post-treat

Pre-treat

Post-treat

Effect of CF

(1)

(2)

(3)

(4)

{(2)-(1)}-{(4)-(3)}182.35184.09186.00173.41**

14.33(89.23)(31.75)(37.78)(36.07)[p=0.277]

4.27

3.93

3.95

4.07

-0.46

(2.28)(1.93)(1.54)(1.98)[p=0.348]

382.81555.75*398.96521.33**50.57(715.31)(374.61)(443.14)(330.59)[p=0.676]

152.78461.22**245.54423.91***130.07(720.65)(421.62)(467.99)(349.40)[p=0.307]

68

33

104

104

Note: Standard deviations are in the parentheses. From columns (1) to (4), we conducted thet-test of equality of means between each column against the Column (3), which presents thecontrol plots in the pre-treatment period. We also report the unconditional DID effect of CF
in the last column, and the p-values in the brackets. ***, ** and * indicate statisticalsignificance level of level of 1%, 5%, and 10%, respectively.

37

test of [(2)-(1)-(3)=0]

0.243

0.012

0.26**
6.18(0.04)(0.17)

-0.51
1.36

0.82

(0.06)(0.25)

4.74-0.12**(0.07)(0.26)

-0.36

0.05

(0.08)(0.33)-1.20***

-0.09

-0.33

0.616

0.06
0.25

(0.04)

0.72(0.07)-0.14*(0.11)-0.11(0.07)

-0.03

(=1)

Bund

0.313

-0.09
1.02

(0.04)

0.75(0.06)

0.03(0.11)-0.17(0.08)

-0.05

(=1)

Canal

0.508

0.07
0.44

(0.04)

0.80(0.05)

0.02(0.07)

0.003(0.07)

-0.09

(=1)

selection

Seed

Transplant

0.096-0.20*
2.76(0.05)

0.62(0.07)-0.09(0.08)-0.47***(0.09)-0.18**

(=1)

0.061

-0.271*
3.49(0.05)

0.64(0.07)-0.01(0.10)-0.29***(0.09)-0.009incubation (=1)

Seed

0.012

-0.30**
6.31(0.05)

0.34(0.07)-0.04(0.07)-0.21***(0.09)

0.13

in row (=1)

Transplant

0.164-58.66
1.93(11.45)272.66(28.95)-27.28(31.12)-55.76*(17.14)31.40*(kg/ha)Fertilizer

controlled for the other characteristics.

Note. Robust standard errors are in parentheses. ***, ** and * indicate statistical significance level of level of 1%, 5%, and 10%, respectively. We alsop-valueχ!!

Effect of CF: (2)-(1)-(3)Control mean & se(3) Non-CF * Post-Treat(2) CF *Post-Treat(1) CF *Pre-Treat(=1)

adoptedpractices

Leveling

No. ofTable 8. Determinants of implementing improved agronomic practices and fertilizer application. (DR method)

0.03

4.41

-55.96**
0.05

3.62

-29.36*(8.65)

111.43(12.44)-41.07***(9.16)-102.41***(12.43)-31.98***(000FCFA/ha)

Hired labor cost per hectare

0.001

9.91

-66.07***(6.31)

73.96(7.97)-37.98***(9.28)-58.23***(19.34)45.82**(000FCFA/ha)

Machine cost per hectare

controlled for the other characteristics.

39

Note. Robust standard errors are in parentheses. ***, ** and * indicate statistical significance level of level of 1%, 5%, and 10%, respectively. We alsop-value

0.12

2.41

test of [(2)-(1)-(3)=0]-0.17(10.13)(0.04))

χ!!

81.59

(12.53)(0.06)

0.90-25.61**(17.75)(0.06)-0.10*-29.87*(21.15)(0.10)-0.26***

51.70**

0.01(person-days/ha)(ha)Effect of CF: (2)-(1)-(3)Control mean & se(3) Non-CF * Post-Treat(2) CF *Post-Treat(1) CF *Pre-Treat

Family labor per hectare

Cultivated land size

Table 9. Determinants of family labor, hired labor, and machinery use. (DR method)Non-CF(0.30)(6.40)

0.07

p-valueRiceprofit

0.995

0.00

-8.04

(40.53)425.49(61.52)118.06*(69.19)69.19(107.18)-40.83

0.567

0.33

75.91

(42.20)303.00(62.56)156.48**(69.04)114.00*(110.38)-118.39(000FCFA/ha) (000FCFA/ha)

Rice income

controlled for the other characteristics.

40

Note. Robust standard errors are in parentheses. ***, ** and * indicate statistical significance level of level of 1%, 5%, and 10%, respectively. We also

0.637

3.23

χ!! test of [(2)-(1)-(3)=0] 0.22

-0.94*(0.17)(3.86)
7.87

4.00(0.34)

187.01(4.82)

0.30-0.54*(0.36)(14.22)
0.12

0.10

3.01

(ton/ha)(FCFA/kg)* -10.76**

Effect of CF: (2)-(1)-(3)Control mean & se

Post-Treat(3)(2) CF *Post-Treat(1) CF *Pre-Treat

Rice yieldPaddy price

Table 10. Determinants of rice price, productivity and profitability. (DR method)Table 11. Basic characteristics of the non-CF farmers’ plot by the use of tractor.
HH head female (=1)
HH head age
HH head any schooling (=1)
Family size
Proportion of male adult members
Proportion of female adult members
Cultivate in July or later (=1)
No. of training attended by familyln asset (000FCFA)Land rent (000FCFA/ha)
Water availability: “scarce” (=1)
Water availability: “good” (=1)
Obs.

TractorManual0.04

0.04(0.21)(0.21)

43.8

43.7(10.3)(12.2)

0.66

0.58(0.47)(0.49)

10.0*

8.5

(6.5)

(5.3)

0.33

0.32

(0.18)(0.16)

0.29

0.29(0.15)(0.12)

0.42***

0.57(0.49)(0.49)

6.5

5.8

(4.3)

(4.2)

3.82

3.77

(1.7)

(1.4)

11.9

5.6

(40.6)(12.3)

0.01

0.03(0.13)(0.19)

0.19***

0.02(0.39)(0.16)

105

103

Note: Standard deviations are in the parentheses. ***, ** and * indicate statisticalsignificance level of level of 1%, 5%, and 10%, respectively.

41

Table 12. Adoption of agronomic practices and fertilizer application among non-CF farmers
by the use of tractor.
No. of adopted agronomic practices
Leveling (=1)
Bund construction (=1)
Canal construction (=1)
Seed selection (=1)
Seed incubation (=1)
Transplanting (=1)
Transplanting in row (=1)
Chemical fertilizer (kg/ha)
Obs.

TractorManual4.85***

4.23(1.55)(1.53)

0.80***

0.64(0.40)(0.48)

0.85***

0.41(0.35)(0.49)

0.84***

0.66(0.36)(0.47)

0.79**

0.89(0.40)(0.31)

0.63*

0.52

(0.48)(0.50)

0.61

0.68(0.48)(0.46)

0.30

0.39(0.46)(0.49)

251.7***

149.3(171.8)(158.0)105

103

Note: Standard deviations are in the parentheses. ***, ** and * indicate statisticalsignificance level of level of 1%, 5%, and 10%, respectively.

42

Table 13. The application of family labor, hired labor, and machinery among non-CF farmers
by the use of tractor.
Cultivated land size (ha)Family labor (person-days/ha)
Hired labor cost (000 FCFA/ha)
Machine cost (000 FCFA/ha)
Obs.

TractorManual0.91***

0.65(0.46)(0.40)

78.0

89.3(101.1)(82.9)

119.0***

61.0(90.7)(95.6)

82.6***

14.6(53.1)(50.9)

105

103

Note: Standard deviations are in the parentheses. *** indicate statistical significance level oflevel of 1%,.

43

Table 14. The application of family labor across different tasks among non-CF farmers by theuse of tractor.
Land preparation (person-days/ha)
Crop establishment (person-days/ha)
Crop care (person-days/ha)
Harvesting (person-days/ha)
Obs.

TractorManual9.1

12.8(21.0)(14.2)

13.4

14.9(30.7)(19.5)

26.7

26.5(32.7)(32.7)

27.6

31.3(44.1)(34.3)

105

103

Notes: Standard deviations are in the parentheses.
Crop establishment consists of seeding and transplanting. Crop care includes weeding,fertilizer application, pesticide and herbicide application, and water control. Harvesting
consists of not only harvesting but also threshing and drying.

44

Table 15. The cost of hired labor across different tasks among Non-CF farmers by the use of
tractor.
Land preparation (000FCFA/ha)
Crop establishment (000FCFA/ha)
Crop care (000FCFA/ha)
Harvesting (000FCFA/ha)
Obs.

TractorManual14.3

16.2(40.7)(37.9)

36.6***

13.2(31.5)(26.0)

14.1*

7.7(23.5)(24.1)

48.3***

21.4(61.2)(39.5)

105

103

Notes: Standard deviations are in the parentheses. * and *** indicate statistical significancelevel of 10% and 1%, respectively.
Crop establishment consists of seeding and transplanting. Crop care includes weeding,fertilizer application, pesticide and herbicide application, and water control. Crop harvest
consists of harvesting, threshing, and drying.

45

Table 16. Rice price, yield, income, and profit among non-CF farmers by the use of tractor.
Paddy price (FCFA/kg)
Rice yield (kg/ha)
Rice income per ha (000FCFA/ha)
Rice profit per ha (000FCFA/ha)
Rice income per plot (000FCFA)
Rice profit per plot (000FCFA)
Obs.

TractorManual185.3**

173.9(37.4)(36.6)

4.39***

3.62(2.01)(1.39)

478.4

441.4

(402.5)(387.8)

361.4

307.4

(415.9)(427.5)460.3***

300.5(515.1)(334.4)381.8**

232.3(519.6)(340.8)105

103

Note: Standard deviations are in the parentheses. t-test is against the control plots in the
pre-treatment period. ***, ** and * indicate statistical significance level of 1%, 5%, and 10%,respectively.

46

0.74
(0.07)(0.24)(0.08)(0.28)

4.21

0.05

0.57**(=1)

adoptedpractices(0.07)

0.32(0.08)

0.52***

Bund (=1)(0.06)

0.68(0.07)

0.17**

Canal (=1)(0.04)

0.90(0.06)-0.13**

selection
(=1)

Seed

(0.07)

0.50(0.09)

0.12

incubation
(=1)

Seed

(0.07)

0.65(0.09)

-0.05

(=1)

Transplant(0.07)

0.37(0.08)-0.10

in row (=1)

Transplant(26.61)

231.13(31.49)46.74

fertilizer

Chemical47

Note. Robust standard errors are in parentheses. *** and ** indicate statistical significance level of 1% and 5%, respectively. The timing of cultivationand other basic characteristics are controlled.

Control mean & se

TractorLeveling

No. ofTable 17. Effect of mechanization in land preparation on adoption of agronomic practices among non-CF farmers. (DR method)

79.46(10.81)(0.07)(14.36)(0.07)

0.75

-7.14

Family labor per hectare(person-days/ha)

0.19**

Cultivated land size(ha)

(8.64)

63.98(12.73)57.59***(5.37)

18.51(6.78)

63.56***

Hired labor cost per hectare Machine cost per hectare(000FCFA/ha)(000FCFA/ha)and other basic characteristics are controlled.

48

Note. Robust standard errors are in parentheses. *** and ** indicate statistical significance level of 1% and 5%, respectively. The timing of cultivation

Control mean & se

Tractormethod)Table 18. Effect of mechanization in land preparation on the application of family labor, hired labor, and machinery among non-CF farmers. (DR14.26(2.74)(1.78)(4.18)(1.16)
12.11

-1.73

(4.02)

29.10(5.78)-4.48

Harvesting (=1)

49

Crop establishment consists of seeding and transplanting. Crop care includes weeding, fertilizer application, pesticide and herbicide application, andwater control. Crop harvest consists of harvesting, threshing, and drying.

(4.76)

23.00(5.61)

2.31

Crop
Crop care (=1)
establishment (=1)

-3.20

Notes. Robust standard errors are in parentheses.

Control mean & se

TractorLand preparation(person-days/ha)

Table 19. Effect of mechanization in land preparation on the application of family labor among non-CF farmers. (DR method)

12.94(2.80)(2.88)(4.21)(5.07)
8.92

23.19***

4.92

Crop establishment(000FCFA/ha)(2.14)

7.86(3.07)

6.50**

Crop care(000FCFA/ha)(6.24)

30.64(9.28)

21.39**

Harvesting(000FCFA/ha)50

water control. Harvesting consists of not only harvesting but also threshing and drying.

Crop establishment consists of seeding and transplanting. Crop care includes weeding, fertilizer application, pesticide and herbicide application, andNotes. Robust standard errors are in parentheses. *** and ** indicate statistical significance level of 1% and 5%, respectively.

Control mean & se

TractorLand preparation(000FCFA/ha)

Table 20. Effect of mechanization in land preparation on the hired labor cost among non-CF farmers. (DR method)

3.62(0.20)(3.58)(0.28)(4.83)

177.150.86***

8.47*

Rice yield(ton/ha)(37.28)450.75(55.58)

40.71

Rice income per ha(000FCFA/ha)(43.11)331.55(58.44)51.43(56.04)348.03(72.78)134.04*(000FCFA)

51(57.83)279.55(73.35)

126.62*

Rice profit per ha Rice income per Rice profit per(000FCFA/ha)plot
plot (000FCFA)

Note. Robust standard errors are in parentheses. *** and * indicate statistical significance level of 1% and 10%, respectively.

Control mean & se

TractorPaddy price(FCFA/kg)

Table 21. Effect of mechanization in land preparation on paddy price, productivity and profitability among non-CF farmers. (DR method)