Mechanization in Cote d'Ivoire: Impacts of Tractorization on Agricultural Intensification

Abstract

It is critically important to intensify farming systems in sub-Saharan Africa by disseminating improved agronomic practices and increasing the application of modern inputs (Chaps. 1 and 2 of this volume). One of the region’s challenges is that proper land preparation is difficult due to the scarcity of draft animals and the underdevelopment of the tractor rental market (Chap. 7). Our analysis of rice production in Cote d’Ivoire reveals that farmers who use two-wheel tractors in land preparation are more likely to adopt proper, labor-intensive rice cultivation practices and apply fertilizer more intensively, thereby raising productivity. Thus, the diffusion of two-wheel tractors appears to be critical to the intensification of rice-farming systems in sub-Saharan Africa.

This chapter draws heavily on Mano et al. (2020).

1 Introduction

In view of the need to modernize agriculture in Africa, where productivity has been stagnant for an extended period, farm mechanization has attracted considerable attention from economists and policymakers (Chap. 7 of this volume). Unlike tropical Asia, the use of draft animals has been limited in this region due to the prevalence of trypanosomiasis, or sleeping sickness (Alsan 2015), coupled with deteriorating animal health services and recurring droughts (Mrema et al. 2008; Takeshima et al. 2013, 2015; Takeshima 2015). The public sector made substantial efforts to promote mechanization by distributing tractors¹ at subsidized prices in sub-Saharan Africa (SSA) in the 1960s and 1970s (Pingali 2007; Adu-Baffour et al. 2019). However, these government-sponsored attempts often failed due to governance challenges such as lack of political interest and elite capture (Daum and Birner 2017), as well as low demand for tractor services among local farmers, leaving tractors idle or scrapped (Pingali et al. 1987). Along with intensified concerns about increased unemployment in the face of growing populations, agricultural mechanization lost its momentum in SSA after the 1970s (Diao et al. 2014).

Renewed interest in agricultural mechanization has recently emerged with an emphasis on the role of the private sector in the provision of tractor services (Mrema et al. 2008; Diao et al. 2014; Daum and Birner 2017; Adu-Bauffour et al. 2019). Although machines are expensive and indivisible, they are purely private goods, and governmental intervention may not be justifiable if there are sizeable private service providers that are not credit-constrained. Proper land preparation by tractors is expected to improve farmers’ agronomic practices, especially by allowing for better tillage, weed control, and water management. Whether this private sector model is successful crucially depends on its profitability for farmers.

While tractor use has been considered a substitute for labor and draft animals in Asia, the association between tractors and labor use is ambiguous in SSA. If the land is thoroughly plowed by tractor in place of manual labor and if this facilitates the adoption of input- and labor-intensive agronomic practices, the adoption of proper rice cultivation practices and the application of modern inputs may increase along with the use of tractors. Such intensification, accompanied by mechanization, would occur if proper land preparation and agronomic practices were complementary. However, we know little about whether farmers who use tractors in land preparation are also more likely to choose farming intensification and how this affects the productivity and profitability of crop production in SSA. This study fills these research gaps by revisiting the association between agricultural mechanization and intensification in farming systems as well as productivity and profitability improvements in SSA. We do this by drawing on a case study of rice farming in Cote d’Ivoire.

This focus on rice production is suitable for our research purpose because the productivity of rice, especially lowland rice, depends on the use of modern inputs and improved rice cultivation practices, such as bund construction, leveling, and transplanting (see Chap. 2 of this volume). Therefore, whether farmers employ these recommended practices and apply appropriate modern input levels in conjunction with their choices concerning tractor use for land preparation is an important issue impacting the realization of the Green Revolution in SSA (Chap. 7).

To examine the association between tractor use and farming intensification, we use plot-level data collected from 111 farmers in ten villages in 2015. We apply a regression framework with village fixed effects and a doubly robust (DR) method to address the inherent differences between the plots plowed with tractors and those cultivated with hand hoes.

We find that the farmers who use tractors for land preparation more intensively adopt proper labor-intensive rice cultivation practices and apply modern inputs, thereby improving their productivity. The results are robust regardless of empirical specifications and estimation methods, providing evidence that mechanization in land preparation will be complimentary to input- and labor-intensive production systems in SSA. This finding is consistent with the emerging literature (Chap. 9 of this volume; Takeshima et al. 2013) and suggests that promoting mechanization in land preparation does not lead to increased rural unemployment in SSA (Binswanger 1986; Panin 1995; Pingali 2007; Adu-Bauffour et al. 2019). Our result contrasts with the experiences of the US since the 1940s, Europe and Japan since the 1950s, and tropical Asia since the 1960s, where rising wage rates have induced the substitution of capital for labor and draft animals (e.g., David and Otsuka 1994; Wang et al. 2016). The current chapter illustrates the potential of a unique agricultural development path in SSA in the absence of draft animals and underdevelopment of the tractor rental market, which is different from the case of Tanzania examined in Chap. 9, where draft animals co-exist with agricultural machines.

The rest of this chapter is organized as follows: Sect. 8.2 explains the study setting and discusses the descriptive statistics. The study’s empirical strategy is presented in Sect. 8.3. Section 8.4 discusses the estimation results regarding the association between mechanization in land preparation and the rice-farming intensification. Section 8.5 provides some concluding remarks and recommendations for future studies.

2 Data

2.1 Study Setting

Our study site is the Yamoussoukro District in Cote d’Ivoire. Located between the country’s business center, Abidjan, and the headquarters of AfricaRice (formerly the West Africa Rice Development Association, or WARDA) in Bouake, it has good access to markets and technical information about agronomic practices. Our site is unique in that, unlike those in other SSA countries, virtually all the sample rice farmers grow WITA 9, a modern variety of rice developed by AfricaRice, and apply significant amounts of chemical fertilizer under the influence of training programs provided by local governments, Japan International Cooperate Agency (JICA), and international organizations such as the World Bank (Takahashi et al. 2019). Some non-governmental organizations may have also learned improved agronomic practices from AfricaRice’s publications and website and then disseminated them to farmers.

However, as in many countries in SSA, proper land preparation is difficult due to the scarcity of draft animals and the underdevelopment of the tractor rental market. According to our interviews with farmers, only a limited number of tractors are available in the region. Furthermore, the supply of tractors is dominated by Chinese products that frequently break down while spare parts are lacking and the maintenance system for repairs remains underdeveloped. Farmers usually contact the tractor owners directly before a crop season. The tractor owners are not usually farmers but wealthy entrepreneurs living in towns who arrange tractors and operators to provide plowing services across villages and regions. In 2013, a local agricultural company was also established to offer tractor services and modern inputs on credit via contract farming.

At the beginning of the main cultivation season, roughly from July to December each year, the tractor demand surges against their limited supply, making it difficult to secure them. Manual land preparation is possible but laborious without sufficient irrigation water, which provides a soft soil mass but is often inadequate. Due to constraints on plowing because of the absence of draft animals and tractors, it is difficult to intensify land use through improved agronomic practices such as bund construction, leveling, transplanting, weeding, and water control.²

2.2 Sampling Framework

A household survey of 111 households was conducted in early 2015. This survey comprised a census of all rice farmers in the ten villages that the local agricultural company targeted to operate contract farming.³ However, according to our interviews with local farmers, this agricultural company was neither familiar with the local production environment nor well organized in contract management. Since the impact of contract farming was limited, this study focuses on the effects of tractor use in land preparation on the adoption of input- and labor-intensive agronomic practices by using the pooled sample comprising farmers who engaged in contract farming and those who did not.⁴

Our sample covered the main cropping season, July to December 2014. Since some farmers cultivate more than one plot, 136 rice plots were surveyed. All the rice plots were under irrigation. The collected data pertain to household socioeconomic characteristics, detailed rice production (including input use, the adoption of agronomic practices, and output), labor time, water access, and past rice cultivation training experience.

2.3 Descriptive Analysis

Table 8.1 presents the basic characteristics of the rice plots by land preparation method, either tractor or manual. A total of 56 plots were plowed using tractors, and 80 plots were plowed manually. The household head was typically a male in his mid-40 s with minimal education, and the average household consisted of around ten members. Family members had previously received agricultural training on several agronomic practices provided by the government and international organizations such as the World Bank. We conducted a t-test of the equality of means between the plots plowed with tractors and those manually. We did not find significant differences in most key variables between the tractor plots and the manual plots except for education, assets, and plot size.⁵ Although we do not have detailed data, agricultural experts knowledgeable about the local rice farming also confirm that plot conditions, including soil quality and plot slope, are largely homogeneous within each irrigation scheme.

Table 8.1 Basic characteristics of rice plots by land preparation method

Table 8.2 illustrates the application of production factors and the amount of applied chemical fertilizer by the land preparation method. Family and hired labor, machinery, and chemical fertilizer were more intensively applied in the plots plowed with tractors. On average, about 298 kg of chemical fertilizer per hectare were used in the tractor plots, compared with 181 kg per hectare in the manual plots.⁶ This highly intensive fertilizer application in Cote d’Ivoire is likely related to the technical training and campaigns provided by the government and international agencies through local extension services and a well-functioning chemical fertilizer market. When tractors were available and land preparation was adequately done, farmers also intensively applied chemical fertilizer.

Table 8.2 The application of production factors by land preparation method

Table 8.3 illustrates the adoption of agronomic practices using the land preparation method. We pay special attention to the rice cultivation practices commonly adopted in rice production in Asia, which led to success in the rice-based Green Revolution (Chap. 2 of this volume; David and Otsuka 1994; Otsuka and Larson 2013, 2016). These agronomic practices, known to be highly complementary, include leveling, bund construction, canal construction, seed selection, seed incubation using paper or straw, and transplanting.

Table 8.3 Adoption of agronomic practices and fertilizer application by land preparation method

The plots adequately plowed with tractors recorded a significantly greater number of adopted agronomic practices (Table 8.3). In particular, the tractor plots were associated with the adoption of improved land preparation practices, such as bund construction and canal construction. As Tables 8.2 and 8.3 indicate, farmers significantly increased labor use to apply more chemical fertilizer and adopt improved rice cultivation practices when tractors were available and land preparation was adequately executed. More precisely, the use of tractors saves labor in land preparation, whereas it was also positively associated with adopting labor-intensive agronomic practices such as more thorough canal and bund construction. This proper land preparation enabled better water control, which improved the effectiveness of fertilizer and the productivity of careful transplanting. Overall, both family and hired labor were used significantly more intensively on the tractor plots than on the manual plots for crop care and harvesting (Mano et al. 2020).⁷

We now turn to the economic performance of rice farming by tractor use (Table 8.4). Income was defined as the value of production minus the paid-out cost, and profit was income minus the imputed cost of family labor.⁸ We observe higher productivity and profitability of the tractor plots compared with the manual plots. In particular, rice yield per hectare, total rice income per plot, and total rice profit per plot were significantly higher on the tractor plots.⁹

Table 8.4 Rice yield, income, and profit by land preparation method

3 Empirical Strategy

We conduct regression analysis to examine the impact of tractor use on rice-cultivation performance. Estimating the causal effect of tractor use on farming system choice, while desirable, is difficult because of a lack of plausible instruments and panel data. Moreover, at the outset of the season, farmers may decide to employ input- and labor-intensive farming systems in conjunction with their choice of whether to use a tractor to prepare the land. These considerations illustrate endogenous technology choice, suggesting that we must refrain from a causal inference regarding tractor use per se (Mundlak et al. 1999, 2012; Larson and Leon 2006). However, we will compare plots endowed with similar attributes but with and without tractor use to isolate the effect of tractor use.

Consider the following cross-sectional regression function:

$${\mathrm{Y}}_{i}={\beta }_{0}+{\beta }_{1}{M}_{i}+{X}_{i}^{^{\prime}}{\beta }_{X}+{\varepsilon }_{i}$$

(8.1)

where: ${\mathrm{Y}}_{i}$ is the outcome variable of plot i, such as the input application, the adoption of rice-cultivation practices, productivity, and profitability in rice farming; ${M}_{i}$ is a dummy variable for tractor use (or mechanization) in land preparation; ${X}_{i}$ is the vector of the basic characteristics of plot i and the cultivator; $\beta$ s are the regression parameters to be estimated, where ${\beta }_{1}$ is assumed to capture the statistical association between tractor use and the outcome variables, which is our primary interest; and ${\varepsilon }_{i}$ is a random error term.

Outcome variable ${\mathrm{Y}}_{i}$ is represented by the input application, consisting of (A1) the imputed cost of family labor (000FCFA/ha), (A2) the cost of hired labor (000FCFA/ha), (A3) the cost of machinery (000FCFA/ha), (A4) the application of chemical fertilizer (kg/ha); the adoption of proper rice-cultivation practices, consisting of (B1) the number of adopted practices, (B2) canal construction, (B3) bund construction, (B4) leveling, (B5) seed selection, (B6) seed incubation, (B7) transplanting; and (C) rice-farming performance, consisting of (C1) rice yield (t/ha), (C2) rice income per hectare (000FCFA/ha), (C3) rice profit per hectare (000FCFA/ha), (C4) total rice income per plot (000FCFA), and (C5) total rice profit per plot (000FCFA).

Covariate ${X}_{i}$ is the vector of the basic characteristics of plot i and its cultivator. These basic characteristics consist of (1) a dummy variable for female-headed households, (2) the age of the household head and its square term, (3) a dummy variable for the household head who received any schooling, (4) the number of household members, (5) the number of agronomic practices that the farmers learned in formal agricultural training programs in the past, (6) the value of agricultural assets,¹⁰ (7) the plot size, (8) the rent or rental value of the cropland, (9) whether the farmer perceived that the water was not adequate, and (10) whether the plot was under contract farming. We selected these variables because they are considered exogenous and are likely to affect outcomes; thus, they are often used as controls in the literature (see Table 8.1 for the summary statistics of these variables). The covariates regarding market access are omitted due to their small variation. The rice plots are concentrated within each sample village, and the villages are located within 30 min of the capital city. To make farmers with and without tractors in land preparation more comparable, we include village fixed effects in the regression model to address the possible correlation in outcomes due to the common production environments within the villages.

Another estimation strategy we use is to explicitly control the selection on observables and match plots with similar characteristics. A range of estimation methods exists, such as propensity score matching, inverse probability weighting, and doubly robust (DR) estimator. The use of these methods is common in the literature on new agricultural technologies and institutions, including contract farming, when there are no plausible instruments or panel data (Takahashi and Barrett 2014; Bellemare and Novak 2016; Mishra et al. 2016; Khan et al. 2019). To address the effect of selection on partially observable characteristics, we apply the DR estimation, or more precisely, inverse-probability-weighted regression adjustment, which combines the regression and propensity score weighting (Wooldridge 2007, 2010, Sect. 21.3.4).¹¹ The DR method is more robust than the propensity score matching estimator and the inverse-probability-weighting estimator because it can provide a consistent estimate as long as either the propensity score for tractor use or the regression function of outcomes ${\mathrm{Y}}_{i}$ in terms of covariates ${\mathrm{X}}_{i}$ is correctly specified (Wooldridge 2010).¹² More specifically, we first estimate the probability of tractor use by using a logit model with a set of covariates, including the basic characteristics of the plot and the cultivator as described above. Then, each expected outcome with and without a tractor is computed by:

$$ \begin{aligned}\widehat{E\left({Y}_{1}\right)}&=\frac{1}{n}\sum_{i=1}^{n}\left[\frac{{M}_{i}}{p\left({X}_{i}\right)}{Y}_{1i}+\left(1-\frac{{M}_{i}}{p\left({X}_{i}\right)}\right)\widehat{{Y}_{1i}}\right]\mathrm{ and}\\ \widehat{E\left({Y}_{0}\right)}&=\frac{1}{n}\sum_{i=1}^{n}\left[\frac{1-{M}_{i}}{1-p\left({X}_{i}\right)}{Y}_{0i}+\left(1-\frac{1-{M}_{i}}{1-p\left({X}_{i}\right)}\right)\widehat{{Y}_{0i}}\right],\end{aligned}$$

where variable $p\left(X\right)$ is the estimated probability of tractor use, $\widehat{{Y}_{1i}}$ and $\widehat{{Y}_{oi}}$ are the predicted values from estimated regression Eq. (8.1) with and without tractors (${M}_{i}=1$ and ${M}_{i}=0$), respectively, and $n$ is the number of sample plots. Taking the difference between the two estimators above, $\widehat{E\left({Y}_{1}\right)}-\widehat{E\left({Y}_{0}\right)}=\widehat{E\left({Y}_{1}-{Y}_{0}\right)}$, we can obtain an unbiased estimate of the statistical association between tractor use and outcomes.

4 Estimation Results

Tables 8.5, 8.6, and 8.7 present the estimated impact of tractor use on input application, the adoption of proper rice-cultivation practices, and rice-farming performance. The estimation results regarding the adoption of agronomic practices obtained with these two methods are similar (see Tables 8.5, 8.6, and 8.7).¹³ Because the central aim of this study was to explore the impact of tractor use on rice-farming intensification while controlling for plot and farmer characteristics, we used the DR estimator in the following analysis, which directly addresses endogenous tractor use based on selection on observables.¹⁴

Table 8.5 Association between tractor use in land preparation and the application of production factors

Table 8.6 Association between tractor use in land preparation and the adoption of agronomic practices

Table 8.7 Association between tractor use in land preparation and productivity and profitability

The use of tractors in land preparation had positive associations with intensified input and labor application per hectare of land, including chemical fertilizer, family labor, and hired labor (see Table 8.5). Tractor use was significantly associated with the number of proper rice-cultivation practices adopted, specifically bund construction and seed incubation (Table 8.6). Bund construction enabled effective water control and increased the effectiveness of fertilizer use.¹⁵ Furthermore, while the use of tractors was not significantly associated with the use of either family or hired labor in land preparation, it increased the application of family labor in crop care and harvesting, as well as hired labor in crop establishment, crop care, and harvesting (see Table 6 of Mano et al. 2020).¹⁶

Table 8.7 presents the association of tractor use with rice yields, as well as incomes and profits from rice farming. Tractor use is significantly and positively associated with rice yields, consistent with the greater application of inputs and labor to more carefully implement improved rice-cultivation practices (see Tables 8.5 and 8.6). Given the mean values of rice yields in the case of manual land preparation, the increase in rice yields associated with tractor use is 39.6%. However, tractor use was not significantly associated with income or profit, perhaps because of the increased cost of the labor input, including family and hired laborers. Another possibility is that the rental price of the tractor service is adjusted to make the profit indifferent between the manual plots and tractor plots, given the limited tractor service availability. These results align with the recent evidence presented by Benin (2015) and Adu-Baffour et al. (2019).

Overall, we confirmed that farmers who use the tractor for land preparation also intensively apply modern inputs, use more labor to implement proper rice-cultivation practices, and improve productivity.

5 Conclusions

Farming intensification is becoming critically important for improving food security in SSA, where agricultural productivity has long been stagnant. This chapter analyzed the statistical association between tractor use in land preparation and the adoption of intensive farming methods. We used primary data drawn from Cote d’Ivoire and studied farmers with good access to water, markets, and skills in improved agronomic practices. We found that tractor use in land preparation is positively associated with intensively applying family and hired labor and chemical fertilizer and the number of adopted proper rice-cultivation practices, specifically bund construction and seed incubation. Tractor use is also found to increase paddy yield per hectare. Mano et al. (2020) also found tractor use was associated with more careful implementation of crop establishment, such as seed preparation and transplanting, and crop care such as weeding, fertilizer application, and water control.

As exemplified by induced innovation theory, the conventional view of agricultural development assumes that capital substitutes for labor as wages increase due, for example, to the development of the non-farm sector (Hayami and Ruttan 1985). However, our analysis of rice farming in Cote d’Ivoire suggests that the availability of tractor services is positively associated with the application of labor and more intensive implementation of improved agronomic practices, suggesting a potentially complementary role for capital and labor, as discussed in Adu-Baffour et al. (2019), Takeshima et al. (2013), and Pingali (2007), among others. Tractor use may have saved labor in land preparation, but more importantly, it also intensified the farming system and increased labor application. This offsets, or more than offsets, the potential reduction of labor use in land preparation. Thus, a complementarity was found between tractor use in land preparation and input- and labor-intensive farming methods. Tractor use appears to serve as a substitute for labor when it replaces draft animals, as in tropical Asia (Binswanger 1978). However, tractor use may complement labor when it replaces manual labor in land preparation, which makes it effective in adopting proper rice-cultivation practices, as in our case.

The generalizability of our results is questionable at this stage, given that our sample is drawn from areas with favorable access to water, markets, and technological information. However, our findings support the emerging literature suggesting complementarity between mechanization in land preparation and the adoption of intensive farming systems in SSA, including Chap. 9 of this volume discussing the case of Tanzania (Takeshima et al. 2013, 2015; Pingali 2007).

The government may be able to promote the private supply of high-quality tractors by establishing a public quality inspection and certification system and promoting the development of a maintenance and repair system (Daum and Birner 2017). It is also vital to train tractor owners and operators to encourage careful maintenance and provide a public-sector extension service because knowledge of improved cultivation practices, including tractor use, is a local public good.

Therefore, future studies should rigorously evaluate whether the following policies will promote the intensification of rice-farming systems in SSA: (1) building an extension system that promotes both the adoption of proper rice-cultivation practices and tractor use, (2) helping develop a tractor service market by providing information on tractor quality through inspections, and (3) training tractor owners and operators in the careful maintenance of tractors and building the expertise of mechanics in tractor-repair services.