The role of contracts in the adoption of irrigation by Brazilian orange growers

Agricultural Water Management 233 (2020) 106078

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The role of contracts in the adoption of irrigation by Brazilian orange growers

T

Fabiana Ribeiro Rossia, Hildo Meirelles de Souza Filhob, Bruno Varella Mirandac, Marcelo José Carrerb,* a

Sustainable Rural Development Coordination (CDRS), Biritiba Mirim, São Paulo, Brazil Federal University of São Carlos (UFSCar), Department of Production Engineering, São Carlos, São Paulo, Brazil c Insper (Instituto de Ensino e Pesquisa São Paulo), São Paulo, Brazil b

A R T I C LE I N FO

A B S T R A C T

Keywords: Contracts Irrigation Bargaining power Technology adoption

This article analyses the drivers of adoption of irrigation systems by orange growers in the State of São Paulo, Brazil. Given the scarcity of similar studies focusing on the Brazilian orange sector, the starting point of our article is the estimation of a translog production function to assess the impact of irrigation on the production of a sample of 98 São Paulo’s orange growers. The results showed that irrigation adoption increased orange production of the farms in the sample by 21.9 %, ceteris paribus. Despite the potential benefits of irrigation, adoption was rather low: only 24.6 % of the orange orchards area in the Brazilian orange belt was irrigated in 2015. Starting from this apparent paradox, we argue that the information asymmetry and conflicts observed in the contractual relationship between growers and juice processing firms reduce the incentives for the adoption of technologies in the Brazilian orange sector. We apply probit models to the same sample to test four hypotheses on drivers of the decision to adopt irrigation. The results of our empirical analysis show that the use of contracts in transactions with processing firms negatively affect the adoption of irrigation. The analysis also identifies other variables that influence positively the adoption of irrigation: participation in producers’ associations, participation in courses on irrigation and dependence on the income from the orange orchard. The results of this study suggest that the design of policies aimed at fostering irrigation adoption must consider potential bargaining power asymmetries in the relationships between the users of irrigation and the buyers of their agricultural production.

1. Introduction

Agriculture USDA, 2019). Several varieties of orange are cultivated in Brazil to comply with quality requirements of the industry. The most representative varieties are Pera Rio, Valencia, Valencia Folha Murcha, Hamlin, Westin and Rubi. Among these, Hamlin is the only variety grown exclusively for the juice processing market (Citrus Defense Fund FUNDECITRUS, 2019). Since the turn of the twenty-first century, several challenges have negatively affected the profitability of orange growers in the state of São Paulo. Farmers have faced increasing production costs that result from the high incidence of pests and diseases such as greening and citrus canker. Moreover, most Brazilian orange growers sell their production to firms with a marked oligopsonistic position in the juice processing industry. Three Brazilian firms dominate the orange juice export market, accounting for 98 % of the Brazilian orange juice exports. Consequently, growers are subjected to the considerable bargaining power accumulated by these large exporters (Ito and

Brazil produced 17.7 tons of orange in the market year 2017/2018, which represented around 34 % of the world production (United States Department of Agriculture USDA, 2019). According to the Brazilian Agricultural Census, almost 56,000 farms had orchards with at least 50 orange trees in 2017 (IBGE, 2017). The Brazilian orange production is clustered in the State of São Paulo which produced 12.9 million tons in 2018 (IBGE, 2018). Around 80 % of the São Paulo’s orange production is directed to the juice processing industry. The remaining supply is traded in the fresh fruit market (Neves et al., 2010). In the market year of 2018/2019, 1.03 million tons of orange juice were produced in Brazil – a volume that represents more than half of the world production (United States Department of Agriculture USDA, 2018). Brazil is a major exporter of orange juice as well, selling over 75 % of the global orange juice exports (United States Department of

⁎

Corresponding author. E-mail address: [email protected] (M.J. Carrer).

https://doi.org/10.1016/j.agwat.2020.106078 Received 30 October 2019; Received in revised form 5 February 2020; Accepted 9 February 2020 Available online 27 February 2020 0378-3774/ © 2020 Elsevier B.V. All rights reserved.

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2. Literature Review and Hypotheses

Zylbersztajn, 2018). Farmers have limited alternative to sell their production, given that the fresh fruit market is relatively small and orange demand is price-inelastic. In 2016, the Brazilian Economic Defense Administrative Council (CADE) approved the terms of a settlement in a cartel investigation against orange juice processors. It was a long investigation which lasted for 17 years and resulted in a payment of 301 million Brazilian Real to CADE. It is also important to take into account that the orange market is characterized by high price volatility. Taken together, these factors led a considerable number of orange growers to abandon the activity. From 1996–2018, the number of orange growers in the state was reduced by more than 79 % (Citrus Defense Fund FUNDECITRUS, 2019). Given this challenging context, the adoption of new practices and technologies in the orange orchards of the state of São Paulo may contribute to a reduction of the rate of exit of the orange market. In particular, a growing research body has suggested that the adoption of irrigation can lead to a substantial increase of the productivity of orange orchards, as well as improving fruit quality – i.e. ratio of juice, the weight and size of oranges (Petillo and Castel, 2004; Duenhas et al., 2005). Given the scarcity of similar studies focused on the Brazilian orange sector, the starting point of our article is an estimation of a translog production function to assess the impact of irrigation on orange production. A survey comprising 98 orange growers of the state of São Paulo provided cross-sectional data at the farm level for the crop season of 2013/2014. According our estimation, irrigation increases orange production by 21.9 %, as showed in section 4. Despite the potential benefits of irrigation, only 24.6 % of the orange orchards area in the Brazilian orange belt – comprised by São Paulo and the southwest of the state of Minas Gerais – was irrigated in 2015 (FUNDECITRUS, 2015). Starting from this apparent paradox, this paper discusses the following research question: what are the factors that explain the low diffusion of irrigation in the Brazilian orange belt? In order to accomplish this task, we estimate probit models to analyse the drivers of adoption of irrigation systems from data of the same survey of 98 orange growers. The nature of the transactions between orange growers and juice processing firms was examined. As several authors describe, conflicts among them in the Brazilian orange belt are recurrent (Figueiredo et al., 2013; Ito and Zylbersztajn, 2016; Carrer and Souza Filho, 2018). In particular, the considerable bargaining power held by juice processing firms allows them to design contracts – and to explicitly manipulate the level of incompleteness of these agreements – to capture a greater share of the quasi-rent derived from orange transactions (Ito and Zylbersztajn, 2018). Hence, our main interest is in assessing how the existence of contracts between orange growers and juice processing firms affects the adoption of irrigation. Following Acemoglu et al.’s (2007) contribution, we argue that the information asymmetries and conflicts observed in the relationship between growers and juice processing firms reduce the incentives for the adoption of more advanced technologies in the orange sector (see also Ménard, 2018). In the pages below, we show that the use of contracts with juice processing firms in the commercialization of oranges reduces the likelihood of adoption of irrigation among the orange farms in our sample. We also test other three hypotheses. First, the participation in producers’ associations positively affects the adoption of irrigation – a result that suggests the importance of collective actions for the sharing of relevant information among farmers. Nevertheless, we do not identify a significative influence of factors such as the access to credit and the use of management tools on irrigation adoption. Taken together, these results suggest that the bargaining power concentrated in the hands of juice producers has contributed to the low rates of irrigation adoption in the Brazilian orange industry.

The adoption of a technology occurs when an individual accepts to use and employs a new technology continuously (Feder et al., 1985). Whenever a technology is developed and introduced in the market, adoption will be heterogeneous: while some entrepreneurs will adopt the technology instantly, others will postpone its adoption and a portion of potential users will never adopt it (Caswell, 1991). In turn, the diffusion of a technology is characterized by the aggregate adoption of a technology – generally in replacement of an old technology used to solve the same set of problems – within a population or area over time (Rogers, 1962). Early studies on technology diffusion have employed “epidemic diffusion models”, which are based on the idea that diffusion depends on the reduction of the informational uncertainty related to the adoption of the technology (Griliches, 1957; Mansfield, 1961). However, researchers soon realized that the expected profits derived from adoption may vary depending on the features of the firm under consideration. In response, scholars have extensively analyzed how particular thresholds affect the economic benefits of use of a given technology. A well-known example is David’s (1969) analysis of the drivers of adoption of mechanical grain reapers in the United States during the nineteenth century. The study, which employs a threshold model of technology diffusion, showed that the size of the farm was a fundamental factor behind the adoption decision. Since farmers had to make a choice between manual methods – with high variable costs – and mechanical grain reapers, a farm had to be large enough to take advantage of the potential economies of scale that derive from the adoption of the technology. The introduction of threshold models has shifted the empirical analysis from the study of an aggregate adoption process to an individual decision-making process. In this sense, threshold models assume that the adoption of a technology results from a profit-maximizing or utility-maximizing behaviour in a heterogeneous population of decision-makers (Foster and Rosenzweig, 2010; Carrer et al., 2017). Individuals will adopt a new technology if adoption increases the net revenue from production. At the same time, different preferences and other external factors will also affect the adoption of a technology (Galliano and Orozco, 2011). For instance, the threshold model allows the identification of several factors that affect the adoption of a new technology at the individual level. These insights can highly influence the formulation of public policies and private strategies, accelerating the diffusion of a new technology (Geroski, 2000). Beyond David’s threshold model basic tenets, several studies assume the influence of other demand and supply factors on the decision of adopting a technology. Examples include factors such as the previous number of users of a technology (Reinganum, 1981), economic learning and learning-by-using (Stoneman, 1981; Rosenberg, 1982), risk attitudes (Kondouri et al., 2006; Salazar and Rand, 2016) and suppliers’ profits (Metcalfe, 1981). While these contributions have enhanced our understanding of how innovation is adopted and diffused, the progressive development of the literature has added complexity to empirical analyses. In particular, the inclusion of variables with dynamic properties has increased the amount of information to be collected and included in empirical models (Berger, 2001). Most studies that analyse the drivers of irrigation adoption use threshold models to identify a critical level of heterogeneity that would differentiate adopters and non-adopters (Caswell, 1991). This broad literature can be divided in three main strands. The first strand studies the “secondary adoption of irrigation”, that is, the factors that lead farmers to replace their current irrigation system, adopting more efficient ones (Fishelson and Rymon, 1989; Dinar and Yaron, 1990; Koundouri et al., 2006; Schuck et al., 2007). In turn, the second strand encompasses studies that identify the drivers of choice of one irrigation system over another. In this strand, researchers generally assume that the decision to adopt irrigation had already been made when farmers 2

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Hypothesis 1. The adoption of irrigation is positively affected by participation in agricultural associations.

chose an irrigation system (Negri and Brooks, 1990; Green et al., 1996; Moreno and Sunding, 2005). After all, profit-maximizer farmers will choose to use an irrigation system only if the technology is perceived to be more profitable than alternative options (Caswell and Zilberman, 1985). Finally, the third strand comprises contributions that assess the “primary adoption of irrigation” – i.e. situations in which the farmer has never used irrigation and can choose whether to adopt the technology. Given the low diffusion of irrigation in the Brazilian orange belt, our hypotheses derive mostly from the theoretical and empirical developments of the “primary adoption of irrigation” strand of literature. Researchers highlight four categories that affect the adoption of irrigation during this stage of the decision-making process: (i) access and sharing of information; (ii) technical and managerial skills; (iii) the organizational and institutional environments; and (iv) structural and agronomic aspects of the farm. In the paragraphs below, we derive hypothesis related to the first three of these four categories identified in the literature. Reflecting the fact that all the farms in our sample come from the same geographical region, the results of our survey show similar structural and agronomic characteristics for each observation. Hence, we do not include hypotheses related to this category in our empirical analysis. Yet, we acknowledge that previous studies have analysed the influence of structural and agronomic aspects such as temperature and precipitation levels (Torkamani and Shajari, 2008; Cunha et al., 2014; Cremades et al., 2015), access to water sources (e.g. He et al., 2007; Alcon et al., 2011; Singh et al., 2015), soil type (Negri et al., 2005; Cremades et al., 2015) and the access to infrastructure (Kulshreshtha and Brown, 1993).

2.2. Technical and managerial skills The appropriate operation of irrigation systems requires technical and managerial skills. Hence, human capital and use of management tools matter (Anderson et al., 1999; Namara et al., 2007; Zhang et al., 2019). Studies show that the educational level of farmers positively affects the likelihood of adoption of a technology (Bagheri and Ghorbani, 2011; Carrer et al., 2017). From a farm management perspective, education may be a necessary but an insufficient condition for an appropriate operation of the bundle of resources within the boundaries of the farm. For instance, the absorptive capacity of an organization – i.e. the ability to recognize the value of a piece of information and use it to pursue economic gain – depends on the establishment of appropriate organizational structures or the existence of know-how (see Cohen and Levinthal, 1990; Vinholis et al., 2016). In the Brazilian orange belt, we expect that these capabilities should be supported by management tools that facilitate the gathering of information. Examples include the use of specialized software and the participation in certification systems that require the provision of relevant data. The available evidence shows that these tools help to foster the adoption of innovations in farms (Bernard and Fan (2004)). Therefore, we expect that: Hypothesis 2. The adoption of irrigation is positively affected by the use of management tools.

2.1. Access and sharing of information

2.3. The organizational and institutional environments

Information usually plays a fundamental role in inducing the adoption of a technology. For instance, the seminal contributions by Griliches (1957) and Mansfield (1961) associate the cumulative adoption of a technology over time with the progressive dissemination of information among its potential users. More available information means an enhanced ability to evaluate the costs and benefits of adoption (Geroski, 2000; Galliano and Orozco, 2011). Moreover, the cumulative adoption of a technology may help farmers to understand how to operate it properly (Namara et al., 2007). As Geroski (2000) points out, a technology has two aspects: the “hardware aspect” – i.e. the physical characteristics – and the “software aspect”, which is the amount of information that is needed to use it effectively. Since knowledge from an adopter’s perspective is built up mainly from the experience of use of a technology, it is reasonable to assume that the establishment of communication channels among farmers foster the transmission of information related to the “software” aspects of irrigation (Genius et al., 2013). The literature identifies several mechanisms that support the diffusion of information on irrigation (Galioto et al., 2020). Following the widespread interest in understanding the relationship between the existence of extension programs and the adoption of a technology, several studies show that farmers who interact with technical assistance staff are more likely to adopt irrigation systems (He et al., 2007; Alcon et al., 2011; Cunha et al., 2014; Cremades et al., 2015; Ferreira, 2015; Zhang et al., 2019). Similarly, training programs on the operation of an irrigation system may provide the necessary information – e.g. practical demonstrations – that would reduce the uncertainty related to the adoption of the technology. Finally, interactions between user and nonusers of a technology can occur either because of geographical proximity or due to the membership to organizations such as associations and cooperatives (Vinholis et al., 2016). Other studies show that the members of farmers’ organizations receive more information, positively affecting the likelihood of irrigation (Kulshreshtha and Brown, 1993; Alcon et al., 2011). Hence, we expect that:

Multiple organizational and institutional factors affect technology adoption and diffusion (Feder et al., 1985; Galliano and Orozco, 2011). In the literature dedicated to analysing the phenomenon in agricultural contexts, the idea that farmers face a series of constraints to adopt new technologies is pervasive. A commonly studied constraint with an institutional component is limited access to credit. Farmers from developing countries often cannot provide a collateral and, therefore, are unable to obtain credit. The broad literature discussing how credit access affects technology adoption identifies several explanatory variables. Examples include factors directly related to the ability of a farmer to provide a collateral – e.g. the size of the farm and the ownership of equipment – and indirect factors such as community ties, proximity between lenders and borrowers and the nature of the household relationships (Negri et al., 2005; Salazar and Rand, 2016; Carrer et al., 2020). Irrespective of the driver of assess to credit, however, we expect that an enhanced ability to borrow resources from public or private organizations will increase the probability of adoption of irrigation, ceteris paribus (He et al., 2007; Alcon et al., 2011). Hypothesis 3. The adoption of irrigation is positively affected by access to rural credit. Finally, we assess the role of contracts in the adoption of irrigation in the Brazilian orange belt. Contracts are an important coordination mechanism in this sector. Vertical integration explains the supply of 40 % of the oranges used by the Brazilian juice processing industry, with large volumes (60 %) being sold by independent growers. Forwards contracts – that range from one to two years of duration – are most commonly used in transactions between orange growers and processing firms. Spot market transactions are unusual in the juice processing market. In the fresh orange segment, however, spot market transactions are the most common transaction. In general, growers sell fresh fruits to packinghouses, which classify, clean and distribute the oranges to both domestic retailers and foreign markets. Farmers may eventually have their own packinghouses. 3

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questionnaire was divided into three main blocks: (i) personal characteristics of the orange farmers; (ii) structural aspects of the production process; (iii) aspects of the decision-making process and individual perceptions on relevant institutional and economic factors. The crosssection data refers to 2013/14 crop year. It is relevant to present some characteristics of the farms in the sample. Oranges are the main output of all sampled farms. In addition to orange, other crops were cultivated in some farms (e.g., lemon, sugar cane, and cattle). On average, oranges were cultivated in 65 % of the available agricultural land of these farms. In 53 farms (54 % of the total sample), oranges were cultivated in 100 % of their available agricultural land, while in 45 farms other crops were cultivated as well. Adopters and non-adopters of irrigation grow many varieties of oranges. The adopters’ orange area consisted of Pera Rio (42 % of the irrigated area), Valencia (29 %), Hamlin (8%), Folha-Murcha (7%), Westin (4%) and other varieties (10 %); while the non-adopters grow Pera Rio (36 % of the orange area), Valencia (31 %), Hamlin (11 %), Natal (1%), Folha-Murcha (7%), Westin (5%) and other varieties (9%). These distributions were not statistically different between the two groups. All varieties, with exception of Hamlin, could be sold in both fresh fruit market and juice processing market. In fact, 76 % of adopters had sold their oranges in both markets and 24 % in the fresh fruit market exclusively; while 67 % of non-adopters had sold in both markets and 33 % in the fresh fruit market exclusively. Our econometric analyses can be subdivided in two steps: (i) translog production function estimation; (ii) probit models estimation. A flexible translog production function was estimated to measure the impact of irrigation, as a technological change, on orange production of the sampled farms. The following specific form describes the proposed econometric model:

Scholars have long assumed that farmers are risk averse (Stiglitz, 1974), a claim that is widely used in the empirical research (Giné and Yang, 2009). In this sense, efficient contracts would balance the benefits from enhanced incentives and the costs from risk bearing (Meyer et al., 1992), potentially fostering the materialization of investments in technology. However, the idea that risk preferences play a fundamental role in shaping contracts in agriculture is not consensual. For instance, Allen and Lueck (1995) use a transaction cost-based framework to explain the adoption of contracts among farmers. People are assumed to be risk neutral under a transaction cost-based logic, with the features of the contracts ensuing from an alignment between the characteristics of the transaction and the necessary instruments to enforce the exchange (Williamson, 1987). Hence, fear of ex-post expropriation of the gains derived from investments in specific assets becomes a driving force in the design of contracts (Klein et al., 1978). Despite these threats, transaction cost-based theorists tend to predict the design of contractual remedies that mitigate the probability of opportunistic behaviour (Allen and Lueck, 2002). Nevertheless, people are often unable to design contracts that secure investments in specific assets. Acemoglu et al. (2007) show that a greater level of contractual incompleteness may disincentive a firm to adopt more advanced technologies, given the frictions that may arise among the parties. In other cases, the design of relatively standardized contracts is insufficient for protecting the quasi-rent derived from specific investments (Ménard, 2018). Furthermore, new dimensions of market power, such as the use of contracts to unilaterally impose restrictions on the actions of other parties, have become more common in agri-food supply chains (see Bonanno et al., 2018). The Brazilian orange belt illustrates this argument. As Ito and Zylbersztajn (2016) point out, orange juice processing firms from the state of São Paulo hold a considerable ability to influence the selection of terms in the contracts with farmers. The high barriers to entry in the juice processing segment limit the alternatives of commercialization for Brazilian orange producers. At the same time, juice processing firms have pursued a strategy based on the vertical integration of part of their orange supply – a move that has further increased their bargaining power (Ito and Zylbersztajn, 2018). The result is a pattern of continuous conflicts between juice processing firms and orange producers, with a frequent use of power by firms to shape contractual obligations and capture a greater share of the total surplus generated in the agri-food chain (Carrer and Souza Filho, 2018). Consequently, we expect that Brazilian orange farmers who sell to juice processing firms using contracts have lower incentives to adopt irrigation technologies.

(

lnyi = β0 + β1 lnx1i + β2 lnx2i + β3 lnx3i + β4 lnx 4i + β11 1 2 ln2x1i + β12 lnx1i lnx2i + β13 lnx1i lnx3i + β14 lnx1i lnx 4i + β22 + β23 lnx2i lnx3i + β24 lnx2i lnx 4i + β33 + β44

(

(

(

)

1 ln2x 2i 2

)

)

1 ln2x + β lnx lnx 3i 3i 4i 2 34

)

1 ln2x + δIrriga + u 4i i 2

(1)

in which yi denotes the orange production of the i-th firm; xi is a vector of logarithms of physical quantities of inputs used by the farm – land (x1, in hectares), labour (x2, in hours), capital (x3, in machine hours) and fertilizers (x4, in kilo); Irriga is a dummy variable for irrigation adoption; β is a vector of the parameters of the production function to be estimated; δ is the (shift) parameter that captures the impact of irrigation adoption on the production function; ui is a random error term, independent and identically distributed; and i denotes the i-th of 98 farms in the sample. The estimation of the parameter δ allows the quantification of the impact of the adoption of irrigation on orange production, given the endowments of factors of production – land, labour, capital and fertilizers. Hence, the shift parameter δ is a measure of the effect of irrigation on the production of the farms in the sample. The parameters were estimated by ordinary least squares (OLS) method. The drivers of irrigation adoption, in turn, were investigated by probit models. The dependent variable in these models is measured as a dummy variable – i.e. 1 corresponds to observations in which the farmer adopted irrigation and 0 corresponds to cases of non-adoption. The probability of adopting irrigation is estimated by:

Hypothesis 4. The adoption of irrigation is negatively affected by the use of contracts with juice processing firms. 3. Materials and methods This article uses a mixed methods “development approach” (Greene et al., 1989), in which a qualitative research inspires the creation of a survey that informs a quantitative analysis. We applied our survey to a random sample of orange farms from the central, southern and northern regions of the orange belt of the State of São Paulo. This state was responsible for 73 % of Brazilian orange production in 2018. The regions studied had 9370 agricultural production units with orange orchards and account for approximately 50 % of this state's total orange production (LUPA, 2017). Simple random sampling was used to calculate the sample size. With a sampling error of 10 % and confidence level of 95 %, a sample with 98 farms was obtained (47 located in the southern, 31 in the central and 20 in the northern region of the citrus belt). The sample is divided in two groups: 34 adopters of irrigation and 64 nonadopters of irrigation. We consider “adopters” the growers who had any type of irrigation system in any fraction of their orange orchards during the crop season of 2013/2014. Face-to-face interviews with the orange growers were performed between March and September of 2014. The

x´β

Prob (y = 1│x) = Φ (x´β ) =

xβ

∫ Φ (t )dt = ∫−∞´

−∞

1 2 1 e − ( 2 t dt) 2п

(2)

in which, Φ (.) represents the cumulative density function and Φ (.) the probability density function of the standard normal distribution, β are the parameters and x a vector of the explanatory variables. 4

(+) (-)

Dummy (1 if farmer sold more than 50 % of orange to processing industry and 0 otherwise) Assumes discrete values ranging from 1 – fully disagree to 5 – fully agree with the following statement: “I trust in my intuition when I choose the best moment to sell oranges”. Assumes discrete values ranging from 1 – fully disagree to 5 – fully agree with the following statement: “I trust in judicial system to enforce my legal rights in commercialization contracts”.

y1*i = y2i β + x1i γ + μi

+ vi

(3)

Table 1 Description of the variables and expected signs for the factors determining irrigation adoption.

Type of measure

in which i = 1, …, 98; y1i is a binary dependent variable (ADOPT), y2i is an endogenous variable (CONTR), x1i is a vector of exogenous variables (ASSOC, MANAG, CRED, EXP, COURS, INCO and SIZE), x2i is a vector of instrumental variables (CHANNEL, RISKP and ENFORC), and the equation for y2i is written in reduced form. β and γ are vectors of structural parameters, and ∏1 and ∏2 are matrices of the reduced form parameters. This is a recursive model; i.e., y2i appears in the equation for y1*i , but y1*i does not appear in the equation for y2i . The estimated equation for y1*i can be rewritten as: y1*i = (x i ∏ + vi ) β + x1i γ + μi (4)We estimated two probit models. In the first model, we assume exogeneity between irrigation adoption and contracts use, while endogeneity is assumed in the second. The Wald test was applied to test the presence of endogeneity. We use the maximum-likelihood method to estimate the probability of irrigation adoption in software LIMDEP 10 and the package “ivprobit” in R. Table 1 presents the description of the variables used in the probit models. It is important to mention that water sources were available in 82 farms of the sample (84 % of all sample) and in all farms of the subsample of irrigation adopters. The role of water availability in the adoption, measured by this information, cannot be tested in our models because 100 % of adopters had water sources. Other variables that could account water sources characteristics, such as volume, quality and proximity were not available for a more comprehensive analysis on this issue. Thus, we assume that water availability was not a barrier to irrigation and other drivers would be important to explain adoption. Our main interest is to test the four hypotheses stated in section 2, mainly the effect of the use of contracts (CONTR), access to credit (CREDIT), participation in agricultural association (ASSOC) and the use of management tools (MANAG) on irrigation adoption. Special interest is devoted to the effect of contracts on the adoption. Additionally, we add four control variables to the models. The variable SIZE measures the size of the orchards. Several empirical studies find that large-sized farms are more likely to employ irrigation (Negri et al., 2005; Bagheri and Ghorbani, 2011; Kamwamba-Mtethiwa et al., 2012; Singh et al., 2015; Zhang et al., 2019). In turn, the variable EXP measures the experience of the farmers in orange production and represents the number of years dedicated to the same activity. Experience, a proxy from accumulated knowledge of the farmer, is expected to be positively correlated with the adoption of irrigation. The variable COURS is a dummy variable which measures the participation of orange growers in courses on irrigation. Several studies assess how the level of education and access to information affect the likelihood of adoption of irrigation (Negri et al., 2005; Bagheri and Ghorbani, 2011; Cunha et al., 2014; Zhang et al., 2019). Finally, the variable INCO measures the percentage of the income of the respondent which derives from the commercialization of oranges. We expect that farmers who are more dependent on the income from their orange orchards will be more likely to invest in irrigation, given the potential optimization in the use of other factors of 5

Dummy (1 if yes; 0 otherwise).

∏

Dependent variable ADOPT Irrigation adoption during the 2013/2014 crop year Explanatory variables CONTR Use of formal contract in transactions with buyers EXP Experience of the grower in the orange activity. ASSOC Participation in agricultural associations. COURS Participation on irrigation courses. INCO Dependence of the orange income. SIZE Size of the farm. MANAG Use of management tools in the farm. CRED Access to rural credit. Instrumental variables CHANNEL Main distribution channel used to sell oranges RISKP Propensity of farmer to assume risks in orange commercialization ENFORC Perception of farmer about the enforcement of contracts

xi

Description

∏1 + x2i ∏2 + vi =

Acronym

y2*i = x1i

(+)

(-) (+) (+) (+) (+) (+) (+) (+)

Expected effect on irrigation adoption

Our main interest is to estimate the effect of the use of contracts on the probability of irrigation adoption. However, endogeneity between irrigation adoption (ADOPT) and use of contracts (CONTR) may arise, which could cause bias in our analysis. For example, low prices received by farmers in their contracts with processing firms can reduce profitability of the orange production. This reduction in profitability may also decrease the probability of irrigation adoption. Then, the decisions of irrigation adoption and contract use can be simultaneous; in this case, the use of contracts would be correlated with the error term of irrigation adoption equation. To deal with this potential endogeneity we estimated an Instrumental Variables Probit Model (IV Probit) by using the Amemiya’s (1978) Generalized Least Squares estimators. Generally, the model is:

Dummy (1 if yes; 0 otherwise). Number of years. Dummy (1 if yes; 0 otherwise). Dummy (1 if yes; 0 otherwise). Dummy (1 if 50 % or more of the total grower’s income comes from orange activity; 0 otherwise). Hectares of land of the orchards. Dummy (1 if farmers used any management tool [software, internet, certifications]; 0 otherwise). Dummy (1 if yes; 0 otherwise).

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production and the reduction of the risk caused by the scarcity of water. Irrigation adoption can be either part of a strategy to reduce the risk of insufficient rainwater in situations of higher income risk – i.e. yield stabilization strategies – or part of a strategy to increase income based on investments in technologies that improve the productivity of orchards – i.e. yield increase strategies. Three instrumental variables (CHANNEL, RISKP, and ENFORC) were used to the estimation of reduced-form equation in the IV Probit Model. These variables must affect the use of contracts, but cannot affect the irrigation adoption. They also must be exogenous. The variable CHANNEL measures the main commercialization channel used by farmer. As presented in the sample description, distribution channel does not differentiate adopters from non-adopters. Nevertheless, the distribution channel may affect the use of contracts because the latter is more frequent in transaction with processing firms. It is expected that farmers who sell most of his/her orange production to processing firms are more likely to use contracts. RISKP is a proxy for the propensity of farmer to accept risks in orange commercialization. The finance behavioral literature shows that the greater a farmer's risk propensity, the less likely he/she is to use contracts (see Franken et al., 2014). We expect negative effect of RISKP on the use of contracts. ENFORC is a proxy for farmers’ perception about the judicial system as a mean to enforce legal rights in contracts. As pointed by North (1994), the enforcement of contracts is fundamental to reduce transaction costs in economic system. Farmers who perceive that contract rights can be enforced would be more confident to accept contracts. Descriptive statistics of the variables used in the analyzes of the drivers of irrigation adoption are displayed in Table 2

Table 3 Results from the translog production function: impact of irrigation adoption on orange yields.

Table 2 Descriptive statistics of the variables used in analyzes for the factors determining irrigation adoption.

Mean 0.353 21.03 0.470 0.353 0.618 235.94 0.382 0.706 0.588 3.529 2.205

SD 0.485 9.96 0.506 0.485 0.493 244.82 0.493 0.462 0.499 1.673 1.572

Non adopters = 64 Min 0 7 0 0 0 19.3 0 0 0 1 1

Max 1 50 1 1 1 840 1 1 1 5 5

Mean 0.516 25.51 0.344 0.047 0.344 165.18 0.156 0.609 0.594 2.330 2.515

SD 0.504 12.13 0.479 0.213 0.479 217.23 0.366 0.492 0.495 1.554 1.563

Min 0 4 0 0 0 5 0 0 0 0 1

Coefficient

Standard Error

Constant lnx1 (hectares) lnx2 (labour hours) lnx3 (machine hours) lnx4 (kg NPK) lnx1 x lnx1 lnx1 x lnx2 lnx1 x lnx3 lnx1 x lnx4 lnx2 x lnx2 lnx2 x lnx3 lnx2 x lnx4 lnx3 x lnx3 lnx3 x lnx4 lnx4 x lnx4 Irriga Model fit F statistic Prob F > F* R-squared N = 98

β0 β1 β2 β3 β4 β11 β12 β13 β14 β22 β23 β24 β33 β34 β44 δ

−0.094 0.539*** 0.036 0.232* 0.247** 0.098 −0.187 0.141 −0.093 0.157 0.226 −0.152 −0.455* 0.018 0.199 0.219***

0.064 0.141 0.106 0.147 0.107 0.422 0.230 0.222 0.294 0.323 0.264 0.191 0.272 0.231 0.292 0.079

106.66 0.000 0.951

The use of mean-scaled variables allows us to interpret each one of the first-order coefficients of the translog function as the partial elasticity of production for the sample mean. Thus, a 1% increase in the area – measured in hectares – results in an increase of 0.54 % in orange production, ceteris paribus, at the sample mean both for inputs and output. Interpreting the coefficients for the other inputs demands a similar approach. The elasticity of scale – i.e. the sum of the first-order coefficients – of the translog function for the sample mean is equivalent to 1.04. Given that this value is very close to 1, we can assume the existence of constant returns of scale for the sample mean. The parameter δ, which represents the most relevant parameter in our production function analysis, has a value of 0.219 and a statistical significance at the 1 percent level. Hence, we can identify a clear positive effect of the adoption of irrigation on the production function of the analyzed farms: this result means that irrigation adoption increases by 21.9 % the orange production of the farms in our sample, ceteris paribus. Overall, our analysis supports the idea that the use of irrigation increases the productivity of the factors of production in the orange farms in the state of São Paulo, Brazil. Table 4 shows the estimates of the probit models used to investigate the drivers of adoption of irrigation. The Wald test suggest the rejection of exogeneity at 5% level. In this respect, the IV Probit Model tends to provide the most consistent estimates. It is important to observe that the main results (parameters signs and statistically significances) – mainly the estimates of the variable “CONTR” – are similar under the two specifications, indicating robustness. The binary IV Probit Model correctly predicts 74.5 % of the adoption decisions, while the probit model predicts 77.5 % of the adoption decisions. Moreover, the p-value of the Wald Test (Prob > Chi2) rejects the null hypothesis that all coefficients of the variables are equal to zero in the two models. Hence, the models can be used to explain the drivers of adoption of irrigation among the orange farmers in our sample. The statistical significances for all the variables in our models are informed as we discuss the results. However, we focus on explaining the results whose statistical significance is at least at the 10 percent level for supporting or rejecting our hypotheses. Among the control variables, SIZE has no statistically significant effect on the likelihood of adoption of irrigation. The result suggests that the possibility of achieving gains of scale is an insufficient driver of

Table 3 summarizes the results of the translog production function model. The F-test of significance (< 0.00) rejects the hypothesis that all of the coefficients are equal to 0 at the 1 percent level. The R2 for the estimated model has a value of 0.95, meaning that irrigation and the factors of production included in the analysis – i.e. land, labour, capital and fertilizers – explain 95 % of the patterns of orange production in the farms in our sample. Following the prevalent practices in the empirical literature, we use the mean scaled variables for the output and for the input factors to estimate the translog function. The estimated first-order coefficients β1, β2, β3 and β4, which account for the four factors of production, have positive sign. Hence, the estimated translog function respects the monotonocity condition for the sample mean. Since the translog production function is not globally concave – i.e. is not characterized by convex isoquants – we check the concavity conditions for the properties of the sample, calculating the first and second derivatives, as well as the determinant of the Hessian matrix for the sample mean. The results show that the concavity conditions are respected for the sample mean.

Variable CONTR EXP ASSOC COURS INCO SIZE MANAG CRED CHANNEL RISKP ENFORC

Parameter

* significant at 10 %. ** significant at 5%. *** Significant at 1%.

4. Results

Adopters = 34

Variable

Max 1 58 1 1 1 1128 1 1 1 1 5

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of it does not imply the possibility of appropriating the results of the investment. Other factors, such as the power asymmetry in the bilateral relationship between the orange producer and the juice processing firm, likely affect the decision as well. Access to credit positively affects the adoption of irrigation among the orange growers in our sample only at the 15 percent level. Thus, Hypothesis 3 is not corroborated. At the same time, we acknowledge that this result demands further scrutiny. Given the substantial amount of physical capital required to implement irrigation, access to financial resources is likely to facilitate the adoption of this technology. Although rural credit in Brazil is subsidized – i.e. interest rates are lower than those found in the marketplace – many farmers are unable to access rural credit lines. Reasons for the existence of such constraints include the lack of collaterals, the existence of previous unpaid debts or the evaluation that some farmers belong to high non-payment risk groups. Disparities in the ability of accessing credit lines might widen the income differentials among farmers – in particular when the adoption of a technology leads to the reduction of costs or higher economic gains (Carrer et al., 2020). In the case of the São Paulo’s orange belt, however, it is not clear whether factors such as credit access or the progressive spread of information would suffice to support an increase in the rate of adoption of irrigation. The reason: given the considerable bargaining power held by the buyers of oranges, potential gains derived from investments in specific assets would likely be captured by the few firms in the juice processing segment. For instance, our empirical analysis shows that the probability of adoption of irrigation by the farmers of our sample is negatively affected by the use of contracts with processing firms, at 5% level of significance. This result supports Hypothesis 4, shedding light on relevant implications of the interaction between bargaining power and efficiency. The qualitative evidence collected during the first stage of this research suggests that farmers who decide to sell their oranges in the fresh fruit market have enhanced incentives to adopt irrigation. In fact, buyers in this market channel pay higher prices and demand higher quality fruits. In our sample, the average price received by the farmers who sold oranges to the fresh fruit market was 10.50 Brazilian Real, compared to 8.60 Brazilian Real received from juice processing firms. The frictions in the transactions between farmers and juice processing firms appear to have reduced the incentives for the adoption of advanced technologies in the production of oranges, resulting in negative effects on productivity.

Table 4 Estimates (standard errors between parentheses) for the determinants of irrigation adoption. Variable

Probit

IV Probit

Constant CONTR INCO MANAG COURS ASSOC EXP CRED SIZE Log-likelihood Wald-chi2 Prob > chi2 R2 McFadden Predicted Overall Percentage

−1.1363** (0.4913) −0.9488*** (0.3272) 1.0182*** (0.3272) 0.5516 (0.3972) 1.3261*** (0.4667) 0.5437* (0.3432) −0.0176 (0.0143) 0.4632 (0.3462) 0.0002 (0.0007) −44.337 37.849 0.0000 0.2991 77.55 %

−0.6533 (0.6923) −3.0172**(1.222) 1.1751***(0.4948) 0.6284(0.5642) 1.1919**(0.6139) 0.8169*(0.5445) −0.0182(0.019) 0.7057(0.5060) 0.0016(0.0011) −45.343 35.837 0.0000 0.2832 74.5 %

* significant at 10 %. ** significant at 5%. *** Significant at 1%.

adoption in the São Paulo’s orange belt. In turn, experience is also not important to explain the adoption of irrigation.1 As expected, the participation in courses on irrigation is positively associated with irrigation adoption at the 5 percent level. Finally, the variable INCO has a positive and significant effect on ADOPT at the 1 percent level. Hence, the likelihood of adopting irrigation is higher among growers who depend more on the revenue from their orange orchards. Now we turn to the discussion of our hypotheses. Consistent with Hypothesis 1, the participation in agricultural associations positively affects the adoption of irrigation in the farms analysed at the 10 percent level. Together with the fact that the control variable COURS – i.e. participation in courses on irrigation – has a positive effect on irrigation adoption at the 1 percent level, this result reinforces the idea that the diffusion of information is an important mechanism to foster the adoption of technologies by farmers (Zhang et al., 2019). The information shared among the members of a farmers’ collective action can shed light on details related to the technical performance and the economic benefits of irrigation, helping to shape a positive judgment on adoption. Moreover, the progressive diffusion of new techniques might create the possibility of broader quality upgrade initiatives – a strategy that can potentially lead to the creation of additional value in the agrifood chain. We find no strong support for Hypothesis 2. More specifically, the use of management tools appears to exert a limited influence in the adoption of irrigation in the São Paulo’s orange belt – the statistical significance of the coefficient is at the 15 percent level. Our results capture a weak link between the existence of capabilities and resources that may support the adoption of a technology and the decision to adopt it. Previous research shows that the ownership of capabilities and resources helps to shape the set of feasible organizational arrangements for agricultural producers (Miranda and Chaddad, 2014). A similar logic can be extended to adoption choices – before a technology is adopted, potential users must know how to take advantage of it. Nevertheless, our findings suggest that knowing how to take advantage

5. Discussion This article shows that the use of contracts between Brazilian orange growers and juice processing firms is negatively associated with the decision of adopting irrigation. This result is counterintuitive: inspired by the work of Williamson (1987), economists dedicated to the analysis of agri-food supply chains and networks have argued that the establishment of a contract implies safeguards that contribute to the materialization of specific investments. The idea that the level of contract incompleteness can influence technology adoption – i.e., more incomplete contracts might disincentivize investments in more advanced technologies (Acemoglu et al., 2007) – is still not fully incorporated in this literature. After all, Williamson’s “discriminating alignment hypothesis” predicts that agents will necessarily find an organizational solution that solves adaptation problems in a bilateral relationship, independent of the fact that contract incompleteness is pervasive (see Granovetter, 1985). Further research must disentangle the multiple dimensions related to a pattern of contract stability, trying to elucidate the complex relationship between power-based and efficiency-based factors in the establishment of formal agreements. The potential research avenues opened by such counterintuitive results are the main contribution of our article. The case of the orange sector in Brazil suggests that distributive issues can be as important as efficiency issues to determine how an agricultural commodity will be

1 It could be argued that farmers who are close to retirement age are unlikely to adopt new technologies, therefore, age would be an important factor. The Pearson correlation index between EXP and age of farmers is 0.72 and statistically significant at 1% level. In this case, inclusion of age and EXP in the same models would lead to multicollinearity problems. Additionally, we estimated different specifications in our probit models to include some proxies for retirement age (e.g., age of farmers and a dummy variable with value = 1 for farmers who are over 60 years of age and 0 otherwise). These variables were also not statistically significant at 10% level in these models.

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produced, creating additional challenges for policy makers. For instance, traditional policy prescriptions, such as the provision of credit and extension services, might be necessary but insufficient conditions for the adoption of irrigation. Whenever growers perceive that the economic results of their efforts might be partially capture by upstream or downstream members of the agri-food supply chain, incentives for the adoption of a new technology might be reduced. At the same time, the participation in formal organizations – e.g. cooperatives – and networks should likely affect the potential impact of policies. Further research may investigate the structural characteristics of the networks that tie orange growers from the Brazilian orange belt. For example, scholars can compare the effects of the participation in cooperatives and other types of organization, such as producers’ associations and pools, in the diffusion of relevant information on agricultural technologies. Likewise, the consequences of the existence of strong ties and weak ties to innovation adoption can be further studied. Further research can also investigate, for example, how asymmetric bargaining power – or orange producer’s perceptions on this level of asymmetry – affects both the choice of a market channel and technology adoption decisions. Another potential topic for future research would be the analysis of the impact of actual conflicts over time on the orange growers’ predisposition to acquire knowledge that is necessary for the adoption of new technologies in their orchards. Many frictions have characterized the contractual relationship between farmers and juice processing firms (Ito and Zylbersztajn, 2016; Carrer and Souza Filho, 2018). Therefore, experience should not necessarily lead to a higher likelihood to employ techniques that would enable a more efficient use of the available resources.

provision of subsidies might be seen with caution: in agri-food chains with highly asymmetric bargaining power, support measures might be appropriated mostly by oligopsonistic firms, limiting the benefits for farmers. Beyond the issues outlined in our Discussion section, we acknowledge the following limitations in our analysis. First, this paper uses cross-sectional data for the single crop season year of 2013/2014. Hence, we could not assess the impact of technological changes or the effects of changes in variables such as orange prices and input prices on irrigation adoption. Furthermore, we could not collect information on the exact location of the farms. Given the relevance of site asset specificity in explaining the emergence of bargaining power in contractual relationships in the agricultural sector, this information might help to depict a more precise picture of the nature of the conflicts in the Brazilian orange industry. Finally, the sample was not large enough to estimate spatial econometric models that would allow an analysis of the potential technological spillovers effects from the adoption of irrigation. These issues can all be addressed in future studies.

6. Conclusions

References

This paper aims to identify the drivers of irrigation adoption by orange growers located in the Centre-North region of the state of São Paulo, Brazil. In the Introduction, we also report the results of an assessment of the impact of irrigation adoption on the productivity of orange orchards – to the best of our knowledge, this is the first article to analyse the issue. The estimated translog production function shows that, given the endowment of the other factors of production included in the analysis, the adoption of irrigation has a positive effect of 21.9 % on orange production of farms. Therefore, we find that the adoption of irrigation has a statistically significant positive effect on the productivity of the farms included in our sample. Of course, such growth in the productivity level of orange orchards may be insufficient to incentivize the diffusion of irrigation. For instance, irrigation adoption rates are rather low. Using primary data from a survey of 98 orange growers, our study provides a potential explanation for such apparent paradox. Considering the continuous conflicts between orange growers and juice processing firms in the Brazilian orange belt, our results suggest that the existence of contracts with processing firms does not increase the likelihood of adoption of new technologies. Our main empirical result is aligned with the shifts from the “traditional” market power framework to one that includes ideas such as buyer market power, and the role of contracts as governance tools to manage transactions in the agri-food chains (see Bonanno et al., 2018). For instance, our results suggest that irrigation adoption in the Brazilian orange belt is associated with the strategy to circumvent the oligopsonistic power of juice processing firms. From the perspective of orange producers, more research is needed to understand the complex interplay between the choice of technology and the choice of distribution channel in the Brazilian orange industry. Moreover, the results indicate that human capital and information play an important role in irrigation adoption. Attendance to courses on irrigation and membership to associations positively affect the adoption of irrigation. These conclusions can influence the design of public policies, particularly to the design of policies with the goal to enhance rural credit supply, extension services and training in farm management. In particular, the

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