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Determining Willingness to Adopt Mechanical Harvesters among Southeastern Blueberry Producers Blueberry consumption in the U.S. has increased threefold over the decade from 2000 to 2010 followed by a fourfold increase in acreage plantings in the Southeastern region (Perez et al., 2011; Morgan et al., 2011). During the same period, the machine harvesting technology of high value Southeastern blueberry cultivars, such as Rabbiteye (Vaccinium ashei) and Southern Highbush (Vaccinium corymbosum X darowii) destined for the fresh market has also improved. Fresh market blueberry harvesting in the Southeast is generally characterized by high demand for large workforces needed to hand pick each ripened blueberry over a six to eleven week ripening window. Labor costs are estimated to be as high as 1,300 worker hours/hectare, representing about 60 percent of average operating costs (Brown et al. 1983; Safley, Cline, and Mainland, 2006). Previous studies have shown that shortages of agricultural workers, either through attrition or immigration enforcement mechanisms, lead to increases in agricultural worker wages, and an increased interest in labor saving machine technologies (Borjas, 2003; Zahniser et al. 2008). These factors accompanied by the improvement in the technology of blueberry harvesters have made machine harvesting a viable alternative to manual harvesting. The objective of this study is to determine factors motivating the adoption of mechanical harvesting (MH) technology among blueberry farmers in the Southeast. The study uses survey and wage data from the four largest Southeastern blueberry growing states of Florida, Georgia, Mississippi, and North Carolina. The unique contribution of the study to the existing literature on significant factors that influence agricultural technology adoption patterns is the inclusion of historical wage variation as a proxy for labor uncertainty related to worker attrition or enforcement related worker shortages. Background Information on Southeastern Blueberry Industry Southeastern blueberry farmers allocate their land between two primary cultivars, Rabbiteye and Southern Highbush, for their characteristics and ripening windows. Because commercial blueberries are self-sterile, farmers then select two or more varieties within that cultivar based on chilling hour restrictions, soil preferences, and pollination capabilities. The native Rabbiteye cultivar has a mature orchard life of between 20 to 25 years with five years from planting until full production, and five years of declining production. A Southeastern U.S. rabbiteye orchard may yield between 6,000 and 10,000 lbs/acre per year in its prime production years, with variations largely dependent on weather conditions and management intensity. Rabbiteye varieties ripen between April to August in the Southeast, with Florida harvest starting in early April and moving north and east as the spring and summer progresses. Southern Highbush cultivars also have an orchard life of between 20-25 years, but the plants are smaller, not as vigorous, and more sensitive to organic matter and low pH levels. Southern Highbush varieties may yield between 3,000 to 8,000 lbs/acre per year in prime production years, with variations largely dependent on weather conditions and management intensity. Southern Highbush ripens earlier than Rabbiteye and is available for the fresh market from March to June in the Southeast. The fruit of the Southern Highbush cultivar is often bigger and has a softer flesh and skin, thus more suited to the fresh market but more easily bruised, than the rabbiteye fruit (Braswell et al., 2009). Modern, over the row, mechanical Blueberry harvesters, such as the Korvan or BEI series, run every five to seven days with three to five passes per orchard for each cultivar. A similar harvesting schedule is followed if hand harvesting. The price of a mechanical harvester ranges between $100,000 to $200,000 depending on functionality, and has a useful lifespan of around 20 years. Mechanically harvested fruit shows a 31.6 percent decrease in price per flat for fresh blueberries and a 49.1 percent decrease in price per lb. for processed blueberries versus hand harvested fruit (Safley, Cline, and Mainland, 2006). Because harvesting machines are inferior to hand-picking in terms of discerning between ripe and unripe berries, average machine harvesters yield is 70 percent of average hand-picking yields due to droppage (Morgan, et al.2011). Furthermore, while mechanical blueberry harvesters are considered a labor saving technology, there is still a need for a human workforce to break the berry clusters prior to the onset of the marketable harvest process. Laborers are needed for grading, sorting, and cleaning remnants of berries broken from mechanical tines within a berry cluster. The price per pound received by the producer declines as the harvest season progresses throughout the year, with Floridian Southern Highbush receiving the highest price per pound for domestic blueberries in March, and prices decreasing as other Southeastern regions begin producing. By mid-summer Southeastern blueberry farmers are competing with Northern, Great Lakes, and Western blueberry regions and farm-gate prices continue their decline (Williamson and Lyrene, 2004). Thus, there is an incentive for profit maximizing blueberry producers to harvest fresh market blueberries as early in their respective season as possible to take advantage of early season prices. For most of the 50 year history of cultivated blueberry production in the Southeast, there has been a relatively accessible, documented, undocumented, or H2-A 1 work visa workforce for hand harvesting during the spring and summer seasons (Martin, 2003). By 2007, 75 percent of the hired farmworkers in the fruit, vegetable, and horticulture crops (FVH, includes berries) were undocumented despite the creation of the E-Verify system ten years earlier by the Illegal Immigration Reform and Immigrant Responsibility Act of 1996 (IIRIRA) (Calvin and Martin, 2010). In fact IIRIRA has not effectively decreased the supply of undocumented farmworkers, nor increased the supply of native born workers, but has increased the burden of potential fines for hiring undocumented farmworkers on producers, increasing their financial liability (Devadoss and Luckstead, 2008). National legislation such as IIRARA coupled with state legislations, such as the 2011 Beason-Hammon Alabama Taxpayer and Citizen Protection Act, Senate Bill 56 which caused 40,000 to 80,000 undocumented and documented Alabama laborers to leave the workforce, cause FVH producers to worry about farm labor shortages. These labor shortages in turn induce labor wage increases and lead producers to reevaluate their production methods toward labor saving mechanization (Passel and Cohn, 2011; Huffman, 2012). Anecdotally, the Congressional testimony of Georgia blueberry farmer C. Horner in 2011 demonstrates the difficulties blueberry farmers in the Southeast have with labor scarcity; he testified that even after hiring native workers and applying for H2-A work visa authorized workers, he was still forced to hire 60 unauthorized farmworkers of the 67 needed to harvest his blueberry orchard (Horner, 2011). 1 H2-A work visas are temporary visas granted by petition “for agricultural employers who anticipate a shortage of domestic workers to bring nonimmigrant foreign workers to the U.S. to perform agricultural labor or services of a temporary or seasonal nature.” (U.S. Department of Labor, 2009). Previous Work Hicks (1932) proposed the hypothesis of induced innovation as a way to demonstrate that increases in the prices of factors of production incentivize innovations in order to decrease those specific factor costs. This hypothesis is often used in the context of factor prices for labor spurring labor saving innovations. Samuelson (1965) observes the tautology of Hick’s (1932) hypothesis in a dynamic setting: a rational cost-minimizing entrepreneur will eventually choose factors of production that minimize costs. Samuelson advanced Hick’s theory by postulating that capital relative to labor as a ratio of the factors of production induces innovation, rather than Hick’s notion that labor and capital are perfect substitutes and innovations are introduced as a way for a profit maximizing entrepreneur to minimize labor costs. Samuelson demonstrated that an entrepreneur experiences long-run equilibrium when both factors of the capital/labor ratio are increasing. Thus, as there exists a long-term trend of increasing costs of labor, research into innovations that are either labor saving or labor augmenting are necessary to maintain capital/labor ratio equilibrium. He suggested that all machines are in fact invented to improve efficiency, but also that machines do not work in a vacuum and require human operators in order to be truly profit maximizing. Kislev and Petersen (1981) hypothesized that there are two main reasons for the switch from manual labor in agriculture to machine labor. These reasons are technical changes in agriculture that are developed by agricultural researchers to render labor less efficient than machines, and manual laborers leave the agricultural sector as a market phenomenon due to wage increases in a substitute labor sector (such as construction or service), and as a result agricultural operators are forced to switch from manual labor to machinery. However, Kislev and Petersen (1981) fail to recognize the causal effect of the switch from manual labor to machine labor in agriculture: low wage, low skill labor being coerced out of the market due to governmental policies regarding low-skill immigration and immigration status enforcement. Marra, Pannell, and Abadi Ghadim (2003) emphasize that there is not a unifying theory on risk preferences, uncertainty, and adoption of agricultural innovations. Studies such as Shapiro, Brorsen, and Doster (1992) discovered that adopters of double cropping techniques were more likely to be self-described as risk averse which directly contradicts Marra and Carlson’s (1990) findings that adopters of double cropping are less likely to be risk averse using an Arrow-Pratt risk formula based on perceived variability in prices and quantities. Straub (2009) suggests that adoption is not just an individual act, but also has a social context based on emotional and cognitive concerns. Individuals make their adoption decisions based on the perception of the technology that they have constructed, which is molded by their communication and socioeconomic status. Thus, early adopters are often distinguished from late adopters by having access to broader amounts of information, higher socioeconomic status, higher educational attainment levels, and are less risk averse than their counterparts. Napasintuwong and Emerson (2004) used a Morishima Elasticity of Substitution (MES) model to demonstrate changes in price and quantity ratios on relative factor share. They conclude that capital (mechanization) is a substitute for both self-employed labor and hired labor in the Florida agricultural market, particularly when the prices of labor increase due to immigration legislation. However, if capital becomes less expensive due to innovation and availability and is adopted, labor becomes a complement of capital and employment could also rise. Zahniser, Hertz, Dixon, and Rimmer (2008) used a simulation based model to look at the effects immigration legislation would have on the agricultural sector and the implications for the substitution of farm machinery for labor. They estimate that there is almost zero substitution between foreign born farmworkers (authorized or unauthorized) and native born farm workers. Thus, simulated policies that affect immigration, particularly unauthorized farm labor, would decrease the long-run agricultural output of the U.S. by 1.7-3.5 percent. Furthermore, agricultural sectors that rely heavily on farm labor, like FVH, would experience larger decreases in output and exports than non-labor intensive sectors like oilseeds and grains. Borjas (2003) used simulation based models to generate wage effects of a purely native born male workforce from 1980 to 2000. He then compared those simulated wage effects with the actual wage data using a native and immigrant (documented and undocumented) workforce over the same period. During that period, Borjas (2003) calculated an eleven percent increase in the labor supply of working males and estimated an own factor price elasticity for labor between -0.3 and -0.4. Borjas (2003) distinguishes workers by their level of educational attainment and notes that employment competition between natives and immigrants exist exclusively within the parameters of these levels. Within the lowest level of educational attainment, high school dropouts, he states that the immigration influx from 1980 to 2000 decreased wages by 8.9 percent for this group. Calvin and Martin (2010) use Borjas’ (2003) simulations to demonstrate that historical influxes of immigrant farmworkers lead to a decrease in overall farmworker wages to the benefit of capital owners by an estimated $8 billion annually. However, due to enforcement mechanisms on farmworkers such as E-Verify, increased border enforcement and deportations, and local legislation such as Alabama SB56, wages demanded by farmworkers have recently increased, renewing interest in mechanical harvesters. Daberkow and McBride (2003) researched the adoption decisions of American farmers to precision agriculture (PA) technologies using a logistic regression model to determine farm and producer characteristics of those who adopt. They categorize their independent variables into farm size, human capital, risk and risk preference, tenure, labor supply with regards to income, credit constraints, and location factors. With regards to farm size Just and Zilberman (1983) determined that the fixed expenses of lumpy agricultural technology adoption can often dissuade smaller landholders from adopting new technologies as compared to larger landholder’s adoption decisions. They surmise that larger landholders often have the ability to experiment with the technology on a portion of their fields before complete adoption, in effect testing the technology, while smaller landholders feel required to use the technology on their entire operation if the technology is a large fixed expense. With regards to educational attainment Koundouri, Nauges, and Tzouvelekas (2006) use a two-stage Probit model to determine that levels of education are significant in modeling irrigation adoption decisions among Greek currant farmers. They correlate educational attainment to extension service visits and information access and find positive significance to the probability of adopting irrigation technologies. Koundouri, Nauges, and Tzouvelekas (2006) infer that higher educational attainment and access to extension information decreases the value of waiting to adopt a technology until another farmer has tested it. With regards to off-farm income Fernandez-Cornejo, Hendricks, and Mishra (2005) modeled the adoption decision process of converting to herbicide tolerant (HT) soybeans in the U.S. using a variety of human capital variables. They found significance with age, number of children in the household, farm typology, and off-farm income. Fernandez-Cornejo, Hendricks, and Mishra (2005) concluded that the probability of adoption of HT soybeans is positively explained by off-farm income, and that the elasticity of off-farm income with respect to the probability of adoption is close to one. While adopting HT soybean seed is divisible, as opposed to adopting a lumpy agriculture technology, the findings concerning off-farm income constitute a contribution to adoption literature in American agriculture. Ghadim, Pannell, and Burton (2005) use Tobit and probit modeling to distinguish between risk perceptions and risk preferences and assert that both are significant factors in explaining adoption decisions, according to their study on chickpea adoption in Australia. They found that risk-averse farmers tended away from adoption of a complementing chickpea crop. They also suggested farmers believed that the risk associated with chickpea adoption is greater than the benefits of crop diversification, thus the perception of the risks associated with chickpea adoption is significant. With regards to credit constraints Feder (1980) states that the larger the credit constraint associated with either the technology being adopted or the factors of production, the more risk averse the farmer becomes decreasing the probability of adoption. Conversely, the presence of credit availability increases the probability of adoption by the farmer, as well as investing in a larger farm. Data and Methods Data for this study was collected from a 2011 Blueberry Industry Survey of the Southeastern Region. It was distributed through mail from February 22, 2011 to March 1, 2011 to 692 members of blueberry grower associations in Florida, Georgia, North Carolina and Mississippi, states which represent the majority of blueberry production in the Southeast. Of the 692 blueberry farms that were mailed surveys, 234 responded for a response rate of 33.8 percent. The 2007 Census of Agriculture calculated 2,145 blueberry farms in the Florida, Georgia, North Carolina and Mississippi, thus the 234 respondents to the Blueberry Industry Survey represent 10.9 percent of blueberry farms in these select states. Additionally, the 2007 Census of Agriculture estimated 20, 792 acres of tame blueberries within the four selected states. The 234 survey respondents aggregated blueberry acreage is 12,386 acres, which represents 59.6 percent of total blueberry acreage in the four surveyed states. (USDA Census of Agriculture, 2009). Wage data was acquired from Department of Labor Bureau of Labor Statistics (BLS) Quarterly Census of Employment and Wages (QCEW). The wage data represents county level average weekly wage data reported quarterly based on the North American Industry Classification System (NAICS). This study used the Natural Resources and Mining industry supersector, which includes the agriculture industry subset, NAICS 11, being the only county level quarterly wage data available (U.S. Bureau of Labor Statistics, 2013). The data was collected from 2001 to 2010 for the harvest period which for the Southeast is the second and third calendar quarters. The quarterly data was then weighted according to Cooperative Extension publications on blueberries harvesting times to generate a harvest-period average wage for each state. Florida wage data is only represented by the second quarter (April through June) wage data (Williamson and Lyrene, 2004). Georgia wage data is a weighted average of 75 percent second quarter and 25 percent third quarter wage data (Scherm and Krewer, 2003). Mississippi (Braswell et al., 2009) and North Carolina (Safley, Cline and Mainland, 2006) wage data are simple averages of second quarter and third quarter wage data. Methodology Producers are assumed to be rational-decision makers and utility maximizer with regard to their technology adoption decision. Random utility theory as presented by McFadden (1980) indicates that perceived utility is maximized conditional to economic constraints. This perceived utility is measured individually for choice j using the following model: = xij′ β + ε ij (1) U ij where U ij is the utility of individual i using the choice j , xij is a vector of farm and farmer characteristics augmented with other important economic variables used in the labor-saving technology decision process, β is a vector of parameters, and ε ij is the random disturbance term associated with choice j . The probability that choice j is made is simply stated as Pr(U ij > U ik ) for all j ≠ k as a function of explanatory variables in a discrete choice process. This discrete choice process: (2) 1, if U ij > U ik ; mechanical harvesters are adopted and utilized Yi = 0, otherwise; mechanical harvesters are not adopted is modeled using a binary logistic regression (logit) model. This binary logit model then provides probabilities of MH technology adoption using the discrete choice and the vector of explanatory variables. The binary logit model estimates likelihood of adoption using: n (3) n Prob(= Yi 1= x) L ∑ x′β = i =1 ∑ x′β e i=1 n ∑ x′β 1 + e i=1 where L() represents a logistic distribution (Liao, 1994). Using this specification, we can estimate the expected probabilities of both financial characteristics and personal beliefs have on the mechanical blueberry adoption decision. Explanatory variables in (3) include farmer characteristics, farm characteristics, financial characteristics and county-level wage rate. Farmer characteristics include variables like age, experience, education level, risk preferences. Farm characteristics include labor costs and production level. The latter is used to account for the fact that the first 3 to 5 years of production (because early low yields) do not justify MH utilization. Financial characteristics include the level of financing, on- and off-farm income and transfer of ownership. The average of the harvest-period wage rate for the last two years prior to 2010 (the year for which the data was collected in the survey) was also included. Finally, historical standard deviation of wage data is used as a broad proxy for labor uncertainty. Definitions and summary statistics of variables used in the logistic regression are presented in Table 1. Dummy variables for farmers who produce only one or both rabbiteye and Southern Highbush cultivars are included to control for the differences between the two cultivars, in particular given that rabbiteye cultivars are historically better suited to machine harvesting. Results Preliminary results from the logistic regression show that farmer characteristics such as age and experience are significant. Further both these variables show a nonlinear relationship with the likelihood of adoption. In other words, the likelihood of adoption is increasing at a decreasing rate with both age and experience. Likelihood of adoption increases with increases in production. The increase is higher in the case when the farmer produces only the rabbiteye cultivar as compared to producing only the Southern Highbush cultivar or both cultivars. This finding corroborates extension studies on rabbiteye production and MH technology usage (Takeda et al. 2008). Willingness to accept risk, as a measure of risk preferences, also has a positive relationship with the likelihood of adoption similar to previous findings (Ghadim, Pannell, and Burton, 2005). For farmers who have both cultivars in production an increase in the standard deviation of wages increases the likelihood of adoption. This finding supports our hypothesis that increased variation in wages, indicating increased labor uncertainty, causes an increase in the likelihood of MH technology adoption. However, increases in harvest-period wages decreases the likelihood of MH technology adoption. Increases in labor costs also decrease the likelihood of MH technology adoption. One argument for these findings is that this could be a temporal bias: the farmer may have already decided to maintain hand harvesting in future seasons, regardless of labor cost. Percentage of financed land and establishment costs, gross sales, plans to transfer ownership, and number of crop insurance purchases all had little explanatory influence. Also the likelihood of MH technology adoption increases with lower educational attainment levels. All findings should be noted as preliminary results and not to be used without the authors’ consent. References Borjas, G. J. (2003). The labor demand curve is downward sloping: reexamining the impact of immigration on the labor market. The quarterly journal of economics, 118(4), 1335-1374. Braswell, John, Steven Stringer, Blair Sampson, and David Ingram. 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The Review of Economics and Statistics, 47(4), 343-356. Scherm, H., and G. Krewer. "Blueberry production in Georgia: Historical overview and recent trends." Small Fruits Review 2.4 (2003): 83-91. Shapiro, B. I., Wade Brorsen, B., & Doster, D. H. (1992). Adoption of double-cropping soybeans and wheat. Southern Journal of Agricultural Economics, 24, 33-33. Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625-649. Takeda, Fumiomi, Gerard Krewer, Elvin L. Andrews, Benjamin Mullinix, and Donald L. Peterson. (2008) "Assessment of the V45 blueberry harvester on rabbiteye blueberry and southern highbush blueberry pruned to v-shaped canopy."HortTechnology 18, no. 1: 130138. Williamson, J. G., & Lyrene, P. M. (2004). The Florida blueberry industry: A decade of growth. In Proc. Fla. State Hort. Soc (Vol. 117, pp. 234-235). Zahniser, Steven, Tom Hertz, Peter Dixon, and Maureen Rimmer. (2012) "Immigration Policy and its Possible Effects on US Agriculture and the Market for Hired Farm Labor: A Simulation Analysis." American Journal of Agricultural Economics 94, no. 2: 477-482. U.S. Department of Agriculture, Census of Agriculture. (2009) Table 34: Berries 2007 and 2002. Accessed August 15, 2013 U.S. Department of Labor, Bureau of Labor Statistics. (2013) Agriculture, Forestry, Fishing and Hunting: NAICS 11. Accessed August 15, 2013 U.S. Department of Labor, Bureau of Labor Statistics. (2013) Quarterly Census of Employment and Wages 2001-2010. Accessed August 15, 2013 Table 1. Variable Description and Summary Statistics Variable Description Type machine harvested any blueberries in 2010, Y=1 Sample Mean (s.d.) Min Max 0.36(0.48) 0 1 years growing blueberries continuous 11.59(11.58) 0 75 Total production1 continuous 263.14(694.2) 0 4,814.5 midpoint age from scale, 6=age 65 or older scale 1-6 55.36(8.96) 21 65 willingness to accept risk, 4=extremely willing Likert 0-4 2.47(0.98) 0 4 number of blueberry crop insurance purchases in last 10 years, 5=purchased all 10 years scale 1-5 2.03(1.37) 1 5 percent blueberry land/establishment costs financed percentage 21.18(34.81) 0 100 percent of family income generated off-farm percentage 57.16(40.93) 0 100 plan to transfer ownership of operation to a family member, 1=plan to transfer dummy 0.67(0.47) 0 1 highest level of education, 6=completed graduate degree scale 1-6 3.73(1.37) 1 6 2010 blueberry operation gross sales (before taxes) 2 8=$1M+ gross sales scale 1-8 211.28(330.15) 10 1000 standard deviation of wages continuous 67.41(39.64) 17.23 257.89 aggregate of 2009 and 2008 wages continuous 1,034.87(341.46) 557.2 2365 2009 labor costs3 continuous 24.61(51.81) 0 354.30 farmer has both rabbiteye and SHB dummy 0.244(0.43) 0 1 Total Production, rabbiteye only4 dummy 13.60(50.28) 0 450.04 number of blueberry crop insurance purchases in last 10 years, rabbiteye only dummy 0 5 2009 labor costs, rabbiteye only5 dummy 1.96(7.82) 0 97.68 willingness to accept risk, both rabbiteye and SHB dummy 0.67(1.26) 0 4 Note: 1, 4 measure in 1,000 lb. increments 2 measured in $1,000 increments, 0.41(0.96) 3, 5 measured in $1,000 increments