Muscadine grape is a species native to the southeastern United States and is well adapted to the warm and humid conditions in this region. Unlike widely consumed “bunch” grapes (primarily Vitis vinifera), muscadine grapes form smaller clusters, unbranched tendrils, and berries with thick skins and a unique fruity aroma, that often abscise from the cluster when ripe (Conner, 2013).
Over the recent decades, advances in plant breeding has ushered in over 100 improved muscadine grape cultivars which include disease-resistant, high-yielding, seedless, self-fertile, and high-juice-quality cultivars that can be used for a range of end products. More recently, some researchers have started deploying “precision breeding” techniques in response to changing consumer preferences for bioengineered food. According to Gray et al. (2014, 2015), recision breeding is a newly enabled approach to plant genetic improvement that transfers only specific desirable traits among sexually compatible relatives via the mitotic cell division pathway to avoid the significant genetic disruption imposed on conventional breeding by meiosis (sexual reproduction). This technology also allows for the development of new cultivars with different attributes (e.g., skin friability, skin thickness, and flesh firmness) with potential to serve the growing and increasingly diverse end-use market segments for muscadine grapes. In addition, the availability of a fairly large cultivar pool makes it possible to find the most suitable location-specific cultivars that will allow growers to maximize production and profits.
Muscadine grapes occupy a niche market where they are marketed as fresh fruit and also processed for jams, juice, and wine. Increasing numbers of studies show that muscadine grapes contain high levels of resveratrol (Ector et al., 1996; Signorelli and Ghidoni, 2005) and other antioxidants that help prevent cardiovascular diseases, fight cancer-causing agents, and enhance the production of estrogen, a female sex hormone (Gu et al., 2006; Olas and Wachowicz, 2005; Signorelli and Ghidoni, 2005). This has led to a significant increase in the demand for the grapes by commercial enterprises for production of dietary supplements, creating new opportunities for growers.
Even though the current and future market outlook has been promising with tremendous potential for growers to reap both economies of scale and scope, investment in muscadine grape production and marketing is yet to catch up. On average, from 2010 to 2014 used production of grapes (in U.S. dollars) in Georgia (the leading muscadine grape producing state) increased by 65% while production (in tons) during the same period only increased by 38% [U.S. Department of Agriculture (USDA), 2015]. In addition, total acreage planted in Georgia increased by only 6.6% from 1500 acres in 2010 to 1600 acres in 2014 (USDA, 2015). This can partly be attributed to the relatively limited awareness and consumption of muscadine grapes nationally and internationally, high cost of entering the current niche market, competition from other crops, limited access to loans, and the absence of information to help farmers make better investment decisions in the face of production and marketing risk.
Farm enterprise budgets remain the primary approach used by extension professionals and growers to gauge the profitability of a farm venture as well as to secure farm loans. Where they exist, such budgets rarely reflect the diversity in technology and risks faced by growers. Most if not all of the existing budgets are based on traditional (nonstochastic) costs, yield, and price estimates, with little or no consideration for uncertainty stemming from production and marketing risk inherent in agriculture. Traditional sensitivity analysis or risk-rated approaches which involve calculating revenues under different scenarios (Byrd et al., 2006; Fonsah and Hudgins, 2007; Fonsah et al., 2007, 2008, 2012) are often based on selected values and subjective expectations of future prices, and fail to consider the dependency between yields and prices in a systematic approach. For niche markets such as muscadine grapes where market data are generally absent, and individual-level data tightly safe-guarded, the data on which budgets and sensitivity analysis are conducted are likely to be even less reliable or representative. The results derived from this approach, especially with the adoption of new technology that often comes with high variability and risk, are more likely to give users a false sense of confidence about expected returns on the investment, resulting in higher rates of insolvency and disappointment when such profits are not realized in the real world.
The development and application of stochastic budget analysis largely has been ignored by applied economists. To the best of our knowledge, few applications of stochastic budget exist in the literature (Falk, 1994; Jason et al., 2007; Peacock et al., 1995; Rayburn, 2009; Werth et al., 1991) despite tremendous advances in computation over recent decades, and none of the studies account for the dependency between output and prices. In addition, all of the existing applications are in animal production with none in the fruit and vegetable industry. The study most related to ours is by Rayburn (2009). The author illustrated the use of statistical functions in Excel (Microsoft, Redmond, WA) to produce a stochastic no-till grass-legume hay budget from a deterministic template. However, this study 1) did not account for structural dependency between yields and prices, 2) employed a normal distribution that has been found not to fit yield distributions, and 3) made no attempt to evaluate and compare investment decisions under the traditional and stochastic approach. A relatively small number of traditional muscadine budgets currently exist in the literature. For instance, Noguera et al. (2005) developed budgets for producing muscadine grapes for wine and juice in Arkansas using a single-wire trellis system; Carpio et al. (2008) extended it to estimate and compare the cost of production and profitability of muscadine grapes in North Carolina under the single-wire and Geneva double-curtain trellis systems with and without irrigation. In both studies, only the traditional budgeting approach and sensitivity analysis was employed.
In this study, we develop and investigate a simple framework for stochastic farm enterprise budgets. Specifically, we estimate the costs, revenues, and profitability measures of producing muscadine grapes in Georgia using both the traditional nonstochastic approach in the first stage (Georgia is currently the largest producer of muscadine grapes in the United States with over 1600 planted acres). Next, we develop a probabilistic framework that accounts for yield and price dependency through price elasticity, and extend the traditional framework to a stochastic approach using Monte Carlo simulations. The later allows us to derive the likelihood of obtaining specific investment outcomes under different degrees of elasticity. Finally, we compare results derived from the traditional (nonstochastic) and stochastic approach.
American Agricultural Economics Association 2000 Commodity cost and returns handbook. A report of the AAEA task force on commodity costs and returns (February). Amer. Agr. Econ. Assn., Ames, IA
Byrd, M.M., Escalante, C.L., Fonsah, E.G. & Wetzstein, M.E. 2006 Financial efficiency of methyl bromide alternatives for Georgia bell pepper industries J. Amer. Soc. Farm Managers Rural Appraisers 69 31 39
Carpio, C.E., Safley, C.D. & Poling, E.B. 2008 Estimated costs and investment analysis of producing and harvesting muscadine grapes in the southeastern United States HortTechnology 18 308 317
Ector, B.J., Magee, J.B., Hegwood, C.P. & Coign, M.J. 1996 Resveratrol concentration in muscadine berries, juice, pomace, purees, seeds and wines Amer. J. Enol. Viticult. 47 57 62
Fonsah, E.G., Ferrer, C.M., Escalante, C. & Culpepper, S. 2012 The use of budget analysis in assisting vegetable growers in the adoption of methyl bromide alternatives for weeds, diseases, and nematodes control for bell pepper in Georgia and the southeast. Univ. Georgia Coop. Ext. Serv. Bul. 1411:1–12
Fonsah, E.G. & Hudgins, J. 2007 Financial and economic analysis of producing commercial tomatoes in the Southeast J. Amer. Soc. Farm Managers Rural Appraisers 70 141 148
Fonsah, E.G., Krewer, G., Harrison, K. & Bruorton, M. 2007 Risk rated economic returns analysis for southern highbush blueberries in soil in Georgia HortTechnology 17 571 579
Fonsah, E.G., Krewer, G., Harrison, K. & Stanaland, D. 2008 Economic returns using risk rated budget analysis for rabbiteye blueberries in Georgia HortTechnology 18 506 515
Gray, D.J., Li, Z.T., Grant, T.N.L., Dean, D.A., Trigiano, R.N. & Dhekney, S.A. 2015 The application of precision breeding (PB) for crop improvement is fully consistent with the plant life cycle: The utility of PB for grapevine Acta Hort. (In press)
Gu, J., Wang, C.Q., Fan, H.H., Ding, H.Y., Xie, X.L. & Xu, Y.M. 2006 Effects of resveratrol on endothelial progenitor cells and their contributions to reendothelialization in intima-injured rats J. Cardiovasc. Pharmacol. 47 711 721
Iowa State University 2005 Estimating farm machinery cost. Machinery management. Iowa State Univ. Ext. Outreach, Agr. Decision Maker PM 710 (A3-29)
Jason, R.E., Sperow, M., D’Souza, G.E. & Rayburn, E.B. 2007 Stochastic simulation of pasture-raised beef production systems and implications for the Appalachian cow-calf sector J. Sustain. Agr. 30 4 27 51
Mississippi State University 2010 Muscadine—2010 Fruit and nut planning budgets. Mississippi State Univ., Dept. Agr. Econ., Budget Rpt. 2010-04
Noguera, E., Morris, J., Striegler, K. & Thomsen, M. 2005 Production budgets for Arkansas wine and juice grapes. Arkansas Agr. Expt. Sta. Res. Rpt. 976
Peacock, K., Nayga, R., Brumfield, R., Bacon, J. & Thatch, D. 1995 The economic feasibility of a New Jersey fresh tomato packing facility: A stochastic simulation approach J. Food Distrib. Res. 26 2 9
Ramirez, O.A. 1997 Estimation and use of a multivariate parametric model for simulating heteroskedastic, correlated, non-normal random variables: The case of corn belt corn, soybean, and wheat yields Amer. J. Agr. Econ. 79 191 205
Rayburn, E.B. 2009 Estimating economic risk using Monte Carlo enterprise budgets. Forage Grazinglands Jan. 2009. doi: 10.1094/FG-2009-0415-01-MG
Signorelli, P. & Ghidoni, R. 2005 Resveratrol as an anticancer nutrient: Molecular basis, open questions and promises J. Nutr. Biochem. 16 449 466
University of Illinois at Urbana-Champaign. 2008 Machinery cost estimates: Farm business management. Dept. Agr. Consumer Econ., Univ. Illinois Urbana–Champaign
U.S. Department of Agriculture (USDA) 2015 Quick Stats. 3 Nov. 2015 <http://www.nass.usda.gov/Quick_Stats/>
Werth, L., Kinder, J., Nielsen, M. & Azzam, S. 1991 Use of a simulation model to evaluate the influence of reproductive performance and management decisions on net income in beef production J. Anim. Sci. 69 4710 4721