Julian M. Alston and Olena Sambucci To effectively evaluate the benefits from viticultural innovations requires comparing the outcomes from vineyards making use of the “new” technology and practices compared with the outcomes that would have been obtained in the same vineyards otherwise, using the existing “old” technology and practices. To do this well requires having a detailed understanding of the production practices that will make use of the new technology and the outcomes it will provide across the range of relevant conditions and production systems, compared with the status quo technology and practices.
In many cases, technological innovations can be evaluated using a partial budget focused on differences in costs per acre or per unit output (for instance, as in our VitisGen work on evaluating the benefits from the development of pest or diseases resistant varieties of grapevines, in which we compare vineyards with the new versus old varieties—see, for example, Fuller et al. 2014a, 2014b). To scale this up to the level of the industry as a whole requires some understanding of which types of growers will adopt the technology, and when, and this can be predicted based on the results from the partial budgets that show the situations in which it will be profitable for growers to adopt the new technology.
Some elements of the precision agriculture technologies and practices to be developed in the Efficient Vineyards (EV) project can be considered in this light—for instance, comparing a system using variable rate management with an alternative that does not. In such an application, the analysis can proceed by quantifying the economic differences between two production systems, with and without the new technology—in terms of their effects on input use and costs; yield, product quality and price, and total revenue—and deducing the consequences for variable profits. This analysis might be informed by the results from experimental trials in vineyards or actual use of the technology in commercial production, depending on the stage of the innovation process.
However, in some cases, we are dealing with information technology whose function is to reduce uncertainty about the state of the production process, as observed by the grower, rather than changing practices directly. Users of such technology derive benefits from improved information status, which leads to them making better decisions on average and consequently incurring lower expected losses from suboptimal decisions. In economics terms, rather than evaluating technologies in terms of increases in variable profits (implicitly assuming a state of perfect knowledge), we are now contemplating technologies designed to assist decision-making under uncertainty (explicitly acknowledging imperfect knowledge), a more difficult process to model and evaluate.
A producer who is maximizing profit under certainty will choose an input (in the case of a single variable input as in Figure 1, Panel a) or a mix of inputs that minimizes costs. It is straightforward to model a change in technology for this case of profit maximization under certainty. When modeling decision-making under uncertainty we have to acknowledge the possibility of producers making errors in their observation of the state of the production process or in their application of productive inputs (Figure 1, Panel b). New technology in this case will affect the distribution of possible profit outcomes rather than simply the maximum. Such innovations are harder to evaluate.
The process of developing data resources and analyzing private and public data at the level of the firm and industry is complementary to the development of the conceptual framework, and is proceeding in parallel. Drawing on the Cost and Return Studies produced by the University of California Cooperative Extension (UCCE, 2000–2015), other work by Fuller et al. (2014b), and feedback from growers and researchers, we developed several sample budgets that include information on average production costs that may be affected by the adoption of new technology (Figure 4). We then used available data to determine which variables to focus on when considering the potential benefits from improved harvest timing, for example.
Our analysis of data from USDA Market News reports (USDA 2017) for table grapes (Figure 5) suggested that the early season price premium is relevant for only a very small share of the volume of a selection of varieties grown in the Coachella Valley. Hence, timing of harvest with a view to earning an early premium is not a big decision factor for the majority of table grape growers, for much of their fruit. At the same time, we were able to identify consistent price premium for berry size, something that EV technology can measure and a grower can control. The next step in modeling the change in economic outcomes for growers of table grapes with the adoption of EV technology will be to collect high resolution data on variables that we find to be relevant to modeling the production and harvesting process as it may be influenced by the adoption of EV technology.
Wine grapes present a different set of challenges: one source of potential benefits from new EV technology is from improvements in fruit quality, which is a concept not currently defined in objectively measurable ways. Our current challenge in this context is to determine a way to relate measurements of berry composition to changes in returns to the growers, in terms of price per ton. Our goal is to relate the changes in observable berry attributes to changes in value of the fruit, which will allow us to evaluate the benefits from using the EV technology to manage berry composition. This is still very much work in progress, but we do know that our findings will be region- and variety-specific. Preliminary analysis of market data for wine grapes in California demonstrates wide variation in price per ton both within and among crush districts (Figure 7 shows box plots of price per ton for Cabernet Sauvignon in 2015).
California Department of Food and Agriculture (CDFA 2016) California Crush Report 2015. Available at: https://www.nass.usda.gov/Statistics_by_State/California/Publications/Grape_Crush/Final/2015/201503gcbtb00.pdf.
Fuller, K.B., J.M. Alston, and O. Sambucci. 2014a. “The Value of Powdery Mildew Resistance in Grapes: Evidence from California.” ARE Update 17(5):1–4. University of California Giannini Foundation of Agricultural Economics, https://s.giannini.ucop.edu/uploads/giannini_public/7d/e7/7de78288-459d-497e-b7cd-f9eb447d0340/v17n5_1_ojkhjes.pdf
Fuller, K.B., J.M. Alston, and O. Sambucci. 2014b. “The Value of Powdery Mildew Resistance in Grapes: Evidence from California.” Wine Economics and Policy 3: 90–107.
University of California Cooperative Extension. (UCCE 2000–2015). Cost and Return Studies. Available at: http://coststudies.ucdavis.edu.
United States Department of Agriculture (USDA 2017) Agricultural Marketing Services Market News Custom Report. Data available at: https://www.ams.usda.gov/market-news/custom-reports.