Mendoza is the main wine-producing province of Argentina, and the government is currently implementing a range of policies that seek to improve grape grower profitability, including a vineyard replanting program. This study uses a dataset of all grape sales recorded in Mendoza from 2007 to 2018, totaling 90,910 observations, to investigate the determinants of grape prices. Key findings include: smaller volume transactions receive lower-average prices per kilogram sold; the discount for cash payments is higher in less-profitable regions; and the effect of wine stock levels on prices is substantial for all varieties. Long-run predicted prices are also estimated for each variety, and region; and these results suggest that policymakers should review some of the varieties currently used in the vineyard replanting program. (JEL Classifications: Q12, Q13, Q18)
The European grapevine moth is one of the most pertinent viticulture pests. In recent years, the moth extended to New World countries, some of which started eradication programs. We used a dataset for Mendoza and a county-fixed effects regression model to estimate the impact of the moth on grape production across the province's counties. Our results suggest that the moth led to a decrease of up to 8% of Mendoza's grape production; however, this may have been worse without strong eradication efforts. We conclude that moth eradication programs may be economically justified in Argentina, and perhaps in other countries. (JEL Classifications: Q10, Q18, C23)
Precipitation patterns are projected to change in different directions across wine regions in Australia, but temperatures are projected to increase in all wine regions, making them less prone to frosts but more prone to heatwaves and more arid. This research aims to estimate how climate change could affect grape yields in Australia. This is, to our knowledge, the first study using a panel data framework to estimate the potential impact of climate change on grape yields. This framework involves a two-step approach in which the first step consists of estimating the impact of weather on grape yields using a fixed effects panel data model, and the second step involves estimating the potential impact of climate change projections using the estimates from the first step. We also estimate a novel hybrid model that interacts weather with climate, potentially accounting for long-run adaptation. The results suggest that climate change by 2050 may lead to higher yields in most regions but lower yields in some of the country’s largest regions. Put differently, an increase in yields may be expected in the coolest regions, while a decrease may be expected in the hottest regions. Consequently, the average yield in Australia may change very little.
Using a dataset with 16 climate variables for locations representing 813 wine regions that cover 99 % of the world’s winegrape area, we employ principal component analysis (PCA) for data reduction and cluster analysis for grouping similar regions. The PCA resulted in three components explaining 89 % of the variation in the data, with loadings that differentiate between locations that are warm/dry from cool/wet, low from high diurnal temperature ranges, low from high nighttime temperatures during ripening, and low from high vapour pressure deficits. The cluster analysis, based on these three principal components, resulted in three clusters defining wine regions globally, with the results showing that premium wine regions can be found across each of the climate types. This is, to our knowledge, the first such classification of virtually all of the world’s wine regions. However, with both climate change and an increasing preference for premium relative to non-premium wines, many of the world’s winegrowers may need to change their mixes of varieties, or source more of their grapes from more appropriate climates.
Background and Aims Cross‐sectional models are useful for estimating the impact that climate and climate change have on grape prices due to changes in grape composition. The aim of this study is to estimate the impact of growing season temperature (GST) on grape prices in Australia using cross‐sectional data. Methods and Results We use data on average price by cultivar and region for a 10 year period. We estimate a model using (area‐) weighted least squares and variables from a principal component analysis to control for 103 characteristics that relate to the production system used in each of 26 regions. Results suggest that a GST increase of 1°C leads to a decrease of 9% in the average price of grapes. A LASSO model that we use as a robustness check suggests similar results: a GST increase of 1°C leads to a decrease of 7.3% in the average price of grapes. Conclusions Failing to control for characteristics that relate to the production system overestimates the impact of GST on grape prices, suggesting that changes to variables in a production system may mitigate deleterious changes to grape composition due to climate change. Significance of the Study This study contributes to the understanding of the issue of omitted variable bias in cross‐sectional models, and how to deal with this issue when analysing the impact of climate and climate change in grape and wine research.
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