Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Bio -based and A ppl ied Economics BAE Copyright: © 2021 E. Lamonaca, F.G. Santeramo, A. Seccia. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: E. Lamonaca, F.G. Santer- amo, A. Seccia (2021). Climate changes and new productive dynamics in the global wine sector. Bio-based and Applied Economics 10(2): 123-135. doi: 10.36253/bae-9676 Received: September 5, 2020 Accepted: December 16, 2020 Published: October 28, 2021 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. ORCID EL: 0000-0002-9242-9001 FGS: 0000-0002-9450-4618 AS: 0000-0003-4549-6479 Climate changes and new productive dynamics in the global wine sector Emilia Lamonaca*, Fabio Gaetano Santeramo, Antonio Seccia University of Foggia, Italy * Corresponding author. E-mail: emilia.lamonaca@unifg.it Abstract. Climate change has the potential to impact the agricultural sector and the wine sector in particular. The impacts of climate change are likely to differ across pro- ducing regions of wine. Future climate scenarios may push some regions into climatic regimes favourable to grape growing and wine production, with potential changes in areas planted with vines. We examine which is the linkage between climate change and productivity levels in the global wine sector. Within the framework of agricul- tural supply response, we assume that grapevines acreage and yield are a function of climate change. We find that grapevines yield suffers from higher temperatures dur- ing summer, whereas precipitations have a varying impact on grapevines depending on the cycle of grapevines. Differently, acreage share of grapevines tends to be favoured by higher annual temperatures, whereas greater annual precipitations tend to be det- rimental. The impacts vary between Old World Producers and New World Producers, also due to heterogeneity in climate between them. Keyword: climate change, acreage response, yield response, Old World producers, New World producers. JEL code: F18, Q11, Q54. 1. INTRODUCTION In both academic research and policymaking agenda there is growing awareness that climate change and the agri-food sector are closely related, and that those links deserve investigation and understanding to analyse the evolu- tion of global agriculture, and to anticipate future challenges such as climate change adaption and mitigation (Falco et al., 2019; Santeramo et al., 2021). Agriculture, on which human welfare depends, is severely affected by cli- mate change. Some adverse effects, already observed, are likely to intensify in the future, contributing to declines in agricultural production in many regions of the world, fluctuations in world market prices, growing levels of food insecurity (Reilly and Hohmann, 1993; Meressa and Navrud, 2020). Adaptation potential and adaptation capability to climate change may exac- erbate differences between regions. In a globalised world, the macro-level impacts of climate change are driven by comparative advantage between regions (Bozzola et al., 2021). If impacts of climate change on productivity http://creativecommons.org/licenses/by/4.0/legalcode 124 Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Emilia Lamonaca, Fabio Gaetano Santeramo, Antonio Seccia differ between regions, then adjustments through pro- duction patterns may dampen the adverse effects of cli- mate change (Costinot et al., 2016; Gouel and Laborde, 2021). Although the agricultural sector is identified as the most sensitive and vulnerable sector to climate change (e.g., Deschenes and Greenstone, 2007), the effects of climate change on the wine sector and on dif- ferent producing regions (i.e., Old World Producers, New World Producers) is still an open question. How do pro- ductivity levels react to changes in climate? Do climate change impacts on production patterns differ between Old World Producers and New World Producers? As suggested by Mozell and Thach (2014), the nar- row climatic zones for growing grapes may be severely affected both by short-term climate variability and long- term climate change. A vast majority of earlier stud- ies on the impacts of climate change have analysed the effects on domestic markets, leaving underinvestigated the effects on world production (Reilly and Hohmann, 1993). In the wine-related literature, previous stud- ies reveal that the impacts of climate change are likely to differ across producing regions of wine. Jones et al. (2005) suggest that, currently, Old World Producers (i.e., European regions) benefit of better growing season tem- peratures than New World Producers. However, future climate scenarios may push some regions into climatic regimes favourable to grape growing and wine produc- tion (Lamonaca and Santeramo, 2021). All in all, there is the potential for relevant changes in areas planted with vines due to changes in climate (Moriondo et al., 2013; Seccia and Santeramo, 2018). Projected scenarios of future climate change at the global and wine region scale are likely to impact the wine market. In particular, spatial changes in viable grape growing regions, and opening new regions to viti- culture would determine new productive scenarios in the wine sector at the global level. Given this background, our contribution aims at understanding how productive patterns allow differ- ent producing regions (e.g., Old World Producers, New World Producers) to respond to changes in climate. Specifically, we examine the linkage between climate change and productivity levels in the global wine sector. In this regard, Rosenzweig and Parry (1994) argue that doubling of the atmospheric carbon dioxide concentra- tion would lead to only a small decrease in global agri- cultural production. In addition, Reilly and Hohmann (1993) suggest that interregional adjustments in produc- tion buffer the severity of climate change impacts both at global and domestic level. From a methodological perspective, the study of agricultural supply response has traditionally decomposed it in terms of acreage and yield responses (e.g., Haile et al., 2016; Kim and Mos- chini, 2018). Our contribution examines how climate change affects acreage and yield response for grape- vines. To this aim, we assume that land allocations are consistent with the choices of a representative farmer who maximises expected profit. We posit that crop- land can be allocated between grapevines and all other crops. Because these two allocation choices exhaust the set of possible land allocations, total county cropland is assumed to be fixed. Thus, the decision problem can be stated as that of choosing acreage. We assume that the acreage shares are a function of expected per acre rev- enue, given by the product between the output price and expected yield, and of climate change. Investigating both the responsiveness of grapevine acreage and yield to cli- mate change allows us to conclude on the global supply response. While our cross-countries analysis is informa- tive on the production patterns in the wine sector at a global scale, it cannot conclude on the effects of climate change at the micro-level (e.g., grape growers, wine pro- ducers). Indeed, a country-level analysis does not cap- ture differences within countries in terms of both grape- vine yield and climate variability, particularly in geo- graphically heterogeneous countries such as the United States, Canada, Russia, China (Kahn et al., 2019). 2. ESTIMATING THE RELATIONSHIP BETWEEN CLIMATE CHANGE AND GRAPEVINES PRODUCTION 2.1 Yield response equation Following Kim and Moschini (2018), we postulate a simple linear equation for yield response. In detail, the expected grapevines yield of county i at time t(yit) is modelled as: yit = α + αi + βTt + γ’Xit,s + εit (1) where αi are country-specific intercepts; Tt is a linear trend variable and β the related parameter; the vector Xit,s includes climate variables specific for county i, time t, and season s (i.e. 30-years rolling average seasonal temperatures and precipitations, Tempit,s and Precit,s), we also posit a quadratic relationship between climate and yields (i.e. Temp2it,s and Prec2it,s); γ’ is the vector of parameter of interest1; α and εit are a constant and the error term. Following the climate literature (e.g., 1 It is worth noting that the parameter captures the climate sensitivity of grapevine yield without considering the implicit adaptation to climate change, differently from analyses based on the Ricardian model of cli- mate change (e.g., Mendelsohn et al., 1994). 125Climate changes and new productive dynamics in the global wine sector Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Kurukulasuriya et al., 2011; Massetti et al., 2016), we use a four-season model, assuming that seasonal differences in temperatures and precipitations are likely to impact grapevines productivity. However, we exclude climate normals of the winter season which is characterised by the dormancy of grapevines; in fact, the annual growth cycle of grapevines begins with bud break in the spring season and culminate in leaf fall in the autumn season. We explore the relationship between grapevines yield and climate variables to estimate the potential effects of climate change using either ordinary least squares (OLS) or quantile regression (QR). The model in equation (1) is estimated in an OLS fashion on the whole sample and on subsamples of Old World Producers and New World Producers. The properties of QR have motivated its application in the context of agriculture and weather, mostly focusing on the impact of climate change on various crop yield distributions (Conradt et a., 2015). The QR facilitates a thorough analysis of the differential impact of climate change across the yield distribution; a QR approach is useful in such situations and for considering asymmetry and heterogeneity in cli- matic impacts (Barnwal and Kotani, 2013). 2.2 Acreage response equation Total county cropland (A) is assumed to be fixed and land allocations are presumed to be consistent with the choices of a representative farmer who maximises expected profit. We posit that agricultural land can be devoted to two alternative uses, grapevines and all oth- er crops. The decision problem can be stated as that of choosing acreage shares sk ≡ Ak ⁄ A, where Ak is the acre- age allocated to the k-th use (k = 1 for grapevines and k = 2 for all other crops). Because A is fixed, increased land allocation to any one crop is equivalent to an increase in its share sk, maintaining the land constraint s1 + s2 = 12. Empirically, observed acreage share of grapevines in county i at time t(sit) is modelled as: sit = λ + λi + θTt + φsit-1 + ψrit + ω’Zit + νit (2) 2 Due to a land constraint, a representative farmer may decide to allo- cate more (less) acreage to grapevine reducing (increasing) the share of acreage devoted to other crops to maximise expected profits. This may be a sort of implicit adaptation to climate conditions. For instance, due to warmer temperatures, acreages devoted to grapevine in Italy may increase to the detriment of acreage intended to other production (e.g., apple tree, pear tree). As suggested in Ricardian literature in climate change economics (e.g., Timmins, 2006; Kurukulasuriya et al., 2011; Bozzola et al., 2018). where the set of conditioning variables includes coun- try-specific trend effects, λi; a time trend, Tt, capturing exogenous technological progress; expected per acre revenue, rit; past acreage shares, sit-1, climate variables, Zit, which may directly affect planting decisions (i.e. 30-years rolling average annual temperatures and pre- cipitations, Tempit and Precit, and their squares, Tem- p2it and Prec2it). The term λ is a set constant terms; θ, φ, and ψ are parameters to be estimated, ω’ is the vector of climate-specific parameters; νit is the error term. The term sit-1 allows us to account for the behaviour of pro- ducers that adjust their acreage when they realise that the desired acreage differs from the acreage realised in the previous year; it captures the dynamic effects on acreage allocation (Santeramo, 2014). Following Kim and Moschini (2018), we interact own output price and expected yields estimated in equation (1), to obtain the expected per acre revenue (i.e., rit = pit ∙ yit). Since our study is a country-level analysis, consistent with Hen- dricks et al. (2014) we assume that the country-level expected prices are exogenous: this assumption allows us to deal with potential endogeneity of prices. In order to compute the expected per acre revenue variables for the acreage response equations, we rely on the OLS estimate of equation (1). We follow an approach similar to Haile et al. (2016) and Kim and Moschini (2018) and estimate the model in equation (2) using a system generalised method-of- moments (GMM) estimator, based on a one-step esti- mation with robust standard errors. In fact, applying OLS estimation to a dynamic panel data regression model, such as in equation (2), results in a dynam- ic panel bias because of the correlation of the lagged dependent variable with the country-fixed effects (Nickell, 1981). Since current acreage is a function of the fixed effects (λi), lagged acreage is also a function of these country-fixed effects. This violates the strict exo- geneity assumption, thus the OLS estimator is upward biased and inconsistent. A solution to this issue con- sists in transforming the data and removing the fixed effects. However, under the within-group transforma- tion, the lagged dependent variable remains correlat- ed with the error term, and therefore the fixed-effects estimator is downward biased and inconsistent. To overcome these problems, the GMM is a more efficient estimator that allows the estimate of a dynamic panel difference model using lagged endogenous and other exogenous variables as instruments. In particular, the system GMM technique transforms the instruments themselves in order to make them exogenous to the fixed effects (Roodman, 2009). ˆ ˆ ˆ ˆ 126 Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Emilia Lamonaca, Fabio Gaetano Santeramo, Antonio Seccia 3. DATA SOURCES AND SAMPLE DESCRIPTION The empirical analysis relies on a rich dataset of his- torical temperature and precipitation data (from 1961 to 2015) and historical trade flows data (from 1996 to 20153) for 14 countries. The selected countries are Argentina, Australia, Brazil, Canada, China, France, Germany, Italy, New Zealand, Russian Federation, South Africa, Spain, the United Kingdom, the United States. They account for more than two-third of the volume of wine production (70% in 2016, Global Wine Markets, 1860 to 2016 data- base). This group of countries includes both Old Works Producers and New World Producers and countries belonging to Northern or Southern Hemisphere4. Table 1 provides descriptive statistics for key vari- ables, also distinguishing between Old World Producers and New World Producers. Historical country-specific monthly average tempera- ture and precipitation data have been collected from the Climate Change Knowledge Portal World Bank (World Bank, 2018). Annual and seasonal climatologies (i.e., roll- ing 30-years averages5) of temperature (in °C) and pre- cipitations (mm) have been constructed using historical weather data. As for seasonal climatologies, monthly data have been clustered into three-month seasons: December (of the previous year) through February as winter, March 3 The longer time period used for climate data allows to build climatol- ogies (i.e. 30-years averages) of temperature and precipitations: in 1996 (the starting point of the final dataset) climate normal is based on a real 30-years average. 4 The list of countries by group is presented in Appendix A.1. 5 Differently from other studies that aggregated to data by weighting each information at the grid level by the amount of agricultural area the grid contains (e.g., Gammans et al., 2017), we use simple average of cli- mate data aggregated at the country level. through May as spring, June through August as summer, and September through November as autumn. These sea- sonal definitions have been adjusted for the fact that sea- sons in the Southern and Northern Hemispheres occur at exactly the opposite months of the year. The annual 30-years average temperature is 10.37 ºC (table 1). Within this group, annual average temperatures are about 1 ºC higher for Old World Producers than for New World Producers, reflecting the fact that New World Producers are mostly located to lower latitudes (figure 1). The difference in average temperatures between Old World Producers and New World Producers tends to be higher during winter (3.97 °C of Old World Producers and 0.77 °C of New World Producers; table 1). The annual 30-years average precipitation is 68.55 mm and is about 5 mm greater in Old World Producers Figure 1. List of countries. Source: elaboration on Anderson and Nelgen (2015). Notes: Old World Producers in blue, New World Producers in red. Table 1. Descriptive statistics for key variables. Variable Unit All producers Old World Producers New World Producers Acreage ha 303,640 (±347,791) 560,850 (±435,259) 160,745 (±162,051) Share of acreage - 0.01 (±0.02) 0.02 (±0.00) 0.001 (±0.001) Yield t/ha 10.50 (±4.59) 3.96 (±1.22) 12.09 (±1.13) Price USD/t 779.27 (±448.80) 528.60 (±40.70) 708.32 (±396.59) 30-years average temperature (annual) °C 10.37 (±8.51) 10.86 (±1.87) 10.10 (±10.52) 30-years average temperature (spring) °C 9.90 (±9.08) 9.70 (±1.54) 10.01 (±11.28) 30-years average temperature (summer) °C 18.76 (±4.76) 18.26 (±2.54) 19.04 (±5.61) 30-years average temperature (autumn) °C 10.92 (±8.21) 11.57 (±2.03) 10.55 (±10.12) 30-years average precipitation (annual) mm 68.55 (±36.13) 71.89 (±17.46) 66.69 (±43.09) 30-years average precipitation (spring) mm 62.35 (±34.87) 67.18 (±11.14) 59.66 (±42.50) 30-years average precipitation (summer) mm 82.17 (±44.21) 61.95 (±19.52) 93.40 (±49.81) 30-years average precipitation (autumn) mm 74.56 (±44.14) 82.93 (±24.25) 69.91 (±51.47) Note: Average values and standard deviation in parentheses. 127Climate changes and new productive dynamics in the global wine sector Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 than in New World Producers. However, seasonal differ- ences are observed: during summer, the level of precipi- tations is much lower in Old World Producers than in New World Producers (table 1). In our sample, we observe a 6% increase in median values of 30-years average temperature over twenty years (figure 2). As suggested in Jones et al. (2005), Old World Pro- ducers benefit of better growing seasons as compared to New World Producers. It should be kept in mind, how- ever, that the strength of seasonality varies significantly across the globe, with seasons being more homogenous around the Equator. Country-specific annual data on areas planted with vines (in ha) and yields of areas planted with vines (in t/ha), collected from the FAOSTAT database, are described in table 1. The FAOSTAT database also pro- vides country-level annual acres for agricultural land. Total agricultural land includes two components: i.e., cropland (arable land and land under permanent crops) and land under permanent meadows and pastures. In the methodological framework, we assume that agricul- tural land can be devoted to two alternative uses, grape- vines and all other crops. The latter category should cap- ture all acres that could have been not planted to grape- vines. Hence, we obtain the category all other uses as the difference between total agricultural land and acres planted with vines. In our model, we also use country- specific annual price data for grapes (USD/t), collected from the FAOSTAT database. In order to obtain the reduced per acre revenue, we interact own output price and expected yields estimated in equation (1). Within our sample, despite the expansion of areas planted with vines in New World Producers during the last decades, acres intended to grape growing are, on average, more than three times larger in Old World Pro- ducers (561 thousands ha with respect to 161 thousands ha, table 1). However, grapevines yields are much larg- er for New World Producers (12.09 t/ha) than for Old World Producers (3.96 t/ha). Yields are often not normally distributed but are negatively skewed (e.g., Swinton and King, 1991). This is also what we find in the distribution of grapevines yield in our sample (figure 3). A distribution of yield differ- ent from a normal distribution may be associated with the frequent occurrence of outliers; for instance, yield realisations may not follow the pattern described by the majority of yield observations (Conradt et al., 2015). It is worth noting that countries with grapevines yields within 25th percentile are Canada, Spain, France, United Kingdom, New Zealand, Russian Federation, whereas countries with yields of grape within 75th per- centile are Argentina, Australia, Brazil, China, Germany, United States, South Africa. 4. RESULTS AND DISCUSSION 4.1 Yield response The estimation results for the yield response, based on equation (1), are reported in tables 2 (OLS estimates)6 and 3 (QR estimates). The results in table 2 show that the higher the average temperatures in producing countries during summer, the lower the grapevines yield. Greater precipitations are beneficial for yield during the early growing season (i.e., spring), but detrimental during the 6 In a sensitivity analysis, we analyse the effects of annual climatic variables on grapevine yields. The results, reported in table A.2 in the Appendix, highlight differences between Old World Producers and New World Producers. While higher annual average temperatures are detri- mental (up a certain threshold) for Old World Producers, New World Producers benefit of greater annual average temperatures and precipita- tions. 55.0 55.2 55.4 55.6 55.8 56.0 56.2 56.4 56.6 56.8 57.0 10.5 10.6 10.7 10.8 10.9 11.0 11.1 11.2 11.3 11.4 11.5 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 Pr ec ip ita tio n (m m ) T em pe ra tu re ( °C ) Temperature Precipitation Figure 2. Median 30-years temperatures and precipitations in 1997- 2015. Source: elaboration on data from CRU of University of East Anglia. Note: data refer to the sample of 14 major producers of wine. Figure 3. Distribution and descriptive statistics for grapevines yield. Min: 1.22 Max: 19.50 Median: 10.57 Mean: 10.50 Std. Dev.: 4.59 Skewness: -0.15 Kurtosis: 2.25 128 Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Emilia Lamonaca, Fabio Gaetano Santeramo, Antonio Seccia late growing season and the harvest time (i.e. summer and autumn). The relationship between summer climate and yields is nonlinear7. The overall effects are mostly driven by the impacts of climate change on grapevines yields of New World Producers. Differently, grapevines yield of Old World Producers seem not affected by cli- mate change. The results are consistent with evidence from vine-related literature. In fact, Merloni et al. (2018) report that higher temperatures can have a negative impact on grapevines yield and quality. An increase in extreme high temperatures in summer may have adverse consequences on grapevines phenology (Briche et al., 2014). In addition, Ramos et al. (2008) suggest that sea- sonal distribution of precipitation matter, with larger rainfall levels being crucial for grapevines at the begin- ning of the growing season (i.e., spring) whereas more stable precipitations are desirable from flowering to rip- ening (i.e., summer and autumn). The OLS approach is applied when the depend- ent variable is normally distributed, whereas QR is employed when the variable is not normally distributed (see figure 3). The QR (median) is more robust to outli- ers than mean regression (OLS)8. Furthermore, QR pro- vides a clearer understanding of the data by assessing the effects of explanatory variables on the location and the scale parameters of the model (Conradt et a., 2015). The results of the QR reported in table 3 mostly confirm the non-linear relationship between grapevines yields and average temperatures in producing countries during summer. No substantial differences are observed across different quantiles of the distribution of grape- 7 The results are robust also controlling for different combinations of fixed effects: the results are reported in tables A.3 and A.4 in the Appendix. We further detect a non-linear relationship between grape- vine yield and summer precipitation controlling for time fixed effects (common to all countries) and country-specific fixed effects. Different- ly, we cannot conclude on the relationship between grapevine yield and detrended climate variables obtained from the yearly weather deviation from the long-run climate (30-year rolling average), as recently pro- posed by Khan et al. (2019). The result is not surprising: while detrend- ed climate variables capture short-run changes in climate conditions (i.e., weather shocks), 30-year rolling average temperatures and precip- itations inform on long-run changes in climate conditions: It is unlikely that weather shocks on a year-by-year basis affect the responsiveness of the viticultural sector, but long-run changes in climate capture struc- tural changes in the sector and are more likely to influence production decisions of a multi-year crop. A comparison between short- and long- run analyses is reported in table A.5 in the Appendix. 8 We conduct a multidimensional outlier detection analysis based on the ‘bacon’ algorithm, which identifies outliers based on the Mahalano- bis distances (Billor et al., 2000, Weber, 2010). The algorithm allows the identification and removal of observations characterised by implausibly large or low entries of key variables. The results of the model estimated without outliers, reported in tables A.6 and A.7 in the Appendix, con- firm the main results, although the effect of temperatures and precipita- tions on grapevine yields tend to be lower. vines yields. Differently, the results reveal that lower yield realisations (i.e., within 25th percentile) tend to be most affected by greater precipitations during the har- vest time (i.e., autumn). It is worth noting that countries with grapevines yields within 25th percentile are mostly cool climate wine regions such as Canada and Russian Federation. Cool regions tend to have also higher rain- fall levels and yields tend to be lower on average, rising production costs (Anderson, 2017). Table 2. Estimation results for grapevines yields, OLS. Variables Dependent variable: yield All producers Old World Producers New World Producers Temperature (spring) 1.4440 -9.5441 -1.4800 (1.7044) (12.7571) (2.1761) Temperature- squared (spring) -0.3044*** 0.3965 -0.2577** (0.0747) (0.5755) (0.1209) Temperature (summer) -16.3650** -22.5187 -1.8786 (7.1026) (14.6183) (11.2236) Temperature- squared (summer) 0.4258** 0.4752 0.3047 (0.1955) (0.3634) (0.3264) Temperature (autumn) 0.6543 -0.6787 -0.5129 (1.9410) (12.3068) (2.3252) Temperature- squared (autumn) 0.0761 -0.0685 0.1321 (0.0888) (0.4882) (0.1181) Precipitation (spring) 0.5227* 0.4326 0.8057* (0.2795) (0.7043) (0.4339) Precipitation- squared (spring) -0.0041*** -0.0035 -0.0052*** (0.0015) (0.0048) (0.0019) Precipitation (summer) -0.3230* -0.0678 -0.0427 (0.1906) (0.3849) (0.3922) Precipitation- squared (summer) 0.0013 -0.0001 0.0005 (0.0009) (0.0022) (0.0013) Precipitation (autumn) -0.3507** -0.3838 -0.4272 (0.1601) (0.4282) (0.3758) Precipitation- squared (autumn) 0.0019** 0.0019 0.0019 (0.0008) (0.0020) (0.0017) Time trend 0.1392*** 0.3459* 0.0109 (0.0477) (0.1756) (0.1007) Observations 280 100 180 R-squared 0.9314 0.9656 0.8930 Notes: OLS estimate of equation (1) on the whole sample (All pro- ducers) and subsamples of Old World Producers and New World Producers. All specifications include country-specific constants. Robust standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. 129Climate changes and new productive dynamics in the global wine sector Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 4.2 Acreage response Table 4 presents the estimation results under the acreage models. All dynamic models (All Producers, Old World Producers and New World Producers) are based on a one-step GMM estimator. The Arellano- Bond test for autocorrelation is used to test for serial correlation in levels. The test results indicate that the null hypothesis of no second-order autocorrela- tion in residuals cannot be rejected, indicating the consistency of the system GMM estimators. Accord- ing to the Sargan test results, we fail to reject the null hypothesis of instrument exogeneity: the system GMM estimators are robust but weakened by many instruments. We fail to find a significant acres-price relationship, which could imply that many grapevines’ producers do not form their price expectations on the basis of infor- mation on expected per acre revenues. More importantly, the estimation results reveal that higher annual temperatures in producing countries are beneficial for grapevines acreage share. This is true for both Old and New World Producers, despite the effects are much larger in Old World Producers. As suggested in Ruml et al. (2012), among the many climatic factors affecting wine production, temperature appears to be most important. Differently, severe rainfall levels is significantly asso- ciated with less grapevines share. The negative effects of greater annual precipitations is entirely associated with New World Producers, whereas the Old World Produc- ers seem not affected by changes in the rainfall levels. Table 3. Estimation results for grapevines yields, quantile regres- sion. Variables Dependent variable: yield 25th percentile 50th percentile 75th percentile Temperature (spring) 0.8721 0.7711 1.3070 (1.9059) (1.8734) (2.3574) Temperature- squared (spring) -0.1418* -0.2405*** -0.3368*** (0.0756) (0.0812) (0.1073) Temperature (summer) -22.4737*** -27.0681*** -23.1306*** (4.5501) (7.0368) (7.0902) Temperature- squared (summer) 0.5454*** 0.7064*** 0.6102*** (0.1219) (0.1864) (0.1763) Temperature (autumn) 3.0239 1.9043 2.2129 (2.1210) (1.2873) (2.4223) Temperature- squared (autumn) -0.1279 -0.0515 0.0525 (0.0813) (0.0611) (0.0998) Precipitation (spring) 0.2402 0.6707** 0.4740 (0.2974) (0.2899) (0.2913) Precipitation- squared (spring) -0.0024 -0.0048*** -0.0035* (0.0018) (0.0017) (0.0018) Precipitation (summer) -0.2866 -0.0272 -0.1956 (0.2024) (0.1155) (0.1925) Precipitation- squared (summer) 0.0014 -0.0001 0.0011 (0.0012) (0.0006) (0.0011) Precipitation (autumn) -0.3157* -0.1921 -0.1535 (0.1691) (0.1477) (0.1627) Precipitation- squared (autumn) 0.0019** 0.0011* 0.0010 (0.0008) (0.0006) (0.0007) Time trend 0.1523*** 0.1796*** 0.1024* (0.0574) (0.0534) (0.0545) Observations 280 280 280 Notes: QR estimate of equation (1) on the whole sample. All speci- fications include country-specific constants. Robust standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. Table 4. Estimation results for grapevines acreage, Old World Pro- ducers and New World Producers. Variables Dependent variable: acreage share All Producers Old World Producers New World Producers Lagged acreage share 0.995*** 0.795*** 0.953*** (0.001) (0.046) (0.012) Expected per acre revenue -0.00003 -0.163 -0.0003 (0.00003) (0.109) (0.001) Temperature (annual) 0.107*** 38.983* 0.131*** (0.019) (22.496) (0.020) Temperature-squared (annual) -0.006*** -0.134 -0.008*** (0.001) (1.574) (0.001) Precipitation (annual) -0.107*** 18.447 -0.122*** (0.033) (11.384) (0.028) Precipitation-squared (annual) 0.001*** -0.120 0.001*** (0.0002) (0.081) (0.0001) Test for AR(1): p-value 0.096 0.106 0.239 Test for AR(2): p-value 0.238 0.326 0.266 Sargan test: p-value 0.134 0.592 0.926 Number of instruments 149 47 123 Notes: One-step generalised method-of-moments (GMM) esti- mate of equation (2) on the whole sample and on subsamples of Old World Producers and New World Producers. All specifica- tions include a constant and a time trend. Coefficients and stand- ard errors estimated are of the order of 10-6 for ‘expected per acre revenue’ and of 10-4 for climate variable. Observations are 198 for all producers, 47 for Old World Producers and 151 for New World Producers. Robust standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. 130 Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Emilia Lamonaca, Fabio Gaetano Santeramo, Antonio Seccia 5. CONCLUDING REMARKS Climate change has the potential to impact the agri- cultural sector and the wine sector in particular (Mozell and Thach, 2014). Most of the previous studies analys- ing the impact of climate change on agriculture do not consider the effects of climate change on world produc- tion, markets and trade patterns (Reilly and Hohmann, 1993). Our analysis allowed us to understand if climate change is able to affect productivity levels of grapevines. Overall, we found that grapevines yield suffers from higher temperatures during summer, whereas precipita- tions have a varying impact on grapevines depending on the cycle of grapevines. In particular, we observed that greater precipitations are beneficial during the ear- ly growing season (spring), but detrimental during the late growing season and the harvest time (summer and autumn). Differently, acreage share of grapevines tends to be favoured by higher annual temperatures, whereas greater annual precipitations tend to be detrimental. The impacts however vary between Old World Produc- ers and New World Producers, also due to heterogene- ity in climate between them: the effects of temperatures are less pronounced for New World Producers, whereas precipitations have no effects for Old World Producers. As suggested in previous studies (e.g., Jones et al., 2005), Old World Producers benefit of better growing season, but climate change may push New World Producers into more favourable climatic regimes. The opening of new regions, benefiting of better climatic regimes, to viticulture would determine new productive scenarios and, as a result, new trade dynam- ics (Macedo et al., 2019). New productive scenarios are likely to favour the production of varietal wines from autochthonous grapes whose quality is strongly related to microclimatic and pedological conditions (Seccia et al., 2017). In addition, changes in trade regulations, that have largely influenced the agri-food market, are modifying also global trade of wine (Santeramo et al., 2019; Seccia et al., 2019). Such dynamics should not be neglected. Future research should be intended to exam- ine how climate change could affect global trade of wine and to understand how importers and exporters could react to new trade dynamics, due to climate change, in terms of trade regulations. ACKNOWLEDGMENT The research has been supported by a Research Grant funded by the International Organisation of Vine and Wine (OIV). The authors are grateful to Dr. Martina Bozzola for collection and organisation of climate data, to Tatiana Svinartchuk, Tony Battaglene, and to the seminar audi- ences at the 9th AIEAA Conference and the EGU Gen- eral Assembly 2021 for helpful comments. REFERENCES Anderson, K. (2017). How might climate changes and pref- erence changes affect the competitiveness of the world’s wine regions? Wine Economics and Policy 6(1): 23-27. Anderson, K., and Nelgen, S. (2015). Global Wine Mar- kets, 1961 to 2009: A Statistical Compendium. Uni- versity of Adelaide Press. Barnwal, P., and Kotani, K. (2013). Climatic impacts across agricultural crop yield distributions: An appli- cation of quantile regression on rice crops in Andhra Pradesh, India. Ecological Economics 87: 95-109. Billor, N., Hadi, A.S. and Velleman, P.F. (2000). BACON: Blocked adaptive computationally efficient outlier nominators. Computational Statistics & Data Analysis 34: 279-298. Bozzola, M., Lamonaca, E., and Santeramo, F.G., (2021). On the impact of climate change on global agri-food trade. Working Paper. Bozzola, M., Massetti, E., Mendelsohn, R., and Capitanio, F. (2018). A Ricardian analysis of the impact of cli- mate change on Italian agriculture. European Review of Agricultural Economics 45(1): 57-79. Briche, E., Beltrando, G., Somot, S., and Quénol, H. (2014). Critical analysis of simulated daily tempera- ture data from the ARPEGE-climate model: applica- tion to climate change in the Champagne wine-pro- ducing region. Climatic Change 123(2): 241-254. Conradt, S., Finger, R., and Bokusheva, R. (2015). Tai- lored to the extremes: Quantile regression for index‐ based insurance contract design. Agricultural Eco- nomics 46(4): 537-547. Costinot, A., Donaldson, D., and Smith, C. (2016). Evolv- ing comparative advantage and the impact of climate change in agricultural markets: Evidence from 1.7 million fields around the world. Journal of Political Economy 124(1): 205-248. Deschenes, O., and Greenstone, M. (2007). The Economic Impacts of Climate Change: Evidence from Agricul- tural Output and Random Fluctuations in Weather. The American Economic Review 97(1): 354–85 Falco, C., Galeotti, M., and Olper, A. (2019). Climate change and migration: Is agriculture the main chan- nel? Global Environmental Change 59: 101995. Gammans, M., Mérel, P., and Ortiz-Bobea, A. (2017). Negative impacts of climate change on cereal yields: 131Climate changes and new productive dynamics in the global wine sector Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 statistical evidence from France. Environmental Research Letters 12(5): 054007. Gouel, C., and Laborde, D. (2021). The crucial role of domestic and international market-mediated adapta- tion to climate change. Journal of Environmental Eco- nomics and Management 106: 102408. Haile, M.G., Kalkuhl, M., and von Braun, J. (2016). Worldwide acreage and yield response to internation- al price change and volatility: a dynamic panel data analysis for wheat, rice, corn, and soybeans. Ameri- can Journal of Agricultural Economics 98(1), 172-190. Hendricks, N.P., Smith, A., and Sumner, D.A. (2014). Crop supply dynamics and the illusion of partial adjustment. American Journal of Agricultural Eco- nomics 96(5): 1469-1491. Jones, G.V., White, M.A., Cooper, O.R., and Storchmann, K. (2005). Climate change and global wine quality. Climatic Change 73(3): 319-343. Kahn, M.E., Mohaddes, K., Ng, R.N.C., Pesaran, M.H., Raissi, M., and Yang, J-C. (2019). Long-term macro- economic effects of climate change: A cross-country analysis. National Bureau of Economic Research Working Paper. Kim, H., and Moschini, G. (2018). The dynamics of sup- ply: US corn and soybeans in the biofuel era. Land Economics 94(4): 593-613. Kurukulasuriya, P., Kala, N., and Mendelsohn, R. (2011). Adaptation and climate change impacts: a structural Ricardian model of irrigation and farm income in Africa. Climate Change Economics 2(2): 149-174. Lamonaca, E., and Santeramo, F.G. (2021). Climate changes and Dynamics of the Agricultural Produc- tions in the Mediterranean Region. EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21- 1104, https://doi.org/10.5194/egusphere-egu21-1104, 2021. Macedo, A., Rebelo, J., and Gouveia, S. (2019). Export propensity and intensity in the wine industry: a frac- tional econometric approach. Bio-based and Applied Economics 8(3): 261-277. Massetti, E., Mendelsohn, R., and Chonabayashi, S. (2016). How well do degree days over the growing season capture the effect of climate on farmland val- ues? Energy Economics 60: 144-150. Meressa, A.M., and Navrud, S. (2020). Not my cup of cof- fee: Farmers’ preferences for coffee variety traits–Les- sons for crop breeding in the age of climate change. Bio-based and Applied Economics 9(3): 263-282. Merloni, E., Camanzi, L., Mulazzani, L., and Malorgio, G. (2018). Adaptive capacity to climate change in the wine industry: A Bayesian Network approach. Wine Economics and Policy 7(2): 165-177. Mendelsohn, R., Nordhaus, W., and Shaw, D. (1994). The impact of global warming on agriculture: a Ricardian analysis. American Economic Review 84: 753–771. Moriondo, M., Jones, G.V., Bois, B., Dibari, C., Ferrise, R., Trombi, G., and Bindi, M. (2013). Projected shifts of wine regions in response to climate change. Cli- matic Change 119(3-4): 825-839. Mozell, M.R., and Thach, L. (2014). The impact of climate change on the global wine industry: Challenges & solutions. Wine Economics and Policy 3(2): 81-89. Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica 1417-1426. Ramos, M.C., Jones, G.V., and Martínez-Casasnovas, J.A. (2008). Structure and trends in climate parameters affecting winegrape production in northeast Spain. Climate Research 38(1): 1-15. Reilly, J., Hohmann, N. (1993). Climate change and agri- culture: the role of international trade. The American Economic Review 83(2): 306-312. Roodman, D. (2009). How to do xtabond2: An introduc- tion to difference and system GMM in Stata. The Sta- ta Journal 9(1): 86-136. Rosenzweig, C., Parry, M.L. (1994). Potential impact of climate change on world food supply. Nature 367(6459): 133-138. Ruml, M., Vuković, A., Vujadinović, M., Djurdjević, V., Ranković-Vasić, Z., Atanacković, Z., Sivčev, B., Marković, N., Matijašević, S., and Petrović, N. (2012). On the use of regional climate models: implications of climate change for viticulture in Serbia. Agricultur- al and Forest Meteorology 158: 53-62. Santeramo, F.G. (2014). On the estimation of supply and demand elasticities of agricultural commodites. Intl Food Policy Res Inst. Santeramo, F.G., Miljkovic, D., and Lamonaca, E. (2021). Agri-food trade and climate change. Economia agro- alimentare/Food Economy 23(1), [In press]. Santeramo, F.G., Lamonaca, E., Nardone, G., and Seccia, A. (2019). The benefits of country-specific non-tariff measures in world wine trade. Wine Economics and Policy 8(1): 28-37. Seccia, A., and Santeramo, F.G. (2018). Impacts of climate change on the wine sector in Italy and mitigation and adaptation strategies. In R. Compés López, V. Sotés Ruiz: El sector vitivinícola frente al desafío del cambio climático. Estrategias públicas y privadas de mitigación y adaptación en el Mediterráneo. Cajamar Caja Rural, pp. 91-115. ISBN-13: 978-84-95531-92-6. Seccia, A., Carlucci, D., Santeramo, F.G., Sarnari, T., and Nardone, G. (2017). On the effects of search attrib- utes on price variability: An empirical investigation on quality wines. BIO Web of Conferences 9, 03014. 132 Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Emilia Lamonaca, Fabio Gaetano Santeramo, Antonio Seccia Seccia, A., Santeramo, F.G., Lamonaca, E., and Nardone, G. (2019). On the effects of bilateral agreements in world wine trade. BIO Web of Conferences 12, 03009. Swinton, S.M., and King, R.P. (1991). Evaluating robust regression techniques for detrending crop yield data with nonnormal errors. American Journal of Agricul- tural Economics 73: 446-451. Timmins, C. (2006). Endogenous land use and the Ricardian valuation of climate change. Environmental and Resource Economics 33: 119–142. Weber, S. (2010). Bacon: An effective way to detect outli- ers in multivariate data using Stata (and Mata). The Stata Journal 10(3): 331-338. World Bank (2018). Metadata of the Climate Change Knowledge Portal. APPENDIX Table A.1. List and description of countries in the sample. Country ISO 3 Wine producer Hemisphere 30-years annual average temperature (°C) 30-years annual average precipitation (mm) Argentina ARG New World Producer Southern 14.44 49.16 Australia AUS New World Producer Southern 21.76 40.47 Brazil BRA New World Producer Southern 25.14 148.20 Canada CAN New World Producer Northern -6.47 38.77 China CHN New World Producer Northern 6.94 48.29 Germany DEU Old World Producer Northern 9.28 61.12 Spain ESP Old World Producer Northern 13.84 50.92 France FRA Old World Producer Northern 11.41 71.61 United Kingdom GBR Old World Producer Northern 8.94 103.42 Italy ITA Old World Producer Northern 12.51 78.70 New Zealand NZL New World Producer Southern 10.06 145.83 Russia RUS New World Producer Northern -5.43 36.64 United Stated USA New World Producer Northern 7.50 55.57 South Africa ZAF New World Producer Southern 18.13 40.89 Source: Wine producer classification follows Anderson and Nelgen (2015). 133Climate changes and new productive dynamics in the global wine sector Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Table A.2. Estimation results for grapevines yields, OLS. Variables Dependent variable: yield All producers Old World Producers New World Producers Temperature (annual) 1.3078 -22.4180*** 5.2902*** (1.4604) (7.5813) (1.8500) Temperature- squared (annual) -0.0215 0.6741*** 0.0892** (0.0344) (0.1969) (0.0423) Precipitation (annual) 0.1755 0.3731 1.1522** (0.4226) (0.9870) (0.4877) Precipitation- squared (annual) -0.0021 -0.0025 -0.0058** (0.0022) (0.0052) (0.0026) Time trend 0.0400 0.2498 -0.0490 (0.0479) (0.1578) (0.0593) Observations 280 100 180 R-squared 0.9148 0.9626 0.8758 Notes: OLS estimate of equation (1) on the whole sample (All pro- ducers) and subsamples of Old World Producers and New World Producers. All specifications include country-specific constants. Robust standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. Table A.3. Estimation results for grapevines yield: controlling for different combinations of fixed effects. Variables Our results Sensitivity analysis Temperature (spring) 1.4440 2.2203 (1.7044) (1.8717) Temperature-squared (spring) -0.3044*** -0.3176*** (0.0747) (0.0794) Temperature (summer) -16.3650** -16.1260** (7.1026) (7.4473) Temperature-squared (summer) 0.4258** 0.4022** (0.1955) (0.2013) Temperature (autumn) 0.6543 0.0276 (1.9410) (2.3118) Temperature-squared (autumn) 0.0761 0.1007 (0.0888) (0.0948) Precipitation (spring) 0.5227* 0.5692** (0.2795) (0.2844) Precipitation-squared (spring) -0.0041*** -0.0041*** (0.0015) (0.0015) Precipitation (summer) -0.3230* -0.3870* (0.1906) (0.2034) Precipitation-squared (summer) 0.0013 0.0015* (0.0009) (0.0009) Precipitation (autumn) -0.3507** -0.3009* (0.1601) (0.1607) Precipitation-squared (autumn) 0.0019** 0.0017** (0.0008) (0.0008) Country fixed effects Yes Yes Time trend Yes No Time fixed effects No Yes Country-time fixed effects No No R-squared 0.9314 0.9386 Notes: OLS estimate of yield response equation. Observations are 280. Robust standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. 134 Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Emilia Lamonaca, Fabio Gaetano Santeramo, Antonio Seccia Table A.4. Estimation results for grapevines acreage: controlling for different combinations of fixed effects. Variables Our results Sensitivity analysis Lagged acreage share 0.995*** 0.995*** (0.001) (0.002) Expected per acre revenue -0.00003 0.002 (0.00003) (0.003) Temperature (annual) 0.107*** 0.095*** (0.019) (0.021) Temperature-squared (annual) -0.006*** -0.006*** (0.001) (0.001) Precipitation (annual) -0.107*** -0.133*** (0.033) (0.047) Precipitation-squared (annual) 0.001*** 0.001*** Country fixed effects Yes Yes Time trend Yes No Time fixed effects No Yes Notes: One-step generalised method-of-moments (GMM) estimate of acreage response equation. Coefficients and standard errors esti- mated are of the order of 10-6 for ‘expected per acre revenue’ and of 10-4 for climate variable. Observations are 198. Robust standard errors are in parentheses. *** Significant at the 1 percent level. Table A.5. Estimation results for grapevines yield: controlling for detrended climate variables. Variables Our results (Long-run analysis) Sensitivity analysis (Short-run analysis) Temperature (spring) 1.4440 0.1500 (1.7044) (0.1418) Temperature-squared (spring) -0.3044*** 0.0164 (0.0747) (0.0900) Temperature (summer) -16.3650** 0.1692 (7.1026) (0.2820) Temperature-squared (summer) 0.4258** -0.2140 (0.1955) (0.1384) Temperature (autumn) 0.6543 0.2483 (1.9410) (0.1584) Temperature-squared (autumn) 0.0761 -0.1644** (0.0888) (0.0820) Precipitation (spring) 0.5227* -0.0050 (0.2795) (0.0088) Precipitation-squared (spring) -0.0041*** -0.0002 (0.0015) (0.0005) Precipitation (summer) -0.3230* 0.0039 (0.1906) (0.0093) Precipitation-squared (summer) 0.0013 -0.0005 (0.0009) (0.0003) Precipitation (autumn) -0.3507** 0.0071 (0.1601) (0.0056) Precipitation-squared (autumn) 0.0019** 0.0002 (0.0008) (0.0002) R-squared 0.9314 0.9177 Notes: OLS estimate of yield response equation. Observations are 280. Detrended climate variables in the sensitivity analysis are obtained from the yearly weather deviation from the long-run cli- mate (30-year rolling average). All specifications include country- specific constants and the time trend. Robust standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. Table A.6. Multidimensional outlier detection analysis. 5th percentile 10th percentile 15th percentile Total number of observations 280 280 280 BACON outliers 0 0 20 Non-outliers remaining 208 208 260 135Climate changes and new productive dynamics in the global wine sector Bio-based and Applied Economics 10(2): 123-135, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9676 Table A.7. Estimation results for grapevines yields: OLS with and without outliers and QR. Variables OLS QR All observations (A) Observations w/out outliers (B) 25th percentile (C) 50th percentile (D) 75th percentile (E) Temperature (spring) 1.4440 1.6527 0.8721 0.7711 1.3070 (1.7044) (1.7917) (1.9059) (1.8734) (2.3574) Temperature-squared (spring) -0.3044*** -0.3114*** -0.1418* -0.2405*** -0.3368*** (0.0747) (0.0765) (0.0756) (0.0812) (0.1073) Temperature (summer) -16.3650** -14.7502* -22.4737*** -27.0681*** -23.1306*** (7.1026) (7.7445) (4.5501) (7.0368) (7.0902) Temperature-squared (summer) 0.4258** 0.3653* 0.5454*** 0.7064*** 0.6102*** (0.1955) (0.2163) (0.1219) (0.1864) (0.1763) Temperature (autumn) 0.6543 0.2605 3.0239 1.9043 2.2129 (1.9410) (2.1218) (2.1210) (1.2873) (2.4223) Temperature-squared (autumn) 0.0761 0.1037 -0.1279 -0.0515 0.0525 (0.0888) (0.0967) (0.0813) (0.0611) (0.0998) Precipitation (spring) 0.5227* 0.5162* 0.2402 0.6707** 0.4740 (0.2795) (0.2777) (0.2974) (0.2899) (0.2913) Precipitation-squared (spring) -0.0041*** -0.0041*** -0.0024 -0.0048*** -0.0035* (0.0015) (0.0015) (0.0018) (0.0017) (0.0018) Precipitation (summer) -0.3230* -0.3643 -0.2866 -0.0272 -0.1956 (0.1906) (0.2388) (0.2024) (0.1155) (0.1925) Precipitation-squared (summer) 0.0013 0.0013 0.0014 -0.0001 0.0011 (0.0009) (0.0009) (0.0012) (0.0006) (0.0011) Precipitation (autumn) -0.3507** -0.3302** -0.3157* -0.1921 -0.1535 (0.1601) (0.1629) (0.1691) (0.1477) (0.1627) Precipitation-squared (autumn) 0.0019** 0.0019** 0.0019** 0.0011* 0.0010 (0.0008) (0.0008) (0.0008) (0.0006) (0.0007) Observations 280 260 280 280 280 R-squared 0.9314 0.9037 Notes: OLS and QR estimate of yield response equation. Robust standard errors are in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. Volume 10, Issue 2 - 2021 Firenze University Press Mediterranean agriculture facing climate change: Challenges and policies Filippo Arfini The long-term fortunes of territories as a route for agri-food policies: evidence from Geographical Indications Cristina Vaquero-Piñeiro Application of Multi-Criteria Analysis selecting the most effective Climate change adaptation measures and investments in the Italian context Raffaella Zucaro, Veronica Manganiello, Romina Lorenzetti*, Marianna Ferrigno Climate changes and new productive dynamics in the global wine sector Emilia Lamonaca*, Fabio Gaetano Santeramo, Antonio Seccia A systematic review of attributes used in choice experiments for agri-environmental contracts Nidhi Raina*, Matteo Zavalloni, Stefano Targetti, Riccardo D’Alberto, Meri Raggi, Davide Viaggi The effect of farmer attitudes on openness to land transactions: evidence for Ireland Cathal Geoghegan*, Anne Kinsella, Cathal O’Donoghue