Bio-based and Applied Economics BAE Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 Copyright: © 2023 M. Tappi, F. Carucci, A. Gagliardi, G. Gatta, M.M. Giuliani, F.G. Santeramo. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: M. Tappi, F. Carucci, A. Gagliardi, G. Gatta, M.M. Giuliani, F.G. Santeramo (2023). Earliness, phenological phases and yield-temperature relation- ships: evidence from durum wheat in Italy. Bio-based and Applied Econom- ics 12(2): 115-125. doi: 10.36253/bae- 13745 Received: September 09, 2022 Accepted: March 06, 2023 Published: August 05, 2023 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. Editor: Fabio Bartolini, Emilia Lamon- aca. ORCID MT: 0000-0003-0682-5719 FC: 0000-0001-6460-6333 AG: 0000-0001-7923-3859 GG: 0000-0001-8381-7785 MMG: 0000-0001-9014-1955 FGS: 0000-0002-9450-4618 Paper presented at the 11th AIEAA Conference Earliness, phenological phases and yield- temperature relationships: evidence from durum wheat in Italy Marco Tappi1,*, Federica Carucci2, Anna Gagliardi1, Giuseppe Gatta1, Marcella Michela Giuliani1, Fabio Gaetano Santeramo1 1 University of Foggia, Department of Agriculture, Food, Natural resources and Engineer- ing (DAFNE), Foggia, Italy 2 University of Tuscia, Department of Agriculture and Forest scieNcEs (DAFNE), Viterbo, Italy *Corresponding author. E-mail: marco.tappi@unifg.it Abstract. The impacts of extreme weather events on crop production are largely het- erogeneous along the timing dimension of the shocks, and the varieties being affected. We investigate the yield-temperature relationships for three categories of earliness of durum wheat: early-maturing, middle-maturing, and late-maturing. We disentangle the time dimension distinguishing five phenological stages, as identified by the Growing Degree Days approach. Our panel regression models show that the starting, growing, and anthesis stages are sensitive to changes in minimum temperatures, regardless of wheat earliness. Raises in maximum temperatures during the starting stage are asso- ciated with increases in yields until a certain threshold above of which decrease; the opposite is true for increases in maximum temperatures in the maturity stage for late- maturing varieties, and in the end stage for early-maturing varieties. Results imply that farmers and policymakers may adopt ex-ante and ex-post risk management strate- gies, i.e., choice of variety to avoid severe yield losses and incentives to crop insurance uptake, respectively. Keywords: climate change, crop insurance, growing degree days, risk management, weather index. JEL codes: G22, Q18, . INTRODUCTION The climate variability and the increased frequency of extreme weather events threaten the agricultural sector (Auci et al., 2021). The simulations on projected yields under climate change conditions show losses in crop production (Challinor et al., 2014). In turn, these, may impact the market dynamics with price increases and changes in firms’ profitability margins (Stevanovic et al., 2016). The risk management interventions subsidised by the Common Agricultural Policy (CAP) of European Union (EU), e.g., crop insurances, mutual funds, may help farmers to cope with the poten- http://creativecommons.org/licenses/by/4.0/legalcode 116 Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 Marco Tappi et al. tial losses due to climatic changes (Severini et al., 2016; Meuwissen et al., 2018; Shirsath et al., 2019; Giampietri et al., 2020; Cordier and Santeramo, 2020; Rippo and Cerroni, 2022), even better if combined with other ex- ante practices, e.g., agroecological strategies (Altieri et al., 2015). The weather-index insurances (WIIs) emerged as promising tools to indemnify farmers affected by weather damages (Anghileri et al., 2022). The work- ing principle of the WIIs is a compensation based on a proxy (the weather index) correlated with potential yield losses (Abdi et al., 2022). The WIIs may contrib- ute solving the market failures due to moral hazard and adverse selection issues, which are common in tradi- tional indemnity insurance (Santeramo, 2019; Bucheli et al., 2022). The main threat to the well-functioning WIIs relies on the possible low correlation between triggered pay-outs and the occurrence of loss events, a peculiar- ity referred to as ‘basis risk’ (Cesarini et al., 2021). The basis risk may assume multiple forms. The temporal basis risk may result from the discrepancy between the timing of the weather index fails and the evolution of the crop growth stages (Masiza et al., 2022). The phe- nology information collected in publicly available data- sets (e.g., through satellite remote sensors) may help reduce the temporal basis risk (Dalhaus et al., 2018; Afshar et al., 2021). Indeed, the phenological stages show different susceptibilities to the weather conditions, a relevant aspect for the weather index definition. As for durum wheat, the timing of the undesired weather events matter. For instance, low temperatures are det- rimental in all stages of growth, but the most severe negative impacts are observed during the reproductive stage (Barlow et al., 2015). High temperatures severely compromise the physiological processes during the flowering and grain filling stages (Rezaei et al., 2015; Makinen et al., 2018, Gagliardi et al., 2020). As a matter of fact, taking into consideration the phenological stag- es within which the weather event occur is crucial to understand the weather-yields relationships better: this concept directly translates into better modelling of the temporal basis risk. Although remote sensing imagery represents a promising technique for identifying phe- nological stages, many factors, such as the atmospheric conditions (e.g., clouds) or the biotic and abiotic envi- ronmental perturbations, may also be relevant to ana- lyse the physiological process (Zeng et al., 2020), but are complex in nature and computation. On the other hand, a fixed calendar approach may be oversimplis- tic and misleading. A second-best solution is to use the Growing Degree Days (GDD), adopted to schedule management activities. It represents a suitable method to predict specific crop stages based on the amount of daily temperature degree (Miller et al., 2001). Conradt et al., 2015 showed that the GDD approach accurately identifies the phenological phases. However, the tim- ing of the phenological stages is not homogenous across varieties. Apart from the studies just mentioned, the lit- erature on the role of varieties in shaping the relation- ships between yield and weather is quite limited. Thus, departing from a vast literature on the yield-weather nexus (Di Falco et al., 2012; Powell and Reinhard, 2016; Delerce et al., 2016; Chavas et al., 2019), we deepen on the heterogeneities that the yield-temperatures relation- ship may show across different phenological stages and earliness of durum wheat, hereafter defined as early- maturing, middle-maturing, and late-maturing. Build- ing up the works of Tappi et al. (2022), who show the need to collect more refined data to investigate the rela- tionships between yields and weather variables, and of Tappi et al. (2022), who focus on the role of temporal and design approaches in yield-weather assessment, the aim of our paper is to assess whether the relation- ships yield-temperature control for three categories of durum wheat earliness (i.e., early-maturing, middle- maturing, and late-maturing) among five phenological stages identified by the GDD approach, focusing on the most representative Italian provinces in terms of durum wheat production. Apart from the new knowledge, our paper has direct implications for farmers aiming to adopt ex-ante risk management strategies (e.g., choice of variety) and for policymakers planning ex-post risk management strategies (e.g., incentives to crop insur- ance uptake). The Italian participation level in crop insurance schemes is still low, limited to few products, and concentrated in few areas (Santeramo, 2018, 2019; Coletta et al., 2018). Therefore, the focus on the yield- temperature relationship may directly speak with the ongoing debate on how to improve the attractiveness of innovative insurances in a more and more warming cli- mate change scenario. DATA AND METHODOLOGY Durum wheat is the main crop in the Mediterra- nean area for making pasta, couscous, semolina, and other products (Carucci et al., 2020). We collected yields and weather data from 2006 to 2020 of 30 main durum wheat-producing Italian provinces, located in Central and Southern Italy (Figure 1, in the Appendix). Specifi- cally, yearly durum wheat yield data (quintals of produc- tion/cultivated hectares) have been collected from the National Institute of Statistics (ISTAT). In contrast, daily weather data have been collected from JRC - Agri4Cast 117Earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in Italy Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 Meteorological database of European Commission that includes maximum temperatures (°C) and minimum temperatures (°C). Descriptive statistics of the dataset are shown in Table 1. More specifically, maximum temperatures show a mean value of 13.5 °C, a median value of 13.6 °C, in a range between -5.3 °C and 33 °C; minimum tempera- tures show a mean value of 5.9 °C, a median value of 6.1 °C, in a range between -11.6 °C and 19.9 °C, Furthermore, maximum temperatures exceed 30 °C in some Southern provinces, e.g., Agrigento, Caltanis- setta, Catania, Enna, Matera, Palermo, Trapani, while minimum temperatures exceed -2 °C in some North- ern provinces, e.g., Bologna, Ferrara, Perugia, Pisa, Ravenna, Rovigo, Siena (Table 2, in the Appendix). We selected weather variables within the timeframe of the wheat production cycle. Several approaches are avail- able to assess econometrically the weather impacts on society and the economy: cross-sections, linear and non-linear panel, long-differences, and partition- ing variation (Hsiang, 2016; Kolstad and Moore, 2020). Cross-sectional and panel regression analyses are the most used to assess the climate impacts on agriculture (Carter et al., 2018). Generally, panel model approach uses crop yields as the output of production function, while the cross-section uses a proxy for land productiv- ity, e.g., revenue or profit (Blanc and Schlenker, 2020). According to Hsiang (2016), climate may affect social outcomes in two ways: directly, i.e., the effects of weath- er in a certain time, and indirectly (i.e., belief effect), i.e., the consequent effects of weather on decisions and actions also referred to as adaptation. Belief effects and other unobservable variables may cause bias in esti- mates (Hsiang, 2016). In this complex scenario and con- sidering the trade-off among econometrics models, the panel approach presents some advantages for control- ling unobserved omitted variables, removing a possible source of bias (Hsiang, 2016; Kolstad and Moore, 2020). Moreover, nonlinear panel models with fixed effects may capture partially long-run adaptive response to climate change (Carter et al., 2018), also contributing to over- coming the main limitations of panel regression: the short-run response to weather fluctuations (Kolstad and Moore, 2020). Therefore, our yield response equation is based on a non-linear panel regression: yit = f(wit;β) + αi + αt + εit (1) where yit represents the vector of durum wheat yield data for the 30 main Italian provinces (i) in terms of production volumes and time horizon covered (t). The function f(wit;β) is explained in the formula (2) below. The estimated coefficients (in bold) are collected in the matrix of first and second-order coefficients noted as β, whereas αi and αt are the vectors of the location-specif- ic and year-specific fixed effects, controlling for unob- served heterogeneity over space and time. The error term is noted by the εit (Hsiang, 2016; Tack et al., 2015; Kolstad and Moore, 2020). Five phenological stages of durum wheat have been identified through the GDD approach, starting from the sowing date in the middle of November for wheat crop cultivated in the Mediter- ranean area (Miller et al., 2001): (i) starting, from emer- gence to two leaves unfolded; (ii) growing, from the end of two leaves unfolded to the beginning of anthesis (first anthers are visible); (iii) anthesis, from the begin- ning of anthesis to beginning of seed fill; (iv) maturity, from the beginning of seed fill to dough stage; (v) end, from dough stage to full maturity. The GDD approach predicts plants stages from seeding to maturity using the accumulation of heat or temperature units above a threshold or base temperature below which no growth occurs (Miller et al., 2001). The function f(wit;β) is expli- cated as follows: (2)1 1 We focused on how the temperatures may affect the yields, considering the precipitations as control factor mainly because its effect on yields is difficult to catch (being affected by other variables such as soil texture, management practices, irrigation, etc.). A single rain event may impact on a smaller portion of territory than changes in temperatures affect- ing entire areas. Therefore, the evaluation of the effect of precipitation on the yields needs of further investigation. Moreover, we controlled for the market shocks, i.e., on how unfavourable years in terms of durum wheat price. The results are robust. Table 1. Descriptive statistics of daily temperatures and yearly yield variables from 2006 to 2020 among 30 main durum-wheat producing Italian provinces. Variable (unit) Obs. Mean Median St. dev Min Max Maximum temperature (°C) 68,832 13.55749 13.63 4.59804 -5.336364 32.98 Minimum temperature (°C) 68,832 5.899284 6.07 3.919422 -11.65 19.95 Yield (q/ha) 68,299 36.81634 33 12.98301 17 81.42377 118 Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 Marco Tappi et al. where tminit and tmaxit, are the daily minimum and maximum temperatures across space (i) and time (t). The index s (s = {1,2,3,4,5}) indicates the phenological stage of durum wheat. The apex x indicates the linear- ity of the term. Furthermore, based on phenology cal- culation combined with the Universal Growth Stag- ing Scale reported in Miller et al. (2001) for the wheat crop, we identified three categories of earliness, i.e., early-maturing, middle-maturing, and late-maturing, also identifying the dates of occurrence of phenological stages (Table 3). We assume that the sowing date is the same for all varieties (i.e., November 152), although it represents a limit of our paper. However, it is useful to assess yield- temperature relationships among different earliness identified by GDD approach. Instead, the daily ther- mal sum that determines the transition from one phe- nological phase to the next, changes. It is interesting to highlight that the shift between early-maturing and late-maturing varieties is just one week. This aspect may play a decisive role in assessing of the yield-temperature relationship and, hence, both on farmers decisions (e.g., choice of earliness) and policymakers to plan risk man- agement policies. RESULTS AND DISCUSSION Results display a strong relationship between durum wheat yields and temperatures among differ- ent earliness, focusing on the each phenological phase (Table 4, more details in the Table 7, in the Appendix). More specifically, minimum temperatures that occur in the starting phase negatively affect the yields in a non- linear way, until 8-9 °C for all categories of earliness, while maximum temperatures seem to have a positive 2 Generally, the sowing date of wheat is set on the middle of November in the Mediterranean area (Allen et al., 1998) effect, until 14-15 °C, above of which the yield decrease (table 4 and table 5; more details in the table 7, in the Appendix). Yield is negatively impacted by minimum temperatures linearly occurring in growing stage (table 4, more details in the table 7, in the Appendix). According to the scientific literature, 85% of world- wide wheat cultivation is yearly affected by spring frost causing severe yield losses due to damage of micro- organelles of the cells, excessive production of reactive oxygen species (ROS) and lipid peroxidation (Hassan et al., 2021). Moreover, low temperatures in the fall sea- son may cause yield losses until 9 percent (Tack et al., 2015). Makinen et al. (2018) found that damages due to frost negatively affect all phenological stages, even more the reproductive phase (i.e., flowering). Howev- er, focusing on the anthesis stage, our results showed contradictory evidence: minimum temperatures seem to positively affect the yields in a non-linear way, although turning points of temperatures showed that the positive relationship is true until 7-9 °C for all vari- eties, above of which yields decrease (table 4 and table 5; more details in the table 7, in the Appendix). It is still interesting to highlight that the effect of minimum temperatures on yields is not affected by earliness. Although the end stage lasts just a week, minimum temperatures may negatively affect the yields of early- maturing (until 10 °C) and middle-maturing varieties. Maximum temperatures occurring in starting stage positively affect the yields of all varieties in nonlinear way until 14-15 °C above of which decrease (table 5). At the same time, the adverse effects have been high- lighted only in maturity for late-maturing varieties and end stages for early-maturing varieties until a certain threshold, i.e., 17 °C and 13 °C, respectively. We also estimated the impacts of statistically sig- nificant weather coefficients among earliness and phe- nological stages, hence, the confidence level of tem- perature distributions. Results show a high confidence level, highlighting no differences among coefficients Table 3. Dates of occurrence and GDD values of durum wheat among phenological stages. starting growing anthesis maturity end start end start end start end start end start end Early-maturing (GDD) Nov, 15 (0) Dec, 1 (168) Dec, 2 (169) Mar, 29 (806) Mar, 30 (807) Apr, 19 (1067) Apr, 20 (1068) May, 16 (1433) May, 17 (1434) May, 22 (1538) Middle-maturing (GDD) Nov, 15 (0) Dec, 5 (188) Dec, 6 (189) Apr, 1 (853) Apr, 2 (854) Apr, 22 (1120) Apr, 23 (1121) May, 20 (1494) May, 21 (1495) May, 26 (1602) Late-maturing (GDD) Nov, 15 (0) Dec, 8 (207) Dec, 9 (208) Apr, 5 (900) Apr, 6 (901) Apr, 25 (1173) Apr, 26 (1174) May, 23 (1555) May, 24 (1556) May, 30 (1665) Note: Referred to the year 2020. 119Earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in Italy Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 (table 6). Therefore, the temperatures’ effects on yields do not vary between earliness within each phenologi- cal phase. It follows that early-maturing varieties are the most susceptible to changes in temperature, although the general relationship between yield and temperature is the same among earliness. Damages due to low tem- peratures are more likely among earliness than losses due to high temperatures. Sure enough, the vegetative stage lasts about four months, while maturity about a month and ends in just a week. Therefore, it is difficult to escape from low temperatures during starting and growing stages. Although wheat crop needs low temper- ature to complete vernalization processes, frost events occurring toward the end of the vegetative phase may cause severe damage such as the tiller, spike number, leaf area reduction and photosynthetic capacity, leading to a heavy yield losses (Xiao et al., 2018). CONCLUSIONS Given the potential impact of climate change on yields, deepening the yield-weather relationships is helping farmers cope with the weather risks. Therefore, we assess the effects of temperatures on durum wheat yields among early-maturing, middle-maturing, and late-maturing varieties. We distinguished the effects across five phenological stages (i.e., starting, growing, anthesis, maturity, and end) identified through the GDD approach, starting from the middle of November as sow- ing date. The levels and changes in temperatures affect durum wheat yields in several ways. More specifically, upward changes in the minimum temperatures are det- rimental for to yields when they occur in the starting and growing phases, regardless of the earliness. Increas- es in maximum temperatures are indeed positively cor- related (until a threshold of 14-15 °C) with the yields if they occur in the starting stage, whereas a negative effect Table 4. Effect of temperatures on yields among phenological stages and earliness of durum wheat. starting growing anthesis maturity end EM MM LM EM MM LM EM MM LM EM MM LM EM MM LM Minimum temperature Maximum temperature Notes: EM, MM, and LM indicate the early-, middle-, and late-maturing durum wheat earliness, respectively. Red cells indicate a negative impact of temperatures on yields, blue cells a positive impact, white cells for the uncaptured relationships. Table 5. Turning points of temperatures among phenological stages and earliness (°C). starting anthesis maturity end EM MM LM EM MM LM EM MM LM EM MM LM Minimum temperature -8+ -8+ -9+ +8- +9- +7- NS NS NS -10+ NS NS Maximum temperature +15- +14- +14- NS NS NS NS NS -17+ -13+ NS NS Notes: EM, MM, and LM, indicate the early-, middle-, and late-maturing durum wheat earliness, respectively. The values show the threshold temperatures beyond which there is a change of sign in the regression estimates (table 7, in the Appendix). NS: not significant. Table 6. Confidence levels of temperatures distribution. starting growing anthesis maturity end em ml em ml em ml em ml em ml Minimum temperature -0.50580 -0.79056 -0.41006 0.16589 0.17650 -1.11070 - - -0.03887 - Maximum temperature 1.50379 0.83624 - - - - - - - - Notes: em indicates the differences among coefficients of early-maturing and middle-maturing varieties divided by standard errors of base- line (i.e., middle-maturing variety); ml indicates the differences among coefficients of middle-maturing and late-maturing varieties divided by standard errors of baseline (i.e., middle-maturing variety). 120 Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 Marco Tappi et al. is found when the event occurs at the maturity for late- maturing varieties or end stage for early-maturing vari- eties. Generally, the impacts of chronic heat stress, i.e., high temperatures for a longer duration, are lower than the heat shocks, i.e., extreme high temperatures for a short duration (Li et al., 2013). However, early-maturing varieties provides a better adaptation under warming conditions (Mondal et at., 2013), also because they may escape from the damages due to high temperatures by anticipating the crop cycle. Cold stress may cause mor- phological, physiological, biochemical, and molecular modifications in wheat. Phenotypic screening of cold- tolerant genes, pre-sowing seed treatments, and exog- enous application of growth hormones may be a suit- able solution tolerating severe low temperature extremes (Hassan et al., 2021). In conclusion, a better knowledge of the yield-temperature relationships, along with a deeper comprehension of the informative content of the secondary data on weather dynamics, may help both the farmers for the application of agronomic strategies, and policymakers for the planning of interventions to boost uptake in innovative crop insurance, such as the WIIs. Promoting greater comprehensibility of contracts’ condi- tions, increasing transparency of indemnities and losses, and also improving the dissemination of risk manage- ment tools among farmers, may improve the trust, hence the adoption of subsidised insurance schemes (Giampi- etri et al., 2020). The main limitation of our study is the neglet of the effects of temperatures events on grain quality, although this is far beyond the scope of the analysis and will be addressed in future research. 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Temporal and Design Approaches to Catch Further Yield-Weather Rela- tionships: Evidence on Durum Wheat in Italy. Work- ing paper Tappi, M., Nardone, G., & Santeramo, F. (2022). On the relationships among durum wheat yields and weath- er conditions: evidence from Apulia region, South- ern Italy. Bio-Based and Applied Economics Journal. 11(2), 123-130. Xiao, L., Liu, L., Asseng, S., Xia, Y., Tang, L., Liu, B., … & Zhu, Y. (2018). Estimating spring frost and its impact on yield across winter wheat in China. Agricultural and Forest Meteorology, 260, 154-164. Zeng, L., Wardlow, B. D., Xiang, D., Hu, S., & Li, D. (2020). A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment, 237, 111511. APPENDIX Figure 1. Main durum wheat-producing provinces in Italy. Note: the main durum wheat-producing Italian provinces in decreasing order are: Foggia (Puglia region), Campobasso (Molise region), Palermo (Sicilia region), Ancona (Marche region), Potenza (Basili- cata region), Matera (Basilicata region), Enna (Sicilia region), Macerata (Marche region), Avellino (Campania region), Catania (Sicilia region), Ferrara (Emilia-Romagna region), Caltanissetta (Sicilia region), Perugia (Umbria region), Bari (Puglia region), Vit- erbo (Lazio region), Bologna (Emilia-Romagna region), Ravenna (Emilia-Romagna region), Brindisi (Puglia region), Siena (Toscana region), Agrigento (Siclia region), Benevento (Campania region), Grosseto (Toscana region), Pisa (Toscara region), Chieti (Abruzzo region), Trapani (Sicilia region), Teramo (Abruzza), Roma (Lazio), Barletta-Andria-Trani (Puglia region), Rovigo (Veneto), Pesaro- Urbino (Marche region) (ISTAT, 2020). 123Towards a new generation of (agri-) food policies Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 Table 2. Descriptive statistics of daily temperatures, cumulative precipitation, and yearly yield variables for 30 main durum wheat producing provinces, 2020 year. Province Variable Obs. Mean Median St. dev Min Max Agrigento Maximum temperature 198 17.27042 16.475 3.656134 10.52143 31.60714 Minimum temperature 198 10.53427 9.921429 3.269273 4.15 21.2 Yield 198 27 27 0 27 27 Ancona Maximum temperature 198 15.0523 14.80455 4.87341 5.245454 28.81818 Minimum temperature 198 6.42034 5.990909 4.345694 -1.463636 16.89091 Yield 198 45.3306 45.3306 0 45.3306 45.3306 Avellino Maximum temperature 198 15.16203 14.54545 4.195631 4.790909 28.70909 Minimum temperature 198 8.191552 8.095455 3.676873 -.0090909 18.63636 Yield 198 32.81769 35 3.921524 25.80645 35 Barletta-Andria- Trani Maximum temperature 198 16.11383 15.70625 4.502608 6.375 28.525 Minimum temperature 198 7.62822 7.15625 3.886316 -1.1625 17.4875 Yield 198 21.92088 22 .1421842 21.66667 22 Bari Maximum temperature 198 15.81414 15.45833 4.549949 6.333333 29.175 Minimum temperature 198 7.309596 6.741667 3.7425 -1.9 15.99167 Yield 198 20.24346 20 .4374887 20 21.02564 Benevento Maximum temperature 198 15.37965 14.735 4.230869 4.54 28.35 Minimum temperature 198 8.102222 7.995 3.768435 -.1 18.14 Yield 198 32.00147 31.97674 .0444387 31.97674 32.08092 Brindisi Maximum temperature 198 16.79045 16.59 4.067458 8.61 28.66 Minimum temperature 198 8.679343 8.05 3.772157 -.2 18.74 Yield 198 34.8064 34.52381 .5077994 34.52381 35.71429 Bologna Maximum temperature 198 14.48802 13.37143 5.873645 2.014286 28.45714 Minimum temperature 198 5.333694 4.835714 4.361542 -2.635714 15.53571 Yield 198 54.32178 55.5577 2.220902 50.35106 55.5577 Caltanissetta Maximum temperature 198 16.8204 15.89 4.028488 9.41 32.56 Minimum temperature 198 9.514748 8.94 3.520087 2.61 22 Yield 198 28 28 0 28 28 Campobasso Maximum temperature 198 15.20285 14.95455 4.480825 3.372727 26.70909 Minimum temperature 198 8.140358 8.2 3.817845 -1.163636 17.9 Yield 198 35.76263 36 .4265517 35 36 Catania Maximum temperature 198 17.3101 16.73889 4.048381 9.516666 31.79445 Minimum temperature 198 8.244501 7.669444 3.742443 .3722222 19.37222 Yield 198 28.57143 28.57143 0 28.57143 28.57143 Chieti Maximum temperature 198 15.16586 14.795 4.622042 3.25 26.89 Minimum temperature 198 7.781061 7.65 3.902836 -1.2 18.08 Yield 198 32.6417 32.84671 .3684098 31.98302 32.84671 Enna Maximum temperature 198 16.58646 15.75455 4.356249 8.218182 32.06364 Minimum temperature 198 8.238797 7.754546 3.665236 .4818182 20.07273 Yield 198 30 30 0 30 30 Ferrara Maximum temperature 198 15.09104 14 6.145688 1.991667 29.18333 Minimum temperature 198 5.57319 5.341667 4.807333 -2.8 17.08333 Yield 198 60.20202 64 6.824827 48 64 Foggia Maximum temperature 198 15.9456 15.52292 4.463743 5.341667 27.81667 Minimum temperature 198 8.216098 7.877083 3.775962 -.8 18.29583 Yield 198 31.25 31.25 0 31.25 31.25 Grosseto Maximum temperature 198 17.01996 16 4.174036 9.141176 27.51765 Minimum temperature 198 7.026352 6.997059 4.34386 -1.876471 16.59412 Yield 198 38.79645 38.84181 .0815015 38.65074 38.84181 124 Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 Marco Tappi et al. Province Variable Obs. Mean Median St. dev Min Max Macerata Maximum temperature 198 14.77406 14.60909 4.793149 5.263637 28.03636 Minimum temperature 198 6.397888 6.027273 4.163119 -.9909091 16.48182 Yield 198 42.00229 42.00229 0 42.00229 42.00229 Matera Maximum temperature 198 16.14141 15.80333 4.373894 6.466667 30.13333 Minimum temperature 198 7.865387 7.27 3.649867 .06 17.63333 Yield 198 29.68525 29.68525 0 29.68525 29.68525 Palermo Maximum temperature 198 16.95558 16.17143 3.899543 9.852381 33.84762 Minimum temperature 198 10.14218 9.728571 3.256468 3.638095 19.40952 Yield 198 25.99503 25.99503 0 25.99503 25.99503 Perugia Maximum temperature 198 14.29614 13.5125 4.94103 4.515 26.96 Minimum temperature 198 5.502298 5.37 4.366943 -2.92 15.535 Yield 198 45.45914 44.86486 1.067896 44.86486 47.36842 Pesaro-Urbino Maximum temperature 198 14.78035 14.34091 4.952188 4.390909 28.68182 Minimum temperature 198 6.921442 6.786364 4.275691 -.9363636 17.32727 Yield 198 38.00858 38.00858 0 38.00858 38.00858 Pisa Maximum temperature 198 16.72483 15.875 4.305614 7.983333 27 Minimum temperature 198 7.081019 7.179167 4.555321 -2.35 16.78333 Yield 198 37.21282 40.33502 5.610488 27.1819 40.33502 Potenza Maximum temperature 198 14.74603 14.36087 4.311705 4.573913 28.35217 Minimum temperature 198 7.983707 7.556522 3.500149 -.1913043 18.28696 Yield 198 27.29257 27.29257 0 27.29257 27.29257 Ravenna Maximum temperature 198 14.83678 14.03182 5.718577 2.609091 28.87273 Minimum temperature 198 5.954132 5.440909 4.503898 -2.563636 16.74545 Yield 198 66.57576 68 2.55931 62 68 Roma Maximum temperature 198 17.05811 16.0775 3.893504 9.67 27.955 Minimum temperature 198 7.608207 7.3925 4.132565 -.35 19.59 Yield 198 29.23737 29 .4265517 29 30 Rovigo Maximum temperature 198 14.97117 13.71818 5.981333 2.472727 28.05455 Minimum temperature 198 5.668916 5.363636 4.904146 -2.936364 17.71818 Yield 198 56.23271 59.41509 5.718627 46.00845 59.41509 Siena Maximum temperature 198 15.97519 14.77813 4.710545 6.7125 27.15 Minimum temperature 198 5.882323 5.953125 4.678793 -3.45 16.81875 Yield 198 37.53158 38 .8417409 36.02664 38 Teramo Maximum temperature 198 14.60795 14.2125 4.647457 3.6875 26.6625 Minimum temperature 198 7.074495 6.925 3.945144 -1.1 17.55 Yield 198 39.80582 39.80582 0 39.80582 39.80582 Trapani Maximum temperature 198 17.92341 17.12143 3.75761 10.22857 33.5 Minimum temperature 198 10.757 10.63571 3.492239 2.428571 21.32857 Yield 198 22.90524 23.80952 1.624959 20 23.80952 Viterbo Maximum temperature 198 16.60761 15.75 4.229603 8.413333 27.76667 Minimum temperature 198 7.143199 6.993333 4.106516 -.5666667 17.82 Yield 198 38.73369 38.02031 1.281932 38.02031 41.02564 125Towards a new generation of (agri-) food policies Bio-based and Applied Economics 12(2): 115-125, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13745 Ta bl e 7. E ffe ct s o f e ar lin es s o n th e re la tio ns hi p be tw ee n du ru m w he at y ie ld a nd w ea th er c on di tio ns . st ar tin g gr ow in g an th es is m at ur ity en d EM M M LM EM M M LM EM M M LM EM M M LM EM M M LM M in im um te m pe ra tu re -0 .2 15 74 ** *- 0. 18 91 5* ** -0 .1 47 59 ** *- 0. 06 01 5* ** -0 .0 52 24 ** *- 0. 05 54 4* ** 0. 15 03 8* ** 0. 13 94 9* * 0. 20 80 2* ** 0. 07 91 8 -0 .0 25 72 0. 00 23 3 -0 .3 42 43 * -0 .3 34 64 * 0. 04 42 9 (0 .0 58 02 ) (0 .0 52 57 ) (0 .0 47 52 ) (0 .0 19 44 ) (0 .0 19 29 ) (0 .0 19 27 ) (0 .0 58 08 ) (0 .0 61 70 ) (0 .0 66 69 ) (0 .0 74 92 ) (0 .0 79 99 ) (0 .0 82 07 ) (0 .1 96 84 ) (0 .2 00 40 ) (0 .2 08 92 ) M in im um te m pe ra tu re (s q) 0. 01 43 1* ** 0. 01 29 8* ** 0. 00 91 7* ** -0 .0 03 22 * -0 .0 04 13 ** -0 .0 02 98 * -0 .0 09 87 ** -0 .0 08 65 * -0 .0 16 92 ** * -0 .0 08 80 * -0 .0 01 62 -0 .0 01 58 0. 01 72 0* 0. 01 59 5 -0 .0 02 85 (0 .0 03 94 ) (0 .0 03 64 ) (0 .0 03 35 ) (0 .0 01 73 ) (0 .0 01 71 ) (0 .0 01 70 ) (0 .0 04 46 ) (0 .0 04 55 ) (0 .0 04 72 ) (0 .0 04 56 ) (0 .0 04 69 ) (0 .0 04 65 ) (0 .0 10 28 ) (0 .0 10 08 ) (0 .0 10 05 ) M ax im um te m pe ra tu re 0. 48 70 6* ** 0. 33 81 1* ** 0. 25 52 8* ** 0. 01 71 2 0. 00 38 7 0. 00 92 1 -0 .0 29 72 0. 01 81 6 0. 13 01 3 0. 00 07 2 -0 .1 54 47 -0 .2 68 02 ** * -0 .3 78 84 * -0 .2 15 56 -0 .0 00 99 (0 .1 14 19 ) (0 .0 99 05 ) (0 .0 87 80 ) (0 .0 35 72 ) (0 .0 34 72 ) (0 .0 33 81 ) (0 .0 78 84 ) (0 .0 83 82 ) (0 .0 89 23 ) (0 .0 93 70 ) (0 .0 97 37 ) (0 .0 99 74 ) (0 .2 28 96 ) (0 .2 17 50 ) (0 .2 27 02 ) M ax im um te m pe ra tu re (s q) -0 .0 17 16 ** *- 0. 01 23 7* ** -0 .0 09 38 ** * 0. 00 18 0 0. 00 26 9* 0. 00 22 2 0. 00 27 5 0. 00 02 8 -0 .0 03 33 -0 .0 00 35 0. 00 45 6* 0. 00 83 6* ** 0. 01 48 3* * 0. 00 98 3* 0. 00 42 3 (0 .0 04 22 ) (0 .0 03 75 ) (0 .0 03 40 ) (0 .0 01 58 ) (0 .0 01 50 ) (0 .0 01 43 ) (0 .0 02 65 ) (0 .0 02 74 ) (0 .0 02 85 ) (0 .0 02 71 ) (0 .0 02 74 ) (0 .0 02 73 ) (0 .0 05 92 ) (0 .0 05 49 ) (0 .0 05 55 ) Pr ov F E Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ti m e tr en d Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s O bs . 6, 49 6 7, 47 2 8, 44 7 34 ,1 05 35 ,2 15 36 ,2 17 10 ,6 67 10 ,5 23 10 ,4 01 12 ,2 35 12 ,0 73 11 ,9 53 3, 00 6 3, 01 6 2, 95 8 N o. o f p ro v 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 N ot es : E M , M M , a nd L M , i nd ic at e th e ea rly -, m id dl e- , a nd la te -m at ur in g du ru m w he at e ar lin es s, re sp ec tiv el y. R es ul ts s ho w th e es tim at es o f t he r eg re ss io ns m od el ( 1) fo r ea ch y ea r. St an da rd e rr or s a re sh ow n in p ar en th es is. P he no lo gi ca l s ta ge s h av e be en id en tifi ed th ro ug h th e G D D a pp ro ac h, st ar tin g fr om N ov em be r 1 5 as so w in g da te . CAP, Farm to Fork and Green Deal: policy coherence, governance, and future challenges Annalisa Zezza Key policy objectives for European agricultural policies: Some reflections on policy coherence and governance issues Silvia Coderoni Towards a new generation of (agri-) food policies Gianluca Brunori Earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in Italy Marco Tappi1,*, Federica Carucci2, Anna Gagliardi1, Giuseppe Gatta1, Marcella Michela Giuliani1, Fabio Gaetano Santeramo1 Does the presence of inner areas matter for the registration of new Geographical Indications? Evidence from Italy Francesco Pagliacci, Francesco Fasano* Learning, knowledge, and the role of government: a qualitative system dynamics analysis of Andalusia’s circular bioeconomy Antonio R. Hurtado1,2,*, Julio Berbel2