Bio -based and A ppl ied Economics BAE Bio-based and Applied Economics 11(2): 123-130, 2022 | e-ISSN 2280-6e172 | DOI: 10.36253/bae-12160 Copyright: © 2022 M. Tappi, G. Nardone, 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, G. Nardone, F.G. Santeramo (2022). On the relationships among durum wheat yields and weath- er conditions: evidence from Apulia region, Southern Italy. Bio-based and Applied Economics 11(2): 123-130. doi: 10.36253/bae-12160 Received: October 4, 2021 Accepted: April 27, 2022 Published: August 30, 2022 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: Simone Cerroni. ORCID MT: 0000-0003-0682-5719 GN: 0000-0003-3816-0993 FGS: 0000-0002-9450-4618 Paper presented at the 10th AIEAA Conference On the relationships among durum wheat yields and weather conditions: evidence from Apulia region, Southern Italy Marco Tappi*, Gianluca Nardone, Fabio Gaetano Santeramo University of Foggia (Italy) * Corresponding author. E-mail: marco.tappi@unifg.it Abstract. The weather index-based insurances may help farmers to cope with climate risks overcoming the most common issues of traditional insurances. However, the weather index-based insurances present the limit of the basis risk: a significant yield loss may occur although the weather index does not trigger the indemnification, or a compensation may be granted even if there has not been a yield loss. Our investi- gation, conducted on Apulia region (Southern Italy), aimed at deepening the knowl- edge on the linkages between durum wheat yields and weather events, i.e., the work- ing principles of weather index-based insurances, occurring in susceptible phenologi- cal phases. We found several connections among weather and yields and highlight the need to collect more refined data to catch further relationships. We conclude opening a reflection on how the stakeholders may make use of publicly available data to design effective weather crop insurances. Keywords: climate change, farming system, phenological phase, risk, weather insur- ance. JEL codes: G22, Q14, Q18, Q54. INTRODUCTION Farming activities are exposed and vulnerable to several risks, among which the weather risks are increasingly frequent and impactful due to cli- mate change (Conradt et al., 2015). Among the several strategies available to reduce the weather impacts on farming systems, e.g., pest control, financial saving, agricultural and structural diversification (Vroege and Finger, 2020), the crop insurance programs can play an important role (Di Falco et al., 2014). In recent years, the attention for the weather index-based insurances (WIBIs) has been growing mainly because these tools may help to overcome some of the challenges associated with traditional indemnity-based insur- ances, e.g., asymmetric information, high transaction costs, moral hazard, and adverse selection (Norton et al., 2013; Dalhaus and Finger, 2016; Belissa et al., 2019; Ceballos et al., 2019). Differently from the traditional insuranc- es, which provide pay-outs depending on actual yield losses, WIBIs indem- http://creativecommons.org/licenses/by/4.0/legalcode 124 Bio-based and Applied Economics 11(2): 123-130, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12160 Marco Tappi, Gianluca Nardone, Fabio Gaetano Santeramo nify the farmers when an index, computed on rainfall or temperature and highly correlated with farms per- formance (e.g., yields), is triggered (Conradt et al., 2015; Dalhaus and Finger, 2016). Therefore, farmers will be indemnified when the index exceeds a pre-determined threshold (Belissa et al., 2019). Moreover, WIBIs can be manipulated neither by the insurers or the insured because they are collected from historical and current dataset provided by recognized bodies (Belissa et al., 2020; Vroege et al., 2021). However, WIBIs present a lim- it, namely basis risk: a significant yield loss may occur even if the weather index does not trigger the payment (Conradt et al., 2015; Dalhaus et al., 2018) or a compen- sation may be granted even if there has not been a yield loss (Heimfarth and Musshoff, 2011). The contribution of our study is at least twofold: first, we provide empirical evidence on how yields and weather conditions are cor- related, more specifically, we deepen the knowledge on the linkages between durum wheat yields and weather events occurring in susceptible phenological stages; sec- ond, we start a reflection on how stakeholders may make use of publicly available data to design an effective crop insurance scheme. We focused on the Apulia region (Southern Italy) which is the main national producer of durum wheat: almost a thousand of tons of produc- tion, i.e., accounting for 25% of the Italian durum wheat production, and about 344 thousand cultivated hectares, i.e., accounting for 28% of the Italian area utilized to grow durum wheat (ISMEA, 2020). THE ITALIAN CROP INSURANCE SYSTEM The Italy boasts a long tradition of public subsidies for agricultural risk management. The “Fondo di Solida- rietà Nazionale” (FSN) was instituted in 1974 to finance both insurance policies and ex-post payments (Enjolras et al., 2012). Moreover, the EU Common Agricultural Policy allocated funds for agricultural insurances (art. 37 of EU Reg. 1305/2013) to cope economic losses due to adverse weather conditions, plant diseases, epizo- oties, and parasitic infestations (Santeramo et al., 2016; Rogna et al., 2021). Despite the public interventions, the participation level to insurance programs remains low (i.e., around 15 percent) mainly due to high costs of bureaucracy (i.e., complexity of procedures), delays in payments, lack of experience with crop insurance con- tracts or lack of high-quality information on existing insurance tools (Santeramo, 2019). The role of Defense Consortia, introduced both to facilitate the match of insurers and farmers in the subsidized crop insurance market and to reduce the asymmetric information, is not negligible. It emerges a North-South territorial dualism that affects farmers participation: Defence Consortia are more effective in Northern Italy than in the Southern Italy and, also, the strong presence of producer organi- zations and cooperatives aggregates the crop insurance’s demand in the Northern Italy (Santeramo et al., 2016). Moreover, farmers who trust more in the intermediaries assisting them are inclined to adopt insurance tools to cope the risk of production loss, while risk averse farm- ers tend to implement other risk management strategies as crop or financial diversification (Trestini et al., 2018). In Italy, only the 9.9 percent of Utilised Agricultural Area is covered by insurance contracts and 20.9 percent of production value is insured (ISMEA, 2021). Accord- ing to a survey conducted by ISMEA in 2018 on low participation to the subsidized agricultural insurance systems, most Italian farmers renounce to subscribe insurance contracts due to economic reasons, highlight- ing the high costs of policies. The share of farmers who believe that their farms are not exposed to specific risks or who have had negative experiences when receiving compensation, losing trust on insurance market systems, is also not negligible. Indeed, Giampietri et al., 2020 found that the trust affects the decision-making process: under uncertainty, the trust may substitute the knowl- edge also overcoming the lack of experience, therefore, strong communication campaigns to improve farm- ers’ participation are recommended. Moreover, focus- ing on the WIBIs, also subsidized by the Measure 17 of National Rural Development Program 2014-2020, a lack of knowledge emerged among big insured farmers, i.e., WIBIs were unknown to 93 percent of them (ISMEA, 2020). Furthermore, some farmers believe that index- based insurances are inadequate to manage the weather risks due to the distrust of the objectivity of the indexes and parameters used, also showing an aversion to any future subscriptions. Clearly, it is necessary to improve the appeal and communication of these innovative risk management tools, also considering that any interven- tion aimed at promoting farmer participation should improve the competition among insurance providers, also reducing at the same time the asymmetric informa- tion and opportunistic behaviour (Menapace et al., 2016; Rogna et al., 2021; Santeramo and Russo, 2021). In this complex scenario, we estimate the yield response equa- tion to investigate the responsiveness of yield to climate, deepening the working principles of weather index- based insurance, through a case study on durum wheat crop in the Apulia region, also animating the debate on the use of publicly available data to the development of an effective and attractive tool to manage climatic risk in agriculture. 125On the relationships among durum wheat yields and weather conditions: evidence from Apulia region, Southern Italy Bio-based and Applied Economics 11(2): 123-130, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12160 DATA AND RESEARCH METHODOLOGY An agronomic review on durum wheat allowed us to identify sensitive phenological stages of durum wheat in Apulia region and those critical weather events occur- ring in certain phenological stages that may cause signif- icant production losses (Table 1). Cold sensitivity is higher during the germination phase that occurs 10-15 days after sowing in which temperatures of few degrees centigrade below zero may cause considerable damages (Baldoni and Giardini, 2000, Angelini, 2007; Disciplinare di Produzione Inte- grata della Regione Puglia, 2021). Likewise, tempera- tures of few degrees centigrade below zero during the stem elongation phase may cause stems death and seri- ous damages to the tissue of the internodes (Baldoni and Giardini, 2000; Angelini, 2007; Disciplinare di Pro- duzione Integrata della Regione Puglia, 2021). Flower- ing stage occurs in late May and lasts about 10 days in which wheat crop is highly sensitive to cold stress that may cause death of f lowers (Angelini, 2007; Baldoni and Giardini, 2000; Disciplinare di Produzione Integra- ta della Regione Puglia, 2021). Heat and drought stress during susceptible flowering and grain filling stages (i.e., after flowering, until the first decade of July) may cause considerable reductions in wheat yield and quality, lead- ing the acceleration of leaf senescence process, reducing photosynthesis, causing oxidative damage, pollen steril- ity, also reducing physiological and metabolic imbalanc- es, photosynthesis, grain numbers and weight (Angelini, 2007; Asseng et al., 2011; Li et al., 2013; Farooq et al., 2014; Rezaei et al., 2015; Zampieri et al., 2017; Makinen et al., 2018). Heavy rainfall during the entire crop cycle may cause significant production losses due to the pro- liferation of pathogens, nutrient leaching, soil erosion, inhibition of oxygen uptake by roots (i.e., hypoxia or anoxia), waterlogging and lodging (Zampieri et al., 2017; Makinen et al., 2018). Furthermore, we collected yearly total production (tons) and area harvested (hectares) data for durum wheat crop from the National Institute of Statistics (ISTAT), from 2006 to 2019, for each province of Apulia region, also calculating the respective yields (tons/ hectare). Then, for the same time-period, we collected 10-days frequency weather data from six synoptic weath- er stations of the Institute for Environmental Protection and Research (ISPRA), one for each province of Apulia region: Bari (BA), Barletta-Andria-Trani (BT), Brindisi (BR), Foggia (FG), Lecce (LE), Taranto (TA). Weather data include 10-days average minimum temperature (°C), i.e., the average of daily minimum temperatures, 10 days average maximum temperature (°C), i.e., the aver- age of daily maximum temperatures, and 10-days cumu- lative precipitation (mm), i.e., the average of daily pre- cipitation. Details on collected variables are shown in Table 2. Our empirical approach is based on a panel data model that includes fixed effect (i.e., it is a major advan- tage of the panel rather than cross-sectional regression) both to control for unobservable variables such as seed varieties or soil quality that may vary across the space, i.e., provinces, and to catch the variation across the time within the Apulian provinces (Tack et al., 2015; Blanc and Schlenker, 2017; Kolstad and Moore, 2020). Table 1. Phenological stages, weather events and critical limits of durum wheat in Apulia region. Phenological stage Weather event Time interval Critical limit Reference Sowing Cold From the first decade of November to the first decade of December Temperature < 0 °C Baldoni and Giardini, 2000; Angelini, 2007; Disciplinare di produzione integrata della Regione Puglia, 2021Germination Cold From the second decade of November to the second decade of December Temperature < 0 °C Stem elongation Cold From the second decade of March to the third decade of April Temperature < 0 °C Baldoni and Giardini, 2000; Angelini, 2007 Flowering Cold From the second decade of May to the first decade of June Temperature < 0 °C Angelini, 2007; Disciplinare di produzione integrata della Regione Puglia, 2021 Heat, drought Temperature > 30-31 °C Angelini, 2007; Rezaei et al., 2015 Grain filling Heat, drought From the second decade of June to the first decade of July Temperature > 34 °C Angelini, 2007; Asseng et al., 2011; Rezaei et al., 2015; Zampieri et al., 2017; Makinen et al., 2018 All phases Excessive rainfall From first decade of November to the first decade of July Rainfall > 40 mm/day Makinen et al., 2018 126 Bio-based and Applied Economics 11(2): 123-130, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12160 Marco Tappi, Gianluca Nardone, Fabio Gaetano Santeramo The relationship between durum wheat yields and weather events is synthesized as follows: yit = f(wit) + μi + θt + εit where yit is the yield over the space (i) and time (t) as function ( f ) of weather (wit), also including fixed effects over space (μi) and time (θt), error term and “controls” refers to other relevant exogenous variables (εit) (Kolstad and Moore, 2020). More specifically, we conducted tem- poral and spatial autocorrelation identifying those con- tiguous provinces having a larger shared borders for a twofold check: (i) verify if the weather events occurring in a province may affect durum wheat yields in the con- tiguous province; (ii) control if the yields may be affect- ed by weather events occurring at time t-1. Undoubtedly, both environmental and agronomic factors may justify the extreme variability of the durum wheat yield across the Apulian provinces: Foggia shows the highest average durum wheat yields while Lecce shows the lowest aver- age yields, although it is characterized by lower yield variability than other provinces as Brindisi that, on the contrary, is more affected by environmental and agro- nomic factors, reason why it may benefit of crop insur- ance programs more than other provinces to cope yields fluctuations (Table 3). RESULTS Our results clearly show that a relationship links weather conditions and production yields in the Apulia region. More specifically, precipitation seem to have a negative effect on durum wheat yields (Table 4). However, controlling by spatial and temporal autocorrelation, the effects of temperatures have been caught. Minimum temperatures negatively affect durum wheat yields, while maximum temperatures positive- ly affect the yields, both in a non-linear way. Indeed, we included the squares of weather variables to catch the nonlinearity, in other terms, the trade-off between weather and yields (Blanc and Schlenker, 2017). Our results clearly highlight that the weather affects the yields in a nonlinear way, therefore, variables have a statistically significant inverted-U shape relationship Table 2. Details on collected variables. Variable (unit) Frequency Time-period Province Weather station - province (no. of obs, SR in km2) Source durum wheat yield (tons/hectares) Yearly 2006-2019 Bari (BA) Barletta-Andria-Trani (BAT) Brindisi (BR) Foggia (FG) Lecce (LE) Taranto (TA) - ISTAT average minimum temperature (°C) average maximum temperature (°C) cumulative precipitation (mm) 10-days Bari - BA (501, 5.138) Trani - BT (144, 1.543) Brindisi - BR (471, 1.839) Monte Sant’Angelo - FG (504, 7.008) Lecce - LE (471, 2.799) Marina di Ginosa – TA (471, 2.437) ISPRA, UCEA,ARPA Notes: missing data have been integrated including Research Unit for Climatology and Meteorology (UCEA) and Regional Agency for the Protection of the Environment (ARPA) datasets. Table includes no. of observations and spatial resolution (SR) of weather stations. Table 3. Durum wheat yields (tons/hectare) among Apulian prov- inces. Average Minimum Maximum Standard deviation Bari 0.234 0.170 0.306 0.045 BAT 0.224 0.200 0.260 0.020 Brindisi 0.285 0.180 0.420 0.071 Foggia 0.314 0.200 0.420 0.047 Lecce 0.189 0.160 0.220 0.018 Taranto 0.244 0.100 0.350 0.057 Notes: data include yearly durum wheat yield from 2006 to 2020. Source: ISTAT, 2020. 127On the relationships among durum wheat yields and weather conditions: evidence from Apulia region, Southern Italy Bio-based and Applied Economics 11(2): 123-130, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12160 (Schlenker and Roberts, 2009; Lobell et al., 2011). Last but not least, minimum temperatures may affect the contiguous provinces. According to the scientific lit- erature, any excess (or deficit) of temperature and pre- cipitation (or their combinations) may cause severe yield losses on durum wheat (Baldoni and Giardini, 2000; Angelini, 2007; Asseng et al., 2011; Li et al., 2013; Farooq et al., 2014; Rezaei et al., 2015; Zampieri et al., 2017; Makinen et al., 2018). Furthermore, we estimated the model for each phenological phase of durum wheat to capture the potential heterogeneity in the effect of weather variables, also controlling by spatial and tem- poral autocorrelation. Our results show that the rela- tionship between weather variables and yields is valid only for some weather variables in certain phenological phases. More specifically, the maximum temperatures and precipitation positively affect durum wheat yield in a nonlinear way when occur in the germination and grain filling stages, respectively (Table 5). Moreover, minimum temperatures may affect the contiguous provinces. Clearly, ten-days data we have col- lected does not highlight the dynamics between weath- er events occurring in certain phenological stages and durum wheat yields mainly because the impacts of daily weather are not captured. Moreover, most variables are not statistically significant: this limit opens a reflection on data disaggregation level and on the need to collect more spatially and temporally refined data, also lay- ing the foundations for the development of an effective index that reflects the responsiveness of the yields to cli- matic conditions to be implemented in the WIBIs. The evidence resulting from our econometric model on phe- nological stages is also in contrast with the literature: germination stage is highly sensitive to cold stress (Bal- Table 4. Effects of weather variables on durum wheat yield. Variables Panel prov FE time trend Panel temporal correlation prov FE time trend Panel spatial correlation prov FE time trend Panel temporal correlation spatial correlation prov FE time trend Temperature (min) -0.00764 -0.00124 -0.46909*** -0.45553** (0.10641) (0.11715) (0.17058) (0.18731) Temperature (min) sq. 0.00049 -0.00023 0.00892* 0.01384** (0.00296) (0.00320) (0.00490) (0.00544) Temperature (max) 0.22572 0.28286* 0.61165** 0.66801** (0.14125) (0.15378) (0.25587) (0.27703) Temperature (max) sq. -0.00523* -0.00612** -0.01530*** -0.02022*** (0.00278) (0.00299) (0.00515) (0.00568) Precipitation -0.01646** -0.01625* -0.03939** -0.04670** (0.00799) (0.00844) (0.01819) (0.01954) Precipitation sq. 0.00008 0.00007 0.00019 0.00024 (0.00006) (0.00006) (0.00017) (0.00018) Yield (lag) - 0.10464*** - -0.09290*** (0.02153) (0.03579) Temperature (min) contig. - - 0.23065*** 0.18642*** (0.06565) (0.07019) Temperature (max) contig. - - 0.00822 0.04557 (0.10765) (0.11545) Precipitation contig. - - 0.00537 0.00771 (0.00704) (0.00837) Observations 1,837 1,638 914 833 Number of id 6 6 4 4 Notes: panel regression model was processed in STATA software. It includes provincial fixed effect, time trend, temporal (i.e., yield lag), and spatial (contiguous weather variables) autocorrelation. Standard errors in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. 128 Bio-based and Applied Economics 11(2): 123-130, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12160 Marco Tappi, Gianluca Nardone, Fabio Gaetano Santeramo doni and Giardini, 2000, Angelini, 2007; Disciplinare di Produzione Integrata della Regione Puglia, 2021), while there are not evidences on heat stress during this stage. However, our study may help the debate suggesting pre- cise directions for the future research. CONCLUSIONS Participating in index-based crop insurance schemes is a key challenge to improve the resilience of farm- ing systems and adopting effective subsidies to enhance participation in the schemes is a pressing goal for poli- cymakers. In this complex scenario, we investigated how temperatures and precipitation are correlated with yields data to reflect on potential designs for the index- based insurance schemes. While not novel (e.g., Chen et al., 2014), we found that weather changes affect durum wheat yields in a nonlinear way and some weather events occurring in certain phenological phases may have an impact on the yields. Our results are important to show that even with aggregated data the evidence is striking. However, focusing on phenological stages, our findings are in contrast with the literature highlighting the complexity of the phenomenon and the need to rely on more temporally and spatially disaggregated data. Although we provided clear evidence on the weather- yield relationship, it is impossible to design a WIBI using 10-days weather data. Therefore, our contribution may help the debate suggesting precise directions for the future research: first, a major effort should be devoted to the collection of weekly or daily weather observations, also identifying empirical damage thresholds that can be verified at farm-level, as well as the collection of produc- tion area or municipal data; a promising approach could be the Growing Degree Days tool so as to calibrate the more precisely the growing stages in a view to a bet- ter explanation of weather risks on crop performances (Conradt et al., 2015; Dalhaus et al., 2018; Lollato et al., 2020); last but not least, the design of the index- Table 5. Effects of weather variables on yield by phase. Variables sowing germination stem elongation flowering grain filling Yield (lag) -0.11883 0.05952 0.17798* -0.04474 0.09403 (0.20660) (0.20523) (0.09219) (0.18593) (0.14041) Temperature (min) 0.95845 -0.00051 0.50020 -1.32087 -0.65587 (2.53724) (1.74362) (1.26379) (4.06620) (3.83238) Temperature (min) sq. -0.01783 0.01530 -0.01201 0.03550 0.02171 (0.11363) (0.08655) (0.05223) (0.10882) (0.08353) Temperature (max) 3.15220 23.00804** -2.73726 7.62398 -1.65011 (12.35641) (10.88917) (2.21349) (8.51643) (6.74553) Temperature (max) sq. -0.15964 -0.76330** 0.06023 -0.15868 0.01396 (0.35336) (0.33477) (0.05582) (0.15987) (0.11320) Precipitation 0.04601 -0.07450 -0.03735 -0.43463 0.42332* (0.12015) (0.11228) (0.07473) (0.42173) (0.24351) Precipitation sq. -0.00034 0.00054 0.00049 0.01188 -0.00826* (0.00088) (0.00084) (0.00101) (0.01680) (0.00463) Temperature (min) contig. 1.05294** 0.86957** 0.62187*** 0.52210 0.55304** (0.41397) (0.35021) (0.17188) (0.35845) (0.23765) Temperature (max) contig. 0.38942 0.17524 -0.06474 0.22627 0.00512 (1.25128) (1.33537) (0.34861) (0.52741) (0.37530) Precipitation contig. -0.05370 0.01278 -0.01394 -0.10017 -0.05635 (0.05168) (0.04199) (0.03275) (0.11446) (0.04998) Observations 42 44 125 43 67 Number of id 4 4 4 4 4 Notes: panel regression model was processed in STATA software. It includes provincial fixed effect, time trend, temporal (i.e., yield lag), and spatial (contiguous weather variables) autocorrelation. Notes: standard errors in parentheses *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level. 129On the relationships among durum wheat yields and weather conditions: evidence from Apulia region, Southern Italy Bio-based and Applied Economics 11(2): 123-130, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-12160 based insurance schemes needs of further investigation because establishing a triggering index is a major chal- lenge for the stakeholders involved in the implementa- tion of the insurance schemes. The debate on crop insur- ance schemes is still vivid, and it will be so also in the next decade due to the central role that the risk man- agement (old and novel) tools will have in the new CAP (Meuwissen et al., 2018; Severini et al., 2019; Cordier and Santeramo, 2020). 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Volume 11, Issue 2 - 2022 Firenze University Press Agriculture, food and global value chains: issues, methods and challenges Margherita Scoppola Mapping global value chain participation and positioning in agriculture and food: stylised facts, empirical evidence and critical issues Silvia Nenci1, Ilaria Fusacchia1,2, Anna Giunta1,2, Pierluigi Montalbano3, Carlo Pietrobelli1,4 On the relationships among durum wheat yields and weather conditions: evidence from Apulia region, Southern Italy Marco Tappi*, Gianluca Nardone, Fabio Gaetano Santeramo A choice model-based analysis of diversification in organic and conventional farms Andrea Bonfiglio*, Carla Abitabile, Roberto Henke Financial performance of connected Agribusiness activities in Italian agriculture Gabriele Dono*, Rebecca Buttinelli, Raffaele Cortignani Pesticides, crop choices and changes in well-being Geremia Gios1,*, Stefano Farinelli2, Flavia Kheiraoui3, Fabrizio Martini4, Jacopo Gabriele Orlando5