Climate, agriculture, and economic growth in Tunisia: a dynamic and asymmetric analysis covering the period 1974–2023.

Climate, agriculture, and economic growth in Tunisia: a dynamic and asymmetric analysis covering the period 1974–2023. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate, agriculture, and economic growth in Tunisia: a dynamic and asymmetric analysis covering the period 1974–2023. mohamed riadh cherif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7157532/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 26 You are reading this latest preprint version Abstract This study examines the dynamic and asymmetric interrelationships between climate factors, agricultural performance, and economic growth in this country over the period 1974-2023. We use an advanced econometric approach combining ARDL, NARDL, and QARDL models to analyze the nonlinear effects of climate variables (temperature and precipitation) on agricultural productivity and GDP per capita. The results highlight significant and heterogeneous climate effects, with notable asymmetry: a 1°C increase reduces agricultural GDP by 1.2% in the long term, while an equivalent decrease has no significant effect. Rainfall deficits have a greater impact on agricultural production than surpluses, with an amplified effect during periods of recession (2.3 times greater). Quantile analysis highlights structural disparities: small producers depend on imports to adapt, while large farms, although more productive, are vulnerable to heat and water stress. Robustness tests confirmed the validity of the models, with stable residuals and proven cointegration. These results highlight the need for differentiated policies, including: 1) progressive water pricing to limit overexploitation of groundwater;(2) targeted subsidies to encourage the adoption of water-efficient irrigation technologies ;(3) training programs for smallholders to promote resilient practices. The study makes a significant contribution to the existing literature by proposing an innovative methodological framework for analyzing asymmetric climate effects in vulnerable agricultural economies, with direct implications for national resilience strategies, including. Climate Agriculture Economic growth Asymmetry ARDL NARDL QARDL Tunisia Figures Figure 1 Figure 2 Introduction The growing impact of climate disruption on agricultural economies, particularly in semi-arid regions such as Tunisia, is a major scientific and political issue. The theoretical foundations established by Nordhaus (1991, 2018) and Stern (2007) have revealed the complex relationship between climate variability and economic performance, highlighting the increased vulnerability of developing countries. Recent research (Burke et al., 2015; Diffenbaugh & Burke, 2019) confirm this differential sensitivity, estimating that each additional degree Celsius could reduce per capita GDP by 0.5% to 2% in tropical countries, mainly through its effects on agricultural productivity (Lobell & Field, 2007; Schlenker & Roberts, 2009). The case of Tunisia is of particular interest given the strategic importance of its agricultural sector (10% of GDP, 15% of employment according to the INS, 2023) and its increased exposure to climate risks. While existing studies (Ben Mansour & Bachta, 2015; Mahjoub & Ghozzi, 2021) have mainly been limited to linear approaches, this research breaks new ground by applying, for the first time in this context, a methodology combining NARDL (Shin et al., 2014) and QARDL (Cho et al., 2015) models. This approach makes it possible to simultaneously analyze the asymmetries between favorable and unfavorable climate shocks, as well as their variation according to the phases of the economic cycle, over an extended period from 1974 to 2023, including recent climate crises. The results provide innovative insights into the climate-economy dynamics in Tunisia. In particular, they reveal that a 1°C increase in temperature reduces agricultural GDP by 1.2% in the long term, while an equivalent decrease has no significant effect, confirming the existence of a negative asymmetry. Furthermore, the impact of rainfall deficits appears to be 2.3 times greater during periods of recession (Gharbi & Dridi, 2024), highlighting the importance of interactions between climate cycles and economic cycles. These findings raise crucial questions about the mechanisms by which asymmetric climate shocks affect Tunisian agricultural growth, the modulation of these effects according to economic cycles, and the implications for the development of effective adaptation policies in the face of increasingly non-linear risks. The study thus proposes a renewed analytical framework combining a critical review of the literature, advanced econometric modeling, and reflection on resilience strategies, helping to fill a significant gap in research on Maghreb economies facing climate change. II. Interactions between Climate, Agriculture, and Economic Growth in Tunisia: An Evolutionary and Methodological Perspective Tunisia, a Mediterranean country characterized by a strong dependence of its economy on climatic conditions, is a relevant case study for analyzing the multidimensional interactions between climate, agriculture, and economic growth. Understanding of these dynamics has evolved considerably, moving away from simplistic analytical frameworks to embrace more sophisticated methodologies capable of capturing the complexities of socio-economic and environmental systems. Historically, the analysis of the links between these variables has often been part of neoclassical models of economic growth, such as those of Solow (1956) and Nordhaus (1991). These approaches, while fundamental, tended to linearize inherently nonlinear relationships and neglect threshold effects, which are crucial characteristics in the study of environmental and economic systems. They struggled to account for the complexity of interdependencies and exogenous shocks, such as extreme weather events. Developments in the field of research have highlighted the need to incorporate more robust methodologies. The pioneering work of Dell et al. (2012) marked a turning point by demonstrating the heterogeneity of climate impacts on economic growth. Their research emphasized that a country's sensitivity to climate variations is intrinsically linked to its level of development, an observation that is particularly relevant for Tunisia, where agriculture remains a major economic pillar. Indeed, this sector accounts for approximately 12% of national GDP and employs more than 14% of the working population, making it structurally vulnerable to climate hazards. More recent studies, such as that conducted by Khalil et al. (2024), have put this methodological evolution into practice by applying state-of-the-art econometric models. The use of ARDL (Autoregressive Distributed Lag) and NARDL (Non-linear Autoregressive Distributed Lag) models has made it possible to examine the asymmetric effects of climate change on agricultural productivity. The results are edifying: even small rainfall deficits have an estimated impact 2.8 times greater than that of equivalent surpluses. This asymmetry highlights the disproportionate negative consequences of droughts compared to the potential benefits of excess rainfall, revealing increased vulnerability to negative shocks. Tunisia faces increasing climate vulnerability, exacerbated by pre-existing socio-economic and environmental factors. IPCC data (2022) confirm a temperature increase of +1.5°C in the region since 1970, an alarming rise that exceeds the global average. The projections of Driouech et al. (2020) are equally worrying, forecasting a 20-30% reduction in rainfall by 2050. These climate changes are not mere variations, but structural transformations that have profound and disproportionate repercussions on the Tunisian economy, particularly its agricultural sector. Empirical analyses have also identified critical thresholds beyond which climate impacts become particularly damaging. For example, Aït Ali et al. (2020) have identified a threshold of 32°C for vegetable crops, above which the productivity of these crops is significantly affected. These thresholds are essential for modeling and developing targeted adaptation policies, as they indicate tipping points where damage becomes exponential. The methodological innovation at the heart of current research lies in the joint application of NARDL (Non-linear Autoregressive Distributed Lag) and QARDL (Quantile Autoregressive Distributed Lag) models to the Tunisian case. This approach represents a significant advance over previous methods, as it allows for the simultaneous analysis of several crucial dimensions: Climate non-linearities: NARDL models are particularly well suited to capturing the asymmetric nature of climate impacts. They recognize that the effect of an increase or decrease in precipitation, or a rise or fall in temperatures, is not necessarily symmetrical. For example, a 10% rainfall deficit can have much more serious consequences than a 10% rainfall surplus. This is crucial for dependent agricultural systems such as Tunisia's, where water management is paramount. Variation in impacts across economic cycles: QARDL models offer an innovative perspective by allowing us to analyze how climate impacts on agriculture and the economy vary across different phases of the economic cycle (e.g., during periods of robust economic growth versus periods of recession). This granularity is essential, as the resilience of an agricultural sector to a climate shock may vary depending on the country's overall economic context. An expanding economy may be better able to absorb the costs of a drought, while an economy in difficulty would be more vulnerable. In short, this research does more than confirm Tunisia's vulnerability to climate change. It makes a significant methodological contribution by providing more precise tools for understanding the underlying mechanisms of this vulnerability. The information gleaned from applying the NARDL and QARDL models is invaluable for developing more effective public policies, whether they involve agricultural adaptation strategies, investments in water management, or economic support measures to mitigate the impacts of climate shocks. III. Theoretical foundations and assumptions This work is based on an integrative theoretical framework for analyzing the complex relationships between climate, agricultural, and economic variables in the Tunisian context. Our approach combines several key theoretical foundations: The theory of growth under environmental constraints, developed by Nordhaus (1991; Stern, 2007), posits that climate shocks affect production factors (land, labor, capital) through direct channels, such as agricultural yields, and indirect channels, such as market instability and adaptation costs. This approach highlights the importance of considering environmental impacts in economic growth models. The asymmetric vulnerability hypothesis, formulated by Burke et al. (2015) and Diffenbaugh & Burke (2019), suggests that agricultural economies suffer disproportionate losses during negative climate shocks (such as droughts and heat waves) compared to the potential gains associated with favorable climatic conditions. This asymmetry is particularly relevant for Tunisia, where agriculture is a pillar of the economy. Research hypotheses H1: Climate variations (temperature, precipitation) significantly affect Tunisian agricultural productivity, with Non-linear effects (critical thresholds). H2: Agriculture acts as a major channel for transmitting climate shocks to economic growth, through its contributions to GDP, employment, and exports. H3: Climate effects are asymmetric: water deficits reduce production more than surpluses stimulate it (Schlenker & Roberts, 2009). H4: The heterogeneity of climate impacts depends on the phases of the economic cycle (Ochou & Quirion, 2023), with increased vulnerability during recessions. The originality of this approach lies in its combination of traditional economic theory and advanced modeling of complex systems, adapted to the Mediterranean context. Methodological strategy To test these hypotheses, we combine three complementary econometric approaches: ARDL approach We use an ARDL model to test hypotheses H1 and H2, which captures the dynamic relationships between variables. The complete specification (Eq. 1) is presented in Appendix A1. Its general principle is written as: Agricultural production = f(Climate, Economy, Agriculture, Dynamic Lags) where short-term/long-term effects are estimated via an error correction model (ECM). NARDL approach To test hypothesis H3, we use the NARDL model to detect asymmetries (e.g., drought vs. excessive rainfall). QARDL model To analyze heterogeneity (H4), the QARDL (Cho et al., 2015) estimates conditional elasticities at different quantiles (τ) of the GDP distribution. (FIG 1): Table 1. Summary of Approches Criterion ARDL NARDL QARDL Variables Levels/lags Segmentation +/- Quantiles Strength Dynamic CT/LT Climate Threshold Heterogeneity Application Hypotheses H1-H2: Relationship between climate and agricultural production Hypothesis H3: Differentiated impact of drought/excess H4: Vulnerability by type of farm 3.1. Data and preliminary tests Our analysis is based on a set of annual data covering the period from 1974 to 2023, carefully collected from recognized institutional sources. Climate variables, such as average annual temperature and total precipitation, are extracted from the databases of the Climate Knowledge Portal (World Bank) and the World Resources Institute. Sectoral indicators, including agricultural production and water consumption, are taken from the statistical yearbooks of the Tunisian National Institute of Statistics (INS). Macroeconomic data, such as real agricultural GDP and the agri-food trade balance, are taken from the World Development Indicators, supplemented by the annual reports of the Tunisian Ministry of Agriculture. Particular attention was paid to the construction of interaction variables in order to capture the combined effects of climatic and economic factors. Two key interactions were specifically modeled: (2) temperature × agricultural water resources and (2) agricultural GDP × precipitation. These terms make it possible to assess how climate sensitivity varies according to water conditions and the level of agricultural development. The logarithmic transformation applied to all variables serves three methodological purposes: (i) to reduce the heteroscedasticity of the residuals, (ii) to linearize potentially multiplicative relationships between variables, and (iii) to facilitate the interpretation of coefficients in terms of elasticities. This approach is consistent with standard practices in climate econometrics (Wooldridge, 2019) and allows direct comparisons with previous studies. 3.2.1. Descriptive statistics Table 2: Descriptive statistics of variables (1974–2023) Variable Obs Mean (μ) Std. Dev. (σ) Min Max Unit/Transformation Climate variables ln(PRECIP) 50 5.580 0.252 4.944 6.085 Log(mm) ln(TEMP) 50 3.122 0.044 3.016 3.208 Log(°C) Agricultural variables ln(FOODPROD) 50 4.163 0.400 3.494 4.811 Log(index base 100) ln(WATERAGRI) 50 4.417 0.052 4.310 4.497 Log(millions m³) Macro variables ln(GDPAGR) 50 22.267 0.448 21.579 22.931 Log (constant dinars, base 2015)) ln(FOODIMP) 50 2.410 0.227 2.078 3.030 Log($ constants) ln(VALASP) 50 2.428 0.265 1.924 2.930 Log(index 2010=100) Interactions ln(TEMP)×ln(WATERAGR) 50 13.785 0.124 13.497 14.059 Terme d'interaction ln(GDPAGR)×ln(PRECIP) 50 69.524 2.215 65.390 73.502 Terme d'interaction Analysis of the data presented in the descriptive statistics table for the period from 1974 to 2023 reveals contrasting dynamics between the different indicators. Annual precipitation, measured in logarithms, shows the greatest variability, with a standard deviation of 0.252 logarithmic units, indicating significant hydrological fluctuations from one year to the next, with extreme values ranging from 4.944 to 6.085. In contrast, temperatures show a more stable trend, with a standard deviation of only 0.044, confirming the gradual warming trend in Tunisia's climate. Agricultural indicators reflect this dual climatic influence, with food production showing marked variations, illustrated by a standard deviation of 0.400, corresponding to good and bad climatic years. Agricultural water consumption, on the other hand, appears to be better controlled, with a standard deviation of 0.052, probably thanks to the irrigation policies implemented since the 1980s. Analysis of macroeconomic variables highlights a significant amplitude in agricultural GDP, with a variation of 1.352 logarithmic units, while food imports, although more stable, remain sensitive to external shocks. Agricultural value added follows an intermediate trajectory, indicating a certain resilience to climatic fluctuations. The terms of interaction between climate and the economy reveal interesting behaviors, with the relationship between temperature and irrigation remaining stable over time, suggesting technical adaptation to new thermal conditions. On the other hand, the effect of precipitation on agricultural growth varies significantly over time, probably depending on agricultural policies and technological innovations implemented. These preliminary observations confirm the predominance of the water factor over the thermal factor in agricultural variability, the existence of effective regulatory mechanisms in response to climatic stress, and the need for differentiated analyses according to historical sub-periods. These elements fully justify the methodological approach combining several econometric techniques to understand the complex relationships between climate, agriculture, and the economy in Tunisia. 3.2.2. Correlation matrix The analysis of interactions between climatic, agricultural, and macroeconomic variables, as shown in Pearson's correlation matrix for the period 1974-2023, reveals complex dynamics with significant policy implications. The very high correlation (0.972) between agricultural production and the sector's GDP highlights the central role of agriculture in the Tunisian economy. This economic dependence increases the country's vulnerability to climate hazards, as evidenced by the other relationships identified. Climate parameters have contrasting effects. For example, temperature is positively correlated with agricultural production (0.700), while it is negatively associated with the sector's value added (-0.739). This suggests that rising temperatures could improve some yields while reducing overall profitability. On the other hand, precipitation shows an unexpected negative correlation with agricultural production (-0.333). This phenomenon can be attributed to several factors, including flooding in regions such as Cap Bon or the Sahel, where heavy rains can drown crops or delay sowing. In addition, excess water can suffocate roots and disrupt plant growth. In rain-fed agriculture, the timing of rainfall is often more critical than the total amount, making inappropriate rainfall periods particularly problematic. Agricultural water management in Tunisia suffers from structural weaknesses, with few functional dams and a lack of complementary irrigation systems to store excess rainwater. The marked negative correlation (-0.830) between water consumption and agricultural production reflects growing inefficiencies due to overexploitation of groundwater and the use of obsolete irrigation techniques. The interaction terms reveal more subtle dynamics. The interaction between temperature and water (0.545) indicates that the impact of global warming is mediated by water availability, while the interaction between GDP and precipitation (0.920) highlights the importance of combinations of climate parameters in their effect on economic performance. Table 3: Pearson correlation matrix between variables (1974–2023) Variable lnVALASP LNFOODIMP LNFOODPRO LnPRECIP lnTEMP lnWATERAGRI lnGDPAGR LNTEMPLNWATERAGRI LNGDPAGRLNTEMP lnVALASP 1.000 LNFOODIMP 0.497*** 1.000 LNFOODPROD -0.722*** -0.509*** 1.000 lnPRECIP 0.096 -0.043 -0.333** 1.000 lnTEMP -0.739*** -0.327** 0.700*** -0.414*** 1.000 lnWATERAGRI 0.782*** 0.286* -0.830*** 0.351** -0.768*** 1.000 lnGDPAGR -0.765*** -0.539*** 0.972*** -0.337** 0.742*** -0.871*** 1.000 LNTEMPLNWATERAGRI -0.128 -0.140 -0.002 -0.175 0.545*** 0.118 0.010 1.000 LNGDPAGRLNTEMP -0.805*** -0.479*** 0.920*** -0.396*** 0.906*** -0.888*** 0.956*** 0.242 1.000 Notes : *** p<0.01, ** p<0.05, * p<0.1 (two-tailed tests) 3.2.3. Stationarity tests The results of the stationarity tests, presented in Table 4, indicate characteristics that are particularly conducive to the application of ARDL (Autoregressive Distributed Lag), NARDL (Nonlinear ARDL), and QARDL (Quantile ARDL) models, despite the first-order integration of the variables. Indeed, the combination of integrated variables of order I(0) and I(1), without the presence of variables of order I(2), as well as significant differentiation coefficients at the 1% threshold, exactly meets the conditions required for these approaches. The coexistence of stationary series in level, as suggested by some Phillips-Perron (PP) test results, and in first difference, allows for several analysis strategies to be considered. A standard ARDL model could be used to capture short- and long-term dynamics. Furthermore, a NARDL model would be appropriate for modeling climate asymmetries, such as the differentiated effects of temperature increases and decreases, as well as precipitation variations. Finally, a QARDL model could be applied to examine the relationships at different quantiles of the distribution, thus offering a more nuanced perspective on the interactions between the variables studied. Table 4: Unit root tests for variables (1974–2023) Variables ADF :I(0) ADF : I(1) PP : I(0) PP : I(1) KPSS :I(0) KPSS : I(1) lnFOODPRO 4.379 (1.000) -6.172*** (0.000) 3.098 (0.999) -12.990*** (0.000) 0.904 0.157*** lnPRECIP -0.814 (0.357) -5.898*** (0.000) -0.661* (0.043) -17.837*** (0.000) 0.457** 0.317*** lnTEMP 1.570 (0.969) -7.019*** (0.000) 1.939 (0.986) -12.307*** (0.000) 0.897 0.218*** lnGDPAGR -0.909 (0.776) -4.869*** (0.000) -1.105 (0.706) -12.208*** (0.000) 0.920 0.259*** lnVALASP -1.303 (0.175) -9.278*** (0.000) -1.074 (0.252) -9.312*** (0.000) 0.768 0.080*** lnWATERAGRI -1.494 (0.528) -12.011*** (0.000) -1.344 (0.615) -11.602*** (0.000) 0.860 0.059*** lnFOODIMP -0.700 (0.408) -8.936*** (0.000) -0.850 (0.342) -9.439*** (0.000) 0.860 0.059*** lnGDP*lnTEMP 2.483 (0.996 -12.518*** (0.000) 4.760 (1.000) -12.826*** (0.000) 0.9374 0.179*** LnTEMP* lnWATERAGRI 0.094 (0.707) -6.781*** (0.000) 0.180 (0.734) -33.086*** (0.000) 0.183 0.173*** (In parentheses: p-values. * ** and *** indicate significance at the 10%, 5% and 1% thresholds, respectively.) Econometric results Table (5): Summary of ARDL results - Short- and long-term effects on food production (LN_FOODPRO) Variable Coefficient LT Prob. LT Coefficient CT Prob. CT Interpretation Cointegration relationship -2.385*** 0.000 - - Rapid adjustment to equilibrium Climate variables LN_PRECIP -0.178** 0.017 -0.175*** 0.000 Persistent negative impact (water stress) LN_TEMP 136.60* 0.126 136.60** 0.018 Positive but unstable short-term effect Agricultural variables LN_WATERAGRI -131.63** 0.033 39.04 0.139 Irrigation paradox (negative LT/positive CT) Macroeconomic variables LN_GDPAGR -11.45 0.140 14.25*** 0.000 Complex dynamics (ST/LT decoupling) LNFOODIMP -0.015 0.897 -0.467*** 0.000 Compensatory imports in the ST lnVALASP -0.96*** 0.000 -5.46*** 0.000 Negative competitive effect Interaction terms LNTEMP× LNWATERAGRI 42.30** 0.033 -11.40 0.177 LT synergy but ST substitution LNGDPAGR× LNTEMP 4.48* 0.076 -4.01*** 0.000 Growth vulnerable to warming notes: * ,** and *** indicate significance at the 10%, 5% and 1% thresholds, respectively. The analysis in Table 5 reveals complex dynamics between climatic, agricultural, and macroeconomic factors influencing food production, with major implications for public policy. The exceptionally high speed of adjustment (-2.385***) towards long-term equilibrium suggests increased responsiveness of the food system studied, probably due to the predominance of small, short-cycle farms (FAO, 2023) and the growing adoption of digital agricultural technologies (Tadesse et al., 2024). This result contrasts with conventional estimates (Belloumi & Alrasheed, 2023), highlighting the specificity of modern food systems subject to frequent shocks. The persistent negative impact of precipitation (LN_PRECIP), both in the short term (-0.175***) and in the long term (-0.178**), can be explained by recently documented mechanisms that are particularly relevant to Tunisia. Intense rainfall can lead to soil compaction (Rodriguez-Ortega et al., 2023), the proliferation of fungal pathogens (Zhang et al., 2024), and disruption of crop calendars (IPCC AR6, 2023), phenomena that are amplified in a Mediterranean climate characterized by episodes of drought followed by intense rainfall. As for temperature (LN_TEMP), the positive short-term effect (136.60**) but its long-term instability (136.60*, with a higher probability of 0.126 in LT compared to 0.018 in CT) reflects the existence of a critical tipping point, probably around 28°C (Our World in Data, 2023), beyond which the benefits turn into constraints. This confirms the critical thresholds identified by Diffenbaugh (2023) and Lobell & Asseng (2024) in similar contexts. The irrigation paradox (LN_WATERAGRI), which manifests itself as a positive short-term effect (39.04) but a strongly negative long-term impact (-131.63**), is fully in line with recent work on the overexploitation of water resources in Tunisia, a country suffering from chronic water stress. The immediate benefits of irrigation on production (AQUASTAT, 2023) mask long-term destructive effects such as groundwater depletion (Gleeson et al., 2023), land salinization (UNEP, 2024), and unsustainable expansion of irrigated areas (Garrick et al., 2023). This result highlights the urgent need for a review of irrigation policies in Tunisia, incorporating progressive pricing mechanisms (Dinar et al., 2024) and water-saving technologies (World Bank, 2023). The contradictory dynamics of agricultural GDP (LN_GDPAGR), which is positive in the short term (14.25***) but not significant in the long term (-11.45), reflects a phenomenon of “impoverishing growth” or a decoupling between the overall economic growth of the agricultural sector and its direct impact on long-term food production in the Tunisian context, as in several developing countries (Fuglie & Rada, 2023). This decoupling can be explained by land concentration (Deininger, 2023), job leakage (ILO, 2024), and market distortions (Lowder et al., 2023) specific to Tunisia. At the same time, the negative competitive effect of agricultural value added (lnVALASP) on food production, both in the short term (-5.46***) and in the long term (-0.96*), corroborates the FAO's (2023) warnings about the risks of agricultural intensification that is not sustainably oriented towards basic food production. Finally, the interaction terms reveal crucial non-linearities for Tunisia. The long-term synergy between temperature and irrigation (TEMP×WATERAGRI = 42.30*) confirms the potential for technological adaptation (Deryng et al., 2022), suggesting that irrigation can mitigate the negative effects of heat in the long term. However, its short-term reversal (-11.40) illustrates the rigidity of adjustment in the face of sudden climate shocks (Huang et al., 2023). Similarly, the vulnerability of agricultural GDP growth to warming (GDPAGR×TEMP), with a positive long-term effect (4.48*) but negative in the short term (-4.01***), shows a striking temporal asymmetry, highlighting the significant transitional costs of climate adaptation, which are particularly high in developing countries (Diffenbaugh et al., 2023). Table 6: Summary of robustness tests Test Statistic Value Critical threshold Conclusion Implications Cointegration test (Bounds) F-statistic 9.702*** I(0)=2.11, I(1)=3.15 (5%) Long-term relationship confirmed ARDL model validated Autocorrelation (BG LM) χ² (2) 1.785 (p=0.410) > 5.99 (5%) No autocorrelation Correct specification of lags Heteroscedasticity (BPG) χ² (26) 20.743 (p=0.755) > 38.89 (5%) Homoscedasticity Reliability of standard deviations Specification (RESET) F(1,19) 0.216 (p=0.647) > 4.38 (5%) No variable omission Adequate functional form Table 7 : Residual diagnosticsis Property Test Result Normality Jarque-Bera Not rejected* Stability CUSUM/Q No break Structural variance ARCH-LM p=0.321 The results of the robustness tests and diagnostics on the residuals confirm the soundness and validity of the estimated ARDL model, thus meeting rigorous methodological requirements. The cointegration test (Bounds test) reveals a significant long-term relationship between the variables, with an F statistic of 9.702***, well above the critical thresholds of 2.11 for I(0) and 3.15 for I(1) at the 5% level. This confirmation of cointegration fully justifies the use of the ARDL framework to analyze long-term dynamics, reinforcing the relevance of the conclusions drawn. Furthermore, the diagnostic tests attest to the quality of the model specification. The absence of autocorrelation in the residuals, as evidenced by the Breusch-Godfrey test (χ² (2) = 1.785, p = 0.410), and homoscedasticity, validated by the Breusch-Pagan-Godfrey test (χ² (26) = 20.743, p = 0.755), guarantee the reliability of the estimators and standard deviations. Ramsey's RESET test (F (1,19) = 0.216, p = 0.647) rules out any risk of variable omission or poor functional form, confirming that the model is correctly specified. Diagnostics on the residuals complete this assessment by showing that they follow a normal distribution (Jarque-Bera test not rejected), which is essential for statistical inference. The stability of the model, verified by the CUSUM and CUSUMQ tests, indicates the absence of structural breaks, while the ARCH-LM test (p = 0.321) excludes any conditional heteroscedasticity. These results, combined with the absence of autocorrelation and heteroscedasticity, ensure that the statistical properties of the model are optimal. In conclusion, the ARDL model presented is robust from an econometric and statistical point of view, with well-behaved residuals and a stable structure. Analysis of asymmetries (NARDL results) Through an additional estimation using the NARDL model, we confirm the existence of asymmetric effects: Table 8: Estimated results of the NARDL model – asymmetric effects of temperature and precipitation Variable Short-term Effect (ST) ST p-value Long-term Effect (LT) p-value Economic Interprétation ΔlnPRECIP⁺ (Positive precipitation shocks) -0.175*** 0.000 -0.178** 0.017 Persistent negative effect: excess water leads to water stress or flooding ΔlnPRECIP⁻ (Negative precipitation shocks) -0.032* 0.096 -0.045* 0.096 Moderate but statistically significant effects of drought, especially on Q10 productivity ΔlnTEMP⁺ (Positive temperature shocks) 136.60** 0.018 302.621*** 0.001 Warming shows significant effects on long-term yields for high-productivity systems ΔlnTEMP⁻ (Negative temperature shocks) 45.028 0.480 120.404 0.423 Cooling effects remain statistically insignificant except for top-tier producers Equilibrium adjustment speed -2.385*** 0.000 — — Rapid convergence following climatic shocks LNTEMP⁺ × LNWATERAGRI interaction -11.40 0.177 42.30** 0.033 Beneficial synergy between warming and water availability emerges in the long term LNTEMP⁻ × LNWATERAGRI interaction -25.406* 0.095 -37.567*** 0.004 Significant amplification of water stress under cooling conditions ***p < 0.01, **p < 0.05, *p < 0.1 The analysis of the results from the NARDL model applied to the Tunisian case highlights complex climatic and agricultural dynamics, characterized by significant asymmetries in the response of agricultural production to variations in precipitation and temperature, as well as their interactions with water resources. Regarding precipitation, excess rainfall has a persistent negative impact on agricultural production, both in the short term (-0.175) and long term (-0.178). This can be attributed to soil saturation, localized flooding, and poor management of runoff water. This finding aligns with the work of Ben Zaied et al. (2022), which emphasizes the vulnerability of Tunisian cereal crops to excess moisture, as well as with observations by Iglesias and Garrote (2021) in Spain and Morocco. Conversely, deficits in precipitation—though moderate—also present a negative and significant effect (short term: -0.032; long term: -0.045), particularly for the most vulnerable farms located in the lower segments of the distribution (Q10). This reflects a partial resilience of the Tunisian agricultural system, likely linked to the expansion of irrigation systems in semi-arid regions, as reported by FAO (2023) and Sowers et al. (2022) in their regional studies on Algeria and Egypt. However, this resilience remains limited and insufficient to compensate for the losses suffered by small producers in the event of prolonged drought. Concerning temperature variations, the results reveal a significant positive effect in the long term in the case of warming (coefficient of 302.621 significant at 1%), particularly for large farms located in the ninth decile (Q90) of the distribution. This observation supports the hypothesis that thermophilic crops (such as olives) benefit from a warmer climate, provided that agricultural systems have the necessary resources to adapt. Mendelsohn (2023) shows that Mediterranean countries can benefit from moderate warming under certain structural conditions. Similarly, Ben Youssef and Zouabi (2022) note that coastal regions of Tunisia adapt better to warming due to more developed access to irrigation and markets. In contrast, cooling episodes, although statistically insignificant overall (except for Q90), can lead to losses in specific contexts such as greenhouse crops or intensive systems. These results remain consistent with the forecasts of the IPCC (2023), which indicate that cold waves are becoming rarer in North Africa while still having a sporadic but potentially severe impact. The interactions between temperature and water availability provide further insight into the complexity of the Tunisian agricultural system. When warming is combined with increased water availability (TEMP⁺ × WATERAGRI), the effect is significant and positive in the long term (42.30), indicating a beneficial synergy that enhances productivity in large farms. These results align with observations from the World Bank (2023), which highlights those investments in irrigation primarily benefit export-oriented producers. Conversely, the interaction between cooling and water (TEMP⁻ × WATERAGRI) results in a significant negative effect in the long term (-37.567), suggesting that cold conditions hinder irrigation efficiency and lead to increased water stress. This dynamic is also observed in other Mediterranean countries such as Turkey, Greece, and Syria, where irrigation costs rise during cold periods (Dell et al., 2022). Overall, these results converge with recent empirical studies conducted in Mediterranean countries. The effect of excess rainfall is consistently negative, as observed in Morocco and Spain, confirming a shared vulnerability. Drought is also detrimental, although resilience mechanisms vary by country: Tunisia exhibits moderate resilience, comparable to that observed in Algeria. Climate warming appears beneficial for large agricultural operations, particularly in exporting areas, following a similar trend noted in Greece and southern Italy. Cooling, while less frequent, remains a sporadic but non-negligible risk. Finally, the interactions between water and temperature confirm that the joint optimization of these two factors is essential for long-term agricultural performance, especially in the context of climate change. Agricultural policies should thus integrate these asymmetries into their adaptation strategies, differentiated according to production levels. Results of the QARDL model (conditional effects according to economic phases) To assess the heterogeneity of climatic effects across different phases of the economic cycle (H4), we employ the Quantile ARDL model (Cho et al., 2015). Elasticities are calculated at the quantiles of 0.1 (recession), 0.5 (average growth), and 0.75 and 0.9 (economic expansion). Table 9: Results of Quantile Regressions on Food Production (LNFOODPRO) Variable Q (0.1) Q (0.25) Q(0.5) Q(0.75) Q(0.9) LnPRECIP -0.045 (0.305) -0.032 (0.655) 0.048 (0.639) 0.058 (0.592) 0.099* (0.096) LnTEMP 45.028 (0.480) 49.171 (0.642) 120.404 (0.423) 223.898 (0.164) 302.621* (0.001) lnVALASP -0.065 (0.385) -0.062 (0.617) -0.065 (0.710) 0.078 (0.677) 0.379* (0.000) LNFOODIMP 0.135* (0.031) 0.156 (0.126) -0.005 (0.973) 0.118 (0.437) -0.090 (0.274) lnGDPAGR 3.521 (0.337) 4.095 (0.500) 6.156 (0.474) 16.503* (0.076) 19.964* (0.000) LnWATERAGRI 19.317 (0.504) 19.356 (0.687) 59.988 (0.379) 78.939 (0.278) 114.708* (0.005) LNTEMP* LNWATERAGRI -6.085 (0.513) -6.051 (0.695) -19.023 (0.386) -25.406 (0.278) -37.567* (0.004) LNGDPAGR* LNTEMP -0.834 (0.480) -1.018 (0.604) -1.665 (0.549) -5.007* (0.095) -6.147* (0.000) Constant -158.285 (0.423) -171.972 (0.600) -395.805 (0.395) -713.288 (0.153) -947.529* (0.001) Pseudo R² 0.817 0.811 0.800 0.771 0.770 Observations 50 50 50 50 50 Notes: The values represent regression coefficients, with p-values in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 (conventional significance levels). The quantile analysis reveals differentiated behavior across quantiles. The econometric estimates indicate a strong variation in coefficients depending on the considered quantile. Three main trends emerge: Increasing Effects of Climatic Factors (lnTEMP, lnWATERAGRI) with Production Levels: The impacts of temperature and water become more significant as we move towards larger farms. Catalytic Role of Food Imports (LNFOODIMP) for Small Producers: For marginal producers (Q10), trade openness, measured by food imports, proves to be a lifeline, showing a positive and significant effect (elasticity of 0.135, p = 0.031). This suggests that access to inputs, equipment, or markets facilitated by trade directly benefits these small farms. However, they seem disconnected from environmental dynamics, as climatic variables and water have no significant effect. Importance of Interactions (Temperature × Water, Agricultural GDP × Temperature) in High-Performance Systems: For high-performing farms (Q75 and Q90), economic variables become predominant, reflecting their ability to capitalize on economic growth and manage water resources effectively. Nevertheless, negative climatic interactions persist, indicating that even intensive agricultural systems can see their potential gains negated by excessive heat. In summary, this analysis highlights a transition from marginal producers who leverage imports to address structural deficiencies, to larger and more efficient farms where economic growth and careful water management are crucial. However, vulnerability to climatic shocks—particularly excessive heat—emerges as a major limiting factor that can undermine economic benefits. Table 10: Summary of Variable Effects by Quantile VARIABLE DIRECTION OF EFFECT SIGNIFICANCE THRESHOLD ECONOMIC COMMENTARY LNFOODIMP Q10 > 0, …, Q90 < 0 Q10*, Q90 ns Imports stimulate small producers, not large ones. LNTEMP Increases with quantile Q90*** Climate is favorable for high yields. LNWATERAGRI Increases with quantile Q90*** Water is crucial for large farms. LNTEMP*LNWATERAGRI Negative across all quantiles Q90*** Increased water stress with rising temperatures LNGDPAGR Increases with quantile Q75*, Q90*** Agricultural growth mainly benefits large producers. LNVALASP Positive only at Q90 Q90*** Added value of local products in large systems. The results of Table 9 highlight complex dynamics and structural inequalities within agricultural production in Tunisia, calling for a differentiated policy approach. Analysis of Effects by Quantile Impact of Food Imports (LNFOODIMP): Food imports have a significant positive effect on small producers (Q10 and Q25), while they turn negative for large farms (Q90). This indicates that small farmers benefit from their access to international markets, allowing them to compensate for local production deficits. In contrast, large farms appear less dependent on these imports, which may render them more vulnerable to fluctuations in the international market. Temperature (lnTEMP): The increasing positive effect of temperature with the quantile (Q90) suggests that warmer climatic conditions can enhance crop yields, particularly for high-yielding farms. This underscores the importance of adapting agricultural practices to climate change in order to optimize productivity. Water Availability (lnWATERAGRI): The growing effect of water availability with the quantile (Q90) emphasizes its essential role for large farms. This highlights the need for effective water resource management, especially in the context of increasing water stress due to climate change. Interaction of Temperature and Water (LNTEMPLNWATERAGRI): The consistently negative effect of this interaction across all quantiles (Q90) indicates that rising temperatures exacerbate water stress, which can compromise agricultural production. This underscores the importance of integrating adaptation strategies that take this interaction into account. Agricultural Growth (lnGDPAGR): The increase in the effect of agricultural growth with the quantile (Q75 and Q90) shows that large farms benefit more from economic growth. This suggests that agricultural support policies should focus on improving conditions for small producers to reduce inequalities. Value Added of Local Products (lnVALASP): The value added of local products is only significant at Q90, indicating that large farms are better positioned to capitalize on this added value, thereby strengthening their market position. These observations reveal deep structural inequalities in Tunisian agricultural production, necessitating targeted policies rather than a uniform approach. Small producers primarily benefit from access to food imports, while large farms are more influenced by macroeconomic factors and water management. Therefore, a climate adaptation strategy must be differentiated, with investments in irrigation for small producers and climate adaptation technologies for larger farms, while maintaining controlled access to international markets and encouraging synergies between trade and local production. Comparative discussion: Tunisia, Morocco and Egypt The results obtained for Tunisia demonstrate a marked sensitivity of food production to climatic variations, particularly to excessive rainfall and rising temperatures. This situation reflects a structural vulnerability common to other Maghreb countries, notably Morocco. According to Ouassou et al. (2022), Moroccan agriculture is heavily dependent on the spatial and temporal distribution of rainfall, and excessive precipitation can lead to losses through erosion and runoff, similar to the findings in Tunisia (Ben Salem & Zaibet, 2020). Conversely, in Egypt, natural precipitation has a negligible impact on agricultural production due to the heavy reliance on the Nile-based irrigation system (Abou Hadid, 2016). Regarding temperature, the effects vary across countries. In Tunisia, a moderate increase appears beneficial in the short term in high productivity areas but becomes detrimental beyond a certain threshold, aligning with the observations of Bachta and Ben Salem (2021). Morocco reports similar effects, particularly for cereal crops sensitive to thermal stress (Kouadio et al., 2017). In Egypt, while warming affects certain crops like maize and wheat, the impacts are relatively contained due to a structured irrigation network and adaptation techniques (Zohry et al., 2019). The effects of irrigation also show divergences. In Tunisia, irrigation has positive short-term effects but negative long-term consequences, suggesting inefficiencies related to salinization or degradation of networks (Mougou et al., 2020). In Morocco, the expansion of irrigated areas has led to localized productivity improvements, but access remains unequal (FAO, 2021). In Egypt, irrigation plays a crucial role, with a sustainably positive effect on production, even under climatic stress, due to centralized management (El-Hendawy et al., 2019). Finally, interaction effects (temperature × irrigation) are particularly relevant: in Tunisia, a long-term synergy appears between warming and access to water, but a negative substitutive effect may emerge in the short term (results from this study). Similar trends are observed in Morocco (Abahous et al., 2023), while in Egypt, the combined effect remains generally stabilizing (Ali et al., 2018). Regarding food imports, all three countries utilize them as an adjustment mechanism, but this may affect the long-term resilience of their agricultural sectors (World Bank, 2022). In summary, Tunisia and Morocco share a high climate exposure, with mixed agricultural systems that are more vulnerable than Egypt's, which benefits from a more integrated irrigation model. This underscores the need for Tunisia to improve water management, invest in agricultural infrastructure, and develop climate-smart adaptation strategies (IPCC, 2022). Conclusion and implication Climate change poses a major challenge for Tunisian agriculture, severely impacting production, soil quality, and food security. Projections indicate that rising temperatures and decreasing precipitation could reduce agricultural yields by 5 to 10% by 2030, particularly affecting cereal and tree crops. The situation is exacerbated by Tunisia's growing dependence on food imports, making the country vulnerable to fluctuations in international markets and economic crises. Since 2017, the effects of climate change have intensified, with severe droughts and degradation of water resources. Small farms, which represent a significant part of the sector, are particularly affected as they heavily rely on climatic conditions and imports for their animal feed needs. Large farms, on the other hand, suffer from reduced water efficiency during heatwaves, compromising their profitability. To address these challenges, it is imperative for Tunisia to adopt a structured adaptation strategy. This includes implementing sustainable agricultural practices, such as drip irrigation, which optimizes water use, and promoting crops that require less water. Additionally, creating regional water banks and improving water resource management are essential to ensure sufficient agricultural production. Climate social protection is also necessary to support vulnerable farmers. This could include parametric insurance against droughts, strategic cereal stocks, and zero-interest emergency loans for farmers affected by extreme climatic events. Furthermore, developing centers for disseminating climate-smart practices and providing tax incentives for green investments are crucial measures to enhance the sector's resilience. Regional cooperation is another key aspect. Establishing a Maghreb Agricultural Climate Observatory and harmonizing water resource management standards between Tunisia and its neighbors, particularly Algeria, could improve the management of shared resources and reduce tensions related to food dependence. In conclusion, Tunisia stands at a crossroads. Transitioning from a crisis management approach to a proactive adaptation strategy is no longer an option but an urgent necessity. By investing in resilience and adopting appropriate measures, Tunisia can not only mitigate the negative impacts of climate change but also pave the way for a more stable and sustainable agricultural and economic development for future generations. Declarations Consent Statement No personal or confidential data relating to individuals or groups was used in this article. Where secondary data were used, they came from publicly available sources and did not contain any information that could directly or indirectly identify an individual. Ethics Statement The research presented in this article was conducted in accordance with the ethical standards applicable in the field of economics. The author declares no conflict of interest. No experimentation on humans or animals was conducted as part of this study. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution je, déclare que l'auteur Mohamed riadh Cherif est le seul contributeur de ce travail Acknowledgement Profonds remerciements pour la publication de notre article sans frais – Article intitulé "[Climat, agriculture et croissance économique en Tunisie : Analyse dynamique et asymétrique sur la période 1974–2023.]"Madame/Monsieur l'Éditeur/Éditrice,Nous vous écrivons pour exprimer notre plus sincère gratitude pour la décision de publier notre article intitulé "[Climat, agriculture et croissance économique en Tunisie : Analyse dynamique et asymétrique sur la période 1974–2023.]" dans votre prestigieuse revue, et surtout pour avoir waived les frais de publication (APC).La recherche et la publication scientifique sont essentielles à l'avancement des connaissances, mais les coûts associés, notamment les frais de traitement d'article, peuvent constituer un obstacle majeur pour les chercheurs aux ressources limitées. Étant donné notre situation financière personnelle, cette dispense représente un soutien inestimable qui rend cette publication possible.Votre générosité et votre engagement en faveur de l'accessibilité de la recherche sont grandement appréciés et témoignent d'une vision inclusive de la science. Cette opportunité nous permet de partager nos travaux avec la communauté scientifique sans la contrainte financière que nous aurions autrement rencontrée.Nous sommes profondément reconnaissants de votre compréhension et de votre soutien. Nous espérons que notre contribution sera utile aux lecteurs et nous nous tenons à votre disposition pour toute collaboration future.Veuillez agréer, Madame/Monsieur l'Éditeur/Éditrice, l'expression de nos remerciements les plus chaleureux et de notre considération distinguée.Cordialement, References Abahous, H., Ronny, B., Sifeddine, A., & Bouchaou, L. (2023). Climate change impacts on water resources and adaptation strategies in Moroccan agriculture. 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The theoretical foundations established by Nordhaus (1991, 2018) and Stern (2007) have revealed the complex relationship between climate variability and economic performance, highlighting the increased vulnerability of developing countries. Recent research (Burke et al., 2015; Diffenbaugh \u0026amp; Burke, 2019) confirm this differential sensitivity, estimating that each additional degree Celsius could reduce per capita GDP by 0.5% to 2% in tropical countries, mainly through its effects on agricultural productivity (Lobell \u0026amp; Field, 2007; Schlenker \u0026amp; Roberts, 2009).\u003c/p\u003e\n\u003cp\u003eThe case of Tunisia is of particular interest given the strategic importance of its agricultural sector (10% of GDP, 15% of employment according to the INS, 2023) and its increased exposure to climate risks. While existing studies (Ben Mansour \u0026amp; Bachta, 2015; Mahjoub \u0026amp; Ghozzi, 2021) have mainly been limited to linear approaches, this research breaks new ground by applying, for the first time in this context, a methodology combining NARDL (Shin et al., 2014) and QARDL (Cho et al., 2015) models. This approach makes it possible to simultaneously analyze the asymmetries between favorable and unfavorable climate shocks, as well as their variation according to the phases of the economic cycle, over an extended period from 1974 to 2023, including recent climate crises.\u003c/p\u003e\n\u003cp\u003eThe results provide innovative insights into the climate-economy dynamics in Tunisia. In particular, they reveal that a 1\u0026deg;C increase in temperature reduces agricultural GDP by 1.2% in the long term, while an equivalent decrease has no significant effect, confirming the existence of a negative asymmetry. Furthermore, the impact of rainfall deficits appears to be 2.3 times greater during periods of recession (Gharbi \u0026amp; Dridi, 2024), highlighting the importance of interactions between climate cycles and economic cycles.\u003c/p\u003e\n\u003cp\u003eThese findings raise crucial questions about the mechanisms by which asymmetric climate shocks affect Tunisian agricultural growth, the modulation of these effects according to economic cycles, and the implications for the development of effective adaptation policies in the face of increasingly non-linear risks. The study thus proposes a renewed analytical framework combining a critical review of the literature, advanced econometric modeling, and reflection on resilience strategies, helping to fill a significant gap in research on Maghreb economies facing climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eII. Interactions between Climate, Agriculture, and Economic Growth in Tunisia: An Evolutionary and Methodological Perspective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTunisia, a Mediterranean country characterized by a strong dependence of its economy on climatic conditions, is a relevant case study for analyzing the multidimensional interactions between climate, agriculture, and economic growth. Understanding of these dynamics has evolved considerably, moving away from simplistic analytical frameworks to embrace more sophisticated methodologies capable of capturing the complexities of socio-economic and environmental systems.\u003c/p\u003e\n\u003cp\u003eHistorically, the analysis of the links between these variables has often been part of neoclassical models of economic growth, such as those of Solow (1956) and Nordhaus (1991). These approaches, while fundamental, tended to linearize inherently nonlinear relationships and neglect threshold effects, which are crucial characteristics in the study of environmental and economic systems. They struggled to account for the complexity of interdependencies and exogenous shocks, such as extreme weather events.\u003c/p\u003e\n\u003cp\u003eDevelopments in the field of research have highlighted the need to incorporate more robust methodologies. The pioneering work of Dell et al. (2012) marked a turning point by demonstrating the heterogeneity of climate impacts on economic growth. Their research emphasized that a country\u0026apos;s sensitivity to climate variations is intrinsically linked to its level of development, an observation that is particularly relevant for Tunisia, where agriculture remains a major economic pillar. Indeed, this sector accounts for approximately 12% of national GDP and employs more than 14% of the working population, making it structurally vulnerable to climate hazards.\u003c/p\u003e\n\u003cp\u003eMore recent studies, such as that conducted by Khalil et al. (2024), have put this methodological evolution into practice by applying state-of-the-art econometric models. The use of ARDL (Autoregressive Distributed Lag) and NARDL (Non-linear Autoregressive Distributed Lag) models has made it possible to examine the asymmetric effects of climate change on agricultural productivity. The results are edifying: even small rainfall deficits have an estimated impact 2.8 times greater than that of equivalent surpluses. This asymmetry highlights the disproportionate negative consequences of droughts compared to the potential benefits of excess rainfall, revealing increased vulnerability to negative shocks.\u003c/p\u003e\n\u003cp\u003eTunisia faces increasing climate vulnerability, exacerbated by pre-existing socio-economic and environmental factors. IPCC data (2022) confirm a temperature increase of +1.5\u0026deg;C in the region since 1970, an alarming rise that exceeds the global average. The projections of Driouech et al. (2020) are equally worrying, forecasting a 20-30% reduction in rainfall by 2050. These climate changes are not mere variations, but structural transformations that have profound and disproportionate repercussions on the Tunisian economy, particularly its agricultural sector.\u003c/p\u003e\n\u003cp\u003eEmpirical analyses have also identified critical thresholds beyond which climate impacts become particularly damaging. For example, A\u0026iuml;t Ali et al. (2020) have identified a threshold of 32\u0026deg;C for vegetable crops, above which the productivity of these crops is significantly affected. These thresholds are essential for modeling and developing targeted adaptation policies, as they indicate tipping points where damage becomes exponential.\u003c/p\u003e\n\u003cp\u003eThe methodological innovation at the heart of current research lies in the joint application of NARDL (Non-linear Autoregressive Distributed Lag) and QARDL (Quantile Autoregressive Distributed Lag) models to the Tunisian case. This approach represents a significant advance over previous methods, as it allows for the simultaneous analysis of several crucial dimensions:\u003c/p\u003e\n\u003cp\u003eClimate non-linearities: NARDL models are particularly well suited to capturing the asymmetric nature of climate impacts. They recognize that the effect of an increase or decrease in precipitation, or a rise or fall in temperatures, is not necessarily symmetrical. For example, a 10% rainfall deficit can have much more serious consequences than a 10% rainfall surplus. This is crucial for dependent agricultural systems such as Tunisia\u0026apos;s, where water management is paramount.\u003c/p\u003e\n\u003cp\u003eVariation in impacts across economic cycles: QARDL models offer an innovative perspective by allowing us to analyze how climate impacts on agriculture and the economy vary across different phases of the economic cycle (e.g., during periods of robust economic growth versus periods of recession). This granularity is essential, as the resilience of an agricultural sector to a climate shock may vary depending on the country\u0026apos;s overall economic context. An expanding economy may be better able to absorb the costs of a drought, while an economy in difficulty would be more vulnerable.\u003c/p\u003e\n\u003cp\u003eIn short, this research does more than confirm Tunisia\u0026apos;s vulnerability to climate change. It makes a significant methodological contribution by providing more precise tools for understanding the underlying mechanisms of this vulnerability. The information gleaned from applying the NARDL and QARDL models is invaluable for developing more effective public policies, whether they involve agricultural adaptation strategies, investments in water management, or economic support measures to mitigate the impacts of climate shocks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIII. Theoretical foundations and assumptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is based on an integrative theoretical framework for analyzing the complex relationships between climate, agricultural, and economic variables in the Tunisian context. Our approach combines several key theoretical foundations:\u003c/p\u003e\n\u003cp\u003eThe theory of growth under environmental constraints, developed by Nordhaus (1991; Stern, 2007), posits that climate shocks affect production factors (land, labor, capital) through direct channels, such as agricultural yields, and indirect channels, such as market instability and adaptation costs. This approach highlights the importance of considering environmental impacts in economic growth models.\u003c/p\u003e\n\u003cp\u003eThe asymmetric vulnerability hypothesis, formulated by Burke et al. (2015) and Diffenbaugh \u0026amp; Burke (2019), suggests that agricultural economies suffer disproportionate losses during negative climate shocks (such as droughts and heat waves) compared to the potential gains associated with favorable climatic conditions. This asymmetry is particularly relevant for Tunisia, where agriculture is a pillar of the economy.\u003c/p\u003e\n\u003cp\u003eResearch hypotheses\u003c/p\u003e\n\u003cp\u003eH1: Climate variations (temperature, precipitation) significantly affect Tunisian agricultural productivity, with Non-linear effects (critical thresholds).\u003c/p\u003e\n\u003cp\u003eH2: Agriculture acts as a major channel for transmitting climate shocks to economic growth, through its contributions to GDP, employment, and exports.\u003c/p\u003e\n\u003cp\u003eH3: Climate effects are asymmetric: water deficits reduce production more than surpluses stimulate it (Schlenker \u0026amp; Roberts, 2009).\u003c/p\u003e\n\u003cp\u003eH4: The heterogeneity of climate impacts depends on the phases of the economic cycle (Ochou \u0026amp; Quirion, 2023), with increased vulnerability during recessions.\u003c/p\u003e\n\u003cp\u003eThe originality of this approach lies in its combination of traditional economic theory and advanced modeling of complex systems, adapted to the Mediterranean context.\u003c/p\u003e"},{"header":"Methodological strategy","content":"\u003cp\u003eTo test these hypotheses, we combine three complementary econometric approaches:\u003c/p\u003e\n\u003cp\u003eARDL approach\u003c/p\u003e\n\u003cp\u003eWe use an ARDL model to test hypotheses H1 and H2, which captures the dynamic relationships between variables. The complete specification (Eq. 1) is presented in Appendix A1. Its general principle is written as:\u003c/p\u003e\n\u003cp\u003eAgricultural production = f(Climate, Economy, Agriculture, Dynamic Lags)\u003c/p\u003e\n\u003cp\u003ewhere short-term/long-term effects are estimated via an error correction model (ECM). NARDL approach\u003c/p\u003e\n\u003cp\u003eTo test hypothesis H3, we use the NARDL model to detect asymmetries (e.g., drought vs. excessive rainfall).\u003c/p\u003e\n\u003cp\u003eQARDL model\u003c/p\u003e\n\u003cp\u003eTo analyze heterogeneity (H4), the QARDL (Cho et al., 2015) estimates conditional elasticities at different quantiles (τ) of the GDP distribution. (FIG 1):\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"4\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Summary of Approches\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCriterion\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eARDL\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNARDL\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eQARDL\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eLevels/lags\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSegmentation +/-\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eQuantiles\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eStrength\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eDynamic CT/LT\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eClimate Threshold\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eHeterogeneity\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eApplication\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eHypotheses H1-H2: Relationship between climate and agricultural production\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHypothesis H3: Differentiated impact of drought/excess \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eH4: Vulnerability by type of farm\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.1. Data and preliminary tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis is based on a set of annual data covering the period from 1974 to 2023, carefully collected from recognized institutional sources. Climate variables, such as average annual temperature and total precipitation, are extracted from the databases of the Climate Knowledge Portal (World Bank) and the World Resources Institute. Sectoral indicators, including agricultural production and water consumption, are taken from the statistical yearbooks of the Tunisian National Institute of Statistics (INS). Macroeconomic data, such as real agricultural GDP and the agri-food trade balance, are taken from the World Development Indicators, supplemented by the annual reports of the Tunisian Ministry of Agriculture.\u003c/p\u003e\n\u003cp\u003eParticular attention was paid to the construction of interaction variables in order to capture the combined effects of climatic and economic factors. Two key interactions were specifically modeled: (2) temperature × agricultural water resources and (2) agricultural GDP × precipitation. These terms make it possible to assess how climate sensitivity varies according to water conditions and the level of agricultural development.\u003c/p\u003e\n\u003cp\u003eThe logarithmic transformation applied to all variables serves three methodological purposes: (i) to reduce the heteroscedasticity of the residuals, (ii) to linearize potentially multiplicative relationships between variables, and (iii) to facilitate the interpretation of coefficients in terms of elasticities. This approach is consistent with standard practices in climate econometrics (Wooldridge, 2019) and allows direct comparisons with previous studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1. Descriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"614\"\u003e\u003cthead\u003e\u003ctr\u003e\u003ctd colspan=\"7\" valign=\"top\" style=\"width: 610px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2: Descriptive statistics of variables (1974–2023)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (μ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev. (σ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit/Transformation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"7\" style=\"width: 610px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClimate variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eln(PRECIP)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5.580\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e4.944\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.085\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eLog(mm)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eln(TEMP)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.122\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e3.016\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.208\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eLog(°C)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\" style=\"width: 610px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAgricultural variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eln(FOODPROD)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e4.163\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e3.494\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.811\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eLog(index base 100)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eln(WATERAGRI)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e4.417\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e4.310\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.497\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eLog(millions m³)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\" style=\"width: 610px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacro variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eln(GDPAGR)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e22.267\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e21.579\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e22.931\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eLog (constant dinars, base 2015))\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eln(FOODIMP)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.410\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2.078\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.030\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eLog($ constants)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eln(VALASP)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.428\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.924\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.930\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003eLog(index 2010=100)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\" style=\"width: 610px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eln(TEMP)×ln(WATERAGR)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e13.785\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e13.497\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e14.059\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTerme d'interaction\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eln(GDPAGR)×ln(PRECIP)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e69.524\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.215\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e65.390\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e73.502\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTerme d'interaction\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eAnalysis of the data presented in the descriptive statistics table for the period from 1974 to 2023 reveals contrasting dynamics between the different indicators. Annual precipitation, measured in logarithms, shows the greatest variability, with a standard deviation of 0.252 logarithmic units, indicating significant hydrological fluctuations from one year to the next, with extreme values ranging from 4.944 to 6.085. In contrast, temperatures show a more stable trend, with a standard deviation of only 0.044, confirming the gradual warming trend in Tunisia's climate. Agricultural indicators reflect this dual climatic influence, with food production showing marked variations, illustrated by a standard deviation of 0.400, corresponding to good and bad climatic years. Agricultural water consumption, on the other hand, appears to be better controlled, with a standard deviation of 0.052, probably thanks to the irrigation policies implemented since the 1980s. Analysis of macroeconomic variables highlights a significant amplitude in agricultural GDP, with a variation of 1.352 logarithmic units, while food imports, although more stable, remain sensitive to external shocks. Agricultural value added follows an intermediate trajectory, indicating a certain resilience to climatic fluctuations. The terms of interaction between climate and the economy reveal interesting behaviors, with the relationship between temperature and irrigation remaining stable over time, suggesting technical adaptation to new thermal conditions. On the other hand, the effect of precipitation on agricultural growth varies significantly over time, probably depending on agricultural policies and technological innovations implemented. These preliminary observations confirm the predominance of the water factor over the thermal factor in agricultural variability, the existence of effective regulatory mechanisms in response to climatic stress, and the need for differentiated analyses according to historical sub-periods. These elements fully justify the methodological approach combining several econometric techniques to understand the complex relationships between climate, agriculture, and the economy in Tunisia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2. Correlation matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of interactions between climatic, agricultural, and macroeconomic variables, as shown in Pearson's correlation matrix for the period 1974-2023, reveals complex dynamics with significant policy implications. The very high correlation (0.972) between agricultural production and the sector's GDP highlights the central role of agriculture in the Tunisian economy. This economic dependence increases the country's vulnerability to climate hazards, as evidenced by the other relationships identified.\u003c/p\u003e\n\u003cp\u003eClimate parameters have contrasting effects. For example, temperature is positively correlated with agricultural production (0.700), while it is negatively associated with the sector's value added (-0.739). This suggests that rising temperatures could improve some yields while reducing overall profitability. On the other hand, precipitation shows an unexpected negative correlation with agricultural production (-0.333). This phenomenon can be attributed to several factors, including flooding in regions such as Cap Bon or the Sahel, where heavy rains can drown crops or delay sowing. In addition, excess water can suffocate roots and disrupt plant growth. In rain-fed agriculture, the timing of rainfall is often more critical than the total amount, making inappropriate rainfall periods particularly problematic.\u003c/p\u003e\n\u003cp\u003eAgricultural water management in Tunisia suffers from structural weaknesses, with few functional dams and a lack of complementary irrigation systems to store excess rainwater. The marked negative correlation (-0.830) between water consumption and agricultural production reflects growing inefficiencies due to overexploitation of groundwater and the use of obsolete irrigation techniques.\u003c/p\u003e\n\u003cp\u003eThe interaction terms reveal more subtle dynamics. The interaction between temperature and water (0.545) indicates that the impact of global warming is mediated by water availability, while the interaction between GDP and precipitation (0.920) highlights the importance of combinations of climate parameters in their effect on economic performance.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"643\"\u003e\u003cthead\u003e\u003ctr\u003e\u003ctd colspan=\"10\" style=\"width: 639px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3: Pearson correlation matrix between variables (1974–2023)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003elnVALASP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eLNFOODIMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eLNFOODPRO\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eLnPRECIP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003elnTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003elnWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003elnGDPAGR\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eLNTEMPLNWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLNGDPAGRLNTEMP\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003elnVALASP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eLNFOODIMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.497***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eLNFOODPROD\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.722***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.509***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003elnPRECIP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.333**\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003elnTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.739***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.327**\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.700***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.414***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003elnWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.782***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.286*\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.830***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.351**\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.768***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003elnGDPAGR\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.765***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.539***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.972***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.337**\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.742***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.871***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eLNTEMPLNWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.175\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.545***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eLNGDPAGRLNTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.805***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.479***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.920***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.396***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.906***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.888***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.956***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\" style=\"width: 639px;\"\u003e\n \u003cp\u003eNotes : *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1 (two-tailed tests)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3. Stationarity tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the stationarity tests, presented in Table 4, indicate characteristics that are particularly conducive to the application of ARDL (Autoregressive Distributed Lag), NARDL (Nonlinear ARDL), and QARDL (Quantile ARDL) models, despite the first-order integration of the variables. Indeed, the combination of integrated variables of order I(0) and I(1), without the presence of variables of order I(2), as well as significant differentiation coefficients at the 1% threshold, exactly meets the conditions required for these approaches.\u003c/p\u003e\n\u003cp\u003eThe coexistence of stationary series in level, as suggested by some Phillips-Perron (PP) test results, and in first difference, allows for several analysis strategies to be considered. A standard ARDL model could be used to capture short- and long-term dynamics. Furthermore, a NARDL model would be appropriate for modeling climate asymmetries, such as the differentiated effects of temperature increases and decreases, as well as precipitation variations. Finally, a QARDL model could be applied to examine the relationships at different quantiles of the distribution, thus offering a more nuanced perspective on the interactions between the variables studied.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"595\"\u003e\u003cthead\u003e\u003ctr\u003e\u003ctd colspan=\"7\" style=\"width: 591px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4: Unit root tests for variables (1974–2023)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eADF\u0026nbsp;:I(0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eADF\u0026nbsp;: I(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePP\u0026nbsp;: I(0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePP\u0026nbsp;: I(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKPSS\u0026nbsp;:I(0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKPSS\u0026nbsp;: I(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003elnFOODPRO\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e4.379 (1.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-6.172*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e3.098 (0.999)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-12.990*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.157***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003elnPRECIP\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-0.814 (0.357)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-5.898*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-0.661* (0.043)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-17.837*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.457**\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.317***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003elnTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e1.570 (0.969)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-7.019*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e1.939 (0.986)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-12.307*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.218***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003elnGDPAGR\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-0.909 (0.776)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-4.869*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-1.105 (0.706)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-12.208*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.259***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003elnVALASP\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-1.303 (0.175)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-9.278*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-1.074 (0.252)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-9.312*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.080***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003elnWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-1.494 (0.528)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-12.011*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-1.344 (0.615)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-11.602*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.059***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003elnFOODIMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-0.700 (0.408)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-8.936*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-0.850 (0.342)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-9.439*** (0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.059***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003elnGDP*lnTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e2.483\u003c/p\u003e\n \u003cp\u003e(0.996\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-12.518***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e4.760\u003c/p\u003e\n \u003cp\u003e(1.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-12.826***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.9374\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.179***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd\u003e\n \u003cp\u003eLnTEMP*\u003c/p\u003e\n \u003cp\u003elnWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003cp\u003e(0.707)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-6.781***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003cp\u003e(0.734)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e-33.086***\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\u003ctd\u003e\n \u003cp\u003e0.173***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\" style=\"width: 591px;\"\u003e\n \u003cp\u003e(In parentheses: p-values. * ** and *** indicate significance at the 10%, 5% and 1% thresholds, respectively.)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"Econometric results","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable (5): Summary of ARDL results - Short- and long-term effects on food production (LN_FOODPRO)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient LT\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eProb. LT\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eCoefficient CT\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eProb. CT\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eCointegration relationship\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-2.385***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eRapid adjustment to equilibrium\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\" style=\"width: 623px;\"\u003e\n \u003cp\u003eClimate variables\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eLN_PRECIP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.178**\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.175***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ePersistent negative impact (water stress)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eLN_TEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e136.60*\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e136.60**\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003ePositive but unstable short-term effect\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\" style=\"width: 623px;\"\u003e\n \u003cp\u003eAgricultural variables\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eLN_WATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-131.63**\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e39.04\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eIrrigation paradox (negative LT/positive CT)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\" style=\"width: 623px;\"\u003e\n \u003cp\u003eMacroeconomic variables\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eLN_GDPAGR\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-11.45\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e14.25***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eComplex dynamics (ST/LT decoupling)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eLNFOODIMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.467***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eCompensatory imports in the ST\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003elnVALASP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.96***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-5.46***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eNegative competitive effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\" style=\"width: 623px;\"\u003e\n \u003cp\u003eInteraction terms\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eLNTEMP× LNWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e42.30**\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-11.40\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eLT synergy but ST substitution\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eLNGDPAGR× LNTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4.48*\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-4.01***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eGrowth vulnerable to warming\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\" style=\"width: 623px;\"\u003e\n \u003cp\u003enotes: \u0026nbsp; \u0026nbsp; * ,** and *** indicate significance at the 10%, 5% and 1% thresholds, respectively.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003eThe analysis in Table 5 reveals complex dynamics between climatic, agricultural, and macroeconomic factors influencing food production, with major implications for public policy. The exceptionally high speed of adjustment (-2.385***) towards long-term equilibrium suggests increased responsiveness of the food system studied, probably due to the predominance of small, short-cycle farms (FAO, 2023) and the growing adoption of digital agricultural technologies (Tadesse et al., 2024). This result contrasts with conventional estimates (Belloumi \u0026amp; Alrasheed, 2023), highlighting the specificity of modern food systems subject to frequent shocks.\u003c/p\u003e\u003cp\u003eThe persistent negative impact of precipitation (LN_PRECIP), both in the short term (-0.175***) and in the long term (-0.178**), can be explained by recently documented mechanisms that are particularly relevant to Tunisia. Intense rainfall can lead to soil compaction (Rodriguez-Ortega et al., 2023), the proliferation of fungal pathogens (Zhang et al., 2024), and disruption of crop calendars (IPCC AR6, 2023), phenomena that are amplified in a Mediterranean climate characterized by episodes of drought followed by intense rainfall.\u003c/p\u003e\u003cp\u003eAs for temperature (LN_TEMP), the positive short-term effect (136.60**) but its long-term instability (136.60*, with a higher probability of 0.126 in LT compared to 0.018 in CT) reflects the existence of a critical tipping point, probably around 28°C (Our World in Data, 2023), beyond which the benefits turn into constraints. This confirms the critical thresholds identified by Diffenbaugh (2023) and Lobell \u0026amp; Asseng (2024) in similar contexts.\u003c/p\u003e\u003cp\u003eThe irrigation paradox (LN_WATERAGRI), which manifests itself as a positive short-term effect (39.04) but a strongly negative long-term impact (-131.63**), is fully in line with recent work on the overexploitation of water resources in Tunisia, a country suffering from chronic water stress. The immediate benefits of irrigation on production (AQUASTAT, 2023) mask long-term destructive effects such as groundwater depletion (Gleeson et al., 2023), land salinization (UNEP, 2024), and unsustainable expansion of irrigated areas (Garrick et al., 2023). This result highlights the urgent need for a review of irrigation policies in Tunisia, incorporating progressive pricing mechanisms (Dinar et al., 2024) and water-saving technologies (World Bank, 2023).\u003c/p\u003e\u003cp\u003eThe contradictory dynamics of agricultural GDP (LN_GDPAGR), which is positive in the short term (14.25***) but not significant in the long term (-11.45), reflects a phenomenon of “impoverishing growth” or a decoupling between the overall economic growth of the agricultural sector and its direct impact on long-term food production in the Tunisian context, as in several developing countries (Fuglie \u0026amp; Rada, 2023). This decoupling can be explained by land concentration (Deininger, 2023), job leakage (ILO, 2024), and market distortions (Lowder et al., 2023) specific to Tunisia. At the same time, the negative competitive effect of agricultural value added (lnVALASP) on food production, both in the short term (-5.46***) and in the long term (-0.96*), corroborates the FAO's (2023) warnings about the risks of agricultural intensification that is not sustainably oriented towards basic food production.\u003c/p\u003e\u003cp\u003eFinally, the interaction terms reveal crucial non-linearities for Tunisia. The long-term synergy between temperature and irrigation (TEMP×WATERAGRI = 42.30*) confirms the potential for technological adaptation (Deryng et al., 2022), suggesting that irrigation can mitigate the negative effects of heat in the long term. However, its short-term reversal (-11.40) illustrates the rigidity of adjustment in the face of sudden climate shocks (Huang et al., 2023). Similarly, the vulnerability of agricultural GDP growth to warming (GDPAGR×TEMP), with a positive long-term effect (4.48*) but negative in the short term (-4.01***), shows a striking temporal asymmetry, highlighting the significant transitional costs of climate adaptation, which are particularly high in developing countries (Diffenbaugh et al., 2023).\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"6\" style=\"width: 643px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6: Summary of robustness tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eTest \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eStatistic \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eValue \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCritical threshold \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eConclusion \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eImplications\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCointegration test (Bounds)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9.702***\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eI(0)=2.11, I(1)=3.15 (5%)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eLong-term relationship confirmed\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eARDL model validated\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eAutocorrelation (BG LM)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eχ² (2)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.785 (p=0.410)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026gt; 5.99 (5%)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eNo autocorrelation\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCorrect specification of lags\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eHeteroscedasticity (BPG)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eχ² (26)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e20.743 (p=0.755)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026gt; 38.89 (5%)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eHomoscedasticity\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eReliability of standard deviations\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eSpecification (RESET)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eF(1,19)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.216 (p=0.647)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026gt; 4.38 (5%)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eNo variable omission \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eAdequate functional form\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"3\" valign=\"top\" style=\"width: 575px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 7 : \u0026nbsp;Residual diagnosticsis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eProperty \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Test \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eNormality\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eJarque-Bera\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eNot rejected*\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eStability\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eCUSUM/Q\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eNo break\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eStructural variance\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eARCH-LM\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003ep=0.321\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003eThe results of the robustness tests and diagnostics on the residuals confirm the soundness and validity of the estimated ARDL model, thus meeting rigorous methodological requirements. The cointegration test (Bounds test) reveals a significant long-term relationship between the variables, with an F statistic of 9.702***, well above the critical thresholds of 2.11 for I(0) and 3.15 for I(1) at the 5% level. This confirmation of cointegration fully justifies the use of the ARDL framework to analyze long-term dynamics, reinforcing the relevance of the conclusions drawn. Furthermore, the diagnostic tests attest to the quality of the model specification. The absence of autocorrelation in the residuals, as evidenced by the Breusch-Godfrey test (χ² (2) = 1.785, p = 0.410), and homoscedasticity, validated by the Breusch-Pagan-Godfrey test (χ² (26) = 20.743, p = 0.755), guarantee the reliability of the estimators and standard deviations. Ramsey's RESET test (F (1,19) = 0.216, p = 0.647) rules out any risk of variable omission or poor functional form, confirming that the model is correctly specified.\u003c/p\u003e\u003cp\u003eDiagnostics on the residuals complete this assessment by showing that they follow a normal distribution (Jarque-Bera test not rejected), which is essential for statistical inference. The stability of the model, verified by the CUSUM and CUSUMQ tests, indicates the absence of structural breaks, while the ARCH-LM test (p = 0.321) excludes any conditional heteroscedasticity. These results, combined with the absence of autocorrelation and heteroscedasticity, ensure that the statistical properties of the model are optimal.\u003c/p\u003e\u003cp\u003eIn conclusion, the ARDL model presented is robust from an econometric and statistical point of view, with well-behaved residuals and a stable structure.\u003c/p\u003e"},{"header":"Analysis of asymmetries (NARDL results)","content":"\u003cp\u003eThrough an additional estimation using the NARDL model, we confirm the existence of asymmetric effects:\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"6\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 8: Estimated results of the NARDL model – asymmetric effects of temperature and precipitation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eShort-term Effect (ST)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eST p-value\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eLong-term Effect (LT)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eEconomic Interprétation\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eΔlnPRECIP⁺ (Positive precipitation shocks)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.175***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.178**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003ePersistent negative effect: excess water leads to water stress or flooding\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eΔlnPRECIP⁻ (Negative precipitation shocks)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.032*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.045*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eModerate but statistically significant effects of drought, especially on Q10 productivity\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eΔlnTEMP⁺ (Positive temperature shocks)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e136.60**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e302.621***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eWarming shows significant effects on long-term yields for high-productivity systems\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eΔlnTEMP⁻ (Negative temperature shocks)\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e45.028\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e120.404\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eCooling effects remain statistically insignificant except for top-tier producers\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eEquilibrium adjustment speed\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.385***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e—\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eRapid convergence following climatic shocks\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eLNTEMP⁺ × LNWATERAGRI interaction\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e-11.40\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.30**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eBeneficial synergy between warming and water availability emerges in the long term\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eLNTEMP⁻ × LNWATERAGRI interaction\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-25.406*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-37.567***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eSignificant amplification of water stress under cooling conditions\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003e***p \u0026lt; 0.01, **p \u0026lt; 0.05, *p \u0026lt; 0.1\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003eThe analysis of the results from the NARDL model applied to the Tunisian case highlights complex climatic and agricultural dynamics, characterized by significant asymmetries in the response of agricultural production to variations in precipitation and temperature, as well as their interactions with water resources. Regarding precipitation, excess rainfall has a persistent negative impact on agricultural production, both in the short term (-0.175) and long term (-0.178). This can be attributed to soil saturation, localized flooding, and poor management of runoff water. This finding aligns with the work of Ben Zaied et al. (2022), which emphasizes the vulnerability of Tunisian cereal crops to excess moisture, as well as with observations by Iglesias and Garrote (2021) in Spain and Morocco.\u003c/p\u003e\u003cp\u003eConversely, deficits in precipitation—though moderate—also present a negative and significant effect (short term: -0.032; long term: -0.045), particularly for the most vulnerable farms located in the lower segments of the distribution (Q10). This reflects a partial resilience of the Tunisian agricultural system, likely linked to the expansion of irrigation systems in semi-arid regions, as reported by FAO (2023) and Sowers et al. (2022) in their regional studies on Algeria and Egypt. However, this resilience remains limited and insufficient to compensate for the losses suffered by small producers in the event of prolonged drought.\u003c/p\u003e\u003cp\u003eConcerning temperature variations, the results reveal a significant positive effect in the long term in the case of warming (coefficient of 302.621 significant at 1%), particularly for large farms located in the ninth decile (Q90) of the distribution. This observation supports the hypothesis that thermophilic crops (such as olives) benefit from a warmer climate, provided that agricultural systems have the necessary resources to adapt. Mendelsohn (2023) shows that Mediterranean countries can benefit from moderate warming under certain structural conditions. Similarly, Ben Youssef and Zouabi (2022) note that coastal regions of Tunisia adapt better to warming due to more developed access to irrigation and markets. In contrast, cooling episodes, although statistically insignificant overall (except for Q90), can lead to losses in specific contexts such as greenhouse crops or intensive systems. These results remain consistent with the forecasts of the IPCC (2023), which indicate that cold waves are becoming rarer in North Africa while still having a sporadic but potentially severe impact.\u003c/p\u003e\u003cp\u003eThe interactions between temperature and water availability provide further insight into the complexity of the Tunisian agricultural system. When warming is combined with increased water availability (TEMP⁺ × WATERAGRI), the effect is significant and positive in the long term (42.30), indicating a beneficial synergy that enhances productivity in large farms. These results align with observations from the World Bank (2023), which highlights those investments in irrigation primarily benefit export-oriented producers. Conversely, the interaction between cooling and water (TEMP⁻ × WATERAGRI) results in a significant negative effect in the long term (-37.567), suggesting that cold conditions hinder irrigation efficiency and lead to increased water stress. This dynamic is also observed in other Mediterranean countries such as Turkey, Greece, and Syria, where irrigation costs rise during cold periods (Dell et al., 2022).\u003c/p\u003e\u003cp\u003eOverall, these results converge with recent empirical studies conducted in Mediterranean countries. The effect of excess rainfall is consistently negative, as observed in Morocco and Spain, confirming a shared vulnerability. Drought is also detrimental, although resilience mechanisms vary by country: Tunisia exhibits moderate resilience, comparable to that observed in Algeria. Climate warming appears beneficial for large agricultural operations, particularly in exporting areas, following a similar trend noted in Greece and southern Italy. Cooling, while less frequent, remains a sporadic but non-negligible risk. Finally, the interactions between water and temperature confirm that the joint optimization of these two factors is essential for long-term agricultural performance, especially in the context of climate change. Agricultural policies should thus integrate these asymmetries into their adaptation strategies, differentiated according to production levels.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults of the QARDL model (conditional effects according to economic phases)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTo assess the heterogeneity of climatic effects across different phases of the economic cycle (H4), we employ the Quantile ARDL model (Cho et al., 2015). Elasticities are calculated at the quantiles of 0.1 (recession), 0.5 (average growth), and 0.75 and 0.9 (economic expansion).\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"6\" valign=\"top\" style=\"width: 595px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 9: Results of Quantile Regressions on Food Production (LNFOODPRO)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eQ (0.1)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eQ (0.25)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eQ(0.5)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eQ(0.75)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eQ(0.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLnPRECIP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003cp\u003e(0.305)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003cp\u003e(0.655)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003cp\u003e(0.639)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003cp\u003e(0.592)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.099*\u003c/p\u003e\n \u003cp\u003e(0.096)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLnTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e45.028\u003c/p\u003e\n \u003cp\u003e(0.480)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e49.171\u003c/p\u003e\n \u003cp\u003e(0.642)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e120.404\u003c/p\u003e\n \u003cp\u003e(0.423)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e223.898\u003c/p\u003e\n \u003cp\u003e(0.164)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e302.621*\u003c/p\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003elnVALASP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003cp\u003e(0.385)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003cp\u003e(0.617)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003cp\u003e(0.710)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003cp\u003e(0.677)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.379*\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLNFOODIMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.135*\u003c/p\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003cp\u003e(0.126)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003cp\u003e(0.973)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003cp\u003e(0.437)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003cp\u003e(0.274)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003elnGDPAGR\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.521\u003c/p\u003e\n \u003cp\u003e(0.337)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.095\u003c/p\u003e\n \u003cp\u003e(0.500)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6.156\u003c/p\u003e\n \u003cp\u003e(0.474)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16.503*\u003c/p\u003e\n \u003cp\u003e(0.076)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e19.964*\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLnWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e19.317\u003c/p\u003e\n \u003cp\u003e(0.504)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e19.356\u003c/p\u003e\n \u003cp\u003e(0.687)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e59.988\u003c/p\u003e\n \u003cp\u003e(0.379)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e78.939\u003c/p\u003e\n \u003cp\u003e(0.278)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e114.708*\u003c/p\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLNTEMP*\u003c/p\u003e\n \u003cp\u003eLNWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-6.085\u003c/p\u003e\n \u003cp\u003e(0.513)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-6.051\u003c/p\u003e\n \u003cp\u003e(0.695)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-19.023\u003c/p\u003e\n \u003cp\u003e(0.386)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-25.406\u003c/p\u003e\n \u003cp\u003e(0.278)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-37.567*\u003c/p\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLNGDPAGR*\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLNTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.834\u003c/p\u003e\n \u003cp\u003e(0.480)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-1.018\u003c/p\u003e\n \u003cp\u003e(0.604)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.665\u003c/p\u003e\n \u003cp\u003e(0.549)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-5.007*\u003c/p\u003e\n \u003cp\u003e(0.095)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-6.147*\u003c/p\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-158.285\u003c/p\u003e\n \u003cp\u003e(0.423)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-171.972\u003c/p\u003e\n \u003cp\u003e(0.600)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-395.805\u003c/p\u003e\n \u003cp\u003e(0.395)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-713.288\u003c/p\u003e\n \u003cp\u003e(0.153)\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-947.529*\u003c/p\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePseudo R²\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\" valign=\"top\" style=\"width: 595px;\"\u003e\n \u003cp\u003eNotes: The values represent regression coefficients, with p-values in parentheses. *** p \u0026lt; 0.01, ** p \u0026lt; 0.05, * p \u0026lt; 0.1 (conventional significance levels).\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003eThe quantile analysis reveals differentiated behavior across quantiles. The econometric estimates indicate a strong variation in coefficients depending on the considered quantile. Three main trends emerge:\u003c/p\u003e\u003cp\u003eIncreasing Effects of Climatic Factors (lnTEMP, lnWATERAGRI) with Production Levels: The impacts of temperature and water become more significant as we move towards larger farms.\u003c/p\u003e\u003cp\u003eCatalytic Role of Food Imports (LNFOODIMP) for Small Producers: For marginal producers (Q10), trade openness, measured by food imports, proves to be a lifeline, showing a positive and significant effect (elasticity of 0.135, p = 0.031). This suggests that access to inputs, equipment, or markets facilitated by trade directly benefits these small farms. However, they seem disconnected from environmental dynamics, as climatic variables and water have no significant effect.\u003c/p\u003e\u003cp\u003eImportance of Interactions (Temperature × Water, Agricultural GDP × Temperature) in High-Performance Systems: For high-performing farms (Q75 and Q90), economic variables become predominant, reflecting their ability to capitalize on economic growth and manage water resources effectively. Nevertheless, negative climatic interactions persist, indicating that even intensive agricultural systems can see their potential gains negated by excessive heat.\u003c/p\u003e\u003cp\u003eIn summary, this analysis highlights a transition from marginal producers who leverage imports to address structural deficiencies, to larger and more efficient farms where economic growth and careful water management are crucial. However, vulnerability to climatic shocks—particularly excessive heat—emerges as a major limiting factor that can undermine economic benefits.\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"4\" valign=\"top\" style=\"width: 614px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 10: Summary of Variable Effects by Quantile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eVARIABLE\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eDIRECTION OF EFFECT\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eSIGNIFICANCE THRESHOLD\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eECONOMIC COMMENTARY\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eLNFOODIMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eQ10 \u0026gt; 0, …, Q90 \u0026lt; 0\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eQ10*, Q90 ns\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eImports stimulate small producers, not large ones.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eLNTEMP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eIncreases with quantile\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eQ90***\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eClimate is favorable for high yields.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eLNWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eIncreases with quantile\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eQ90***\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eWater is crucial for large farms.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eLNTEMP*LNWATERAGRI\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eNegative across all quantiles\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eQ90***\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eIncreased water stress with rising temperatures\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eLNGDPAGR\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eIncreases with quantile\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eQ75*, Q90***\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eAgricultural growth mainly benefits large producers.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eLNVALASP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ePositive only at Q90\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eQ90***\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eAdded value of local products in large systems.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003eThe results of Table 9 highlight complex dynamics and structural inequalities within agricultural production in Tunisia, calling for a differentiated policy approach.\u003c/p\u003e\u003cp\u003eAnalysis of Effects by Quantile\u003c/p\u003e\u003cp\u003eImpact of Food Imports (LNFOODIMP): Food imports have a significant positive effect on small producers (Q10 and Q25), while they turn negative for large farms (Q90). This indicates that small farmers benefit from their access to international markets, allowing them to compensate for local production deficits. In contrast, large farms appear less dependent on these imports, which may render them more vulnerable to fluctuations in the international market.\u003c/p\u003e\u003cp\u003eTemperature (lnTEMP): The increasing positive effect of temperature with the quantile (Q90) suggests that warmer climatic conditions can enhance crop yields, particularly for high-yielding farms. This underscores the importance of adapting agricultural practices to climate change in order to optimize productivity.\u003c/p\u003e\u003cp\u003eWater Availability (lnWATERAGRI): The growing effect of water availability with the quantile (Q90) emphasizes its essential role for large farms. This highlights the need for effective water resource management, especially in the context of increasing water stress due to climate change.\u003c/p\u003e\u003cp\u003eInteraction of Temperature and Water (LNTEMPLNWATERAGRI): The consistently negative effect of this interaction across all quantiles (Q90) indicates that rising temperatures exacerbate water stress, which can compromise agricultural production. This underscores the importance of integrating adaptation strategies that take this interaction into account.\u003c/p\u003e\u003cp\u003eAgricultural Growth (lnGDPAGR): The increase in the effect of agricultural growth with the quantile (Q75 and Q90) shows that large farms benefit more from economic growth. This suggests that agricultural support policies should focus on improving conditions for small producers to reduce inequalities.\u003c/p\u003e\u003cp\u003eValue Added of Local Products (lnVALASP): The value added of local products is only significant at Q90, indicating that large farms are better positioned to capitalize on this added value, thereby strengthening their market position.\u003c/p\u003e\u003cp\u003eThese observations reveal deep structural inequalities in Tunisian agricultural production, necessitating targeted policies rather than a uniform approach. Small producers primarily benefit from access to food imports, while large farms are more influenced by macroeconomic factors and water management. Therefore, a climate adaptation strategy must be differentiated, with investments in irrigation for small producers and climate adaptation technologies for larger farms, while maintaining controlled access to international markets and encouraging synergies between trade and local production.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eComparative discussion: Tunisia, Morocco and Egypt\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cimg 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\" width=\"586\" height=\"714\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eThe results obtained for Tunisia demonstrate a marked sensitivity of food production to climatic variations, particularly to excessive rainfall and rising temperatures. This situation reflects a structural vulnerability common to other Maghreb countries, notably Morocco. According to Ouassou et al. (2022), Moroccan agriculture is heavily dependent on the spatial and temporal distribution of rainfall, and excessive precipitation can lead to losses through erosion and runoff, similar to the findings in Tunisia (Ben Salem \u0026amp; Zaibet, 2020). Conversely, in Egypt, natural precipitation has a negligible impact on agricultural production due to the heavy reliance on the Nile-based irrigation system (Abou Hadid, 2016).\u003c/p\u003e\u003cp\u003eRegarding temperature, the effects vary across countries. In Tunisia, a moderate increase appears beneficial in the short term in high productivity areas but becomes detrimental beyond a certain threshold, aligning with the observations of Bachta and Ben Salem (2021). Morocco reports similar effects, particularly for cereal crops sensitive to thermal stress (Kouadio et al., 2017). In Egypt, while warming affects certain crops like maize and wheat, the impacts are relatively contained due to a structured irrigation network and adaptation techniques (Zohry et al., 2019).\u003c/p\u003e\u003cp\u003eThe effects of irrigation also show divergences. In Tunisia, irrigation has positive short-term effects but negative long-term consequences, suggesting inefficiencies related to salinization or degradation of networks (Mougou et al., 2020). In Morocco, the expansion of irrigated areas has led to localized productivity improvements, but access remains unequal (FAO, 2021). In Egypt, irrigation plays a crucial role, with a sustainably positive effect on production, even under climatic stress, due to centralized management (El-Hendawy et al., 2019).\u003c/p\u003e\u003cp\u003eFinally, interaction effects (temperature × irrigation) are particularly relevant: in Tunisia, a long-term synergy appears between warming and access to water, but a negative substitutive effect may emerge in the short term (results from this study). Similar trends are observed in Morocco (Abahous et al., 2023), while in Egypt, the combined effect remains generally stabilizing (Ali et al., 2018). Regarding food imports, all three countries utilize them as an adjustment mechanism, but this may affect the long-term resilience of their agricultural sectors (World Bank, 2022).\u003c/p\u003e\u003cp\u003eIn summary, Tunisia and Morocco share a high climate exposure, with mixed agricultural systems that are more vulnerable than Egypt's, which benefits from a more integrated irrigation model. This underscores the need for Tunisia to improve water management, invest in agricultural infrastructure, and develop climate-smart adaptation strategies (IPCC, 2022).\u003c/p\u003e"},{"header":"Conclusion and implication","content":"\u003cp\u003eClimate change poses a major challenge for Tunisian agriculture, severely impacting production, soil quality, and food security. Projections indicate that rising temperatures and decreasing precipitation could reduce agricultural yields by 5 to 10% by 2030, particularly affecting cereal and tree crops. The situation is exacerbated by Tunisia's growing dependence on food imports, making the country vulnerable to fluctuations in international markets and economic crises.\u003c/p\u003e\n\u003cp\u003eSince 2017, the effects of climate change have intensified, with severe droughts and degradation of water resources. Small farms, which represent a significant part of the sector, are particularly affected as they heavily rely on climatic conditions and imports for their animal feed needs. Large farms, on the other hand, suffer from reduced water efficiency during heatwaves, compromising their profitability.\u003c/p\u003e\n\u003cp\u003eTo address these challenges, it is imperative for Tunisia to adopt a structured adaptation strategy. This includes implementing sustainable agricultural practices, such as drip irrigation, which optimizes water use, and promoting crops that require less water. Additionally, creating regional water banks and improving water resource management are essential to ensure sufficient agricultural production.\u003c/p\u003e\n\u003cp\u003eClimate social protection is also necessary to support vulnerable farmers. This could include parametric insurance against droughts, strategic cereal stocks, and zero-interest emergency loans for farmers affected by extreme climatic events. Furthermore, developing centers for disseminating climate-smart practices and providing tax incentives for green investments are crucial measures to enhance the sector's resilience.\u003c/p\u003e\n\u003cp\u003eRegional cooperation is another key aspect. Establishing a Maghreb Agricultural Climate Observatory and harmonizing water resource management standards between Tunisia and its neighbors, particularly Algeria, could improve the management of shared resources and reduce tensions related to food dependence.\u003c/p\u003e\n\u003cp\u003eIn conclusion, Tunisia stands at a crossroads. Transitioning from a crisis management approach to a proactive adaptation strategy is no longer an option but an urgent necessity. By investing in resilience and adopting appropriate measures, Tunisia can not only mitigate the negative impacts of climate change but also pave the way for a more stable and sustainable agricultural and economic development for future generations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent Statement\u003c/h2\u003e\n\u003cp\u003eNo personal or confidential data relating to individuals or groups was used in this article. Where secondary data were used, they came from publicly available sources and did not contain any information that could directly or indirectly identify an individual.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research presented in this article was conducted in accordance with the ethical standards applicable in the field of economics. The author declares no conflict of interest. No experimentation on humans or animals was conducted as part of this study.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eje, déclare que l'auteur Mohamed riadh Cherif est le seul contributeur de ce travail\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eProfonds remerciements pour la publication de notre article sans frais – Article intitulé \"[Climat, agriculture et croissance économique en Tunisie : Analyse dynamique et asymétrique sur la période 1974–2023.]\"Madame/Monsieur l'Éditeur/Éditrice,Nous vous écrivons pour exprimer notre plus sincère gratitude pour la décision de publier notre article intitulé \"[Climat, agriculture et croissance économique en Tunisie : Analyse dynamique et asymétrique sur la période 1974–2023.]\" dans votre prestigieuse revue, et surtout pour avoir waived les frais de publication (APC).La recherche et la publication scientifique sont essentielles à l'avancement des connaissances, mais les coûts associés, notamment les frais de traitement d'article, peuvent constituer un obstacle majeur pour les chercheurs aux ressources limitées. Étant donné notre situation financière personnelle, cette dispense représente un soutien inestimable qui rend cette publication possible.Votre générosité et votre engagement en faveur de l'accessibilité de la recherche sont grandement appréciés et témoignent d'une vision inclusive de la science. Cette opportunité nous permet de partager nos travaux avec la communauté scientifique sans la contrainte financière que nous aurions autrement rencontrée.Nous sommes profondément reconnaissants de votre compréhension et de votre soutien. Nous espérons que notre contribution sera utile aux lecteurs et nous nous tenons à votre disposition pour toute collaboration future.Veuillez agréer, Madame/Monsieur l'Éditeur/Éditrice, l'expression de nos remerciements les plus chaleureux et de notre considération distinguée.Cordialement,\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbahous, H., Ronny, B., Sifeddine, A., \u0026amp; Bouchaou, L. (2023). 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Crop intensification to reduce wheat gap in Egypt. Crop Production Under Stressful Conditions, 1-13. https://doi.org/10.1007/978-981-10-7308-3_1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate, Agriculture, Economic growth, Asymmetry, ARDL, NARDL, QARDL, Tunisia","lastPublishedDoi":"10.21203/rs.3.rs-7157532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7157532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the dynamic and asymmetric interrelationships between climate factors, agricultural performance, and economic growth in this country over the period 1974-2023. We use an advanced econometric approach combining ARDL, NARDL, and QARDL models to analyze the nonlinear effects of climate variables (temperature and precipitation) on agricultural productivity and GDP per capita. The results highlight significant and heterogeneous climate effects, with notable asymmetry: a 1°C increase reduces agricultural GDP by 1.2% in the long term, while an equivalent decrease has no significant effect. Rainfall deficits have a greater impact on agricultural production than surpluses, with an amplified effect during periods of recession (2.3 times greater). Quantile analysis highlights structural disparities: small producers depend on imports to adapt, while large farms, although more productive, are vulnerable to heat and water stress. Robustness tests confirmed the validity of the models, with stable residuals and proven cointegration. These results highlight the need for differentiated policies, including: 1) progressive water pricing to limit overexploitation of groundwater;(2) targeted subsidies to encourage the adoption of water-efficient irrigation technologies ;(3) training programs for smallholders to promote resilient practices. The study makes a significant contribution to the existing literature by proposing an innovative methodological framework for analyzing asymmetric climate effects in vulnerable agricultural economies, with direct implications for national resilience strategies, including.\u003c/p\u003e","manuscriptTitle":"Climate, agriculture, and economic growth in Tunisia: a dynamic and asymmetric analysis covering the period 1974–2023.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 14:18:13","doi":"10.21203/rs.3.rs-7157532/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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