temperature and precipitation increase, as in the remainder of the paper. The difference stands with the amplitudes of the estimates. In this sample, a one percent temperature increase raises aggregated pesticide use by 2.21%, which is about 150% greater than in Table 2. We find similar larger precipitation impact on aggregated use. For fungicide and herbicide use too, the effect of temperature and precipitation is consistently at least 150 to 200% greater than for the whole sample. Table 7 shows that these zip codes specializing in cereals and oilseed crops are actually the most sensitive to weather shocks.

Second, our preferred estimates also closely align with those obtained for zip codes specializing in forages and pastures (Table 7, Panel B.). This is probably explained by the fact that these latter zip codes represent 42% of our sample. Consequently, our preferred estimates fall in between the estimates obtained in panels A. and B. Here, a one percent temperature increase leads zip codes specializing in forages and pastures to an approximately one percent increase in the use of all pesticide categories, while a one percent precipitation increase results in an approximate one-third of a percentage point increase. In other words, zip codes specializing in forages and pastures exhibit about half the level of sensitivity to similar weather shocks compared to those specializing in cereals and oilseed crops.

Finally, the results obtained for zip codes specializing in fruit production or vines are fairly consistent with those obtained for the other regions, at least for precipitation (Table 7, Panels C. and D.). Estimates for temperature are different yet. Specifically, we observe negative or null effects of temperature on the use of fungicides and herbicides in zip codes specializing in fruit production and vines. This outcome aligns well with the results identified in Table 6 regarding the weather impacts in autumn, when these crops are the main ones remaining to be harvested. Additionally, we find that they exhibit much greater insecticide use adjustments to temperature (compared to the remainder of the sample). This suggests that fruits and vines are more sensitive to insect damage, or that farmers have greater incentives to protect them – these crops are indeed much more profitable than the others. Because fruit production areas use ten to twenty times more insecticides than other zip codes (see Table 7), these results actually suggest that the use of insecticides in France is primarily influenced by temperature shocks in zip codes specializing in fruit production.

One possible explanation for these distinct outcomes in the zip codes specializing in fruits and vines is that they are located in warmer regions than others. Zip codes specializing in vines are for instance located in areas that are three to four degrees Celsius warmer than the average zip code in our sample (Table 7). Our latter findings may thus not solely be attributed to agricultural specialization, but rather to potential non-linear impacts of temperature on pesticide use. We further examine this possibility in Section 5.6.

## 5.5 Ruling out alternative mechanisms

We interpreted our previous results as if farmers respond to weather shocks by adjusting pesticide use at the *intensive margin* only. This explanation could however be threatened by two possible alternative mechanisms. First, farmers could adjust to weather shocks at the extensive margin by changing crop allocations (Graveline and Mérel, 2014; Cui, 2020). As such, because crops do not necessarily rely on the same pesticide intensity (see Table 7), crop allocation changes could mechanically modify overall pesticide use, ultimately introducing
composition issues in the measurement of our dependent variable in equation (4). Second, farmers could also respond to weather shocks by adjusting at the super-extensive margin (Graveline and Mérel, 2014; Cui, 2020), by either restricting or expanding total farmland. That is, variations in our dependent variable may partly come from changes in the denominator (adjusted UAA). In either case, our previous interpretations of our estimates may not be entirely supported as they could encompass several mechanisms. Because we cannot formally test whether farmers respond to weather shocks at the intensive margin only, our approach in this section aims to quantify the extent of the extensive and super-extensive margin responses in order to rule out potential confounding effects of these alternative mechanisms on our estimates of interest.

Formally, we re-estimate equation (4) by changing our dependent variable, conserving all the other estimation elements. The first step is to consider the adjusted UAA as our new dependent variable. Results indicating that farmland area *reduces* in response to higher temperature or precipitation would challenge the validity of our preferred estimates suggesting that hotter or wetter growing seasons increase pesticide use, as they can reflect super-extensive margin response instead of a true intensive margin response. As a second step, we investigate whether farmers adjust at the extensive margin by estimating the impacts of weather on the shares of major agricultural uses within the zip codes. Evidence suggesting adaptation at the extensive margin may alter the interpretation of our results, in particular if we see that farmers respond to higher temperature or precipitation by shifting their crop allocations towards more pesticide-intensive crops (such as fruits or vines; see Table 7). Table 8 displays the results obtained from such complementary estimations.

<table><caption>Table 8: Weather elasticities of agricultural areas</caption><thead><tr><th rowspan="2"></th><th>Super-extensive</th><th colspan="4">Extensive</th></tr><tr><th>Adjusted UAA</th><th>Cereals</th><th>Feedcrops</th><th>Fruits</th><th>Vines</th></tr></thead><tbody><tr><th>Average Temperature</th><td>0.049<br>(0.036)</td><td>0.006<br>(0.004)</td><td>0.034***<br>(0.003)</td><td>-0.070***<br>(0.020)</td><td>0.046***<br>(0.013)</td></tr><tr><th>Total Precipitation</th><td>0.029***<br>(0.011)</td><td>0.002*<br>(0.001)</td><td>0.003***<br>(0.001)</td><td>0.007*<br>(0.004)</td><td>0.006***<br>(0.002)</td></tr></tbody></table>

NOTE. Elasticities are computed at sample mean values using WLS estimates and equation (5) with alternative dependent variables (see column names). The standard errors are clustered at the zip code level and corrected for spatial dependency using the Conley spatially-robust correction. Standard errors are computed using the delta method and shown in brackets. *, **, *** indicate p-values lower than 0.1, 0.05 and 0.01. Cereals include wheat, barley and maize, rape, sunflower, oilseeds, protein crops and pulses. Feedcrops include fodder and temporary grasslands. Fruits includes orchards, nuts, olive trees, rice and vegetables.

Table 8 shows that the adjusted UAA is not sensitive to changes in temperature during the growing season. That is, French farmers do not adapt at the super-extensive margin in response to temperature shocks. Because it indicates a constant denominator in the estimation of equation (4), this result goes in favor of our previous interpretations that farmers mainly adjust their pesticide purchases at the intensive margin. Results for the response at the extensive margin also supports this interpretation. Indeed, results from Table 8 suggest minor changes of crop allocations in response to higher temperature. For example, the share of cereals – which constitute the half of total UAA on average – is not sensitive to temperature shocks. Actually, farmers primarily respond to higher temperature by slightly decreasing the share of fruit and replacing it by smaller expansions of vines and feedcrops. Given that these two crops consume fewer
composition issues in the measurement of our dependent variable in equation (4). Second, farmers could also respond to weather shocks by adjusting at the super-extensive margin (Graveline and Mérel, 2014; Cui, 2020), by either restricting or expanding total farmland. That is, variations in our dependent variable may partly come from changes in the denominator (adjusted UAA). In either case, our previous interpretations of our estimates may not be entirely supported as they could encompass several mechanisms. Because we cannot formally test whether farmers respond to weather shocks at the intensive margin only, our approach in this section aims to quantify the extent of the extensive and super-extensive margin responses in order to rule out potential confounding effects of these alternative mechanisms on our estimates of interest.

Formally, we re-estimate equation (4) by changing our dependent variable, conserving all the other estimation elements. The first step is to consider the adjusted UAA as our new dependent variable. Results indicating that farmland area *reduces* in response to higher temperature or precipitation would challenge the validity of our preferred estimates suggesting that hotter or wetter growing seasons increase pesticide use, as they can reflect super-extensive margin response instead of a true intensive margin response. As a second step, we investigate whether farmers adjust at the extensive margin by estimating the impacts of weather on the shares of major agricultural uses within the zip codes. Evidence suggesting adaptation at the extensive margin may alter the interpretation of our results, in particular if we see that farmers respond to higher temperature or precipitation by shifting their crop allocations towards more pesticide-intensive crops (such as fruits or vines; see Table 7). Table 8 displays the results obtained from such complementary estimations.

<table><caption>Table 8: Weather elasticities of agricultural areas</caption><thead><tr><th rowspan="2"></th><th>Super-extensive</th><th colspan="4">Extensive</th></tr><tr><th>Adjusted UAA</th><th>Cereals</th><th>Feedcrops</th><th>Fruits</th><th>Vines</th></tr></thead><tbody><tr><th>Average Temperature</th><td>0.049<br>(0.036)</td><td>0.006<br>(0.004)</td><td>0.034***<br>(0.003)</td><td>-0.070***<br>(0.020)</td><td>0.046***<br>(0.013)</td></tr><tr><th>Total Precipitation</th><td>0.029***<br>(0.011)</td><td>0.002*<br>(0.001)</td><td>0.003***<br>(0.001)</td><td>0.007*<br>(0.004)</td><td>0.006***<br>(0.002)</td></tr></tbody></table>

NOTE. Elasticities are computed at sample mean values using WLS estimates and equation (5) with alternative dependent variables (see column names). The standard errors are clustered at the zip code level and corrected for spatial dependency using the Conley spatially-robust correction. Standard errors are computed using the delta method and shown in brackets. *, **, *** indicate p-values lower than 0.1, 0.05 and 0.01. Cereals include wheat, barley and maize, rape, sunflower, oilseeds, protein crops and pulses. Feedcrops include fodder and temporary grasslands. Fruits includes orchards, nuts, olive trees, rice and vegetables.

Table 8 shows that the adjusted UAA is not sensitive to changes in temperature during the growing season. That is, French farmers do not adapt at the super-extensive margin in response to temperature shocks. Because it indicates a constant denominator in the estimation of equation (4), this result goes in favor of our previous interpretations that farmers mainly adjust their pesticide purchases at the intensive margin. Results for the response at the extensive margin also supports this interpretation. Indeed, results from Table 8 suggest minor changes of crop allocations in response to higher temperature. For example, the share of cereals – which constitute the half of total UAA on average – is not sensitive to temperature shocks. Actually, farmers primarily respond to higher temperature by slightly decreasing the share of fruit and replacing it by smaller expansions of vines and feedcrops. Given that these two crops consume fewer
pesticides per unit area than fruit (see Table 7), this means that the changes in crop allocations induced by higher temperatures would actually conduct farmers to use *fewer pesticides* on average. This goes *against* our previous estimates, which are all significantly positive (Table 2). Given the mixed results on the adaptation at the extensive and super-extensive margins induced by temperature changes, we believe that our previous temperature estimates do reflect farmers’ adjustment at the intensive margin. More precisely, they actually reflect the *lower bound* of the farmers’ intensive margin response to temperature in terms of pesticide use.

Looking now at precipitation, Table 8 indicates that farmers do adapt at the super-extensive margin in response to higher precipitation. This means that we cannot solely attribute our previous estimates to adjustment at the intensive margin. That being said, we find that farmers *extend* their adjusted UAA in response to wetter growing seasons. This implies that, if farmers did not additionally react at the intensive margin, our previous estimates would have been negative. Yet, because they are positive (Table 2), this implies that the numerator has increased more than the denominator in response to higher precipitation. In other words, our previous estimates actually reflect the *lower bound* of the farmers’ intensive margin responses to greater precipitation. The results at the extensive margin are more ambiguous. They do not reflect strong crop allocation changes, with all crop shares slightly increasing in response to higher precipitation (about ten times smaller in amplitude than those highlighted for temperature). Because these effects are small and only slightly significant (two of them are not significantly different from zero at the 95% statistical level), we believe that these effects are of minor importance for the present debates. At least, they do not reflect any shift from pesticide-extensive crops towards pesticide-intensive crops, indicating that our preferred precipitation estimates should not suffer from strong composition effects.

In summary, Table 8 indicates that adaptation mechanisms at the extensive and super-extensive margins are – at most – limited, and go – in any case – in the opposite directions to the signs of our preferred estimates. In other words, these extra findings imply that our preferred estimates are likely to represent conservative estimates of farmers’ intensive margin responses to weather shocks.

## 5.6 Non-linear temperature impacts

Now that we have presented robust and consistent results using average temperatures during the growing season, we turn to the presentation of the non-linear impact estimates. Previous analyses already reported non-linear concave effects of precipitation on pesticide use (see Table A2 in the online Appendices). We further investigate the non-linear effects of temperature within the growing season by estimating equation (6) using three functional forms (piecewise functions, bins and 9th-order polynomial functions; see online Appendix A5). Figure 4 presents the resulting estimates on pesticide purchase.

Figure 4 reveals that the farmers’ pesticide use responses to temperature vary across its entire distribution. It consistently demonstrates, for all pesticide categories, that moderate temperatures have a slightly linear positive effect on pesticide use, but that extreme temperatures strongly reduce it. Such linear responses for moderate temperatures are consistent with that identified in Section 5.1. However, we have not
pesticides per unit area than fruit (see Table 7), this means that the changes in crop allocations induced by higher temperatures would actually conduct farmers to use *fewer pesticides* on average. This goes *against* our previous estimates, which are all significantly positive (Table 2). Given the mixed results on the adaptation at the extensive and super-extensive margins induced by temperature changes, we believe that our previous temperature estimates do reflect farmers’ adjustment at the intensive margin. More precisely, they actually reflect the *lower bound* of the farmers’ intensive margin response to temperature in terms of pesticide use.

Looking now at precipitation, Table 8 indicates that farmers do adapt at the super-extensive margin in response to higher precipitation. This means that we cannot solely attribute our previous estimates to adjustment at the intensive margin. That being said, we find that farmers *extend* their adjusted UAA in response to wetter growing seasons. This implies that, if farmers did not additionally react at the intensive margin, our previous estimates would have been negative. Yet, because they are positive (Table 2), this implies that the numerator has increased more than the denominator in response to higher precipitation. In other words, our previous estimates actually reflect the *lower bound* of the farmers’ intensive margin responses to greater precipitation. The results at the extensive margin are more ambiguous. They do not reflect strong crop allocation changes, with all crop shares slightly increasing in response to higher precipitation (about ten times smaller in amplitude than those highlighted for temperature). Because these effects are small and only slightly significant (two of them are not significantly different from zero at the 95% statistical level), we believe that these effects are of minor importance for the present debates. At least, they do not reflect any shift from pesticide-extensive crops towards pesticide-intensive crops, indicating that our preferred precipitation estimates should not suffer from strong composition effects.

In summary, Table 8 indicates that adaptation mechanisms at the extensive and super-extensive margins are – at most – limited, and go – in any case – in the opposite directions to the signs of our preferred estimates. In other words, these extra findings imply that our preferred estimates are likely to represent conservative estimates of farmers’ intensive margin responses to weather shocks.

## 5.6 Non-linear temperature impacts

Now that we have presented robust and consistent results using average temperatures during the growing season, we turn to the presentation of the non-linear impact estimates. Previous analyses already reported non-linear concave effects of precipitation on pesticide use (see Table A2 in the online Appendices). We further investigate the non-linear effects of temperature within the growing season by estimating equation (6) using three functional forms (piecewise functions, bins and 9th-order polynomial functions; see online Appendix A5). Figure 4 presents the resulting estimates on pesticide purchase.

Figure 4 reveals that the farmers’ pesticide use responses to temperature vary across its entire distribution. It consistently demonstrates, for all pesticide categories, that moderate temperatures have a slightly linear positive effect on pesticide use, but that extreme temperatures strongly reduce it. Such linear responses for moderate temperatures are consistent with that identified in Section 5.1. However, we have not
previously identified such a negative relationship for extreme temperatures, indicating that the concave effect
of temperature is identified far away from the average. We examine these relationships in more detail here.

<figure><img src="image_5.png" /><figcaption>Figure 4: Impacts of temperature distribution on pesticide purchases during the growing season. NOTE. Graphs display changes in pesticide purchase in kg/ha if crops are exposed for one day to a particular 1°C temperature interval where we sum the fraction of a day during which temperatures fall within each interval. The 90%, 95% and 99% confidence bands (from light to dark blue) are adjusted for spatial correlation using the Conley spatially-robust correction. Curves are centered so that the exposure-weighted impact is zero. Histograms at the bottom of each graph display the average temperature exposure among all zip codes.</figcaption></figure>

Figure 4 indicates that exposure to temperatures up to about 25°C has minor impacts on use of all
pesticide categories. Pesticide purchase does indeed weakly increase from 8°C up to this threshold for
all categories, with most effects not distinguishable from zero. Purchases only significantly increase for
temperatures higher than 25°C. For example, an additional day of exposure to 27 to 30°C would increase
aggregated pesticide purchases by about 2% relative to average exposure to 16 to 18°C (reference bin). All
functions show a negative influence of extreme temperatures on the purchase of all pesticide categories. For
example, an additional day of exposure to temperatures above 33°C would decrease fungicide purchase by
10 to 25%. Our findings are similar regarding herbicide purchase, which starts to be negatively affected
previously identified such a negative relationship for extreme temperatures, indicating that the concave effect of temperature is identified far away from the average. We examine these relationships in more detail here.

<figure><img src="image_6.png" /><figcaption>Figure 4: Impacts of temperature distribution on pesticide purchases during the growing season. NOTE. Graphs display changes in pesticide purchase in kg/ha if crops are exposed for one day to a particular 1°C temperature interval where we sum the fraction of a day during which temperatures fall within each interval. The 90%, 95% and 99% confidence bands (from light to dark blue) are adjusted for spatial correlation using the Conley spatially-robust correction. Curves are centered so that the exposure-weighted impact is zero. Histograms at the bottom of each graph display the average temperature exposure among all zip codes.</figcaption></figure>

Figure 4 indicates that exposure to temperatures up to about 25°C has minor impacts on use of all pesticide categories. Pesticide purchase does indeed weakly increase from 8°C up to this threshold for all categories, with most effects not distinguishable from zero. Purchases only significantly increase for temperatures higher than 25°C. For example, an additional day of exposure to 27 to 30°C would increase aggregated pesticide purchases by about 2% relative to average exposure to 16 to 18°C (reference bin). All functions show a negative influence of extreme temperatures on the purchase of all pesticide categories. For example, an additional day of exposure to temperatures above 33°C would decrease fungicide purchase by 10 to 25%. Our findings are similar regarding herbicide purchase, which starts to be negatively affected
by temperatures above 30°C. An additional day of exposure to temperatures above 30°C would decrease herbicide purchase by 5 to 15%. Though less precisely estimated, insecticide purchase is also negatively affected by temperatures above 33°C.

These results are consistent with the literature. First, the lower pesticide use induced by extreme temperatures is consistent with the documented negative impacts of heat on (i) weed, fungi and insect growth conditions, (ii) pesticide efficacy and (iii) attainable yields (Patterson et al., 1999; Delcour et al., 2015; Deutsch et al., 2018; Kawasaki, 2023). Our results on insecticide use are particularly consistent with Möhring et al. (2022) and Larsen and McComb (2021) who both identified negative impacts of extreme temperatures on insecticide use. They are also close to Rhodes and McCarl (2020), who found that a high number of hot days (33°C and above) negatively impact insecticide purchases on soybeans and winter wheat. We further expand on the aforementioned findings by demonstrating that the adverse effects of extreme temperatures not only apply to insecticide use, but also apply to that of fungicides and herbicides. While the negative impacts of heat on pesticide use are strong and significant, it is essential to weight these results by their very low frequency in our sample. This raises the question of whether the more frequent occurrence of extreme temperature events associated with climate change will decrease pesticide use in the future. We explore these potential future effects in the following section.

# 6 Simulations of climate change impacts

In this section, we use our previous estimates to project future pesticide use under forthcoming climate conditions. Specifically, we multiply our preferred estimates by the difference in average temperature and precipitation during the growing season between 2014 and 2019 and those projected between 2050 and 2055, assuming all other factors to be constant.<fnref n="23" /> We derive information about future temperature and precipitation from the spatially-explicit projections from the ALADIN climate model of Météo-France under the medium emission pathways scenario (RCP 4.5 scenario).<fnref n="24" /> Note that the selection of this climate scenario is not intended to provide an accurate forecast of actual pesticide outcomes in 2050. Rather, it serves the purpose of demonstrating the projected implications of our model based on a plausible climate scenario. ALADIN projects a general increase in temperature and a rarefaction of precipitation during the growing season in 2050-2055 under the RCP 4.5 scenario, with an expected rise of average temperature by 1.41°C and an expected decrease of average precipitation by 30 mm (see online Appendix A26). The results of projections of future pesticide use are displayed in Figure 5 and Table 9.

<footnote n="23">²³We notably assume constant crop allocation and UAA. If results from Section 5.5 indicate that farmers adjust their total agricultural area and crop allocations to weather shocks in the growing seasons, these adaptation patterns are rather limited and our estimates mostly reflect intensive margin responses.</footnote>

<footnote n="24">²⁴ALADIN's climate change projections have the unique advantage of being tailored to the same 8 km × 8 km SAFRAN unit as the historical weather conditions that we used elsewhere in the paper. Note that we specifically use the projections provided by the ALADIN63 module, which draws on the same methods as those applied to obtain the historical weather data used in this paper.</footnote>
by temperatures above 30°C. An additional day of exposure to temperatures above 30°C would decrease herbicide purchase by 5 to 15%. Though less precisely estimated, insecticide purchase is also negatively affected by temperatures above 33°C.

These results are consistent with the literature. First, the lower pesticide use induced by extreme temperatures is consistent with the documented negative impacts of heat on (i) weed, fungi and insect growth conditions, (ii) pesticide efficacy and (iii) attainable yields (Patterson et al., 1999; Delcour et al., 2015; Deutsch et al., 2018; Kawasaki, 2023). Our results on insecticide use are particularly consistent with Möhring et al. (2022) and Larsen and McComb (2021) who both identified negative impacts of extreme temperatures on insecticide use. They are also close to Rhodes and McCarl (2020), who found that a high number of hot days (33°C and above) negatively impact insecticide purchases on soybeans and winter wheat. We further expand on the aforementioned findings by demonstrating that the adverse effects of extreme temperatures not only apply to insecticide use, but also apply to that of fungicides and herbicides. While the negative impacts of heat on pesticide use are strong and significant, it is essential to weight these results by their very low frequency in our sample. This raises the question of whether the more frequent occurrence of extreme temperature events associated with climate change will decrease pesticide use in the future. We explore these potential future effects in the following section.

# 6 Simulations of climate change impacts

In this section, we use our previous estimates to project future pesticide use under forthcoming climate conditions. Specifically, we multiply our preferred estimates by the difference in average temperature and precipitation during the growing season between 2014 and 2019 and those projected between 2050 and 2055, assuming all other factors to be constant.<fnref n="23" /> We derive information about future temperature and precipitation from the spatially-explicit projections from the ALADIN climate model of Météo-France under the medium emission pathways scenario (RCP 4.5 scenario).<fnref n="24" /> Note that the selection of this climate scenario is not intended to provide an accurate forecast of actual pesticide outcomes in 2050. Rather, it serves the purpose of demonstrating the projected implications of our model based on a plausible climate scenario. ALADIN projects a general increase in temperature and a rarefaction of precipitation during the growing season in 2050-2055 under the RCP 4.5 scenario, with an expected rise of average temperature by 1.41°C and an expected decrease of average precipitation by 30 mm (see online Appendix A26). The results of projections of future pesticide use are displayed in Figure 5 and Table 9.

<footnote n="23">²³We notably assume constant crop allocation and UAA. If results from Section 5.5 indicate that farmers adjust their total agricultural area and crop allocations to weather shocks in the growing seasons, these adaptation patterns are rather limited and our estimates mostly reflect intensive margin responses.</footnote>

<footnote n="24">²⁴ALADIN's climate change projections have the unique advantage of being tailored to the same 8 km × 8 km SAFRAN unit as the historical weather conditions that we used elsewhere in the paper. Note that we specifically use the projections provided by the ALADIN63 module, which draws on the same methods as those applied to obtain the historical weather data used in this paper.</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 5: Pesticide use projections in 2050 under RCP4.5 climate change scenario. NOTE. Graphs display estimated changes in pesticide use in percentage if crops are exposed to hypothetical changes in temperature and precipitation during the growing season (from March 1st to August 31st) according to our preferred estimates and to the information provided by the ALADIN climate model for the RCP 4.5 emission pathways scenario between 2050 and 2055.</figcaption></figure>

Figure 5 shows the tailored projections of pesticide use in 2050-2055 compared to 2014-2019 averages when using our preferred estimates obtained with average weather conditions during the growing season (see Section 5.1). It shows that farmers in different regions will react differently to the heterogeneous temperature and precipitation changes. In particular, the south-eastern part of France stands out among the other French regions. Total pesticide purchases will increase in this particular region by up to 35%, about three times more than in the other parts of France. This large increase seems particularly driven by farmers' responses in terms of fungicide and herbicide use. Indeed, although fungicide and herbicide use increases in most locations, it particularly surges in this area. By comparison, Figure 5 shows that insecticide use in this region will respond very heterogeneously to future climate conditions, with some locations increasing insecticide use by up to 15%, while others might decrease its use by up to 35%.
<figure><img src="image_7.png" /><figcaption>Figure 5: Pesticide use projections in 2050 under RCP4.5 climate change scenario. NOTE. Graphs display estimated changes in pesticide use in percentage if crops are exposed to hypothetical changes in temperature and precipitation during the growing season (from March 1st to August 31st) according to our preferred estimates and to the information provided by the ALADIN climate model for the RCP 4.5 emission pathways scenario between 2050 and 2055.</figcaption></figure>

Figure 5 shows the tailored projections of pesticide use in 2050-2055 compared to 2014-2019 averages when using our preferred estimates obtained with average weather conditions during the growing season (see Section 5.1). It shows that farmers in different regions will react differently to the heterogeneous temperature and precipitation changes. In particular, the south-eastern part of France stands out among the other French regions. Total pesticide purchases will increase in this particular region by up to 35%, about three times more than in the other parts of France. This large increase seems particularly driven by farmers' responses in terms of fungicide and herbicide use. Indeed, although fungicide and herbicide use increases in most locations, it particularly surges in this area. By comparison, Figure 5 shows that insecticide use in this region will respond very heterogeneously to future climate conditions, with some locations increasing insecticide use by up to 15%, while others might decrease its use by up to 35%.
Panel A. of Table 9 sums up the overall climate effects when using our preferred estimates obtained with average temperature during the growing season (see columns “Avg. Temp.”). Panels B. and C. respectively decompose the total climate change impacts into those attributed to temperature and those attributed to precipitation. Moreover, Table 9 also displays the results with the piecewise linear function parameters reported in Section 5.6 on the non-linear temperature impacts (see columns “Cum. Temp.” in Table 9).

Table 9 provides a clear picture. According to our preferred approach, French farmers would respond to future temperature and precipitation by increasing their aggregated pesticide use by between 7% and 15% on average (Panel A.). This aggregate increase is actually driven by the farmers’ responses in terms of herbicide and fungicide use, which would also increase by between 8 and 14%, while maintaining their insecticide use at average 2014-2019 levels. These effects are predominantly driven by the impacts of warmer temperatures. Indeed, Table 9 indicates that higher temperatures projected in the RCP4.5 scenario would increase total pesticide, fungicide and herbicide use by 13 to 15% (Panel B.), while precipitation changes would reduce them by 2 to 4% (Panel C.). In contrast, the climate impact on insecticides is null because the (small) temperature and precipitation impacts offset each other. While slightly smaller in magnitude, these results are consistent when using the piecewise estimates. As such, the more frequent extreme heat events under the RCP4.5 scenario only offset a marginal proportion of the impacts of higher moderate temperatures.

<table><caption>Table 9: Projections of changes in pesticide use in 2050 under a RCP4.5 climate change scenario</caption><thead><tr><th rowspan="2"></th><th colspan="2">Total Pesticides</th><th colspan="2">Fungicides</th><th colspan="2">Herbicides</th><th colspan="2">Insecticides</th></tr><tr><th>Avg. Temp.</th><th>Cum. Temp.</th><th>Avg. Temp.</th><th>Cum. Temp.</th><th>Avg. Temp.</th><th>Cum. Temp.</th><th>Avg. Temp.</th><th>Cum. Temp.</th></tr></thead><tbody><tr><td colspan="9">PANEL A. TOTAL IMPACTS</td></tr><tr><td>Changes in use (%)</td><td>11.074***<br>(1.761)<br>[6.947,15.201]</td><td>7.839***<br>(1.447)<br>[5.002, 10.675]</td><td>11.028***<br>(3.760)<br>[3.658,18.399]</td><td>6.236***<br>(2.261)<br>[1.804,10.668]</td><td>11.549***<br>(1.680)<br>[8.256,14.843]</td><td>9.150**<br>(2.164)<br>[6.502,11.799]</td><td>-0.384<br>(2.943)<br>[-6.152,5.384]</td><td>-1.720<br>(2.164)<br>[-5.962,2.522]</td></tr><tr><td colspan="9">PANEL B. TEMPERATURE IMPACTS</td></tr><tr><td>Changes in use (%)</td><td>13.847***<br>(2.133)<br>[9.666,18.029]</td><td>9.720***<br>(1.411)<br>[6.954, 12.486]</td><td>15.012***<br>(3.760)<br>[7.642,22.382]</td><td>9.078***<br>(2.334)<br>[4.503, 13.652]</td><td>13.482***<br>(1.680)<br>[10.189,16.775]</td><td>10.261***<br>(1.273)<br>[7.766,12.756]</td><td>1.479<br>(2.942)<br>[-4.289,7.247]</td><td>-0.414<br>(2.113)<br>[-4.555,3.728]</td></tr><tr><td colspan="9">PANEL C. PRECIPITATION IMPACTS</td></tr><tr><td>Changes in use (%)</td><td>-2.774***<br>(0.448)<br>[-3.652,-1.895]</td><td>-1.881***<br>(0.468)<br>[-2.798,-0.964]</td><td>-3.984***<br>(0.635)<br>[-5.228,-2.739]</td><td>-2.842***<br>(0.616)<br>[-4.049, -1.635]</td><td>-1.933***<br>(0.438)<br>[-2.792,-1.073]</td><td>-1.110**<br>(0.461)<br>[-2.015,-0.206]</td><td>-1.863***<br>(0.899)<br>[-3.037,-0.690]</td><td>-1.306**<br>(0.598)<br>[-2.478,-0.135]</td></tr></tbody></table>

NOTE. The figures indicate the percentage changes in pesticide use under hypothetical increases in temperature and precipitation during the growing season (from March 1st to August 31st) using our preferred estimates (Avg. Temp.), the piecewise linear function (Cum. Temp.) and average RCP4.5 ALADIN projections between 2050 and 2055. The figures are average effects in France expressed as percentages of initial use. Standard errors are corrected for spatial correlation using Conley (1999) and shown in brackets. 95% confidence intervals are displayed in square brackets. *, **, *** indicate p-values lower than 0.1, 0.05 and 0.01.

# 7 Concluding remarks

A recent and abundant literature has measured the effects of weather shocks on crop yields to assess the impacts of climate change on future crop production implicitly accounting for farmers' adaptation (Schlenker and Roberts, 2009; Blanc and Schlenker, 2017). However, efforts to explicitly measure these adaptation behaviors have been limited in practice (Hsiang, 2016). This paper proposes to elucidate such adaptation
Panel A. of Table 9 sums up the overall climate effects when using our preferred estimates obtained with average temperature during the growing season (see columns “Avg. Temp.”). Panels B. and C. respectively decompose the total climate change impacts into those attributed to temperature and those attributed to precipitation. Moreover, Table 9 also displays the results with the piecewise linear function parameters reported in Section 5.6 on the non-linear temperature impacts (see columns “Cum. Temp.” in Table 9).

Table 9 provides a clear picture. According to our preferred approach, French farmers would respond to future temperature and precipitation by increasing their aggregated pesticide use by between 7% and 15% on average (Panel A.). This aggregate increase is actually driven by the farmers’ responses in terms of herbicide and fungicide use, which would also increase by between 8 and 14%, while maintaining their insecticide use at average 2014-2019 levels. These effects are predominantly driven by the impacts of warmer temperatures. Indeed, Table 9 indicates that higher temperatures projected in the RCP4.5 scenario would increase total pesticide, fungicide and herbicide use by 13 to 15% (Panel B.), while precipitation changes would reduce them by 2 to 4% (Panel C.). In contrast, the climate impact on insecticides is null because the (small) temperature and precipitation impacts offset each other. While slightly smaller in magnitude, these results are consistent when using the piecewise estimates. As such, the more frequent extreme heat events under the RCP4.5 scenario only offset a marginal proportion of the impacts of higher moderate temperatures.

<table><caption>Table 9: Projections of changes in pesticide use in 2050 under a RCP4.5 climate change scenario</caption><thead><tr><th rowspan="2"></th><th colspan="2">Total Pesticides</th><th colspan="2">Fungicides</th><th colspan="2">Herbicides</th><th colspan="2">Insecticides</th></tr><tr><th>Avg. Temp.</th><th>Cum. Temp.</th><th>Avg. Temp.</th><th>Cum. Temp.</th><th>Avg. Temp.</th><th>Cum. Temp.</th><th>Avg. Temp.</th><th>Cum. Temp.</th></tr></thead><tbody><tr><td colspan="9">PANEL A. TOTAL IMPACTS</td></tr><tr><td>Changes in use (%)</td><td>11.074***<br>(1.761)<br>[6.947,15.201]</td><td>7.839***<br>(1.447)<br>[5.002, 10.675]</td><td>11.028***<br>(3.760)<br>[3.658,18.399]</td><td>6.236***<br>(2.261)<br>[1.804,10.668]</td><td>11.549***<br>(1.680)<br>[8.256,14.843]</td><td>9.150**<br>(2.164)<br>[6.502,11.799]</td><td>-0.384<br>(2.943)<br>[-6.152,5.384]</td><td>-1.720<br>(2.164)<br>[-5.962,2.522]</td></tr><tr><td colspan="9">PANEL B. TEMPERATURE IMPACTS</td></tr><tr><td>Changes in use (%)</td><td>13.847***<br>(2.133)<br>[9.666,18.029]</td><td>9.720***<br>(1.411)<br>[6.954, 12.486]</td><td>15.012***<br>(3.760)<br>[7.642,22.382]</td><td>9.078***<br>(2.334)<br>[4.503, 13.652]</td><td>13.482***<br>(1.680)<br>[10.189,16.775]</td><td>10.261***<br>(1.273)<br>[7.766,12.756]</td><td>1.479<br>(2.942)<br>[-4.289,7.247]</td><td>-0.414<br>(2.113)<br>[-4.555,3.728]</td></tr><tr><td colspan="9">PANEL C. PRECIPITATION IMPACTS</td></tr><tr><td>Changes in use (%)</td><td>-2.774***<br>(0.448)<br>[-3.652,-1.895]</td><td>-1.881***<br>(0.468)<br>[-2.798,-0.964]</td><td>-3.984***<br>(0.635)<br>[-5.228,-2.739]</td><td>-2.842***<br>(0.616)<br>[-4.049, -1.635]</td><td>-1.933***<br>(0.438)<br>[-2.792,-1.073]</td><td>-1.110**<br>(0.461)<br>[-2.015,-0.206]</td><td>-1.863***<br>(0.899)<br>[-3.037,-0.690]</td><td>-1.306**<br>(0.598)<br>[-2.478,-0.135]</td></tr></tbody></table>

NOTE. The figures indicate the percentage changes in pesticide use under hypothetical increases in temperature and precipitation during the growing season (from March 1st to August 31st) using our preferred estimates (Avg. Temp.), the piecewise linear function (Cum. Temp.) and average RCP4.5 ALADIN projections between 2050 and 2055. The figures are average effects in France expressed as percentages of initial use. Standard errors are corrected for spatial correlation using Conley (1999) and shown in brackets. 95% confidence intervals are displayed in square brackets. *, **, *** indicate p-values lower than 0.1, 0.05 and 0.01.

# 7 Concluding remarks

A recent and abundant literature has measured the effects of weather shocks on crop yields to assess the impacts of climate change on future crop production implicitly accounting for farmers' adaptation (Schlenker and Roberts, 2009; Blanc and Schlenker, 2017). However, efforts to explicitly measure these adaptation behaviors have been limited in practice (Hsiang, 2016). This paper proposes to elucidate such adaptation
behaviors by focusing on pesticide use as an illustrative case. Using an original, exhaustive dataset of purchases of all active substances in France, we show that farmers do adjust their pesticide use to weather shocks. In particular, we find that farmers react to similar weather shocks by purchasing far more fungicides and herbicides than insecticides. Our preferred estimates obtained using average weather conditions during the growing season indicate that a one percent temperature increase leads farmers to purchase additional +1.70% of fungicides, +1.72% of herbicides, but only +0.37% of insecticides. These results are robust to many sensitivity analyses. The lack of evidence of any storage behaviors from one year to another suggests that our dependent variables – that are fundamentally data on pesticide purchases – are actually likely to represent pesticide use. We also show that our preferred estimates are likely to represent the lower bound estimates of the farmers’ intensive margin responses to weather shocks. This means that our preferred estimates actually represent the combined effects of weather on (i) pest pressure, (ii) pesticide efficacy and (iii) attainable yields, but do not include change in crop allocation nor in total agricultural area. Additional analyses indicate that our preferred estimates are largely driven by weather shocks during spring, at the time when farmers apply pesticides, and that zip codes specializing in cereals and oilseed crops are much more sensitive to weather shocks than other regions. We also document the fact that the sensitivity of pesticide applications to weather shocks is greater for those with higher risks to human health in the case of herbicides and fungicides, and for those with higher risks to the environment in the case of insecticides. Finally, we identify non-linear, concave weather effects on pesticide use, which appears close to the sample average for precipitation, but far from this point for temperature.

Projections based on our preferred estimates indicate that, *ceteris paribus*, French farmers are likely to increase pesticide use by 7% to 15% on average by 2050 in response to a RCP4.5 climate change scenario. This has significant implications for French and European policymakers. Despite their efforts over the past decades to halve pesticide use (Schebesta and Candel, 2020), our results indeed suggest that achieving such reductions becomes even more challenging in the context of climate change. While some stakeholders might have hoped that climate change would have decreased the incentives to use pesticides (due to anticipated reductions in crop yields; see IPPC, 2021, for example), our findings suggest instead that the climate-induced increase in pest pressure or pesticide efficacy – i.e. the two other channels captured by our estimates – would actually lead rational farmers to *apply more pesticides*. To anticipate these trends, our results call for a strengthening of existing pesticide policies, in particular those in France. Indeed, despite relying on pesticide taxes modulated by toxic risks, our results suggest that the current French tax rates would be insufficient to reduce pesticide use in the country. Given that we predict that the largest climate-induced surges in pesticide applications would concern the most toxic products (those primarily affected by French and European regulations), we believe that this calls in particular for a proactive increase on these substances' tax rates. However, because such taxes would result in hundreds of millions of euros of losses for the French food and farming sector (Bareille and Gohin, 2020),<fnref n="25" /> it is likely that they would not be socially accepted.

<footnote n="25">²⁵The reduction of pesticide use is however likely to lead to numerous positive non-market effects. Compiling previous assessments, Bâ et al. (2015) consider that pesticide pollution costs annually from €7.0 billion to €28.4 billion in France (mostly due to the negative health outcomes of insecticide use). Using total sales, this value implicitly</footnote>
behaviors by focusing on pesticide use as an illustrative case. Using an original, exhaustive dataset of purchases of all active substances in France, we show that farmers do adjust their pesticide use to weather shocks. In particular, we find that farmers react to similar weather shocks by purchasing far more fungicides and herbicides than insecticides. Our preferred estimates obtained using average weather conditions during the growing season indicate that a one percent temperature increase leads farmers to purchase additional +1.70% of fungicides, +1.72% of herbicides, but only +0.37% of insecticides. These results are robust to many sensitivity analyses. The lack of evidence of any storage behaviors from one year to another suggests that our dependent variables – that are fundamentally data on pesticide purchases – are actually likely to represent pesticide use. We also show that our preferred estimates are likely to represent the lower bound estimates of the farmers’ intensive margin responses to weather shocks. This means that our preferred estimates actually represent the combined effects of weather on (i) pest pressure, (ii) pesticide efficacy and (iii) attainable yields, but do not include change in crop allocation nor in total agricultural area. Additional analyses indicate that our preferred estimates are largely driven by weather shocks during spring, at the time when farmers apply pesticides, and that zip codes specializing in cereals and oilseed crops are much more sensitive to weather shocks than other regions. We also document the fact that the sensitivity of pesticide applications to weather shocks is greater for those with higher risks to human health in the case of herbicides and fungicides, and for those with higher risks to the environment in the case of insecticides. Finally, we identify non-linear, concave weather effects on pesticide use, which appears close to the sample average for precipitation, but far from this point for temperature.

Projections based on our preferred estimates indicate that, *ceteris paribus*, French farmers are likely to increase pesticide use by 7% to 15% on average by 2050 in response to a RCP4.5 climate change scenario. This has significant implications for French and European policymakers. Despite their efforts over the past decades to halve pesticide use (Schebesta and Candel, 2020), our results indeed suggest that achieving such reductions becomes even more challenging in the context of climate change. While some stakeholders might have hoped that climate change would have decreased the incentives to use pesticides (due to anticipated reductions in crop yields; see IPPC, 2021, for example), our findings suggest instead that the climate-induced increase in pest pressure or pesticide efficacy – i.e. the two other channels captured by our estimates – would actually lead rational farmers to *apply more pesticides*. To anticipate these trends, our results call for a strengthening of existing pesticide policies, in particular those in France. Indeed, despite relying on pesticide taxes modulated by toxic risks, our results suggest that the current French tax rates would be insufficient to reduce pesticide use in the country. Given that we predict that the largest climate-induced surges in pesticide applications would concern the most toxic products (those primarily affected by French and European regulations), we believe that this calls in particular for a proactive increase on these substances' tax rates. However, because such taxes would result in hundreds of millions of euros of losses for the French food and farming sector (Bareille and Gohin, 2020),<fnref n="25" /> it is likely that they would not be socially accepted.

<footnote n="25">²⁵The reduction of pesticide use is however likely to lead to numerous positive non-market effects. Compiling previous assessments, Bâ et al. (2015) consider that pesticide pollution costs annually from €7.0 billion to €28.4 billion in France (mostly due to the negative health outcomes of insecticide use). Using total sales, this value implicitly</footnote>
In this context, alternative public policies targeting these most toxic substances may be explored. One
possibility would be to redistribute the resulting tax revenues to farmers via lump-sum payments (Chèze
et al., 2020), but alternative policy instruments beyond mere financial incentives should also be considered
(Finger and Möhring, 2022).

All our results are identified thanks to fine-grained pesticide purchase measured at about 9 km × 9 km
on similarly spatially detailed weather shocks. To our knowledge, we are the first to perform this kind of
econometric assessment at such a detailed resolution. While this spatial resolution presents clear advantages,
it also introduces some limitations. First, the lack of exhaustive data on crop yields and pest pressure at
such a detailed spatial definition prevents us from separately identifying the weather impacts on (i) pest
pressure, (ii) pesticide efficacy and (iii) attainable yields. Second, while we have provided several elements
indicating that our measurements of pesticide purchases are likely to reflect pesticide use, we acknowledge
that our analyses would benefit from data on actual pesticide use (particularly on a crop-specific basis),
rather than relying solely on purchase data. Unfortunately, the few existing databases on pesticide use are
however only available at coarser spatial resolutions, which would have threatened our identification strategy.
Third, the impacts that we have identified are short-term in nature, and further research is essential to unveil
long-term adaptations to climate change. This includes investigating changes in cropping areas (Cui, 2020),
or expansion of the total agricultural area (Graveline and Mérel, 2014). While we argue that our estimates
are not immediately affected by these adaptations in the short term, they may influence farmers’ pesticide
use decisions in the longer run (Burke and Emerick, 2016). Incorporating these elements is necessary to
enhance predictions of pesticide use under future climate conditions and, in fine, improve our assessment of
the costs of climate change (Carleton et al., 2022).

<footnote>corresponds to a social cost of pesticides between 100 and 400 €/kg. Bareille and Gohin (2020) however show
that the reduction in pesticide use in France can be accompanied by negative side-effects on other non-market
dimensions (e.g. increase in carbon emissions or increase in fertilizer pollution).</footnote>
In this context, alternative public policies targeting these most toxic substances may be explored. One
possibility would be to redistribute the resulting tax revenues to farmers via lump-sum payments (Chèze
et al., 2020), but alternative policy instruments beyond mere financial incentives should also be considered
(Finger and Möhring, 2022).

All our results are identified thanks to fine-grained pesticide purchase measured at about 9 km × 9 km
on similarly spatially detailed weather shocks. To our knowledge, we are the first to perform this kind of
econometric assessment at such a detailed resolution. While this spatial resolution presents clear advantages,
it also introduces some limitations. First, the lack of exhaustive data on crop yields and pest pressure at
such a detailed spatial definition prevents us from separately identifying the weather impacts on (i) pest
pressure, (ii) pesticide efficacy and (iii) attainable yields. Second, while we have provided several elements
indicating that our measurements of pesticide purchases are likely to reflect pesticide use, we acknowledge
that our analyses would benefit from data on actual pesticide use (particularly on a crop-specific basis),
rather than relying solely on purchase data. Unfortunately, the few existing databases on pesticide use are
however only available at coarser spatial resolutions, which would have threatened our identification strategy.
Third, the impacts that we have identified are short-term in nature, and further research is essential to unveil
long-term adaptations to climate change. This includes investigating changes in cropping areas (Cui, 2020),
or expansion of the total agricultural area (Graveline and Mérel, 2014). While we argue that our estimates
are not immediately affected by these adaptations in the short term, they may influence farmers’ pesticide
use decisions in the longer run (Burke and Emerick, 2016). Incorporating these elements is necessary to
enhance predictions of pesticide use under future climate conditions and, in fine, improve our assessment of
the costs of climate change (Carleton et al., 2022).

<footnote>corresponds to a social cost of pesticides between 100 and 400 €/kg. Bareille and Gohin (2020) however show
that the reduction in pesticide use in France can be accompanied by negative side-effects on other non-market
dimensions (e.g. increase in carbon emissions or increase in fertilizer pollution).</footnote>
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Rhodes, L. A. and McCarl, B. A. (2020). An analysis of climate impacts on herbicide, insecticide, and fungicide expenditures. *Agronomy*, 10(5):745. 1, 3.1, 5.4, 5.6

Schebesta, H. and Candel, J. J. (2020). Game-changing potential of the EU’s Farm to Fork Strategy. *Nature Food*, 1(10):586–588. 1, 7

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