We have used historic and future climate data, historic corn growth interval durations, yield and planted area for each of the nine IA districts ( Table 1 ). According to the USDA-NASS definition, “districts” represent a group of counties in a state on the basis of “geography, climate and cropping practices”. Hence it has been assumed here that districts are a homogenous group of counties with respect to corn growth conditions including weather and genotype.

The historic daily minimum and maximum temperature and rainfall data from 1981-2018 for each US county included in this study were downloaded from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) daily dataset. The National Climate Data Centre (NCDC) is commonly used as a weather data source in the agronomic literature, however the PRISM data are preferred here because the NCDC has data gaps for outlying (rural) areas (Johnston and Matlock, 2011). The agricultural district level temperature is derived as the weighted average of the county temperatures.

For future climate data we have used the downscaled climate projections from the NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP) dataset (Thrasher et al., 2022) for 25 Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We downloaded the daily minimum, maximum temperature and rainfall data for the period 2015-2099 data for each of the nine agricultural districts in IA for two future greenhouse gas emissions scenarios: SSP2-4.5 and SSP5-8.5, the “medium” and “high” twenty-first century greenhouse gas concentration trajectories respectively (O’Neill et al., 2017; Riahi et al., 2017).

Historic corn yield, corn planted area and growth interval data for the IA districts from 1981-2018 was downloaded from USDA-NASS website. The USDA-NASS defines yield as bushels of corn produced per acre. USDA-NASS produces a weekly national Crop Progress (CP) report during the growing season (April – November) for selected crops including corn. For corn growth interval data, it is not reasonable to assume that all the corn in a district will grow concurrently as it is unlikely that all the corn in a district will be planted on the same date. Following the difference in planting date, the corn in a district is likely to be at different stages of corn growth at anyone reporting date. It is assumed that corn in a particular district attains a particular stage of corn growth when 50% of the crop in the district is reported to have attained that corn growth stage. The CP reports do not report the date when 50% of the crops attain a particular corn growth stage but instead report the percentage of the total district corn crop at various corn growth stages on the reporting day. We estimate the calendar date that corresponds to 50% growth for each corn growth stage by interpolating between the reported calendar dates bracketing the 50% attained phenological growth. The interpolation was carried out using the logistic function which is a well-known plant growth function (Yin et al., 2002). This interpolation process provided an estimated calendar date when 50% of the corn crop in a ‘district attains a particular corn growth stage during each growing season from 1981-2018.

The results in Table 2 show that historically corn yield in IA was negatively impacted by high average monthly temperature during July – September (Rows 3-4, Table 2 ). This is in line with the relationship between yield and high growing season temperatures reported in previous studies (Lobell and Field, 2007; Schlenker and Roberts, 2009; Mishra and Cherkauer, 2010). Our results highlight the yield sensitivity to the July mean temperature in particular, when a 1°C increase in temperature reduced yield by 6.3% on average (regression coefficient of -6.3% in Row 3, Table 2 ). The sensitivity to the August mean temperature is slightly lower when a 1°C increase in temperature reduced yield by 4.6% on average (Row 4, Table 2 ). In terms of the diurnal temperature range, we find that the historical yield was most sensitive to the June diurnal range with a regression coefficient of -4.1% (Row 8, Table 2 ) implying that and a widening of the June diurnal range by 1°C reduced yield by 4.1% on average. The negative impact of widening diurnal range on yield is well recorded (Sunoj et al., 2016; Sadok and Jagadish, 2020; Niu et al., 2021). However, the April diurnal range had an opposite impact (Row 7, Table 2 ) where a widening of the April diurnal range increased yield by 1.5%. As expected, the positive coefficients (Rows 10 and 11, Table 2 ) confirm that the average monthly rainfall during the growing season had a beneficial impact on the historical yield. Rainfall in June/July has been particularly important and a 1% increase in average June/July rainfall contributed to a 0.04% (Rows 10 and 11, Table 2 ) increase in the average yield historically. Rows 12-14 in Table 2 measure the impact on yield of dry conditions brought on by abnormally low monthly rainfall (below the 5^th percentile) from June – August. The negative coefficients in Rows 12-14 of Table 2 show that historically the yield declined respectively by 4.2% and 4.0% due to abnormally low rainfall in June and July respectively, while abnormally low August rainfall had the biggest impact reducing yield by 8% on average.

In terms of the genetic variables included in our regression model Δ G d t , we find that historically a 1% increase in the GDD accumulation during the vegetative interval increased the historic yield by 1.7% on average (Row 17, Table 2 ) whereas a 1% increase in the GDD accumulation during the reproductive interval increased yield by 0.9% (Row 18, Table 2 ). Our results further show that planting a 1 day earlier increased the historic yield by 1.3% (regression coefficient -1.3% in Row 15, Table 2 ); increasing the length of the reproductive interval by 1 day increased the yield by 0.6% (Row 16, Table 2 ). Additionally, our model shows that the increase in net planted area in IA historically also led to an improvement in the yield with a 1% increase in net planted area resulting in a 0.2% improvement in the historic yield on average (Row 19, Table 2 ).

We next combine the impact of the climate and genetic variables to summarise the historic impact of climate and genetic variables for IA in Figure 1 . There are 9 districts in IA: 3 northern, 3 central and 3 southern districts. Instead of showing the results for each of the 9 individual districts, the results in Figure 1 are shown for the combined northern, central and southern districts to remove the noise within the districts and observe trends in the results. The results in Figure 1 show that the impact of the combined climate variables during 1981-2018 on the average yield was noticeably worse in the northern districts (-1.3% per annum) relative to the southern districts (0.1% per annum). On the other hand, the average yield improvement due to the genetic variables in the northern districts (2.3% per annum) was noticeably higher than in the southern districts (1.2% per annum) during 1981-2018. Overall, for IA (averaged across all districts), the combined climate variables accounted for a decline of 0.7% per annum, while the combined genetic variables accounted for an increase of 1.8% per annum in yield from 1981-2018.

In this section we use the climate change predictions to model the future impact on corn yield at the state level in IA. The GCMs used in this study show that the average growing season temperature in IA is projected to increase by 2.4 – 2.9°C by mid-century and 3.4–6.3°C by late-century. On the other hand, the average spring temperature (March-April) is predicted to increase by 1.9– 2.3°C by mid-century (3.0 – 5.0°C by late-century) which is less than the predicted increase in the average growing season temperature. The relatively slower spring warming is significant because this will mean that farmers might not be able to plant as early as they would like to (with the current technology) to escape the much hotter temperature in the growing season. The GCMs further predict that the growing season temperature will also become progressively more extreme as we move further towards the end of the century. Climate projections show that future average monthly rainfall in IA is expected to increase in the spring months and reduce slightly in the summer by mid-century. Spring rainfall will also become more extreme as we approach mid-century which could lead to delayed planting. The growing season rainfall is also projected to become more extreme. Using the regression coefficients from our model and multiplying these by the projected change in future temperature and rainfall variables, we find that IA corn yield will decline by 1.4% to 1.7% per annum by mid-century (1.2% to 2.1% per annum by late century) for medium/high emissions scenarios relative to the historic average yield. ( Figure 2 ).

It is not known how genetic development and will adapt to counteract the impact of climate change on yield in the future. Indeed, some studies report that the historic gains in agricultural yield improvement appear to be slowing down (Rizzo et al., 2022). For the purpose of projecting the impact of genetic variables on future yield in IA, we have considered three scenarios for future development in genetic variables: base, optimistic and pessimistic. The base scenario is a constant 1.8% per annum average yield improvement (this is the average historic improvement across IA), the optimistic scenario is a constant 2.3% per annum average yield improvement (this is the historical improvement for the northern IA districts) and the pessimistic scenario is a constant 1.2% per annum average yield improvement (this is the historical improvement for the southern IA districts).

Table 3 shows the projected impact of climate change and genetic variables on IA corn yield for the base, optimistic and pessimistic genetic development scenarios. The projections are shown for mid and late 21^st century under both the medium and high emissions scenarios. Instances where the impact on yield of genetic variables is not sufficient to offset the negative impact of future climate change are highlighted red in the table. The results in Table 3 show that improvement in genetic variables will not be able to offset the projected impact of future climate change in IA under the pessimistic scenario. Even under the base genetic development scenario, if we continue with high emissions, by late 21^st century the improvement in genetic variables will not be able to offset the negative impact of climate change on yield. Worryingly even for instances where the climate impact is completely offset by genetic development, food security is likely to be challenged by the growing demand driven by the projected human population growth of 50% by mid-century (relative to the start of the 21^st century) (United Nations, 2019).