Tohoku University Research Center for Policy Design Discussion Paper

TUPD-2024-004

Substitution of Human and Physical Capitals in Farm Adaptation to
Extreme Temperatures: Evidence from Corn Yields in US

Yi-Chun Ko
Asian Growth Research Institute
Graduate School of Economics and Management, Tohoku University

Shinsuke Uchida
Graduate School of Economics, Nagoya City University
Graduate School of Economics and Management, Tohoku University

Akira Hibiki
Graduate School of Economics and Management, Tohoku University

March 2024

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Substitution of Human and Physical Capitals in Farm Adaptation to
Extreme Temperatures: Evidence from Corn Yields in US
By Yi-Chun Ko*, Shinsuke Uchida**, and Akira Hibiki***
Abstract
This study delves into the factors that underly farmer’s adaptation offarm production to extreme weather. Specifically, we examine how
farmer’s age mitigates the negative effects of extreme temperatures oncrop yields. Our findings reveal a nonlinear relationship between age
and the ability to adapt, wherein the adaptation capability generally
increases and then decreases with age. Furthermore, we explore how
farmer’s age interacts with farm technology, such as irrigation, toinfluence farmer’s adaptation simultaneously. Interestingly, age
effects are less pronounced in irrigated areas, where the likelihood of
exposure to climate risk is comparably low. This suggests that humancapital plays a critical role in introducing adaptation measures inareas at high risk of exposure to extreme temperatures.
Keywords: Adaptation, Adaptation capability, Climate change, Corn,
Crops yields, Extreme temperatures, Farmer’s age, Irrigation
JEL classification: Q10, Q51, Q54*

Asian Growth Research Institute,〒803-0814 11-4, Otemachi, Kokura Kita-ku, Kitakyushu, Fukuoka, Japan (E-mail: yichunko@agi.or.jp);
Graduate School of Economics and Management (Research Center for Policy Design), Tohoku University**
Graduate School of Economics, Nagoya City University,〒 467-8501 Yamanohata 1, Mizuho-cho, Mizuho-ku, Nagoya, Aichi, Japan (E-mail:suchida@econ.nagoya-cu.ac.jp); Graduate School of Economics and Management (Research Center for
Policy Design), Tohoku University; Asian Growth Research Institute***
Graduate School of Economics and Management, Tohoku University,〒 980-8576 27-1, Kawauchi, Aoba-ku, Sendai, Miyagi, Japan (E-mail: hibiki@tohoku.ac.jp); Asian
Growth Research InstituteThis research was performed by the Environment Research and Technology Development Fund(JPMEERF20S11821) of the Environmental Restoration and Conservation Agency of Japan.
This research was also performed by Society for Environmental Economics and Policy Studies.

1

l. Introduction
Crop production has been at risk from climate change. Existing literature indicates thatcrops are susceptible to extreme heat, and temperature rise is likely to reduce crop yields(e.g., Schlenker and Roberts 2009; Burke and Emerick 2016; Chen et al. 2016). Farmers
can adapt to mitigate yield loss by such as installing irrigation, changing planting dates,and developing new heat–tolerant seed varieties, but the degree to adapt depends onfarmers’ ability and available farm technology.
This study examines how farmers’ ability and farm technology influence theadaptation capability of farm production to extreme weather events. It has not been fully
explored what type of farmers are more likely to adapt and reduce extreme temperatureeffects on crop yields.1 We specifically investigate how farmers’ age and irrigation use
reduce the negative effects of extreme temperatures on crop yields.
Aging lowers cognitive and physical skills that could augment productivity (Tauer
1984; Maestas et al. 2016; Eggertsson et al. 2019; Lee and Shin 2019; Park et al. 2021).
Aging also functions as a barrier to the adoption of new climate–resilient technologies,
because new technologies are too complex (Park 2000; Salthouse et al. 2003; Salthouse
2012; Klein et al. 2015; Hunter et al. 2016) or near–retirement farmers are less likely toinvest in them (Czaja and Sharit 1998; Barnes et al. 2019; Shang et al. 2021). On the other
hand, aging has a positive aspect on production as farmers accumulate more experiences
and knowledge from learning–by–doing. Tauer (1984) found an inverted U–shape
relationship between agricultural productivity and farmer’s age, which can be led by these
relative merits and demerits of aging. We hypothesize that farmer’s age has a similar
nonlinear effect on crop yields under the risk of extreme temperatures.
We further explore how the farmer’s ability and the farm technology influence theadaptation capability simultaneously. Irrigation technology can relieve heat stress bycooling the canopy temperature (Siebert et al. 2014) and thereby control a resilient
environment to alleviate negative temperature effects (e.g., Troy et al. 2015; Tack et al.
2017; Zaveri and Lobell 2019). Such physical capital may be able to complement humancapital decay by aging. In other words, we postulate that a decline in the degree ofadaptation to extreme temperatures due to farmer’s aging can be reinforced by the

Many studied the determinants of adaptation to climate change. Kgosikoma et al. (2018) found that
farmers’ gender, age, household size, poverty, and knowledge about climate change significantly influence
their adaptation. Apart from the above, level of education, access to extension and credit, and membershipto farmers’ groups are also found to determine farmers’ choices (Deressa et al. 2009; Shikuku et al 2017).
However, it is unclear whether such determinants help farmers mitigate the negative impacts of climatechange.
12presence of irrigation technology. To the best of our knowledge, this represents the firstsystematic attempt to understand the collective influence of farmer age and irrigation
technology on crop yield resilience in the face of climate challenges.
We test our hypotheses by using US corn yield data from 2002 to 2017. Corn is oneof the primary cereal grains in the world,2 and is the most widely produced feed grain in
the United States. 3 Corn is susceptible to heat stress and drought. Under the global
warming scenario, corn yields will significantly decline across the world (IPCC 2022).
Adaptation to the warming scenario is urged to mitigate such a negative warming effect,and enhancing the degree of adaptation is a key to facilitate adaptation.
Figure 1 plots annual county–average corn yields in natural logarithm and farmer’s
age in irrigated and rainfed (non–irrigated) areas in US corn–producing counties. The
distribution of age appears similar across the irrigated and rainfed counties. We also
observe an inverted U–shape relationship between age and corn yields in both irrigatedand rainfed counties. We test that this relationship comes through which age affects
farmer’s adaptation capability that mitigates the negative temperature effect on cornyields. In addition, the curvature of the U–shape fitting is different between irrigated andrainfed counties. This difference can indicate that the negative aging effect on the yield–
temperature relationship is offset by irrigation technology.
Aging is inevitable in modern society. Figure 2 shows the dynamics of Americancorn farmer’s age in our sample period. The average age of farmers has increased from
52.2 to 54.4 years old over the decade. The negative aging effect on the yield–temperature
relationship in Figure 1 likely becomes larger in recent years. Revealing the effect ofadaptation capability to a temperature rise is important to prevent further potential
damages from global warming and aging.
We find that age nonlinearly influences farmer’s adaptation capability to reduce thenegative temperature effect: Capability generally increases and then decreases with age(inverted U–shaped age effects). Interestingly, age effects are smaller in irrigated areas
where exposure to climate risk is relatively low. This suggests that human capital plays acritical role in introducing adaptation measures in areas at high risk of exposure to
According to OECD-FAO Agricultural Outlook 2019–2028 (https://www.oecd-ilibrary.org/agricultureand-food/oecd-fao-agricultural-outlook-2019-2028_agr_outlook-2019-en), corn is the most widely usedcereal in both developed and developing countries, with the largest growth in area and yield worldwide. In
addition, US is the largest corn producer and a major player in the global corn trade market, exporting 10–
20 percent of its annual production. See USDA Foreign Agricultural Service (FAS) U.S. Corn Exports in
2021 for more detailed information (https://www.fas.usda.gov/commodities/corn).
3
Corn accounts for more than 95 percent of total feed grain production in US. It is also used for livestock
feed and industrial products such as fuel ethanol. See United States Department of Agricultural (USDA)
National Agricultural Statistics Service (NASS) Statistics by Subject for detailed information(https://www.nass.usda.gov/Statistics_by_Subject/index.php?sector=CROPS).
2

3

extreme temperatures.
The remainder of this paper is organized with the delineation of empiricalmethodology and data in Section ll and the discussion of estimation results in Section lll.
Section IV concludes.

Figure 1: County average corn yields and farmer’s age by irrigated and rainfed areas
in US corn production counties over 2002–2017
Notes: We draw a quadratic fit on county average corn yields and farmer’s age over 2002–2017 withthe weight of average corn acreage over the same period. They are obtained respectively from Schlenker
and Roberts (2009) and US Agricultural Censuses. See the data section for a detailed description.

4

Figure 2: Farmer’s age dynamics over Agricultural Census years
Notes: Annual county–average corn farmer’s age is attained from US
Agricultural Censuses 2002–2012.4 See the data section for a detailed
description.

ll. Methodology and Data
A. Methodology
We start to delineate the yield response function to temperatures by following Burke andEmerick (2016) who used the panel analysis and GDD (growing degree days) method toestimate the yield–temperature relationship. Temperature effects on crop yields are often
measured by GDD in the literature. GDD accounts for the cumulative heat that crops
receive over the growing season. To further account for the nonlinear yield–temperature
relationship, Burke and Emerick (2016) construct GDD≤𝑇 which measures cumulative
heat between the lower and upper bound daily temperature threshold, T, where the lower
and upper bound thresholds are set as 0℃ and 28℃, respectively. Similarly, GDD>𝑇Considering that farmer characteristics are predominant in affecting adaptation behaviors, the current
census year data is matched to yield and weather data for the following 5 years. For example, census year
data for 2012 is matched to yield and weather data for 2013–2017.
45

measures the cumulative heat above T over the growing season.5 To examine the effectof farmer’s age on the yield–temperature relationship, we test our hypotheses byintroducing interaction terms of these GDD variables with the measure of farmer’s
adaptation ability (farmer’s age) in the models described below.
The effect of temperatures and adaptation measures is estimated by the panel
approach as given by:log(Y𝑖𝑡 ) = 𝛼0 + 𝛽1 GDD≤𝑇 𝑖𝑡 + 𝛽2 (GDD≤𝑇 𝑖𝑡 × X𝑖𝑡 ) + 𝛽3 GDD>𝑇 𝑖𝑡+𝛽4 (GDD>𝑇 𝑖𝑡 × X𝑖𝑡 ) + 𝛿X𝑖𝑡 + 𝐙𝑖𝑡 𝛾 + C𝑖 + λ𝑠𝑡 + ε𝑖𝑡 ,(1)

where Y𝑖𝑡 is corn yield in county i in year t, X𝑖𝑡 represents farmer’s age, a vector
𝐙𝑖𝑡 includes the other explanatory variables of precipitation 6 and farm characteristics(irrigation use, farmer’s age, and farm size), the county fixed effects C𝑖 account for
county–specific confounding factors such as geological conditions, the state by year fixed
effects λ𝑠𝑡 account for technological change and policy interventions, and ε𝑖𝑡 indicatesthe error term.
B. Data
Agricultural data on corn acreage and yields and weather data on temperatures andprecipitation are drawn from Schlenker and Roberts (2009).7 Farm characteristics datasuch as corn acreage for irrigation, number of corn farms, etc. is obtained from US
Agricultural Censuses via Inter–university Consortium for Political and Social Researchcollection. Additionally, age data for corn farmers is obtained through a special request

For example, if T = 19 and a set of daily temperatures is -1, 15, 18, 21, and 24, GDD≤𝑇 is equal to 0, 15,
18, 19, and 19, and GDD>𝑇 is equal to 0, 0, 0, 2, and 5.
6
Following Burke and Emerick (2016), we set the growing season threshold precipitation at 50cm. A
below-threshold precipitation variable is constructed by taking the difference between the cumulative
precipitation for the growing season and the threshold precipitation for the growing season interacted withan indicator variable for precipitation being below the threshold. An above-threshold precipitation variable
is constructed similarly. Their cross terms with the adaptation ability measure are also included in Zit.
7
The agricultural data used in Schlenker and Roberts (2009) is from United States Department ofAgriculture (USDA). The USDA’s National Agricultural Statistics Service (NASS) provides annual dataon corn acreage and yields at the county level. The weather data is also drawn from Schlenker and Roberts(2009) and include daily interpolated values of precipitation totals and maximum and minimum
temperatures for 4 km grid cells covering the entire United States. These weather data are aggregated to thecounty-day level by averaging daily values for grid cells in each crop-growing county, which are estimatedfrom satellite data.
5

6

process.8 Following the previous literature, our analysis focuses on counties east of the100th meridian and on weather conditions in the growing season from April to September.
Figures 3 and 4 display the spatial distribution of county average corn yields andaverage corn farmer’s age over the study period in Figure 1, respectively. We notice thatcounties with lower yields are located primarily in warmer regions in the southern US.
The distribution of corn yields seems negatively associated with that of the average corn
farmer’s age. This indicates that corn yields are lower in a warmer climate where more
older farmers involve in corn production. In contrast, the spatial distribution of theaverage share of land irrigated for corn production does not seem associated with that ofcorn yields or corn farmer’s age (Figure 5). Corn production is highly irrigated,particularly in the western and southeastern US including Arkansas, Louisiana, Tennessee,
southern Georgia, and central Florida, where corn yields are high while corn farmer’s age
is also in the high range. We use this variation to evaluate the irrigation effect on therelationship between age, extreme temperatures, and yields.

Figure 3. County average corn yield (bushels/acre) over 2002–2017
Notes: Data are for US counties east of the 100th meridian.

We would like to express our gratitude to the Data Lab of the USDA’s NASS for generously providing
the age data used in this study. Their assistance was invaluable in enhancing the quality and depth of ourresearch.
87

Figure 4. County average corn farmer age (years old) over 2002–2017
Notes: See Notes in Figure 3.

Figure 5. County average share of land irrigated for corn production (%)
over 2002–2017
Notes: See Notes in Figure 3.

8lll. Empirical Results
A. Yield–Temperature Response Function
We first provide results from estimating the yield response function to temperatures.
Columns 1–4 produce results using temperature, precipitation, and other control variables(share of corn farmers under age 35, share of corn farmers over age 65, share of corn
irrigated land, and corn farm size) with different fixed effects (county and year fixed
effects, county and state by year fixed effects, county fixed effect and state–specificquadratic year trend, and county fixed effect and county–specific quadratic year trend,
respectively). Results show very similar parameter estimates of the responsiveness ofcorn yields to temperatures.9,10
We find that corn yields respond positively to temperatures below the threshold of
28℃ and negatively to temperatures above the threshold. Our preferred specification of
the panel model in column (2) shows that exposure to an additional degree day below thethreshold yields a marginal increase in corn yields by 0.01 percent, albeit insignificantly,
while exposure to an additional degree day above the threshold results in a decrease incorn yields by 0.55 percent. Consistent with Burke and Emerick (2016), these results
indicate that US corn yields are particularly vulnerable to high temperatures. Figure 6
displays the yield–temperature response function in column (2) with the 95 percent
confidence intervals.
We also estimate heterogeneous temperature effects on corn yields in irrigated andrainfed counties. We assign counties with no irrigation use in 2002–2017 as rainfed
counties, and irrigated counties otherwise. We further classify irrigated counties into highirrigation and low-irrigation categories based on the 75th percentile. The yield–
temperature response functions parallel to Figure 6 are depicted in Figure 7 (andparameter estimates are provided in Appendix Table A4). We find in rainfed counties thatthe corn yields appear more vulnerable to hot temperatures (with a steeper slope).

Appendix Table A2 shows that the results are consistent regardless of whether other control variables
are included.
10
The reduced number of observations is attributed to the availability of data on farm characteristics. In
Appendix Table A3, we present evidence to indicate that the absence of census data does not have any
significant bearing on the results obtained from our analysis.
99Table 1. Results of the nonlinear temperature effects on corn yields
GDD below threshold

GDD above threshold

Precip below threshold

Precip above threshold

(1)

(2)

(3)

(4)

0.00020.00010.0003***0.0003***(0.0001)(0.0001)(0.0001)(0.0001)-0.0046***-0.0055***-0.0047***-0.0046***(0.0006)(0.0009)(0.0005)(0.0005)

0.00090.0029*0.0041*0.0039*(0.0021)(0.0015)(0.0021)(0.0023)-0.0016**-0.0013**-0.0014**-0.0013**(0.0007)(0.0005)(0.0006)(0.0006)

Observations
16,985
16,985
16,985
16,985
Fixed effects
Cty, Yr
Cty, State-Yr
Cty, State-tr
Cty, Cty-tr
Control var.
Yes
Yes
Yes
Yes
0.71280.81100.70140.6823Adjusted 𝑅2
Notes: ***, **, and * denote 1 percent, 5 percent, and 10 percent significant levels, respectively.
Specifications are estimated with different fixed effects shown at the bottom; see main text for details. Data
are for US counties east of the 100th meridian, 2002–2017. Standard errors clustered at the state level arereported in parentheses. Regressions are weighted by the 2002–2017 average corn area. Observations are
dropped due to singleton observations.

Figure 6. Corn yield response function to temperatures
Notes: Parameter estimates in Table 1 are converted to the marginal change in log cornyields with respect to a day of exposure to a given ℃ temperature relative to a day spentat a threshold temperature of 28℃. The solid line represents the yield–temperature
response function. The shaded area represents 95 percent confidence intervals.

10

Figure 7. Corn yield response function to temperatures in
irrigated vs. rainfed counties
Notes: Corn yield response function to temperatures in Figure 6 is estimated separately in
irrigated (categorized into high irrigation and low irrigation) and rainfed counties. Indicatorvariables representing zero and nonzero irrigated share of corn acreage (classified into highirrigation and low irrigation) are interacted with all explanatory variables. In the visualization,
we exclusively present the results for high irrigated and rainfed counties. The sensitivity ofcrop yield to temperatures in low irrigated counties falls between that of high irrigated andrainfed counties. Table point estimates are provided in Appendix Table A4, while graphicalresults for low irrigated counties can be obtained upon request.

B. Farmer’s Adaptation, Farmer’s Adaptation Ability and theYield–Temperature Relationship

Farmer’s adaptation is important to mitigate the negative temperature effects on crop
yields. As an example, high irrigation utilization likely mitigates the negative effects ofhot temperatures on crop yields (e.g., Troy et al. 2015; Tack et al. 2017; Zaveri and Lobell
2019). To ensure that our results are consistent with the existing literature (since we use
a different timeline), we perform the same hypothesis tests on corn yields.
Table 2 presents the results for the effect of irrigation on the yield–temperature
relationship, where we use the share of irrigated corn acreage over total corn acreage.
11

Consistent with the previous studies mentioned above, irrigation usage reduces thenegative temperature effects on corn yields. A parameter estimate of GDD above thethreshold (-0.0067) indicates that exposure to an additional degree day above thethreshold significantly decreases corn yields by 0.67 percent. Note that this negative
temperature effect is larger than 0.55 percent in Table 1, suggesting that irrigation
technology mitigates the negative temperature effects on corn yields. One percent
increase in the share of irrigated corn acreage mitigates the negative hot temperatureeffect by 0.0066 percent. At the extreme case where corn production is entirely irrigated(i.e., the share of irrigated corn acreage is 100 percent), corn yields are almost fully
resilient against extremely hot temperatures above 28℃.11
Our study further submits that farmer’s adaptation is facilitated by farmer’s
adaptation ability. We test whether farmer’s aging lowers farmer’s adaptation capability
and subsequently affects the sensitivity of crop yields to extreme temperatures. Thispotential decline may stem from diminishing cognitive and physical skills and/or a lackof incentives for investment in adaptation measures. The graphical results of theestimation in Figure 8 show that age marginally mitigates the negative temperature effects
at a diminishing rate.12 Farmers at the age of 35–64 are most capable of reducing thenegative hot temperature effects, while such capability diminishes as they getyounger/older. 13 The point estimates for hot temperature effects are provided inAppendix Table A5. Specifically, at the age range of 35–64, the negative impact of hot
temperatures is estimated at 0.46 percent, approximately 1.2 times lower than the estimateof 0.55 percent reported in Table 1.
To explore the heterogeneous age effect under different climate risks, we segregate
estimation results in irrigated (high irrigated and low irrigated) and rainfed counties. We
assign counties with no irrigation use in 2002–2017 as rainfed counties, and irrigated(high irrigated: average irrigation uses between 0–30 percent; low irrigated: average
irrigation uses above 30 percent, which is about 75 percentiles) counties otherwise.

Given corn’s high sensitivity to hot temperatures and minimal sensitivity to colder temperatures in theUS, our analysis in this section predominantly concentrates on the impacts of hot temperatures. Results
regarding the impacts of cold temperatures are accessible upon request.
12
We also find the inverted U–shape relationship between age and corn yields, which is consistent with
Tauer (1984).
13
The foundational age bracket considered in this study ranges 35–64 years old.
11

12

Table 2. Irrigation effects on the corn yield response function to hot temperatures
(1)

(2)

(3)

(4)

-0.0063***-0.0067***-0.0060***-0.0060***(0.0007)(0.0009)(0.0005)(0.0006)

GDD above threshold

0.0064***0.0066***0.0063***0.0062***× irrigation(0.0010)(0.0010)(0.0010)(0.0010)

Irrigation

1.8074***2.0638***1.9771***1.6164***(0.3573)(0.4891)(0.4427)(0.4705)

Observations

16,985

16,985

16,985

16,985

Fixed effects
Control var.
Adjusted 𝑅2

Cty, Yr
Yes
0.7478Cty, State-Yr
Yes
0.8314Cty, State-tr
Yes
0.7328Cty, Cty-tr
Yes
0.7178GDD above threshold

Notes: See Notes in Table 1. Irrigation unit: 0–1 (e.g., 0.1 means 10 percent).

Figure 8. Age effects on the corn yield response function to hot temperatures
Notes: Parameter estimates in Appendix Table A5 are transformed into the marginal change inlog corn yields concerning an increase in younger/older farmers relative to middle–aged
farmers. We set the value of older farmers to 0 on the left–hand side and the value of younger
farmers to 0 on the right–hand side. The midpoint represents when all farmers are middle–
aged. The dots represent the impact of age on the yield–temperature response function, whilelines depict the 95 percent confidence intervals. Age unit: 0–1 (e.g., 0.1 means 10 percent).

13

In irrigated areas, exposure to climate risk is relatively low as argued above with
Table 2. Figure 9 presents evidence to suggest that the age effect in irrigated regions isless pronounced when compared to rainfed regions. The point estimates for hot
temperature effects are provided in Appendix Table A6. Our findings indicate that in
rainfed areas, for every one percent reduction in the age of farmers below 35/above 65
years old, there is a corresponding reduction of 0.0048/0.0025 percent in the adverse
impact of hot temperature on crop yield, as compared to farmers aged between 35–64
years old. Conversely, the corresponding percentage decrease in irrigated areas is minimal,
closer to 0. This suggests that age–related adaptation capability plays a critical role inintroducing adaptation measures in areas at high risk of exposure to extreme temperatures.

Figure 9. Age effects on the corn yield response function to hot temperatures:
irrigated vs. rainfed counties
Notes: See Notes in Figure 8. In the visualization, we exclusively present the results forhigh irrigated and rainfed counties. The age impact on the sensitivity of crop yield totemperatures in low irrigated counties falls between that of high irrigated and rainfed
counties. Table point estimates are provided in Appendix Table A6, and graphical results
for low irrigated counties are available upon request. Age unit: 0–1 (e.g., 0.1 means 10
percent).

14

IV. Conclusion
This study investigates how farmer’s age and irrigation use affect the yield–temperature
relationship by using the panel and GDD methods. Our findings indicate that both age
and irrigation practices are crucial factors that alleviate the adverse effects of extreme
temperatures on crop yields. Increased irrigation usage leads to a reduction in yield losses
due to extreme temperature events. Furthermore, our analysis reveals that age nonlinearly
influences farmer’s adaptation capability to reduce the negative temperature effect:
Capability generally increases and then decreases with age (inverted U–shaped age
effects). Notably, the impact of age is less pronounced in irrigated areas, where exposure
to climate risk is relatively low. This suggests that human capital plays a critical role inintroducing adaptation measures in areas at high risk of exposure to extreme temperatures.
These findings have significant policy implications for avoiding further declines incrop yields under climate change scenarios. Adopting irrigation technology is effective in
reducing the negative temperature effects on crop yields. In addition, to enhance theadaptation capability of crop production in the absence of irrigation technology, sufficientinvestment in human capital is essential. Facilitating the transfer of knowledge andexperience from older to younger farmers is an important aspect of this endeavor.
Additionally, effective communication of climate change risks is crucial for both
inexperienced and older farmers, with extension services playing a pivotal role in thisregard.

15

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Appendix
Table A1. Summary statistics
Obs.

Mean

SD

Min

Max

Corn yield (bushels/acre)

16,985137.02

36.72

10.36

246.67

GDD below threshold (D)16,9853603.67

470.562221.764839.20

GDD above threshold (D)16,985

79.52

74.74

0.005

569.12

Precip below threshold (cm)

16,985

-1.68

4.34

-43.030.00

Precip above threshold (cm)

16,985

15.21

14.82

0.00

93.05

Age below 35 (0–1)

16,985

0.10

0.05

0.00

0.70

Age 35–64 (0–1)

16,985

0.68

0.08

0.20

1.00

Age above 65 (0–1)

16,985

0.22

0.08

0.00

0.80

Irrigated corn land (0–1)

16,985

0.13

0.24

0.00

1.00

Corn farm size (acres/ farm)

16,985217.87157.442.82

1559.70

Notes: Only Apr. to Sep. data is used for weather variables. D: degree days. 0–1: 0–100 percent (e.g., 0.1
means 10 percent).

Table A2. Results of the nonlinear temperature effects on corn yields using temperatureand precipitation variables only
GDD below threshold

GDD above threshold

Observations
Fixed effects
Control var.
Adjusted 𝑅2

(1)

(2)

(3)

(4)

0.00020.00010.0003***0.0003***(0.0001)(0.0001)(0.0001)(0.0001)-0.0046***-0.0055***-0.0047***-0.0046***(0.0006)(0.0009)(0.0005)(0.0005)

16,985
Cty, Yr
No
0.7112

16,985
Cty, State-Yr
No
0.8105

16,985
Cty, State-trNo0.701016,985
Cty, Cty-tr
No0.6816

Notes: See Notes in Table 1.

19

Table A3. Results of the nonlinear temperature effects on corn yields using all counties
GDD below threshold

GDD above threshold

Observations
Fixed effects
Control var.
Adjusted 𝑅2

(1)

(2)

(3)

(4)

0.0002*0.00010.0003***0.0003***(0.0001)(0.0001)(0.0001)(0.0001)-0.0048***-0.0054***-0.0049***-0.0049***(0.0006)(0.0009)(0.0005)(0.0006)

22,804Cty, YrNo
0.723122,804Cty, State-Yr
No
0.818422,804Cty, State-tr
No0.712322,804Cty, Cty-tr
No0.6880Notes: See Notes in Table 1.

Table A4. Parameter estimates of the corn yield response functions to temperatures in
irrigated vs. rainfed counties
(1)

(2)

(3)

(4)

-0.0001-0.0002*0.000020.00002(0.0001)(0.0001)(0.00004)(0.0001)-0.0019***-0.0027***-0.0019***-0.0019***(0.0004)(0.0006)(0.0003)(0.0004)

0.0003**0.00010.0004***0.0004***(0.0001)(0.0001)(0.0001)(0.0001)-0.0060***-0.0063***-0.0058***-0.0058***(0.0007)(0.0008)(0.0005)(0.0005)

0.00020.00020.0004***0.0003***(0.0001)(0.0002)(0.0001)(0.0001)-0.0057***-0.0069***-0.0057***-0.0054***(0.0009)(0.0009)(0.0008)(0.0009)

16,985
Cty, Yr
Yes
0.745016,985
Cty, State-Yr
Yes
0.8714

16,985
Cty, State-tr
Yes
0.7621

16,985
Cty, Cty-tr
Yes
0.7635High irrigated counties
GDD below threshold

GDD above threshold

Low irrigated counties
GDD below threshold

GDD above threshold

Rainfed counties
GDD below threshold

GDD above threshold

Observations
Fixed effects
Control var.
Adjusted 𝑅2

20

Notes: See Notes in Table 1. We assign counties with no irrigation use in 2002–2017 as rainfed counties,
and irrigated (high irrigated: average irrigation uses between 0–30 percent; low irrigated: average irrigation
uses above 30 percent, which is about 75 percentiles) counties otherwise.

Table A5. Age effects on the corn yield response function to temperatures
(1)

(2)

(3)

(4)

-0.0037***-0.0046***-0.0039***-0.0041***(0.0011)(0.0011)(0.0010)(0.0014)

GDD above threshold-0.0002-0.0018-0.0001

0.0010× below 35(0.0027)(0.0020)(0.0027)(0.0037)

GDD above threshold-0.0039-0.0033-0.0033-0.0024

× above 65(0.0041)(0.0019)(0.0038)(0.0053)

Observations
Fixed effects
Control var.
Adjusted 𝑅2

16,985
Cty, Yr
Yes
0.7142

16,985
Cty, State-Yr
Yes
0.812016,985
Cty, State-tr
Yes
0.7036

16,985
Cty, Cty-tr
Yes0.6857GDD above threshold

Notes: See Notes in Table 1. Age (below 35 and above 65) unit: 0–1 (e.g., 0.1 means 10 percent).

Table A6. Age effects on the corn yield response function to temperatures: irrigated vsrainfed counties(1) High irrigated counties(2) Low irrigated counties(3) Rainfed counties-0.0030***-0.0068***-0.0059***(0.0009)(0.0012)(0.0009)

GDD above threshold

0.0017-0.0002-0.0048*× below 35(0.0019)(0.0023)(0.0027)

GDD above threshold

0.00110.0022-0.0024

× above 65(0.0019)(0.0024)(0.0024)

GDD above threshold

Observations
Fixed effects
Control var.
Adjusted 𝑅2

16,985
Cty, State-Yr
Yes
0.8296

Notes: See Notes in Table 1. Age (below 35 and above 65) unit: 0–1 (e.g., 0.1 means 10 percent). We assign
counties with no irrigation use in 2002–2017 as rainfed counties, and irrigated (high irrigated: average
irrigation uses between 0–30 percent; low irrigated: average irrigation uses above 30 percent, which is about
75 percentiles) counties otherwise.

21