Double cropping as an adaptation to climatechange in the United StatesMatthew Gammansa, Pierre Mérela, and Ariel Ortiz-Bobeab

aDepartment of Agricultural and Resource Economics, University of California, Davis, CA 95616; bCharles H. Dyson School of Applied Economics and Management,Cornell University, Ithaca, NY 14853

January 6, 2020

Growing evidence indicates that climate change will reduce the yields of key crops in the United States. Biophys-ical crop models suggest that warming will also increase opportunities for double cropping by reducing time tomaturity and/or expanding the frost-free period. These models are tailored towards assessing agronomic suitabil-ity and do not account for economic incentives facing farmers. We implicitly account for such factors by linkinghigh-resolution land cover data with detailed soil and climate data to explain farmers’ propensity to double-cropsoybeans with winter wheat in the Eastern United States. Double-cropping propensity exhibits a robust non-linear relationship with respect to growing degree days, with higher temperatures increasing double croppingin cool regions but having little effect on, or even decreasing, double cropping in warm regions. Our estimatesimply that under current economic conditions, a 3°C warming would result in relatively modest double-croppingincreases in cooler regions, translating to a net increase of 4.6 percentage points in the overall share of soybeanacreage double-cropped with wheat. A fixed-effects panel model of county yields further indicates that yields ofdouble-cropped soybeans are about 12.1% lower than those of single-cropped soybeans, an estimate consistentwith agronomic evidence. Accounting for both effects, we project that climate change will induce a shift in landuse from single to double cropping that increases calorie production by up to 5% under a 3°C warming scenarioaccording to our preferred model, offsetting a small fraction of predicted yield declines.

agriculture | climate change | double cropping | adaptation | soy

Climate change is projected to disrupt produc-tion of major crop commodities and reduce theyields of key crops in the United States (US) [1–5]. Simultaneously, world demand for caloriesis expected to increase as a result of growingpopulation and rising incomes [6, 7]. The abilityof the agricultural sector to meet this demanddepends, in part, on its ability to adapt produc-tion to climatic changes. Adaptation to climatechange could take many forms. Farmers mayshift planting dates [8], alter irrigation and fer-tilization patterns [9], adopt new varietals, orswitch crops entirely [10]. However, recent trendsin agricultural productivity in the US suggestthat Midwestern agriculture has become more,not less, sensitive to climatic shocks [11, 12]. Inthis context, investigating the potential of ex-plicit adaptation strategies to reduce the climaticsensitivity of agriculture appears paramount.

One potentially promising adaptation channelis harvesting multiple crops sequentially within a

calendar year. If climate change relaxes currentconstraints on cropping frequency, the caloriegain from an additional crop harvest may morethan offset losses from lower yields.

Globally, multi-cropping is prevalent in manyagricultural regions, particularly tropical or sub-tropical lowlands [13]. As temperate regionswarm under climate change, double cropping(DC) may become an attractive adaptive option[14–16]. For instance, ref. [15] predicts that inthe US, climate change will increase the area suit-able for DC by 0.35 million km2, an area largerthan the agricultural area of France. Other recentwork has found that climate change may increasethe feasibility of wheat and rice DC in Japan [17],maize-maize DC in Mediterranean climates [18],and wheat-maize or barley-rapeseed DC in China[19–21]. However, it is not clear that warmingsystematically leads to an increase in croppingfrequency. An observational study of the MatoGrasso region of Brazil found that a 1°C warming

Gammans et al.

led to a reduction in cropping frequency equiva-lent to a 3.2% decrease in agricultural production[22]. The effect that climate change will have oncropping frequency remains uncertain for manyimportant agricultural regions.

A common feature of most existing studies onthe climatic adaptation potential of DC is their re-liance on process-based crop models. While suchmodels can account with great detail for changesin agronomic suitability, they do not accountfor economic factors relevant to farmer decisions.Simply put, multi-cropping may be technicallyfeasible yet economically undesirable, perhapsbecause of crop prices, government policies, orproduction costs. For instance, ref. [16] evaluatescurrent US suitability for the most common DCsystem to be about 192,000 km2, whereas theUSDA estimates the current total DC area to beonly 33,200 km2 [23]. To account for economicincentives not captured in previous work, thispaper utilizes observed land cover to assess thepotential of DC to offset the projected negativeeffects of climate change on US calorie supply.

In the US, the most prevalent multi-croppingsystem involves a winter wheat harvest followedby a soybean harvest in the same calendar year[23]. Over the period 2008–2018, winter wheat-soy DC represented roughly 79% of the entiremulti-cropped area. The next most commoncombinations were winter wheat/corn and winterwheat/sorghum. Additional data on the preva-lence of various DC combinations in the US isprovided in SI Appendix, Section 1.

Figure 1 shows recent acreage in winter wheat-soy DC across the Eastern US as well as the shareof soy agriculture held in a winter wheat-soy sys-tem. The unit of analysis is a 4-by-4 km gridcell. Figures displaying total soy area and theshare of soy that is single cropped are shown in SIAppendix, Section 1. DC is most prevalent in theLower Mississippi River Basin region from south-ern Arkansas to southern Illinois, although thereare many regions with substantial DC acreage.Over the period 2008–2018, DC represented 6.0%of the total area in soybeans across the EasternUS.

Recent work relying on biophysical crop mod-els indicates that climate change will more thandouble the share of US cultivated area agronomi-cally suitable for winter wheat-soy DC [16]. Weuse observed rates of winter wheat-soy DC in theEastern US obtained from satellite imagery toquantify the relationship between climate andDC propensity and infer the effects of futureclimatic changes on DC prevalence and calorieproduction.

Fig. 1. Map of current DC practices. Panel A shows the average area of winterwheat-soy DC in each 4-by-4 km grid cell over the 2008-2018 sample period. PanelB shows the average share of total soy area that is double cropped within each gridcell. Areas shaded in light grey grow soybeans but do not have DC. Areas shaded indark grey do not grow soybeans.

ResultsWe use cross-sectional regression analysis on landcover and climatic data spanning more than130,000 4-by-4 km grid cells to quantify how cli-mate (here, temperature and precipitation) af-fects the share of soy agriculture held in a winterwheat-soy DC system in the Eastern US. The

2 | Gammans et al.

Fig. 2. Marginal effect of climate on DC propensity. Panel A (resp. B) shows theeffect of GDD (resp. precipitation) on the DC share. In each panel, a histogram ofthe climates that contain DC is shown in green at the bottom. The mean predictedDC share within a climate interval (100 GDD for Panel A and 25 mm for Panel B) isshown by the purple curve. The blue curve shows the marginal impact of an increasein the climate value (1,000 GDD for Panel A and 250 mm for Panel B) evaluated atthe mean DC share in a given climate interval. A 95% confidence interval is shownin light blue.

model regresses the logistic transformation of theshare of double-cropped soybeans on quadraticfunctions of climate variables, controlling for soilcomposition as well as factors that vary by statethrough the use of fixed effects. As such, ourmodel exploits deviations from state and soil-type means in climate and double-cropping shareto identify the climate-DC relationship.

DC propensity responds non-linearly to tem-perature exposure.Figure 2 depicts the esti-mated marginal effect of climate variables onDC propensity. Panel (a) of the figure focuses ontemperature exposure and shows that the shareof double-cropped soybeans responds non-linearlyto cumulative annual growing degree days. Thehorizontal axis measures annual growing degreedays (GDD) above 5°C across the calendar year,averaged over a 25-year period. The histogram atthe bottom shows the GDD distribution acrossgrid cells in our sample that contain DC, in 100-GDD intervals. The purple curve depicts the pre-dicted share of soybean acreage double-croppedwith wheat, averaged over grid cells falling intoeach 100-GDD bin, averaged over grid cells fallinginto each 100-GDD bin. The brown curve inthe panel second from the top depicts estimatedquadratic response, overlaid over an alternativestep-function specification that allows for the ef-fect of GDD on the logistic transformation ofshare to vary flexibly across climates. The bluecurve at the top depicts the marginal impact ofGDD on the share of double-cropped soybeans,as implied by the quadratic response curve. Theimpact is scaled by a factor of 1,000, meaningthat the ordinate shows the change in the DCshare caused by an increase in 1,000 GDD. Foreach GDD bin, the marginal impact is computedusing our regression estimates as well as infor-mation on the predicted DC share for grid cellswith climate falling in that bin. That is, informa-tion from the purple and brown curves are usedto construct the blue curve. A 95% confidenceinterval is shown in light blue.1

Figure 2 shows that an increase in annual GDDdramatically increases DC propensity in coolerbaseline climates, but significantly decreases it inwarmer baseline climates. Our estimates implythat DC propensity, as captured by the sharedouble cropped, peaks at approximately 4,600GDD, roughly the climate of Memphis, Tennessee.This climate corresponds to the point at which

1The quadratic response curve by itself implies a decreasing marginal effect of GDD on the trans-formed DC share, going from positive to negative values. The fact that the predicted (and actual)shares are zero at the extreme points of the climate distribution further implies the non-monotonicityin the marginal response apparent in the blue curve, which depicts the marginal effect of GDD onthe DC share itself.

3 | Gammans et al.

the blue curve crosses the horizontal axis.The non-linearity of the GDD-DC propensity

relationship is also apparent in models estimatedon subsamples of our data based on longitude,that is, the Eastern vs. Western portions of oursample. The corresponding figures are shown inSI Appendix, Section 5.

Precipitation has little effect of DC propensityacross most of the precipitation distribution.Panel (b) of Figure 2 depicts the marginal effectsof annual precipitation on DC propensity. Thebottom histogram depicts the distribution of gridcells across 25 mm precipitation intervals. Thepurple curve shows the average DC share acrossgrid cells with precipitation falling in each bin.The brown curve shows the estimated quadraticand categorical step-function responses. The cat-egorical step-function estimates imply that cli-mates receiving less than 1050 mm of annualprecipitation are significantly less likely to havedouble cropping than a climate that receives 1500mm of annual precipitation. However, because arelatively small fraction of the variation in doublecropping comes from these drier climates, thiseffect is not captured with the quadratic specifi-cation. The ordinate in the top graph representsthe marginal effect of precipitation on the DCshare, evaluated for an additional 250 mm ofrainfall using the average predicted share acrossgrid cells whose climate falls within each 25 mmbin. The figure shows that rainfall does not havea large or statistically significant effect on DCpropensity across the precipitation distribution.Given that the effect of precipitation on DC ap-pears insignificant for most baseline climates, andsince future trends in precipitation are less clearthan future temperature trends [24], our counter-factual analysis of climate change impacts focuseson temperature effects.

Holding total soybean acreage constant, fu-ture warming is predicted to increase the dou-ble-cropped soy area in the Eastern US.We re-port the predicted effect of climate change on DCcultivation for 2, 3, and 4°C uniform warmingscenarios. Figure 3 shows the predicted changes

Fig. 3. Predicted effect of warming on DC area. Panels A, B, and C show the changein DC area within each PRISM grid cell under 2, 3, and 4°C warming, respectively.Each grid cell is 16 km2. Areas shaded in dark grey areas are not included in oursample as they do not grow soybeans.

4 | Gammans et al.

in DC acreage for each grid cell in our sample.For a 2°C warming scenario, modest increases inDC acreage in cooler Northern or mountainousregions are only partially offset by reductions inDC acreage in warmer Southern regions. Rela-tive to the 2°C scenario, 3°C and 4°C scenariosimply large additional increases in DC acreagein Indiana and northern Illinois. Across warmingscenarios, we find the largest increases in DCacreage in Illinois and the largest decreases inDC acreage in the Mississippi Delta region, par-ticularly in Arkansas. We find additional modestincreases in DC across the upper Midwest andthe Delmarva Peninsula and small decreases inDC acreage across the Carolinas and Georgia.

Our estimates imply that warming scenariosof 2, 3 and 4°C are associated with net increasesin DC area of 8,370, 11,630 and 13,580 km2, re-spectively, relative to a current baseline of 15,180km2 of winter wheat-soy DC. These increasescorrespond to 55.1, 76.6, and 89.5% of the cur-rent cultivated area of double-cropped soybeans,respectively.

Longitudinal county-level evidence suggeststhat DC decreases soybean yield by 12.1% onaverage.The adoption of DC expands the to-tal area effectively under cultivation, increasingcaloric production. However, the adoption ofDC adds constraints to the production process,notably in terms of planting schedule and nutri-ent and water competition, which may result inlower yields. To account for this potential “DCyield penalty,” we analyze whether increases inthe DC share are associated with lower soybeanyields using available county-level yield data. Wefind that planting wheat prior to soybeans de-creases soybean yields by 12.1% on average, anestimate in line with agronomic evidence [25–28]but new to the statistical yield literature. Froma pure calorie perspective, this penalty is smallrelative to the additional calories afforded by theadditional wheat harvest, although its effect onprofitability may be large.

The climate-induced change in DC propensityonly partially offsets yield-driven decreases

Fig. 4. Probability distribution of the adaptive value of DC. Distributions of the cal-culated adaptive value across 1,000 random draws of our estimated parameters for2, 3, and 4°C warming scenarios are shown in, respectively, blue, yellow, and red.For each distribution, we show a corresponding boxplot with whiskers stretching fromthe 5th to 95th percentile of the simulated adaptive values. The interior box stretchesfrom the 25th to 75th percentile, with the median denoted within the box by a solidline.

in calorie production in US soybean agricul-ture.Combining our predicted DC area changes,our estimated DC yield penalty, and informationregarding the caloric contents of wheat and soy,we calculate the adaptive value of DC. We definethis adaptive value as the percentage change intotal calories from soy systems attributable tochanges in DC propensity induced by climatechange. This value can then be compared to thechange in calorie supply due to climate-inducedsoybean and wheat yields effects, holding DC con-stant at its baseline rate. Because we find thatclimate change is likely to increase DC area, andcalorie supply is higher under DC despite the soy-bean yield penalty, we find that DC has positiveadaptive values across all warming scenarios.

Figure 4 depicts the distribution of climate-induced adaptive values, expressed in percentagechange of calorie production from soybean agri-culture (inclusive of calories from winter wheat inDC systems). Each density plot shows the distri-bution of outcomes for a given uniform warmingscenario, where the stochasticity comes from sam-pling amongst values of our estimated regressionparameters. As such, the distribution reflectsstatistical uncertainties related to our empiricalDC propensity and yield-penalty models.

For uniform warming scenarios of 2, 3, and4°C, and across most parameter draws, changes

5 | Gammans et al.

in DC lead to an increase in total calorie pro-duction, by up to 5%, and more under warmerclimates. The reason why the large net increasesin DC area predicted under warming translateinto somewhat moderate calorie effects is that DCcalorie production represents a relatively smallshare of baseline calories from soy agriculture.

An alternative DC adoption model withoutstate fixed effects also implies that climate-in-duced changes in DC will slightly offset yield--driven calorie declines.Results from a modelthat does not include state fixed effects are pro-vided in SI Appendix, Section 3.2. A model with-out state fixed effects takes advantage of a largershare of cross-sectional climatic variation, butit is susceptible to estimation bias if there areunobserved factors varying regionally that ex-plain DC propensity and are correlated with (butnot caused by) climate. Similar to our preferredspecification, this model implies a non-linear rela-tionship between GDD and DC propensity. Esti-mates imply increases in DC area of 5,830, 8,080,and 9,580 km2 under 2, 3, and 4°C warming sce-narios, respectively. These changes in DC areatranslate into net adaptive values that are smalland positive, with central estimates ranging from3% to 4% depending on the warming scenario.

DiscussionOur findings suggest that climate change is likelyto increase the prevalence of winter wheat-soyDC in the Eastern US. We find that warmingincreases the DC share across much of the UpperMidwest and Mid-Atlantic, as the frost-free pe-riod expands and the growing season constraintis relaxed. Our results imply that these increasesin DC are tempered by decreases in DC acreagein Southern areas. These regions are not con-strained by the length of the growing season undercurrent or expected future climate. This decreaseis potentially explained by the deleterious effectsof warming on winter wheat, particularly duringthe winter vernalization stage [16].

Several papers have investigated the climateadaptation potential of DC using biophysical

models [16, 18, 20]. Of these, ref. [16] also stud-ies winter wheat-soy DC in the Eastern US andfinds that moderate climate change will relax thegrowing-season constraint in many soy-producingregions, increasing the total area suitable forDC. Our estimated effects of 3°C warming aresupportive of this view, with small projected in-creases in DC area in many, though not all, of thestates classified as “phenologically constrained”in their study. However, the magnitude of theincrease predicted by ref. [16] is far larger thanour findings here. Although their study usesclimate change scenario projections, while oursuses more crude uniform warming scenarios, thewarming experienced under Representative Car-bon Pathway 45 scenario in 2100 is roughly 2°C[29]. Under this scenario, ref. [16] finds thatthe area suitable for DC will more than double,while we find increases of only 55.1% under a 2°Cuniform warming relative to current practices.One possible explanation for this discrepancy isthe difference between agronomic and economicsuitability. Even if climate change expands con-siderably the area that is agronomically suitablefor DC cultivation, farmers may not find DCprofitable relative to single cropping. Since werely on observed rates of DC, our approach bet-ter accounts for variation in DC that cannot beexplained solely by phenological constraints. Ad-ditionally, by including state fixed effects, wecontrol for economic or geographic variables thatmay be correlated with climate and affect farm-ers’ likelihood to adopt DC. These may includedifferences across states in input or output prices,access to wheat-processing facilities, or farm pol-icy, notably in terms of crop insurability pursuantto USDA Risk Management Agency rules [23].

From a methodological standpoint, the paperclosest to this study is ref. [22], which uses satel-lite land cover data to investigate the role thatyearly temperature variation plays in DC adop-tion and DC abandonment in the Mato Grassoregion of Brazil. A crucial difference is that ref.[22] uses a panel approach that relies on year-to-year changes in temperature and DC area todescribe the relationship between temperature

6 | Gammans et al.

and DC adoption. While weather fluctuationscan affect cropping frequency, we believe that thedecision to double crop is fundamentally based onfarmers’ expectations about local climate. Thissuggests that a cross-sectional regression of DCpropensity on climate variables is conceptuallyappropriate.

One concern about implementing a cross-sectional regression analysis is that unobservedfactors that influence the decision to double cropmight be correlated with climate, introducingbias in our estimates of climate effects [30–33].To address this potential issue, we harness thehigh resolution of our data and include state fixedeffects along with a rich set of controls on soilcharacteristics. This strategy means that we esti-mate the effect of climate on DC propensity fromwithin-state variations in climate conditional onthis rich set of controls.

What effect will climate change have on crop-ping intensity? The evidence is mixed, with refs.[16, 17, 19, 20] finding large increases in DCsuitability and ref. [22] finding negative effects.While these discrepancies may partly stem frommethodological differences, they also reflect thatwarming’s effect on cropping intensity depends onbaseline climate and the specific crops considered.Given this consideration, the effect of climatechange on cropping intensity should be evaluatedon a setting-by-setting basis. Future researchcould focus on currently unstudied contexts orseek to better understand how methodologicalchoices have affected prior findings.

The small positive adaptive values of DC re-ported in this study should be compared to thelarge and negative effects of climate change onUS soy and wheat yields. For a 3°C uniformwarming scenario, ref. [3] reports a decrease insoy yields of 17% and ref. [4] reports a decreasein Kansas winter wheat yields of 24%. Estimatesfrom our yield-penalty model and our preferredDC adoption model imply that changes in DCpractices will increase the calorie production ofsoy agriculture by an additional 5.0%. The theadoption model without state fixed effects impliesa smaller increase in calorie production, 3.5%. It

thus seems unlikely that changes in DC will fullymitigate the large and negative effects of climatechange on US soybean and wheat yields predictedby prior studies.

Our estimates as well as those in ref. [3] assumethat areas held in soy agriculture remain constantunder future warming. Despite this caveat, tothe extent that winter wheat-soy DC is a closesubstitute to single-cropped soy, our findings sug-gest that land use change will only slightly offsetthe negative calorie impacts of climate change inthe US.

MethodsThis paper addresses the role that changes in DCpropensity will have on the calorie productionof US soy agriculture. Eq. (1) defines calorieproduction for some location i as the sum ofcalories from single-cropped soybeans and caloriesfrom the DC system:

Ci = Ai((1− τi)ysoy

i + τi[(1− θ)ysoyi + yDCwheat

i ])

(1)

where Ci is total calories produced in location i,Ai is the soybean area, ysoy

i (resp., yDCwheati ) is the

yield of single-cropped soybeans (resp., double-cropped wheat) expressed in calorie units, θ is theDC penalty (assumed to be constant across spaceand climates), and τi is the share of soybeansthat are double-cropped with wheat. Takingthe derivative of this expression with respect tolocal climate (denoted µi) while holding soybeanarea constant, the local response of net calorieproduction to climate can be expressed as follows:

(2)dCidµi

= Ai∂τi∂µi

[yDCwheati − θysoy

i ]

+Ai(1− θτi)∂ysoy

i

∂µi+Aiτi

∂yDCwheati

∂µi.

Because we are interested in calorie effects thatare channeled through changes in DC practices,we focus on the first term of the expression:∂τi

∂µi[yDCwheati −θysoy

i ], which we define as the adap-tive value of DC. This term is the effect of climate

7 | Gammans et al.

on the DC share, ∂τi

∂µi, multiplied by the difference

in calorie production at location i between DCand single-cropped soy systems, accounting forthe DC penalty θ. The second term capturesthe effect of climate on soybean yields, an ef-fect well documented in the existing literature[3, 5]. The final term represents the effect ofclimate on double-cropped wheat yields. Thispaper provides empirical estimates of both ∂τi

∂µi

and θ, allowing us to calculate the adaptive valueof DC for US soy agriculture.

Data.Data on DC prevalence were constructedfrom the USDA Cropland Data Layer (CDL),which contain satellite land cover data at the 30m-pixel-level for the contiguous US from 2008 to2018. Temperature and precipitation data comefrom the PRISM gridded dataset and are at a 4km resolution [34]. For soils information, we relyon the dataset constructed by [35] which providesdetailed information on soil characteristics atvarious spatial resolutions and depths down to100 m. We use data on the clay, sand, and siltmakeup of soil at 5 cm depth. We match thegridded soil data with the PRISM climate gridusing a nearest-neighbor resampling approach.We obtain county-level soy and wheat yields fromUSDA/NASS.

Our sample includes all states that lie entirelyeast of 100th meridian, with the exception ofFlorida and the New England region, both ofwhich are minor soy producers. Omitting theGreat Plains states avoids many regions thatgrow primarily wheat, rather than soy.

To construct our cross-sectional DC share, foreach PRISM grid cell we take a simple averageof winter wheat-soy and soy area over our 11-year sample. Due to the potential for measure-ment error in the land cover pixel data, we as-sume that the true area is zero if the pixel dataimplies an area less than 0.01 km2 per PRISMgrid cell. (This would correspond to isolatedfields of around 2.5 acres, which seems unrealisti-cally small.) Results for an alternative minimumthreshold of 0.1 km2 are similar and are shownin SI Appendix, Section 4. The mean DC sharein a grid cell with positive soy area (sg) is de-

fined as the average area in winter wheat-soy DCdivided by the total soy area (including single-and double-cropped soy). We use two climatevariables: annual precipitation, pg, and growingdegree days above 5°C, GDDg, to explain DCpropensity. These climate variables are the aver-age of yearly values from 1981-2005. Note thatboth precipitation and GDD are cumulative overthe entire calendar year, not just the growing sea-son. This allows for the possibility that farmersadjust planting and harvesting dates to best suittheir climate.

Empirical approach.To investigate the effect ofclimate on DC propensity, ∂τi/∂µi, we estimatea fractional logistic cross-sectional model at thePRISM grid-cell level:

(3)G−1(sg) = βGDD1 GDDg + βGDD2 (GDDg)2

+ βp1pg + βp2 (pg)2 + γr + φs + εg

where G−1 is the inverse of the logit transfor-mation and sg is the DC share within PRISMgrid cell g. Using the fractional logit aproach,introduced by ref. [36], has the advantage ofaccommodating observations with a share of 0or 1. As a result, our model utilizes variation inboth the extent and existence of double croppingthroughout the universe of soy-growing locations.This transformation also allows climate variablesto have varying marginal effects depending on thebaseline climate and soil-type and ensures thatpredicted DC shares always lie between 0 and 1.Our preferred specification includes a state fixedeffect, γr, and a large set of soil type fixed effects,φs, which control for soil type with fixed effectsbased on clay/sand/silt percentages. Standarderrors εg are clustered at the state level, allowingfor arbitrary correlations across grid cells withinthe same state. To ensure that our findings arenot driven by our choice of the quadratic specifica-tion for climate variables, we also estimate modelsthat define a categorical step-function climate re-sponse, F (climatei) = ∑H

0 βsteph 1(climatei ∈ h),

where h is some interval of climate values and Ftakes the value βsteph when the a grid cell’s climateis in interval h. We use 100 GDD intervals for

8 | Gammans et al.

temperature and 50 mm intervals for precipita-tion. As a robustness check, we also estimatemodels that include a quadratic function of frost-free season length. Given the extremely high cor-relation between GDD and season length, thesemodels do little to improve model fit. Therefore,we focus on quadratic models without seasonlength in our impact calculation.

To calculate the DC penalty on soy yields, θ,we utilize a county-level panel regression thatexploits variation in the DC share about countyand state-by-year fixed effects to explain countyyield changes. The regression also controls forcounty weather throughout the May-Septembersoy growing season. The statistical model is:

log(yct) = θsct+J∑j=1

βjxctj +h(pct)+γc+γkt+ εct

(4)

where sct is the share of total soy that is double-cropped with wheat in county c in year t and yct isaverage soy yield, inclusive of single- and double-cropped soybeans. Weather is controlled for usinga step function parameterized by β = (β1, . . . , βJ)that allows for a different marginal effect of expo-sure time within each 3°C interval j, xctj , startingfrom exposure between 6°C and 9°C up to expo-sure above 39°C. Temperature exposure effectsare therefore relative to exposure to growing-season temperatures below 6°C. The coefficientβj identifies the percentage change in averagecrop yield caused by replacing one day of expo-sure to temperatures below 6°C by one day ofexposure to temperatures within interval j. Thefunction h parameterizes a quadratic response oflog yield to cumulative growing-season precipi-tation (pct). County (γc) and state-by-year (γkt)fixed effects are also included. Standard errors areclustered at the state-year level to address spatialcorrelation in yields and assume conditional in-dependence across years. Regression coefficientsare reported in SI Appendix, Section 2. Notably,our estimated response to temperature exposureis broadly consistent with ref. [3], although ourpanel is considerably shorter.

Impact calculation and uncertainty analysis.We use our cross-sectional regression estimatesto predict the effect of various warming scenarioson DC practices. We compute predicted grid-cell shares under both baseline climate (s0

g) andhypothetical climates (s1

g) with 2, 3, and 4°Cuniform warming. We multiply the differencebetween baseline and counterfactual predictedshares (s1

g − s0g) by the current soy area to ob-

tain the change in DC area at the PRISM grid-cell level. We assume that the double-croppedwheat yield, yDCwheat

i , is equal to the county’s av-erage wheat yield, ywheat

c . We calculate the yieldof single-cropped soybeans for all cells within agiven county using the county’s average soy yield(ysoyc ), the county’s average DC share (τc), and

the estimate of the DC penalty (θ). In caseswhere county-level yields are unobserved (mostlyfor wheat), we use state-level average yields. Weconvert yields from bushel to kilocalorie unitsusing the conversion factors provided in ref. [37].Combining these elements with our estimate ofthe DC penalty, we obtain a grid-cell-level es-timate of the first term of Eq. (2), namely theadaptive value of DC on Eastern US soy agricul-ture holding soy and wheat yields constant:

(5)∆Cg

∣∣∣y

soyg ,yDCwheat

g

≈ Ag(s1g − s0

g

) ywheatc

− θysoyc

1− τcθ

.We then aggregate these grid-level changes tothe level of the Eastern US and express themrelative to baseline calorie production from soyagriculture according to the formula:

(6)%C =

∑g ∆Cg

∣∣∣y

soyg ,yDCwheat

g∑cAc (ysoy

c + τcywheatc )

Eq. (3), Eq. (5), and Eq. (6) imply that ourmeasure of calorie impact is a highly non-linearfunction of estimated model parameters. To ac-count for the uncertainty surrounding our param-eter estimates, we randomly draw 1,000 sets of

9 | Gammans et al.

parameters from Eq. (3) using the central esti-mates and the estimate of the variance-covariancematrix. In parallel, we randomly draw 1,000 val-ues of θ from the parameter and variance esti-mates of Eq. (4). For each draw, we calculate theadaptive value of DC, generating a probabilitydistribution under each warming scenario. Theresults of this exercise are shown in Figure 4.

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10 | Gammans et al.

Double cropping as an adaptation to climate change in

the United States

Supporting Information Appendix

Matthew Gammans, Pierre Merel and Ariel Ortiz-Bobea

Contents

1 Data appendix 2

2 Regression results tables and figures 4

3 Results with alternative specifications 93.1 Model with alternative GDD threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.2 Model without state fixed effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.3 Season length included . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.4 Finer soil controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4 Robustness to choice of minimum area threshold 19

5 East versus West subsamples 22

1

1 Data appendix

Land cover data come from USDA Cropland Data Layer (CDL) (USDA-NASS, 2018). This dataset relieson satellite imagery to classify land cover. Although certain regions and crops are available in earlier years,2008 is the first year with coverage across the contiguous United States (US). The data is available at 30m-pixel-level resolution. Climate data come from the PRISM gridded dataset and are at the 4 km-grid-cellresolution. CDL pixels are assigned to the PRISM grid cell in which their centroid lies. PRISM grid cellsare assigned to the county in which their centroid lies. Figure S.1 shows the area of soy within each PRISMcell. Figures showing the area of winter wheat-soy DC and the share of soy that is double cropped are shownin the main text. The PRISM dataset provides daily gridded values of minimum temperature, maximumtemperature, and precipitation. These daily values are used to construct growing degree days (GDD) acrossthe entire calendar year with a minimum threshold of 5◦C and no maximum threshold. Yearly GDD andcumulative precipitation values are averaged across 1981–2005 to construct our climate variables. Figure S.2shows climate GDD levels for our baseline period 1981-2005, as well as for three uniform warming scenarios.For our county-level panel regression, county-level values of weather are constructed by weighting PRISMgrid cells within the county by their share of the county’s total farmland. Exposure to each 1◦C temperatureinterval is derived from the daily data by fitting a double-sine curve between the minimum and maximumtemperatures of consecutive days.

Soy and wheat yield data come from USDA/NASS and are at the county level. County-years with missingyield values are omitted from our panel regression. For our climate change impact calculations, we calculatemean county yield by dividing the average harvest in years where it is reported by the average acreage inthese same years. We do this calculation only for years within our 2008–2018 sample period. If a county’sharvest and acreage are missing, the county is assigned its state’s average yield over the 2008-2018 period.Given that our findings are primarily driven by changes in DC area and not by differences in yield betweenDC and single cropped soy, we believe this approach is preferable to omitting these counties entirely, despitethe fact that state yields are likely to be systematically higher than the yields of counties for which data areunavailable.

Figure S.1: Current soy cultivation. Panel A displays the total soy area within each 4 km-by-4 km PRISMgrid cell and Panel B displays the share of soy area within each grid cell that is single cropped. Light greyareas do not contain soy agriculture.

2

Figure S.2: Mean annual growing degree days over the 1981-2005 baseline period (Panel A) and severaluniform warming scenarios (Panels B-D)

Soil data come from Ramcharan et al. (2018) and are available at https://doi.org/10.18113/S1KW2H.We use data on the percentage of clay, sand, and silt at the 5 cm soil depth. Since these three variables sumto 1, we only explicitly control for clay and sand percentages due to multicollinearity. Figure S.3 shows thedistribution of soil types across our sample. Each unique color in Figure S.3 corresponds to a fixed effectthat will be estimated in our empirical analysis. Thus, we do not impose that similar soil classes necessarilyhave a similar effect on DC propensity. In this sense, our approach to soils is more flexible than the approachof Xie et al. (2018).

3

Figure S.3: Map of soil fixed effects. Each color represents a fixed effect that

This paper focuses on winter wheat-soy DC in the Eastern US. Table S.1 shows the average yearly areabetween 2008 to 2018 of all DC combinations included in the CDL. Winter wheat-soy is by far the mostprevalent combination and constitutes 79% of all double-cropped area nationwide.

Table S.1: Most prevalent double cropping combinations in the US

Crop Combination Mean area (sq km) Share of all double croppingWinter wheat-soybeans 18,245 0.79Winter wheat-corn 1,428 0.06Winter wheat-sorghum 1,395 0.06Winter wheat-cotton 1,011 0.04Oats-corn 436 0.02Barley-soybeans 316 0.01Other combinations 458 0.02

2 Regression results tables and figures

The results of DC propensity regressions are presented in Table S.2. Column 1 displays the results of ourpreferred specification which includes state fixed effects and does not include season length. Columns 2-4show the results of additional specifications that do not include state fixed effects and/or include seasonlength controls or county fixed effects. The results of these models are discussed in more detail in Section 3of this Appendix.

Coefficients for soil composition and state fixed effects for our preferred specification are presented inmap form in Figures S.4 and S.5, respectively. Figure S.4 shows that the high-silt soils of Southern Illinoisand Western Kentucky are associated with higher DC propensity, while soil with more clay are associatedwith lower DC propensity. This comports with the fact that wheat requires well-ventilated soils and areas inwhich a high wheat yield can be realized are more likely to adopt DC.1 Figure S.5 shows that even conditional

1In the process of conducting this research, we spoke with Carrie Knott, an extension associate professor at the Universityof Kentucky. Her expertise led her to believe that double cropping differences within Kentucky are attributable largely to soildifferences, a belief that is consistent with our empirical results.

4

on soils climate, some regions, such as Pennsylvania, Delaware, and Maryland, are more likely to engagein DC than others, such as Iowa. This may reflect regional differences in economic or social drivers of DCadoption, such as access to wheat markets or farmers’ education or experience level.

Figure S.4: Map of estimated soil composition fixed effects. Values are relative to high-clay soil type, mostcommonly found in Southeast Georgia and Western Michigan.

Figure S.5: Map of estimated state fixed effects. Values are relative to Alabama.

5

Table S.2: DC propensity fractional logistic regression results

Dependent variable: DC propensity

(1) (2) (3) (4)

100 ∗ prec (mm) 0.10 0.13∗ 0.26∗ 0.16(0.71) (0.07) (0.12) (0.71)

(100 ∗ prec)2 (mm) -0.06 -0.06∗ -0.13 -0.08(0.03) (0.03) (0.04) (0.03)

1000*GDD 12.2∗∗∗ 9.69∗∗∗ 6.36∗∗∗ 9.32∗

(2.11) (1.41) (0.43) (2.77)

(1000*GDD)2 -1.31∗∗∗ -1.08∗∗∗ -0.72∗∗∗ -0.99∗∗

(0.23) (0.17) (0.05) (0.31)

100*SeasonLength (days) 8.44∗∗

(4.2)

(100*SeasonLength)2 (days) -1.78∗∗

(0.89)

Soil Composition FE Y Y Y YState FE Y N N YCounty FE N N Y NObservations 130,327 130,327 130,327 130,327

∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01Note: standard errors clustered at the state level

6

The results of the yield penalty panel regression are presented in Table S.3. The temperature controlvariables are visually represented in Figure S.6. We include both our preferred specification, as well as analternative specification that includes the area of soy planted in a county-year as a control. This is to addressthe concern that land on which DC is adopted is fundamentally different than non-DC land in a mannerthat affects yields. Although increased soy acreage is associated with higher yields, it does not affect thecoefficient on DC share. Coefficients on high-temperature intervals, those above 33◦C, are jointly significantlynegative. Although the estimated decline in yield at high temperatures is not as large as the decline foundin Schlenker and Roberts (2009), our results are qualitatively similar, with exposure to temperatures above30◦C being harmful. As in Schlenker and Roberts (2009) we find a non-monotonic, concave relationshipbetween precipitation and yield.

Figure S.6: Estimated yield-temperature relationship from yield-penalty model. The blue line indicates theestimated effect of replacing one day in the given temperature interval with a day in the omitted interval(below 6◦C). A 95% confidence interval is shown in grey.

7

Table S.3: Yield penalty regression results

Dependent variable: log(yield)

DC share -0.121∗∗ -0.121∗∗

(0.043) (0.043)

Exposure to 6-9◦C (days) 0.009 0.008(0.010)

Exposure to 9-12◦C (days) −0.001 −0.002(0.008)

Exposure to 12-15◦C (days) 0.005 0.003(0.007)

Exposure to 15-18◦C (days) 0.007 0.006(0.007)

Exposure to 18-21◦C (days) 0.003 0.002(0.007)

Exposure to 21-24◦C (days) 0.002 0.001(0.007)

Exposure to 24–27◦C (days) 0.007 0.006(0.007)

Exposure to 27-30◦C (days) 0.007 0.006(0.007)

Exposure to 30-33◦C (days) −0.004 −0.005(0.007)

Exposure to 33-36◦C (days) −0.018∗∗ −0.019∗∗

(0.007)

Exposure to 36-39◦C (days) −0.025∗∗ −0.026∗∗

(0.010)

Exposure to above 39◦C (days) −0.029 −0.030(0.019)

1000*prec (mm) 0.90∗∗∗ 0.90∗∗∗

(0.12) (0.12)

(1000*prec)2 (mm) -0.68∗∗∗ -0.68∗∗∗

(0.086) (0.086)

Planted Area (million acres) 0.95∗

(0.45)

Observations 12,358 12,358Adjusted R2 0.792 0.798

∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01Note: standard errors clustered at the state level

8

3 Results with alternative specifications

In this section we present the results of a number of alternative specification to ensure the robustness of ourkey findings to various modelling choices.

3.1 Model with alternative GDD threshold

In our preferred specification, we use GDD above a minimum threshold of 5◦C as our temperature climatevariable. We test an alternative model that uses a minimum threshold of 0◦C. We recalculate a 1981–2005average of cumulative annual GDD. We then re-estimate our main DC propensity model with this alternativeGDD variable. The estimated marginal effect of climate variables on DC propensity for this model are shownin Figure S.7. The GDD response is nearly a perfect horizontal translation of our main specification responseshown in the main text. The precipitation response is also nearly identical.

Figure S.7: Marginal effect of climate on DC propensity for model with a minimum GDD threshold of 0◦C.Panel A (resp. B) shows the effect of GDD (resp. precipitation) on the the DC share. In both panels,a histogram of the climates within our sample are shown in green at the bottom. Above this, the meanDC share within a climate interval (100 GDD for Panel A and 25 mm for Panel B) is shown by the purplecurve. The brown curve shows the estimated response, overlaid on estimates from an alternative categoricalstep-function specification. The blue curve shows the marginal impact of an increase in the climate value(1,000 GDD for Panel A and 250 mm for Panel B) evaluated at the mean predicted DC share in a givenclimate interval. A 95% confidence interval is shown in light blue.

9

3.2 Model without state fixed effects

Our preferred model uses within-state differences in climate to explain cross-sectional differences in DCpropensity, conditional on soil composition. This approach is desirable as it controls for a wide range ofgeographic, economic, and social factors that may contribute to regional differences in farmers’ decisions todouble crop. For example, if there are regional differences in farmer education level or differences in the costsof accessing wheat markets, a model that omits fine-scale location controls may conflate the effect climatewith the effect of these confounding factors that are potentially correlated with climate. One drawback ofthis approach is that it removes climatic variation within our data. If our climate data are measured witherror, then our resulting estimates may be biased towards zero as the inclusion of fixed effects could amplifyclassical measurement error. It should be noted that, because our dependent variables are 25-year averagesof yearly weather, some noise will likely be averaged out across years. Nevertheless, in this section we reportresults from a model that omits state fixed effects.

Figure S.8 shows the estimated marginal response of DC propensity to climate for models without statefixed effects. The response of DC propensity to GDD is shown in Panel (a). As in our preferred specification,the estimated relationship is non-linear with DC propensity increasing with additional GDD at lower climatevalues and decreasing with additional GDD in the hottest climates. In contrast with our preferred specifi-cation, a model without state fixed effects suggests that additional precipitation significantly decreases DCpropensity in wetter climates, as shown in Panel (b). In our preferred specification, the peak of the estimatedDC propensity-GDD relationship is roughly 4,600 GDD. In a model without county fixed effects, the peak isroughly 4,500 GDD. Compared with our preferred model, most Northern regions are predicted to see slightlysmaller increases in DC area. Figure S.9 shows the predicted DC area changes under 2, 3, and 4◦C uniformwarming scenarios. Figure S.10 shows a distribution of the adaptive values of DC on calorie productionacross random draws of our estimated parameters. Under all warming scenarios, the distributions are lesspositive than our preferred specification results, though still quite similar. This is driven by less dramaticincreases in DC area in Norther Midwest regions. Regardless of specification, we find that increases DC willslightly increases calorie production under climate change but not offset the large yield declines associatedwith warming.

10

Figure S.8: Marginal effect of climate on DC propensity for model without county fixed effects. Panel A(resp. B) shows the effect of GDD (resp. precipitation) on the the DC share. In both panels, a histogramof the climates within our sample are shown in green at the bottom. Above this, the mean DC share withina climate interval (100 GDD for Panel A and 25 mm for Panel B) is shown by the purple curve. The browncurve shows the estimated response, overlaid on estimates from an alternative categorical step-functionspecification. The blue curve shows the marginal impact of an increase in the climate value (1,000 GDD forPanel A and 250 mm for Panel B) evaluated at the mean predicted DC share in a given climate interval. A95% confidence interval is shown in light blue.

11

Figure S.9: Predicted effect of warming on DC area based on a model without county fixed effects. Panels A,B, and C show the change in DC area within each PRISM grid cell under 2, 3, and 4◦C warming, respectively.Each grid cell is 16 km2. Areas shaded in white have predicted DC changes very close to zero. Areas shadedin dark grey are omitted from our sample as they do not contain soy agriculture.

12

Figure S.10: Probability distribution of the adaptive value of DC for a model without county fixed effects.Distributions of the calculated adaptive value across 1,000 random draws of our estimated parameters for2, 3, and 4◦C warming are shown in, respectively, blue, green, and red. For each distribution, we show acorresponding boxplot with whiskers stretching from the 5th to 95th percentile of the simulated adaptivevalues. The interior box stretches from the 25th to 75th percentile, with the median denoted within the boxby a solid line.

3.3 Season length included

Previous studies (e.g. Seifert and Lobell (2015)) have identified season length, the length of time betweenthe last frost of winter and the first frost of the following winter, as an important constraint on the successof DC systems. Our GDD variable largely captures season length, as warm locations tend to also have longgrowing seasons. Indeed, the correlation between climate GDD and season length is 0.97. In our preferredspecification, we do not explicitly model season length, but allow its effect to be be implicitly captured byour GDD variable. Here we present results for models that include a quadratic function of season length.To compute climate values of season length we compute the number of days between the last frost of winterand the first frost of the following winter for each calendar year in our baseline period of 1981–2005 andtake a simple average over the period. For each warming scenario, we compute hypothetical season lengthsfor every year in the 1981–2005 period and average these. Results of this model are nearly identical to ourpreferred model results and are shown in Figures S.11 and S.12.

13

Figure S.11: Predicted effect of warming on DC area based on a model that includes season length. Panels A,B, and C show the change in DC area within each PRISM grid cell under 2, 3, and 4◦C warming, respectively.Each grid cell is 16 km2. Areas shaded in white have predicted DC changes very close to zero. Areas shadedin dark grey are omitted from our sample as they do not contain soy agriculture.

14

Figure S.12: Probability distribution of the adaptive value of DC for a model that includes season length.Distributions of the calculated adaptive value across 1,000 random draws of our estimated parameters for2, 3, and 4◦C warming are shown in, respectively, blue, green, and red. For each distribution, we show acorresponding boxplot with whiskers stretching from the 5th to 95th percentile of the simulated adaptivevalues. The interior box stretches from the 25th to 75th percentile, with the median denoted within the boxby a solid line.

3.4 Finer soil controls

In our preferred specification, we assume that there is no effect of altering soil composition within 20%-intervals (portrayed graphically in Figure S.3). For example, an observation with a 38%-41%-21% clay-sand-silt soil composition would share a soil fixed effect with an observation that had a 22%-53%-35% mixture.Given that this simple classification leads to 15 unique potential soil classes, we do not view it as particularlyrestrictive. Nevertheless, in this section we present results for models that discretize soil composition at 10%-intervals, leading to 55 potential unique soil categories, of which 43 appear in our data. Figure ?? shows amap of these categories.

Figure S.15 shows the estimated marginal response of DC propensity to climate for models with finersoil fixed effects. The response of DC propensity to GDD is shown in Panel (a) and is extremely similar toour preferred specification response, although less precisely estimated. Figure S.15 shows the predicted DCarea changes under 2, 3, and 4◦C uniform warming scenarios, again nearly identical to those based on ourpreferred specification. Figure S.15 shows a distribution of the adaptive values of DC on calorie productionacross random draws of our estimated parameters. Reassuringly, the median simulated adaptive values arequite similar those of our preferred specification. However, the reduced precision in our regression estimatestranslated to considerably more uncertainty.

15

Figure S.13: Map of soil fixed effects. Each color represents a fixed effect that

Figure S.14: Predicted effect of warming on DC area based on a model that includes season length and omitscounty fixed effects. Panels A, B, and C show the change in DC area within each PRISM grid cell under2, 3, and 4◦C warming, respectively. Each grid cell is 16 km2. Areas shaded in white have predicted DCchanges very close to zero. Areas shaded in dark grey are omitted from our sample as they do not containsoy agriculture.

16

Figure S.15: Predicted effect of warming on DC area based on a model that includes season length and omitscounty fixed effects. Panels A, B, and C show the change in DC area within each PRISM grid cell under2, 3, and 4◦C warming, respectively. Each grid cell is 16 km2. Areas shaded in white have predicted DCchanges very close to zero. Areas shaded in dark grey are omitted from our sample as they do not containsoy agriculture.

17

Figure S.16: Probability distribution of the adaptive value of DC for a model that includes season length andomits county fixed effects. Distributions of the calculated adaptive value across 1,000 random draws of ourestimated parameters for 2, 3, and 4◦C warming are shown in, respectively, blue, green, and red. For eachdistribution, we show a corresponding boxplot with whiskers stretching from the 5th to 95th percentile ofthe simulated adaptive values. The interior box stretches from the 25th to 75th percentile, with the mediandenoted within the box by a solid line.

18

4 Robustness to choice of minimum area threshold

To address potential measurement error in the CDL land cover data, all results presented thus far assumethat, if the area of a land cover type within a PRISM grid cell in a given year is less 0.01 km2, it has beenclassified erroneously – as it would be unlikely for a farmer to plant such a small field. Thus, we assign anarea of zero in these cases. In this section, we adopt an even more conservative approach, requiring that aPRISM grid cell contain at least 0.1 km2 of a crop variety. In general, we view a lower threshold as moreconservative as many smalls fall below this more stringent threshold (Kuethe, 2014). Figure S.17 showsthe marginal response of DC share to climate variables. The GDD response, shown in Panel (a), exhibitsa steeper increase than our preferred specification, while the precipitation response, shown in Panel (b), isquite similar to the response estimated on the larger sample. Under this alternative minimum area thresholdassumption, we find a panel yield penalty of -11.0%, in line with the -12.1% reported in the main text. DCarea impacts and adaptive value results are shown in Figures S.18 and S.19 respectively. Across warming,scenarios, we find slightly larger adaptive values than in our preferred specification. Still, the findings aresimilar in that, while DC seems like a potentially effective adaptation, it’s effect is small relative to the largedecreases in agricultural yields predicted under future climate change.

Figure S.17: Marginal effect of climate on DC propensity when imposing a minimum area of 0.1 km2.Panel A (resp. B) shows the effect of GDD (resp. precipitation) on the the DC share. In both panels,a histogram of the climates within our sample are shown in green at the bottom. Above this, the meanDC share within a climate interval (100 GDD for Panel A and 25 mm for Panel B) is shown by the purplecurve. The brown curve shows the estimated response, overlaid on estimates from an alternative categoricalstep-function specification. The blue curve shows the marginal impact of an increase in the climate value(1,000 GDD for Panel A and 250 mm for Panel B) evaluated at the mean predicted DC share in a givenclimate interval. A 95% confidence interval is shown in light blue.

19

Figure S.18: Predicted effect of warming on DC area when requiring a minimum area of 0.1 km2 to be treatedas a true observation. Panels A, B, and C show the change in DC area within each PRISM grid cell under2, 3, and 4◦C warming, respectively. Each grid cell is 16 km2. Areas shaded in white have predicted DCchanges very close to zero. Areas shaded in dark grey are omitted from our sample as they do not containsoy agriculture.

20

Figure S.19: Probability distribution of the adaptive value of DC when imposing a minimum area of 0.1km2. Distributions of the calculated adaptive value across 1,000 random draws of our estimated parametersfor 2, 3, and 4◦C warming are shown in, respectively, blue, green, and red. For each distribution, we showa corresponding boxplot with whiskers stretching from the 5th to 95th percentile of the simulated adaptivevalues. The interior box stretches from the 25th to 75th percentile, with the median denoted within the boxby a solid line.

21

5 East versus West subsamples

In this section we report the results of separately estimating our model on eastern and western subsamplesof our data. We separate our data at the 84th meridian, roughly along the Appalachian Mountains. FigureS.20 shows the marginal response of DC share to climate variables for our western subsample. Both the GDDand precipitation results are largely in line with our preferred specification findings. Figure S.21 shows themarginal response of DC share to climate variables for our eastern subsample. Again, DC propensity exhibitsa non-monotonic response to GDD, peaking around 4,500 GDD. For this eastern subsample, we find thatlevels of annual precipitation above 1,100 millimeters have a significant negative effect on DC propensity.

Figure S.20: Marginal effect of climate on DC propensity using only observations west of the 84th meridian.Panel A (resp. B) shows the effect of GDD (resp. precipitation) on the the DC share. In both panels,a histogram of the climates within our sample are shown in green at the bottom. Above this, the meanDC share within a climate interval (100 GDD for Panel A and 25 mm for Panel B) is shown by the purplecurve. The brown curve shows the estimated response, overlaid on estimates from an alternative categoricalstep-function specification. The blue curve shows the marginal impact of an increase in the climate value(1,000 GDD for Panel A and 250 mm for Panel B) evaluated at the mean predicted DC share in a givenclimate interval. A 95% confidence interval is shown in light blue.

22

Figure S.21: Marginal effect of climate on DC propensity using only observations east of the 84th meridian.Panel A (resp. B) shows the effect of GDD (resp. precipitation) on the the DC share. In both panels,a histogram of the climates within our sample are shown in green at the bottom. Above this, the meanDC share within a climate interval (100 GDD for Panel A and 25 mm for Panel B) is shown by the purplecurve. The brown curve shows the estimated response, overlaid on estimates from an alternative categoricalstep-function specification. The blue curve shows the marginal impact of an increase in the climate value(1,000 GDD for Panel A and 250 mm for Panel B) evaluated at the mean predicted DC share in a givenclimate interval. A 95% confidence interval is shown in light blue.

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Schlenker, W. and Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to U.S.crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37):15594–15598.

Seifert, C. A. and Lobell, D. B. (2015). Response of double cropping suitability to climate change in theUnited States. Environmental Research Letters, 10(2):024002.

USDA-NASS (2018). USDA National Agricultural Statistics Service Cropland Data Layer. Technical report,USDA-NASS, Washington, DC. https://nassgeodata.gmu.edu/CropScape.

Xie, L., Lewis, S. M., Auffhammer, M., and Berck, P. (2018). Heat in the Heartland: Crop Yield and CoverageResponse to Climate Change Along the Mississippi River. Environmental and Resource Economics, pages1–29.

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