LETTERSPUBLISHED ONLINE: 29 JANUARY 2012 | DOI: 10.1038/NCLIMATE1356

Extreme heat effects on wheat senescence in IndiaDavid B. Lobell1*, Adam Sibley1 and J. Ivan Ortiz-Monasterio2

An important source of uncertainty in anticipating the effectsof climate change on agriculture is limited understandingof crop responses to extremely high temperatures1,2. Thisuncertainty partly reflects the relative lack of observationsof crop behaviour in farmers’ fields under extreme heat. Weused nine years of satellite measurements of wheat growthin northern India to monitor rates of wheat senescencefollowing exposure to temperatures greater than 34 ◦C. Wedetect a statistically significant acceleration of senescencefrom extreme heat, above and beyond the effects of increasedaverage temperatures. Simulations with two commonly usedprocess-based crop models indicate that existing modelsunderestimate the effects of heat on senescence. As theonset of senescence is an important limit to grain filling, andtherefore grain yields, crop models probably underestimateyield losses for +2 ◦C by as much as 50% for some sowingdates. These results imply that warming presents an evengreater challenge to wheat than implied by previous modellingstudies, and that the effectiveness of adaptations will dependon how well they reduce crop sensitivity to very hot days.

Wheat is harvested annually on more than 220million hectaresof cropland, making it the most widely grown crop in the world.As a crop that prefers relatively cool temperatures, wheat is sownthroughout much of the world in late autumn or early winterand harvested before early summer. The temperature profile of thewheat growing season in many regions therefore rises towards theend, with the hottest conditions experienced during grain filling1.

High temperatures affect crop growth at many stages of devel-opment and through several different mechanisms. Grain yieldsare affected both by changes in grain number, which is determinedfrom 30 days before flowering (or anthesis) until shortly afteranthesis, and grain size, which is determined during grain filling.Towards the end of the season, when hot conditions are commonin many regions, the most pronounced effect of warming is toshorten the duration of grain filling3,4. High temperatures can alsoincrease the rate of grain filling, but only slightly at temperaturesabove 20 ◦C, which fails to compensate for the shortened durationand leads to an overall reduction in grain size1,2,5,6. Above 30 ◦C,warming can slow grain-filling rates, in part because the leafphotosynthetic apparatus can be damaged at extreme canopytemperatures, resulting in an acceleration of senescence7–11.

In response to these factors, farmers typically select varietiesthat possess a maturity rating well suited to the local climate.That is, they maximize the period of growth during favourabletemperatures while maturing in time to escape excessive heat.Despite the selection of suitable varieties, however, temperaturefluctuations from year to year can cause significant changes inyields. For example, recent simulations of wheat yield in Australiafound that a growing season that is 2 ◦C warmer than average hasyields that are typically less than 50% of those in years 2 ◦C cooler

1Department of Environmental Earth System Science and Program on Food Security and the Environment, Stanford University, Stanford, California 94305,USA, 2International Maize and Wheat Improvement Center (CIMMYT), Global Conservation Agriculture Program, Apdo. Postal 6-641, 06600 MexicoD.F., Mexico. *e-mail: dlobell@stanford.edu.

than average, even without differences in precipitation2. Similarly,wheat-yield variations in India are widely attributed to temperatureeffects, with yields in 2010 reportedly hampered owing to a suddenrise in temperature causing forcedmaturity12.

Although crop-simulation models typically include equationsto model the effects of temperature on both development andgrain-filling rates, models differ in exactly how these mechanismsare treated, particularly for extreme temperatures. For example, theAgricultural Production Systems Simulator (APSIM)model used inref. 2 includes a separate equation to speed up senescence for tem-peratures above 34 ◦C, which results in a decline in photosynthesisand grain-filling rates, whereasmodels such as the widely used CropEnvironment Resource Synthesis (CERES)model do not13.

Model differences such as this arise because responses to extremeheat have been investigated in only a small number of experimentaltrials. These trials vary in many aspects, including the variety used,air humidity, soil moisture, the speed at which temperatures areincreased from ambient levels, and the timing, severity and durationof heat exposure in the life cycle1,7,14,15. Such differencesmake it hardto interpret the often large spread in observed effects of extremeheat on grain size, development, senescence or yield. For example,studies carried out in greenhouses can experience unusually highlevels of humidity, which inhibit transpiration and cause canopytemperatures to rise markedly above ambient temperatures14. Asa result of these and other confounding factors, modellers areunderstandably unclear on whether certain processes are importantenough to include and, if so, how to include them.

The treatment of extreme heat effects becomes especially impor-tant whenmodels are used to project the impacts of climate change.The occurrence of extreme heat events is already increasing inmanyparts of the world16, and will continue to do so throughout the nextfew decades regardless of changes in policies affecting greenhouse-gas emissions17. Even changes that were once considered rather ex-treme scenarios, such as a 4 ◦C increase in global mean temperatureover pre-industrial levels (withmuch larger warming inmany crop-ping regions), could happen as soon as the early 2060s (ref. 18).

The high frequency of heat events in plausible future scenariosunderscores the importance of understanding crop responsesto extreme temperatures. Additional experiments are certainlyneeded, but alternative approaches can also be helpful. Here, weintroduce one such approach, which uses satellite data to developa large data set on wheat phenology and daily temperatures in theIndo-Gangetic Plains (IGP) in India. This data set is then usedto identify the unique effects of extreme heat on wheat throughregression analysis. Predictions from the regression model for theeffects of different amounts of warming are then compared withpredictions from two process-based crop models, CERES-Wheatand APSIM. These comparisons are used to explore whetherpast projections of wheat responses to warming in this region,which generally ignore the effects of extreme heat, have accurately

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LETTERS NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1356

Punjab

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Figure 1 | The study region of the IGP in northern India. a, The location of the main study area (outlined) and names of three primary states. b, Thegreen-up date (day of year) estimated by MODIS for harvest year 2001. c, The green-season length (days from green-up to senescence) estimated byMODIS for the same year. White areas indicate grid cells with less than 40% wheat, which were not included in this study. A total of 1,638,127 individualestimates of green-up and season length were used over the study period.

captured the implications of climate change for the future viabilityof wheat production in the region.

Results and discussionThe study focused on the portion of the IGP in India (Fig. 1a), whichis one of themost intensivewheat growing regions in theworld, withnearly 100% of the wheat area irrigated and average fertilizer ratesof 145 kgNha−1 (ref. 19). Patterns of wheat green-up and greenseason length (GSL) derived from theModerate Resolution ImagingSpectroradiometer (MODIS) satellite data (Fig. 1) agreed well withprevious ground-based studies of sow-date gradients in the studyregion20,21. In particular, wheat is sown earlier in the northwest stateof Punjab, and later by a month or more at the eastern edge of UttarPradesh and into Bihar (Supplementary Fig. S1 shows a distributionof sow dates across the region for all years). The successive delaysas one moves eastwards results from several factors, includinglater sowing and harvesting of rice and slow drainage of fields inlow-lying areas22,23. Estimates ofGSL indicated that later-sown areastended to have shorter growing cycles, resulting in amuch narrowerrange of harvest dates than sowing dates. This is expected on thebasis of previous work showing that wheat develops more quicklyin the warmer temperatures experienced for later sowing, and thatday length and vernalization sensitivities causemost cultivars grownin the region to developmore slowly when sown earlier24.

The effects of temperature were assessed by a regression of GSLon measures of cumulative exposure to normal-growing-degreedays (GDD; between 0 and 30 ◦C) and extreme-growing-degreedays (EDD; above 34 ◦C). To control for the fact that day lengthinfluences development rates, separate regressions were carriedout for early, middle and late sowing dates. A simple plot of theaverage GSL for high and low values of EDD at each value ofGDD illustrates that GSL is shortened by both high GDD and EDD(Fig. 2a). Regressions for each of the three common dates resultedin a statistically significant effect of both GDD and EDD on GSL,with higher values of each leading to shorter seasons (Fig. 2b).The coefficients were larger in absolute value for EDD than GDD,indicating that a further degree of warming has a stronger effect onGSL as temperatures exceed 34 ◦C. All coefficients were statisticallysignificant even after accounting for spatial correlation (p< 0.05),consistent across the use of two satellite data sets (SupplementaryFig. S3), and robust to the inclusion of rainfall and district-levelfixed effects in the regression (Supplementary Table S1).

The inferred acceleration of senescence is consistent withvarious greenhouse experiments that have documented damage to

photosynthetic cells and reductions in photosynthetic rates andviable leaf area when plants are exposed to extreme heat afteranthesis, for example, refs 10,11. However, these experimentalstudies provide limited guidance on the quantitative effects ofextreme heat, because they investigate a small number of treatmentsthat are difficult to relate to field conditions. The geospatial datasets used here reflect the behaviour of the wheat varieties grown atpresent in actual field conditions under actual farmer management.They therefore serve as a valuable confirmation of past experiments,and provide a basis for quantifying potential responses to futurechanges in extreme heat.

The MODIS-based regression models for GSL indicate thatwarming the region by 2 ◦C would shorten the photosyntheticallyactive part of the growing season by roughly nine days, withslight variations depending on sow date (Fig. 3a). Simulations withCERES-Wheat and APSIM indicate significantly less shorteningof the season, particularly for later sowing dates. For example,with 2 ◦C warming and a sow date of 25 November, the MODISregression shortens the season by roughly nine days compared withsix for CERES and only three for APSIM. For the sow date of10 December, the APSIM season actually becomes longer for a 2 ◦Cwarming, which is surprising given that the model contains specificequations to accelerate senescence for extreme heat. This unusualbehaviour is driven by the thermal-time calculations in APSIM,which like the original version (but unlike the present version)of CERES-Wheat has a triangular response of thermal time totemperature, with a peak value at 26 ◦C. Temperatures above 26 ◦Cat any point in the season cause a slowing of overall development inAPSIM, and inwarming scenarios a significant portion of the seasonis above this value. Similar artefacts have recently been observed forsimulations of rice development at high temperatures that are abovethose for which the crop models are calibrated25. This erroneousslowing of development is probably one reason why CERES-Wheatnow maintains thermal-time accumulation at maximum rates fortemperatures up to 50 ◦C.

The underestimations of season shortening imply that bothCERES and APSIM are underestimating potential yield lossesfor warming in this region, given that reduced season lengthis a key mechanism of yield loss under warming. In particular,extreme heat exposure in this region occurs towards the end ofthe cycle (Supplementary Fig. S2), which shortens grain-fillingduration and slows photosynthesis and grain-filling rates. Usingthe relationship between season length and yield change in ourCERES simulations, we estimated the yield losses associated with

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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1356 LETTERS

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Figure 2 | The effects of GDD and EDD on GSL in the study area for2000–2009. a, Average GSL for grid cells with green-up on the weekcentred on 11 December, shown for different GDD and for the top (red) andbottom (blue) quartile of EDD at each GDD. High GDD shortens GSL (up to∼2,600 ◦C per day), and high EDD results in further shortening. Shadingindicates±2σ . b, Estimated coefficients for GDD, EDD and growing seasonprecipitation (PRE) in a regression to predict GSL for three commongreen-up dates using MODIS data. Error bars indicate 5–95% confidenceinterval, which accounts for heteroskedatic and spatially autocorrelatederrors. Coefficients for GDD and EDD remained significantly negative(p<0.05) after including district-level fixed effects (SupplementaryTable S1) or using an alternative satellite data set (Supplementary Fig. S3).Number of observations (n)=209,391, n=253,767 and n= 165,257 for thethree respective dates.

the predicted shortening from the regression model (Fig. 3b).Compared with both CERES and APSIM, losses predicted from theMODIS regression were significantly larger for the two later sowdates. At themost common sowing date at present of 25 November,for instance, the median yield decline for a+2 ◦C scenario was 14%for CERES and 10% for APSIM, whereas the MODIS regressionindicated a yield loss of 20%. Differences were less pronouncedfor the earlier sowing date, which tended to occur in the westernportion of the study region where there is less exposure to extremeheat in the growing season (Supplementary Fig. S2).

Overall, the response of wheat senescence to warming evidentin the MODIS data indicate greater sensitivities of season lengthand wheat yield to warming than implied by two commonly usedcropmodels.Whether these results hold beyond the crop and regionconsidered in this study is a question for future research to address,and will probably depend on the degree to which warming resultsin increased exposure to heat above critical thresholds. A more

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Figure 3 | Comparison of MODIS-based responses to crop models.a, Estimated response of season length to+2 ◦C warming based onregression coefficients from MODIS analysis (shown in Fig. 2b) and twocommon crop models (CERES-Wheat and APSIM-Wheat). b, The same asin a but showing percentage estimated yield losses. As we did not estimateyields directly with MODIS, the yield losses for MODIS were based on therelationship between season-length shortening and yield loss as simulatedby CERES. Error bars in both panels show 5–95% confidence interval basedon 1,000 bootstrap samples for MODIS estimates and 5–95% interval for27 simulations (three sites, nine years) for the crop models.

general point exemplified by this study is that phenology patternscaptured in satellite data over the past decade provide a usefulnew data set with which to evaluate the performance of existingcrop models. Although these models have traditionally been testedwith greenhouse or field-level data, the use of satellite data isespecially relevant to the broader scale questions that crop modelsare increasingly used to address.

MethodsEstimates of green-up and senescence dates and GSL across the IGP were obtainedusing vegetation index products derived from two remote-sensing platforms inconjunction with established phenology metrics (see Supplementary Information).We restricted our analysis to land areas in India above 24◦ N that have at least 40%area sownwithwheat according to a globalmap of wheat-harvested area26.

Daily minimum and maximum temperatures (Tmin and Tmax) were estimatedat each 1-km grid cell using a combination of the Global Summary of the Day(GSOD) data set from the National Climate Data Center (http://www.ncdc.noaa.gov/cgi-bin/res40.pl?page=gsod.html) and the high-resolutionmaps of climatologyprovided in the WorldClim database (http://worldclim.org). WorldClim Tmin

and Tmax maps provide long-term average values on a monthly basis, which weinterpolated to daily values by fitting a cosine curve at each grid cell. From thesedaily climatology values we compute daily Tmin and Tmax anomalies for each GSODstation across India. The anomalies are then interpolated to 1-km grid cells using athin-plate spline with latitude (◦), longitude (◦) and elevation (km) as covariates.

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LETTERS NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1356

Anomalies are interpolated rather than actual station measurements to minimizethe effects of missing data27.

GDD was calculated from hourly temperature values obtained by fitting a sinecurve to daily Tmin and Tmax.

GDDbase,opt=

N∑t=1

DDt , DD=

0 if Tt <Tbase

T−Tbase if Tbase ≤Tt ≤Topt

Topt−Tbase if Tt >Topt

where t represents the hourly time step, N is the total number of hours in theseason and DD represents degree days. We used a base temperature of 0 ◦C anda maximum temperature of 30 ◦C. Furthermore, we computed the accumulationof degree days over 34 ◦C (Tbase = 34 ◦C, Topt =∞), termed EDD. N was basedon the average length of GSL for a given sowing date, rather than the GSL ofeach individual pixel, as the latter would lead to endogeneity in an analysis ofGDD and EDD effects on GSL (that is, shorter seasons would have lower GDD byconstruction). For simplicity, we present results for three representative green-upwindows: the weeks centred on days 330, 345 and 360 of the year, which span alarge fraction of the green-up dates in the region (Supplementary Fig. S1). Eachpixel/year combination that fell into one of these three weeks was segregated into aseparate group, for which average and standard deviation of senescence dates werecalculated. The period from average green-up date to average senescence plus onestandard deviation was used as the interval in which to calculate GDD and EDDfor every pixel in the group.

To estimate effects of heat on GSL, a linear regression was applied to each ofthe three aforementioned groups of green-up dates:

GSL=β0+βGGDD+βEEDD+βRRAIN

Total rainfall for the growing season (RAIN) was included because rainfall mightbe correlated with extreme heat and could alleviate moisture stress and thereforedelay senescence. RAIN was estimated using gridded rainfall from NASA (http://power.larc.nasa.gov/). To account for the effects of autocorrelation among the1-km pixels in our study, standard errors for the regression coefficients werecomputed using a heteroskedasticity and autocorrelation consistent covariancematrix, following the procedure in ref. 28. As an additional robustness check, theregression was also carried out using district fixed effects to avoid the influenceof omitted variables related to location, such as fertilizer rates or variety selection(Supplementary Table S1).

To explore the possible effects of climate change on GSL, using both ourregression model and existing crop models, we selected three sites from each groupof planting dates. From each group one site was drawn from the fifth, fiftieth andninety-fifth percentiles in EDD accumulation to ensure that our sites representthe full range of possible extreme heat exposure. Daily temperatures at every sitewere raised by 1 ◦C, and GDD and EDD recomputed. The regression equationswere then used to predict change in GSL relative to baseline. This process wasrepeated for 2–4 ◦C warming.

Finally, using these same sites and temperature records, we ran CERES-Wheatand APSIM to obtain process-based model estimates of season shortening and yieldloss. Models were run without nitrogen or water stress. In CERES we used cultivarparameters developed for a similar wheat-growing region of Mexico29. For APSIMwe chose one of the default cultivars, Zippy, as Zippy vernalization and photoperiodsensitivity parameters are reasonable for the area. To be consistent with our methodof obtaining GSL from satellite data, we used daily model outputs in both cases toidentify the dates when 10% of maximum leaf area was reached on each end of thegrowing season. We also compared the yield outputs for baseline and elevated tem-perature to calculate percentage yield loss for each year and location in our sampleset. Regressing these yield losses against growing season shortening gives an estimateof the amount of yield onemight expect to lose for each day of shortening inGSL.

Received 11 August 2011; accepted 1 December 2011;published online 29 January 2012

References1. Wardlaw, I. & Wrigley, C. Heat tolerance in temperate cereals: An overview.

Aust. J. Plant Physiol. 21, 695–703 (1994).2. Asseng, S., Foster, I. & Turner, N. C. The impact of temperature variability on

wheat yields. Glob. Change Biol. 17, 997–1012 (2011).3. Ritchie, J. T. & NeSmith, D. S. inModeling Plant and Soil Systems Vol. 31

(eds Hanks, J. & Ritchie, J. T.) 5–29 (American Society of Agronomy, 1991).4. Tashiro, T. &Wardlaw, I. F. A comparison of the effect of high temperature on

grain development in wheat and rice. Ann. Bot. 64, 59–65 (1989).5. Wardlaw, I. &Moncur, L. The response of wheat to high temperature following

anthesis. I. The rate and duration of kernel filling. Funct. Plant Biol. 22,391–397 (1995).

6. Sofield, I., Evans, L., Cook, M. & Wardlaw, I. Factors influencing the rate andduration of grain filling in wheat. Funct. Plant Biol. 4, 785–797 (1977).

7. Zhao, H., Dai, T., Jing, Q., Jiang, D. & Cao, W. Leaf senescence and grain fillingaffected by post-anthesis high temperatures in two different wheat cultivars.Plant Growth Regul. 51, 149–158 (2007).

8. Harding, S. A., Guikema, J. A. & Paulsen, G. M. Photosynthetic decline fromhigh temperature stress during maturation of wheat: I. Interaction withsenescence processes. Plant Physiol. 92, 648–653 (1990).

9. Reynolds, M. P., Balota, M., Delgado, M. I. B., Amani, I. & Fischer, R. A.Physiological and morphological traits associated with spring wheat yieldunder hot, irrigated conditions. Aust. J. Plant Physiol. 21, 717–730 (1994).

10. Al-Khatib, K. & Paulsen, G. M. Mode of high temperature injury to wheatduring grain development. Physiol. Plant. 61, 363–368 (1984).

11. Al-Khatib, K. & Paulsen, G. M. High-temperature effects on photosyntheticprocesses in temperate and tropical cereals. Crop Sci. 39, 119–125 (1999).

12. Gupta, R. et al. Wheat productivity in indo-gangetic plains of India during2010: Terminal heat effects and mitigation strategies. PACA Newsletter 14,1–11 (2010).

13. Wilkens, P. & Singh, U. inModeling Temperature Response in Wheat and Maize(ed. White, J. W.) 1–7 (CIMMYT, 2001).

14. Stone, P. & Nicolas, M. Wheat cultivars vary widely in their responses of grainyield and quality to short periods of post-anthesis heat stress. Funct. Plant Biol.21, 887–900 (1994).

15. Ferris, R., Ellis, R., Wheeler, T. & Hadley, P. Effect of high temperature stressat anthesis on grain yield and biomass of field-grown crops of wheat. Ann. Bot.82, 631–639 (1998).

16. Zwiers, F. W., Zhang, X. & Feng, Y. Anthropogenic influence on longreturn period daily temperature extremes at regional scales. J. Clim. 24,881–892 (2011).

17. Meehl, G. A. et al. in IPCC Climate Change 2007: The Physical Science Basis(eds Solomon, S. et al.) (Cambridge Univ. Press, 2007).

18. Betts, R. A. et al. When could global warming reach 4 ◦C? Phil. Tran. R. Soc. A369, 67–84 (2011).

19. Food and Agriculture Organization of the United Nations. Fertilizer Use byCrop in India (FAO, 2005).

20. Randhawa, A., Dhillon, S. & Singh, D. Productivity of wheat varieties asinfluenced by the time of sowing. J. Res. Punjab Agr. Univ. 18, 227–233 (1981).

21. Aggarwal, P. K. & Kalra, N. Analyzing the limitations set by climatic factors,genotype, and water and nitrogen availability on productivity of wheat. 2.Climatically potential yields and management strategies. Field Crop. Res. 38,93–103 (1994).

22. Chandna, P. et al. Increasing the Productivity of Underutilized Lands by TargetingResource Conserving Technologies-A GIS/Remote Sensing Approach: A CaseStudy of Ballia District, Uttar Pradesh, in the Eastern Gangetic Plains 43(CIMMYT, 2004).

23. Fujisaka, S., Harrington, L. & Hobbs, P. Rice-wheat in South Asia: Systemsand long-term priorities established through diagnostic research. Agr. Syst. 46,169–187 (1994).

24. Ortiz-Monasterio, J. I., Dhillon, S. S. & Fischer, R. A. Date of sowing effectson grain-yield and yield components of irrigated spring wheat cultivars andrelationships with radiation and temperature in Ludhiana, India. Field Crop.Res. 37, 169–184 (1994).

25. van Oort, P. A. J., Zhang, T., de Vries, M. E., Heinemann, A. B. & Meinke, H.Correlation between temperature and phenology prediction error in rice(Oryza sativa L.). Agr. Forest Meteorol. 151, 1545–1555 (2011).

26. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographicdistribution of crop areas, yields, physiological types, and net primaryproduction in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).

27. Mitchell, T. D. & Jones, P. D. An improved method of constructing adatabase of monthly climate observations and associated high-resolution grids.Int. J. Climatol. 25, 693–712 (2005).

28. Hsiang, S. M. Temperatures and cyclones strongly associated with economicproduction in the Caribbean and Central America. Proc. Natl Acad. Sci. USA107, 15367–15372 (2010).

29. Lobell, D. B. et al. Analysis of wheat yield and climatic trends in Mexico.Field Crop. Res. 94, 250–256 (2005).

AcknowledgementsWe thank the APSIM team for providing their model and S. Hsiang for providing thecode to estimate heteroskedasticity- and autocorrelation-consistent standard errors. Thiswork was supported by the Rockefeller Foundation and NASA New Investigator grantno. NNX08AV25G to D.B.L.

Author contributionsD.B.L. conceived the study, D.B.L. and A.S. analysed data, A.S. carried out crop modelsimulations, andD.B.L., A.S. and J.I.O-M. interpreted results andwrote the paper.

Additional informationThe authors declare no competing financial interests. Supplementary informationaccompanies this paper on www.nature.com/natureclimatechange. Reprints andpermissions information is available online at http://www.nature.com/reprints.Correspondence and requests formaterials should be addressed toD.B.L.

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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE1356

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

Supplementary Information for

“Extreme heat effects on wheat senescence in India” by Lobell, Sibley, and Ortiz-Monasterio

Satellite Data Processing Methods:

We obtained two time series of vegetation index (VI) products spanning the study area for 2000-2009. The first was obtained by combining the MOD13A2 (Terra) and MYD13A2 (Aqua) MODIS products (available at https://lpdaac.usgs.gov). Each gives the maximum value of the enhanced VI (EVI) over a 16 day composite window, with an eight day offset between the two products, yielding EVI estimates at eight day intervals. A contemporaneous time series of 10 day composite normalized difference VI (NDVI) data from the SPOT VEGETATION sensor (available at http://free.vgt.vito.be) was used as a secondary source, to ensure that results were robust to the choice of instrument record. Both products cover the entire study area at a spatial resolution of 1km.

For both VI time series we fit double logistic functions to each time series on a pixel-by-pixel basis using the Timesat software 1. The double logistic curve has been used extensively to model vegetation phenology as its shape closely resembles the VI signature of plants during a growing season 2. From our fitted functions we define green-up in each year as the point when the fitted curve reaches 10% of its maximum amplitude for that year; senescence was defined as the equivalent point on the declining portion of the function. Green season length (GSL) was computed each year as the number of days between green-up and senescence.

References:

1 Jönsson, P. & Eklundh, L. TIMESAT--a program for analyzing time-series of satellite sensor data* 1. Computers & Geosciences 30, 833-845 (2004).

2 Fischer, A. A Simple-Model For the Temporal Variations of Ndvi At Regional- Scale Over Agricultural Countries - Validation With Ground Radiometric Measurements. International Journal of Remote Sensing 15, 1421-1446 (1994).

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Table S1. Regression coefficients for models with and without district fixed-effects for three different green-up dates. Values in parentheses indicate standard errors, and stars indicate statistical significance (**: p< 0.05). Standard errors were computed to account for heteroskedatic and spatially auto-correlated errors.

Standard Regression Fixed Effects Model

GDD EDD Precip GDD EDD Precip

Day 330 -0.03** -0.122** 0.013** -0.017** -0.131** 0.017**

(0.002) (0.034) (0.063) (0.003) (0.029) (0.006)

Day 345 -0.031** -0.216** -0.009** -0.007** -0.24** 0.004

(0.003) (0.05) (0.055) (0.004) (0.038) (0.008)

Day 360 -0.04** -0.151** -0.057** -0.262** -0.25** 0.008

(0.004) (0.042) (0.063) (0.004) (0.04) (0.01)

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Supplementary Figure Captions:

1. Histogram of green-up dates estimated from MODIS for harvest years 2000-2009. Shaded bars indicate three 7-day windows used for regression analysis.

2. Average number of days within each month of the growing season when maximum daily temperatures exceeded 34 °C for 2000-2009 in study region. No areas experienced 34 °C during December-February (top right). Extreme heat occurs mainly during the grain filling period of wheat, in March and April.

3. Estimate of regression coefficients for GDD, EDD, and growing season rainfall in a model to predict GSL for three common green-up dates using SPOT-VGT data. Error bars indicate 5-95% confidence interval, and were computed to account for heteroskedatic and spatially auto-correlated errors. (Same as Figure 2b in main paper but for SPOT-VGT instead of MODIS data)

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Figure S1. Histogram of green-up dates estimated from MODIS for harvest years 2000-2009. Shaded bars indicate three 7-day windows used for regression analysis.

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Figure S2. Average number of days within each month of the growing season when maximum daily temperatures exceeded 34 °C for 2000-2009 in study region. No areas experienced 34 °C during December-February (top right). Extreme heat occurs mainly during the grain filling period of wheat, in March and April.

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Figure S3. Estimate of regression coefficients for GDD, EDD, and growing season rainfall in a model to predict GSL for three common green-up dates using SPOT-VGT data. Error bars indicate 5-95% confidence interval, and were computed to account for heteroskedatic and spatially auto-correlated errors. (Same as Figure 2b in main paper but for SPOT-VGT instead of MODIS data)

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