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  • 1. LETTERS PUBLISHED ONLINE: 29 JANUARY 2012 | DOI: 10.1038/NCLIMATE1356Extreme heat effects on wheat senescence in IndiaDavid B. Lobell1 *, Adam Sibley1 and J. Ivan Ortiz-Monasterio2An important source of uncertainty in anticipating the effects than average, even without differences in precipitation2 . Similarly,of climate change on agriculture is limited understanding wheat-yield variations in India are widely attributed to temperatureof crop responses to extremely high temperatures1,2 . This effects, with yields in 2010 reportedly hampered owing to a suddenuncertainty partly reflects the relative lack of observations rise in temperature causing forced maturity12 .of crop behaviour in farmers’ fields under extreme heat. We Although crop-simulation models typically include equationsused nine years of satellite measurements of wheat growth to model the effects of temperature on both development andin northern India to monitor rates of wheat senescence grain-filling rates, models differ in exactly how these mechanismsfollowing exposure to temperatures greater than 34 ◦ C. We are treated, particularly for extreme temperatures. For example, thedetect a statistically significant acceleration of senescence Agricultural Production Systems Simulator (APSIM) model used infrom extreme heat, above and beyond the effects of increased ref. 2 includes a separate equation to speed up senescence for tem-average temperatures. Simulations with two commonly used peratures above 34 ◦ C, which results in a decline in photosynthesisprocess-based crop models indicate that existing models and grain-filling rates, whereas models such as the widely used Cropunderestimate the effects of heat on senescence. As the Environment Resource Synthesis (CERES) model do not13 .onset of senescence is an important limit to grain filling, and Model differences such as this arise because responses to extremetherefore grain yields, crop models probably underestimate heat have been investigated in only a small number of experimentalyield losses for +2 ◦ C by as much as 50% for some sowing trials. These trials vary in many aspects, including the variety used,dates. These results imply that warming presents an even air humidity, soil moisture, the speed at which temperatures aregreater challenge to wheat than implied by previous modelling increased from ambient levels, and the timing, severity and durationstudies, and that the effectiveness of adaptations will depend of heat exposure in the life cycle1,7,14,15 . Such differences make it hardon how well they reduce crop sensitivity to very hot days. to interpret the often large spread in observed effects of extreme Wheat is harvested annually on more than 220 million hectares heat on grain size, development, senescence or yield. For example,of cropland, making it the most widely grown crop in the world. studies carried out in greenhouses can experience unusually highAs a crop that prefers relatively cool temperatures, wheat is sown levels of humidity, which inhibit transpiration and cause canopythroughout much of the world in late autumn or early winter temperatures to rise markedly above ambient temperatures14 . Asand harvested before early summer. The temperature profile of the a result of these and other confounding factors, modellers arewheat growing season in many regions therefore rises towards the understandably unclear on whether certain processes are importantend, with the hottest conditions experienced during grain filling1 . enough to include and, if so, how to include them. High temperatures affect crop growth at many stages of devel- The treatment of extreme heat effects becomes especially impor-opment and through several different mechanisms. Grain yields tant when models are used to project the impacts of climate change.are affected both by changes in grain number, which is determined The occurrence of extreme heat events is already increasing in manyfrom 30 days before flowering (or anthesis) until shortly after parts of the world16 , and will continue to do so throughout the nextanthesis, and grain size, which is determined during grain filling. few decades regardless of changes in policies affecting greenhouse-Towards the end of the season, when hot conditions are common gas emissions17 . Even changes that were once considered rather ex-in many regions, the most pronounced effect of warming is to treme scenarios, such as a 4 ◦ C increase in global mean temperatureshorten the duration of grain filling3,4 . High temperatures can also over pre-industrial levels (with much larger warming in many crop-increase the rate of grain filling, but only slightly at temperatures ping regions), could happen as soon as the early 2060s (ref. 18).above 20 ◦ C, which fails to compensate for the shortened duration The high frequency of heat events in plausible future scenariosand leads to an overall reduction in grain size1,2,5,6 . Above 30 ◦ C, underscores the importance of understanding crop responseswarming can slow grain-filling rates, in part because the leaf to extreme temperatures. Additional experiments are certainlyphotosynthetic apparatus can be damaged at extreme canopy needed, but alternative approaches can also be helpful. Here, wetemperatures, resulting in an acceleration of senescence7–11 . introduce one such approach, which uses satellite data to develop In response to these factors, farmers typically select varieties a large data set on wheat phenology and daily temperatures in thethat possess a maturity rating well suited to the local climate. Indo-Gangetic Plains (IGP) in India. This data set is then usedThat is, they maximize the period of growth during favourable to identify the unique effects of extreme heat on wheat throughtemperatures while maturing in time to escape excessive heat. regression analysis. Predictions from the regression model for theDespite the selection of suitable varieties, however, temperature effects of different amounts of warming are then compared withfluctuations from year to year can cause significant changes in predictions from two process-based crop models, CERES-Wheatyields. For example, recent simulations of wheat yield in Australia and APSIM. These comparisons are used to explore whetherfound that a growing season that is 2 ◦ C warmer than average has past projections of wheat responses to warming in this region,yields that are typically less than 50% of those in years 2 ◦ C cooler which generally ignore the effects of extreme heat, have accurately1 Department of Environmental Earth System Science and Program on Food Security and the Environment, Stanford University, Stanford, California 94305,USA, 2 International Maize and Wheat Improvement Center (CIMMYT), Global Conservation Agriculture Program, Apdo. Postal 6-641, 06600 MexicoD.F., Mexico. *e-mail: CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | 1 © 2012 Macmillan Publishers Limited. All rights reserved.
  • 2. LETTERS NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1356 a b 34 c 34 Punjab Haryana 32 32 Uttar Pradesh Latitude (° N) Latitude (° N) 30 30 28 28 26 26 74 76 78 80 82 84 74 76 78 80 82 84 Longitude (° E) Longitude (° E) 310 320 330 340 350 360 80 100 120 140 160 Green-up date (day of year) Green-season length (no. days)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 viability photosynthetic cells and reductions in photosynthetic rates andof wheat production in the region. viable leaf area when plants are exposed to extreme heat after anthesis, for example, refs 10,11. However, these experimentalResults and discussion studies provide limited guidance on the quantitative effects ofThe study focused on the portion of the IGP in India (Fig. 1a), which extreme heat, because they investigate a small number of treatmentsis one of the most intensive wheat growing regions in the world, with that are difficult to relate to field conditions. The geospatial datanearly 100% of the wheat area irrigated and average fertilizer rates sets used here reflect the behaviour of the wheat varieties grown atof 145 kg N ha−1 (ref. 19). Patterns of wheat green-up and green present in actual field conditions under actual farmer management.season length (GSL) derived from the Moderate Resolution Imaging They therefore serve as a valuable confirmation of past experiments,Spectroradiometer (MODIS) satellite data (Fig. 1) agreed well with and provide a basis for quantifying potential responses to futureprevious ground-based studies of sow-date gradients in the study changes in extreme heat.region20,21 . In particular, wheat is sown earlier in the northwest state The MODIS-based regression models for GSL indicate thatof Punjab, and later by a month or more at the eastern edge of Uttar warming the region by 2 ◦ C would shorten the photosyntheticallyPradesh and into Bihar (Supplementary Fig. S1 shows a distribution active part of the growing season by roughly nine days, withof sow dates across the region for all years). The successive delays slight variations depending on sow date (Fig. 3a). Simulations withas one moves eastwards results from several factors, including CERES-Wheat and APSIM indicate significantly less shorteninglater sowing and harvesting of rice and slow drainage of fields in of the season, particularly for later sowing dates. For example,low-lying areas22,23 . Estimates of GSL indicated that later-sown areas with 2 ◦ C warming and a sow date of 25 November, the MODIStended to have shorter growing cycles, resulting in a much narrower regression shortens the season by roughly nine days compared withrange of harvest dates than sowing dates. This is expected on the six for CERES and only three for APSIM. For the sow date ofbasis of previous work showing that wheat develops more quickly 10 December, the APSIM season actually becomes longer for a 2 ◦ Cin the warmer temperatures experienced for later sowing, and that warming, which is surprising given that the model contains specificday length and vernalization sensitivities cause most cultivars grown equations to accelerate senescence for extreme heat. This unusualin the region to develop more slowly when sown earlier24 . behaviour is driven by the thermal-time calculations in APSIM, The effects of temperature were assessed by a regression of GSL which like the original version (but unlike the present version)on measures of cumulative exposure to normal-growing-degree of CERES-Wheat has a triangular response of thermal time todays (GDD; between 0 and 30 ◦ C) and extreme-growing-degree temperature, with a peak value at 26 ◦ C. Temperatures above 26 ◦ Cdays (EDD; above 34 ◦ C). To control for the fact that day length at any point in the season cause a slowing of overall development ininfluences development rates, separate regressions were carried APSIM, and in warming scenarios a significant portion of the seasonout for early, middle and late sowing dates. A simple plot of the is above this value. Similar artefacts have recently been observed foraverage GSL for high and low values of EDD at each value of simulations of rice development at high temperatures that are aboveGDD illustrates that GSL is shortened by both high GDD and EDD those for which the crop models are calibrated25 . This erroneous(Fig. 2a). Regressions for each of the three common dates resulted slowing of development is probably one reason why CERES-Wheatin a statistically significant effect of both GDD and EDD on GSL, now maintains thermal-time accumulation at maximum rates forwith higher values of each leading to shorter seasons (Fig. 2b). temperatures up to 50 ◦ C.The coefficients were larger in absolute value for EDD than GDD, The underestimations of season shortening imply that bothindicating that a further degree of warming has a stronger effect on CERES and APSIM are underestimating potential yield lossesGSL as temperatures exceed 34 ◦ C. All coefficients were statistically for warming in this region, given that reduced season lengthsignificant even after accounting for spatial correlation (p < 0.05), is a key mechanism of yield loss under warming. In particular,consistent across the use of two satellite data sets (Supplementary extreme heat exposure in this region occurs towards the end ofFig. S3), and robust to the inclusion of rainfall and district-level the cycle (Supplementary Fig. S2), which shortens grain-fillingfixed effects in the regression (Supplementary Table S1). duration and slows photosynthesis and grain-filling rates. Using The inferred acceleration of senescence is consistent with the relationship between season length and yield change in ourvarious greenhouse experiments that have documented damage to CERES simulations, we estimated the yield losses associated with2 NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | © 2012 Macmillan Publishers Limited. All rights reserved.
  • 3. NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1356 LETTERS a a 135 MODIS CERES Shortening of season length (no. days) 130 APSIM 8 Season length (days) 125 6 120 4 115 110 EDD quartile 2 First Fourth 105 0 2,200 2,300 2,400 2,500 2,600 2,700 2,800 10 November 25 November 10 December GDD Sow date b b MODIS 20 CERES APSIM 0 Coefficient estimates 15 Yield loss (%) ¬0.1 10 ¬0.2 GDD 5 EDD PRE ¬0.3 0 26 November 11 December 26 December 10 November 25 November 10 December Centre of green-up values (one week) Sow dateFigure 2 | The effects of GDD and EDD on GSL in the study area for Figure 3 | Comparison of MODIS-based responses to crop models.2000–2009. a, Average GSL for grid cells with green-up on the week a, Estimated response of season length to +2 ◦ C warming based oncentred on 11 December, shown for different GDD and for the top (red) and regression coefficients from MODIS analysis (shown in Fig. 2b) and twobottom (blue) quartile of EDD at each GDD. High GDD shortens GSL (up to common crop models (CERES-Wheat and APSIM-Wheat). b, The same as∼2,600 ◦ C per day), and high EDD results in further shortening. Shading in a but showing percentage estimated yield losses. As we did not estimateindicates ±2σ . b, Estimated coefficients for GDD, EDD and growing season yields directly with MODIS, the yield losses for MODIS were based on theprecipitation (PRE) in a regression to predict GSL for three common relationship between season-length shortening and yield loss as simulatedgreen-up dates using MODIS data. Error bars indicate 5–95% confidence by CERES. Error bars in both panels show 5–95% confidence interval basedinterval, which accounts for heteroskedatic and spatially autocorrelated on 1,000 bootstrap samples for MODIS estimates and 5–95% interval forerrors. Coefficients for GDD and EDD remained significantly negative 27 simulations (three sites, nine years) for the crop models.(p < 0.05) after including district-level fixed effects (SupplementaryTable S1) or using an alternative satellite data set (Supplementary Fig. S3). general point exemplified by this study is that phenology patternsNumber of observations (n) = 209,391, n = 253,767 and n = 165,257 for the captured in satellite data over the past decade provide a usefulthree respective dates. new data set with which to evaluate the performance of existing crop models. Although these models have traditionally been testedthe predicted shortening from the regression model (Fig. 3b). with greenhouse or field-level data, the use of satellite data isCompared with both CERES and APSIM, losses predicted from the especially relevant to the broader scale questions that crop modelsMODIS regression were significantly larger for the two later sow are increasingly used to address.dates. At the most common sowing date at present of 25 November, Methodsfor instance, the median yield decline for a +2 ◦ C scenario was 14% Estimates of green-up and senescence dates and GSL across the IGP were obtainedfor CERES and 10% for APSIM, whereas the MODIS regression using vegetation index products derived from two remote-sensing platforms inindicated a yield loss of 20%. Differences were less pronounced conjunction with established phenology metrics (see Supplementary Information).for the earlier sowing date, which tended to occur in the western We restricted our analysis to land areas in India above 24◦ N that have at least 40%portion of the study region where there is less exposure to extreme area sown with wheat according to a global map of wheat-harvested area26 . Daily minimum and maximum temperatures (Tmin and Tmax ) were estimatedheat in the growing season (Supplementary Fig. S2). at each 1-km grid cell using a combination of the Global Summary of the Day Overall, the response of wheat senescence to warming evident (GSOD) data set from the National Climate Data Center ( the MODIS data indicate greater sensitivities of season length gov/cgi-bin/ and the high-resolution maps of climatologyand wheat yield to warming than implied by two commonly used provided in the WorldClim database ( WorldClim Tmincrop models. Whether these results hold beyond the crop and region and Tmax maps provide long-term average values on a monthly basis, which we interpolated to daily values by fitting a cosine curve at each grid cell. From theseconsidered in this study is a question for future research to address, daily climatology values we compute daily Tmin and Tmax anomalies for each GSODand will probably depend on the degree to which warming results station across India. The anomalies are then interpolated to 1-km grid cells using ain increased exposure to heat above critical thresholds. A more thin-plate spline with latitude (◦ ), longitude (◦ ) and elevation (km) as covariates.NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | 3 © 2012 Macmillan Publishers Limited. All rights reserved.
  • 4. LETTERS NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1356Anomalies are interpolated rather than actual station measurements to minimize 8. Harding, S. A., Guikema, J. A. & Paulsen, G. M. Photosynthetic decline fromthe effects of missing data27 . high temperature stress during maturation of wheat: I. Interaction with GDD was calculated from hourly temperature values obtained by fitting a sine senescence processes. Plant Physiol. 92, 648–653 (1990).curve to daily Tmin and Tmax . 9. Reynolds, M. P., Balota, M., Delgado, M. I. B., Amani, I. & Fischer, R. A. if Tt < Tbase Physiological and morphological traits associated with spring wheat yield   N  0  GDDbase,opt = DDt , DD = T − Tbase if Tbase ≤ Tt ≤ Topt under hot, irrigated conditions. Aust. J. Plant Physiol. 21, 717–730 (1994). t =1  Topt − Tbase if Tt > Topt  10. Al-Khatib, K. & Paulsen, G. M. Mode of high temperature injury to wheat during grain development. Physiol. Plant. 61, 363–368 (1984).where t represents the hourly time step, N is the total number of hours in the 11. Al-Khatib, K. & Paulsen, G. M. High-temperature effects on photosyntheticseason and DD represents degree days. We used a base temperature of 0 ◦ C and processes in temperate and tropical cereals. Crop Sci. 39, 119–125 (1999).a maximum temperature of 30 ◦ C. Furthermore, we computed the accumulation 12. Gupta, R. et al. Wheat productivity in indo-gangetic plains of India duringof degree days over 34 ◦ C (Tbase = 34 ◦ C, Topt = ∞), termed EDD. N was based 2010: Terminal heat effects and mitigation strategies. PACA Newsletter 14,on the average length of GSL for a given sowing date, rather than the GSL of 1–11 (2010).each individual pixel, as the latter would lead to endogeneity in an analysis of 13. Wilkens, P. & Singh, U. in Modeling Temperature Response in Wheat and MaizeGDD and EDD effects on GSL (that is, shorter seasons would have lower GDD by (ed. White, J. W.) 1–7 (CIMMYT, 2001).construction). For simplicity, we present results for three representative green-up 14. Stone, P. & Nicolas, M. Wheat cultivars vary widely in their responses of grainwindows: the weeks centred on days 330, 345 and 360 of the year, which span a yield and quality to short periods of post-anthesis heat stress. Funct. Plant Biol.large fraction of the green-up dates in the region (Supplementary Fig. S1). Each 21, 887–900 (1994).pixel/year combination that fell into one of these three weeks was segregated into a 15. Ferris, R., Ellis, R., Wheeler, T. & Hadley, P. Effect of high temperature stressseparate group, for which average and standard deviation of senescence dates were at anthesis on grain yield and biomass of field-grown crops of wheat. Ann. Bot.calculated. The period from average green-up date to average senescence plus one 82, 631–639 (1998).standard deviation was used as the interval in which to calculate GDD and EDD 16. Zwiers, F. W., Zhang, X. & Feng, Y. Anthropogenic influence on longfor every pixel in the group. return period daily temperature extremes at regional scales. J. Clim. 24, To estimate effects of heat on GSL, a linear regression was applied to each of 881–892 (2011).the three aforementioned groups of green-up dates: 17. Meehl, G. A. et al. in IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, 2007). GSL = β0 + βG GDD + βE EDD + βR RAIN 18. Betts, R. A. et al. When could global warming reach 4 ◦ C? Phil. Tran. R. Soc. A 369, 67–84 (2011).Total rainfall for the growing season (RAIN) was included because rainfall might 19. Food and Agriculture Organization of the United Nations. Fertilizer Use bybe correlated with extreme heat and could alleviate moisture stress and therefore Crop in India (FAO, 2005).delay senescence. RAIN was estimated using gridded rainfall from NASA (http:// 20. Randhawa, A., Dhillon, S. & Singh, D. Productivity of wheat varieties To account for the effects of autocorrelation among the influenced by the time of sowing. J. Res. Punjab Agr. Univ. 18, 227–233 (1981).1-km pixels in our study, standard errors for the regression coefficients were 21. Aggarwal, P. K. & Kalra, N. Analyzing the limitations set by climatic factors,computed using a heteroskedasticity and autocorrelation consistent covariance genotype, and water and nitrogen availability on productivity of wheat. 2.matrix, following the procedure in ref. 28. As an additional robustness check, the Climatically potential yields and management strategies. Field Crop. Res. 38,regression was also carried out using district fixed effects to avoid the influence 93–103 (1994).of omitted variables related to location, such as fertilizer rates or variety selection 22. Chandna, P. et al. Increasing the Productivity of Underutilized Lands by Targeting(Supplementary Table S1). Resource Conserving Technologies-A GIS/Remote Sensing Approach: A Case To explore the possible effects of climate change on GSL, using both our Study of Ballia District, Uttar Pradesh, in the Eastern Gangetic Plains 43regression model and existing crop models, we selected three sites from each group (CIMMYT, 2004).of planting dates. From each group one site was drawn from the fifth, fiftieth and 23. Fujisaka, S., Harrington, L. & Hobbs, P. Rice-wheat in South Asia: Systemsninety-fifth percentiles in EDD accumulation to ensure that our sites represent and long-term priorities established through diagnostic research. Agr. Syst. 46,the full range of possible extreme heat exposure. Daily temperatures at every site 169–187 (1994).were raised by 1 ◦ C, and GDD and EDD recomputed. The regression equations 24. Ortiz-Monasterio, J. I., Dhillon, S. S. & Fischer, R. A. Date of sowing effectswere then used to predict change in GSL relative to baseline. This process was on grain-yield and yield components of irrigated spring wheat cultivars andrepeated for 2–4 ◦ C warming. relationships with radiation and temperature in Ludhiana, India. Field Crop. Finally, using these same sites and temperature records, we ran CERES-Wheat Res. 37, 169–184 (1994).and APSIM to obtain process-based model estimates of season shortening and yield 25. van Oort, P. A. J., Zhang, T., de Vries, M. E., Heinemann, A. B. & Meinke, H.loss. Models were run without nitrogen or water stress. In CERES we used cultivar Correlation between temperature and phenology prediction error in riceparameters developed for a similar wheat-growing region of Mexico29 . For APSIM (Oryza sativa L.). Agr. Forest Meteorol. 151, 1545–1555 (2011).we chose one of the default cultivars, Zippy, as Zippy vernalization and photoperiod 26. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographicsensitivity parameters are reasonable for the area. To be consistent with our method distribution of crop areas, yields, physiological types, and net primaryof obtaining GSL from satellite data, we used daily model outputs in both cases to production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).identify the dates when 10% of maximum leaf area was reached on each end of the 27. Mitchell, T. D. & Jones, P. D. An improved method of constructing agrowing season. We also compared the yield outputs for baseline and elevated tem- database of monthly climate observations and associated high-resolution grids.perature to calculate percentage yield loss for each year and location in our sample Int. J. Climatol. 25, 693–712 (2005).set. Regressing these yield losses against growing season shortening gives an estimate 28. Hsiang, S. M. Temperatures and cyclones strongly associated with economicof the amount of yield one might expect to lose for each day of shortening in GSL. production in the Caribbean and Central America. Proc. Natl Acad. Sci. USA 107, 15367–15372 (2010).Received 11 August 2011; accepted 1 December 2011; 29. Lobell, D. B. et al. Analysis of wheat yield and climatic trends in Mexico.published online 29 January 2012 Field Crop. Res. 94, 250–256 (2005).References Acknowledgements1. Wardlaw, I. & Wrigley, C. Heat tolerance in temperate cereals: An overview. We thank the APSIM team for providing their model and S. Hsiang for providing the Aust. J. Plant Physiol. 21, 695–703 (1994). code to estimate heteroskedasticity- and autocorrelation-consistent standard errors. This2. Asseng, S., Foster, I. & Turner, N. C. The impact of temperature variability on work was supported by the Rockefeller Foundation and NASA New Investigator grant wheat yields. Glob. Change Biol. 17, 997–1012 (2011). no. NNX08AV25G to D.B.L.3. Ritchie, J. T. & NeSmith, D. S. in Modeling 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 Author contributions grain development in wheat and rice. Ann. Bot. 64, 59–65 (1989). D.B.L. conceived the study, D.B.L. and A.S. analysed data, A.S. carried out crop model5. Wardlaw, I. & Moncur, L. The response of wheat to high temperature following simulations, and D.B.L., A.S. and J.I.O-M. interpreted results and wrote the paper. 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 and Additional information duration of grain filling in wheat. Funct. Plant Biol. 4, 785–797 (1977). The authors declare no competing financial interests. Supplementary information7. Zhao, H., Dai, T., Jing, Q., Jiang, D. & Cao, W. Leaf senescence and grain filling accompanies this paper on Reprints and affected by post-anthesis high temperatures in two different wheat cultivars. permissions information is available online at Plant Growth Regul. 51, 149–158 (2007). Correspondence and requests for materials should be addressed to D.B.L.4 NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | © 2012 Macmillan Publishers Limited. All rights reserved.
  • 5. SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE1356 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 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 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).NATURE CLIMATE CHANGE | 1 © 2012 Macmillan Publishers Limited. All rights reserved.
  • 6. Table S1. Regression coefficients for models with and without district fixed-effects for three differentgreen-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 PrecipDay 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) © 2012 Macmillan Publishers Limited. All rights reserved.
  • 7. 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) © 2012 Macmillan Publishers Limited. All rights reserved.
  • 8. Figure S1. Histogram of green-up dates estimated from MODIS for harvest years 2000-2009. Shaded barsindicate three 7-day windows used for regression analysis. © 2012 Macmillan Publishers Limited. All rights reserved.
  • 9. Figure S2. Average number of days within each month of the growing season when maximum dailytemperatures exceeded 34 °C for 2000-2009 in study region. No areas experienced 34 °C duringDecember-February (top right). Extreme heat occurs mainly during the grain filling period of wheat, inMarch and April. © 2012 Macmillan Publishers Limited. All rights reserved.
  • 10. Figure S3. Estimate of regression coefficients for GDD, EDD, and growing season rainfall in a model topredict 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-correlatederrors. (Same as Figure 2b in main paper but for SPOT-VGT instead of MODIS data) © 2012 Macmillan Publishers Limited. All rights reserved.