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How is CO2 affecting yields and technological progress? A statistical analysis

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Abstract

This paper analyzes the impact of climate, crop production technology, and atmospheric carbon dioxide (CO2) on current and future crop yields. The analysis of crop yields endeavors to advance the literature by estimating the effect of atmospheric CO2 on observed crop yields. This is done using an econometric model estimated over pooled historical data for 1950–2009 and data from the free air CO2 enrichment experiments. The main econometric findings are: 1) Yields of C3 crops (soybeans, cotton, and wheat) directly respond to the elevated CO2, while yields of C4 crops (corn and sorghum) do not, but they are found to indirectly benefit from elevated CO2 in times and places of drought stress; 2) The effect of technological progress on mean yields is non-linear; 3) Ignoring atmospheric CO2 in an econometric model of crop yield likely leads to overestimates of the pure effects of technological progress on crop yields of about 51, 15, 17, 9, and 1 % of observed yield gain for cotton, soybeans, wheat, corn and sorghum, respectively; 4) Average climate conditions and climate variability contribute in a statistically significant way to average crop yields and their variability; and 5) The effect of CO2 fertilization generally outweighs the effect of climate change on mean crop yields in many regions resulting in an increase of 7–22, 4–47, 5–26, 65–96, and 3–35 % for yields of corn, sorghum, soybeans, cotton, and wheat, respectively.

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Notes

  1. In the FACE experiments, air enriched with CO2 is blown into the rings where crops are grown in the real field (not in the chamber). Then, a computer-control system uses the wind speed and CO2 concentration information to adjust the CO2 flow rates to maintain the desired CO2 concentration. Finally, crop yield in the elevated CO2 rings are compared to that in the control rings with non-elevated CO2 (ambient) environment. Details of the FACE experiments are provided in Long et al. (2006).

  2. The ENSO events, which are associated with differing sea-surface temperatures (SSTs) patterns between the eastern equatorial Pacific and the southern Pacific (Southern Oscillation Index, SOI), is an important controlling factors in global interannual climate variability (Hastenrath 1995; Phillips et al. 1999) and in crop yields (e.g., Hansen et al. 1998).

  3. For robustness, the observational data were tested for unit roots, although after we pooled the observational and FACE data, our data does not fully have a panel data structure. Using a Fisher-type test (Choi 2001) and a Levin-Lin-Chu test (Levin et al. 2002) test, all series except CO2, which is I(1), are stationary in the level, I(0). However, after we apply the panel unit root tests to the residuals we find it is stationary in the level, I(0), implying that our model might not have the problem of spurious regression (Granger 1981). We thank Dr. David Bessler, Texas A&M University, for his useful suggestion on how to approach this testing.

  4. The log-likelihood function of equation (1) is:

    \( l\mathrm{nL}=-\frac{1}{2}\left[\mathrm{n}* \ln \left(2\uppi \right)+{\displaystyle \sum_{\mathrm{i}=1}^{\mathrm{n}} \ln \left(\mathrm{h}{\left({\mathrm{X}}_{\mathrm{i}},\upalpha \right)}^2\right)+{\displaystyle \sum_{\mathrm{i}=1}^{\mathrm{n}}\frac{{\left({\mathrm{y}}_{\mathrm{i}}-\mathrm{f}\left({\mathrm{X}}_{\mathrm{i}},\upbeta \right)\right)}^2}{\mathrm{h}{\left({\mathrm{X}}_{\mathrm{i}},\upalpha \right)}^2}}}\right] \) under the assumption that yi ~ N(f(Xi, β), h(Xi, α)2) and εi ~ N(0, 1).

  5. The PDSI is a standardized measure of surface moisture conditions, ranging from about -10 to +10 with negative values denoting dry conditions and positive values indicating wet conditions (Palmer 1965).

  6. We thank Dr. Chi-Chung Chen, National Chung-Hsing University, Taiwan for his useful suggestion regarding the selection of ENSO phases.

  7. We thank Dr. Bruce A. Kimball from USDA-ARS, Arid-Land Agricultural Research Center, Arizona; Dr. Donald R. Ort; Dr. Lisa Ainsworth; and Dr. Andrew Leakey from University of Illinois at Urbana Champaign in providing us the FACE experimental datasets.

  8. To derive these estimates we fix all other independent variables at their 2008 levels and vary time and the CO2 concentration variable from the 1950 to 2100 levels.

  9. The A1B scenario reflects a move away from fossil fuel reliance with lower emissions than today’s levels but less reduction than in the more optimistic B1 and B2 scenarios. For our time frame (2050) the choice of SRES scenarios does not make much difference as emissions and climate change implications of different SRES scenarios do not diverge significantly as noted in The citation “IPCC (2007a)” (original) has been changed to “IPCC (2007a)”. Please check if appropriate.IPCC (2007a).

  10. Timmermann et al. (1999) produce estimates that the El Nino phase will shift from occurring 23.8 % of the time to 33.9 % of the time, while La Niña phase will shift from 25 to 31 %, and Neutral from 51.5 to 35.1 %.

  11. In Long et al. (2006) and Leakey (2009), as atmospheric CO2 increases from about 367 to 550 ppm, yields of wheat, soybeans, and C-4 crop are projected to increase 13, 14, and 0%, respectively, while chamber studies find that yields of wheat, soybeans, and C-4 crop are forecasted to increase 31, 32, and 18%, respectively.

  12. For a definition of the regions see note 1 on Table 5.

References

  • Ainsworth EA, Long SP (2005) What have we learned from 15 years of free air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol 165:351–372

    Article  Google Scholar 

  • Allen LH Jr, Kakani VG, Vu JCV, Boote KJ (2011) Elevated CO2 increases water use efficiency by sustaining photosynthesis of water-limited maize and sorghum. J Plant Physiol 168(16):1909–1918

    Article  Google Scholar 

  • Attavanich W (2011) Essays on the effect of climate change on agriculture and agricultural transportation Ph.D. Dissertation, Texas A&M University

  • Attavanich W, McCarl BA, Ahmedov Z, Fuller S, Vedenov DV (2013) Effects of climate change on US grain transport. Nat Clim Chang 3:638–643

    Article  Google Scholar 

  • Bazzaz FA (1990) The response of natural ecosystems to the rising global CO2 levels. Annu Rev Ecol Syst 21:167–196

    Article  Google Scholar 

  • Bunce JA (2012) Responses of cotton and wheat photosynthesis and growth to cyclic variation in carbon dioxide concentration. Photosynthetica 50:395–400

    Article  Google Scholar 

  • Chen CC, McCarl BA, Schimmelpfennig DE (2004) Yield variability as influenced by climate: a statistical investigation. Clim Change 66:239–261

    Article  Google Scholar 

  • Choi I (2001) Unit root tests for panel data. J Int Money Financ 20:249–272

    Article  Google Scholar 

  • Dai A (2010) Drought under global warming: a review. Wiley Interdiscip Rev Clim Chang 2(1):45–65

    Article  Google Scholar 

  • Deschenes O, Greenstone M (2007) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. Am Econ Rev 97(1):354–385

    Article  Google Scholar 

  • Fisher AC, Hanemann WM, Roberts MJ, Schlenker W (2012) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. Comment Am Econ Rev 102(7):3749

    Article  Google Scholar 

  • Granger CWJ (1981) Some properties of time series data and their use in econometric model specification. J Econ 16:121–130

    Article  Google Scholar 

  • Hansen JW, Hodges AW, Jones JW (1998) ENSO influences on agriculture in the Southeastern United States. J Clim 11:404–411

    Article  Google Scholar 

  • Hastenrath S (1995) Recent advances in tropical climate prediction. J Clim 8:1519–1532

    Article  Google Scholar 

  • Hazell PBR (1984) Sources of increased instability and U.S. cereal production. Am J Agric Econ 66(3):302–311

    Article  Google Scholar 

  • Huang H, Khanna M (2010) An econometric analysis of U.S. crop yield and cropland acreage: implications for the impact of climate change. Paper presented at the joint annual meeting of the AAEA, CAES, and WAEA, Denver, Colorado, July 25–27

  • IPCC (2007a) Climate change 2007: The physical science basis. In: Solomon S, Qin D, Manning M et al (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, UK

    Google Scholar 

  • IPCC (2007b) Climate change 2007impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof PJ et al (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, UK

    Google Scholar 

  • IPCC (2012) In: Field CB, Barros V, Stocker TF et al (eds) Special report on managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge

    Google Scholar 

  • Just R, Pope RD (1978) Stochastic specification of production function and economic implications. J Econ 7:67–86

    Article  Google Scholar 

  • Just R, Pope RD (1979) Production function estimation and related risk considerations. Am J Agric Econ 61:277–284

    Article  Google Scholar 

  • Kimball BA (2006) The effects of free-air CO2 enrichment of cotton, wheat, and sorghum. In: Nösberger J, Long SP, Norby RJ et al (eds) Managed ecosystems and CO2 case studies, processes, and perspectives. Springer, Berlin, pp 47–70

    Google Scholar 

  • Kimball BA (2010) Lessons from FACE: CO2 effects and interactions with water, nitrogen, and temperature. In: Hillel D, Rosenzweig C (eds) Handbook of climate change and agroecosystems: impacts, adaptation, and mitigation. Imperial College Press, London UK, pp 87–107

    Chapter  Google Scholar 

  • Leakey ADB (2009) Rising atmospheric carbon dioxide concentration and the future of C-4 crops for food and fuel. Proc Biol Sci R Soc 276:2333–2343

    Article  Google Scholar 

  • Levin A, Lin CF, Chu CSJ (2002) Unit root tests in panel data: Asymptotic and finite-sample properties. J Econ 108:1–24

    Article  Google Scholar 

  • Liu H, Yang L, Wang Y, Huang J, Zhu J, Yunxia W, Dong G, Liu G (2008) Yield formation of CO2-enriched hybrid cultivar Shanyou 63 under fully open-air field conditions. Field Crop Res 108:93–100

    Article  Google Scholar 

  • Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616–620

    Article  Google Scholar 

  • Long SP, Ainsworth EA, Leakey ADB, Nösberger J, Ort DR (2006) Food for thought: Lower-than-expected crop yield stimulation with rising CO2 concentrations. Science 312(5782):1918–1921

    Article  Google Scholar 

  • Ma H, Zhu J, Xie Z, Liu G, Zeng Q, Han Y (2007) Responses of rice and winter wheat to free-air CO2 enrichment (China FACE) at rice/wheat rotation system. Plant Soil 294(1–2):137–146

    Article  Google Scholar 

  • Manderscheid R, Erbs M, Weigel HJ (2014) Interactive effects of free-air CO2 enrichment and drought stress on maize growth. Eur J Agron 52:11–21

    Article  Google Scholar 

  • McCarl BA, Villavicencio X, Wu X (2008) Climate change and future analysis: is stationarity dying? Am J Agric Econ 20:1241–1247

    Article  Google Scholar 

  • Mearns LO, Katz RW, Schneider SH (1984) Extreme high-temperature events: changes in their probabilities with changes in mean temperature. J Clim Appl Meteorol 23:1601–1613

    Article  Google Scholar 

  • NOAA (2012a) The U.S. climate division dataset. http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries1.pl Accessed 1 May 2012

  • NOAA (2012b) NNDC climate data online. NOAA Satellite and Information Service and National Climate Data Center. http://www7.ncdc.noaa.gov/CDO/cdoselect.cmd?datasetabbv=GSOD&countryabbv=US&georegionabbv=&resolution=40 Accessed 1 May 2012

  • NOAA (2012c) Cold and warm episodes by season. Climate prediction center, national weather service. http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml Accessed 10 April 2012

  • NOAA (2012d) Trends in atmospheric carbon dioxide. Global monitoring division, earth system research laboratory. http://www.esrl.noaa.gov/gmd/ccgg/trends/ Accessed 1 September 2012

  • Ottman MJ, Kimball BA, Pinter PJ et al (2001) Elevated CO2 increases sorghum biomass under drought conditions. New Phytol 150:261–273

    Article  Google Scholar 

  • Palmer WC (1965) Meteorological drought. Research paper No. 45. office of climatology, U.S. Weather Bureau, Washington

    Google Scholar 

  • Parry ML, Rosenzweig C, Iglesias A, Livermore M, Fischer G (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob Environ Chang 14(1):53–67

    Article  Google Scholar 

  • Phillips J, Rajagopalanb B, Caneb M, Rosenzweiga C (1999) The role of ENSO in determining climate and maize yield variability in the U.S. Cornbelt. Int J Climatol 19:877–888

    Article  Google Scholar 

  • Poorter H (1993) Interspecific variation in the growth response of plants to an elevated ambient CO2 concentration. Plant Ecol 104(1):77–97

    Article  Google Scholar 

  • Pregibon D (1980) Goodness of link tests for generalized linear models. Appl Stat 29:15–24

    Article  Google Scholar 

  • Reilly JM, Graham J, Abler DG et al (2002) Changing climate and changing agriculture: report of the agricultural sector assessment team. Cambridge University Press, UK

    Google Scholar 

  • Roberts MJ, Schlenker W (2010) Is agricultural production becoming more or less sensitive to extreme heat? Evidence from US corn and soybean yields. NBER Working Paper No. 16308

  • Rose SK, McCarl BA (2008) Greenhouse gas emissions, stabilization and the inevitability of adaptation: challenges for agriculture. Choices 23(1):15–18

    Google Scholar 

  • Saha A, Havenner A, Talpaz H (1997) Stochastic production function estimation: small sample properties of ML versus FGLS. Appl Econ 29:459–469

    Article  Google Scholar 

  • Schlenker W, Lobell DB (2010) Robust negative impacts of climate change on African agriculture. Environ Res Lett 5(1):014010

    Article  Google Scholar 

  • Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc Natl Acad Sci 106(37):15594–15598

    Article  Google Scholar 

  • Semenov MA, Porter JR (1995) Climatic variability and the modelling of crop yields. Agr Forest Meteorol 73:265–283

    Article  Google Scholar 

  • Tack JB, Harri A, Coble KH (2012) More than mean effects: modeling the effect of climate on the higher order moments of crop yields. Am J Agric Econ 94(5):1037–1054

    Article  Google Scholar 

  • Thompson LM (1975) Weather variability, climate change, and grain production. Science 188:535–541

    Article  Google Scholar 

  • Timmermann A, Oberhuber J, Bacher A et al (1999) Increased El Niño frequency in a climate model forced by future greenhouse warming. Nature 398(April):694–697

    Google Scholar 

  • Tubiello FN, Jagtap S, Rosenweig C, Goldberg R, Jones JW (2002) Effects of climate change on U.S. crop production: simulation results using two different GCM scenarios. Part I: wheat, potato, corn, and citrus. Climate Res 20(3):259–270

    Article  Google Scholar 

  • Tubiello FN et al (2007) Crop response to elevated CO2 and world food supply: a comment on “Food for thought…” by Long et al., Science 312:1918–1921, 2006. Eur J Agron 26(3):215–223

    Article  Google Scholar 

  • USDA-NASS (2012) Data and statistics—quick stats. http://www.nass.usda.gov/Data_and_Statistics/Quick_Stats/index.asp

  • Yang L, Liu H, Wang Y, Zhu J, Huang J, Liu G, Dong G, Wang Y (2009) Yield formation of CO2-enriched intersubspecific hybrid rice cultivar Liangyoupeijiu under fully open-air field condition in a warm sub-tropical climate. Agric Ecosyst Environ 129:193–200

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Economics, Faculty of Economics, Kasetsart University, 50 Phahonyothin Rd., Chatuchak, Bangkok, Thailand, 10900

    Witsanu Attavanich

  2. Department of Agricultural Economics, Texas A&M University, 2124 TAMU, College Station, TX, 77843-2124, USA

    Bruce A. McCarl

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  1. Witsanu Attavanich
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  2. Bruce A. McCarl
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Correspondence to Witsanu Attavanich.

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Attavanich, W., McCarl, B.A. How is CO2 affecting yields and technological progress? A statistical analysis. Climatic Change 124, 747–762 (2014). https://doi.org/10.1007/s10584-014-1128-x

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