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Climate Change and Food Security pp 85–98Cite as

Crop Responses to Climate: Time-Series Models

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Part of the book series: Advances in Global Change Research ((AGLO,volume 37))

Abstract

Time series of annual crop production levels, at scales ranging from experimental trials to regional production totals, are widely available and represent a useful opportunity to understand crop responses to weather variations. This chapter discusses the main techniques of building models from time series and the tradeoffs involved in the many decisions required in the process. A worked example using United States maize production is used to illustrate key concepts.

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Notes

  1. 1.

    Footnote 2

  2. 2.

    However, note that if the yield anomalies in Fig. 5.1b showed no sign of heteroskedasticity, then introducing the log transformation could lead to heteroskedasticity by suppressing values at the beginning of the record.

  3. 3.

    Footnote 4

  4. 4.

    The recent IPCC Fourth Assessment Report states: “Projected changes in the frequency and severity of extreme climate events will have more serious consequences for food and forestry production, and food insecurity, than will changes in projected means of temperature and precipitation (high confidence).”

  5. 5.

    Footnote 6

  6. 6.

    Extremely low and high values are nearly always bad for crops, so that the optimum value is found somewhere between.

References

  • Chatfield C (1996) The analysis of time series. CRC, Boca Raton, FL

    Google Scholar 

  • Easterling W, Aggarwal P, Batima P, Brander K, Erda L, Howden M, Kirilenko A, Morton J, Soussana JF, Schmidhuber J, Tubiello F (2007). Chapter 5: food, fibre, and forest products. in: climate change 2007: impacts, adaptation and vulnerability contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK and New York, NY, USA

    Google Scholar 

  • Frost C, Thompson SG (2000) Correcting for regression dilution bias: comparison of methods for a single predictor variable. J R Stat Soc A Sta 163(2):173–189

    Article  Google Scholar 

  • Hansen L (1991) Farmer response to changes in climate: the case of corn production. J Agr Econ Res 43(4):18–25

    Google Scholar 

  • Hu Q, Buyanovsky G (2003) Climate effects on corn yield in Missouri. J. Appl Meteorol 42(11):1626–1635

    Article  Google Scholar 

  • Lobell DB, Ortiz-Monasterio JI (2007) Impacts of day versus night temperatures on spring wheat yields: a comparison of empirical and CERES model predictions in three locations. Agron J 99(2):469–477

    Article  Google Scholar 

  • Lobell DB, Field CB, Cahill KN, Bonfils C (2006) Impacts of future climate change on California perennial crop yields: model projections with climate and crop uncertainties. Agric For Meteorol 141(2–4):208–218

    Article  Google Scholar 

  • Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319(5863):607–610

    Article  CAS  Google Scholar 

  • Peng S, Huang J, Sheehy J, Laza R, Visperas R, Zhong X, Centeno G, Khush G, Cassman K (2004) Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci U S A 101(27):9971–9975

    Article  CAS  Google Scholar 

  • Porter JR, Semenov MA (2005) Crop responses to climatic variation. Philos Trans R Soc B-Biol Sci 360(1463):2021–2035

    Article  Google Scholar 

  • Schlenker W, Roberts MJ (2006) Nonlinear effects of weather on corn yields. Rev Agric Econ 28(3):391–398

    Article  Google Scholar 

  • Sheehy JE, Mitchell PL, Ferrer AB (2006) Decline in rice grain yields with temperature: models and correlations can give different estimates. Field Crop Res 98(2–3):151–156

    Article  Google Scholar 

  • Thompson LM (1986) Climatic-change; weather variability; and corn production. Agron J 78(4):649–653

    Article  Google Scholar 

  • Zhang T, Zhu J, Yang X, Zhang X (2008) Correlation changes between rice yields in North and Northwest China and ENSO from 1960 to 2004. Agric For Meteorol 148(6–7):1021–1033

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    David Lobell

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  1. David Lobell
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Editors and Affiliations

  1. Stanford University, CA, USA

    David Lobell  & Marshall Burke  & 

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Lobell, D. (2010). Crop Responses to Climate: Time-Series Models. In: Lobell, D., Burke, M. (eds) Climate Change and Food Security. Advances in Global Change Research, vol 37. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2953-9_5

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