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Changing yields in the Central United States under climate and technological change

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Abstract

This paper projects the race between technologically driven increases in crop yields and changing climatic conditions in the central USA, one of the world’s most productive agricultural regions. Using the highest, average, and lowest decadal rates of technologically driven increases in crop yields over the 1980 to 2017 period, we develop spatially explicit yield scenarios to the end of the twenty-first century under RCP4.5 and RCP8.5. We find that with static technological innovation, severe climate change will decrease yields by an average of 22.4% (26.1 bu. ac−1) for maize, 27.9% (8.83 bu. ac−1) for soybeans, and 20% (7.14 bu. ac−1) for winter wheat in the central USA; however, with even the lowest rates of technological yield growth, yields increase by an average of 25.0% (40.5 bu. ac−1) for maize and 30.2% (14.2 bu. ac−1) for soybeans. We conclude that technology has the potential to overcome the negative impacts of climate change on the yields of maize, soybeans, and winter wheat in the central USA, but if these increases are to be environmentally sustainable, technological developments must be information-intensive rather than input-intensive.

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References

  • Alston JM, Anderson MA, James JS, Pardey PG (2010) Persistence pays: U.S. agricultural productivity growth and the Benefits from Public R&D Spending. Springer, New York

    Book  Google Scholar 

  • Amundson R, Berhe AA, Hopmans JW, Olson C, Sztein AE, Sparks DL (2015) Soil and human security in the 21st century. Science 348:1261071–1261071

    Article  Google Scholar 

  • Attavanich W, McCarl BA (2014) How is CO 2 affecting yields and technological progress? A statistical analysis. Clim Chang 124(4):747–762

    Article  Google Scholar 

  • Bador M, Donat MG, Geoffroy O, Alexander LV (2018) Assessing the robustness of future extreme precipitation intensification in the CMIP5 ensemble. J Clim 31:6505–6525. https://doi.org/10.1175/JCLI-D-17-0683.1

    Article  Google Scholar 

  • Bentsen M et al (2013) The Norwegian earth system model, NorESM1-M – part I: description and basic evaluation of the physical climate. Geosci Model Dev 6:687–720. https://doi.org/10.5194/gmd-6-687-2013

    Article  Google Scholar 

  • Bita CE, Gerats T (2013) Plant tolerance to high temperature in a changing environment: scientific fundamentals and production of heat stress-tolerant crops. Front Plant Sci. https://doi.org/10.3389/fpls.2013.00273

  • Blanc É (2017) Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models. Agricultural and Forest Meteorology 236:145–161

  • Blanc E, & Sultan B (2015) Emulating maize yields from global gridded crop models using statistical estimates. Agricultural and Forest Meteorology 214, 134–147

  • Bongiovanni R, Lowenberg-Deboer J (2004) Precision agriculture and sustainability. Precis Agric 5:359–387

    Article  Google Scholar 

  • Brookes G, Barfoot P (2016) GM crops: global socio-economic and environmental impacts 1996–2014. PG Economics Ltd, Dorchester

    Google Scholar 

  • Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Naeem S (2012) Biodiversity loss and its impact on humanity. Nature 486(7401):59–67. https://doi.org/10.1038/nature11148

    Article  Google Scholar 

  • Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N (2014) A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang 4:287–291

    Article  Google Scholar 

  • Corn and Soybean Digest, 2018. https://www.cornandsoybeandigest.com/soybeans/georgia-producer-sets-new-world-soybean-yield-record. Accessed 9–26-18

  • Craine JM, Elmore AJ, Wang L, Aranibar J, Bauters M, Boeckx P, Zmudczyńska-Skarbek K (2018) Isotopic evidence for oligotrophication of terrestrial ecosystems. Nature Ecology & Evolution 2(11):1735. https://doi.org/10.1038/s41559-018-0694-0

    Article  Google Scholar 

  • Dufresne J-L et al (2012) Climate change projections using the IPSL-CM5 earth system model: from CMIP3 to CMIP5. Clim Dyn 40:2123–2165. https://doi.org/10.1007/s00382-012-1636-1

    Article  Google Scholar 

  • Eilers PHC, Marx BD (1996) Flexible smoothing with B-splines and penalties. Stat Sci 11(2):89–121

    Article  Google Scholar 

  • Elser J, Bennett E (2011) Phosphorus cycle: a broken biogeochemical cycle. Nature 478:29–31

    Article  Google Scholar 

  • Evans LT (1993) Crop evolution, adaptation and yield. Cambridge University Press, Cambridge

    Google Scholar 

  • Fargione JE, Plevin RJ, Hill JS (2010) The ecological impact of biofuels. Annu Rev Ecol Evol Syst 41:351–377. https://doi.org/10.1146/annurev-ecolsys-102209-144720

    Article  Google Scholar 

  • Farmer JD, LaFond F (2016) Hoe predictable is technological progress? Res Policy 45:647–665

    Article  Google Scholar 

  • Fischer, T., D. Byerlee, G. Edmeasdes, 2014. Crop yields and global food security. Australian Centre for International Agriculktural Research

  • Fuglie K (2017) R&D capital, R&D spillovers, and productivity growth in world agriculture. Appl Econ Perspect Policy 40(3):421–444. https://doi.org/10.1093/aepp/ppx045

    Article  Google Scholar 

  • Hillier J, Hawes C, Squire G, Hilton A, Wale S, Smith P (2009) The carbon footprint of food crop production. Int J Agric Sustain 7:107–118. https://doi.org/10.3763/ijas.2009.0419

    Article  Google Scholar 

  • Hooper DU, Adair EC, Cardinale BJ, Byrnes JEK, Hungate BA, Matulich KL et al (2012) A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486(7401):105–108. https://doi.org/10.1038/nature111 https://doi.org/10.1111/j.1365-2664.2006.01270.x

    Article  Google Scholar 

  • James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning (Vol. 112, p. 18). Springer, New York

    Book  Google Scholar 

  • Khatodia, S., K. Bhatotia, N. Passricha, S.M.P. Khurana and N. Tuteja, 2016. The CRISPR/Cas Gemone-editing Tool: Application in Improvement of Crops. Front Plant Sci doi:https://doi.org/10.3389/pls.2016.00506

  • Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199

    Article  Google Scholar 

  • Konikow LF (2013) Groundwater depletion in the United States (1900−2008). US Geol Surv Sci Investig Rep 2013−5079:63 p. http://pubs.usgs.gov/sir/2013/5079

  • Liang XZ, You W, Chambers RG, Schmoldt DL, Gao W, Liu C, Liu YA, Sun C, Kennedy JA (2017) Determining climate effects on US total agricultural productivity. Proc Natl Acad Sci 114(12), E2285–E2292. https://doi.org/10.1073/pnas.1615922114

  • Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:613–616

    Article  Google Scholar 

  • Mann CC (2018) The wizard and the prophet. Knopf, New York

    Google Scholar 

  • Mascioli NR, Fiore AM, Previdi M, Correa M (2016) Temperature and precipitation extremes in the United States: quantifying the responses to anthropogenic aerosols and greenhouse gases. J Clim 29:2689–2701. https://doi.org/10.1175/JCLI-D-15-0478.1

    Article  Google Scholar 

  • Mesonet. (2017). agweather connection. [online] Available at: https://www.mesonet.org/mesonet_connection/V2_No8.pdf [Accessed 2 Nov. 2017]

  • Miller, P., Lanier, W., and Brandt, S. (2001). Using growing degree days to predict plant stages. AgExtension Commun. Coord. Communication. Serv. Mont. State Univ.-Bozeman Bozeman MT.

  • Moore FC, Lantz U, Baldos C, Hertel T (2017) Economic impacts of climate change on agriculture: a comparison of process-based and statistical yield models. Environ Res Lett 12:065008

    Article  Google Scholar 

  • National Corn Growers Association 2018. https://www.dtnpf.com/agriculture/web/ag/news/crops/article/2017/12/19/hula-sets-new-world-corn-yield-542 . Accessed 5–26-18

  • NDAWN: North Dakota Agricultural Weather Network. (2017). Corn Growing Degree Days. [online] Available at: https://ndawn.ndsu.nodak.edu/help-corn-growing-degree-days.html [Accessed 2 Nov. 2017]

  • PRISM Climate Group (2004). PRISM Climate Data. Available at: http://www.prism.oregonstate.edu/

  • R Core Team (2017). R: a language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. URL https://www.R-project.org/

  • Rabalais NN, Turner RE, Wiseman WJ Jr (2002) Gulf of Mexico hypoxia, a.K.a. “the dead zone”. Annu Rev Ecol Syst 33:235–263

    Article  Google Scholar 

  • Ramakutty N, Evan AT, Manfreda C, Foley JA (2008) Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob Biogeochem Cycles. https://doi.org/10.1029/2007GB002952

  • Ray DK, Ramankutty N, Mueller ND, West PC, Foley JA (2012) Recent patterns of crop yield growth and stagnation. Nat Commun 3(1). https://doi.org/10.1038/ncomms2296

  • Ray DK, Gerber JS, MacDonald GK, West PC (2015) Climate variation explans a third of global crop yield variability. Nat Commun 6. https://doi.org/10.1038/ncomms6989

  • Rocheta E, Sugiyanto M, Johnson F, Evans J, Sharma A (2014) How well do general circulation models represent low-frequency variability? Water Resour Res 50:2108–2123

    Article  Google Scholar 

  • Rockström J, Steffen W, Noone K, Persson Å, Chapin FS, Lambin EF, Foley JA (2009) A safe operating space for humanity. Nature 461(7263):472–475. https://doi.org/10.1038/461472a

    Article  Google Scholar 

  • Rosenzwieg C, Tubiello FN, Goldberg R, Mills E, Bloomfield J (2002) Increased crop damage in the US from excess precipitation under climate change. Glob Environ Chang 12:197–202

    Article  Google Scholar 

  • Schauberger B, Archontoulis S, Arneth A, Balkovic J, Ciais P, Deryng D, Elliott J, Folberth C, Khabarov N, Müller C et al (2017) Consistent negative response of US crops to high temperatures in observations and crop models. Nat Commun 8:13931

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Schlesinger WH, Bernhardt ES (2013) Biogeochemistry: an analysis of global change. Academic Press, San Diego, CA

    Google Scholar 

  • Schoof JT (2015) High-resolution projections of 21st century daily precipitation for the contiguous U.S. J Geophys Res-Atmos 120:3029–3042

    Article  Google Scholar 

  • Schoof JT, Pryor SC, Robeson SM (2007) Downscaling daily maximum and minimum temperatures in the Midwestern USA: a hybrid empirical approach. Int J Climatol 27:439–454

    Article  Google Scholar 

  • Stoebner TJ, Lant CL (2014) Geographic determinants of rural land covers and the agricultural margin in the Central United States. Appl Geogr 55:138–154

    Article  Google Scholar 

  • Swift MJ, Izac A-MN, van Noordwijk M (2004) Biodiversity and ecosystem services in agricultural landscapes—are we asking the right questions? Agriculture. Ecosyst Environ 104(1):113–134. https://doi.org/10.1016/j.agee.2004.01.013

    Article  Google Scholar 

  • Tainter, JAD, Strumsky, TG Taylor, M Arnold, J Lobo 2018. Depletion vs. innovation: the fundamental question of sustainability. Pp65-93 in Burlando, R and A. Tartaglia (eds.) Physical Limits to Economic Growth: Perspectives of economic, social, and complexity science. Routledge: New York

  • Tester M, Landridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327:818–822

    Article  Google Scholar 

  • Tilman D, Balzer C, Hill J, Befort BL (2011) Global food demand and the sustainable intensification of agriculture. Proc Natl Acad Sci 108:20260–20264. https://doi.org/10.1073/pnas.1116437108

    Article  Google Scholar 

  • Troy TJ, Kipgen C, Pal I (2015) The impact of climate extremes and irrigation on US crop yields. Environ Res Lett 10(5):054013. https://doi.org/10.1088/1748-9326/10/5/054013

    Article  Google Scholar 

  • USDA-FAS, United States Departmental of Agriculture, Foreign Agricultural Service. (2017). Grain: World Markets and Trade

  • USDA National Agricultural Statistics Service Cropland Data Layer. 2019. Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/. (Accessed August 2018). USDA-NASS, Washington, DC.

  • USGCRP (2017) In: Wuebbles DJ, Fahey DW, Hibbard KA, Dokken DJ, Stewart BC, Maycock TK (eds) Climate science special report: fourth National Climate Assessment, volume 1. U.S. Global Change Research Program, Washington, DC, 470pp

    Google Scholar 

  • Svitashev S, Schwartz C, Lenderts B, Young JK, & Cigan AM (2016) Genome editing in maize directed by CRISPR–Cas9 ribonucleoprotein complexes. Nature communications 7, 13274

  • van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31. https://doi.org/10.1007/s10584-011-0148-z

    Article  Google Scholar 

  • Wang S, Heisey P, Schimmelpfennig D, Ball, E (2015) Agricultural productivity growth in the United States: Measurement, trends, and drivers. USDA Economic Research Report No. (ERR-189). https://www.ers.usda.gov/publications/pub-details/?pubid=45390

  • Wilks DS (1999) Multisite downscaling of daily precipitation with a stochastic weather generator. Clim Res 11:125–136

    Article  Google Scholar 

  • Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J Royal Stat Soc(B) 73(1):3–36

    Article  Google Scholar 

  • Yukimoto S et al (2012) A new global climate model of the meteorological research institute: MRI-CGCM3 – model description and basic performance. J Meteorol Soc Jpn 90A:23–64

    Article  Google Scholar 

  • Zhang W, Ricketts TH, Kremen C, Carney K, Swinton SM (2007) Ecosystem services and dis-services to agriculture. Ecol Econ 64(2):253–260. https://doi.org/10.1016/j.ecolecon.2007.02.024

    Article  Google Scholar 

  • Zhao C, Liu B, Piao S, Wang X, Lobell DB, Huang Y, ... & Durand JL. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences, 114(35), 9326–9331.

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Burchfield, E., Matthews-Pennanen, N., Schoof, J. et al. Changing yields in the Central United States under climate and technological change. Climatic Change 159, 329–346 (2020). https://doi.org/10.1007/s10584-019-02567-7

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