Skip to main content
SpringerLink
Log in

Crop Response to Climate: Ecophysiological Models

  • Chapter
  • First Online:
Climate Change and Food Security

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

Abstract

To predict the possible impacts of global warming and increased CO2 on agriculture, scientists use computer-based models that attempt to quantify the best-available knowledge on plant physiology, agronomy, soil science and meteorology in order to predict how a plant will grow under specific environmental conditions. The chapter reviews the basic features of crop models with emphasis on physiological responses to temperature and CO2 and explains how models are used to predict potential impacts of climate change, including options for adaptation. The closing section reviews major issues affecting the reliability of model-based predictions. These include the need for accurate inputs, the challenges of improving the underlying physiological knowledge, and the need to improve representations of genetic variation that likely will affect adaptation to climate change.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adejuwon JO (2006) Food crop production in Nigeria. II. Potential effects of climate change. Climate Res 32:229–245

    Article  Google Scholar 

  • Ainsworth EA et al. (2008) Next generation of elevated [CO2] experiments with crops: a critical investment for feeding the future world. Plant Cell Environ 31:1317–1324

    Article  CAS  Google Scholar 

  • Alexandrov VA, Hoogenboom G (2000) Vulnerability and adaptation assessments of agricultural crops under climate change in the Southeastern USA. Theor Appl Climatol 67:45–63

    Article  Google Scholar 

  • Alexandrov V, Eitzinger J, Cajic V, Oberforster M (2002) Potential impact of climate change on selected agricultural crops in north-eastern Austria. Glob Change Biol 8:372–389

    Article  Google Scholar 

  • Allen RG (1986) A Penman for all seasons. J Irr Drain Eng 112:348–368

    Article  Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1988) Crop evapotranspiration – guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. Rome, Italy

    Google Scholar 

  • Andales AA, Batchelor WD, Anderson CE (2000) Incorporating tillage effects into a soybean model. Agric Syst 66:69–98

    Article  Google Scholar 

  • Anothai J, Patanothai A, Pannangpetch K, Jogloy S, Boote KJ, Hoogenboom G (2008a) Reduction in data collection for determination of cultivar coefficients for breeding applications. Agric Syst 96:195–206

    Article  Google Scholar 

  • Anothai J, Patanothai A, Jogloy S, Pannangpetch K, Boote KJ, Hoogenboom G (2008b) A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of crop performance trials. Field Crop Res 108:169–178

    Article  Google Scholar 

  • Anwar MR, O’Leary G, McNeil D, Hossain H, Nelson R (2007) Climate change impact on rainfed wheat in south-eastern Australia. Field Crop Res 104:139–147

    Article  Google Scholar 

  • Asseng S, Keating BA, Fillery IRP, Gregory PJ, Bowden JW, Turner NC, Palta JA, Abrecht DG (1998) Performance of the APSIM-wheat model in Western Australia. Field Crops Res 57:163–179

    Article  Google Scholar 

  • Bindraban PS (1999) Impact of canopy nitrogen profile in wheat on growth. Field Crop Res 63:63–77

    Article  Google Scholar 

  • Boote KJ, Pickering NB (1994) Modeling photosynthesis of row crop canopies. Hortscience 29:1423–1434

    Google Scholar 

  • Boote KJ, Minguez MI, Sau F (2002) Adapting the CROPGRO legume model to simulate growth of faba bean. Agron J 94:743–756

    Article  Google Scholar 

  • Bostick WM, Koo J, Walen VK, Jones JW, Hoogenboom G (2004) A web-based data exchange system for crop model applications. Agron J 96:853–856

    Article  Google Scholar 

  • Brassard J-P, Singh B (2008) Impacts of climate change and CO2 increase on agricultural production and adaptation options for southern Quebec, Canada. Mitigation and adaptation strategies for global change. Climate Res 13:241–265

    Google Scholar 

  • Bunce J (2005) Seed yield of soybeans with daytime or continuous elevation of carbon dioxide under field conditions. Photosynthetica 43:435–438

    Article  Google Scholar 

  • Crafts-Brandner SJ, Salvucci ME (2004) Analyzing the impact of high temperature and CO2 on net photosynthesis: biochemical mechanisms, models and genomics. Field Crop Res 90:75–85

    Google Scholar 

  • De Wit CT (1965) Photosynthesis of leaf canopies. Agricultural research report 663. Pudoc, Wageningen

    Google Scholar 

  • Desjardins RL, Allen LH, Lemon ER (1978) Variations of carbon dioxide, air temperature, and horizontal wind within and above a maize crop. Boundary-Layer Meteorol 14:369–380

    Article  Google Scholar 

  • Duncan WG, Loomis RS, Williams WA, Hanau R (1967) A model for simulating photosynthesis in plant communities. Hilgardia 38:181–205

    Google Scholar 

  • Easterling WE, Mearns LO, Hays CJ, Marx D (2001) Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: part II. Accounting for adaptation and CO2 direct effects. Climatic Change 51:173–197

    Article  CAS  Google Scholar 

  • Easterling WE et al (2007) Food, fibre and forest products. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) 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, pp 273–313

    Google Scholar 

  • Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149:78–90

    Article  CAS  Google Scholar 

  • Gitay H, Brown S, Easterling W, Jallow B (2001) Ecosystems and their goods and services. In: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Climate change 2001: impacts, adaptation, and vulnerability. Third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK

    Google Scholar 

  • Godwin DC, Singh U (1998) Cereal growth, development and yield. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer, Dordrecht, the Netherlands

    Google Scholar 

  • Grant RF, Kimball BA, Brooks TJ, Wall GW, Pinter PJ Jr, Hunsaker DJ, Adamsen FJ, Lamorte RL, Leavitt SW, Thompson TL, Matthias AD (2001) Modeling interactions among carbon dioxide, nitrogen, and climate on energy exchange of wheat in a free air carbon dioxide experiment. Agron J 93:638–649

    Article  Google Scholar 

  • Grimm SS, Jones JW, Boote KJ, Hesketh JD (1993) Parameter estimation for predicting flowering date of soybean cultivars. Crop Sci 33:137–144

    Article  Google Scholar 

  • Guerra LC, Hoogenboom G, Garcia y Garcia A, Banterng P, Beasley Jr JP (2008) Determination of cultivar coefficients for the CSM-CROPGRO-Peanut model using variety trial data. Trans ASAE 51:1471–1481

    Google Scholar 

  • Hammer G et al (2006) Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci 11:587–593

    Article  CAS  Google Scholar 

  • Hanks J, Ritchie JT (1991) Modeling plant and soil systems. ASSA, CSSA, SSSA, Madison, WI

    Google Scholar 

  • Hartkamp AD, Hoogenboom G, White JW, Gilbert R, Benson T, Barreto HJ, Gijsman A, Tarawali S, Bowen W (2002) Adaptation of the CROPGRO growth model to velvet bean as a green manure cover crop: II. Model testing and evaluation. Field Crop Res 78:27–40

    Article  Google Scholar 

  • Hay R, Porter J (2006) The physiology of crop yield, 2nd edn. Blackwell, Oxford, UK

    Google Scholar 

  • Hoogenboom G, White JW (2003) Improving physiological assumptions of simulation models by using gene-based approaches. Agron J 95:82–89

    Article  Google Scholar 

  • Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Batchelor WD, Hunt LA, Boote KJ, Singh U, Uryasev O, Bowen WT, Gijsman AJ, du Toit A, White JW, Tsuji GY (2004) Decision support system for agrotechnology transfer version 4.0 [CD-ROM]. University of Hawaii, Honolulu, HI

    Google Scholar 

  • Hunt LA, White JW, Hoogenboom G (2001) Agronomic data: advances in documentation and protocols for exchange and use. Agric Syst 70:477–492

    Article  Google Scholar 

  • Jamieson PD, Brooking IR, Semenov MA, McMaster GS, White JW, Porter JR (2007) Reconciling alternative models of phenological development in winter wheat. Field Crop Res 103:36–41

    Article  Google Scholar 

  • Jones PG, Thornton PK (2003) The potential impacts of climate change on maize production in Africa and Latin America in 2055. Global Environ Chang 13:51–59

    Article  Google Scholar 

  • Jones CA, Bland WL, Ritchie JT, Williams JR (1991) Simulation of root growth. In: Hanks J, Ritchie JT (eds) Modeling plant and soil systems. ASA-CSSA-SSSA, Madison, WI, pp 91–123

    Google Scholar 

  • Jones JW et al (2003) The DSSAT cropping system model. Eur J Agron 18:235–265

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Loomis RS, Rabbinge R, Ng E (1979) Explanatory models in crop physiology. Annu Rev Plant Physiol 30:339–367

    Article  Google Scholar 

  • Mavromatis T, Boote KJ, Jones JW, Irmak A, Shinde D, Hoogenboom G (2001) Developing genetic coefficients for crop simulation models with data from crop performance trials. Crop Sci 41:40–51

    Article  Google Scholar 

  • Mavromatis T, Boote KJ, Jones JW, Wilkerson GG, Hoogenboom G (2002) Repeatability of model genetic coefficients derived from soybean performance trails across different states. Crop Sci 42:76–89

    Article  Google Scholar 

  • Messina CD, Jones JW, Boote KJ, Vallejos CE (2006) A gene-based model to simulate soybean development and yield responses to environment. Crop Sci 46:456–466

    Article  CAS  Google Scholar 

  • Minguez MI, Ruiz Ramos M, Diaz Ambrona CH, Quemada M, Sau F (2007) First-order impacts on winter and summer crops assessed with various high-resolution climate models in the Iberian Peninsula. Climatic Change 81:343–355

    Article  Google Scholar 

  • Monteith JL, Unsworth MH (1990) Principles of environmental physics. Edward Arnold, London

    Google Scholar 

  • Passioura JB (1996) Simulation models: science, snake oil, education, or engineering? Agron J 88:690–694

    Article  Google Scholar 

  • Penning De Vries FWT, Brunsting AHM, Van Laar HH (1974) Products, requirements and efficiency of biosynthesis a quantitative approach. J Theor Biol 45:339–377

    Article  CAS  Google Scholar 

  • Porter JR, Gawith M (1999) Temperatures and the growth and development of wheat: a review. Eur J Agron 10:23–36

    Article  Google Scholar 

  • Reekie JYC, Hickleton PR, Reekie EG (1994) Effects of elevated CO2 on time to flowering in four short-day and four long-day species. Can J Bot 72:533–538

    Article  Google Scholar 

  • Reynolds JF, Acock B (1985) Predicting the response of plants to increasing carbon dioxide: a critique of plant growth models. Ecol Model 29:107–129

    Article  Google Scholar 

  • Reynolds JF, Acock B (1997) Modularity and genericness in plant and ecosystem models: modularity in plant models. Ecol Model 94:7–16

    Article  Google Scholar 

  • Ritchie JT (1998) Soil water balance and plant water stress. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer, Dordrecht, the Netherlands, pp 41–54

    Google Scholar 

  • Ritchie JT, NeSmith DS (1991) Temperature and crop development. In: Hanks J, Ritchie JT (eds) Modeling plant and soil systems. ASSA, CSSA, SSSA, Madison, WI, pp 5–30

    Google Scholar 

  • Rosenzweig C (1985) Potential CO2-induced climate effects on North American wheat-producing regions. Climatic Change 7:367–389

    Article  Google Scholar 

  • Singh U, Matthews RB, Griffin TS, Ritchie JT, Hunt LA, Goenaga JT (1998) Modeling growth and development of root and tuber crops. In: Tsuji GY, Hoogenboom G, Thornton PK (eds)Understanding options for agricultural production. Kluwer, Dordrecht, the Netherlands, pp 1

    Google Scholar 

  • Soler CMT, Sentelhas PC, Hoogenboom G (2007) Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. Eur J Agron 27:165–177

    Article  Google Scholar 

  • Soler CMT, Maman N, Zhang X, Mason SC, Hoogenboom G (2008) Determining optimum planting dates for pearl millet for two contrasting environments using a modelling approach. J Agric Sci 146:445–459

    Article  Google Scholar 

  • Spaeth SC, Sinclair TR (1985) Linear increase in soybean harvest index during seed-filling. Agron J 77:207–211

    Article  Google Scholar 

  • Stockle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. Eur J Agron 18:289–307

    Article  Google Scholar 

  • Sung S, Amasino RM (2004) Vernalization and epigenetics: how plants remember winter. Curr Opin Plant Biol 7:4–10

    Article  CAS  Google Scholar 

  • Thomas JF, Harvey CN (1983) Leaf anatomy of four species grown under long-term continuous CO2 enrichment. Bot Gaz 144:303–309

    Article  Google Scholar 

  • Tingem MR, Rivington M, Bellocchi G, Azam-Ali S, Colls J (2008) Effects of climate change on crop production in Cameroon. Climate Res 36:65–77

    Article  Google Scholar 

  • Tsuji GY, Hoogenboom G, Thornton PK (eds.) (1998) Understanding options for agricultural production, Kluwer, Dordrecht, the Netherlands

    Google Scholar 

  • Tubiello FN, Amthor JS, Boote KJ, Donatelli M, Easterling W, Fischer G, Gifford RM, Howden M, Reilly J, Rosenzweig C (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 26:215–223

    Article  CAS  Google Scholar 

  • von Caemmerer (2000) Biochemical models of leaf photosynthesis. CSIRO, Collingwood, Australia

    Google Scholar 

  • White JW, Hoogenboom G (1996) Simulating effects of genes for physiological traits in a process-oriented crop model. Agron J 88:416–422

    Article  Google Scholar 

  • White JW, Hoogenboom G (2005) Integrated viewing and analysis of phenotypic, genotypic, and environmental data with “GenPhEn Arrays”. Eur J Agron 23:170–182

    Article  Google Scholar 

  • White JW, Hoogenboom G, Jones JW, Boote KJ (1995) Evaluation of the dry bean model Beangro V1.01 for crop production research in a tropical environment. Exp Agric 31:241–254

    Article  Google Scholar 

  • White JW, Hoogenboom G, Hunt LA (2005) A structured procedure for assessing how crop models respond to temperature. Agron J 97:426–439

    Article  Google Scholar 

  • White JW, Boote KJ, Hoogenboom G, Jones PG (2007) Regression-based evaluation of ecophysiological models. Agron J 99:419–427

    Article  Google Scholar 

  • White JW, Herndl M, Hunt LA, Payne TS, Hoogenboom G (2008) Simulation-based analysis of effects of Vrn and Ppd loci on flowering in wheat. Crop Sci 48:678–687

    Article  Google Scholar 

  • Williams JR, Jones CA, Kiniry JR, Spanel DA (1989) The EPIC crop growth model. Trans ASAE 32:497–511

    Google Scholar 

  • Zhang X-C (2005) Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agricultural and Forest Meteorology 135:215–229

    Google Scholar 

  • Ziska LH, Bunce JA (2007) Predicting the impact of changing CO2 on crop yields: some thoughts on food. New Phytol 175:607–618

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. US Arid Land Agricultural Research Center, USDA-ARS, 21881 North Cardon Lane, Maricopa, AZ, 85224, USA

    Jeffrey W. White

  2. Biological and Agricultural Engineering, The University of Georgia, 165 Gordon Futral Court, Griffin, GA, 30223-1731, USA

    Gerrit Hoogenboom

Authors
  1. Jeffrey W. White
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerrit Hoogenboom
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeffrey W. White .

Editor information

Editors and Affiliations

  1. Stanford University, CA, USA

    David Lobell  & Marshall Burke  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

White, J.W., Hoogenboom, G. (2010). Crop Response to Climate: Ecophysiological 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_4

Download citation

Publish with us

Policies and ethics

Search

Navigation