Abstract
The decision-making environment of farmers in Quebec is very complex as in most agricultural territories. In their decision-making process, farmers must deal not only with a set of socio-economic factors at different spatial and temporal scales, but also with biophysical elements likely to have a significant influence on the level of agricultural productivity. Among these, climatic conditions are fundamental to the development, growth and yield of crops. In a context of climate change and variability mainly characterized by rising temperatures and more frequent extreme events, climate conditions will become increasingly important in any process of informed decision-making on the farm.
From this perspective, the objective of this paper is twofold: (1) to present the potential impacts of climate change on corn yields for two municipalities in the West Monte´re´gie region of the province of Quebec; and (2) in the light of analyses carried out on some phenological stages of corn, to focus on some adaptation options likely to reduce the impact of anticipated climate conditions on corn yields in both municipalities.
To achieve these objectives, a methodological approach is adopted that combines five climate scenarios from the Canadian Regional Climate Model for the 2010–2039 horizon with the CERES-Maize crop model embedded in DSSAT.
Compared to the 1961–1990 reference period and considering the cultivars currently used, the impacts of climate change vary from one municipality to another. Within the municipalities, they depend on the climate scenario considered and the fertilizing effect of carbon dioxide. However, in general terms, with the current cultivars, a yield decrease mainly due to an acceleration of the physiological maturity process has been anticipated for the future period under investigation.
By considering some cultivars that are better adapted to the anticipated climatic conditions for the 2010–2039 period, corn yields are projected to increase for most climate scenarios in the two municipalities. If the modelling exercise of climate change impacts on corn yields has provided useful insights into the technical adjustments to be made in order to reduce the negative impacts of climate change on crop productivity in the West Monte´re´gie region, it is nonetheless important to keep in mind that their adoption is not straightforward and is strongly linked to other types of stressors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anothai, J., Patanothai, A., Pannangpetch, K., Jogloy, S., Boote, K. J., & Hoogenboom, G. (2008). Reduction in data collection for determination of cultivar coefficients for breeding applications. Agricultural Systems, 96, 195–206.
Bristow, R. L., & Campbell, G. S. (1984). On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agriculture Forest Meteorology, 31, 159–166.
Bruce, S., Leedman, A., & Sims, J. (2006). The Australian National Agricultural Monitoring System – A national climate risk management application. The Australian Society of Agronomy. http://www.regional.org.au/au/asa/2006/poster/technology/4715_brucese.htm. Consulted on 8 May 2008.
Caya, D., & Laprise, R. (1999). A semi-implicit semi-lagrangian regional climate model: The Canadian RCM. Monthly Weather Review, 127, 341–362.
Fay, T. H., Greeff, J. C., Eisenberg, B. E., & Groenveld, H. T. (2006). Testing the model for one predator and two mutualistic prey species. Ecological Modelling, 196, 245–255.
Hoogenboom, G., Tsuji, G. Y., et al. (1995). Decision support system to study climate change impacts on crop production. In C. Rosenzweig, L. H. Allen Jr., L. A. Harper, S. E. Hollinger, & J. W. Jones (Eds.), Climate change and agriculture: Analysis of potential international impacts (ASA special publication no. 59, pp. 51–75). Madison: American Society of Agronomy.
Hunt, L. A., Pararajasingham, S., Jones, J. W., Hoogenboom, G., Imamura, D. T., & Ogoshi, R. M. (1993). GENCALC: Software to facilitate the use of crop models for analyzing field experiments. Agronomy Journal, 85, 1090–1094.
Institut de Recherche et de Développement en Agroenvironnement (IRDA). (2007). Base dedonnées pédologiques numériques.
Intergovernmental Panel on Climate Change (IPCC). (2001). Climate change 2001: The scientific basis. In Contribution of working group I to the third assessment report. p. 944.
Intergovernmental Panel on Climate Change (IPCC). (2007). Climate Change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment. Report of the Intergovernmental Panel on Climate Change [S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor & H. L. Miller (Eds.)] (pp. 996). Cambridge/New York: Cambridge University Press.
IPCC. (2013). Summary for policymakers. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge/New York: Cambridge University Press.
Jones, J. W., Jagtap, S. S., Hoogenboom, G., & Tsuji, G. Y. (1990). The structure and function of DSSAT. In Proc. of ISBNAT Symp. Decision Support System for Agrotechnology Transfer., 17 October 1989 (pp. 1–14). Hawaii Univ., Honolulu.
Jones, J. W., Hoogenbon, G., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265.
La Financière Agricole du Québec. (2000). L’état des cultures au Québec. Rapport final 2000.
Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., Boote, K. J., Ruane, A. C., Thorburn, P. J., Cammarano, D., Hatfield, J. L., Rosenzweig, C., Aggarwal, P. K., Angulo, C., Basso, B., Bertuzzi, P., Biernath, C., Brisson, N., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R., Grant, R. F., Lee, H., Hooker, J., Hunt, L. A., Ingwersen, J., Izaurralde, R. C., Kersebaum, K. C., Müller, C., Kumar, S. N., Nendel, C., O’leary, G., Olesen, J. E., Osborne, T. M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M. A., Shcherbak, I., Steduto, P., Stöckle, C. O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., White, J. W., & Wolf, J. (2014). Multimodel ensembles of wheat growth: Many models are better than one. Global Change Biology, 21(2), 911–925.
Mayer, D. G., & Butler, D. G. (1993). Statistical validation. Ecological Modelling, 68, 21–32.
Ministère de l’Agriculture des Pêcheries et de l’Alimentation du Québec (MAPAQ). (2006). Profil bioalimentaire de la Montérégie. 92p.
Morissette, H. (1972). Les conditions du développement agricole au Québec. Québec: Les Presses de l’Université Laval.
Music, B., & Caya, D. (2007). Evaluation of the hydrological cycle over the Mississipi river Bassin as simulated by the canadian regional climate model (CRCM). Journal of Hydrometeorology, 8, 969–988.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., & Zhang, H. (2013). Anthropogenic and natural radiative forcing. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge/New York: Cambridge University Press.
Parry, M., Rosenzweig, C., Iglesias, A., Fischer, G., & Livermore, M. (1999). Climate change and world food security: A new assessment. Global Environmental Change, 9, S51–S67.
Plummer, D. A., Caya, D., Frigon, A., Cote, H., Giguere, M., Paquin, D., Biner, S., Harvey, R., & de Elia, R. (2006). Climate and climate change over North America as simulated by the Canadian RCM. Journal of Climate, 19(13), 3112–3132.
R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.URL http://www.R-project.org/
Ritchie, J. T., Singh, U., Godwin, D. C., & Bowen, W. T. (1998). Cereal growth, development and yield. In G. Y. Tsuji, G. Hoogenboom, & P. K. Thornton (Eds.), Understanding options for agricultural production (pp. 79–88). Dordrecht: Kluwer Academic Publishers.
Savoie, M. (2006). Étude du caractère local des impacts du changement climatique sur les rendements en agriculture au Québec. Travail de maîtrise. Québec: Département de géographie, Université de Montréal.
Tsuji, G. Y., Hoogenboom, G., & Thornton, P. K. (1998). Understanding options for agricultural production. Dordrecht: Kluwer.
van Ittersum, M. K., Leffelaar, P. A., van Keulen, H., Kropff, M. J., Bastiaans, L., & Goudriaan, J. (2003). On approaches and applications of the Wageningen crop models. European Journal of Agronomy, 18, 201–234.
Vanclay, J. K., & Skovsgaard, J. P. (1997). Evaluating forest growth models. Ecological Modelling, 98, 1–12.
Villeneuve, C., & Richard, F. (2007). Vivre les changements climatiques: Quoi de neuf? Éditions MultiMondes
Wood, R. (2008). Natural ups and downs. Nature, 453, 43–44.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Délusca, K., Bryant, C.R. (2016). Climate Change and Corn Productivity in West Montérégie, Quebec: From Impacts Anticipation to Some Adaptation Potentialities. In: Bryant, C., Sarr, M., Délusca, K. (eds) Agricultural Adaptation to Climate Change. Springer, Cham. https://doi.org/10.1007/978-3-319-31392-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-31392-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31390-0
Online ISBN: 978-3-319-31392-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)