Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-17T10:28:26.479Z Has data issue: false hasContentIssue false
  • English
  • Français

Using the CSM–CERES–Maize model to assess the gap between actual and potential yields of grain maize

Published online by Cambridge University Press:  31 May 2016

Q. JING ,
J. SHANG ,
T. HUFFMAN ,
B. QIAN ,
E. PATTEY ,
J. LIU ,
T. DONG ,
C. F. DRURY  and
N. TREMBLAY
Q. JING
Affiliation:
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
J. SHANG
Affiliation:
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
T. HUFFMAN
Affiliation:
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
B. QIAN*
Affiliation:
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
E. PATTEY
Affiliation:
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
J. LIU
Affiliation:
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
T. DONG
Affiliation:
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
C. F. DRURY
Affiliation:
Harrow Research and Development Centre, Agriculture and Agri-Food Canada, Harrow, ON N0R 1G0, Canada
N. TREMBLAY
Affiliation:
Saint-Jean-sur-Richelieu Research and Development Centre, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada
*
*To whom all correspondence should be addressed. Email: budong.qian@agr.gc.ca
Get access Rights & Permissions [Opens in a new window]

Summary

Maize in Canada is grown mainly in the south-eastern part of the country. No comprehensive studies on Canadian maize yield levels have been done so far to analyse the barriers of obtaining optimal yields associated with cultivar, environmental stress and agronomic management practices. The objective of the current study was to use a modelling approach to analyse the gaps between actual and potential (determined by cultivar, solar radiation and temperature without any other stresses) maize yields in Eastern Canada. The CSM–CERES–Maize model in DSSAT v4·6 was calibrated and evaluated with measured data of seven cultivars under different nitrogen (N) rates across four sites. The model was then used to simulate grain yield levels defined as: yield potential (YP), water-limited (YW, rainfed), and water- and N-limited yields with N rates 80 kg/ha (YW, N-80N) and 160 kg/ha (YW, N-160N). The options were assessed to further increase grain yield by analysing the yield gaps related to water and N deficiencies. The CSM–CERES–Maize model simulated the grain yields in the experiments well with normalized root-mean-squared errors <0·20. The model was able to capture yield variations associated with varying N rates, cultivar, soil type and inter-annual climate variability. The seven calibrated cultivars used in the experiments were divided into three grades according to their simulated YP: low, medium and high. The simulation results for the 30-year period from 1981 to 2010 showed that the average YP was 15 000 kg/ha for cultivars with high yield potential. The YP is generally about 6000 kg/ha greater than the actual yield (YA) at each experimental site in Eastern Canada. Two-thirds of this gap between YP and YA is probably associated with water stress, as a gap of approximately 4000 kg/ha between the YW and the YP was simulated. This gap may be reduced through crop management, such as introducing irrigation to improve the distribution of available water during the growing season. The simulated yields indicated a gap of about 3000 and 1000 kg/ha between YW and YW,N-80N for cultivars with high YP and low YP, respectively. The gap between YW and YW,N-160N decreased to <2000 kg/ha for high Yp cultivars with little difference for the low Yp cultivars. The different yield gaps among cultivars suggest that cultivars with high YP require high N rates but cultivars with low YP may need only low N rates.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 155 , Issue 2 , March 2017 , pp. 239 - 260
Check for updates
Copyright
Copyright © Her Majesty the Queen in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada. 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

AAFC (Agriculture and Agri-Food Canada) (2015). National Ecological Framework for Canada V2·2. Available from: http://open.canada.ca/data/en/dataset/3ef8e8a9-8d05-4fea-a8bf-7f5023d2b6e1 (verified 23 February 2016).Google Scholar
Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56. Rome: FAO.Google Scholar
Beaudoin, N., Launay, M., Sauboua, E., Ponsardin, G. & Mary, B. (2008). Evaluation of the soil crop model STICS over 8 years against the ‘on farm’ database of Bruyères catchment. European Journal of Agronomy 29, 46–57.Google Scholar
Ben Nouna, B., Katerji, N. & Mastrorilli, M. (2000). Using the CERES–Maize model in a semi-arid Mediterranean environment. Evaluation of model performance. European Journal of Agronomy 13, 309–322.Google Scholar
Berendse, F. (1990). Organic matter accumulation and nitrogen mineralization during secondary succession in heathland ecosystems. Journal of Ecology 78, 413–427.Google Scholar
Bernier, M.-H., Madramootoo, C. A., Mehdi, B. B. & Gollamudi, A. (2010). Assessing on-farm irrigation water use efficiency in Southern Ontario. Canadian Water Resources Journal 35, 115–130.Google Scholar
Blum, A. (2005). Drought resistance, water-use efficiency, and yield potential – are they compatible, dissonant, or mutually exclusive? Australian Journal of Agricultural Research 56, 1159–1168.Google Scholar
Bouman, B. A. M. & van Laar, H. H. (2006). Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agricultural Systems 87, 249–273.Google Scholar
Bréda, N. J. J. (2003). Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. Journal of Experimental Botany 54, 2403–2417.Google Scholar
Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y. M., Cellier, P., Debaeke, P., Gaudillère, J. P., Hénault, C., Maraux, F., Seguin, B. & Sinoquet, H. (2003). An overview of the crop model STICS. European Journal of Agronomy 18, 309–332.Google Scholar
Bruulsema, T. W., Tollenaar, M. & Heckman, J. R. (2000). Boosting crop yields in the next century. Better Crops 84, 9–11.Google Scholar
Cardwell, V. B. (1982). Fifty years of Minnesota corn production: sources of yield increase. Agronomy Journal 74, 984–990.Google Scholar
Cassman, K. G., Dobermann, A. & Walters, D. T. (2002). Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio 31, 132–140.Google Scholar
Cassman, K. G., Dobermann, A. R., Walters, D. T. & Yang, H. (2003). Meeting cereal demand while protecting natural resources and improving environmental quality. Annual Review of Environment and Resources 28, 315–358.Google Scholar
Ciampitti, I. A. & Vyn, T. J. (2012). Physiological perspectives of changes over time in maize yield dependency on nitrogen uptake and associated nitrogen efficiencies: a review. Field Crops Research 133, 48–67.Google Scholar
DEKALB (2015). 2013 Corn: Individual Plot Yield Report. Winnipeg, Manitoba, Canada: DEKALB. Available from: https://www.dekalb.ca/eastern/en/iplot?setname=BTF16&year=2013 (verified 23 February 2016).Google Scholar
Dobermann, A., Witt, C., Abdulrachman, S., Gines, H. C., Nagarajan, R., Son, T. T., Tan, P. S., Wang, G. H., Chien, N. V., Thoa, V. T. K., Phung, C. V., Stalin, P., Muthukrishnan, P., Ravi, V., Babu, M., Simbahan, G. C., Adviento, M. A. A. & Bartolome, V. (2003). Estimating indigenous nutrient supplies for site-specific nutrient management in irrigated rice. Agronomy Journal 95, 924–935.Google Scholar
Drury, C. F., Tan, C. S., Gaynor, J. D., Oloya, T. O. & Welacky, T. W. (1996). Influence of controlled drainage-subirrigation on surface and tile drainage nitrate loss. Journal of Environmental Quality 25, 317–324.CrossRefGoogle Scholar
Drury, C. F., Yang, X. M., Reynolds, W. D. & McLaughlin, N. B. (2008). Nitrous oxide and carbon dioxide emissions from monoculture and rotational cropping of corn, soybean and winter wheat. Canadian Journal of Soil Science 88, 163–174.Google Scholar
Drury, C. F., Tan, C. S., Welacky, T. W., Reynolds, W. D., Zhang, T. Q., Oloya, T. O., McLaughlin, N. B. & Gaynor, J. D. (2014). Reducing nitrate loss in tile drainage water with cover crops and water-table management systems. Journal of Environmental Quality 43, 587–598.Google Scholar
Duvick, D. N. (1997). What is yield? In Developing Drought and Low N-tolerant Maize (Eds Edmeades, G. O., Bänziger, B., Mickelson, H. R. & Pena-Valdivia, C. B.), pp. 332–335. EI Batan, Mexico: CIMMYT.Google Scholar
Duvick, D. N. (2005 a). The contribution of breeding to yield advances in maize (Zea mays L.). Advances in Agronomy 86, 83–145.Google Scholar
Duvick, D. N. (2005 b). Genetic progress in yield of United States maize (Zea mays L.). Maydica 50, 193–202.Google Scholar
Egli, D. B. & Hatfield, J. L. (2014). Yield and yield gaps in central U.S. corn production systems. Agronomy Journal 106, 2248–2254.Google Scholar
Evans, L. J. & Cameron, B. H. (1983). The Brookston series in southwest Ontario: characteristics, classification and problems in defining a soil series. Canadian Journal of Soil Science 63, 339–352.Google Scholar
Ewert, F. (2004). Modelling plant responses to elevated CO2: how important is leaf area index? Annals of Botany 93, 619–627.CrossRefGoogle ScholarPubMed
FAO (2014). FAOSTAT Rome: FAO. Available from: http://faostat3.fao.org/download/Q/QC/E (verified 23 February 2016).Google Scholar
Gabrielle, B., Da-Silveira, J., Houot, S. & Michelin, J. (2005). Field-scale modelling of carbon and nitrogen dynamics in soils amended with urban waste composts. Agriculture, Ecosystems and Environment 110, 289–299.Google Scholar
Godwin, D. C. & Jones, C. A. (1991). Nitrogen dynamics in soil-plant systems. In Modeling Plant and Soil Systems (Eds Hanks, J. & Ritchie, J. T.), pp. 287–321. Agronomy Monograph no. 31. Madison, WI: ASA, CSSA, and SSSA.Google Scholar
Godwin, D. C. & Singh, U. (1998). Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In Understanding Options for Agricultural Production (Eds Tsuji, G. Y., Hoogenboom, G. & Thornton, P. K.), pp. 55–77. System Approaches for Sustainable Agricultural Development vol. 7. Dordrecht, Netherlands: Kluwer Academic Publishers.CrossRefGoogle Scholar
Hatfield, J. L. & Walthall, C. L. (2015). Meeting global food needs: realizing the potential via genetics × environment × management interactions. Agronomy Journal 107, 1215–1226.Google Scholar
Hoogenboom, G., Jones, J. W., Wilkens, P. W., Porter, C. H., Boote, K. J., Hunt, L. A., Singh, U., Lizaso, J. I., White, J. W., Uryasev, O., Ogoshi, R., Koo, J., Shelia, V. & Tsuji, G. Y. (2015). Decision Support System For Agrotechnology Transfer (DSSAT). Version 4.6 (www.DSSAT.net). Prosser, Washington: DSSAT Foundation.Google Scholar
Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. & Yang, J. (2015). Upscaling modelled crop yields to regional scale: a case study using DSSAT for spring wheat on the Canadian prairies. Canadian Journal of Soil Science 95, 49–61.Google Scholar
Ibarra, S., Broughton, R. S. & Bonnell, R. B. (1999). On-farm management of irrigation for maize in Eastern Ontario. Canadian Water Resources Journal 24, 15–23.Google Scholar
IPCC (2007). Climate change 2007: the physical science basis. In Contributions of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Eds Solomon, S., Qin, D. & Manning, M.). Cambridge, UK and New York, NY: Cambridge University Press.Google Scholar
ISQ (Institut de la statistique du Québec) (2015). Agricutlure and Biofood Industry – Field Crops. Québec, Canada: ISQ. Available from: http://www.stat.gouv.qc.ca/statistiques/agriculture/grandes-cultures/index_an.html (verified 8 October 2015).Google Scholar
Jackson, P., Robertson, M., Cooper, M. & Hammer, G. L. (1996). The role of physiological understanding in plant breeding: from a breeding perspective. Field Crops Research 49, 11–37.Google Scholar
Jamieson, P. D., Porter, J. R. & Wilson, D. R. (1991). A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research 27, 337–350.Google Scholar
Jégo, G., Pattey, E., Bourgeois, G., Drury, C. F. & Tremblay, N. (2011). Evaluation of the STICS crop growth model with maize cultivar parameters calibrated for Eastern Canada. Agronomy for Sustainable Development 31, 557–570.Google Scholar
Jones, C. A. & Kiniry, J. R. (1986). CERES-Maize: A Simulation Model of Maize Growth and Development. College Station, Texas: Texas A&M Unversity Press.Google Scholar
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J. & Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal of Agronomy 18, 235–265.Google Scholar
Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I., Hargreaves, J. N. G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J. P., Silburn, M., Wang, E., Brown, S., Bristow, K. L., Asseng, S., Chapman, S., McCown, R. L., Freebairn, D. M. & Smith, C. J. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267–288.Google Scholar
Levy, P. E. & Jarvis, P. G. (1999). Direct and indirect measurements of LAI in millet and fallow vegetation in HAPEX-Sahel. Agricultural and Forest Meteorology 97, 199–212.CrossRefGoogle Scholar
Liu, H. L., Yang, J. Y., Drury, C. F., Reynolds, W. D., Tan, C. S., Bai, Y. L., He, P., Jin, J. & Hoogenboom, G. (2011 a). Using the DSSAT–CERES–Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production. Nutrient Cycling in Agroecosystems 89, 313–328.Google Scholar
Liu, H. L., Yang, J. Y., Tan, C. S., Drury, C. F., Reynolds, W. D., Zhang, T. Q., Bai, Y. L., Jin, J., He, P. & Hoogenboom, G. (2011 b). Simulating water content, crop yield and nitrate-N loss under free and controlled tile drainage with subsurface irrigation using the DSSAT model. Agricultural Water Management 98, 1105–1111.Google Scholar
Liu, H.-L., Yang, J.-Y., He, P., Bai, Y.-L., Jin, J.-Y., Drury, C. F., Zhu, Y.-P., Yang, X.-M., Li, W.-J., Xie, J.-G., Yang, J.-M. & Hoogenboom, G. (2012). Optimizing parameters of CSM–CERES–Maize model to improve simulation performance of maize growth and nitrogen uptake in northeast China. Journal of Integrative Agriculture 11, 1898–1913.Google Scholar
Liu, S., Yang, J. Y., Drury, C. F., Liu, H. L. & Reynolds, W. D. (2014). Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario, Canada. Canadian Journal of Soil Science 94, 435–452.Google Scholar
Lizaso, J. I., Batchelor, W. D. & Westgate, M. E. (2003 a). A leaf area model to simulate cultivar-specific expansion and senescence of maize leaves. Field Crops Research 80, 1–17.Google Scholar
Lizaso, J. I., Batchelor, W. D., Westgate, M. E. & Echarte, L. (2003 b). Enhancing the ability of CERES–Maize to compute light capture. Agricultural Systems 76, 293–311.Google Scholar
MacDonald, K. B. & Kloosterman, B. (1984). The Canada Soil Information System (CanSIS): General User's Manual. LRRI Contribution no. 83-59-E. Ottawa, Ontario, Canada: Land Resource Research Centre, Research Branch, Agriculture Canada.Google Scholar
Mastrorilli, M., Katerji, N. & Nouna, B. B. (2003). Using the CERES-Maize model in a semi-arid Mediterranean environment. Validation of three revised versions. European Journal of Agronomy 19, 125–134.Google Scholar
Nelson, W. L. & Reetz, H. F. Jr. (1986). Herman Warsaw and his successful search for higher corn yields. Journal of Agronomic Education 15, 68–70.Google Scholar
Nouna, B. B., Katerji, N. & Mastrorilli, M. (2003). Using the CERES-Maize model in a semi-arid Mediterranean environment. New modelling of leaf area and water stress functions. European Journal of Agronomy 19, 115–123.Google Scholar
Nyiraneza, J., Ziadi, N., Zebarth, B. J., Sharifi, M., Burton, D. L., Drury, C. F., Bittman, S. & Grant, C. A. (2012). Prediction of soil nitrogen supply in corn production using soil chemical and biological indices. Soil Science Society of America Journal 76, 925–935.Google Scholar
OMAFRA (Ontario Ministry Of Agriculture, Food And Rural Affairs) (2014). Macronutrients – Nitrogen Management. Guelph, ON, Canada: OMAFRA. Available from: http://www.omafra.gov.on.ca/english/crops/pub811/1fertility.htm#nitrogen (verified 23 February 2016).Google Scholar
OMAFRA (Ontario Ministry Of Agriculture, Food And Rural Affairs) (2015). Estimated Area, Yield, Production and Farm Value of Specified Field Crops, Ontario, 2011–2014. Guelph, ON, Canada: OMAFRA. Available from: http://www.omafra.gov.on.ca/english/stats/crops/estimate_new.htm (verified 23 February 2016).Google Scholar
Payne, R. (2014). A Guide to ANOVA and Design in GenStat, 17th edn. Hemel Hempstead, UK: VSN International.Google Scholar
Priestley, C. H. B. & Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review 100, 81–92.Google Scholar
Rajsic, P. & Weersink, A. (2008). Do farmers waste fertilizer? A comparison of ex post optimal nitrogen rates and ex ante recommendations by model, site and year. Agricultural Systems 97, 56–67.Google Scholar
Ritchie, J. T. (1998). Soil water balance and plant water stress. In Understanding Options for Agricultural Production (Eds Tsuji, G., Hoogenboom, G. & Thornton, P.), pp. 41–54. Systems Approaches for Sustainable Agricultural Development vol. 7. Dordrecht, The Netherlands: Springer.Google Scholar
Ritchie, J. T. & Otter, S. (1985). Description and performance of CERES-Wheat: a user-oriented wheat yield model. ARS – United States Department of Agriculture, Agricultural Research Service 38, 159–175.Google Scholar
Sadras, V. O., Grassini, P. & Steduto, P. (2010). Status of Water Use Efficiency of Main Crops, SOLAW Background Thematic Report – TR07. p. 41. Rome: FAO.Google Scholar
Seligman, N. C. & Van Keulen, H. (1981). PAPRAN: a simulation model of annual pasture production limited by rainfall and nitrogen. In Simulation of Nitrogen Behaviour of Soil-Plant Systems (Eds Frissel, M. J. & Van Veen, J. A.), pp. 192–221. Wageningen, The Netherlands: PUDOC.Google Scholar
Shoji, S. & Kanno, H. (1994). Use of polyoelfin-coated fertilizers for increasing fertilzier efficiency and reducing nitrate leaching and nitrous-oxide emissions. Fertilizer Research 39, 147–152.Google Scholar
Soil Landscapes Of Canada Working Group (2010). Soil Landscapes of Canada v3·2. (Digital Map and Database at 1:1 Million Scale). Ottawa, ON, Canada: Agriculture and Agri-Food Canada. Available from: http://sis.agr.gc.ca/cansis/nsdb/slc/v3.2/index.html (verified 23 February 2016).Google Scholar
Soler, C. M. T., Sentelhas, P. C. & 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. European Journal of Agronomy 27, 165–177.Google Scholar
Statistics Canada (2014). Corn: Canada's Third Most Valuable Crop. Ottawa, ON, Canada: Statistics Canada. Available from: http://www.statcan.gc.ca/pub/96-325-x/2014001/article/11913-eng.htm (verified 23 February 2016).Google Scholar
Stenberg, P., Linder, S., Smolander, H. & Flower-Ellis, J. (1994). Performance of the LAI-2000 plant canopy analyzer in estimating leaf area index of some Scots pine stands. Tree Physiology 14, 981–995.Google Scholar
Stöckle, C. O., Donatelli, M. & Nelson, R. (2003). CropSyst, a cropping systems simulation model. European Journal of Agronomy 18, 289–307.Google Scholar
Subedi, K. D. & Ma, B. L. (2009). Assessment of some major yield-limiting factors on maize production in a humid temperate environment. Field Crops Research 110, 21–26.Google Scholar
Tollenaar, M. & Lee, E. A. (2002). Yield potential, yield stability and stress tolerance in maize. Field Crops Research 75, 161–169.Google Scholar
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.Google Scholar
van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P. & Hochman, Z. (2013). Yield gap analysis with local to global relevance – A review. Field Crops Research 143, 4–17.Google Scholar
Van Wart, J., Kersebaum, K. C., Peng, S., Milner, M. & Cassman, K. G. (2013). Estimating crop yield potential at regional to national scales. Field Crops Research 143, 34–43.Google Scholar
Wirsenius, S., Azar, C. & Berndes, G. (2010). How much land is needed for global food production under scenarios of dietary changes and livestock productivity increases in 2030? Agricultural Systems 103, 621–638.Google Scholar
Witt, C., Pasuquin, J. M. & Dobermann, A. (2006). Towards a site-specific nutrient management approach for maize in Asia. Better Crops with Plant Food 90, 28–31.Google Scholar
Yan, W. & Hunt, L. A. (1998). Genotype by environment interaction and crop yield. Plant Breeding Reviews 16, 135–178.Google Scholar
Yang, J. M., Yang, J. Y., Liu, S. & Hoogenboom, G. (2014). An evaluation of the statistical methods for testing the performance of crop models with observed data. Agricultural Systems 127, 81–89.Google Scholar
Ziadi, N., Bélanger, G., Gastal, F., Claessens, A., Lemaire, G. & Tremblay, N. (2009). Leaf nitrogen concentration as an indicator of corn nitrogen status. Agronomy Journal 101, 947–957.Google Scholar