Environmental Science and Pollution Research

, Volume 25, Issue 28, pp 28413–28430 | Cite as

Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan

  • Ishfaq Ahmed
  • Muhammad Habib ur Rahman
  • Shakeel Ahmed
  • Jamshad Hussain
  • Asmat Ullah
  • Jasmeet Judge
Research Article


Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha−1, respectively. Model was further used to assess climate variability. Historical climate data (1980–2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha−1 for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region.


CERES-Maize Sowing date Crop phenology Grain yield Climate variability 


  1. Abbas G, Ahmad S, Ahmad A, Nasim W, Fatima Z, Hussain S, Rehman MH, Khan MA, Hasanuzzaman M, Fahad S, Boote KJ, Hoogenboom G (2017) Quantification the impacts of climate change and crop management on phenology of maize-based cropping system in Punjab, Pakistan. Agric For Meteorol 247:42–55CrossRefGoogle Scholar
  2. Abdrabbo MAA, Hashem FA, Elsayed ML et al (2013) Evaluation of CSM-CERES-maize model for simulating maize production in northern delta of Egypt. Life Sci J 10:3179–3192Google Scholar
  3. Anderson CL, Cronholm A, Biscaye P (2017) 14 Do changes in farmers’ seed traits align with climate change? A case study of maize in Chiapas, Mexico. Handb Behav Econ Smart Decis Ration Decis within Bounds Reason 251Google Scholar
  4. Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn PJ, Rötter RP, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal PK, Angulo C, Bertuzzi P, Biernath C, Challinor AJ, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt LA, Ingwersen J, Izaurralde RC, Kersebaum KC, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen JE, Osborne TM, Palosuo T, Priesack E, Ripoche D, Semenov MA, Shcherbak I, Steduto P, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White JW, Williams JR, Wolf J (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Chang 3:827–832CrossRefGoogle Scholar
  5. Bassu S, Brisson N, Durand J et al (2014) How do various maize crop models vary in their responses to climate change factors? Glob Chang Biol 20:2301–2320CrossRefGoogle Scholar
  6. Baumer O, Rice J (1988) Methods to predict soil input data for Drainmod, ASAE paper 88-2564. American Society of Engineers, St. Joseph, MichiganGoogle Scholar
  7. Bergamaschi H, Dalmago GA, Bergonci JI, Bianchi CAM, Müller AG, Comiran F, Heckler BMM (2004) Water supply in the critical period of maize and the grain production. Pesqui Agropecuária Bras 39:831–839CrossRefGoogle Scholar
  8. Boote KJ, Porter C, Jones JW et al (2016) Sentinel site data for crop model improvement—definition and characterization. Improv Model Tools Assess Clim Chang Eff Crop Response:125–158Google Scholar
  9. Bowman RA (1997) Field methods to estimate soil organic matter. Conservation tillage fact sheet# 5-97. USDA-ARS and NRCS, Akron. Available: Accessed Jan 2018
  10. Chisanga CB, Phiri E, Shepande C, Sichingabula H (2015a) Evaluating CERES-Maize model using planting dates and nitrogen fertilizer in Zambia. J Agric Sci 7:1–19. CrossRefGoogle Scholar
  11. Chisanga CB, Phiri E, Shepande C, Sichingabula H (2015b) Evaluating CERES-Maize model using planting dates and nitrogen fertilizer in Zambia. J Agric Sci 7:79Google Scholar
  12. Cicchino M, Edreira JI, Otegui ME (2010) Heat stress during late vegetative growth of maize: effects on phenology and assessment of optimum temperature. Crop Sci 50:1431–1437CrossRefGoogle Scholar
  13. Coumou D, Rahmstorf S (2012) A decade of weather extremes. Nat Clim Chang 2:491–496CrossRefGoogle Scholar
  14. Craufurd PQ, Wheeler TR (2009) Climate change and the flowering time of annual crops. J Exp Bot 60:2529–2539CrossRefGoogle Scholar
  15. Dogan E, Clark GA, Rogers DH et al (2006) On-farm scheduling studies and CERES-maize simulation of irrigated corn. Appl Eng Agric 22:509–516CrossRefGoogle Scholar
  16. Ferris R, Ellis RH, Wheeler TR, Hadley P (1998) Effect of high temperature stress at anthesis on grain yield and biomass of field-grown crops of wheat. Ann Bot 82:631–639CrossRefGoogle Scholar
  17. Field CB (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University PressGoogle Scholar
  18. Gabaldon Leal C, Lorite IJ, Minguez Tudela MI et al (2015) Strategies for adapting maize to climate change and extreme temperatures in Andalusia, Spain. Clim Res 65:159–173CrossRefGoogle Scholar
  19. Government of Pakistan (2017) Economic survey of Pakistan. Econ Advis Wing Financ Div Govt Pakistan:29–30Google Scholar
  20. Hatfield JL, Prueger JH (2015) Temperature extremes: effect on plant growth and development. Weather Clim Extrem 10:4–10CrossRefGoogle Scholar
  21. He J, Jones JW, Graham WD, Dukes MD (2010) Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agric Syst 103:256–264CrossRefGoogle Scholar
  22. Hoogenboom G, Porter CH, Shelia V, et al (2016) Decision support system for agrotechnology transfer (DSSAT) version 4.7 ( DSSAT Foundation, Gainesville, Florida, USA
  23. Huang J, Ji M, Xie Y, Wang S, He Y, Ran J (2016) Global semi-arid climate change over last 60 years. Clim Dyn 46:1131–1150CrossRefGoogle Scholar
  24. Hunt LA, Boote KJ (1998) Data for model operation, calibration, and evaluation. Understanding options for agricultural production. Springer, In, pp 9–39Google Scholar
  25. IPCC (2014) Climate change 2014: mitigation of climate change. Cambridge University PressGoogle Scholar
  26. Jones CA, Kiniry JR, Dyke PT (1986) CERES-maize: a simulation model of maize growth and development. Texas A& M University PressGoogle Scholar
  27. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:235–265CrossRefGoogle Scholar
  28. Jones JW, He J, Boote KJ et al (2011) Estimating DSSAT cropping system cultivar-specific parameters using Bayesian techniques. Methods Introd Syst Model Agric Res:365–394Google Scholar
  29. Lin Y, Wu W, Ge Q (2015) CERES-Maize model-based simulation of climate change impacts on maize yields and potential adaptive measures in Heilongjiang Province, China. J Sci Food Agric 95:2838–2849. CrossRefGoogle Scholar
  30. Liu X, Andresen J, Yang H, Niyogi D (2015) Calibration and validation of the hybrid-maize crop model for regional analysis and application over the US Corn Belt. Earth Interact 19:1–16CrossRefGoogle Scholar
  31. Lobell DB, Bänziger M, Magorokosho C, Vivek B (2011) Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat Clim Chang 1:42–45CrossRefGoogle Scholar
  32. Lobell DB, Hammer GL, McLean G, Messina C, Roberts MJ, Schlenker W (2013) The critical role of extreme heat for maize production in the United States. Nat Clim Chang 3:497–501CrossRefGoogle Scholar
  33. Msowoya K, Madani K, Davtalab R, Mirchi A, Lund JR (2016) Climate change impacts on maize production in the warm heart of Africa. Water Resour Manag 30:5299–5312CrossRefGoogle Scholar
  34. Mubeen M, Ahmad A, Wajid A, Khaliq T, Hammad HM, Sultana SR, Ahmad S, Fahad S, Nasim W (2016) Application of CSM-CERES-Maize model in optimizing irrigated conditions. Outlook Agric 45:173–184CrossRefGoogle Scholar
  35. Naheed G, Mahmood A (2006) Water requirement of wheat crop in Pakistan. Pakistan J Meteorol 6:89–97Google Scholar
  36. Nasim W (2010) Modeling the impact of climate change on nitrogen use efficiency in sunflower (helianthus Annuus L.) under different agroclimatic conditions of Punjab-Pakistan. Diss. Faculty of Agriculture/University of Agriculture, FaisalabadGoogle Scholar
  37. Ngwira AR, Aune JB, Thierfelder C (2014) DSSAT modelling of conservation agriculture maize response to climate change in Malawi. Soil Tillage Res 143:85–94CrossRefGoogle Scholar
  38. Popelka M (2012) Genetic architecture of stay-green in maize. Purdue University, USA, pp 100–240Google Scholar
  39. Prasanna BM (2011) Maize in Asia-trends, challenges and opportunities. Addressing Climate Change Effects and Meeting Maize Demand for Asia; Asian Maize Conference, 11; Book of Extended Summaries; Nanning, Guangxi (China); 7-11 Nov. 2011. In: ^ TAddressing Climate Change Effects and Meeting Maize Demand for Asia; Asian Maize Conference, 11; Book of Extended Summaries; Nanning, Guangxi (China); 7-11 Nov. 2011^ AZaidi, PH Babu, R. Cairns, J. Jeffers, D. Kha, LQ Krishna, GK Krishna, V. McDonaldGoogle Scholar
  40. Rawls WJ, Brakensiek DL, Saxtonn KE (1982) Estimation of soil water properties. Trans ASAE 25:1316–1320CrossRefGoogle Scholar
  41. Sangoi L (2001) Understanding plant density effects on maize growth and development: an important issue to maximize grain yield. Ciênc Rural 31:159–168CrossRefGoogle Scholar
  42. Saseendran SA, Ma L, Nielsen DC, Vigil MF, Ahuja LR (2005) Simulating planting date effects on corn production using RZWQM and CERES-Maize models. Agron J 97:58–71CrossRefGoogle Scholar
  43. 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 27:165–177CrossRefGoogle Scholar
  44. Tubiello FN, Soussana J-F, Howden SM (2007) Crop and pasture response to climate change. Proc Natl Acad Sci 104:19686–19690CrossRefGoogle Scholar
  45. Rahman MH, Ahmad A, Wajid A, et al (2017) Application of CSM-CROPGRO-Cotton model for cultivars and optimum planting dates: evaluation in changing semi-arid climate. Field Crop Rese.
  46. Rahman MH, Ahmad A, Wang X et al (2018) Multi-model projections of future climate and climate change impacts uncertainty assessment for cotton production in Pakistan. Agric For Meteorol 253:94–113CrossRefGoogle Scholar
  47. Ureta C, Martinez-Meyer E, Gonzalez EJ, Alvarez-Buylla ER (2016) Finding potential high-yield areas for Mexican maize under current and climate change conditions. J Agric Sci 154:782–794CrossRefGoogle Scholar
  48. Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194CrossRefGoogle Scholar
  49. Willmott CJ, Robeson SM, Matsuura K (2012) A refined index of model performance. Int J Climatol 32:2088–2094CrossRefGoogle Scholar
  50. Yin XG, Wang M, Kong QX, Wang ZB, Zhang HL, Chu QQ, Wen XY, Chen F (2015) Impacts of high temperature on maize production and adaptation measures in Northeast China. Ying yong sheng tai xue bao= J Appl Ecol 26:186–198Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ishfaq Ahmed
    • 1
  • Muhammad Habib ur Rahman
    • 2
  • Shakeel Ahmed
    • 3
  • Jamshad Hussain
    • 1
  • Asmat Ullah
    • 4
  • Jasmeet Judge
    • 5
  1. 1.Agro-climatology Laboratory, Department of AgronomyUniversity of AgricultureFaisalabadPakistan
  2. 2.Department of AgronomyMNS-University of AgricultureMultanPakistan
  3. 3.Department of AgronomyBZUMultanPakistan
  4. 4.Agronomic Research StationKaror-LayyahPakistan
  5. 5.Center for Remote Sensing, Agricultural & Biological Engineering DepartmentUniversity of FloridaGainesvilleUSA

Personalised recommendations