Scientific Research and Essays
Subscribe to SRE
Full Name*
Email Address*

Article Number - A74E94366446


Vol.12(18), pp. 167-187 , September 2017
https://doi.org/10.5897/SRE2017.6521
ISSN: 1992-2248


 Total Views: 0
 Downloaded: 0

Review

Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review



Charles B. Chisanga
  • Charles B. Chisanga
  • Ministry of Agriculture, Box 70232, Ndola, Ndola, Zambia.
  • Google Scholar
Elijah Phiri
  • Elijah Phiri
  • Department of Soil Science, School of Agricultural Sciences, University of Zambia, Box 32379, Lusaka, Zambia.
  • Google Scholar
Vernon R. N. Chinene
  • Vernon R. N. Chinene
  • Department of Soil Science, School of Agricultural Sciences, University of Zambia, Box 32379, Lusaka, Zambia.
  • Google Scholar







 Received: 13 June 2017  Accepted: 06 September 2017  Published: 30 September 2017

Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0


Review of literature related to the impact of climate change on maize (Zea mays L.) yield using Global Climate Models (GCMs), statistical downscaling, and crop simulation (APSIM-maize-and-CERES-maize models) models are discussed. GCMs can simulate the current and future climatic scenarios. Crop yield projections using crop models require climate inputs at higher spatial resolution than that provided by GCMs. The computationally inexpensive statistical downscaling technique is widely used for this translation. Studies on regional climate modeling have mostly focused on Southern Africa and West Africa, with very few studies in Zambia. Additionally, the integrated use of climate and crop models have received relatively less attention in Africa compared to other parts of the world. Conversely, the AgMIP protocols have been implemented in Sub-Saharan Africa (SSA) (Ethiopia, Kenya, Tanzania, Uganda and South Africa) and South Asia (SA) (Sri Lanka). In Zambia, however, the protocols have not been applied at either regional or local scale. Applying crop and statistical downscaling models requires calibration and validation, and these are crucial for correct climate and crop simulation. The review shows that although uncertainties exist in the design of models, and parameters, soil, climate and management options, the climate would adversely affect maize yield production in SSA. The potential effect of climate change on maize production can be studied using crop models such as agricultural production simulator (APSIM) and decision support system for agrotechnology (DSSAT) models. There is need to use integrated assessment modeling to study future climate impact on maize yield. The assessment is essential for long-term planning in food security and in developing adaptation and mitigation strategies in the face of climate variability and change.

 

Key words: Review, AgMIP, climate scenario, climate change, variability, crop simulation model, bias correction, dynamical downscaling, Global Climate Model (GCM), statistical downscaling.

Abbreviation:

 Atmospheric Oceanic Global Circulation Models (AOGCM). United Kingdom Transient climate experiment (UKTR). European Centre-Hamburg model version 1 (ECHAM1). Global  Sea  Ice  and  Sea  Surface Temperature (GISSTR). Canadian Centre for Climate (Modelling and Analysis) (CCC). Intergovernmental Panel on Climate Change and Task Group on Data and Scenario Support for Impact and Climate Analysis (IPCC-TGICA)


Ahmed M, Asif M, Sajad M, Khattak JZK, Ijaz W, Fayyaz-ul-Hassan, Wasaya A, Chun JA (2013). Could the agricultural system be adapted to climate change? A review. Aust. J. Crop Sci 7:1642-1653.

 

Araya A, Girma A, Getachew F (2015a). Exploring impacts of climate change on maize yield in two contrasting agro-ecologies of Ethiopia. Asian J. Appl. Sci. Eng. 4:2305-2915. 

 
 

Araya A, Hoogenboom G, Luedeling E, Hadgu KM, Kisekka I, Martorano LG (2015b). Assessment of maize growth and yield using crop models under present and future climate in southwestern Ethiopia. Agric. For. Meteorol. 214:252-265. 
Crossref

 
 

Arslan A, Mccarthy N, Lipper L, Asfaw S, Cattaneo A, Kokwe M (2015a). Climate Smart Agriculture: Assessing the Adaptation Implications in Zambia. J. Agric. Econ. 66:753-780.
Crossref

 
 

Arslan A, McCarthy N, Lipper L, Asfaw S, Cattaneo A, Kokwe M (2015b). Food security and adaptation impacts of potential climate smart agricultural practices in Zambia. Agricultural Development Economics Division

 
 

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. 3:627-632. 

 
 

Basso B, Cammarano D, Carfagna E (2013). Review of crop yield forecasting methods and early warning systems. In Proceedings of the First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics, FAO Headquarters, Rome, Italy pp. 18-19.

 
 

Bassu S, Brisson N, Durand JL, Boote K, Lizaso J, Jones JW, Rosenzweig C, Ruane AC, Adam M, Baron C, Basso B, Biernath C, Boogaard H, Conijn S, Corbeels M, Deryng D, De Sanctis G, Gayler S, Grassini P, Hatfield J, Hoek S, Izaurralde C, Jongschaap R, Kemanian AR, Kersebaum KC, Kim S-H, Kumar NS, Makowski D, Müller C, Nendel C, Priesack E, Pravia MV, Sau F, Shcherbak I, Tao F, Teixeira E, Timlin D, Waha K (2014). How do various maize crop models vary in their responses to climate change factors? Glob. Chang. Biol. 20:2301-2320. 
Crossref

 
 

Bationo A, Hartemink A, Lungu O, Naimi M, Okoth P, Smaling E, Thiombiano L, Waswa B (2012a). Knowing the African Soils to Improve Fertilizer Recommendations. In: Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Dordrecht: Springer Netherlands, pp 19-42. doi:http://dx.doi.org/10.1007/978-94-007-2960-5_3
Crossref

 
 

Bationo A, Kihara J, Adesina A (2012b). Beyond Biophysical Recommendations: Towards a New Paradigm. In: Kihara J, Fatondji D, Jones JW, Hoogenboom G, Tabo R, Bationo A eds. Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT),. Dordrecht: Springer Netherlands, pp 169-184. 
Crossref

 
 

Bationo A, Kihara J, Adesina A (2012c). Beyond Biophysical Recommendations: Towards a New Paradigm. In: Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Dordrecht: Springer Netherlands, 169-184.
Crossref

 
 

Bhatt BP, Haris AA, Chhabra V (2014). Crop models and their utility for studies on land use systems of Eastern Region. Int. J. Food Agric. Vet. Sci. 4:24-35.

 
 

Bhuvandas N, Timbadiya PV, Patel PL, Porey PD (2014). Review of downscaling methods in climate change and their role in hydrological studies. Int. J. Environ. Ecol. Geol. Mar. Eng. 8:713-718.

 
 

Charron I (2014). A Guidebook on Climate Scenarios: Using Climate Information to Guide Adaptation Research and Decisions. Ouranos, 86p.

 
 

Chen J, Brissette FP, Leconte R (2012). WeaGETS – a Matlab-based

 
 

Chinene VRN (1985). Generation of field data for validation of crop models in Zambia. In: Woode PR ed. XI International Forum on soil taxonomy and agrotechnology transfer, Zambia, July 15 - August 1, 1985. Lusaka, Zambia, 181–186.

 
 

Chisanga CB (2014). Evaluation of the CERES-Maize model in simulating maize (Zea mays L.) growth, development and yield at different planting dates and nitrogen rates in a subtropical environment of Zambia. MSc thesis. The University of Zambia.

 
 

Chisanga CB, Phiri E, Chinene VRN (2017). Statistical Bias Correction of Fifth Coupled Model Intercomparison Project Data from the CGIAR Research Program on Climate Change , Agriculture and Food Security - Climate Portal for Mount Makulu , Zambia. Br. J. Appl. Sci. Technol. 21:1-16. doi: 
Crossref

 
 

Chisanga CB, Phiri E, Chizumba S, Sichingabula H (2015). Evaluating CERES-Maize Model Using Planting Dates and Nitrogen Fertilizer in Zambia. J. Agric. Sci. 7:1-20.
Crossref

 
 

CSIRO and Bureau of Meteorology (2015). Climate Change in Australia Information for Australia's Natural Resource Management Regions: Technical Report, CSIRO and Bureau of Meteorology, Australia.

 
 

De Salvo M, Begalli D, Signorello G (2013). Measuring the effect of climate change on agriculture: A literature review of analytical models. J. Dev. Agric. Econ. 5:499-509. 
Crossref

 
 

Devak M, Dhanya CT (2014). Downscaling of Precipitation in Mahanadi Basin, India. Int. J. Civ. Eng. Res. 5:111-120.

 
 

Dimes JP, Cooper P, Rao KPC (2008). Climate Change Impact on Crop Productivity in the Semi-Arid Tropics of Zimbabwe in the 21st Century. In: Proceedings of the Workshop on Increasing the Productivity and Sustainability of Rain-Fed Cropping Systems of Poor Smallholder Farmers, Tamale, 22-25 September 2008, 189-198. Tamale, 11.

 
 

Donatelli M, Srivastava AK, Duveiller G, Niemeyer S (2012). Estimating Impact Assessment and Adaptation Strategies under Climate Change Scenarios for Crops at EU27 Scale. In: Seppelt R, Voinov AA, Lange S, Bankamp D eds. 2012 International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Sixth Biennial Meeting. Leipzig, Germany: International Environmental Modelling and Software Society (iEMSs), 8.

 
 

Dos-Santos CAC (2011). Trends in indices for extremes in daily air temperature over Utah, USA. Rev. Bras. Meteorol. 26:19-28.
Crossref

 
 

Flato G, Marotzke J, Abiodun B, Braconnot P, Chou SC, Collins W, Cox P, Driouech F, Emori S, Eyring V, Forest C, Gleckler P, Guilyardi E, Jakob C, Kattsov V, Reason C, Rummukainen M (2013). Evaluation of Climate Models. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, Stechow C von, Zwickel T, Minx JC eds. Climate Change 2013: The Physical Science Basis. The contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, U. K, 741-866. 

 
 

Fosu-mensah BY (2013). Modelling the impact of climate change on maize (Zea mays L.) yield under rain-fed Conditions in sub-humid Ghana. United Nations University Institute for Natural Resources in Africa (UN-INRA).

 
 

Fumpa-Makano R (2011). Forests and Climate Change Integrating Climate Change Issues into National Forest Programmes and Policy Frameworks. Lusaka. Government of the Republic of Zambia (GRZ) and United Nations Development Programme (UNDP) (2007). Enabling activities for the preparation of Zambia's second national communication to the United Nations Framework Convention on Climate Change (UNFCCC) Project.

 
 

Gudmundsson L (2016). qmap: Statistical transformations for post-processing climate model output. R package version 1.0-4. :36.

 
 
   

Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012). Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrol. Earth Syst. Sci. 16:3383-3390. 
Crossref

 
 

Haerter JO, Hagemann S, Moseley C, Piani C (2011). Climate model bias correction and the role of timescales Climate model bias correction and the role of timescales. Hydrol Earth Syst. Sci. Discuss 7:7863-7898. 
Crossref

 
 

Hao Z, Ju Q, Jiang W, Zhu C (2013). Characteristics and scenarios projection of climate change on the tibetan plateau. Sci. World J. vol. 2013, Article ID 129793, 9 pages.
Crossref

 
 
   
 

Hassan Z, Shamsudin S, Harun S (2014). Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theor. Appl. Climatol. 116:243-257. 
Crossref

 
 

Hawkins E, Osborne TM, Ho CK, Challinor AJ (2013a). Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe. Agric. For. Meteorol. 170:19-31. 

 
 

Hawkins E, Osborne TM, Ho CK, Challinor AJ, Kit C, Challinor AJ, Ho CK, Challinor AJ (2013b). Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe. Agric. For. Meteorol. 170:19-31. 

 
 

Hawkins E, Osborne TM, Kit C, Challinor AJ, Ho CK, Challinor AJ (2013c). Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe. Agric. For. Meteorol. 170:19-31. 

 
 

He L, Asseng S, Zhao G, Wu D, Yang X, Zhuang W (2015). Agricultural and Forest Meteorology Impacts of recent climate warming, cultivar changes, and crop management on winter wheat phenology across the Loess Plateau of China. Agric. For. Meteorol. 200:135-143.
Crossref

 
 

Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F (2013). A trend-preserving bias correction – The ISI-MIP approach. Earth Syst. Dyn. 4:219-236. doi: 
Crossref

 
 

Herrero M, Ringler C, van de Steeg J, Koo J, Notenbaert A (2010). Climate variability and climate change and their impacts on Kenya's agricultural sector. ILRI, Nairobi. Kenya:1-56.

 
 

Hewitson BC, Crane RG (2006). Consensus between GCM climate change projections with empirical downscaling: Precipitation downscaling over South Africa. Int J Climatol 26:1315-1337.
Crossref

 
 

Ho CK, Stephenson DB, Collins M, Ferro CAT, Brown SJ (2012). Calibration Strategies: A Source of Additional Uncertainty in Climate Change Projections. Am. Meteorol. Soc. 93(1):21-26. 

 
 

Hoogenboom G, White JW, Messina CD (2004). From genome to crop: Integration through simulation modeling. Field Crops Res. 90(1):145-163. doi: 
Crossref

 
 

Internationale Zusammenarbeit (GIZ) GmbH (2014). Integrating Climate

 
 

Iglesias A (2006). Climate change and agriculture. In: CGE Hands-on Training Workshop on V&A Assessment of the Asia and Pacific Region. Jakarta, 20–24.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2001). Climate change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: Cambridge Univ. Press, Cambridge, U. K.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2007a). Climate Change 2007: Impacts, Adaptation and Vulnerability: contribution of Working Group II to the Fourth Assessment Report of the IPCC.

 
 

Geneva, Switzerland.

 
 

IPCC (2007b). IPCC Fourth Assessment Report (AR4). Geneva, Switzerland: Cambridge Univ. Press, Cambridge, U. K.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2007c). Climate Change 2007: The Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge; New York : Cambridge University Press, 2007.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2007d). Climate change 2007 : Impacts, Adaptation and Vulnerability : Working Group II contribution to the Fourth Assessment Report of the IPCC Intergovernmental Panel on Climate Change.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2007e). Climate Change 2007 - The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC. Cambridge Univ. Press, Cambridge, U. K.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2013a). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovern- mental Panel on Climate Change. Geneva, Switzerland: Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2013b). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the IPCC. Geneva, Switzerland: Cambridge Univ. Press, Cambridge, U. K.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2014a). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

 
 

Intergovernmental Panel on Climate Change (IPCC) (2014b). Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

 
 

Irwin SE, Sarwar R, King, Leanna M, Simonovic SP (2012). Assessment of climatic vulnerability in the Upper Thames River basin: Downscaling with LARS-WG. Ontario.

 
 

Jintrawet A (2015). Soil data as a component towards the development of seasonal crop yield forecasts and local community adaptation options: An integrative approach under TRF-DSS Research and Development Network. In: International Year of Soils 2015: August 20, 2015. Cha Am, Hua Hin, Phetchaburi, Thailand, 1–6.

 
 

Johnston RZ (2013). Using the ceres-maize model to create a geographically explicit grid based estimate of corn yield under climate change scenarios. BSc thesis. University of Arkansas, Fayetteville.

 
 

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. J. Agron. 18:235-265. 
Crossref

 
 

Jones CA, Kiniry JR (James R, Dyke PT (1986). CERES-Maize : a simulation model of maize growth and development. Texas, USA: Texas A & M University Press.

Jones M, Singels A, Ruane A. (2014). Simulated impacts of climate change on water use and yield of irrigated sugarcane in South Africa. Short Non-Refereed Pap 86:184-189. 

 

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

 
 

Kang Y, Khan S, Ma X (2009). Climate change impacts on crop yield, crop water productivity and food security – A review. Prog. Nat. Sci. 19:1665-1674. 
Crossref

 
 

Kapetanaki G, Rosenzweig C (1997). Impact of climate change on maize yield in central and northern Greece: A simulation study with CERES-Maize. Mitig. Adapt. Strat. Glob. Chang. 1:251-271. 
Crossref

 
 

Kassie BT, Van Ittersum MK, Hengsdijk H, Asseng S, Wolf J, Rötter RP (2014). Climate-induced yield variability and yield gaps of maize (Zea

 
 

Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes J, Silburn M, Wang E, Brown S, Bristow K, Asseng S, Chapman S, McCown R, Freebairn D, Smith C (2003). An overview of APSIM, a model designed for farming

 
 

Khadka D, Pathak D (2016). Climate change projection for the marsyangdi river basin, Nepal using statistical downscaling of GCM and its implications in geodisasters. Geoenviron. Disasters 3:15.

 
 

Kim SH, Kim J, Walko R, Myoung B, Stack D, Kafatos M (2015a). Climate Change Impacts on Maize-yield Potential in the Southwestern United States. Procedia Environ Sci 29:279–280. 

 
 

Kim KB, Kwon HH, Han D (2015b). Bias correction methods for regional climate model simulations considering the distributional parametric uncertainty underlying the observations. J Hydrol 530:568–579. 

 
 

Kiniry JR, Williams JR, Vanderlip RL, Atwood JD, Reicosky DC, Mulliken J, Cox WJ, Mascagni HJJ, Hollinger SE, Wiebold WJ (1997). Evaluation of Two Maize Models for Nine U. S. Locations. Agron. J. 89:421-426.
Crossref

 
 

Kum D, Lim KJ, Jang CH, Ryu J, Yang JE, Kim SJ, Kong DS, Jung Y (2014). Using Three Bias-Correction Methods. Adv. Meteorol. 2014:12.
Crossref

 
 

Lapp S, Sauchyn D, Wheaton E (2008). Institutional Adaptations to Climate Change Project: Future Climate Change Scenarios for the South Saskatchewan River Basin. Prairie Adaptation Research Collaborative. Canada, 2008

 
 

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.
Crossref

 
 

Liu Y, Yang SJ, Li SQ, Chen F (2012). Application of the Hybrid-Maize model for limits to maize productivity analysis in a semiarid environment. Sci. Agric. 69:300-307. 
Crossref

 
 

Liu HLL, Yang JYY, Tan CSS, Drury CFF, Reynolds WDD, Zhang TQQ, Bai YLL, Jin J, He P, Hoogenboom G (2011). Simulating water content, crop yield and nitrate-N loss under free and controlled tile drainage with subsurface irrigation using the DSSAT model. Agric. Water Manag. 98:1105-1111. 
Crossref

 
 

Lizaso JI, Boote KJ, Jones JW, Porter CH, Echarte L, Westgate ME, Sonohat G (2011). CSM-IXIM: A New Maize simulation model for DSSAT version 4.5. Agron. J. 103:766-779.
Crossref

 
 

Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030. Sci. 319:607-610.
Crossref

 
 

Makadho JM (1996). Potential effects of climate change on corn production in Zimbabwe. Clim. Res. 6:147-151.
Crossref

 
 

McSweeney CF, Jones RG, Booth BBB, Lee RW, Rowell DP, Jones CFMRG, Rowell RWLDP (2012). Selecting ensemble members to provide regional climate change information. J. Clim. 25:7100-7121. 
Crossref

 
 

Mearns LO, Giorgi F, Whetton P, Pabon D, Hulme M, Lal M (2003). Guidelines for Use of Climate Scenarios Developed from Regional Climate Model Experiments. View.

 
 

Mearns LO, Hulme M (2001). Climate Scenario Development. In: Climate Change 2001: The Physical Science Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. pp 739-768. 

 
 

Mohanty M, Probert ME, Reddy KS, Dalal RC, Mishra AK, Subba Rao A, Singh M, Menzies NW (2012). Simulating soybean-wheat cropping system: APSIM model parameterization and validation. Agric. Ecosyst. Environ. 152:68-78.
Crossref

 
 

Motha RP (2011). Use of Crop Models for Drought Analysis. In: Sivakumar MVK, Motha RP, Wilhite DA, Wood DA eds. Agriculture Drought Indices: Proceedings of an Expert Meeting, June 2-4, 2010. Murcia, Spain: World Meteorological Organisation, pp 138-148.

 
 

Msongaleli BM, Rwehumbiza F, Tumbo SD, Kihupi N (2015). Impacts of Climate Variability and Change on Rainfed Sorghum and Maize: Implications for Food Security Policy in Tanzania. J. Agric. Sci. 7:124-142.
Crossref

 
 

Ministry of Tourism, Environment and Natural Resources (MTENR) (2010). National Climate Change Response Strategy (NCCRS) Ministry of Tourism, Environment and Natural Resources. Government of the Republic of Zambia. Lusaka, Zambia.

 
 

Ministry of Tourism, Environment and Natural Resources (MTENR), Global Environment Facility (GEF), United Nations Development Programme (UNDP) (2007). Formulation of the National Adaptation Programme of Action on Climate Change. Lusaka, Zambia: Ministry of Tourism, Environmental and Natural Resources.

 
 

Muchanga M (2012). A Survey of Public Particpation in Planning for Climate Change Adaptation among Selected Areas of Zambia's Lusaka Province. Am. Int. J. Contemp. Res. 2:81-90.

 
 

Myoung B, Kim SH, Stack DH, Kim J, Kafatos MC (2015). Temperature, Sowing and Harvest Dates, and Yield Potential of Maize in the Southwestern US. Procedia Environ. Sci. 29:276. 
Crossref

 
 

Nakicenovic N, Alcamo J, Davis G, Vries B de, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, Rovere EL La, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner HH, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, Rooijen S van, Victor N, Dadi Z (2000). Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: Cambridge University Press, Cambridge.

 
 

Navarro-Racines CE, Tarapues-Montenegro JE (2015). Bias-correction in the CCAFS-Climate Portal: A description of methodologies. Decision and Policy Analysis (DAPA) Research Area. Cali, Colombia: International Center for Tropical Agriculture (CIAT).

 
 

Nelson GC, Valin H, Sands RD, Havlík P, Ahammad H, Deryng D, Elliott J, Fujimori S, Hasegawa T, Heyhoe E, Kyle P, Von Lampe M, Lotze-Campen H, Mason d'Croz D, van Meijl H, van der Mensbrugghe D, Müller C, Popp A, Robertson R, Robinson S, Schmid E, Schmitz C, Tabeau A, Willenbockel D (2014). Climate change effects on agriculture: economic responses to biophysical shocks. PNAS 111:3274-3279. 
Crossref

 
 

Paeth H, Hall NMJ, Gaertner MA, Alonso MD, Moumouni S, Polcher J, Ruti PM, Fink AH, Gosset M, Lebel T, Gaye AT, Rowell DP, Moufouma-Okia W, Jacob D, Rockel B, Giorgi F, Rummukainen M (2011). Progress in regional downscaling of west African precipitation. Atmos. Sci. Lett. 12:75-82.
Crossref

 
 

Palijah S (2015). The implications of climate variability and change on rural household food security in Zambia: experiences from Choma District, Southern Province. MA thesis. University of Nairobi, Kenya

 
 

Pang XP, Gupta SC, Moncrief JF, Rosen CJ, Cheng HH (1998). Evaluation of Nitrate Leaching Potential in Minnesota Glacial Outwash Soils using the CERES-Maize Model. J. Environ. Quality 27:75-85. 
Crossref

 
 

Pang XP, Letey J, Wu L (1997). Yield and Nitrogen Uptake Prediction by CERES-Maize Model under Semiarid Conditions. Soil Sci. Soc. Am. J. 61:254-256.
Crossref

 
 

Pierce DW, Cayan DR, Maurer EP, Abatzoglou JT, Hegewisch KC (2015). Improved Bias Correction Techniques for Hydrological Simulations of Climate Change. J. Hydrometeorol. 16(6):2421-2442. 
Crossref

 
 

Piontek F, Müller C, Pugh TAM, Clark DB, Deryng D, Elliott J, De F, González JC, Flörke M, Folberth C, Franssen W, Frieler K, Friend AD, Gosling SN, Hemming D, Khabarov N, Kim H, Lomas MR, Masaki Y, Mengel M, Morse A, Neumann K, Nishina K, Ostberg S, Pavlick R, Ruane AC, Schewe J, Schmid E, Stacke T, Tang Q, Tessler ZD, Tompkins AM, Warszawski L, Wisser D, Schellnhuber HJ (2014). Multisectoral climate impact hotspots in a warming world. Proceedings of the National Academy of Sciences 111(9): 3233-3238.
Crossref

 
 

Ramirez-Villegas J, Jarvis A (2010). Downscaling Global Circulation Model Outputs: The Delta Method Decision and Policy Analysis.

 
 

Ramirez-Villegas J, Jarvis A, Läderach P (2013). Empirical approaches for assessing impacts of climate change on agriculture: The EcoCrop model and a case study with grain sorghum. Agric. For. Meteorol. 170:67-78. 
Crossref

 
 

Rauff KO, Bello R (2015). A Review of Crop Growth Simulation Models
Crossref

 
 

Reidsma P, Wolf J, Kanellopoulos A, Schaap BF, Mandryk M, Verhagen J, van Ittersum MK (2015). Climate Change Impact and Adaptation

 
 

Rosenzweig C, Iglesias A (1998). The use of crop models for international climate change impact assessment. In Understanding options for agricultural production. pp 267-292. Springer Netherlands.
Crossref

 
 

Rosenzweig C, Jones J, Antle J, Hatfield J (2015). Protocols for AgMIP Regional Integrated Assessments Version 6.0. p 62.

 
 

Rosenzweig C, Jones J, Hatfield J, Antle J (2013a). Guide for Regional Integrated Assessments: Handbook of Methods and Precedures Version 5.

 
 

Rosenzweig C, Jones JW, Hatfield JR, Antle JM, Ruane AC, Boote KJ, Thorburn PJ, Valdivia RO, Porter CH, Janssen S, Wiebe K, Mutter CZ, Lifson S, Contreras EM, Athanasiadis I, Baigorria G, Cammarano D, Descheemaeker K, Hoogenboom G, Lizaso J, McDermid S, Wallach D, Adiku SDK, Ahmad A, Beletse Y, Dileepkumar G, Kihara J, Masikati P, Ponnusamy P, Subash N, Rao KPC, Zubair L (2014). Regional Integrated Assessments of Farming Systems in Sub-Saharan Africa and South Asia. Summary Report: Phase 1.

 
 

Rosenzweig C, Jones JW, Hatfield JL, Ruane A, Thornburn KJ, Antle JM, Nelson GC, Porter C, Janssen S, Basso B, Ewert F, Wallach D, Baigorria G, Winter JM (2013b). The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies. Agric. For. Meteorol. 170:166-182.
Crossref

 
 

Rötter R, Höhn J (2015). An overview of climate change impact on crop production and its variability in Europe, related uncertainties and research challenges. In: Elbehri A ed. Climate change and food systems: global assessments and implications for food security and trade. Rome, Italy: Food Agriculture Organization of the United Nations (FAO), 40.

 
 

Rötter RP, Tao F, Höhn JG, Palosuo T (2015). Use of crop simulation modelling to aid ideotype design of future cereal cultivars. J. Exp. Bot. 66:3463-3476. doi: 
Crossref

 
 

Ruiter A (2012). Delta-change approach for CMIP5 GCMs. Internship Report version 3. De Bilt.

 
 

Ruiz-Ramos M, Mínguez MI (2010). Evaluating uncertainty in climate change impacts on crop productivity in the Iberian Peninsula. Clim. Res. 44:69-82. doi: 
Crossref

 
 

Salvacion AR (2011). Simulating Impact of Climate Change in Crop Productivity Using Future Climate Projections and DSSAT Crop Simulation Models: Guide Module. Laguna, Philippines: School of Science and Management, University of the Philippines Los Ba-os. 

 
 

Santoso H, Idinoba M, Imbach P (2008). Climate Scenarios: What we need to know and how to generate them. Bogor, Indonesia.

 
 

Sarkar R (2009). Use of DSSAT to model cropping systems. CAB Rev. Perspect. Agric. Vet. Sci. Nutr. Nat. Resour. 4:1-12.
Crossref

 
 

Sarkar R, Kar S (2006). Evaluation of management strategies for sustainable rice – wheat cropping system, using DSSAT seasonal analysis. J. Agric. Sci. 144:421-434. 
Crossref

 
 

Sebastian K (2014). ATLAS of African Agriculture Research and Development: Revealing agriculture's place in Africa. Washington, DC: International Food Policy Research Institute. 

 
 

Semenov MA, Barrow EM (2002). LARS-WG: A Stochastic Weather Generator for Use in Climate Impact Studies version 3. User Manual. User Manual, Hertfordshire, UK:27.

 
 

Sharma D, Gupta A Das, Babel MS (2007). Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand. Hydrol. Earth Syst. Sci. Discuss 11:1373-1390. 

Sichingabula HM (1998). Rainfall variability, drought and implications of its impacts on Zambia, 1886-1996. IAHS-AISH Publ:125–134.

 

Sinclair TR, Seligman NG (1995). Crop Modeling: From Infancy to Maturity. Agron. J. 88:698-704. 

 
 

Soler CM, Sentelhas PC, Hoogenboom G (2005). Thermal time for phenological development of four maize hybrids grown off-season in

 
 

Soler CMT, Sentelhas PC, Hoogenboom G (2007). Application of the

 
 

Steduto P, Hsiao TC, Raes D, Fereres E (2009). AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles. Agron. J. 101:426-437. 

 
 

Stockdale TN, Alves O, Boer G, Deque M, Ding Y, Kumar A, Kumar K, Landman W, Mason S, Nobre P, Scaife A, Tomoaki O, Yun WT (2010). Understanding and Predicting Seasonal-to-Interannual Climate Variability -The Producer Perspective. Procedia Environ. Sci. 1:55-80.
Crossref

 
 

Surampalli RY, Zhang TC, Ojha CSP, Gurjar BR, Tyagi RD, Kao CM (eds.) (2012). Climate change: modeling, mitigation and adapting. American Society of Civil Engineers (ASCE). 

 
 

Switanek MB, Troch PA, Castro CL, Leuprecht A, Chang H, Mukherjee R, Demaria EMC (2016). Scaled distribution mapping : a bias correction method that preserves raw climate model projected changes. Hydrol. Earth Syst. Sci. Discuss 30:1-31.
Crossref

 
 

Tachie-Obeng E, Akponikpè PBI, Adiku S (2013). Considering effective adaptation options to impacts of climate change for maize production in Ghana. Environ. Dev. 5:131-145. 
Crossref

 
 

The World Bank (2007). Agriculture for Development. World development report 2008. Washington DC 20433: The World Bank. 

 
 

Thorp KR, Youssef MA, Jaynes DB, Malone RW, Ma L (2009). DRAINMOD-N II: Evaluated for an agricultural system in Iowa and compared to RZWQM-DSSAT. Transactions of the ASABE 52(5):1557-1573.
Crossref

 
 

Trzaska S, Schnarr E (2014). A Review of Downscaling Methods for Climate Change Projections. African and Latin American Resilience to Climate Change (ARCC).

 
 

Tsimba R (2011). Development of a decision support system to determine the best maize (Zea mays. L) hybrid-planting date option under typical New Zealand management systems. Massey University.

 
 

Tsimba R, Edmeades GO, Millner JP, Kemp PD (2013). The effect of planting date on maize grain yields and yield components. Field Crop Res. 150:135-144. 
Crossref

 
 

Tumbo SD, Kahimba FC, Mbilinyi BP, Rwehumbiza FB, Mahoo HF, Mbungu WB, Enfors E, Planning L, Centre SR (2012). Impact of Climate Change on Agricultural Production in Semi-Arid Areas of Tanzania: A Case of Same District. Afr. Crop Sci. J. 20:453-463.

 
 

Turral H, Burke J, Faurès JM, Faurés JM (2011). Climate change, water and food security. FAO. Rome, Italy.

 
 

UNDP (2010). Adaptation to the effects of drought and climate change in Agro-ecological Regions I and II in Zambia. Lusaka, Zambia.

 
 

UNFCCC (2012). Agriculture. In: CGE Training Materials for Vulnerability and Adaptation Assessment. 67.

 
 

Uthes S, Ndah HT, Triomphe B, Schuler J, Zander P (2011). Conservation Agriculture in AFRICA : Analysing and Foreseeing its Impact - Comprehending its Adoption. 

View

 
 

Valdivia RO, Antle JM, Rosenzweig C, Ruane AC, Vervoort J, Ashfaq M, Hathie I, Hathie SHK, Mulwa R, Nhemachena C, Ponnusamy P, Rasnayaka H, Singh H (2015). Representative Agricultural Pathways and Scenarios for Regional Integrated Assessment of Climate Change Impact, vulnerability and adaptation. In: Rosenzweig C, Hillel

 
 

Vučetić V (2006). Modelling of the maize production and the impact of climate change on maize yields in Croatia. Grič 3, Zegreb, Croatia.

 
 

Wang Q (2015). Linking APCC Seasonal Climate Forecasts to a Rice-Yield Model for South Korea. South Korea: APEC Climate Center.

 
 

Wang E, Cresswell H, Bryan B, Glover M, King D (2009). Modelling farming systems performance at catchment and regional scales to support natural resource management. NJAS - Wageningen J. Life Sci. 57:101-108.
Crossref

 
 

Wenjiao S, Fulu T, Zhao Z (2013). A review on statistical models for identifying climate contributions to crop yields. J. Geography Sci. 23:567-576. 

 
 

Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO, Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004). Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. available from the DDC of IPCC TGCIA, 27

 
 

Wilby RL, Dawson CW (2007). SDSM 4.2-A decision support tool for the assessment of regional climate change impacts, Version 4.2 User Manual. Lancaster Univ Lancaster/Environment Agency England Wales: pp 1-94.

 
 

Wilby RL, Dawson CW, Barrow EM (2002). SDSM - A Decision Support Tool for the Assessment of Regional Climate Change Impacts. Environ. Model Softw. 17:145-157. 
Crossref

 
 

Wilby RL, Wigley TML (1997). Downscaling general circulation model output: a review of methods and limitations. Prog. Phys. Geography 21:530-548.
Crossref

 
 

Yang HS, Dobermann A, Lindquist JL, Walters DT, Arkebauer TJ, Cassman KG (2004). Hybrid-maize - A maize simulation model that combines two crop modeling approaches. Field Crop Res. 87:131-154. 
Crossref

 
 

Yang J, Greenwood DJ, Rowell DL, Wadsworth GA, Burns IG (2000). Statistical methods for evaluating a crop nitrogen simulation model, N ABLE. Agric. Syst. 64:37-53.
Crossref

 
 

Yin C, Li Y, Urich P (2013). SimCLIM 2013 Data Manual.

 
 

Zare H, Fallah MH, Asadi S, Mojab A, Bannayan M (2016). Assessment of DSSAT and WOFOST sensitivity to temperature derived from AgMERRA. In: International Crop Modelling Symposium, iCROPM, 15-17 March 2016. Berlin, pp 434-435.

 
 

Zinyengere N, Crespo O, Hachigonta S, Tadross M (2014). Local impacts of climate change and agronomic practices on dry land crops in Southern Africa. Agric. Ecosyst. Environ. 197:1-10. 
Crossref

 
 
 

 


APA Chisanga, C. B., Phiri, E., & Chinene, V. R. N. (2017). Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review. Scientific Research and Essays, 12(18), 167-187.
Chicago Charles B. Chisanga, Elijah Phiri and Vernon R. N. Chinene. "Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review." Scientific Research and Essays 12, no. 18 (2017): 167-187.
MLA Charles B. Chisanga, Elijah Phiri and Vernon R. N. Chinene. "Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review." Scientific Research and Essays 12.18 (2017): 167-187.
   
DOI https://doi.org/10.5897/SRE2017.6521
URL http://academicjournals.org/journal/SRE/article-abstract/A74E94366446

Subscription Form