With the changing climate, reliable climate projections are essential for agriculture risk management. The present study aims to explore the output of a regional climate model (RCM) at different climatic regimes and its applications in crop simulation models. Here, a comparative study of the cotton crop growth and yield response for Akola in the central and Hisar northern agroclimatic zone of India represents rainfed and irrigated growing regions of cotton, respectively. The RegCM4 projections and its bias-corrected values of temperature and precipitation data for the period 1971–2005 are compared with the observations to assess its reliability with the crop simulation models as weather inputs. The results signify that the RCM model is wet, which implies that, it shows high rainfall intensity in terms of frequency as a number of rainy days and amount. The model also shows night warming as there is a significant decline in maximum temperature and minimal decline in minimum temperature, thus there is a reduced diurnal temperature difference. Overall model highly underestimates temperature and overestimates rainfall. Strikingly reduced numbers of intense warm and cold events are simulated. Model is highly biased for rainfall events >0 mm/day and 5mm/day, and moderately biased for rainfall >5 mm/day. Precipitation bias-correction, using quantile mapping approach, shows excellent agreement at an annual scale. But precipitation variability could not be captured that well as it is a ‘distribution-based method'. However, it worked well in the irrigated Hisar region than the rainfed Akola region. The bias-corrected RegCM4 climate inputs are utilized in Decision Support System for Agro-technology Transfer (DSSAT) simulations for cotton yields, Leaf Area Index (LAI) and ball number at maturity/m2 (NM) for both regions. Bias-corrected outputs are in better agreement with corresponding observations than non-bias-corrected outputs in both regions. Future research could apply these simulated model data complemented with reliable bias correction techniques to explicitly study climate change's impact on crop productivity.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price includes VAT (USA)
Tax calculation will be finalised during checkout.
Aggarwal P K, Kalra N, Chander S and Pathak H 2006 InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I: Model description; Agric. Syst. 89 1–25.
Ahmad S, Abbas Q, Abbas G, Fatima Z, Naz S, Younis H, Khan R, Nasim W, Habib ur Rehman M, Ahmad A and Rasul G 2017 Quantification of climate warming and crop management impacts on cotton phenology; Plants 6 7.
Ahmed K F, Wang G, Silander J, Wilson A M, Allen J M, Horton R and Anyah R 2013 Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the US northeast; Glob. Planet. Change 100 320–332.
Anapalli S, Fisher D, Reddy K, Pettigrew W, Sui R and Ahuja L 2016a Vulnerabilities and adapting irrigated and rainfed cotton to climate change in the Lower Mississippi Delta Region; Climate 4 55.
Anapalli S S, Pettigrew W T, Reddy K N, Ma L, Fisher D K and Sui R 2016b Climate-optimized planting windows for cotton in the Lower Mississippi Delta Region; Agronomy 6(4) 46.
Anwar M R, Li Liu D, Farquharson R, Macadam I, Abadi A, Finlayson J, Wang B and Ramilan T 2015 Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia; Agric. Syst. 132 133–144.
Araya A, Hoogenboom G, Luedeling E, Hadgu K M, Kisekka I and Martorano L G 2015 Assessment of maize growth and yield using crop models under present and future climate in southwestern Ethiopia; Agric. Meteorol. 214(21) 252–265.
Beamish A L, Nijland W, Edwards M, Coops N C and Henry G H 2016 Phenology and vegetation change measurements from true colour digital photography in high Arctic tundra; Arct. Sci. 2 33–49.
Casanueva A, Bedia J, Herrera S, Fernández J and Gutiérrez J M 2018 Direct and component-wise bias-correction of multi-variate climate indices: The percentile adjustment function diagnostic tool; Clim. Change 147 411–425.
Chai T and Draxler R R 2014 Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature; Geosci. Model. Dev. 7 1247–1250.
Choudhary A, Dimri A P and Maharana P 2018 Assessment of CORDEX-SA experiments in representing precipitation climatology of summer monsoon over India; Theor. Appl. Climatol. 134(1) 283–307.
Dason A A, Raj D, Sivananam L, Muthusankaranarayanan A and Rajagopalan S 1975 Influence of weed competition at different stages of crop growth on the yield of rainfed cotton; Madras Agric. J. 62(8) 508–512.
Deshpande L 2007 Long Staple Cotton Scenario in Maharashtra and Future Prospects; Project Coordinator & Head, AICRP on Cotton, Coimbatore.
Gao X and Giorigi F 2017 Use of the RegCM system over East Asia: Review and perspectives; Engineering 3 766–772.
Panjwani S, Kumar S N, Ahuja L and Islam A 2020 Evaluation of selected global climate models for extreme temperature events over India; Theor. Appl. Climatol. 140(2) 1–8.
Giorgi F, Coppola E, Solmon F, Mariotti L, Sylla M B, Bi X, Elguindi N, Diro G T, Nair V, Giuliani G and Turuncoglu U U 2012 RegCM4: Model description and preliminary tests over multiple CORDEX domains; Clim. Res. 52 7–29.
Giorgi F and Gutowski Jr W J 2015 Regional dynamical downscaling and the CORDEX initiative; Annu. Rev. Env. Resour. 40 467–490.
Gudmundsson L 2014 Qmap: Statistical Transformations for Post-Processing Climate Model Output.
Gudmundsson L, Bremnes J B, Haugen J E and 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(9) 3383–3390.
Guinn G 1986 Hormonal relations during reproduction; In: Cotton Physiology, The Cotton Foundation, Memphis, TN, pp. 113–136.
Gutowski W J, Decker S G, Donavon R A, Pan Z, Arritt R W and Takle E S 2003 Temporal–spatial scales of observed and simulated precipitation in central U.S. climate; J. Climate 16(22) 3841–3847.
Hebbar K B, Venugopalan M V, Khadi B M and Aggarwal P K 2007 Impact from climate change on cotton production (abstracts). National Conference on Impact of Climate Change with Particular Reference to Agriculture organized by Agro-Climate Research Centre, Tamil Nadu Agricultural University during 22–24, August at Coimbatore.
Hebbar K B, Venugopalan M V, Prakash A H and Aggarwal P K 2013 Simulating the impacts of climate change on cotton production in India; Clim. Change 118 701–713.
Hoogenboom G, Porter C H, Shelia V, Boote K J, Singh U, White J W, Hunt L A, Ogoshi R, Lizaso J I, Koo J, Asseng S, Singels A, Moreno L P and Jones J W 2019 Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7.5 (https://DSSAT.net). DSSAT Foundation, Gainesville, Florida, USA.
ICAC 2009 Global Warming and Cotton Production – Part II; The International Cotton Advisory Committee Recorder 27 9–12.
IPCC 2014 Climate Change 2014: Synthesis Report; Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds) Pachauri R K and Meyer L A, IPCC, Geneva, Switzerland, 151p.
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 and Ritchie J T 2003 The DSSAT cropping system model; Europian J. Agron. 18(3–4) 235–265.
Khargkharate V K, Ghanbahadur M, Pathrikar V B, Gaikwad J M and Shaikh S A 2017 Production potential of hirsutum COTTON (AKH-081) as affected by plant density and nutrient management under rainfed condition of Vidarbha Region; Int. J. Pure Appl. Biosci. 5 1189–1194.
Kumar R, Bhattoo M S, Punia S S, Bhusal N and Yadav S 2014 Performance of different Bt cotton (Gossypium hirsutum L.) hybrids under varying dates of sowing; J. Cotton Res. Dev. 28 263–264.
Liu Y, Wang E, Yang X and Wang J 2010 Contributions of climatic and crop varietal changes to crop production in the North China Plain, since 1980s; Glob. Change Biol. 16 2287–2299.
Liu Z, Yuan Y L, Liu S Q, Yu X N and Rao L Q 2006 Screening for high-temperature tolerant cotton cultivars by testing in vitro pollen germination, pollen tube growth and boll retention; J. Integrat. Plant Biol. 48 706–714.
Loka D A 2012 Effect of water-deficit stress on cotton during reproductive development; Ph.D. Dissertation, University of Arkansas, Fayetteville, Ark.
Mall R K, Singh N, Singh K K, Sonkar G and Gupta A 2018 Evaluating the performance of RegCM4.0 climate model for climate change impact assessment on wheat and rice crop in diverse agro-climatic zones of Uttar Pradesh, India; Clim. Change 149 503–515.
Maraun D 2016 Bias-correcting climate change simulations – a critical review; Curr. Clim. Change Rep. 2 211–220.
Mauney J R 1986 Cotton Physiology (No. 1). Cotton Foundation.
Mclaughlin J E and Boyer J S 2004 Glucose localization in maize ovaries when kernel number decreases at low water potential and sucrose is fed to the stems; Ann. Bot. 94 75–86.
Miley W N and Oosterhuis D M 1990 Nitrogen nutrition of cotton: Practical Issues, Chap. 1, American Society of Agronomy (ASA), Wiley and Sons, Madison, WI, pp. 1–24.
Metzger M J, Leemans R and Schroter D A 2005 multidisciplinary multi-scale framework for assessing vulnerabilities to global change; Int. J. Appl. Earth Observ. Geoinform. 7 253–267.
Mishra V, Kumar D, Ganguly A R, Sanjay J, Mujumdar M, Krishnan R and Shah R D 2014 Reliability of regional and global climate models to simulate precipitation extremes over India; J. Geophys. Res. Atmos. 119(15) 9301–9323.
Nath A, Karunakar A P, Kumar A, Yadav A, Chaudhary S and Singh S P 2017 Evaluation of the CROPGRO-soybean model (DSSAT v 4.5) in the Akola region of Vidarbha, India; Ecol. Environ. Conserv. 23 153–159.
Nelson G C and Shively G E 2014 Modeling climate change and agriculture: An introduction to the special issue; Agric. Econ. 45 1–2.
Oosterhuis D M 1999 Yield response to environmental extremes in cotton; Special Reports-University of Arkansas Agricultural Experiment Station 193 30–38.
Pareek N, Roy S, Saha S and Nain A S 2017 Calibration and validation of Aquacrop model for wheat crop in Tarai region of Uttarakhand; J. Pharmacogn. Phytochem. 6 1442–1445.
Pathak T B, Jones J W, Fraisse C W, Wright D and Hoogenboom G 2012 Uncertainty analysis and parameter estimation for the CSM-CROPGRO-Cotton model; Agron. J. 104 1363–1373.
Pattnayak K C, Panda S K, Saraswat V and Dash S K 2018 Assessment of two versions of regional climate model in simulating the Indian Summer Monsoon over South Asia CORDEX domain; Clim. Dyn. 50 3049–3061.
Pettigrew W T 2004 Physiological consequences of moisture deficit stress in cotton; Crop Sci. 44 1265–1272.
Qian B, Jong R D, Huffman T, Wang H and Yang J 2016 Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies; Theor. Appl. Climatol. 123 651–669.
Rajczak J and Schär C 2017 Projections of future precipitation extremes over Europe: A multimodel assessment of climate simulations; J. Geophys. Res. Atmos. 122(20) 10,773–10,800.
Reddy K R, Doma P R, Mearns L O, Boone M Y, Hodges H F, Richardson A G and Kakani V G 2002 Simulating the impacts of climate change on cotton production in the Mississippi delta; Clim. Res. 22 271–281.
Reddy K R, Hodges H F and Reddy V R 1992 Temperature effects on cotton fruit retention; Agron. J. 84 26–30.
Reddy K R and Zhao D 2005 Interactive effects of elevated CO2 and potassium deficiency on photosynthesis, growth, and biomass partitioning of cotton; Field Crops Res. 94(2–3) 201–213.
Sankaranarayanan K, Praharaj C S, Nalayini P, Bandyopadhyay K K and Gopalakrishnan N 2010 Climate change and its impact on cotton (Gossypium sp.); Indian J. Agric. Sci. 80 561–575.
Shi Y, Wang G and Gao X 2018 Role of resolution in regional climate change projections over China; Clim. Dyn. 51(5) 2375–2396.
Shikha A, Dimri A P, Maharana P, Singh K K and Niwas R 2018 Cotton crop in changing climate; Curr. Sci. 115(5) 948–954.
Shikha A, Singh K K, Dimri A P, Niwas R and Maharana P 2019 Model-based approach to study the response of Bt-cotton towards elevated temperature and carbon dioxide in the semi-arid region of Hisar; J. Clim. Change 5 35–50.
Shikha A and Bhuyan S 2017 Cotton crop: Various aspects and transition from past, present and future; Int. J. Agric. Environ. Sci. 4 27–31.
Singh R P, Vara Prasad P V, Sunita K, Giri S N and Reddy K R 2007 Influence of high temperature and breeding for heat tolerance in cotton: A review; Adv. Agron. 93 313–385.
Stewart J M, Oosterhui D, Heitholt J J and Mauney J R (eds) 2009 Physiology of Cotton. Springer Science and Business Media.
Taylor K E, Stouffer R J and Meehl G A 2012 An overview of CMIP5 and the experiment design; Bull. Am. Meteorol. Soc. 93 485–498.
Teutschbein C and Seibert J 2012 Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?; Hydrol. Earth Syst. Sci. Discuss. 17 5061–5077.
Thrasher B L, Maurer E P, McKellar C and Duffy P B 2012 Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping; Hydrol. Earth Syst. Sci. Discuss. 9(4) 5515–5529.
Turco M, Llasat M C, Herrera S and Gutierrez J M 2017 Bias-correction and downscaling of future RCM precipitation projections using a MOS-Analog technique; J. Geophys. Res. Atmos. 122(5) 2631–2648.
Turner N C, Hearn A B, Begg J E and Constable G A 1986 Cotton (Gossypium hirsutum L.) physiological and morphological responses to water deficits and their relationship to yield; Field Crops Res. 14 153–170.
Zhao T, Bennett J C, Wang Q J, Schepen A, Wood A W, Robertson D E and Ramos M H 2017 How suitable is quantile mapping for postprocessing GCM precipitation forecasts?; J. Clim. 30 3185–3196.
For the present study, data from the field study has been a part of the FASAL project. ‘Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL)’, which is operational at Ministry of Agriculture, under Govt. of India in collaboration with India Meteorological Department (IMD), Space Application Centre (SAC) Mahalanobis National Crop Forecasting Centre (MNCFC) and Institute of Economic Growth (IEG). For this IMD collaborates with Agromet Field units (AMFUs) located at different State Agricultural Universities (SAUs), ICAR, IITs, etc., to develop yield forecasts. For the climate projections, we would like to gratefully acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, the former coordinating body of CORDEX and responsible panel for CMIP5. The climate modelling groups are sincerely thanked for producing and making available their model output. The authors thank the Earth System Grid Federation (ESGF) infrastructure and the Climate Data Portal hosted at the Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology (IITM) for providing CORDEX South Asia data.
Communicated by Kavirajan Rajendran
About this article
Cite this article
Shikha, A., Dimri, A.P., Singh, K.K. et al. Evaluating the performance of RegCM4 in studies on irrigated and rainfed cotton crops. J Earth Syst Sci 130, 198 (2021). https://doi.org/10.1007/s12040-021-01705-z
- Climate change
- quantile mapping