Skip to main content

Advertisement

Log in

Assessing the influence of climate model biases in predicting yield and irrigation requirement of cassava

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

The present study is conducted to assess the influence of climate model biases in the predictions of yield and water requirement of cassava in one of the major cassava growing regions in India. Simple linear bias correction methods are used for temperature, and non-linear corrections are used for other meteorological variables. The WOFOST and CROPWAT models are used to predict the crop yield and water requirement of cassava using the scenarios of 2030, 2050, and 2070 for the representative concentration pathway 4.5 derived from the Long Ashton Research Station Weather Generator (LARS-WG). The percentage change in crop yield predictions with and without bias corrections of meteorological variables ranges from 7.6 to 10.8%, 1.6 to 5.4%, and − 3.0 to 4.0% respectively for 2030, 2050, and 2070. The bias corrections made an increment in the gross irrigation requirements of cassava with 16.5, 17.8, and 16.0% in 2030, 2050, and 2070 respectively, compared to the values without bias corrections. The outcome of this study indicates that raw meteorological variables directly from the climate models result over-/underestimation of yield and irrigation requirements of cassava, and the bias corrections help to issue reliable crop yield predictions. Results show zero yield reductions of cassava until 2050, and beyond that, there can be reductions in the crop yield. The gross irrigation requirements of cassava increase in the future to achieve higher productivity. However, this study needs to extend to other major growing regions in India to derive a general conclusion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Availability of data and materials

NASA POWER data (https://power.larc.nasa.gov/data-access-viewer/) with 0.5° × 0.5° resolution derived for the study location was bias corrected with reference to the observations. Further, future scenarios of 2030, 2050, and 2070 were derived using the Long Ashton Research Station Weather Generator, LARS-WG (Semenov and Porter 1994; Semenov et al. 1998). The crop and soil data needed for the study were collected from the annual reports (2012–2015) of ICAR-CTCRI.

References

  • Annual Report (2012–2013) In: George J and Sunitha S (ed) Indian Council of Agricultural Research (ICAR)-Central Tuber Crops Research Institute (CTCRI), Thiruvananthapuram, Kerala, India. pp 124

  • Annual Report (2013–2014) In: George J and Sunitha S (ed)  Indian Council of Agricultural Research (ICAR)-Central Tuber Crops Research Institute (CTCRI), Thiruvananthapuram, Kerala, India. pp 159

  • Annual Report (2014–2015) In: George J, Sunitha S, Immanuel S (ed) Indian Council of Agricultural Research (ICAR)-Central Tuber Crops Research Institute (CTCRI), Thiruvananthapuram, Kerala, India. pp 168

  • Awal R, Fares A, Bayabil H (2018) Assessing potential climate change impacts on irrigation requirements of major crops in the Brazos Headwaters Basin. Texas Water 10:1610. https://doi.org/10.3390/w10111610

    Article  Google Scholar 

  • Bakker AMR, Bessembinder JJE, de Wit AJW, van den Hurk JJM, Hoek SB (2014) Exploring the efficiency of bias corrections of regional climate model output for assessment of future cop yields in Europe. Reg Environ Change 14:865–877

    Google Scholar 

  • Boogaard HL, Van Diepen CA, Rötter RP, Cabrera JMCA, Van Laar HH (1998) User’s guide for the WOFOST 7.1 crop growth simulation model and WOFOST control center 1.5. Technical Document 52.Winand Staring Centre,Wageningen, the Netherlands, 144 pp

  • Brovkin V, Boysen L, Raddatz T, Gayler V, Loew A, Claussen M (2013) Evaluation of vegetation cover and land-surface albedo in MPI-ESM CMIP5 simulations. J Adv Model Earth Syst 5(1):48–57

    Article  Google Scholar 

  • Byju G, Suja G (2020) Chapter 5—Mineral nutrition of cassava. AdvAgron 159:169–235

    Google Scholar 

  • Cammanaro D, Rivington M, Matthews KB, Miller DG, Bellocchi G (2017) Implications of climate model biases and downscaling on crop model-simulated climate change impacts. European Journal of Agronomy 88: 63–75

  • Collins et al (2011) Development and evaluation of an Earth-System model—HadGEM2. Geosci Model Dev 4:1051–1075

    Article  Google Scholar 

  • Galmarini S, Cannon AJ, Ceglar A, Christensen OB et al (2019) Adjusting climate model biases for agricultural impact assessment: How to cut the mustard. ClimServ 13:65–69

    Google Scholar 

  • Hadinia H, Pirmoradian N, Ashrafzadeh A (2017) Effect of changing climate on rice water requirement in Guilan, north of Iran. J Water Clim Chang. https://doi.org/10.2166/wcc.2016.025

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hazeleger et al (2012) EC-Earth V2.2: description and validation of a new seamless earth system prediction model. ClimDyn 39:2611–2629

    Google Scholar 

  • Islam R, Islam MdM, Islam MdN, Islam MN, Sen S, Kamal R (2020) Climate change adaptation strategies: a prospect toward crop modeling and food security management. Model Earth Syst Environ 6:769–777

    Article  Google Scholar 

  • Jakariya Md, Sarker SR, Sayem SM, Saad S, Islam MdN, Rahman A, Alam MdS, Ali MS, Akter D (2020) Nexus among rice production and environmental factors in the coastal region of Bangladesh: a stochastic modeling approach for future forecasting. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-00969-6

    Article  Google Scholar 

  • Liu DL, Wang B, Evans J, Ji F, Waters C, Macadam I, Yang X, Beyer K (2018) Propagation of climate model biases to biophysical modeling can complicate assessments of climate change impact in agricultural systems. Int J Climatol 39:424–444

    Article  Google Scholar 

  • Madhukar A, Dashora K, Kumar V (2020) Investigating historical climate impacts on wheat yield in India using a statistical modeling approach. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-00932-5

    Article  Google Scholar 

  • Martin S (1992) CROPWAT-A computer program for irrigation planning and management FAO Irrigation and Drainage Paper No. 46. FAO Land and Water Development Division. Food and Agriculture Organization of the United Nations, Rome, Italy

    Google Scholar 

  • Mishra AK, Ines AVM, Singh VP, Hansen JW (2013) Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation. Stoch Environ Res Risk Assess 27:449–457

    Article  Google Scholar 

  • Musayev S, Burgess E, Mellor J (2018) A global performance assessment of rainwater harvesting under climate change. ResourConservRecycl 132:62–70

    Google Scholar 

  • Pushpalatha R, Byju G (2020) Is cassava a climate “smart” crop? A review in the context of bridging future food demand gap. Trop Plant Biol. https://doi.org/10.1007/s12042-020-09255

    Article  Google Scholar 

  • Pushpalatha R, Sunitha SA, George J, Shiny R, Byju G (2020) Development of optimal irrigation schedules and crop water production function for cassava: study over three major growing areas in India. IrrigSci. https://doi.org/10.1007/s00271-020-00669-0

    Article  Google Scholar 

  • Ruiz-Ramos M, Sánchez E, Gallardo C, Mínguez MI (2011) Impacts of projected maximum temperature extremes for C21 by an ensemble of regional climate models on cereal cropping systems in the Iberian Peninsula. Nat Hazards Earth SystSci 11:3275–3291

    Article  Google Scholar 

  • Ruiz-Ramos M, Rodriguez A, Dosio A, Goodess CM, Harpham C, Minguez MI, Sanchez E (2016) Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century. Climatic Change 134:283–297

    Article  Google Scholar 

  • Saranraj P, Behera SS, Ray RC (2019) Traditional foods from topical root and tuber crops. Innovations in traditional foods, Chapter 7. In: Galanakis CM (ed) Food waste recovery group. ISEKI Food Association, Vienna Austria

    Google Scholar 

  • Semenov MA, Porter JR (1994) The Implications and Importance of Non-Linear Responses in Modelling of Growth and Development of Wheat. In: Grasman, J. and van Straten G (eds.) Predictability and Non-Linear Modelling in Natural Sciences and Economics, Wageningen.

  • Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107

    Article  Google Scholar 

  • Shagega FP, Munishi SE, Kongo VM (2020) Prediction of future climate in Ngerengere river catchment, Tanzania. PhysChem Earth Parts A/B/C 112:200–209

    Article  Google Scholar 

  • Sunil A, Deepth B, Mirajkar AB, Adarsh S (2020) Modeling future irrigation water demands in the context of climate change: a case study of Jayakwadi command area, India. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-00955-y

    Article  Google Scholar 

  • Toros H, Mokari M, Abbasnia M (2019) Regional variability of temperature extremes in the maritime climate of Turkey: a case study to develop agricultural adaptation strategies under climate change. Model Earth Syst Environ 5:857–865

    Article  Google Scholar 

  • Watanabe S, Hajima T, Sudo K, Nagashima T et al (2011) MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev 4:845–872

    Article  Google Scholar 

  • Zhang T, Li T, Yang X, Simelton E (2016) Model biases in rice phenology under warmer climates. Sci Rep. https://doi.org/10.1038/srep27355

    Article  Google Scholar 

  • Zhou T, Wu P, Sun S, Li X, Wang Y, Luan X (2017) Impact of future climate change on regional crop water requirement—A case study of Hetao Irrigation district. China Water 9:429. https://doi.org/10.3390/w9060429

    Article  Google Scholar 

Download references

Acknowledgements

We are thankful to Women Scientist Scheme, Department of Science & Technology, India (DST WOS-A); ICAR-Central Tuber Crops Research Institute (ICAR-CTCRI), Thiruvananthapuram, India; and All India Coordinated Research Project on Tuber Crops (AICRP-TC) for the complete support to conduct this study.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

RP designed and performed the work. GB supervised and analysed the results.

Corresponding author

Correspondence to Byju Gangadharan.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pushpalatha, R., Gangadharan, B. Assessing the influence of climate model biases in predicting yield and irrigation requirement of cassava. Model. Earth Syst. Environ. 7, 307–315 (2021). https://doi.org/10.1007/s40808-020-01038-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-020-01038-8

Keywords

Navigation