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Impact of climate change on the potential geographical suitability of cassava and sweet potato vs. rice and potato in India

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Abstract

The current study focused on determining the potential geographical suitability of cassava and sweet potato, two major tropical root crops of India, using the species distribution model, MaxEnt and QGIS. The model showed excellent performance based on the AUC (area under the ROC curve) values (> 0.8) obtained during training and testing. District wise geographical suitability is analysed, and the results indicate an increase in the geographical suitability of cassava in the future with a percentage increase of 42, 41; 32, 43; and 33, 32 for 2030, 2050, and 2070, respectively, for the two representative concentration pathways (RCPs) 4.5 and 8.5. The same for sweet potato is 32, 25; 27, 31; and 23, 21, respectively. The geographical suitability of rice and potato is also tested in India in the future to compare the results of cassava and sweet potato. The percentage increase in rice suitability is 17, 15; 15, 17; and 13, 11 for 2030, 2050, and 2070, respectively, for the two RCPs about its current growing locations. The increase in geographical suitability of potato is 10, 11; 10, 9; and 10, 9%, respectively. The outcome of this study shares information about the highly suitable districts for cassava, sweet potato, rice, and potato across the Indian subcontinent in the future. It can assist the decision managers in diversifying crops to highly suitable areas to ensure food security.

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Availability of data and materials

The bioclimatic variables of current and future data are downloaded from Worldclim 2.1 (Fick and Hijmans 2017) and are masked for the Indian subcontinent; further these data are converted into ‘asc’ format for the MaxEnt model using the R and QGIS softwares. The district wise crop presence point data are collected from the report of All India Coordinated Research Project on Tuber Crops (AICRP-TC) of 2017–2018 (George and Sunitha, 2017–2018) and Horticultural Statistics of Govt. of India (Saxena et al. 2018).

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References

  • Balasubramanian M, Birundha VD (2012) Climate change and its impact on India. IUP J Environ Sci V I(1):31–46

    Google Scholar 

  • Boucher O, Servonnat J, Albright AL, Aumont O, Balkanski Y, Bastrikov V, et al. (2020) Presentation and evaluation of the IPSL-CM6A-LR climate model. J Adv Model Earth Syst 12: e2019MS002010. https://doi.org/10.1029/2019MS002010

  • Byju G, Suja G (2020) Chapter 5 - Mineral nutrition of cassava. Advances in Agronomy 159: 169–235.

  • Chaudhari PR, Tamrakar N, Singh L, Tandon A, Sharma D (2018) Rice nutritional and medicinal properties: A review article. J Pharma Phytochem 7(2):150–156

    Google Scholar 

  • Dladla LNT, Modi AT, Mabhaudhi T, Chibarabada TP (2019) Yield, water use, and water use efficiency of sweet potato under different environments. Acta Horticulturae 1253. ISHS. https://doi.org/10.17660/ActaHortic.2019.1253.38.

  • Du Z, He Y, Wang H, Wang C, Duan Y (2021) Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the Maxent model. J Arid Environ 184: 104328

  • FAOSTAT (2021) Production; Cassava, sweet potato, yams, taro; world; 1961–2019 (Online). Food and Agriculture Organization of the United Nations. Downloaded data http://www.fao.org/faostat/en/#data/QC. Accessed 01 Fabruary 2021.

  • Fick SE, Hijmans RJ (2017) WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315

    Article  Google Scholar 

  • George J, Sunitha S (2017–2018) All India Coordinated Research Project on Tuber Crops (AICRP-TC): Annual Report. CTCRI|QSF|RP-416. Pp. 232

  • Hajima T, Watanabe M, Yamamoto A, Tatebe H, Noguchi MA, Abe M et al (2020) Development of the MIROC-ES2L Earth system model and the evaluation of biochemical processes and feedbacks. Geoscientific Model Development 13:2197–2244

    Article  Google Scholar 

  • Kaky E, Nolan V, Alatawi A, Gilbert F (2020) A comparison between Ensemble and Maxent species distribution modelling approaches for conservation: a case study with Egyptian medical plants. Ecol Inf 60: 101150

  • Kumar S, Graham J, West AM, Evangelista PH (2014) Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India. Comput Electron Agric 103:55–62

    Article  Google Scholar 

  • Merow C, Smith MJ, Silander A (2013) A practical guide to Maxent for modelling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069

    Article  Google Scholar 

  • Mohankumar CR (2000) Agro-techniques of sweetpotato. Fourth International Training Course on Integrated production and Processing Technologies for sweetpotato, 21–28 November, Trivandrum, Kerala, India, pp 59–69.

  • Mussoline WA, Wilkie AC (2017) Feed and fuel: the dual-purpose advantage of an industrial sweet potato. J. of the Science of Food and Agriculture 97(5): 1567–1575.

  • Narayan A, Prasad R, Singh PP, Singh RS (2018) Elephant foot yam: money spinning tuber crop for doubling farmer’s income of Bihar. Int J Curr Microbiol App Sci 7:1014–1021

    Google Scholar 

  • Palao LK, Naziri D, Balanza JG, Campilan DM (2019) Transformational adaptation of key root and tuber crops in Asia: species distribution modelling for assessing crop suitability in response to climate change. Final Report. Food Resilience Through Root and Tuber Crops in Upland and Coastal Communities of the Asia-Pacific (FOODSTART+) Project. Lima (Peru). International Potato Center. 34 p

  • Philips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modelling of species geographic distributions. Ecol Model 190:231–259

    Article  Google Scholar 

  • Pushpalatha R, Gangadharan B (2020) Is cassava a climate “smart”crop? A review in the context of bridging future food demand gap. Tropical Plant Biology 13:201–211

    Article  Google Scholar 

  • Sabitha S, Byju G, Sreekumar J (2016) Projected changes in mean temperature and total precipitation and climate suitability of cassava (Manihot esculenta) in major growing environments of India. Indian J Agric Sci 86(5):647–653

    Google Scholar 

  • Saxena M, Kumar P, Gupta RP, Bhargav H, Thakur B, Reddy N, Karale M, Singh R, Gilotra P (2018) Horticultural Statistics at a glance. Government of India, Ministry of Agriculture and Farmer’s Welfare, Department of Agriculture, Horticulture Statistics Division. Pp.490

  • Séférian R, Nabat P, Michou M, Saint-Martin D, Voldoire A, Colin J et al (2019) Evaluation of CNRM Earth-System model, CNRM-ESM2-1: role of Earth system processes in present-day and future climate. J Adv Model Earth Syst. https://doi.org/10.1029/2019MS001791

    Article  Google Scholar 

  • Shiny R, Sreekumar J, Byju G (2019) Coupled multi-model climate and climate suitability change predictions for major cassava growing regions of India under two representative concentration pathways. J Trop Agric 57(2):140–151

    Google Scholar 

  • Shiogama H, Abe M, Tatebe H (2019) MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898

  • Swart NC, Cole JNS, Kharin VV, Lazare M, Scinocca JF, Gillett NP et al (2019) The Canadian Earth System Model Version 5 (CanESM5.0.3). Geosci Model Dev 12:4823–4873

    Article  Google Scholar 

  • Tatebe H, Ogura T, Nitta T, Komuro Y, Ogochi K, Takemura T et al (2019) Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development 12:2727–2765

    Article  Google Scholar 

  • Taylor M, Lebot V, McGregor A, Redden RJ (2018) Chapter 15- Sustainable production of roots and tuber crops for food security under climate change. Edits. Yadav SS, Redden RJ, Hatfield JL, Ebert AW, Hunter D. Food Security and Climate Change.

  • Urvois T, Auger-Rozenberg MA, Roques A, Rossi JP, Kerdelhue C (2021) Climate change impact on the potential geographical distribution of two invading Xylosandrus ambrosia beetles. Sci Rep 11:1339

    Article  Google Scholar 

  • Wu T, Chu M, Dong M, Fang Y, Jie W, Li J, Li W, Liu Q, Shi X, Xin X, Yan J, Zhang F, Zhang J, Zhang Li, Zhang Y (2018) BCC-CSM2MR model output prepared for CMIP6 CMIP piControl. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.3016

  • Xie X, Zhang T, Wang L, Huang Z (2017) Regional water footprints of potential biofuel production in China. Biotechnol Biofuels 10:95

    Article  Google Scholar 

  • Xu D, Zhuo Z, Wang R, Ye M, Pu B (2019) Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling. Global Ecology and Conservation 19:e00691

  • Yadav M, Arora N, Dawar S, Bodh PC, Singla S, Sharma A (2018) Agricultural Statistics at a glance, Govt. Of India, Ministry of Agriculture and Farmers Welfare, Department of Agriculture, Cooperation and Farmers Welfare, Directorate of Economics and Statistics. Pp. 502

  • Yukimoto S, Koshiro T, Kawai H, Oshima N, Yoshida K, Urakawa S, et al. (2019) MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.621

  • Zhao H, Zhang H, Xu C (2020) Study on Taiwania cryptomerioides under climate change: Maxent modeling for predicting the potential geographical distribution. Global Ecology and Conservation 24: e01313

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Acknowledgements

We are thankful to the 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 fulfil this study. We are also acknowledging Fick and Hijmans (2017) for the Worldclim 2.1—historic and future climate data. We acknowledge Dr. Govindan Kutty M, Indian Institute of Space Science & Technology, India, for the grammatical modification of the revised text.

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Dr. Raji Pushpalatha performed the work. Dr. Byju Gangadharan supervised and analysed the results. Ms. Shiny Rajan provided us with the shapefile of India.

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Correspondence to Gangadharan Byju.

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All the procedures performed in this study were in accordance with the ethical standards of the institute.

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Raji, P., Shiny, R. & Byju, G. Impact of climate change on the potential geographical suitability of cassava and sweet potato vs. rice and potato in India. Theor Appl Climatol 146, 941–960 (2021). https://doi.org/10.1007/s00704-021-03763-1

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