Geographical Information System and Management of Horticultural Genetic Resources with Special Reference to India

  • Natarajan Sivaraj
  • V. Kamala
  • M. Thirupathi Reddy
  • S. R. Pandravada
  • B. Sarath Babu
  • P. E. Rajasekharan
  • S. P. Ahlawat
  • V. Ramanatha Rao


The Indian sub-continent is endowed with unique combination of habitats, ecosystems and plant species of horticultural importance which together make up rich and diverse horticultural genetic resources. The relative abundance and richness of horticultural and other species is another criterion to measure the degree of diversity. Management of horticultural genetic resources (HGR) at national level involves collation of enormous data and its analysis crucial to the effectiveness of its organizational process and adding extensively to the value of natural resources. Innovations in geospatial technology are underutilized in the management of horticultural crop genetic resources in India and many other countries around the world. Geospatial technology and geographic information system (GIS) technology could be leveraged to obtain suitable results to meet the challenges and facilitate enhanced decision support including planning for horticultural resources management. Sustainable management of horticultural genetic resources is of great concern as increasing population and rapid technological strides are putting enormous pressure on the country’s nutritional and food security. Potential use of GIS and other geospatial technologies in HGR management is discussed and highlighted in this chapter.


Conservation GIS Germplasm Horticulture Management 


  1. Abdul Nizar, M., Dikshit, N., & Sivaraj, N. (2014). DIVA-geographic information system approaches for assessment of diversity and distribution pattern of Abelmoschus species from Maharashtra, India. Advances in Applied Research, 6(1), 28–34.CrossRefGoogle Scholar
  2. Adair, R., Johnson, R. C., Hellier, B., & Kaiser, W. (2006). Collecting taper tip onion (Allium acuminatum Hook.) in the Great Basin using traditional and GIS methods native. Plants Journal, 7(2), 141–148.Google Scholar
  3. Addeo, G., Guastadisegni, G., & Pisante, N. (2001). Land and water quality for sustainable and precision farming. In Proceedings of the 1st World Congress on sustainable agriculture, Madrid, pp. 1–4.Google Scholar
  4. Aggelopoulou, K. D., Wulfsohn, D., Fountas, S., Gemtos, T. A., Nanos, G. D., & Blackmore, S. (2010). Spatial variation in yield and quality in a small apple orchard Prec. Agriculture, 11, 538–555.Google Scholar
  5. Anitha, K., Sunil, N., Sivaraj, N., Chakrabarty, S. K., Babu Abraham, Viond Kumar, Suresh Kumar, G., Varaprasad, K. S., & Khetarpal, R. K. (2010). Spatial distribution of seed borne fungi on Pongamia pinnata: A DIVA-GIS analysis with reference to Macrophomina phaseolina. Indian Journal of Plant Protection, 38(1), 67–72.Google Scholar
  6. Archana, P. R., Abraham, Z., & Sivaraj, N. (2015). Diversity analysis of Kaempferia galanga L. germplasm from South India using DIVA-GIS approach. Industrial Crops and Products, 63, 433–439.Google Scholar
  7. Archana, P. R., Sivaraj, N., & Kumar, A. (2016). Chemical diversity among Andrographis paniculata Nees (Kalmegh) and assessing climate suitable regions for elite germplasm distribution in India. Medicinal Plants, 8(4), 267–275.Google Scholar
  8. Arora, R. K. (1991). Plant diversity in Indian gene Centre. In R. S. Paroda & R. K. Aorora (Eds.), Plant genetic resources-conservation and management (pp. 25–54). New Delhi: IPGRI, Regional Office for South Asia.Google Scholar
  9. Arora, R. K., & Pandey, A. (1996). Wild edible plants of India: Diversity, conservation and use. New Delhi: National Bureau of Plant Genetic Resources.Google Scholar
  10. Babu Abraham, Kamala, V., Sivaraj, N., Sunil, N., Pandravada, S. R., Vanaja, M., & Varaprasad, K. S. (2010). DIVA-GIS approaches for diversity assessment of pod characteristics in black gram (Vigna mungo L. Hepper). Current Science, 98(5), 616–619.Google Scholar
  11. Begum, H., Reddy, M. T., Malathi, S., Reddy, B. P., Narshimulu, G., Nagaraju, J., & Siddiq, E. A. (2013). Molecular analysis of intracultivar polymorphism in ‘Peddarasam’ mango (Mangifera indica L.) using microsatellite markers. Asia-Pacific Journal of Molecular Biology and Biotechnology, 21(3), 97–113.Google Scholar
  12. Bonman, J. M., Bockelman, H. E., Jin, Y., Hijmans, R. J., & Gironella, A. I. N. (2007). Geographic distribution of stem rust resistance in wheat landraces. Crop Science, 47, 1955–1963.CrossRefGoogle Scholar
  13. Bourdeix, R., Guarino, L., Mathur, P. N., & Baudouin, L. (2005). Mapping of coconut genetic diversity. In Pons Batugal, V. Ramanatha Rao, & Jeffrey Oliver (Eds.), Coconut genetic resources (pp. 32–43). New Delhi: IPGRI.Google Scholar
  14. Bydekerke, L., Van Ranst, E., Vanmechelen, L., & Groenemans, R. (1998). Land suitability assessment for cherimoya in southern Ecuador using expert knowledge and GIS. Agriculture, Ecosystems & Environment, 69(2), 89–98.CrossRefGoogle Scholar
  15. Carnaval, A. C., & Moritz, C. (2008). Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic forest. Journal of Biogeography, 35, 1187–1201.CrossRefGoogle Scholar
  16. Chang, S. R., Chiu, H. L., Chiou, W. L., & Chen, C. W. (2015). Establishment and application prospect of the geographic information system for the wild relatives of horticultural crops. Journal of Taiwan Agricultural Research, 64(4), 279–289.Google Scholar
  17. Chong, Y. M., Balasundram, S. K., & Mohd Hanif, A. H. (2017). Detecting and monitoring plant nutrient stress using remote sensing approaches: A review. Asian Journal of Plant Sciences, 16(1), 1–8, 201.
  18. Cissé, M., Dornier, M., Sakho, M., N’Diaye, A., Reynes, M., & Sock, O. (2009). Le bissap (Hibiscus sabdariffa L.): composition et principales utilisations. Fruits, 64(3), 179–193.CrossRefGoogle Scholar
  19. Cobley, L. S. (1968). An introduction to botany of tropical crops (pp. 95–98). London: Longman.Google Scholar
  20. Dikshit, N., Nizar, MA, Sivaraj, N., & Chand, D. (2014, August 8–10). Diversity in bottlegourd (Lagenaria siceraria (Mol.) Stand.) germplasm in Maharashtra. In: Souvenir and book of abstracts. National Seminar on “Strategies for improvement enhancing productivity and utilization of Cucurbits” held at Bhubaneshwar, p. 86.Google Scholar
  21. Easterling, W. E., Crosson, P. R., Rosenberg, N. J., McKenny, M. S., Katz, L. A., & Lemon, K. M. (1993). Agricultural impacts of and responses to climate-change in the Missouri- Iowa- Nebraska-Kansas (MINK) region. Climatic Change, 24, 23–61.CrossRefGoogle Scholar
  22. Eitzinger, A., Läderach, P., Carmona, S., Navarro, C., & Collet, L. (2013). Prediction of the impact of climate change on coffee and mango growing areas in Haiti. Full technical report. Centro Internacional de Agricultura Tropical (CIAT), Cali, Colombia.Google Scholar
  23. Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J., Mc, C., Peterson, A. T., Phillips, J., Richardson, K., Scachetti-Pereira, R., Schapire, E., Soberon, J., Williams, S., Wisz, M., & Zimmermann, E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.CrossRefGoogle Scholar
  24. Elith, J., Phillips, S. J., Hastie, T., Dudik, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43–57.CrossRefGoogle Scholar
  25. ElSaidy, S. M., Ismail, I. A., & EL-Zoghbi, M. (1992). A study on Roselle extraction as a beverage or as a source for anthocyanins. Zagazig Journal of Agricultural Research, 19, 831–839.Google Scholar
  26. Ganeshaiah, K. N., Barve, N., Nilima, N., Chandrashekara, K., Swamy, M., & Uma Shaanker, R. (2003). Predicting the potential geographical distribution of the sugarcane wooly aphid using GARP and DIVA-GIS. Current Science, 85, 1526–1528.Google Scholar
  27. Gautam, P. L. (2004). Trends in plant genetic resource management. In B. S. Dhillon, R. K. Tyagi, & A. Lal (Eds.), Plant genetic resource management (pp. 18–30). New Delhi: Narosa Publishing House.Google Scholar
  28. Gixhari, B., Ismaili, H., Vrapi, H., Elezi, F., Dias, S., & Sulovari, H. (2012). Geographic distribution and diversity of fruit tree species in Albania. International Journal of Ecosystems and Ecology Sciences (IJEES), 2(4), 355–360.Google Scholar
  29. Greene, S. L., & Hart, T. (1996). Plant genetic resource collection: An opportunity for the evolution of global data sets. Accessed 02.08.2017.
  30. Greene, S., Hart, T., & Afonin, A. (1999). Using geographic information to acquire wild crop germplasm: II. Post collection analysis. Crop Science, 39, 843–849.CrossRefGoogle Scholar
  31. Guarino, L. (1995). Geographic information systems and remote sensing for the plant germplasm collector. In L. Guarino, V. Ramanatha Rao, & R. Reid (Eds.), Collecting plant genetic diversity. Technical guidelines (pp. 315–328). Wallingford: CAB International.Google Scholar
  32. Guarino, L., Maxted, N., & Sawkins, M. (1999). Analysis of geo-referenced data and the conservation and use of plant genetic resources. In S. L. Greene & L. Guarino (Eds.), Linking genetic resources and geography: Emerging strategies for conserving and using crop biodiversity (CSSA special publication no. 27) (pp. 1–24). Madison: ASA and CSSA.Google Scholar
  33. Guarino, L., Jarvis, A., Hijmans, R. J., & Maxted, N. (2002). Geographic information systems (GIS) and the conservation and use of PGR. In J. M. M. Engels, V. R. Rao, A. H. D. Brown, & M. T. Jackson (Eds.), Managing plant genetic diversity (pp. 387–404). Rome: IPGRI.Google Scholar
  34. Gunjeet Kumar, Sivaraj, N., Kamala, V., Gangopadhyay, K. K., Sushil Pandey, Panwar, N. S., Dhariwal, O. P., Meena, B. L., SK, T., & Dutta, M. (2013). Diversity analysis in eggplant germplasm in India using DIVA-GIS approach. Indian Journal of Horticulture, 70(4), 519–525.Google Scholar
  35. Hart, T. S., Greene, S. L., & Afonin, A. (1996). Mapping for germplasm collections: Site selection and attribution. In: Proceedings of the third international conference on integrating GIS and environmental modeling. NCGIA, Santa Barbara, CA.Google Scholar
  36. Hijmans, R. J., & Graham, C. (2006). The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology, 12, 2272–2281.CrossRefGoogle Scholar
  37. Hijmans, R. J., & Spooner, D. M. (2001). Geographic distribution of wild potato species. American Journal of Botany, 88, 2101–2112.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Hijmans, R. J., Garrett, K. A., Huaman, Z., Zhang, D. P., Schreuder, M., & Bonierbale, M. (2000). Assessing the geographic representativeness of genebank collections: The case of Bolivian wild potatoes. Conservation Biology, 14, 1755–1176.CrossRefGoogle Scholar
  39. Husna, A. K. N., Balasundram, S. K., & Tan, C. P. (2015). Fluorescence sensing as a tool to estimate palm oil quality and yield. Science and Technology Vitivinicola Journal, 30, 58–56.Google Scholar
  40. Jarvis, A., Ferguson, M. E., Williams, D. E., Guarino, L., Jones, P. G., Stalker, H. T., Valls, J. F. M., Pittman, R. N., Simpson, C. E., & Bramel, P. (2003). Biogeography of wild Arachis: Assessing conservation status and setting future priorities. Crop Science, 43, 1100–1108.CrossRefGoogle Scholar
  41. Jones, P. G., Beebe, S. E., Tohme, J., & Galway, N. W. (1997). The use of geographical information systems in biodiversity exploration and conservation. Biodiversity and Conservation, 6, 947–958.CrossRefGoogle Scholar
  42. Jones, P. G., Guarino, L., & Jarvis, A. (2002). Computer tools for spatial analysis of PGR data: 2. FloraMap. PGR Newsletter, 130, 1–6.Google Scholar
  43. Kamala, V., Gupta, A. J., Sivaraj, N., Pandravada, S. R., Sunil, N., Varaprasad, K. S., & Lawande, K. E. (2011a). Diversity analysis of onion germplasm collections from north Telangana region of Andhra Pradesh. Indian Journal of Plant Genetic Resources, 24(2), 163–171.Google Scholar
  44. Kamala, V., Gupta, A. J., Rajput, A., Sivaraj, N., Pandravada, S. R., Sunil, N., Varaprasad, K. S., & Lawande, K. E. (2011b). Diversity in bulb traits in onion germplasm collected from Chhattisgarh and Maharashtra. Indian Journal of Horticulture, 71(4), 499–504.Google Scholar
  45. Kamala, V., Aghora, T. S., Sivaraj, N., Rao, T., Pandravada, S. R., Sunil, N., Mohan, N., Varaprasad, K. S., & Chakrabarty, S. K. (2014). Germplasm collection and diversity analysis in Yardlong bean (Vigna unguiculata sub sp. sesquipedalis) from coastal Andhra Pradesh and Odisha. Indian Journal of Plant Genetic Resources, 27(2), 171–177.CrossRefGoogle Scholar
  46. Kamala, V., Sivaraj, N., Rameash, K., Pandravada, S. R., & Sarath Babu, B. (2016). Assessing potential pockets Vis a Vis climate suitability for sustainable cultivation of Yardlong bean in Andhra Pradesh and Odisha. Abstract Book. 1st International Agrobiodiversity Congress, New Delhi, p. 155.Google Scholar
  47. Kays, S. J. (2011). Cultivated vegetables of the world: A multilingual onomasticon (p. 184). University of Georgia, Wageningen Academic Publishers, The Netherlands.Google Scholar
  48. Linta Vincent, Sivaraj, N., Anushma, P. L., Ganeshan, S., & Rajasekharan, P. E. (2015). Diversity, distribution, collection and conservation of Amaranth germplasm from Andhra Pradesh. In: Conference: 3rd International symposium on underutilized plant species- exploration and conservation for future generation, At KVK, Agricultural College and Research Institute, Madurai.Google Scholar
  49. Mahadevan, N., Shivali, & Kamboj, P. (2009). Hibiscus sabdariffa Linn. An overview. Natural Product Radiance, 8(1), 77–83.Google Scholar
  50. Miller, A. J., & Knouft, J. H. (2006). GIS based characterization of the geographic distribution of wild and cultivated populations of the Meso American fruit tree Spondias purpurea (Anacardiaceae). American Journal of Botany, 93(12), 1757–1767.CrossRefGoogle Scholar
  51. Morton, F. J. (1987). Roselle (Hibiscus sabdariffa L.). In C. F. Dowling Jr. (Ed.), Fruits of warm Climates. Miami: Creative Resources Systems.Google Scholar
  52. Nayar, M. P. (1980). Endemism and pattern of distribution of endemic genera (angiosperm) in India. Journal of Economic and Taxonomic Botany, 1, 99–110.Google Scholar
  53. Ottai, M. E. S., Aboud, K. A., Mahmoud, I. M., & El-Hariri, D. M. (2006). Stability analysis of roselle cultivars (Hibiscus sabdariffa L.) under different nitrogen fertilizer environments. World Journal of Agricultural Sciences, 2(3), 333–339.Google Scholar
  54. Parra-Quijano, M., Iriondo, J. M., & Torres, E. (2012). Review. Applications of ecogeography and geographic information systems in conservation and utilization ofPGR. Spanish Journal of Agricultural Research, 10(2), 419–442.CrossRefGoogle Scholar
  55. Parthasarathy, U., George, J., Saji, K. V., Srinivasan, V., Madan, M. S., Mathur, P. N., & Parthasarathy, V. A. (2008). Spatial analysis for piper species distribution in India. PGR Newsletter, 147, 1–5.Google Scholar
  56. Parthasarthy, U., Johny, A. K., Jayarajan, K., & Parthasarthy, V. A. (2007). Site suitability for turmeric production in India, a GIS interpretation. Natural Product Radiance, 6(2), 142–147.Google Scholar
  57. Pederson, G. A., Fairbrother, T. E., & Greene, S. L. (1996). Cyanogenesis and climate relationships in U.S. white clover germplasm collection and core subset. Crop Science, 36, 427–433.CrossRefGoogle Scholar
  58. Phillips, S. J., Dudik, M., & Schapire, R. E. (2004). A maximum entropy approach to species distribution modeling. In: Proceedings of the Twenty-First International conference on machine learning. Banff, Canada, pp. 655–662.Google Scholar
  59. Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.CrossRefGoogle Scholar
  60. Priya Devi, S., Thangam, M., Ramchandrudu, K., Ashok Kumar, J. & Singh, N. P. (2013). Genetic diversity of Kokum (Garcinia indica) in Goa-tree and fruit characters. Technical bulletin no. 33, ICAR (RC), Goa.Google Scholar
  61. Purseglove, J. W. (1974). Tropical crops dicotyledons. Volume1 and 2 (pp. 370–372). London: Longman Group.Google Scholar
  62. Questad, E. J., Kellner, J. R., Kinney, K., Cordell, S., Asner, G. P., Thaxton, J., Diep, J., Uowolo, A., Brooks, S., Inman-Narahari, N., SA, E., & Tucker, B. (2014). Mapping habitat suitability for at-risk plant species and its implications for restoration and reintroduction. Ecological Applications, 24(2), 385–395.CrossRefGoogle Scholar
  63. Rajappa Joga, Sivaraj, N., Puran Chandra, Baba Karmakar, & Mohapatra, K. P. (2017). Assessment of potential pockets on climate suitability for sustainable cultivation of hill banana Kait syieng (AAB genome group) in India. In Book of lead, invited lectures and abstracts. National Seminar on “Smart farming for enhancing input use efficiency, income and environmental security (SFEIES-2017)” held at ICAR Research Complex, Umiam, Meghalaya, 19–21 September, pp. 129–130.Google Scholar
  64. Rameash, K., Sivaraj, N., Sarath Babu, B., & Chakrabarty, S. K. (2015). Screening brinjal genotypes for resistance to shoot and fruit borer, Leucinodes orbonalis and analysing the geographical divergence of resistance through DIVA-GIS. The Bioscan, 10(2), 923–928.Google Scholar
  65. Rameash, K., Sivaraj, N., Pandravada, S. R., & Sarath Babu, B. (2016). Modelling potential habitat distribution of chilli germplasm resistant to thrips, Scirtothrips dorsalis in India. In: Anitha Kodaru, K. Rameash, D. Sridevi, B. Sarath Babu, & K. S. Varaprasad (Eds.), Extended summaries. Conference on National priorities in plant health management (p. 202).Google Scholar
  66. Reddy, M. T., Begum, H., Sunil, N., Pandravada, S. R., Sivaraj, N., & Kumar, S. (2014). Preliminary characterization and evaluation of landraces of Indian spinach (Basella spp. L.) for agro-economic and quality traits. Plant Breeding and Biotechnology, 2(1), 48–63.CrossRefGoogle Scholar
  67. Reddy, M. T., Begum, H., Sunil, N., Pandravada, S. R., Sivaraj, N., & Kumar, S. (2015a). Predicting potential habitat distribution of sorrel (Rumex vesicarius L.) in India from presence-only data using maximum entropy model. Open Access Library Journal, 2, e1590. Scholar
  68. Reddy, M. T., Begum, H., Rao, N. H., Sunil, N., Pandravada, S. R., & Sivaraj, N. (2015b). Genetic diversity and variability in landraces for key agro-economic traits in vegetable Roselle (Hibiscus sabdariffa var. sabdariffa L.). Jordan Journal of Biological Sciences, 8(2), 113–125.CrossRefGoogle Scholar
  69. Reddy, M. T., Begum, H., Sunil, N., Pandravada, S. R., & Sivaraj, N. (2015c). Assessing climate suitability for sustainable vegetable Roselle (Hibiscus sabdariffa var. sabdariffa L.) cultivation in India using MaxEnt model. Agricultural and Biological Sciences Journal, 1(2), 62–70.Google Scholar
  70. Reddy, M. T., Begum, H., Sunil, N., Pandravada, S. R., Sivaraj, N., & Kumar, S. (2015d). Mapping the climate suitability using maxent modeling approach for Ceylon spinach (Basella alba L.) cultivation in India. The Journal of Agricultural Sciences, 10(2), 87–97.CrossRefGoogle Scholar
  71. Reddy, M. T., Pandravada, S. R., Sivaraj, N., & Sunil, N. (2016). Characterization of Indian landrace germplasm and morphological traits desirable for designing a customer-driven variety in okra (Abelmoschus esculentus L. Moench). Journal of Global Agriculture and Ecology, 6(1), 7–34.Google Scholar
  72. Rosenzweig, C., Allen, L. H., Jr., Harper, L. A., Hollinger, S. E., & Jones, J. W. (1995). Climate change and Agriculture: Analysis of potential international impacts (ASA Special Publication Number 59). Madison: American Society of Agronomy.Google Scholar
  73. Sankaran, S., & Ehsani, R. (2011). Visible-near infrared spectroscopy based citrus greening detection: Evaluation of spectral feature extraction techniques. Crop Protection, 30, 1508–1513.CrossRefGoogle Scholar
  74. Scheldeman, X., & van Zonneveld, M. (2010). Training manual on spatial analysis of plant diversity and distribution. Rome: Bioversity International.Google Scholar
  75. Semwal, D. P., & Ahlawat, S. P. (2015). Application of geoinformatics in PGR studies. E-Publication (NBP-16-03). New Delhi: ICAR-National Bureau of Plant Genetic Resources.Google Scholar
  76. Semwal, D. P., Bhandari, D. C., Bhatt, K. C., & Singh, R. (2013). Diversity distribution pattern in collected germplasm of rapeseed-mustard using GIS in India. Indian Journal of Plant Genetic Resources, 26(1), 76–81.Google Scholar
  77. Sivaraj, N., Sunil, N., Pandravada, S. R., Kamala, V., Vinod Kumar, Rao, B. V. S. K., Prasad, R. B. N., & Varaprasad, K. S. (2009). DIVA-GIS approaches for diversity assessment of fatty acid composition in linseed (Linum usitatissimum L.) germplasm collections from peninsular India. Journal of Oilseeds Research, 26, 13–15.Google Scholar
  78. Sivaraj, N., Sunil, N., Pandravada, S. R., Kamala, V., Rao, B. V. S. K., Prasad, R. B. N., Nayar, E. R., Joseph John, K., Abraham, Z., & Varaprasad, K. S. (2010). Fatty acid composition in seeds of Jack bean [Canavalia ensiformis (L.) DC] and Sword bean [Canavalia gladiate (Jacq.) DC] germplasm from South India: A DIVA-GIS analysis. Seed Technology, 32(1), 46–53.Google Scholar
  79. Sivaraj, N., Sunil, N., Pandravada, S. R., Kamala, V., Vinod Kumar, Abraham, B., Rao, B. V. S. K., RBN, P., & Varaprasad, K. S. (2012a). Variability in linseed (Linum usitatissimum) germplasm collections from peninsular India with special reference to seed traits and fatty acid composition. Indian Journal of Agricultural Sciences, 82(2), 102–105.Google Scholar
  80. Sivaraj, N., Pandravada, S. R., Dikshit, N., Abdul Nizar, M., Kamala, V., Sunil, N., & Chakrabarty, S. K. (2012b). Sustainable management of safflower (Carthamus tinctorius L.) genetic resources in India. Journal of Oilseeds Research, 29(Special Issue), 45–49.Google Scholar
  81. Sivaraj, N., Rameash, K., & Sarath Babu, B. (2016a). Maximum entropy (MaxEnt) modelling approach for predicting potential climate suitable locations of popular banana varieties in India: I. Poovan (AAB). International Journal of Applied & Pure Science and Agriculture, 2(2), 270–276.Google Scholar
  82. Sivaraj, N., Rameash, K., & Sarath Babu, B. (2016b). Mapping the climate suitability using maximum entropy modelling approach for red banana cultivation in India. International Journal of Applied & Pure Science and Agriculture, 2(2), 277–283.Google Scholar
  83. Sivaraj, N., Rameash, K., & Sarath Babu, B. (2016c). Assessing potential pockets on climate suitability for sustainable Hill Banana (Virupakshi–AAB variety) cultivation in India. Advances in Applied Research, 8(1), 1–7.CrossRefGoogle Scholar
  84. Spandana, B., Sivaraj, N., John Prasanna Rao, G., Anuradha, G., Sivaramakrishnan, S., & Jabeen, F. (2012). Diversity analysis of sesame germplasm using DIVA-GIS. Journal of Spices and Aromatic Crops, 21(2), 145–150.Google Scholar
  85. Sthapit, B. R., Ramanatha Rao, V., & Sthapit, S. R. (2012). Tropical fruit tree species and climate change. New Delhi: Bioversity International.Google Scholar
  86. Sultan, S. M., Dar, S. A., Dand, S. A., & Sivaraj, N. (2014). Diversity of common bean in Jammu and Kashmir, India: A DIVA geographic information system and cluster analysis. Journal of Applied and Natural Science, 6(1), 226–233.CrossRefGoogle Scholar
  87. Sunil, N., Sivaraj, N., Pandravada, S. R., Kamala, V., Raghuram Reddy, P., & Varaprasad, K. S. (2008). Genetic and geographical divergence in horsegram germplasm from Andhra Pradesh, India. PGR: Characterization and Utilization, 7(1), 84–87.Google Scholar
  88. Sunil, N., Sivaraj, N., Anitha, K., Abraham, B., Vinod Kumar, Sudhir, E., Vanaja, M., & Varaprasad, K. S. (2009). Analysis of diversity and distribution of Jatropha curcas L. germplasm using Geographic Information System (DIVA-GIS). Genetic Resources and Crop Evolution, 56, 115–119.CrossRefGoogle Scholar
  89. Tittensor, D. P., Baco, A. R., Brewin, P. E., Clark, M. R., Consalvey, M., Hall-Spencer, J., Rowden, A. A., Schlacher, T., Stocks, K. I., & Rogers, A. D. (2009). Predicting global habitat suitability for stony corals on seamounts. Journal of Biogeography, 36, 1111–1128.CrossRefGoogle Scholar
  90. Tubiello, F. N., Donatelli, M., Rosenzweig, C., & Stockle, C. O. (2000). Effects of climate change and elevated CO2 on cropping systems: Model predictions at two Italian locations. European Journal of Agronomy, 13, 179–189.CrossRefGoogle Scholar
  91. Tubiello, F. N., Donatelli, M., Rosenzweig, C., & Stockle, C. O. (2002). Effects of climate change on US crop production: Simulation results using two different GCM scenarios. Part 1: Wheat, potato, maize and citrus. Climate Research, 20, 256–270.CrossRefGoogle Scholar
  92. Utpala, P., Johny, A. K., Parthasarathy, V., Jayarajan, K., & Madan, M. S. (2006). Diversity of ginger cultivation in India- a GIS study. Journal of Spices and Aromatic Crops, 15(2), 93–99.Google Scholar
  93. van Zonneveld, M., Thomas, E., Galluzzi, G., & Scheldeman, X. (2011). Mapping the ecogeographic distribution of biodiversity and GIS tools for plant germplasm collection. In L. Guarino et al. (Eds.), Collecting plant genetic diversity: Technical guidelines. Wallingford: CABI.Google Scholar
  94. Varaprasad, K. S., Sivaraj, N., Ismail, M., & Pareek, S. K. (2007). GIS mapping of selected medicinal plants diversity in the southeast coastal zone for effective collection and conservation. In K. Janardhan Reddy, B. Bahadur, B. Bhadraiah, & M. L. N. Rao (Eds.), Advances in medicinal plants (pp. 69–78). Chennai: Universities Press (India).Google Scholar
  95. Varaprasad, K. S., Sivaraj, N., Pandravada, S. R., Kamala, V., & Sunil, N. (2008). GIS mapping of agrobiodiversity in Andhra Pradesh. In Proceedings of Andhra Pradesh Akademi of sciences. Special issue on plant wealth of Andhra Pradesh, pp. 24–33.Google Scholar
  96. Vavilov, N. I. (1951). The origin, variation, immunity and breeding of cultivated plants. New York: Ronald Press.CrossRefGoogle Scholar
  97. Verbruggen, H., Tyberghein, L., Pauly, K., Vlaeminck, C., Van Nieuwenhuyze, K., Kooistra, W., Leliaert, F., & De Clerck, O. (2009). Macroecology meets macroevolution: Evolutionary niche dynamics in the seaweed Halimeda. Global Ecology and Biogeography, 18, 393–405.CrossRefGoogle Scholar
  98. Williams, J. N., Seo, C. W., Thorne, J., Nelson, J. K., Erwin, S., O’Brien, J. M., & Schwartz, M. W. (2009). Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions, 15, 565–576.CrossRefGoogle Scholar
  99. Wollan, A. K., Bakkestuen, V., Kauserud, H., Gulden, G., & Halvorsen, R. (2008). Modelling and predicting fungal distribution patterns using herbarium data. Journal of Biogeography, 35, 2298–2310.CrossRefGoogle Scholar
  100. Zhang, X., Liu, F., He, Y., & Gong, X. (2013). Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging. Biosystems Engineering, 115, 56–65.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Natarajan Sivaraj
    • 1
  • V. Kamala
    • 1
  • M. Thirupathi Reddy
    • 2
  • S. R. Pandravada
    • 1
  • B. Sarath Babu
    • 1
  • P. E. Rajasekharan
    • 3
  • S. P. Ahlawat
    • 4
  • V. Ramanatha Rao
    • 5
  1. 1.ICAR-National Bureau of Plant Genetic Resources, Regional StationHyderabadIndia
  2. 2.Vegetable Research Station, Sri Konda Laxman Telangana State Horticultural UniversityHyderabadIndia
  3. 3.Division of Plant Genetic ResourcesICAR-Indian Institute of Horticultural ResearchBengaluruIndia
  4. 4.ICAR-National Bureau of Plant Genetic ResourcesNew DelhiIndia
  5. 5.Global Research for Development Support Ventures (GRSV)BengaluruIndia

Personalised recommendations