Potential benefits of drought and heat tolerance in groundnut for adaptation to climate change in India and West Africa

  • Piara Singh
  • S. Nedumaran
  • B. R. Ntare
  • K. J. Boote
  • N. P. Singh
  • K. Srinivas
  • M. C. S. Bantilan
Original Article

Abstract

Climate change is projected to intensify drought and heat stress in groundnut (Arachis hypogaea L.) crop in rainfed regions. This will require developing high yielding groundnut cultivars that are both drought and heat tolerant. The crop growth simulation model for groundnut (CROPGRO-Groundnut model) was used to quantify the potential benefits of incorporating drought and heat tolerance and yield-enhancing traits into the commonly grown cultivar types at two sites each in India (Anantapur and Junagadh) and West Africa (Samanko, Mali and Sadore, Niger). Increasing crop maturity by 10 % increased yields up to 14 % at Anantapur, 19 % at Samanko and sustained the yields at Sadore. However at Junagadh, the current maturity of the cultivar holds well under future climate. Increasing yield potential of the crop by increasing leaf photosynthesis rate, partitioning to pods and seed-filling duration each by 10 % increased pod yield by 9 to 14 % over the baseline yields across the four sites. Under current climates of Anantapur, Junagadh and Sadore, the yield gains were larger by incorporating drought tolerance than heat tolerance. Under climate change the yield gains from incorporating both drought and heat tolerance increased to 13 % at Anantapur, 12 % at Junagadh and 31 % at Sadore. At the Samanko site, the yield gains from drought or heat tolerance were negligible. It is concluded that different combination of traits will be needed to increase and sustain the productivity of groundnut under climate change at the target sites and the CROPGRO-Groundnut model can be used for evaluating such traits.

Keywords

Climate change factors Genetic improvement Heat and drought tolerance Peanut CROPGRO-Peanut model 

References

  1. Aggarwal PK (2008) Global climate change and Indian agriculture: impacts, adaptation and mitigation. Indian J Agric Sci 78:911–919Google Scholar
  2. Alagarswamy G, Boote KJ, Allen LH Jr, Jones JW (2006) Evaluating the CROPGRO-Soybean model ability to simulate photosynthesis response to carbon dioxide levels. Agron J 98:34–42CrossRefGoogle Scholar
  3. Anothai J, Patanothai A, Pannangpetch K, Jogloy S, Boote KJ, Hoogenboom G (2009) Multi-environment evaluation of peanut lines by model simulation with the cultivar coefficients derived from a reduced set of observed field data. Field Crop Res 110:111–121CrossRefGoogle Scholar
  4. Batjes NH (2012) ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global grid (ver 1.2), report 2012/01 with data set. ISRIC—World Soil Information, Wageningen, p 57Google Scholar
  5. Birthal PS, Parthasarathy Rao P, Nigam SN, Bantilan MCS, Bhagavatula S (2010) Groundnut and soybean economies in Asia: facts, trends and outlook. International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, Andhra Pradesh, India. (ISBN: 978-92-9066-531-1), p 92Google Scholar
  6. Boote KJ, Jones JW (1986) Applications of, and limitations to, crop growth simulation models to fit crops and cropping systems to semi-arid environments. In: Bidinger FR, Johansen C (eds) Drought research priorities for the dryland tropics. International Crops Research Institute for the Semi-Arid Tropics, Patancheru, pp 63–75Google Scholar
  7. Boote KJ, Tollenaar M (1994) Modeling genetic yield potential. In: Boote KJ, Bennett JM, Sinclair TR, Paulsen GM (eds) Physiology and determination of crop yield. ASA-CSSA-SSSA, Madison, pp 533–565Google Scholar
  8. Boote KJ, Jones JW, Hoogenboom G, Pickering NB (1998) Simulation of crop growth: CROPGRO model. In: Tsuji GY, Hoogenboom G, Thornton P (eds) Understanding the option for agricultural production. Kluwer Academic Publishers, London, pp 99–128CrossRefGoogle Scholar
  9. Boote KJ, Kropff MJ, Bindraban PS (2001) Physiology and modeling of traits in crop plants: implications for genetic improvement. Agric Syst 70:395–420CrossRefGoogle Scholar
  10. Boote KJ, Jones JW, Batchelor WD, Nafziger ED, Myers O (2003) Genetic coefficients in the CROPGRO-soybean model: links to field performance and genomics. Agron J 95:32–51CrossRefGoogle Scholar
  11. Boote KJ, Allen LH Jr, Vara Prasad PV, Jones JW (2010) Testing effects of climate change in crop models. In: Hillel D, Rosenzweig C (eds) Handbook of climate change and agroecosystems. Imperial College Press, London, pp 109–129CrossRefGoogle Scholar
  12. Boote KJ, Ibrahim AMH, Lafitte R, McCulley R, Messina C, Murray SC, Specht JE, Taylor S, Westgate ME, Glasener K, Bijl CG, Giese JH (2011) Position statement on crop adaptation to climate change. Crop Sci 51:2337–2343CrossRefGoogle Scholar
  13. Cooper P, Rao KPC, Singh P, Dimes J, Traore PS, Rao KP, Dixit P, Twomlow S (2009) Farming with current and future climate risk: advancing a ‘hypothesis of hope’ for rain-fed agriculture in the semi-arid tropics. J SAT Agric Res 7:1–19Google Scholar
  14. Craufurd PQ, Prasad PVV, Kakani VG, Wheeler TR, Nigam SN (2003) Heat tolerance in groundnut. Field Crop Res 80:63–77CrossRefGoogle Scholar
  15. Easterling WE (1996) Adapting North American agriculture to climate change in review. Agric For Meteorol 80:1–53CrossRefGoogle Scholar
  16. FAO (2012) URL:http://faostat.fao.org/site/567/default.aspx#ancor. Accessed on: 15 April 2012
  17. Farquhar GD, von Caemmerer S (1982) Modeling of photosynthetic response to environment. In: Lange OL, Nobel PS, Osmond CB, Zeigler H (eds) Encyclopedia of plant physiology, new series, vol 12B. Physiological plant ecology II, vol 12B. Springer, Berlin, pp 549–587CrossRefGoogle Scholar
  18. Fischer G, Shah M, Tubiello FN, van Velhuizen H (2005) Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080. Phil Trans Royal Soc B 360:2067–2073CrossRefGoogle Scholar
  19. Gilbert RA, Boote KJ, Bennett JM (2002) On-farm testing of the PNUTGRO crop growth model in Florida. Peanut Sci 29:58–65CrossRefGoogle Scholar
  20. Hammer GL, Butler DG, Muchow RC, Meinke H (1996) Integrating physiological understanding and plant breeding via crop modeling and optimization. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 419–441Google Scholar
  21. Hammer GL, Kropff MJ, Sinclair TR, Porter JR (2002) Future contributions of crop modeling: from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. Eur J Agron 18:15–31CrossRefGoogle Scholar
  22. Hammer GL, Sinclair TR, Chapman S et al (2004) On systems thinking, systems biology and the in silico plant. Plant Physiol 134:909–911CrossRefGoogle Scholar
  23. Hammer GL, Chapman S, van Oosterom E, Podlich D (2005) Trait physiology and crop modeling as a framework to link phenotypic complexity to underlying genetic systems. Aust J Agric Res 56:947–960CrossRefGoogle Scholar
  24. Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Boote KJ, Hunt LA, Singh U, Lizaso JL, White JW, Uryasev O, Royce FS, Ogoshi R, Gijsman AJ, Tsuji GY (2010) Decision support system for agrotechnology transfer (DSSAT) version 4.5 [CD–ROM]. University of Hawaii, HonoluluGoogle Scholar
  25. Howden SM, Soussana JF, Tubiello FN, Chhetri N, Dunlop M, Meinke H (2007) Adapting agriculture to climate change. PNAS 104:19691–19696CrossRefGoogle Scholar
  26. IPCC (2001) Climate change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate. Cambridge University Press, Cambridge, p 881Google Scholar
  27. IPCC (2007) 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 University Press, Cambridge, p 996Google Scholar
  28. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) DSSAT cropping system model. Eur J Agron 18:235–265CrossRefGoogle Scholar
  29. Jongrungklang N, Toomsan B, Vorasoot N, Jogloy S, Boote KJ, Hoogenboom G, Patanothai A (2011) Rooting traits of peanut genotypes with different yield responses to pre-flowering drought stress. Field Crop Res 120:262–270CrossRefGoogle Scholar
  30. Lal S, Deshpande SB, Sehgal J (1994) Soil series of India. Soils bulletin 40. National Bureau of Soil Survey and Land Use Planning, Nagpur, p 648Google Scholar
  31. Landivar JA, Baker DN, Jenkins JN (1983) Application of GOSSYM to genetic feasibility studies. II. Analyses of increasing photosynthesis, specific leaf weight and longevity of leaves in cotton. Crop Sci 23:504–510CrossRefGoogle Scholar
  32. Messina CD, Jones JW, Boote KJ, Vallejos CE (2006) A gene-based model to simulate soybean development and yield responses to environment. Crop Sci 46:456–466CrossRefGoogle Scholar
  33. Naab JB, Singh P, Boote KJ, Jones JW, Marfo KO (2004) Using the CROPGRO-peanut model to quantify yield gaps of peanut in the Guinean savanna zone of Ghana. Agron J 96:1231–1242CrossRefGoogle Scholar
  34. Nautiyal PC, Ravindra V, Rathnakumar AL, Ajay BC, Zala PV (2012) Genetic variations in photosynthetic rate, pod yield and yield components in Spanish groundnut cultivars during three cropping seasons. Field Crop Res 125:83–91CrossRefGoogle Scholar
  35. Ntare BR, Williams JH, Dougbedji F (2001) Evaluation of groundnut genotypes for heat tolerance under field conditions in a Sahelian environment using a simple physiological model for yield. J Agric Sci 136:81–88CrossRefGoogle Scholar
  36. Painawadee M, Jogloy S, Kesmala T, Akkasaeng C, Patanothai A (2009) Identification of traits related to drought tolerance in peanut (Arachis hypogaea L.). Asian J Plant Sci 8:120–128CrossRefGoogle Scholar
  37. Prasad PVV, Boote KJ, Allen LH Jr, Thomas JMG (2003) Supra-optimal temperatures are detrimental to peanut (Arachis hypogaea L.) reproductive processes and yield at ambient and elevated carbon dioxide. Glob Chang Biol 9:1775–1787CrossRefGoogle Scholar
  38. Prasad PVV, Kakani VG, Upadhyaya HD (2009) Growth and production of groundnut. In: Verheye WH (ed) Soils, plant growth and crop production, encyclopedia of life support systems. Eolss Publishers, OxfordGoogle Scholar
  39. Putto C, Pathanothai A, Jogloy S, Pannangpetch K, Boote KJ, Hoogenboom G (2009) Determination of efficient test sites for evaluation of peanut breeding lines using the CSM-CROPGRO-peanut model. Field Crop Res 110:272–281CrossRefGoogle Scholar
  40. Ritchie JT (1998) Soil water balance and plant stress. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer Academic Publishers, Dordrecht, pp 41–54CrossRefGoogle Scholar
  41. Singh P, Boote KJ, Virmani SM (1994a) Evaluation of the groundnut model PNUTGRO for crop response to plant population and row spacing. Field Crops Res 39:163–170CrossRefGoogle Scholar
  42. Singh P, Boote KJ, Yogeswara Rao A, Iruthayaraj MR, Sheikh AM, Hundal SS, Narang RS, Singh P (1994b) Evaluation of the groundnut model PNUTGRO for crop response to water availability, sowing dates, and seasons. Field Crops Res 39:147–162CrossRefGoogle Scholar
  43. Singh P, Boote KJ, Kumar U, Srinivas K, Nigam SN, Jones JW (2012) Evaluation of genetic traits for improving productivity and adaptation of groundnut to climate change in India. J Agron Crop Sci 198:399–413CrossRefGoogle Scholar
  44. Soil Conservations Service (1972) National engineering handbook, hydrology section 4, chapters 4–10Google Scholar
  45. Songsri P, Jogloy S, Vorasoot N, Akkasaeng C, Patanothai A, Holbrook CC (2008) Root distribution of drought-resistant peanut genotypes in response to drought. J Agron Crop Sci 194:92–103CrossRefGoogle Scholar
  46. Suleiman AA, Ritchie JT (2003) Modeling soil water redistribution during second-stage evaporation. Soil Sci Soc Am J 67:377–386CrossRefGoogle Scholar
  47. Suriharn B, Patanothai A, Boote KJ, Hoogenboom G (2011) Designing a peanut ideotype for a target environment using the CSM-CROPGRO-Peanut model. Crop Sci 51:1887–1902CrossRefGoogle Scholar
  48. Tardieu F (2003) Virtual plants: modelling as a tool for genomics of tolerance to water deficit. Trends Plant Sci 8:9–14CrossRefGoogle Scholar
  49. Tubiello FN, Soussana J, Howden SM (2007) Crop and pasture response to climate change. PNAS 105:19686–19690CrossRefGoogle Scholar
  50. Whisler FD, Acock B, Baker DN, Fye RE, Hodges HF, Lambert JR, Lemmon HE, McKinion JM, Reddy VR (1986) Crop simulation models in agronomic systems. Adv Agron 40:141–208CrossRefGoogle Scholar
  51. White JW, Hoogenboom G (2003) Gene-based approaches to crop simulation: past experiences and future opportunities. Agron J 95:52–64CrossRefGoogle Scholar
  52. Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteor Soc 63:1309–1313CrossRefGoogle Scholar
  53. Yin X, Kropff MJ, Stam P (1999) The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley. Heredity 82:415–421CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Piara Singh
    • 1
  • S. Nedumaran
    • 1
  • B. R. Ntare
    • 2
  • K. J. Boote
    • 3
  • N. P. Singh
    • 1
  • K. Srinivas
    • 1
  • M. C. S. Bantilan
    • 1
  1. 1.International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)PatancheruIndia
  2. 2.International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)BamakoMali
  3. 3.Agronomy DepartmentUniversity of Florida, IFASGainesvilleUSA

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