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Yield potential of upland rice varieties under reproductive-stage drought and optimal water regimes in Nigeria

Published online by Cambridge University Press:  28 March 2018

C. O. Anyaoha ,
M. Fofana ,
V. E. Gracen ,
P. B. Tongoona ,
E. T. Blay ,
M. Semon  and
B. Popoola
C. O. Anyaoha*
Affiliation:
West Africa Centre for Crop Improvement, University of Ghana, Accra, Ghana
M. Fofana
Affiliation:
Africa Rice Center, Ibadan, Nigeria
V. E. Gracen
Affiliation:
West Africa Centre for Crop Improvement, University of Ghana, Accra, Ghana Cornel Universities, Ithaca, New York, NY, USA
P. B. Tongoona
Affiliation:
West Africa Centre for Crop Improvement, University of Ghana, Accra, Ghana
E. T. Blay
Affiliation:
West Africa Centre for Crop Improvement, University of Ghana, Accra, Ghana
M. Semon
Affiliation:
Africa Rice Center, Ibadan, Nigeria
B. Popoola
Affiliation:
Africa Rice Center, Ibadan, Nigeria
*
*Corresponding author. E-mail: kriskoty@yahoo.com
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Abstract

The goal of most rice improvement programs is the enhancement of farmers’ yield using less land and limited water. This study evaluated 77 upland rice genotypes under optimal upland growing conditions in the field and ranked the genotypes using base indices. Subsequently, eighteen cultivars selected from the field trial were screened under drought in rainout-shelter conditions. The traits evaluated for index selection were yield, days to flowering, plant height, number of panicles and filled grains. Under field conditions, based on the sum of economic weight assigned to five traits used to compute the selection index, IR 68704-145-1-1-B and IR 63380-16 were the best genotypes. In the rainout-shelter experiment, Ofada 2 (508 gm−2) had the highest grain yield under non-stress conditions while ITA 117 (152.38 gm−2) had the highest grain yield under drought stress. The base index was efficient for selecting superior genotypes with the best combination for all the traits considered. Susceptibility to drought stress of the landraces leads to poor grain yield.

Keywords

climate changedrought stresslandracesnon-stresslodgingrecovery ability
Type
Research Article
Information
Plant Genetic Resources , Volume 16 , Issue 4 , August 2018 , pp. 378 - 385
Copyright
Copyright © NIAB 2018 

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References

Adebayo, AA, Onu, JI, Adebayo, EF and Anyanwu, SO (2012) Farmers awareness, vulnerability and adaptation to climate change in Adamawa State Nigeria. British Journal of Arts and Social Sciences 9: 104–115.Google Scholar
Adeyemi, P and Vodouhe, SR (1996) Amélioration de la Productivité des Variétés Locales de Oryza Glaberrima Steud. par des croisements intra et interspécifiques avec Oryza sativa Linn in Hybridations interspécifiques au Bénin. Bouaké (Côte d'Ivoire): ADRAO, pp. 159–175.Google Scholar
Ajah, J and Ajah, FC (2014) Socio-economic determinant of small-scale rice farmers output in Abuja; Nigeria. Asian Journal of Rural Development 4: 16–24.Google Scholar
Akpokodje, G, Lançon, F and Erenstein, O (2001) Nigeria's rice economy: State of the art. In NISER/WARDA Nigerian Rice Economy Stakeholders Workshop, Ibadan, November, pp. 8–9.Google Scholar
Bernier, J, Kumar, A, Ramaiah, V, Spaner, D and Atlin, G (2007) A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Science 47: 507–516.Google Scholar
Bernier, J, Atlin, GN, Serraj, R, Kumar, A and Spaner, D (2008) Breeding upland rice for drought resistance. Journal of the Science of Food and Agriculture 88: 927–939.Google Scholar
Dalton, TJ (2004) A household hedonic model of rice traits: economic values from farmers in West Africa. Agricultural Economics 31(2–3): 149–159Google Scholar
Dixit, S, Singh, A and Kumar, A (2014) Rice breeding for high grain yield under drought: a strategic solution to a complex problem. International Journal of Agronomy: 15. doi: 10.1155/2014/863683.Google Scholar
Falconer, DS (1989) Introduction to Quantitative Genetics, 3rd edn. London: Longman Science and Technolgy, p. 11.Google Scholar
FAO (2004) Fact sheet No 5 on International Year of Rice. Available at http://www.rice2004.org.Google Scholar
Guimaraẽs, EP (2009) Rice Breeding. Springer, Cereals (Ed.) M.J. Carena 2009, XIV, 426 pp. 40 illis., 13 in color., Hardcover. ISN: 978-0-72294-8.Google Scholar
Guimarães, CM, Stone, LF, Castro, APD and Morais Júnior, OPD (2015) Physiological parameters to select upland rice genotypes for tolerance to water deficit. Pesquisa Agropecuária Brasileira 50: 534–540.Google Scholar
Kumar, A, Bernier, J, Verulkar, S, Lafitte, HR and Atlin, GN (2008) Breeding for drought tolerance: direct selection for yield, response to selection and use of drought-tolerant donors in upland and lowland-adapted populations. Field Crops Research 107: 221–231.Google Scholar
Kumar, A, Dixit, S, Ram, T, Yadaw, RB, Mishra, KK and Mandal, NP (2014) Breeding high-yielding drought-tolerant rice: genetic variations and conventional and molecular approaches. Journal of Experimental Botany 65:6265–78.Google Scholar
Lafitte, HR, Price, AH and Courtois, B (2004) Yield response to water deficit in an upland rice mapping population: associations among traits and genetic markers. Theoretical and Applied Genetics 109: 1237–1246.Google Scholar
Lin, CY (1978) Index selection for genetic improvement of quantitative characters. Theoretical and Applied Genetics 52: 49–56.Google Scholar
Liu, XQ, Rong, JY and Liu, XY (2008) Best Linear unbiased prediction for linear combinations in general mixed linear models. Journal of Multivariate Analysis 99: 1503–1517.Google Scholar
Longtau, SR (2003) Multi-agency partnerships in West African Agriculture. A review and description of rice production systems in Nigeria. Jos, Nigeria: Eco-systems Development Organization (EDO-DFID) report, 50.Google Scholar
Nassir, AL and Adewusi, KM (2012) Performance of established and improved interspecific rice genotypes under variable soil moisture. Experimental Agriculture & Horticulture 12: 1–11.Google Scholar
Ogunbayo, SA, Ojo, DK, Sanni, KA, Akinwale, MG, Toulou, B, Shittu, A and Gregorio, GB (2014) Genetic variation and heritability of yield and related traits in promising rice genotypes (Oryza sativa L.). Journal of Plant Breeding and Crop Science 6: 153–159.Google Scholar
Oguntunde, PG (1998) Evaluating the methods of estimating evapotranspiration potential in humid and sub humid stations of Nigeria (Doctoral dissertation, Federal University of Technology Akure).Google Scholar
Ologbon, OAC, Ikheloa, EE and Akerele, EO (2012) Adoption of ‘Ofada'rice variety and technical efficiency of rice-based production systems in Ogun state, Nigeria. World Journal of Agricultural Sciences 8: 624–631.Google Scholar
Price, AH, Cairns, JE, Horton, P, Jones, HG and Griffiths, H (2002) Linking drought-resistance mechanisms to drought avoidance in upland rice using a QTL approach: progress and new opportunities to integrate stomatal and mesophyll responses. Journal of Experimental Botany 53: 989–1004.Google Scholar
PrOpCom, New Nigeria Foundation (2007) Ofada Baseline Survey.” Submitted to PrOpCom. Nigeria: PrOpCom.Google Scholar
Rabiei, B, Valizadeh, M, Ghareyazie, B and Moghaddam, M (2004) Evaluation of selection indices for improving rice grain shape. Field Crops Research 89: 359–367.Google Scholar
Saka, JO and Lawal, BO (2009) Determinants of adoption and productivity of improved rice varieties in southwestern Nigeria. African Journal of Biotechnology 8: 4923–4932.Google Scholar
Sellammal, R, Robin, S and Raveendran, M (2014) Association and heritability studies for drought resistance under varied moisture stress regimes in backcross inbred population of rice. Rice Science 21: 150–161.Google Scholar
Shamsudin, NAA, Swamy, BM, Ratnam, W, Cruz, MTS, Sandhu, N, Raman, AK and Kumar, A (2016) Pyramiding of drought yield QTLs into a high quality Malaysian rice cultivar MRQ74 improves yield under reproductive stage drought. Rice 9: 1.Google Scholar
Showemimo, FAG, Gregorio, OV, Nukwungwu, M, Maji, AT, Adigbo, SO, Olaoye, OJ, Akintokun, PO, Bodunde, JG, Idowu, OTH and Awe, CA (2011) Release of two dual purpose of ofada rice varieties (FUNAABOR-1 AND FUNAABOR-2) by federal university of agriculture, abeokuta (FUNAAB). Journal of Agricultural Science and Environment 11: 122–123.Google Scholar
Sié, M, Kaboré, KB, Dakouo, D, Dembélé, Y, Moukoumbi, YD, Ba, MN and Traoré, A (1999) Caractérisation des hybrides interspécifiques (O. Glaberrima þ O. Sativa) pour leur adaptabilité à la riziculture de bas-fond. Formulaire de requête d ‘un financement spécial pour un projet d ‘un groupe d ‘action, 6. (non pubie).Google Scholar
Singh, BN, Fagade, S, Ukwungwu, MN, Williarn, C, Jagtap, SS, Oladimeji, O, Effisue, A and Okhidievbie, O (1997) Rice growing environments and biophysical constraints in different agro-ecological zones of Nigeria. Meteorological Journal 2: 35–44.Google Scholar
Smith, HF (1936) A discriminant function for plant selection. Annals of Eugenics 7: 240–250.Google Scholar
USDA (2017) Grain: World markets and trade.Google Scholar
Venuprasad, R, Cruz, MS, Amante, M, Magbanua, R, Kumar, A and Atlin, GN (2008) Response to two cycles of divergent selection for grain yield under drought stress in four rice breeding populations. Field Crops Research 107: 232–244.Google Scholar
Venuprasad, R, Lafitte, HR and Atlin, GN (2007) Response to direct selection for grain yield under drought stress in rice. Crop Science 47: 285–293.Google Scholar
Vikram, P, Swamy, BM, Dixit, S, Singh, R, Singh, BP, Miro, B, Kohli, A, Henry, A, Singh, NK and Kumar, A (2015) Drought susceptibility of modern rice varieties: an effect of linkage of drought tolerance with undesirable traits. Scientific Reports 5: 14799.Google Scholar
Vikram, P, Swamy, BM, Dixit, S, Trinidad, J, Cruz, MT, Maturan, PC, Amante, M and Kumar, A (2016) Linkages and interactions analysis of major effect drought grain yield QTLs in rice. PLoS ONE 11: e0151532.Google Scholar
Williams, JS (1962) The evaluation of a selection index. Biometrics 18: 375–393.Google Scholar
Zu, X, Lu, Y, Wang, Q, Chu, P, Miao, W, Wang, H and La, H (2017) A new method for evaluating the drought tolerance of upland rice cultivars. The Crop Journal 5, 488–498.Google Scholar
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