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Genotype × environment interaction on the yield of spring oilseed rape (Brassica napus) under rainfed conditions in Argentine Pampas

Published online by Cambridge University Press:  13 August 2019

L. E. Puhl
Affiliation:
Departament of Quantitative Methods and Information Systems, School of Agriculture, University of Buenos Aires, Av. San Martin 4453 (C1417DSE) Buenos Aires, Argentina
D. J. Miralles
Affiliation:
Departament of Crop Production, School of Agriculture, University of Buenos Aires, Av. San Martín 4453 (C1417DSE) Buenos Aires, Argentina IFEVA, University de Buenos Aires and CONICET, School of Agriculture, Av. San Martin 4453 (C1417DSE) Buenos Aires, Argentina
C. G. López
Affiliation:
Faculty of Agricultural Sciences, National University of Lomas de Zamora, Juan XXIII and Ruta 4, Llavallol (B1836), Buenos Aires Province, Argentina Faculty of Agricultural Sciences, IIPAAS, Institute of Research on Agricultural Production, Environment and Health, National University of Lomas de Zamora, Juan XXIII and Ruta 4, Llavallol (B1836), Buenos Aires Province, Argentina
L. B. Iriarte
Affiliation:
INTA Barrow, Integrated Experimental Farm, Ruta 3 Km 488, (7500) Tres Arroyos, Buenos Aires Province, Argentina
D. P. Rondanini*
Affiliation:
Departament of Crop Production, School of Agriculture, University of Buenos Aires, Av. San Martín 4453 (C1417DSE) Buenos Aires, Argentina Faculty of Agricultural Sciences, IIPAAS, Institute of Research on Agricultural Production, Environment and Health, National University of Lomas de Zamora, Juan XXIII and Ruta 4, Llavallol (B1836), Buenos Aires Province, Argentina CONICET, National Council of Scientific and Technological Research, Av. Godoy Cruz 2290 (C1425FQB) Buenos Aires, Argentina
*
Author for correspondence: D. P. Rondanini, E-mail: rondanin@agro.uba.ar

Abstract

Oilseed rape seed yield has increased in the last 40 years in most countries, but this yield gain has not been accompanied by greater yield stability. The current study aimed to quantify the genotype by environment (G × E) interaction on oilseed rape yield, identify genotypes with broad adaptability and the main environmental drivers related to seed yield. A weighted two-stage mixed-model analysis was applied to official multi-environment trials of nine spring genotypes (G), in three locations (L) during 6 years (Y) on central and southern Argentine Pampas under rainfed conditions. Best linear unbiased prediction of seed yield ranged from 0.37 to 3.73 kg/ha. Fixed effect L × Y was highly significant and G variability was estimated as 130 kg/ha of standard deviation. Contrasting genotypes were identified by Shukla's stability index and two of those showed the best yield performance in the wettest year. Factor analysis explained 0.75 of total variation and discriminated genotypes with broad and specific adaptability, as well as combined environments according to the similarities in seed yield of the evaluated genotypes. Environmental loadings of Factor 2 were linearly associated with cumulative rainfall in the post-flowering period (up to 230 mm). It is concluded that (i) a significant G × L × Y interaction underlies the high variability of seed yield, (ii) two genotypes (G6 and G7) with high yield stability were identified, and (iii) G × E effects are associated with post-flowering rainfall.

Type
Crops and Soils Research Paper
Copyright
Copyright © Cambridge University Press 2019 

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References

Acorsi, CRL, Guedes, TA, Coan, MMD, Pinto, RJB, Scapim, CA, Pacheco, CAP, Guimaraes, PEO and Casela, CR (2017) Applying the generalized additive main effects and multiplicative interaction model to analysis of maize genotypes resistant to grey leaf spot. Journal of Agricultural Science, Cambridge 155, 939953.Google Scholar
Allard, RW and Bradshaw, AD (1964) Implications of genotype-environmental interactions in applied plant breeding. Crop Science 4, 503507.Google Scholar
Berry, PM and Spink, JH (2006) A physiological analysis of oilseed rape yields: past and future. Journal of Agricultural Science, Cambridge 144, 381392.Google Scholar
Brandle, JE and McVetty, PBE (1988) Genotype x environment interaction and stability analysis of seed yield of oilseed rape grown in Manitoba. Canadian Journal of Plant Science 68, 381388.Google Scholar
Chapman, SC and de la Vega, AJ (2002) Spatial and seasonal effects confounding interpretation of sunflower yields in Argentina. Field Crops Research 73, 107120.Google Scholar
Cruz, CD (1998) GENES: software for experimental statistics in genetics. Genetics and Molecular Biology 21, 135138.Google Scholar
Cruzate, G, Panigatti, JL and Moscatelli, G (2008) Suelos y Ambientes de Buenos Aires. Buenos Aires, Argentina: GeoINTA. Available at http://www.geointa.inta.gob.ar/wp-content/uploads/downloads/Laminas_de_Suelos/Buenos-Aires_3.jpg (Accessed 16 July 2019).Google Scholar
Dardanelli, JL, Balzarini, MG, Martínez, MJ, Cuniberti, M, Resnik, S, Ramunda, SF, Herrero, R and Baigorri, H (2006) Soybean maturity groups, environments and their interaction define mega-environments for seed composition in Argentina. Crop Science 46, 19391947.Google Scholar
de la Vega, AJ, Chapman, SC and Hall, AJ (2001) Genotype by environment interaction and indirect selection for yield in sunflower. I. Two-mode pattern analysis of oil and biomass yield across environments in Argentina. Field Crops Research 72, 1738.Google Scholar
Escobar, M, Berti, M, Matus, I, Tapia, M and Johnson, B (2011) Genotype × environment interaction in canola (Brassica napus L.) seed yield in Chile. Chilean Journal of Agricultural Research 71, 175186.Google Scholar
FAO (2019) FAOSTAT. Rome, Italy: FAO, Statistics Division.Google Scholar
Gehringer, A, Snowdon, R, Spiller, T, Basunanda, P and Friedt, W (2007) New oilseed rape (Brassica napus) hybrids with high levels of heterosis for seed yield under nutrient-poor conditions. Breeding Science 57, 315320.Google Scholar
Gogel, B, Smith, A and Cullis, B (2018) Comparison of one-and two-stage mixed model analysis of Australia's National Variety Trial Southern Region wheat data. Euphytica 214, article no. 44. https://doi.org/10.1007/s10681-018-2116-4.Google Scholar
Gomez, NV and Miralles, DJ (2011) Factors that modify early and late reproductive phases in oilseed rape (Brassica napus L.): its impact on seed yield and oil content. Industrial Crops and Products 34, 12771285.Google Scholar
He, D, Wang, E, Wang, J and Lilley, JM (2017) Genotype × environment × management interactions of canola across China: a simulation study. Agricultural and Forest Meteorology 247, 424433.Google Scholar
Henderson, CR (1984) Applications of Linear Models in Animal Breeding. Guelph, Ontario, Canada: University of Guelph.Google Scholar
Hu, X (2014) Combined yield comparison and stability evaluation of rape genotypes using the mixed model. Field Crops Research 167, 1118.Google Scholar
Iriarte, LB and Valetti, OE (2008) Cultivo de Colza. Buenos Aires, Argentina: Ediciones INTA (in Spanish).Google Scholar
Johnson, RA and Wichern, DW (1992) Applied Multivariate Statistical Analysis. Englewood Cliffs, New Jersey, USA: Prentice-Hall.Google Scholar
Kirkegaard, JA, Lilley, JM, Brill, RD, Ware, AH and Walela, CK (2018) The critical period for yield and quality determination in canola (Brassica napus L.). Field Crops Research 222, 180188.Google Scholar
Leal Filho, W, Alves, F, Caeiro, S and Azeiteiro, UM (2014) International Perspectives on Climate Change: Latin America and Beyond. Cham, Switzerland: Springer.Google Scholar
Liu, L, Gan, Y, Bueckert, R and Van Rees, K (2011) Rooting systems of oilseed and pulse crops. II. Vertical distribution patterns across the soil profile. Field Crops Research 122, 248255.Google Scholar
Messina, CD, Hansen, JW and Hall, AJ (1999) Land allocation conditioned on El Niño–Southern Oscillation phases in the Pampas of Argentina. Agricultural Systems 60, 197212.Google Scholar
Moghaddam, MJ and Pourdad, SS (2011) Genotype × environment interactions and simultaneous selection for high oil yield and stability in rainfed warm areas rapeseed (Brassica napus L.) from Iran. Euphytica 180, 321335.Google Scholar
Mortazavian, SMM and Azizi-Nia, S (2014) Nonparametric stability analysis in multi-environment trial of canola. Turkish Journal of Field Crops 19, 108117.Google Scholar
Murakami, DM and Cruz, CD (2004) Proposal of methodologies for environment stratification and analysis of genotype adaptability. Crop Breeding and Applied Biotechnology 4, 711.Google Scholar
Nowosad, K, Liersch, A, Popławska, W and Bocianowski, J (2016) Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 208, 187194.Google Scholar
Panigatti, JL, Cruzate, G, Tasi, H and Bedendo, D (2008) Suelos y Ambientes de Entre Ríos. Buenos Aires, Argentina: GeoINTA. Available at http://www.geointa.inta.gob.ar/wp-content/uploads/downloads/Laminas_de_Suelos/Entre-R%C3%ADos_3.jpg (Accessed 16 July 2019).Google Scholar
Pater, D, Mullen, JL, McKay, JK and Schroeder, JI (2017) Screening for natural variation in water use efficiency traits in a diversity set of Brassica napus L. identifies candidate variants in photosynthetic assimilation. Plant and Cell Physiology 58, 17001709.Google Scholar
Patterson, HD and Thompson, R (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58, 545554.Google Scholar
Peltonen-Sainio, P and Jauhiainen, L (2008) Association of growth dynamics, yield components and seed quality in long-term trials covering rapeseed cultivation history at high latitudes. Field Crops Research 108, 101108.Google Scholar
Peltonen-Sainio, P, Jauhiainen, L, Trnka, M, Olesen, JE, Calanca, P, Eckersten, H, Eitzinger, J, Gobin, A, Kersebaum, KC, Kozyra, J, Kumar, S, Dalla Marta, A, Micale, F, Schaap, B, Seguin, B, Skjelvåg, AO and Orlandini, S (2010) Coincidence of variation in yield and climate in Europe. Agriculture, Ecosystems and Environment 139, 483489.Google Scholar
Pilarczyk, W (2013) The relation between G×E interaction and the number of locations in series of oilseed rape trials. Biometrical Letters 50, 5360.Google Scholar
Ploschuk, RA, Miralles, DJ, Colmer, TD, Ploschuk, EL and Striker, GG (2018) Waterlogging of winter crops at early and late stages: impacts on leaf physiology, growth and yield. Frontiers in Plant Science 9, article no. 1863. doi: 10.3389/fpls.2018.01863.Google Scholar
Rahman, H (2013) Review: breeding spring canola (Brassica napus L.) by the use of exotic germplasm. Canadian Journal of Plant Science 93, 363373.Google Scholar
Riffkin, P, Potter, T and Kearney, G (2012) Yield performance of late-maturing winter canola (Brassica napus L.) types in the High Rainfall Zone of southern Australia. Crop and Pasture Science 63, 1732.Google Scholar
Riffkin, P, Christy, B, O'Leary, G and Partington, D (2016) Contribution of phase durations to canola (Brassica napus L.) grain yields in the High Rainfall Zone of southern Australia. Crop and Pasture Science 67, 359368.Google Scholar
Rondanini, DP, Gomez, NV, Agosti, MB and Miralles, DJ (2012) Global trends of rapeseed grain yield stability and rapeseed-to-wheat yield ratio in the last four decades. European Journal of Agronomy 37, 5665.Google Scholar
SAS Institute (2011) The SAS System for Windows. Release 9.2. Cary, NC, USA: University Edition SAS Inst.Google Scholar
Shukla, GK (1972) Some statistical aspects of partitioning genotype–environmental components of variability. Heredity 29, 237245.Google Scholar
Smith, AB, Cullis, BR and Thompson, R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. Journal of Agricultural Science, Cambridge 143, 449462.Google Scholar
Smith, AB, Ganesalingam, A, Kuchel, H and Cullis, BR (2015) Factor analytic mixed models for the provision of grower information from national crop variety testing programs. Theoretical and Applied Genetics 128, 5572.Google Scholar
Tahira, AR, Khan, MA and Amjad, M (2013) Stability analysis of canola (Brassica napus) genotypes in Pakistan. Global Advanced Research Journal of Agricultural Science 2, 270275.Google Scholar
Takashima, NE, Rondanini, DP, Puhl, LE and Miralles, DJ (2013) Environmental factors affecting yield variability in spring and winter rapeseed genotypes cultivated in the southeastern Argentine Pampas. European Journal of Agronomy 48, 88100.Google Scholar
Wan, J, Griffiths, R, Ying, J, McCourt, P and Huang, Y (2009) Development of drought-tolerant canola (Brassica napus L.) through genetic modulation of ABA-mediated stomatal responses. Crop Science 49, 15391554.Google Scholar
Wollmer, AC, Pitann, BK and Mühling, H (2018) Waterlogging events during stem elongation or flowering affect yield of oilseed rape (Brassica napus L.) but not seed quality. Journal of Agronomy and Crop Science 204, 165174.Google Scholar
Zhang, H, Berger, JD and Milroy, SP (2013) Genotype × environment interaction studies highlight the role of phenology in specific adaptation of canola (Brassica napus) to contrasting Mediterranean climates. Field Crops Research 144, 7788.Google Scholar
Zhang, H, Berger, JD and Herrmann, C (2017) Yield stability and adaptability of canola (Brassica napus L.) in multiple environment trials. Euphytica 213, article no. 155. DOI https://doi.org/10.1007/s10681-017-1948-7.Google Scholar
Zhu, M, Monroe, JG, Suhail, Y, Villiers, F, Mullen, J, Pater, D, Hauser, F, Jeon, BW, Bader, JS, Kwak, JM, Schroeder, JI, McKay, JK and Assmann, SM (2016) Molecular and systems approaches towards drought-tolerant canola crops. New Phytologist 210, 11691189.Google Scholar