GENOTYPE AND GENOTYPE-BY-ENVIRONMENT IMPACTS ON GRAIN QUALITY OF PEANUT GENOTYPES

BALZARINI, M.; BALDESSARI, J.; VIGLIANO, M; MARTÍNEZ, M.J.

Manfredi, Córdoba

Simposio; 1° Simposio Internacional de Mejoramiento Genético Vegetal; 2021

INTA ? Centro Regional Córdoba Córdoba, Argentina

Genotype and Genotype-by-environment impacts on Grain Quality of Peanut GenotypesVigliano, M.1, Balzarini, M.2-3, Baldessari, J.3, De la Barrera, G. 3, G. Aguilar, R.1, Silva, M.1, Sandrinelli, R.2,Alvarez, C4, Martínez, M.J*11Grain Quality Laboratory, EEA INTA Manfredi, Córdoba. Argentina. 2Faculty of Agricultural Sciences, NationalUniv. of Córdoba, Córdoba. Argentina and UFYMA (INTA-CONICET).3Peanut Plant breeding department, EEAINTA Manfredi, Córdoba, Argentina. 4Soil and Water Laboratory, EEA INTA Manfredi, Córdoba. Argentina.*e-mail: martinez.mariajose@inta.gob.arCórdoba, Argentina, is the biggest world exporter of high quality peanuts for snacks, confectionary and other food uses.A total of 20 peanut genotypes (18 advanced lines from INTA Breeding Program) and two commercial cultivars,GRANOLEIC (high-oleic (HO) cultivar) and ASEM400 (Non-HO), were evaluated on field trials for grain quality across6 environments during the 2017/2018 and 2019/2020 crop seasons located at S. Eufemia, V. Valeria, Moldes andBulnes, all in Córdoba. The objectives of this work were to assess: 1) genotype performances regarding grain quality,and 2) the relative contribution of genotype (G), environment (E), and GxE interaction effects on quantitative traitsrelated to grain composition. Chemical analyses were carried out at the Grain Laboratory of INTA-Manfredi, using twofield replicates (200 g/sample) per G from each E. There were determined the contents of oil, protein, ashes andcarbohydrates by diffence and fatty acids, tocopherols, and sugars were determined by chromatography, all methodsfollowing the AOCS (1998) specifications. A random effect model was fitted to obtain REML estimates of G, E and GxEvariance components for each quantitative trait. For traits with the higher GxE effects, the interaction was explored byAMMI biplots, stability measures, and correlated with climatic and edaphic environmental variables from each E. Thegrain composition of peanut genotypes and the statistical significance of G effect, as well as G, E and GxE contributionsto total trait variability are shown in Table 1. Except delta tocopherol, all other grain quality characters varied significantlyamong the genotypes. Genotype effect was higher for fatty acids composition, carbohydrates, gamma and betatocopherols. The line G13 had the highest O/L (15.4) relationship, the highest oleic acid (77.52), and the lowest IY(80.5). The rest of the selected lines were Non-HO. Proximate composition quality traits had lower genetic components;oil, protein and ashes were mainly influenced by environmental factors such as soil and climate. The highest oil contentwas recorded in Moldes, with higher air temperatures, lower precipitations, and higher water deficit (-500 mm) duringseed fill. At that location it was also determined, the lowest protein content. This could be related not only to the waterdeficit and shortest growing season (150d), but also to low organic matter (OM), nitrate (-NO3), and extractablephosphorus (Pe) in soil. The highest G×E interaction occurred for the sugar composition, being ASEM400 the genotypethat contributed most to GxE, with sugar content increasing while higher water deficit during the seed fill (Moldes).Advanced line G5, had a similar sugar content to ASEM400 but with the highest fructose average and stability. Asconclusion, Line G13 seems to offer the peanut industry a good HO alternative to the widely grown GRANOLEICO.Acknowledgements: Project INTA (2019PE-E7-517) PIODO134-2017 MINCyT Córdoba, Convenio ASEM-INTA,CONICET. To Ing Agr. Raquel Balbo and Patricia Fabro