Register      Login
Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE

Secondary traits explaining sorghum genotype by environment interactions for grain yield

Ana J. P. Carcedo A , Pedro A. Pardo B and Brenda L. Gambin A C
+ Author Affiliations
- Author Affiliations

A Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR), Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Campo Experimental Villarino S/N, Zavalla (S2125ZAA), Prov. de Santa Fe, Argentina.

B Advanta Semillas SAIC, Ruta 33 km 636, CC559, (2900), Venado Tuerto, Santa Fe, Argentina.

C Corresponding author. Email: bgambin@unr.edu.ar; gambin@iicar-conicet.gov.ar

Crop and Pasture Science 68(7) 599-608 https://doi.org/10.1071/CP17015
Submitted: 9 January 2017  Accepted: 13 July 2017   Published: 17 August 2017

Abstract

Effective plant improvement depends on understanding grain yield genotype by environment (G × E) interactions. Studies focusing on more heritable (secondary) traits provide a way for interpreting the nature of these interactions and assist selection by adapting hybrids to specific adaptation patterns. The objective of our study was to explore some specific traits to help describe G × E interactions for yield in grain sorghum. A set of 22 representative hybrids were grown at eight different environments varying mainly in water and nitrogen availability. Studied traits were yield, phenology (time to anthesis and grain-filling duration), numerical yield components (grain number and individual grain weight) and physiological components (biomass at maturity and harvest index).

The G × E interaction to G component variance represented 3.48 for grain yield, 1.03 for grain-filling duration, 0.87 for biomass at maturity, 0.71 for time to anthesis, and less than 0.5 for the rest of the traits. Although the G × E interaction for yield was large, the relative genotypic contribution of most studied traits suggests that G × E interaction is not a major impediment for attaining high selection responses to these traits. Pattern analysis applied to G × E best linear unbiased predictors defined three genotype and three environmental groups. Environments were grouped suggesting different water stress levels during early or pre-flowering stages, whereas genotype groups depicted different yield responses across environmental groups. Phenology differences among genotypes explained a large portion of the G × E interaction throughout its influence on grain weight. Late flowering genotypes performed poorly in terms of grain weight and yield across all environments, showing that these materials are not the best option for our production system. Longer grain filling contributed to grain weight and yield at environments with low stress levels, particularly when combined with intermediate or short maturity. Early materials contributed to grain weight and yield at the highest stressful environments. We provide useful information to sorghum breeders at temperate environments, and described secondary traits that could assist selection at particular environments.

Additional keywords: breeding, indirect selection, Sorghum bicolor (L.) Moench.


References

Abakemal D, Shimelis H, Derera J (2016) Genotype-by-environment interaction and yield stability of quality protein maize hybrids developed from tropical highland adapted inbred lines. Euphytica 209, 757–769.
Genotype-by-environment interaction and yield stability of quality protein maize hybrids developed from tropical highland adapted inbred lines.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28XjvVymtLs%3D&md5=3a22487a9250f59309e26d7d4b0a36e0CAS |

Assefa Y, Staggenborg SA (2010) Grain sorghum yield with hybrid advancement and changes in agronomic practices from 1957 through 2008. Agronomy Journal 102, 703–706.
Grain sorghum yield with hybrid advancement and changes in agronomic practices from 1957 through 2008.Crossref | GoogleScholarGoogle Scholar |

Bassi FM, Bentley AR, Charmet G, Ortiz R, Crossa J (2016) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Science 242, 23–36.
Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXhsVGntrnM&md5=b0fcffb10443989c48f49375f33f0effCAS |

Bates D, Maechler M, Bolker B, Walker S (2014) lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1–7. Available at: http://CRAN.R-project.org/package=lme4 (accessed 15 June 2016).

Chapman SC, Cooper M, Butler DG, Henzel RG (2000a) Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield. Australian Journal of Agricultural Research 51, 197–207.
Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield.Crossref | GoogleScholarGoogle Scholar |

Chapman SC, Cooper M, Hammer GL, Butler DG (2000b) Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields. Australian Journal of Agricultural Research 51, 209–221.
Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields.Crossref | GoogleScholarGoogle Scholar |

Cockerham CC (1963) Estimation of genetic variances. In ‘Statistical genetics and plant breeding’. (Eds WD Hanson, HF Robinson) pp. 53–94. (NAS-NRC: Washington, DC)

Cooper M, DeLacy IH (1994) Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments. Theoretical and Applied Genetics 88, 561–572.
Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2c7isV2hsQ%3D%3D&md5=eb5321d8edf65d25ec1428579066f54bCAS |

Cooper M, Woodruff DR, Eisemann RL, Brennan PS, DeLacy IH (1995) A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes. Theoretical and Applied Genetics 90, 492–502.
A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2c7htlWkug%3D%3D&md5=654c72b4c3e561d0e99d42a44dfa11aeCAS |

Cooper M, Stucker RE, DeLacy IH, Harch BD (1997) Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Science 37, 1168–1176.
Wheat breeding nurseries, target environments, and indirect selection for grain yield.Crossref | GoogleScholarGoogle Scholar |

Cooper M, Gho C, Leafgren R, Tang T, Messina C (2014) Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. Journal of Experimental Botany 65, 6191–6204.
Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXitl2gsbs%3D&md5=319147e63d71a66d05010df3894b9674CAS |

Craufurd PQ, Flower DJ, Peacock JM (1993) Effect of heat and drought stress on sorghum (Sorghum bicolor). I. Panicle development and leaf appearance. Experimental Agriculture 29, 61–76.
Effect of heat and drought stress on sorghum (Sorghum bicolor). I. Panicle development and leaf appearance.Crossref | GoogleScholarGoogle Scholar |

Curti RN, de la Vega AJ, Andrade AJ, Bramardi SJ, Bertero HD (2014) Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina. Field Crops Research 166, 46–57.
Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina.Crossref | GoogleScholarGoogle Scholar |

de la Vega AJ, Hall AJ (2002a) Effects of planting date, genotype and their interactions on sunflower yield: I. Determinants of oil-corrected grain yield. Crop Science 42, 1191–1201.
Effects of planting date, genotype and their interactions on sunflower yield: I. Determinants of oil-corrected grain yield.Crossref | GoogleScholarGoogle Scholar |

de la Vega AJ, Hall AJ (2002b) Effects of planting date, genotype and their interactions on sunflower yield: II. Components of oil yield. Crop Science 42, 1202–1210.
Effects of planting date, genotype and their interactions on sunflower yield: II. Components of oil yield.Crossref | GoogleScholarGoogle Scholar |

de Mendiburu F (2014) Agricolae: Statistical procedures for agricultural research. R package version 1.2–4. Available at: https://cran.r-project.org/package=agricolae (accessed 15 June 2016).

DeLacy IH, Basford KE, Cooper M, Bull JK, McLaren CG (1996) Analysis of multi-environment trials – An historical perspective. In ‘Plant adaptation and crop improvement’. (Eds M Cooper, GL Hammer) pp. 39–123. (CABI Publishing: Wallingford, UK)

Dickerson GE (1962) Implications of genetic-environmental interaction in animal breeding. British Society of Animal Science 4, 47–63.

Donatelli M, Hammer GL, Vanderlip RL (1992) Genotype and water limitation effect on phenology, growth, and transpiration efficiency in grain sorghum. Crop Science 32, 781–786.
Genotype and water limitation effect on phenology, growth, and transpiration efficiency in grain sorghum.Crossref | GoogleScholarGoogle Scholar |

Eisemann RL, Cooper M, Woodruff DR (1990) Beyond the analytical methodology-better interpretation and exploitation of genotype-by-environment interaction in breeding. In ‘Genotype-by-environment interaction and plant breeding’. (Ed. MS Kang) pp. 108–117. (Louisiana State University: Baton Rouge, LA)

Fox PN, Rosielle AA (1982) Reducing the influence of environmental main-effects on pattern analysis of plant breeding environments Euphytica 31, 645–656.
Reducing the influence of environmental main-effects on pattern analysis of plant breeding environmentsCrossref | GoogleScholarGoogle Scholar |

Gambín BL, Borrás L (2007) Plasticity of sorghum kernel weight to increased assimilate availability. Field Crops Research 100, 272–284.
Plasticity of sorghum kernel weight to increased assimilate availability.Crossref | GoogleScholarGoogle Scholar |

Gambín BL, Borrás L (2011) Genotypic diversity in sorghum inbred lines for grain-filling patterns and other related agronomic traits. Crop & Pasture Science 62, 1026–1036.
Genotypic diversity in sorghum inbred lines for grain-filling patterns and other related agronomic traits.Crossref | GoogleScholarGoogle Scholar |

Gambin BL, Coyos T, Di Mauro M, Borrás L, Garibaldi LA (2016) Exploring genotype, management and environmental variables influencing grain yield of late-sown maize in central Argentina. Agricultural Systems 146, 11–19.
Exploring genotype, management and environmental variables influencing grain yield of late-sown maize in central Argentina.Crossref | GoogleScholarGoogle Scholar |

Gizzi G, Gambin BL (2016) Eco-physiological changes in sorghum hybrids released in Argentina over the last 30 years. Field Crops Research 188, 41–49.
Eco-physiological changes in sorghum hybrids released in Argentina over the last 30 years.Crossref | GoogleScholarGoogle Scholar |

Hammer GL, Carberry PS, Muchow RC (1993) Modelling genotypic and environmental control of leaf area dynamics in grain sorghum. I. Whole plant level. Field Crops Research 33, 293–310.
Modelling genotypic and environmental control of leaf area dynamics in grain sorghum. I. Whole plant level.Crossref | GoogleScholarGoogle Scholar |

Heiniger RW, Vanderlip RL, Kofoid KD (1993) Caryopsis weight patterns within the sorghum panicle. Crop Science 33, 543–549.
Caryopsis weight patterns within the sorghum panicle.Crossref | GoogleScholarGoogle Scholar |

Ivory DA, Kaewmeechai S, DeLacy IH, Basford KE (1991) Analysis of the environmental component of genotype × environment interaction in crop adaptation evaluation. Field Crops Research 28, 71–84.
Analysis of the environmental component of genotype × environment interaction in crop adaptation evaluation.Crossref | GoogleScholarGoogle Scholar |

Lefkovitch LP (1985) Multi-criteria clustering in genotype-environment interaction problems. Theoretical and Applied Genetics 70, 585–589.
Multi-criteria clustering in genotype-environment interaction problems.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2c7ptFKitA%3D%3D&md5=8efcc9b35cf581d0e011ac465a129929CAS |

Liang S, Ren G, Liu J, Zhao X, Zhou M, McNeil D, Ye G (2015) Genotype-by-environment interaction is important for grain yield in irrigated lowland rice. Field Crops Research 180, 90–99.
Genotype-by-environment interaction is important for grain yield in irrigated lowland rice.Crossref | GoogleScholarGoogle Scholar |

Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik, K (2013) Cluster: Cluster analysis basics and extensions. R Package version 1.14.4. Available at: https://cran.r-project.org/package=cluster (accessed 15 June 2016).

Mason SC, Kathol D, Eskridge KM, Galusha TD (2008) Yield increase has been more rapid for maize than for grain sorghum. Crop Science 48, 1560–1568.
Yield increase has been more rapid for maize than for grain sorghum.Crossref | GoogleScholarGoogle Scholar |

Muir W, Nyquist WE, Xu S (1992) Alternative partitioning of the genotype-by-environment interaction. Theoretical and Applied Genetics 84, 193–200.
Alternative partitioning of the genotype-by-environment interaction.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2c7ktVGhsQ%3D%3D&md5=7de0ff72bdb38a675252d746a8cb5bbfCAS |

Pepper GE, Prine GM (1972) Low light intensity effects on grain sorghum at different stages of growth. Crop Science 12, 590–593.
Low light intensity effects on grain sorghum at different stages of growth.Crossref | GoogleScholarGoogle Scholar |

Qin J, Xu R, Li H, Yang C, Liu D, Liu Z, Zhang L, Lu W, Frett T, Chen P, Zhang M, Qiu L (2015) Evaluation of productivity and stability of elite summer soybean cultivars in multi-environment trials. Euphytica 206, 759–773.
Evaluation of productivity and stability of elite summer soybean cultivars in multi-environment trials.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXhsFartLvF&md5=783a81a3860bc79b3964c048f2bd3c30CAS |

Rakshit S, Ganapathy KN, Gomashe SS, Rathore A, Ghorade RB, Nagesh Kumar MV, Ganesmurthy K, Jain SK, Kamtar MY, Sachan JS, Ambekar SS, Ranwa BR, Kanawade DG, Balusamy M, Kadam D, Sarkar A, Tonapi VA, Patil JV (2012) GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data. Euphytica 185, 465–479.
GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data.Crossref | GoogleScholarGoogle Scholar |

Robinson GK (1991) That BLUP is a good thing: the estimation of random effects. Statistical Science 6, 15–32.
That BLUP is a good thing: the estimation of random effects.Crossref | GoogleScholarGoogle Scholar |

Shorter R, Mungomery RE (1981) Analysis of variance of data from multi-environment trials. In ‘Interpretation of plant response and adaptation to agricultural environments’. (Eds DE Byth, VE Mungomery) pp. 12–26. (Australian Institute of Agricultural Science: Brisbane)

Soil Survey Staff (2014) ‘Keys to soil taxonomy.’ 12th edn. (USDA-Natural Resources Conservation Service: Washington, DC)

Unger PW, Baumhardt RL (1999) Factors related to dryland grain sorghum yield increases: 1939 through 1997. Agronomy Journal 91, 870–875.
Factors related to dryland grain sorghum yield increases: 1939 through 1997.Crossref | GoogleScholarGoogle Scholar |

van Oosterom EJ, Hammer GL (2008) Determination of grain number in sorghum. Field Crops Research 108, 259–268.
Determination of grain number in sorghum.Crossref | GoogleScholarGoogle Scholar |

Vanderlip RL, Reeves HE (1972) Growth stages of sorghum [Sorghum bicolor, (L.) Moench]. Agronomy Journal 64, 13–16.
Growth stages of sorghum [Sorghum bicolor, (L.) Moench].Crossref | GoogleScholarGoogle Scholar |

Ward JH (1963) Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244.
Hierarchical grouping to optimize an objective function.Crossref | GoogleScholarGoogle Scholar |

Williams WT (1976) ‘Pattern analysis in agricultural science.’ (Elsevier Scientific Publishing Company: Amsterdam)

Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47, 643–655.
GGE biplot vs. AMMI analysis of genotype-by-environment data.Crossref | GoogleScholarGoogle Scholar |

Zavala-García F, Bramel-Cox PJ, Eastin JD, Witt MD, Andrews DJ (1992) Increasing the efficiency of crop selection for unpredictable environments. Crop Science 32, 51–57.
Increasing the efficiency of crop selection for unpredictable environments.Crossref | GoogleScholarGoogle Scholar |

Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agronomy Journal 80, 388–393.
Statistical analysis of a yield trial.Crossref | GoogleScholarGoogle Scholar |