Genotype by environment interaction and adaptability of photoperiod-sensitive biomass sorghum hybrids

Delgado, I. D.; Gonçalves, Flávia Maria Avelar; Parrella, R. A. da C.; Castro, Fernanda Maria Rodrigues; Nunes, José Aírton Rodrigues

doi:10.1590/1678-4499.20190028

Cited by 8 publications

(9 citation statements)

References 17 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Finally, the bi‐segmented statistical model proposed by Toler (Delgado et al., 2019; Toler & Burrows, 1998) was fitted to describe the adaptability and phenotypic stability, which is given by the following equation:

Y_{ij} = {normalα}_{normali} + [Z_{j} β_{1 normali} + (() 1 - Z_{normalj}) β_{2 normali}] {normalμ}_{normalj} + {normalδ}_{ij} + e_{ij}

where Y ij is the mean of genotype i in environment j; α i is the parameter that reflects the value of the response of genotype i in the mean environment [ μ j = 0, where μ j is the non‐observable (latent) regression variable, but which is a parameter of the model]; β 1i and β 2i are non‐linear regression coefficients that measure the response of genotype i to the variations in the environments of lower and higher quality, respectively; Z j is a variable that assumes the value Z j = 1 if μ j ≤ 0 and Z j = 0 if μ j > 0; μ j is the parameter that measures environmental quality; δ ij is the deviation from this regression; and e ij is the mean experimental error.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Selection of elite Rhizobium strains by biometric techniques for inoculation in common bean

et al. 2021

View full text Add to dashboard Cite

Variation in plant responses to inoculation with rhizobia strains in different environments can be analyzed with the aid of biometric techniques. The Annicchiarico methodology and the Additive Main Effects and Multiplicative Interaction Analysis (AMMI) and Toler models are here applied to the genotype × environment (GE) interaction to determine the adaptability and phenotypic stability of N2‐fixing bacteria in symbiosis with common bean (Phaseolus vulgaris L.) in environments with high, moderate, and low acidity and with deficiencies or excessive concentrations of nutrients. A randomized block design was used in eight environments, with four replications and seven bacterial genotypes, including two controls with native soil bacterial communities (with or addition without mineral‐N fertilization). Individual and combined analyses of variance and biometric analyses were conducted on the yield values. CIAT899, UFLA02‐127, and native rhizobia were the most stable genotypes in relation to environments (AMMI models). CIAT899 and native rhizobia are the least adapted (Toler models), whereas the genotype UFLA02‐127 has the best response to the environments (Toler models). The Annicchiarico method showed that the reliability of adoption of UFLA02‐127 and native rhizobia fertilized with mineral N exceeds 85%, indicating high probability of increase in common bean yield if adopted in the field. Conversely, native rhizobia have <65% reliability.

show abstract

Y_{ij} = {normalα}_{normali} + [Z_{j} β_{1 normali} + (() 1 - Z_{normalj}) β_{2 normali}] {normalμ}_{normalj} + {normalδ}_{ij} + e_{ij}

Section: Methodsmentioning

confidence: 99%

“…Finally, the bi-segmented statistical model proposed by Toler (Delgado et al, 2019;Toler & Burrows, 1998) was fitted to describe the adaptability and phenotypic stability, which is given by the following equation:…”

Section: Toler Modelsmentioning

confidence: 99%

Selection of elite Rhizobium strains by biometric techniques for inoculation in common bean

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The trait assessed was the dry mass yield of the plot, which was converted into tonne ha −1 . The crop management is described in detail by Delgado et al (2019).…”

Section: Real Datamentioning

confidence: 99%

Bayesian GGE model for heteroscedastic multienvironmental trials

Oliveira

Silva

et al. 2022

Crop Science

Self Cite

View full text Add to dashboard Cite

The dissection of genotype × environment interaction (GEI) is a crucial aspect of the final stages of plant breeding pipelines and recommendation of cultivars. Linearbilinear models used to analyze this interaction, such as the additive main effects and multiplicative interaction (AMMI) and genotype plus GEI (GGE), often assume homogeneity of the residual variances across environments which affects the estimates and therefore, interpretations and conclusions. Our main objective was to propose a GGE model that considers heteroscedasticity across environments using Bayesian inference and to evaluate its implications in the interpretation of real and simulated data. The GGE model assuming common variance was also fitted for comparison purposes. The great flexibility of the Bayesian inference is transferred to the biplots, allowing the construction of credible regions for genotypic and environmental scores. The inference on the stability and adaptability of genotypes might change when heteroscedasticity is ignored. When real data are used, different patterns of correlations between environments also affect the representativeness and discrimination of the target environment. The modeling of heteroscedasticity allowed the clustering of environments into subgroups, with similar effects for GEI. The proposed GGE model was more adequate and realistic to deal with scenarios of heterogeneous variance in multienvironment trials, which can be useful for exploiting the GEI.Abbreviations: AE, average environment; AEA, axis of the average environment; AEC, average environment coordination; AMMI, additive main effects and multiplicative interaction model; BGGE, Bayesian GGE model under homogeneity of residual variances across sites; BGGEH, Bayesian GGE model under heterogeneity of residual variances across sites; FA, Factor-Analytic Model; G, Genotype; GEI, Genotype × environment interaction; GGE, Genotype main effect + genotype × environment interaction; HPD, Highest posterior density; IG, ideal genotype; MCMC, Markov chain Monte Carlo; MET, Multienvironmental trial; MLM, mixed linear model; SS GGE , total sum of squares for G + GEI; SVD, Singular value decomposition.

show abstract

“…Pengaruh interaksi genotipe x lingkungan terhadap karakter-karakter hasil dan komponen hasilperlu diperhatikan untuk melihat perubahan keragaan karakter sorgum pada lingkungan yang berbeda. Interaksi genotipe x lingkungan dilaporkan berpengaruh terhadap sifat ketahanan terhadap penyakit karat daun (Rifka et al, 2020), biomassa sorgum (Delgado et al, 2019), dan kandungan biji sorgum seperti kandungan protein, lemak, amilosa dan tanin (Trikoesoemaningtyas et al, 2015). Kondisi lingkungan tersebut dapat berupa kondisi tanah dan musim pada saat penanaman.…”

Section: Pendahuluanunclassified

“…Koefisien keragaman lingkungan (KK) pada penelitian ini cenderung kecil yaitu 4.38-20.54%. Semakin besar nilai KK, menunjukkan keragaan suatu karakter lebih besar dipengaruhi oleh galat lingkungan (Delgado et al, 2019).…”

Section: Hasil Dan Pembahasanunclassified

Interaksi Genotipe x Lingkungan pada Karakter dan Komponen Hasil Galur-galur Sorgum IPB

Wibawa

TRIKOESOEMANINGTYAS

Wirnas

2021

J. Agron. Indonesia

View full text Add to dashboard Cite

Sorgum (Sorghum bicolor (L.) Moench) merupakan tanaman multiguna yang prospektif untuk dikembangkan di Indonesia sebagai sumber pangan, pakan dan bioenergi. Nilai Brix pada nira batang dan karakter malai pada sorgum merupakan karakter hasil yang perlu diperhatikan dalam perakitan varietas sorgum multiguna. Penelitian ini bertujuan untuk memperoleh informasi keragaman nilai Brix pada nira batang dan karakter malai serta komponen hasil dari galur-galur sorgum multiguna IPB dan responnya terhadap perbedaan kondisi lingkungan. Materi genetik yang digunakan adalah 16 galur sorgum generasi F7 hasil seleksi pedigree dan bulk secara terpisah dari tiga populasi hasil persilangan dan 4 varietas sorgum nasional, ditanam menggunakan rancangan acak kelompok di dua kondisi lingkungan yaitu musim kemarau tahun 2019 dan musim hujan tahun 2020. Hasil penelitian menunjukkan karakter hasil yaitu bobot biji per malai dan nilai Brix pada nira batang selain dipengaruhi oleh genotipe dan lingkungan, juga dipengaruhi oleh interaksi genotipe x lingkungan. Analisis korelasi menunjukkan tidak ada hubungan yang signifikan antara nilai Brix pada nira batang dengan bobot biji per malai. Masing-masing berkorelasi nyata positif dengan tinggi tanaman, sehingga sorgum yang tinggi cenderung menghasilkan bobot biji per malai dan nilai Brix yang tinggi. Genotipe PI 150/N-043-16-5-5-P dan PI 150/K-19-6-2-B merupakan genotipe dengan tinggi tanaman, bobot biji per malai dan nilai Brix yang tinggi dibanding genotipe lainnya, dan setara dengan varietas Bioguma 1, Bioguma 3, dan Numbu. Kata kunci: bioenergi, bobot biji per malai, nilai Brix, pakan, pangan

show abstract

Genotype by environment interaction and adaptability of photoperiod-sensitive biomass sorghum hybrids

Cited by 8 publications

References 17 publications

Selection of elite Rhizobium strains by biometric techniques for inoculation in common bean

Selection of elite Rhizobium strains by biometric techniques for inoculation in common bean

Bayesian GGE model for heteroscedastic multienvironmental trials

Interaksi Genotipe x Lingkungan pada Karakter dan Komponen Hasil Galur-galur Sorgum IPB

Contact Info

Product

Resources

About