Model Ammi Untuk Analisis Interaksi Genotipe X Lokasi (Indra Herlangga)

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Model Ammi Untuk Analisis Interaksi Genotipe X Lokasi (Indra Herlangga)

  1. 1. AlfianFutuhulHadi<br />HalimatusSa’diyah<br />Model AMMI UntukAnalisisInteraksiGenotipeX Lokasi<br />
  2. 2. ABSTRAK<br />Additive Main Effects And Multiplicative Interaction Model (AMMI model) nowadays is used to assess in plant breeding, especially to asses the Genotype X Environment Interaction (GEI) on a mega-environmental experiment. AMMI is a statistical technique to analyze two ways factorial experiment. AMMI combine analysis of variance for additive main effect and multivariate analysis of principal component by bilinear modeling for interaction effect. AMMI also produce graphic –which is called AMMI-biplot or GGE-biplot– that can give more information about the GEI than ordinary ANOVA method. We can use the AMMI-biplot to determine the genotype is stable or have a specific adaptation on an environment.<br />
  3. 3. PENDAHULUAN<br />Percobaanmultilokasimerupakanpercobaan yang seringdigunakandalampenelitianpemuliaantanamanuntukmengkajiinteraksigenotipe X lingkungan. Kajian ini penting dalam pemuliaan tanaman karena hasilnyadapatdigunakanuntukmendugadanmenyeleksigenotipe-genotipe yang berpenampilanstabil(stability of genotypes) padaberbagailingkunganberbedaatauberadaptasipadasuatulingkunganspesifik<br />Additive main effects and multiplicative interaction model (AMMI model) merupakansuatumetodemultivariat yang relatifbarudigunakanakhir-akhirinidalampenelitian-penelitianpemuliaantanaman untuk mengkaji GEI pada suatu percobaan multilokasi. model AMMI merupakansuatu model gabungandaripengaruhaditifpadaanalisisragamdanpengaruhmultiplikatifpadaanalisiskomponenutama.<br />
  4. 4. KESIMPULAN<br />AMMI dangrafikbiplot AMMI mampumemberikanlebihbanyakinformasitentanginteraksigenotipe X lokasidibandingkandenganmetode ANOVA yang biasadilakukan. NamunAMMI tidaklepasdarikelemahan/kerugian.<br />Pendekatan AMMI tidakmenyediakanpengujianhipotesissepertipendekatankonvensional lain. Karenaitu, AMMI-biplotbaikuntukeksplorasimembangkitkanhipotesisdaripadapengambilankeputusanhipotesis.<br />

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