Pemanfaatan Analisis Komponen Utama dalam Mendeskripsikan Ciri Morfoagronomi Padi yang Berkorelasi dengan Yield

Rice is an important food crop with a crucial role in global food security. Study on correlation between morphoagronomic traits and yield is an important step in the development of superior rice varieties. Research data were collected from 20 rice varieties grown in the experimental field. A total of 10 morphoagronomic trait variables were measured, including plant height, number of productive tillers, flowering age, dry weight of biomass, hollow grain, 1,000-grain weight, grain weight, panicle number, panicle length, and harvest index. Data were analyzed using variance, correlation, and principal component analyses. The results of PCA showed that 3 principal components (PC) explained 80.88% of the data variation. PC1 was associated with plant height, flowering age, biomass dry weight, empty grain, panicle length, grain weight per clump. PC2 was associated with 1,000-grain weight and harvest index, while PC3 was associated with number of productive tillers and number of panicles. Correlation analysis showed that 1,000-grain weight was positively correlated with harvest index, while biomass dry weight was negatively correlated. These results suggest that all observed morphoagronomic traits except biomass dry weight have an important role in increasing rice yield. Furthermore, the results of this study provide basic informations for the development of high-yielding rice varieties.

Authors

Institutions

Publication Details

Journal
Jurnal Sumberdaya Hayati
Published
2024-09-19
DOI
https://doi.org/10.29244/jsdh.10.3.136-142
Primary Topic
Computer Science and Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pemanfaatan Analisis Komponen Utama dalam Mendeskripsikan Ciri Morfoagronomi Padi yang Berkorelasi dengan Yield

Puji Lestari, Fata Muslim Fikri, Miftahudin
Jurnal Sumberdaya Hayati
Computer Science and Engineering
article

Pemanfaatan Analisis Komponen Utama dalam Mendeskripsikan Ciri Morfoagronomi Padi yang Berkorelasi dengan Yield

Puji Lestari, Fata Muslim Fikri, Miftahudin
article en

Abstract

Rice is an important food crop with a crucial role in global food security. Study on correlation between morphoagronomic traits and yield is an important step in the development of superior rice varieties. Research data were collected from 20 rice varieties grown in the experimental field. A total of 10 morphoagronomic trait variables were measured, including plant height, number of productive tillers, flowering age, dry weight of biomass, hollow grain, 1,000-grain weight, grain weight, panicle number, panicle length, and harvest index. Data were analyzed using variance, correlation, and principal component analyses. The results of PCA showed that 3 principal components (PC) explained 80.88% of the data variation. PC1 was associated with plant height, flowering age, biomass dry weight, empty grain, panicle length, grain weight per clump. PC2 was associated with 1,000-grain weight and harvest index, while PC3 was associated with number of productive tillers and number of panicles. Correlation analysis showed that 1,000-grain weight was positively correlated with harvest index, while biomass dry weight was negatively correlated. These results suggest that all observed morphoagronomic traits except biomass dry weight have an important role in increasing rice yield. Furthermore, the results of this study provide basic informations for the development of high-yielding rice varieties.

Jurnal Sumberdaya HayatiVol. 10(3)
IPB University (ID)
Openalex Percentile: Top 85%
Computer Science and Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.