Dataset for stability of high biomass and yield in maize under normal and intercropping conditions based on biplot, genotype stability index and land equivalent ratio

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2024-12-01 DOI:10.1016/j.dib.2024.111161

Mansyur , D. Ruswandi

{"title":"Dataset for stability of high biomass and yield in maize under normal and intercropping conditions based on biplot, genotype stability index and land equivalent ratio","authors":"Mansyur , D. Ruswandi","doi":"10.1016/j.dib.2024.111161","DOIUrl":null,"url":null,"abstract":"<div><div>Research on high-yielding and biomass of maize hibrids which adaptive to intercropping environment is important in the context of modern agriculture faced with the challenges of climate change. The field evaluation was conducted in Arjasari, West Java, Indonesia, for two seasons in three different cropping systems, namely: maize sole cropping, maize+soybean intercropping and maize+sweet potato intercropping. The evaluation applied a randomized completed block design with three replications. The article provides a data set of Combined Anova Table and biplot graphic of GGE and AMMI. Combined of Anova Table is provided to identify the effect of genotype, environment and their interaction for the traits observed. Thus biplot of GGE and AMMI is provided to identify representative environment and mega-environment for testing and development of hybrid maize; and to evaluate their adaptability in sole cropping as well as in intercropping with soybean and sweetpotato. The data in this article can be utilized by farmers to choose specific or stable maize hybrids for different cropping system.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111161"},"PeriodicalIF":1.0000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340924011235","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Research on high-yielding and biomass of maize hibrids which adaptive to intercropping environment is important in the context of modern agriculture faced with the challenges of climate change. The field evaluation was conducted in Arjasari, West Java, Indonesia, for two seasons in three different cropping systems, namely: maize sole cropping, maize+soybean intercropping and maize+sweet potato intercropping. The evaluation applied a randomized completed block design with three replications. The article provides a data set of Combined Anova Table and biplot graphic of GGE and AMMI. Combined of Anova Table is provided to identify the effect of genotype, environment and their interaction for the traits observed. Thus biplot of GGE and AMMI is provided to identify representative environment and mega-environment for testing and development of hybrid maize; and to evaluate their adaptability in sole cropping as well as in intercropping with soybean and sweetpotato. The data in this article can be utilized by farmers to choose specific or stable maize hybrids for different cropping system.

查看原文本刊更多论文

基于双标图、基因型稳定指数和土地等效比的正常和间作条件下玉米高产生物量稳定性数据集

研究适应间作环境的玉米杂交种的高产和生物量对面临气候变化挑战的现代农业具有重要意义。在印度尼西亚西爪哇省的Arjasari进行了为期两个季节的三种不同种植制度的田间评价，即玉米单作、玉米+大豆间作和玉米+甘薯间作。评价采用随机完整区组设计，有3个重复。本文提供了一组GGE和AMMI的组合方差分析表和双标图。结合方差分析表分析了基因型、环境及其互作对所观察性状的影响。通过GGE和AMMI双标图，确定杂交玉米试验开发的代表性环境和超大环境；并评价其在单作和大豆、甘薯间作中的适应性。本文的数据可供农民根据不同的种植制度选择特定的或稳定的玉米杂交种。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.