Data set for estimating combining abilities for yield and quality attributes in summer tomato using line by tester analysis in Bangladesh

IF 1 Q3 MULTIDISCIPLINARY SCIENCES Data in Brief Pub Date : 2024-10-31 DOI:10.1016/j.dib.2024.111063

Mohammad Matin Akand , Mohammed Abu Taher Masud , Md. Azizul Hoque , Mohammad Mostafa Kamal , Mohammad Rezaul Karim , Bahauddin Ahmed

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Abstract

This article provides a dataset for line × tester analysis in the F1 generation of summer tomatoes using open-source R statistical software and the ‘agricolae’ package. The dataset includes seven inbred lines as female parents (L) and two testers as male parents (T) with diverse genetic bases and heat tolerance qualities. Fourteen cross combinations were produced through L × T (7 × 2) mating design, involving hybridization between lines (f) and testers (m) in a one-to-one fashion. To assess the heterosis of the crosses, all parents (both lines and testers) were included along with the crosses and evaluated in the same experimental field for 16 traits using a randomized complete block design (RCBD) with two replications. The line × tester analysis estimates the ANOVA, including parents, combining ability, genetic components, and the contribution of parental lines to genetic variation in the hybrids. This dataset is valuable for breeders in subtropical countries to develop efficient breeding strategies for hybrid summer tomato varieties.

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利用逐行测试分析法估算孟加拉国夏季番茄产量和质量属性组合能力的数据集

本文利用开源 R 统计软件和 "agricolae "软件包提供了一个数据集，用于分析夏季番茄 F1 代中的品系×测试者。数据集包括作为雌性亲本（L）的七个近交系和作为雄性亲本（T）的两个测试者，它们具有不同的遗传基础和耐热性。通过 L × T（7 × 2）交配设计产生了 14 个杂交组合，其中包括品系（f）和测试者（m）之间一对一的杂交。为了评估杂交组合的异交性，所有亲本（包括品系和测试者）都被纳入杂交组合，并在同一试验田中采用随机完全区组设计（RCBD）对 16 个性状进行了评估。品系×测试者分析估计了方差分析，包括亲本、结合能力、遗传成分以及亲本品系对杂交种遗传变异的贡献。该数据集对亚热带国家的育种者制定夏季杂交番茄品种的高效育种策略很有价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.