Dataset on the influence of zinc foliar application and vermicompost on agromorphogenic traits of Aloe vera

This article presents dataset on agromorphogenic traits of Aloe vera treated by foliar application of Zn (zinc) and vermicompost. Data from yield and yield related characters with morphological traits were collected to assess the effect of vermicompost and Zn. The data showed in this dataset article contained 17 agronomic and morphological traits. The collected data were analyzed using excel, statistix 10.0 and STAR software. The analyzed data presented with the help of ANOVA (analysis of variance), mean comparison, correlation co-efficient and principal component analysis (PCA). Data of microclimate, correlation co-efficient and biplot distribution of principal components were presented graphically. The aim of the article is to ensure the data easily accessible and as a brief source of agricultural management information for crop development and production for researcher.


a b s t r a c t
This article presents dataset on agromorphogenic traits of Aloe vera treated by foliar application of Zn (zinc) and vermicompost. Data from yield and yield related characters with morphological traits were collected to assess the effect of vermicompost and Zn. The data showed in this dataset article contained 17 agronomic and morphological traits. The collected data were analyzed using excel, statistix 10.0 and STAR software. The analyzed data presented with the help of ANOVA (analysis of variance), mean comparison, correlation co-efficient and principal component analysis (PCA). Data of microclimate, correlation co-efficient and biplot distribution of principal components were presented graphically. The aim of the article is to ensure the data easily accessible and as a brief source of agricultural management information for crop development and production for researcher.

Value of the Data
• The data provide the primary effect of Zn and vermicompost on growth, yield and yield related traits of Aloe vera . • The dataset give basic information for researchers to develop management practise for Aloe vera production in this climatic region and optimization of fertilizer dose for Aloe vera cultivation. • Data can be used to develop adaptation strategies for Aloe vera as a new field crop in the northern region of Bangladesh.

Micro-climate data
Maximum, minimum and average temperature with humidity during experimental period were presented in Fig. 1 . The raw data for micro-climate is presented in the supplementary file "Raw data of micro-climate".   Table 1 . presents the data on analysis of variance (ANOVA) of nine agronomic traits (Plant height, number of leaves, leaf length, leaf breadth, largest leaf length, largest leaf breadth, single mature leaf weight, largest leaf weight, total leaf weight per plant) of Aloe vera . Mean comparison of different treatment and interaction between Zn and vermicompost on nine agronomic traits of Aloe vera showed in Tables 3 and 5 . The raw data for nine agronomic traits ( Tables 1 , 3 , and 5 ) is presented in the supplementary file "Raw data of nine agronomic traits". Analysis of variance (ANOVA) of eight morphological traits (Number of sucker, number of tiller, tiller height, tiller weight, sucker height, sucker weight, number of lateral root and length of tap root) of Aloe vera are shown in Table 2 . Mean comparison of different treatment and interaction between Zn and vermicompost on eight morphological traits of Aloe vera are presented in Tables 4 and 6 . The raw data for ( Tables 2 , 4 and 6 ) morphological traits is presented in the supplementary file "Raw data of eight morphological traits".

The effect of Zn foliar application and vermicompost on agromorphogenic traits of Aloe vera
Data on agronomic and morphological traits were collected to investigate the effect of Zn and vermicompost ( Tables 3 and 4 ) and the combination of treatments effect ( Tables 5 and 6 ). Correlation analysis of nine agronomic traits are depicted in Fig. 2 . The raw data for correlation analysis and analyzed data of correlation co-efficient is presented in the supplementary file "correlation analysis". Table 7 , shows the principal component analysis of agronomic traits of Aloe vera . Table 8 , presents eigenvector of agronomic traits of Aloe vera . The principal compo-

Plant production
Aloe vera cultivar was collected from local market of Rangpur, Bangladesh. Aloe vera plants were grown in nursery bed for sucker production. Around two month old uniform sucker of Aloe vera with a size 8 to 10 cm were selected to transfer into plastic pot from nursery bed. The transplanting date was 1 September 2020. The size of plastic pot was 10 inch diameter and 10 inch height with 10 kg soil capacity. Vermicompost were properly mixed with soil prior to transplant of sucker in net house [1] . The experiment was set up in the net house of Bangladesh Institute of Research and Training on Applied Nutrition (BIRTAN), Regional station, Rangpur, Bangladesh in September 2020 to February 2021. Different intercultural operations and standard agronomic practices were performed with the method explained for Aloe vera cultivation [2] .Observations were recorded after six month of transplanting.

Data collection
Agronomic characters like (PH: plant height, NL: number of leaves, LL: leaf length, LB: leaf breadth, LLL: largest leaf length, LLB: largest leaf breadth, SMLW: single mature leaf weight, LLW: largest leaf weight, TLWP: total leaf weight per plant) and morphological traits (NS: number of sucker, NT: number of tiller, TH: tiller height, SH: sucker height, SW: sucker weight, NLR: number of lateral root, LTR: length of tap root) were measured [ 3 , 4 ]. Average data of plant characters were recorded from three plants of each replications after six month of transplanting. Leaf harvesting and data collection date was 28 February 2021.

Statistical analysis
Statistix 10.0 software was used to preformed an analysis of variance (ANOVA) at P < 0.05; mean values differentiating by Tukey HSD All-Pairwise Comparisons test at P < 0.05. Correlation co-efficient analysis and principal component analysis were conducted by Statistical tool for Agricultural Research (STAR) software, version 2.0.1 (2014). Figure on micro climatic data were drawn by EXCEL software.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that have, or could be perceived to have, influenced the work reported in this article.

Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Supplementary Materials
Supplementary material associated with this article can be found in the online version at doi: 10.1016/j.dib.2021.107436 .