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Agronomy
Special Issues
Design and Statistical Analysis in Agricultural, Biological and Environmental Sciences
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Design and Statistical Analysis in Agricultural, Biological and Environmental Sciences

A special issue of Agronomy (ISSN 2073-4395).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5834

Special Issue Editors

Dr. George Menexes

E-Mail Website
Guest Editor
Laboratory of Agronomy, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: development and application of statistical methods for the analysis of data resulting from experiments in the field of agriculture and, in general, from biological sciences; methodology of scientific research and agricultural experimentation; methods of multidimensional analysis of quantitative and categorical data; methods of multivariate statistical analysis; the development of statistical software/code
Dr. Angelos Markos

E-Mail Website
Guest Editor
Department of Primary Education, School of Education Sciences, Democritus University of Thrace, Alexandroupolis, Greece
Interests: multidimensional analysis of quantitative and categorical data; methods of multivariate statistical analysis; big data analysis; development of statistical software/code

Special Issue Information

Dear Colleagues,

Scientific research in biological sciences, and especially, in agriculture, is a field that requires up-to-date methodological schemes both at the design and the statistical analysis stages of the experiments and studies. The efficient use of resources in agricultural research, as well as valid statistical analyses and inductive inferences, is always an important and “hot” issue that offers challenges for new research opportunities. Back in the early twentieth century, Sir Ronald Fisher, with his work at the Rothamsted Experimental Station (1919-1933), introduced the principles of experimentation in all applied sciences and created the foundations for modern statistical science. Nowadays, in the big data era, there is a plethora of multivariate and multidimensional data, data “cubes”, and a broad range of statistical and data analysis methods for managing and analyzing data arising in different branches of agricultural research. Thus, the aim of this Special Issue of Agronomy is to highlight the importance of and the opportunities for applied statistics in agricultural, biological, and environmental sciences. In addition, applications with new or improved methodological schemes and novel strategies in planning research, managing, analyzing, interpreting and visualizing data are currently of primary importance in the field. We welcome original research papers with methodological and/or statistical and/or data analysis orientation and content, systematic reviews, scoping reviews, and meta-analyses. We believe that there must be a balance between design and analysis, since both are equally important. 

Dr. George Menexes
Dr. Angelos Markos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multivariate and multidimensional data analysis
  • big data
  • design of experiments

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Published Papers (3 papers)

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Research

15 pages, 8588 KiB  
Article
An In-Depth Presentation of the ‘rhoneycomb’ R Package to Construct and Analyze Field-Experimentation ‘Honeycomb Selection Designs’
by Anastasios Katsileros, Nikolaos Antonetsis, Maria-Georgia Gkika, Eleni Tani, Penelope J. Bebeli and Ioannis Tokatlidis
Agronomy 2023, 13(8), 2145; https://doi.org/10.3390/agronomy13082145 - 16 Aug 2023
Viewed by 1170
Abstract
The Honeycomb Selection Design (HSD) is an innovative experimental method whose main feature is the even and systematic entry arrangement. Its systematicity, if combined with the absence of inter-plant competition that maximizes the phenotypic expression and differentiation of individual plants, enables the implementation [...] Read more.
The Honeycomb Selection Design (HSD) is an innovative experimental method whose main feature is the even and systematic entry arrangement. Its systematicity, if combined with the absence of inter-plant competition that maximizes the phenotypic expression and differentiation of individual plants, enables the implementation of single-plant selection as early as the initial generations of genetic segregation, facilitating plant breeders to identify superior genotypes. Due to the specificity of entry allocation and the complexity of statistical data analysis, a specialized software becomes necessary. This article provides a detailed presentation of the ‘rhoneycomb’, a free and open-source R package concerning the construction, visualization, and analysis of HSDs. Full article
(This article belongs to the Special Issue Design and Statistical Analysis in Agricultural, Biological and Environmental Sciences)
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13 pages, 1537 KiB  
Article
The Impact of Data Envelopment Analysis on Effective Management of Inputs: The Case of Farms Located in the Regional Unit of Pieria
by Asimina Kouriati, Anna Tafidou, Evgenia Lialia, Angelos Prentzas, Christina Moulogianni, Eleni Dimitriadou and Thomas Bournaris
Agronomy 2023, 13(8), 2109; https://doi.org/10.3390/agronomy13082109 - 11 Aug 2023
Cited by 2 | Viewed by 2015
Abstract
Technical efficiency is considered a useful advisory tool for managers whose main goal is to maximize profit and minimize costs. Data envelopment analysis is a widely accepted methodology for technical efficiency estimation in the sector of agriculture. For that reason and with the [...] Read more.
Technical efficiency is considered a useful advisory tool for managers whose main goal is to maximize profit and minimize costs. Data envelopment analysis is a widely accepted methodology for technical efficiency estimation in the sector of agriculture. For that reason and with the view to extract useful conclusions regarding farmers’ effective management of inputs, this study aims to present the DEA method through its implementation in a set of farms located in the regional unit of Pieria. To conduct this analysis, relevant data were collected through a survey in which 40 farms participated. The output variable was chosen to be each farm’s total amount of sales, while the inputs were selected in a way to represent the main factors of production, such as (1) land in acres, (2) labor in hours, and (3) variable costs in EUR. The results showed that the examined farms need to reduce the inputs used by 34.6% to operate more efficiently from the point of view of the CRS model. Therefore, farmers should be motivated to reduce the inputs used, something that can be done through the provision of specialized advisory services. This will, of course, be helped by both the local authorities and the policies of the country in which the rational use of inputs seems to be necessary. This study may contribute to the relevant literature, agriculture, and the area since management suggestions are formulated for the farmers of Pieria’s regional unit. Full article
(This article belongs to the Special Issue Design and Statistical Analysis in Agricultural, Biological and Environmental Sciences)
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14 pages, 3502 KiB  
Article
Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data
by Thomas M. Koutsos, Georgios C. Menexes, Ilias G. Eleftherohorinos and Thomas K. Alexandridis
Agronomy 2023, 13(7), 1685; https://doi.org/10.3390/agronomy13071685 - 22 Jun 2023
Viewed by 1570
Abstract
Block Kriging (a spatial interpolation method) and log10 transformation were compared for their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate mean values in all harvested maize plants grown in three randomly taken field plots and for harvested plants [...] Read more.
Block Kriging (a spatial interpolation method) and log10 transformation were compared for their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate mean values in all harvested maize plants grown in three randomly taken field plots and for harvested plants after removing the “edge or margin” ones. The results showed that log10 transformation reduced CVs of all harvested original fresh weight (FW) plant data in the three plots from 35.6–41.6% (original data) to 6.0–7.5%, while the respective CVs due to Block Kriging were reduced to 14.5–19.9%. The back-log10-transformed means of all harvested FW plant data were reduced by 6.8–9.4%, while the respective reduction for plants excluding the margin ones was 1.3–8.3%. The Block Kriging means for all harvested FW plant data were reduced only by 0.3–0.4%, while the respective means of the harvested plants excluding margin ones were increased by 0.4–4.3%. These findings strongly suggest that Block Kriging should be preferred over the log10 transformation method (used so far by agroscientists) as it managed to effectively reduce variability in crop data and estimate missing values that provide more precise and reliable estimates of corn yield for farmers. Full article
(This article belongs to the Special Issue Design and Statistical Analysis in Agricultural, Biological and Environmental Sciences)
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