Statistical Advances and Modeling in Agriculture

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2464

Special Issue Editor


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Guest Editor
1. Institute of Plant Breeding and Genetic Resources, ELGO-DIMITRA, Thermi, GR-57001 Thessaloniki, Greece
2. Department of Computer Science, School of Sciences and Engineering, University of Nicosia, Nicosia 2417, Cyprus
Interests: applied statistics; biostatistics; predictive models; mathematical modeling; multivariate statistical analysis; time series analysis; generalized linear models; machine learning

Special Issue Information

Dear Colleagues,

Biostatistics has played a crucial role over the years, by supporting and enhancing research contributions within the field of agriculture. Technological advances have now widely facilitated the acquisition of high-dimensional biological data sets. Currently, a major scientific challenge lies in understanding these data sets, efficiently extracting relevant information, and producing new knowledge. Characteristic examples include, but are not limited to, data related to genome-wide DNA methylation, RNA sequencing (transcriptomics), quantitative proteomics, and metagenomics. At the same time, statistical advances now enable us to use more elaborate analyses to answer traditional scientific questions in the field.

This Special Issue aims to promote statistical advances and modeling in agriculture. We invite you to submit articles on applied statistics, computational statistics, machine learning, causal discovery/models, novel algorithms, new methodological approaches, etc., in all areas of agriculture, involving high-dimensional data and special or standard datasets. Research articles employing standard statistical methodology and modeling are also welcomed, with an emphasis on the statistical aspects of the performed research.

Dr. Theodoros Moysiadis
Guest Editor

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

  • algorithms
  • applied statistics
  • biostatistics
  • causal discovery
  • computational statistics
  • machine learning
  • regression
  • metabolomics
  • proteomics
  • transcriptomics

Published Papers (2 papers)

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Research

17 pages, 8632 KiB  
Article
Comparing Spatial Sampling Designs for Estimating Effectively Maize Crop Traits in Experimental Plots
by Thomas M. Koutsos and Georgios C. Menexes
Agronomy 2024, 14(2), 280; https://doi.org/10.3390/agronomy14020280 - 26 Jan 2024
Viewed by 852
Abstract
The current study investigates the performance of various sampling designs in providing accurate estimates for crucial maize yield traits (intended for silage) including plant height, fresh/dry/ear weight, number of maize ears per plant, and total ear weight per plant, using spatial maize data. [...] Read more.
The current study investigates the performance of various sampling designs in providing accurate estimates for crucial maize yield traits (intended for silage) including plant height, fresh/dry/ear weight, number of maize ears per plant, and total ear weight per plant, using spatial maize data. The experiment took place in an experimental field area at Aristotle University (AUTH) farm during the 2016 growing season. Nine sampling designs were statistically analyzed and compared with spatial data from an Italian maize hybrid (AGN720) to identify the most suitable and effective sampling design for dependable maize yield estimates. The study’s results indicate that, among the different sampling techniques, Stratified Random Sampling is the most effective and reliable method for obtaining accurate maize yield estimates. This new approach not only provides precise estimates but also requires fewer measurements, making it suitable for experiments where not all plants have emerged. These findings suggest that Stratified Random Sampling can be employed effectively as an alternative to harvesting the entire plot for effectively estimating maize crop traits in experimental plots. Full article
(This article belongs to the Special Issue Statistical Advances and Modeling in Agriculture)
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23 pages, 2269 KiB  
Article
Exploring the Robustness of Causal Structures in Omics Data: A Sweet Cherry Proteogenomic Perspective
by Maria Ganopoulou, Aliki Xanthopoulou, Michail Michailidis, Lefteris Angelis, Ioannis Ganopoulos and Theodoros Moysiadis
Agronomy 2024, 14(1), 8; https://doi.org/10.3390/agronomy14010008 - 19 Dec 2023
Viewed by 911
Abstract
Causal discovery is a highly promising tool with a broad perspective in the field of biology. In this study, a causal structure robustness assessment algorithm is proposed and employed on the causal structures obtained, based on transcriptomic, proteomic, and the combined datasets, emerging [...] Read more.
Causal discovery is a highly promising tool with a broad perspective in the field of biology. In this study, a causal structure robustness assessment algorithm is proposed and employed on the causal structures obtained, based on transcriptomic, proteomic, and the combined datasets, emerging from a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. ‘Tragana Edessis’ tissues. The algorithm assesses the impact of intervening in the datasets of the causal structures, using various criteria. The results showed that specific tissues exhibited an intense impact on the causal structures that were considered. In addition, the proteogenomic case demonstrated that biologically related tissues that referred to the same organ induced a similar impact on the causal structures considered, as was biologically expected. However, this result was subtler in both the transcriptomic and the proteomic cases. Furthermore, the causal structures based on a single omic analysis were found to be impacted to a larger extent, compared to the proteogenomic case, probably due to the distinctive biological features related to the proteome or the transcriptome. This study showcases the significance and perspective of assessing the causal structure robustness based on omic databases, in conjunction with causal discovery, and reveals advantages when employing a multiomics (proteogenomic) analysis compared to a single-omic (transcriptomic, proteomic) analysis. Full article
(This article belongs to the Special Issue Statistical Advances and Modeling in Agriculture)
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