[{"id":926,"title":"Pattern Mining in Grassland Science","sha":"e7c21212d54de1a106689ba17610735d","user_id":null,"state":"published","body":"This article presents ideas on how pattern mining can be applied in grassland science, where there is a high demand for rule pattern recognition, evaluation, and selection. In principle, vegetation data are well suited for pattern mining because they are available in a table structure that can be easily read by a program. Some of the application areas are listed below:\r\n\r\n1. Suggestion of suitable plants to increase biodiversity in a field. \r\n\r\n2. Identify and analyze patterns that favor the occurrence of harmful organisms in order to develop effective counterstrategies in pasture management. \r\n\r\n3. Optimization of cattle grazing of grasslands, as environmental factors can also be related to plant traits with pattern mining. Animal specific factory can be included if necessary.\r\n\r\n4. Optimization of organic fertilization on extensive areas (also sub-area specific) in order to harvest high yields for food purposes on the one hand, but not to endanger biodiversity on the other hand.\r\n\r\n5. Identification of limitations and disadvantages of pattern mining and to adapt the algorithms accordingly.\r\n\r\n6. Combine expert knowledge (farmers, consultants, scientistsn and other stakeholders) with data driven algorithms to enhance Grassland Sciences.","created_at":"2022-03-11T13:54:14.358Z","updated_at":"2022-06-15T06:00:38.166Z","zenodo_id":6643842,"doi":"https://doi.org/10.5281/zenodo.6643842","subject":null,"tags":["grasland science","pattern mining","agriculture"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":858,"title":"agricolaeplotr: An R package for visualising experimental designs in science and engineering","sha":"7d5007cfc09d9b9f7958eb6d8ffb5dac","user_id":null,"state":"published","body":"Experimental designs are widely used in the life sciences to substantiate hypotheses through experiments. In the life sciences, this is an indispensable part of gaining scientific knowledge. In addition, open source software is increasingly used in science and engineering. One important programme is R, which is used as a statistical programme for evaluating experiments. The most important R package for designing experiments is the agricolae package, which creates many common experimental designs. However, apart from simple matrix sketches for selected designs, the package does not contain any visualisations of the experimental designs. This gap will be closed by the planned agricolaeplotr package. \r\n\r\nThe planned workflow: The user can enter an object as input, which contains an experimental design from the agricolae package and gets a field map output with a few parameters. \r\nThis shows the spatial dimensions of the experiment, as well as the location of the individual plots in the experiment. In addition, the plot numbers are output as labels, and the plots are coloured according to the levels of a factor (see attachment). This can be further processed, as pure ggplot2 functions are used. These maps can be used to communicate with project stakeholders in science and education.\r\n","created_at":"2021-07-04T16:39:26.157Z","updated_at":"2021-09-13T06:00:54.858Z","zenodo_id":5502175,"doi":"https://doi.org/10.5281/zenodo.5502175","subject":null,"tags":["r","package","agriculture","visualisation","science","engineering"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":"fieldmap_three_factors.pdf","attachment_content_type":"application/pdf","attachment_file_size":15333,"attachment_updated_at":"2021-07-04T16:39:26.137Z"},{"id":883,"title":"Agrimath - Mathematical Proof of Methods Used in Agricultural Sciences","sha":"42c4bf3373de5ab37f626cffff520163","user_id":null,"state":"published","body":"Scientific publications in peer-reviewed journals represent the backbone of science, because information in this medium ideally passes through a review process and is released for publication after peer review. This increases credibility, as a selection process usually identifies and sorts out faulty work. However, even in retrospect, after the paper has been published, errors can occur that remained unknown or unmentioned in the scientific community.\r\n\r\nThe idea of the project is to mathematically evaluate methods in agricultural science based on classical forms of mathematical proof. Emphasis will be placed on grassland science as the main subject of methodology. The methods used will be mathematically proven and, if necessary, revised after contradiction. Correct techniques should also be named so that further work can be done with them. \r\n\r\nThe goal is a consistent mathematical evaluation of the toolbox of agricultural scientists to give the work additional credibility. The explicit non-goal is to merely showcase authors of papers that use methodologies that are mathematically proven to be inappropriate or biased. Or offending agricultural science projects and their stakeholders.\r\nThe findings will be read primarily by agronomists in an effort to improve methodology.","created_at":"2021-09-26T04:27:34.222Z","updated_at":"2021-10-28T06:04:26.481Z","zenodo_id":5606093,"doi":"https://doi.org/10.5281/zenodo.5606093","subject":null,"tags":["agriculture","grassland sciences","mathematics","proof","paper"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":273,"title":"Applying Genomic tools to Cannabis Breeding ","sha":"46acc95d5b851cf28facc88ff54b30e7","user_id":null,"state":"published","body":"Humans share a long history with Cannabis, perhaps going back even further back than the dawn of agriculture. As such this plant has traveled the world alongside humanity, consequent introductions of novel variants into the pool of genetics available to farmers and breeders has been both diverse and poorly recorded. While a general consensus has been reached in terms of the general classification of Cannabis into four groups: European Hemp and Chinese Hemp (which can be characterized by low psychoactive potential) and \"sativa\" and \"indica\" drug-type plants, the numerous other traits displayed by the genus have been given little attention.  The availability of genomic resources for Cannabis now enables us to implement marker assisted selection programs aimed at identifying the particular genetic variant(s) responsible for traits of interest, such as cannabinoid, terpenoid and anthocyanin profiles responsible for the pharmacological, recreational and coloration properties of common strains. As this information becomes available to breeders, one can only expect that the monopoly enjoyed by some clandestine and licensed operations will be thwarted by brute force produced by the creative and intuitive farmers that have cultivated this plant and introduced and maintained genetic variation into the gene pool of this fascinating domesticated plant. Major benefits include the acceleration of targeted homozygosity.","created_at":"2016-01-08T18:21:49.457Z","updated_at":"2022-04-19T06:00:34.586Z","zenodo_id":92720,"doi":"http://dx.doi.org/10.5281/zenodo.44530","subject":null,"tags":["cannabis","breeding","hemp","mj","genomics","marker assisted selection","agriculture","domestication","single nucleotide polymorphism","quantitative trait loci","homozygosity"],"vote_count":0,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":"AlpineRocket.jpg","attachment_content_type":"image/jpeg","attachment_file_size":218629,"attachment_updated_at":"2016-01-08T18:21:48.730Z"}]