Andreas Hund: Catalogue data in Autumn Semester 2021 |
| Name | PD Dr. Andreas Hund |
| Name variants | Andreas Hund |
| Field | Crop Science |
| Address | Professur für Kulturpflanzenwiss. ETH Zürich, FMG C 25 Eschikon 33 8315 Lindau SWITZERLAND |
| Telephone | +41 44 632 38 29 |
| andreas.hund@usys.ethz.ch | |
| URL | https://kp.ethz.ch/people/person-detail.OTA3Njc=.TGlzdC8xNDQyLDExMzQ4NjQxMzg=.html |
| Department | Environmental Systems Science |
| Relationship | Privatdozent |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 751-3603-00L | Current Challenges in Plant Breeding  Number of participants limited to 15. | 2 credits | 2G | B. Studer, A. Hund | |||||||||||||||||
| Abstract | The seminar 'Current challenges in plant breeding' aims to bring together national and international experts in plant breeding to discuss current activities, latest achievements and future prospective of a selected topic/area in plant breeding. The topic this year will be: 'Plant Breeding a(nd) Data Science'. | ||||||||||||||||||||
| Learning objective | The educational objectives cover both thematic competences and soft skills: Thematic competences: - Deepening of scientific knowledge in plant breeding - Critical evaluation of current challenges and new concepts in plant breeding - Promotion of collaboration and Master thesis projects with practical plant breeders Soft skills: - Independent literature research to get familiar with the selected topic - Critical evaluation and consolidation of the acquired knowledge in an interdisciplinary team - Establishment of a scientific presentation in an interdisciplinary team - Presentation and discussion of the teamwork outcome - Establishing contacts and strengthening the network to national and international plant breeders and scientist | ||||||||||||||||||||
| Content | Interesting topics related to plant breeding will be selected in close collaboration with the working group for plant breeding of the Swiss Society of Agronomy (SSA). | ||||||||||||||||||||
| Lecture notes | None | ||||||||||||||||||||
| Literature | Peer-reviewed research articles, selected according to the topic. | ||||||||||||||||||||
| Prerequisites / Notice | Participation in the BSc course 'Pflanzenzüchtung' is strongly recommended, a completed course in 'Molecular Plant Breeding' is highly advantageous. | ||||||||||||||||||||
| 751-3801-00L | Experimental Design and Applied Statistics in Agroecosystem Science | 3 credits | 2G | A. Hund, W. Eugster, C. Grieder, R. Kölliker | |||||||||||||||||
| Abstract | Different experimental designs will be discussed and various statistical tools will be applied to research questions in agroecosystem sciences. Statistical methods range from simple analysis of variance to mixed-models and multivariate statistics. Surveys and manipulative field and laboratory experiments are addressed and students learn to analyse data using a hands-on approach. | ||||||||||||||||||||
| Learning objective | Students will know various statistical analyses and their application to science problems in their study area as well as a wide range of experimental design options used in environmental and agricultural sciences. They will practice to use statistical software packages (R), understand pros and cons of various designs and statistics, and be able to statistically evaluate their own results as well as those of published studies. | ||||||||||||||||||||
| Content | The course program uses a learning-by-doing approach ("hands-on minds-on"). The topics are introduced as short lectures, but most of the work is done on the computer using different packages of R – a software for statistical computing and graphics. In addition to contact hours exercises must be finalized and handed in for grading. The credit points will be given based on successful assessments of selected exercises. The tentative schedule contains the following topics: Introduction to experimental design and applied statistics in R Data handling and data exploration with tidyverse Designs of field and growth chamber experiments theory Design creation with DiGGer Fitting linear mixed-effects models with lme4 Marginal means estimation and post-hoc tests with emmeans Nonlinear regression fits Statistical learning techniques Principle component analysis, canonical correpondence analysis (CCA), cluster analysis Random forest This course does not provide the mathematical background that students are expected to bring along when signing up to this course. Alternatively, students can consider some aspects of this course as a first exposure to solutions in experimental design and applied statistics and then deepen their understanding in follow-up statistical courses. | ||||||||||||||||||||
| Lecture notes | Handouts will be available (in English) | ||||||||||||||||||||
| Literature | A selection of suggested additional literature, especially for German speaking students will be presented in the introductory lecture. | ||||||||||||||||||||
| Prerequisites / Notice | This course is based on the course Mathematik IV: Statistik, passed in the 2nd year and the Bachelor's course "Wissenschaftliche Datenauswertung und Datenpräsentation" (751-0441-00L) | ||||||||||||||||||||
Competencies |
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