Methods in Plant Ecophysiology

Plant ecophysiology refers to the study of how plants physiologically respond to their surrounding environment. This course aims at strengthening both the theoretical knowledge and practical skills of students on methodology commonly used in plant ecophysiology.

This course is obligatory for students that plan to do their M.Sc. thesis in the pre-harvest track of the chair group Horticulture and Product Physiology (HPP; course is not required for post-harvest theses). However, all students that are interested in exploring how plants respond to their environment -- and how to measure these responses -- are welcome to take the course as well. This course consists of lectures, where the theoretical framework for topics such as gas exchange, chlorophyll fluorescence, plant-water relations, and remote sensing will be covered. Lectures are complemented with tutorials, where students will learn how to work with and analyze data related to plant ecophysiology. Furthermore, students will collect their own data during hands-on practicals, which feature a carousel of varied topics. There is ample student-teacher interaction throughout the course in the practicals and in-class assignments. Students are expected to actively prepare for all course sessions (especially the practicals) and to earnestly contribute to in-class discussions.

Lectures will cover the foundations of gas exchange theory and how infrared gas analysis is implemented; stomatal anatomy and function; the response of photosynthesis to environmental factors such as light intensity and spectrum, temperature, humidity, and CO2 concentration; remote sensing; source-sink interactions; plant-water relations; and plant photomorphology. Tutorials will focus on experimental design, data management, fitting of photosynthetic parameters, quantifying the light environment, and exploring plant-water relations. Finally, students will work in groups during the practicals to collect data on gas exchange, chlorophyll fluorescence, and plant morphology. Students will later implement their knowledge in experimental design, statistical analysis, and data visualization to analyze the data and draw conclusions from their data. This work will culminate in the development of a scientific poster that will be presented at the end of the course.

• Execute, analyse, and critically evaluate response curves of net photosynthesis rate to light intensity and CO2 concentration. Explain the meaning of derived parameters of both types of curves

• Execute, analyse, and critically evaluate measurements of chlorophyll fluorescence and derived parameters

• Measure, analyse, and critically evaluate light intensity and spectrum in a plant production environment. Calculate and explain the meaning of key parameters derived from light measurements

• Measure common parameters of plant morphology and describe how they respond to changes in their growing environment

• Demonstrate a general understanding of how physiological processes in plants and environmental conditions can be measured

• Document and store data correctly, such that it can be re-used by others. Evaluate, analyse, and visualise data produced by others

• Formulate research questions and hypotheses that can be answered using data collected in the course. Construct, present, and discuss results in a scientific poster

• ? (5%) Participation in practical groupwork (2-3 students). Attendance/preparedness checked for all group members in each session. If not passed, student will need to complete an alternative assignment.
• ? (20%) The poster will be completed in groups (2-3 people); if the student does not pass they will need to complete an alternative (individual) assignment.
• ? (75%) Final exam.

ZSS06100 Laboratory Safety Students are expected to have a general background in at least one of the following subjects per category:

• plant biology, plant physiology, botany, crop development, crop production, photosynthesis, biochemistry, and/or quantitative ecology;
• experimental design, applied statistics, advanced statistics, biostatistics and/or environmental data analysis.;
• data analysis; Proficiency in working in Excel or coding languages such as R or Python.;
• reading scientific articles and academic writing skills in English.

• See Brightspace page.