Performing Department
(N/A)
Non Technical Summary
Drought impacts the tightly interrelated systems of carbon and water transport, yielding a complex problem for plants. During water stress, plants close the pores on their leaves to prevent water loss, but this also prevents carbon uptakethrough those same pores. Plants ultimately succumb to water stress by carbon starvation through reduced carbon capture ("suffering from hunger") and hydraulic failure through reduced water transport("suffering from thirst"). However, the physiological mechanisms underlying the different ways in which plants respond to water stress remain poorly understood. This knowledge gap hinders our ability to predict how plants will respond over the second half of the 21st century as the intensity and severity of droughts are projected to increase. This work will focus on the challenge of water stress for agricultural crops due to the biological, social, and economic consequences. The overarching goal is to 1) advance our understanding of carbon and water dynamics in crops under water stress, 2) improve our ability to predict which crops are most at risk of damage under global change, and 3) inform management strategies for irrigation and crop selection to optimize agricultural production. To accomplish this goal, tools from plant physiology will be combined with cutting-edge microCT imaging and machine learning technology to study the physiological responses of economically important woody perennial crops under experimental drought and field conditions. This work will advance our understanding of crop plant stress physiology, and has broader implications for ensuring the health and production of agricultural crops in a changing world.
Animal Health Component
20%
Research Effort Categories
Basic
80%
Applied
20%
Developmental
0%
Goals / Objectives
Major goals:The goals of thisproject (from the original fellowship award) are to advance our understanding of carbon and water dynamics in agricultural crops in response to stress, improve our ability to predict which agricultural crops are most at risk of damage under global change, and inform management strategies to optimize and maintain agricultural production. To accomplish these goals, wehave been using and will continue to use tools from plant physiology with cutting-edge microCT imaging to study California's economically important woody perennial crops in both greenhouse-based and field-based experiments.Remaining objectives: We completed a greenhouse-based drought experiment in which carbohydrate metabolism was compared between almonds, pistachios, and grapes. Following the drought treatment, the plants were scanned with X-ray microCT imaging at the Advanced Light Source to quantify the distribution and depletion of starch in the stem in response to water stress. Stem tissue was also collected for the measurement of nonstructural carbohydrates. We will analyze these samples for nonstructural carbohydrates in the laboratory. Furher, the microCT imaging has yielded a massive dataset that will require data processing using machine learning technology.We also collected field samples from hundreds of plants in a commercial vineyard and orchard where crop water use and stress are being mapped with remote sensing to inform irrigation management and heat wave mitigation. In the vineyard, nonstructural carbohydrates were measured in wine grapes to experimentally determine how varying irrigation practices prior to extreme heat events influences whole-vine physiology and carbon allocation. These data willbe analyzed. In the orchard, hundreds of samples from four almond varieties were collected biannually from trees within the footprint of an eddy covariance flux tower. These samples will be processed in the laboratory for nonstructural carbohydrates and for subsequent calculation of carbon budgets. Additionally, stem cores willbe collected from the trees and annual growth measurements will also be taken. These additional measurements will be essential for understanding whole-tree carbon allocation and linking tree-level physiology with ecosystem-level flux data at the site.
Project Methods
Methods: Thegreenhouse-based drought experiment allowed us to quantify the distribution and depletion of starch in the stem in response to water stress. ThemicroCT images of the stem will be processedusing machine learning technology. The data will be analyzed using statistical methods in R. The data will be interpreted with support from colleagues, farmers/growers, and experts in the field.We also collected field samples from hundreds of plants in a commercial vineyard and orchard. In the vineyard, nonstructural carbohydrates were measured in wine grapes to experimentally determine how varying irrigation practices prior to extreme heat events influences whole-vine physiology and carbon allocation. These data were analyzed with statistical methods in R.In the orchard, hundreds of samples from four almond varieties were collected biannually from trees within the footprint of an eddy covariance flux tower. These samples will be processed in the laboratory for nonstructural carbohydrates and for subsequent calculation of carbon budgets. Additionally, annual growth measuremnets from stem cores will also be taken. The tree-level growth measurements will be correlated with other pre-existing datasuch as ecosystem level tower fluxes, yield, and flower counts.Efforts and Evaluation:Our efforts to disseminate our project outcomes totarget audiencesinclude sharing our findings with growers and farm advisors through informal discussion and annual meetings, with the scientific community through peer-reviewed publications and conference presentations, with undergraduate and graduate students through direct involvement in the research activities as well asthrough lectures in courses, and with the general public through outreach programs and social media. The project has been successfully completed by approximately 60% and is on track for continued success. The experimental and fieldwork components have yielded data that will be analyzed and prepared for dissemination in oral and written form.The continued success of the project will be measured by several key milestones, including 1) analyzing nonstructural carbohydrates, 2) analyzing microCT images with machine learning, 3) recruiting and mentoring undergraduate students, 4) performing statistical analyses, 5) presenting these data at annual conferences, and 6) preparing and submitting manuscripts to peer-reviewed journals. Research progress will be further communicated through lab meetings and seminars, and to the general public through participation in outreach and engagement on social media platforms.