Date of Award

Summer 8-15-2022

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Master of Science (MS)

Department

Botany and Plant Pathology

Advisor

YongJiang Zhang

Second Committee Member

Matthew Wallhead

Third Committee Member

Parinaz Rahimzadeh-Bajgiran

Additional Committee Members

José Eduardo Meireles

Abstract

The wild blueberry is one of the major crops of Maine, with significant economic value and potential health benefits. Due to global climate change, drought impacts have been increasing significantly in recent years in the northeast region of the USA, causing significant economic losses in the agricultural sectors. It has been predicted to increase further in the future. Changing patterns of the elevated atmospheric temperatures, increased rainfall variabilities, and more frequent drought events have made the wild blueberry industry of Maine vulnerable, suggesting the adoption of novel approaches to mitigate the negative impacts of global climate changes. Also, wild blueberry fields show high spatial heterogeneity, making precise and effective management difficult. Our research focuses on quantifying the spatial heterogeneity in functional traits of wild blueberries, analyzing the impact of historical drought on wild blueberry production, and testing the use of drone-based thermal sensors to quantify spatial heterogeneity in water stress across wild blueberry fields.

In chapter two, we aimed to quantify the inter-genotype variation in several structural, functional, and yield-related traits and to establish the relationship between functional traits and yield-related traits. We conducted a study during the 2019 harvest season measuring several structural, functional, and yield traits from two wild blueberry farms. We found high variations in structural, functional, and yield-related traits among genotypes but not between the two fields, confirming the spatially heterogeneous nature within wild blueberry fields. We also found negative associations of the leaf mass per unit area and midday leaf temperature with the yield, whereas the leaf chlorophyll concentration was positively associated with the yield. Additionally, we found quadratic relationships between some yield-related traits and stem length, with the optimum stem length for yield at 25 cm. Our results suggest that some leaf and stem functional traits can be used to predict wild blueberry yields.

In chapter three, we analyzed historical drought patterns using a drought index Standardised Precipitation-Evapotranspiration Index (SPEI). We assessed drought impacts on production (yield) and remotely sensed vegetation indices; Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) of the wild blueberry fields in Maine, USA. Despite a significant warming pattern, we found no significant changes in SPEI in the past 71 years. We also analyzed the impact of short and long-term water conditions (SPEI) during the growing season on the wild blueberry vegetation condition and production. We found that drought has had a significant impact on vegetation status and production historically. Further, the relationship between the relatively long-term SPEI and vegetation indices EVI and NDVI was significantly more substantial than short-term SPEI, suggesting water conditions in a relatively long-term probably determine crop health. We also compared an irrigated and non-irrigated wild blueberry field at the same location (Deblois, Maine). We found that irrigation decoupled the relationship between SPEI and vegetation indices and yield, suggesting the need for effective irrigation strategies to mitigate drought impacts.

In chapter four, we tested the use of remotely sensed canopy temperature-based crop water stress index (CWSI) to remotely and non-destructively detect the water status of wild blueberries. By detecting crop water status using the CWSI, irrigation can be intelligently controlled in the highly spatially heterogeneous wild blueberry fields to increase efficiency and profitability. A drone-based thermal sensor was used to acquire the canopy temperature data remotely and then calculate CWSI. CWSI calculated from bio-indicator based Twet and Tdry reference was found to be effective (R² = 0.78: p < 0.05) in detecting leaf water potential (LWP), which is superior compared to the statistical Twet and empirical Tdry reference-based CWSI. The CWSI-LWP model-based crop water status (LWP) maps showed high variability in crop water stress within irrigated and non-irrigated fields, suggesting the need for precise water stress monitoring and management in wild blueberry fields.

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