This thesis consists of three sections that provide detailed knowledge of nutrient estimation and management in wild blueberry production. The first section investigated the main and interactive effects of long term fertilizer (NPK) enrichments on soil mineral nitrogen, organic nitrogen and carbon, microbial biomass nitrogen and carbon, net mineralization and net nitrification in wild blueberry soils. The second section studied the optimization of wild blueberry growth, development, foliar nutrients and harvestable yields by using response surface methodology. The third section examined nutrient estimation technologies using field spectroscopy. The remote sensing data was analysed with a combination partial least squares regression and variable selection algorithms (Chemometric analysis).
The results indicated elevated nitrification activity under nitrogen enrichments, mainly performed by heterotrophs, report unusually high levels of dissolved organic carbon (> 150 C ha-1), a fungal dominated soil system and high concentration of soluble organic nitrogen in the crop year of production. Nitrification and high dissolved organic carbon levels were observed in connection with possible nitrogen saturation and potential environmental hazards. The results imply a need for nitrification inhibition measures.
Results from field studies examining the main and interactive effects of soil applied N, P and K suggested that applications of nitrogen (35 kg ha-1), phosphorus (40 kg
ha-1) and potassium (30 kg ha-1) were required to optimize growth, development and harvestable yields of wild blueberry. Under these fertilizer rates, the corresponding predicted harvestable yield was 4,126 kg ha-1 that is as much as 13% higher than would be produced by commonly used fertilizer rate in the industry. This study presented new leaf nutrient ranges for sprout and crop years for wild blueberry fields in Atlantic Canada. Hyperspectral remote sensing technologies were used for estimating macro and micro nutrients. This study provides critical information on wavelengths important for nutrient estimation in reflectance spectra (400-2500 nm). The results and inferences from this thesis may be employed to improve crop production, increase economic returns and health of soil and sustainability of wild blueberry production in Nova Scotia. / This study was undertaken to examine the response of the wild blueberry plant to soil applied fertilizers and encompasses soil nitrogen and carbon pools, plant growth and development, leaf nutrient concentrations and harvestable yields. In addition, given the vast area in which wild blueberry fields are located, the study also examined the feasibility of assessing plant nutrient status through the use of remote sensing hyperspectral technologies. Our results emphasize the importance of monitoring for soil nitrogen and carbon pools in the context of accelerated nitrogen cycling, nitrogen saturation, the fine-tuning of current leaf nutrient ranges in Atlantic Canada in connection to fertilizer rates, the possibility of estimating leaf nutrient contents by remote sensing technologies all with the aim of optimizing wild blueberry yields.
In terms of statistical techniques, this thesis used response surface methodologies with a central composite design as a means of discovering, the main and interactive effects of soil applied fertilizers to determine the most appropriate soil nitrogen levels and leaf nutrient ranges that correlate to the highest harvestable yields. The remote sensing data used to estimate leaf nutrients concentrations, various models that combined chemometrics and response surface methodologies for determining model efficiencies with aim of getting informative wavelengths in wild blueberry fields.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/45049 |
| Date | 10 December 2013 |
| Creators | Maqbool, Rizwan |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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