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    Enhancing grassland nitrogen estimation : a multiscale approach through optical reflectance spectroscopy and hybrid modeling techniques : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2025-01-21) Dehghan-Shoar, Mohammad Hossain
    Optical remote sensing technology has emerged as a powerful tool for assessing vegetation characteristics, particularly nitrogen (N) concentration (N%) in heterogeneous grasslands. Accurate estimation of N% is crucial for farmers, as it directly influences grassland productivity and plays a key role in sustainable land management. Accurate N assessments optimize fertilizer use, boosting productivity, lowering costs, and enhancing environmental modeling to address impacts such as N leaching and greenhouse gas emissions. Despite significant progress, challenges and knowledge gaps remain, highlighting the need for continued research to fully harness remote sensing’s potential in agricultural management and its impact on livestock productivity. This thesis aims to advance the accurate estimation of grassland N% by integrating physically-based, empirical-statistical, and hybrid models using optical reflectance spectroscopy data. The research focuses on three primary objectives: 1. To estimate N% in grasslands using optical reflectance spectroscopy, data will be collected across multiple scales, including ground-, leaf-, canopy-, and satellite-scale observations. 2. To improve the universality and adaptability of grassland N% models through a hybrid approach that combines data from various optical sensors across multiple scales. 3. To account for and quantify uncertainties in grassland N% prediction models. The thesis addresses the challenge of uncertainty by conducting a comprehensive analysis of its sources and developing methods, such as Physically Informed Neural Networks (PINN), to account for them. Key strategies include data fusion techniques for integrating diverse data sources and improving atmospheric correction methods. A unified methodology combining empirical-statistical and physically-based approaches is proposed to enhance generalization. Machine learning algorithms play a pivotal role in feature selection and optimization, further improving model accuracy and transferability. The developed methods undergo evaluation using independent validation data collected from heterogeneous grasslands across different periods and locations. Results demonstrate that integrating physically-based and empirical-statistical approaches significantly improves model accuracy and transferability, providing a deeper understanding of the factors influencing vegetation traits. This thesis highlights the importance of advanced techniques, including machine learning, deep learning algorithms, Radiative Transfer Models (RTM), and data fusion methods, for precisely characterizing vegetation traits, contributing to more sustainable and efficient grassland management practices.
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    Effect of application times of urease inhibitor (Agrotain®) on NH emissions from urine patches : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Soil Science at Massey University, Manawatu, New Zealand
    (Massey University, 2014) Rodriguez Gelos, Maria Jimena
    In grazed pastures about 80% of urine nitrogen (N) in the form of urea is rapidly hydrolysed and is subjected to ammonia (NH3) losses. The use of urease inhibitors (UI) has been used as a mitigation tool to decrease the rate of NH3 volatilization from fertilizer urea and animal urine. In previous New Zealand trials the UI effect in reducing NH3 emissions from urine has been measured by applying urine mixed with the urease inhibitor to the pasture soil thus increasing the chance to better inhibit the urease enzyme. However, these trials do not represent a realistic grazing scenario where only urine is deposited onto the soil. This current research aimed to identify the best time to spray the Agrotain® above soil pasture to reduce NH3 losses from urine patches. A field experiment was carried out on dairy farm # 4 at Massey University, Palmerston North, New Zealand. The treatments were: a control (without urine and Agrotain®), urine alone at 530 kg N ha–1 and urine plus Agrotain®. The UI was applied to the chambers and soil plots 5 and 3 days prior to urine deposition, on the same day and 1, 3 and 5 after urine deposition in autumn (April 2013). NH3 losses were measured using the dynamic chamber method. After the application of the treatments, NH3(g) volatilization was determined in the acid traps, and soil mineral N (NH4+-N and NO3--N) and pH were measured from soil plots at different times over a period of 30 days. The application of the inhibitor prior to urine deposition reduced NH3 losses with reductions of 27.6% and 17.5% achieved for UAgr-5 and UAgr-3, respectively; and there was also a reduction in both soil NH4+-N concentration and soil pH in comparison with urine alone or with the treatments where Agrotain® was applied after urine deposition. Application of Agrotain® on the same day as urine reduced NH3 losses by 9.6% but this was not statistically significant from treatments when Agrotain® was applied after urine. The application of Agrotain® after urine deposition had no effect on NH3 losses from urine.