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    Transforming scientific research and development in precision agriculture : the case of hyperspectral sensing and imaging : a thesis presented in partial fulfilment of the requirements for the degree of Doctor in Philosophy in Agriculture at Massey University, Manawatū, New Zealand
    (Massey University, 2021) Cushnahan, Megan
    There has been increasing social and academic debate in recent times surrounding the arrival of agricultural big data. Capturing and responding to real world variability is a defining objective of the rapidly evolving field of precision agriculture (PA). While data have been central to knowledge-making in the field since its inception in the 1980s, research has largely operated in a data-scarce environment, constrained by time-consuming and expensive data collection methods. While there is a rich tradition of studying scientific practice within laboratories in other fields, PA researchers have rarely been the explicit focal point of detailed empirical studies, especially in the laboratory setting. The purpose of this thesis is to contribute to new knowledge of the influence of big data technologies through an ethnographic exploration of a working PA laboratory. The researcher spent over 30 months embedded as a participant observer of a small PA laboratory, where researchers work with nascent data rich remote sensing technologies. To address the research question: “How do the characteristics of technological assemblages affect PA research and development?” the ethnographic case study systematically identifies and responds to the challenges and opportunities faced by the science team as they adapt their scientific processes and resources to refine value from a new data ecosystem. The study describes the ontological characteristics of airborne hyperspectral sensing and imaging data employed by PA researchers. Observations of the researchers at work lead to a previously undescribed shift in the science process, where effort moves from the planning and performance of the data collection stage to the data processing and analysis stage. The thesis develops an argument that changing data characteristics are central to this shift in the scientific method researchers are employing to refine knowledge and value from research projects. Importantly, the study reveals that while researchers are working in a rapidly changing environment, there is little reflection on the implications of these changes on the practice of science-making. The study also identifies a disjunction to how science is done in the field, and what is reported. We discover that the practices that provide disciplinary ways of doing science are not established in this field and moments to learn are siloed because of commercial constraints the commercial structures imposed in this case study of contemporary PA research.
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    Predicting spatiotemporal yield variability to aid arable precision agriculture in New Zealand : a case study of maize-grain crop production in the Waikato region : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agriculture and Horticulture at Massey University, Palmerston North, New Zealand
    (Massey University, 2020) Jiang, Guopeng
    Precision agriculture attempts to manage within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. To achieve this, delineation of field-specific management zones (MZs), representing significantly different yield potentials are required. To date, the effectiveness of utilising MZs in New Zealand has potentially been limited due to a lack of emphasis on the interactions between spatiotemporal factors such as soil texture, crop yield, and rainfall. To fill this research gap, this thesis aims to improve the process of delineating MZs by modelling spatiotemporal interactions between spatial crop yield and other complementary factors. Data was collected from five non-irrigated field sites in the Waikato region, based on the availability of several years of maize harvest data. To remove potential yield measurement errors and improve the accuracy of spatial interpolation for yield mapping, a customised filtering algorithm was developed. A supervised machine-learning approach for predicting spatial yield was then developed using several prediction models (stepwise multiple linear regression, feedforward neural network, CART decision tree, random forest, Cubist regression, and XGBoost). To provide insights into managing spatiotemporal yield variability, predictor importance analysis was conducted to identify important yield predictors. The spatial filtering method reduced the root mean squared errors of kriging interpolation for all available years (2014, 2015, 2017 and 2018) in a tested site, suggesting that the method developed in R programme was effective for improving the accuracy of the yield maps. For predicting spatial yield, random forest produced the highest prediction accuracies (R² = 0.08 - 0.50), followed by XGBoost (R² = 0.06 - 0.39). Temporal variables (solar radiation, growing degree days (GDD) and rainfall) were proven to be salient yield predictors. This research demonstrates the viability of these models to predict subfield spatial yield, using input data that is inexpensive and readily available to arable farms in New Zealand. The novel approach employed by this thesis may provide opportunities to improve arable farming input-use efficiency and reduce its environmental impact.