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Statistical and In-field Challenges Involved in Quantifying Crop Nitrogen Use Efficiency (NUE) and Spatial Soil Fertility in Central Alberta
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- Author / Creator
- Jamil, Musfira
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Modern agriculture faces the conundrum of a looming threat of food scarcity and heightened pressure on natural resources to address and sustain increasing food demand. Improving nutrient use efficiency is crucial to sustainable food production. It can be helpful in tackling this critical challenge while delivering the required benefits on social, environmental, and economic fronts. Given the limited availability of readily accessible available soil nitrogen (N) and the high cost of synthetic nitrogenous fertilizers, nitrogen use efficiency (NUE) becomes central to the effectiveness of any management practice aimed at sustainable agriculture. In this study, I evaluated the statistical challenges involved in defining NUE as a ratio of grain productivity to available soil nitrate (AN). Ratio analyses and different regression models were used to compare NUE. Measures of goodness of fit showed that quadratic regression (QR) models were comparatively more robust in estimating NUE. This finding elucidated a fundamental limitation in most analyses of NUE as a ratio matrix, as it negated the assumption of isometry crucial to validity of the derived conclusions. Nonetheless, results from QR analysis can be extrapolated to extract information of practical significance, such as the agronomically optimum N rate (AONR) and economic optimum N rate (EONR). Moreover, sample size calculations elucidated the need for a large number of plots to distinguish genotypes differing for NUE; therefore, imposing a logistic constraint to accurately assess differences in NUE.
Strategies for improving nutrient management in croplands such as the 4R Nutrient Stewardship offer a promising avenue to address the seemingly contrasting goals of modern agriculture. In this study, I compared multiple linear regression, a non-geostatistical technique, to different geo-statistical techniques, including ordinary kriging (OK), ordinary cokriging (OCK), and regression kriging (RK) to decipher the spatial structure of soil fertility parameters. Based on cross-validation
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estimates, OK in most cases proved to be the model choice to predict soil nutrients, including available nitrogen, readily available phosphorus, and available potassium. In contrast, RK was the best performing method to estimate cation exchange capacity, pH, and organic matter. Landscape position did not show a strong spatial correlation with soil fertility parameters and grain productivity, as terrain attributes failed to substantively improve the corresponding predicted estimates. -
- Subjects / Keywords
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- Graduation date
- Spring 2020
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
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