The impact of weather change on nitrous oxide emission with spatial pattern detection and large data approximation - Ofori, Francis Ohene (Kansas State University, 2019)
The correlations between agriculture, climate change, and greenhouse gas concentration are multiplex and manifold. Agriculture has been a focus due to its vital connection with climate and food supply. It could have substantial implications for the economy and agricultural management to study the detection of spatial pattern in regional climate change and the impact of weather change on greenhouse gases, specifically on nitrous oxide emission of state crops. To capture the spatial pattern of significant regional climate change, a Process-based Geographical Algorithm Machine (PGAM) procedure is proposed by viewing the spatio-temporal data sets as a realizations of underlying random fields. Past and future climate scenarios of daily weather are simulated using multiple Global Circulation Models (GCMs). The simulation differences and consistency of precipitation, minimum, and maximum temperature in the state of Kansas produced by these climate models are assessed using the spatial Kolmogorov-Smirnov test. The climate change index described by a temporal distance metric from PGAM is used to study the adverse effect on nitrous oxide emission connected with agricultural management practices based on a linear mixed-effect model. This project further delves into the effect of weather change on nitrous oxide emission using large data approximation technique; however, the size of the data set creates issues in estimation and prediction. This is because it involves the determinant and inversion of the $n \times n$ covariance matrix of the data process. Thus, an approximation technique for reducing the dimension of the covariance matrix is required. We theoretically and numerically investigate the conditions under which computational intensity and prediction accuracy are balanced by adopting a projection approach. An optimal rank selection method is proposed to achieve good efficiency in terms of Kullback-Leibler divergence and mean square prediction error while reducing the computational cost. The accuracy and performance of the proposed method are evaluated via both simulation and spatial regression analysis of nitrous oxide emission.
Keywords: Spatial pattern detection; Geographical algorithm machine; Large Data Approximation