Predictive Cropland Data Layer and Uncertainty Measures

The National Agricultural Statistics Service (NASS)
of the United States Department of Agriculture
(USDA) uses High-Order Markov
Chains (HOMC) to analyze crop rotation patterns
over time and project future crop-specific
planting. However, HOMCs often face issues with
sparsity and identifiability due to the representation
of categorical data as indicator variables. As the
number of HOMCs needed for analysis increases, the
parametric space's dimension grows exponentially.
Parsimonious representations reduce the number
of parameters but often produce less accurate
predictions. To better represent the complexity of
the data, a deep neural network model is suggested.
To measure the degree of uncertainty surrounding
categorical predictions, two uncertainty measures
are also offered.

Categorical prediction

Neural networks

Uncertainty quantification Introduction 

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2024