Manuscript Library

Ferhatoglu, C.; Miller, B.A. Choosing Feature Selection Methods for Spatial Modeling of Soil Fertility Properties at the Field Scale. Agronomy 2022, 12, 1786. https://doi.org/10.3390/agronomy12081786 Abstract With the growing availability of environmental covariates, feature selection (FS) is becoming an essential task for applying machine learning (ML) in digital soil mapping (DSM). In this study, the effectiveness of six types…

Miller, B.A. and J. Juilleret. The colluvium and alluvium problem: Historical review and current state of definitions. Earth-Science Reviews 209: 103316. doi: 10.1016/j.earscirev.2020.103316. Abstract In the history of alluvium and colluvium, the definitions have been shifted and rearranged several times, and this evolution is ongoing. Although field books, textbooks, and dictionaries provide standardized references, the…

Kyebogola, S., C.L. Burras, B.A. Miller, O. Semalulu, R.S. Yost, M.M. Tenywa, A.W. Lenssen, P. Kyomuhendo, C. Smith, C.K. Luswata, M.J. Gilbert Majaliwa, L. Goettsch, C.J. Pierce Colfer, R.E. Mazur. Comparing Uganda’s indigenous soil classification system with World Reference Base and Soil Taxonomy. Geoderma Regional. doi: 10.1016/j.geodrs.2020.e00296. Abstract This study examines three soil classification systems…

Khaledian, Y. and B.A. Miller. Selecting appropriate machine learning methods for digital soil mapping. Applied Mathematical Modelling 81: 401-418. doi: 10.1016/j.apm.2019.12.016. Abstract Digital soil mapping (DSM) increasingly makes use of machine learning algorithms to identify relationships between soil properties and multiple covariates that can be detected across landscapes. Selecting the appropriate algorithm for model building…

Miller, B.A., E.C. Brevik, P. Pereira, and R.J. Schaetzl. Progress in Soil Geography I: Reinvigoration. Progress in Physical Geography: Earth and Environment 43(6): 827-854. doi: 10.1177/0309133319889048. Abstract The geography of soil is more important today than ever before. Models of environmental systems and myriad direct field applications depend on accurate information about soil properties and…

Marques, K., J.A. Demattê, B.A. Miller, and I. Lepsch. Geomorphometric segmentation of complex slope elements to improve soil mapping in southeast Brazil. Geoderma Regional 14: e00175. doi: 10.1016/j.geodrs.2018.e00175. Abstract Hillslope elements have considerable potential in predicting soil properties and types in the landscape, making them likely to be a useful basis for detailed soil mapping….

This landscape was originally interpreted as loess mixed with underlying sands. This paper re-evaluates this landscape through a spatial analysis of data from auger samples and soil pits. To better estimate the loamy sediment’s initial textures, we utilized “filtered” laser diffraction data, which remove much of the coarser sand data. Our new model for the…

Despite the soil science discipline in the USA hitting hard times in the 1980s and 1990s, there were still many positive advances within soil science in the USA during these two decades. There was an increased use of geophysical instrumentation, remote sensing, geographic information systems (GIS), and global positioning systems (GPS), and research began in…

This study examines the spatial patterns and accuracies of predictions made by different spatial modelling methods on sample sets taken at two different scales. These spatial models are then tested on independent validation sets taken at three different scales. Each spatial modelling method produced similar, but unique, maps of soil organic carbon content (SOC%). Kriging…

Categories of cartographic scale correspond to the selection of environmental soil predictors used to initially create historical soil maps. Paradigm shifts in soil mapping and classification can be best explained by not only their correlation to historical improvements in scientific understanding, but also by differences in purpose for mapping, and due to advancements in geographic…