1946 Fertilizer Recommendations for Field Crops, Permanent Pastures and Hay Fields

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Report of Feasibility Study Task Force for Marietta Truck Growers Association

Exact date of working paper unknown

Sheet erosion studies on Cecil clay

"November 1936." Includes bibliographical references (p. 51-52). Also available in microfilm under: State agricultural papers

Soil Characterization Using Textural Features Extracted from GPR Data

Soils can be non-intrusively mapped by observing similar patterns within ground-penetrating radar (GPR) profiles. We observed that the intricate and often indiscernible textural variability found within a complex GPR image possesses important parameters that help delineate regions of similar soil characteristics. Therefore, in this study, we examined the feasibility of using textural features extracted from GPR data to automate soil characterizations. The textural features were matched to a fingerprint database of previous soil classifications of GPR textural features and the corresponding ground truths of soil conditions. Four textural features (energy, contrast, entropy, and homogeneity) were selected for inputs into a neural-network classifier. This classifier was tested and verified using GPR data obtained from two distinctly different field sites. The first data set contained features that indicate the presence or lack of sandstone bedrock in the upper 2 m of a shallow soil profile of fine sandy loan and loam. The second data set contained columnar patterns that correspond to the presence or the lack of vertical preferential-flow paths within a deep loess soil. The classifier automatically grouped each of these data sets into one of the two categories. Comparing the results of classification using extracted textural features to the results obtained by visual interpretation found 93.6% of the sections that lack sandstone bedrock correctly classified in the first set of data, and 90% of the sections that contain pronounced columnar patterns correctly classified in the second set of data. The classified profile sections were mapped using integrated GPR and GPS data to show surface boundaries of different soil categories. These results indicate that extracted textural features can be utilized for automatic characterization of soils using GPR data

The extraordinary evolutionary history of the reticuloendotheliosis viruses

The reticuloendotheliosis viruses (REVs) comprise several closely related amphotropic retroviruses isolated from birds. These viruses exhibit several highly unusual characteristics that have not so far been adequately explained, including their extremely close relationship to mammalian retroviruses, and their presence as endogenous sequences within the genomes of certain large DNA viruses. We present evidence for an iatrogenic origin of REVs that accounts for these phenomena. Firstly, we identify endogenous retroviral fossils in mammalian genomes that share a unique recombinant structure with REVs—unequivocally demonstrating that REVs derive directly from mammalian retroviruses. Secondly, through sequencing of archived REV isolates, we confirm that contaminated Plasmodium lophurae stocks have been the source of multiple REV outbreaks in experimentally infected birds. Finally, we show that both phylogenetic and historical evidence support a scenario wherein REVs originated as mammalian retroviruses that were accidentally introduced into avian hosts in the late 1930s, during experimental studies of P. lophurae, and subsequently integrated into the fowlpox virus (FWPV) and gallid herpesvirus type 2 (GHV-2) genomes, generating recombinant DNA viruses that now circulate in wild birds and poultry. Our findings provide a novel perspective on the origin and evolution of REV, and indicate that horizontal gene transfer between virus families can expand the impact of iatrogenic transmission events

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Pennsylvania Folklife Vol. 26, No. 4

• Isaac Ziegler Hunsicker: Ontario Schoolmaster and Fraktur Artist • Walls and Fences in Susquehanna County, Pennsylvania • Glossary of Pennsylvania German Terms Related to Construction and Tobacco Agriculture • Pennsylvania German Astronomy and Astrology XV: Benjamin Franklin\u27s Almanacs • Wilhelm Nast and the German Universalists • Vegetables in the Pennsylvania Cuisine: Folk-Cultural Questionnaire No. 47https://digitalcommons.ursinus.edu/pafolklifemag/1072/thumbnail.jp