By-products, damaged feeds and nontraditional feed sources for swine

"In Missouri, corn and soybean oil meal are the principal feed ingrents used in formulating swine rations. Rations using corn and soybean meal remain the standard to which other ingredients are compared. COnsiderable use is made of other ingredients, however, depending on cost."--First page.John C. Rea, Ronald O. Bates and Trygve L. Veum (Department of Animal Science, College of Agriculture)Revised 10/87/6

Predicting soil permanganate oxidizable carbon (POXC) by coupling DRIFT spectroscopy and artificial neural networks (ANN)

Infrared spectroscopy has transformed soil property quantification by enabling low-cost, high-throughput analysis of soils, enabling mapping and monitoring of this non-renewable resource. However, less evaluated are newly emerging indicators of soil health. Furthermore, as soil spectral libraries expand in size, commonly employed linear models such as partial least squares regression (PLSR) may be challenged by the number and diversity of spectra. Artificial neural networks (ANN) are an emerging deep learning approach that can offer advantages in quantification of soil properties by utilizing non-linear relationships among spectra and soil components. We compared ANN versus PLSR models for predicting an increasingly used soil health indicator, permanganate oxidizable C (POXC), as well as more routinely predicted soil variables (e.g., clay, soil organic C [SOC]), across a gradient of soil organic matter furnished by a deforestation chronosequence in Kenya (n = 144). Candidate ANN architectures were first methodologically evaluated and described to identify best-practices for the application of ANN to soil spectroscopy. Predictions by the resulting ANN relative to PLSR were similar or slightly improved for routinely measured variables that represent soil organic matter (SOC, C:N) and physical properties (clay, silt, sand, bulk density). The accuracy of POXC predictions were similar for ANN (RMSE 102 mg kg−1) and PLSR (RMSE 106 mg kg−1). However, models drew on shared but also distinct wavenumbers, indicating differential use of information in soil infrared spectra by non-linear versus linear chemometric models. Even in relatively small spectral datasets of similar soil types expected to favor PLSR, ANN shows comparable predictive performance. To help guide future applications of ANN in soil spectroscopy, we propose a systematic procedure to select ANN model hyperparameters

RANK AND QUANTITY MOBILITY IN THE EMPIRICAL DYNAMICS OF INEQUALITY

Horizontal and vertical measures of inequality are related through mobility. The paper draws attention to two types of mobility: quantity mobility, which refers to mobility in income itself, and rank mobility, which refers to mobility in the position in the distribution of income. Individually matched census data for earnings in Israel are used to illustrate these concepts empirically. Mobility is measured between 1983 and 1995. It is shown that earnings in Israel are highly mobile. The high degree of earnings mobility implies that horizontal measures of inequality considerably overstate the underlying level of inequality. The method of errors in variables is used to distinguish between current and permanent mobility and inequality. Permanent earnings are more equal than current earnings and less mobile. Finally, the methodology is applied to PSID. It is shown that earnings were more mobile in Israel than in the United States. Copyright 2004 Blackwell Publishing Ltd.