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The spectral sensing of nitrogen (N)-related traits and the grain yield of maize (Zea mays L.) is widely used in agricultural practice because it is rapid, non-destructive, and cost-efficient. However, there exists a lack of agronomically supported spectral detection limits. The agronomic aspects have not yet been fully considered based on commonly used statistical measures such as the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean absolute error (MAE) and should therefore be extended. In the present study, we evaluated regression models of spectral indices derived from an unmanned aerial vehicle (UAV) by capturing N-related traits such as above-ground and grain N uptake, the nitrogen nutrition index (NNI), and grain yield covering data sets of two years, sites, and four developmental stages of maize. The results suggest that an agronomic evaluation exclusively adopting widely used statistical measures is not fully adequate. The R² is essentially influenced by differentiation of the trait, which in turn depends on year effects and growth stages. Further statistics such as RMSE and MAE average the error and lead to an under- and overestimation for most observations. In this investigation, we defined an appropriate agronomical error interval for above-ground and grain N uptake, NNI, and grain yield of ± 40 and ± 25 kg N ha−1, ± 0.2, and ± 1.4 t ha−1, with a probability of at least 80%. These interval limits are consistent across years and growth stages. The consistency occurs because most spectral indices are dominated by biomass. Across all of them, the best-performing spectral indices combine GREEN, REDEDGE, and NIR bands. For spectral indices using the RED band, the range of the agronomic error interval performed equally with a slightly worse probability of data points inside the error limits. Agronomically based error limits should be included in addition to common statistical measures in the spectral assessment of N-related traits of maize and grain yield to optimize the ex-ante and/or ex-post analysis of N-fertilization. [ABSTRACT FROM AUTHOR]

Pulsed UV light inactivation of Salmonella Typhimurium on unpackaged and vacuum-packaged chicken breast, Listeria monocytogenes on unpackaged and vacuum-packaged chicken frankfurters, and Salmonella Enteritidis on shell eggs was explained by log-linear and Weibull models using inactivation data from previous studies. This study demonstrated that the survival curves of Salmonella Typhimurium and L. monocytogenes were nonlinear exhibiting concavity. The Weibull model was more successful than the log-linear model in estimating the inactivations for all poultry products evaluated, except for Salmonella Enteritidis on shell eggs, for which the survival curve was sigmoidal rather than concave, and the use of the Weibull model resulted in slightly better fit than the log-linear model. The analyses for the goodness of fit and performance of the Weibull model produced root mean square errors of 0.059 to 0.824, percent root mean square errors of 3.105 to 21.182, determination coefficients of 0.747 to 0.989, slopes of 0.842 to 1.042, bias factor values of 0.505 to 1.309, and accuracy factor values of 1.263 to 6.874. Overall, this study suggests that the survival curves of pathogens on poultry products exposed to pulsed UV light are nonlinear and that the Weibull model may generally be a useful tool to describe the inactivation patterns for pathogenic microorganisms affiliated with poultry products. [ABSTRACT FROM AUTHOR]