Statistical modeling of warm-spell duration series using hurdle models

Regression models for counts could be applied to the earth sciences, for instance when studying trends of extremes of climatological quantities. Hurdle models are modified count models which can be regarded as mixtures of distributions. In this paper, hurdle models are applied to model the sums of lengths of periods of high temperatures. A modification to the common versions presented in the literature is presented, as left truncation as well as a particular treatment of zeros is needed for the problem. The outcome of the model is compared to those of simpler count models

Statistical analysis of possible trends for extreme floods in northern Sweden

With ongoing climate change, analysis of trends in maximum annual daily river flow is of interest. Flow magnitude and timing during the year were investigated in this study. Observations from 11 unregulated rivers in northern Sweden were analysed, using extreme-value distributions with time-dependent parameters. The Mann-Kendall test was used to investigate possible trends. The extreme-value statistics revealed no significant trends for the stations considered, but the Mann-Kendall test showed a significant upward trend for some stations. For timing of maximum flow (day of the year), the Mann-Kendall test revealed significant downward trends for two stations (with the longest records). This implies that the day of the maximum flow is occurring earlier in the year in northern Sweden

A tale of two stations: a note on rejecting the Gumbel distribution

The existence of an upper limit for extremes of quantities in the earth sciences, e.g. for river discharge or wind speed, is sometimes suggested. Estimated parameters in extreme-value distributions can assist in interpreting the behaviour of the system. Using simulation, this study investigated how sample size influences the results of statistical tests and related interpretations. Commonly used estimation techniques (maximum likelihood and probability-weighted moments) were employed in a case study; the results were applied in judging time series of annual maximum river flow from two stations on the same river, but with different lengths of observation records. The results revealed that sample size is crucial for determining the existence of an upper bound

Applications of discrete factor analysis

A recently proposed method for factor analysis of discrete data is extended to better handle overdispersion. Three empirical examples from veterinary sciences, musicology and agriculture are investigated, involving true count data as well as ordinal data. Comparisons are made with results from related statistical techniques, e.g., principal component analysis

Tales of the Wakeby Tail and Alternatives When Modelling Extreme Floods

Estimation of return levels, based on extreme-value distributions, is of importance in the earth and environmental sciences. The selection of an appropriate probability distribution is crucial. The Wakeby distribution has shown to be an interesting alternative. By simulation studies, we investigate by various means of minimum distance to distinguish between common distributions when modelling extreme events in hydrology. Estimation of parameters is performed by L-moments. Moreover, time series of annual maximum floods from major unregulated rivers in Northern Sweden were analysed with respect to fitting an appropriate distribution. The results of the simulation study shows that the Wakeby distribution has the best fit of the tail for a wide range of sample sizes. For the analysis of extreme floods, the Wakeby distribution is in the majority of cases the best fit by means of minimum distance. However, when considering estimation of return levels by competing distributions, results can vary considerably for longer return periods

Statistical modeling of warm-spell duration series using hurdle models

Regression models for counts could be applied to the earth sciences, for instance when studying trends of extremes of climatological quantities. Hurdle models are modified count models which can be regarded as mixtures of distributions. In this paper, hurdle models are applied to model the sums of lengths of periods of high temperatures. A modification to the common versions presented in the literature is presented, as left truncation as well as a particular treatment of zeros is needed for the problem. The outcome of the model is compared to those of simpler count models.Peer Reviewe

Loss of beef during primary production at Swedish farms 2002-2021

Loss of animals is a considerable waste of resources in the meat supply chain, where quantitative data are scarce but critical for guiding improvements. In this study, we used material flow analysis to track the amount of beef diverted away from the food supply chain at the farm level. The beef losses (absolute and as the proportion of yearly initial production) were estimated from data on assisted and unassisted deaths of cattle on Swedish farms obtained from the central register of bovine animals for 2002-2021 combined with official statistics on slaughter weight. The fallen animals were grouped according to age, sex and breed, to enable estimations of the lost amount of carcass weight, both in total and per animal group. The yearly loss during primary production 2017-2021 was on average 13,000 ton carcass weight, or 8.5% of the initial production. No decreasing trend for the loss rate could be determined after 2015, when the Agenda 2030 target 12.3 (Halved food waste and reduced early losses) was introduced. Female dairy breeds showed greater beef losses than dairy males or beef breeds and crossbreeds of both sexes, and their beef losses mostly occurred at 4-5 years of age, thus constituting the hot spot group for lost beef. The results can serve as a base for directed reduction efforts

Cross-validation of stagewise mixed-model analysis of Swedish variety trials with winter wheat and spring barley

In cultivar testing, linear mixed models have been used routinely to analyze multienvironment trials. A single‐stage analysis is considered as the gold standard, whereas two‐stage analysis produces similar results when a fully efficient weighting method is used, namely when the full variance–covariance matrix of the estimated means from Stage 1 is forwarded to Stage 2. However, in practice, this may be hard to do and a diagonal approximation is often used. We conducted a cross‐validation with data from Swedish cultivar trials on winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.) to assess the performance of single‐stage and two‐stage analyses. The fully efficient method and two diagonal approximation methods were used for weighting in the two‐stage analyses. In Sweden, cultivar recommendation is delineated by zones (regions), not individual locations. We demonstrate the use of best linear unbiased prediction (BLUP) for cultivar effects per zone, which exploits correlations between zones and thus allows information to be borrowed across zones. Complex variance–covariance structures were applied to allow for heterogeneity of cultivar × zone variance. The single‐stage analysis and the three weighted two‐stage analyses all performed similarly. Loss of information caused by a diagonal approximation of the variance–covariance matrix of adjusted means from Stage 1 was negligible. As expected, BLUP outperformed best linear unbiased estimation. Complex variance–covariance structures were dispensable. To our knowledge, this study is the first to use cross‐validation for comparing single‐stage analyses with stagewise analyses

Survival of Campylobacter jejuni in frozen chicken meat and risks associated with handling contaminated chicken in the kitchen

Most Campylobacter infections in humans are sporadic cases, often connected to private households. Chicken meat is believed to be the main source of human exposure to Campylobacter and there are significant risks of cross-contamination when handling Campylobacter-contaminated chicken in the kitchen. One post-harvest pre-ventive measure to reduce Campylobacter concentrations on chicken meat is freezing. This study examined survival of different sequence types of C. jejuni during freezing and risk factors during handling of C. jejuni-contaminated chicken meat in the kitchen. Chicken fillets were artificially contaminated before freezing with two different sequence types of C. jejuni (ST-257 and ST-918), at concentrations in the meat of 4.1 log10 CFU/g (low) and 5.3 log10 CFU/g (high). Risk factors in the kitchen were assessed by swabbing gloves before and after washing, to simulate hands before and after washing. Utensils such as scissors and forceps used for cutting were also sampled, while a cutting board was sampled twice to simulate before and after wiping.The greatest decrease in Campylobacter concentrations in the freezer occurred in the first four days and the decrease then flattened off. After 49 days in the freezer, concentrations on meat contaminated with high and low levels of ST-257 decreased by 2.0 log10 CFU/g and 1.5 log10 CFU/g, respectively, while concentrations on chicken meat contaminated with a high and low level of ST-918 decreased by 1.0 log10 CFU/g and 0.7 log10 CFU/ g, respectively. Campylobacter was isolated from all simulated environmental samples. The highest load in the environment of both sequence types was unwashed gloves and the first sampling of the unwiped cutting board. Transfer from gloves and the cutting board was lower after washing/wiping, but high concentrations (>= 2 log10 CFU/mL rinse fluid) of Campylobacter persisted for all samples contaminated with ST-918 and for 18 of 20 samples contaminated with ST-257.In conclusion, there are differences between Campylobacter sequence types in their ability to withstand freezing stress and Campylobacter remaining on hands after washing and on cutting boards after wiping is a likely source of cross-contamination in the kitchen

An Image-Analysis-Based Method for the Prediction of Recombinant Protein Fiber Tensile Strength

Silk fibers derived from the cocoon of silk moths and the wide range of silks produced by spiders exhibit an array of features, such as extraordinary tensile strength, elasticity, and adhesive properties. The functional features and mechanical properties can be derived from the structural composition and organization of the silk fibers. Artificial recombinant protein fibers based on engineered spider silk proteins have been successfully made previously and represent a promising way towards the large-scale production of fibers with predesigned features. However, for the production and use of protein fibers, there is a need for reliable objective quality control procedures that could be automated and that do not destroy the fibers in the process. Furthermore, there is still a lack of understanding the specifics of how the structural composition and organization relate to the ultimate function of silk-like fibers. In this study, we develop a new method for the categorization of protein fibers that enabled a highly accurate prediction of fiber tensile strength. Based on the use of a common light microscope equipped with polarizers together with image analysis for the precise determination of fiber morphology and optical properties, this represents an easy-to-use, objective non-destructive quality control process for protein fiber manufacturing and provides further insights into the link between the supramolecular organization and mechanical functionality of protein fibers