## 2 Material and Methods  
This research utilized a data set with workers' compensation claims between 2008 and 2016 from a leading insurance company in the Midwest of the United States. Among all occupational records, the agribusinesses with the highest frequencies of occupational incidents were extracted (13,867 claims). Statistical analysis was completed with JMP Pro statistical software (JMP(r), Version 13.2, SAS Institute Inc., Cary, NC, USA, 1989-2007). Frequency distributions were used to categorize the variables. The descriptive analysis of the data showed that out of 13,867 incident records, 50% had occurred in grain elevator operations, 18% in refined fuel, 11% in feed mills for livestock operation, 8% in food distributors, 8% in fertilizer blending/distribution, 3% in poultry hatchery/grower/processor, and 2% in grain milling operations. Considering the injury nature group, 98% involved a specific injury and only 2% were occupational disease and multiple injuries. Table 1 shows the frequency and percentage of total cases of injured body group, type of incident (main cause of incident), and the injured occupations. The _injured body part_ group refers to the main classification of an injury afflicting a specific part of the body and has seven levels. Each level has some detailed information. For example, the _upper extremities_ body group includes _elbow(s)_, _lower arm_, _finger(s)_, _shoulder(s)_, etc. _Cause of injury_ group has ten categories, each including some specific cause. For example, the _struck or injured by_ group includes specific causes of _hand tool or machine in use_, _falling or flying objects_, _object being lifted or handled_, etc. The _nature of injury_ group has three levels, each consisting of a specific detail of the injury. For example, the _specific injuries_ include a detailed nature of the injury, such as _laceration_, _fracture_, _amputation_, _concussion_, etc.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictor** & **Freq.** & **\% Total** & **Predictor** & **Freq.** & **\% Total** \\ \hline Injured body group & & & Occupation & & \\ Upper extremities & 4778 & 34.4 & Grain elevator operations & 2474 & 17.8 \\ Trunk & 3096 & 22.3 & Farm machinery operations & 2077 & 14.9 \\ Lower extremities & 3079 & 22.2 & Grain milling & 1751 & 12.6 \\ Head & 1878 & 13.5 & Chauffeurs and helpers & 1470 & 10.6 \\ Multiple & 529 & 3.81 & Hay grain or feed dealers & 1450 & 10.4 \\ Body & 321 & 2.31 & Gas and oil dealers & 1272 & 9.17 \\ Neck & 186 & 1.34 & Grocery store—retail & 1014 & 7.31 \\  & & & Auto service repair & 438 & 3.16 \\ Cause of injury group & & & Store wholesale & 397 & 2.85 \\ Strain or injury by & 3883 & 28.0 & Clerical officer & 367 & 2.64 \\ Fall, slip, or trip injury & 3585 & 25.8 & Salespersons & 316 & 2.28 \\ Struck or injured by & 1602 & 11.5 & Store: meat, fish, poultry & 275 & 1.98 \\ miscellaneous & 1349 & 9.72 & Fertilizer dry mixing & 194 & 1.40 \\ Cut, puncture, or scrape & 1235 & 8.90 & Food sundries manufacturing & 184 & 1.32 \\ Vehicle & 585 & 4.21 & Poultry/egg producer & 178 & 1.28 \\ Heat or cold exposure & 512 & 3.69 & & & \\ Caught in, under, or between & 452 & 3.26 & Industry & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of individual predictors.  
There are two numerical values in the data: age and experience. The average age of the injured at the incident date was 43 years old (min = 15, max = 84.5), and the average years of experience was 6 years (min = 0, max = 48).