Supplementary Material - Regression trees to identify combinations of farming practices that afford the best overall intrinsic quality of milk

Published: 15 September 2022| Version 1 | DOI: 10.17632/3xdpk76v8b.1
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Supplementary file S1 Adaptations made on the assessments from Rey-Cadilhac et al. (2021) Supplementary file S2 Correlation between dimension scores and both overall quality and indicator scores of the UHT milk assessment. Table S1 Milking machine routine score calculation Table S2 Teat cleaning routine score calculation Table S3: Definition of the modalities of the variable Complexity of the milking equipment pipeline Table S4 Description of modalities of the variable Diet_forage Table S5 Description of the indicators at the base of the sensory, technological, health and nutrional dimensions of the cheese assessment Table S6 Correlation coefficients between quantitative farming practices variables Table S7 Correlation ratios between quantitative and qualitative farming practices variables Table S8 Cramer's V coefficients between qualitative farming practices variables Figure S1 Variable importance plots obtained from random forest analysis for construction of overall cheese quality (A), and cheese sensory (B), technological (C), health (D) and nutritional (E) dimensions regression trees Figure S2 Variable importance plots obtained from random forest analysis for construction of overall UHT milk quality (A), and UHT milk sensory (B), technological (C), health (D) and nutritional (E) dimension regression trees Figure S3 Regression trees explaining UHT milk sensory, technological, health and nutritional dimensions from farming practices.

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Supplementary material of the article : Regression trees to identify combinations of farming practices that afford the best overall intrinsic quality of milk

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Dairy Cattle, Regression Tree, Milk Quality, Farming Systems

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