Use of quali-quantitative feeding practices criteria in typology of smallholders’ dairy production systems

Dias, Angélica; Fischer, Vívian

doi:10.37496/rbz5020200283

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…They also found a positive relationship between sociodemographic characteristics of dairy farmers and productivity parameters. Dias and Fischer (2021), observed a positive correlation between grain feeding and milk productivity per cow and area. Furthermore, the authors observed a negative correlation between the use of low-quality forage and productivity per cow and area.…”

Section: Multiple Linear Regression Of Extracted Factorsmentioning

confidence: 93%

Factors associated with grain feeding in dairy farms located in the central-western region of Paraná State, Brazil

Lima

Damasceno

Borges

et al. 2021

Semina: Ciênc. Agrár.

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This study aimed to investigate factors associated with grain feeding and determine the typology of dairy farms that use high-grain diets. Twenty-two farm operators were interviewed in three municipalities located in the central-western region of Paraná state, Brazil. Information on reproductive and nutritional management practices, sociodemographic characteristics, and farm performance was collected. Data were analyzed using exploratory factor analysis, hierarchical cluster analysis, and multiple linear regression. Three factors (F1, F2, and F3) were extracted, which together explained 82.61% of the total variance. F1 comprised diet quality, technology, and breeding composition. F2 comprised labor and size. F3 comprised feed quality and schooling. Farms were classified into four groups and compared in terms of factor scores and performance parameters. Group 1 had the highest mean score on F1 (0.715), group 4 on F2 (1.642), and group 2 on F3 (1.116). Groups 4 and 1 had the highest milk productivity (2043.50 and 399.52 L day−1, respectively) and labor efficiency (418.16 and 148.63 L worker−1 day−1, respectively). Group 4 also had the highest mean number of cows per worker (25.52 cows worker−1). Regression analysis revealed that diet quality, technology, and breeding composition (F1) explained the variance in cow productivity. Labor and size, (F2) explained the variance in number of cows per worker. Daily productivity and labor efficiency were explained by both F1 and F2. Feed quality and farm operator’s level of schooling did not explain the variation in any of the variables. We found that roughage quality, breeding technology, and herd breed composition are the major factors associated with grain feeding. Farmers who feed cows high-quality roughage throughout the year and invest in genetic improvement and selective breeding strategies are more likely to adopt high-grain feeding and have high milk productivity.

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Section: Multiple Linear Regression Of Extracted Factorsmentioning

confidence: 93%