Influence of forage production area, concentrate supply, and workforce on productive results in milk production systems

Koerich, Guilherme; Damasceno, Júlio César; Bánkuti, Ferenc Istvan; Parré, José Luiz; Santos, Geraldo Tadeu dos

doi:10.1590/rbz4820170177

Cited by 7 publications

(12 citation statements)

References 13 publications

(19 reference statements)

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“…Si bien se han utilizado distintas variables para la tipificación de unidades de producción, las que mejores resultados aportan para el análisis de los sistemas de producción de leche son el tamaño de la unidad de producción y el nivel de producción (9) . Este método de análisis, en la actualidad, es uno de los más empleados, como queda evidenciado en recientes estudios de caso de Brasil, Colombia y México (10)(11)(12)(13) , debido a que permite encontrar más relaciones entre las variables estudiadas. Lo anterior tiene especial relevancia debido a que la producción de lácteos se lleva a cabo en todas las regiones agroecológicas del país y se tienen desde las altamente tecnificadas hasta las de subsistencia (12) .…”

Section: Introductionunclassified

Características de la producción de leche en La Frailesca, Chiapas, México

Vera

Vargas-Canales

Quintero-Salazar³

et al. 2021

Rev. Mex. Cienc. Pecu.

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Históricamente, la región Frailesca ha sido una de las principales zonas ganaderas del estado de Chiapas, por lo que se puede intuir que la estructura actual de su sistema lechero es una buena aproximación de la del estado. Este trabajo tuvo como objetivo la caracterización y análisis de las unidades de producción que forman parte del sistema lechero de la región Frailesca con la intención de describir su estructura en cuanto a tamaño y tipo de unidades de producción. Para tal fin se utilizaron parámetros de producción, de costos de producción e indicadores de rentabilidad. Se determinaron las características principales de las unidades de producción en cuanto a su tamaño y condiciones de operación por localidad de pertenencia, a fin de construir una tipología de productores y de realizar un contraste por localidad. Se encontró que la estructura productiva de la región tiene como base unidades de pequeña escala que constituyen alrededor del 76.7 % del total de las unidades de la muestra, mientras que los medianos productores representan un 14.6 % del total y los grandes únicamente el 8.7 %. Entre estos tipos de productores existe una diferencia significativa en cuanto al tamaño del hato productivo, rendimiento, costos y rentabilidad. En síntesis, la estructura del sistema productivo de la región Frailesca, como otros sistemas de lechería tropical en el país, está constituida por unidades de producción de baja escala con características de unidades familiares con uso intensivo de mano de obra, bajo nivel tecnológico y altos costos de suplementación.

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Section: Introductionunclassified

Características de la producción de leche en La Frailesca, Chiapas, México

Vera

Vargas-Canales

Quintero-Salazar³

et al. 2021

Rev. Mex. Cienc. Pecu.

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“…Other studies achieved similar results. Koerich et al (2019), using similar methods to analyze dairy systems in Paraná State, found a positive relationship between variables related to forage production area and concentrate supply and dairy system productivity. They also found a positive relationship between sociodemographic characteristics of dairy farmers and productivity parameters.…”

Section: Multiple Linear Regression Of Extracted Factorsmentioning

confidence: 98%

“…After the analytical procedures, 14 variables were maintained in the analysis (Table 1). Factors were named according to the variables that defined them (Koerich et al, 2019).…”

Section: Exploratory Factor Analysismentioning

confidence: 99%

“…Multiple linear regression was performed to identify significant factors and their explanatory capacity. Factor scores of each dairy farm were considered independent variables, and the six performance parameters were considered dependent variables (Koerich et al, 2019). Stepwise procedures based on the F-test (p < 0.05) were used to select factors for the model (Çamdevýren, Demýr, Kanik, & Keskýn, 2005;Koerich et al, 2019) (equation 4):…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

“…Regression coefficients were tested by Student's t-test. The coefficient of determination (R 2 ) was used as a standard criterion of predictive success (Çamdevýren et al, 2005;Koerich et al, 2019). These procedures allowed finding associations between extracted factors and performance parameters.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

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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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