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Resumen
Context: Demand for dairy products is expected to continue driving intensification in dairy systems. Little is known about the productive and economic performance and risk of intensification strategies either within grazing systems or confinement dairy systems in Argentina. Objective: This study investigated four strategies to double milk production for the average grazing dairy system of Argentina (BASE), using either grazing or confinement systems. [ver mas...]
dc.contributor.authorBaudracco, Javier
dc.contributor.authorLazzarini, Belén
dc.contributor.authorRossler, Noelia
dc.contributor.authorGastaldi, Laura Beatriz
dc.contributor.authorJauregui, José Martí­n
dc.contributor.authorFariña, Santiago
dc.date.accessioned2022-02-22T15:53:12Z
dc.date.available2022-02-22T15:53:12Z
dc.date.issued2022-03
dc.identifier.issn0308-521X
dc.identifier.otherhttps://doi.org/10.1016/j.agsy.2022.103366
dc.identifier.urihttp://hdl.handle.net/20.500.12123/11243
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0308521X22000026
dc.description.abstractContext: Demand for dairy products is expected to continue driving intensification in dairy systems. Little is known about the productive and economic performance and risk of intensification strategies either within grazing systems or confinement dairy systems in Argentina. Objective: This study investigated four strategies to double milk production for the average grazing dairy system of Argentina (BASE), using either grazing or confinement systems. Physical and economic performance and risk associated with each alternative was explored using a modelling approach. Investment of capital required to establish each alternative was estimated. Methods: Four scenarios that double milk production per farm from a BASE scenario were designed and modelled using a whole-farm model named e-Dairy: two grazing dairy systems with different milk yield per cow per year: GR6750 (6750 L/cow per year) and GR7500 (7500 L/cow per year) and two confinement systems, an open dry yard (DRYLOT) and a compost bedded pack (COMPOST). Stochastic budgeting was used to model the combined influence of variation in milk, price and crops yield. Outputs of the stochastic analysis are shown in the form of cumulative distribution functions (CDF). Results and conclusions: All the intensification alternatives increased milk production per ha from 7800 L, in BASE system, to 18,209 and 26,758 L in grazing and confinement systems, respectively. Intensified scenarios required an investment of capital between two and three times higher than the BASE scenario. All scenarios had positive economic results. The BASE scenario showed both the lowest farm operating profit and the lowest return on assets ($99/ha per year and 4.1%, respectively). Intensified grazing systems had the highest return on assets (above 12%), while the COMPOST system showed the highest farm operating profit ($1121/ha per year) and the lowest return on assets (7.5%) of the intensification alternatives explored. According to stochastic simulations, the COMPOST and DRYLOT scenarios would expose farmers to a greater maximum loss than BASE and grazing scenarios when negative farm operating profit occurred. However, cumulative distribution functions of profit showed that they would have higher profit than BASE and grazing scenarios along most of the CDF curve.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherElsevieres_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.sourceAgricultural Systems 197 : 103366 (March 2022)es_AR
dc.subjectLechees_AR
dc.subjectMilkeng
dc.subjectProducciónes_AR
dc.subjectProductioneng
dc.subjectModelos Estocásticoses_AR
dc.subjectStochastic Modelseng
dc.subjectGranjas Lecherases_AR
dc.subjectDairy Farmseng
dc.subjectAnálisis de Riesgoses_AR
dc.subjectRisk Analysiseng
dc.subjectInversioneses_AR
dc.subjectInvestmenteng
dc.subjectArgentinaes_AR
dc.subject.otherLeche Doblees_AR
dc.subject.otherDouble Milkeng
dc.titleStrategies to double milk production per farm in Argentina: Investment, economics and risk analysises_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.origenEEA Rafaelaes_AR
dc.description.filFil: Baudracco, Javier. Universidad Nacional del Litoral. Facultad de Ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Baudracco, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: Lazzarini, Belén. Universidad Nacional del Litoral. Facultad de Ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Rossler, Noelia. Universidad Nacional del Litoral. Facultad de Ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Gastaldi, Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina.es_AR
dc.description.filFil: Jauregui, José Martí­n. Universidad Nacional del Litoral. Facultad de Ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Fariña, Santiago. Instituto Nacional de Investigación Agropecuaria (INIA); Uruguayes_AR
dc.subtypecientifico


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