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| 1. |  | GUARIN, J. R.; MARTRE, P; EWERT, F.; WEBBER, H.; DUERI, S.; CALDERINI, D.; REYNOLDS, M.; MOLERO, G.; MIRALLES, D.; GARCIA, G.; SLAFER, G.; GIUNTA, F.; PEQUENO, D. N. L.; STELLA, T.; AHMED, M.; ALDERMAN, P. D.; BASSO, B.; BERGER, A.; BINDI, M.; BRACHO-MUJICA, G.; CAMMARANO, D.; CHEN, Y.; DUMONT, B.; REZAEI, E. E.; FERERES, E.; FERRISE, R.; GAISER, T.; GAO, Y.; GARCIA-VILA, M.; GAYLER, S.; HOCHMAN, Z.; HOOGENBOOM, G.; HUNT, L. A.; KERSEBAUM, K. C.; NENDEL, C.; OLESEN, J. E.; PALOSUO, T.; PRIESACK, E.; PULLENS, J. W. M.; RODRÍGUEZ, A.; RÖTTER, R. P.; RUIZ RAMOS, M.; SEMENOV, M. A.; SENAPATI, N.; SIEBERT, S.; SRIVASTAVA, A. M.; STÖCKLE, C.; SUPIT, I.; TAO, F.; THORBURN, P.; WANG, E.; WEBER, T. K. D.; XIAO, L.; ZHANG, Z.; ZHAO, C.; ZHAO, J.; ZHAO, Z.; ZHU, Y.; ASSENG, S. Evidence for increasing global wheat yield potential. [Letter]. Environmental Research Letters, 12 December 2022, Volume 17, 124045. OPEN ACCESS. doi: https://doi.org/10.1088/1748-9326/aca77c Article history: Received 13 June 2022; Accepted 30 November 2022; Published 12 December 2022. -- Corresponding author: Jose Rafael Guarin, E-mail: j.guarin@columbia.edu -- LICENSE: Original content from this work may be used under the...| Biblioteca(s): INIA Las Brujas. |
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| 2. | | MARCAIDA, M.; ASSENG, S.; EWERT, F.; BASSU, S.; DURAND, J.L.; LI, T.; MARTRE, P.; ADAM, M.; AGGARWAL, P.K.; ANGULO, C.; BARON, C.; BASSO, B.; BERTUZZI, P.; BIERNATH, C.; BOOGAARD, H.; BOOTE, K.J.; BOUMAN, B.; BREGAGLIO, S.; BRISSON, N.; BUIS, S.; CAMMARANO, D.; CHALLINOR, A.J.; CONFALONIERI, R.; CONIJN, J.G.; CORBEELS, M.; DERYNG, D.; DE SANCTIS, G.; DOLTRA, J.; FUMOTO, T.; GAYDON, D.; GAYLER, S.; GOLDBERG, R.; GRANT, R.F.; GRASSINI, P.; HATFIELD, J.L.; HASEGAWA, T.; HENG, L.; HOEK, S.; HOOKER, J.; HUNT, L.A.; INGWERSEN, J.; IZAURRALDE, R.C.; JONGSCHAAP, R.E.E.; JONES, J.W.; KEMANIAN, R.A.; KERSEBAUM, K.C.; KIM, S.-H.; LIZASO, J.; MÜLLER, C.; NAKAGAWA, H.; NARESH KUMAR, S.; NENDEL, C.; O'LEARY, G.J.; OLESEN, J.E.; ORIOL, P.; OSBORNE, T.M.; PALOSUO, T.; PRAVIA, V.; PRIESACK, E.; RIPOCHE, D.; ROSENZWEIG, C.; RUANE, A.C.; RUGET, F.; SAU, F.; SEMENOV, M.A.; SHCHERBAK, I.; SINGH, B.; SINGH, U.; SOO, H.K.; STEDUTO, P.; STÖCKLE, C.; STRATONOVITCH, P.; STRECK, T.; SUPIT, I.; TANG, L.; TAO, F.; TEIXEIRA, E.I.; THORBURN, P.; TIMLIN, D.; TRAVASSO, M.; RÖTTER, R.P.; WAHA, K.; WALLACH, D.; WHITE, J.W.; WILKENS, P.; WILLIAMS, J.R.; WOLF, J.; YIN, X.; YOSHIDA, H.; ZHANG, Z.; ZHU, Y. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. Agricultural and Forest Meteorology, 2015, v.214-215, p. 483-493. Article history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015.| Biblioteca(s): INIA Las Brujas; INIA Treinta y Tres. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
02/02/2022 |
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Actualizado : |
02/02/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
BAUDRACCO, J.; LAZZARINI, B.; ROSSLER , N.; GASTALDI, L.; JAUREGUI, J.; FARIÑA, S. |
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Afiliación : |
JAVIER BAUDRACCO, IciAgro Litoral, Universidad Nacional del Litoral-CONICET, FCA. R.P. Kreder 2805, Esperanza, 3080, Argentina; BELÉN LAZZARINI, Universidad Nacional del Litoral (Facultad de Ciencias Agrarias), Argentina; NOELIA ROSSLER, Universidad Nacional del Litoral (Facultad de Ciencias Agrarias), Argentina; LAURA GASTALDI, INTA (E.E.A. Rafaela), Argentina; JOSÉ JAUREGUI, Universidad Nacional del Litoral (Facultad de Ciencias Agrarias), Argentina; SANTIAGO FARIÑA, INIA (Instituto Nacional de Investigación Agropecuaria). |
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Título : |
Strategies to double milk production per farm in Argentina: Investment, economics and risk analysis. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Agricultural Systems, 2022, Volume 197, Article number 103366. doi: https://doi.org/10.1016/j.agsy.2022.103366 |
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ISSN : |
0308-521X |
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DOI : |
10.1016/j.agsy.2022.103366 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 July 2021; Received in revised form 8 December 2021; Accepted 13 January 2022; Available online 24 January 2022. |
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Contenido : |
ABSTRACT.- 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.cu 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. Significance: Farmers who decide to intensify their systems will have higher profit compared with BASE scenario, but should be prepared to afford higher investment and also to cope with increased variability of profit under price or climate risk. If the switch from a BASE scenario was to be implemented at a national scale, it would impact on surplus milk that might cause significant changes beyond farm gate. Further research is required to investigate the environmental impact of intensification alternatives.
© 2022 Elsevier Ltd MenosABSTRACT.- 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.cu 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 betw... Presentar Todo |
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Palabras claves : |
Dairy system; Intensification; Milk yield; Return on investment; Stochastic. |
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Asunto categoría : |
L01 Ganadería |
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Marc : |
LEADER 03853naa a2200277 a 4500
001 1062730
005 2022-02-02
008 2022 bl uuuu u00u1 u #d
022 $a0308-521X
024 7 $a10.1016/j.agsy.2022.103366$2DOI
100 1 $aBAUDRACCO, J.
245 $aStrategies to double milk production per farm in Argentina$bInvestment, economics and risk analysis.$h[electronic resource]
260 $c2022
500 $aArticle history: Received 21 July 2021; Received in revised form 8 December 2021; Accepted 13 January 2022; Available online 24 January 2022.
520 $aABSTRACT.- 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.cu 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. Significance: Farmers who decide to intensify their systems will have higher profit compared with BASE scenario, but should be prepared to afford higher investment and also to cope with increased variability of profit under price or climate risk. If the switch from a BASE scenario was to be implemented at a national scale, it would impact on surplus milk that might cause significant changes beyond farm gate. Further research is required to investigate the environmental impact of intensification alternatives. © 2022 Elsevier Ltd
653 $aDairy system
653 $aIntensification
653 $aMilk yield
653 $aReturn on investment
653 $aStochastic
700 1 $aLAZZARINI, B.
700 1 $aROSSLER , N.
700 1 $aGASTALDI, L.
700 1 $aJAUREGUI, J.
700 1 $aFARIÑA, S.
773 $tAgricultural Systems, 2022, Volume 197, Article number 103366. doi: https://doi.org/10.1016/j.agsy.2022.103366
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