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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
27/08/2020 |
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Actualizado : |
21/05/2021 |
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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 : |
ZARZA, R.; REBUFFO, M.; LA MANNA, A.; BALZARINI, M. |
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Afiliación : |
RODRIGO TABARE ZARZA FUENTES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MONICA IRENE REBUFFO GFELLER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALEJANDRO FRANCISCO LA MANNA ALONSO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MÓNICA BALZARINI, Universidad Nacional de Córdoba (National University of Córdoba), School of Agricultural Sciences, Córdoba, Argentina. |
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Título : |
Red clover (Trifolium pratense L.) seedling density in mixed pastures as predictor of annual yield. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Field Crops Research, 1 October 2020, Volumen 256, 107925. DOI: https://doi.org/10.1016/j.fcr.2020.107925 |
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ISSN : |
0378-4290 |
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DOI : |
10.1016/j.fcr.2020.107925 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 30 January 2020/ Revised 17 June 2020/ Accepted 27 July 2020/ Available online 15 August 2020.The field experiments were funded by INIA (the national agricultural research institute) (Project PA 010). The present work is a part of the thesis submitted by R. Zarza to the Postgraduate program of FCA-UNC.Corresponding author:E-mail addresses: rzarza@inia.org.uy (R. Zarza), monicarebuffo11@gmail.com (M. Rebuffo), alamanna@inia.org.uy (A. La Manna),mbalzari@agro.uncor.edu (M. Balzarini). |
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Contenido : |
Abstract:
Biomass predictive models can be useful tools to design management strategies for mixed pastures of red clover (Trifolium pratense) with either grasses or herbs in intensive grazing systems. This paper proposes mixed regression models to predict the annual yield of two mixtures based on red clover seedling density (CSD) and environmental effects (low, intermediate, high-yield environments). Two mixtures of red clover with either chicory (Cichorium intybus) or prairie grass (Bromus catharticus) were sown in Uruguay in a multi-environment experiment with six sowing rates to generate varying levels of species seedling densities. The CSD was recorded at 3, 7 and 12 weeks after sowing (WS). Yield prediction models in the initial establishment year (Y1) and second-year (Y2) were fitted with CSD at the three count times. CSD increased proportionally to the sowing rates in all environments. The CSD, even at 3 WS, provided a good prediction of expected annual yield (error mean <15 %). The fitted models estimated the probability of exceeding the threshold of 10,000 kg DM ha?1 annual yield based on CSD observed at 3 WS in all three environments. There is a high probability of harvesting more than the threshold, even when the CSD at 3 WS is higher than 50 seedlings m-2 in the high-yield environment. Forage prediction models based on CSD, fitted for environments of different yield potential, will contribute to improved management of mixed pastures in intensive grazing systems. |
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Palabras claves : |
BLUP OF ENVIRONMENTAL EFFECTS; CHICORY; FORAGE YIELD; GRASSLAND; PLANT STAND; PRAIRIE GRASS; RENDIMIENTO DE FORRAJE. |
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Thesagro : |
ACHICORIA; FORRAJES; PASTURAS; TREBOL ROJO; TRIFOLIUM PRATENSE. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 02996naa a2200337 a 4500
001 1061287
005 2021-05-21
008 2020 bl uuuu u00u1 u #d
022 $a0378-4290
024 7 $a10.1016/j.fcr.2020.107925$2DOI
100 1 $aZARZA, R.
245 $aRed clover (Trifolium pratense L.) seedling density in mixed pastures as predictor of annual yield.$h[electronic resource]
260 $c2020
500 $aArticle history: Received 30 January 2020/ Revised 17 June 2020/ Accepted 27 July 2020/ Available online 15 August 2020.The field experiments were funded by INIA (the national agricultural research institute) (Project PA 010). The present work is a part of the thesis submitted by R. Zarza to the Postgraduate program of FCA-UNC.Corresponding author:E-mail addresses: rzarza@inia.org.uy (R. Zarza), monicarebuffo11@gmail.com (M. Rebuffo), alamanna@inia.org.uy (A. La Manna),mbalzari@agro.uncor.edu (M. Balzarini).
520 $aAbstract: Biomass predictive models can be useful tools to design management strategies for mixed pastures of red clover (Trifolium pratense) with either grasses or herbs in intensive grazing systems. This paper proposes mixed regression models to predict the annual yield of two mixtures based on red clover seedling density (CSD) and environmental effects (low, intermediate, high-yield environments). Two mixtures of red clover with either chicory (Cichorium intybus) or prairie grass (Bromus catharticus) were sown in Uruguay in a multi-environment experiment with six sowing rates to generate varying levels of species seedling densities. The CSD was recorded at 3, 7 and 12 weeks after sowing (WS). Yield prediction models in the initial establishment year (Y1) and second-year (Y2) were fitted with CSD at the three count times. CSD increased proportionally to the sowing rates in all environments. The CSD, even at 3 WS, provided a good prediction of expected annual yield (error mean <15 %). The fitted models estimated the probability of exceeding the threshold of 10,000 kg DM ha?1 annual yield based on CSD observed at 3 WS in all three environments. There is a high probability of harvesting more than the threshold, even when the CSD at 3 WS is higher than 50 seedlings m-2 in the high-yield environment. Forage prediction models based on CSD, fitted for environments of different yield potential, will contribute to improved management of mixed pastures in intensive grazing systems.
650 $aACHICORIA
650 $aFORRAJES
650 $aPASTURAS
650 $aTREBOL ROJO
650 $aTRIFOLIUM PRATENSE
653 $aBLUP OF ENVIRONMENTAL EFFECTS
653 $aCHICORY
653 $aFORAGE YIELD
653 $aGRASSLAND
653 $aPLANT STAND
653 $aPRAIRIE GRASS
653 $aRENDIMIENTO DE FORRAJE
700 1 $aREBUFFO, M.
700 1 $aLA MANNA, A.
700 1 $aBALZARINI, M.
773 $tField Crops Research, 1 October 2020, Volumen 256, 107925. DOI: https://doi.org/10.1016/j.fcr.2020.107925
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