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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
17/04/2024 |
Actualizado : |
17/04/2024 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
BAIETTO, A.; HIRIGOYEN, A.; HERNÁNDEZ, J.; DEL PINO, A. |
Afiliación : |
ANDRÉS BAIETTO, Soil and Water Department, Faculty of Agronomy, University of the Republic, Montevideo, 12900, Uruguay; ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JORGE HERNÁNDEZ, Soil and Water Department, Faculty of Agronomy, University of the Republic, Montevideo, 12900, Uruguay; AMABELIA DEL PINO, Soil and Water Department, Faculty of Agronomy, University of the Republic, Montevideo, 12900, Uruguay. |
Título : |
Litterfall production modeling based on climatic variables and nutrient return from stands of Eucalyptusgrandis Hill ex Maiden and Pinustaeda L. |
Complemento del título : |
Original paper. |
Fecha de publicación : |
2024 |
Fuente / Imprenta : |
Journal of Forestry Research. 2024, Volume 35, e61. https://doi.org/10.1007/s11676-024-01706-w |
ISSN : |
1007-662X |
DOI : |
10.1007/s11676-024-01706-w |
Idioma : |
Inglés |
Notas : |
Article history: Received 13 June 2023, Accepted 17 August 2023, Published 21 March 2024. -- Correspondence: Baietto, A.; Forest Department, Faculty of Agronomy, University of the Republic, Montevideo, Uruguay; email:abaietto@fagro.edu.uy -- Corresponding editor: Yanbo Hu. -- Funding: This study was funded by Lumin S.A. and the Agencia Nacional de Investigación e Innovación (ANII) [POS_NAC_2016_1_130479]. -- Supplementary Information: The online version contains supplementary material available at https:// doi. org/ 10. 1007/s11676- 024- 01706-w |
Contenido : |
ABSTRACT.- Native grasslands in the Pampas of South America are increasingly being replaced by Eucalyptus and Pinus stands. The short rotation regimes used for the stands require high nutrient levels, with litterfall being a major source of nutrient return. To model the litterfall production using climatic variables and assess the nutrient return in 14-year-old Eucalyptusgrandis and Pinustaeda stands, we measured litter production over 2 years, using conical litter traps, and monitored climatic variables. Mean temperature, accumulated precipitation, and mean maximum vapor pressure deficit at the seasonal level influenced litterfall production by E.grandis; seasonal accumulated precipitation and mean maximum temperature affected litterfall by P.taeda. The regression tree modeling based on these climatic variables had great accuracy and predictive power for E.grandis (N = 33; MAE (mean absolute error) = 0.65; RMSE (root mean square error) = 0.91; R2 = 0.71) and P.taeda (N = 108; MAE = 1.50; RMSE = 1.59; R2 = 0.72). The nutrient return followed a similar pattern to litterfall deposition, as well as the order of importance of macronutrients (E.grandis: Ca > N > K > Mg > P; P.taeda: N > Ca > K > Mg > P) and micronutrients (E.grandis and P.taeda: Mn > Fe > Zn > Cu) in both species. This study constitutes a first approximation of factors that affect litterfall and nutrient return in these systems. © Northeast Forestry University 2024. |
Palabras claves : |
Afforestation; Climate modeling; Litterfall; Myrtaceae; Nutrient recycling; Pinaceae; SISTEMA FORESTAL - INIA. |
Asunto categoría : |
K01 Ciencias forestales - Aspectos generales |
Marc : |
LEADER 02885naa a2200277 a 4500 001 1064593 005 2024-04-17 008 2024 bl uuuu u00u1 u #d 022 $a1007-662X 024 7 $a10.1007/s11676-024-01706-w$2DOI 100 1 $aBAIETTO, A. 245 $aLitterfall production modeling based on climatic variables and nutrient return from stands of Eucalyptusgrandis Hill ex Maiden and Pinustaeda L.$h[electronic resource] 260 $c2024 500 $aArticle history: Received 13 June 2023, Accepted 17 August 2023, Published 21 March 2024. -- Correspondence: Baietto, A.; Forest Department, Faculty of Agronomy, University of the Republic, Montevideo, Uruguay; email:abaietto@fagro.edu.uy -- Corresponding editor: Yanbo Hu. -- Funding: This study was funded by Lumin S.A. and the Agencia Nacional de Investigación e Innovación (ANII) [POS_NAC_2016_1_130479]. -- Supplementary Information: The online version contains supplementary material available at https:// doi. org/ 10. 1007/s11676- 024- 01706-w 520 $aABSTRACT.- Native grasslands in the Pampas of South America are increasingly being replaced by Eucalyptus and Pinus stands. The short rotation regimes used for the stands require high nutrient levels, with litterfall being a major source of nutrient return. To model the litterfall production using climatic variables and assess the nutrient return in 14-year-old Eucalyptusgrandis and Pinustaeda stands, we measured litter production over 2 years, using conical litter traps, and monitored climatic variables. Mean temperature, accumulated precipitation, and mean maximum vapor pressure deficit at the seasonal level influenced litterfall production by E.grandis; seasonal accumulated precipitation and mean maximum temperature affected litterfall by P.taeda. The regression tree modeling based on these climatic variables had great accuracy and predictive power for E.grandis (N = 33; MAE (mean absolute error) = 0.65; RMSE (root mean square error) = 0.91; R2 = 0.71) and P.taeda (N = 108; MAE = 1.50; RMSE = 1.59; R2 = 0.72). The nutrient return followed a similar pattern to litterfall deposition, as well as the order of importance of macronutrients (E.grandis: Ca > N > K > Mg > P; P.taeda: N > Ca > K > Mg > P) and micronutrients (E.grandis and P.taeda: Mn > Fe > Zn > Cu) in both species. This study constitutes a first approximation of factors that affect litterfall and nutrient return in these systems. © Northeast Forestry University 2024. 653 $aAfforestation 653 $aClimate modeling 653 $aLitterfall 653 $aMyrtaceae 653 $aNutrient recycling 653 $aPinaceae 653 $aSISTEMA FORESTAL - INIA 700 1 $aHIRIGOYEN, A. 700 1 $aHERNÁNDEZ, J. 700 1 $aDEL PINO, A. 773 $tJournal of Forestry Research. 2024, Volume 35, e61. https://doi.org/10.1007/s11676-024-01706-w
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| Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
Fecha actual : |
12/11/2015 |
Actualizado : |
09/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
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. |
Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. |
Fecha de publicación : |
2015 |
Fuente / Imprenta : |
Agricultural and Forest Meteorology, 2015, v.214-215, p. 483-493. |
ISSN : |
0168-1923 |
DOI : |
10.1016/j.agrformet.2015.09.013 |
Idioma : |
Inglés |
Notas : |
Article history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015. |
Contenido : |
ABSTRACT.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in the considered sites. Compared to wheat, required levels of [CO2]increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulatingclimate change impacts increase more with temperature than with elevated [CO2].
© 2015 Elsevier B.V. All rights reserved. MenosABSTRACT.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in... Presentar Todo |
Palabras claves : |
Climate change; CROP MODEL; Emulator; MAIZE; Meta-model; MODELIZACIÓN DE LOS CULTIVOS; RICE; Statistical model; WHEAT; Yield. |
Thesagro : |
ARROZ; CAMBIO CLIMÁTICO; MAÍZ; MODELOS ESTADISTICOS; TRIGO. |
Asunto categoría : |
A50 Investigación agraria |
Marc : |
LEADER 05363naa a2201417 a 4500 001 1053856 005 2019-10-09 008 2015 bl uuuu u00u1 u #d 022 $a0168-1923 024 7 $a10.1016/j.agrformet.2015.09.013$2DOI 100 1 $aMARCAIDA, M. 245 $aA statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration. 260 $c2015 500 $aArticle history: Received 6 March 2015 / Received in revised form 29 July 2015 / Accepted 20 September 2015 / Available online 1 October 2015. 520 $aABSTRACT. Ensembles of process-based crop models are increasingly used to simulate crop growth for scenariosof temperature and/or precipitation changes corresponding to different projections of atmospheric CO2concentrations. This approach generates large datasets with thousands of simulated crop yield data. Suchdatasets potentially provide new information but it is difficult to summarize them in a useful way due totheir structural complexities. An associated issue is that it is not straightforward to compare crops and tointerpolate the results to alternative climate scenarios not initially included in the simulation protocols.Here we demonstrate that statistical models based on random-coefficient regressions are able to emulateensembles of process-based crop models. An important advantage of the proposed statistical models isthat they can interpolate between temperature levels and between CO2concentration levels, and canthus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulatedby 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to thesedatasets, and are then used to analyze the variability of the yield response to [CO2] and temperature.Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effectof a temperature increase of +2◦C in the considered sites. Compared to wheat, required levels of [CO2]increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulatingclimate change impacts increase more with temperature than with elevated [CO2]. © 2015 Elsevier B.V. All rights reserved. 650 $aARROZ 650 $aCAMBIO CLIMÁTICO 650 $aMAÍZ 650 $aMODELOS ESTADISTICOS 650 $aTRIGO 653 $aClimate change 653 $aCROP MODEL 653 $aEmulator 653 $aMAIZE 653 $aMeta-model 653 $aMODELIZACIÓN DE LOS CULTIVOS 653 $aRICE 653 $aStatistical model 653 $aWHEAT 653 $aYield 700 1 $aASSENG, S. 700 1 $aEWERT, F. 700 1 $aBASSU, S. 700 1 $aDURAND, J.L. 700 1 $aLI, T. 700 1 $aMARTRE, P. 700 1 $aADAM, M. 700 1 $aAGGARWAL, P.K. 700 1 $aANGULO, C. 700 1 $aBARON, C. 700 1 $aBASSO, B. 700 1 $aBERTUZZI, P. 700 1 $aBIERNATH, C. 700 1 $aBOOGAARD, H. 700 1 $aBOOTE, K.J. 700 1 $aBOUMAN, B. 700 1 $aBREGAGLIO, S. 700 1 $aBRISSON, N. 700 1 $aBUIS, S. 700 1 $aCAMMARANO, D. 700 1 $aCHALLINOR, A.J. 700 1 $aCONFALONIERI, R. 700 1 $aCONIJN, J.G. 700 1 $aCORBEELS, M. 700 1 $aDERYNG, D. 700 1 $aDE SANCTIS, G. 700 1 $aDOLTRA, J. 700 1 $aFUMOTO, T. 700 1 $aGAYDON, D. 700 1 $aGAYLER, S. 700 1 $aGOLDBERG, R. 700 1 $aGRANT, R.F. 700 1 $aGRASSINI, P. 700 1 $aHATFIELD, J.L. 700 1 $aHASEGAWA, T. 700 1 $aHENG, L. 700 1 $aHOEK, S. 700 1 $aHOOKER, J. 700 1 $aHUNT, L.A. 700 1 $aINGWERSEN, J. 700 1 $aIZAURRALDE, R.C. 700 1 $aJONGSCHAAP, R.E.E. 700 1 $aJONES, J.W. 700 1 $aKEMANIAN, R.A. 700 1 $aKERSEBAUM, K.C. 700 1 $aKIM, S.-H. 700 1 $aLIZASO, J. 700 1 $aMÜLLER, C. 700 1 $aNAKAGAWA, H. 700 1 $aNARESH KUMAR, S. 700 1 $aNENDEL, C. 700 1 $aO'LEARY, G.J. 700 1 $aOLESEN, J.E. 700 1 $aORIOL, P. 700 1 $aOSBORNE, T.M. 700 1 $aPALOSUO, T. 700 1 $aPRAVIA, V. 700 1 $aPRIESACK, E. 700 1 $aRIPOCHE, D. 700 1 $aROSENZWEIG, C. 700 1 $aRUANE, A.C. 700 1 $aRUGET, F. 700 1 $aSAU, F. 700 1 $aSEMENOV, M.A. 700 1 $aSHCHERBAK, I. 700 1 $aSINGH, B. 700 1 $aSINGH, U. 700 1 $aSOO, H.K. 700 1 $aSTEDUTO, P. 700 1 $aSTÖCKLE, C. 700 1 $aSTRATONOVITCH, P. 700 1 $aSTRECK, T. 700 1 $aSUPIT, I. 700 1 $aTANG, L. 700 1 $aTAO, F. 700 1 $aTEIXEIRA, E.I. 700 1 $aTHORBURN, P. 700 1 $aTIMLIN, D. 700 1 $aTRAVASSO, M. 700 1 $aRÖTTER, R.P. 700 1 $aWAHA, K. 700 1 $aWALLACH, D. 700 1 $aWHITE, J.W. 700 1 $aWILKENS, P. 700 1 $aWILLIAMS, J.R. 700 1 $aWOLF, J. 700 1 $aYIN, X. 700 1 $aYOSHIDA, H. 700 1 $aZHANG, Z. 700 1 $aZHU, Y. 773 $tAgricultural and Forest Meteorology, 2015$gv.214-215, p. 483-493.
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