Risks of neglecting phenology when assessing climatic controls of primary production
We evaluated the effect that integrating annual aboveground net primary production (ANPP) along different 12-month periods has on temporal models of productivity (ANPP as a linear function of annual precipitation). We studied Argentinean Patagonia, which encompasses a variety of climates and biomes....
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| Otros Autores: | , |
| Formato: | Artículo |
| Lenguaje: | Inglés |
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| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2020bandieri.pdf LINK AL EDITOR |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
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| 022 | |a 1432-9840 | ||
| 024 | |a 10.1007/s10021-019-00393-7 | ||
| 040 | |a AR-BaUFA | ||
| 100 | 1 | |a Bandieri, Lucas M. |u CONICET - Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC). Puerto Madryn, Chubut, Argentina. |9 69618 | |
| 245 | 0 | 0 | |a Risks of neglecting phenology when assessing climatic controls of primary production |
| 520 | |a We evaluated the effect that integrating annual aboveground net primary production (ANPP) along different 12-month periods has on temporal models of productivity (ANPP as a linear function of annual precipitation). We studied Argentinean Patagonia, which encompasses a variety of climates and biomes. Using MODIS normalized difference vegetation index (NDVI) to estimate green biomass, we assessed the date of maximum annual NDVI for 2000–2016. One quarter of Patagonia (West/South region) exhibited a well-defined seasonality, with maximum NDVI during spring–summer, whereas the rest (Central/East region) showed a much less well-defined maximum NDVI, generally during fall. Then we calculated temporal models for each pixel, considering both annual and seasonal precipitation (PPT), in two ways: (i) centered models, integrating NDVI for a period centered at the actual growing season, that is, July–June for West/South region and January–December for Central/East region, and (ii) displaced models, switching the NDVI integration period. Our results indicate that, with the centered models, 84% of the Central/East region exhibited significant temporal models, but only 52% of the West/South region did. For the displaced models, 60% (40%) of pixels of Central/ East (West/South) region changed their best predictor of ANPP. In general, the best predictor changed from current-year PPT to current-plusprevious- year PPT or from current-year fall to previous-year fall. Our results suggest that more attention must be paid in choosing the integration period for annual ANPP. This is more than a formal matter since the putative best predictor of ANPP can dramatically change depending on the assumed phenology. | ||
| 653 | |a GROWING SEASON | ||
| 653 | |a NDVI | ||
| 653 | |a NPP | ||
| 653 | |a PATAGONIA | ||
| 653 | |a PHENOLOGY | ||
| 653 | |a PRECIPITATION | ||
| 653 | |a SEASONALITY | ||
| 653 | |a TEMPORAL MODELS | ||
| 700 | 1 | |9 6385 |a Fernández, Roberto Javier |u Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. |u CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. | |
| 700 | 1 | |a Bisigato, Alejandro Jorge |u CONICET - Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC). Puerto Madryn, Chubut, Argentina. |u Universidad Nacional de la Patagonia San Juan Bosco (UNPSJB). Puerto Madryn, Chubut, Argentina. |9 8005 | |
| 773 | 0 | |t Ecosystems |w SECS000773 |g vol.23 (2020), p.164-174, tbls., grafs., mapas | |
| 856 | |f 2020bandieri |i en reservorio |q application/pdf |u http://ri.agro.uba.ar/files/intranet/articulo/2020bandieri.pdf |x ARTI202003 | ||
| 856 | |z LINK AL EDITOR |u https://link.springer.com | ||
| 942 | |c ARTICULO | ||
| 942 | |c ENLINEA | ||
| 976 | |a AAG | ||