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. 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.
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| Subjects: | GROWING SEASON, NDVI, NPP, PATAGONIA, PHENOLOGY, PRECIPITATION, SEASONALITY, TEMPORAL MODELS, |
| Online Access: | http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=47691 http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= |
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KOHA-OAI-AGRO:476912022-10-26T12:04:26Zhttp://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=47691http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=AAGRisks of neglecting phenology when assessing climatic controls of primary productionBandieri, Lucas M.Fernández, Roberto JavierBisigato, Alejandro Jorgetextengapplication/pdfWe 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.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.GROWING SEASONNDVINPPPATAGONIAPHENOLOGYPRECIPITATIONSEASONALITYTEMPORAL MODELSEcosystems |
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GROWING SEASON NDVI NPP PATAGONIA PHENOLOGY PRECIPITATION SEASONALITY TEMPORAL MODELS GROWING SEASON NDVI NPP PATAGONIA PHENOLOGY PRECIPITATION SEASONALITY TEMPORAL MODELS |
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GROWING SEASON NDVI NPP PATAGONIA PHENOLOGY PRECIPITATION SEASONALITY TEMPORAL MODELS GROWING SEASON NDVI NPP PATAGONIA PHENOLOGY PRECIPITATION SEASONALITY TEMPORAL MODELS Bandieri, Lucas M. Fernández, Roberto Javier Bisigato, Alejandro Jorge Risks of neglecting phenology when assessing climatic controls of primary production |
| description |
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. |
| format |
Texto |
| topic_facet |
GROWING SEASON NDVI NPP PATAGONIA PHENOLOGY PRECIPITATION SEASONALITY TEMPORAL MODELS |
| author |
Bandieri, Lucas M. Fernández, Roberto Javier Bisigato, Alejandro Jorge |
| author_facet |
Bandieri, Lucas M. Fernández, Roberto Javier Bisigato, Alejandro Jorge |
| author_sort |
Bandieri, Lucas M. |
| title |
Risks of neglecting phenology when assessing climatic controls of primary production |
| title_short |
Risks of neglecting phenology when assessing climatic controls of primary production |
| title_full |
Risks of neglecting phenology when assessing climatic controls of primary production |
| title_fullStr |
Risks of neglecting phenology when assessing climatic controls of primary production |
| title_full_unstemmed |
Risks of neglecting phenology when assessing climatic controls of primary production |
| title_sort |
risks of neglecting phenology when assessing climatic controls of primary production |
| url |
http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=47691 http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= |
| work_keys_str_mv |
AT bandierilucasm risksofneglectingphenologywhenassessingclimaticcontrolsofprimaryproduction AT fernandezrobertojavier risksofneglectingphenologywhenassessingclimaticcontrolsofprimaryproduction AT bisigatoalejandrojorge risksofneglectingphenologywhenassessingclimaticcontrolsofprimaryproduction |
| _version_ |
1756046811440611328 |