Estimating forage quantity and quality under different stress and senescent biomass conditions via spectral reflectance
Assessing forage quantity and quality through remote sensing can facilitate grassland and pasture management. However, the high spatial and temporal variability of canopy conditions may limit the predictive accuracy of models based on reflectance measurements. The objective of this work was to develop this type of models, and to challenge their capacity to predict plant properties under a wide range of environmental conditions. We manipulated Paspalum dilatatum canopies through different stress treatments [flooding, drought, nutrient availability, and control] and by artificially varying the amount of senescent biomass. We measured canopy reflectance and constructed simple models, based on either normalized vegetation indices or a few selected wavebands, to estimate biomass and two variables related to forage quality: proportion of photosynthetic vegetation and biomass C:N ratio. General models satisfactorily predicted plant properties for the whole set of environmental conditions, but failed under specific conditions such as drought [for estimates of plant biomass], fertilization [for estimates of C:N ratio], and different levels of senescent tillers [for estimates of the proportion of photosynthetic vegetation]. Where general models failed, specific models, based on different bands, achieved satisfactory accuracy. The general models performed better when based on a few selected bands than when based on two-band vegetation indices, having better accuracy [higher R2] and parsimony [lower BIC]. However specific models performed similarly for both approaches [similar R2 and BIC]. These results indicate that these plant properties can be predicted from reflectance information under a broad range of conditions, but not for some particular conditions, where ancillary data or more complex models are probably needed to increase predictive accuracy.
| Main Authors: | , , , |
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| Format: | Texto biblioteca |
| Language: | spa |
| Subjects: | ACCURACY ASSESSMENT, ANGIOSPERM, BIOMASS, C [PROGRAMMING LANGUAGE], CANOPY, CANOPY REFLECTANCE, DROUGHT, ENVIRONMENTAL CONDITIONS, ESTIMATION, FORAGE, MODELING, NDVI, NUTRIENT AVAILABILITY, PARSIMONY ANALYSIS, PARTICULAR CONDITION, PASPALUM DILATATUM, PASTURE MANAGEMENT, PLANT COMMUNITY, PREDICTION, PREDICTIVE ACCURACY, QUALITATIVE ANALYSIS, QUANTITATIVE ANALYSIS, REFLECTION, REMOTE SENSING, SPATIAL AND TEMPORAL VARIABILITY, SPECTRAL REFLECTANCE, SPECTRAL REFLECTANCES, VEGETATION, |
| Online Access: | http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=47110 |
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| Summary: | Assessing forage quantity and quality through remote sensing can facilitate grassland and pasture management. However, the high spatial and temporal variability of canopy conditions may limit the predictive accuracy of models based on reflectance measurements. The objective of this work was to develop this type of models, and to challenge their capacity to predict plant properties under a wide range of environmental conditions. We manipulated Paspalum dilatatum canopies through different stress treatments [flooding, drought, nutrient availability, and control] and by artificially varying the amount of senescent biomass. We measured canopy reflectance and constructed simple models, based on either normalized vegetation indices or a few selected wavebands, to estimate biomass and two variables related to forage quality: proportion of photosynthetic vegetation and biomass C:N ratio. General models satisfactorily predicted plant properties for the whole set of environmental conditions, but failed under specific conditions such as drought [for estimates of plant biomass], fertilization [for estimates of C:N ratio], and different levels of senescent tillers [for estimates of the proportion of photosynthetic vegetation]. Where general models failed, specific models, based on different bands, achieved satisfactory accuracy. The general models performed better when based on a few selected bands than when based on two-band vegetation indices, having better accuracy [higher R2] and parsimony [lower BIC]. However specific models performed similarly for both approaches [similar R2 and BIC]. These results indicate that these plant properties can be predicted from reflectance information under a broad range of conditions, but not for some particular conditions, where ancillary data or more complex models are probably needed to increase predictive accuracy. |
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