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 devel...

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Otros Autores: Durante, Martín, Oesterheld, Martín, Piñeiro, Gervasio, Vassallo, María Mercedes
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Lenguaje:Español
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Acceso en línea:http://ri.agro.uba.ar/files/intranet/articulo/2014durante1.pdf
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Aporte de:Registro referencial: Solicitar el recurso aquí
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245 1 0 |a Estimating forage quantity and quality under different stress and senescent biomass conditions via spectral reflectance 
520 |a 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. 
653 0 |a ACCURACY ASSESSMENT 
653 0 |a ANGIOSPERM 
653 0 |a BIOMASS 
653 0 |a C [PROGRAMMING LANGUAGE] 
653 0 |a CANOPY 
653 0 |a CANOPY REFLECTANCE 
653 0 |a DROUGHT 
653 0 |a ENVIRONMENTAL CONDITIONS 
653 0 |a ESTIMATION 
653 0 |a FORAGE 
653 0 |a MODELING 
653 0 |a NDVI 
653 0 |a NUTRIENT AVAILABILITY 
653 0 |a PARSIMONY ANALYSIS 
653 0 |a PARTICULAR CONDITION 
653 0 |a PASPALUM DILATATUM 
653 0 |a PASTURE MANAGEMENT 
653 0 |a PLANT COMMUNITY 
653 0 |a PREDICTION 
653 0 |a PREDICTIVE ACCURACY 
653 0 |a QUALITATIVE ANALYSIS 
653 0 |a QUANTITATIVE ANALYSIS 
653 0 |a REFLECTION 
653 0 |a REMOTE SENSING 
653 0 |a SPATIAL AND TEMPORAL VARIABILITY 
653 0 |a SPECTRAL REFLECTANCE 
653 0 |a SPECTRAL REFLECTANCES 
653 0 |a VEGETATION 
700 1 |a Durante, Martín  |9 26775 
700 1 |9 8019  |a Oesterheld, Martín 
700 1 |9 22554  |a Piñeiro, Gervasio 
700 1 |a Vassallo, María Mercedes  |9 22854 
773 |t International Journal of Remote Sensing  |g vol.35, no.9 (2014), p.2963-2981 
856 |u http://ri.agro.uba.ar/files/intranet/articulo/2014durante1.pdf  |i En reservorio  |q application/pdf  |f 2014durante1  |x MIGRADOS2018 
856 |u http://www.tandfonline.com/  |x MIGRADOS2018  |z LINK AL EDITOR 
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