Abstract
Abstract: The objective of this work was to identify the spectral bands, vegetation indices, and periods of the canola crop season in which the correlation between spectral data and biophysical indicators (total shoot dry matter and grain yield) is most significant. The experiment was carried out during the 2013 and 2014 crop seasons at Embrapa Trigo, in the state of Rio Grande do Sul, Brazil. A randomized complete block design was used, with four replicates, and the treatments consisted of five doses of nitrogen topdressing. Plant dry matter, grain yield, and phenology were measured. The canola spectral response was evaluated by measuring the canola canopy reflectance using a spectroradiometer, and, with this data, the SR, NDVI, EVI, SAVI, and GNDVI vegetation indices were determined. Pearson’s correlations between the spectral and biophysical variables of canola showed that the red (620 to 670 nm) and near-infrared (841 to 876 nm) bands were the best to estimate the dry matter. The vegetative period is the most indicated to obtain the most significant correlations for canola. All the used vegetation indices are adequate for estimating the dry matter and grain yield of canola.
Highlights
IntroductionGrain yield can be predicted by agrometeorologicalspectral models in which the spectral variable is usually represented by vegetation indices, considered remote indicators of vegetation vigor
Research programs should focus on grain yield, since, from an economic viewpoint, it is the most important variable to the farmer because it determines the economic return of the crop.Grain yield can be predicted by agrometeorologicalspectral models in which the spectral variable is usually represented by vegetation indices, considered remote indicators of vegetation vigor
The accumulated shoot dry matter was higher for all treatments in the 2013 crop year compared with 2014 (Table 1)
Summary
Grain yield can be predicted by agrometeorologicalspectral models in which the spectral variable is usually represented by vegetation indices, considered remote indicators of vegetation vigor. The visible and near infrared due to the accumulation of biomass (Jensen, 2009). The increased biomass causes the reflectance to increase due to increased multiple scattering and additive reflectance (Jensen, 2009; Ponzoni et al, 2012). Agrometeorological-spectral models, with some variants in the selected terms and the type of adjusted equations, have been proposed in the literature (Rosa et al, 2010; Mabilana et al, 2012). Despite the importance of grain yield studies, models to estimate canola grain yields adjusted to the specific management and climate conditions of the state of Rio Grande do Sul are not yet available in the literature
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