Abstract

Pasture management studies seldom use remote sensing, especially for Andropogon gayanus cv. Planaltina. This work aimed to investigate whether a single-chip digital camera converted into multispectral equipment could measure and perceive differences in the normalized difference vegetation index (NDVI) in Andropogon gayanus cv. Planaltina grass subjected to different doses of nitrogen. In a greenhouse with a randomized block design, the research team subjected the cultivar to 5 different doses of nitrogen (treatments), with four replications. The group compared the NDVI measurements of canopies carried out with a GreenSeeker® HCS-100 active optical sensor and a Canon PowerShot A495 single-chip camera that was converted into a multispectral device by replacing the glass filter that blocks the passage of the infrared by a filter that allows the passage of the red and near infrared wavelengths. Both measurements were correlated with nitrogen doses, stem and leaf blades dry matter and dead material production, and cultivar height. The study concluded that the multispectral camera, as well as the active optical sensor, measured the NDVI and noticed differences in the production of stem, leaf blades and dead material dry matter, and Andropogon gayanus cv. Planaltina plant height when subjected to different doses of nitrogen.

Highlights

  • Brazil stands out in the world agribusiness as one of the top food producers and exporters

  • Farmers determine the availability of dry matter, which will be the reference for forage supply estimates in the entire pasture (DEMINICIS, 2015), and, based on it, they adjust the stocking rate

  • We suggest that the normalized difference vegetation index (NDVI) values obtained by the camera can assist in the spatial and temporal monitoring of pastures

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Summary

Introduction

Brazil stands out in the world agribusiness as one of the top food producers and exporters In this context, Brazilian cattle breeding plays an essential role since it has the second largest herd in the world. Farmers use estimates of available grass biomass to determine stocking rates for grazing animals (SANTOS et al, 2008) This prediction is usually performed by sampling the grass, in a chart with a known area. In this sample, farmers determine the availability of dry matter, which will be the reference for forage supply estimates in the entire pasture (DEMINICIS, 2015), and, based on it, they adjust the stocking rate

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