Yerba mate (Ilex paraguariensis) as a source of antioxidants with soybean grain in supplementation of lactating ewes reared in tropical pastures

Soares, Évelyn Silva de Melo; Ítavo, Camila Celeste Brandão Ferreira; Ítavo, L. C. V.; Nazário, Carlos Eduardo Domingues; Melo, Gleice Kelli Ayardes de; Arco, Thais Fernanda Farias de Souza; Miguel, Aline Aparecida da Silva; Godoy, C. de; Andrade, Priscila Bernardo de; Osório, J.A.C.; Vital, Ana Carolina Pelaes; Santos, Geraldo Tadeu dos

doi:10.1007/s11250-022-03427-w

Cited by 2 publications

(1 citation statement)

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“…[1][2][3][4]. Additionally, destructive methods are also employed to assist in obtaining the accumulation rate and availability of forage mass in research assessing the effect of different defoliation intensities on animal performance in continuous stocking, rotational stocking, and integrated production systems [5][6][7][8][9], as well as in research on feed supplements (e.g., mineral, energy, protein) with forage for ruminant production [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Methods Used to Determine Forage Mass and Nutritional Condition in Tropical Pastures

Fernandes,

Santos,

Gurgel

et al. 2023

AgriEngineering

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The quantification of forage availability in tropical grasses is generally done in a destructive and time-consuming manner, involving cutting, weighing, and waiting for drying. To expedite this process, non-destructive methods can be used, such as unmanned aerial vehicles (UAVs) equipped with high-definition cameras, mobile device images, and the use of the normalized difference vegetation index (NDVI). However, these methods have been underutilized in tropical pastures. A literature review was conducted to present the current state of remote tools’ use in predicting forage availability and quality in tropical pastures. Few publications address the use of non-destructive methods to estimate forage availability in major tropical grasses (Megathyrsus maximus; Urochloa spp.). Additionally, these studies do not consider the fertility requirements of each cultivar and the effect of management on the phenotypic plasticity of tillers. To obtain accurate estimates of forage availability and properly manage pastures, it is necessary to integrate remote methods with in situ collection of soil parameters. This way, it will be possible to train machine learning models to obtain precise and reliable estimates of forage availability for domestic ruminant production.

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