无损测定热带牧场牧草质量和营养状况的方法

Patrick Bezerra Fernandes, Camila Alves dos Santos, Antonio Leandro Chaves Gurgel, Lucas Ferreira Gonçalves, Natália Nogueira Fonseca, Rafaela Borges Moura, Kátia Aparecida de Pinho Costa, Tiago do Prado Paim
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引用次数: 0

摘要

热带草地牧草可用性的量化通常以破坏性和耗时的方式完成,包括切割,称重和等待干燥。为了加快这一过程,可以使用非破坏性的方法,例如配备高清摄像机的无人机(uav),移动设备图像,以及使用归一化植被指数(NDVI)。然而,这些方法在热带牧场未得到充分利用。本文综述了热带牧场牧草可用性和质量远程预测工具的应用现状。很少有出版物涉及使用非破坏性方法来估计主要热带禾草(Megathyrsus maximus;Urochloa spp)。此外,这些研究没有考虑每个品种的育性需求和管理对分蘖表型可塑性的影响。为了准确估计牧草可利用性和合理管理牧场,有必要将远程方法与现场土壤参数采集相结合。通过这种方式,将有可能训练机器学习模型,以获得对国内反刍动物生产的饲料可用性的精确可靠的估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-Destructive Methods Used to Determine Forage Mass and Nutritional Condition in Tropical Pastures
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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