Grid Grazing:Virtual Fencing and Uav for Innovative Grazing Management

Hamidi, Dina; Hütt, Christoph; Komainda, Martin; Grinnell, Natascha Alexandria; Horn, Juliane; Riesch, Friederike; Hamidi, Masud; Traulsen, Imke; Isselstein, J.

doi:10.2139/ssrn.4329495

Cited by 1 publication

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“…Precision livestock farming (PLF) has been proposed as an innovative approach to meeting these multifaceted challenges, particularly in managing cattle dairy and meat production more effectively 4 . Within PLF, sensor technologies are becoming indispensable for optimizing grazing management and improving pasture utilization 5 , 6 . In this respect, virtual fencing has been proposed as one major advantage of facilitated grazing 7 .…”

Section: Introductionmentioning

confidence: 99%

UAV LiDAR-based grassland biomass estimation for precision livestock management

Hütt,

Isselstein,

Komainda

et al. 2024

J. Appl. Rem. Sens.

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We present an approach for grassland management using uncrewed aerial vehicles (UAV) LIDAR data and statistical modeling techniques integrated within a softwarebased multi-level information system (SMI). The primary objective is to utilize UAV LiDAR data and statistical modeling techniques within an SMI to accurately estimate compressed sward height (CSH) and above-ground biomass for precision farming applications. As a case study, four UAV LiDAR flights were conducted over rotational grazing farmland, and the collected data were processed to a point cloud. A statistical model was developed to estimate CSH values (R 2 ¼ 0.59, RMSE = 5.9 cm) using LiDAR metrics of the point cloud data. In addition, destructive sampling of grassland facilitated the calibration process, enabling the modeling of biomass based on the CSH values, specifically expressed as above-ground herbage dry biomass (R 2 ¼ 0.89, RMSE ¼ 0.2669 Mg ha −1 ). The collected data further enabled the approximation of biomass across the entire area of interest, which covered ∼200 ha, utilizing a 2.5 × 2.5 m polygon grid. The data were subsequently transferred to an SMI, which operates on the same grid and complements the information, thus offering a comprehensive foundation for decision-making, optimizing grazing systems, and efficient resource allocation. We contribute to advancing precision farming and sustainable grassland management.

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Section: Introductionmentioning

confidence: 99%