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- ItemDynamic cooling strategy based on individual animal response mitigated heat stress in dairy cows(Amsterdam : Elsevier, 2020) Levit, H.; Pinto, S.; Amon, T.; Gershon, E.; Kleinjan-Elazary, A.; Bloch, V.; Ben Meir, Y.A.; Portnik, Y.; Jacoby, S.; Arnin, A.; Miron, J.; Halachmi, I.Technological progress enables individual cow's temperatures to be measured in real time, using a bolus sensor inserted into the rumen (reticulorumen). However, current cooling systems often work at a constant schedule based on the ambient temperature and not on monitoring the animal itself. This study hypothesized that tailoring the cooling management to the cow's thermal reaction can mitigate heat stress. We propose a dynamic cooling system based on in vivo temperature sensors (boluses). Thus, cooling can be activated as needed and is thus most efficacious. A total of 30 lactating cows were randomly assigned to one of two groups; the groups received two different evaporative cooling regimes. A control group received cooling sessions on a preset time-based schedule, the method commonly used in farms; and an experimental group, which received the sensor-based (SB) cooling regime. Sensor-based was changed weekly according to the cow's reaction, as reflected in the changes in body temperatures from the previous week, as measured by reticulorumen boluses. The two treatment groups of cows had similar milk yields (44.7 kg/d), but those in the experimental group had higher milk fat (3.65 vs 3.43%), higher milk protein (3.23 vs 3.13%), higher energy corrected milk (ECM, 42.84 vs 41.48 kg/d), higher fat corrected milk 4%; (42.76 vs 41.34 kg/d), and shorter heat stress duration (5.03 vs 9.46 h/day) comparing to the control. Dry matter intake was higher in the experimental group. Daily visits to the feed trough were less frequent, with each visit lasting longer. The sensor-based cooling regime may be an effective tool to detect and ease heat stress in high-producing dairy cows during transitional seasons when heat load can become severe in arid and semi-arid zones.
- ItemIn-situ fruit analysis by means of LiDAR 3D point cloud of normalized difference vegetation index (NDVI)(Amsterdam [u.a.] : Elsevier, 2023) Tsoulias, Nikos; Saha, Kowshik Kumar; Zude-Sasse, ManuelaA feasible method to analyse fruit at the tree is requested in precise production management. The employment of light detection and ranging (LiDAR) was approached aimed at measuring the number of fruit, quality-related size, and ripeness-related chlorophyll of fruit skin. During fruit development (65 – 130 day after full bloom, DAFB), apples were harvested and analysed in the laboratory (n = 225) with two LiDAR laser scanners measuring at 660 and 905 nm. From these two 3D point clouds, the normalized difference vegetation index (NDVILiDAR) was calculated. The correlation analysis of NDVILiDAR and chemically analysed fruit chlorophyll content showed R2 = 0.81 and RMSE = 3.63 % on the last measuring date, when fruit size reached 76 mm. The method was tested on 3D point clouds of 12 fruit trees measured directly in the orchard, during fruit growth on five measuring dates, and validated with manual fruit analysis in the orchard (n = 4632). Point clouds of individual apples were segmented from 3D point clouds of trees and fruit NDVILiDAR were calculated. The non-invasively obtained field data showed good calibration performance capturing number of fruit, fruit size, fruit NDVILiDAR, and chemically analysed chlorophyll content of R2 = 0.99, R2 = 0.98 with RMSE = 3.02 %, R2 = 0.65 with RMSE = 0.65 %, R2 = 0.78 with RMSE = 1.31 %, respectively, considering the related reference data at last measuring date 130 DAFB. The new approach of non-invasive laser scanning provided physiologically and agronomically valuable time series data on differences in fruit chlorophyll affected by the leaf area to number of fruit and leaf area to fruit fresh mass ratios. Concluding, the method provides a tool for gaining production-relevant plant data for, e.g., crop load management and selective harvesting by harvest robots.