Validation of a commercial collar‐based sensor for monitoring eating and ruminating behaviour of dairy cows

The use of sensor technologies to monitor cows’ behavior is becoming commonplace in the context of dairy production. This study aimed at validating a commercial collar‐based sensor system, the AFICollar® (Afimilk, Kibbutz Afikim, Israel), designed to monitor dairy cattle feeding and ruminating behavior. Additionally, the performances of two versions of the software for behavior classification, the current software AFIfarm® 5.4 and the updated version AFIfarm® 5.5, were compared. The study involved twenty Holstein‐Friesian cows fitted with the collars. To evaluate the sensor performance under different feeding scenarios, the animals were divided into four groups and fed three different types of feed (total mixed ration, long hay, animals allowed to graze). Recordings of hourly rumination and feeding time produced by the sensor were compared with visual observation by scan sampling at 1 minute intervals using Spearman correlation, concordance correlation coefficient (CCC), Bland–Altman plots and linear mixed models for assessing the precision and accuracy of the system. The analyses confirmed that the updated software version V5.5 produced better detection performance than the current V5.4. The updated software version produced high correlations between visual observations and data recorded by the sensor for both feeding (r = 0.85, CCC = 0.86) and rumination (r = 0.83, CCC = 0.86). However, the limits of agreement for both behaviors remained quite wide (feeding: −19.60 min/h, 17.46 min/h; rumination: −15.80 min/h, 15.00 min/h). Type of feed did not produce significant effects on the agreement between visual observations and sensor recordings. Overall, the results indicate that the system can provide farmers with adequately accurate data on feeding and rumination time, and can be used to support herd management decisions. Despite all this, the precision of the system remained relatively lim-ited, and should be improved with further developments in the classification algorithm

Validation of a commercial collar‐based sensor for monitoring eating and ruminating behaviour of dairy cows

The use of sensor technologies to monitor cows’ behavior is becoming commonplace in the context of dairy production. This study aimed at validating a commercial collar-based sensor system, the AFICollar® (Afimilk, Kibbutz Afikim, Israel), designed to monitor dairy cattle feeding and ruminating behavior. Additionally, the performances of two versions of the software for behavior classification, the current software AFIfarm® 5.4 and the updated version AFIfarm® 5.5, were compared. The study involved twenty Holstein-Friesian cows fitted with the collars. To evaluate the sensor performance under different feeding scenarios, the animals were divided into four groups and fed three different types of feed (total mixed ration, long hay, animals allowed to graze). Recordings of hourly rumination and feeding time produced by the sensor were compared with visual observation by scan sampling at 1 minute intervals using Spearman correlation, concordance correlation coefficient (CCC), Bland–Altman plots and linear mixed models for assessing the precision and accuracy of the system. The analyses confirmed that the updated software version V5.5 produced better detection performance than the current V5.4. The updated software version produced high correlations between visual observations and data recorded by the sensor for both feeding (r = 0.85, CCC = 0.86) and rumination (r = 0.83, CCC = 0.86). However, the limits of agreement for both behaviors remained quite wide (feeding: −19.60 min/h, 17.46 min/h; rumination: −15.80 min/h, 15.00 min/h). Type of feed did not produce significant effects on the agreement between visual observations and sensor recordings. Overall, the results indicate that the system can provide farmers with adequately accurate data on feeding and rumination time, and can be used to support herd management decisions. Despite all this, the precision of the system remained relatively limited, and should be improved with further developments in the classification algorithm

Building performance analysis of a dairy factory in South Iraq: appraisal of a local bio-based envelope

Received: February 4th, 2021 ; Accepted: April 24th, 2021 ; Published: April 29th, 2021 ; Correspondence: [email protected] have a relevant impact on the environment, and building materials cause environmental impacts during all life cycle stages: production, utilization, management and demolition. The global request for more efficient buildings with less environmental impacts has grown during the last years. Among various technologies, thermal insulation has proven to be helpful in reducing emissions by increasing energy conservation. This paper intends to show how the Building Performance Analysis (BPA) supports the decision-making process in many areas where common insulation materials are not available and there is a general reluctance to use local natural materials. A building located in the city of Al Chubaish in Dhi Qar Province in Iraq is examined as a case study. The construction is designed for processing buffalo milk. It was built in the first decade of the century, during the Iraqi conflict, using only the materials available at that time, most of which, concrete bricks, mortar and plaster. Currently, this dairy factory is a very inefficient structure in terms of energy saving. But because its elementary form, it is a perfect example to investigate how a simple exterior wall insulation can improve building performance in extreme environmental conditions. Accordingly, two different models have been created. One is the replica of the real building without any upgrading. The second instead presents a thermal insulation realized with reed bio-based material locally available. Through advanced simulation engines and building performance analysis data integrated into Autodesk Revit, each model has been tested to identify significant improvements in terms of energy savings in this particular stressed background

Comparison of spatial-temporal analysis modelling with purely spatial analysis modelling using temperature data obtained by remote sensing

Received: March 25th, 2021 ; Accepted: May 20th, 2021 ; Published: October 5th, 2021 ; Correspondence: [email protected] in climatic elements directly affect the productivity of agricultural activities. Temperature is one of the climatic elements that varies in space and time.Therefore, understanding spatial variations in temperature is essential for many activities. Given the above, the objective of this work was to compare the performance of the proposed spatiotemporal analysis model with that of purely spatial analysis using temperature data obtained by remote sensing. The experimental data were arranged in a grid with 403 spatial locations, with 22 samples collected in a 24-hour period. The statistical software R Core Team (2020) was used to perform the analysis. The packages used in the analyses were ‘geoR’, ‘CompRandFld’, ‘scatterplot3d’, and ‘fields’. For making the maps, the software ArcGIS was used. The behavioural analysis of spatiotemporal dependence indicated, through the covariogram graph of the data, that there is a strong spatial dependence. For the cases of purely spatial analysis of phenomena, a separate spatial model for each time is justified because this type of model presents a smaller prediction error and requires simpler processing than the space-time model. It was possible to compare the space-time analysis with the purely spatial analysis using temperature data obtained by remote sensing images. The data modelled with the purely spatial analysis had, on average, lower error than those with the space-time model