Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2023
Forfattere
Isabell EischeidSammendrag
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Forfattere
Andres Perea Lara Macon Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung'e Huiping Cao Andres F. Cibils Richard Estell Glenn Duff Santiago A. UtsumiSammendrag
Detection of parturition of rangeland cows remotely may be possible using low cost LoRa WAN monitoring systems that are capable of logging and transmitting cow activity and position data in real time. This study evaluated candidate algorithms for early detection of parturition using longitudinal data of cow activity and position collected by GPS and triaxial accelerometers. Trials were conducted at the USDA Jornada Experimental Range from November to December 2022. Five Raramuri Criollo and five Angus x Hereford mature cows were equipped with LoRa WAN tracking collars instrumented with GPS and triaxial accelerometers and monitored through late gestation (> 7 months) while grazing rangeland pastures of 1,230 and 2,200 ha, respectively. Animal location (latitude and longitude) and activity count (Ac) obtained from GPS and accelerometers data, respectively, were collected by receiving stations that transmitted data in real time through a LoRa WAN network. Collars transmitted GPS positions at one-hour intervals and Ac data at two-minute intervals. An operator routinely inspected focal cows in herds to register parturition within approximately 12 h accuracy. Sensor data for 21 days prior to calving were processed to calculate distance traveled (m/h) and activity rate (Ac/h). For each hour interval, the adjusted activity Index IN = activity/distance (Ac/m) was computed to disentangle motion changes not associated with walking activity. Two algorithms were tested. The first considered the temporal deviation (D) of IN for a given hour (X0), compared with the average IN of the same hour in the previous seven days: D = INX0 /(INX-1+ INX-2 + …+ INX-7)/7). The second considered the normalized probability (N) of D for a given hour (X0) compared with the same hour over the previous seven days: N = (INX0-(INX-1+ INX-2 + …+ INX-7)/7)/sd.(INX-0, INX-1, …, INX-7). A threshold for high probability of calving was set when at least three consecutive hours with D >3 or N >0.95 were detected. Both algorithms correctly triggered alerts on actual calving days. Thus, lack of detection or false detections of calving indicated that the sensitivity and specificity for calving detection were both 100%. The normalized method (N) triggered delayed calving alerts in two cases. Furthermore, greater (P < 0.05) number of consecutive hours with D > 3 (5.6 ± 2.1) around actual calving time were detected vs. the number of consecutive hours with N > 0.95 (3.9 ± 1.2), suggesting that the former algorithm was also able to detect longer duration of behaviors associated with calving. Results indicate possibilities for remote detection of the onset and duration of calving behavior (parturition + first nursing hours) of beef cows managed on large rangeland pastures that impose operational challenges for visual inspection of cows during calving. Further tests with a greater number of cows and management systems would be needed to confirm this hypothesis.
Forfattere
Andres Perea Lara Macon Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung'e Sara Campa Madrid Huiping Cao Andres F. Cibils Richard Estell Glenn Duff Santiago A. UtsumiSammendrag
Monitoring cattle on rangelands is a daunting task that can be improved by using wearable sensors that are capable of transmitting motion and position data in real time and at low cost. This study tested the performance of machine learning (ML) classifiers to discriminate among foraging activities of cows based on triaxial accelerometer data collected in real-time by LoRa WAN networks. Trials were conducted at the New Mexico State University Chihuahuan Desert Rangeland Research Center and the USDA Jornada Experimental Range in Doña Ana County, NM. A total of 24 Brangus, Brahman, Raramuri Criollo and Angus x Hereford mature cows fitted with LoRa WAN tracking collars housing GPS and triaxial accelerometers were monitored across four periods during the 2022 summer and fall seasons on desert rangeland pastures. Trackers integrated and transmitted activity count (Ac) data from accelerometers at one-minute intervals. Video recording of focal cows (n = 24) was undertaken during daylight hours (0630 to 2000 h) from a distance of ~30 m to minimize interference with natural behaviors. A total of 168 hours of video were recorded and inspected by an experienced observer to label video files according to a classification tree of four main activities: grazing (GR), walking (WA), resting (RE) and ruminating (RU), and two states: active (AC) or static (ST). Individualized activities and states were considered when cows performed the same predefined activity or state for more than 30 secs. Retrieved sensor data from collar trackers were labeled by state and activity according to labels collected from video records. This classification resulted in a dataset containing 9,222 events, including 3,928 for GR, 2,286 for WA, 2,032 for RE, and 976 for RU, as well as 6,214 labels for AC and 3,008 labels for ST. Deep learning through Multilayer Perceptron Classifiers (MLPC) were coded and implemented using a split configuration of 70% of the data for training and 30% for testing, respectively. In preliminary runs, models had reduced ability to properly discriminate among RE (F1 = 0.42) and RU (F1 = 0.43) Thus, RE and RU were merged on subsequent tests, resulting in 3,928 labels for GR, 2,286 labels for WA, and 3,008 labels for merged RE. Deep learning models successfully classified between AC vs. ST behavior with an overall F1 performance score of 0.96. Further use of the same deep learning models successfully classified among GR, WA, and RE activities with an overall F1 performance score of 0.91, suggesting satisfactory application of the trained models to monitor cattle grazing activities on desert rangeland.
Sammendrag
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Sammendrag
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Forfattere
Lars Aksel OpsahlSammendrag
In Norway we now get more up-to-date maps for land resource map (AR5), because the domain experts on agriculture in the municipalities in Norway have got access to a easy to use client. This system includes a simple web browser client and a database built on Postgis Topology. In this talk we will focus on, what is it with Postgis Topology that makes it easier to build user friendly and secure tools for updating of land resource maps like AR5. We will also say a couple of words about advantages related to traceability and data security, when using Postgis Topology. In another project, where we do a lot ST_Intersection and ST_Diff on many big Simple Feature layers that covers all of Norway, we have been struggling with Topology exceptions, wrong results and performance for years. Last two years we also tested JTS OverlayNG, but we still had problems. This year we are switching to Postgis Topology and tests so far are very promising. We also take a glance on this project here in this talk. A Postgis Topology database modell has normalised the data related to borders and surfaces as opposed to Simple Feature where this is not the case. Simple Feature database modell may be compared to not using foreign keys between students and classes in a database model, but just using a standard spreadsheet model where each student name are duplicated in each class they attend. URL’s that relate this talk https://gitlab.com/nibioopensource/pgtopo_update_gui https://gitlab.com/nibioopensource/pgtopo_update_rest https://gitlab.com/nibioopensource/pgtopo_update_sql https://gitlab.com/nibioopensource/resolve-overlap-and-gap
Forfattere
Jostein FrydenlundSammendrag
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Forfattere
Jostein FrydenlundSammendrag
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Forfattere
Jostein FrydenlundSammendrag
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Forfattere
Anne-Grete Buseth BlankenbergSammendrag
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