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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

Sammendrag

Avlingsnivået i økologisk dyrka eng er avhengig av ytre vekstfaktorar som jord og vêr, både om vinteren og i vekstsesongen. Vidare er botanisk samansetjing i enga, særleg innhald av kløver, gjødslingsnivå, alder på enga og tal slåttar viktig. Avlingskvaliteten er i stor grad påverka av dei same faktorane. Tidleg førsteslått, hyppig slått og høg andel kløver gir høg fôrverdi, høgt proteininnhald og høg proteinavling.

Sammendrag

Kløverinnslaget er viktig for avlingsmengd og proteininnhald i økologisk dyrka eng. Kløver har også høg fordøyelegheit. Kyr et gjerne kløver og har høgare fôropptak og mjølkeproduksjon når surfôret er kløverrikt. På grunn av høgt proteininnhald i kløver, kan proteinutnyttinga i mjølkeproduksjonen gå ned med aukande innslag av kløver i rasjonen.

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Sammendrag

Virtual fencing is a promising alternative to contain livestock dispersal without using physical barriers. This technology uses smart-wearable collars that deliver predictable warning tones to animals when they approach virtual boundaries paired with mild electric pulses. Virtual fencing allows for dynamic management of livestock grazing, based on site-specific variations in the quality and quantity of forages. However, several factors can affect the efficacy of virtual fencing, including the length of prior experience with virtual fencing, climatic conditions, forage availability inside and outside virtual fencing paddocks and collar configuration schedules. Lactation requirements and social interactions between collared cows and uncollared calves can also influence the efficacy of the technology. Virtual fencing trials were conducted at the New Mexico State University’s Chihuahuan Desert Rangeland Research Center from August 27 to December 21 of 2022 to evaluate the efficacy of virtual fencing to manage rangeland cows during late lactation and following weaning. Twenty-six Brangus cows previously trained to use NoFence C2 collars (NoFence, Batnfjordsøra, Norway), were monitored for 30 days during late lactation and 28 days after weaning. Collared cows and uncollared calf pairs were allocated to four virtual fence pastures in late lactation and after weaning, with pasture duration (4.2 ± 0.6 d), size (72 ± 19 ha) and perimeter (4,523 ± 352 m) varying according to forage availability and access to fresh drinking water. Audio cues, electric pulses and ratio of electric pulses to audio cues before and after weaning were compared by ANOVA in a Completely Randomized Design replicated across pre-weaning and post-weaning pastures (n = 8). The average number of electric pulses per cow was greater (P < 0.0004) for pre-weaning (3.7 ± 0.2) than for post-weaning post-weaning (1.6 ± 0.3) pastures. The number of audio warnings per cow was also greater (P < 0.0001) for pre-weaning (52 ± 3.3) than post-weaning (34 ± 3.3) pastures. Conversely, cows had decreased (P < 0.0001) ratios of electric pulses relative to audio tones on post-weaning (4.8 ± 0.5%) than pre-weaning (7.0 ± 0.8%) pastures. These results suggest that cows responded better to virtual fencing after weaning, likely because weaned cows were no longer affected by social interactions with uncollared calves. Furthermore, cows after weaning apparently relied on warning tones and fewer electric pulses to interact safely with virtual fences. However, it is important to note that sources of variation not accounted for or controlled by the present experimental design may have also affected the recorded interactions with virtual fences in the present study.

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Sammendrag

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.