Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2023
Abstract
No abstract has been registered
Abstract
More than 2/3rds of Norway’s agricultural area are grassland, and more than half of it is over 5 years old. Renewing old grassland increases annual yield but causes yield loss during renewal. Parts of the increased yield is due to replacement of low-productive species with high production species and cultivars, replacing biodiversity with productivity. Finding the optimal rate of renewal requires long term experiments to compare the sustainability of different strategies. Therefore, three field experiments were established to investigate the effect of difference renewal and harvest strategies on grass yield and quality, on similar mineral soil at Særheim (58.5°N, 5.6°E) in 1968 and Fureneset (61.3°N,5.0°E) in 1974, and on peat soil at Svanhovd (69.5°N,30.0°E) in 1968. Until 1991, the experiment included non-renewed treatments, and renewal every 3rd or 6th year. It was cut either two or three times a year, with autumn grazing on parts of the two-cut regime. The experiment was simplified in 1992, with the establishment of another non-renewed treatment, all treatments being cut 3 times a year (2 at Svanhovd), no grazing but contrasting slurry and compound fertilizer applications. This phase lasted until 2011, followed by period with no renewal and minimal registration. The third phase started in 2016, with renewal of all treatments at Fureneset and Særheim, except the permanent grassland from 1968/1974. Duration between renewals was doubled, and fertilizer applications revised. Presenting results from the third phase, we show that five to six years are required to recoup and significantly over-yield the non-renewed grassland. We will use soil chemical and physical properties, fertilizer application and yield gaps as well as ecological succession from sown seed mixture in 2017 till 2022 grassland to discuss the why we needed six years for all renewed treatments to over-yield permanent grassland from 1974.
Authors
Narta ElshaniAbstract
Sheep production systems in Norway present complexity in the same way as other systems partaking in the climate challenges. Sustainability of these systems cannot be defined through single-impact indicators; hence a broader range of sustainability dimensions and trade-offs must be assessed. The present research uses the Sustainability Assessment and Monitoring RouTine (SMART): a multi-criteria sustainability assessment based on the Sustainability Assessment of Food and Agriculture Systems (SAFA) Guidelines which gathers data on the farms’ performance through 327 indicators across 4 dimensions. Eight sheep farms in Norway were selected for assessment: four low-land coastal farms, and four inland mountain farms. Management practices which support sustainability were identified in all farms: high animal welfare, high number of days of access to pasture for the livestock, no/low use of synthetic chemicals, good water management, and high quality of life for farmers. Management practices which hinder sustainability and key areas for improvement were also identified: increased onfarm energy production, decreased use of externally sourced concentrate feed, and increased farmers’ knowledge about externally sourced inputs. Some differences between the coastal and inland farms were also identified which were related to number of days of access to pasture for livestock, water consumption, participation for farmers in trainings and additional education, and political involvement. Using the SMART-Farm tool aided the process of identifying practices and systematically evaluating them through a global sustainability perspective. Aggregated results from the SMART-Farm assessment indicated a high degree of goal achievement across dimensions. The farms scored on average above 80% on the Environmental Integrity and the Social Well-Being, and lower on the Economic Resilience and the Good Governance dimensions (76% & 71% respectively). To evaluate these results, a qualitative expert elicitation method was employed; this provided insight into shortcomings which were a result of the context-generic approach that the tool has and lack of inclusion of stakeholder participation in indicator selection and aggregation process. These shortcomings are important to consider when interpreting the results of numeral integration assessments which are used for decision-making. However, evaluating these scores was also a valuable outcome in itself since it uncovered knowledge gaps about the topic of sustainability of sheep farming in Norway.
Authors
Andres F. Cibils Richard Estell Sheri Spiegal Shelemia Nyamuryekung'e Matthew M. McIntosh Danielle M. Duni Oscar A. Herrera Conegliano Felipe A. Rodriguez Almeida Octavio Roacho Estrada Lisandro J. Blanco Michael C. Duniway Santiago A. Utsumi Alfredo L. GonzalezAbstract
No abstract has been registered
Authors
Shelemia Nyamuryekung'e Andrew Cox Andres Perea Richard Estell Andres F. Cibils John P. Holland Tony Waterhouse Glenn Duff Micah Funk Matthew M. McIntosh Sheri Spiegal Brandon Bestelmeyer Santiago UtsumiAbstract
Virtual fencing systems have emerged as a promising technology for managing the distribution of livestock in extensive grazing environments. This study provides comprehensive documentation of the learning process involving two conditional behavioral mechanisms and the documentation of efficient, effective, and safe animal training for virtual fence applications on nursing Brangus cows. Two hypotheses were examined: (1) animals would learn to avoid restricted zones by increasing their use of containment zones within a virtual fence polygon, and (2) animals would progressively receive fewer audio-electric cues over time and increasingly rely on auditory cues for behavioral modification. Data from GPS coordinates, behavioral metrics derived from the collar data, and cueing events were analyzed to evaluate these hypotheses. The results supported hypothesis 1, revealing that virtual fence activation significantly increased the time spent in containment zones and reduced time in restricted zones compared to when the virtual fence was deactivated. Concurrently, behavioral metrics mirrored these findings, with cows adjusting their daily travel distances, exploration area, and cumulative activity counts in response to the allocation of areas with different virtual fence configurations. Hypothesis 2 was also supported by the results, with a decrease in cueing events over time and increased reliance with animals on audio cueing to avert receiving the mild electric pulse. These outcomes underscore the rapid learning capabilities of groups of nursing cows in responding to virtual fence boundaries.
Authors
Shelemia Nyamuryekung'e Glenn Duff Santiago A. Utsumi Richard Estell Matthew M. McIntosh Micah Funk Andrew Cox Huiping Cao Huiying Chen Sheri Spiegal Andres Perea Sajidur Rahman Andres F. CibilsAbstract
LoRa-WAN sensors were used to compare methods for determining walking distances by grazing cattle in near real-time. The accuracy of relying on a global positioning system (GPS) alone or in combination with motion data derived from triaxial accelerometers was compared using stationary control trackers (Control) placed in fixed field locations (n=6) or vs. trackers (Animal) mounted on cows (n=6) grazing on pasture at the New Mexico State University’s Clayton Livestock Research Center. Trackers communicated motion data at 1-minute intervals and GPS positions at 15-minute intervals for seven days. Daily distance walked was determined using: 1) raw GPS data (RawDist), 2) data with erroneous GPS locations removed (CorrectedDist), or 3) data with erroneous GPS locations removed and with GPS data associated with the static state excluded (CorrectedDist_Act). Distances were analyzed via one-way ANOVA to compare Control vs. Animal deployment effects. No difference (P=0.43) in walking distance was detected between Control vs. Animal for RawDist. However, distances calculated for CorrectedDist differed (P<0.01) between the two tracker deployments. Due to the random error of GPS measurements, CorrectedDist for stationary devices differed (P=0.01) from zero. The walking distance calculated by CorrectedDist_Act differed (P<0.01) between Control vs. Animal trackers, with distances for Control trackers not differing (P=0.44) from zero. The fusion of GPS and accelerometer data was a more suitable method for calculating walking distance by grazing cattle. This result may highlight the value of combining more than one source of independent sensor data in Precision Livestock Farming applications.
Authors
Matthew M. McIntosh Andres F. Cibils Shelemia Nyamuryekung'e Richard Estell Andrew Cox Danielle M. Duni Qixu Gong Tony Waterhouse John P. Holland Huiping Cao Laura Boucheron Huiying Chen Sheri Spiegal Glenn Duff Santiago A. UtsumiAbstract
Objective: Precision livestock farming technologies show great promise for the management of extensive, arid rangelands, but more practical knowledge is needed to allow ranchers to determine potential applications and limitations for adoption. We tested a long-range wide area network (LoRa-WAN) precision livestock system over 3 mo (April–June 2020) in a ranch in southwest New Mexico, USA. The system monitors cattle position and movements, precipitation, and water trough water levels at pasture and ranch scales, in real time. Materials and Methods: Here we describe the components of the system and share what we have learned from our preliminary experiences. This system included a solar-power LoRa-WAN receiving station with the corresponding gateway, radio frequency antenna (824–894 MHz), and Wi-Fi bridge for data transmission into the Internet. The testbed network for testing LoRa-WAN sensors included 43 GPS-trackers deployed on lactating beef cows and 2 environmental sensors used to monitor precipitation regimens and trough water levels, respectively. Results and Discussion: The system collected data consistently for the trough levels and precipitation, whereas data from the cow GPS-trackers was highly heterogeneous. On average, 46 ± 4% of daily data packets logged by GPS-trackers were successfully transmitted through the LoRa-WAN system, exceeding 80% of successful transmission in several cases. This report documents the necessary infrastructure, performance, and maintenance of system components, which could be of significant information value to ranchers and researchers with a desire to deploy similar monitoring systems. Implications and Applications: This Technical Note documents the implemetation of a LoRa-WAN monitoring system at the ranch scale for a 3-mo period. The system has allowed the ranch manager and assisting staff to efficiently manage cattle inventories and promptly address animal welfare concerns. However, further research is required to assess the scalability of this system across commercial operating cattle ranches in the Southwest United States, thereby unlocking its potential for broader adoption and effect.
Authors
Danielle M. Duni Matthew M. McIntosh Shelemia Nyamuryekung'e Andres F. Cibils Michael C. Duniway Richard Estell Sheri Spiegal Alfredo L. Gonzalez Melakeneh G. Gedefaw Matthew Redd Robert Paulin Caitriana M. Steele Santiago A. Utsumi Andres PereaAbstract
No abstract has been registered
Authors
Santiago A. Utsumi Shelemia Nyamuryekung'e Andres Perea Maximiliano Spetter Andrew Cox Lara Macon Richard Estell Glenn DuffAbstract
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.
Authors
Andres Perea Lara Macon Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung'e Huiping Cao Andres F. Cibils Richard Estell Glenn Duff Santiago A. UtsumiAbstract
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.