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
Cover crops are used to increase carbon sequestration in soils. However, an increase of organic matter in soils not only increases carbon stocks but also affects nitrogen availability. This can trigger N2O emissions, particularly during wintertime, when standing plant biomass from cover crops decays. N2O emissions associated with cover crops could potentially cancel out the carbon gain. In this study, N2O emissions were measured over two years in a field experiment in SE Norway with barley and various cover crops (perennial and Italian ryegrass, oilseed radish, summer and winter vetch, phacelia and a mixture of different herbs) and compared with controls without cover crops. Manual chambers were used in summer during the growth of the main crop, while winter emissions were measured more frequently by a field robot to capture freeze-thaw induced emission peaks. Both winters had poor snow cover and the highest N2O emissions were measured during freeze-thaw cycles in early spring. Nitrogen-rich cover crops with poor overwintering (oilseed radish) increased wintertime emissions, whereas perennial cover crops with good overwintering (perennial ryegrass and herb mixture) tended to reduce N2O emissions compared to controls. This suggests that the overall climate effect of cover crops in hemiboreal cereal production depends on cover crop species and winter conditions.
Abstract
The use of cover crops in cereal production as a climate smart agricultural practice is generally used to increase carbon sequestration in soils. However, increased plant biomass in wintertime can trigger N2O emissions due to decay during freeze-thaw cycles. So far little is known about N2O winter emissions from cover crops which, in the worst case, could cancel out the carbon gain by cover crops. Here we report N2O emissions from a two-year field experiment in SE Norway with barley and various cover crops (perennial and Italian ryegrass, oilseed radish, summer and winter vetch, phacelia and a mixture of different herbs) measured against controls without cover crops. A field robot was used for measuring N2O emissions at high temporal resolution during off-season, i.e., the period from cereal crop harvest to cereal crop sowing. During the first winter, the snow cover was poor and the significantly higher N2O emissions were measured from oilseed radish during spring thaw whereas perennial ryegrass reduced emissions. A second winter is measured and N2O emissions from both years will be presented. In addition, continuous measurements are needed to assess the effect of diurnal freeze-thaw cycles on N2O emissions before scaling up to annual N2O emission fluxes and comparing with C sequestration.
Authors
J. O’Malley J.A. Finn C.S. Malisch P.R. Adler M. Bezemer A. Black Åshild Gunilla Ergon J. Eriksen S. Filley A. Fiorini P. Golinski G. Grange J. Hakl Y. He N. Hoekstra P. Högy O. Huguenin-Elie M. Ibanez R. Jiaxin J. Jing J. Jungers Z. Kadžiulienė D. Krol J Lajeunesse G. Louarn S. Meyer T. Moloney G. Peratoner C. Porqueddu C. Reynolds Ievina Sturite M.N. Thivierge F. Zhu C. BrophyAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Claire Morgan-Davies Germain Tesniere Cathy Dwyer Grete H. M. Jørgensen Eliel Gonzalez-Garcia Sotiris Patsios Evangeline Sossidou Tim Keady Brid McClearn Fiona Kenyon Gerrardo Caja Lise Grøva Mauro Decandia Ludovic Cziszter Ilan Halachmi Jean Marc GautierAbstract
No abstract has been registered
Authors
Ingerd Skow Hofgaard Guro Brodal Heidi Udnes Aamot Marit Almvik Aina Lundon Russenes Morten Lillemo Simon Graham EdwardsAbstract
Oat harvested from plants infested with plant pathogenic fungi within the Fusarium head blight (FHB) complex may sometimes contain high levels of mycotoxins, which makes the grain unsuitable for food and feed. Fusarium graminearum, a deoxynivalenol (DON) producer, and Fusarium langsethiae, a T-2 toxin (T2) and HT-2 toxin (HT2) producer, are commonly occurring in Norwegian oats. We have analysed grains of Nordic oat varieties and breeding lines for the content of mycotoxins and DNA of Fusarium species belonging to the FHB disease complex (Hofgaard et al. 2022). The grains were harvested from field trials located in South-East Norway in the years 2011-2020. The ranking of oat varieties according to HT2+T2 levels corresponded with the ranking according to the DNA levels of F. langsethiae. However, this ranking did not resemble the ranking for DON and F. graminearum DNA. Our results implies that a moderate resistance to DON producers does not guarantee a moderate resistance to HT2+T2 producers. Separate tests are therefore necessary to determine the resistance towards DON and HT2+T2 producers in oats. This creates practical challenges for the screening of FHB resistance in oats as todays’ screening focuses on resistance to F. graminearum and DON. We identified oat varieties with generally low levels of both mycotoxins and FHB pathogens which should be promoted to mitigate mycotoxin risk in Norwegian oats.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
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.
Authors
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. UtsumiAbstract
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.