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
Authors
Vilde Lytskjold Haukenes Johan Asplund Lisa Åsgård Jørund Rolstad Ken Olaf Storaunet Mikael OhlsonAbstract
Fire in the boreal forests emits substantial amounts of organically bound carbon (C) to the atmosphere and converts a fraction of the burnt organic matter into charcoal, which in turn is highly refractory and functions as a long-term stable C pool. It is well established that the boreal forest charcoal pool is sufficiently large to play a significant role in the global C cycle. However, there is a need for spatially representative estimates of how large proportions of the forest floor C pool are made up of charcoal across different plant communities in the boreal forest ecosystem. Thus, we have quantified the amounts of C separately in charcoal and the organic layers of the forest floor across fine spatial scales in a boreal forest landscape with a well-documented fire history. We found that the proportion of charcoal C made up an average of 1.2% of the total forest floor C, and the charcoal proportions showed a high small-scale spatial variability and were concentrated in the organic–mineral soil interface. Proportions of charcoal C decreased with increasing time since last fire. Deeper soils, denser soils, and local concave areas had the highest proportions of charcoal C, whereas historical fire frequencies and current differences in vegetation did not relate to the proportions of charcoal C.
Abstract
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.
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.
Authors
Alice Budai Daniel Rasse Teresa Gómez de la Bárcena Hugh Riley Vegard Martinsen Ievina Sturite Adam Thomas O'Toole Samson Øpstad Thomas CottisAbstract
No abstract has been registered
Abstract
Wood modification using polyesterification of sorbitol and citric acid is a novel environmentally friendly strategy for wood protection improving its dimensional stability and acts against fungal deterioration. Inelastic Raman scattering is sensitive to the molecules of high polarizability and both lignocellulose and aliphatic esters formed during the treatment are polar. Therefore, in the present study, the quality control of the treatment using a handheld Raman spectrometer equipped with 830 nm laser is suggested as a rapid and reliable approach. Raman spectra from six wood modification levels (resulting in different weight percent gain, WPG) of three different wood species (Silver birch, Scots pine and Norway spruce) as well as three sample preparation strategies (intact, sanded and milled wood samples) were collected, and further analyzed using a chemometric method. Best performing models based on Powered Partial Least Squares Regression predicted the WPG level at R2 = 0.85, 0.95 and 0.98 for birch, pine and spruce, respectively. In addition, a clear separation between hard and soft wood species was also captured. Especially for softwood species, the sample preparation method affected the model accuracy, revealing the best performance in milled material. It is concluded that by using handheld Raman spectrometer it is possible to perform accurate quality control of wood modified by polyesterification of citric acid and sorbitol.
Abstract
The aim of this study was to contribute to development of organic fertiliser products based on fish sludge (i.e. feed residues and faeces) from farmed smolt. Four dried fish sludge products, one liquid digestate after anaerobic digestion and one dried digestate were collected at Norwegian smolt hatcheries in 2019 and 2020. Their quality as fertilisers was studied by chemical analyses, two 2-year field experiments with spring cereals and soil incubation combined with a first-order kinetics N release model. Cadmium (Cd) and zinc (Zn) concentrations were below European Union maximum limits for organic fertilisers in all products except one (liquid digestate). Relevant organic pollutants (PCB7, PBDE7, PCDD/F + DL-PCB) were analysed for the first time and detected in all fish sludge products. Nutrient composition was unbalanced, with low nitrogen/phosphorus (N/P) ratio and low potassium (K) content relative to crop requirements. Nitrogen concentration in the dried fish sludge products varied (27–70 g N kg-1 dry matter), even when treated by the same technology but sampled at different locations and/or times. In the dried fish sludge products, N was mainly present as recalcitrant organic N, resulting in lower grain yield than with mineral N fertiliser. Digestate showed equally good N fertilisation effect as mineral N fertiliser, but drying reduced N quality. Soil incubation in combination with modelling is a relatively cheap tool that can give a good indication of N quality in fish sludge products with unknown fertilisation effects. Carbon/N ratio in dried fish sludge can also be used as an indicator of N quality.
Authors
Trond Mæhlum Bente Føreid Maria Dietrich Melesse Eshetu_Moges Trine Hvoslef-Eide Ola Lødøen Vethe Petter D. JenssenAbstract
This report (D2.5) presents a qualitative and quantitative assessment for nutrients and energy regarding circular fertilizers and biogas production from waste resources. A transformation towards sustainable food production for the growing urban population requires improved circular urban nutrient management. Urban agriculture (UA), like any agricultural system, needs input of resources in terms of growth media, nutrients, and water. Resources that are often imported into cities, especially in the form of food, generate urban waste. Current environmental, social, and economic challenges of cities are seen as opportunities that can be derived locally, as this project demonstrates. The domestic organic waste and wastewater contains energy (thermal and chemical) and nutrients that could play a role in the urban circular economy if proper technology and management are applied. Urban organic waste contains relevant nutrients including nitrogen (N) and phosphorus (P), as well as organic matter, yet less than 5% of the global urban resources are presently recycled. One recycling approach is the composting of urban organic wastes, recovery of nutrients from source-separated urine and anaerobic digestate of blackwater, and biogas and biochar produced as sources of energy. At the NMBU showcase different technologies were assessed to demonstrate how to achieve sustainable and circular urban farming systems. Qualitative and quantitative information about organic fertilizers, making budgets for the nutrient contents of waste resources and organic fertilizer and comparing this with the nutrient needs of the plants in the relevant cultivation area, as shown in this report, can provide better fertilization and less loss to the environment. We need more information on the fertilizer value of waste resources and how these nutrients can be best utilised. Due to the increased interest, more information about health and environmental challenges by implementing circular UA should be obtained
Authors
Ruchiru D. Herath Uttam Pawar Dushyantha M. Aththanayake Kushan D. Siriwardhana Dimantha I. Jayaneththi Chatura Palliyaguru Miyuru Gunathilake Upaka RathnayakeAbstract
Climate change, urbanization, and many anthropogenic activities have intensified the floods in today’s world. However, poor attention was given to mitigation strategies for floods in the developing world due to funding and technical limitations. Developing flood inundation maps from historical flood records would be an important task in mitigating any future flood damages. Therefore, this study presents the predictive capability of the Rainfall-Runoff-Inundation (RRI) model, a 2D coupled hydrology-inundation model, and to build flood inundation maps utilizing available ground observation and satellite remote sensing data for Kalu River, Sri Lanka. Despite the lack of studies in predicting flood levels, Kalu River is an annually flooded river basin in Sri Lanka. The comparative results between ground-based rainfall (GBR) measurement and satellite rainfall products (SRPs) from the IMERG satellite have shown that SRPs underestimate peak discharges compared to GBR data. The accuracy and the reliability of the model were assessed using ground-measured discharges with a high coefficient of determination (R2 = 0.89) and Nash–Sutcliffe model efficiency coefficient (NSE = 0.86). Therefore, the developed RRI model can successfully be used to simulate the inundation of flood events in the KRB. The findings can directly be applied to the stakeholders.