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|Developing sensor technologies to inform breeding approaches to reduce damaging behaviour in laying hens and pigs: The GroupHouseNet approach
Rodenburg, T.B. ; Bennewitz, J. ; Haas, E.N. De; Košťál, L. ; Pichová, K. ; Piette, D. ; Tetens, J. ; Visser, B. ; Klerk, B. De; Sluis, M. Van Der; Zande, L.E. Van Der; Siegford, J. ; Toscano, M. ; Norton, T. ; Guzhva, O. ; Ellen, E.D. - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 467 - 470.
Automatic tracking - Damaging behaviour - Genetic selection
The European COST Action GroupHouseNet aims to provide synergy for preventing damaging behaviour in group-housed pigs and laying hens. One area of focus of this network is how genetic and genomic tools can be used to breed animals that are less likely to develop damaging behaviour directed at their pen-mates. Reducing damaging behaviour in large groups is a challenge, because it is difficult to identify and monitor individual animals. With the current developments in sensor technologies and animal breeding, there is the possibility to identify individual animals, monitor individual behaviour, and link this information to the genotype. Using a combination of sensor technologies and genomics enables us to select against damaging behaviour in pigs and laying hens.
|Defining resilient pigs after a Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) challenge using activity and feeding data from accelerometers
Zande, L.E. Van Der; Dunkelberger, J.R. ; Rodenburg, Bas ; Mathur, P.K. ; Cairns, W.J. ; Keyes, M.C. ; Eggert, J.M. ; Little, E.A. ; Dee, S.A. ; Knol, E.F. - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 471 - 475.
Accelerometers - Behaviour - Pig - PRRS - Resilience - RMSE
Porcine reproductive and respiratory syndrome (PRRS) is an infectious viral disease in pigs. PRRS causes reproductive failure in sows and respiratory infections in growing pigs. To improve pig health and minimise economic losses, resilient pigs are preferred within the herd. Resilient pigs still become infected, yet are able to recover following infection, showing less variation in activity and feeding. In this study, 232 commercial crossbred pigs were equipped with individual accelerometer ear tags to monitor the number of active, feeding, and hyperactive events per individual per hour. At eight weeks of age, pigs were inoculated with PRRS virus 1-7-4. Data from accelerometers were collected 23 days prior to challenge and 42 days post-infection (dpi). Expected levels of activity, feeding, and hyperactivity were estimated by regressing behavioural traits on observed datapoints prior to challenge. This regression line was extended to 42 dpi. Then, deviations from the regression line were quantified as Root Mean Square Error (RMSE) for each individual during the following time periods: pre-challenge, 0-13 dpi, and 13-42 dpi. All traits decreased and RMSE increased post-challenge. These results are consistant with clinical signs of PRRS, including lethargy and loss of appetite. In addition, association of these traits with survival was also investigated. RMSE prior to PRRS-infection was not predictive of survival after infection. However, RMSE of feeding and activity during the peak challenge period (0-13 dpi) was predictive of survival, where pigs with less deviation in behaviour were more resilient to the PRRS challenge.
|TrackLab 2: A new solution for automatic recording of location, activity and social behaviour of group-housed animals
Gijssel, A. Van; Loke, B.J. ; Ouweltjes, W. ; Rodenburg, T.B. ; Visser, E.K. ; Noldus, L.P.J.J. - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 677 - 683.
Health - Livestock - Monitoring - Multi-modal - TrackLab - Welfare
The popularity of precision livestock farming is largely driven by a desire to optimise productivity, profitability and comfort. At the same time, there are growing societal concerns about animal welfare and animal health in relation to food safety and human health. These concerns can be addressed by academic and applied research into animal welfare and health indicators and increasingly by the utilisation of welfare and health metrics in operational farm management systems. TrackLab 2 is the latest tool for the measurement of livestock welfare and health indicators. It is designed to integrate and process multi-modal data for the capture of welfare and health indicators such as social behaviour, place-preference, activity, feeding and physiology. It was beta tested on four sites in the dairy cattle, poultry and pig farming domain. These first explorative tests revealed that TrackLab metrics are useful for both scientific, applied and commercial livestock research. TrackLab hardware is working well for large animals (cows, calves, pigs, sheep, poultry) but needs to be optimised for use on young birds and piglets. TrackLab 2 is also the first version to be applied in the operational farming context. The utilisation of welfare and health metrics in the operational context, to a level that exceeds the productivity focus, can prove a valuable asset in addressing societal concerns and enhancing livestock farming sustainability.
|Assessing individual activity levels in two broiler lines using an ultra-wideband tracking system
Sluis, M. Van Der; Klerk, B. De; Ellen, E.D. ; Haas, Y. De; Hijink, T. ; Rodenburg, T.B. - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 903 - 906.
Activity - Broilers - Tracking - Ultra-wideband
Individual data on activity of broilers is valuable for breeding programmes, as activity may serve as proxy for multiple health, welfare and performance indicators. However, in current husbandry systems, broilers are often kept in large groups, which makes it difficult to identify and monitor them at the individual level. Sensor technologies, such as ultra-wideband (UWB) tracking systems, might offer solutions. This paper investigated the recorded distances of an UWB tracking system that was applied to broilers, as a first step in assessing the potential of an UWB tracking system for studying individual levels of activity in broilers housed in groups. To this end, the distances moved as recorded by the UWB system were compared to distances recorded on video, using Kinovea video tracking software. There was a moderately strong positive correlation between the output of the UWB system and video tracking, although some under- and over- estimations were observed. Even though the recorded distances from the UWB system may not completely match the true distances moved, the UWB system appears to be well-suited for studying differences in activity between individual broilers when measured with the same system settings.
|Associating body condition score and parity with sub-optimal mobility in pasture-based dairy cows
O'Connor, A.H. ; Bokkers, E.A.M. ; Boer, I.J.M. De; Hogeveen, H. ; Sayers, R. ; Byrne, N. ; Ruelle, E. ; Shalloo, L. - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 798 - 802.
Body condition - Claw disorder - Grass-based system - Lameness - Parity
Sub-optimal mobility in dairy cows can be broadly defined as abnormal gait which causes a deviation from the optimal walking pattern of a cow. Sub-optimal mobility is also associated with significant economic and environmental consequences, which have yet to be extensively researched or quantified in pasture-based systems. However, to quantify sub-optimal mobility in terms of its impacts economically and environmentally, and indeed to aid in the development of automated detection sensors for sub-optimal mobility, a clear understanding of the characteristics of a cow with sub-optimal mobility is required. So far, automated detection sensors have been successful for detecting moderate to severe forms of sub-optimal mobility. However, there is a need for a better understanding of the cow-level traits associated with all forms of sub-optimal mobility, including mild forms, to incorporate this into future development of automated detection sensors for sub-optimal mobility. Therefore, the aim of our study was to determine the associations between hoof disorders (both type and presence), body condition score, and all levels of sub-optimal mobility in pasture-based dairy cows using data from a large sample of Irish dairy farms. Mobility scores, body condition scores (BCS), claw disorder (presence and severity), and parity records were available for 6,927 dairy cows from 52 pasture-based herds. Binomial logistic regression analysis was completed to determine the associations between claw disorder (presence and severity), BCS, parity and sub-optimal mobility. The output variable was sub-optimal mobility (mobility score ≥ 1) and the predictor variables were specific claw disorders and their severities, BCS, and parity. Our results indicate that all severities of claw disorders, low BCS, and higher parity cows are all associated with an increased risk for sub-optimal mobility.
|Field trial to demonstrate the intelligent dairy assistant (IDA) system on dairy farms
Rutten, C.J. ; Molenaar, N. ; Hogewerf, P. ; Gosliga, S.P. Van; Lokhorst, C. - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 277 - 283.
Artificial intelligence - Health - Learning - Oestrus - Sensor
Connecterra's Intelligent Dairy Assistant (IDA) is a novel Internet of Things based on a management support system for dairy farms. IDA uses sensor technology, cloud computing and artificial intelligence to support dairy farmers with insights on oestrus and health management. IDA analyses cow behaviour (originating from a 3D accelerometer on a neck collar) and herd patterns, and learns from the farmer's feedback. Within the Horizon 2020 project Internet for Food and Farm (www.IoF2020.eu) a field trial was conducted. The goal was to demonstrate that the IDA approach to generate actionable insights works. Therefore, we tracked KPI's on farm economics, animal health and fertility on two commercial dairy farms. We report the results of our trial from January to December 2018. Both farms had a herd of 100 cows, from which 50 cows were equipped with IDA. The farm KPI's were measured separately for the groups with and without. In comparison to the without group, the with group had, on average, its expected calving interval 5.92 and 0.88 days lower on Farm 1 and 2, respectively. Likewise, treatments with antibiotics 1.15 and 2.60 days shorter and 305 day milk yield 434 kg higher (Farm 1) and 405 kg lower (Farm 2) in the with group. For milk production the results are inconclusive as the groups were not balanced on milk yield before the trial started. We experienced in this trial, by qualitative feedback of the farmers, that the IDA approach worked and more observations are needed for scientific proof.
|Using a data lake in animal sciences
Schokker, D. ; Athanasiadis, I.N. ; Visser, B. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2019
In: Precision Livestock Farming 2019. - Organising Committee of the 9th European Conference on Precision Livestock Farming (ECPLF), Teagasc, Animal and Grassland Research and Innovation Centre (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 140 - 144.
Animal experiment - Data lake - Scalability - Sensor data
In the livestock domain, Big Data is becoming more common and is being anchored into the mind-set of researchers. With the increasing availability of large amounts of data of varying nature, there is the challenge of how to store, combine, and analyse these data efficiently. With this study, we explored the possibility of using a data lake for storing and analysing sensor data, using an animal experiment as the use case, to improve scalability and interoperability. The use case was an experiment within Breed4Food (a public-private partnership), in which the gait score of 200 turkeys was determined. In the experiment, a gait score was traditionally assigned to each animal by a highly-skilled person who visually inspected them walking. Next to it, a set of sensor data streams was recorded for each animal, specifically inertial measurement units (IMUs), a 3D-video camera, and a force plate, with the ambition to explore the effectiveness of these data streams as predictors for estimating the gait score. The resulting sensor output, i.e. raw data, were successfully stored in its original format in the data lake. Subsequently, for each sensor output we performed extract, transform, and load activities, by executing custom-made scripts to generate tab or comma separated files. Lastly, by using Apache Spark it was possible to easily perform parallel processing of the data, allowing for fast computing. In conclusion, we managed to set up a data lake, load animal experimental data and run preliminary analyses. The data lake allowed for easy scale up of both data loading and analyses, which is desired for dynamic analyses pipelines, especially when more data are collected in the future.
|Monitoring pig behaviour by RFID registrations
Mol, R.M. De; Hogewerf, P.H. ; Verheijen, R.G.J.A. ; Dirx, N.C.P.M.M. ; Fels, J.B. Van Der - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 315 - 321.
Drinking behaviour - Eating behaviour - Pigs - RFID
Automation of the monitoring of pig behaviour can support management; deviating behaviour can be an indicator for disease or other disturbances. Registrations of RFID at specific places in a pen can be used to estimate the individual pig behaviour in order to detect abnormalities in drinking and eating behaviour. LF RFID tags were applied to pigs in the period from weaning until the start of the fattening phase. This was done in several experiments at the Dutch Swine Innovation Centre in the South of the Netherlands. In each experiment, 12 pigs were monitored, each equipped with a RFID tag in the right ear. Three readers were installed at the drinking place: (1) and at the feeding trough (2) video recordings were available to validate the results from the processed RFID readings. The performance of the system depended on the established reading distance of the readers. Tag readings were combined into visits by applying a bout criterion. Visits were combined into meals by applying a meal criterion. A good correspondence between tag readings and observed visits was found. A longer reading distance resulted in more tag readings that did not coincide with eating or drinking (but with resting behaviour nearby). Combining the two readers at the feeding trough as if they were one gave better results. The derived visits and meals can be used for detection of deviations on the number per pig per day (or per pig per part of the day). This automated detection can be a valuable tool in pig farming.
|Machine learning to realize phosphate equilibrium at field level in dairy farming
Mollenhorst, H. ; Haan, M.H.A. De; Oenema, J. ; Hoving-Bolink, A.H. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 41 - 44.
Boosting - Crop yield - Machine learning - Manure - Phosphorus - Regression tree
An important factor in circular agriculture is efficient application of animal manure. Therefore, input and output of nutrients, like phosphorus (P), need to be balanced. Currently, manure application is regulated with rather fixed P application norms as a generic translation of P yields of grassland and maize. Predicting P yields based on field specific, historical data could be an important step to better balance P input and output. This study's objective was to predict P yields based on field and weather data, using machine learning. The dataset contained 640 records of yearly crop yields per field between 1993-2016 with information on P input and output, irrigation, and soil status at field level as well as local weather data. Generalized boosted regression (GBR) was used to predict P yields for the last five years based on information from all previous years. Model performance was evaluated per year as well as together by plotting observed versus predicted values of all five years in one plot. This final plot was compared to a plot with the currently used generic application norms. Model performance per year showed that GBR could predict the trend from low to high rather well (correlations of ~0.8). Results of the five years together showed that GBR performance was better than the generic application norms (correlation 0.68 vs 0.59; RMSE 7.3 vs 8.2). In conclusion, GBR contributed to defining more flexible P application norms with the aim to realize a phosphate equilibrium.
|At-market sensor technologies to develop proxies for resilience and efficiency in dairy cows
Ouweltjes, W. ; Haas, Y. De; Kamphuis, C. - \ 2019
In: Precision Livestock Farming 2019. - Teagasc (Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019 ) - ISBN 9781841706542 - p. 246 - 253.
Feed efficiency - Precision livestock farming - Proxies - Resilience
We hypothesise that at-market sensor technologies can be used to develop proxies for complex traits such as resilience and feed efficiency (FE). This was tested by comparing variables describing sensor data patterns (“curve-parameters”) from resilient or FE cows with non-resilient or non-FE cows. Sensor data included data from weighing scales, activity (steps) and rumination activity from neck collars, and milk production from the parlour or the milking robot. Curve-parameters were calculated for each sensor for each lactation for which data was available and included the mean, standard deviation (std), slope, skewness, and the autocorrelation. Data originated from a Wageningen Research farm, and included data from 1,800 cows with calvings between 1995-2016. During this time frame, there were 98 lactations with sufficient feed intake recordings to compute FE at lactation level (DMI (kg) / milk yield (kg)), and to rank them accordingly. The 1,800 cows that could be ranked according to their lifetime resilience (ability to re-calf in combination with the number of health and insemination events) based on scores for each of the, in total, 5,771 lactations. Subsequently, the 20% or 10% most and least FE or resilient lactations, respectively, were selected. Curve-parameters of these selected lactations were compared. Results imply that using a single sensor, or a single curve parameter, is likely to be insufficient as a proxy for resilience of efficiency. Future research should focus on studying which combination of curve parameters and sensors are most informative as proxy for these two complex traits.
Towards sustainable European grassland farming with Inno4Grass: an infrastructure for innovation and knowledge sharing
Krause, A. ; Becker, Talea ; Feindt, Peter H. ; Huyghe, C. ; O'Donovan, Michael ; Peeters, A. ; Pol, A. van den - \ 2018
In: Sustainable meat and milk production from grasslands. - Zürich : European Grassland Federation EGF (Grassland Science in Europe ) - ISBN 9781841706436 - p. 925 - 936.
European agriculture is facing tremendous challenges related to the rapid decrease of farm populations, competitiveness on open markets and the preservation of natural resources on finite areas. Grasslands, which are highly significant for nature conservation, often face land-use competition with arable cropping, urbanisation and other uses. Farmers need dedicated innovations to improve grasslands economic performance and their effective implementation in practice. This requires co-creation of knowledge between researchers and farmland practitioners, as was broadly pointed out by the European Commission. This paper describes a novel approach to create a collaborative space for grassland innovations contributing to profitability of European grassland farms while preserving environmental benefits. Innovative modes of collaboration between practice and science are enabled by an international thematic network across eight European member states. A methodolog y serves to collect farmers’ innovative ideas and to stimulate collaboration among various stakeholders (farmers’ groups, extension services, education and research) including cross-border collaborations, where grassland-related knowledge is made available for local conditions. This interactive innovation model fosters knowledge exchange and establishes a farmland-specific Information Management System. The aim is to stimulate a renewed, collaborative innovation culture for EU grasslands. The methods are conceptualised and put into practice by the Thematic Network project Inno4Grass funded under Horizon 2020.
Grazing for carbon
Pol, A. van den; Chabbi, A. ; Cordovil, C.M.D.S. ; Vliegher, A. de; Die Dean, M. ; Hennessy, D. - \ 2018
In: Sustainable meat and milk production from grasslands Cork : European Grassland Federation EGF (Grassland Science in Europe ) - ISBN 9781841706436 - p. 682 - 684.
The potential of grasslands as a carbon (C) sink in Europe is large despite the number of uncertainties related to the effect of grazing systems on C sequestration. The EIP-AGRI Focus Group (FG) ‘Grazing for Carbon’, a temporary group of 20 selected European experts from research and practice, shared knowledge and experience from different disciplines on the relationship between grazing and soil C. The FG explored grazing management strategies, drivers and barriers for different grazing systems, as well as tools and business models to support them successfully. The overall aim was to identify how to increase the soil C content in grazing systems. Six priorities were addressed: the effects and trade-offs associated with approaches to sequestering C in different grazing systems, the effect of grazing on C and soil nutrients, the role of plant mixtures and native species, general guidelines for optimal grazing, effective monitoring of soil C as a tool for soil quality evaluation and incentives to promote the adoption of grazing systems to optimise soil C content.
Social and economic impacts of grass based ruminant production
Pol, A. van den; Becker, Talea ; Botana Fernandez, Adrian ; Hennessy, Thia ; Peratoner, Giovanni - \ 2018
In: Sustainable meat and milk production from grasslands. - Zürich : European Grassland Federation EGF (Grassland Science in Europe ) - ISBN 9781841706436 - p. 697 - 708.
Grass based ruminant production provides multiple benefits to farmers and to wider society. This paper addresses key economic and social factors of grass based ruminant production and illustrates them with national and regional examples from different parts of Europe. Farmers are key actors when it comes to maintaining and improving grass based production systems since they decide on the day-to-day management of the farm. The traditional farm economy model is a model where the income of farmers is a function of the price of the animal products sold, subsidies/direct payments and the associated costs of production. The multiple benefits of grass based production systems to society lead to promising new business models, where farmers are financially rewarded for their added value contributions. This is already put into practise as several societal initiatives have been started, to support rewards for ecosystem services delivered. When developing stimulating initiatives, the mind-set of the farmer should be taken into account, since this is an important influencing factor. Special attention should be paid to young farmers since they represent the next generation of farming.
Grazing for Carbon : End report. EIP-AGRI
Pol, A. van den; Chabbi, A. ; Vliegher, A. de; Hennessy, D. ; Hutchings, N. ; Klumpp, Katja - \ 2018
Brussels : EIP-AGRI - 32 p.
Effect of nitrogen and phosphorus fertilisation and their interaction on nitrogen-phosphorus ratio of grass
Curth-van Middelkoop, J.C. - \ 2018
In: Sustainable meat and milk productions from grasslands. - Cork : European Grassland Federation EGF (EGF Grassland science in Europe ) - ISBN 9781841706436 - p. 565 - 567.
In many EU countries fertilisation of grassland with nitrogen (N) and phosphorus (P) is limited by legislation. In earlier research in grassland experiments usually one nutrient was varied and a non-limiting amount of the other was applied. The interaction of the effects of N and P has been less addressed. Additionally, grass is sometimes presumed to have a constant N:P ratio. In the Netherlands, in the period 1994 - 2003, four separate field experiments were undertaken on four soil types. In these field experiments permanent grassland was fertilised with different N and P levels over a period of five and six years. Phosphorus fertilisation level had a negative effect and N fertilisation level had a positive effect on N:P ratio. At higher N levels, the influence of P fertilisation was stronger. Over time the (negative) influence of P fertilisation level also became stronger. The influence of N did not change systematically over time. Both N and P fertilisation levels should be taken into account to estimate N:P ratio.
Eurodairy: a bottom-up approach to transfer innovations on grass to European farmers
Brocard, V. ; Rankin, J. ; Korevaar, H. ; Menghi, A. ; Keatinge, R. - \ 2018
In: Sustainable meat and milk production from grasslands / Horan, B., Hennessy, D., O'Donovan, M., Kennedy, E., McCarthy, B., Finn, J.A., O'Brien, B., Wageningen : Wageningen Academic Publishers (Grassland Science in Europe ) - ISBN 9781841706436 - p. 944 - 946.
Amazing Grazing: science in support of future grass based dairy systems
Schils, R.L.M. ; Philipsen, A.P. ; Holshof, G. ; Zom, R.L.G. ; Hoving, I.E. ; Reenen, C.G. van; Werf, J.T.N. van der; Galama, P.J. ; Sebek, L.B. ; Klootwijk, C.W. ; Eekeren, N. van; Hoekstra, N.J. ; Stienezen, M.W.J. ; Pol, A. van den - \ 2018
In: Sustainable meat and milk production from grasslands. - Wageningen : Wageningen Academic Publishers (Grassland Science in Europe ) - ISBN 9781841706436 - p. 336 - 338.
The Amazing Grazing project addresses the challenges that Dutch farmers face in grazing systems with high feed supplementation and high stocking rates on available grazing area. The project consists of six interlinked components (soil, grass growth, grass supply, grass intake, supllementation and behaviour), that are arranged around two grazing and three cutting experiments, as well as three farmer consultation groups. The grazing experiment showed that fresh grass intakes of approximately 6 kg DM cow -1 d-1 are feasible in intensive grazing systems with high feed supplementation levels. Tools for grass monitoring and planning, as well as cow behaviour monitoring, are being developed to support farmer decisions.
Amazing Grazing: substantial fresh grass intake in restricted grazing systems with high stocking rates
Holshof, G. ; Zom, R.L.G. ; Schils, R.L.M. ; Pol, A. van den; Klootwijk, C.W. - \ 2018
In: Sustainable meat and milk productions from grasslands. - European Grassland Federation EGF (Grassland science in Europe ) - ISBN 9781841706436 - p. 234 - 237.
Due to larger herds on smaller grazing platforms, grazing has been decreasing in the the Netherlands. It is a challenge for farmers to achieve high fresh grass intake in modern grazing systems with high livestock densities and high supplementation levels. Two grazing systems were studied during two consecutive years: strip grazing (SG) and compartmented continuous grazing (CCG), both with 7.5 cows ha-1 on the grazing platform. Cows had daily access to the paddock for 6-8 h during daytime. During the night, supplementary feed was provided (5 - 12 kg DM cow -1 day-1; up to 8 kg DM day-1 of supplement, only maize silage was fed, above 8 kg DM a mixture of maize and grass silage was fed). Comprehensive data was collected on sward and animal performance focusing on grass intake. Both 2016 and 2017 showed an average grass intake ranging from 5.5-6.5 kg DM cow-1 day-1. The systems showed no significant difference with respect to grass intake and milk production. Each year, on averange 174% of the area of the CCG and 233% of the ares of SG was mown for silage. The results of this experiment show that grass intake can be sustantial (on average 1037 kg DM cow-1 during the grazing season) in restricted grazing systems with high stocking rates.
Amazing Grazing: N use efficiency of 60 individual dairy cows under intensive grazing
Klootwijk, C.W. ; Zom, R.L.G. ; Pol, A. van den; Middelaar, C.E. van; Holshof, G. ; Boer, I.J.M. de - \ 2018
In: Sustainable meat and milk production from grasslands. - Wageningen Academic Publishers (Grassland Science in Europe ) - ISBN 9781841706436 - p. 81 - 84.
The Dutch dairy sector aims to improve nitrogen (N) use efficiency (NUEN) of intensive dairy farms while supporting grazing. To gain insight into the NUEN of intensive dairy farms, we need insight into the NUEN at cow level. We performed a 2 x 2 factorial grazing trial with 60 Holstein Friesian cows (7.5 cows ha-1), in which we compared NUEN of individual cows under two grazing systems, i.e. compartmented continious grazing (CCG) and strip grazing (SG) and two levels of dietary rumen-degradable protein balance (OEB), i.e. low and high (a difference of 500 g OEB cow-1 day-1). Grass and supplementary intakes and faecal and milk outputs were quantified and analysed for N content, during two weeks in July and September 2016. Results showed a higher NUEN for cows in CCG (39%) compared to cows in SG (36%) in July, due to lower grass (N) intake in CCG. Low OEB showed a higher NUEN (40%) compared to high OEB (34%). Our results are key to exploring strategies to improve NUEN of farms that apply innovative grazing systems.
HEx : A heterologous expression platform for the discovery of fungal natural products
Harvey, Colin J.B. ; Tang, Mancheng ; Schlecht, Ulrich ; Horecka, Joe ; Fischer, Curt R. ; Lin, Hsiao Ching ; Li, Jian ; Naughton, Brian ; Cherry, James ; Miranda, Molly ; Li, Yong Fuga ; Chu, Angela M. ; Hennessy, James R. ; Vandova, Gergana A. ; Inglis, Diane ; Aiyar, Raeka S. ; Steinmetz, Lars M. ; Davis, Ronald W. ; Medema, Marnix H. ; Sattely, Elizabeth ; Khosla, Chaitan ; Onge, Robert P.S. ; Tang, Yi ; Hillenmeyer, Maureen E. - \ 2018
Science Advances 4 (2018)4. - ISSN 2375-2548
For decades, fungi have been a source of U.S. Food and Drug Administration-approved natural products such as penicillin, cyclosporine, and the statins. Recent breakthroughs in DNA sequencing suggest that millions of fungal species exist on Earth, with each genome encoding pathways capable of generating as many as dozens of natural products. However, the majority of encoded molecules are difficult or impossible to access because the organisms are uncultivable or the genes are transcriptionally silent. To overcome this bottleneck in natural product discovery, we developed the HEx (Heterologous EXpression) synthetic biology platform for rapid, scalable expression of fungal biosynthetic genes and their encoded metabolites in Saccharomyces cerevisiae. We applied this platform to 41 fungal biosynthetic gene clusters from diverse fungal species from around the world, 22 of which produced detectable compounds. These included novel compounds with unexpected biosynthetic origins, particularly from poorly studied species. This result establishes the HEx platform for rapid discovery of natural products from any fungal species, even those that are uncultivable, and opens the door to discovery of the next generation of natural products.