Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Towards field specific phosphate applications norms with machine learning
Mollenhorst, H. ; Haan, M.H.A. de; Oenema, J. ; Hoving, A.H. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2018
- 1 p.
A flexible data architecture to automate collection of (near) real-time methane sensor data at commercial dairy farms
Kamphuis, C. ; Haas, Y. de; Bergh, E. van den - \ 2018
- 1 p.
Using Data Lake Stack in Animal Sciences
Schokker, D. ; Athanasiadis, I.N. ; Visser, B. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2018
- 1 p.
A flexible data architecture to automate collection of (near) real-time methane sensor data at commercial dairy farms
Kamphuis, Claudia - \ 2018
Early prediction of phenotypic survival to the second lactation in Dutch and Flemish Holstein heifers using genomic and phenotypic data
Heide, E.E.M. van der; Veerkamp, R.F. ; Kamphuis, C. ; Ducro, B.J. - \ 2018
In: Proceedings of the 11th World Congress on Genetics Applied to Livestock Production. - - 6 p.
Due to uncertainty about survival and future performance of replacement heifers, many farmers rear a surplus of heifers. By predicting survival at an early age, uncertainty about heifer survival could be reduced, and fewer replacement heifers would be needed. A dataset of 1907 Holstein heifers born between 2012 and 2013 with 50 genomic breeding values (GEBV) and various phenotypic variables was used to predict survival to second lactation, at two moments in life; at birth, and at age of 18 months. While it was not possible to reliably predict survival outcome of individual heifers, the surviving heifers ranked higher on average than non-surviving heifers at birth (0.87 (SD = 0.047) vs 0.84 (SD =0.059), and at 18 months (0.89 (SD =0.066) vs 0.85 (SD = 0.080). The best prediction of survival in both cases was obtained by combining phenotypic information and gEBV, demonstrating the potential for farmers to combine both information sources to predict the probability of survival for their replacement heifer management. Keywords: phenotypic prediction, dairy cattle, survival
Near and at-market farm tehnologies to develop proxies for resilience and efficiency
Kamphuis, Claudia - \ 2018
Bovine subclinical mastitis reduces milk yield and economic return
Gonçalves, J.L. ; Kamphuis, C. ; Martins, C.M.M.R. ; Barreiro, J.R. ; Tomazi, T. ; Gameiro, A.H. ; Hogeveen, H. ; Santos, M.V. dos - \ 2018
Livestock Science 210 (2018). - ISSN 1871-1413 - p. 25 - 32.
Contagious - Environmental - Milk price - Milk quality - Subclinical mastitis
The effect of different pathogens was studied by evaluating the contralateral (healthy and infected) mammary quarters of 146 lactating cows. The impact of SM on economic return (quarter milk yield × milk price) was determined by applying milk payment estimates on milk collected from healthy vs. infected glands. Cows were considered infected when they had at least 2 out of 3 weekly composite SCC results > 200 × 103 cells/mL and a microbiological culture (MC) positive result from composite foremilk samples, collected in the third week of sampling. Infected cows were evaluated a second time within 15 days and had milk yield measured at the quarter level and foremilk samples collected by aseptic technique for analysis of MC, milk composition and SCC. Of the 611-composite milk samples, 397 (65%) were culture-negative, and 214 (35%) were culture-positive and the most frequent isolated bacteria were Corynebacterium spp. (7.9%), coagulase negative staphylococci (5.8%), Staphylococcus aureus (5.3%), Streptococcus uberis (4.6%), Streptococcus agalactiae (3.9%), other environmental streptococci (2.4%), Gram-negative isolates (2.4%), Enterococcus spp. (1.4%) and Streptococcus dysgalactiae (0.7%). A total of 55 pairs of healthy contralateral quarters (control) were compared, and no difference was observed between them when evaluating SCC, milk yield, fat and protein concentration and economic return. A total of 124 pairs of healthy had lower SCC (274.9 × 103 cells/mL) than infected contralateral quarters (SCC of 1038.5 × 103 cells/mL). At the quarter level, IMI caused by minor pathogens had no effect on SCC, milk yield and economic return. Subclinical mastitis caused by contagious and environmental pathogens increased SCC and decreased milk yield when compared with healthy contralateral quarters. Moreover, quarters infected by contagious pathogens had increased concentrations of milk protein and fat when compared with healthy contralateral quarters. Therefore, the milk economic return was lower in quarters with SM caused by environmental pathogens (US$ 0.18/quarter.milking) and contagious (US$ 0.22/quarter.milking) when compared with healthy contralateral quarters. The milk losses ranged from 0.07 kg/quarter.milking to 1.4 kg/quarter.milking and the economic losses ranged from US$ 0.02–0.4/quarter.milking according to the pathogen causing SM.
Economics of precision dairy monitoring techniques
Hogeveen, H. ; Rutten, N. ; Kamphuis, C. ; Voort, M. van der - \ 2017
In: Conference on Precision Dairy Farming. - - p. 87 - 97.
Het celgetal en andere mastitisindicatoren
Lam, T. ; Sandman-Berends, I. ; Kamphuis, C. ; Schukken, Y. ; Vliegher, S. de - \ 2017
In: Handboek Uiergezondheid Rund / Lam, Theo, De Vliegher, Sarne, Nijmegen : Communication In Practice - ISBN 9789082232127 - p. 71 - 86.
Principles to determine the economic value of sensors technologies used on dairy farms
Voort, M. van der; Hogeveen, H. ; Kamphuis, C. - \ 2017
In: Large Dairy Herd Management / Beede, David. K., ADSA - ISBN 9780963449139 - p. 1293 - 1303.
Traditional mixed linear modelling versus modern machine learning to estimate cow individual feed intake
Kamphuis, C. ; Riel, J.W. van; Veerkamp, R.F. ; Mol, R.M. de - \ 2017
In: Precision Livestock Farming '17. - - p. 366 - 376.
precision feeding - dairy cows - Big Data - prediction - machine learning
Three modelling approaches were used to estimate cow individual feed intake
(FI) using feeding trial data from a research farm, including weekly recordings
of milk production and composition, live-weight, parity, and total FI.
Additionally, weather data (temperature, humidity) were retrieved from the
Dutch National Weather Service (KNMI). The 2014 data (245 cows; 277
parities) were used for model development. The first model (M1) applied an
existing formula to estimate energy requirement using parity, fat and protein
corrected milk, and live-weight, and assumed this requirement to be equal to
energy intake and thus FI. The second model used ‘traditional’ Mixed Linear
Regression, first using the same variables as in M1 as fixed effects (MLR1), and
then by adding weather data (MLR2). The third model applied Boosted
Regression Tree, a ‘modern’ machine learning technique, again once with the
same variables as M1 (BRT1), and once with weather information added
(BRT2). All models were validated on 2015 data (155 cows; 165 parities) using
correlation between estimated and actual FI to evaluate performance. Both
MLRs had very high correlations (0.91) between actual and estimated FI on 2014
data, much higher than 0.46 for M1, and 0.73 for both BRTs. When validated on
2015 data, correlations dropped to 0.71 for MLR1 and 0.72 for MLR2, and
increased to 0.71 for M1 and 0.76 for both BRTs. FI estimated by BRT1 was, on
average, 0.35kg less (range: -7.61 – 13.32kg) than actual FI compared to 0.52kg
less (range: -11.67 – 19.87kg) for M1. Adding weather data did not improve FI
Big Data en voorspellen van voeropname
Kamphuis, Claudia - \ 2017
presentation powerpoint
Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows
Rutten, C.J. ; Kamphuis, C. ; Hogeveen, H. ; Huijps, K. ; Nielen, M. ; Steeneveld, W. - \ 2017
Computers and Electronics in Agriculture 132 (2017). - ISSN 0168-1699 - p. 108 - 118.
Calving management - Dairy farming - Wearable sensors

Management during calving is important for the health and survival of dairy cows and their calves. Although the expected calving date is known, this information is imprecise and farmers still have to check a cow regularly to identify when it starts calving. A sensor system that predicts the moment of calving could help farmers efficiently check cows for calving. Observation of a cow prior to calving is important because dystocia can occur, which requires timely intervention to mitigate adverse effects on both cow and calf. In this study, 400 cows on a Dutch dairy farm were equipped with sensors. The sensor was a single device in an ear tag, which synthesised cumulative activity, rumination activity, feeding activity, and temperature on an hourly basis. Data were collected during a one-year period. During this period, the starting moment of 417 calvings was recorded using camera images of the calving pen taken every 5 min. In total, 114 calving moments could be linked with sensor data. The moment at which calving started was defined as the first camera snapshot with visible evidence that the cow was having contractions or had started labor. Two logit models were developed: a model with the expected calving date as independent variable and a model with additional independent variables based on sensor data. The areas under the curves of the Receiver Operating Characteristic were 0.885 and 0.929 for these models, respectively. The model with expected calving date only had a sensitivity of 9.1%, whereas the model with additional sensor data has a sensitivity of 36.4%, both with a fixed false positive rate of 1%. Results indicate that the inclusion of sensor data improves the prediction of the start of calving; therefore the sensor data has value for the prediction of the moment of calving. The model with the expected calving date and sensor data had a sensitivity of 21.2% at a one-hour time window and 42.4% at a three-hour time window, both with a false positive rate of 1%. This indicates that prediction of the specific hour in which calving started was not possible with a high accuracy. The inclusion of sensor data improves the accuracy of a prediction of the start of calving, compared to a prediction based only on the expected calving date. Farmers can use the alerts of the predictive model as an indication that cows should be supervised more closely in the next hours.

Effect on bovine subclinical mastitis on milk yield and composition at the mammary quarter level
Gonsalves, J. ; Martins, C.M. ; Barreiro, J.R. ; Tomazi, T. ; Gameiro, A.H. ; Kamphuis, C. ; Hogeveen, H. ; Santos, M.V. dos - \ 2016
Shorter sampling periods and accurate estimates of milk volume and components are possible for pasture based dairy herds milked with automated milking systems
Kamphuis, C. ; Burke, J. ; Taukiri, S. ; Petch, S.F. ; Turner, S.A. - \ 2016
Journal of Dairy Research 83 (2016)3. - ISSN 0022-0299 - p. 326 - 333.
Dairy cows grazing pasture and milked using automated milking systems (AMS) have lower milking frequencies than indoor fed cows milked using AMS. Therefore, milk recording intervals used for herd testing indoor fed cows may not be suitable for cows on pasture based farms. We hypothesised that accurate standardised 24 h estimates could be determined for AMS herds with milk recording intervals of less than the Gold Standard (48 hs), but that the optimum milk recording interval would depend on the herd average for milking frequency. The Gold Standard protocol was applied on five commercial dairy farms with AMS, between December 2011 and February 2013. From 12 milk recording test periods, involving 2211 cow-test days and 8049 cow milkings, standardised 24 h estimates for milk volume and milk composition were calculated for the Gold Standard protocol and compared with those collected during nine alternative sampling scenarios, including six shorter sampling periods and three in which a fixed number of milk samples per cow were collected. Results infer a 48 h milk recording protocol is unnecessarily long for collecting accurate estimates during milk recording on pasture based AMS farms. Collection of two milk samples only per cow was optimal in terms of high concordance correlation coefficients for milk volume and components and a low proportion of missed cow-test days. Further research is required to determine the effects of diurnal variations in milk composition on standardised 24 h estimates for milk volume and components, before a protocol based on a fixed number of samples could be considered. Based on the results of this study New Zealand have adopted a split protocol for herd testing based on the average milking frequency for the herd (NZ Herd Test Standard 8100:2015).
Precision Dairy Farming 2016
Kamphuis, C. ; Steeneveld, W. - \ 2016
Wageningen : Wageningen Academic Publishers - ISBN 9789086862832 - 459 p.
The supply of new innovative precision dairy farming technologies is steadily increasing. It aims to help farmers to be more labour efficient and to support them in their daily management decisions. At the same time, since many technologies are developed from an engineering perspective, adoption of these technologies is sometimes limited since knowledge on economic benefits and farmers' needs is often incomplete. This book covers the current status of precision dairy farming technologies and what farmers expect from them. It also includes insights and future perspectives on managing, analysing, and combining sensor information. Moreover, new innovative ideas that may better fit farmers' needs and expectation are introduced, ranging from technologies or innovations that aim at improved animal health and welfare, to those technologies that result in a more efficient use of feed and improved grazing management.
Effects of a free-stall barn layout on the efficiency of Dutch dairy farms with an automatic milking systems
Heurkens, D. ; Kamphuis, C. ; Kamp, A.J. van der - \ 2016
In: Precision Dairy Farming 2016. - Wageningen : Wageningen Academic Publishers - ISBN 9789086862832 - p. 157 - 162.
Development of an investment model for automated mastitis detection systems
Eastwood, C.R. ; DeVeer, J. ; Neal, M. ; Rue, B.T. De la; Kamphuis, C. - \ 2016
In: Precision Dairy Farming 2016. - Wageningen : Wageningen Academic Publishers - ISBN 9789086862832 - p. 439 - 444.
Economic of Precision Dairy Farming Technologies: Principles to Determine the Economic Value of Sensor Technologies used on Dairy Farms
Voort, Mariska van der; Hogeveen, H. ; Kamphuis, C. - \ 2016
Precision Management Technologies
Field validation of protocols developed to evaluate in-line mastitis detection systems
Kamphuis, C. ; Dela Rue, B.T. ; Eastwood, C.R. - \ 2016
Journal of Dairy Science 99 (2016)2. - ISSN 0022-0302 - p. 1619 - 1631.
Automated mastitis detection - In-line sensors - Performance evaluation - Standard protocols

This paper reports on a field validation of previously developed protocols for evaluating the performance of in-line mastitis-detection systems. The protocols outlined 2 requirements of these systems: (1) to detect cows with clinical mastitis (CM) promptly and accurately to enable timely and appropriate treatment and (2) to identify cows with high somatic cell count (SCC) to manage bulk milk SCC levels. Gold standard measures, evaluation tests, performance measures, and performance targets were proposed. The current study validated the protocols on commercial dairy farms with automated in-line mastitis-detection systems using both electrical conductivity (EC) and SCC sensor systems that both monitor at whole-udder level. The protocol for requirement 1 was applied on 3 commercial farms. For requirement 2, the protocol was applied on 6 farms; 3 of them had low bulk milk SCC (128 × 103cells/mL) and were the same farms as used for field evaluation of requirement 1. Three farms with high bulk milk SCC (270 × 103 cells/mL) were additionally enrolled. The field evaluation methodology and results were presented at a workshop including representation from 7 international suppliers of in-line mastitis-detection systems. Feedback was sought on the acceptance of standardized performance evaluation protocols and recommended refinements to the protocols. Although the methodology for requirement 1 was relatively labor intensive and required organizational skills over an extended period, no major issues were encountered during the field validation of both protocols. The validation, thus, proved the protocols to be practical. Also, no changes to the data collection process were recommended by the technology supplier representatives. However, 4 recommendations were made to refine the protocols: inclusion of an additional analysis that ignores small (low-density) clot observations in the definition of CM, extension of the time window from 4 to 5 milkings for timely alerts for CM, setting a maximum number of 10 milkings for the time window to detect a CM episode, and presentation of sensitivity for a larger range of false alerts per 1,000 milkings replacing minimum performance targets. The recommended refinements are discussed with suggested changes to the original protocols. The information presented is intended to inform further debate toward achieving international agreement on standard protocols to evaluate performance of in-line mastitis-detection systems.

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