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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    Why not investing in sensors is logical for dairy farmers
    Steeneveld, W. ; Rutten, C.J. ; Oude Lansink, A.G.J.M. ; Hogeveen, H. - \ 2017
    - p. 55 - 65.
    Adoption - Dairy - Economics - Investment - Sensors

    There are many claims regarding the potential of sensors for dairy farming, but in general the adoption on farms is still low. For sensor manufacturers, it is important to realize that uncertainty about future technological progress may influence the adoption of sensors. The aim of the current study was to explain why dairy farmers do not (yet) adopt sensors on their farms. To explain this, the uncertainty of the investment decision for highly adopted sensors (automated estrus detection) and recently released and thus hardly adopted sensors (automated body condition scoring (BCS)) was illustrated. The illustration makes use of the real options theory to analyze the timing of investment decisions. The results indicated that investing now in automated estrus detection resulted in higher economic returns than investing 5 years from now, while for the automated BCS postponing the investment resulted in higher economic returns compared to investing now. These results show that farmers indicating that they did not (yet) invest in sensors because they are waiting for improved versions made rational decisions. Based on the illustration it is economically worthwhile to postpone investments in sensors for which there is much uncertainty. Also, the current high adoption of automated estrus detection sensors is logical because the net present value of investing now is higher than the net present value of investing in 5 years. This study illustrated that uncertainty about the benefits of a sensor system, potential future improvement of sensor technology, and expected better management information from a sensor system, form rational economic reasons to postpone investment in sensor systems.

    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.
    The utility of sensor technology to support reproductive management on dairy farms
    Rutten, C.J. - \ 2017
    Wageningen University. Promotor(en): H. Hogeveen; M. Nielen, co-promotor(en): W. Steeneveld. - Wageningen : Wageningen University - ISBN 9789463431934 - 232
    dairy cattle - dairy farms - sensors - reproduction - reproductive behaviour - animal health - calving - activity - management - dairy farming - technology - agricultural economics - melkvee - melkveebedrijven - sensors - voortplanting - voortplantingsgedrag - diergezondheid - kalven - activiteit - bedrijfsvoering - melkveehouderij - technologie - agrarische economie

    Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. Some sensors like activity meters, electrical conductivity, weight floors and somatic cell count sensors are commercially available. Adoption has in general been low and mainly driven by the AMS, with a clear exception for estrus detection. In practice, the economic benefits of using sensor systems has not been proven. So, to make sensors live up to their full potential there is a need for research to shift from technical development towards practical applications and integration with operational farm management. Estrus detection sensors can have a good detection performance and are currently applied by farmers in practice, therefore this thesis focusses on sensors that support reproductive management. The main objective of this thesis is to study the utility of sensor technology to support reproductive management on dairy farms. This main objective was split in five sub objectives that each study a part of the main objective and were discussed in the separate chapters of this thesis.

    We demonstrated that utility of sensors for reproductive management can be found in economic benefits (estrus and calving detection), reduction of labor (calving and estrus detection) and more detailed management information (prognosis of insemination success). So, automated estrus detection aids reproductive management.

    From this thesis the following conclusions can be drawn:

    The developed theoretical framework describes four levels of sensor development, which should all be included in proper development of sensor systems. The literature review showed that no studies developed sensor systems with regard to management and decision support.

    It was possible to improve the prediction of the start of calving compared to a model that only uses the expected calving date. However, predicting the start of calving within an hour was not possible with a high sensitivity and specificity.

    There was financial merit in the use of calving detection, because the sensor system enables more timely intervention by the farmer. The uncertainty about the positive effects was large, which caused a wide range in the simulated financial benefits.

    Investment in a sensor for estrus detection was on average profitable with a return on investment of 11%. Profitability was influenced most by the heuristic culling rules and the expected increase of the estrus detection rate between detection by visual observation and the sensor.

    Routinely collected farm data can be used to estimate a prognosis on insemination success and be used to determine whether an individual cow has a higher or lower than average likelihood of insemination success. Integration of this prognostic model with an estrus detection sensor has potential.

    Currently farmers only adopt sensors for estrus detection or because they were standard with an AMS. A reason for this is that sensor systems do not produce clear information for farmers. Sensor technology should be focused on management support of applications. Labor benefits of sensors are important for adoption of sensors by farmers, farmers value flexibility, increased family time and less physical workload as benefits. However, economic evaluations of technical solutions are unable to quantify these benefits. Sensor research should consider the preference of farmers regarding labor. For the appraisal of sensor technology new methods to value labor benefits of sensor are needed. Furthermore, in sensor development societal acceptance should be an important consideration. Animal rights activists may frame the use of sensors as a form of industrialized farming. Only using technical arguments and considerations to explain the benefits of sensors will hamper the societal acceptance of modern dairy farming. Application of sensors on dairy farms should be communicated smartly to society in terms that relate the values of citizens.

    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.

    A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data
    Rutten, C.J. ; Steeneveld, W. ; Vernooij, J.C.M. ; Huijps, K. ; Nielen, M. ; Hogeveen, H. - \ 2016
    Journal of Dairy Science 99 (2016)8. - ISSN 0022-0302 - p. 6764 - 6779.
    Dairy - Insemination success - Prognostic model - Reproduction

    A prognosis of the likelihood of insemination success is valuable information for the decision to start inseminating a cow. This decision is important for the reproduction management of dairy farms. The aim of this study was to develop a prognostic model for the likelihood of successful first insemination. The parameters considered for the model are readily available on farm at the time a farmer makes breeding decisions. In the first step, variables are selected for the prognostic model that have prognostic value for the likelihood of a successful first insemination. In the second step, farm effects on the likelihood of a successful insemination are quantified and the prognostic model is cross-validated. Logistic regression with a random effect for farm was used to develop the prognostic model. Insemination and test-day milk production data from 2,000 commercial Dutch dairy farms were obtained, and 190,541 first inseminations from this data set were used for model selection. The following variables were used in the selection process: parity, days in milk, days to peak production, production level relative to herd mates, milk yield, breed of the cow, insemination season and calving season, log of the ratio of fat to protein content, and body condition score at insemination. Variables were selected in a forward selection and backward elimination, based on the Akaike information criterion. The variables that contributed most to the model were random farm effect, relative production factor, and milk yield at insemination. The parameters were estimated in a bootstrap analysis and a cross-validation was conducted within this bootstrap analysis. The parameter estimates for body condition score at insemination varied most, indicating that this effect varied most among Dutch dairy farms. The cross-validation showed that the prognosis of insemination success closely resembled the mean insemination success observed in the data set. Insemination success depends on physiological conditions of the cow, which are approximated indirectly by production and reproduction data that are routinely recorded on the farm. The model cannot be used as a detection model to distinguish cows that conceive from cows that do not. The model validation indicates, however, that routinely collected farm data and test-day milk yield records have value for the prognosis of insemination success in dairy cows.

    The potential of using sensor data to predict the moment of calving for dairy cows
    Rutten, C.J. ; Steeneveld, W. ; Kamphuis, C. ; Huijps, K. ; Hogeveen, H. - \ 2015
    In: Precision Livestock Farming Applications / Halachmi, Ilan, Wageningen : Wageningen Academic Publishers - ISBN 9789086862689 - p. 161 - 167.
    On dairy farms, management of calving is important for the health of dairy cows and the survival rate of calves born. Although an expected calving date is known, farmers need to check their cows regularly to estimate the moment when a cow will start calving. A sensor system which predicts the moment of calving could help farmers to check cows effectively for the occurrence of dystocia. In this study, a total of 450 cows on two farms were equipped with Agis SensOor sensors (Agis Automatisering B.V., Harmelen, the Netherlands), which measure rumination activity, activity and temperature hourly. Data were collected over a one-year period. During that period, the exact moment of 417 calvings was recorded using camera images of the calving pen taken every 5 minutes. In total 110 calvings could be linked with sensor data. The moment when calving started was defined as the hour in which the camera images showed the cow having contractions or labour initially started. Two logit models were developed: a reduced model with the expected calving date as the independent variable and a full model which additionally included independent variables based on sensor data. The areas under the Receiver Operating Characteristic curves were 0.682 and 0.878 for the reduced and full model with, at a false positive rate of 10%, sensitivities of 22 and 69%, respectively. Results indicated that the inclusion of sensor data improved prediction of the start of calving and thus that the sensor data used have some potential for predicting the moment of calving.
    Development of a predictive model for the onset of calving
    Rutten, C.J. ; Steeneveld, W. ; Huijps, K. ; Hogeveen, H. - \ 2015
    - p. 397 - 405.
    The potential of using sensor data to predict the moment of calving for dairy cows
    Rutten, C.J. ; Steeneveld, W. ; Kamphuis, C. ; Hogeveen, H. - \ 2014
    In: Book of Abstracts of the 65th Annual Meeting of the European Federation of Animal Science. - EAAP (Book of Abstracts 20) - p. 150 - 150.
    An ex ante analysis on the use of activity meters for automated estrus detection, to invest or not to invest?
    Rutten, C.J. ; Steeneveld, W. ; Inchaisri, C. ; Hogeveen, H. - \ 2014
    Journal of Dairy Science 97 (2014)11. - ISSN 0022-0302 - p. 6869 - 6887.
    timed artificial-insemination - dairy farms - decision-making - information-technology - reproductive programs - stochastic simulation - cows - cattle - herds - model
    The technical performance of activity meters for automated detection of estrus in dairy farming has been studied, and such meters are already used in practice. However, information on the economic consequences of using activity meters is lacking. The current study analyzes the economic benefits of a sensor system for detection of estrus and appraises the feasibility of an investment in such a system. A stochastic dynamic simulation model was used to simulate reproductive performance of a dairy herd. The number of cow places in this herd was fixed at 130. The model started with 130 randomly drawn cows (in a Monte Carlo process) and simulated calvings and replacement of these cows in subsequent years. Default herd characteristics were a conception rate of 50%, an 8-wk dry-off period, and an average milk production level of 8,310 kg per cow per 305 d. Model inputs were derived from real farm data and expertise. For the analysis, visual detection by the farmer (“without” situation) was compared with automated detection with activity meters (“with” situation). For visual estrus detection, an estrus detection rate of 50% and a specificity of 100% were assumed. For automated estrus detection, an estrus detection rate of 80% and a specificity of 95% were assumed. The results of the cow simulation model were used to estimate the difference between the annual net cash flows in the “with” and “without” situations (marginal financial effect) and the internal rate of return (IRR) as profitability indicators. The use of activity meters led to improved estrus detection and, therefore, to a decrease in the average calving interval and subsequent increase in annual milk production. For visual estrus detection, the average calving interval was 419 d and average annual milk production was 1,032,278 kg. For activity meters, the average calving interval was 403 d and the average annual milk production was 1,043,398 kg. It was estimated that the initial investment in activity meters would cost €17,728 for a herd of 130 cows, with an additional cost of €90 per year for the replacement of malfunctioning activity meters. Changes in annual net cash flows arising from using an activity meter included extra revenues from increased milk production and number of calves sold, increased costs from more inseminations, calvings, and feed consumption, and reduced costs from fewer culled cows and less labor for estrus detection. These changes in cash flows were caused mainly by changes in the technical results of the simulated dairy herds, which arose from differences in the estrus detection rate and specificity between the “with” and “without” situations. The average marginal financial effect in the “with” and “without” situations was €2,827 for the baseline scenario, with an average IRR of 11%. The IRR is a measure of the return on invested capital. Investment in activity meters was generally profitable. The most influential assumptions on the profitability of this investment were the assumed culling rules and the increase in sensitivity of estrus detection between the “without” and the “with” situation.
    Analysis of investment in an estrus detection system for dairy farms
    Rutten, C.J. ; Steeneveld, W. ; Inchaisri, C. ; Hogeveen, H. - \ 2013
    In: Proceedings of the Precision Dairy Conference and Expo. - Rochester : University of Minnesota - p. 177 - 178.
    Sensor systems for dairy cow health management: A review
    Rutten, C.J. ; Velthuis, A.G.J. ; Steeneveld, W. ; Hogeveen, H. - \ 2013
    In: Proceedings of the Precision Dairy Conference and Expo. - Rochester : University of Minnesota - p. 89 - 90.
    Overview of published sensor systems for detection of oestrus and lameness in dairy cows
    Rutten, C.J. ; Velthuis, A.G.J. ; Steeneveld, W. ; Hogeveen, H. - \ 2013
    In: Proceedings of the 6th European Conference on Precision Livestock Farming. - - p. 163 - 171.
    Analysis of investment in an oestrus detection system for dairy farms
    Rutten, C.J. ; Steeneveld, W. ; Inchaisri, C. ; Hogeveen, H. - \ 2013
    In: Proceedings of the European Conference of Precision Livestock Farming. - - p. 124 - 132.
    Analysis of investment in an estrus detection system for dairy
    Rutten, Niels - \ 2013
    Sensor systems for dairy cow health management: a review
    Rutten, Niels - \ 2013
    Overview of published sensor systems for detection of oestrus and lameness in dairy cows
    Rutten, Niels - \ 2013
    Analysis of investment in a oestrus detection system for dairy farms
    Rutten, Niels - \ 2013
    Can sensor technology benefit mastitis control
    Rutten, Niels - \ 2013
    Tochtdetectie met sensoren rendeert
    Rutten, C.J. ; Steeneveld, W. ; Hogeveen, H. - \ 2013
    Veeteelt 30 (2013)16. - ISSN 0168-7565 - p. 26 - 27.
    melkveehouderij - melkkoeien - vruchtbaarheid - oestrus - detectie - sensors - rendement - investering - dairy farming - dairy cows - fertility - oestrus - detection - sensors - returns - investment
    Sensoren die de veehouder helpen bij het opsporen van tochtige koeien, doen meestal hun werk, blijkt uit diverse onderzoeken. Maar verdient de investering zichzelf terug? Uit onderzoek van de faculteit Diergeneeskunde blijkt dat het antwoord 'ja' is.
    Invited review: Sensors to support health management on dairy farms
    Rutten, C.J. ; Velthuis, A.G.J. ; Steeneveld, W. ; Hogeveen, H. - \ 2013
    Journal of Dairy Science 96 (2013)4. - ISSN 0022-0302 - p. 1928 - 1952.
    automatic milking systems - clinical mastitis detection - economic decision-making - lactating holstein cows - somatic-cell count - estrus detection - electrical-conductivity - bovine-milk - subclinical mastitis - ruminal ph
    Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow’s status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found. Key words: automated
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