36 results on '"van der Voort, Mariska"'
Search Results
2. Quantifying the economic and animal welfare trade-offs of classification models in precision livestock farming for sub-optimal mobility management
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Edwardes, Francis, van der Voort, Mariska, Hogeveen, Henk, Edwardes, Francis, van der Voort, Mariska, and Hogeveen, Henk
- Abstract
Precision livestock farming (PLF) offers a sensor-based management approach to potentially mitigate the negative economic and animal welfare consequences of sub-optimal mobility (SOM). Human-based SOM classification is often done using more than two classes (i.e., mobility scores 1–5, where 1 = optimal and 5 = severely impaired mobility), while binary classification is ultimately used in sensor-based classification. Previous economic research shows that classifying SOM as a binary problem in sensor-based management has little to no economic value while non-binary SOM classification may be more economically beneficial. However, the animal welfare implications of a non-binary SOM classification approach are unknown. In this study, we assess whether economic and welfare gains can be achieved by using 3-class SOM classifiers (i.e., sensors) for sensor-based SOM management compared with the current no-sensor SOM management. With respect to mobility scores 1–5, three SOM classes (K1 = non-SOM, K2 = SOM, and K3 = severe-SOM) along with two management scenarios, with four different classifiers each, were defined. Mobility scores 1–5 were grouped into one of three SOM classes depending on the classifier. In management scenario one, mobility scores 1 and 2, were grouped to K1, while mobility score 3 was grouped to K2 and mobility scores 4 and 5 to K3. In management scenario two, mobility scores 2 and 3 were grouped to K2. In both management scenarios, alerts for cows classified to SOM class K2 were generated every 7 days based on an alert prioritisation criterion, while alerts for cows classified to SOM class K3 were generated daily. Treatment options followed the generation of either weekly or daily alerts. For each of the eight classifiers (i.e., 4 classifiers per management scenario) 600 classification outcomes were defined. A bio-economic simulation model was used to simulate the economic and welfare effects of the various classifiers and classification outcomes respectiv
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- 2024
3. The costs of chronic mastitis : A simulation study of an automatic milking system farm
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Bonestroo, John, Fall, Nils, Hogeveen, Henk, Emanuelson, Ulf, Klaas, Ilka Christine, van der Voort, Mariska, Bonestroo, John, Fall, Nils, Hogeveen, Henk, Emanuelson, Ulf, Klaas, Ilka Christine, and van der Voort, Mariska
- Abstract
Mastitis is a production disease in dairy farming that causes economic losses. Especially chronic mastitis (i.e., mastitis cases continuing longer than 28 days) can substantially affect the risk of transmission of intramammary infections (IMI) and total milk production losses. Insights into the impact of chronic mastitis on production and farm economics are needed to guide chronic mastitis decision-making. We aimed to estimate the costs of chronic mastitis with a Monte Carlo simulation model in which the costs of chronic mastitis were estimated as part of the total mastitis costs. The model simulated milk yields, IMI dynamics, somatic cell count (SCC), and pregnancy status on an average Dutch dairy farm with 100 cow places over 9 years. The model was parameterized using information from the literature and actual sensor data from automatic milking system (AMS) farms. The daily subclinical milk production losses were modeled using a generalized additive model and sensor data. Transmission of IMI was modeled as well. The model results indicated median total costs of mastitis of € 230 per generic IMI case (i.e., a weighted average of all pathogens). The most substantial cost factors were the extra mastitis cases due to transmission, culling, and milk production losses. Other significant costs originated from dry cow treatments and diverted milk. The model also indicated median total costs due to chronic mastitis of € 118 (51 % of the total mastitis costs). The share of chronic mastitis relative to the total mastitis costs was substantial. Transmission of contagious bacteria had the largest share among the chronic mastitis costs (51 % of the costs of chronic cases). The large share of chronic mastitis costs in the total mastitis costs indicates the economic importance of these mastitis cases. The results of the study point to the need for future research to focus on chronic mastitis and reducing its presence on the AMS dairy farm.
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- 2023
4. Impact of information and communication technologies on fertilizer and pesticide use efficiency of China's grain production
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Zhu, Qiubo, van der Voort, Mariska, Ren, Guangcheng, Bai, Junfei, Zhu, Qiubo, van der Voort, Mariska, Ren, Guangcheng, and Bai, Junfei
- Abstract
Based on a panel data from National Rural Fixed Point Survey (NRFP), this study explored the effects of information and communication technologies (ICTs) on the fertilizer and pesticide use efficiency of China's grain production and the mechanisms using stochastic frontier analysis (SFA) and two-way fixed-effects model with two-stage least square (2SLS) estimation. The results show that the average fertilizer use efficiency (FE), pesticide use efficiency (PE) and comprehensive fertilizer and pesticide use efficiency (CFPE) of grain production were 0.286, 0.404, and 0.364 respectively during 2003–2011. It should be noted that the CFPE decreased by 48.39% during 2003–2011 and showed a descending trend in all regions. ICTs had significant positive effects on FE, PE, and CPFE which could be explained by providing farmers more sustainable knowledge and hence shifting farming practices from overusing fertilizer towards using farmyard manure as a substitute. ICTs' positive effects were more pronounced for farmers with higher-level education and in central region. Additionally, ICTs had significant spillover effects, extending from users to nonusers within the villages. These results suggest that ICTs could be considered as an effective way to increase the fertilizer and pesticide use efficiency and promote the sustainable development of agriculture in China.
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- 2023
5. Estimating the Effect of a Bovine Viral Diarrhea Virus Control Program: An Empirical Study on the Performance of Dutch Dairy Herds
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Yue, Xiaomei, primary, Wu, Jingyi, additional, van der Voort, Mariska, additional, Steeneveld, Wilma, additional, and Hogeveen, Henk, additional
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- 2022
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6. Estimating the Effect of a Bovine Viral Diarrhea Virus Control Program: An Empirical Study on the Performance of Dutch Dairy Herds
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Yue, Xiaomei, Wu, Jingyi, van der Voort, Mariska, Steeneveld, Wilma, Hogeveen, Henk, Yue, Xiaomei, Wu, Jingyi, van der Voort, Mariska, Steeneveld, Wilma, and Hogeveen, Henk
- Abstract
More and more European countries have implemented a bovine viral diarrhea virus (BVDV) control program. The economic effects of such programs have been evaluated in simulations, but empirical studies are lacking, especially in the final stage of the program. We investigated the economic (gross margin) and production effects (milk yield, somatic cell count, and calving interval) of the herds obtaining BVDV-free certification based on longitudinal annual accounting and herd performance data from Dutch dairy herds between 2014 and 2019, the final stages of the Dutch national BVDV-free program. This study was designed as a case-control study: two types of case herds were defined for two analyses. The case herds in the first analysis are herds where the BVDV status changed from “BVDV not free” to “BVDV free” during the study period. The not-free status refers to a herd that participated in the BVDV-free program but had not yet obtained the BVDV-free certification. In the second analysis, the case herds started participating in the Dutch BVDV-free program during the study period and obtained the BVDV-free certification. Control herds in both analyses were BVDV-free during the entire study period. Potential bias between the covariates of the two herd groups was reduced by matching case and control herds using the propensity score matching method. To compare the differences between case and control herds before and after BVDV-free certification, we used the time-varying Difference-in-Differences estimation (DID) methodology. The results indicate that there was no significant change in milk yield, somatic cell count, calving interval, and gross margin upon BVDV-free certification. There are several possible explanations for the non-significant effects observed in our study, such as the final stage of the BVDV control program, not knowing the true BVDV infection situation in case herds and not knowing if control measures were implemented in case herds prior to participating i
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- 2022
7. Forecasting chronic mastitis using automatic milking system sensor data and gradient-boosting classifiers
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Bonestroo, John, van der Voort, Mariska, Hogeveen, Henk, Emanuelson, Ulf, Klaas, Ilka Christine, Fall, Nils, Bonestroo, John, van der Voort, Mariska, Hogeveen, Henk, Emanuelson, Ulf, Klaas, Ilka Christine, and Fall, Nils
- Abstract
Although most of the losses due to mastitis per case in dairy production are estimated to be caused by clinical cases, subclinical cases, especially chronic, can also be problematic due to milk production losses and the risk of transmission of pathogens. Knowing which subclinical mastitis cases will become chronic at an early stage would be helpful in intervening in these cases. Automatic milking systems (AMS) can collect data on mastitis indicators such as conductivity, Somatic cell count (SCC), and blood in the milk for each milking. The aim of this study was to develop a sensor-based prediction model using SCC, conductivity, blood in the milk, parity, milk diversion, time interval between milkings, milk yield and DIM that forecasts the chronicity in subclinical mastitis cases after an initial increase in SCC. We used sensor data from 14 European and North American dairy farms (with herd sizes of lactating cows ranging from 55 to 638 cows and herd mean parities between 2.00 and 3.19) with an AMS and an online cell counter, measuring SCC. Typically, a threshold of 200,000 SCC/ml has been used to distinguish cows with subclinical mastitis from healthy cows. We used gradient-boosting trees and sensor data to forecast whether the SCC would decrease structurally below 200,000 SCC/ml in 50 days after the day at which the prediction was performed. Data from 30 and 15 days prior to the day where the forecast was made, was used. The model was trained on data from seven randomly selected dairy farms from the dataset and the data of the remaining seven dairy farms were used to estimate the predictive performance. These results were compared with two approaches that simulate how farmers would diagnose chronic mastitis with a simple prediction rule based on close-to-daily SCC (frequent sampling approach), and on less frequent monthly SCC (monthly sampling approach). We used accuracy, Matthew's correlation coefficient (MCC), and Area under the Curve (AUC) as metrics to assess t
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- 2022
8. Simulating the mechanics behind sub-optimal mobility and the associated economic losses in dairy production
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Edwardes, Francis, van der Voort, Mariska, Halasa, Tariq, Holzhauer, Menno, Hogeveen, Henk, Edwardes, Francis, van der Voort, Mariska, Halasa, Tariq, Holzhauer, Menno, and Hogeveen, Henk
- Abstract
Hoof disorders and sub-optimal mobility (SOM) are economically important health issues in dairy farming. Although the dynamics of hoof disorders have an important effect on cow mobility, they have not been considered in previous simulation models that estimate the economic loss of SOM. Furthermore, these models do not consider the varying severities of SOM. The objective of this study was to develop a novel bio-economic simulation model to simulate the dynamics of 8 hoof disorders: digital dermatitis (DD), interdigital hyperplasia (HYP), interdigital dermatitis/heel-horn erosion (IDHE), interdigital phlegmon (IP), overgrown hoof (OH), sole haemorrhage (SH), sole ulcer (SU) and white-line disease (WLD), their role in SOM, and estimate the economic loss of SOM in a herd of 125 dairy cows. A Reed-Frost model was used for DD and a Greenwood model for the other 7 hoof disorders. Economic analysis was conducted per mobility score according to a 5-point mobility scoring method (1 = perfect mobility; 5 = severely impaired mobility) by comparing a scenario with SOM and one without SOM. Parameters used in the model were based on literature and expert opinion and deemed credible during model validation rounds. Results showed that the mean cumulative incidence for maximum mobility scores 2–5 SOM episodes were respectively 34, 16, 7 and <1 episodes per 100 cows per pasture period and 39, 19, 8, <1 episodes per 100 cows per housing period. The mean total annual economic loss due to SOM resulting from the hoof disorders under study was €15,342: €122 per cow per year. The economic analysis uncovered direct economic losses that could be directly linked to SOM episodes and indirect economic losses that could not be directly linked to SOM episodes but arose due to the presence of SOM. The mean total annual direct economic loss for maximum mobility score 2–5 SOM episodes was €1129, €3098, €4354 and €480, respectively. The mean total annual indirect economic loss varied considerably bet
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- 2022
9. Machine learning-based farm risk management : A systematic mapping review
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Ghaffarian, Saman, van der Voort, Mariska, Valente, João, Tekinerdogan, Bedir, de Mey, Yann, Ghaffarian, Saman, van der Voort, Mariska, Valente, João, Tekinerdogan, Bedir, and de Mey, Yann
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Farms face various risks such as uncertainties in the natural growth process, obtaining adequate financing, volatile input and output prices, unpredictable changes in farm-related policy and regulations, and farmers‘ personal health problems. Accordingly, farmers have to make decisions to be prepared for such situations under risk or mitigate their impacts to maintain essential functions. Increasingly, a data-driven perspective is warranted where machine learning (ML) has become an essential tool for automatic extraction of useful information to support decision-making in farm management as well as risk management. ML's role in farm risk management (FRM) has recently increased with advances in technology and digitalization. This paper provides a literature review in the form of a systematic mapping study to identify the publications, trends, active research communities, and detailed reviews on the use of ML methods for FRM. Accordingly, nine research/mapping questions are designed to extract the required information. In total, we retrieved 1819 papers, of which 746 papers were selected based on the defined exclusion criteria for a detailed review. We categorized the studies based on the addressed risk types (e.g., production risk), assessments that addressed risk components (e.g., resilience), used ML types (e.g., supervised learning) and algorithms ranging from regression modeling to deep learning, addressed ML tasks (e.g., classification), data types (e.g., images), and farm types (e.g., crop-based farm). The results reveal that there is a significant increase in employing ML methods including deep learning and convolutional neural networks for FRM in recent years. The production risk and impact/damage assessment are the most frequently addressed risk type and assessment that addressed risk components in ML-FRM, respectively. In addition, research gaps and open problems are identified and accordingly insights and recommendations from risk management and machine le
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- 2022
10. Estimating the nonlinear association of online somatic cell count, lactate dehydrogenase, and electrical conductivity with milk yield
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Bonestroo, John, van der Voort, Mariska, Fall, Nils, Emanuelson, Ulf, Klaas, Ilka Christine, Hogeveen, Henk, Bonestroo, John, van der Voort, Mariska, Fall, Nils, Emanuelson, Ulf, Klaas, Ilka Christine, and Hogeveen, Henk
- Abstract
Reduction of milk yield is one of the principal components in the cost of mastitis. However, past research into the association between milk yield and mastitis indicators is limited. Past research has not been based on online or in-line daily measurements and has not fully explored nonlinearity and the thresholds at which milk yield starts to decrease. In dairy herds with automated milking systems equipped with sensors, mastitis indicators of individual cows are measured on an intraday frequency, which provides unprecedented avenues to explore such effects in detail. The aim of this observational study was primarily to investigate the nonlinear associations of lactate dehydrogenase (LDH), electrical conductivity (EC), and somatic cell count (SCC) with milk yield at various stages of lactation, parity, and mastitis chronicity status (i.e., whether the cow had SCC ≥200,000 SCC/mL for the last 28 d). We also investigated thresholds at which mastitis indicators (LDH, EC, and SCC) started to be negatively associated with milk yield. We used data from 21 automated milking system herds measuring EC and online SCC. Of these herds, 7 of the 21 additionally measured online LDH. We operationalized milk yield as milk synthesis rate in kilograms per hour. Applying a generalized additive model, we estimated the milk synthesis rate as a function of the 3 mastitis indicators for 3 different subgroups based on parity, stage of lactation, and mastitis chronicity. Partial dependence plots of the mastitis indicators were used to evaluate the milk synthesis rate to study if the milk synthesis rate was associated with mastitis indicators at a specific level. Results showed that milk synthesis rate decreased with increasing SCC, LDH, and EC, but in a nonlinear fashion. The thresholds at which milk synthesis rate started to decrease were 2.5 LnSCC (12,000 SCC/mL) to 3.75 LnSCC (43,000 SCC/mL), 0 to 1 LnLDH (1−2.7 U/L), and 5.0 to 6.0 mS/cm for EC. Additionally, another substantial decrease
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- 2022
11. Estimating the nonlinear association of online somatic cell count, lactate dehydrogenase, and electrical conductivity with milk yield
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Bonestroo, John, primary, van der Voort, Mariska, additional, Fall, Nils, additional, Emanuelson, Ulf, additional, Klaas, Ilka Christine, additional, and Hogeveen, Henk, additional
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- 2022
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12. The effect of new bovine viral diarrhea virus introduction on somatic cell count, calving interval, culling, and calf mortality of dairy herds in the Dutch bovine viral diarrhea virus–free program
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Yue, Xiaomei, primary, van der Voort, Mariska, additional, Steeneveld, Wilma, additional, van Schaik, Gerdien, additional, Vernooij, Johannes C.M., additional, van Duijn, Linda, additional, and Hogeveen, Henk, additional
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- 2021
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13. Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review
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Ghaffarian, Saman, primary, Valente, João, additional, van der Voort, Mariska, additional, and Tekinerdogan, Bedir, additional
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- 2021
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14. The effect of bovine viral diarrhea virus introduction on milk production of Dutch dairy herds
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Yue, Xiaomei, Steeneveld, Wilma, van der Voort, Mariska, van Schaik, Gerdien, Vernooij, Johannes C.M., van Duijn, Linda, Veldhuis, Anouk M.B., Hogeveen, Henk, FAH Evidence based Veterinary Medicine, dFAH AVR, FAH veterinaire epidemiologie, Dep Gezondheidszorg Landbouwhuisdieren, FAH Evidence based Veterinary Medicine, dFAH AVR, FAH veterinaire epidemiologie, and Dep Gezondheidszorg Landbouwhuisdieren
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Yield (finance) ,viruses ,animal diseases ,Bedrijfseconomie ,WASS ,Biology ,Antibodies, Viral ,Virus ,Herd immunity ,03 medical and health sciences ,bovine viral diarrhea virus introduction ,Animal science ,Milk yield ,Business Economics ,Genetics ,Animals ,control program ,milk production ,Viral diarrhea ,030304 developmental biology ,0303 health sciences ,Diarrhea Viruses, Bovine Viral ,Dairy herds ,0402 animal and dairy science ,virus diseases ,04 agricultural and veterinary sciences ,Milk production ,040201 dairy & animal science ,Dairying ,Milk ,bovine viral diarrhea virus ,Herd ,Bovine Virus Diarrhea-Mucosal Disease ,Cattle ,Female ,Animal Science and Zoology ,Food Science - Abstract
Dairy cows are negatively affected by the introduction of bovine viral diarrhea virus (BVDV), and consequently, produce less milk. Existing literature on potential milk production losses is based on relatively outdated data and hardly evaluates milk production loss in relation to a new BVDV infection in a surveillance system. This study determined the annual and quarterly loss in milk production of BVDV introduction in 3,126 dairy herds participating in the Dutch BVDV-free program between 2007 and 2017. Among these herds, 640 were "breakdown-herds" that obtained and subsequently lost their BVDV-free status during the study period, and 2,486 herds obtained and retained their BVDV-free status during the study period. Milk yields before and after BVDV introduction were compared through annual and quarterly linear mixed models. The fixed variables for both models included herd type (breakdown-herd or free-herd), bovine viral diarrhea status (on an annual and quarterly basis), year, season, and a random herd effect. The dependent variable was the average daily milk yield on the test day. To define the possible BVDV-introduction dates, 4 scenarios were developed. In the default scenario, the date of breakdown (i.e., loss of the BVDV-free status) was assumed as the BVDV-introduction date. For the other 3 scenarios, the BVDV-introduction dates were set at 4, 6, and 9 mo before the date of breakdown, based on the estimated birth date of a persistently infected calf. In the default scenario, the loss in milk yield due to BVDV introduction occurred mainly in the first year after breakdown, with a reduction in yield of 0.08 kg/cow per day compared with the last year before breakdown. For the other 3 scenarios, the greatest yield reduction occurred in the second year after BVDV introduction, with a loss of 0.09, 0.09, and 0.1 kg/cow per day, respectively. For the first 4 quarters after BVDV introduction in the default scenario, milk yield loss was 0.14, 0.09, 0.02, and 0.08 kg/cow per day, respectively. These quarterly results indicated that milk yield loss was greatest in the first quarter after BVDV introduction. Overall, BVDV introduction had a negative, but on average a relatively small, effect on milk yield for herds participating in the BVDV-free program. This study will enable dairy farmers and policymakers to have a clearer understanding of the quantitative milk production effect of BVDV on dairy farms in a control program.
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- 2021
15. Digital twins in agri-food : Societal and ethical themes and questions for further research
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Van Der Burg, Simone, Kloppenburg, Sanneke, Kok, Esther J., Van Der Voort, Mariska, Van Der Burg, Simone, Kloppenburg, Sanneke, Kok, Esther J., and Van Der Voort, Mariska
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Digital Twins are computational representations of both living and non-living entities and processes, which can be used to analyse and simulate interventions in these entities and processes. When developing Digital Twins, it is important to anticipate on the societal, ethical and safety impacts they may have. Since in the agri-food domain Digital Twins are still in its infancy, it is possible to include societal values from the beginning onwards, during the research and development process. In this paper, we present four themes (i.e. resources, representations, actions and implementations) to organise the anticipation of and reflection on potential impacts of Digital Twins in the agri-food domain. Using insights from the smart farming literature, we assess for each theme which issues and questions require further research and attention, in order to develop an agenda for responsible research and innovation on Digital Twins.
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- 2021
16. The effect of new bovine viral diarrhea virus introduction on somatic cell count, calving interval, culling, and calf mortality of dairy herds in the Dutch bovine viral diarrhea virus–free program
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Yue, Xiaomei, van der Voort, Mariska, Steeneveld, Wilma, van Schaik, Gerdien, Vernooij, Johannes C.M., van Duijn, Linda, Hogeveen, Henk, Yue, Xiaomei, van der Voort, Mariska, Steeneveld, Wilma, van Schaik, Gerdien, Vernooij, Johannes C.M., van Duijn, Linda, and Hogeveen, Henk
- Abstract
Bovine viral diarrhea virus (BVDV) infection has a major effect on the health of cows and consequently on herd performance. Many countries have implemented control or eradication programs to mitigate BVDV infection and its negative effects. These negative effects of BVDV infection on dairy herds are well documented, but there is much less information about the effects of new introduction of BVDV on dairy herds already participating in a BVDV control program. The objective of our study was to investigate the effect of a new BVDV introduction in BVDV-free herds participating in the Dutch BVDV-free program on herd performance. Longitudinal herd-level surveillance data were combined with herd information data to create 4 unique data sets, including a monthly test-day somatic cell count (SCC) data set, annual calving interval (CIV) and culling risk (CR) data sets, and a quarterly calf mortality rate (CMR) data set. Each database contained 2 types of herds: herds that remained BVDV free during the whole study period (defined as free herds), and herds that lost their BVDV-free status during the study period (defined as breakdown herds). The date of losing the BVDV-free status was defined as breakdown date. To compare breakdown herds with free herds, a random breakdown date was artificially generated for free herds by simple random sampling from the distribution of the breakdown month of the breakdown herds. The SCC and CIV before and after a new introduction of BVDV were compared through linear mixed-effects models with a Gaussian distribution, and the CR and CMR were modeled using a negative binomial distribution in generalized linear mixed-effects models. The explanatory variables for all models included herd type, BVDV status, year, and a random herd effect. Herd size was included as an explanatory variable in the SCC, CIV, and CMR model. Season was included as an explanatory variable in the SCC and CMR model. Results showed that free herds have lower SCC, CR, CMR, and
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- 2021
17. Diagnostic properties of milk diversion and farmer-reported mastitis to indicate clinical mastitis status in dairy cows using Bayesian latent class analysis
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Bonestroo, John, Fall, Nils, van der Voort, Mariska, Klaas, Ilka Christine, Hogeveen, Henk, Emanuelson, Ulf, Bonestroo, John, Fall, Nils, van der Voort, Mariska, Klaas, Ilka Christine, Hogeveen, Henk, and Emanuelson, Ulf
- Abstract
The development of digital farming gives bovine mastitis research and management tools access to large datasets. However, the quality of registered data on clinical mastitis cases or treatments may be inadequate (e.g. due to missing records). In automatic milking systems, the decision to divert milk from the bulk milk tank during milking is registered (i.e. milk diversion indicator) for every milking and could potentially indicate a clinical mastitis case. This study accordingly estimated the diagnostic performance of a milk diversion indicator in relation to farmer-recorded clinical mastitis cases in the absence of a “gold standard”. Data on milk diversion and farmer-reported clinical mastitis from 3,443 lactations in 13 herds were analyzed. Each cow lactation was split into 30-DIM periods in which it was registered whether milk was diverted and whether clinical mastitis was reported. One 30-DIM period was randomly sampled for each lactation and this was the unit of analysis, this procedure was repeated 300 times, resulting in 300 datasets to create autocorrelation-robust results during analysis. We used Bayesian latent class analysis to assess the diagnostic properties of milk diversion and farmer-reported clinical status. We analyzed different episode lengths of milk diversion of 1 or more milk diversion days until 10 or more milk diversion days for two scenarios: farmers with poor-quality (51% sensitivity, 99% specificity) and high-quality (90% sensitivity, 99% specificity) mastitis registrations. The analysis was done for all 300 datasets. The results showed that for the scenario where the quality of clinical mastitis reporting was high, the sensitivity was similar for milk-diversion threshold durations of 1–4 days (0.843 to 0.793 versus 0.893). Specificity increased when the number of days of milk diversion increased and was ≥98% at a milk-diversion threshold durations of 8 or more consecutive milk diversion days. In the scenario where the quality of clinical
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- 2021
18. Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review
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Ghaffarian, Saman, Valente, João, Van Der Voort, Mariska, Tekinerdogan, Bedir, Ghaffarian, Saman, Valente, João, Van Der Voort, Mariska, and Tekinerdogan, Bedir
- Abstract
Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when
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- 2021
19. Data underlying the manuscript: The effect of new bovine viral diarrhea virus (BVDV) introduction on somatic cell count, calving interval, culling rate and calf mortality of dairy herds in the Dutch BVDV-free program
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Yue, Xiaomei, Steeneveld, Wilma, van der Voort, Mariska, van Schaik, Gerdien, Vernooij, Hans, van Duijn, Linda, Hogeveen, Henk, Yue, Xiaomei, Steeneveld, Wilma, van der Voort, Mariska, van Schaik, Gerdien, Vernooij, Hans, van Duijn, Linda, and Hogeveen, Henk
- Abstract
Supplemental material for the paper titled "The effect of new bovine viral diarrhea virus (BVDV) introduction on somatic cell count, calving interval, culling rate and calf mortality of dairy herds in the Dutch BVDV-free program".
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- 2021
20. Progression of different udder inflammation indicators and their episode length after onset of inflammation using automatic milking system sensor data
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Bonestroo, John, primary, van der Voort, Mariska, additional, Fall, Nils, additional, Hogeveen, Henk, additional, Emanuelson, Ulf, additional, and Klaas, Ilka Christine, additional
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- 2021
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21. The effect of bovine viral diarrhea virus introduction on milk production of Dutch dairy herds
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FAH Evidence based Veterinary Medicine, dFAH AVR, FAH veterinaire epidemiologie, Dep Gezondheidszorg Landbouwhuisdieren, Yue, Xiaomei, Steeneveld, Wilma, van der Voort, Mariska, van Schaik, Gerdien, Vernooij, Johannes C.M., van Duijn, Linda, Veldhuis, Anouk M.B., Hogeveen, Henk, FAH Evidence based Veterinary Medicine, dFAH AVR, FAH veterinaire epidemiologie, Dep Gezondheidszorg Landbouwhuisdieren, Yue, Xiaomei, Steeneveld, Wilma, van der Voort, Mariska, van Schaik, Gerdien, Vernooij, Johannes C.M., van Duijn, Linda, Veldhuis, Anouk M.B., and Hogeveen, Henk
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- 2020
22. Data underlying the publication: The effect of bovine viral diarrhea virus introduction on milk production of Dutch dairy herds
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Yue, Xiaomei, Steeneveld, Wilma, van der Voort, Mariska, van Schaik, G., Vernooij, J.C.M., van Duijn, C., Veldhuis, A.M.B., Hogeveen, Henk, Yue, Xiaomei, Steeneveld, Wilma, van der Voort, Mariska, van Schaik, G., Vernooij, J.C.M., van Duijn, C., Veldhuis, A.M.B., and Hogeveen, Henk
- Abstract
Supplemental material for the paper titled "The impact of bovine viral diarrhea virus introduction on milk production of Dutch dairy herds".
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- 2020
23. Assessment of the value of information of precision livestock farming: A conceptual framework
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Rojo-Gimeno, Cristina, primary, van der Voort, Mariska, additional, Niemi, Jarkko K., additional, Lauwers, Ludwig, additional, Kristensen, Anders Ringgaard, additional, and Wauters, Erwin, additional
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- 2019
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24. Assessment of the value of information of precision livestock farming:A conceptual framework
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Rojo-Gimeno, Cristina, van der Voort, Mariska, Niemi, Jarkko K., Lauwers, Ludwig, Kristensen, Anders Ringgaard, Wauters, Erwin, Rojo-Gimeno, Cristina, van der Voort, Mariska, Niemi, Jarkko K., Lauwers, Ludwig, Kristensen, Anders Ringgaard, and Wauters, Erwin
- Abstract
Although precision livestock farming (PLF) technologies ensure various dimensions of more precise information, the question arises to what extent additional preciseness provides more value. Literature gives insufficient anchor points to estimate the value of information (VOI) obtained with PLF technologies. This study proposes a conceptual framework with building blocks to determine the VOI. Next, the framework is used to describe factors and existing gaps in the VOI assessment. This, finally, leads to reflections and recommendations about how to assess and improve the VOI of PLF. Literature reveals that the VOI surpasses the mere use of more precise information to take decisions, but encompasses a path from data collection to decisions with particular outcomes. The framework interlinks three building blocks: (i) data processing, (ii) decision making and (iii) impact analysis with factors influencing the VOI such as the process to transform data into information, level of precision, decision rules, social influences, the accuracy of information, herd size and prevalence of the condition measured. Besides profitability, outcomes from decisions include the impact on animal welfare, environment, food safety, and food security. The data-to-value framework allows for a better assessment of VOI and its potentials, and provides anchor points to design useful and valuable PLF technologies. The framework also helps to determine the role of advisors in interpreting the more precise information and in formulating farmer-tailored advice to apply the most optimal practices. Both technology design and advisors’ role may enhance the VOI of future PLF developments and applications.
- Published
- 2019
25. Dynamic forecasting of individual cow milk yield in automatic milking systems
- Author
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Jensen, Dan B., primary, van der Voort, Mariska, additional, and Hogeveen, Henk, additional
- Published
- 2018
- Full Text
- View/download PDF
26. Dynamic forecasting of individual cow milk yield in automatic milking systems
- Author
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Jensen, Dan B., van der Voort, Mariska, Hogeveen, Henk, Jensen, Dan B., van der Voort, Mariska, and Hogeveen, Henk
- Abstract
Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.
- Published
- 2018
27. Economics for the veterinary practitioner: From burden to blessing
- Author
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Hogeveen, H., van Soest, F.J.S., and van der Voort, Mariska
- Subjects
Business Economics ,Bedrijfseconomie ,Life Science ,WASS - Published
- 2016
28. Using production economics for relating animal diseases with farm performances: a case of gastrointestinal nematode infections in adult dairy cattle
- Author
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van der Voort, Mariska, Van Huylenbroeck, Guido, Lauwers, Ludwig, and Charlier, Johannes
- Subjects
Agriculture and Food Sciences ,production theory ,animal health economics ,farm-specific ,interdisciplinary framework - Abstract
The changing socio-economic environment challenges dairy farmers to remain competitive and increase or maintain animal health. Making the optimal economic-epidemiological decision demands for an interdisciplinary approach in which the individual dairy farmer is the point of focus. Dairy farmers are the main decision makers in case of production diseases in dairy cattle, because in most cases production diseases are of management origin. However, production diseases are not always perceived as being important, because of hidden losses and gradual enter into the daily business of the farm. When controlling production diseases, the negative impact of the disease must therefore be clear, but also the economic benefit of possible disease control strategies. A range of publications and methods are available to determine the economic impact of an animal disease and/or disease control strategy. Existing applications, however, have some limitations hampering effective economic-epidemiological decision making. First, they often fail in taking into account some basic production-economic principles. They do not consider the production function and assume linear relations between inputs and outputs by using average figures. The challenge is to establish more accurate economic estimates considering the relationship between inputs and outputs of the farm. Second, existing studies often produce generic results, ignoring farm-specific differences. Due to the differences between farms, the published average economic impact of a disease or control strategy may be different from what individual farmers experience. Missing the farm specificity in economic analysis makes it more difficult to take the optimal decision at individual farm level. The objective of this dissertation is to explore how economic and epidemiological information can be combined within a production economic framework at individual farm level in order to allow farm-specific decision support on animal diseases. The case of gastrointestinal (GI) nematode infections is used to answer this objective. The conceptual framework that is presented in this dissertation, combines recent insights from veterinary science and farm economics. The framework introduces the use of the production function, which considers the relation between input(s) and output(s) of the production process. On dairy farms, examples of inputs are feed, labour and animal health costs, while outputs can be meat and milk. The advantage of using the production function is (1) that both input(s) and output(s) of the farm are taken into account and that the likely non-linear relation between production and the animal disease can be considered, (2) the unique position of the farms is considered in relation to the performance benchmark and the relation between this position and the level of infection, can be determined. This positioning and relating production to infection allows, at individual farm level, to optimize the level of infection and the economic farm performances. This framework is the starting point for empirical and analytical research to evaluate the economic impact of GI nematode infections and their control strategies. To make the framework operational, efficiency analysis, cluster analysis, partial budgeting and whole-farm simulations are combined in this dissertation. Efficiency analysis is used to determine the farms unique position in the input-output framework and to relate this position to the level of GI nematode infection and control strategies. Cluster analysis groups farms that are similar in their input-output transformation. This allows for analysing whether the relation between economic performance and the level of GI nematode infection depends on this input-output transformation. While efficiency analysis uses relative performance measures, partial budgeting allows for presenting the absolute effect of the level of infection and control strategies on conventional technical and economic key performance indicators (KPIs). And finally, a whole-farm simulation model is used to determine the effect of control strategies on the production parameters of the farm. Application of the methods requires farm-specific data on the farm´s infection level as well as on technical and economic performances. Therefore, in this dissertation, individual farm accountancy data are linked to individual farm GI nematode infection data. The various methods that are used incorporate multiple degrees of farm specificity in the evaluation of the relation between GI nematode infections and the economic farm performances. This dissertation shows that GI nematode infections reduce the technical efficiency of the farms. The size of the effect, however, differs from farm to farm. Low efficient farms can gain, by a similar reduction in infection, higher increases in milk production with the given input amounts, compared to high efficient farms. Although lowering the level of infection results in a higher increase of milk production in low efficient farms, for high efficient farms it can be the last bit to become completely efficient. When the unique position of farms in the input-output space is considered in relation to infection and grazing management, clear differences between farms are also observed. Three groups of farms can be distinguished based on their position in input-output space and for each group different economic-epidemiological improvement paths are derived. At the current price levels, improvement of the allocative efficiency (e.g., reflects the ability of a farm to use inputs in cost minimising proportions, given their respective prices) implies a higher level of GI nematode infection in two of the three groups. Only for the group with a high efficiency level and a high level of infection, reducing infection seems of economic interest. The high variation between farms within groups indicates that the epidemiological-economic relationship is even different between individual farms in each group. Overall, the results indicate a high degree of farm specificity is required when analysing the relationship between farm performances and infection. A decrease in GI nematode infection increases the technical efficiency of farms and results in an economic-epidemiological win-win situation. The increase in technical efficiency, due to a given infection reduction, becomes larger when relatively more concentrates and roughage are used. On the other hand, technical efficiency increases less when more pasture is used compared to other inputs. This dissertation also shows that the use of relatively more pasture per cow is associated with a higher level of infection. On the contrary, increasing pasture in combination with the use of relatively more concentrates and roughage is associated with better economic performances. Although an economic-epidemiological win-win situation is detected between improving TE and reducing infection, for some farms a trade-off exists between reducing the level of infection and optimizing AE. When grazing management to control GI nematode infections is implemented, the gross margin decreases. This is due to an increase of the feed costs, due to higher use of concentrates and higher costs for pasture, and a relatively low increase in milk production. Although grazing management can reduce the level of infection, they result in a lower economic performance. The application of the integrative conceptual framework allows to gain additional insights about the relationship between GI nematode infections and economic performance at farm level. Current advice on controlling infections are mainly based on mere parasitological findings on the farm. Considering also economic implications provides therefore added value to existing decision making. For the development of an economic-epidemiological decision support tool, farm-specific data on infection and the farm performances are needed. These data must be combined with the data of other farms, because the methods that are used in the framework define farm-specific relations based on a data set of several farms. For decision support in practice, the different methods considered in this dissertation need to be combined in a practical and user-friendly tool. This is not straightforward, because the farmer cannot be expected to be familiar with all methods. The results of efficiency analysis, concerning the possible improvement margins and the contribution of a lower level of infection and control strategies, may best be communicated with traditional KPIs the farmer is familiar with. For the development of a decision support tool and its successful implementation into practice, the challenge is to construct a tool that complies with a number of critical success factors. Critical success factors that are distinguished in literature are perceived usefulness, accessibility, flexibility, credibility, maintenance and adaptability. In addition, including stakeholders during the design, evaluation and implementation of decision support tools is very important. This dissertation shows that combining economic and epidemiological information for decision support is possible, but not self-evident. Insights are provided on the combined use of economic and epidemiological data and different methods on the translation of the results for decision support in practice. This dissertation shows the need for epidemiological and economic information from a representative set of farms, a combination of positive and normative methods and further research on a practical decision support system that is capable of embedding these methods. This decision support system should be focussed on the individual farm level, because the farm performances and also the relationship between GI nematode infections and farm performances are highly farm-specific.
- Published
- 2015
29. Decision making on helminths in cattle : Diagnostics, economics and human behaviour
- Author
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Charlier, Johannes, De Waele, Valérie, Ducheyne, Els, van der Voort, Mariska, Vande Velde, Fiona, Claerebout, Edwin, Charlier, Johannes, De Waele, Valérie, Ducheyne, Els, van der Voort, Mariska, Vande Velde, Fiona, and Claerebout, Edwin
- Abstract
Helminth infections of cattle affect productivity in all classes of stock, and are amongst the most important production-limiting diseases of grazing ruminants. Over the last decade, there has been a shift in focus in the diagnosis of these infections from merely detecting presence/absence of infection towards detecting its impact on production. This has been facilitated by studies observing consistent negative correlations between helminth diagnostic test results and measures of productivity. Veterinarians are increasingly challenged to consider the economic aspects of their work, and the use of these tests should now be integrated in economic evaluation frameworks for improved decision making. In this paper, we review recent insights in the farm-specific economic impact of helminth infections on dairy cattle farms as well as in farmer attitudes and behaviour regarding helminth control. Combining better economic impact assessments of helminth infections together with a deeper understanding of the non-economic factors that drive a farmer's animal health decisions should result in more effective control strategies and increased farmer satisfaction.
- Published
- 2016
30. Decision making on helminths in cattle: diagnostics, economics and human behaviour
- Author
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Charlier, Johannes, primary, De Waele, Valérie, additional, Ducheyne, Els, additional, van der Voort, Mariska, additional, Vande Velde, Fiona, additional, and Claerebout, Edwin, additional
- Published
- 2015
- Full Text
- View/download PDF
31. ParaCalc® : a novel tool to evaluate the economic importance of worm infections on the dairy farm
- Author
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Charlier, Johannes, van der Voort, Mariska, Hoogeveen, Henk, and Vercruysse, Jozef
- Subjects
Veterinary Sciences - Published
- 2011
32. A stochastic frontier approach to study the relationship between gastrointestinal nematode infections and technical efficiency of dairy farms
- Author
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van der Voort, Mariska, primary, Van Meensel, Jef, additional, Lauwers, Ludwig, additional, Vercruysse, Jozef, additional, Van Huylenbroeck, Guido, additional, and Charlier, Johannes, additional
- Published
- 2014
- Full Text
- View/download PDF
33. Beliefs, intentions, and beyond : a qualitative study for the adoption of sustainable parasite control in Flanders’ cattle industry
- Author
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Vande Velde, Fiona, Charlier, Johannes, Cauberghe, Veroline, Hudders, Liselot, Claerebout, Edwin, van der Voort, Mariska, and Hogeveen, Henk
- Subjects
Social Sciences ,Veterinary Sciences - Published
- 2017
34. Re-reconceptualising the ‘behavioural approach’ in agricultural studies : beyond a cognitive socio-psychological perspective
- Author
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Vande Velde, Fiona, Charlier, Johannes, Cauberghe, Veroline, Hudders, Liselot, Claerebout, Edwin, van der Voort, Mariska, and Hogeveen, Henk
- Subjects
Social Sciences ,Veterinary Sciences - Published
- 2017
35. The effect of bovine viral diarrhea virus introduction on milk production of Dutch dairy herds.
- Author
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Yue X, Steeneveld W, van der Voort M, van Schaik G, Vernooij JCM, van Duijn L, Veldhuis AMB, and Hogeveen H
- Subjects
- Animals, Antibodies, Viral, Bovine Virus Diarrhea-Mucosal Disease epidemiology, Cattle, Dairying, Female, Bovine Virus Diarrhea-Mucosal Disease physiopathology, Diarrhea Viruses, Bovine Viral, Milk
- Abstract
Dairy cows are negatively affected by the introduction of bovine viral diarrhea virus (BVDV), and consequently, produce less milk. Existing literature on potential milk production losses is based on relatively outdated data and hardly evaluates milk production loss in relation to a new BVDV infection in a surveillance system. This study determined the annual and quarterly loss in milk production of BVDV introduction in 3,126 dairy herds participating in the Dutch BVDV-free program between 2007 and 2017. Among these herds, 640 were "breakdown-herds" that obtained and subsequently lost their BVDV-free status during the study period, and 2,486 herds obtained and retained their BVDV-free status during the study period. Milk yields before and after BVDV introduction were compared through annual and quarterly linear mixed models. The fixed variables for both models included herd type (breakdown-herd or free-herd), bovine viral diarrhea status (on an annual and quarterly basis), year, season, and a random herd effect. The dependent variable was the average daily milk yield on the test day. To define the possible BVDV-introduction dates, 4 scenarios were developed. In the default scenario, the date of breakdown (i.e., loss of the BVDV-free status) was assumed as the BVDV-introduction date. For the other 3 scenarios, the BVDV-introduction dates were set at 4, 6, and 9 mo before the date of breakdown, based on the estimated birth date of a persistently infected calf. In the default scenario, the loss in milk yield due to BVDV introduction occurred mainly in the first year after breakdown, with a reduction in yield of 0.08 kg/cow per day compared with the last year before breakdown. For the other 3 scenarios, the greatest yield reduction occurred in the second year after BVDV introduction, with a loss of 0.09, 0.09, and 0.1 kg/cow per day, respectively. For the first 4 quarters after BVDV introduction in the default scenario, milk yield loss was 0.14, 0.09, 0.02, and 0.08 kg/cow per day, respectively. These quarterly results indicated that milk yield loss was greatest in the first quarter after BVDV introduction. Overall, BVDV introduction had a negative, but on average a relatively small, effect on milk yield for herds participating in the BVDV-free program. This study will enable dairy farmers and policymakers to have a clearer understanding of the quantitative milk production effect of BVDV on dairy farms in a control program., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)
- Published
- 2021
- Full Text
- View/download PDF
36. Decision making on helminths in cattle: diagnostics, economics and human behaviour.
- Author
-
Charlier J, De Waele V, Ducheyne E, van der Voort M, Vande Velde F, and Claerebout E
- Abstract
Helminth infections of cattle affect productivity in all classes of stock, and are amongst the most important production-limiting diseases of grazing ruminants. Over the last decade, there has been a shift in focus in the diagnosis of these infections from merely detecting presence/absence of infection towards detecting its impact on production. This has been facilitated by studies observing consistent negative correlations between helminth diagnostic test results and measures of productivity. Veterinarians are increasingly challenged to consider the economic aspects of their work, and the use of these tests should now be integrated in economic evaluation frameworks for improved decision making. In this paper, we review recent insights in the farm-specific economic impact of helminth infections on dairy cattle farms as well as in farmer attitudes and behaviour regarding helminth control. Combining better economic impact assessments of helminth infections together with a deeper understanding of the non-economic factors that drive a farmer's animal health decisions should result in more effective control strategies and increased farmer satisfaction.
- Published
- 2016
- Full Text
- View/download PDF
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