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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

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.

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.

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

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

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

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

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

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.

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

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

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

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".

Supplemental material for the paper titled "The impact of bovine viral diarrhea virus introduction on milk production of Dutch dairy herds".

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.

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.

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.