Multi-criteria assessment of ethical aspects in fresh tomato systems: Plant genomics technology innovation and food policy uses

Product assessment for imperceptible characteristics like environmental impact, healthfulness, naturalness, and fairness is a helpful tool in product innovation and for enhancing socially responsible conduct. In this study we apply multiple criteria analysis for the assessment of fresh tomatoes in terms of consumer perceptions regarding the above characteristics. The generated indices provide an explicit and comprehensive representation of consumer perceptions. Existing tomato products from the Dutch market are ranked alongside (reasonable conjectures of) potential products to be developed with the use of plant genomics technology. The results are interpreted to provide insights into the socially optimal use of (plant genomics) technology for fresh tomato production. Policy uses are highlighted.Ethical assessment, corporate societal responsibility, multiple criteria., Demand and Price Analysis, Research and Development/Tech Change/Emerging Technologies,

A Systematic Evaluation of Measures Against Highly Pathogenic Avian Influenza (HPAI) in Indonesia

Over the past years, many different control measures have been implemented to prevent HPAI infection. The national plan with numerous measures lead to problems in terms of prioritization and budget allocation. Our study objectives are to (i) establish an inventory of measures on HPAI control in Indonesia since the first actions were taken in 2004, (ii) evaluate preferences for different HPAI control measures applied in the West Java province at the district level during 2013–2017, and (iii) establish a basis for further qualitative and quantitative research to improve control for an endemic HPAI in Indonesia. This research was carried out according to the following five steps (i) development of an HPAI management framework for an endemic state, (ii) inventorization of measures directed at HPAI and description of the development of HPAI in Indonesia, (iii) development of a questionnaire for the experts involved, (iv) systematic evaluation of preferences for short- and long-term HPAI strategies and measures applied in the West Java Province based on expert opinion, and (v) data analysis. The study systematically evaluated in total 27 measures. The results of this study show that the animal disease management framework is helpful as a systematic structure to distinguish and evaluate strategies and measures. In our framework, we defined the following strategies: prevention, monitoring, control, mitigation, eradication, and human protection. The findings of our research show that the primary aims of the government were to safeguard humans from HPAI transmission by mitigating HPAI disease in livestock. The measures with the highest priority were preventive vaccination of poultry, biosecurity, and stamping-out infected flocks. This showed that the government predominantly chose a vaccination-based HPAI mitigation strategy. However, the chosen strategy has a low implementation feasibility. A collaboration between the responsible stakeholders farmers may increase the feasibility of the chosen strategy in the future. Furthermore, our findings provide a basis for research into the motivation of farmers to implement different measures as well as into the expected impact of different measures to develop an effective and efficient mitigation approach

TRACEABILITY AND CERTIFICATION IN MEAT SUPPLY CHAINS

Food safety problems such as the BSE and dioxin crises focused attention on traceability systems and the certification of such systems. This study analyzes the status and perspectives of traceability systems and certification schemes, and reviews their potential costs and benefits. Results indicate that traceability and certification in meat supply chains comprise a very dynamic area with an increasing impact. Necessary transparency, control of livestock epidemics, increasing due diligence, and a declining role for governments are critical factors. Findings also reveal there is a general focus on the technical characteristics of traceability and certification, and there is a lack of economic considerations. Therefore, specific topics are emphasized for an economic research agenda, such as an analysis of the break-even point for the level of detail of traceability systems, the reconsideration of liability and recall insurance schemes, and regulatory incentives to motivate adoption by free-riders.certification, cost-benefit analysis, livestock production, supply chain, traceability, Industrial Organization, Livestock Production/Industries,

A PUSH AND PULL INTERVENTION TO CONTROL AVIAN INFLUENZA: A LESSON LEARNED FROM THE WESTERN JAVA POULTRY SECTOR

HPAI H5N1 is considered endemic in Indonesian poultry and poses a major challenge to animal and human health authorities. The complex structure of the Indonesian poultry meat value chain is an important reason for the limited efficacy of HPAI control in Indonesia so far. The paper objective is to describe how to implement a push-and-pull strategy in the poultry supply chain to control HPAI infection in Western Java. More specifically, this study investigates the poultry value chain in Western Java in relation to consumers’ behavior and governance of the value chain. Implementation of biosecurity and HPAI control measures was strongly related to the governance structure of the chain, with interactions that accentuating the risk of HPAI. In conclusion, a push strategy, as an incentive mechanism, should be designed in such a way that it pays attention to the interactions between actors in a value chain and their impact on introduction and transmission of disease. Moreover, a pull strategy as an incentive mechanism for consumers forcing producers to improve their production environment into higher levels of biosecurity is expected to be less effective than a push strategy targeting producers. Keywords: avian influenza, biosecurity, consumer preferences, willingness to pay, a push and pull strateg

Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis

AbstractRapid detection of dairy cow mastitis is important so corrective action can be taken as soon as possible. Automatically collected sensor data used to monitor the performance and the health state of the cow could be useful for rapid detection of mastitis while reducing the labor needs for monitoring. The state of the art in combining sensor data to predict clinical mastitis still does not perform well enough to be applied in practice. Our objective was to combine a multivariate dynamic linear model (DLM) with a naïve Bayesian classifier (NBC) in a novel method using sensor and nonsensor data to detect clinical cases of mastitis. We also evaluated reductions in the number of sensors for detecting mastitis. With the DLM, we co-modeled 7 sources of sensor data (milk yield, fat, protein, lactose, conductivity, blood, body weight) collected at each milking for individual cows to produce one-step-ahead forecasts for each sensor. The observations were subsequently categorized according to the errors of the forecasted values and the estimated forecast variance. The categorized sensor data were combined with other data pertaining to the cow (week in milk, parity, mastitis history, somatic cell count category, and season) using Bayes’ theorem, which produced a combined probability of the cow having clinical mastitis. If this probability was above a set threshold, the cow was classified as mastitis positive. To illustrate the performance of our method, we used sensor data from 1,003,207 milkings from the University of Florida Dairy Unit collected from 2008 to 2014. Of these, 2,907 milkings were associated with recorded cases of clinical mastitis. Using the DLM/NBC method, we reached an area under the receiver operating characteristic curve of 0.89, with a specificity of 0.81 when the sensitivity was set at 0.80. Specificities with omissions of sensor data ranged from 0.58 to 0.81. These results are comparable to other studies, but differences in data quality, definitions of clinical mastitis, and time windows make comparisons across studies difficult. We found the DLM/NBC method to be a flexible method for combining multiple sensor and nonsensor data sources to predict clinical mastitis and accommodate missing observations. Further research is needed before practical implementation is possible. In particular, the performance of our method needs to be improved in the first 2 wk of lactation. The DLM method produces forecasts that are based on continuously estimated multivariate normal distributions, which makes forecasts and forecast errors easy to interpret, and new sensors can easily be added

Estimating the combined costs of clinical and subclinical ketosis in dairy cows

Clinical ketosis (CK) and subclinical ketosis (SCK) are associated with lower milk production, lower reproductive performance, an increased culling of cows and an increased probability of other disorders. Quantifying the costs related to ketosis will enable veterinarians and farmers to make more informed decisions regarding the prevention and treatment of the disease. The overall aim of this study was to estimate the combined costs of CK and SCK using assumptions and input variables from a typical Dutch context. A herd level dynamic stochastic simulation model was developed, simulating 385 herds with 130 cows each. In the default scenario there was a CK probability of almost 1% and a SCK probability of 11%. The herds under the no risk scenario had no CK and SCK, while the herds under the high-risk scenario had a doubled probability of CK and SCK compared to the default scenario. The results from the simulation model were used to estimate the annual cash flows of the herds, including the costs related to milk production losses, treatment, displaced abomasum, mastitis, calf management, culling and feed, as well as the returns from sales of milk and calves. The difference between the annual net cash flows of farms in the no risk scenario and the default scenario provides the estimate of the herd level costs of ketosis. Average herd level costs of ketosis (CK and SCK combined) were €3,613 per year for a default farm and €7,371 per year for a high-risk farm. The costs for a single CK case were on average €709 (with 5 and 95 percentiles of €64 and €1,196, respectively), while the costs for a single SCK case were on average €150 (with 5 and 95 percentiles of €18 and €422, respectively) for the default farms. The differences in costs between cases occurred due to differences between cases (e.g., cow culled vs cow not culled, getting another disease vs not getting another disease).</p

Forecasting chronic mastitis using automatic milking system sensor data and gradient-boosting classifiers

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 distin- guish 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 the forecasting performance of the chronic mastitis prediction model. On average, the forecast model, using 30 days of sensor data prior to the day of prediction, outperformed the approaches according to the accuracy (chronic mastitis prediction model: 0.888, frequent sampling approach: 0.848, and monthly sampling approach: 0.865), MCC (chronic mastitis prediction model: 0.712, frequent sampling approach: 0.630, and monthly sampling approach: 0.552), and AUC metrics (chronic mastitis prediction model: 0.964 and frequent sampling approach: 0.941) metrics. The results also indicate that shortening the input requirement from 30 days of prior sensor data to 15 days has a limited effect on the performance of the model. Overall, this study shows that it is possible with a high accuracy to predict the future chronic mastitis status using past sensor data and machine learning models

Diagnostic properties of milk diversion and farmer-reported mastitis to indicate clinical mastitis status in dairy cows using Bayesian latent class analysis

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 mastitis reporting was low, the sensitivity of milk diversion and reported clinical mastitis cases was similar at milk-diversion threshold durations of 1–7 days (0.687 to 0.448 versus 0.503 to 0.504) while specificity exceeded the 98% at milk-diversion threshold durations of 7 or more consecutive milk diversion days. In both scenarios, a milk diversion threshold duration of 4–7 days achieved the most desirable combined sensitivity and specificity. This study concluded that milk diversion can be a valid alternative to farmer-reported clinical mastitis as it performs similarly in indicating actual clinical mastitis