The aim of this review is to focus the attention on the nutrition ecology of the heavy metals and on the major criticisms related to the heavy metals content in animal feeds, manure, soil and animal-origin products. Heavy metals are metallic elements that have a high density that have progressively accumulated in the food chain with negative effects for human health. Some metals are essential (Fe, I, Co, Zn, Cu, Mn, Mo, Se) to maintain various physiological functions and are usually added as nutritional additives in animal feed. Other metals (As, Cd, F, Pb, Hg) have no established biological functions and are considered as contaminants/ undesirable substances. The European Union adopted several measures in order to control their presence in the environment, as a result of human activities such as: farming, industry or food processing and storage contamination. The control of the animal input could be an effective strategy to reduce human health risks related to the consumption of animal-origin products and the environmental pollution by manure. Different management of raw materials and feed, animal species as well as different legal limits can influence the spread of heavy metals. To set up effective strategies against heavy metals the complex interrelationships in rural processes, the widely variability of farming practices, the soil and climatic conditions must be considered. Innovative and sustainable approaches have discussed for the heavy metal nutrition ecology to control the environmental pollution from livestockrelated activities.

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Current farm sizes do not allow the precise identification and tracking of individual cows and their health and behavioral records. Currently, the application of information technology within intensive dairy farming takes a key role in proper routine management to improve animal welfare and to enhance the comfort of dairy cows. An existing application based on information technology is represented by the GEA CowView system (GEA Farm Technologies, Bönen, Germany). This system is able to detect and monitor animal behavioral activities based on positioning, through the creation of a virtual map of the barn that outlines all the areas where cows have access. The aim of this study was to validate the accuracy, sensitivity, and specificity of data provided by the CowView system. The validation was performed by comparing data automatically obtained from the CowView system with those obtained by a manual labeling procedure performed on video recordings. Data used for the comparisons were represented by the zone-related activities performed by the selected dairy cows and were classified into 2 categories: activity and localization. The duration in seconds of each of the activities/localizations detected both with the manual labeling and with the automated system were used to evaluate the correlation coefficients among data; and subsequently the accuracy, sensitivity, specificity, and positive and negative predictive values of the automated monitoring system were calculated. The results of this validation study showed that the CowView automated monitoring system is able to identify the cow localization/position (alley, trough, cubicles) with high reliability in relation to the zone-related activities performed by dairy cows (accuracy higher than 95%). The results obtained support the CowView system as an innovative potential solution for the easier management of dairy cows.

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The world population will significantly increase by 2050