Real-time automatic integrated monitoring of barn environment and dairy cattle behaviour: Technical implementation and evaluation on three commercial farms

Due to increasing herd sizes and automation on dairy farms there is an important need for automated monitoring of cow production, health, and welfare. Despite much progress in automatic monitoring techniques, there is still a need to integrate data from multiple sources to create a comprehensive overview and accurate diagnosis of a cow’s state. To aid the technological development of data integration, a prototype of an open and customizable automatic system that integrates data from multiple sensors relating to barn environment and cow behaviour was developed. The system integrates data from sensors that measure barn climate (e.g., temperature, humidity, wind speed), air quality (e.g., CO2 concentration), water use and temperature, the moisture and temperature of the litter and cow behaviour (e.g., lying, eating, ruminating). An external weather system and video recording system are also included. The system’s architecture consists of four main elements: sensors, nodes, gateways, and backend. The data are recorded by sensors, then locally processed on custom-developed sensor nodes, and then transmitted via radio channels to local gateways that combine the data from multiple nodes and transmit them to distributed digital storage (“the cloud”) via a 3G/4G cellular network. On the cloud, the data are further processed and stored in a database. The data are then presented to the user continuously and in real time on a dashboard that can be accessed via the internet. In the design of the local wireless network, care was taken to avoid data packet collision and thus to minimize data loss. To test the system’s performance, the system was installed and operated on three commercial dairy cattle farms for one year. The system provided high data stability with minimal loss and outliers, showing that the system is reliable and suitable for long term application on commercial dairy farms. The system’s architecture, communication network, and data processing and visualization applications form an open framework for research and development purposes, allowing it to be customized and fine-tuned before being deployed as a management assistant on commercial dairy farms. Missing elements that should be added in the future are the integration of the data from the milking parlour and cow identification. Algorithms to integrate information from multiple sensors can be added to provide a comprehensive system that monitors all aspects related to cow welfare, health, and production automatically, remotely and in real time, thereby supporting farmers in important management decision-making

Technical, Economic, and Environmental Assessment of a Collective Integrated Treatment System for Energy Recovery and Nutrient Removal from Livestock Manure

The aim of this 5-year study was to evaluate the technical, economic, and environmental performances of a collective-based integrated treatment system for bioenergy production and nutrients removal to improve the utilization efficiency and reduce the environmental impact of land applied livestock manure. The study involved 12 livestock production units located in an intensive livestock area designated as nitrate vulnerable zone with large N surplus. The treatment system consisted of an anaerobic digestion unit, a solid–liquid separation system, and a biological N removal process. Atmospheric emissions and nutrient losses in water and soil were examined for the environmental assessment, while estimated crop removal and nutrient utilization efficiencies were used for the agronomic assessment. The integrated treatment system achieved 49% removal efficiency for total solids (TS), 40% for total Kjeldahl nitrogen (TKN), and 41% for total phosphorous (TP). A surplus of 58kWh/t of treated manure was achieved considering the electricity produced by the biogas plant and consumed by the treatment plant and during transportation of raw and treated manure. A profit of 1.61 €/t manure treated and an average reduction of global warming potential by 70% was also achieved. The acidification potential was reduced by almost 50%. The agronomic use of treated manure eliminated the TKN surplus and reduced the TP surplus by 94%. This collective integrated treatment system can be an environmentally and economically sustainable solution for farms to reduce N surplus in intensive livestock production areas

Climate change and socio-economic assessment of PLF in dairy farms: Three case studies

Precision Livestock Farming (PLF) techniques include sensors and tools to install on livestock farms and/or animals to monitor them and support the decision making process of farmers, finally early detecting alerting conditions and improving the livestock efficiency. Direct consequences of this monitoring include enhanced animal welfare, health and productivity, improved farmer lifestyle, knowledge, and traceability of livestock products. The indirect consequences, instead, include improved Carbon Footprint and socio-economic indicators of livestock products. In this context, the aim of this paper is to develop an indicator applicable to dairy cattle farming that takes into account concurrently these indirect consequences. The indicator was developed combining the three sustainability pillars (with specific criteria): environmental (carbon footprint), social (5 freedoms of animal welfare and antimicrobial use) and economic (cost of technology and manpower use). The indicator was then tested on 3 dairy cattle farms located in Italy, where a baseline traditional scenario (BS) was compared with an alternative scenario (AS) where PLF techniques and improved management solutions were adopted. The results highlighted that the carbon footprint reduced in all AS by 6-9 %, and the socio-economic indicators entailed improvements in animals and workers welfare with some differences based on the tested technique. Investing in PLF techniques determines positive effects on all/almost all the criteria adopted for the sustainability indicator, with case-specific aspects to consider. Being a user-friendly tool that supports the testing of different scenarios, this indicator could be used by stakeholders (policy makers and farmers in particular) to identify the best direction towards investments and incentive policies

Assessment of the influence of energy density and feedstock transport distance on the environmental performance of methane from maize silages

In Europe, thanks to public subsidy, the production of electricity from anaerobic digestion (AD) of agricultural feedstock has considerably grown and several AD plants were built. When AD plants are concentrated in specific areas (e.g., Northern Italy), increases of feedstock' prices and transport distances can be observed. In this context, as regards low-energy density feedstock, the present research was designed to estimate the influence of the related long-distance transport on the environmental performances of the biogas-to-electricity process. For this purpose the following transport systems were considered: farm trailers and trucks. For small distances (<5km), the whole plant silage shows the lowest impact; however, when distances increase, silages with higher energy density (even though characterised by lower methane production per hectare) become more environmentally sustainable. The transport by trucks achieves better environmental performances especially for distances greater than 25km

Development of a New Wearable 3D Sensor Node and Innovative Open Classification System for Dairy Cows' Behavior

Simple Summary In order to keep dairy cows under satisfactory health and welfare conditions, it is very important to monitor the animals in their living environment. With the support of technology, and, in particular, with the installation of sensors on neck-collars, cow behavior can be adequately monitored, and different behavioral patterns can be classified. In this study, an open and customizable device has been developed to classify the behaviors of dairy cows. The device communicates with a mobile application via Bluetooth to acquire raw data from behavioral observations and via an ad hoc radio channel to send the data from the device to the gateway. After observing 32 cows on 3 farms for a total of 108 h, several machine learning algorithms were trained to classify their behaviors. The decision tree algorithm was found to be the best compromise between complexity and accuracy to classify standing, lying, eating, and ruminating. The open nature of the system enables the addition of other functions (e.g., localization) and the integration with other information sources, e.g., climatic sensors, to provide a more complete picture of cow health and welfare in the barn. Monitoring dairy cattle behavior can improve the detection of health and welfare issues for early interventions. Often commercial sensors do not provide researchers with sufficient raw and open data; therefore, the aim of this study was to develop an open and customizable system to classify cattle behaviors. A 3D accelerometer device and host-board (i.e., sensor node) were embedded in a case and fixed on a dairy cow collar. It was developed to work in two modes: (1) acquisition mode, where a mobile application supported the raw data collection during observations; and (2) operating mode, where data was processed and sent to a gateway and on the cloud. Accelerations were sampled at 25 Hz and behaviors were classified in 10-min windows. Several algorithms were trained with the 108 h of behavioral data acquired from 32 cows on 3 farms, and after evaluating their computational/memory complexity and accuracy, the Decision Tree algorithm was selected. This model detected standing, lying, eating, and ruminating with an average accuracy of 85.12%. The open nature of this system enables for the addition of other functions (e.g., real-time localization of cows) and the integration with other information sources, e.g., microenvironment and air quality sensors, thereby enhancing data processing potential

Dairy Cow Behavior Is Affected by Period, Time of Day and Housing

Dairy cow behavior is affected by external and endogenous factors, including time of year, barn microclimate, time of day and housing. However, little is known about the combined effects of these factors. Data were collected on eight farms in Northern Italy during summer, winter and a temperate season. The temperature-humidity index (THI) was recorded using environmental sensors, whereas cow behavior was monitored using leg accelerometers and cameras. Period, time of day and their interaction all significantly affected lying, standing and feeding behavior. However, although THI had a significant negative effect on lying and a positive effect on standing during daytime (all p &lt; 0.001), during nighttime, it only had a significant negative effect on lying duration and mean lying bout duration (p &lt; 0.001 for both). There was also significant variation between farms in all behavioral parameters, as well as interactions with period and time of day. For instance, farm differences in lying duration were more pronounced during daytime than during nighttime. These findings show how housing can interact with other factors, such as period of the year and time of day, and illustrate the influence of barn structure and farm management on cow behavior and, consequently, their welfare

Investigating on the environmental sustainability of organic animal products? The case of organic eggs

The organic farming of laying hens is experiencing a growing trend in Italy, following an increase in consumer demand for organic eggs. The present study aimed to investigate the environmental performance of organic egg production for the first time in the Italian context. To this end, the Life Cycle Assessment (LCA) of organic egg production in a farm rearing laying hens located in Northern Italy was performed. The analysis was carried out in a cradle to farm gate perspective, with 1 kg of eggs selected as functional unit. Primary data relating to animal performances and resources consumed was collected on site, and subsequently integrated with secondary data, including estimates of manure-related emissions. In order to model in a representative way the organic feed consumed, data relating to typical cropping systems of the country has been used for the various ingredients, keeping the organic production method specifications into account. Inventory data was then converted on an annual basis and characterized using the ILCD method, and twelve impact categories were assessed. Moreover, the influence on impact results of different allocation choices and efficiency in terms of hen-day egg production were explored with a sensitivity analysis. The main environmental burden for organic egg production showed to be feed production and supply, with a share ranging from 49% to 87% over all the evaluated impact categories. Other hotspots are pullets rearing, responsible for a share between 10 and 14% over all categories, and manure-related emissions, which weighed significantly for PM (35%), TA (39%) and TE (39%). A value for CC of 1.56 kg CO2 eq/kg shelled eggs was obtained, thanks to good production performances together with some benefits given by organic feed use, particularly the avoidance of mineral fertilizer consumption and of land use change related emission. At the same time, the results show clearly that environmental improvements should be sought primarily in the same feed area. This must be done both on-farm, which was highlighted also by the sensitivity analysis on hen-day egg production, and at the supply chain level, acting on the impact related to crop production and pullets rearing phases. Starting from the results, some environmental weaknesses and strengths of organic farming have been discussed. Future studies must further investigate the impact of this rearing system in a wider perspective and explore possible scenarios of mitigation practices