Big Data approaches as a support for precision livestock farming techniques

With the advent of new technologies it is increasingly easier to find data of different nature from even more accurate sensors that measure the most disparate physical quantities and with different methodologies. The collection of data thus becomes progressively important and takes the form of archiving, cataloging and online and offline consultation of information. Over time, the amount of data collected can become so relevant that it contains information that cannot be easily explored manually or with basic statistical techniques. The use of Big Data therefore becomes the object of more advanced investigation techniques, such as Machine Learning and Deep Learning. In this work some applications in the world of precision zootechnics and heat stress accused by dairy cows are described. Experimental Italian and German stables were involved for the training and testing of the Random Forest algorithm, obtaining a prediction of milk production depending on the microclimatic conditions of the previous days with satisfactory accuracy. Furthermore, in order to identify an objective method for identifying production drops, compared to the Wood model, typically used as an analytical model of the lactation curve, a Robust Statistics technique was used. Its application on some sample lactations and the results obtained allow us to be confident about the use of this method in the future

Microventilation system improves the ageing conditions in existent wine cellars

Background and Aims: The importance of indoor environmental conditions in a cellar is well known and continuously investigated. The process of wine ageing consists of several steps, during which temperature (T) and relative humidity (RH) play a fundamental role as the quality of the final product is strongly related to stable and suitable environmental conditions. Critical factors, such as mould growth or wine evaporation, have emerged when ventilation has proved to be insufficient or poorly designed. The limitation of stagnant areas and the homogeneity inT and RH provide for proper wine conservation; however, unwanted local conditions can occur in the zones with insufficient air exchange. Methods and Results: Considering these aspects, a controlled microventilation and monitoring system was installed in a case study cellar, and T and RH were monitored for 1 year. The data have been analysed to investigate criticalities of the environmental conditions. The ventilation was activated in specific critical conditions to increase the homogeneity of the T and RH in the critical zones. The results show that the microventilation system improves the homogeneity of both T and RH without affecting the average values. Conclusions: The study demonstrated the efficacy of the system and indicated possible modifications to improve system performance. Significance of the Study: The system proved to be a useful tool for both improving the environmental conditions and providing useful information to the winemakers about the ageing conditions

Lesson learned in big data for dairy cattle: advanced analytics for heat stress detection

This report provides an overview of the strategies for data management and data analysis developed within the EU project EIT Food DairySust “Big data and advanced analytics for sustainable management of the dairy cattle sector”. The main ambition of this project is to improve sustainability and animal welfare, besides productivity, in dairy farming, through advanced data analytics for every level of stakeholders. Good data management, in terms of acquisition, processing, harmonization and imputation, is required for good modelling for early diagnosis and for the identification of optimal prevention strategies, particularly in fields where monitoring can collect very heterogeneous data, and for which agreed protocols have not yet been standardized. The project investigated the “ecosystem” of data and application strategies for sharing computer resources and information in a secure and organic manner. This research first developed an optimal computational ecosystem based on the integration and harmonization of heterogeneous data types. Classical and advanced modelling strategies were used and compared. The results are suitable to provide the stakeholders with improved decision-making process about animal welfare and sustainability of the production. This report focuses on the implementation of a numerical model for the assessment of the impact of heat stress on milk production and provides a feedback on it

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions

Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016–2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production

A Smart Monitoring System for Self-sufficient Integrated Multi-Trophic AquaPonic

The Integrated Smart Monitoring and Control System (ISMaCS) is designed to allow the acquisition of large physical and environmental features in an agriculture and aquaculture integrated context and make data available for checking, assistance and analysis. The system is able to work in different environments, to collect data and make them available remotely in real time. It is designed to operate in the structures of the PRIMA project where aquaculture and plant cultivation are integrated in indoor and outdoor environments. This system allows the diagnosis of the operating conditions of the monitored plants

A Smart Monitoring System for Self-sufficient Integrated Multi-Trophic AquaPonic

The Integrated Smart Monitoring and Control System (ISMaCS) is designed to allow the acquisition of large physical and environmental features in an agriculture and aquaculture integrated context and make data available for checking, assistance and analysis. The system is able to work in different environments, to collect data and make them available remotely in real time. It is designed to operate in the structures of the PRIMA project where aquaculture and plant cultivation are integrated in indoor and outdoor environments. This system allows the diagnosis of the operating conditions of the monitored plants

Assessment of milk yield loss induced by heat stress in dairy cows. Researching the sustainability and the animal welfare

This study was developed within the EIT Food European project DAIRYSUST “Big data and advanced analytics for sustainable management of the dairy cattle sector”, running in 2021- 2022. The aim of the project is to improve sustainability, animal welfare and productivity in dairy farming through the use of advanced data analytics. Livestock farms routinely produce and monitor data relating to environmental conditions, animal behaviour, and production parameters. The development of data-driven platforms and solutions which bring together all the separate data could be used to enhance decision-making and improve the sustainability of the agri-food system. This project is developing a system which integrates and harmonises the different data types. The outcomes are planned to be used by stakeholders in the dairy farming sector to improve their decision-making processes relating to sustainability, animal welfare and productivity. In this context, the study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, with the main goal to assess the trend in daily milk yield of individual cows in relation to environmental conditions. The model has been calibrated and tested on the data collected on dairy farms which expressed their availability in collaborating in the project. The results show that the model can detect the drop in the cow’s milk yield due to extreme hot conditions inducing heat stress effects and milk yield loss. In fact, the average relative error provided by the model in the predictions, is 2% of the total milk production in the test days. The results confirm that the obtained Random Forest Model represents a reliable and viable tool for the evaluation of future production scenarios of dairy cows in presence of heat stress environmental conditions. The model proposed may thus help to develop and improve decision support systems for farmers to increase both milk yield and animal welfare and, on the other hand, to reduce the resources needed, hence increasing sustainability of the dairy sector