Addressing Data Bottlenecks in the Dairy Farm Industry

Simple Summary

A better understanding of the current challenges and opportunities regarding data management and data governance in the dairy industry is key to design and define effective data utilization. Thus, a survey was conducted to understand the attitudes of farmers and non-farmers. Respondents strongly agreed that data sharing is a valuable enterprise. They recognized that raw data collected at the farm should be the property of the farmer, and that incentives could motivate farmers to continue, or increase, their data sharing, but most of them were unfamiliar with data collection protocols. Although most farmers are already sharing data, most of them have not signed a data share agreement and feel they do not have data control, once their data are accessed by others. Most respondents exhibited concern about critical data issues, such as ownership, confidentiality, security, lack of integration, and even lack of awareness of the importance of data integration. Farmers indicated that they would be encouraged to adopt a new technology if it is easy to implement and has the potential to improve herd or farm management and profit, whereas they would be discouraged if the technology is expensive, difficult to use, or they do not have clear information about its use.

Abstract

A survey to explore the challenges and opportunities for dairy farm data management and governance was completed by 73 farmers and 96 non-farmers. Although 91% of them find data sharing beneficial, 69% are unfamiliar with data collection protocols and standards, and 66% of farmers feel powerless over their data chain of custody. Although 58% of farmers share data, only 19% of them recall having signed a data share agreement. Fifty-two percent of respondents agree that data collected on farm belongs only to the farmer, with 25% of farmers believing intellectual property products are being developed with their data, and 90% of all said companies should pay farmers when making money from their data. Farmers and non-farmers are somewhat concerned about data ownership, security, and confidentiality, but non-farmers were more concerned about data collection standards and lack of integration. Sixty-two percent of farmers integrate data from different sources. Farmers’ most used technologies are milk composition (67%) and early disease detection (56%); most desired technologies are body condition score (56%) and automatic milking systems (46%); most abandoned technologies are temperature and activity sensors (14%) and automatic sorting gates (13%). A better understanding of these issues is paramount for the industry’s long-term sustainability.

Towards a Vectorial Approach to Predict Beef Farm Performance

Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance.

Dairy Cows Activity under Heat Stress: A Case Study in Spain

Heat stress plays a role in livestock production in warm climates. Heat stress conditions impair animal welfare and compromise the productive and reproductive performance of dairy cattle. Under heat stress conditions, dairy cattle modify their behavior. Thus, the assessment of behavior alterations can be an indicator of environmental or physiological anomalies. Moreover, precision livestock farming allows for the individual and constant monitoring of animal behavior, arising as a tool to assess animal welfare. The purpose of this study was to evaluate the effect of heat stress on the behavior of dairy cows using activity sensors. The study was carried out in Tinajeros (Albacete, Spain) during the summer of 2020. Activity sensors were installed in 40 cows registering 6 different behaviors. Environmental conditions (temperature and humidity) were also monitored. Hourly data was calculated for both animal behavior and environmental conditions. Temperature and Heat Index (THI) was calculated for each hour. The accumulated THI during the previous 24 h period was determined for each hour, and the hours were statistically classified in quartiles according to the accumulated THI. Two groups were defined as Q4 for no stress and Q1 for heat stress. The results showed that animal behavior was altered under heat stress conditions. Increasing THI produces an increase in general activity, changes in feeding patterns and a decrease in rumination and resting behaviors, which is detrimental to animal welfare. Daily behavioral patterns were also affected. Under heat stress conditions, a reduction in resting behavior during the warmest hours and in rumination during the night was observed. In conclusion, heat stress affected all behaviors recorded as well as the daily patterns of the cows. Precision livestock farming sensors and the modelling of daily patterns were useful tools for monitoring animal behavior and detecting changes due to heat stress.

Machine Learning in Agriculture: A Comprehensive Updated Review

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

Measures Matter-Determining the True Nutri-Physiological Value of Feed Ingredients for Swine

Many types of feed ingredients are used to provide energy and nutrients to meet the nutritional requirements of swine. However, the analytical methods and measures used to determine the true nutritional and physiological ("nutri-physiological") value of feed ingredients affect the accuracy of predicting and achieving desired animal responses. Some chemical characteristics of feed ingredients are detrimental to pig health and performance, while functional components in other ingredients provide beneficial health effects beyond their nutritional value when included in complete swine diets. Traditional analytical procedures and measures are useful for determining energy and nutrient digestibility of feed ingredients, but do not adequately assess their true physiological or biological value. Prediction equations, along with ex vivo and in vitro methods, provide some benefits for assessing the nutri-physiological value of feed ingredients compared with in vivo determinations, but they also have some limitations. Determining the digestion kinetics of the different chemical components of feed ingredients, understanding how circadian rhythms affect feeding behavior and the gastrointestinal microbiome of pigs, and accounting for the functional properties of many feed ingredients in diet formulation are the emerging innovations that will facilitate improvements in precision swine nutrition and environmental sustainability in global pork-production systems.

A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy

Simple Summary

The climate crisis is accompanied by an increasing number of heat waves that negatively affect the behavior of dairy cows and their welfare. To understand if and how this is affecting farms in Northern Italy, a survey was carried out on eight cattle farms located in the Lombardy region. Three periods were monitored for one year (thermoneutral, hot and cold seasons) using environmental sensors installed in the barn and accelerometers mounted on the hind leg of groups of cows. From the results, it emerged that cows react to high air temperature and humidity conditions by reducing their lying time, which negatively affects milk production. Four out of the eight investigated farms showed that the negative effects caused by heat stress were evident. Hence, the farmer should consider the possibility of improving the barn structure, for example with an efficacious forced ventilation system. Cattle welfare is the first step towards healthy and productive cows.

Abstract

Due to its increasing pressure on dairy cows, studies that investigate how to cope with heat stress are needed. The heat stress affects multiple aspects of cows’ lives, among which their behavior and welfare. In this study, a survey was carried out in eight farms located in Northern Italy to monitor and evaluate the environmental aspects of the barns and the behavioral responses of dairy cows. For one year, three periods were monitored: thermoneutral (T_S), hot (H_S) and cold (C_S) seasons. Temperature and relative humidity were measured by environmental sensors, and lying vs. standing time, number of lying bouts and their average duration were collected by accelerometers. The temperature-humidity index (THI) was quantified inside and outside of the barn. Results show that at the increase of the THI, behavioral adaptations occurred in all the farms, especially with a reduction of lying time and an increase of respiration rate. Four of the eight farms need interventions for improving the cows’ welfare. Here, environmental problems should be solved by introducing or improving the efficacy of the forced ventilation or by modifying the barn structure. Monitoring dairy barns with sensors and Precision Livestock Farming techniques can be helpful for future livestock farming to alert farmers on the need for their interventions to respond immediately to unwanted barn living conditions.

Variations in the body surface temperature of sows during the post weaning period and its relation to subsequent reproductive performance

Objective

A study was made investigate factors affecting body surface temperature changes after weaning in sows, whether these can be used to aid detection of natural estrus and how they relate to subsequent reproductive performance.

Methods

A total of 132 sows were selected during summer from a breeding farm, with mean parity of 3.6±2.3 and 28.5±0.9 days lactation length. Four daily measurements (6:00, 8:00, 16:00, and 18:00) of vulva (VST), udder (UST), ear base and central back skin temperatures for individual sows were taken by an infrared thermometer, continuing up to 8 days post weaning.

Results

The VST obtained from sows showing estrus at 4 days post-weaning (4DPW), 5DPW, and 6DPW showed a peak at the fourth day post-weaning, but then started to decrease. The VST of sows not detected in standing heat (NDPW) remained at a lower level during the experiment, but UST was increased soon after weaning. The VST-UST temperature differences during daytime of sows that were showing behavioural standing heat on 4DPW, 5DPW, 6DPW, and 7DPW were 0.46°C±0.123°C, 0.71°C±0.124°C, 0.66°C ±0.171°C, and 0.58°C±0.223°C, respectively. The NDPW sows had the highest UST observed, but also the lowest VST so that a more negative value of temperature difference (−0.31°C) was seen during first few days post-weaning. A total of 119 sows were observed to show standing heat and were bred. The later the estrus, the smaller the litter size (p = 0.005).

Conclusion

Sows which did not show behavior indicative of stable standing heat after weaning had a VST which remained at a lower level, but the UST increased soon after weaning. Therefore, for sow heat detection under field conditions, the changes of VST and UST and difference between the two should be considered together to increase the accuracy of detection.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in "big data" analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.