DATA-DRIVEN DECISION MAKING IN LIVESTOCK MANAGEMENT 

Pashu Sandesh, 26 Feb 2024

Manoj kumar¹, S.K.Rewani², Rahul Mourya³, Vikas Galav⁴

^1,3MVSc Scholar, Department of Veterinary and A.H. Extension Education, PGIVER, RAJUVAS, Bikaner

²Assistant Professor, Department of Veterinary and A.H. Extension Education, PGIVER, RAJUVAS, Bikaner

          ⁴Assistant Professor, Department of Pathology, PGIVER, RAJUVAS, Bikaner 

Abstract

Data-driven decision-making is revolutionizing livestock management, offering farmers unprecedented insights into their operations. This article explores the pivotal role of data analytics in optimizing livestock health, productivity, and sustainability. By leveraging advanced technologies such as sensors, RFID tags, and satellite imagery, farmers can collect vast amounts of real-time data on animal behaviour, health indicators, and environmental conditions. Through sophisticated analytics, this data can be transformed into actionable insights, enabling farmers to make informed decisions on feed allocation, breeding strategies, disease detection, and resource utilization. Moreover, predictive modelling techniques empower farmers to anticipate challenges and proactively implement preventive measures, reducing risks and maximizing profitability. However, challenges such as data integration, privacy concerns, and accessibility barriers must be addressed to unlock the full potential of data-driven livestock management. By embracing data-driven approaches, farmers can enhance efficiency, sustainability, and resilience in livestock operations, ushering in a new era of precision agriculture.

Keywords

Data, Decision Making, RFID, Innovation.

Introduction 

The rise in population and income development is resulting in the rise of the demand for a wider variety of foods globally. Human nutrition studies have revealed that people are moving towards more affluent food consumption habits (Wiedmann et al., 2020). Livestock management is one area that will gain a lot from this growth. This shift in demand from foods of plant origin to livestock products like meat, eggs, and milk, combined with the ever-growing population, must be addressed without causing any further load on the environment or exhausting the diminishing global resources. In addition to this, agriculture's age-old and conventional methods are also undergoing exceptional changes in this era of technical developments. Introducing the world of data-driven decision-making, a typical example of mankind seeking efficiency, that is transforming how farmers and ranchers manage their animal populations, ensuring both increased productivity and the welfare of the animals in their care. The livestock industries are showing keen interest in examining how data-driven decision-making may be used to increase productivity (Koketsu and Iida, 2020). Decisions of the progressive farmers in the field of agriculture no longer only rely on common practices and intuition nowadays, thanks to technology for the tools available to farmers today which enable them to collect, process, and interpret a wealth of data coming from every aspect of their livestock operations. A significant amount of real-time data is gathered by sensors designed to keep tabs on animal health, environmental factors, and feeding patterns which when analyzed, reveal intricate insights that have the potential to completely reshape the farming industry. This article will try to enlighten how by utilizing the power of data we can promote a new era of accuracy and well-informed decisions in the business When decisions are made on livestock farms utilizing data-driven decision-making, they are based on predictions created using the data gathered on the farm and across the supply chain. Data analytics and machine learning (ML) approaches are used to make accurate forecasts and support decision-making. According to Neethirajan (2020), ML models are now being used to forecast various factors relevant to decision-making, such as sales and feed performance. The ability to predict the problems and opportunities to correct them beforehand has never been more real. From improving feed formulas to spotting illness early symptoms this data-driven revolution is a symbol of a change towards ethical and ecological farming, where animal welfare is supported by facts rather than presumptions. The potential benefits come with challenges, as with every revolutionary innovation. To maintain a competitive edge in the agriculture industry as a whole data collection and analytics are becoming increasingly important. For ML models to function properly, structured data must be available and easily accessible (Lee and Shin, 2020). To put better data analytics into practice, it is crucial to look into the full data analytics pipeline (Pääkkönen and Pakkala, 2015). However, as a flaw of this technology for the application of this into current agricultural workflows one needs to pay more attention to data security, and privacy issues as the computerized system can be hacked or manipulated.

Collecting and Gathering Livestock Data

Livestock data collection includes a wide range of information, such as animal health records, eating patterns, environmental variables, and reproductive data. The progress in the Overall technological aspect has favoured the emergence of IoT and Cloud paradigms, resulting in the emergence of various 

technology-related concepts such as Precision Agriculture (measures and responds to the variability of agricultural data gathered by sensors), Smart Farming (applies information and data technologies to perform a more comprehensive analysis of the farming system, taking into account location, historical, real-time data, and other factors) (Wolfert et al., 2017) and IoT (the process of linking several heterogeneous things, such as machinery, cars, or buildings, with electronic devices, such as sensors and actuators, over various communication protocols to capture and retrieve data). Smart collars, temperature sensors, and RFID tags are examples of IoT devices that provide real-time monitoring of important parameters, allowing farmers to precisely observe animal behaviours and detect early signs of illness. These technologies generate a continual stream of data that aids in making timely, educated decisions.

Radio Frequency Identification (RFID) tags provide a technique for tracking and identifying inpidual animals. When RFID tags are attached to each animal, they allow for more precise tracking of movement patterns, behaviours, and health statuses. Gas sensors to measure potentially dangerous levels of gases in 493 the air inside barns. GPS and geolocation sensors are very useful for livestock that are free to roam in open grazing systems. Farmers may track the locations and movements of their livestock with these gadgets. Sensors are also used to track several elements of livestock behaviour and physiology like hygrometers and air thermometers etc used in the barns. Activity monitors capture movements and behavioural patterns and provide insights into animal health and reproduction cycles. Health monitors with sensors for heart rate, respiration rate, and body temperature have the advantage of detecting health issues early, allowing for prompt interventions and limiting illness spread among the herd. The integration of data collected by these sensors and gadgets goes beyond simply observation and into action. Information is delivered to centralized platforms for analysis and transformation into meaningful insights via data transmission and analytics. This technique enables farmers to make informed real-time decisions, leading to better resource allocation, optimised feeding strategies, and faster remedies for animal health issues.

Benefits of Data-Driven Decisions in Livestock Management

    The incorporation of data-driven decision-making in livestock management has ushered in a new era of accuracy and efficiency in agricultural practices in recent years. Farmers and livestock managers may optimize resources, increase animal health and well-being, and improve farm sustainability by 

leveraging the power of data analytics and technology.

1. Capital Optimization: Farmers may modify feeding schedules and adjust diets to fulfil the 

inpidual demands of each animal by analyzing past data. In (2018) Dunn and associates discovered that data-driven feeding techniques resulted in a 15% reduction in feed 

expenditures and a 10% increase in overall cattle output.

2. Prediction of Market Demand: Farmers can change their production plans to meet shifting 

demands by analyzing past data on customer preferences and market trends. This eliminates 

overproduction and food waste while assuring a consistent supply of high-quality products.

3. Environmental Sustainability: Farmers may construct more efficient housing conditions that 

reduce energy consumption and the environmental imprint of livestock operations by analyzing data on environmental elements such as temperature, humidity, and air quality.

4. Preventive Health Care: Data analytics aid in the early detection of health disorders and the 

execution of timely interventions in proactive health management. Smith et al. (2020), for example, revealed that real-time monitoring of dairy cow behaviour using IoT devices resulted 

in a 25% reduction in veterinary expenditures due to early disease diagnosis.

5. Improved Reproduction Methods: According to the findings of Garca-Ruiz et al. (2019),

combining genetic data into breeding decisions resulted in a 12% increase in milk yield and a 

9% improvement in reproductive rates in dairy cows. Farmers can use genetic data analysis to 

select animals with superior qualities for breeding, resulting in healthier progeny and enhanced herd genetics.

Challenges and Considerations

The incorporation of data-driven decisions in livestock management has various benefits, but 

it also introduces new obstacles and issues that must be addressed to ensure successful implementation and ethical practice. These difficulties transcend technical, ethical, and practical dimensions, and need careful consideration and strategic planning. Compliance with data protection standards is critical for retaining consumer and stakeholder trust. Farmers and livestock management must take strong cybersecurity precautions to avoid data breaches and unauthorized access. Along with this, Poor data quality, whether caused by sensor failure or human mistake, can also lead to inaccurate conclusions and actions so to maintain the accuracy of obtained data it is critical to create data validation methods and perform frequent maintenance. Furthermore, in the challenges of these technologies one can add 

the implementation cost of these sensors and devices which only the high-earning farmers can afford, also the poor farmers lack the knowledge and skills to implement these technologies. Adopting data-driven decisions necessitates a strong technology foundation not all farmers, particularly in rural areas, may have access to advanced technology or dependable internet connectivity. To promote equal adoption of these practices, it is critical to bridge the digital gap. At last farmers and stakeholders can reap the benefits of data-driven decisions while guaranteeing ethical, efficient, and sustainable livestock management practices by identifying these issues and taking a strategic approach.

Conclusion

Globalization has facilitated agricultural trading worldwide. Adoption of data-driven decisions 

in livestock management stands out as a beacon of progress and potential in the shifting landscape of modern agriculture. Farmers and stockbreeders must upstage themselves by offering high-quality products, information about the origin of the product, and the processes it has gone through in the value chain till it reaches the retail site. To that purpose, the agri-food business must adopt technology like the Internet of Things and other smart agricultural technologies that provide monitoring and traceability. However, amid these exciting developments, it is critical to recognize the problems and concerns that come with the adoption of data-driven decisions. A comprehensive approach is required to ensure data security and privacy, as well as to bridge technological pisions and address ethical problems. Collaboration among stakeholders, including farmers, researchers, and technology specialists, is critical in overcoming these obstacles and achieving seamless integration of data and agriculture. Finally, the future holds enormous promise for data-driven decisions in cattle management. It is a road towards greater efficiency, animal welfare, and environmental sustainability. We go on a transforming journey as we embrace this trip with a spirit of innovation, accountability, and teamwork.

Reference

Dunn, S. M., Mader, T. L., Schinckel, A. P.,        & Einstein, M. E. (2018). Using on-farm data to 

develop precision feeding recommendations for growing pigs. Journal of Animal Science, 

96(5), 1749-1760.

García-Ruiz, A., Cole, J. B., VanRaden, P. M., Wiggans, G. R., Ruiz-López, F. J., & Van Tassell, C. P. (2019). Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proceedings of the National 

Academy of Sciences, 116(9), 3338-3343.

Koketsu, Y., Iida, R. (2020). Farm data analysis for lifetime performance components of 

sows and their predictors in breeding herds. Porc. Health. Manag. 

Lee, I., Shin, Y.J. (2020). Machine learning for enterprises: applications, algorithm selection, 

and challenges. Bus. Horiz. 63, 157–170. 

Neethirajan, S.(2020). The role of sensors, big data and machine learning in modern animal 

farming. Sens. Bio Sens. Res. 

Pääkkönen, P., Pakkala, D., 2015. Reference architecture and classification of technologies, 

products and services for big data systems. Big Data Res. 

Wolfert, S., L. Verdouw, Ge., C., Bogaardt, M.J.(2017), Big data in smart farming–a review, Agricultural Systems 153 69–80.

Smith, M. L., Bowman, M. E., & Ryan, C. M. (2020). Precision livestock farming: Potential 

applications for swine veterinarians. Journal of Swine Health and Production, 28(4), 205-211.