Management in the Era of Big Data

Issues and Challenges

Joanna Paliszkiewicz

• Publisher: Auerbach Publications
• Year: 2020
• ISBN:
  9781000093674
• Edition: 1
• Topic: Business
• Subtopic: Management
• Index: Business

This book is a wonderful collection of chapters that posits how managers need to cope in the Big Data era. It highlights many of the emerging developments in technologies, applications, and trends related to management's needs in this Big Data era.

—Dr. Jay Liebowitz, Harrisburg University of Science and Technology

This book presents some meaningful work on Big Data analytics and its applications. Each chapter generates helpful guidance to the readers on Big Data analytics and its applications, challenges, and prospects that is necessary for organizational strategic direction.

—Dr. Alex Koohang, Middle Georgia State University

Big Data is a concept that has caught the attention of practitioners, academicians, and researchers. Big Data offers organizations the possibility of gaining a competitive advantage by managing, collecting, and analyzing massive amounts of data. As the promises and challenges posed by Big Data have increased over the past decade, significant issues have developed regarding how data can be used for improving management. Big Data can be understood as large amounts of data generated by the Internet and a variety of connected smart devices and sensors.

This book discusses the main challenges posed by Big Data in a manner relevant to both practitioners and scholars. It examines how companies can leverage Big Data analytics to act and optimize the business. This book brings together the theory and practice of management in the era of Big Data. It offers a look at the current state of Big Data, including a comprehensive overview of both research and practical applications.

By bringing together conceptual thinking and empirical research on the nature, meaning, and development of Big Data in management, this book unifies research on Big Data in management to stimulate new directions for academic investigation as well as practice.

II

Big Data in Management: Applications, Prospects, and Challenges

Chapter 7

Big Data in Modern Farm Management

Monika Gębska

Warsaw University of Life Sciences—SGGW

Contents

7.1 Introduction

7.2 Farm Management before the Era of Big Data

7.3 Big Data in Animal Production Management

7.4 Advantages of Big Data in Farm Management

7.5 Disadvantages of Big Data in Farm Management

7.6 Farmers’ Perception of Big Data

7.7 Conclusions and Proposition for Future Research

References

7.1 Introduction

Farm management is becoming more and more complicated, and yet its effects are of fundamental importance for food production. Farms are getting bigger, more modern, strongly connected with the market which is strongly regulated and unstable. In addition, the effects of agricultural production depend to a large extent on its adaptation to natural conditions, especially soil quality and climate. Farmers will face many challenges in this respect due to adverse climate changes—weather variability, rainfall irregularity, more frequent droughts (Food and Agriculture Organization, 2017). FAO (2017) also draws attention to the fact that agricultural production will hinder the spread of plant and livestock diseases in the future as a result of increased cross-border human mobility.

Meanwhile, agriculture’s job is to feed a growing world population. According to United Nations forecasts, by 2050 the population will increase by 2 billion people, which will require an increase in agricultural production by up to 60% over the next 30 years (United Nations, 2019). This will not be easy due to the limited resources, especially agricultural land and manual workforce.

In this context, researchers and practitioners are increasingly paying attention to the need to produce food in a more sustainable way (Kemp, Girdwood, Parton and Charry, 2004), taking into account current conditions and future needs. Therefore, agricultural activities are increasingly subject to legal regulations in the field of environmental protection (Krol, 2015), animal welfare (Vapnek and Chapman, 2010), or bio-insurance (Dutta, Mueller, Smith, Das and Aryal, 2015). Society expects food to be safe, available, cheap (Piližota, 2012), and produced to many standards, including ethics (Food Standards Agency, 2016; Piližota, 2012) while protecting landscape and biodiversity (Berry, Delgado, Pierce and Khosla, 2004; McConnell and Burger, 2018). Meanwhile, farmers must take into account the profitability of production and the achievement of income, and yet the economic effects of agricultural activities are very variable (Beckman and Schimmelpfennig, 2015) and depend on numerous decisions taken during a long, complicated production cycle.

Because of the number of variables, a farmer must take into account when making decisions, farm management is more difficult than for most enterprises (Kemp, Girdwood, Parton and Charry, 2004). Advanced information technologies such as Big Data, which have already been successfully implemented in other sectors of the economy, can help farmers in managing the farm (Lokers, Knapen, Janssen, van Randen and Jansen, 2016). According to the definition of De Mauro, Greco and Grimaldi (2016, p. 131), “Big Data is the Information asset characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value”.

Big Data, thanks to extensive analytical models, gives the opportunity to carry out a detailed analysis of agricultural activities and facilitate making the right decisions in time. As a result, they can make agricultural production more efficient, profitable, and also friendly to society, the environment, animals and, as a result, more sustainable (Waga and Rabah, 2014).

In this context, an essential question is how farmers will be able to manage farms and meet the multiple challenges such as food security, climate change, responsibly water and soil management, biodiversity, animal welfare while improving productivity, lowering at the same time its environmental footprint resulting from greenhouse gas emissions (Sayer and Cassman, 2013).

For the farmers to meet all their expectations, they will need to focus on increasing production in sustainable ways. This is a big challenge and only farmers who manage their farms in reasonably effective way will be successful. Probably many farmers having small farms will leave farming as the costs of production are higher than the net returns making it unprofitable (Sawant, Urkude and Jawale, 2016). Studies conducted by many researchers suggest that efficient data management and processing can be a good solution to problems faced by farmers. It helps decision makers to make appropriate choices, plan agricultural activities, and take preventive and curative measures as needed (Sawant, Urkude and Jawale, 2016).

The aim of this chapter is to present ways of using Big Data in modern farm management based on a critical literature review and to propose future research in the area. Research question used in this study is: Why we need Big Data in agriculture and how Big Data may help manage the farm in a modern way?

The bibliographic analysis in the area under study was performed. First, a keyword-based search was carried out from the Google Scholar. As search keywords, following query was applied: “Big Data” AND “Management” AND [“Precision Agriculture “OR” Smart Farming “OR” Agriculture “OR” Animal Production “OR” Milk production “OR” Pigs “OR” Cows “OR Broiler “OR” Poultry “OR” Meat production”]. From this effort, abstracts of 485 initially identified papers published in English in the years 2010–2019 were checked in terms of content and relationship with the topic of the article. Finally, only 84 were used and fully analyzed.

In the first part of this chapter, its data sources are presented, and next, the use of Big Data in crop and animal production is described. Further, advantages and disadvantages of Big Data adaptation in farm management are presented. Farmers’ opinions in these regards are presented. Finally, conclusions and future research directions are proposed.

7.2 Farm Management before the Era of Big Data

Farm management is defined as the “process by which resources and situations are manipulated by the farm manager in trying, with less than complete information, to achieve his goals” (Dillon, 2008). It means making and implementing all the decisions made by organizing and operating a farm for maximum production and profit.

As in the past and today in traditional farming, many decisions are made based on historical data, experience, and intuition (Nuthall and Old, 2018). These decisions determine the use of means of production, including fertilizers, pesticides, feed, as well as the dates of agrotechnical convergences, irrigation, and veterinary treatments. However, as early as the 1990s, the need to develop methods to support farmers’ decision making and predicting their results was noticed. Activities in this direction began with modeling of plant growth and crop size (Basso, Ritchie, Pierce, Braga and Jones, 2001). Most of the models used were based on linear regression analysis and multiple linear regression analysis (Kravchenko and Bullock, 2000) and were used to make decisions at the operational level.

In the last 30 years, there has been rapid technical and technological progress resulting mainly from the construction and miniaturization of computers and the invention of the Internet. New devices and systems have also been created that are also applicable in agriculture. The creation of the Global Position System (GPS) (Shanwad, Patil, Dasog, Mansur and Shashidhar, 2002) and radio-frequency identification (RFID) technology using radio waves to identify people and objects at a distance (Luvisi, 2016) was particularly important. Since the method of combining the use of RFID with the Internet has been invented, it has been possible for ...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Foreword by Jay Liebowitz, D.Sc.
• Foreword by Alex Koohang, PhD
• Preface
• Editor
• Contributors
• Section I Big Data and Management: Theoretical Foundations
• Section II Big Data in Management: Applications, Prospects, and Challenges
• Index