Big Data For Smallholder Farmers

There is massive scope for linking farmers to valuable knowledge and agricultural services through Information and Communication Technologies (ICTs) as a pathway to driving improved productivity and market access, creating opportunities for higher incomes and better food security as a result. In Uganda, as in many sub-Saharan countries, lack of timely and accurate access to information about weather, financial services, crop management, markets and climate coping mechanisms is a serious obstacle for smallholder farmers, preventing them from achieving higher yields and selling their produce for better prices. To address these challenges, an innovative initiative is showcasing how ICTs can be used to capture satellite-based information on a wide range of agricultural indicators, packaging it into tailor-made messages for farmers in local languages.

According to Forbes, we generate almost 2.5 quintillion bytes of data every day. The next generation of agriculture heavily depends on data. The ability to capture, sort, analyze and extract actionable intelligence from large data sets to reveal patterns (human, climate, market) and related trends is an important emerging field. The increase in the use of Internet of Things (IoT) devices would only add to this data deluge. The Economist rightly called data as the world’s most valuable resource , while some calling data the new oil . Agriculture would be one of the major users of IoTs. How can individuals, organizations and governments build capacities and processes in place to take advantage of this huge influx of data. This coupled with existing data streams (weather, satellite imagery, markets etc.,) would create an ecosystem which if managed efficiently would provide rich dividents especially in the agriculture sector where the right information at the right time will make a great influence in the livelihoods of people involved in agriculture and allied activities. This publication looks at how various initiatives are leveraging data, related to agriculture value chains, to influence decision making and efficient service delivery together with addressing key building blocks such as interoperability, data sharing, data security and the necessary policies and regulations that are needed to be implemented to sustain the data ecosystem.

Smallholder farming systems contribute a substantial quantity of the food consumed in many lower and middle-income countries and contribute to the national and local economies. Despite the importance of smallholder farming, a transformation is needed in order to deliver food security and decent incomes for the farmers themselves and at the national level. This transformation must also be sustainable in terms of environmental impacts and social equity in order to be successful in the long term. The pressures of population growth, climate change, and land fragmentation compound the problem. Addressing these overlapping issues is a big challenge. One obstacle is the lack of good quality granular data linking these issues together. Household surveys are the workhorse method for gathering such data, but there are well-known problems that prevent household survey data from building up a “big picture” and delivering insights beyond the geographical boundary of each individual study. Such obstacles include the lack of access to datasets, differences in survey design, and respondent biases. Agile, data-oriented research tools can help to overcome these challenges. We use the term “agile” to imply methods that do not attempt exhaustive measurements, which are designed to be easy to use, and which entail some degree of flexibility in terms of adaptation to local conditions and integration with other tools or methods. Often these methods also nudge the behavior of tool users towards best practices. In recent years various research tools and approaches have been published which fit within our definition of “agile data-oriented research tools”. The domains these tools function in include monitoring and evaluation, intervention targeting, tailored information delivery, citizen science, credit scoring, and user feedback collection; all with the over-arching aim to improve data quality and access for those studying the sustainable development of smallholder farming systems. The goal of this Research Topic is to better define that niche, the ecosystem of tools and current practices, and to explore how such approaches can provide the underpinning knowledge required for the transformation of smallholder farming systems. One example of an agile data-oriented research tool is the Rural Household Multi-Indicator Survey (RHoMIS). It is a modular, digital system for building household surveys addressing the common topics in smallholder development. It was purposefully designed to give a broad overview of the farm system whist keeping survey duration to a minimum, to be user-friendly in implementation, and to be sufficiently flexible to function in a broad variety of locations and projects. Since 2015 it has been used by 30 organizations in 32 countries to interview over 34,000 households. The tool and database are open access and a community of practice is developing around the tool. We particularly welcome contributions that engage with the RHoMIS tool and data. However, we also describe the tool in order to provide an example of what is meant by an agile data-oriented research tool, and welcome contributions focusing on other tools or methodologies. We encourage the submission of manuscripts addressing the above topic, and those which fit within one of the following three sub-themes: (i) Perspectives or review articles which explore the niche, best practices, or promising approaches in agile data-oriented research tools for smallholder farm system transformation. Also, technology and code articles that describe new tools are welcomed. (ii) Original research articles presenting analyses based on data derived from agile data-oriented tools used at the project level. Examples include impact evaluations, adoption studies, targeting studies, or adaptive management, and should reflect on the additional benefit leveraged by the agile method applied. (iii) Original research articles that make use of the large amounts of data generated by such agile methods and/or link between agile data and other data sources. Examples include meta-analyses of data from multiple studies, layering data collected from different agile tools, or linking agile data to remote sensing or large-scale modeling outputs.

This book is open access under a CC BY 4.0 license. This book sheds new light on a selection of big data scenarios from an interdisciplinary perspective. It features legal, sociological and economic approaches to fundamental big data topics such as privacy, data quality and the ECJ’s Safe Harbor decision on the one hand, and practical applications such as smart cars, wearables and web tracking on the other. Addressing the interests of researchers and practitioners alike, it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.All contributions are based on papers submitted in connection with ABIDA (Assessing Big Data), an interdisciplinary research project exploring the societal aspects of big data and funded by the German Federal Ministry of Education and Research.This volume was produced as a part of the ABIDA project (Assessing Big Data, 01IS15016A-F). ABIDA is a four-year collaborative project funded by the Federal Ministry of Education and Research. However the views and opinions expressed in this book reflect only the authors’ point of view and not necessarily those of all members of the ABIDA project or the Federal Ministry of Education and Research.

This book aims at identifying and understanding the key factors and mechanisms involved in the diffusion and utilization of big data (BD) in key policy areas such as agriculture, health care and the environment in developing countries. The book has eight chapters and covers: (1) the current status, opportunities and challenges of big data in developing countries; (2) the big data ecosystem in developing countries; (3) big data in environmental protection and resource conservation; (4) big data in health-care delivery and outcomes; (5) big data in agriculture; (6) big data's role in increasing smallholder farmers' access to finance; and (7) data privacy and security issues facing smallholder farmers and poor communities in developing countries.

The digital age has transformed business opportunities and strategies in a resolutely practical and data-driven project universe. This book is a comprehensive and analytical source on entrepreneurship and Big Data that prospective entrepreneurs must know before embarking upon an entrepreneurial journey in this present age of digital transformation. This book provides an overview of the various aspects of entrepreneurship, function, and contemporary forms. It covers a real-world understanding of how the entrepreneurial world works and the required new analytics thinking and computational skills. It also encompasses the essential elements needed when starting an entrepreneurial journey and offers inspirational case studies from key industry leaders. Ideal reading for aspiring entrepreneurs, Entrepreneurship and Big Data: The Digital Revolution is also useful to students, academicians, researchers, and practitioners.

Spore magazine 190: Farm data: Serving smallholder farmers in a digital age Increasing amounts of agricultural data are being produced at faster speeds, using a greater variety of technologies and innovations than ever before. But what is the value of information sharing for smallholders, and what are the risks? SPORE is the quarterly magazine of the Technical Centre for Agricultural and Rural Cooperation (CTA), offering a global perspective on agribusiness and sustainable agriculture. CTA operates under the Cotonou Agreement between the countries of the Africa, Caribbean and Pacific (ACP) group and the European Union and is financed by the EU.

Big Data has the power to change all aspects of agriculture, environmental protection and healthcare, especially in developing countries, by allowing new levels of analysis and tailoring of impacts. How big datawill impact will benefit smallholder farmers relative to global multinationals. The book considers how big data can changing the way lenders assess creditworthiness of potential borrowers.Data privacy and security issues are important issues. The key ideas, concepts and theories presented are explored, illustrated and contrasted through in-depth case studies of developing world-based big data companies and deployment and utilization big data in agriculture, environmental protection and healthcare.