DIGITAL FARMER PROFILES: Reimagining Smallholder Agriculture AUTHORS
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DIGITAL FARMER PROFILES: Reimagining Smallholder Agriculture AUTHORS Bobbi Gray, Grameen Foundation Lee Babcock, Grameen Foundation Leo Tobias, Grameen Foundation Mona McCord, Grameen Foundation Ana Herrera, Grameen Foundation Cecil Osei, Grameen Foundation Ramiro Cadavid, Grameen Foundation ACKNOWLEDGEMENTS Digital Development for Feed the Future is a collaboration between the Global Development Lab and the Bureau for Food Security, both within the United States Agency for International Development (USAID), and is focused on integrating a suite of coordinated digital tools and technologies into Feed the Future activities to accelerate agriculture-led economic growth and improved nutrition. Feed the Future is America’s initiative to combat global hunger and poverty. Many thanks go to all the service providers and others (listed in Annex 1) who gave of their time to contribute to this landscape assessment through interviews and email exchanges and for directing our team to additional resources. We hope we have adequately presented their work, experiences, and opinions. The authors of this publication would like to express their gratitude to the many Grameen Foundation team members who assisted in conducting interviews and identifying resources for this research: Maria Hernandez, Gaurav Chakraverty, Simon Okot, Evelyne Banura, Brigitta Nyawira, and Benjamin Kimosop. Also, of the Grameen Foundation, we would like to express our gratitude to Lauren Hendricks, Sybil Chidiac, Jessie Tientcheu, Gigi Gatti, and Emily Romero for their thought leadership and support, and to Brent Farrar, Bee Wuethrich, and Liselle Yorke for their graphic design and communications assistance. Additional thanks go to Bee Wuethrich of Grameen Foundation for her copyediting. Finally, we would like to voice our appreciation to Ellen Galdava and Abdul Bari Farahi from FHI 360 and Kwasi Donkor and Christopher Burns of USAID for their support, review, and input into this publication. November, 2018 TABLE OF CONTENTS Abbreviations ............................................................................................................................................................................v Glossary .....................................................................................................................................................................................vi Executive Summary ...............................................................................................................................................................1 I. Introduction ............................................................................................................................................................................3 II. Defining Smallholder Farmers ....................................................................................................................................5 III. Service Provider Models—Data Generators ...................................................................................................8 IV. Types of Data Collected .........................................................................................................................................11 V. Data Capture Methods ...............................................................................................................................................19 VI. Data Storage: Cloud Computing and Blockchain .......................................................................................24 VII. Data Analytics ................................................................................................................................................................27 VIII. Qualitative Analysis and Insights .........................................................................................................................35 IX. Data Sharing ....................................................................................................................................................................37 X. Data Use .............................................................................................................................................................................41 XI. What is Innovation in Farmer Profile Data Management? ....................................................................46 XII. Conclusion & Next Steps .......................................................................................................................................50 XIII. Case Studies on Innovative Farmer Profile Data Management .....................................................53 Ricult Case Study ....................................................................................................................................................55 Grameen Foundation Case Study .................................................................................................................62 CGIAR’s Platform for Big Data in Agriculture .......................................................................................68 Bibliography .............................................................................................................................................................................72 Annexes ....................................................................................................................................................................................80 Annex 1: Key Informant Interviews ..............................................................................................................80 Annex 2: Key questions for farmer profile data asset management ..........................................82 Photo by USAID NEAT ABBREVIATIONS AGRA Alliance for a Green Revolution in Africa API Application Programming Interface ARET Agriculture Risk Evaluation Tool B2B Business to Business B2C Business to Customer BI Business Intelligence CBA Commercial Bank of Africa CGAP Consultative Group to Assist the Poor CGIAR Consultative Group for International Agricultural Research CIAT International Center for Tropical Agriculture (part of CGIAR network) CoP Community of Practice CTA The Technical Centre for Agricultural and Rural Cooperation FAIR Findable, Accessible, Interoperable, Reusable FAO Food and Agriculture Organization FI Financial Institution FUNDER Foundation for Rural Business Development GCAP Ghana Commercial Agricultural Project GPS Global Positioning System GSMA Groupe Speciale Mobile Association HCD Human-centered Design i2i Impact 2 Insight IARI Indian Agriculture Research Institute IVR Interactive Voice Response KYC Know Your Customer LST Land Surface Temperature LSMS Living Standards Measurement Study MNO Mobile Network Operator NDVI Normalized Difference Vegetation Index NGO Non-governmental Organization OBD Outbound Dialing SMS Short Message Service VGI Volunteered Geographic Information WAAPP West Africa Agricultural Productivity Program GLOSSARY Algorithm A set of rules to be followed in calculations or other problem-solving operations, especially by a computer. Artificial Intelligence Emerged in the 1950s. The theory and development of computer systems to be able to perform tasks normally requiring human intelligence. Open, harmonized, interoperable, and integrated datasets from multiple Big Data* domains aimed to accelerate agricultural research and data use in service of development goal. Blockchain A secure distributed immutable database shared by all parties in a distributed network where transaction data can be recorded. Business Intelligence Using data generated by service users to make decisions about product/service design. Thoughtful use of data (often big data) to inform farmer decisions and actions. Data-driven Agriculture It means having the right data, at the right time, to make better decisions that improve long-term profitability. Deep Learning Emerged in 2010s; a technique for implementing machine learning. Farmer Ecosystem An interconnected and coordinated network of support services, information, suppliers, buyers and actors that meet the needs of farming households. Farmer Profile Data collected on a farmer and his or her farm that is used by a service provider or multiple service providers to design and direct products or services. The interconnection via the internet of computing devices embedded in Internet of Things everyday objects, enabling them to send and receive data; e.g., in soil, farm tools and waterways. Land Surface Temperature The temperature of the land itself rather than the ambient air above it (as is used in most typical temperature recording). An algorithm that is trained, given input data, and then run on new data to predict Machine Learning the output. As the system processes more data, it learns from its mistakes. Emerged in 1980s. An approach for achieving artificial intelligence and is used in predictive, prescriptive, and cognitive analytics. Normalized Difference Vegetation Index A measure of plant health derived from satellite imagery. A set of concepts and categories in a subject area or domain that shows their Ontology properties and the relations between them. Currently perceived as being necessary for interoperability of data. Outbound Dialing Also called voice SMS, is a pre-recorded message sent to mobile phones. An integrated crop management system that attempts to match the kind and amount of inputs with the actual crop needs for small areas within a farm field. Precision Agriculture Precision