Summit on Data Science in Agriculture Summary – USDA NIFA (.gov)

Report on the Summit on Data Science in Agriculture and Its Alignment with Sustainable Development Goals (SDGs)

Introduction

In October 2016, the National Institute of Food and Agriculture (NIFA), in collaboration with the National Science Foundation (NSF)-supported Midwest Big Data Hub at the University of Illinois Urbana-Champaign and the College of Agriculture and Life Sciences at Iowa State University, convened the Summit on Data Science in Agriculture. The summit aimed to leverage big data to address critical challenges in food and agricultural sciences, directly contributing to the achievement of several Sustainable Development Goals (SDGs), including Zero Hunger (SDG 2), Climate Action (SDG 13), and Industry, Innovation, and Infrastructure (SDG 9).

Objectives of the Summit

1. Assess the current state of data in agriculture and harness the unprecedented opportunities presented by big data to tackle major food and agricultural challenges, supporting SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production).
2. Develop priority areas for future workshops focused on the generation, management, and integration of big data within food and agricultural systems, advancing SDG 9 (Industry, Innovation, and Infrastructure).
3. Launch the Food and Agriculture Cyberinformatics and Tools (FACT) Initiative to foster stakeholder engagement and promote data-driven innovation in agriculture, aligning with SDG 17 (Partnerships for the Goals).

Launch of the FACT Initiative

The FACT initiative, now known as the Data Science for Food and Agricultural Systems (DSFAS) program, was announced as a competitive USDA-NIFA program. It supports projects focused on advancing data science applications in agriculture, thereby promoting sustainable agricultural practices and innovation consistent with SDG 2 and SDG 9.

Key Themes and Discussions

• Agri-business Collaboration and Data Exchange Facilities: Emphasizing partnerships to enhance data sharing and integration, contributing to SDG 17.
• Anticipated Benefits and Opportunities of Data Science: Highlighting how data science can improve food security and agricultural productivity, supporting SDG 2 and SDG 1 (No Poverty).
• Predictive Modeling for Big Data and Genomics: Utilizing advanced analytics to optimize crop yields and resilience, aligning with SDG 2 and SDG 3 (Good Health and Well-being).
• Smart and Connected Communities: Innovating future cities through data-driven agricultural solutions, relevant to SDG 11 (Sustainable Cities and Communities).
• Modeling Climate and Environmental Effects on Food Security: Addressing climate change impacts on agriculture, directly supporting SDG 13 (Climate Action) and SDG 15 (Life on Land).

Conclusion

The Summit on Data Science in Agriculture underscored the critical role of data science in transforming food and agricultural systems. By fostering innovation, collaboration, and sustainable practices, the initiatives and discussions at the summit contribute significantly to advancing multiple Sustainable Development Goals, ensuring a resilient and food-secure future.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 2: Zero Hunger
   • The article focuses on agriculture, food security, and addressing major challenges in food and agricultural sciences, which directly relates to ending hunger and promoting sustainable agriculture.
2. SDG 9: Industry, Innovation and Infrastructure
   • The emphasis on data science, big data, predictive modeling, and innovative technologies in agriculture aligns with fostering innovation and building resilient infrastructure.
3. SDG 13: Climate Action
   • Modeling climate and environmental effects on food security connects to taking urgent action to combat climate change and its impacts.
4. SDG 17: Partnerships for the Goals
   • The collaboration between USDA, NSF, universities, and stakeholders reflects partnerships to strengthen the means of implementation and revitalize global partnerships.

2. Specific Targets Under Those SDGs

1. SDG 2: Zero Hunger
   • Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers through sustainable food production systems and resilient agricultural practices.
   • Target 2.4: Ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production.
2. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors, including agriculture, through innovation and data science.
3. SDG 13: Climate Action
   • Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries, including agriculture.
4. SDG 17: Partnerships for the Goals
   • Target 17.16: Enhance the global partnership for sustainable development, complemented by multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology, and financial resources.

3. Indicators Mentioned or Implied to Measure Progress

1. For SDG 2 Targets
   • Indicator 2.3.1: Volume of production per labor unit by classes of farming/pastoral/forestry enterprise size.
   • Indicator 2.4.1: Proportion of agricultural area under productive and sustainable agriculture.
2. For SDG 9 Targets
   • Indicator 9.5.1: Research and development expenditure as a proportion of GDP.
   • Indicator 9.5.2: Number of researchers per million inhabitants.
   • Implied indicators related to the use and integration of big data and data science tools in agriculture.
3. For SDG 13 Targets
   • Indicator 13.1.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population.
   • Indicator 13.1.2: Number of countries with national and local disaster risk reduction strategies.
   • Implied use of climate and environmental modeling data to assess food security risks.
4. For SDG 17 Targets
   • Indicator 17.16.1: Number of countries reporting progress in multi-stakeholder development effectiveness monitoring frameworks that support the achievement of the sustainable development goals.
   • Implied measurement of partnerships and collaborative initiatives such as the FACT/DSFAS program.

4. Table of SDGs, Targets and Indicators

Source: nifa.usda.gov