Research Findings

Research Findings

The AGData Acceleration Facility invests in innovations and partnerships to catalyze the improved collection, processing, storage, and use of ground-truth data, targeting key bottlenecks and technical challenges that inhibit the large-scale collection of ground-truth datasets and their use. This page contains all outputs from the ECAAS AGData Acceleration Facility research.

Stakeholder Landscape Assessment

The Stakeholder Landscape Assessment identifies and maps the ecosystem of public, private, and civil society organizations operating in the crop analytics space for smallholder agricultural systems.

Stakeholder Database

The ECAAS Stakeholder Database includes information on nearly 200 organizations working in agricultural ground data collection, processing, sharing, use, and application.

State of the Science for Ground Truthing in Crop Analytics

The State of the Science report reviews the use and collection of ground truth observations to enable crop analytics. The report focuses on three key crop variables: field boundaries, crop type, and yield estimation.

ECAAS Field Mapper for ODK

View the guidelines for advancing crop analytics using ODK Collect. The purpose of the form is to standardize the collection of agricultural reference data. Designed and improved through consultations, the new toolkit is equipped with improved user support features and modifications for easy data capture. Please, check out the new form and deploy the tool with your own data capture efforts!

Innovation Investment Priorities for Smallholder-focused Crop Analytics

The AGData Acceleration Facility Innovation Agenda outlines priority areas of innovation for advancing smallholder-focused crop analytics.

Scalable Approaches to Field Data Collection using Human-Centered Design

By: 6th Grain & Farm.Ink

Read the initial lessons learned and final report to see how 6th Grain and overcame challenges and delivered state-of-the-art agricultural mapping services to Kenyan farmers.

Next-Gen Crop Production Analytics

By: IFPRI, ICRISAT, University of Twente, & aWhere

Field-based crop cut experiment (CCE) data is a gold standard of ground truth data for crop analytics, yet it is time-consuming and challenging to scale over large areas. Satellite remote sensing-based estimates can cover a large area, yet their accuracy is highly subject to the availability and quality of ground truth data. 

Read an introduction about the project, and learn more about how Dynamic Area Sampling Frames and 3D Smartphone Imaging can improve the value and utility of ground truth data for improving crop production analytics.

Creating Open Agricultural Maps and Groundtruth Data to Better Deliver Farm Extension Services

By: Farmerline & Clark University

This project collected crop-type data in order to test the sustainably over large areas. Read the Service Scale-Up plan to learn how the team planned to efficiently deliver farm extension services directly to smallholders in Ghana. Then read the initial lessons learned from the gathering crop type maps for Maize and Rice.