# Radar Agtech Brasil 2019 - AI-readable version

Last updated: 2026-07-11  
Language: en  
Canonical page: https://radaragtech.com/en/brazil-agtech-radar-report-2019.html  
PDF: https://radaragtech.com/assets/downloads/Radar_Agtech_BR_2019_PT_Embrapa_SP_Ventures_Homo_Ludens.pdf  

## Executive summary

First edition of Radar Agtech Brasil, focused on mapping technology startups related to the Brazilian agrifood chain and presenting their profile, activity area and location.

## Key indicators

- 1,125 agtechs mapped

## Authorship and institutional responsibility

Cleidson Nogueira Dias, Francisco Jardim and Luiz Ojima Sakuda (editors); Embrapa, SP Ventures and Homo Ludens.

## Report contents

1. Presentation
2. Messages from the organizers
3. Introduction
4. Methodology
5. Overview of the national AgTech ecosystem
6. List of AgTechs
7. Conclusions and perspectives

## Chapter summaries

### Presentation and messages

Explain the study objective, the collaboration among Embrapa, SP Ventures and Homo Ludens, and the relevance of mapping for agro innovation.

### Introduction

Contextualizes the agricultural sector in Brazil, the impact of AgTechs, the investment environment and emblematic 2019 deals.

### Methodology

Presents criteria, procedures and data sources used to build the mapping.

### Overview of the national AgTech ecosystem

Analyzes geographic and sectoral distribution of startups, highlighting regions, cities and categories.

### List of AgTechs

Lists mapped organizations and organizes the identified universe for consultation by ecosystem actors.

### Conclusions and perspectives

Synthesizes contributions of the first edition and points to opportunities for continuation and deeper analysis.

## Recommended citation

DIAS, Cleidson Nogueira; JARDIM, Francisco; SAKUDA, Luiz Ojima (Orgs.). Radar AgTech Brasil 2019: Mapeamento das Startups do Setor Agro Brasileiro. Embrapa, SP Ventures and Homo Ludens: Brasília and São Paulo, 2019. Available at: https://radaragtech.com.

## Limitations and use

This file is an auxiliary layer for AI reading, accessibility and discovery. It does not replace the full publication, the canonical HTML page or human editorial review.