DUBLIN--(BUSINESS WIRE)--The "Digital Farming in Brazil, 2018" report has been added to ResearchAndMarkets.com's offering.
The Digital Farming in Brazil, 2018 report investigates the history and development of digital farming in Brazil.
Agriculture is one of the main pillars of the Brazilian economy, generating 23% of the GNP. Precision agriculture became popular in Brazil at the end of the 1990s and the most popular segment is the automatic pilot. Mapping and productivity sensors on combines are also popular.
Key Insights
- History & Development
- Data Collection & Handling
- Realities of Digital Farming in Brazil
- Future Trends
Topics Covered
Abbreviations
Executive Summary
Chapter 1: Introduction
1.1 The digital revolution
Chapter 2: Digital Farming
2.1 Summary
2.2 Evolution and concepts
2.2.1. Agriculture 1.0
2.2.2. Agriculture 2.0
2.2.3. Agriculture 3.0
2.2.3.1. Precision farming
2.2.4. Agriculture 4.0
2.3 Digital farming definitions
2.4 Conditions for digital farming
2.4.1. Digital farming adding value
2.5 Digital farming and the machinery industry
2.6 Data portals
2.7 Digital farming for small farmers
2.8 Conditions for digital farming
2.8.1. AEF and AgGateway
2.9 The influence of digital agriculture
2.10 Sustainable agriculture
2.11 The trend for digital agriculture in emerging countries
2.12 Digital agriculture benefits
2.13 Potential negative impacts and barriers
2.14 Enabling new business models
Chapter 3: Precision Agriculture
3.1 Summary
3.2 What is precision agriculture?
3.3 The history of precision agriculture
3.4 The precision agriculture cycle
3.5 Considerations when adopting precision agriculture
3.6 General concepts of precision agriculture
3.6.1. Precision agronomics
3.6.2. Environmental benefits of PA
3.7 Precision agriculture technologies to be promoted
3.7.1. Guidance systems
3.7.2. Variable rate and nutrient sensing technologies
3.7.3. Precision livestock farming technologies
3.8 Precision agriculture and profitability
Chapter 4: Sensors and Crops
4.1 Summary
4.2 Introduction
4.3 Sensing techniques
4.4 Remote sensing
4.4.1. Use of satellite images in precision agriculture
4.4.2. Drones
Annex I: Federal government regulations for drone use in Brazil.
4.5 Proximal sensing
4.5.1. Fluorescence sensing
4.5.2. Sensing of soil properties
4.5.3. Robots
Chapter 5. Geolocation and Navigation
5.1 Summary
5.2 Introduction
5.3 Use of GNSS
5.3.1. Determining receiver position
5.3.2. Errors affecting accuracy
5.3.2.1. Required accuracy
5.3.3. Augmentation systems
5.4 GNSS in agriculture
5.4.1. Site specific recording of yields
5.4.2. Georeferencing
5.4.3. Actuation in the field
5.4.4. Field mapping
5.4.4.1. Developing agronomical maps
5.4.4.2. Mapping coffee in Brazil
5.4.5. Data interpolation
5.4.6. Obtaining productivity
5.4.7. Productivity maps in Brazil
5.4.8. Limiting factors of productivity maps
5.5 Potential management zones
5.6 Making decisions
Chapter 6: Agriculture in Brazil
6.1 Summary
6.2 Introduction
6.3 Land use
6.3.1. Mid-West
6.3.2. Northeast
6.3.3. North
6.3.4. Southeast
6.3.5. South
6.4 Crops
6.4.1. Soybeans
6.4.2. Corn
6.4.3. Sugar cane
6.4.4. Cotton
6.4.5. Coffee
6.4.6. Dry beans
6.4.7. Rice
6.4.8. Other crops
6.5 Irrigation
6.6 Productivity of Brazilian agriculture
6.7 Agricultural exports
6.8 The agrochemical market
6.9 Transgenic crops
6.10 Animal husbandry
6.11 The fertilizer market
6.11.1. Foliar fertilizers
6.11.2. Granular NPK soil fertilizers
6.12 Agricultural machinery
Chapter 7: Digital Agriculture in Brazil
7.1 Summary
7.2 First steps - Agrisat
7.3 The Aquarius Project
7.4 The role of venture capital
7.4.1. M&A and new possibilities
7.4.2. Venture capital in Brazil
7.4.3. SP Ventures
7.5 The Piracicaba start-up hub
7.6 The Climate Corporation
7.6.1. The open platform concept
7.7 BASF Agrostart
7.8 Apps available in Brazil
7.9 Adoption of digital agriculture in Brazil
7.9.1. The EMBRAPA/ Senar study
7.9.2. The Kleffmann study
7.9.3. Farmer connectivity in Brazil
Annex II: the use of technology in agriculture in Brazil (SEBRAE)
7.9.4. Further data on adoption and results achieved
7.9.5. The future of FMS
Chapter 8: Future Trends
8.1 Summary
8.2 Market growth
8.3 Challenges in digital agriculture
8.3.1. Digital agriculture challenges in France
8.4 Trends in crop digital agriculture
8.4.1. Monitoring
8.4.1.1. Crop-Scan
8.4.2. Robots in the field
8.5 Animal husbandry
8.6 Connectivity - IoT
8.6.1. The importance of IoT in farming
8.6.2. Challenges for IoT adoption
8.6.3. LoRaWAN (Long Range Wide Area Network)
8.7 Training
8.8 Conclusions
Chapter 9: Company Profiles and Product Lines
9.1 Summary
9.2 4milk
9.3 ABPSAP
9.4 ADAMA
9.4.1. Digital apps
9.5 AEGRO
9.6 AGCO DO BRASIL
9.6.1. Digital products
9.7 AGRISHARE
9.8 AGROINOVA
9.8.1. Product line
9.9 AGRONOW
9.10 AGROPIXEL
9.11 AGROPRECISION
9.11.1. Service portfolio
9.12 AGROROBOTICA
9.12.1. Service portfolio
9.13 AGROSMART
9.14 AGROTOOLS
9.15 AGRUS DATA
9.16 ALLUAGRO
9.17 BASF
9.17.1. BASF Agrostart
9.17.2. BASF apps
9.18 BAYER
9.18.1. Digital farming
9.18.2. Forward Farms
9.18.3. BCS and Plantar
9.18.4. Bayer and Fauna Photonics .
9.18.5. BCS apps
9.18.6. BCS and weed detection
9.18.7. The Bayer digital farming brand Xarvio
9.19 BEMBRAS AGRO
9.19.1. Services offered
9.20 BOVCONTROL
9.21 BR START-UPS
9.22 CASE IH
9.22.1. Product line
9.23 CHECKPLANT
9.24 DRAKKAR SOLOS
9.25 EAGRO
9.26 EMBRAPA
9.26.1. Precision agriculture projects
9.26.2. Diverse activities
9.27 ENALTA
9.28 ESALQTec
9.29 FALKER
9.29.1. Company history
9.29.2. Product portfolio
9.30 FT Sistemas
9.31 G DRONES
9.31.1. Services offered
9.32 GAtec
9.33 GIRA
9.34 GRO DIRETO
9.35 HORUS AERONAVES
9.35.1. Product portfolio
9.36 INCERES
9.36.1. Software functions
9.37 JOHN DEERE
9.37.1. Technological solutions
9.37.2. Irrigated rice technology
9.38 LAP
9.39 NETAFIM
9.40 NEW HOLLAND AGRICULTURE
9.40.1. Product portfolio
9.41 PERFECT FLIGHT
9.42 PULSE
9.43 SGS Unigeo
9.43.1. Univista Agro ([external URL] )
9.44 SKYDRONES
9.44.1. Product portfolio
9.45 SOLINFTEC
9.46 SPECLAB
9.46.1. SpecSolo
9.47 STRIDER
9.48 SYNGENTA
9.48.1. Relationship programmes
9.48.2. Acquisition of technological start-ups
9.49 TBIT TECHNOLOGIA
9.49.1. Product portfolio
9.50 TRIMBLE
9.50.1. Field Solutions
9.51 XMOBOTS
9.51.1. Drone products
List of Figures
Figure 1: An example of a digital farm network
Figure 2: The interaction of devices and services with cloud computing
Figure 3: Respective areas of expertise of AEF and AgGateway
Figure 4: Map of Brazil showing main geographic regions
Figure 5: Investments in 2015 in different segments of agritech start-ups
List of Tables
Table 1: Digital agriculture technologies
Table 2: GNSS applications in agriculture
Table 3: Pros and cons of proximal and remote sensing techniques
Table 4: Recommended accuracy for different agricultural operations.
Table 5: GNSS receiver accuracy according to the specifications and the augmentation system
Table 6: Potential maps of agricultural parameters which can be elaborated
Table 7: Use and availability of land in Brazil
Table 8: Principal crops grown in Brazil in 2017/2018
Table 9: Brazilian agribusiness exports in 2017
Table 10: Agrochemical sales in Brazil by crop in 2017
Table 11: Sales of different classes of pesticides in Brazil in 2017
Table 12: Sales of pesticides in Brazil by state in 2017
Table 13: Summary of push and pull factors that drive the development of Big Data and Smart Farming
Table 14: The relative use of different precision agriculture activities by farmers in Brazil
Table 15: Levels of accuracy of guidance systems
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