This book describes how models are used to monitor crops and soils in precision agriculture, and how they are used to support farmers’ decisions. The introductory section starts with an overview of precision agriculture from the early days of yield monitoring in the 1980s to the present, with a focus on the role of models. The section continues with descriptions of the different kinds of models and the opportunities for their application in precision agriculture. The section concludes with a chapter on socio-economic drivers and obstacles to the adoption of precision agriculture technologies. The middle section of the book explores the state-of-the-art in modeling for precision agriculture. Individual chapters focus on the major processes in precision agriculture: water use, nitrogen and other amendments, as well as weeds, pests and diseases. The final section contains a series of short chapters that each describe a commercial, model-based service that is currently available to farmers. The book aims to provide useful information to graduate-level professionals that want to broaden their knowledge of precision agriculture; to scientists who want to learn about using academic knowledge in practical farming; and to farmers, farm consultants and extension workers who want to increase their understanding of the science behind some of the commercial software available to the farming community.
Author(s): Davide Cammarano, Frits K. van Evert, Corné Kempenaar
Series: Progress in Precision Agriculture
Publisher: Springer
Year: 2023
Language: English
Pages: 301
City: Cham
Preface
Contents
Part I: Modelling for Precision Agriculture
Introduction
1 Rationale
2 Crop Modelling
3 Precision Agriculture
4 Use of Models in Precision Agriculture
4.1 Regression Models
4.2 Simple Dynamic Models
4.3 Crop Growth Models
4.4 Digital Twins
4.5 Machine Learning
5 Chapters of the Book
6 Current State of Modelling for Precision Agriculture and Work Needed
6.1 Data
6.2 Models
6.3 Actuation
7 Conclusion
References
Process-Based Modelling of Soil-Crop Interactions for Site-Specific Decision Support in Crop Management
1 Introduction
2 General Character of Process-Based Agro-ecosystem Models
3 Modelling Spatial Variation
4 Site Sensitivity of Agro-ecosystem Models
5 Examples of Model Use for Precision Agriculture
5.1 Identification of Site-Specific Management Using Long-Term Simulations
5.2 Inverse Modelling to Derive Unknown Properties
5.3 Operational Use for Site-Specific Management Operations
6 Conclusions
References
Models in Crop Protection
1 Introduction to Plant Disease Models and Modelling Approaches
1.1 What Is a Model?
1.2 Types of Models and Modelling Approaches
1.2.1 Qualitative vs. Quantitative Models
1.2.2 Deterministic vs. Stochastic Models
1.2.3 Empirical vs. Fundamental Models
1.2.4 Simulation vs. Predictive Models
1.3 A Brief History of Plant Disease Modelling
2 Development of Process-Based Models
2.1 Definition of the Intended Use of the Model
2.2 Conceptualization of the System
2.3 Development of the Mathematical Framework
2.4 Model Evaluation
3 Models for IPM
3.1 Decision-Making in IPM
3.2 Strategic Disease Management
3.2.1 A First Example: Epidemics of Grape Downy Mildew
3.2.2 A Second Example: Effect of Biocontrol on Epidemic Development
3.3 Tactical Disease Management
3.3.1 A First Example: Prediction of Secondary Infections of Grape Downy Mildew
3.3.2 A Second Example: Prediction of Stem Rust Infections on Wheat
3.4 Multi-modelling for Decision-Making
3.4.1 Modelling the Host Plant
3.4.2 Modelling the Effects of Fungicides
3.4.3 Combining Models
4 Conclusions and Perspectives
References
Development and Adoption of Model-Based Practices in Precision Agriculture
1 Introduction
2 Design, Development and Delivery of MBPs
2.1 Design
2.2 Development
2.3 Delivery
3 Social Factors Affecting Adoption of MBPs
3.1 Demographic Factors
3.2 Societal Factors
3.3 Farm Size
4 Behavioural Approaches to the Adoption of Models for Precision Farming
4.1 Risk Attitudes
4.2 Behavioural Factors Beyond Risk Attitudes
5 An Empirical Approach to Research on Adoption of Models
6 Discussion
References
Part II: State of the Art
Process-Based Models and Simulation of Nitrogen Dynamics
1 Introduction
2 Modelling Nitrogen
2.1 Nitrogen Dynamics in the Soil (Supply)
2.2 N Dynamic in Plants (Demand)
3 Case Studies
3.1 Case Study 1: A Mediterranean Environment
3.2 Case Study 2
4 Conclusions
References
Modelling Soil Water Dynamics
1 Introduction
2 Water Movement in Soils
2.1 Law of Conservation of Matter
2.2 Darcy-Buckingham
2.3 Richards Equation
2.4 Difference with Tipping-Bucket
2.5 How to Solve Richards Equation?
3 Boundary and Initial Conditions
4 Constitutive Hydraulic Properties of Soils
5 Example 1: Simulated Infiltration Versus Analytical Solution
6 Root Water Uptake
7 Additional Phenomena: Processes
8 Soil-Water Simulation Models
9 Example 2: Water Content in the Root Zone Predicted from Ensemble Weather Forecasts
10 Spatial Variation
11 Epilogue
References
Data Fusion in a Data-Rich Era
1 Introduction
2 Need for Data Fusion in Precision Agriculture
2.1 Principles of Geostatistical Data Fusion and Change of Support
2.2 Application of Geostatistical Data Fusion in Precision Agriculture
2.3 Applications of Data Fusion in Remote Sensing
2.4 Summing Up
References
Data Assimilation of Remote Sensing Data into a Crop Growth Model
1 Introduction
2 Data Assimilation Techniques
2.1 Forcing
2.2 Recalibrating
2.2.1 Reinitiating
2.2.2 Re-parameterization
2.3 Updating
3 A Guideline on Applying DA Techniques
4 CSM-RS Assimilation in the Framework of Precision Agriculture
5 Future Perspective
6 Conclusion
References
Part III: Case Studies
Adapt-N (Yara International)
1 Introduction
2 Technique
3 Usage
4 Advantages to Growers
5 Environmental Benefits
6 Transparency
7 Adoption
8 Conclusion
References
Granular Agronomy Nitrogen Management
1 Introduction
2 Service Offering
3 Setup
3.1 Field Boundaries
3.2 Data Hierarchy
3.3 Decision Zones
3.3.1 Yield Targets
3.3.2 Soils
3.4 Management Plans
3.5 Initial Nitrogen
3.6 Soil Monitoring Depth
3.7 Planting Information
3.8 Irrigation
4 Operation
4.1 Granular Crop Model
4.2 Season Chart
4.3 Weather
4.3.1 Current Year
4.3.2 Forecast
4.3.3 Historical
4.4 Variable-Rate Prescriptions
4.5 Daily Reports
5 Future Development
5.1 Crops
5.2 Environmental Concerns
5.3 Variable-Rate Nitrogen Approaches
6 Conclusion
References
xarvio Digital Farming Solutions
1 Introduction
2 Possible Use Cases
3 Crop Modelling at xarvio Digital Farming Solutions
4 Crop Model Framework Used by xarvio Digital Farming
References
WatchITgrow
1 Introduction
2 Content and Functionalities
2.1 Monitoring Crop Productivity and Health with Satellite Data
2.2 Improved Sampling
2.3 Application Maps
2.4 Yield Potential Maps
2.5 Remote Planning
2.6 Weather and Soil Data
2.7 Yield Forecasting
2.8 Field Registration
2.9 Automatic Data Exchange
2.10 Connecting Different Actors in the Chain
3 Outlook: Towards Data-Driven Advice
References
Precision Agriculture in Rice Farming
1 Introduction
2 Kubota´s Role in Japanese Smart Agriculture
2.1 Current Situation and Issues of Japanese Agriculture
2.2 The Challenges Faced by Professional Farmers and Kubota´s Position
3 Using Data for Precision Farming
3.1 Farming Support System: Kubota Smart Agri System (KSAS)
3.2 Future Developments in KSAS
4 Automation for Super Labor Savings
4.1 Automatic/Unmanned Agricultural Machinery
4.2 Future Plans for the Evolution of Autonomous and Unmanned Agricultural Machinery
5 Outlook
References
AgSkyNet: Harnessing the Power of Sky and Earth for Precision Agriculture
1 Introduction
1.1 Integrated Pest Assessment with Data-Driven Insights
1.2 Integration of Remote Sensing and Crop Growth Simulation Models to Estimate Crop Yield and Stubble Burning Severity
1.3 Hyperspectral Sensing for Pesticide Residue Detection in Green Tea Leaves
References
Dacom Precision Agriculture System
1 Introduction
1.1 Fungal Disease Prediction Model
1.2 Crop Growth Model
1.3 Development of Dacom Software System
References
Akkerweb and farmmaps: Development of Open Service Platforms for Precision Agriculture
1 Introduction
1.1 Development of Akkerweb and farmmaps Platforms
1.2 Features of the Platforms
1.3 Technical Implementation
1.3.1 Authentication/Authorization
1.3.2 Server Side
1.3.3 Server Side, Data Persistence Layer
1.3.4 Client Side
1.3.5 Client Server Communication
2 Models and Apps on Akkerweb/farmmaps
2.1 WatBal Soil Moisture Balance Model
2.2 Tipstar Water- and Nitrogen-Limited Potato Crop Growth Model
2.3 NemaDecide
2.4 Soil Herbicide Variable-Rate Application App
2.5 Variable-Rate Application App to Kill Potato Haulm
2.6 Late Blight App
2.7 Nitrogen Side Dress System (NSS) in Ware and Starch Potatoes
2.8 farmmaps Dashboard
3 Outlook
References
Index
