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Regeneratieve landbouw

Unlocking Insights for Precision Agriculture with SoilBeat

Het geïndustrialiseerde landbouwsysteem heeft een verwoestend effect gehad op de bodemgezondheid. Het gebruik van synthetische pesticiden en meststoffen heeft de basis van de landbouw aangetast, waardoor onze bodem is uitgeput van belangrijke voedingsstoffen en leven. De bodem is een ongelooflijk veerkrachtig, levend wezen. Het bestaat uit klei-, zand- en slibdeeltjes en bevat de levende wortels van planten, myceliumdraden, dieren zoals wormen en nematoden, en tonnen micro-organismen. Het gebruik van kunstmest heeft ook aanzienlijke milieuschade veroorzaakt, bijvoorbeeld door het negatieve effect op de stikstofuitstoot.

Onderstaande video legt dit duidelijk uit en legt uit waarom we de milieuschade moeten beperken.

Regeneratieve landbouw biedt een duurzame oplossing. Het is een systeem voor de productie van voedsel en biomassa dat zich richt op het herstel van de gezondheid van de bodem en de biodiversiteit, terwijl het tegelijkertijd onze afhankelijkheid van synthetische inputs zoals herbiciden, pesticiden en kunstmest vermindert. Het eindresultaat is een fundament van veerkrachtige grond die consistente, hoogwaardige opbrengsten en een gezonder milieu produceert.

What is holistic data?

Holistic data in agronomy refers to the integration and analysis of information from a variety of sources, including:

  • Soil data: Soil tests revealing nutrient levels, organic matter, texture, and other properties.

  • Plant sap and tissue analysis: Real-time and historical data on plant nutrient uptake and overall health.

  • Field observations: Visual assessments of crop growth, development, and signs of stress or disease.

  • Historical data: Records of past crop yields, weather patterns, and management practices.

  • Other relevant data: Information on pest and disease pressure, market prices, and regulatory requirements.

By combining these diverse datasets, agronomists gain a comprehensive and dynamic view of the agricultural system, enabling them to make more informed and precise decisions.

Why Holistic Data Matters

  • Early Problem Detection and Prevention:

Integrating soil data with plant sap analysis can reveal nutrient deficiencies before they become visible as symptoms in the field.


Field observations combined with historical pest and disease data can help predict and prevent outbreaks.

  • Precise and Targeted Interventions:

By understanding the specific needs of each field, agronomists can tailor fertilizer recommendations, irrigation schedules, and pest control measures to optimize resource use and minimize environmental impact.


A study published in the Agronomy Journal found that site-specific nutrient management based on holistic data increased corn yields by 10% compared to conventional practices.

  • Improved Yield and Quality:

Addressing nutrient deficiencies, pests, and diseases early and effectively leads to healthier plants and higher yields.


Research in Crop Science showed that integrating soil and tissue analysis with crop modeling improved nitrogen fertilizer recommendations, resulting in a 15% yield increase in wheat.

  • Data-Driven Decision-Making:

Holistic data allows for the development of predictive models that can forecast crop yields, optimize irrigation, and guide other management decisions. A case study in Precision Agriculture demonstrated that using machine learning algorithms to analyze holistic data improved soybean yield predictions by 20%.

  • Long-Term Sustainability:

By tracking soil health, nutrient cycling, and crop performance over time, agronomists can identify sustainable practices that protect the environment and ensure long-term productivity.

A report by the Food and Agriculture Organization (FAO) emphasized the importance of holistic data for monitoring and promoting sustainable agriculture practices.

SoilBeat: Empowering Agronomists with Holistic Data

SoilBeat is a powerful tool designed to help agronomists harness the full potential of holistic data. It addresses the challenges of data integration, management, and analysis, providing a comprehensive platform for data-driven agriculture.

Key Features of SoilBeat:

  • Data Integration: SoilBeat seamlessly combines data from various sources, including PDFs, spreadsheets, lab results, field observations (through a mobile app), and integrations with other agricultural software. This eliminates the need for manual data entry and ensures a unified view of all relevant information.

  • Extensive Visualization: SoilBeat offers a wide range of visualization options, allowing agronomists to easily compare and analyze different datasets, identify trends, and uncover correlations.

  • Recommendation Engine: SoilBeat's advanced algorithms leverage historical data, current conditions, and expert knowledge to generate tailored recommendations for fertilizer applications, irrigation, and other management practices. These recommendations can be customized to align with specific agronomic goals and preferences.

  • Microlearning Modules: SoilBeat provides educational resources to help agronomists interpret complex data and make informed decisions.

  • Collaboration Tools: SoilBeat facilitates collaboration between agronomists, farmers, and other stakeholders, enabling seamless data sharing and communication.

Soil Beat Panel Review

Overcoming Challenges with SoilBeat

SoilBeat addresses the key challenges of holistic data management

Data Integration:

By unifying data from disparate sources, SoilBeat eliminates the need for manual data entry and ensures a comprehensive view of all relevant information.

Data Management:

SoilBeat's cloud-based platform provides secure and scalable storage for large volumes of data, ensuring easy access and organization.

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Data Analysis:

SoilBeat's advanced analytics tools, including machine learning and AI, empower agronomists to extract valuable insights from complex datasets without requiring specialized expertise.

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