Maximizing Crop Performance with Biosolutions: An Agronomic Approach

Introduction: Bridging the Gap Between Lab and Field Results

In the pursuit of sustainable agriculture, biosolutions—including biostimulants and biocontrol products—are emerging as essential alternatives to traditional chemical inputs. However, a significant challenge remains: the discrepancy between results achieved in controlled laboratory environments and those observed in real-world field conditions. This inconsistency often leads to skepticism among farmers, reducing their trust in the efficacy of biosolutions.

This gap underlines the need for a paradigm shift in how we evaluate and deploy biosolutions. By transitioning from a conventional testing approach, which isolates variables and measure impact of few factors, to a more comprehensive agronomic approach, we can better understand the environmental and agronomic conditions under which biosolutions can deliver optimal results.

Contaxtualize biostimulant testing data

A better agronomic and environment characterization : the solution for analyzing field data results of Biosolutions. A cornerstone of this agronomic approach is the accurate profiling of field data. Bloomeo Biosolutions software emphasizes thischalenge, which is critical to understand the conditions that influence the performance of biosolutions.

Contextualization involves gathering and analyzing data on several key factors:

• Localization of the trials: The geographic location significantly influences climate patterns and environmental conditions, which affect biosolution efficacy.
• Soil type: Different soil types interact uniquely with biosolutions, impacting nutrient uptake and root system development.
• Climate conditions: Variables such as temperature, humidity, and UV exposure are essential for predicting how biosolutions will perform under different weather scenarios.
• Plant characteristics: Factors like plant variety, immune response, and architecture play crucial roles in how biosolutions interact with crops.
• Biotic stressors: The type and pressure of pests or pathogens are critical determinants of biosolution effectiveness.
• Interactions with other organisms: Soil and aerial microflora, along with interactions with other plants, significantly influence biosolution outcomes.
• Nutrition: Fertilization, irrigation, and soil physico-chemical properties are pivotal in determining the success of biosolutions.
• Application of the product: The technique, quality, and timing of application, as well as factors like washing off due to rain, are crucial for achieving desired results.

The Bloomeo integrates data from sensors, drones, and satellites through APIs, offering higher-resolution insights essential for certain biosolutions. Additionally, Bloomeo allows users to input field data via desktop or mobile applications, enabling real-time recording of events like exceptional weather, animal damage, and specific product application methods.

By aggregating and standardizing this information, Bloomeo creates a detailed geospatial reference for each field. Machine learning models then detect and correct outliers, ensuring that the contextual data is both accurate and actionable.

After the era of phenotyping and genotyping Bloomeo is opening the gates of envirotyping.

Let’s redefine the experimental protocols to analyze the conditions of success

As we move towards an agronomic approach, the necessity for flexible and innovative experimental protocols becomes clear. Traditional testing methods, which often rely on rigid, one-dimensional experiments, are inadequate for capturing the complex interactions that occur in real-world agricultural settings.

2 strategies can be set up :

- A multifactorial approach is essential. This involves testing combinations of influencing factors with biosolutions to identify synergistic effects. For example, pairing a biostimulant with a biocontrol agent can enhance plant growth while protecting against pests, leading to higher overall yields. Additionally, the use of cover crops or service plants, which can act as biocontrol agents by outcompeting weeds or supporting beneficial insects, exemplifies the need for innovative protocols.

- A systemic approach, can be considered to evaluate the interconnections between various biological, environmental, and socio-economic factors that influence the performance of biosolutions considering it as an holistic solutions to optimize crop production and ecosystem health. The design of such trials compare less modalities and is supported by advanced digital solutions

New statistical paradigm to valorize agronomic trials

In this new paradigm, statistical analysis plays a crucial role. Robust statistical methods are necessary to analyze the vast amounts of data generated by multifactorial trials. New experimental designs require to adapt descriptive statistics algorithms.

The democratization of mixed linear models brings new opportunities to analyze the effects of factors on the trial performance and better modelized environmental and agronomic effects that are rarely fixed.

By employing advanced statistical tools, researchers can identify patterns, correlations, and causal relationships that would otherwise remain hidden.

This data-driven approach allows for more accurate predictions of biosolution efficacy under various field conditions. Indeed Predicitive models and use of machine learning offer interesting results if the trials results are better characterized with agronomic and environmental context.

Integrating Farmer Data for Comprehensive Field Insights

Farmers are often curious to test new products if they can secure their production and limit the use of chemical products. And the companies producing Biosolutions can always rely on a large network of farmers keen to make feedbacks on product performances.

Integrating data from partner farmers is another critical component of the agronomic approach. While this data may be less structured than controlled trial data, it provides valuable insights into how biosolutions perform across a diverse range of environments. It can also be a challenge to add agronomic and environmental context to these feedbacks like for example, the soil nature, the previous crop, the cultivation process etc.

In that ambition to facilitate the data collection process, by involving farmers in Bloomeo, not only it gathers more varied data but also engages potential future customers, fostering trust and collaboration.

Conclusion: Data-Driven Success in Biosolutions for Sustainable Agriculture

In conclusion, the success of biosolutions in agriculture depends on our ability to understand the complex interplay of factors influencing their effectiveness in the field. By shifting from conventional testing to an agronomic approach that emphasizes agronomic and environment characterization, innovative experimental protocols, robust statistical analysis, and farmer collaboration, stakeholders can significantly improve crop yields and sustainability.

The Doriane Bloomeo Biosolutions software is designed to support the biocontrol and biostimulation sectors with this data-driven approach. To learn more about how Bloomeo can help optimize your crop management strategies, visit our Bloomeo Biosolutions page.

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