Decision Support Systems (DSS): Why Horta Makes a Difference in the Current Landscape

As we have previously discussed, the integration of Decision Support Systems (DSS) in regenerative agriculture is becoming increasingly essential (in this regard, we invite you to read our previous in-depth article, available here).

The purpose of this article is twofold: on the one hand, we aim to delve into the practical advantages of using DSS to fully understand their concrete potential and how they can effectively help save both time and money, while also enabling the production of higher-quality and more sustainable products; on the other hand, we seek to demonstrate how Horta S.r.l. stands out from the competition thanks to solid mechanistic models and robust scientific and field research.

How is a DSS Defined?

For those who are not yet familiar, it is important to clarify what a DSS is and its role in the agri-food sector.

A Decision Support System (DSS) is an integrated human-computer platform that utilizes data from various sources to provide agri-food businesses with informed advice, supporting their decision-making process in different operational circumstances.

A key characteristic of a DSS is that it does not give direct instructions or commands; instead, it offers a range of options and recommendations, leaving the final decision to the professionals.

A DSS not only provides a list of options for ongoing activities but also helps decision-makers improve performance in future tasks.

Through advanced data analysis and predictive models, the system enables businesses to anticipate trends, manage risks, and optimize resource use, thus contributing to more efficient and sustainable production.

Decision Support Systems (DSS): Why Horta Makes a Difference in the Current Landscape

Decision Support Systems (DSS) have now become common tools in the agri-food sector, with numerous companies offering them as part of their product lines.

However, none of these organizations have the experience and expertise that Horta has gained in the development and implementation of DSS.

So, what makes Horta's DSS so unique and superior compared to the others?

The Distinctive Advantages of Horta's DSS

• Reliable models: The information provided by Horta's DSS is the result of rigorous research and development activities, validated and monitored directly in the field. This ensures that operational decisions are based on solid and scientifically proven data, offering farmers and agri-food companies a secure foundation on which to build their strategies.
• Holistic approach: Horta's systems take all aspects of farming techniques into account, offering a comprehensive and integrated view of agricultural management. This approach allows for the optimization of every phase of the production process, from planting to harvesting, improving both the quality and quantity of the crops.
• Simplicity and clarity: Despite the complexity of the analyses performed, Horta S.r.l. 's DSS convert complex processes into simple and immediately usable operational choices. This feature helps farmers and agri-food supply chains to make quick and informed decisions without having to deal with unnecessarily complicated technicalities.

The Scientific Approach Behind Horta's DSS

Horta's DSS are digital farming systems that provide specific, effective, and timely advice, along with warning alerts.

But how do they manage to offer such precise and reliable support? By integrating data and information from various sources, Horta's DSS build a comprehensive and detailed picture of the agricultural situation.

Here’s how:

• Land: The physico-chemical characteristics of the soil are crucial in determining the water and nutrient needs of crops. Horta’s DSS use this information to optimize irrigation and fertilization management.
• Database: A constantly updated database of fertilizers and plant protection products, applicable based on the phenological stage and specific needs of the crops, ensures that the recommendations are always up-to-date and compliant with current regulations.
• Variety: The characterization of individual crop varieties, considering growth rates and nutritional requirements, allows for personalized advice that maximizes production potential.
• Climate: A continuous flow of validated meteorological data and weather forecasts feeds the predictive models of the DSS. This allows users to anticipate adverse weather events and better manage environmental conditions.
• Boundaries: The precise geolocation of agricultural plots takes into account the geographical and topographical characteristics of the cultivation site. This improves the accuracy of predictions and operational recommendations.

How is a DSS developed at Horta?

The DSS developed by Horta cover a wide range of crops, as illustrated in the figure below. For each of these crops, Horta has created models that are perfectly tailored to the specific needs of that cultivation.

In other words, each DSS is custom-made for the reference crop, addressing the main issues that may arise. These systems cover both biotic issues, such as fungi and insects, and abiotic issues, such as frost or heat stress.

What makes Horta’s DSS so unique and superior to others is the use of mechanistic models, which are perfectly tailored to the specific needs of each crop. But what is meant by a mechanistic model, and why is it a differentiating factor?

A mechanistic model is a mathematical model that simulates biological, physical, or chemical processes based on an understanding of the fundamental mechanisms that govern them. Unlike statistical or empirical models, which only observe correlations between inputs and outputs, mechanistic models focus on the fundamental principles and laws that describe the behavior of a system.

In the agricultural context, a mechanistic model considers biological and ecological processes such as plant growth, nutrient uptake, evapotranspiration, and disease dynamics. It is based on detailed knowledge of the interactions between plants, soil, climate, and other environmental factors. These models aim to accurately represent how plants and ecosystems respond to different external conditions and how various factors influence their productivity and health.

The mechanistic models developed by Horta follow a series of key steps to ensure an accurate representation of biological systems, applicable in real agricultural contexts. This in-depth scientific approach is what sets Horta apart from other DSS providers. While many systems on the market rely on empirical or statistical models, which may not fully capture the complexity of biological systems, Horta’s mechanistic models offer a higher level of precision and reliability in predictions and recommendations.

Here is a description of the various steps involved in the modelling of biological systems at Horta:

What are the practical benefits of using Horta's DSS?

While we have previously focused on the theoretical and methodological aspects, it is essential to highlight, in numerical terms, the tangible benefits that Horta’s Decision Support Systems (DSS) offer to farmers and the agri-food supply chains.

These benefits are not just marketing claims but tangible results that demonstrate how Horta’s DSS genuinely save time and money, while also improving the sustainability of agricultural products.

Practical benefits for agri-food supply chains through DSS adoption:

Tomatoes

• 10% increase in Brix levels

The Brix scale measures the sugar content in an aqueous solution, directly correlating with the sweetness and quality of tomatoes.

A 10% increase in Brix levels indicates a significant improvement in fruit quality. This result is due to the optimized irrigation and nutrient management facilitated by the DSS, which ensures that plants receive water and nutrients exactly when needed, promoting better photosynthetic efficiency and sugar accumulation.

• 32% increase in water productivity (kg of dry matter/m3 of water)

Water productivity reflects the efficiency of water use in biomass production. A 32% increase indicates that more dry matter is produced per unit of water consumed. This improvement suggests that the DSS schedules irrigation based on plant needs and environmental conditions, reducing water waste and improving overall water use efficiency.

Wheat

• 16% reduction in carbon footprint (CO?eq t/t)

A 16% reduction in the carbon footprint per ton of wheat produced represents a significant environmental benefit. This reduction can be attributed to the optimization of fertilizer use, the reduction in plant protection product (PPP) usage, and more efficient field operations, all guided by the DSS. This results in a reduction of greenhouse gas emissions associated with wheat production.

• 12% reduction in fertilizer use

Excessive fertilizer use can cause environmental problems such as soil degradation and water pollution due to nutrient runoff. A 12% reduction in fertilizer use indicates that the DSS provides precise recommendations for nutrient application based on the actual needs of the crops. This approach ensures that plants receive the right amount of nutrients without excess, promoting soil health and reducing environmental impact.

Wine Grapes

• 35% reduction in plant protection product (PPP) use (kg/year)

A significant 35% reduction in PPP use highlights the DSS's ability to improve pest and disease management. By predicting pest infestations and disease risks through predictive models and monitoring environmental conditions, the system allows for targeted application of PPPs only when necessary. This reduces not only the costs of chemical products but also the environmental impact of pesticides on non-target organisms and ecosystems.

• 41% reduction in carbon footprint (CO?eq t/t)

The substantial 41% reduction in the carbon footprint in wine grape production underscores the effectiveness of the DSS in promoting sustainable viticulture. This result can be attributed to optimized vineyard management practices, such as precise fertilization, efficient irrigation, and reduced chemical interventions, which decrease energy consumption and associated emissions.

Corn

• 28% reduction in mycotoxins

Mycotoxins are harmful compounds produced by certain fungi that can contaminate corn and pose serious risks to human and animal health.

A 28% reduction in mycotoxin levels indicates that the DSS effectively monitors environmental conditions conducive to fungal growth and provides timely alerts for preventive actions. This proactive approach improves food safety and crop quality, which are essential for meeting regulatory standards and consumer expectations.

If you want to know more about regenerative agriculture, we invite you to watch our video on YouTube!