Success Stories
Success Stories
Discover How Our Solutions are Making a Real Impact
Discover How Our Solutions are Making a Real Impact
Problem
Seed companies face significant challenges in estimating hybrid yields in environments where direct trials haven’t been conducted. This limits the ability to optimize recommendations and accurately position products in new agricultural zones, especially when dealing with critical environmental variables like climate change.
Solution
In collaboration with Pioneer, NODES™ implemented predictive modeling tools to extrapolate data to regions without direct trials. Based on environmental profiles, including the analysis of the crop life cycle, identifying the optimal genetic-environment combinations. This approach enabled early alerts for unfavorable environmental conditions and accurately predicted yields in new locations.
Impact
Thanks to NODES™, Pioneer optimized its trial network, reducing the number needed without compromising result quality. This allowed them to:
Anticipate climate risks with a high probability of low yields early on for specific zones.
Improve strategic decision-making, reducing costs and maximizing return on investment.
Provide internal and commercial teams with high-precision tools to position hybrids in the best conditions, enhancing the end-user experience and strengthening global market competitiveness.
Problem
Seed companies face significant challenges in estimating hybrid yields in environments where direct trials haven’t been conducted. This limits the ability to optimize recommendations and accurately position products in new agricultural zones, especially when dealing with critical environmental variables like climate change.
Solution
In collaboration with Pioneer, NODES™ implemented predictive modeling tools to extrapolate data to regions without direct trials. Based on environmental profiles, including the analysis of the crop life cycle, identifying the optimal genetic-environment combinations. This approach enabled early alerts for unfavorable environmental conditions and accurately predicted yields in new locations.
Impact
Thanks to NODES™, Pioneer optimized its trial network, reducing the number needed without compromising result quality. This allowed them to:
Anticipate climate risks with a high probability of low yields early on for specific zones.
Improve strategic decision-making, reducing costs and maximizing return on investment.
Provide internal and commercial teams with high-precision tools to position hybrids in the best conditions, enhancing the end-user experience and strengthening global market competitiveness.
Problem
Seed companies face significant challenges in estimating hybrid yields in environments where direct trials haven’t been conducted. This limits the ability to optimize recommendations and accurately position products in new agricultural zones, especially when dealing with critical environmental variables like climate change.
Solution
In collaboration with Pioneer, NODES™ implemented predictive modeling tools to extrapolate data to regions without direct trials. Based on environmental profiles, including the analysis of the crop life cycle, identifying the optimal genetic-environment combinations. This approach enabled early alerts for unfavorable environmental conditions and accurately predicted yields in new locations.
Impact
Thanks to NODES™, Pioneer optimized its trial network, reducing the number needed without compromising result quality. This allowed them to:
Anticipate climate risks with a high probability of low yields early on for specific zones.
Improve strategic decision-making, reducing costs and maximizing return on investment.
Provide internal and commercial teams with high-precision tools to position hybrids in the best conditions, enhancing the end-user experience and strengthening global market competitiveness.
Problem
The development and adoption of new crop varieties face critical challenges in the industry, including high trial costs and long delays in identifying materials that meet specific quality and yield standards. This affects the efficiency and competitiveness of industrial processes dependent on agricultural inputs.
Solution
NODES™ uses advanced artificial intelligence and historical data to optimize crop selection based on their predicted performance in industrial environments. The target environment prediction tool quickly identifies varieties that meet quality, yield, and adaptability demands, reducing reliance on extensive physical trials.
Impact
In collaboration with industry leaders like Boortmalt, NODES™ has transformed industrial processes by reducing the number of field-tested varieties by 50%, speeding up the availability of high-potential raw materials. This improves operational efficiency, ensures consistently high-quality inputs, and reduces waste in critical stages such as malting or food processing. By integrating predictive technology, companies achieve greater resilience against climate change and optimize the sustainability of the supply chain.
Problem
The development and adoption of new crop varieties face critical challenges in the industry, including high trial costs and long delays in identifying materials that meet specific quality and yield standards. This affects the efficiency and competitiveness of industrial processes dependent on agricultural inputs.
Solution
NODES™ uses advanced artificial intelligence and historical data to optimize crop selection based on their predicted performance in industrial environments. The target environment prediction tool quickly identifies varieties that meet quality, yield, and adaptability demands, reducing reliance on extensive physical trials.
Impact
In collaboration with industry leaders like Boortmalt, NODES™ has transformed industrial processes by reducing the number of field-tested varieties by 50%, speeding up the availability of high-potential raw materials. This improves operational efficiency, ensures consistently high-quality inputs, and reduces waste in critical stages such as malting or food processing. By integrating predictive technology, companies achieve greater resilience against climate change and optimize the sustainability of the supply chain.
Problem
The development and adoption of new crop varieties face critical challenges in the industry, including high trial costs and long delays in identifying materials that meet specific quality and yield standards. This affects the efficiency and competitiveness of industrial processes dependent on agricultural inputs.
Solution
NODES™ uses advanced artificial intelligence and historical data to optimize crop selection based on their predicted performance in industrial environments. The target environment prediction tool quickly identifies varieties that meet quality, yield, and adaptability demands, reducing reliance on extensive physical trials.
Impact
In collaboration with industry leaders like Boortmalt, NODES™ has transformed industrial processes by reducing the number of field-tested varieties by 50%, speeding up the availability of high-potential raw materials. This improves operational efficiency, ensures consistently high-quality inputs, and reduces waste in critical stages such as malting or food processing. By integrating predictive technology, companies achieve greater resilience against climate change and optimize the sustainability of the supply chain.
Problem
Traditional genetic improvement faces significant challenges: high costs, long field trial cycles, and limited ability to quickly identify elite genetics. This makes it hard to respond agilely to market demands and changing climatic conditions.
Solution
During the 2022-2023 campaign, NODES™ applied computational breeding tools to analyze historical data up to the 2021-2022 campaign. Out of the 279 materials evaluated in the Quimarsem program, our model predicted with 93% accuracy the 65 hybrids selected as top performers from a total of 70 advanced ones. This predictive capability allowed for optimized resource allocation and focused efforts on materials with the highest potential.
Impact
Thanks to NODES™, Quimarsem significantly reduced the costs and time associated with traditional trials, achieving precise selection of elite genetics with fewer resources. This not only accelerated the development of new varieties but also ensured a higher return on investment by bringing highly competitive hybrids to market faster, strengthening the efficiency of the entire production chain.
Problem
Traditional genetic improvement faces significant challenges: high costs, long field trial cycles, and limited ability to quickly identify elite genetics. This makes it hard to respond agilely to market demands and changing climatic conditions.
Solution
During the 2022-2023 campaign, NODES™ applied computational breeding tools to analyze historical data up to the 2021-2022 campaign. Out of the 279 materials evaluated in the Quimarsem program, our model predicted with 93% accuracy the 65 hybrids selected as top performers from a total of 70 advanced ones. This predictive capability allowed for optimized resource allocation and focused efforts on materials with the highest potential.
Impact
Thanks to NODES™, Quimarsem significantly reduced the costs and time associated with traditional trials, achieving precise selection of elite genetics with fewer resources. This not only accelerated the development of new varieties but also ensured a higher return on investment by bringing highly competitive hybrids to market faster, strengthening the efficiency of the entire production chain.
Problem
Traditional genetic improvement faces significant challenges: high costs, long field trial cycles, and limited ability to quickly identify elite genetics. This makes it hard to respond agilely to market demands and changing climatic conditions.
Solution
During the 2022-2023 campaign, NODES™ applied computational breeding tools to analyze historical data up to the 2021-2022 campaign. Out of the 279 materials evaluated in the Quimarsem program, our model predicted with 93% accuracy the 65 hybrids selected as top performers from a total of 70 advanced ones. This predictive capability allowed for optimized resource allocation and focused efforts on materials with the highest potential.
Impact
Thanks to NODES™, Quimarsem significantly reduced the costs and time associated with traditional trials, achieving precise selection of elite genetics with fewer resources. This not only accelerated the development of new varieties but also ensured a higher return on investment by bringing highly competitive hybrids to market faster, strengthening the efficiency of the entire production chain.
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