Artificial Intelligence

Crop protection can no longer keep pace with nature. How do we catch up?

A tractor sprays pesticides on wheat crops in Arapongas, Brazil.

A tractor sprays pesticides on wheat crops in Arapongas, Brazil. Image: Reuters/Rodolfo Buhrer

Anthony Klemm
Chief Executive Officer, Enko Chem
  • Crop protection is facing greater challenges from climate change, pest resistance and intensifying regulation.
  • The traditional R&D system in the sector, taking over a decade to make new products available, is no longer fit for purpose.
  • Adaptive R&D systems that prioritize early discovery facilitated by AI can help improve speed and efficiency without sacrificing regulatory oversight.

Crop protection is facing greater challenges from climate change, pest resistance and intensifying regulation. The traditional R&D system, often taking over a decade to deliver new products, is no longer aligned with the pace of biological change. Adaptive R&D systems are emerging as a practical way to improve capital efficiency by focusing resources on higher-probability molecules earlier in development.

Crop protection is fundamental to global food security, yet it remains underappreciated outside agriculture. Each year, pests destroy an estimated 20-40% of global crop production, contributing to higher food prices, supply instability and economic strain. These pressures are increasing as climate change accelerates pest evolution and creates more volatile growing conditions.

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The urgency is amplified by rising global food demand, biodiversity loss and increasing expectations for sustainability. Biodiversity loss alone represents an estimated $10 trillion annual impact when accounting for healthcare and agricultural effects. At the same time, roughly one-third of all food produced for human consumption is lost or wasted globally.

Against this backdrop, the industry’s ability to innovate is under pressure. The removal of older chemistries has increased reliance on fewer, highly effective products, accelerating resistance. Field evaluation remains constrained by seasonal cycles, while climate variability adds further complexity. At the same time, global regulatory requirements continue to expand, increasing cost, timelines and uncertainty.

Over the past 50 years, R&D models have produced new crop protection products at an average of 12.3 years, at costs exceeding $300 million per active ingredient. This pace is no longer aligned with the challenges agriculture faces. The industry must rethink how innovation is delivered.

The limits of traditional R&D

For decades, innovation followed a linear path from discovery through to commercialization. This model delivered results, but it was built for a different era. Today, it is misaligned with the pace of biological and environmental change. Development timelines exceed a decade, while resistance can emerge in just a few years. The gap is widening.

Constraints that define the system are unlikely to change quickly. Products must be validated across multiple crop cycles. Safety assessments continue to rely on animal testing. Regulatory frameworks are expanding globally. These are necessary, but they limit where speed can be gained.

Where adaptive R&D can deliver

Adaptive R&D is not simply about moving faster across the entire pipeline. It is about concentrating innovation where it is most feasible today: early discovery.

At this stage, constraints from field seasons, regulatory requirements and large-scale validation are significantly reduced. This allows for rapid, iterative experimentation, where integrated computational tools and high-throughput validation can compress cycle times from several years to one to two.

AI-enhanced discovery plays a central role in this shift. By identifying patterns, prioritizing novel modes of action and filtering candidate pools before synthesis, it enables organizations to eliminate low-probability options earlier. Leading programmes are already reducing candidate pools by more than 90% prior to synthesis while improving early success rates.

This fundamentally reframes the problem. Rather than attempting to accelerate the entire pipeline – much of which is structurally constrained – adaptive R&D improves how capital is allocated at the earliest stages. Resources are directed toward higher-probability candidates, reducing downstream attrition and increasing the likelihood that successful products emerge.

Failures in this system are not sunk costs, but inputs into continuous learning cycles. The result is a more capital-efficient model that expands the innovation space while improving decision quality.

Impact will come from focusing on what can change within meaningful time frames. Regulatory standards will remain rigorous. Field validation will continue to depend on seasonal cycles. Safety requirements will not fundamentally shift in the near term.

The opportunity is to act where constraints are lowest: early discovery. By concentrating innovation efforts here, the industry can improve speed and efficiency today, without relying on longer-term changes to regulatory frameworks or testing paradigms. This is where adaptive R&D can deliver immediate, measurable impact.

Why resiliency still matters

Speed and efficiency must be paired with resiliency. This requires diversified pipelines, multiple modes of action, and validation across geographies and crops. Sustainability considerations must also be integrated early in the discovery process.

Agriculture operates under constant biological evolution, climate volatility and geopolitical pressure. Organizations that adopt adaptive R&D with discipline can improve capital efficiency, accelerate discovery, and increase the likelihood that new solutions reach farmers in a relevant timeframe.

The question is no longer whether the industry can move faster. It is whether it can redesign how innovation happens – focusing capital, learning and decision-making where they can have the greatest impact now. Those that do not will fall further behind.

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