Encouraging Data Adoption in Agriculture

Agriculture has always been the foundation of India’s economy, sustaining millions of livelihoods and ensuring food security for a growing population. Yet, despite its crucial role, the sector has long struggled with inefficiencies, unpredictable yields, and limited access to financial and technological resources. In response to these challenges, the Indian government has taken a transformative step through the 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐀𝐠𝐫𝐢𝐜𝐮𝐥𝐭𝐮𝐫𝐞 𝐌𝐢𝐬𝐬𝐢𝐨𝐧, a visionary initiative aimed at integrating cutting-edge technologies such as artificial intelligence, the Internet of Things, and big data analytics into the agricultural terrain. This mission is not just about digitization but about creating a robust ecosystem where farmers can leverage digital tools to 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲, 𝐬𝐞𝐜𝐮𝐫𝐞 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐢𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐚𝐜𝐜𝐞𝐬𝐬 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐚𝐠𝐫𝐢𝐜𝐮𝐥𝐭𝐮𝐫𝐚𝐥 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬. The government’s ambitious plan to issue 11 crore 𝐅𝐚𝐫𝐦𝐞𝐫 𝐈𝐃𝐬 by 2026-27 under the Digital Agriculture Mission marks a significant shift toward organized and data-driven farming. As of March 2025, over 4.85 crore unique Farmer IDs have already been generated, each linked to Aadhaar and land records, streamlining access to 𝐠𝐨𝐯𝐞𝐫𝐧𝐦𝐞𝐧𝐭 𝐬𝐮𝐛𝐬𝐢𝐝𝐢𝐞𝐬, 𝐜𝐫𝐨𝐩 𝐢𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐜𝐫𝐞𝐝𝐢𝐭 𝐟𝐚𝐜𝐢𝐥𝐢𝐭𝐢𝐞𝐬 𝐬𝐮𝐜𝐡 𝐚𝐬 𝐭𝐡𝐞 𝐊𝐢𝐬𝐚𝐧 𝐂𝐫𝐞𝐝𝐢𝐭 𝐂𝐚𝐫𝐝 . This structured approach is expected to not only reduce bureaucratic delays but also enhance financial transparency, ensuring that benefits reach the intended recipients without leakages. With its phased expansion, the survey covered 436 districts during the Kharif season of 2024 and extended to 461 districts during the 𝐑𝐚𝐛𝐢 𝐬𝐞𝐚𝐬𝐨𝐧. By June 2025, a nationwide rollout of this digital crop survey is expected, allowing policymakers to make data-backed decisions on resource allocation, market pricing, and supply chain efficiencies. The integration of real-time data will empower the agricultural sector with predictive analytics, 𝐡𝐞𝐥𝐩𝐢𝐧𝐠 𝐟𝐚𝐫𝐦𝐞𝐫𝐬 𝐩𝐥𝐚𝐧 𝐭𝐡𝐞𝐢𝐫 𝐜𝐫𝐨𝐩𝐬 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐞𝐦𝐚𝐧𝐝, 𝐜𝐥𝐢𝐦𝐚𝐭𝐞 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬, 𝐚𝐧𝐝 𝐬𝐨𝐢𝐥 𝐡𝐞𝐚𝐥𝐭𝐡 𝐚𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭𝐬. The launch of AI-powered initiatives such as the 𝐊𝐢𝐬𝐚𝐧 𝐞-𝐌𝐢𝐭𝐫𝐚 𝐜𝐡𝐚𝐭𝐛𝐨𝐭 provides farmers with real-time assistance on best farming practices, weather forecasts, and pest control measures. Furthermore, AI and machine learning models are being deployed under the National Pest Surveillance System to detect early signs of pest infestations, enabling timely intervention and minimizing crop losses. The adoption of IoT-enabled smart irrigation systems is further optimizing water usage, ensuring sustainable and efficient farming practices, particularly in drought-prone regions. The future of farming is digital—precision, productivity, and prosperity for every farmer.

💡 🧑🌾 The #1 Thing Holding Back Digital Farming The biggest barrier to innovation isn’t money or tools—it’s our failure to prove value. New research from the University of Nebraska-Lincoln reveals a startling truth: 75% of farmers cite lack of information about digital agriculture value as their top adoption barrier. Not cost. Not complexity. Trust gaps and knowledge silos are strangling progress in one of humanity’s most critical industries.  Here’s what 500+ Nebraska farmers told us about why DA tools stall:  ➡️ 75% don’t see clear ROI from digital ag   ➡️ 65% lack skilled labor to implement tech   ➡️ 63% say they’re too time-crunched to experiment  The kicker? Technology cost ranked — below “overwhelming choice of tools” and “too few field days.” We’ve been solving the wrong problem.  🚀 Three takeaways for agribusiness leaders: 1. Stop selling features. Farmers need concrete, localized success stories —not another sensor demo.   2. Bridge the labor gap. UNL’s Digital Farming Lab is creating training pipelines. Partner with them. Look for similar activities in your region.   3. Simplify decision fatigue. That “60% overwhelmed by options” statistic? It’s a roadmap for curation.  This isn’t just Nebraska’s challenge. A 2024 global review confirms: Farmers adopt tech fastest when they see peer-validated results. Your unique algorithm means nothing until Joe in Grand Island proves it boosts yields or save costs consistently. So here’s my challenge to you: What’s one tangible way we can turn “why should I?” into “show me how” for growers? Read the details here: https://lnkd.in/eUcs5Xat

Did you know that a single drone flying over a field can now think, analyze, and support decisions almost like a farmer? What you see here is more than a drone spraying crops. It is the combination of AI and precision agriculture working together in real time. This is where farming is heading, and I am deeply interested in how farmers actually experience and trust these tools. Today, drones are not just capturing images. With AI, they can analyze crop health, detect early signs of stress, identify pest or disease patterns, and even recommend actions. Instead of reacting late, farmers can act early and with confidence. This changes everything. Instead of applying fertilizer or chemicals across the whole field, AI-guided drones can target specific areas that need intervention. That reduces costs, saves time, and protects the environment. But more importantly, it improves decision making at the farm level. From my work and research, I have seen a key challenge. The technology is advancing fast, but adoption depends on trust, usability, and relevance. Farmers are not just looking for tools. They are looking for systems that understand their reality, their land, and their experience. This is why I believe AI in agriculture must be farmer-centered. It is not enough for a system to give recommendations. It needs to explain why. It needs to be transparent. It needs to adapt to local contexts. That is exactly the idea behind what I am building with tools like FarmerChat, where the goal is not just to provide answers, but to build confidence in those answers. Imagine a future where a farmer uses a drone to scan the field, receives AI-powered insights instantly, and gets clear, practical recommendations they can trust. That is not far away. In many places, it is already starting. But we have to be intentional. We have to design these systems with farmers, not just for them. If we do that right, AI will not replace farmers. It will strengthen their decisions, protect their resources, and transform agriculture into a more resilient and intelligent system. The future of farming is not just digital. It is human-centered AI in action.

Digital tools powered by AI are growing fast, but are they reaching the farmers who need them most? At the 𝗚𝟮𝟬 𝗠𝗲𝗲𝘁𝗶𝗻𝗴 𝗼𝗳 𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗖𝗵𝗶𝗲𝗳 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 (𝗠𝗔𝗖𝗦), I had the honour of sharing FAO’s perspective on the twin transformations offered by #DigitalAgriculture, #AI innovation and #bioeconomy, and why they must go hand in hand to build resilient agrifood systems in Africa and beyond. In my intervention, I outlined four priorities to make this transformation inclusive and impactful: -     𝗖𝗼-𝗰𝗿𝗲𝗮𝘁𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 at the community level with farmers, not just for them. FAO’s Digital Villages initiative exemplifies this concept by connecting user-driven solutions with local knowledge platforms (such as Farmer Field Schools and Living Labs) for dissemination and scaling. -      𝗘𝗺𝗽𝗼𝘄𝗲𝗿 𝘆𝗼𝘂𝘁𝗵 𝗮𝗻𝗱 𝘄𝗼𝗺𝗲𝗻 agri𝗽𝗿𝗲𝗻𝗲𝘂𝗿𝘀 by unlocking finance and offering incubation and mentorship programs— a must for Africa’s young and dynamic population -     𝗕𝗿𝗶𝗱𝗴𝗲 𝘁𝗵𝗲 𝗴𝗮𝗽 between intention and action by applying behavioural science to understand real barriers to adoption. -     𝗠𝗼𝘃𝗲 𝗳𝗿𝗼𝗺 𝗽𝗶𝗹𝗼𝘁𝗶𝘁𝗶𝘀 𝘁𝗼 𝗽𝗼𝗹𝗶𝗰𝘆-𝗲𝗻𝗮𝗯𝗹𝗲𝗱 𝘀𝗰𝗮𝗹𝗲, supported by strong digital innovation ecosystems and mission-oriented approaches — like accelerating the agroecological transition. Bioeconomy and digitalization are not separate agendas. They reinforce each other: through precision tools, data-driven decision-making, circular practices, and inclusive innovation ecosystems. My key recommendations to MACS: ◾ Support the establishment of Regional Innovation Scaling Hubs to link local pilots with national policies, research institutions and private sector partners. ◾ Fund cross-country behavioural science programmes to document and tailor digital adoption strategies. ◾ Support open-source digital solutions and build digital infrastructure to lower the costs of access and adoption, foster cooperation and connect innovators. ◾ Convene a G20 dialogue on ethical AI, data governance, and digital inclusion, building on FAO’s roadmap for Digital Agriculture and AI innovation. The future of agrifood systems lies in 𝗼𝗽𝗲𝗻, 𝗶𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗲, 𝗮𝗻𝗱 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻, where 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗯𝗶𝗼𝗲𝗰𝗼𝗻𝗼𝗺𝘆 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗰𝗼𝗻𝘃𝗲𝗿𝗴𝗲 𝘁𝗼 𝘀𝗲𝗿𝘃𝗲 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗽𝗹𝗮𝗻𝗲𝘁, 𝗮𝗻𝗱 𝗽𝗿𝗼𝘀𝗽𝗲𝗿𝗶𝘁𝘆. #DigitalAgriculture #Bioeconomy #AgInnovation #AI4Ag #BeSci Henry van Burgsteden, Nevena Alexandrova, Erik van Ingen, Aurélie Toillier, Cristiano Consolini Cortney Price Hans Van Meijl Moteku Nthabi Moses Azong Cho Dr Hlami Ngwenya  

A packed room and strong engagement at AgriFutures evokeAG for the National AgriTech Strategic Plan. AusAgritech convened this critical discussion alongside the release of the White Paper to outline the opportunity for a future where AgriTech is embedded at the core of agricultural productivity. Over the past five years, Australia has invested approximately $620 million in agricultural innovation, agtech accelerators, smart farms, drought hubs, digital-ag platforms and innovation events. While this investment has catalysed the industry, the AusAgritech White Paper's core message is clear, we don't need to spend more. We need to spend differently, with clearer intent and far greater accountability. That means shifting from funding things that look like progress (digital platforms, accelerators, reports) to funding the mechanisms that actually drive adoption, create free market demand for AgriTech, and build a cycle of extension, trust and new capability. 👩🌾 Make adoption the metric, not innovation theatre. Public investment must shift toward technologies, companies, and ecosystem builders capable of demonstrating verified adoption outcomes. That sounds blunt because it needs to be. Companies created, events hosted and webinars completed are not success measures. On farms change is the measure. Productivity lifted is the measure. Outcomes over optics. 💰 Redirect capital to the "valley of scale," not just the valley of death. Innovation often starts in our research institutions, but it's scaled by commercial companies who need consistent and reliable access to capital. We need a range of public and private capital to fund the messy middle, scaling teams, markets, and products to build the capability and trust that creates a flywheel of adoption. If our capital models don't fund the full cycle, we'll keep exporting our IP and importing the returns. 🚜 Rebuild regional extension capability as core infrastructure. Extension is not a nice-to-have. It's the bridge. The uncomfortable truth is that extension capacity has been steadily eroded, and we keep pretending shiny national platforms will replace trusted humans in the paddock. Better outcomes start by funding regional advisory capability that blends ag and tech literacy, and backing producer-led farming system networks that de-risk adoption through demonstrated ROI. Thank you to everyone who took the time to attend and contribute, and to Meg Lovegrove and Rob Hulme for leading the discussion. The National AgriTech Strategy is being built by industry for industry, and we value your ongoing engagement and support. I'd love to hear your perspectives on the National AgriTech Stategic Plan, White Paper 👇

Revolutionizing Agriculture Through Data-Driven Decisions and Technology!! Agriculture is undergoing a significant transformation driven by data analytics and digital technology. Despite innovations like drones, sensors, and geospatial technology aimed at improving productivity, farmers often face challenges adopting them due to high costs and inadequate awareness of their value. To ensure success, the agri-ecosystem must involve farmers, policymakers, AgTech companies, and financial institutions collaborating to promote data-driven decisions. The Power of Agricultural Data: Modern agriculture deals with complex supply chains and requires massive amounts of data encompassing crop cycles, weather, and market trends. This data helps farmers determine optimal planting strategies and input usage for sustainable farming. Companies like Absolute, and Cropin leverage cutting-edge technologies like IoT and AI to support precision agriculture, offering solutions for efficient irrigation, pest detection, and crop monitoring. Soil Testing and Irrigation Optimization: Real-time soil monitoring technologies enable farmers to make better decisions by tracking moisture and nutrient levels. Affordable portable tools and services like Upaj help farmers optimize fertilizer and water use, enhancing soil health and reducing costs. Insurance and Financial Tools: Data-driven insurance tools minimize risks by offering parametric coverage based on weather conditions, providing quicker payouts and mitigating crop losses. Additionally, financial institutions use data analytics to assess farmers’ creditworthiness and offer lower-interest loans, boosting productivity. Bridging the Digital Divide: Governments must improve communication of support programs through data-driven channels and local representatives to help farmers access subsidies and crop advisories. User-friendly apps and initiatives can empower farmers with critical information to make informed decisions. In conclusion, embracing digital agriculture offers unparalleled potential for sustainability and productivity, requiring collaboration between governments, technology providers, and farmers. In a recent interview I shared a detailed perspective on the points above.

The biggest yield booster of this decade won’t be a new seed or fertiliser, it will be… Soil Intelligence. Farming has always been about seeds, water, and sunlight. Now, it’s about data too. Today, soil health isn't just a metric - it’s becoming the gold standard. Imagine this: • In Maharashtra, precision irrigation using soil sensors has reduced water use by 40%, boosted crop yields by 18%, and cut electricity costs by 30%. • In Punjab, soil nutrient sensors dropped fertiliser use by 30%, lifted yields by 15%, and improved soil quality by 10%. • In Tamil Nadu, real-time soil monitoring helped farmers reduce crop losses by 25%, optimize water use by 20%, and raise their incomes by 15%. This is the power of real-time soil data. With Soil Guru Pro, we’ve turned soil testing into: • Real-time, actionable insights • Nutrient dashboards that inform smarter decisions • Farm-specific monitoring that targets action where it’s needed most And it goes beyond tech - it’s a career igniter. Agri-students, FPO leaders, and rural youth are stepping into agronomy with pride. They’re earning, advising, and becoming trusted advisors in their communities, not just reading soil, but telling its story. When you democratize soil knowledge, you don’t just boost yields, you create local experts, strength, and enduring trust. This is the new agronomy: • Transparent • Data-driven • Farmer-first The question isn’t if the data revolution will redefine farming. It’s how soon every acre, every youth, and every community benefits. Could agronomy become the next aspirational career across India's rural landscape? FICCI, Startup India, Ministry of Agriculture & Farmers Welfare, Government of India #farming #GROWit #farmers #soilhealth #soildevice

Excellent piece, Matt Waits. Your framing of interoperability and the need to shift from siloed applications to adaptable capabilities resonates deeply. I’ve been lamenting this for the last few months, as many of my AgTech friends have heard. Having spent the first part of my career banking mobility and enterprise data companies, and the past decade deeply entrenched in AgTech, I now find myself operating the largest fruit and nut tree nursery in the country. That vantage point has sharpened my view that adoption isn’t just about features—it’s about integration, data context, and implementation discipline. We’re actively building our own nursery tech stack, but progress has been frustrating. Not due to resistance to technology, but because the systems don’t align with how our data is structured or how decisions are made on the ground. Most of our tech partners still design in isolation, without a clear understanding of what it means to interoperate in a mixed-system environment. This is where I see real potential for AI—not as a silver bullet, but as a practical bridge. AI agents can help translate between systems, map data semantics, and deliver more usable insights. The potential to streamline onboarding and implementation of new systems is even more tantalizing. But they can’t do it without access to structured, machine-readable data. I appreciate Matt’s call to action for AgTech software companies: publishing clear data structures, exposing APIs thoughtfully, and documenting reference data in a way machines (and humans) can understand is foundational. #agtech #ai Sachi Desai Shane Thomas Rhishi P. Walt Duflock F3 Innovate

5 steps to engage farmers Whether you're introducing a new technology or transitioning to Regenerative Agriculture Most field programs fail for a surprisingly simple reason: they assume growers adopt new practices in a straight line. But agricultural adoption doesn’t behave like corporate decision-making. It’s non-linear. It’s driven by - social proof, - perceived risk, - and local ROI Not by - lab tests, - presentations, - annual plans, - or brand promises. And every time companies ignore this reality, the result is the same: . friction, . low adoption, . inconsistent implementation, . and higher supply risk. That’s what I call the Grower Adoption Ladder a practical way to understand where farmers really are before any program or technology rollout. The Grower Adoption Ladder: 1️⃣ Awareness — “I’ve heard of it.” Usually from another grower, a trusted agronomist, or a local influencer. Awareness is social, not corporate. 2️⃣ Interest — “Does this solve my problem?” Not the company’s problem. Not the NGO’s narrative. Their own operational pain point. 3️⃣ Trial — small, low-risk test. This is where most programs fall apart. Growers only move when the perceived risk becomes manageable — and when someone is close enough to help troubleshoot. 4️⃣ Adoption — expand if ROI is real. No ROI, no adoption. Sometimes the ROI is economic, sometimes agronomic, sometimes emotional (less uncertainty, more security). 5️⃣ Advocacy — growers influence growers. The most powerful stage. Peer-to-peer validation travels further and faster than any corporate initiative. How this works in real life: New technology / Agtech / new product Most companies develop the product in a lab or in an office. Once it works technically, they jump directly into step 4  expecting rapid adoption. But farmers don’t operate like this. They won’t adopt based on technical performance alone. They only move when they feel protected at step 3 — the low-risk trial. This gap between corporate logic and field reality is one of the biggest reasons why new technologies stall. ➡️ What actually works: • micro-pilots, not mass launches • region-specific performance, not generic claims • reliability and consistency, not marketing stories • solving one local problem at a time • positioning trials as risk-reduction, not innovation The hard truth If you don’t know where your growers are on the ladder, you can’t design a program that works no matter the budget. Programs fail not because growers resist change, but because companies misread when and how growers are ready to move. Understanding this ladder changes everything from field engagement to product launches to regenerative sourcing programs. Which step is the most challenging?

🌍🤖 AI isn’t just hype in agriculture—it’s delivering real, measurable sustainability gains. A new report in Nature Portfolio validates this shift, and applying a GTM lens reveals actionable lessons for today’s #AgTech Sales & Marketing professionals: Here are my top 4 #GoToMarket takeaways from this study: #1 🌱 The influence of neighboring farmers’ practices and recommendations is ENOURMOUS.... When one farm adopts advanced tech, its neighbors benefit AND adopt solutions that deliver value. So makes certain to sell the network, not just the node—think partnerships with co-ops, industry groups, and regional agencies. But above all else, do whatever necessary to deliver recurring value to your customers! #2 ⚡ Efficiency and sustainability go hand-in-hand. AI tools drive real resource savings (water, fertilizer, energy) and verifiable emission reductions. Position your solution as both a profit and compliance tool, especially as regulatory and retailer pressures rise. #3 📚 Upskill and educate. Success isn’t just about software; it’s about the people using it. Prioritize enablement, consulting, and training in your GTM plans to ensure customer adoption and advocacy. #4 🚫 Don’t rely solely on subsidies. The research warns that heavy government support can sometimes slow innovation. For long-term wins, build your value proposition around business outcomes, not just incentives or grants. 🔗 https://lnkd.in/gRhp5jDq 💬 Curious how your team is positioning AI in agriculture today? What’s the biggest challenge you see in driving adoption?