Technology Diffusion in Agriculture

 Extract from WIPO report:

Precision agriculture technologies

PATs use sensors, satellite navigation, and data analytics to optimize farming operations. In general, there are three broad categories for PATs: (i) the data collection (sensors, satellite navigation), (ii) the data processing and/or analysis (yield monitoring, soil mapping), and (iii) the decision-making guidance (auto-steering tractors, variable-rate applications of fertilizers and pesticides). (23)

Farmers in Australia, Canada, Europe and the United States lead in the adoption of PATs. (24)

The US pioneered PATs in the 1980s, with adoption accelerating once global positioning systems (GPS) became widely available after 1983. (25) Most of the technologies used were related to grid sampling, fertilizer mapping, and pH as well as yield measurement. Since the 2000s, American farmers have been adopting auto-guidance system and variable rate technologies (VRTs) to reduce the cost of managing their farms. (26)

However, the adoption of PATs remains gradual. Studies show that farmers typically adopt individual PAT components rather than a complete system. (27) This is partly due to the high upfront cost of purchasing PATs.

Less than one-third of US farmers use any PAT tools whatsoever and adoption occurs in modules rather than complete systems. (28) In Europe, for example, entry level PATs include automatic milking systems, digital field records and automatic steerage systems. (29)

In addition, the PATs predominantly adopted vary according to agricultural need. Water scarcity led to the adoption of micro-irrigation in India, for example, whereas farmers in the US and Australia focus more on adopting guidance systems for large-scale cropping.

digital technology diffusion

Extract from WIPO report. 

Digital technologies such as submarine cables, broadband networks, data-driven platforms and AI have become the backbone of modern economies. Yet, not all economies realize the promise of digital transformation. This chapter traces why connectivity and digital capabilities advance rapidly in some regions while others remain constrained by infrastructure gaps, affordability barriers, skills shortages, and regulatory hurdles. It shows that unlocking inclusive digital diffusion requires more than new technologies—it demands coordinated investments, balanced IP governance, and policies that ensure all countries and communities can participate in the opportunities of the digital age.

Many digital technologies are considered GPTs, (General Purpose Technologies) the internet being a classic example. The patent landscape for digital technologies is highly concentrated. Most DT patent applications come from five major jurisdictions; namely, China, the United States, Japan, the Republic of Korea and the European Patent Office. Together, they account for most global filings. This concentration creates uneven diffusion patterns, as technology often follows the investment and licensing channels controlled by leading patent holders. At the same time, the growing market concentration of major digital platforms raises new policy challenges. A small number of global technology firms increasingly control key digital infrastructures, data resources and IP portfolios, shaping the direction and speed of diffusion. Ensuring dynamic competition therefore requires regulatory frameworks that prevent excessive market dominance, encourage interoperability and promote open innovation. Balancing the legitimate protection of IP rights with measures that safeguard competition and facilitate entry for smaller and local innovators remains a central policy priority for inclusive digital transformation.

What is R&D in AI

 

Talk of AI pervades the air in Delhi and all over media. As GOI has huge plans to pump R&D in private sector, there is need to understand R&D in AI.

FY2026 NITRD Program Component Areas (PCAs)

The FY2026 PCAs described on this page are those used by NITRD agencies in compiling the PCA budget information for the NITRD and NAIIO Supplement to the President’s FY2026 Budget.

• ACNS – Advanced Communication Networks and Systems
• AI – Artificial Intelligence
• CHuman – Computing-Enabled Human Interaction, Communication, and Augmentation
• CNPS – Computing-Enabled Networked Physical Systems
• CSP – Cyber Security and Privacy
• EdW – Education and Workforce
• ENIT – Electronics for Networking and Information Technology
• EHCS – Enabling R&D for High-Capability Computing Systems
• HCIA – High-Capability Computing Infrastructure and Applications
• IRAS – Intelligent Robotics and Autonomous Systems
• LSDMA – Large-Scale Data Management and Analysis
• SPSQ – Software Productivity, Sustainability, and Quality

AI R&D will intersect with multiple PCAs. For example:

• R&D on general methods for machine vision would fall under AI, while R&D on robots, even if the robots employ machine vision, would fall under IRAS. Note that R&D on intelligent autonomous systems that exist only in cyberspace, with no physical embodiment, would be reported under AI.
• R&D on algorithms for computational linguistics would fall under AI, while R&D on the broad problem of human-machine interaction, even if it contains an element of natural language processing, would fall under CHuman.
• R&D on the cybersecurity challenges unique to AI, such as the ability to exploit flaws in an AI system’s goals would fall under AI, whereas AI supporting cybersecurity research would fall under CSP.
• R&D on special neuromorphic computing architectures or chips optimized for neural nets would fall under AI, whereas general research in neuromorphic computing would fall under EHCS.
• R&D that is primarily machine learning would fall under AI, while R&D on the larger data management and analysis ecosystem, even if it contains an element of machine learning, would fall under LSDMA.

Source-https://www.nitrd.gov/program-component-areas/nitrd-pcas-2026/#AI

Circular biorefineries for rural India: turning rice straw and bagasse into biofuels

 

India’s abundant rice straw and sugarcane bagasse remain underused and are often burned, worsening air pollution. This review examines how circular biorefineries can convert these lignocellulosic residues into biofuels, advancing energy security, rural incomes, and environmental goals. We interrogate the value chain, from feedstock aggregation and densification to conversion and deployment, comparing physico-chemical pretreatments (e.g., steam explosion and alkaline) with emerging green options and clarifying trade-offs among delignification, fermentable-sugar yield, and inhibitor formation. We evaluate biochemical (enzymatic hydrolysis, and fermentation) and thermochemical (gasification and pyrolysis) routes to a diversified product slate. Evidence favors decentralized, village-scale mini-biorefineries led by Farmer–Producer Organizations, contingent on affordable enzymes, robust microbial catalysts, supportive policy, innovative finance, and disciplined supply-chain governance, a pragmatic roadmap for India’s circular bioeconomy.

Paper-Yadav, Anurag, and Kusum Yadav. “Circular Biorefineries for Rural India: Turning Rice Straw and Bagasse into Biofuels.” Academia Green Energy, vol. 2, no. 4, Academia.edu Journals, 2025, doi:10.20935/AcadEnergy7949.

Hydrogen Industry Outlook 2026

 

• KAKINADA PROJECT WILL BE INDIA’S FIRST COMMERCIAL-SCALE GREEN AMMONIA & GREEN HYDROGEN FACILITY. AM Green has already secured a strong export-oriented offtake pipeline. This includes a binding offtake agreement with Uniper, Germany for upto 500 KTPA starting Q2 2028. A Memorandum of Understanding (MoU) with RWE for approximately 300 KTPA is already in place. Further, 100 KTPA for BASF, 100 KTPA for Keppel, and a host of other players are in the offing.
• PANIPAT GREEN HYDROGEN UNIT ON TRACK FOR DECEMBER 2027 COMPLETION.
• VOC PORT TARGETS 2029 FOR FIRST PHASE OF GREEN HYDROGEN PRODUCTION.
• ELCOGEN AND THE CASE FOR SOLID OXIDE TECHNOLOGY IN INDIA’S HYDROGEN FUTURE.
• 2.4 KTPA GREEN HYDROGEN PLANT SET FOR Q2 FY 26-27 COMMISSIONING.
• GOPALPUR PROJECT FIRST PHASE ON TRACK FOR LATE 2028/EARLY 2029 COMMISSIONING.
• VIJAIPUR PLANT UTILIZES 10 MW PEM ELECTROLYSER TO PRODUCE 4.3 TPD OF HYDROGEN WITH 99.99% PURITY

Download report-https://www.indianchemicalnews.com/assets/img/H2IO-1.pdf