From Raw Produce to Real Profit: The Entrepreneurial Shift in Smallholder Farming

 Across the world, farmers are navigating one of the most challenging periods in recent history. Economic instability, political tensions, rising production costs, and climate shocks are reshaping agricultural markets. Smallholder farmers, who depend heavily on seasonal harvests, feel these disruptions more deeply than anyone else. When global prices fluctuate and input costs rise, selling raw products immediately after harvest often brings low and unstable income.

This is where value addition becomes a powerful strategy. By transforming raw produce, drying, processing, grading, packaging, or creating simple by-products, farmers can capture more value, reduce post-harvest losses, and access better-paying markets. Engaging in value addition is not merely a technical activity; it reflects a form of entrepreneurial intention, where farmers make proactive decisions, identify opportunities, and respond to market needs instead of selling at the first available price.

Cultivating this mindset does not require redefining farmers as “entrepreneurs” in the formal sense. Instead, it means strengthening everyday decision-making: comparing selling options, understanding consumer preferences, improving product quality, and taking small calculated risks. These are all entrepreneurial behaviours rooted in local wisdom and traditional practices.

In today’s digital era, even basic digital literacy in local languages, using phones to check prices, share product photos, or connect with nearby buyers, can dramatically expand farmers’ market reach. Digital tools help bridge the gap between rural producers and consumers, enabling fairer prices and greater control over sales.

Ultimately, value addition empowers smallholder farmers to move from vulnerability to resilience. By adopting an entrepreneurial mindset shaped by their own context and learning, farmers can improve efficiency, withstand global shocks, and build more stable and profitable livelihoods.

The Imai–Tingley Framework: Understanding How Agricultural Interventions Work

 

In agricultural extension, we often ask, “Did the program work?”
But the smarter question is, “How did it work and through what pathways?”
That’s where the Imai–Tingley Framework for Causal Mediation Analysis comes in. Developed by Kosuke Imai, Luke Keele, and Dustin Tingley, it helps researchers uncover why and how interventions influence farmers’ behavior and outcomes.

What It Does?

Traditional regression shows whether an intervention affects an outcome. The Imai–Tingley framework goes further by dividing the total effect into:

        Indirect effect (ACME): the part explained by a mediator (like knowledge, trust, or empowerment)

        Direct effect (ADE): the remaining influence not through that mediator

It’s grounded in the potential outcomes framework, handles nonlinear models, and is implemented  in R using the mediation package.

Why It Matters in Agricultural Extension

Extension programs rarely change outcomes directly. They work through social, psychological, and institutional mechanisms, changing farmers’ knowledge, confidence, or networks.
This framework helps researchers measure those invisible channels of change.
It turns impact evaluation into mechanism evaluation.

Real-World Examples

• Training and Knowledge: Does climate-smart agriculture training increase adoption through improved knowledge?
• Peer Networks: Do farmers adopt new seeds because of extension agents — or because of peer discussions?
• Gender Empowerment: Do women-focused programs improve food security through empowerment?
• Digital Trust: Does mobile advisory adoption depend on trust in digital information?
• Institutional Pathways: Do FPO policies raise income through better market access?

Each case identifies how change happens, not just if it happens.

Blending Numbers with Narratives

Quantitative mediation estimates the “how much.” Qualitative insights explain the “why.”Together, they build stronger, evidence-based stories of agricultural transformation.

The Imai-Tingley Framework helps agricultural researchers move away from asking, “Did it work?” to “How and for whom did it work?” By revealing the pathways of change, knowledge, trust, empowerment, networks, it guides smarter design of future extension programs.

The Imai-Tingley framework vs the Structural Equation Modeling

🔹 SEM Path Analysis

• Focuses on associations between variables.
• Explains how variables are statistically related (direct and indirect effects).
• Commonly used for theoretical model testing (e.g., SmartPLS, AMOS).
• Interpretation: “Farmers who use digital extension tend to adopt more practices, and adoption is linked to higher yield.”

Associational, not strictly causal.

🔹 Imai–Tingley Causal Mediation

• Focuses on causal mechanisms.
• Decomposes the total effect of a treatment into:

Direct effect: digital extension → yield (not through adoption)

Indirect effect: digital extension → adoption → yield

• Uses counterfactual (what-if) logic to estimate how much of the yield change is caused by adoption.

The relationship is causal, not just correlational.

The main difference between SEM path analysis and the Imai–Tingley causal mediation framework is their analytical focus. Path analysis examines the statistical associations between variables to test theoretical relationships, demonstrating how constructs such as digital extension use, adoption of improved practices, and yield are interconnected. However, it does not confirm causality. In contrast, the Imai–Tingley framework uses a counterfactual, causal inference approach to decompose the total effect of an intervention into direct and indirect (mediated) effects. It determines how much of the yield improvement is caused by adoption behavior, making it more suitable for identifying causal mechanisms.

* Dr. Paul Mansingh, J 
*Professor & Head, Department of Agricultural Extension & Economics, 

VIT School of Agricultural Innovations and Advanced Learning (VAIAL), 
Vellore Institute of Technology, Vellore 632014
Mr. Atsu Frank Yayra Ihou
Teaching Cum Research Assistant, 

Department of Agricultural Extension & Economics, 

VIT School of Agricultural Innovations and Advanced Learning (VAIAL), 

Vellore Institute of Technology, Vellore 632014

 

Regression (Prediction Analysis) Vs. Simulation Modeling

 1. Regression (Prediction Analysis)

• Definition: Regression is a statistical tool used to find relationships between variables and to predict the value of a dependent variable based on independent variables.
• Purpose: To explain and quantify how factors influence an outcome, and to predict future outcomes.
• Nature: Data-driven, based on historical or survey data.

Agricultural Extension Example:

Suppose you want to study farmers’ adoption of drip irrigation. Using regression, you can examine how factors like education level, farm size, access to credit, and extension contact influence adoption. The model can predict the likelihood of adoption for a farmer with given characteristics.

Example regression result: “A one-unit increase in extension contact frequency increases the probability of adoption by 15%.”

2. Simulation Modeling

• Definition: Simulation modeling is a computational technique that creates a virtual model of a system and experiments with different scenarios to understand how it behaves.
• Purpose: To mimic real-world processes, test “what-if” scenarios, and understand system dynamics under changing conditions.
• Nature: Model-driven, often built on assumptions, rules, and interactions, not only on past data.

Agricultural Extension Example:

Imagine if we want to study how information about a new pest management technology spreads in a village. Using an agent-based simulation model, you can create virtual “agents” (farmers) with different social networks, risk preferences, and trust levels. You then simulate how adoption spreads if:

• Extension workers train 10% of progressive farmers,
• or if a subsidy is introduced,
• or if a pest outbreak happens.

This allows to test different strategies without waiting years for real-world results.

Key Difference in Agricultural Extension Terms

• Regression: Answers “What factors influence adoption, and how strongly?”
• Simulation: Answers “What will happen if we change extension strategies, policies, or external conditions?”

In short:

• Regression = Predicts adoption probability based on past patterns.
• Simulation = Experiments with scenarios to guide decision-making.

How Regression Works (Change in One Variable at a Time)

• Regression is built on historical data.
• It estimates the average effect of one independent variable on the dependent variable while keeping other factors constant (the “ceteris paribus” assumption).
• For example, in a logistic regression on drip irrigation adoption:

“For every additional extension contact, the probability of adoption increases by 15%, holding farm size, credit access, and education constant.”

• Limitation: Regression can show correlations and predict probabilities but cannot easily capture dynamic interactions, feedback loops, or changing conditions over time.

How Simulation Modeling Works (System Behavior with Multiple Factors)

• Simulation is like creating a virtual laboratory where many factors operate at the same time, interact, and evolve.
• Instead of assuming “all else equal,” simulation allows you to change many variables simultaneously and observe how the system adapts dynamically.
• It can incorporate:

Ø  Feedback loops (e.g., early adopters influence neighbors).

Ø  Non-linear relationships (e.g., adoption accelerates once a threshold is reached).

Ø  Time dynamics (e.g., how adoption changes year by year).

• Example (Agent-Based Simulation in Extension):

Ø  You create a virtual village of 100 farmers with differences in risk attitude, farm size, and social network ties.

Ø  You introduce a pest outbreak and simulate how quickly information spreads if 10% vs 30% of farmers are trained initially.

Ø  The model shows diffusion patterns over time (S-shaped adoption curves, network effects, clustering).

Key Difference in Handling Change

• Regression: Says “If X increases by 1 unit, Y changes by β units (on average), assuming other things fixed.”
• Simulation: Says “Let’s change X (or several Xs together) and see how the whole system evolves over time, considering interactions, feedback, and randomness.”

Quick Analogy for Agricultural Extension

• Regression = Taking a photograph of reality: It tells you the statistical relationship at one point in time.
• Simulation = Making a video of reality: It shows how the system plays out dynamically under different conditions.

Regression usually looks at one factor’s effect while holding others constant. Simulation allows multiple factors to change simultaneously, interact with each other, and influence outcomes dynamically.

Dr. Paul Mainsingh J, Professor & HOD
Department of Agricultural Extension and Economics
VIT School of Agricultural Innovations and Advanced Learning 
Vellore Institute of Technology
Vellore - 632014

How FPOs Help Connect Farmers to PFRDA / Pension Systems

 

1. Channel for outreach and awareness

PFRDA is working with FPOs as strategic partners to raise awareness among farmers about pension schemes like the National Pension System (NPS) and other social security products. FPOs have direct access to rural farmers, which makes them suitable intermediaries for disseminating information, conducting workshops, and mobilizing enrolments.

2. Aggregating small farmers/pooling mechanisms

Many farmers are smallholders with low and irregular incomes. Individually, enrolling them and managing payments (contributions) is operationally expensive. FPOs can pool farmers under one umbrella and act as a facilitator for collective enrolment, contribution collection, and monitoring. The FPO can coordinate periodic payments (monthly, quarterly) from member farmers, making the process more manageable.

3. Simplified enrolment / KYC / administrative support

FPOs can serve as a local institutional point to handle the paperwork, KYC verification, and record-keeping for farmer enrolments under pension schemes. They can also partner with Points of Presence (PoPs) or registration agents in the pension architecture to reduce transaction costs and friction. At FPO Conclaves, PFRDA discusses simplification of onboarding through these institutional linkages.

4. Trust, legitimacy, and social capital

Farmers tend to trust their FPO more than distant authorities. An FPO endorsement can overcome reluctance or skepticism. The FPO’s reputational capital helps in ensuring compliance (i.e. that farmers actually make regular contributions) and continuity.

5. Tailored product design and incentives

PFRDA can design pension products (flexible contribution, lower minimums, flexible withdrawal) in collaboration with FPOs so they suit agrarian income patterns (seasonality, income shocks). FPOs can negotiate or co-finance on behalf of small farmers (for instance subsidizing initial contributions, or absorbing admin costs) to incentivize uptake. In speeches, PFRDA officials have explicitly framed NPS for farmers as a “low-cost, accessible retirement product” suited for the farming community.

6. Monitoring, grievance redressal, and feedback

Because FPOs are local, they can help monitor compliance, follow up on missed payments, resolve problems, and provide feedback to PFRDA or regulators for product/policy improvements.

Challenges and Constraints

Irregular and low cash flows: Many farmers have seasonal income; making consistent pension contributions is difficult. Administrative burden: Even with aggregation, managing KYC, record-keeping, and reconciliations for many small farmers may strain FPO capacity. Awareness and trust: Many farmers might not sufficiently understand pension products; doubts about risk, returns, and benefits need effective communication. Regulation & compliance: Ensuring that FPOs act in a proper fiduciary role (handling funds, remitting contributions) will require regulatory safeguards and oversight. Scale and reach: In remote areas where FPOs are weak or non-existent, the network is limited. Technology and infrastructure gaps: Digital connectivity, literacy, and access to banking or fintech tools are uneven in rural areas.

Dr. Paul Mansingh J, Professor & HOD
Department of Agricultural Extension & Economics
VIT School of Agricultural Innovations and Advanced Learning 
Vellore Institute of Technology 
Vellore, 632014

EMPOWERING WOMEN IN INDIA’S VALUE-ADDED AGRICULTURE SECTOR

 India’s agricultural landscape is undergoing a transformative shift, one that’s not just about crops and commodities, but about innovation, entrepreneurship, and empowerment. At the heart of this change is a powerful force: women. From rural farms to bustling MSME’s and start-ups, women are redefining value addition in agriculture and driving inclusive economic growth. Startups are, in fact, a vital part of the transformation in India’s agricultural landscape. While MSMEs (Micro, Small & Medium Enterprises) represent established small-scale businesses, startups bring fresh energy, disruptive innovation, and scalable tech-driven solutions.

What Is Value Addition in Agriculture?

Value addition refers to enhancing the economic worth of agricultural products through processing, packaging, branding, and diversification. For example: mangoes turned into jams, millets into health mixes, or turmeric into wellness capsules. This transformation boosts farmer incomes, reduces waste, and opens doors to domestic and global markets.

Why Value Addition Matters

• Income Boost: Farmers earn more by selling processed goods than raw produce.
• Job Creation: MSMEs and food processing units generate employment, especially in rural areas.
• Women Empowerment: Women-led enterprises are bridging gender gaps and reshaping local economies.
• Export Growth: Value-added products like organic spices and dairy sweets are gaining traction globally.
• Food Security: Processing reduces post-harvest losses and extends shelf life.

Women: The Backbone of Agro-Entrepreneurship

Did you know that 85% of rural women are engaged in agriculture? Many are now stepping into roles as processors, marketers, and business owners. As of 2024, women own 39% of India’s MSMEs, with a strong presence in food processing, textiles, and handicrafts. But the momentum doesn’t stop there, women-led startups are rapidly gaining ground. According to the Department for Promotion of Industry and Internal Trade (DPIIT), over 75,935 startups in India now include at least one woman director, many of which are innovating in agri-tech, organic food, and rural supply chains. Government initiatives have played a key role in this surge. The Startup India Seed Fund Scheme (SISFS) has approved ₹227.12 crore in funding for 1,278 women-led startups, while the Alternative Investment Fund (AIF) has facilitated over ₹3,107 crore in investments across 149 women-led ventures. Additionally, the Credit Guarantee Scheme for Startups (CGSS) has extended ₹24.6 crore in loan guarantees to women entrepreneurs since April 2023.These startups are not only transforming traditional farming practices but also creating scalable, tech-driven solutions that enhance value addition, reduce waste, and open new market opportunities. Their contributions are positioning India as a global leader in smart and sustainable agriculture.

Their Impact Includes:

• Integrating traditional knowledge into modern value chains.
• Innovating in packaging and product diversification.
• Targeting niche markets like organic foods and handmade textiles.

Strategies to Empower Women in Value Addition

1. Skill Development: Training in food processing, branding, and digital marketing.
2. Access to Finance: Schemes like MUDRA and Stand-Up India offer collateral-free loans.
3. Technology Adoption: Promoting smart agriculture and e-commerce platforms.
4. Policy Support: Government initiatives like PMFME and Make in India provide infrastructure and incentives.

Safe Workspaces: Women-only industrial parks and childcare support in manufacturing hubs.

India vs. Global Giants: Competing with China

(Source: WTO Global Agricultural Trade Report 2025, FAO Global Agricultural Outlook 2025, USDA Foreign Agricultural Service 2025, Ministry of Commerce & Industry, Government of India 2025)

China leads global manufacturing with over 30% output. India, contributing 2.9%, is catching up, thanks to MSMEs, policy reforms, and women entrepreneurs. To compete globally, India must:

• Expand domestic manufacturing.
• Strengthen export logistics.
• Promote women-led innovation in value-added sectors.

Tamil Nadu: A Rising Star in Agro-Based Value Addition

Tamil Nadu is leading the charge with:

• Mega Food Parks in Perambalur, Theni, and Tuticorin.
• Women-led enterprises in millets, turmeric, coconut, and banana processing.
• Export hubs linked to ports like Chennai and Tuticorin.

Top Value-Added Commodities:

• Millets: Health mixes, cookies, flour.
• Turmeric: Capsules, oils, cosmetics.
• Coconut: Virgin oil, chips, coir products.
• Banana: Chips, fiber textiles.

Spices & Flowers: Essential oils, spice mixes.

Union Budget & Policy Highlights (2025–26)

The 2025–26 budget reflects a strategic push toward modernizing agriculture and fostering inclusivity. Announced by the Union Government of India, this central budget includes a 12% increase in agriculture allocations, prioritizing climate-smart farming and promoting women-led Farmer Producer Organizations (FPOs). Investments are being channelled into cold chain infrastructure and digital agriculture to enhance efficiency and reduce post-harvest losses. Despite these advancements, gaps remain in research and development funding and in ensuring accessible credit for smallholder farmers, which are critical areas for sustained growth.

The Road Ahead

Empowering women in value-added agriculture is more than a social initiative; it’s a transformative economic strategy. With targeted support and inclusive policies, India has the potential to boost its GDP by up to 30%, as projected by the IMF. Strengthening women’s roles in agro-processing and entrepreneurship can position India as a global leader in processed agri-exports. This approach also aligns with the Sustainable Development Goals (SDGs), paving the way for equitable and resilient growth across rural and urban landscapes.

  

Paul Mansingh J, Professor & HOD 

Department of Agricultural Extension & Economics,

VIT School of Agricultural Innovations and Advanced Learning (VAIAL),

Vellore Institute of Technology,

Vellore 632014

E PRIYAVADHANA

Ph.D. Scholar, Department of Agricultural Extension, 

Faculty of Agriculture, Annamalai University, 

Chidambaram, Tamil Nadu 608002

Erode Manjal: The Fragrance of Heritage, The Silence After GI Recognition

 A turmeric tale that asks – can pride alone sustain a legacy without action?

        

The Erode district is the pride of the country, which is called the “Turmeric City of India”.  For generations, farmers have nurtured turmeric with skill, patience, and respect for the land. Locally known as “ Erode Manjal”, this spice is more than just an agricultural crop; it is a part of the region's culture, economy, and identity.

In 2019, the Erode district got Geographical Indication (GI) recognition for the famous  Erode Manjal for its traditional Chinna Nadan turmeric variety, known for its rich golden yellow color, high curcumin content, and strong medicinal value. Traditionally, families used it not only as a spice in their kitchens but also in Ayurveda, Siddha, and home remedies for health and healing.

But Erode Manjal is not limited to just one variety. Over time, farmers have also cultivated other types such as Selection, 8, 10, PCT 8,10, Salem Manjal, and Rajahmundry turmeric. Each variety carries its own strength; some give higher yield, some have brighter color, some last longer in storage. While the Chinna Nadan stands unique for its purity and medicinal richness, many have shifted towards high-yielding varieties that are more sustainable in modern farming conditions.

On the ground, the reality is stark. There is another popular variety, Periya Nadan, that has disappeared. Only a few farmers continue with Chinna Nadan, as its low yield and high pest/disease issues make it unsustainable. Most farmers choose high-yielding, bright yellow varieties that offer them better returns and resilience.

Although the GI tag stands as an achievement, trade and marketing remain static, and there is a lack in the usage of the GI tag. Demand has not shifted, awareness has not increased, and price premiums have not materialized. Consumers do not ask for it, traders do not highlight it, and farmers do not benefit from cultivating it.

To make GI recognition meaningful, we must build a dedicated post-registration mechanism. We need committees that drive strategy, marketing plans that reach consumers, e-commerce platforms that connect sellers to buyers, export facilitation that opens global markets, and awareness campaigns that highlight authenticity and heritage. Only then can GI products like Erode Chinna Nadan turmeric transform from a paper certificate into a living symbol of India’s agricultural heritage and cultural pride. Not only Erode Manjal, but we must speak for all the GI-tagged products registered all over India.

*Paul Mansingh, J & Nirosha R

*Professor & Head, Department of Agricultural
Extension & Economics, VIT School of Agricultural
Innovations and Advanced Learning (VAIAL),
Vellore Institute of Technology, Vellore 632014
Teaching Research Assistant Cum, Department
of Agricultural Extension & Economics,
VIT School of Agricultural Innovations and Advanced
Learning (VAIAL), Vellore Institute of Technology, Vellore 632014

🐍 The Cobra Effect: When Solutions Create Bigger Problems

     

In the world of policymaking, good intentions don’t always lead to good outcomes. Sometimes, they backfire spectacularly. This paradox is known as the Cobra Effect, and it’s more relevant today than ever.

 A Lesson from History

During colonial rule in India, the British government offered a bounty for every dead cobra to reduce their numbers. However, instead of solving the problem, people began breeding cobras to profit from them. When the policy was scrapped, the now-useless cobras were released, leading to an even bigger infestation.

This ironic twist is a textbook example of how well-meaning policies can spiral into unintended consequences.

🌾 The Cobra Effect in Indian Agriculture

Fast forward to modern India, and we see similar patterns in agricultural policy:

• Fertilizer Subsidies: Intended to boost productivity, they’ve led to excessive urea use, damaging soil health and long-term fertility.
• 💧 Free Electricity for Irrigation: Aimed at helping farmers, it’s caused rampant groundwater extraction, pushing states like Punjab and Haryana into water crises.
• 🌾 Minimum Support Price (MSP): Designed for income stability, it’s encouraged monocropping of rice and wheat, leading to stubble burning, resource depletion, and environmental stress.

These policies weren’t flawed in intent, but they lacked the foresight to anticipate behavioral responses and ecological consequences.

🧠 What Can Be Done?

To avoid falling into the Cobra trap, we need:

• 🔄 Integrated Policy Design

• 📊 Robust Monitoring Mechanisms

• 🌱 Sustainability Safeguards

• 🧭 Adaptive Governance

Policies must evolve with context, behavior, and environmental feedback. Otherwise, we risk reinforcing the very problems we aim to solve.

Let’s design smarter policies that don’t just look good on paper, but work well in practice. Because sometimes, the road to unintended consequences is paved with good intentions.

#CobraEffect #PolicyDesign #Agriculture #Sustainability #Governance #India #EnvironmentalPolicy #SmartSolutions #PublicPolicy #SystemsThinking

Dr. Paul Mansingh J 

Professor & HOD 
Agricultural Extension and Economics 
#VIT School of Agricultural Innovations and Advanced Learning (#VAIAL)
Vellore Institute of Technology