Mr. Deepak Pareek, Managing Partner and Chief Consultant, of HnyB Tech-Incubations discusses about data driven decision to finalise what to grow and when to grow.
Despite significant digital transformation across various sectors, agriculture remains largely untouched due to multiple factors. Many perceive agricultural challenges as issues solely for farmers or governments to address. However, since our food originates from agriculture, this is a pressing concern for everyone. A recent study indicates that food insecurity has escalated, with over 800 million people worldwide lacking access to sufficient food for a healthy life. By 2030, this number could rise to 1 billion, predominantly in Asia and Africa. To meet global demand, we need more than a 60% increase in agricultural production, but climate effects in recent years have led to decreased productivity. Food insecurity is primarily due to climate change, low productivity, and changing dietary habits. India must feed approximately 1.4 billion people, with 300 million facing food security challenges. In 2022, the Global Food Security Index ranked India 68th out of 113 major countries, and in 2023, the Global Hunger Index placed India at 111th out of 125 countries.
Since 85% of Indian farmers are small and marginal, adopting new technologies for higher productivity is challenging for them. Despite India being a digital nation with broadband internet reaching 950 million people and 1.2 billion mobile connections at low costs, these benefits have not fully extended to agriculture. We need a truly sustainable agri-food system that is data-driven, insightful, scalable, holistic, and distributed. Unlike the past, when the lack of data hindered decision-making, today we have real-time information facilitating rapid choices. Technology can assist farmers in deciding what to grow, when, and how. It must also be profitable, as farmers have struggled financially in recent years, leading many to lose interest in agriculture due to lack of profit.
In the 19th century, farmers used rudimentary technology but often earned more profit than today's farmers. Technological advancements like the Green Revolution increased production through fertilizers and pesticides but led to a substantial fall in profits due to higher cultivation costs. In the 1980s, genetically modified crops were introduced; while India hasn't fully embraced GMOs, many countries have, boosting productivity. However, commodity prices haven't risen enough to offset additional costs, leaving farmers financially strained. In the late 2000s, agricultural mechanization with tractors and harvesters was promoted, yet profits remained low due to increased cultivation expenses. When farmers are advised to adopt technologies like drones or sensors, they often feel confused and fail to see their value. We must explain the advantages and encourage data-driven decisions to improve profitability. We can also anticipate a shift to aeroponics, hydroponics, and organic farming using bio-fertilizers or bio-stimulants to reduce input costs and enhance soil health.
Modern agriculture involves numerous complex activities, making technology adoption slower compared to other industries. Downstream, there are multiple value chains for seeds, packaging, distribution, fertilizers, pesticides, and more, each with its own complexities. Collecting data is essential for insights on input application, seed quantity, soil condition, nutrient availability, pest attacks, and climate-induced stress. Understanding the roles of retailers, aggregators, wholesalers, and end-users like food processing industries is also crucial. The agri-ecosystem now includes not only farmers, aggregators, and input companies but also government policies, various input companies, Farmer Producer Organizations (FPOs), and Farmer Producer Companies (FPCs) where farmers collaborate for better value chain integration. Service providers like AgTech companies, public extension system, NGOs and input companies advise farmers. There are schemes and insurance options more accessible to farmers, and financial institutions offer vital credit for purchasing inputs and cultivation. Technology has started playing a crucial role in providing farmers with information for making farming decisions.
Data can be interpreted in various ways by farmers or other ecosystem participants. Agricultural data encompasses farmer demographics, farm profiles, crop cycles, farming activities, financial transactions, and consumption patterns to facilitate better data-driven decisions. With a deluge of data available, it's essential to identify which data to collect so farmers can use mobile applications to monitor moisture, nutrient availability, and temperature for precision agriculture. Many players are decoding genetics to improve seed quality, developing heat-tolerant and salinity-tolerant seeds capable of growing with minimal resources. Agricultural data is characterized by the five V's: Volume (large amounts of data), Variety (data from sensors and mobile apps), Veracity (accuracy of data for correct decision-making), Velocity (rapid data transmission on various agricultural aspects), and Value (significant worth due to farm scale, climatic zones, and timing of activities).
Digital trends shaping agriculture today include blockchain, which brings transparency to value chains; AI and ML, which help identify stress points that are difficult for farmers to detect; and the Internet of Things (IoT), using sensors connected to the cloud. We believe digital agriculture will rapidly advance due to the market size and growth potential. Governments are formulating policies to aid farmers, who are a significant voter base. India is developing a strong digital infrastructure, with more people connected to the internet and mobile apps compared to global levels. We collect farm data for actionable insights, aiding in understanding weather, climate, soil databases, crop models, market information, agri-inputs, best practice inventories, farmer information, integrated farm models, water balance models, soil nutrition models, and precision agriculture algorithms for data-driven decisions. Many international players like ITK, Plantix, Xarvio, Phytech, Blue River, and OneSoil are identifying trends in farm monitoring, traceability, farm automation, finance, and credit. In India, companies like Absolute, Cropin, AgroStar, SatSure, Stellapps, and TraceX are doing innovative work in digital agriculture.
What factors are considered when selecting crops for specific reason? What soil parameters should be analysed to make informed decisions about crop selection?
To maximize profitability, it's crucial for farmers to understand historical data from the past five years, including weather patterns, commodity pricing, yields of different crop varieties, pest infestation history, and soil nutrient profiles. This information enables them to make informed decisions about which crops to plant and when, aligning harvest times with periods of high market prices.
Selecting the right crop variety is essential. Farmers should consider the specific conditions of their land—such as region, soil type (clay, loamy, or sandy), carbon content, and nutrient levels—rather than simply following what others are cultivating. Soil testing before sowing provides valuable insights into pH, nutrient (macro and micro) levels and electrical conductivity (EC), indicating which crops are best suited to the soil's properties. This helps prevent the mistake of growing the wrong crop or variety, which can lead to low prices and reduced profitability.
Understanding the soil's organic carbon content and water retention capacity is also important, even if testing involves some cost. Investing in soil analysis can significantly impact overall profitability by ensuring optimal crop selection and healthier yields. Modern tools now offer fast and affordable testing solutions, helping farmers decide on the right type of crop and variety with minimal expense. Services like InSoil provide best-in-class support for detailed soil analysis.
How can soil health data contribute to optimising fertigation and irrigation strategies? Are there technologies available for real time soil monitoring?
Soil health data is crucial for optimizing fertigation and irrigation strategies. By conducting soil testing, farmers can identify specific nutrient deficiencies, allowing them to supply only the necessary nutrients in precise quantities. This targeted approach prevents over-fertilization, reduces costs on expensive fertilizers, and avoids yield reductions caused by nutrient imbalances.
Understanding soil pH and electrical conductivity (EC) helps adjust fertigation plans to ensure optimal nutrient uptake by crops. Monitoring soil moisture levels is essential for efficient irrigation; over- or under-watering can stress plants and decrease yields. Implementing drip or sprinkler irrigation systems equipped with moisture sensors allows for precise water management, applying water only when and where it’s needed. Real-time soil monitoring technologies are primarily available for soil moisture through sensors integrated with irrigation systems. Continuous real-time tracking of other soil nutrients is less common due to cost and technological limitations. However, affordable portable tools are available for quick assessments of pH, EC, and basic nutrient content, providing timely data without the need for constant monitoring.
Periodic soil testing—before sowing, mid-season, and pre-harvest—is effective for guiding fertigation and irrigation decisions. While not real-time, these tests offer sufficient information to adjust strategies and optimize crop performance. Services like Upaj provide cost-effective real-time monitoring solutions, helping farmers make informed decisions.
In summary, utilizing soil health data enables farmers to optimize nutrient and water use, improve crop health, and increase profitability. While real-time monitoring is mainly feasible for soil moisture, periodic testing of other soil parameters provides the necessary insights to adjust fertigation and irrigation strategies effectively.
How can data driven decisions help in optimising the use of water resources for irrigation? Can data analytics help in minimizing waste and maximising resources utilisation?
Data-driven decisions enable precise management of water resources by leveraging real-time information and analytics. By using technologies like drip irrigation systems equipped with sensors, farmers can monitor soil moisture levels accurately and apply water only when and where it's needed. This approach conserves water, addresses the challenge of erratic rainfall leading to low reservoir levels, and enhances productivity. Implementing such systems in crops like rice and adopting new agricultural practices can significantly reduce water abuse. Additionally, data can inform irrigation schedules based on weather forecasts, ensuring that water usage aligns with actual crop needs and environmental conditions.
Data analytics plays a crucial role in minimizing waste and maximizing the utilization of resources. By analyzing data across the agricultural value chain, farmers and stakeholders can improve logistics planning to ensure products are delivered in good condition, reducing post-harvest losses due to inadequate infrastructure. Precision agriculture, supported by digital analytics, helps in reducing waste by optimizing the use of inputs like water, fertilizers, and pesticides. This not only lowers the cost of cultivation but also addresses the specific needs of the crops and soil. Data analytics aids in making informed decisions that enhance efficiency, reduce unnecessary expenditure, and support sustainable farming practices.
How do insurance and financial tools help in risk management based on the data analysis?
Insurance and financial tools enhance risk management in agriculture by utilizing data analysis to provide targeted support to farmers. Government schemes often focus on drought-prone or flood-affected areas, offering insurance and financial assistance to those most vulnerable. Digital technology and remote sensing enable the collection of micro-level data, which helps in accurately assessing damage caused by adverse weather conditions. This data-driven approach ensures that compensation is effectively distributed to affected farmers.
Parametric insurance is a prime example of data-informed risk management. In this model, farmers pay a premium, and compensation is automatically triggered based on specific data-driven parameters, such as rainfall levels or temperature extremes. If these predefined conditions occur, the farmer receives compensation without the need for lengthy claims processes. This system relies on accurate data analytics and remote sensing, making it more efficient, cost-effective, and accessible for farmers.
For financial institutions, data analysis mitigates the challenges of lending to farmers. Traditionally, it has been difficult for lenders to monitor how farmers use funds and to assess their reliability. By analyzing data on a farmer's activities, crop performance, and adherence to best practices—possibly monitored through remote sensors—financial institutions can better evaluate creditworthiness. Reliable farmers can receive credit at lower interest rates, and their activities can be remotely monitored to ensure proper use of funds. Additionally, data allows for provisions to account for potential losses, further reducing risk for both the lender and the farmer.
Benefits of Data-Driven Insurance and Financial Tools:
Accurate Risk Assessment: Detailed data helps in evaluating the specific risks faced by farmers, leading to more effective insurance coverage.
Efficient Compensation: Automated payouts in parametric insurance reduce the time and bureaucracy involved in claims, providing quicker relief to farmers.
Improved Credit Access: Data on farmer reliability and crop success enables lenders to offer better loan terms and monitor loan usage.
Cost Reduction: Digital tools lower the costs associated with risk assessment and monitoring, making financial services more affordable for farmers.
Data analysis significantly enhances the effectiveness of insurance and financial tools in agriculture by providing precise, actionable insights. This leads to better risk management, ensuring that farmers receive timely support and that financial institutions can lend with greater confidence. The integration of digital tools not only benefits individual farmers but also contributes to the overall resilience and sustainability of the agricultural sector.
Are there user-friendly apps the farmers can use to access and interpret relevant data? How does government policies and subsidies cater to decision making process for crop selection? How can farmers stay informed about government support programs through data driven channels?
There are lot of such apps that help in information on soil health, agronomical advisory, finance, and insurance. There are government based apps used in Telangana and Andhra Pradesh. There is a portal that provides data on pricing, sowing stages to help the farmers take decision on what crops to grow and which financial model to use. Farmers in Punjab and Haryana are growing paddy irrespective of soil not supporting their cultivation as the paddy is procured from here. Lot of farmers take up drip irrigation initially, but later they drop it as they are unable to maintain the same. We need government policies that helping farmers to grow crops as suitable to their farm. I have not seen a structured way of conveying the information about government programs to the farmers, the reasons being language issue, and the local representatives of the taluk or gram panchayat not passing on the information to the farmers. The extension workers should be apprising the farmers about the government policies, but it is not efficient. These reasons make it difficult for farmers to get access to government support programs.
Deepak Pareek
Email: deepak@hnyb.in
Phone: +91 9898269489
Despite significant digital transformation across various sectors, agriculture remains largely untouched due to multiple factors. Many perceive agricultural challenges as issues solely for farmers or governments to address. However, since our food originates from agriculture, this is a pressing concern for everyone. A recent study indicates that food insecurity has escalated, with over 800 million people worldwide lacking access to sufficient food for a healthy life. By 2030, this number could rise to 1 billion, predominantly in Asia and Africa. To meet global demand, we need more than a 60% increase in agricultural production, but climate effects in recent years have led to decreased productivity. Food insecurity is primarily due to climate change, low productivity, and changing dietary habits. India must feed approximately 1.4 billion people, with 300 million facing food security challenges. In 2022, the Global Food Security Index ranked India 68th out of 113 major countries, and in 2023, the Global Hunger Index placed India at 111th out of 125 countries.
Since 85% of Indian farmers are small and marginal, adopting new technologies for higher productivity is challenging for them. Despite India being a digital nation with broadband internet reaching 950 million people and 1.2 billion mobile connections at low costs, these benefits have not fully extended to agriculture. We need a truly sustainable agri-food system that is data-driven, insightful, scalable, holistic, and distributed. Unlike the past, when the lack of data hindered decision-making, today we have real-time information facilitating rapid choices. Technology can assist farmers in deciding what to grow, when, and how. It must also be profitable, as farmers have struggled financially in recent years, leading many to lose interest in agriculture due to lack of profit.
In the 19th century, farmers used rudimentary technology but often earned more profit than today's farmers. Technological advancements like the Green Revolution increased production through fertilizers and pesticides but led to a substantial fall in profits due to higher cultivation costs. In the 1980s, genetically modified crops were introduced; while India hasn't fully embraced GMOs, many countries have, boosting productivity. However, commodity prices haven't risen enough to offset additional costs, leaving farmers financially strained. In the late 2000s, agricultural mechanization with tractors and harvesters was promoted, yet profits remained low due to increased cultivation expenses. When farmers are advised to adopt technologies like drones or sensors, they often feel confused and fail to see their value. We must explain the advantages and encourage data-driven decisions to improve profitability. We can also anticipate a shift to aeroponics, hydroponics, and organic farming using bio-fertilizers or bio-stimulants to reduce input costs and enhance soil health.
Modern agriculture involves numerous complex activities, making technology adoption slower compared to other industries. Downstream, there are multiple value chains for seeds, packaging, distribution, fertilizers, pesticides, and more, each with its own complexities. Collecting data is essential for insights on input application, seed quantity, soil condition, nutrient availability, pest attacks, and climate-induced stress. Understanding the roles of retailers, aggregators, wholesalers, and end-users like food processing industries is also crucial. The agri-ecosystem now includes not only farmers, aggregators, and input companies but also government policies, various input companies, Farmer Producer Organizations (FPOs), and Farmer Producer Companies (FPCs) where farmers collaborate for better value chain integration. Service providers like AgTech companies, public extension system, NGOs and input companies advise farmers. There are schemes and insurance options more accessible to farmers, and financial institutions offer vital credit for purchasing inputs and cultivation. Technology has started playing a crucial role in providing farmers with information for making farming decisions.
Data can be interpreted in various ways by farmers or other ecosystem participants. Agricultural data encompasses farmer demographics, farm profiles, crop cycles, farming activities, financial transactions, and consumption patterns to facilitate better data-driven decisions. With a deluge of data available, it's essential to identify which data to collect so farmers can use mobile applications to monitor moisture, nutrient availability, and temperature for precision agriculture. Many players are decoding genetics to improve seed quality, developing heat-tolerant and salinity-tolerant seeds capable of growing with minimal resources. Agricultural data is characterized by the five V's: Volume (large amounts of data), Variety (data from sensors and mobile apps), Veracity (accuracy of data for correct decision-making), Velocity (rapid data transmission on various agricultural aspects), and Value (significant worth due to farm scale, climatic zones, and timing of activities).
Digital trends shaping agriculture today include blockchain, which brings transparency to value chains; AI and ML, which help identify stress points that are difficult for farmers to detect; and the Internet of Things (IoT), using sensors connected to the cloud. We believe digital agriculture will rapidly advance due to the market size and growth potential. Governments are formulating policies to aid farmers, who are a significant voter base. India is developing a strong digital infrastructure, with more people connected to the internet and mobile apps compared to global levels. We collect farm data for actionable insights, aiding in understanding weather, climate, soil databases, crop models, market information, agri-inputs, best practice inventories, farmer information, integrated farm models, water balance models, soil nutrition models, and precision agriculture algorithms for data-driven decisions. Many international players like ITK, Plantix, Xarvio, Phytech, Blue River, and OneSoil are identifying trends in farm monitoring, traceability, farm automation, finance, and credit. In India, companies like Absolute, Cropin, AgroStar, SatSure, Stellapps, and TraceX are doing innovative work in digital agriculture.
What factors are considered when selecting crops for specific reason? What soil parameters should be analysed to make informed decisions about crop selection?
To maximize profitability, it's crucial for farmers to understand historical data from the past five years, including weather patterns, commodity pricing, yields of different crop varieties, pest infestation history, and soil nutrient profiles. This information enables them to make informed decisions about which crops to plant and when, aligning harvest times with periods of high market prices.
Selecting the right crop variety is essential. Farmers should consider the specific conditions of their land—such as region, soil type (clay, loamy, or sandy), carbon content, and nutrient levels—rather than simply following what others are cultivating. Soil testing before sowing provides valuable insights into pH, nutrient (macro and micro) levels and electrical conductivity (EC), indicating which crops are best suited to the soil's properties. This helps prevent the mistake of growing the wrong crop or variety, which can lead to low prices and reduced profitability.
Understanding the soil's organic carbon content and water retention capacity is also important, even if testing involves some cost. Investing in soil analysis can significantly impact overall profitability by ensuring optimal crop selection and healthier yields. Modern tools now offer fast and affordable testing solutions, helping farmers decide on the right type of crop and variety with minimal expense. Services like InSoil provide best-in-class support for detailed soil analysis.
How can soil health data contribute to optimising fertigation and irrigation strategies? Are there technologies available for real time soil monitoring?
Soil health data is crucial for optimizing fertigation and irrigation strategies. By conducting soil testing, farmers can identify specific nutrient deficiencies, allowing them to supply only the necessary nutrients in precise quantities. This targeted approach prevents over-fertilization, reduces costs on expensive fertilizers, and avoids yield reductions caused by nutrient imbalances.
Understanding soil pH and electrical conductivity (EC) helps adjust fertigation plans to ensure optimal nutrient uptake by crops. Monitoring soil moisture levels is essential for efficient irrigation; over- or under-watering can stress plants and decrease yields. Implementing drip or sprinkler irrigation systems equipped with moisture sensors allows for precise water management, applying water only when and where it’s needed. Real-time soil monitoring technologies are primarily available for soil moisture through sensors integrated with irrigation systems. Continuous real-time tracking of other soil nutrients is less common due to cost and technological limitations. However, affordable portable tools are available for quick assessments of pH, EC, and basic nutrient content, providing timely data without the need for constant monitoring.
Periodic soil testing—before sowing, mid-season, and pre-harvest—is effective for guiding fertigation and irrigation decisions. While not real-time, these tests offer sufficient information to adjust strategies and optimize crop performance. Services like Upaj provide cost-effective real-time monitoring solutions, helping farmers make informed decisions.
In summary, utilizing soil health data enables farmers to optimize nutrient and water use, improve crop health, and increase profitability. While real-time monitoring is mainly feasible for soil moisture, periodic testing of other soil parameters provides the necessary insights to adjust fertigation and irrigation strategies effectively.
How can data driven decisions help in optimising the use of water resources for irrigation? Can data analytics help in minimizing waste and maximising resources utilisation?
Data-driven decisions enable precise management of water resources by leveraging real-time information and analytics. By using technologies like drip irrigation systems equipped with sensors, farmers can monitor soil moisture levels accurately and apply water only when and where it's needed. This approach conserves water, addresses the challenge of erratic rainfall leading to low reservoir levels, and enhances productivity. Implementing such systems in crops like rice and adopting new agricultural practices can significantly reduce water abuse. Additionally, data can inform irrigation schedules based on weather forecasts, ensuring that water usage aligns with actual crop needs and environmental conditions.
Data analytics plays a crucial role in minimizing waste and maximizing the utilization of resources. By analyzing data across the agricultural value chain, farmers and stakeholders can improve logistics planning to ensure products are delivered in good condition, reducing post-harvest losses due to inadequate infrastructure. Precision agriculture, supported by digital analytics, helps in reducing waste by optimizing the use of inputs like water, fertilizers, and pesticides. This not only lowers the cost of cultivation but also addresses the specific needs of the crops and soil. Data analytics aids in making informed decisions that enhance efficiency, reduce unnecessary expenditure, and support sustainable farming practices.
How do insurance and financial tools help in risk management based on the data analysis?
Insurance and financial tools enhance risk management in agriculture by utilizing data analysis to provide targeted support to farmers. Government schemes often focus on drought-prone or flood-affected areas, offering insurance and financial assistance to those most vulnerable. Digital technology and remote sensing enable the collection of micro-level data, which helps in accurately assessing damage caused by adverse weather conditions. This data-driven approach ensures that compensation is effectively distributed to affected farmers.
Parametric insurance is a prime example of data-informed risk management. In this model, farmers pay a premium, and compensation is automatically triggered based on specific data-driven parameters, such as rainfall levels or temperature extremes. If these predefined conditions occur, the farmer receives compensation without the need for lengthy claims processes. This system relies on accurate data analytics and remote sensing, making it more efficient, cost-effective, and accessible for farmers.
For financial institutions, data analysis mitigates the challenges of lending to farmers. Traditionally, it has been difficult for lenders to monitor how farmers use funds and to assess their reliability. By analyzing data on a farmer's activities, crop performance, and adherence to best practices—possibly monitored through remote sensors—financial institutions can better evaluate creditworthiness. Reliable farmers can receive credit at lower interest rates, and their activities can be remotely monitored to ensure proper use of funds. Additionally, data allows for provisions to account for potential losses, further reducing risk for both the lender and the farmer.
Benefits of Data-Driven Insurance and Financial Tools:
Accurate Risk Assessment: Detailed data helps in evaluating the specific risks faced by farmers, leading to more effective insurance coverage.
Efficient Compensation: Automated payouts in parametric insurance reduce the time and bureaucracy involved in claims, providing quicker relief to farmers.
Improved Credit Access: Data on farmer reliability and crop success enables lenders to offer better loan terms and monitor loan usage.
Cost Reduction: Digital tools lower the costs associated with risk assessment and monitoring, making financial services more affordable for farmers.
Data analysis significantly enhances the effectiveness of insurance and financial tools in agriculture by providing precise, actionable insights. This leads to better risk management, ensuring that farmers receive timely support and that financial institutions can lend with greater confidence. The integration of digital tools not only benefits individual farmers but also contributes to the overall resilience and sustainability of the agricultural sector.
Are there user-friendly apps the farmers can use to access and interpret relevant data? How does government policies and subsidies cater to decision making process for crop selection? How can farmers stay informed about government support programs through data driven channels?
There are lot of such apps that help in information on soil health, agronomical advisory, finance, and insurance. There are government based apps used in Telangana and Andhra Pradesh. There is a portal that provides data on pricing, sowing stages to help the farmers take decision on what crops to grow and which financial model to use. Farmers in Punjab and Haryana are growing paddy irrespective of soil not supporting their cultivation as the paddy is procured from here. Lot of farmers take up drip irrigation initially, but later they drop it as they are unable to maintain the same. We need government policies that helping farmers to grow crops as suitable to their farm. I have not seen a structured way of conveying the information about government programs to the farmers, the reasons being language issue, and the local representatives of the taluk or gram panchayat not passing on the information to the farmers. The extension workers should be apprising the farmers about the government policies, but it is not efficient. These reasons make it difficult for farmers to get access to government support programs.
Deepak Pareek
Email: deepak@hnyb.in
Phone: +91 9898269489