Why “Data” is the next BIG opportunity in Indian Agriculture?
Two most notable acquisitions in 2016 till date have been Bayer’s $ 66 bn buyout of Monsanto and Verizon’s $4.83 bn buyout of Yahoo. While these mergers come from seemingly two very different industries, there are some common underlying currents in these mega-corporations.
Let’s look at Monsanto first. Monsanto has taken a lead in developing integrated digital platform for farmers to access and analyse agronomic data. The platform aims to track farm operations including soil moisture, weather, crop data to make better predictions. Since Monsanto’s acquisition of Climate Corp in 2013, Monsanto has partnered with companies like Vermis Technologies (for soil mapping) and invested (through Monsanto Growth Ventures) in ag data companies like Resson (for image analytics). Clearly, data is integral part of Monsanto’s growth strategy.
Likewise, Verizon is working on ag-software platform supported by Internet-of-Things (IoT) on the farm field including weather stations, sensors and devices for real-time capturing and reporting of data. The Company is already working with vineyards in the California’s Monterey Country and Napa Valley (Source: Company Website). It has tied with a crop modeling company called ITK to provide intelligence on irrigation management and sustainable farming practices to the farm owners of the vineyards.
There is no doubt that giants like Monsanto and Verizon are targeting ag-data as one of key growth drivers. “Data” is now becoming most precious commodity in global agricultural market. What does it mean for Indian Agriculture?
A: Is India ready for data-centric agricultural economy?
The answer is unequivocal “Yes”. The panacea for majority of problems in Indian agriculture lies in “data”. The problems of inflation, wastage, low productivity and lack of institutional farmer financing can be addressed through data. Here’s how?
Food inflation can be solved by timely availability of data. Food inflation is a persistent problem in Indian economy. While demand patterns in food category are more predictable; estimating supply is bit of a challenge. Usually, the cause of sudden, unpredictable and sharp rise in food inflation is lack of requisite and timely supply.
We often see more price volatility in perishable crops like potato, tomato, coriander and onion. In recent times, even staples like oilseeds and pulses have also shown sharp pricing twists both in upside and downside. The solution to this problem is timely availability of data for a) sowing, b) harvest and c) production.
Farmer advisory can be issued if sowing is much more than what market can absorb. Likewise, stocks can be released and import orders can be placed in time if harvest / production data shows lower throughput than market demand.
A real-time assessment of likely throughput for any crop can reduce existing data asymmetry which results in volatility in prices. Thus, the problems of inflationary pressure (in case lower than expected output) and panic among farmers (in case of more than expected output) can be addressed with timely and accurate data.
The current forecasting methods include time-series analysis for predicting crop output. Past data will become less relevant for future predictions in the context of climate change and fast changing weather patterns across the globe. So need of the hour is to have tools to get the data on a real-time basis.
So how does one capture real time data for millions of hectares of arable land?
The answer lies in satellite imagery. It has the potential to capture images of farmer fields to 1 m x 1m resolution (20 – 25 pixels), which is improving further with invent of technology. These images can capture various data points such as Leaf Area Index, plant height, canopy etc which is indicative of crop vigour and hence can be used to accurately estimate farm yield.
Since the launch of NASA’s satellite Landsat-8 in 2013, many companies including Geosys, Planetlabs, Skybox have launched satellites who are providing satellite images to millions of farmers. The data can be bought at approx. cost $ 0.01 to $ 0.02 per hectare.
I am not sure about the of the capability to existing satellites to cover 161 mn hectares of arable land in India at desirable resolution levels. Given the importance of agriculture to Indian economy and India’s capability in launching satellites (as demonstrated time and again by ISRO’s scientists), there is a merit in government exploring the option of launching dedicated satellite with focus on agricultural applications.
In short, food inflation, though cannot be controlled fully but can be predicted with reasonable accuracy with data and technology intervention as discussed above.
Post-harvest wastage of farm produce can be reduced with intervention of data. The criminal wastage of food in the supply chain runs into billions of dollars. Multiple handling points along with lack of quality control points from farm to consumer is the root cause of wastage. For example, apples traveling in trucks from Himachal and Kashmir bear more pressure (because of overloading / poor crate designs) than they can absorb and likewise Alphonso mangoes transported in extreme summer from Konkan to Mumbai go through heat stress even in temperature controlled trucks.
Is there a way, one can monitor quality of produce in transportation and storage? It is possible to capture real-time data through web-enabled devices which can solve the problem. There are devices / remote sensors which can monitor factors causing wastage during storage (pest, rodents, moisture), transportation (high temperature, excessive pressure, humidity). Alarms and data patterns available from the devices can help develop solutions to control factors resulting in wastage. For example, if we have rodent-specific sensors in warehouses to detect / trace their movement, the traps can be design and configured to prevent loss of grain. This innovation is worth investing as rodents alone eat about 2-4% (approx. 5 -10 million tonnes per annum) of grain produced in India.
Soil fertility and productivity can be improved with data application. Poor productivity in India in most crops can be largely attributed to lack of soil fertility. Soil fertility in India is further going down due to erroneous application of fertilisers (more Nitrogen (N) and less of Phosphorus (P) and Potash (K) than the recommended mix). NPK mapping at each field is necessary for the right prescription including type of seed, seed rate, irrigation, plant growth regulators, fertilisers etc.
Again data obtained from a combination of on-field devices and satellite imagery can estimate nutrient value of soil at a given point of time. This can be complemented with data obtained from soil health cards (which can be digitized).
The data on crop imagery can also be captured through farmer’s smartphones and shared with agronomist to find solution of pest attacks or poor growth. Temperature and humidity data from weather stations can be overlapped to see impact of temperature / humidity on crop growth.
Not only agriculture but even animal husbandry industry can benefit a lot by use and application of data. Data from RFID tags can track cow movement and data from collar tags can be used to monitor body / neck movement which can help in heat detection and understand cow health. These data points can be used to improvise feed intake, lactation cycles and ultimately increase milk productivity.
Financing to farmers is another challenge which data can solve. The current priority sector lending to farmers stands at approx. USD 135 bn. However, majority of bankers still face the challenge of determining credit-worthiness of farmers due to lack of KYC records. Lending to farmers can become very efficient, logical and data-driven, if the bankers have access to data on likely crop output from farmer’s field (which can be determined as mentioned earlier). Likewise, insurance companies can ascertain risk premium if they have access to weather, soil, pest and output data.
In summary, use of data has the potential to solve most ag-supply-chain problems. Inclusion and incision of “Data” can be a game changer for Indian agriculture. The question is: are Indian farmers ready for it? Who will pay for data? Do we have talent to deliver on ag-data models?
B. Readiness of Indian farmers and entrepreneurs for data-centric models
My interaction with farmers in states like Rajasthan, Madhya Pradesh, Himachal gives me confidence that farmers are ready to embrace any technology which can improve farm economics. Of course, there is a need to make them aware about the potential upsides and risk mitigation possible with data. For example, in a pilot project conducted by an ag-data company; many farmers found that their estimation of own farm area was different from farm area estimated through geo-tagging. Once they knew the differential to the last digit, they were able to improvise the input application.
In general, farmers are open to adapting technology. Farmers are downloading apps for real-time access to data such as market yard prices. Though transactions and data sharing through such apps are limited, they are bound to go up with increasing use of vernacular language and improving UI and UX.
It’s unlikely that farmer is going to pay for the data (at least in the near future) till they see clearly economic benefits. If farmer is not going to pay, then the question is who will pay? Here are the potential buyers of data:
Agri input and output companies: The data analytics will be very useful for farm input companies (like agrochemicals, seeds, fertilisers, implements) as well as aggregators, processing companies, retailers who are buying farm produce from the farmers.
The input companies can be more prescriptive with data available on soil and crop health. Output companies can use data to ascertain and pre-book their supplies with estimates of crop output available in advance of harvest.
Banks and insurance companies: Both can save lot of cost by analyzing farm data to enable farmer KYC, credit worthiness and risk profile of farmer and the field.
Government: Various ministries and departments in central and state governments can also buy data to improve accuracy, frequency and timeliness of the data collected and reported by them (such as advance estimates of sowing area and production).
Thus, there are enough buyers who will be willing to pay for data. The premium can be built with more analytics, syndication and advisory.
Last but not the least, do we have enough ag-data-preneurs who see the potential and have the capability to seize this opportunity?
My mentoring relationship with many ag-data start-ups give me confidence that there is enough talent and risk-taking appetite in India. Likes of CropIn, Skymet, AgRisk have made inroads in this space and there are many who are soiling their hands in fields to build applications to capture data. Also many farm to consumer and direct to farm startups are converging to data as logical extension of their businesses. Globally, the success of the model is demonstrated by likes of Farmer’s edge, Farmlink, PrecisionHawk; who have scaled up and attracted significant funding in short period of time.
The challenge remains in upscaling data-transaction values (usually calculated in Rs. Per hectare) which can be addressed by building analytics and developing syndicated products. These models require patient capital as gestation periods are long. Also, small farm holdings pose some challenges in making some of the applications viable, which can be mitigated with land consolidation going forward
C. Time for “Data-revolution” in Indian Agriculture
To conclude, Indian farm economy can benefit enormously by availability of timely, accurate and actionable data. Investment in ag-data will benefit the farmer the most by making agriculture more predictable and remunerative for him. Data intervention in agriculture can go a long way in realizing Prime Minister’s dream of doubling farmer’s income in five years.
India is also blessed with a vibrant IT and analytics industry who over last three decades have brought huge amount efficiencies to corporations across the world. The same talent teamed up with agronomists and scientists can bring much-required-efficiency to Indian agriculture.
Over a period of time, we should strive to develop an open-source-digital-platform in agriculture (capturing farm, farmer, soil, crop data) which can open doors for more applications and rural entrepreneurship.
“Green revolution” in sixties and seventies in the last century made us self-sufficient in food needed for the country. Indian population has more than doubled since then. Time is ripe for “Data revolution” which can make Indian agriculture more efficient to cater to growing food demand for the country.
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