1. Indian Agricultural Sector
On the face of it, the Indian agricultural sector presents some staggering numbers. It provides livelihood to 58% of India’s population with a Gross Value Addition of 265.51 billion USD(agriculture, forestry & fishing combined)1. At 283.37 tons, India had a record production of food grains in 2018-191.
While these numbers may seem impressive on an absolute basis, they fail to promise if we delve a little deeper. Although this sector employs(directly/indirectly) about 58% of the population, it contributes only about 15.87% to the country’sGDP2. This brings us to a key parameter, that is, agricultural productivity. The most popular metric to measure agricultural productivity is agricultural Value Added per Worker. A comparison of the top 20 economies on this metric reveals that India’s agricultural productivity has great scope of improvement(Illustrated in Figure 1).
All the countries with the lowest agricultural value-added per worker are mostly developing/underdeveloped economies with high population densities viz. India, Indonesia, China, etc.
India fares the lowest amongst the top 20economies (by GDP) in terms of agricultural value-added per worker. Clearly, there are certain problems that need to be addressed. Now, let us look at the key problems faced by the Indian agricultural sector.
Figure 1: Agricultural Value Added Per Worker* (2018)
1.1 Key problems faced by the Indian Agricultural Sector
A number of factors curtails India’s agricultural output – while some of them are systemic or historical in nature, the others are environmental or technological (See Figure 2). The systemic factors have evolved over centuries of agricultural activity dating back to ancient times while the geographical features of agrarian land and weather patterns primarily dictate the environmental factors. The technological factors have emerged primarily due to lack of advancement of agricultural techniques, and affordability of machinery and equipment. Let us look at all these factors one by one:
a. Cropping pattern: In many areas, continued application of obsolete cropping patterns inhibits agricultural productivity. Practices like mono-cropping not only lead to lesser output but also lead to soil degradation.
b. Land ownership/ Fragmented land holdings: The average size of landholding in India is less than 2 hectares. This makes it difficult to achieve economies of scale and introduce new technologies and machinery
c. Land tenure: Due to absentee landlordism (despite the abolished zamindari system), the tenure of land holdings for farmers is not secure. This makes it an adverse environment for the application of modern farming techniques, crop rotation, etc.
d. Agricultural credit: There is lack of systematic financing provisioning for farmers. Co-operatives and other financial institutions have not been able to eliminate village money lenders who lend money at exorbitant interest rates, thereby making finance unaffordable for farmers.
a. Erratic Monsoon: One of the key factors influencing agricultural productivity in India is the unpredictable behavior of monsoons. This problem is aggravated due to the lack of irrigation facilities across India.
b. Soil infertility: Increasing pressure on agricultural land in India has led to overuse of fertilizers, increase in tillage, abandonment of traditional organic soil revival techniques, and insufficient rotation of crops. This has resulted in soil degradation and loss of fertility.
c. Water sources: Water sources are not effectively linked to fulfill demand for irrigation to all farming areas.
d. Topography: The diverse topography of India’s land makes it essential to identify the right crops for the various soil variants and climatic conditions.
a. Lack of farm equipment: Farm mechanization in India is low despite growth over the decades. A good measure to gauge mechanization is power availability per hectare, which is low in India.
b. Lack of new farming techniques: Due to lack of awareness regarding new farming techniques and over-adherence to old traditional ways of agriculture, farmers in India have not been successful in widely adopting new farming techniques.
c. Lack of water supply: There is lack of efficient ways of water supply for irrigation. Groundwater supplies more than half of India’s demand for irrigation mostly by flooding through open channels. This, however, is an inefficient means of water supply as it leads to depletion of the water table. 39% of wells in India are already showing decrease in groundwater levels.
d. Lack of storage facilities: Even if production factors are enhanced, lack of storage facilities inhibits production. Estimates suggest that about 1.35 billion USD worth of food grains are wasted in India every year due to lack of storage facilities.
a. Lack of agriculture marketing: A number of factors lead to the unorganized nature of the agricultural industry, for example, the small scale of operations managed by small households, over-dependence on monsoon and other natural water sources, etc. This makes proper marketing of Indian agricultural products difficult.
b. Agriculture pricing: Unlike other industries, here, the farmer is more often a price taker rather than being the price maker. This is because of the ownership contracts, people involved in logistics as well as other intermediaries. Illiteracy among farmers is also a key factor that makes it difficult for farmers to get a fair value for their produce.
Figure 2: Factors curtailing India's agriculture
Typically, in the Indian context, a single rural household with all its members are dependent on farming as their single source of livelihood. In such a scenario, it becomes even more necessary to tackle the above-mentioned problems with a comprehensive strategy. While addressing most of these factors need policy interventions, but tackling some of them can be easier through the adoption of Analytics & Smart Farming.In the subsequent sections, we will explore Smart Farming, and how it can alleviate some of the problems discussed above, with the power of analytics in conjunction with IoT & Cloud.
2. Smart Farming
Smart Farming refers to the application of modern Information and Communication Technologies (ICT) in agriculture. It promises to revolutionize the world of agriculture through the application of solutions such as Internet of Things(IoT), actuators and sensors, geo-positioning systems, drones or unmanned aerial vehicles(UAVs), precision equipment, robotics, etc. backed and powered by technologies such as Big Data, Analytics, and Cloud.
Smart Farming has a real potential to deliver more efficient and sustainable agricultural production, through data-driven insights and decisions, and better resource management.
From the farmer’s point of view, Smart Farming will provide the farmer the means for better decision making and more efficient operations and management. Smart farming is associated to three fields of technology, which are inter-related:
As is evident from its very definition, SmartFarming is closely interlinked with IoT, cloud &analytics. IoT is one of the pillars of smart farming. Primarily, it’s utility lies in generating data from various sources pertaining to environmental conditions, seed quality, and quantity, etc. However, amongst all its use cases, four of them are most prominent (Shown in Figure 3):
Figure 3: IoT & Smart Farming
Figure 4: High-level architecture of a Smart Farm
Edge Analytics and Computing is a promising alternative to cloud as it tackles all of the three above-mentioned challenges by eliminating the need for all the data to be transferred to the cloud and back. This reduces cloud-computing costs, additionally, edge analytics and computing enhances network efficiency, which leads to increase in processing speed.
2.1. Role of Analytics in Smart Farming
Analytics has a wide span of use cases and application areas across the agricultural value chain. However, for the sake of simplicity, we will confine our discussion only to crop production and agricultural financing and insurance.
Analytics solutions leverage Big data, IoT, Cloud Computing, and GPS technologies to generate relevant data, which in turn is used to derive actionable insights. This helps farmers andfinancial corporations make better data-driven decisions.
To unlock the power of analytics, basic information from Management Information Systems should be requisitioned. This will ultimately help in driving precision agriculture and better decision making to realize benefits. The framework given in Figure 5 describesAnalytics-enabled Smart Farming and its benefits.
Figure 5: Role of Analytics in Smart Farming
Figure 6: Analytics skills relevant for Smart Farming
2.2 Use cases of Analytics in Smart Farming
Analytics in smart farming has a number of use cases in conjunction with IoT– some of which are:
3. Putting it all together – ‘Smart Farm Operating Model’
A highly effective Smart Farm Operating Model is illustrated in Figure 7. The day-to-day monitoring and control of crop and environmental parameters affecting the crop, plus the regular data-driven crop planning is classified into the core SmartFarm operations, while other ancillary activities associated with the farm such as marketing, inbound/outbound logistics, and crop finance and insurance are bucketed into ‘other Smart Farm operations’. Together, these two buckets constitute what we perceive as a Smart Farm ecosystem.
Typically, there would be sensors and IoT devices placed across the field to collect environmental and crop health parameters which would be relayed to the data cloud via a gateway. The data cloud, is primarily responsible for data storage, processing and analytics. Reporting dashboards allow farmers/decision-makers view the data and key insights. At an immediate level, this can help them use a User Interface or an app to trigger on-field smart devices like actuators/motors or temperature controllers. At a macro level, analytics helps them use the production data, the environmental data along with demand data to plan for the future.
Given the wealth of data that the on-field sensors provide, when demand-related data is added to it,it can help reveal more insights, which are relevant for the marketing function. This data is also relevant for inbound logistics (supply, procurement as well as storage of seeds, fertilizers, manure, etc.) as well as outbound logistics (distribution to the market). Basically, it helps enable decisions on when to store and when to sell. Last but not the least, the plethora of production, climate, crop health, and demand data and insights can help financial institutions and insurance companies gauge risks better to come up with better propositions for farmers.
Figure 7: Smart Farm Operating Model
4. Global implementations of Smart Farming solutions
There have been several initiatives across the globe in the implementation of Smart Farming Powered by Analytics. Some of the solutions and their implementation details are listed in Table 1 .
Table 1: Global Smart Farming solutions and implementation details
5. Key challenges in Smart Farming adoption
5.1 Commercial viability
Figure 8: How to address key hurdles in Smart Farming adoption
Looking at the various aspects of Smart Farming, we can infer that while Smart Farming powered by analytics can be a boon to agricultural productivity in India, there are, however, areas that need attention.
A key area to be worked upon is the strategy to ensure economic feasibility and ease of adoption. Taking cues from implementations across the world, a prudent approach would be to start small– with pilots in small farming districts. Even though every market is unique, there are learnings from every implementation that can be taken forward. Once, a robust framework is developed, the solution can then be scaled across regions.
When it comes to affordability, which prima facie, seems like a big challenge, it must be noted that the global implementations mentioned in this paper are spread across both developed and developing economies. So, with the right framework and roadmap in place, it is likely that smart Farming will enable Indian farmers to produce more and better with less, and thereby earn more and enhance their standard of living.
Global Business Manager, Analytics & AI Consulting, Wipro
Debojit is currently working in areas of strategy, analytics, and AI consulting for a leading banking firm in the Middle East. Prior to his current engagement, as part of Wipro’s Global 100Program, he has had a diverse exposure in areas like IT delivery, pre-sales, communications domain consulting, data privacy and analytics & AI consulting. Debojit holds an MBA degree from IIMBangalore, India, and a B.Tech in Electrical &Electronics from NIT Jamshedpur, India.
Consulting Partner, Analytics & AI Consulting, Wipro
Manish has more than 21 years of industry experience in Business Strategy, BusinessConsulting, Business Transformation, and DigitalTransformation. In the last decade, he has primarily consulted with clients on new digital payments product launches as well as digital transformation programs. Manish did his MBA in International Strategy and Brand Management from Goizueta Business School, Emory University, Atlanta and BE in Electronics &Telecommunication from the University of Pune, India.