How IT technology revolution would make agriculture supply chain more sustainable

Raw materials

1.  Around 4/5th of Indian farmers have holdings less than one hectare and smaller requirements,  therefore lower bargaining power against the local traders/middlemen while procuring agricultural inputs (fertilizers, seeds, finance etc.). Transporting small quantities all the way from bigger urban markets (wholesale) is not economically viable. So farmers end up paying higher prices, often receiving lower quality inputs.

BMI: Pooling

1. A ‘pooling solution/platform’ inspired by likes of Uber Pool or BlaBlaCars.com , where the platform creates an efficient market for rural (as well as urban) commercial transportation where a logistic service provider such as Truck driver would serve pooled demand of material (inputs and produce)  resulting in economies of scale.
2. Smart enforceable contracts enabled by blockchain would efficiently serve this increased need of governance and curb disputes and delays in transactions.
3. Also with cheap and advanced sensors available in market, evaluation of quality of inputs and produce would become fast, objective and transparent, minimizing moral hazard in these transactions. Using internet farmer would be able to quickly check the market price and blockchain ensures farmers gets appropriate MSP (Minimum Selling Price) specified for his quality of produce.

2.  Often farmers can’t  afford the high investment required. E.g. ROI for mechanized farming eqpmt. for small farms is not attractive. At the same time temporary (1-3 days) hiring of labour for operations like harvesting is getting costlier with rising GDP/Capita

BMI: Servicization, Pooling

1. Similar to solution ‘A’, an online marketplace can we envisaged, where equipment owners (farmers/non-farmers) would lease/share mechanized farming services to other farmers.  Regions where terrain structure or small plot sizes won’t allow mechanization, specialized services business model (e.g. Food Security Army in Kerela, India) would emerge. Food security Army pools demand by contracting with farmers of entire region and trains a very small but efficient labourforce to provide services such as harvesting.

3.  Lack of access to appropriate technology and information is one of the biggest problem for small farmers:

a.  It takes a longer time and more hurdles for small farmers to gain information on new technologies, inputs and trainings which have the potential to increase yield.   

b.  Small farmers being poor and semi-literate are very hesitant to invest in new technologies which are expensive and risky.  It is similar issue to new crop varieties that promise high yield but fail to meet expectation due to newly emergent pests

c.  Often, small farmers don’t have critical information on weather forecasts well in time, resulting in significant drop in the yield

BMI: Splitting, Information risk, Customization

1. Risk involved in the decisions (crop selection/rotation, time of sowing/harvesting etc.) at various steps during farming can be reduced by making more data available. For example –using UAVs such as Drones to monitor crop anomalies in real time and accurate plant population count, using sensors (IoT) to monitor health of soil (practiced in Japan for indoor farming), and using AI and Machine learning to better monitor and predict weather to avoid crop losses.
2. Automated Irrigation system could reduce the wastage of water as it adjusts water supply based on soil conditions.
3. Driverless tractors could be another way to reduce the amount of time and effort spent by farmers and allow more acreage to be worked for longer periods of time. Again pooling of theses resources is the only way for farmers to be able to afford these new technologies.
4. The question of “when to sow” is quite critical in farming. An application of technology is when Scientists in ICRISAT and Microsoft engineers came up with an app that sends a SMS trigger to farmers in Andhra Pradesh to sow (based on application that uses cloud based predictive analytics)

Mismatch of supply and demand

1.  There is always a plaguing mismatch between demand (timing & size) from end consumer and supply by farmers. Many highly priced produces are not available while facing supply glut in others crops

BMI: Resequencing, Information risk, Focus, Real Options

1. Aggregated data captured from various sensors via IoT would quite accurately predict the national production/supply. Post-harvesting operations (transportation, storage etc.) can be optimized better having granular supply prediction.
2. To predict/manage demand better, systems could be developed where (pre-)orders are communicated to farmers through messages/calls by end consumers, and consolidated and redistributed through distributed ledger in Blockchain. It gives real options to consumers and better estimates of demand to agri-sector. This effectively resequences production based on demand, and coupled with advanced big data analytics -would reduce demand-supply information gap helping farmers to decide which crop to focus on.

1.  It is not practical/economical to transport the small produce to the urban markets/Mandis. Farmers end-up selling the produce to local traders at substantially lower prices.

2.  Often sale is a barter settlement against the informal loans/advances that farmers take from local traders.

BMI: Pooling

1. Pooling solution described in point ‘A’ is applicable here.  For example: a.  GreenAgtech co. in India ensures middlemen (who usurp 50% of total profits) are removed through Amazon like system and b. IBM has an app that incorporates multiple languages and uses cloud based database to connect buyers and producers based on proximity in the fragmented, monopolized market environment bringing transparency to the whole process.

Value Chain Step: STORAGE

Lack of storage facility

1.  In the absence of local storage facilities, small farmers are forced to sell their produce immediately after harvest. Because this happens at a large scale, prices fall due to supply glut in the market, especially in case of perishable produce such as fruits, vegetables and dairy products.

2.  Standing crop is much less perishable than harvested one. Lack of coordination amongst farmers about harvesting schedule further amplifies the glut.

3.  Also huge volume of produce is wasted having not stored well.

4.  As small farmers are compelled to sell their produce in distress, their profits are squeezed

BMI: Information risk (reducing arrival rate variance), Pooling

1. Refer point ‘A’
2. ‘A Blockchain based shared sowing/harvest calendar’ can reduce the variance of produce arrival in markets, avoiding peaks/gluts. Such a calendar would have series of slots uniformly distributed over sowing/harvesting season where farmers will opt for available slot and plan accordingly. It improves the utilization rate and establishes a rigid governance system. For example such a calendar is already being followed in certain agri-industries. E.g. Sugar mills in Maharashtra in India work closely with farmers to govern ‘who harvests when’ ensuring steady supply of sugarcane to their mills.