FOCUS 2) Sustainable transition towards more sustainable agri-food systems How can innovation and digital solutions accelerate such transition of the agi-food systems?
SUMMARY
Digital tools achieved tangible results in large-middle farms increasing profitability and reducing risk through better agronomic recommendations & supply chain efficiency. Small-holder farmers represent the largest world food production segment and aren’t yet fully benefiting from the potential of digital. COVID-19 showed the fragility of the current food production & supply system. It showed also the potential of digital tools to increase resiliency and adaptability. Governments, local cooperatives, large food buyers should support small-holder farmers adoption of digital tools in a structured & ROI focused way, to increase the world food production system resiliency and efficiency. Such adoption should focus on the food system short term sustainability, long term resiliency and farmers sustainability & resiliency.
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BEFORE COVID-19
Setting the tech stage: while since year 2000, the combination of digital tools and internet have significantly changed the processes of several sectors, agriculture was relatively "untouched", lagging behind digital adoption.
Recently several enabling technologies (such as: IoT (Internet of Things), cloud computing, smartphones, mobile networks reaching remote areas, easy APPs, analytics, AI, drones, robots…) have been converging filling the “digital GAP” to make adoption in agriculture profitable.
This process has already started years ago with the large & middle size farms engaging in digital tools. Most recently the sector is getting more mature slowly switching the focus from “Tech-enthusiasm” to “Tech-Return on Investments”. This attitude is essential for small holder farmers that can’t afford investment in assets with undefined return on investments.
DURING COVID-19
Negative impact: I found extremely interesting reading the several online consultations on the FSN Forum reporting consequences of COVID-19 lockdown in different countries. The ones I’m more interested are:
Past harvest losses (due to):
- Manpower unavailability to harvest/store (mainly because of workers going back to their villages)
- “last mile” supply chain malfunctioning (either because of the farmer unavailability or the missing pick-up of harvest to the fields)
Future harvest losses (due to):
- Manpower unavailability to seed fields (workers going back to their villages)
- (Unavailability of agri-inputs & tools (mainly seeds): this has been scarcely reported yet, but its impact shouldn’t be underestimated in the future)
Positive impact: on the other hands some reported success stories of local farmers being able to cope with the above difficulties, in certain cases even taking profit from it. Most of them reported digital tools as key enabling factors.
LESSON LEARNED & WAY TO GO
- The recent months showed the fragility of the current “food chain processes” from remote fields to local & international supply chain. Some process reengineering is required.
- Digital tools aren’t magic sticks but can make the difference (and did make it according to several reports). That’s the way they should be considered: enabling tools to create new more resilient processes
SOME IDEAS of DIGITAL TOOLS ADOPTION (based on personal experiences)
Enabling technologies:
IoT: weather stations & in situ sensors to provide real-time/near-real-time micro-environmental conditions;
Satellites: near-real time climate data along with historical localized climate data;
Cloud Analytics & Big Data: leveraging existing Agronomic algorithms with real-time local data, assessing risk for farmers and insurances, supporting supply chain & logistic, increasing prices efficiencies, and much more
Smartphones (APPs) or traditional GSM phones (USSD): last mile of bidirectional communication to reach farmers with recommendations/information and enabling farmers to share offers & recommendations.
APPLICATIONS
Agronomic recommendations “top-down”: better agronomic actions matching climate data (local & global) with agronomic algorithms to support farmers: crop variety choice, seeding time, harvest time, treatments against pest & diseases, irrigation patterns when water is available. Real cases (personal experiences) showed increases up to 300-400% of production with 50% water consumption reduction. Others adoption resulted in 30% treatments reduction while equally reducing the agronomic risk of pest & diseases. Coordinated actions of local governments, farmers cooperatives and large buyers can support the adoption to remote areas of such tools replicating such results.
Harvest profitability “two-ways”: crop selection & seeding/harvest-time according to analytics based on local & global market demand along with the supply chain capacity. AI and Big Data can support real time adjustments and mitigating impact of exceptional events (such as floods, droughts, pandemic, …); if the system accommodates all food-provisioning players (including small-holder farmers granting them the same level of information and benefits), it would be definitely more resilient.
Digital micro-insurance: leveraging the combination of enabling tools to create efficient, low cost, highly adaptable insurance product to protect small-holder farmers from exceptional events (including pandemic). The current “old school” approach isn’t sustainable, but the convergence of the mentioned digital tools are proving that (weather index) micro-insurance is indeed sustainable also for small holder farmers and the entire ecosystem requiring in future less public subsidies.
(FUTURISTIC) resilience systems adopting “cooperative human artificial intelligence”: several groups of farmers communicate daily through WhatsApp and similar social network to exchange information and recommendations. AI along with Big Data can potentially be trained to interpret local communications matching them with the farmers economic results, their harvests (shared in an anonymized way respecting farmer privacy), supply chain efficiency, impact of remote producing area on global commodities. Continuous learning AI model can be in future adopted to identify patterns in the combination of Big Data & the local communities farmers chats creating a sort of “cooperative human artificial intelligence” increasing resiliency and self-adapting to global and local events.
Mr. Marco Brini