The Future of Food Security is in the Trash—And...
Monica Munn

Monica Munn Senior Program Associate, The Rockefeller Foundation

Abe Tarapani

Abe Tarapani Director of Business Development, Premise Data

Florent Silve

Florent Silve Sr. Economist & Data Scientist, Premise Data

July 14, 2016

The Future of Food Security is in the Trash—And in Technology

Monica Munn

Monica Munn Senior Program Associate, The Rockefeller Foundation

Abe Tarapani

Abe Tarapani Director of Business Development, Premise Data

Florent Silve

Florent Silve Sr. Economist & Data Scientist, Premise Data

July 14, 2016

u - bahn / metro subway

Food waste and spoilage is one of the biggest—and most preventable—issues plaguing food security today. The stats are staggering: in developing countries, 40 percent of losses occur before the food even hits the market, amounting to $310 billion in lost revenue. In Kenya, Nigeria, and Tanzania alone: almost half of all fruits and vegetables grown are not consumed.

“Recovering food lost during and immediately following a harvest can increase the income of smallholder farmers by as much as 15 percent.”

Reducing food loss has benefits beyond solely increasing the access of underserved populations to a greater source of sustenance. For example, recovering food lost during and immediately following a harvest can increase the income of smallholder farmers by as much as 15 percent. Increased income, in turn, translates into greater domestic consumption and its associated multiplier effect on economic growth.

The heartening news is that food loss and waste are preventable in part thanks to a rise in recent years of technological-driven tools that identify and combat drivers of waste and spoilage throughout crop value chains. A core challenge—and opportunity—is figuring out which tools yield the best results and where.

Going mobile for mangoes in Kenya

Android App - Premise

In Kenya, through our YieldWise initiative, we have been working in conjunction with TechnoServe to reduce the post-harvest loss of mangoes, both through the introduction of specific tools and technologies, and by strengthening and increasing the connection to additional markets for smallholder farmers.

One of the challenges we face, however, is understanding on a granular level how pricing dynamics in the mango value chain are changing in relation to (and potentially as a result of) TechnoServe’s implementation activities, resulting reduced spoilage and thus increased market supply.

Given this context—and our desire to recognize any such price changes as quickly as possible —we turned to Premise, a technology company based in San Francisco with a large operational footprint in Africa. Over the past five months, we’ve piloted Premise’s mobile data capture and monitoring platform as a solution for conducting high-frequency monitoring of localized pricing dynamics in the mango value chain in Kenya.

Our Approach

Premise and TechnoServe collaborated to define a survey to track mango prices that spanned nine cities and included four distinct varieties of mango, and 15 additional fruit, vegetable and food staples.

Premise Data - Observations

From December 2015 through April 2016, more than 350 Premise contributors generated in excess of 18,000 unique timestamped & geotagged observations. Trend and geospatial analyses were provided via a set of online dashboards developed by Premise.

Premise and TechnoServe also identified 20 wholesale traders who agreed to self-report information about their day-to-day operations including the quantity, price and volume of mango sales, rejection rates, and unsold volumes. Traders were also asked about their sourcing constraints, primary reasons for product spoilage, and technologies used to reduce waste.

Key insights and optimism for the future

The advantage of leveraging this kind of technology is the speed at which our implementing partners can monitor externalities such as market price changes over time in relation to their ongoing activities.

The most notable findings:

  • Premise determined a higher wholesale mango price statistic than officially available through the Kenyan State Department of Agriculture, indicating an opportunity to further investigate wholesale price economics. Transparency of wholesale market pricing is critical to both smallholder farmers and retailers as they seek to negotiate a fair rate for selling and/or buying mangoes and key inputs.
  • The mean retail price of fresh whole mango during Q1 2016 was largely stable at ~20 KES and prices for the broader basket of fruit and vegetable staples did not experience any significant movement. While El Niño effects resulted in overall reduced production in the region, the monitoring effort did not identify any specific effect on retail prices. The link between production volumes and retail pricing can often signal early symptoms of market failures or inefficiencies.
  • Up to 5.5 tons of mangos—or 15 percent of average sales volume—were rejected because of low quality, and another 9 tons of mangos, or 4 percent, were thrown out because they went unsold. Such data can provide implementers such as TechnoServe with insight into where in the value chain they might consider investigating to further identify the local drivers of food waste.
  • In mid-March, 87 percent of traders reported facing fruit fly issues, which are a frequent source of produce spoilage, highlighting opportunities to extend a successful farm-level intervention to traders such as introducing flytraps.
  • Only two of the twenty traders surveyed reported using cold storage or any kind of technology to reduce spoilage despite short shelf-lives for mangoes, which hints at some of the opportunity for introducing new technologies and educating local actors in the supply chain on how to use them.

Aggregate Ratios of Mango Discarded and Rejected (All Traders)

Our main takeaway from this pilot is that higher frequency information about market conditions can serve as an important tool to help implementing partners assess outcomes of their work. And, better visibility into the value chain means quicker feedback loops in service of smarter experimentation and ultimately, better outcomes.

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