Pluto, Niger, and Four Recommendations to Improve Food Security

10 May 2016
Authored by:
Dr. Sarah Tisch, Director of Food Security & M&E, Crown Agents USA
Wayan Vota, Senior Mobile Advisor, FHI360


With the data still streaming back from the New Horizons spacecraft, scientists may now have more data about dust storms on Pluto than dust storms in Niger.

This counter-intuitive observation obviously speaks to dramatic improvements in technology; less obviously, it speaks to stubborn challenges surrounding the process and politics of capturing data on a global scale and turning it into sound policy at the national and sub-national level.
   
Stubborn challenges have real consequences. Global food security is no exception. Each year, 3.1 million children die from hunger-related causes, roughly 100 million children in developing countries are underweight, and 793 million people around the world are undernourished. And, as the FAO data below indicates, progress on food security issues has been very uneven.
 

FAO Graph, Food Security

There is a human imperative for the global community to get food security right. That’s one reason our organizations, Crown Agents USA and FHI 360, are partnering with the CSIS Global Food Security Project and Kimetrica to convene some of the world’s leading experts for a practical, action-oriented discussion of how the global community can act with greater precision, speed, and effectiveness to mitigate the impact of food security crises.

In advance of that conversation, we want to share some attainable approaches that will lead to better food security policy and implementation.

First, at the strategic level, food security approaches need to include standard operating procedures for operationalizing data. Multilateral, bilateral, national, and subnational stakeholders need to have a shared, basic playbook for what to do when the data suggests that a food security crisis is emerging or deepening. The key here is shared- this action-oriented information needs to be in the hands of as many key stakeholders as possible. Second, effective resource management needs to be a strategic priority. Immediate relief is needed in a mix of cash and food commodities, but the tipping point when the immediate response solution gets in the way of recovery is not yet known.  The impact on local systems needs to be followed so a more nimble shift can be made to support the power of communities and local institutions to get things done.


“Food is a symbol of power, it is and has been used to reward and punish, and we need to plan for the possibility that our data could be politicized though good policy and communication.”

 
To design for these strategic requirements, we need to question several assumptions. 

1. The first assumption is that agroclimatic data tells us everything we need to know about local food security conditions. In fact, while the availability, precision, and timeliness of agroclimatic data about local conditions are necessary, there is now recognition that factors such as the consequences of conflict, labor markets, food supply systems, nutrition, livelihoods, and social inclusion, among many others shed light on the dynamics of food security. We need to get just as good at identifying and measuring these factors, so that households, communities, and governments at the local level are able survive and thrive during food insecure periods. 

2. The second assumption worth questioning is the hardwired response to ship tons of food. We have lots of examples of this distorting local markets and exacerbating political tensions and, while it’s declining, it’s still an instinct we have to control. 

3. The third is using urgency to crowd out alternative approaches. Smart standard operating procedures and technologies that speed granular data collection allow alternatives to be considered as efficiently as tried-and-true approaches. 

4. The fourth assumption is the belief that the right data automatically births the right decisions. Development professionals are reliably offended when their smart, data-driven recommendations are met with inaction and indifference. Food is a political weapon, it has been used to reward allies and punish detractors from time memorial, and we need to find ways to incentivize data-driven decision making over political expediency. 


“It is amazing that this level of predictive analytics has been developed in this short a time. It is also striking that that the data has this level of trust. Now that we have taken this giant step, what do we do next?"


How does the global community turn these strategic principles and questioned assumptions into effective action? While there are a number of expensive, aspirational investments that merit consideration, here we suggest four lean, practical recommendations:

1) Use technology to access data on other drivers of food insecurity. Right now, global food security data uses regularly collected and vetted agroclimatic data as its foundation—building from this base we suggest investing in data collection at the same levels of rigor on other factors that could be driving food insecurity. This will provide information needed for investments by local people, the private sector, governments, and donors to build greater resiliency to growing climate change, natural disaster shocks, and conflict.  

2) Make the improved data more visible, accessible, and operational. Sharing the data with national, state, and local actors will increase demand for smart, effective solutions. Public dashboards are an example of this recommendation in action.

3) Push the newly obtained and improved data sub nationally. Rather than posting the data and hoping it will be looked at, develop systems to actively push the data to subnational actors. Taking Ethiopia as an example, the goal would be to get the data to stakeholders at the woreda level.

4) Create improved mechanisms for dialogue and action planning. When the data is pushed, it needs to be accompanied by an opportunity to discuss implications and develop specific operational action plans by identifying key stakeholder responsibilities and tracking that accountability through to completion at sub-national and district levels.  

It is amazing that the current level of predictive analytics has been developed in this short a time. It is also striking that that the analytics has this level of trust. 

Now that we have taken this giant step, what do we do next? 

That’s the conversation we’ll start exploring on May 11th. Its outcomes have profound human implications, and we look forward to hearing your voice in the discussion.

 

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