The Poverty of Data in African Agriculture

Africa accounts for 60% of the world’s arable uncultivated land, but despite this incredible agricultural potential, 1 in 4 Africans go hungry every year. Although governments, non-profit organizations, and other stakeholders are committed to reducing food insecurity and developing African agriculture, their efforts have been hampered by a scarcity that mirrors the physical shortage of sustenance.  A drought of data and information is having far-reaching and complex effects in many sub-Saharan African nations as they work to end hunger and improve agriculture.


There are numerous impediments that limit agricultural production and sustainability in sub-Saharan Africa including:

  • Unproductive farming systems
  • Lack of agricultural innovation
  • Limited research capacity and infrastructur
  • Inconsistent or uninformed agricultural policies
  • Poorly managed biotic and abiotic stressors on crops.
  • Lack of accessible education on farming best practices
  • Inadequate information management tools for farmers and regulatory bodies

Identifying the numerous problems farmers face is not easy simply because quality agriculture data is so sparse.  Even large-scale intervention efforts such as the United Nations Millennium Development Goals Project have experienced setbacks because of lack of quality data. The 2010 MDG Project Report noted the challenges of measuring the progress of sub-Saharan Africa in the absence of robust survey information.

“The lack of good quality surveys carried out at regular intervals and delays in reporting survey results continue to hamper the monitoring of poverty. Gaps are particularly acute in sub-Saharan Africa, where more than half of countries lack sufficient data to make comparisons over the full range of the MDGs…”


Building statistical capacity in Africa may be a necessary step before real improvement in the agricultural sector can occur.  Of the 44 countries in sub-Saharan Africa rated by the Food and Agricultural Organization, only two are considered to have high standards in data collection, while standards in 21 countries remain low. The validity of existing statistics has been called into question which leads to ill-informed and inconsistent policy decisions that may do more harm than good.

The absence of agricultural data is a serious, but often overlooked problem; however certain strategies could greatly improve the way data are collected and analyzed.  Below are several suggested approaches that would transform the state of agriculture data in Africa:

How to improve quality data acquisition and analysis

  • Leveraging mobile technology as data gathering tools
  • Developing more accessible data collection systems
  • Creating agencies and providing training to monitor progress
  • Integrating crop data with climate data to create data visualization and predictive models
  • Improving data sharing coordination between governmental agencies and nonprofits
  • Standardizing data collection and visualization methods for a common open access platform

Of these proposed solutions, one of the most novel is the potential use of mobile phones for data collection and tracking.  Where countries may lack the human or monetary resources to carry out effective survey taking or census, the high penetrance of mobile phones makes it possible to collect data from numerous  farmers in a rapid and cost efficient manner. This solution also standardizes the format of collected data and would improve the process and accuracy of analysis and interpretation. Mobile technology may also be used to take many data points over the course of planting and harvesting seasons so that trends could be identified and treatment mitigation strategies may be formulated for crop disease outbreaks and pest management.

Tremendous effort has been put forth to grow agricultural productivity in sub-Saharan Africa, and improving the way agriculture data is collected, visualized and analyzed will only make those efforts more fruitful.

Benefits of better data

  • More informed policy-making and regulation
  • More efficient data-driven farming practices that use data to improve crop yields and decrease crop losses
  • Better understanding of what programs and investments lead to measureable improvements in agricultural productivity
  • Improved market analysis leading to greater returns for smallholder farmers
  • Increased incentives to develop new innovations

Creative strategies for ending Africa’s poverty of quality data will hasten the march toward strong agriculture development and food security.

Future Farming

Crop Heat Map

Crops of the future can be monitored with smartphones and will alert the farmer if toxins are spreading

Imagine you are a Kenyan maize farmer. Your entire lifeblood is tied to your harvest’s percent yield. Imagine your neighbor’s crop gets infected with Aspergillus flavus. Aspergillus is a mold, responsible for producing one of the most deadly, naturally occuring carcinogens known – aflatoxin. The government doesn’t allow the sale of produce with  aflatoxin levels above ten parts per billion (ppb). Recently a study found that aflatoxin contamination is more widespread than previously thought especially  in eastern and south western sites.  For example, in eastern regions 31 percent of samples collected from farmers’ fields in February 2010 had aflatoxin levels greater than ten parts per billion,  which is not only over the Kenyan government limit but also the United Nations World Food Programme.  In southwestern sites, 40 percent of samples from farmers’ fields during  the same period had aflatoxin levels above the legal limit.

However, you aren’t worried because you’ve have been tested all season long with a mobile diagnostic tool from Mobile Assay Inc.  that quantifies aflatoxin. Much like a tricorder, you can cheaply test in the field and around the perimeter. The app allows you to timestamp and log the info so you can manage the data later at your computer with the latest statistical data models.  Comparing climate data like humidity, precipitation and data gathered from your neighbor using Mobile Assay Inc. diagnostic tool, you are able to monitor a heat map similar to today’s doppler radar.

Sound like the future? Maybe not. Startup companies like Mobile Assay are getting funding from partners like the Bill & Melinda Gates Foundation. They already have this Smartphone tool  (called mReader™) and cloud aspect for their customers. Together with the foundation, they are working with places like Jomo Kenyatta University of Agriculture and Technology, or JKUAT, to help solve this complex problem.

According to the United Nations’ Food and Agriculture Organization (FAO), 25 percent of world food crops are affected by aflatoxin, and countries that are situated between 40ºN and 40ºS of the equator all around the globe are most at risk (Source: Meridian Institute).

Future Farming

Smart farming of the future will utilize diagnostic testing on smartphones and the Cloud

New technology like this could go a long way towards solving the world’s food safety problems. Because of the low-cost of  Smartphones even developing countries can afford them. According to the the International Telecommunication Union, 96% of the world population has a mobile subscription (7.1 billion). That’s up a staggering 23% since just two years ago at 5.4 billion. For the latest in mobile diagnostic testing, visit

Mobile Image Ratiometry

Mobile Image Ratiometry: A New Method for Instantaneous Analysis of Rapid Test Strips

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Authors: Donald C. Cooper 1,2, Bryan Callahan 2, Phil Callahan 2

Journal: Nature Precedings

Citation: Nature Precedings : doi:10.1038/npre.2012.6827.

Institute for Behavioral Genetics. Department of Psychology and Neuroscience, University of Colorado, Boulder. 1480 30th St. Boulder, CO 80303. Correspondence author email:


Here we describe Mobile Image Ratiometry (MIR), a new method for the automated quantification of standardized rapid immunoassay strips using consumer-based mobile smartphone and tablet cameras. To demonstrate MIR, we developed a standardized method using rapid immunotest strips directed against cocaine (COC) and its major metabolite, benzoylecgonine (BE). We performed image analysis of three brands of commercially available dye-conjugated anti-COC/BE antibody test strips in response to three different series of cocaine concentrations ranging from 0.1 to 300 ng/ml and BE concentrations ranging from 0.003 to 0.1 ng/ml. These data were then used to create standard curves to allow quantification of COC/BE in biological samples. MIR quantification of COC and BE proved to be a sensitive, economical, and faster alternative to more costly methods, such as gas chromatography-mass spectrometry, tandem mass spectrometry, or high pressure liquid chromatography. MIR is a valuable tool that provides instant data acquisition, tracking and analysis for the emerging field of mobile platform informatics (MPI) and smartphone informatics (SPI).PastedGraphic-8

Each COC and BE standard provided colored signal bands that were quantified and used to create a standard curve. For the test strips obtained from Craig Medical, an exponential function provided the best-fitting curve for both the COC and BE data. Sensitivity for COC ranged from 3 to 30 ng/ml, whereas sensitivity for BE ranged from 0.003 to 0.1 ng/ml. Thus, the Craig Medical test strips were 250 times more sensitive towards BE than COC. Cocaine sensitivity for Medimpex test strips ranged from 0.1 to 2 ng/ml, whereas sensitivity for Q Test strips ranged from 5 to 100 ng/ml. Thus, the Medimpex test strips were approximately 10 times more sensitive to cocaine compared to those from Craig Medical and the Q Test strips approximately 3 times less sensitive to cocaine compared to those from Craig Medical. MIR analysis produced fast, repeatable and highly sensitive detection of COC and BE.


In this paper, we describe MIR, which uses low cost immunoassay strips, a smart phone or tablet computer camera, and automated image analysis to detect and quantify cocaine and benzoylecgonine. MIR has many possible applications when and can be used for almost any number of immunoassay test strips. Many immunoassay test strips exist which test for anything from drugs of abuse to water contaminants and infectious agents, such as bacteria or parasites. Foremost, MIR represents a powerful tool for use in developing countries where resources and trained personnel are limited and immunoassay test strips and cell phones are relatively inexpensive and require little training. Results can be photographed by individuals, transmitted to a central server for archiving and analysis, and the results sent back within minutes. Smart phones and tablet computers can automatically tag photos with coordinates, allowing end-users to track results geographically. The development of MIR (Mobile Assay Inc., is one example that reflects the advancement in the field of Mobile Platform Informatics (MPI), which includes tablets and smart phones. New smart tools for MPI are advancing as mobile devices develop new capability to capture and quantify information previously acquired through costly specialized equipment. In the future it is anticipated that these tools will allow low-cost consumer-based devices to serve as multifunctional data testing, tracking and analyzing devices with applications in a variety of industries.


immunoassay test strips analysis

Generation of Cocaine and Benzoylecgonine Standard curves

In order to quantify COC/BE levels a series of known concentrations were made to generate a standard curve. Unknown samples may be compared to the standard curve, which allows quantification. See for detailed results.

immunoassay test strips and cell phones

Automatic web based quantification
The ability to transmit an image from a wireless or cellular device and receive results instantly, is crucial for an effective mobile diagnostic tool. To that end, we created MIR analysis, a patent pending application that automatically reports colloidal gold signal on standard immunotest strips. The MIR subtracts background noise, selects the signal bands, plots the pixel density ratio of the bands and measures the area underneath each peak. The result is immediately reported on the mobile device and if necessary they are sent to a secure cloud-based server for further analysis and storage. See for detailed analysis.

Testing background illumination

Photos of test strips may be taken under many different lighting conditions. We tested this by applying ddH20 to Craig Medical test strips and taking images using the Sprint HTC 3.2 Megapixel camera phone at 1 hour at various levels of background illumination. The luminosity (51, 75, 100, and 154 average luminosity) was determined using Adobe Photoshop CS3, and signal bands were quantified as described above. See for detailed analysis.

We would like to thank Leah Leverich, Ph.D. for her continued technical assistance.


Institute for Behavioral Genetics/Department of Psychology and Neuroscience, University of Colorado, Boulder. 1480 30th St. Boulder, Co 80303.
Mobile Assay Inc.,