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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…”

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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 MobileAssay.com.

Mobile Image Ratiometry for the Detection of Botrytis cinerea (Gray Mold)

nature precedingsnatureprecedings

Authors: Donald C. Cooper 1,2

Attribution: 3.0 2012 Nature Precedings

ABSTRACT

Mobile Platform Informatics (MPI) and Smartphone Informatics (SPI) methods like Mobile Image Ratiometry (MIR) are potentially transformative point-of-use instantaneous analysis tools that are useful across a variety of industries. In agriculture, MIR-compatible immuno test strips allow early detection of a number of biotic stressors before devastating crop losses occur. Here we describe a low-cost and easy-to-use Smartphone and/or tablet-based protocol (Mobile Assay Inc., www.mobileassay.com) for the detection and on-sight instantaneous analysis of B. cinerea, a fungus that causes significant damage to a variety of plants and flowers. Early detection and tracking of the B. cinerea fungus before the visible gray mold appears has the potential to increase agricultural productivity especially in the developing world.

INTRODUCTION

Botrytis cinerea (gray mold) is a necrotrophic fungus that commonly appears as blossom blights and fruit rots. It is a very common organism that readily grows on dead, declining plant tissue and organic matter. Diagnosing gray mold requires careful examination and testing. Pathogens like B. cinerea have caused costly storage yield losses of 20 to 30% in Colorado onion crops, 50% in Idaho and 60% in Europe. To make testing for pathogens like B. cinerea more convenient, researchers at the University of Colorado, Boulder have developed Mobile Image Ratiometry (MIR; Mobile Assay Inc. MobileAssay.com Boulder, CO), which allows users with Smartphone or tablet-based cameras and calibrated test strips to make rapid and accurate disease diagnoses on the spot (Fig 1). Having a quick test and result makes plant management decisions more efficient and effective. Proper identification of the disease needs to be made in a timely fashion, before the infection gets out of control leading to catastrophic losses. MIR technology allows rapid quantification and cloud-based analysis of immuno-based test strips like those available for the diagnosis of B. cinerea. This newly developed rapid testing technology provides a result in less than 10 minutes and is designed to eliminate disease misdiagnoses and establish whether or not the symptoms are physiological or caused by a chemical or pathogen. This on the spot quantification also permits location geo-tagging to facilitate precise field location damage identification. Thus an entire crop can be tested and the B. cinerea outbreaks plotted on the crop map.

Symptoms of B. cinerea

Plants can be attacked at any stage of growth. B. cinerea usually appears first as lesions on leaves and stems that rapidly lead to the development of gray, furry spores. In onions, B. cinereaproduces symptoms that often appear after the bulbs are stored. The fungus produces tan to brown stain of outer bulb scales. The pathogen either directly penetrates the scale to initiate the lesion or grows down into the scale from the leaf sheath or the leaf itself. Other species of Botrytis such as B. allii produce gray mold may form in the neck area causing it to become sunken as well as drying out the entire bulb. The fungus may partially rot the bulb before it is observed externally. Infected scales become soft and brown in color leaving the plant susceptible to secondary infection by other pathogens. The MIR immunostrip only detects B. cinerea, but the technology could be expanded in the future to include other pathogens such as B. allii. In addition, infection in flower petals produces a rapidly spreading infection that causes fruit tissue to disintegrate into a liquid mass.

In grapes, small circular water-soaked spots are the first to appear. These spots may be faintly clear and relatively indistinct, however, when the grapes are rubbed, the skin over these spots cracks and reveals the firm inner pulp. Gradually affected fruit softens and turns brown. In dense bunches, B. cinerea may spread rapidly until entire bunches are rotted. MIR-based quantification and analysis can be applied to a variety of plants susceptible to Botrytis including grapes, potatoes, onions, berries, lettuce, flowers (Fig 2) wheat, yams, and chick peas. The main factors that promote B. cinerea infection and the MIR-based protocol for its detection are listed below.

Five Conditions that Favor B. cinerea Infectionbotrytis1blog-botrytis1-300x224

  1. High relative humidity (>90%) and cool temperatures 50-75oF near harvest
  2. Poor plant management (e.g. dead leaves) and sanitation (e.g. debris)
  3. High levels of nitrogen from fertilization
  4. Inadequate air circulation among the plants
  5. Light rain and condensation that lasts for several days

Testing for B. cinerea

Serious B. cinerea fungal infection can usually be visually observed in the field or vineyard. However, low-level infections and infections that arise during plant or fruit storage can be difficult to identify visually and can only be detected using immuno-based tests like an enzyme-linked immunosorbant assay (ELISA) or a lateral flow rapid test strip. Isolating infected plants is the standard method for eliminating B. cinerea infection, but it is often difficult to identify which plants carry the spores. Undetected B. cinerea can create a serious infectious outbreak that can be catastrophic. An ideal diagnostic test would be inexpensive, mobile, and simple to use while allowing growers to randomly sample plants in storage, in transit, or in the field in order to detect and begin treating the disease prior to visual symptoms. Such a test could be used by growers, packagers, and buyers at the plant receiving stage to provide an objective measure of B. cinerea infection and to gauge the effectiveness of any eradication program.

Procedure for MIR-Based B. cinerea Testing in the Field

To use MIR, only minimal training with a Smartphone or tablet with a camera and a test strip are required. The Smartphone app does not require the purchase of specialized and expensive laboratory instrument. Costly and time-consuming laboratory testing is not required. Tests may be performed at any chosen location in the field. To make a determination whether B. cinerea is present, a sample is taken from plant tissue showing signs of infection. If possible, a control sample should be taken from an uninfected area of the same plant. Samples are placed in a pouch or vial and macerated with liquid buffer. The liquid samples from the control and test regions are then tested with MIR and a test strip. A photo is taken of the test strip for automated analysis using the MIR application. A properly executed test strip procedure indicates one band (Control) and a second band (Test) if the sample tissue is positive for B. cinerea. The intensity of the second band is proportional to the degree of infection in the sample and can be quantified by MIR analysis. In addition to the results being available on the spot at test time, the results for each test are wirelessly transmitted securely to a cloud server for storage, detailed reporting, and analysis.

Mobile platform informatics (MPI) and Smartphone informatics (SPI) tools like MIR are designed to allow early detection of B. cinerea before infestation and devastating crop losses occur. As new MIR-compatible rapid tests are developed it will be possible to perform instantaneous analysis for other biotic stressors. Biotic stressors such as viruses, fungi, bacteria and other pathogens are a significant problem for agricultural productivity. Farmers with small plots of land in developing countries have sparse resources to prevent these stressors and can, therefore, experience devastating crop losses before and after harvests. MIR-compatible tests have the capacity for early detection and mitigation of these losses, which has the potential to be transformative for farmers in the developing world.

PROGRESS AND COLLABORATIONS

To see up to date progress on this project or if you are interested in contributing to this project visit: Neuro-Cloud.net Donald C. Cooper is Co-Founder and Chairman of Mobile Assay, Inc. The Author would like to acknowledge Howard Schwartz for reviewing and editing the manuscript – Dept. of Bioagr. Sciences & Pest Mgmt., Colorado State University, Fort Collins, Colorado 80523 USA

REFERENCES

Center for Neuroscience, Department of Neuroscience, University of Colorado at Boulder, Boulder Colorado 80303, USA
Correspondence should be sent to D.Cooper@Colorado.edu