Feature Photo by Marie-Soleil Turmel
When we started Our Sci in 2017, one of the first tools we built was a low-cost, handheld Reflectometer. The Reflectometer was used by the Quick Carbon project at Yale University to map soil organic carbon (SOC) at the landscape scale, by the Bionutrient Institute to predict nutrients in food crops (e.g. antioxidant content in lettuce) and by international research groups to assess soil carbon content on smallholder farms in Malawi.
Over the past several years, manufacturing of the Reflectometer has been stalled due to chip and other supply chain shortages. We used that time to address limitations observed in early versions of the Reflectometer. We added normalization factors to each device during the manufacturing process to reduce device-to-device variability and developed an in-field calibration to improve device consistency over time. This year we have been able to manufacture more than 80 new Reflectometers, with more to follow in the coming months.
Now that we have new Reflectometers available, we’re working with Catholic Relief Services (CRS) and the International Maize and Wheat Improvement Center (CIMMYT) to deploy and test the Reflectometer as a decision support tool in numerous countries in southern Africa and Central America. The Reflectometer is appealing to these organizations because its low cost ($500) enables scalability and it can return results in real time, even without internet connection. Real time, offline functionality is critical for stakeholders who may be in difficult-to-reach areas without any internet connection.
Specifically, our partners are interested in how well the Reflectometer can provide two outcomes:
Like any spectroscopy tool, the Reflectometer correlates measured soil carbon values to spectral patterns (in this case reflection) from the Reflectometer in a training dataset. The first step is to develop a calibration dataset in the region of interest. In both peer-reviewed papers linked above, Reflectometer-based models performed better when paired with other soil and field metadata. We recommend the following data be included in a calibration dataset if possible:
Once the training set is complete, we develop two distinct types of model outputs:
Recently, I had the pleasure of traveling to Guatemala City to meet with and train CRS staff and partners (pictured above and below). Prior to my trip, Our Sci and CRS had collaborated to develop a training dataset and build soil carbon models. The training focused on the Reflectometer, SurveyStack, and the in-field process:
The in-person training was a great opportunity to meet with the local experts who will be using the Reflectometer in the field. Together, we will customize the messaging output from the classification models and they will be ready to deploy the Reflectometer to offer decision support to smallholders farmers in Guatemala.
There are numerous areas where we hope to continue to improve the utility of the Reflectometer:
If you are interested in learning more about the Reflectometer or about these initiatives, please check out our Reflectometer tutorials or email me at firstname.lastname@example.org.
December 12, 2023Read More
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