91原创

To help smallholder farmers in sub-Saharan Africa, it is important for extension agents and government workers to know which crops are being grown in specific fields. In a country such as Nigeria, one of Africa鈥檚 largest agricultural producers and home to millions of smallholder farmers, it is critical to understand and assess what is being grown on those farms to help them reach their full potential.

John Uponi, a doctoral student at the 91原创, recently served as the lead author on a paper published in Environmental Research: Food Systems that looked at 鈥�.鈥�

This paper specifically looked at Oyo State in southwest Nigeria, but Uponi hopes to apply the techniques across Nigeria.

鈥淲e were able to generate that spatial data at a high resolution for this particular state that shows where maize and cassava are being grown,鈥� said Uponi, a doctoral student in Kyle Davis鈥� lab group in the College of Earth, Ocean and Environment. 鈥淣ow we are trying to expand this work to the entire country. This data would be useful to the Nigerian Ministry of Agriculture and others working in the agricultural sector by providing high-resolution information on where these crops are grown.鈥�

Uponi said they focused on maize and cassava because they are two staple crops grown on farms throughout Nigeria.

The goal is to have the monitoring be done over several years so that decision makers can monitor which fields are growing cassava one year and then growing maize another year or vice versa.

If they see a decline in areas that grew maize or cassava, they can identify the challenges the farms are facing and how extension workers can reach specific places to deliver interventions and changes in farming patterns.

鈥淯ltimately, when we are done with the entire work, it will be the first national product that shows high-resolution estimates of where these crops are grown,鈥� said Uponi.

In the past, researchers and decision makers just had statistics from a few data collections which didn鈥檛 give a complete overview of exactly where these crops were being grown.

Uponi and Davis used a variety of methods to figure out what the farmers were growing.

In Oyo State, they spent a week driving around and used GoPro cameras, capturing images of the various crops and farming dynamics across the different farming areas.

鈥淭his allowed us to save time because we're able to cover much ground within the fields and we can get as much data as possible,鈥� said Uponi.

The GoPro images had GPS locations, allowing the researchers to visually see the files and identify which farms were growing maize, which were growing cassava, and which were intercropping both.

Working with the International Institute of Tropical Agriculture, where Uponi worked before arriving at 91原创, they used a drone to get images of those farming hotspots, which were used in addition to the GoPro images.

鈥淚n the paper, we had some images showing exactly ground truth data of what an intercropped farm looks like and what a mono-cropped farm looks like,鈥� said Uponi. 鈥淲e were able to not just give the results, we were able to show it visually, using pictures to show what is on the ground. We were able to validate our data and our model based on the images.鈥�

Uponi is hopeful now that they鈥檝e applied this method to one area of Nigeria, they can move on to the whole country and, eventually, all of Africa.

鈥淣igeria is the largest food producer in Africa and if we're able to do this kind of work for Nigeria, then it would be easier to go into other countries,鈥� said Uponi. 鈥淢any African countries share similar smallholder farming systems, mixed-cropping practices, and data limitations. We're saying if we鈥檙e able to get this data for Nigeria, then it will be easier for us to transfer these methods to other African countries.鈥�