Sustainable Agriculture and Renewable Energy: A Data-Driven Approach
In today’s data-driven world, geospatial information systems (GIS) are becoming increasingly instrumental in solving complex problems across various sectors. One area where GIS shows enormous potential is in sustainable agriculture. In collaboration with VIA, an illustreets partner, we are exploring groundbreaking GIS-based projects that focus on the agriculture sector in developing economies.
MarketMap: A one-click feasibility study tool
One of the significant issues in expanding sustainable agriculture technologies, such as solar mills or pumps, is the lack of detailed data on ideal locations for these technologies. Standardized information like diesel mill locations is rarely mapped, especially in countries like Nigeria.
VIA, funded by AGriDi (European Union), partnered with Sosai Renewable Energies in Nigeria to develop a digital tool named “MarketMap.” The tool utilizes building location data and cluster analysis to predict suitable locations for agriculture technologies like milling, drying, irrigation pumps and refrigeration.
How it works
MarketMap begins by collecting data through an integration of illustreets with Open Data Kit (ODK). This method allows for seamless collection of real-time information, such as the location of applicants interested in loan programs for agriculture technologies. During building and settlement analysis, MarketMap had to solve the challenge of handling large datasets like MS Buildings. This was done by grouping buildings into settlement areas through clustering. This makes it easier to identify potential market centers based on building concentrations.
The tool calculates the available agricultural land by eliminating nonagricultural areas from the overall land data. This helps estimate how much of each technology — be it mills, pumps or refrigerators — is required in a given area. By using data like the applicant’s location and their loan interest, MarketMap also provides dynamic financial models. It estimates the feasibility of a loan based on the expected customer base within a certain radius (default is 2 kilometers (km)) of the applicant’s business.
The tool is incredibly flexible, allowing the user to adjust almost every aspect of the financial model in real time. This includes details like interest rates, the loan period, catchment areas and even the specific capacity of the equipment they’re interested in. MarketMap covers multiple business scenarios, such as solar mills, solar refrigeration, solar crop drying and solar irrigation. Each case has its own set of rules and logic to provide the most accurate advice. An integral part of MarketMap is its reporting feature. It provides applicants with a detailed report that includes cash flow charts and estimated income, profit and repayment calculations over the life of the system. The report even features a photo of the applicant, adding a personal touch to the data-driven process.
National capacity planning: Haiti case study
Scaling up the MarketMap concept, the next challenge was to apply this to an entire nation, thereby optimizing the capacity planning for sustainable agriculture technologies.
In collaboration with the Haiti government, VIA extended the MarketMap logic to develop a national model for Haiti. This application estimates the demand and supply for main crops like maize, rice, sorghum and sugar, thereby suggesting the ideal locations and capacity for mills across the country.
How it works
The first task was to collect the data, including the agricultural regions where each crop is grown, and the overall production figures for the country. This data is provided by Haiti’s agriculture department. The number of buildings in each region was also analyzed to help us gauge demand for the crops.
In the model, we used the collected data to proportionally allocate national production tonnage to different agricultural regions. For example, if maize was predominantly grown in Region A, that region was assigned a corresponding share of the national maize production. This created a detailed map of supply across Haiti.
Next, we looked at demand. The number of buildings in each agricultural region served as a proxy for population and, by extension, demand for crops. We then matched this against the supply data, helping us categorize each cluster area as either a net demand or a net surplus region.
Optimization of mill capacities
For this model, we ran three scenarios based on varying levels of aggregation areas: 50 hectares (ha), 500 ha and 5,000 ha. In the 50 ha scenario, the model suggested a larger number of smaller, more decentralized mills. Conversely, the 5,000 ha scenario recommended fewer, but more centralized, higher-capacity mills. Despite the differences in distribution, all scenarios provided the same total milling capacity, presenting options for either centralizing or decentralizing milling infrastructure. This offers the advantage of reducing travel time for farmers, depending on which approach is taken.
While the focus was on agriculture, the same model could be adapted for other needs, like estimating ice requirements for fishing boats based on annual fish production data. This makes our GIS-based model a versatile tool, not just for agriculture, but also for harmonizing other sectors like energy and water.
GIS is proving to be an invaluable tool in bridging the gap between technology and sustainable agriculture. Projects like MarketMap and the Haiti national capacity planning model are prime examples of how GIS can harmonize the needs of energy, agriculture and water sectors. With the aid of illustreets’ powerful analytical engine, real-time adjustments to various parameters are possible, allowing for dynamic, data-driven decision-making.
Are you interested in leveraging GIS for your agricultural projects? Explore these tools and reach out to us to learn more about how you can implement these solutions in your community. Feel free to comment below with your thoughts and experiences, and let’s pave the way for more sustainable, technology-driven agriculture.