DataGood creates predictive model

DataGood delivers predictive model based on our farming production and weather history.

Aura Sevilla with her tomato crop
Aura Sevilla with her tomato crop

León, Nicaragua

During the first half of this year, ViviendasLeon worked with DataGood, a student organization working at the intersection of data science and social good, to develop a tool to improve our agricultural program outcomes. The team of students analyzed almost 4 years of farming data to help build this platform.

The first deliverable was an analysis of the 2017-2020 farming data. DataGood found the following:

  1. Crop Performance: 
    1. Disease over time has decreased since data started being collected. 
    2. Average diseased rates between the different crops vary in the single digits ranging from 4.6 - 0.3 infection rate.
    3. Pest management interventions like Ace and Ajo y Cebo have high percentages of results when treating pests. 
  2. Geospatial data:
    1. Average disease maps from 2017-2020 helped identify problem areas in the communities that may need extra support in regards to pest and disease management. 
  3. Weather Insights:
    1. High rates of illness occurred in fruits during times with higher temperatures and wind indexes. 
    2. Drops in dew point lessened moisture in the air and coincided with more illness.
    3. Vegetables seem to be the most resilient food product since it was the least likely to have increased incidents of illness tied to weather factors.

The second deliverable built on the information from the initial data. The DataGood team created a predictive model for future harvests. The field team would not ground conditions such as location, weather, disease, and other information to provide us with the appropriate crops to cultivate informed by these conditions. 

ViviendasLeón is excited to use this new tool to help us use current conditions to plan our harvests to maximize our resources. We would like to thank the DataGood team for their hard work and support of rural communities in Central America.

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