fairfolio
Saved Projects

El Betweeno: Morisita-Horn Index Correlates Rainfall Variability with Crop Yield

CSEF · 2016 Environmental Science Third Award

Madeline J. Song · Torrey Pines High School, San Diego, San Diego County

Overview

Objectives/Goals The 2015-2016 El Niño is predicted to be one of the strongest of its kind since it its devastating 1997-1998 occurrence. This project uses Morisita-Horn Index (Morisita's) as a novel approach to conducting spatial analysis of rainfall trends. This project has three goals: 1) create a computer program that conducts spatial analysis of color-coded rainfall maps, 2) apply Morisita-Horn index to processed maps in order to analyze year-to-year rainfall variability, and 3) use crop yield data along with Morisita's-derived rainfall variability to predict crop yield following the 2015-2016 El Niño. Methods/Materials Rainfall maps (United States, 2009, 2010, 2014, 2015) were downloaded from the National Weather Service's precipitation database and yearly crop yields were obtained from the United States Department of Agriculture Crop Summaries (2011, 2015). The Multi-Step Color Detection Computer Program was written for this project in order to process existing data (rainfall maps) into a user-friendly tool capable of utilizing Morisita's in spatial analysis. This multi-step computer program includes gridding, color detection/classification, and calibration. Results 4 gridded maps were generated and used for image analysis and color classification. 16 color algorithms were generated and tested for accuracy in image analysis of rainfall maps. 19 prediction ratios were generated to predict crop yields following the 2015-2016 El Niño storm. Conclusions/Discussion This project provides farmers and consumers with a user-friendly tool which they can use to plan and undertake timely interventions before potentially devastating changes in climate and food security. For many individuals across the world, climate volatility is a direct cause of food insecurity. This project unites mathematics, computer science, agricultural science, and climatology in hopes that collaboration between different fields can accelerate the global fight against world hunger.

Summary statement

Morisita-Horn Index was applied to rainfall maps with use of computer image analysis software in order to analyze the effects of rainfall variability on crop yields in the years following the 2015-2016 El Niño.

Awards (1)

Competition history

  • CSEF 2016 Environmental Science · Entry S1110

Resources

Related projects

Closest projects by meaning, across every fair and year in the corpus.

Browse more like this

Source: California Science & Engineering Fair public projects

Save projects to your library

Sign in with Google to keep track of projects you find interesting, organized into folders. An account also raises your daily allowance for “Has this been done?”, and lets you create a key for the MCP server with a much higher limit than anonymous use. Browsing stays public.

Continue with Google