Anticipatory Understanding of Resilient Agriculture to Climate
Clicks: 96
ID: 282050
2024
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
28.5
/100
96 views
33 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #140 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
With billions of people facing moderate or severe food insecurity, the
resilience of the global food supply will be of increasing concern due to the
effects of climate change and geopolitical events. In this paper we describe a
framework to better identify food security hotspots using a combination of
remote sensing, deep learning, crop yield modeling, and causal modeling of the
food distribution system. While we feel that the methods are adaptable to other
regions of the world, we focus our analysis on the wheat breadbasket of
northern India, which supplies a large percentage of the world's population. We
present a quantitative analysis of deep learning domain adaptation methods for
wheat farm identification based on curated remote sensing data from France. We
model climate change impacts on crop yields using the existing crop yield
modeling tool WOFOST and we identify key drivers of crop simulation error using
a longitudinal penalized functional regression. A description of a system
dynamics model of the food distribution system in India is also presented,
along with results of food insecurity identification based on seeding this
model with the predicted crop yields.
| Reference Key |
schlichting2024anticipatory
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | David Willmes; Nick Krall; James Tanis; Zachary Terner; Fernando Tavares; Chris Miller; Joe Haberlin III; Matt Crichton; Alexander Schlichting |
| Journal | arXiv |
| Year | 2024 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.