{"id":3661,"date":"2024-03-25T19:07:00","date_gmt":"2024-03-25T19:07:00","guid":{"rendered":"https:\/\/new.www.purdue.edu\/newsroom\/?p=3661"},"modified":"2024-08-05T12:25:53","modified_gmt":"2024-08-05T16:25:53","slug":"machine-learning-model-demonstrates-effect-of-public-breeding-on-rice-yields-in-climate-change","status":"publish","type":"post","link":"https:\/\/www.purdue.edu\/newsroom\/2024\/Q1\/machine-learning-model-demonstrates-effect-of-public-breeding-on-rice-yields-in-climate-change","title":{"rendered":"Machine-learning model demonstrates effect of public breeding on rice yields in climate change"},"content":{"rendered":"

WEST LAFAYETTE, Ind. —<\/p> \n

Climate change, extreme weather events, unprecedented records in temperatures and higher, acidic oceans make it difficult to predict the long-term fate of modern crop varieties. <\/p>\n<\/div>\n\n\n

In a paper published<\/a> in the March 18, 2024, issue of the Proceedings of the National Academy of Sciences, Diane Wang<\/a>, an assistant professor in Purdue\u2019s Department of Agronomy<\/a>, and her post-doctoral researcher Sajad Jamshidi<\/a>, reported on a predictive model they\u2019ve developed that uses machine-learning algorithms to predict how rice yields will be affected by climate change. Their work was completed in collaboration with researchers at Cornell University and the Dale Bumpers National Rice Research Center<\/a>.<\/p>\n\n\n\n

\u201cWith these kinds of large-scale statistical models, you’re basically taking a set of predictors \u2014 like weather or genetics \u2014 and mapping them to solve for an outcome. Here, we are interested in predicting yield,\u201d Wang said.<\/p>\n\n\n\n

The U.S. is in the top five exporters of rice, making rice production across several southern states important to diets around the world. Wang and Jamshidi\u2019s work lays a foundation for artificial intelligence predictions in rice and other crops, potentially helping agriculture hone breeding practices where crop varieties are most vulnerable to climate change.<\/p>\n\n\n\n

Through this model, the team found that modern varieties of rice are likely to do \u201cless badly\u201d than older varieties in a future impacted by climate change. Public breeding programs, like those based at universities, are largely behind the success of present-day rice. Their development of new varieties has broadened the gene pool for U.S. rice while also incorporating specific, targeted traits. Wang said this study underscores the importance of the historic and ongoing contributions of these public breeding programs.<\/p>\n\n\n\n

\u201cThe ensemble model predicts that modern groups of rice varieties will do less badly than groups of older varieties, but I would be careful to say we\u2019ve finished our job,\u201d Wang said. \u201cThere is a lot of uncertainty with respect to future climates, and these kinds of models are just one tool to explore scenarios.\u201d<\/p>\n\n\n\n

Rice has a small genome compared with other crops. That and the availability of historical data and old-variety seeds made it the ideal study system to design a predictive model. The team obtained historical temperatures and weather data as well as what Wang called the \u201cserendipitous discovery of variety acreage reports.\u201d<\/p>\n\n\n\n

The southern U.S. rice-growing states of the Mississippi Delta region have recorded what variety of rice was grown in what proportion at the county level since the 1970s. Many of these acreage reports were sent to the team as typewritten documents. The group then was able to obtain, from collaborators at the Dale Bumpers National Rice Research Center, seeds from old rice varieties that are no longer commonly grown.<\/p>\n\n\n\n

These rice varieties were analyzed at the genetic level, and Wang and collaborators grouped varieties based on alleles, or gene variations, that they shared. They translated this information from the variety acreage reports into county-level \u201cbags of alleles\u201d and then trained machine-learning models using the allele groups and county-level yields with historical environmental data, like temperature and precipitation.<\/p>\n\n\n\n

Jamshidi\u2019s efforts in building this model are especially novel because the final model combines 10 methods of machine learning to create an ensemble model that can process information with a more multifaceted approach. The ensemble model\u2019s output offers more accurate results under the same predictors. <\/p>\n\n\n\n

Not only will this study provide a framework to build models for other crops with similar predictors, but Wang sees another possible direction for this research. Carrying out physical experiments by growing both old and modern rice varieties under predicted conditions could serve as an additional evaluation of the model, as well as give hints to the genetic and physiological makeup causing the difference in resilience between the variety groups.<\/p>\n\n\n\n

Wang said, \u201cThese kinds of predictions are really the first step. The model has given us some potential outcomes, but now someone has to run the follow-up experiments to get at underlying mechanisms.\u201d <\/p>\n\n\n\n

Wang and her lab continue to study the interactions between crops\u2019 genetics and their environment, and they are using modeling and other technologies to create a more predictable future for agriculture. <\/p>\n\n\n\n

About Purdue University<\/h2>\n\n\n\n

Purdue University is a public research institution demonstrating excellence at scale. Ranked among top 10 public universities and with two colleges in the top four in the United States, Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and locations, including nearly 50,000 in person on the West Lafayette campus. Committed to affordability and accessibility, Purdue\u2019s main campus has frozen tuition 13 years in a row. See how Purdue never stops in the persistent pursuit of the next giant leap \u2014 including its first comprehensive urban campus in Indianapolis, the new Mitchell E. Daniels, Jr. School of Business, and Purdue Computes \u2014 at https:\/\/www.purdue.edu\/president\/strategic-initiatives<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

WEST LAFAYETTE, Ind. — Climate change, extreme weather events, unprecedented records in temperatures and higher, acidic oceans make it difficult to predict the long-term fate of modern crop varieties.  In a paper published in the March 18, 2024, issue of the Proceedings<\/p>\n","protected":false},"author":7,"featured_media":3663,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[263,7],"tags":[],"department":[6],"source":[29],"purdue_today_topic":[],"coauthors":[171],"class_list":["post-3661","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-purdue-computes","category-research-excellence","department-agriculture","source-purdue-news"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/posts\/3661","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/comments?post=3661"}],"version-history":[{"count":3,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/posts\/3661\/revisions"}],"predecessor-version":[{"id":3667,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/posts\/3661\/revisions\/3667"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/media\/3663"}],"wp:attachment":[{"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/media?parent=3661"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/categories?post=3661"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/tags?post=3661"},{"taxonomy":"department","embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/department?post=3661"},{"taxonomy":"source","embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/source?post=3661"},{"taxonomy":"purdue_today_topic","embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/purdue_today_topic?post=3661"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.purdue.edu\/newsroom\/wp-json\/wp\/v2\/coauthors?post=3661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}