Alexandra Huddell, Brian Needelman, Eugene P. Law, Victoria J. Ackroyd, Muthukumar V. Bagavathiannan, Kevin Bradley, Adam S. Davis, Jeffery A. Evans, Wesley Jay Everman, Michael Flessner, Nicholas Jordan, Lauren M. Schwartz-Lazaro, Ramon G. Leon, John Lindquist, Jason K. Norsworthy, Lovreet S. Shergill, Mark VanGessel, Steven B. Mirsky
{"title":"利用早季生物量和天气预测黑麦覆盖作物的生物量","authors":"Alexandra Huddell, Brian Needelman, Eugene P. Law, Victoria J. Ackroyd, Muthukumar V. Bagavathiannan, Kevin Bradley, Adam S. Davis, Jeffery A. Evans, Wesley Jay Everman, Michael Flessner, Nicholas Jordan, Lauren M. Schwartz-Lazaro, Ramon G. Leon, John Lindquist, Jason K. Norsworthy, Lovreet S. Shergill, Mark VanGessel, Steven B. Mirsky","doi":"10.1002/ael2.20121","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n \n <p>Farmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early-season and late-season cover crop biomass. Employing a random forest model, we predicted late-season cereal rye biomass with a margin of error of approximately 1,000 kg ha<sup>−1</sup> based on early-season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early-season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.</p>\n </section>\n \n <section>\n \n <h3> Core Ideas</h3>\n \n <div>\n <ul>\n \n <li>Cereal rye winter cover crop biomass modeled on data from 35 site-years.</li>\n \n <li>We found a strong relationship between early and late-season biomass.</li>\n \n <li>Random forest model with early-season biomass and weather data performed well.</li>\n \n <li>Similar approach could improve decision support tools for cover crop management.</li>\n </ul>\n </div>\n </section>\n </div>","PeriodicalId":48502,"journal":{"name":"Agricultural & Environmental Letters","volume":"9 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.20121","citationCount":"0","resultStr":"{\"title\":\"Early-season biomass and weather enable robust cereal rye cover crop biomass predictions\",\"authors\":\"Alexandra Huddell, Brian Needelman, Eugene P. Law, Victoria J. Ackroyd, Muthukumar V. Bagavathiannan, Kevin Bradley, Adam S. Davis, Jeffery A. Evans, Wesley Jay Everman, Michael Flessner, Nicholas Jordan, Lauren M. Schwartz-Lazaro, Ramon G. Leon, John Lindquist, Jason K. Norsworthy, Lovreet S. Shergill, Mark VanGessel, Steven B. Mirsky\",\"doi\":\"10.1002/ael2.20121\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <section>\\n \\n \\n <p>Farmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early-season and late-season cover crop biomass. Employing a random forest model, we predicted late-season cereal rye biomass with a margin of error of approximately 1,000 kg ha<sup>−1</sup> based on early-season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early-season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Core Ideas</h3>\\n \\n <div>\\n <ul>\\n \\n <li>Cereal rye winter cover crop biomass modeled on data from 35 site-years.</li>\\n \\n <li>We found a strong relationship between early and late-season biomass.</li>\\n \\n <li>Random forest model with early-season biomass and weather data performed well.</li>\\n \\n <li>Similar approach could improve decision support tools for cover crop management.</li>\\n </ul>\\n </div>\\n </section>\\n </div>\",\"PeriodicalId\":48502,\"journal\":{\"name\":\"Agricultural & Environmental Letters\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.20121\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Agricultural & Environmental Letters\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ael2.20121\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural & Environmental Letters","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ael2.20121","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Farmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early-season and late-season cover crop biomass. Employing a random forest model, we predicted late-season cereal rye biomass with a margin of error of approximately 1,000 kg ha−1 based on early-season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early-season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.
Core Ideas
Cereal rye winter cover crop biomass modeled on data from 35 site-years.
We found a strong relationship between early and late-season biomass.
Random forest model with early-season biomass and weather data performed well.
Similar approach could improve decision support tools for cover crop management.
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