[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117334-en":3,"doc-seo-117334-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117334,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Leveraging Soil Mapping and Machine Learning to Improve Spatial Adjustments in Plant Breeding Trials","Spatial adjustments refine plot seed-yield estimates across crops and geographies, helping address field heterogeneity in plant breeding trials. Soil gradients, including nutrient variation, drive much of this variability, yet the contribution of different soil factors has lacked systematic study. Using 43,545 plots from a public soybean program over three years, the work compares moving means, P-spline, and XGBoost, showing soil-feature-based adjustments improve efficiency by 81%, reduce selection similarity by 30%, and lower Moran’s I from 0.13 to 0.01 on average across experiments.","Received: 19 February 2024  \nAccepted: 19 June 2024  \nDOI: 10.1002/csc2.21336  \nORIGINAL ARTICLE  \nCrop Breeding & Genetics  \nLeveraging soil mapping and machine learning to improve spatial adjustments in plant breeding trials  \nMatthew E. Carroll1   Luis G. Riera2  Bradley A. Miller1   Philip M. Dixon3 Baskar Ganapathysubramanian2   Soumik Sarkar2  Asheesh K. Singh1   \n1Department of Agronomy, Iowa State University, Ames, Iowa, USA  \n2Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA  \n3Department of Statistics, Iowa State University, Ames, Iowa, USA  \nCorrespondence  \nSoumik Sarkar, Department of Mechanical Engineering, Iowa State Univdersity, Ames, IA, USA.  \nEmail: [soumiks@iastate.edu](soumiks@iastate.edu)  \nAsheesh K. Singh, Department of Agronomy, Iowa State University, Ames, Iowa, USA. Email: [singhak@iastate.edu](singhak@iastate.edu)  \nAssigned to Associate Editor Francisco Ernesto Gomez.  \nFunding information  \nNorth Central Soybean Research Program; Iowa Soybean Association; USDA CRIS project IOW04714; AI Institute for Resilient Agriculture, Grant/Award Number:  \nUSDA-NIFA \\#2021-67021-35329; COALESCE: COntext Aware LEarning for Sustainable CybEr-Agricultural Systems, Grant/Award Number: CPS Frontier \\# 1954556; Smart Integrated Farm Network for Rural Agricultural Communities (SIRAC), Grant/Award Number: NSFS&CC \\#1952045; RF Baker Center for  \nAbstract  \nSpatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving means (MM) and P-Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Within the trial, spatial variability primarily comes from soil feature gradients, such as nutrients, but a study of the importance of various soil factors including nutrients is lacking. We analyzed plant breeding progeny row (PR) and preliminary yield trial (PYT) data of a public soybean breeding program across 3 years consisting of 43,545 plots. We compared several spatial adjustment methods: unadjusted (as a control), MM adjustment, P-spline adjustment, and a machine learning-based method called XGBoost. XGBoost modeled soil features at: (a) the local field scale for each generation and per year, and (b) all inclusive field scale spanning all generations and years. We report the usefulness of spatial adjustments at both PR and PYT stages of field testing and additionally provide ways to utilize interpretability insights of soil features in spatial adjustments. Our work shows that using soil features for spatial adjustments increased the relative efficiency by 81%, reduced the similarity of selection by 30%, and reduced the Moran’s I from 0.13 to 0.01 on average across all experiments. These results empower breeders to further refine selection criteria to make more accurate selections and select for macro-and micro-nutrients stress tolerance.  \nPlain Language Summary  \nPlant breeding trials are a key component of crop improvement for yield, quality, and stress resistance. Breeding trials typically are grown on small plots of land and are highly affected by the area in the field where they are planted due to field trends. We  \nAbbreviations: ML, machine learning; MM, moving means; PYT, preliminary yield trial; PR, progeny row; XGBoost, extreme gradient boosting. Matthew E. Carroll and Luis G. Riera, as co-first authors, made equal contributions to the paper.  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \n© 2024 The Author(s) . Crop Science published by Wiley Periodicals LLC on behalf of Crop Science Society of America.  \nCARROLL ET AL.  \nPlant Breeding; Plant Sciences Institute, Iowa State University; NRT-DESE: P3-Predictive Phenomics of Plants, Grant/Award Number: ","cbCaik1HT7CZEpjo","https://ap.wps.com/l/cbCaik1HT7CZEpjo","pdf",1680795,1,18,"English","en",105,"# Abstract\n## Spatial adjustment methods\n## Soil feature modeling with XGBoost\n## Impact on selection and spatial dependence\n# 1 Introduction\n## Field heterogeneity in breeding decisions\n## Role of spatial adjustment in early-stage trials","[{\"question\":\"Why are spatial adjustments important in plant breeding trials?\",\"answer\":\"Spatial adjustments improve comparisons of entries and checks under non-uniform field conditions, reducing bias in selection decisions driven by field heterogeneity rather than genetic value.\"},{\"question\":\"Which spatial adjustment methods are compared in this study?\",\"answer\":\"The study compares an unadjusted control, moving means (MM), P-spline adjustment, and a machine learning method using XGBoost to model soil effects.\"},{\"question\":\"How does XGBoost use soil information in the analysis?\",\"answer\":\"XGBoost models soil features at the local field scale for each generation and year, and also at an inclusive field scale spanning all generations and years, to estimate how soil explains yield variability.\"}]","Leveraging Soil Mapping and Machine Learning to Improve Spatial Adjustments in Plant Breeding Trials | 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are spatial adjustments important in plant breeding trials?","Question",{"text":75,"@type":76},"Spatial adjustments improve comparisons of entries and checks under non-uniform field conditions, reducing bias in selection decisions driven by field heterogeneity rather than genetic value.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which spatial adjustment methods are compared in this study?",{"text":80,"@type":76},"The study compares an unadjusted control, moving means (MM), P-spline adjustment, and a machine learning method using XGBoost to model soil effects.",{"name":82,"@type":73,"acceptedAnswer":83},"How does XGBoost use soil information in the analysis?",{"text":84,"@type":76},"XGBoost models soil features at the local field scale for each generation and year, and also at an inclusive field scale spanning all generations and years, to estimate how soil explains yield 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