[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117004-en":3,"doc-seo-117004-105":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},117004,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Leveraging Soil Mapping and Machine Learning to Improve - Spatial Adjustments in Plant Breeding Trials","Spatial adjustments improve the estimation of plot seed yield across crops and geographies by correcting for field heterogeneity. Moving mean and P-spline are common spatial adjustment methods, yet the role of specific soil factors remains insufficiently quantified. Using three years of public soybean breeding data with 43,545 plots, the study compares unadjusted, moving means, P-spline, and XGBoost-based machine learning approaches. XGBoost models soil gradients at local and all-inclusive field scales, enabling interpretable insights and supporting refined selection criteria for nutrient stress tolerance traits.","bioRxiv preprint doi: [https://doi.org/10.1101/2024.01.03.574114](https://doi.org/10.1101/2024.01.03.574114); this version posted January 4, 2024. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  \navailable under aCC-BY-NC-ND 4.0 International license.  \n1 Leveraging Soil Mapping and Machine Learning to Improve  \n2 Spatial Adjustments in Plant Breeding Trials  \n3 Matthew E. Carroll 1†, Luis G. Riera2†, Bradley A. Miller 1 , Philip M. Dixon3 , Baskar  \n4 Ganapathysubramanian2 , Soumik Sarkar2,* , and Asheesh K. Singh 1,*  \n5 1Department of Agronomy, Iowa State University, Ames, Iowa, USA.  \n6 2Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA.  \n7 3Department of Statistics, Iowa State University, Ames, Iowa, USA.  \n8 * Corresponding authors. Email: [singhak@iastate.edu & soumiks@iastate.edu](singhak@iastate.edu & soumiks@iastate.edu)  \n9 †These authors contributed equally to this work.  \n10 Abstract  \n11 Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies.  \n12 Moving mean and P-Spline are examples of spatial adjustment methods used in plant breeding trials to deal  \n13 with field heterogeneity. Within trial spatial variability primarily comes from soil feature gradients, such  \n14 as nutrients, but study of the importance of various soil factors including nutrients is lacking. We analyzed  \n15 plant breeding progeny row and preliminary yield trial data of a public soybean breeding program across  \n16 three years consisting of 43,545 plots. We compared several spatial adjustment methods: unadjusted (as  \n17 a control), moving means adjustment, P-spline adjustment, and a machine learning based method called  \n18 XGBoost. XGBoost modeled soil features at (a) local field scale for each generation and per year, and (b)  \n19 all inclusive field scale spanning all generations and years. We report the usefulness of spatial adjustments  \n20 at both progeny row and preliminary yield trial stages of field testing, and additionally provide ways to  \n21 utilize interpretability insights of soil features in spatial adjustments. These results empower breeders to  \n22 further refine selection criteria to make more accurate selections, and furthermore include soil variables to  \n23 select for macro-and micro-nutrients stress tolerance.  \n24 1 Introduction  \n25 Plant breeders make selection decisions within their programs to advance lines with the highest genetic  \n26 value for the target population of environments [1] . Breeders must address non-uniform field conditions  \n27 (i.e., field heterogeneity) in the selection decision making process, as an incorrect decision causes economic  \n28 strain on the program and limits success [2] . Spatial adjustment methods have been proposed to alleviate  \n29 the challenges of non-uniform field conditions. These methods allow breeders to make more appropriate  \n30 comparisons of entries to each other, as well as to checks in yield plot testing. Spatial adjustments set up  \nbioRxiv preprint doi: [https://doi.org/10.1101/2024.01.03.574114](https://doi.org/10.1101/2024.01.03.574114); this version posted January 4, 2024. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made  \navailable under aCC-BY-NC-ND 4.0 International license.  \n31 an effective and efficient selection process in the plot testing stages. Spatial adjustments are particularly  \n32 applicable in unreplicated trials such as early stage yield testing of hybrids in cross-pollinating crop species, 33 and for purelines in progeny row (PR) and preliminary yield trials (PYT) stages in self-pollinating crops.  \n34 Reducing the size of the error associated with each genotype (i.e., pureline) gives breeders the a","cbCaioknawCpDDgY","https://ap.wps.com/l/cbCaioknawCpDDgY","pdf",2906852,1,28,"English","en",105,"# Abstract\n## Introduction\n## Spatial adjustment methods and field heterogeneity\n## Soil feature modeling with XGBoost\n## Use of interpretability insights for selection","[{\"question\":\"Why are spatial adjustments used in plant breeding trials?\",\"answer\":\"Spatial adjustments reduce the impact of field heterogeneity on plot yields, enabling more appropriate comparisons among genotypes and checks.\"},{\"question\":\"Which spatial adjustment methods are compared in this study?\",\"answer\":\"The study compares unadjusted controls, moving means adjustment, P-spline adjustment, and an XGBoost machine learning method.\"},{\"question\":\"How does XGBoost contribute to soil-informed spatial adjustments?\",\"answer\":\"XGBoost models soil features at both local field scales and broader field scales spanning multiple generations and years, and the approach supports interpretability of soil-variable effects.\"}]","Leveraging Soil Mapping and Machine Learning to Improve - 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