[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119869-en":3,"doc-seo-119869-105":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":20,"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},119869,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Disentangling Jenny’s equation by machine learning","The work revisits the soil-landscape paradigm and Jenny’s equation, a conceptual framework linking soil types with forming factors from climate, organisms, relief, parent material, and time. Because the equation involves qualitative, categorical variables and an under-specified function, it cannot be solved directly with standard mathematical tools. The study demonstrates how machine learning can predict soil types from measurable environmental factors and shows improved performance over conventional statistical analyses using the same inputs.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nDisentangling Jenny’s equation by machine learning  \nF. Prieto‑Castrillo1,7, M. Rodríguez‑Rastrero2,7, F. Yunta 3,7, F. Borondo4,7 & J. Borondo 5,6,7*  \nThe so‑called soil‑landscape model is the central paradigm which relates soil types to their forming factors through the visionary Jenny’s equation. This is a formal mathematical expression that would permit to infer which soil should be found in a specific geographical location if the involved relationship was sufficiently known. Unfortunately, Jenny’s is only a conceptual expression, where the intervening variables are of qualitative nature, not being then possible to work it out with standard mathematical tools. In this work, we take a first step to unlock this expression, showing how Machine Learning can be used to predictably relate soil types and environmental factors. Our method outperforms other conventional statistical analyses that can be carried out on the same forming factors defined by measurable environmental variables.  \nIn 1960 the Nobel Prize in Physics Eugene Wigner published a fascinating paper1 on The unreasonable effectiveness of mathematics in the natural sciences. Despite the generality of the title, the text was mainly restricted to physics, but the belief that mathematical equations are the best way to go in translating relationships and interactions has always been at the deepest root of science.  \nOther disciplines, like biology or geology, have traditionally kept outside this stream, by accepting paradigms consisting of a seemingly endless resort to a multiplicative use of taxonomy, trying to cope with the tremendous diversity in the subject. This situation suffered a dramatic change in biology at the turn of the century, when the appearance of complex networks theory2,3 brought long-awaited tools to help tackling some of their problems4,5; this being also true in sociology6,7. The new approach shifted the focus from diversity to the web of interactions among species or individuals. This approach became even more successful with the advent of Machine Learning (ML) .  \nSimilarly, in Soil Science, understanding soil state-and-change in response to different natural or humans factors still remains a great, yet important, challenge8. The so-called Soil-Landscape Model, graphically described in Fig. 1, is the operational paradigm9 on which field surveys are based. The model assumes that the soil state is a function of the complex interaction of some (landscape) forming factors10, which creates a pattern of layers, on the decimeter scale, more or less parallel to the earth surface, called soil horizons11, 12.  \nThe classical denomination of soil horizons relies on remarkably subjective criteria imprinted by different researchers, something that motivated the introduction of the so-called ’diagnostic horizons’, based on measurable physical, chemical, and morphological properties12, 13, aiming to establish a classification (soil taxa) . Diagnostic horizons reflect soil properties in a simpler way and they are susceptible to spatial representation14.  \nIn this scenario, first Dokuchaev and later Jenny11, 15 developed a seminal milestone of the paradigm, proposing a formalization of the soil forming factors, in the form of the famous clorpt mathematical expression  \n= 􀀞 􀀝􀀜 􀀛 􀀚 􀀙 􀀘 􀀞􀀞􀀞􀀝 􀀞 (1)  \nIn it, S is a currently existing (local16) soil, expressed either as a specific taxon or as one ofits diagnostic horizons. On the r.h.s. of the equation, cl (atmospheric climate), o (organisms), r (relief and landforms) and p (parent material) constitute spatially located environmental factors17, and the time factor, t, indicates the duration of the interaction among them18. Had all symbols in Jenny’s equation have a unique and precise numerical meaning, this equation would have been the central computational expression in pedology19, surely what Dokuchaev and Jenny originally had in mind in","cbCaimSoRj0L6liX","https://ap.wps.com/l/cbCaimSoRj0L6liX","pdf",2635957,1,14,"English","en",105,"# Introduction\n## Soil-Landscape Model and Jenny’s Equation\n## Limits of Classical Mathematical Formalization\n# Method and Machine Learning Approach\n## Predicting Soil Types from Environmental Variables\n## Comparison With Conventional Statistical Analyses\n# Background and Rationale\n## Mathematical Effectiveness in Natural Sciences\n## Network Theory and the Rise of Machine Learning","[{\"question\":\"What problem does Jenny’s equation present in soil science?\",\"answer\":\"Jenny’s equation is conceptual and uses qualitative, often categorical variables, so the involved function cannot be defined or computed directly with standard mathematical tools.\"},{\"question\":\"How does the proposed work use machine learning?\",\"answer\":\"It applies machine learning to extract the unknown functional relationship and to predictably relate soil types and environmental factors derived from measurable variables.\"},{\"question\":\"How does the machine learning method compare with conventional statistics?\",\"answer\":\"The study reports that the machine learning approach outperforms other conventional statistical analyses built on the same forming factors.\"}]","Disentangling Jenny’s equation by machine learning | 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problem does Jenny’s equation present in soil science?","Question",{"text":75,"@type":76},"Jenny’s equation is conceptual and uses qualitative, often categorical variables, so the involved function cannot be defined or computed directly with standard mathematical tools.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed work use machine learning?",{"text":80,"@type":76},"It applies machine learning to extract the unknown functional relationship and to predictably relate soil types and environmental factors derived from measurable variables.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the machine learning method compare with conventional statistics?",{"text":84,"@type":76},"The study reports that the machine learning approach outperforms other conventional statistical analyses built on the same forming 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