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The approach compares eleven nitrogen levels at four Italian sites and links key drivers of performance. Results support maximized yield and income and support scalable sustainable agronomic decision-making using spatial data.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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durum wheat in Italy  \nMarco Fiorentini1 · Calogero Schillaci2 · Michele Denora3 · Stefano Zenobi1 ·  \nPaola A. Deligios1 · Rodolfo Santilocchi1 · Michele Perniola3 · Luigi Ledda1 · Roberto Orsini1  \nAccepted: 16 May 2024  \n© Department of Agricultural, Food and Environmental Sciences (D3A), Agronomy and Crop Science, Marche Polytechnic University, Ancona, Department of European and Mediterranean Cultures, Environment, and Cultural Heritage, University of Basilicata, and European Commission Joint Research Centre, under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024  \nAbstract  \nPurpose This research aims to develop a meta-machine learning model to optimize soil and nitrogen management for durum wheat in Italy. It addresses the challenges of increased food production on limited land amidst rising input costs, geopolitical changes, and climate change. The goal is to aid decision-makers in achieving maximum crop yield and income margins through effective agronomic strategies.  \nMethods The study developed a meta-machine learning model, integrating classification and regression models, and tested it at four sites in Marche and Basilicata, Italy, over several years. The model incorporated data from remote sensing, crop phenology, soil chemical properties, weather data, soil management, and nitrogen levels. A Random Forest model was used to classify crop phenology, while a Neural Network model predicted yield. Eleven nitrogen levels were compared across these sites.  \nResults The Random Forest model achieved an accuracy of 0.98, kappa of 0.96, and recall of 0.98 for predicting crop phenology. The Neural Network model for yield prediction had an R squared of 0.90 and a Root Mean Square Error of 0.59 t ha-1 . Key factors identified for model accuracy were temperature, precipitation, NDVI, and nitrogen input. Simulations of 30 soil management and fertilization combinations revealed that no-tillage management increased grain yield. The Marginal Fertilizer Yield Index determined optimal nitrogen application.  \nConclusions The meta-machine learning model accurately predicted durum wheat yield and identified effective agronomic strategies, demonstrating the potential for broader application in field conditions. The model offers a promising approach to sustainable agriculture and climate change mitigation by utilising publicly available spatial datasets.  \nExtended author information available on the last page of the article  \n1 3  \nGraphical abstract  \nKeywords Machine learning · Decision support system · Remote sensing · Nitrogen fertilization · Soil management · Durum wheat  \nIntroduction  \nGlobal population growth, soil sealing, rising raw material costs and increase of climate extremes are putting a strain on the world’s agricultural sector. The world population is projected to reach 8.5 billion by 2030, with further increases to 9.7 billion by 2050 and 10.4 billion by 2100. However, a recent paper suggests that the world population may reach its peak soon (Le Page, 2023). The rapid population growth is expected to lead to heightened urbanization, bringing a surge in demand for food, raising concerns about food safety. The agricultural sector confronted with the crucial task of increasing food production while having access to limited agricultural land. Additionally, rising production input costs, influenced by geopolitical and climate changes, add to the list of challenges. These factors collectively put stress on the agricultural sector, promoting the agricultural scientific community to actively seek solutions to address these pressing issues. The scientific community is focusing on the development of innovative cropping systems (De Menna et al., 2016), on the spread of conservative agriculture (Capmourteres et al., 2018), on the application of the principles of organic farming (Deligios et al.,","cbCaifoJEJNrH0oz","https://ap.wps.com/l/cbCaifoJEJNrH0oz","pdf",3929061,28,"English","# Abstract\n# Purpose\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"What is the main objective of the meta-machine learning model in this research?\",\"answer\":\"To optimize soil and nitrogen management for durum wheat in Italy by improving yield and income through effective agronomic strategies.\"},{\"question\":\"Which data sources and modeling approaches are used?\",\"answer\":\"The model integrates remote sensing, crop phenology, soil chemical properties, weather, soil management, and nitrogen levels, using Random Forest for phenology and a Neural Network for yield prediction.\"},{\"question\":\"What were the key findings from the model evaluation and simulations?\",\"answer\":\"Random Forest achieved high accuracy for crop phenology, the Neural Network predicted yield with strong fit, and simulations showed that no-tillage increased grain yield while the Marginal Fertilizer Yield Index supported optimal nitrogen application.\"}]","Fertilization and soil management machine learning based sustainable agronomic prescriptions for durum wheat in Italy | PDF",71]