[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128108-en":3,"doc-seo-128108-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128108,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","COMPARISON OF THE MACHINE LEARNING AND AQUACROP MODELS FOR QUINOA CROPS - Tesis de ingeniería","Low crop efficiency in Peru is closely linked to poor water-resource management. This study estimates the irrigation water needed for quinoa by comparing Machine Learning models with the AquaCrop software. Meteorological and crop descriptive data from Jauja were processed, using a simulation period from June to December 2020. Results show AdaBoost performs best, with mean and standard deviation closely matching AquaCrop. ANOVA indicates a higher p-value and lower error via MAE, and the 190-day simulation estimates 472.35 mm of irrigation water for red quinoa.","Universidad de Lima Facultad de Ingeniería Carrera de Ingeniería Industrial  \nCOMPARISON OF THE MACHINE LEARNING AND AQUACROP MODELS FOR QUINOA CROPS  \nTesis para optar el Título Profesional de Ingeniero Industrial  \nRossy Jackeline Chumbe Llimpe  \nCódigo 20170388  \nStefany Dennis Silva Paucar  \nCódigo 20172664  \nAsesor  \nJuan Carlos Quiroz Flores  \nLima – Perú  \nDiciembre de 2024  \n\n| Propuesta\u003Cbr>Carrera Ingeniería Industrial |\n| --- |\n| Título\u003Cbr>COMPARISON OF THE MACHINE LEARNING AND AQUACROP MODELS FOR QUINOA\u003Cbr>CROPS |\n| Autor(es)\u003Cbr>[20170388@aloe.ulima.edu.pe](20170388@aloe.ulima.edu.pe)\u003Cbr>Universidad de Lima [20172664@aloe.ulima.edu.pe](20172664@aloe.ulima.edu.pe)[ ](20172664@aloe.ulima.edu.pe)Universidad de Lima[ygarcia@ulima.edu.pe](ygarcia@ulima.edu.pe)[ ](ygarcia@ulima.edu.pe)Universidad de Lima |\n| Resumen: Una de las principales causas de la baja eficiencia de los cultivos en el Perú es la mala gestiónde los recursos hídricos; por lo que el presente artículo tiene como objetivo principal estimar la cantidad de agua de riego requerida en cultivos de quinua mediante una comparación entre los modelos de Machine Learning y AquaCrop. Para el desarrollo de este estudio se procesaron datos meteorológicos de la provincia de Jauja y descriptivos del cultivo de quinua y se estableció un periodo de simulación de junio a diciembredel 2020. De la simulación realizada se determinó que el mejor modelo para predecir el agua de riegorequerida es el modelo AdaBoost en el cual se observó que la media y desviación estándar de los modelos AdaBoost (Mean = 19.681 y Std. Dev. = 4.665) se comportan de manera similar a AquaCrop (Media = 19.838 y Std. Dev. = 5.04) . Además, el resultado del análisis de varianza (ANOVA) fue que el modelo AdaBoost tiene el mejor indicador de valor p con un valor de 0.962 y un margen de error menor en relaciónal indicador MAE con un valor de 0.629. Asimismo, se identificó que para el periodo de simulación de 190 días se requirieron 472.35mm de agua para realizar el proceso de riego en cultivos de quinua roja.\u003Cbr>Palabras Clave: Adaboost, sistema de irrigación, manejo del agua, análisis estadístico, análisis predictivo.\u003Cbr>Abstract: One of the main causes of low crop efficiency in Peru is poor management of water resources; that is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and Aquacrop models. For the development of this study, meteorological data from the province of Jauja and descriptive data of quinoa crops were processed and a simulation period was established from June to December. From the simulation carried out, it was determined that the best model to predict the required irrigation water is the Ada Boost model in which it was observed that the mean and standard deviation of the Ada Boost models (Mean = 19.681 and Std. Dev. = 4.665) behave similarly to AquaCrop (Mean = 19.838 and Std. Dev. = 5.04) . In addition, the result of the analysis of variance (ANOVA) was that the AdaBoost model has the best p-value indicator with a value of 0.962 and a smaller margin of error in relation to the MAE indicator with a value of 0.629. Likewise, it was identified that for the simulation period of 190 days, 472.35mm of water was required to carry out the irrigation process in red quinoa crops.\u003Cbr>Keywords: AdaBoost; irrigation system; water management; statistical analysis; predictive analysis. |\n| Línea de investigación IDIC – ULIMA\u003Cbr>4. Recursos naturales y medio ambiente |\n| Área y Sub-áreas de Investigación:\u003Cbr>2. Operations Research & Analysis |\n| Objetivo (s) de Desarrollo Sostenible (ODS) :\u003Cbr>Objetivo 12: Garantizar modalidades de consumo y producción sostenibles |\n\n\n| PLANTEAMIENTO DEL PROBLEMA\u003Cbr>Hoy en día los procesos agrícolas están expuestos a sobrecostos, debido a su alta dependencia a las condiciones climáticas, ocasionados por sequías, heladas, inundaciones y plagas (Ministerio de","cbCaitRZ64M17Gb2","https://ap.wps.com/l/cbCaitRZ64M17Gb2","pdf",236232,2,1,7,"English","en",105,"# Propuesta de investigación\n## Planteamiento del problema\n## Objetivo del estudio\n## Justificación\n# Metodología y resultados de la comparación","[{\"question\":\"本研究の主な目的は何ですか？\",\"answer\":\"キヌア栽培に必要な灌漑用水量を、Machine LearningモデルとAquaCropソフトウェアを比較して推定することです。\"},{\"question\":\"どのモデルが最も良い予測性能を示しましたか？\",\"answer\":\"シミュレーションの結果、AdaBoostモデルが最も良い予測性能を示しました。平均・標準偏差がAquaCropと近い値になり、ANOVAではp値が良好でした。\"},{\"question\":\"190日間のシミュレーションで必要とされた灌漑用水量はいくらですか？\",\"answer\":\"190日間のシミュレーションでは、赤キヌア栽培に必要な灌漑用水量は472.35mmと報告されています。\"}]","COMPARISON OF THE MACHINE LEARNING AND AQUACROP MODELS FOR QUINOA CROPS - Tesis de ingeniería | PDF",1785944879,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"comparison-of-the-machine-learning-and-aquacrop-models-for-quinoa-crops-engineering-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/comparison-of-the-machine-learning-and-aquacrop-models-for-quinoa-crops-engineering-thesis/128108/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"本研究の主な目的は何ですか？","Question",{"text":76,"@type":77},"キヌア栽培に必要な灌漑用水量を、Machine LearningモデルとAquaCropソフトウェアを比較して推定することです。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"どのモデルが最も良い予測性能を示しましたか？",{"text":81,"@type":77},"シミュレーションの結果、AdaBoostモデルが最も良い予測性能を示しました。平均・標準偏差がAquaCropと近い値になり、ANOVAではp値が良好でした。",{"name":83,"@type":74,"acceptedAnswer":84},"190日間のシミュレーションで必要とされた灌漑用水量はいくらですか？",{"text":85,"@type":77},"190日間のシミュレーションでは、赤キヌア栽培に必要な灌漑用水量は472.35mmと報告されています。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]