Predicción de fechas óptimas para la evaluación de tizón tardío de papa usando algoritmos de árboles de decisión y bosques aleatorios

Publicación a texto completo no autorizada por el autorManifiesta el uso de algoritmos de árboles de decisión y bosques aleatorios como instrumentos matemáticos y estadísticos-heurísticos para la predicción de fechas óptimas en evaluación de tizón tardío. Dichos algoritmos utilizan los índices de ganancia de información (entropía de la información) y los índices de Gini para ajustar al máximo la predicción. Para el desarrollo y análisis de los resultados de los árboles de decisión se utilizan las implementaciones conocidas como C4.5 y CART; mientras que para los bosques aleatorios se emplea RandomForest.Tesi

Inferencia bayesiana aproximada del modelo espacio-temporal usando NNGP

Los modelos espacio-temporales nos permiten estudiar la distribución espacial de una variable en el tiempo. Por ejemplo, se puede estudiar la distribución espacial del material particulado en un país a través de los años, dado que las concentraciones de material particulado en estaciones cercanas pueden ser similares y la concentración en una estación en un año puede depender de la concentración en la misma estación el año anterior anterior. En esta tesis se propone usar un modelo espacio-temporal a través del proceso gaussiano de vecinos más cercanos. Para implementar este modelo y aplicarlo en grandes bases de datos se propone usar inferencia bayesiana a través del método de integración aproximada de Laplace (INLA). La bondad de ajuste del modelo y su eficiencia se estudia a través de simulaciones. Finalmente se aplica el modelo implementado a una base de datos reales.Spatio-temporal models allow us to study the spatial distribution of a variable over time. For example, we can study the spatial distribution of particulate matter in a country through the years, given that the concentrations of particulate matter in nearby stations can be similar and the concentration in a station in a year can depend on the concentration in the same station in the previous year. In this thesis, we proposed to use a spatio-temporal model through the nearest neighbor Gaussian process. In order to implement and apply the hierarchical model in large databases, it is proposed to use Bayesian inference through Integrated nested Laplace approximation(INLA). The goodness of fit and efficiency of the model is studied through simulations. Finally, the model is applied to real data set

AgroFIMS v.1.0 - User manual

The Agronomy Field Information Management System (AgroFIMS) has been developed on CGIAR’s HIDAP (Highly Interactive Data Analysis Platform) created by CGIAR’s International Potato Center, CIP. AgroFIMS draws fully on ontologies, particularly the Agronomy Ontology (AgrO)1. It consists of modules that represent the typical cycle of operations in agronomic trial management (seeding, weeding, fertilization, harvest, and more) and enables the creation of data collection sheets using the same ontology-based set of variables, terminology, units and protocols. AgroFIMS therefore enables a priori harmonization with metadata and data interoperability standards and adherence to the FAIR Data Principles essential for data reuse and increasingly, for compliance with funder mandates - without any extra work for researchers. AgroFIMS is therefore of value to anyone (scientist, researcher, agronomist, etc.) who wishes to easily design a standards-compliant agronomic research fieldbook following the FAIR Data Principles. AgroFIMS also allows users to collect data electronically in the field, thereby reducing errors. Currently this is restricted to the KDSmart Android platform, but we expect to enable this capability with other platforms such as the Open Data Kit (ODK) and Field Book in v.2.0. Once data is collected using KDSmart, the data can be uploaded back to AgroFIMS for data validation, statistical analysis, and the generation of statistical analysis reports. V.2.0 will allow easy upload of the data from AgroFIMS to an institutional or compliant repository of the user’s choice

AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data

Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing

AgroFIMS v.2.0 - User manual.

This documentation provides instructions to help you get familiarized with the Agronomy Field Information Management System (AgroFIMS) and to produce a fieldbook that you can use to collect well-described, standards-compliant data in the field. AgroFIMS allows users to create fieldbooks to collect agronomic data. The fieldbook is already tied to a metadata standard (the CG Core Metadata Schema, aligned with the industry standard Dublin Core Metadata Schema and required by CGIAR and many other repositories). The data variables and protocol parameters in AgroFIMS fieldbooks align with semantic standards like the Agronomy Ontology (AgrO). This a priori compliance with data standards facilitates data to be Findable, Accessible, Interoperable, and Reusable (FAIR) at collection, making it easier to interpret and aggregate. Data collection is currently available via the Android-based KDSmart or Field Book applications, and the collected data can be imported back to AgroFIMS for statistical analysis and reports. By mid-2021 you will be able to easily upload this collected data through AgroFIMS to a Dublin Core or CG Core-compliant Dataverse repository. To enable access, exchange, and integration of agronomic data across systems and applications we have made available the Agronomy API or AgrAPI, which is a RESTful web service API specification. The AgrAPI blueprint can be implemented in different programming languages, but is currently implemented in the R statistical programming language, allowing you to analyze your data with the R packages and graphics of your choice