![[WEBINAR] Digital soil mapping: Applications for fertilizer management](https://bigdata.cgiar.org/wp-content/uploads/2020/04/Digital-soil-mapping_-Applications-for-fertilizer-management-1080x675.png)
We live in the age of ‘Big Data’. While these new tools are transforming lives at a rapid pace, there are still many opportunities to apply big data approaches to improve on previous efforts in agricultural analytics. In this webinar, we present two approaches to build on traditional analytical to link soil data to agronomic and fertilizer recommendations at the landscape-scale.
![[WEBINAR] Design process of an IoT architecture for agriculture at the Alliance of Bioversity International and CIAT](https://bigdata.cgiar.org/wp-content/uploads/2020/03/Design-process-of-an-IOT-architecture-for-agriculture-at-CIAT-webinar-flyer-2-1080x675.png)
Sensor technologies and ‘Internet of Things’ (IoT) approaches are understood to hold great promise for agricultural research for development, yet researchers may have a difficult time navigating a diverse and complex array of service providers, technologies, and approaches. In order to begin to address this bottleneck, the CGIAR Platform for Big Data in Agriculture conducted an end-to-end design process for selecting services, software platforms, data transmission solutions, and the sensors themselves in light of requirements gathered from crop breeders and agronomists.
![[WEBINAR] WOFOST (WOrld FOod STudies): A simulation model for quantitative analysis of growth and production of annual field crops](https://bigdata.cgiar.org/wp-content/uploads/2020/03/WOFOST-Webinar-2-1080x675.png)
In this webinar, organized by the Crop Modeling Community of Practice, a panel of WOFOST experts formed by Allard de Wit and Hendrik Boogaard will introduce WOFOST, explain the basic principles and show some applications including a hands-on training by means of Jupyter Notebooks. After their presentation, webinar attendees will have the opportunity to ask any WOFOST-related questions.
![[WEBINAR] Machine Learning with Metadata and Experimental Citizen Science in R](https://bigdata.cgiar.org/wp-content/uploads/2020/02/Machine-Learning-with-Metadata-and-Experimental-Citizen-Science-in-R-Webinar-1-1080x675.png)
Machine learning is the scientific study of algorithms and statistical models that provides the computer the ability to automatically learn and improve from experience. In this workshop, we will work in a set of tools developed by Bioversity-CIAT to facilitate the analysis of metadata and experimental citizen science data, from collating data of different sources, gathering environmental variables, to model selection and visualization. All in a single pipeline in R that can be automated to improve predictions and recommendations for agriculture.
![[WEBINAR] Climate Similarity Analysis with R](https://bigdata.cgiar.org/wp-content/uploads/2020/02/Climate-Similarity-Analysis-with-R-Webinar-1-1080x675.png)
Using global climate projection data from multiple models, the climate analogues tool developed by CCAFS takes climate and rainfall predictions for a particular site and searches for places with similar conditions at present. In this webinar, Julian Ramirez will introduce the Climate Analogues Tool developed in R and show how this tool is being used to generate the Extrapolation Domains for climate adaptation options and help the audience to use the tool in own analysis.