The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Nonlinear hierarchical models for predicting cover crop biomass using Normalized Difference Vegetation Index
Incorporating cover crops into agricultural systems can improve soil structural properties, increase nutrient availability, reduce erosion and loss of agrochemicals, and suppress weeds. These benefits are a function of the amount of cover crop biomass that enters the soil. The ability to easily and inexpensively quantify the spatial variability of cover crop biomass is needed to better understand and predict its potential as an input to agricultural systems. Here, we explore the use of Normalized Difference Vegetation Index (NDVI) as a source of information for improving accuracy and precision of cover crop biomass prediction. We focus on developing models that account for biomass variability within and among fields. These models are used to produce digital data layers of predicted biomass and associated uncertainty. We propose hierarchical nonlinear models with field random effects and a residual variance function to accommodate strong heteroscedasticity. These models are motivated using aboveground biomass of red clover (Trifolium pratense L) measured on three different dates in five fields in southwest Michigan. Model adequacy was assessed using the Deviance Information Criterion. Given this criterion, the "best" fitting model included field effects and a polynomial function to account for non-constant residual variance. Importantly, we demonstrate that accounting for heteroscedasticity in the model fitting is critical for capturing uncertainty in subsequent biomass prediction. (C) 2010 Elsevier Inc. All rights reserved.
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