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
The evolution and evaluation of dairy cattle models for predicting milk production: an agricultural model intercomparison and improvement project (AgMIP) for livestock
The contemporary concern about anthropogenic release of greenhouse gas (GHG) into the environment and the contribution of livestock to this phenomenon have sparked animal scientists' interest in predicting methane (CH4) emissions by ruminants. We contend that improving the adequacy of mathematical nutrition model estimates of production of meat and milk is a sine qua non condition to reliably determine ruminants' worldwide contribution to GHG. Focusing on milk production, we address six basic nutrition models or feeding standards (mostly empirical systems) and five complex nutrition models (mostly mechanistic systems), describe their key characteristics, and highlight their similarities and differences. We also present derivative systems. We compiled a database of milk production information from 37 published studies from six regions of the world, totalling 173 data points: 19 for Africa, 45 for Asia, 16 for Europe, 12 for Latin America, 44 for North America and 37 for Oceania. Four models were used to predict milk production in lactating dairy cows, and the adequacy of their predictions was measured against the observed milk production from our database. Even though these mathematical nutrition models shared similar assumptions and calculations, they have different conceptual and structural foundations inherent to their intended purposes. A direct comparison among these models was further complicated by the different models requiring unique inputs that are very often not available, and the low reliability of the inputs prevents an unbiased assessment of the model predictions. Very few studies have collected the necessary information to run more mechanistic systems, and users have to rely on standard information to populate many model inputs. Study effect was a critical source of variation that limited our ability to conclusively evaluate the models' applicability under different scenarios of production around the world. Only after study variation was removed from the database did the adequacy of the model predictions of milk production improved, but deficiencies still existed. On the basis of these analyses, we conclude that not all models were suitable for predicting milk production and that simpler systems might be more resilient to variations in studies and production conditions around the world. Improving the predictability of milk production by mathematical nutrition models is a prerequisite to further development of systems that can effectively and correctly estimate the contribution of ruminants to GHG emissions and their true share of the global warming event.
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