Engineering Papers⌕ Search
Home/Archive/NASA NTRS

NASA NTRS · 20150018280

Representing Extremes in Agricultural Models

Ruane, Alex
View original source ↗Export metadata ↓

Abstract

AgMIP and related projects are conducting several activities to understand and improve crop model response to extreme events. This involves crop model studies as well as the generation of climate datasets and scenarios more capable of capturing extremes. Models are typically less responsive to extreme events than we observe, and miss several forms of extreme events. Models also can capture interactive effects between climate change and climate extremes. Additional work is needed to understand response of markets and economic systems to food shocks. AgMIP is planning a Coordinated Global and Regional Assessment of Climate Change Impacts on Agricultural Production and Food Security with an aim to inform the IPCC Sixth Assessment Report.

Explore related subjects

Agricultural systemsComputer simulationClimate extremes

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ruane, Alex. 2015-07-28. Representing Extremes in Agricultural Models. https://ntrs.nasa.gov/citations/20150018280

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

NASA NTRS2016

Towards a New Generation of Agricultural System Data, Models and Knowledge Products: Design and Improvement

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Agricultural systems↗