Location: Soil Management ResearchTitle: Big agronomic data validates an oxymoron: Sustainable intensification under climate change
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2018
Publication Date: N/A
Technical Abstract: Crop science is increasingly embracing big data to reconcile the apparent rift between intensification of food production and sustainability of a steadily stressed production base. A strategy based on long-term agroecosystem research and modeling simulation of crops, crop rotations and cropping systems was advocated to generate big data that can address complex research and development questions regarding agroecosystems functionality (AF: biomass, grain yield and yield gap per crop, crop rotation and cropping system) and natural resources sustainability (NRS: carbon sequestration, soil erosion, drainage, runoff, and N-leakage) under four future climate change scenarios with increasing emissions (i.e., Representative Concentration Pathways; RCP 2.6, 4.5, 6.0 and 8.5) as described by the Intergovernmental Panel on Climate Change (IPCC). A calibrated and validated agricultural production systems simulator (APSIM), using long-term data on local weather, grain and forage crops and major and minor soil series, generated >100 GB of data on AF and NRS using 99-year of past (recorded) and future (generated) weather data under each of the IPCC climate change scenarios. Big data was mined for causation and for inter- and intra-relationships within and among components of AF and NRS. Yield gap (YG, relative to largest yield) and the probability of sustained delivery of agroecosystem services (pAES) relevant to AF and NRS, were estimated and presented in a multi-dimensional matrix to highlight paths to sustainable intensification under each climate change scenario, and for each crop, crop rotation and cropping system. A dynamic prediction profiler demonstrates how to select factor levels that can optimize each response variable (i.e., YG or pAES) based on realistic assumptions.