Rapid aggregation of global gridded crop model outputs to facilitate cross-disciplinary analysis of climate change impacts in agriculture

We discuss an on-line tool that facilitates access to the large collection of climate impacts on crop yields produced by the Agricultural Model Intercomparison and Improvement Project. This collection comprises the output of seven crop models which were run on a global grid using climate data from five different general circulation models under the current set of representative pathways. The output of this modeling endeavor consists of more than 36,000 publicly available global grids at a spatial resolution of one half degree. We offer flexible ways to aggregate these data while reducing the technical barriers implied by learning new download platforms and specialized formats. The tool is accessed trough any standard web browser without any special bandwidth requirement

An AgMIP Framework for Improved Agricultural Representation in Integrated Assessment Models

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications

An AgMIP framework for improved agricultural representation in integrated assessment models

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications

The Assessment of Impacts and Risks of Climate Change on Agriculture (AIRCCA) model:a tool for the rapid global risk assessment for crop yields at a spatially explicit scale

A main channel through which climate change is expected to affect the economy is the agricultural sector. Large spatial variability in these impacts and high levels of uncertainty in climate change projections create methodological challenges for assessing the consequences this sector could face. Crop emulators based on econometric fixed-effects models that can closely reproduce biophysical models are estimated. With these reduced form crop emulators, we develop AIRCCA, a user-friendly software for the assessment of impacts and risks of climate change on agriculture, that allows stakeholders to make a rapid global assessment of the effects of climate change on maize, wheat and rice yields. AIRCCA produces spatially explicit probabilistic impact scenarios and user-defined risk metrics for the main four Intergovernmental Panel on Climate Change’s (IPCC) emissions scenarios

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives. © 2019, The Author(s)

CGIAR modeling approaches for resource constrained scenarios: IV Models for analyzing socio‐economic factors to improve policy recommendations

International crop-related research as conducted by the CGIAR uses crop modelingfor a variety of purposes. By linking crop models with economic models andapproaches, crop model outputs can be effectively used as inputs into socioeco-nomic modeling efforts for priority setting and policy advice using ex-ante impactassessment of technologies and scenario analysis. This requires interdisciplinarycollaboration and very often collaboration across a variety of research organizations.This study highlights the key topics, purposes, and approaches of socioeconomicanalysis within the CGIAR related to cropping systems. Although each CGIARcenter has a different mission, all CGIAR centers share a common strategy of strivingtoward a world free of hunger, poverty, and environmental degradation. This meansresearch is mostly focused toward resource-constrained smallholder farmers. Thereview covers global modeling efforts using the IMPACT model to farm householdbio-economic models for assessing the potential impact of new technologies onfarming systems and livelihoods. Although the CGIAR addresses all aspects of foodsystems, the focus of this review is on crop commodities and the economic analysislinked to crop-growth model results. This study, while not a comprehensive review,provides insights into the richness of the socioeconomic modeling endeavors withinthe CGIAR. The study highlights the need for interdisciplinary approaches to addressthe challenges this type of modeling faces

Climate change and global crop yield: impacts, uncertainties and adaptation

As global mean temperature continues to rise steadily, agricultural systems are projected to face unprecedented challenges to cope with climate change. However, understanding of climate change impacts on global crop yield, and of farmers’ adaptive capacity, remains incomplete as previous global assessments: (1) inadequately evaluated the role of extreme weather events; (2) focused on a small subset of the full range of climate change predictions; (3) overlooked uncertainties related to the choice of crop modelling approach and; (4) simplified the representation of farming adaptation strategies. This research aimed to assess climate change impacts on global crop yield that accounts for the knowledge gaps listed above, based on the further development and application of the global crop model PEGASUS. Four main research topics are presented. First, I investigated the roles of extreme heat stress at anthesis on crop yield and uncertainties related to the use of seventy-two climate change scenarios. I showed large disparities in impacts across regions as extreme temperatures adversely affects major areas of crop production and lower income countries, the latter appear likely to face larger reduction in crop yields. Second, I coordinated the first global gridded crop model intercomparison study, comparing simulations of crop yield and water use under climate change. I found modelled global average crop water productivity increases by up to 17±20.3% when including carbon fertilisation effects, but decreases to –28±13.9% when excluding them; and identified fundamental uncertainties and gaps in our understanding of crop response to elevated carbon dioxide. Third, to link climate impacts with adaptation, I introduced the recently developed concept of representative agricultural pathways and examined their potential use in models to explore farming adaptation options within biophysical and socio-economic constraints. Finally, I explored tradeoffs between increasing nitrogen fertiliser use to close the global maize yield gap and the resulting nitrous oxide emissions. I found global maize production increases by 62% based on current harvested area using intensive rates of nitrogen fertiliser. This raises the share of nitrous oxide emissions associated with maize production from 20 to 32% of global cereal related emissions. Finally, these results demonstrated that in some regions increasing nitrogen fertiliser application, without addressing other limiting factors such as soil nutrient imbalance and water scarcity, could raise nitrous oxide emissions without enhancing crop yield

Development and analysis of spring plant phenology products: 36 years of 1-km grids over the conterminous US

Time series of phenological products provide information on the timings of recurrent biological events and on their temporal trends. This information is key to studying the impacts of climate change on our planet as well as for managing natural resources and agricultural production. Here we develop and analyze new long term phenological products: 1 km grids of the Extended Spring Indices (SI-x) over the conterminous United States from 1980 to 2015. These new products (based on Daymet daily temperature grids and created by using cloud computing) allow the analysis of two primary variables (first leaf and first bloom) and two derivative products (Damage Index and Last Freeze Day) at a much finer spatial resolution than previous gridded or interpolated products. Furthermore, our products provide enough temporal depth to reliably analyze trends and changes in the timing of spring arrival at continental scales. Validation results confirm that our products largely agree with lilac and honeysuckle leaf and flowering onset observations. The spatial analysis shows a significantly delayed spring onset in the northern US whereas in the western and the Great Lakes region, spring onset advances. The mean temporal variabilities of the indices were analyzed for the nine major climatic regions of the US and results showed a clear division into three main groups: early, average and late spring onset. Finally, the region belonging to each group was mapped. These examples show the potential of our four phenological products to improve understanding of the responses of ecosystems to a changing climat

Representative Agricultural Pathways and Scenarios for Regional Integrated Assessment of Climate Change Impacts, Vulnerability, and Adaptation

The global change research community has recognized that new pathway and scenario concepts are needed to implement impact and vulnerability assessment where precise prediction is not possible, and also that these scenarios need to be logically consistent across local, regional, and global scales (Moss et al., 2008, 2010). For global climate models, representative concentration pathways (RCPs) have been developed that provide a range of time-series of atmospheric greenhouse-gas concentrations into the future (Moss et al., 2008, 2010; van Vuuren et al., 2012a). For impact and vulnerability assessment, new socio-economic pathway and scenario concepts have also been developed (Kriegler, 2012; van Vuuren et al., 2012b), with leadership from the Integrated Assessment Modeling Consortium (IAMC)..