High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based ...crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day >30 °C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures >30 °C. Elevated CO
can only weakly reduce these yield losses, in contrast to irrigation.
Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the ...Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.
We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model ...Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400–1,400 Pcal (8–24% of present-day total) when CO2 fertilization effects are accounted for or 1,400–2,600 Pcal (24–43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20–60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600–2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.
The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population ...density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than five‐fold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia.
The commodity market super-cycle and food price crisis have been associated with rampant food insecurity and the Arab spring. A multitude of factors were identified as culprits for excessive ...volatility on the commodity markets. However, as it regards fertilizers, a clear attribution of market drivers explaining the emergence of extreme price events is still missing. In this paper, we provide a quantitative assessment of the price spike of the global phosphorus fertilizer market in 2008 focusing on diammonium phosphate (DAP). We find that fertilizer market policies in India, the largest global importer of phosphorus fertilizers and phosphate rock, turned out to be a major contributor to the global price spike. India doubled its import of P-fertilizer in 2008 at a time when prices doubled. The analysis of a wide set of factors pertinent to the 2008 price spike in phosphorus fertilizer market leads us to the discovery of a price spike magnification and triggering mechanisms. We find that the price spike was magnified on the one hand by protective trade measures of fertilizer suppliers leading to a 19% drop in global phosphate fertilizer export. On the other hand, the Indian fertilizer subsidy scheme led to farmers not adjusting their demand for fertilizer. The triggering mechanism appeared to be the Indian production outage of P-fertilizer resulting in the additional import demand for DAP in size of about 20% of annual global supply. The main conclusion is that these three factors have jointly caused the spike, underscoring the need for
improvements in fertilizer market regulation on both national and international levels.
Wheat is the third largest crop globally and an essential source of calories in human diets. Maintaining and increasing global wheat production is therefore strongly linked to food security. A large ...geographic variation in wheat yields across similar climates points to sizeable yield gaps in many nations, and indicates a regionally variable flexibility to increase wheat production. Wheat is particularly sensitive to a changing climate thus limiting management opportunities to enable (sustainable) intensification with potentially significant implications for future wheat production. We present a comprehensive global evaluation of future wheat yields and production under distinct Representative Concentration Pathways (RCPs) using the Environmental Policy Integrated Climate (EPIC) agro-ecosystem model. We project, in a geographically explicit manner, future wheat production pathways for rainfed and irrigated wheat systems. We explore agricultural management flexibility by quantifying the development of wheat yield potentials under current, rainfed, exploitable (given current irrigation infrastructure), and irrigated intensification levels. Globally, because of climate change, wheat production under conventional management (around the year 2000) would decrease across all RCPs by 37 to 52 and 54 to 103Mt in the 2050s and 2090s, respectively. However, the exploitable and potential production gap will stay above 350 and 580Mt, respectively, for all RCPs and time horizons, indicating that negative impacts of climate change can globally be offset by adequate intensification using currently existing irrigation infrastructure and nutrient additions. Future world wheat production on cropland already under cultivation can be increased by ~35% through intensified fertilization and ~50% through increased fertilization and extended irrigation, if sufficient water would be available. Significant potential can still be exploited, especially in rainfed wheat systems in Russia, Eastern Europe and North America.
•Assessment of global wheat production under Representative Concentration Pathways•Region-specific agricultural management flexibility to counteract climate impacts•Production with management as in 2000 would decrease by 54 to 103Mt in the 2090s.•Yet, the exploitable and potential production gap will stay above 350 and 580 Mt.
Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of ...14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.
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.
The social cost of carbon (SCC) is estimated by integrated assessment models (IAMs) and is widely used by government agencies to value climate policy impacts. Although there is an ongoing debate ...about obtained numerical estimates and related uncertainties, little attention has been paid so far to the SCC calculation method itself. This work attempts to fill the gap by providing the theoretical background and economic interpretation of the SCC calculation approach implemented in the DICE (Dynamic Integrated Climate-Economy) IAM. Our analysis indicates that the present calculation method is unable to reflect the linkages between two key IAM components—complex interconnected systems—climate and economy, both influenced by emission abatement policies. Within the modeling framework of DICE, the presently estimated SCC valuates emissions, which are beyond policy control, against consumption of products, which cannot be produced by the economy. This makes the SCC irrelevant for application in climate-economic policies and, therefore, calls for a replacement by a more appropriate indicator. An apparent SCC alternative, which can be considered for policy formulation, is the direct output of the DICE model, the socially optimal marginal abatement cost (SMAC), which corresponds to technological possibilities at the optimal level of carbon emissions abatement. In policymaking, because of the revealed SCC deficiency, great attention needs to be paid to the use of estimates obtained earlier.
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