With current annual production at over 600 million tonnes, wheat is the third largest crop in the world behind corn and rice, and an essential source of carbohydrates for millions of people. While wheat is grown over a wide range of environments, it is common in the major wheat-producing countries for grain filling to occur when soil moisture is declining and temperature is increasing. Average global temperatures have increased over the last decades and are predicted to continue rising, along with a greater frequency of extremely hot days. Such events have already been reported for major wheat growing regions in the world. However, the direct impact of past temperature variability and changes in averages and extremes on wheat production has not been quantified. Attributing changes in observed yields over recent decades to a single factor such as temperature is not possible due to the confounding effects of other factors. By using simulation modelling, we were able to separate the impact of temperature from other factors and show that the effect of temperature on wheat production has been underestimated. Surprisingly, observed variations in average growing-season temperatures of AE2 1C in the main wheat growing regions of Australia can cause reductions in grain production of up to 50%. Most of this can be attributed to increased leaf senescence as a result of temperatures 434 1C. Temperature conditions during grain filling in the major wheat growing regions of the world are similar to the Australian conditions during grain filling. With average temperatures and the frequency of heat events projected to increase world-wide with global warming, yield reductions due to higher temperatures during the important grain-filling stage alone could substantially undermine future global food security. Adaptation strategies need to be considered now to prevent substantial yield losses in wheat from increasing future heat stress.

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project's Global Gridded Crop Model Intercomparison based on CMIP5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new 21st-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to −6% (SSP126) and from +1% to −24% (SSP585)-explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO 2 concentrations and expanded high-latitude gains. The 'emergence' of climate impacts consistently occurs earlier in the new projectionsbefore 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Southwestern Australia, with a semiarid Mediterranean climate, has been extensively cleared of native vegetation for winter-growing agricultural species. The resultant reduction in evapotranspiration has increased land salinisation. Through detailed meteorological and vegetation measurements over both agricultural and native vegetation, the bunny fence experiment is addressing the impact on the climate of replacing native perennial vegetation with wintergrowing annual species. Such measurements will give a better understanding of the interaction between the land surface and the atmosphere and are important for improved parameterization of the boundary layer in climate models.

Regional climate models are sensitive to the forcing data used, as well as order to better inform future long-term regional climate simulations for this re-8 gion. We show that the choice of radiation scheme had a strong influence on both 9 temperature and precipitation; the choice of planetary boundary layer scheme has 10 a particularly large influence on minimum temperatures; and, the choice of cumu-11 lus scheme or more complex micro-physics did not strongly influence precipitation 12 simulations. More importantly, we show that the same radiation scheme, when 13 used with different driving data, can lead to different results.

No abstract

No abstract

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO2 emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO2 emissions. Using high-resolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decision-making, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), Miscanthus × giganteus (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively.

A two-layer steady-state resistance model is compared with routine meteorological data collected from the Western Australian wheat belt during 2000-06. Major difficulties in implementing such a model are the correct parameterization for the incoming longwave radiation and estimation of daily soil moisture, neither of which are routinely measured. These difficulties are addressed by testing parameterizations for incoming longwave radiation calibrated to local conditions and incorporating a soil-water balance model based on routine weather data. The modified model has RMSE and biases ranging from 2.48 to 3.18C and 20.28 to 0.88C, respectively, across the wheat belt when comparing all minimum nocturnal temperatures. The model is shown to predict frost events approximately 55% of the time and illustrates that frost damage to foliage may occur when screen temperatures are , 28C.