Abstract: Heat stress impacts on crop growth and yield have been investigated by controlled-environment experiments, however little is known about the impacts under field conditions at large spatial and temporal scales, particularly in a setting with farmers’ autonomous adaptations. Here, using detailed experiment Observations at 34 national agricultural meteorological stations spanning from 1981 to 2009 in the Huang-Huai-Hai Plain (HHHP) of China, we investigated the changes in climate and heat stress during wheat reproductive growing period (from heading to maturity) and the impacts of climate change and heat stress on reproductive growing duration (RGD) and yield in a setting with farmers’ autonomous adaptations. We found that RGD and growing degree days above 0 degrees C (GDD) from heading to maturity increased, which increased yield by similar to 14.85%, although heat stress had negative impacts on RGD and yield. During 1981-2009, high temperature (>34 degrees C) degree days (HDD) increased in the northern part, however decreased in the middle and southern parts of HHHP due to advances in heading and maturity dates. Change in HDD, together with increase in GDD and decrease in solar radiation (SRD), jointly increased wheat yield in the northern and middle parts but reduced it in the southern part of HHHP. During the study period, increase in GDD and decrease in SRD had larger impacts on yield than change in HDD. However, with climate warming of 2 degrees C, damage of heat stress on yield may offset a large portion of the benefits from increases in RGD and GDD, and eventually result in net negative impacts on yield in the northern part of HHHP. Our study showed that shifts in cultivars and wheat production system dynamics in the past three decades reduced heat stress impacts in the HHHP. The insights into crop response and adaptation to climate change and climate extremes provide excellent evidences and basis for improving climate change impact study and designing adaptation measures for the future. (C) 2015 Elsevier B.V. All rights reserved.

Abstract: Process-based models (PBM) for simulation of weather dependent grass growth can assist farmers and plant breeders in addressing the challenges of climate change by simulating alternative roads of adaptation. They can also provide management decision support under current conditions. A drawback of existing grass models is that they do not take into account the effect of winter stresses, limiting their use for full-year simulations in areas where winter survival is a key factor for yield security. Here, we present a novel full-year PBM for grassland named BASGRA. It was developed by combining the LINGRA grassland model (Van Oijen et al., 2005a) with models for cold hardening and soil physical winter processes. We present the model and show how it was parameterized for timothy (Phleum pratense L.), the most important forage grass in Scandinavia and parts of North America and Asia. Uniquely, BASGRA simulates the processes taking place in the sward during the transition from summer to winter, including growth cessation and gradual cold hardening, and functions for simulating plant injury due to low temperatures, snow and ice affecting regrowth in spring. For the calibration, we used detailed data from five different locations in Norway, covering a wide range of agroclimatic regions, day lengths (latitudes from 59 degrees to 70 degrees N) and soil conditions. The total dataset included 11 variables, notably above-ground dry matter, leaf area index, tiller density, content of C reserves, and frost tolerance. All data were used in the calibration. When BASGRA was run with the maximum a-posteriori (MAP) parameter vector from the single, Bayesian calibration, nearly all measured variables were simulated to an overall normalized root mean squared error (NRMSE) <0.5. For many site x experiment combinations, NRMSE was <0.3. The temporal dynamics were captured well for most variables, as evaluated by comparing simulated time courses versus data for the individual sites. The results may suggest that BASGRA is a reasonably robust model, allowing for simulation of growth and several important underlying processes with acceptable accuracy for a range of agroclimatic conditions. However, the robustness of the model needs to be tested further using independent data from a wide range of growing conditions. Finally we show an example of application of the model, comparing overwintering risks in two climatically different sites, and discuss future model applications. Further development work should include improved simulation of the dynamics of C reserves, and validation of winter tiller dynamics against independent data. (C) 2016 Elsevier B.V. All rights reserved.

Abstract: The production of durum wheat in the Mediterranean basin is expected to experience increased variability in yield and quality as a consequence of climate change. To assess how environmental variables and agronomic practices affect grain protein content (GPC), a novel approach based on monthly gridded input data has been implemented to develop empirical model, and validated on historical time series to assess its capability to reproduce observed spatial and inter-annual GPC variability. The model was applied in four Italian regions and at the whole national scale and proved reliable and usable for operational purposes also in a forecast ‘real-time’ mode before harvesting. Precipitable water during autumn to winter and air temperature from anthesis to harvest were extremely important influences on GPC; these and additional variables, included in a linear model, were able to account for 95% of the variability in GPC that has occurred in the last 15 years in Italy. Our results are a unique example of the use of modelling as a predictive real-time platform and are a useful tool to understand better and forecast the impacts of future climate change projections on durum wheat production and quality.