Abstract: Increased climate variability and higher mean temperatures are expected across many world regions, both of which will contribute to more frequent extreme high temperatures events. Empirical evidence increasingly shows that short episodes of high temperature experienced around flowering can have large negative impacts on cereal grain yields, a phenomenon increasingly referred to as heat stress. Crop models are currently the best tools available to investigate how crops will grow under future climatic conditions, though the need to include heat stress effects has been recognized only relatively recently. We reviewed literature on both how key crop physiological processes and the observed yields under production conditions are impacted by high temperatures occurring particularly in the flowering and grain filling phases for wheat, maize and rice. This state of the art in crop response to heat stress was then contrasted with generic approaches to simulate the impacts of high temperatures in crop growth models. We found that the observed impacts of heat stress on crop yield are the end result of the integration of many processes, not all of which will be affected by a “high temperature” regime. This complexity confirms an important role for crop models in systematizing the effects of high temperatures on many processes under a range of environments and realizations of crop phenology. Four generic approaches to simulate high temperature impacts on yield were identified: (1) empirical reduction of final yield, (2) empirical reduction in daily increment in harvest index, (3) empirical reduction in grain number, and (4) semi-deterministic models of sink and source limitation. Consideration of canopy temperature is suggested as a promising approach to concurrently account for heat and drought stress, which are likely to occur simultaneously. Improving crop models’ response to high temperature impacts on cereal yields will require experimental data representative of field production and should be designed to connect what is already known about physiological responses and observed yield impacts. (C) 2014 Elsevier B.V. All rights reserved.

Abstract: AimTo derive location-specific parameters that reflect the geographic differences among cultivars in vernalization requirements, sensitivity to day length (photoperiod) and temperature, which can be used to simulate the phenological development of wheat and maize at the global scale. LocationGlobal. Methods Based on crop calendar observations and literature describing the large-scale patterns of phenological characteristics of cultivars, we developed algorithms to compute location-specific parameters to represent this large-scale pattern. Vernalization requirements were related to the duration and coldness of winter, sensitivity to day length was assumed to be represented by the minimum and maximum day lengths occurring at a location, and sensitivity to temperature was related to temperature conditions during the vegetative development phase of the crop. Results Application of the derived location-specific parameters resulted in high agreement between simulated and observed lengths of the cropping period. Agreement was especially high for wheat, with mean absolute errors of less than 3 weeks. In the main maize cropping regions, cropping periods were over- and underestimated by 0.5-1.5 months. We also found that interannual variability in simulated wheat harvest dates was more realistic when accounting for photoperiod effects. Main conclusions The methodology presented here provides a good basis for modelling the phenological characteristics of cultivars at the global scale. We show that current global patterns of growing season length as described in cropping calendars can be largely reproduced by phenology models if location-specific parameters are derived from temperature and day length indicators. Growing seasons can be modelled more accurately for wheat than for maize, especially in warm regions. Our method for computing parameters for phenology models from temperature and day length offers opportunities to improve the simulation of crop productivity by crop simulation models developed for large spatial areas and for long-term climate impact projections that account for adaptation in the selection of varieties

Abstract: Uncertainty in weather conditions for the forthcoming growing season influences farmers’ decisions, based on their experience of the past climate, regarding the reduction of agricultural risk. Early within-season predictions of grain yield can represent a great opportunity for farmers to improve their management decisions and potentially increase yield and reduce potential risk. This study assessed 3 methods of within-season predictions of durum wheat yield at 10 sites across the Mediterranean Basin. To assess the value of within-season predictions, the model SiriusQuality2 was used to calculate wheat yields over a 9 yr period. Initially, the model was run with observed daily weather to obtain the reference yields. Then, yield predictions were calculated at a monthly time step, starting from 6 mo before harvest, by feeding the model with observed weather from the beginning of the growing season until a specific date and then with synthetic weather constructed using the 3 methods, historical, analogue or empirical, until the end of the growing season. The results showed that it is possible to predict durum wheat yield over the Mediterranean Basin with an accuracy of normalized root means squared error of <20%, from 5 to 6 mo earlier for the historical and empirical methods and 3 mo earlier for the analogue method. Overall, the historical method performed better than the others. Nonetheless, the analogue and empirical methods provided better estimations for low-yielding and high-yielding years, thus indicating great potential to provide more accurate predictions for years that deviate from average conditions.