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Francone, C., Katul, G. G., Cassardo, C., & Richiardone, R. (2012). Turbulent transport efficiency and the ejection-sweep motion for momentum and heat on sloping terrain covered with vineyards. Agricultural and Forest Meteorology, 162-163, 98–107.
Abstract: In boundary layer flows, it is now recognized that the net momentum and mass exchange rates are dominated by the statistical properties of ejecting and sweeping motion often linked to the presence of coherent turbulent structures. Over vineyards, three main factors impact the transport properties of such coherent motion: presence of sloping terrain, variations in leaf area index (LAI) during the growing season, and thermal stratification. The effect of these factors on momentum and heat transport is explored for three vineyard sites situated on different slopes. All three sites experience similar seasonal variation in LAI and mean wind conditions. The analysis is carried out using a conventional quadrant analysis technique and is tested against two models approximating the joint probability density function (JPDF) of the flow variables. It is demonstrated that a Gaussian JPDF explains much of the updraft and downdraft statistical contributions to heat and momentum transport efficiencies for all three sites. An incomplete or truncated third-order cumulant expansion method (ICEM) of the JPDF that retains only the mixed moments and ignores the skewness contributions describes well all the key properties of ejections and sweeps for all slopes, LAI, and stability classes. The implication of these findings for diagnosing potential failures of gradient-diffusion theory over complex terrain is discussed. Because only lower order moments are needed to describe the main characteristics of the JPDF, the use of the Moving Equilibrium Hypothesis (MEH) to predict these moments from the locally measured sensible heat flux and friction velocity is explored. Provided the planar fit coordinate transformation is applied to the data, the MEH can describe these statistical moments at all three sites regardless of terrain slopes and LAI values. (C) 2012 Elsevier B.V. All rights reserved.
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Ferrise, R., Toscano, P., Pasqui, M., Moriondo, M., Primicerio, J., Semenov, M. A., et al. (2015). Monthly-to-seasonal predictions of durum wheat yield over the Mediterranean Basin. Clim. Res., 65, 7–21.
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.
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Faye, B., Webber, H., Naab, J. B., MacCarthy, D. S., Adam, M., Ewert, F., et al. (2018). Impacts of 1.5 versus 2.0 degrees C on cereal yields in the West African Sudan Savanna. Environ. Res. Lett., 13(3), 034014.
Abstract: To reduce the risks of climate change, governments agreed in the Paris Agreement to limit global temperature rise to less than 2.0 degrees C above pre-industrial levels, with the ambition to keep warming to 1.5 degrees C. Charting appropriate mitigation responses requires information on the costs of mitigating versus associated damages for the two levels of warming. In this assessment, a critical consideration is the impact on crop yields and yield variability in regions currently challenged by food insecurity. The current study assessed impacts of 1.5 degrees C versus 2.0 degrees C on yields of maize, pearl millet and sorghum in the West African Sudan Savanna using two crop models that were calibrated with common varieties from experiments in the region with management reflecting a range of typical sowing windows. As sustainable intensification is promoted in the region for improving food security, simulations were conducted for both current fertilizer use and for an intensification case (fertility not limiting). With current fertilizer use, results indicated 2% units higher losses for maize and sorghum with 2.0 degrees C compared to 1.5 degrees C warming, with no change in millet yields for either scenario. In the intensification case, yield losses due to climate change were larger than with current fertilizer levels. However, despite the larger losses, yields were always two to three times higher with intensification, irrespective of the warming scenario. Though yield variability increased with intensification, there was no interaction with warming scenario. Risk and market analysis are needed to extend these results to understand implications for food security.
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Eyshi Rezaei, E., Webber, H., Gaiser, T., Naab, J., & Ewert, F. (2015). Heat stress in cereals: Mechanisms and modelling. European Journal of Agronomy, 64, 98–113.
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.
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Eyshi Rezaei, E., Siebert, S., & Ewert, F. (2015). Impact of data resolution on heat and drought stress simulated for winter wheat in Germany. European Journal of Agronomy, 65, 69–82.
Abstract: Heat and drought stress can reduce crop yields considerably which is increasingly assessed with crop models for larger areas. Applying these models originally developed for the field scale at large spatial extent typically implies the use of input data with coarse resolution. Little is known about the effect of data resolution on the simulated impact of extreme events like heat and drought on crops. Hence, in this study the effect of input and output data aggregation on simulated heat and drought stress and their impact on yield of winter wheat is systematically analyzed. The crop model SIMPLACE was applied for the period 1980-2011 across Germany at a resolution of 1 km x 1 km. Weather and soil input data and model output data were then aggregated to 10 km x 10 km, 25 km x 25 km, 50 km x 50 km and 100 km x 100 km resolution to analyze the aggregation effect on heat and drought stress and crop yield. We found that aggregation of model input and output data barely influenced the mean and median of heat and drought stress reduction factors and crop yields simulated across Germany. However, data aggregation resulted in less spatial variability of model results and a reduced severity of simulated stress events, particularly for regions with high heterogeneity in weather and soil conditions. Comparisons of simulations at coarse resolution with those at high resolution showed distinct patterns of positive and negative deviations which compensated each other so that aggregation effects for large regions were small for mean or median yields. Therefore, modelling at a resolution of 100 km x 100 km was sufficient to determine mean wheat yield as affected by heat and drought stress for Germany. Further research is required to clarify whether the results can be generalized across crop models differing in structure and detail. Attention should also be given to better understand the effect of data resolution on interactions between heat and drought impacts. (C) 2015 Elsevier B.V. All rights reserved.
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