| Home | [1–10] << 11 12 13 14 15 >> |
|
Webber, H., Ewert, F., Olesen, J. E., Müller, C., Fronzek, S., Ruane, A. C., et al. (2018). Diverging importance of drought stress for maize and winter wheat in Europe. Nat. Comm., 9, 4249.
Abstract: Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
|
|
Rodriguez, A., Ruiz-Ramos, M., Palosuo, T., Carter, T. R., Fronzek, S., Lorite, I. J., et al. (2019). Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations. Agricultural and Forest Meteorology, 264, 351–362.
Abstract: unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivwn L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.
|
|
Bai, H., Tao, F., Xiao, D., Liu, F., & Zhang, H. (2016). Attribution of yield change for rice-wheat rotation system in China to climate change, cultivars and agronomic management in the past three decades. Clim. Change, 135(3-4), 539–553.
Abstract: Using the detailed field experiment data from 1981 to 2009 at four representative agro-meteorological experiment stations in China, along with the Agricultural Production System Simulator (APSIM) rice-wheat model, we evaluated the impact of sowing/transplanting date on phenology and yield of rice-wheat rotation system (RWRS). We also disentangled the contributions of climate change, modern cultivars, sowing/transplanting density and fertilization management, as well as changes in each climate variables, to yield change in RWRS, in the past three decades. We found that change in sowing/transplanting date did not significantly affect rice and wheat yield in RWRS, although alleviated the negative impact of climate change to some extent. From 1981 to 2009, climate change jointly caused rice and wheat yield change by -17.4 to 1.5 %, of which increase in temperature reduced yield by 0.0-5.8 % and decrease in solar radiation reduced it by 1.5-8.7 %. Cultivars renewal, modern sowing/transplanting density and fertilization management contributed to yield change by 14.4-27.2, -4.7- -0.1 and 2.3-22.2 %, respectively. Our findings highlight that modern cultivars and agronomic management compensated the negative impacts of climate change and played key roles in yield increase in the past three decades.
Keywords: nitrogen-use efficiency; crop yields; winter-wheat; temperature; responses; impacts; decline; models; trends; plain
|
|
Schauberger, B., Rolinski, S., & Müller, C. (2016). A network-based approach for semi-quantitative knowledge mining and its application to yield variability. Environ. Res. Lett., 11(12), 123001.
Abstract: Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.
|
|
Ruane, A. C., Hudson, N. I., Asseng, S., Camarrano, D., Ewert, F., Martre, P., et al. (2016). Multi-wheat-model ensemble responses to interannual climate variability. Env. Model. Softw., 81, 86–101.
Abstract: We compare 27 wheat models’ yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models’ climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R-2 <= 0.24) was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Published by Elsevier Ltd.
|