Abstract: The crop growth model HERMES was used to model crop rotation cycles at 12 experimental sites in the Czech Republic. A wide range of crops (spring and winter barley, winter wheat, maize, potatoes, sugar beet, winter rape, oats, alfalfa and grass), cultivated between 1981 and 2009 under various soil and climatic conditions, were included. The model was able to estimate the yields of field crop rotations at a reasonable level, with an index of agreement (IA) ranging from 0.82 to 0.96 for the calibration database (the median coefficient of determination (R-2) was 0.71), while IA for verification varied from 0.62 to 0.93 (median R-2 was 0.78). Grass yields were also estimated at a reasonable level of accuracy. The estimates were less accurate for the above-ground biomass at harvest (the medians for IA were 0.76 and 0.72 for calibration and verification, respectively, and analogous medians of R-2 were 0.50 and 0.49). The soil mineral nitrogen (N) content under the field crops was simulated with good precision, with the IA ranging from 0.49 to 0.74 for calibration and from 0.43 to 0.68 for verification. Generally, the soil mineral N was underestimated, and more accurate results were achieved at locations with intensive fertilization. Simulated yields, soil N, water and organic carbon (C) contents were compared with long-term field measurements at Ne. mc. ice, located within the fertile Moravian lowland. At this station, all of the observed parameters were reproduced with a reasonable level of accuracy. In the case of the organic C content, HERMES reproduced a decrease ranging from c. 85 to 77 tonnes (t)/ha (for the 0-0.3 m soil layer) between the years 1980 and 2007. In spite of its relatively simple approach and restricted input data, HERMES was proven to be robust across various conditions, which is a precondition for its future use for both theoretical and practical purposes.

Abstract: Global climate change is predicted to shift seasonal temperature and precipitation patterns. An increasing frequency of extreme weather events such as heat waves and prolonged droughts is predicted, but there are high levels of uncertainty about the nature of local changes. Crop adaptation will be important in reducing potential damage to agriculture. Crop diversity may enhance resilience to climate variability and changes that are difficult to predict. Therefore, there has to be sufficient diversity within the set of available cultivars in response to weather parameters critical for yield formation. To determine the scale of such ‘weather response diversity’ within barley (Hordeum vulgare L.), an important crop in northern conditions, the yield responses of a wide range of modern and historical varieties were analysed according to a well-defined set of critical agro-meteorological variables. The Finnish long-term dataset of MTT Official Variety Trials was used together with historical weather records of the Finnish Meteorological Institute. The foci of the analysis were firstly to describe the general response of barley to different weather conditions and secondly to reveal the diversity among varieties in the sensitivity to each weather variable. It was established that barley yields were frequently reduced by drought or excessive rain early in the season, by high temperatures at around heading, and by accelerated temperature sum accumulation rates during periods 2 weeks before heading and between heading and yellow ripeness. Low temperatures early in the season increased yields, but frost during the first 4 weeks after sowing had no effect. After canopy establishment, higher precipitation on average resulted in higher yields. In a cultivar-specific analysis, it was found that there were differences in responses to all but three of the studied climatic variables: waterlogging and drought early in the season and temperature sum accumulation rate before heading. The results suggest that low temperatures early in the season, delayed sowing, rain 3-7 weeks after sowing, a temperature change 3-4 weeks after sowing, a high temperature sum accumulation rate from heading to yellow ripeness and high temperatures (25 degrees C) at around heading could mostly be addressed by exploiting the traits found in the range of varieties included in the present study. However, new technology and novel genetic material are needed to enable crops to withstand periods of excessive rain or drought early in the season and to enhance performance under increased temperature sum accumulation rates prior to heading.

Abstract: The determination of the impact of climate change on crop yield at a regional scale requires the development of new modelling methodologies able to generate accurate yield estimates with reduced available data. In this study, different simulation approaches for assessing yield have been evaluated. In addition to two well-known models (AquaCrop and Stewart function), a methodological proposal considering a simplified approach using an empirical model (SOM) has been included in the analysis. This empirical model was calibrated using rainfed sunflower experimental field data from three sites located in Andalusia, southern Spain, and validated using two additional locations, providing very satisfactory results compared with the other models with higher data requirements. Thus, only requiring weather data (accumulated rainfall from the beginning of the season fixed on September 1st, and maximum temperature during flowering) the approach accurately described the temporal and spatial yield variability observed (RMSE = 391 kg ha(-1)). The satisfactory results for assessing yield of sunflower under semi-arid conditions obtained in this study demonstrate the utility of empirical approaches with few data requirements, providing an excellent decision tool for climate change impact analyses at a regional scale, where available data is very limited.