Abstract: Integrated conservation and management of agricultural areas affected by the current global warming represents a priority at international level following the implementation of the principles of sustainable agriculture and adaptation measures. Transylvanian Plain (TP), with an area of 395,616 ha is of great agricultural importance for Romania, but with an afforestation degree of only 6.8% and numerous degradation phenomena of farmland, it has the lowest degree of sustainability to climate change. Monitoring of agro-climatic indicators and their evolution in between 2008-2014 and the analysis of the obtained data underlie the technological development of recommendations tailored to current favorable conditions for the main crops. Results obtained show that: the thermal regime of the soils in TP is of mesic type and the hydric regime is ustic; multiannual average of temperature in soil at 10 cm depth is 11.40ºC, respectively at 50 cm depth is 10.24ºC; the average yearly air temperature is 11.17ºC; multiannual average of soil moisture is 0.227 m3/m3; Multiannual average value of precipitation is 466.52 mm. During the studied period, compared with data series available (1961-1990; 1901-2000), clear decrease of the average quantities of rainfall especially during critical periods for crops, and increases in average temperatures for the entire year can be noticed. Between June and August the highest temperature difference were recorded, differences of +3.09°C to +3.65°C. There is an increase phenomenon of drought and heat; determined indicators show that most values, 61.11%, are commensurate with a semiarid climate. Aggression peaks are in February-April, July, and October-November, and for the whole period, in 19.43% of the cases are favorable and very favorable conditions for triggering erosion. Recommended agro-technical measures to limit and counteract the effects of drought, as a climatic phenomenon with major risk to agriculture in TP, refer to: i) use of a biological material resistant to water stress and heat; ii) use of management practices favorable for accumulation of, conservation and the efficient use of water from rainfall; iii) operating a system of conservation agriculture based on soil protection and desertification avoidance.

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.

Abstract: In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. (C) 2012 Elsevier B.V. All rights reserved.