Abstract: This study reports economic thresholds for the lettuce aphid Nasonovia ribisnigri (Mosley), based exclusively on cosmetic damage, that is, presence or absence of aphids at harvest time. Field trials were conducted in La Poveda Experimental Farm, Madrid (Spain) during autumn (2004 and 2005) and spring (2005 and 2006). Plants were arranged in plots and just before the formation of lettuce hearts they were infested with different densities of N. ribisnigri. Two days later, half of each plot was treated with tau-fluvalinate (Klartan24AF) and the other half remained as an untreated control. Economic thresholds were obtained from nonlinear regressions calculated between the percentage of commercial plants at the end of the crop cycle for both, treated and untreated semiplots, and the different initial densities of N. ribisnigri per plant. Two criteria were used to consider a commercial lettuce plant: a conservative estimate (0 aphids/plant) and a lax one (< 5 aphids/plant). Thus, an economic threshold was established for each season and criterium. The economic thresholds that were obtained with the most and least conservative criteria were in spring 0.06 and 0.12 aphids per plant, and in autumn 0.07 and 0.13 aphids per plant, respectively. These results show that to avoid cosmetic damage, insecticide sprays are required when a very low aphid density is detected in lettuce seedlings soon after transplant.

Abstract: The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.

Abstract: ARMOSA is a dynamic simulation model which was developed to simulate crop growth and development, water and nitrogen dynamics under different pedoclimatic conditions and cropping systems in the arable land. The model is meant to be a tool for the evaluation of the impact of different crop management practices on soil nitrogen and carbon cycles and groundwater nitrate pollution. A large data set collected over three to six years from six monitoring sites in Lombardia plain was used to calibrate and validate the model parameters. Measured meteorological data, soil chemical and physical characterizations, crop-related data of different cropping systems allowed for a proper parameterization. Fit indexes showed the reliability of the model in adequately predicting crop-related variables, such as above ground biomass (RRMSE=11.18, EF=0.94, r=0.97), Leaf Area Index maximum value (RRMSE=8.24, EF=0.37, r=0.72), harvest index (RRMSE=19.4, EF=0.32, r=0.74), and crop N uptake (RRMSE=20.25, EF=0.69, r=0.85). Using two different one-year data set from each monitoring site, the model was calibrated and validated, getting to encouraging results: RRMSE=6.28, EF=0.52, r=0.68 for soil water content at different depths, and RRMSE=34.89, EF=0.59, r=0.75 for soil NO3-N content along soil profile. The simulated N leaching was in full agreement with measured data (RRMSE=26.62, EF=0.88, r=0.98).