Abstract: Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.

Abstract: Given the energetic, demographic and the climatic challenges faced today, we designed a combined food and energy (CFE) production system integrating food, fodder and mixed belts of Salix, Alnus and Corylus sp. as bioenergy belts. The objective was to assess the shoot dry weight-stem diameter allometric relationship based on stem diameter at 10 (SD10) and 55 cm (SD55) from the shoot base in the mixed bioenergy belts. Allometric relations based on SD10 and SD55 explained 90-96% and 90-98% of the variation in shoot dry weights respectively with no differences between the destructive and the non-destructive methods. The individual stool yields varied widely among the species and within willow species with biomass yield range of 37.60-92.00 oven dry tons (ODT) ha (1) in 4-year growth cycle. The biomass yield of the bioenergy belt, predicted by allometric relations was 48.84 ODT ha 1 in 4-year growth cycle corresponding to 12.21 ODT ha (1) year (1). The relatively high biomass yield is attributed to the border effects and the ‘fertilizing effect’ of alder due to nitrogen fixation, benefitting other SWRC components. On termination of 4-year growth cycle, the bioenergy belts were harvested and the biomass yield recorded was 12.54 ODT ha (1) year (1), in close proximity to the biomass yield predicted by the allometric equations, lending confidence and robustness of the model for biomass yield determination in such integrated agro-ecosystem. (C) 2014 Elsevier Ltd. All rights reserved.

Abstract: The numerical crop growth model Italian Vineyard Integrated Numerical model for Estimating physiological values (IVINE) was developed in order to evaluate environmental forcing effects on vine growth. The IVINE model simulates vine growth processes with parameterizations, allowing the understanding of plant conditions at a vineyard scale. It requires a set of meteorology data and soil water status as boundary conditions. The primary model outputs are main phenological stages, leaf development, yield, and sugar concentration. The model requires setting some variety information depending on the cultivar: At present, IVINE is optimized for Vitis vinifera L. Nebbiolo, a variety grown mostly in the Piedmont region (northwestern Italy). In order to evaluate the model accuracy, IVINE was validated using experimental observations gathered in Piedmontese vineyards, showing performances similar or slightly better than those of other widely used crop models. The results of a sensitivity analysis performed to highlight the effects of the variations of air temperature and soil water potential input variables on IVINE outputs showed that most phenological stages anticipated with increasing temperatures, while berry sugar content saturated at about 25.5 °Bx. Long-term (60 years, in the period 1950–2009) simulations performed over a Piedmontese subregion showed statistically significant variations of most IVINE output variables, with larger time trend slopes referring to the most recent 30-year period (1980–2009), thus confirming that ongoing climate change started influencing Piedmontese vineyards in 1980.