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Francioni, M., D’Ottavio, P., Lai, R., Trozzo, L., Budimir, K., Foresi, L., et al. (2019). Seasonal Soil Respiration Dynamics and Carbon-Stock Variations in Mountain Permanent Grasslands Compared to Arable Lands. Agriculture-Basel, 9(8), 165.
Abstract: Permanent grasslands provide a wide array of ecosystem services. Despite this, few studies have investigated grassland carbon (C) dynamics, and especially those related to the effects of land-use changes. This study aimed to determine whether the land-use change from permanent grassland to arable lands resulted in variations in the soil C stock, and whether such variations were due to increased soil respiration or to management practices. To address this, seasonal variations of soil respiration, sensitivity of soil respiration to soil temperature (Q(10)), and soil C stock variations generated by land-use changes were analyzed in a temperate mountain area of central Italy. The comparisons were performed for a permanent grassland and two adjacent fields, one cultivated with lentil and the other with emmer, during the 2015 crop year. Soil respiration and its heterotrophic component showed different spatial and temporal dynamics. Annual cumulative soil respiration rates were 6.05, 5.05 and 3.99 t C ha(-1) year(-1) for grassland, lentil and emmer, respectively. Both soil respiration and heterotrophic soil respiration were positively correlated with soil temperature at 10 cm depth. Derived Q(10) values were from 2.23 to 6.05 for soil respiration, and from 1.82 to 4.06 for heterotrophic respiration. Soil C stock at over 0.2 m in depth was 93.56, 48.74 and 46.80 t C ha(-1) for grassland, lentil and emmer, respectively. The land-use changes from permanent grassland to arable land lead to depletion in terms of the soil C stock due to water soil erosion. A more general evaluation appears necessary to determine the multiple effects of this land-use change at the landscape scale.
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Maiorano, A., Martre, P., Asseng, S., Ewert, F., Müller, C., Rötter, R. P., et al. (2016). Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles. Field Crops Research, 202, 5–20.
Abstract: To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.
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