Lorenzo Brilli

Modelling olive trees and grapevines in a changing climate

The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2-enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed. Empirical models are generally unreliable for their possible application in a changing climate.Complex process-based models have already the potential to provide reliable simulations for a changing climate.There is a clear need to improve the simulation of crop processes in response to increased CO2 and higher temperatures.Process-based models should be improved to simulate soil biochemical processes.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Conservation Agriculture and Climate Change

This chapter review aims at developing a clear understanding of the impacts and benefits of conservation agriculture (CA) with respect to climate change, and examining if there are any misleading findings at present in the scientific literature. Most of the world’s agricultural soils have been depleted of organic matter and soil health over the years under tillage-based agriculture (TA), compared with their state under natural vegetation. This degradation process can be reversed and this chapter identifies the conditions that can lead to increase in soil organic matter content and improvement in soil health under CA practices which involve minimum soil disturbance, maintenance of soil cover, and crop diversity. The chapter also discusses the need to refer to specific carbon pools when addressing carbon sequestration, as each carbon category has a different turnover rate. With respect to greenhouse gas emissions, sustainable agricultural systems based on CA principles are described which result in lower emissions from farm operations as well as from machinery manufacturing processes, and that also help to reduce fertilizer use. This chapter describes that terrestrial carbon sequestration efficiently be achieved by changing the management of agricultural lands from high soil disturbance, as TA practices to low disturbance, as CA practices, and by adopting effective nitrogen management practices to provide a positive nitrogen balance for carbon sequestration. However, full advantages of CA in terms of carbon sequestration can usually be observed only in the medium to longer term when CA practices and associated carbon sequestration processes in the soil are well established.

Simulation of olive grove gross primary production by the combination of ground and multi-sensor satellite data

Abstract We developed and tested a methodology to estimate olive (Olea europaea L.) gross primary production (GPP) combining ground and multi-sensor satellite data. An eddy-covariance station placed in an olive grove in central Italy provided carbon and water fluxes over two years (2010–2011), which were used as reference to evaluate the performance of a GPP estimation methodology based on a Monteith type model (modified C-Fix) and driven by meteorological and satellite (NDVI) data. A major issue was related to the consideration of the two main olive grove components, i.e. olive trees and inter-tree ground vegetation: this issue was addressed by the separate simulation of carbon fluxes within the two ecosystem layers, followed by their recombination. In this way the eddy covariance GPP measurements were successfully reproduced, with the exception of two periods that followed tillage operations. For these periods measured GPP could be approximated by considering synthetic NDVI values which simulated the expected response of inter-tree ground vegetation to tillages.

C and N models Intercomparison – benchmark and ensemble model estimates for grassland production

Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international bench- marking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty in model predictions (Asseng et al. , 2013). Simulations of C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (Sandor et al. , 2016) and complicated by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide (C-N MIP) and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C – N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model- intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.

Influence of Interannual Meteorological Variability on Yeast Content and Composition in Sangiovese Grapes

Kloeckera apiculata and Candida zemplinina represent almost the totality of non-Saccharomyces yeasts in grape and fresh musts. These yeasts can accumulate secondary metabolites that are commonly known to increase the aromatic complexity of wine; thus, variations in their total number and ratio may lead to changes in wine taste and flavor. These variations are determined by numerous variables, including climate conditions and viticultural practices that can affect the environment of the yeasts and, in turn, their quantity and composition. This may consequently give rise to changes in the final sensory characteristics of a wine. This work assessed the long-term relationship (1997–2012) between yeast quantity and composition and the main meteorological variables (air temperature, relative humidity, and rainfall) in a Sangiovese vineyard located at the Brunello di Montalcino Wine Consortium (Tuscany). Results indicated that weather conditions 25 to 30 days before harvesting were correlated with total yeasts, particularly rainfall and relative humidity (r ~0.8). Moreover, K. apiculata and C. zemplinina were found to be correlated with temperature 10 days before grape harvest at the same time as leaf pulling (r = −0.66 and r = 0.52, respectively). These results suggest that both climate and management may affect microbial community and its composition.

The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ± 74 g C m-2 yr-1 (animal density reduction) and -81 ± 74 g C m-2 yr-1 (N and animal density reduction), against the baseline of -30.5 ± 69.5 g C m-2 yr-1 (LSU [livestock units] ≥ 0.76 ha-1 yr-1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m-2 yr-1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU-1 yr-1 across sites). The highest N2O-N intensities (N2O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.