Antonello Bonfante

A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability

We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heat-waves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches.

Future Soil Issues

This chapter aims to address future soil issues from a specific viewpoint, namely the need of our country. It starts by analysing both Italy’s physical landscape along with the social and economic structure and its population. From this basis, the chapter focuses on country limitations and potentialities and identifies the most important country-specific contributions by soil science aiming towards the well-being of Italy. We claim that future soil scientist must give major contributions in the followings: (1) spatial planning of the landscape (oriented to urban planning), (2) archaeology and natural heritage, (3) agriculture and forestry combining productivity and environmental protection, (4) hydrogeological risks, (5) integrated landscape management. In order to get these results, the authors anticipate that soil science requires a novel vision, novel approaches and most importantly a novel education combining in-depth specialized knowledge with a very good but broad and basic soil knowledge.

Spatial analysis of clay content in soils using neurocomputing and pedological support: a case study of Valle Telesina (South Italy)

The spatial analysis of soil properties by means of quantitative methods is useful to make predictions at sampled and unsampled locations. Two most important characteristics are tackled, namely the option of using complex and nonlinear models in contrast with (also very simple) linear approaches, and the opportunity to build spatial inference tools using horizons as basic soil components. The objective is to perform the spatial analysis of clay content for validation purposes in order to understand whether nonlinear methods can manage soil horizons, and to quantitatively measure how much they outperform simpler methods. This is addressed in a case study in which relatively few records are available to calibrate (train) such complex models. We built three models which are based on artificial neural networks, namely single artificial neural networks, median neural networks and bootstrap aggregating neural networks with genetic algorithms and principal component regression (BAGAP). We perform a validation procedure at three different levels of soil horizon aggregations (i.e. topsoil, profile and horizon pedological supports). The results show that neurocomputing performs best at any level of pedological support even when we use an ensemble of neural nets (i.e. BAGAP), which is very data intensive. BAGAP has the lowest RMSE at any level of pedological support with

SWAP, CropSyst and MACRO comparison in two contrasting soils cropped with maize in Northern Italy

\hbox {RMSE}_\mathrm{BAGAP}^{Topsoil} = 7.2\,\%

A physically oriented approach to analysis and mapping of terroirs

RMSEBAGAPTopsoil=7.2%,

The role of soil series in quantitative land evaluation when expressing effects of climate change and crop breeding on future land use

\hbox {RMSE}_\mathrm{BAGAP}^{Profile} = 7.8\,\%