Root-driven modification of soil hydraulic properties may influence crop water availability and resilience under climatic variability, yet genotype-specific effects on rhizosphere hydrology remain poorly understood.This study examined how contrasting root architectures of four wheat genotypes, two modern cultivars (Paragon, Bologna) and two landraces (Watkins 238, Senatore Cappelli), modify soil hydraulic behaviour under field conditions. Baseline (pre-sowing), bulk (fallow), and rhizosphere soils were characterised for water retention, unsaturated hydraulic conductivity, and pore size distributions derived from fitted retention curves. Root morphological traits were quantified using crown imaging and root scanning.Genotypes differed markedly in both root traits and their associated hydraulic responses. Paragon, Watkins 238, and Senatore Cappelli exhibited bimodal pore size distributions, characterised by two peaks in the pore radius probability density function, whereas Bologna, baseline, and bulk soil displayed unimodal behaviour with a single dominant pore domain. Watkins 238 achieved the most significant improvement in hydraulic properties, increasing plant-available water to 0.21 m3 m−3 (vs. 0.15 m3 m−3 in baseline), driven by a lower permanent wilting point (0.12 m3 m−3) and higher field capacity (0.34 m3 m−3). Watkins 238 also displayed higher unsaturated conductivity, with hydraulic conductivity at field capacity and wilting point significantly higher than baseline and Bologna, and an area under the K(ψ) curve of 46.1, more than double that of other genotypes.Root network area, root diameter, and branching frequency were significantly correlated with water retention parameters and conductivity metrics (p < 0.05 to p < 0.001), indicating that root systems reshape the pore network and soil hydrological properties. These findings suggest genotype-specific modification of hydraulic processes and highlight opportunities to leverage root traits to enhance water availability and resilience under water-limited conditions.

Agrivoltaic (AV) systems represent a promising strategy for integrating renewable energy production and agricultural activity on the same land unit, while contributing to soil water conservation under increasingly frequent drought conditions. This preliminary, single-site case study investigates the effects of a horizontal biaxial tracking AV system on soil water dynamics in a durum wheat field in the Po Valley (Borgo Virgilio, Mantua, Italy) over a full monitoring period, covering the final crop growth stages and the post-harvest bare soil phase (May–December 2024). Monitoring of soil temperature, volumetric water content (VWC), and soil water potential (SWP) was conducted at four depths (15, 30, 45, and 60 cm) at one representative monitoring station per treatment, comparing soil under AV cover (AVC) and in unshaded conditions (UC), located 10 m apart. Paired VWC and SWP measurements were used to derive site-specific soil water characteristic curves (SWCCs) and to calibrate the agro-hydrological model CRITERIA-1D, which was used to estimate available water (AW) in the first 80 cm of depth for both treatments. Measured VWC values were higher in the AVC profile than in the UC profile at all monitored depths throughout the May–September period, with differences persisting, although at lower values through October–December. Estimated AW was consistently higher under AVC than in UC during both the dry and wet periods. Despite higher VWC, the AVC profile showed more negative average SWP values at all depths during summer. This pattern is consistent with the shape of the derived SWCCs and may point to differences in water-retaining capacity between the two profiles, possibly related to structural modifications induced by 13 years of AV system operation. These preliminary findings suggest that AV systems could potentially improve soil water availability in the root zone of rainfed cereal crops and propose the hypothesis that long-term AV cover may act as a driver of changes in soil hydraulic properties, with implications for the sustainability and climate resilience of dryland farming systems. However, given the design of this case study, with only one monitoring point per treatment, the observed differences reflect the specific monitored locations and cannot fully disentangle the AV treatment effect from pre-existing spatial heterogeneity in soil properties. The preliminary results obtained in this study should therefore not be generalised beyond the specific conditions of this case study, and the interpretations proposed here should be treated as unproven hypotheses rather than established conclusions. Further studies with spatial replication and multi-year monitoring are needed to confirm these patterns.

In a contest of climate change and increasing world population, the optimization of agricultural inputs such as water and fertilizer is of utmost importance to assure crop quality and to limit the impact of agriculture. For these reasons, the need for robust methods of agricultural modeling and forecasting has never been clearer. In particular, forecasting the water budget is a key tool for reducing water wastage and maximizing agricultural production. Although process-based models are largely used in off-line simulation and studies, their operational use in forecasting the irrigation requirements of a specific crop remains complex and the level of accuracy achieved is often insufficient since, if used alone, process-based simulation systems fail to capture the soil and plant dynamic behaviors. To overcome these limitations we propose an integrated system coupling Orchard3D-Lab, an innovative three-dimensional process-based model specifically devised for fruit trees, with a state initialization procedure that exploits a two-dimensional probe grid. The system is capable of auto-tuning its parameters on a specific soil and of providing a precise forecast that can support precision watering policies on a weekly horizon. A large set of tests has been conducted on Kiwifruit in an experimental farm in Northern Italy. Besides accuracy, tests proved the robustness of the system even in the presence of a limited set of examples for parameter auto-tuning. This makes our approach concretely applicable in real-world settings.

Plant roots play a fundamental role in maintaining soil health. Although a broad range of root traits have been reported, few studies have attempted to link root morphology with soil structure. Here, we used shovelomics to characterize the root morphology of a wheat cultivar (Paragon), and two landraces (Senatore-Cappelli, and Watkins238), and advanced soil pore and root network X-ray computed tomography to assess their impact on soil morphology at cylinder and aggregate scales. Bare soil was analyzed as a control. Minkowski functionals and percolation theory parameters were computed to characterize soil pore network morphology. Bioporosity at the cylinder scale was significantly different for all cultivars compared to the bare soil. Bare soil presented the largest structural pore volume and the smallest biopore volume, this suggesting rapid degradation of biopores. At the cylinder scale, biopore characteristics were significantly different between Senatore-Cappelli and Watkins238, with Senatore-Cappelli exhibiting more pores with diameters >1 mm. The parameters from percolation theory revealed notable differences between the rhizospheric and bare soil samples. We found significant differences between genotypes, finding statistically significant correlations among root morphology parameters and pore network geometry.Total imaged porosity and total root volume were limited descriptors of the effect of roots on soil structure, which is better quantified by pore network connectivity measurements. Our findings confirm previous studies on the relationship between root traits and soil properties and highlight the potential of our experimental approach to explore how different genotypes may influence soil morphology, paving the way for future applications in plant phenotyping.

This paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards’ equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known.

Facing global warming’s consequences is a major issue in the present times. Regarding the climate, projections say that heavy rainfalls are going to increase with high probability together with temperature rise; thus, the hazard related to rainfall-induced shallow landslides will likely increase in density over susceptible territories. Different modeling approaches exist, and many of them are forced to make simplifications in order to reproduce landslide occurrences over space and time. Process-based models can help in quantifying the consequences of heavy rainfall in terms of slope instability at a territory scale. In this study, a narrative review of physically based deterministic distributed models (PBDDMs) is presented. Models were selected based on the adoption of the infinite slope scheme (ISS), the use of a deterministic approach (i.e., input and output are treated as absolute values), and the inclusion of new approaches in modeling slope stability through the ISS. The models are presented in chronological order with the aim of drawing a timeline of the evolution of PBDDMs and providing researchers and practitioners with basic knowledge of what scholars have proposed so far. The results indicate that including vegetation’s effects on slope stability has raised in importance over time but that there is still a need to find an efficient way to include them. In recent years, the literature production seems to be more focused on probabilistic approaches.

The research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An innovative statistical function was developed in order to predict the evolution of soil–water content from precipitation and air temperature. The data were analysed in a framework of robust statistics by using a combination of robust parametric and non-parametric models. Specifically, a statistical model, which includes the typical seasonal trend of field data, has been set up. The proposed model showed that relevant features present in the field of experimental data can be obtained and correctly described for predictive purposes.

Root reinforcement, provided by plants in soil, can be exerted by a mechanical effect, increasing soil shear strength for the presence of roots, or by a hydrological effect, induced by plant transpiration. No comparisons have been still carried out between mechanical and hydrological reinforcements on shallow slope stability in typical agroecosystems. This paper aims to compare these effects induced by sowed fields and vineyards and to assess their effects towards the shallow slope staibility. Root mechanical reinforcement has been assessed through Root Bundle Model-Weibull. Root hydrological reinforcement has been evaluated using an empirical relationship with monitored or modelled pore water pressure. Each reinforcement has been inserted in a stability model to quantify their impacts on susceptibility towards shallow landslides. Considering the same environment, corresponding to a typical agroecosystem of northern Italian Apennines, land use has significant effects on saturation degree and pore water pressure, influencing hydrological reinforcement. Root hydrological reinforcement effect is higher in summer, although rainfall-induced shallow landslides rarely occur in this period due to dry soil conditions. Instead, in wet and cold periods, when shallow landslides can develop more frequently, the stabilizing contribution of mechanical reinforcement is on average higher than the hydrological reinforcement. In vineyards, the hydrological reinforcement effect could be observed also during autumn, winter and spring periods, giving a contribution to slope stability also in these conditions. This situation occurs when plants uptake enough water from soil to reduce significantly pore water pressure, guaranteeing values of hydrological reinforcement of 1-3 kPa at 1 m from ground, in agreement with measured mechanical root reinforcement (up to 1.6 kPa). These results suggest that both hydrological and mechanical effects of vegetation deserve high regard in susceptibility towards shallow landslides, helping in selection of the best land uses to reduce probability of occurrence of these failures over large territories.

The aim of this research is testing a new statistical model able to describe the interaction between soil and atmosphere. The model is based on a robust parametric LTS (Least Trimmed Squares) method and harmonic functions. It has been developed taking into account field measurements of quantities involved in both infiltration and evapo-transpiration phenomena, such as volumetric water content, soil-water potential, air temperature, rainfall amounts and solar radiation. The proposed statistical model allows assessing the volumetric water content at different sites using only time series of daily rainfall amount as input data. The model was applied in different test sites, whose data were assumed by the International Soil Moisture Network (ISMN). In fact, ISMN allows getting free time series of soil and meteorological data from monitoring stations all over the world. This note shows how the proposed model is accurate with respect to field data in estimating the volumetric water content in different soils, climates and depths. Future implications of this research will regard water content predictions, especially in areas where field data are scarce. Since the proposed LTS algorithm is very efficient and the computational workload is rather low, the possibility of coupling it with a slope stability analysis over large areas will be investigated, in order to get a distributed real-time model for shallow landslides susceptibility.

AbstractChapter 8 focus on a cornerstone of modern statistics, Bayesian inference. Here Bayesian inference is applied for description of autoregressive processes. After introducing the main concepts, examples applied to real data of temperature and CO2 concentration in Antarctica, as well as radar detection, are presented. Bayesian analysis of the Poisson process is presented with the waiting-time paradox. The Chapter ends with an application to lighthouse detection as a remarkable example of Bayesian inference.

AbstractThe book ends with Chapter 12, which discussed the very definition of a random process, the mathematical definition of randomness and a discussion about definition of entropies. This discussion is developed into a general framework and its implications for scientific inquire.

Abstract Poisson’s processes are presented. Derivation of the well-known distribution is presented as well as homogeneous and non homogeneous Poisson processes are discussed. The distribution of rare events is discussed and examples are presented.

Abstract The analysis of random processes requires a thorough understanding of spectrum and noise analysis. In Chapter 6, Fourier transforms for deterministic and stochastic time series are presented, with application to spectrum analysis. The spectrum analysis for stochastic signal is presented with application to white and coloured noise. The Singular Spectrum Analysis technique is also presented, for analysis and removal of trend.

AbstractIn Chapter 3, the reader finds an in-depth description of the fundamental theory of stochastic processes. The Chapter introduces concepts of continuous and discrete random variables, stationarity, ergodicity, recurrent and transient states, Markov processes and Markov chains. Examples from mathematics and physics are presented to exemplify random processes such as the Buffon’s needle and the Ehrenfest Urn Model.

One the cornerstone of random processes, random walk, is described in Chapter 4. The concepts of absorbing and reflecting barriers are presented along with the gambler’s ruin example, as well as a two-dimensional random walk code and discussion about random walk applied to the process of Brownian motion.

AbstractChapter 5 enters into stochastic time series analysis with the description of moving average, autoregressive and autoregressive moving average processes. Seasonal time series analysis is introduced with examples applied to measures temperature and the hydrological cycle.

AbstractThe modelling of stochastic processes depends on the accuracy of the estimators derived in the process analysis. The problem of accuracy is discussed in Chapter 10, with examples on averaging of time series, the batch means method, moving bootstrap and other techniques to improve accuracy in random processes modelling.

AbstractRandom process are used as tools for random search in minimization algorithm, as an alternative to gradient-based searching algorithm used for instance in least square optimization. Genetic algorithms are presented in the Chapter 9, with application to non linear fitting, autoregressive moving average models. As an example of improved optimization with respect to other approaches, the travelling salesman problem is here solved with genetic algorithms.

Laser diffraction analysis is a fast, reliable and automated method that provides detailed and highly resolved soil and sediment particle size distribution. In recent studies, the methods were compared against independent methods based on direct observation of particles by digital imaging. The data showed that laser diffraction results were in better agreement with the digital imaging independent method than with sedimentation-based methods. However, analysis was performed over a limited number of samples. In this study, 47 soil samples with a wide range of textural properties were analyzed with Laser Diffraction, Pipette, Sieving, Sedigraph and Digital Imaging methods. Detailed statistical analysis using Altman plots and Honest Significant Difference tests demonstrated (at 95% significance) that the five methods do not show statistically significant differences for grain sizes above 100 mu m. However, in the lower end of the size range, i.e. less or equal to 50 mu m, Laser Diffraction showed much better agreement with the reference method selected for comparison, which was Digital Imaging. New regression equations were derived with slope coefficients for linear regressions between Pipette and Laser of 0.2952 (R-2 = 0.8625) for clay, 1.4261 (R-2 = 0.5746) for silt and 1.031 (R-2 = 0.6586) for sand, classified with the International Soil Science Society (ISSS) system. For the United States Department of Agriculture (USDA) classification system, the slopes were: 0.261 (R-2 = 0.8625) for clay, 1.3493 (R-2 = 0.8179) for silt and 1.063 (R-2 = 0.888) for sand. These data were consistent with previous studies. Based on regression and equivalent diameters, Laser Diffraction data were represented on textural triangles for classification, allowing for employing Laser Diffraction for soil texture classification. Two alternative for representing the Laser Diffraction data in textural triangles were employed: (1) using regression equations to convert data to be represented on the standard triangles and (2) modify the upper limit for the clay range, from 2 to 8 mu m. Finally, based on the additional evidence presented in this research, demonstrating that the Laser Diffraction was in better agreement with the optical method with respect to traditional sedimentation methods, it is suggested that the standards for particle size analysis be changed from sedimentation to Laser Diffraction methodologies.

Assessing hazard of rainfall-induced shallow landslides represents a challenge for the risk management of urbanized areas for which the setting up of early warning systems, based on the reconstruction of reliable rainfall thresholds and rainfall monitoring, is a solution more practicable than the delocalization of settlements and infrastructures. Consequently, the reduction in uncertainties affecting the estimation of rainfall thresholds conditions, leading to the triggering of slope instabilities, is a fundament task to be tackled. In such a view, coupled soil hydrological monitoring and physics-based modeling approaches are presented for estimating rainfall thresholds in two different geomorphological environments prone to shallow landsliding. Based on the comparison of results achieved for silty–clayey soils characterizing Oltrepò Pavese area (northern Italy) and ash-fall pyroclastic soils mantling slopes of Sarno Mountains ridge (southern Italy), this research advances the understanding of the slope hydrological response in triggering shallow landslides. Among the principal results is the comprehension that, mainly depending on geological and geomorphological settings, geotechnical and hydrological properties of soil coverings have a fundamental control on the timing and intensity of hydrological processes leading to landslide initiation. Moreover, results obtained show how the characteristics of the soil coverings control the slope hydrological response at different time scales, making the antecedent soil hydrological conditions a not negligible factor for estimating landslide rainfall thresholds. The approaches proposed can be conceived as an adaptable tool to assess hazard to initiation of shallow rainfall-induced landslides and to implement early-warning systems from site-specific to distributed (catchment or larger) scales.