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The 3rd International Electronic Conference on Agronomy (IECAG 2023) was held from October 15 to October 30, 2023. The conference brought together participants from various countries, including students, academics, scientists, and experts in agronomy and related fields. The conference focused on addressing current challenges, exploring innovative solutions, and promoting global collaboration within the agronomy community. The conference included presentations and discussions on a wide range of agronomy research topics, such as disease control, agro-ecology innovation, and digital farming. The conference also featured keynote speeches from distinguished scientists and was supported by various sponsors and media partners. [Extracted from the article]

Climate and management affect grassland plant diversity but studies vary regarding the magnitude of changes in plant species richness. Here we develop a comprehensive understanding of species richness modification due to management (mowing) and climate (warming) variations worldwide, and present the results of two meta-analyses from 999 and 1793 records (articles). Recorded articles had at least one experiment with a case-control design. The results show that both mowing (43 articles) and warming (34 articles) modify species richness, which on average increased by c. 32% with once-a-year mowing (against no mowing) and declined by c. 13% with warming (against ambient temperature). Our meta-analysis on the mowing regime supports the humped-back model, with one or two cuts per year being the level of disturbance optimising species richness. We also observed that warming-induced reduction in species richness is lower in dry climates (< 300 mm yr-1) and at low elevations (< 1000 m a.s.l.). Where available, we accounted for harvested biomass as a concomitant variable and we found that overall it decreased by c. 21% (mowing) and increased by c. 11% (warming). The evidence provided of an opposite response of species richness and harvested biomass to disturbance is consistent with the competitive-exclusion hypothesis of negatively correlated patterns between the two outcomes (high taxonomic diversity with low biomass production, and vice versa). The reported difficulties in finding representative studies in previous meta-analyses and in the present ones highlight the need to orient future research towards long-term experiments on the combined effects of mowing and warming for a more robust inference of environmental and management constraints on grassland performance. [ABSTRACT FROM AUTHOR]

The Biology and Life Sciences Forum requires that all papers published in their conference proceedings undergo peer review by expert referees. The review process is single-blind, and submissions are managed through the Sciforum platform. Out of 234 submissions, 81 were accepted and 58 were published, resulting in an acceptance rate of 0.35. Each paper received an average of one review, with a total of 48 reviewers involved. The review process also included checks for compliance with MDPI standards on plagiarism. The author of the document declares no conflict of interest. [Extracted from the article]

The advent of instrumental and digital weather observatories has not eliminated the substantial gaps within surface meteorological time series. In particular, the lack of long-term homogeneous snowfall records may be a major impediment for hydrologic studies. We tested the hypothesis that reliable estimates of snowy days per year (SDY) can be produced for peninsular Italy, based on weather and site measurements from a limited set of stations centrally located in the Central Mediterranean Area. The core weather data are those from three observatories: Naples (40° 51′ N, 14° 15′ E, 150 m a.s.l.), Rome (41° 53′ N, 12° 28′ E, 100 m a.s.l.) and Montevergine (40° 56′ N, 14° 43′ E, 1280 m a.s.l.). A linear multivariate regression model (LMRM) was developed with candidate predictors (elevation, snowfall days and winter temperatures) for SDY reconstructions in the Benevento Valley (calibration site), whose homogenised SDY time series covers the period 1869–2018 (the longest in Southern Italy, extending back to 1832 thanks to documentary proxies but with missing values from 1911 to 1949). Three validation sites across peninsular Italy were considered: Vesuvius (40° 49′ N, 14° 24′ E, 605 m a.s.l.), Perugia (43° 05′ N, 12° 30′ E, 205 m a.s.l.) and Padua (45° 23′ N, 11° 51′ E, 15 m a.s.l.). The percent relative mean absolute error (%RMAE) for Benevento Valley was 20.2%, and though higher model errors were encountered at validation sites, they were in an acceptable range (32.6, 39.3 and 39.5% at Vesuvius, Perugia and Padua, respectively). Our SDY reconstruction in the Benevento Valley parallels, the pattern of water discharge occurred in the region during the same period, whose fluctuations result in changes of the pattern of snowfall days (i.e. decreasing snowfall days in recent times was accompanied by decreasing groundwater levels). This corroborates that the approach used to reconstruct SDY data takes dominant climate controlling factors of hydrological changes. We conclude that the LMRM, a statistically developed model, is physically meaningful and may be reasonably used for estimating SDY in peninsular Italy roughly down from the sub-Alpine range. [ABSTRACT FROM AUTHOR]

Process‐based crop and grassland models estimating carbon (C) and nitrogen (N) dynamics are widely used to investigate best management practices in agriculture. They integrate several processes in a complex structure, but studies where modules corresponding to specific processes extracted from the whole model structure are assessed independently are uncommon. With the support of documented aerobic incubation trials in manure‐amended soils, a sensitivity analysis was performed on the C–N cycling processes of four modules (MOD1–4), corresponding to the models APSIM, EPIC, FASSET and STICS. The results showed that the parameter 'substrate use efficiency' had the most effect on the predicted values of net CO2 emissions and net N mineralization, together with the C/N ratio of the soil microbial biomass. They explained 74–75% on average of both output variances, whereas parameters determining manure C and N partitioning and first‐order decomposition constants of manure pools explained, on average, an additional 17–19%. Efforts should be focused on calibrating these parameters for more accurate simulations. The greater sensitivity of both outputs to parameters related to manure pools in more complex modules (MOD2–4) facilitates their adaptation to specific contexts, whereas MOD1 probably requires that parameters related to soil pools are also adapted to specific applications. Parameter interactions were limited, becoming noticeable only in situations of N‐limited soil organic matter decomposition. Models MOD1 and MOD3 allowed the C/N ratio of the soil microbial biomass to vary temporarily; therefore, they were less sensitive to mineral N availability and more easily adapted to a wide range of situations. This study provides essential information to support the development of state‐of‐the‐art biogeochemical models. Highlights: We compared four C–N modules embedded in process‐based biogeochemical models.We used sensitivity analysis to assess the simulation of manure decomposition in soil.We identified a few parameters that influenced CO2 emissions and N mineralization.We found that substrate use efficiency explained most of the output variance for all models. [ABSTRACT FROM AUTHOR]

Highlights • We assessed the grassland model CoSMo whit data from different plant mixtures. • The model was able to reproduce time trends of species composition and yield. • Simulations were performed to analyse climate change impact on grassland dynamics. Abstract Plant diversity supports the high aptitude of grassland covers to provide fodder production for animal feeding and contribute to the storage of carbon, while also granting pollination and aesthetics of landscapes. This ability depends on the management intensity of the grassland system, physical constraints and climatic characteristics. Most of current grassland models simulating the impact of weather conditions, soil fertility and the intensity of use of grassland systems (by grazing and/or mowing) on fodder production and carbon-nitrogen fluxes inherit eco-physiological and biophysical details from crop models without considering explicitly temporal changes in taxonomic and functional composition. The dynamic grassland model CoSMo (COmmunity Simulation MOdel) includes a set of rules to simulate explicitly seasonal changes in species composition of managed grasslands under various soil, climate and management drivers, and it is consistent with the degree of complexity of the most generic crop simulators. The incorporation of CoSMo functionalities into the standalone crop simulators CropSyst and WOFOST is proposed as a way to: (i) reduce the uncertainty in estimations of harvested aboveground biomass (AGB) and (ii) simulate dynamically the relative abundance of species (for respectively the whole community and individual species). Considering three mixtures of grassland species (with increasing complexity from Mix 1 to Mix 3) in a mown hay meadow in central Italy (Massa Marittima, 43° 03′ N, 10° 53′ E, 380 m a.s.l.), we show that CoSMo overall improved the model ability to reproduce observed AGB in Mix 1 and 2 (e.g. with CropSyst in Mix 2 relative root mean square error [ RRMSE ] lowered from 35.01 to 25.27%) while the model performance for Mix 3 appeared as not unambiguously linked to considering plant diversity. An assessment of the relative abundance estimates for the whole community indicates 30%< RRMSE <40% with both CoSMo-coupled CropSyst and WOFOST in Mix 1 and Mix 2, while it increases up to ∼60% in Mix 3. The consequences of explicitly accounting for plant diversity for simulated grassland outputs depend on the conditions evaluated, which require further studies. However, our results suggest that grassland modelling omitting plant diversity dynamics are likely over- or underestimating harvested biomass of the plant community, thus biasing projections of future fodder production and estimates of animal feed supplies. This work proves for the first time that CoSMo can support the simulation of grassland systems beyond its theoretical framework. [ABSTRACT FROM AUTHOR]

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phenotyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized. [ABSTRACT FROM AUTHOR]

There is increasing evidence that the impact of climate change on the productivity of grasslands will at least partly depend on their biodiversity. A high level of biodiversity may confer stability to grassland ecosystems against environmental change, but there are also direct effects of biodiversity on the quantity and quality of grassland productivity. To explain the manifold interactions, and to predict future climatic responses, models may be used. However, models designed for studying the interaction between biodiversity and productivity tend to be structurally different from models for studying the effects of climatic impacts. Here we review the literature on the impacts of climate change on biodiversity and productivity of grasslands. We first discuss the availability of data for model development. Then we analyse strengths and weaknesses of three types of model: ecological, process-based and integrated. We discuss the merits of this model diversity and the scope for merging different model types. [ABSTRACT FROM AUTHOR]

The geo-statistical Kriging method is conventionally used in the spatial dimension to predict missing values in a series by utilizing information from neighbouring data, supported by the hypothesis that mathematical expectation is a function of distance between observations. By using a data-driven machine learning-based inferencing and exploration framework, this research applies a Kriging-based interpolation in the temporal dimension to fill in data gaps in time-series of air temperatures. It assesses its performance for artificial gap scenarios (ranging in length from single one to six consecutive data points) generated using data with both daily and hourly resolutions from five sites in Europe (Laqueuille, France; Grillenburg, Germany; Monte Bondone, Italy; Oensingen, Switzerland; Rothamsted, United Kingdom) and one in France overseas (Sedael, Réunion Island). Results show that the method is capable of predicting missing temperatures with acceptable accuracy, especially with the hourly resolution and for non-high elevation sites: modeling efficiency (EF ≤ 1, optimum) >0.8, with the exception of Monte Bondone, placed at >2000 m a.s.l. (EF < 0). With daily data, maximum temperature was correctly predicted at all sites (0.6 ≤ EF < 0.9), while some less accuracy (down to EF < 0.4) was noted when predicting missing daily minimum temperatures. In conclusion, the method appears suitable to be applied to fill in hourly temperature gaps, requiring more stringent hypotheses concerning daily data and mountain sites (but further studies are required to draw concluding recommendations). [ABSTRACT FROM AUTHOR]

A gridded 0.25° reconstruction of rainfall kinetic energy (RKE) over the UK, on the basis of pluviometric observations and reanalysis back to 1765, shows that autumn RKE doubled in 1991-2013 (∼2 MJ m) compared to 1948-1990 (∼1 MJ m). A shift eastward is underway, which includes southern and northern portions of the country. Analyzing the long-running England and Wales precipitation series, we conclude that it is likely that increased precipitation amounts associated with more frequent convective storms created conditions for higher energy events. [ABSTRACT FROM AUTHOR]

Changes in the spatial and temporal patterns of extreme rainfall may have important effects on the magnitude and timing of rainfall erosivity, which in turn lead to even severe soil degradation phenomena. The Mediterranean belt is characterized by strong climatic variability and specific seasonal features, where dry periods are often interrupted by pulsing storms. Identifying the thresholds associated with extreme rainfall events is among the most important challenges for this region. To discern the spatial patterns of rainfall erosivity hazard in the Rhone region (eastern France), this study establishes thresholds in the power of rainstorms. An indicator Kriging approach was employed for computing probability maps of the annual rainfall erosivity exceeding the threshold of 1800 MJ mm ha−1 h−1, the latter being twice greater than the standard deviation. The interdecadal spatial patterns of hazard were assessed for recent decades (1991–2010) and the precedents ones (1961–1990). Climate fluctuations of rainfall erosivity revealed possible signals of increased storminess hazard across the region in recent times. We also discussed changes in the rainfall erosivity hazard forcing as related to climatic changes in daily rain rate, especially in autumn when the erosivity is likely affected by more intense storminess occurring across the southern part of the Rhone region. [ABSTRACT FROM AUTHOR]

The use of a variety of metrics is advocated to assess model performance but correlated metrics may convey the same information, thus leading to redundancy. Starting from this assumption, a method was developed for selecting, from among a collection of performance indicators, one or more subsets providing the same information as the entire set. The method, based on the definition of “stable correlation”, was applied to 23 performance indicators of agrometeorological models, calculated on large sets of simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Two subsets were determined: {Squared Bias, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index, Modified Modelling Efficiency}, {Persistence Model Efficiency, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index}. The method needs corroboration but is statistically founded and can support the implementation of standardized evaluation tools. [ABSTRACT FROM AUTHOR]

Hydrological disasters, such as floods, can have dire consequences for human societies. Historical information plays a key role in detecting whether particular types of hydrological disasters have increased in frequency and/or magnitude and, if so, they are more likely attributable to natural or human-induced climatic and other environmental changes. The identification of regions with similar flood conditions is essential for the analysis of regional flooding regimes. To this end, we here present the longest existing flood reconstruction for the Eastern Liguria Area (ELA) in northwestern Italy, covering 1582 to 2022 CE, which offers a case study representative of the central Mediterranean region. An Annual Flood Intensification Index was developed to transform the historical data into a continuous annual hydrological time-series contained by a homogeneous data structure for the study-area. We found two change-points (trend breaks) in the reconstructed time-series, in 1787 and 1967, with only occasional heavy floods comparable to present-day disasters occurring before the first change-point, and an increasing intensification of floods after the second change-point up to the present day. The recent intensification of flooding in the ELA, associated with changes in land use and land cover, also appears to coincide with phases in which hydrological hazards have become more changeable and extreme in disaster-affected areas. This is evidenced by river basin responses to human-induced disturbances. [ABSTRACT FROM AUTHOR]

The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs. [ABSTRACT FROM AUTHOR]

This work presents the reconstruction of a time series of annual winter air temperatures across Central and Southern Italy for the period 1500-2010 that largely overlaps the Little Ice Age (LIA) period (1300-1850). A detailed analysis was undertaken on winter mean temperature data using both observations (1871-2010) and proxy-based reconstructions (1500-1870). Based on this homogeneized reconstructed series, a time-dependency in low-frequency time-pattern of temperatures (70- and 130-year cycles) was suggested although the temporal oscillation was not merely periodic. The LIA was characterized by marked climatic variability over this part of Southern Europe, with particular emphasis during the so-called 'Maunder Minimum' (MM), between 1645 and 1715. The interannual variability of low temperatures, in particular, makes the MM an outstanding climatic period. There is some consistency that patterns of warming conditions observed in recent times also occurred in the past. Quasiperiodic cycles appear as a consequence of stochastic resonance emerging in long time scales but the variability inherent to the series of winter temperatures, although likely generated by processes internal to the climate system, is difficult to forecast because the system is chaotic and affected by unpredictable noise. [ABSTRACT FROM AUTHOR]

This study proposes a forecast skill scheme for winter temperatures in the central Mediterranean Sub-regional Area. An original series of mean winter (December to February) temperatures spanning the period 1698-2010 is the basis for developing long-term climate prediction. A procedure was identified where a predictable structure was first provided by reducing noise via the Empirical Mode Decomposition method and then an Ensemble Climate Prediction (ECP) was applied. The predictability limit of the decomposed series was assessed by applying a collection of methods from linear and nonlinear time series analysis (Hurst coefficients, Lyapunov exponents and wavelet patterns). The analysis was based on a set of tools that are suitable to discover the manifestation of a possible trajectory of projected temperature change. The progress of the winter temperature forecast was inferred by an Exponential Smoothing with a Damped (ESD) multiplicative trend model. ECP-ESD hindcast experiments were tested and ensemble forecasts run until the year 2040. ECP-ESD yields an ensemble mean path that consists: (1) of a sharp decline in winter temperature in the current decade; (2) more widespread predictions for 2020-2030, with fluctuations near the present multi-decadal average; and (3) a sharp warming for the 2030-2040 decade. Our results are compatible with projections for European winters and major climatic indices such as the Pacific Decadal Oscillation and the Atlantic Multi-decadal Oscillation. [ABSTRACT FROM AUTHOR]

This paper has exploited, for Central and Southern Italy (Mediterranean Sub-regional Area), an unprecedented historical dataset as an attempt to model seasonal (winter and summer) air temperatures in pre-instrumental time (back to 1500). Combining information derived from proxy-documentary data and large-scale simulation, a statistical downscaling approach in the form of mixed regression model was developed to adapt larger-scale estimations (regional component) to the sub-regional temperature pattern (local component). It interprets local temperature anomalies by means of monthly based Temperature Anomaly Scaled Index in the range −5 (very cold conditions in June) to 2 (very warm conditions). The modelled response agrees well with the independent data from the validation sample (Nash-Sutcliffe efficiency coefficient, >0.60). The advantage of the approach is not merely increased accuracy in estimation. Rather, it relies on the ability to extract (and exploit) the right information to replicate coherent temperature series in historical times. [ABSTRACT FROM AUTHOR]

Abstract: The Pasture Simulation model (PaSim) was used to simulate agronomic and environmental services at 12 grassland sites in France, comparing near past climate conditions (1970–1999) and projections for near (2020–2049) and far future (2070–2099). Modelled climate change impacts were assessed using statistical and threshold-based analysis on managed (intensive, extensive) grasslands (permanent, sown), comparing shallow and deep soils, and for an ensemble of emission scenarios, climate models, and downscaling/initialization methods. Results show a significant drift of all sites towards more arid climates by the end of the century. Significant reductions in annual water drainage and herbage protein content are projected, together with new opportunities for annual and seasonal herbage production, in spring and winter especially. Simulated future conditions also show an increased interannual and seasonal variability of production. Contrary to expectations, current natural conditions reflecting large water availability are not expected to reduce future risks of negative climate change impacts on grassland systems. Notably, milk production from aftermath grazing of permanent grasslands established on deep soils at Mirecourt (humid site) is estimated to fall down below one-third of the current median value in four out of 30 years (∼13%) for 2070–2099, whereas similar shortfalls were not observed with the baseline climate. Balancing risks and opportunities, permanent extensive grasslands can be viewed as a trade-off between the continuity of grassland service provision and the mitigation of greenhouse gas emissions. [Copyright &y& Elsevier]

We study the interdecadal-scale variability over the Himalayas/Tibetan Plateau (H/TP) to deepen the understanding of Earth's important ecosystem after the recent controversy about glaciers melting in the Himalayan Mountains. We present a new statistical reconstruction of annual temperature variability back to 1901 for the Pyramid Automated Weather Station of the Ev-K2-CNR Committee, located at the base of the Mount Everest. Our reconstruction using recorded weather station data compared with the Climate Research Unit temperature pattern data indicate that in recent decades the warming trend over the H/TP has been faster than during any equivalent period in the last century. Positive temperature anomalies (mostly observed after 1970) were found to be correlated with the Atlantic Multidecadal Oscillation and the Pacifical Decadal Oscillation indices, but unevenly correlated with the Northern Hemisphere (NH) land-surface temperature. Such decoupled response between H/TP and NH suggests the mechanisms of global warming are only marginally influencing the H/TP climate. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This work presents an attempt to reconstruct the Mediterranean Italy climate across one of the coldest interval (ad 1645-1715) of the 'Little Ice Age' (LIA), characterized by a prolonged episode of low solar acitivty known as 'Maunder minimum' (MM). LIA was characterized by marked climatic variability over this part of southern Europe, particularly during the MM. Using a model based on adding subregional details to regional-scale reconstruction, we estimated for this period a mean winter temperature of ∼1°C lower than in the baseline period 1961-1990 (∼8°C), and the anomaly further lowered down to about -4°C in the winter of 1683-1684. In addition, the interannual variability of extreme low temperatures makes the MM an outstanding climatic period. There is also some consistency of the empirical evidence with the modelling results that, for high-resolution wintertime seasonal reconstruction, central Mediterranean Sub-regional Area shows a colder climate than that represented by coarser simulation of European temperatures during the LIA period. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents and assesses the Decadal Rainfall Erosive Multiscale Model (DREMM), in which extreme precipitation data (95th percentile) are used to estimate decadal-scale rainfall–runoff erosivity values compatible with the Universal Soil Loss Equation and its revision — (R)USLE. The test area was a large region including central Europe and Mediterranean countries, in which 111 decades from 88 weather stations (ranging from about 3 to 1680m above sea level) with rain and (R)USLE rainfall–runoff erosivity data were available. The construction of the model is simplified to a location-explicit term and to the understanding that the most erosive rainfalls are those recorded during the summertime and the beginning of autumn (May–October). These precipitation events are suitable for use in spatial and temporal climate variability studies on decadal time-scales. Once parameterized to capture decadal rainfall–runoff erosivity variability over central Europe and the Mediterranean, the DREMM was run to produce spatial patterns of rainfall–runoff erosivity in Germany and Bulgaria, compared to maps from the (R)USLE approach for 1961–1990. Implications for rainfall–runoff erosivity modelling were discussed concluding that a limited number of parameters may be sufficient to represent decadal rainfall–runoff erosivity for the central European and Mediterranean region. A transferable approach is recommended by employing the DREMM to assess the spatial impact of rainfall extremes. Its use to characterize the long-term dynamics of rainfall–runoff erosivity may also be of value in climate investigation. [Copyright &y& Elsevier]

Rainfall and overland flow are fundamental processes for Earth's ecosystems but can also be land disturbing forces, particularly when triggered by extreme hydro-meteorological events. Examples of these extremes are rainstorms and related phenomena due to rainfall aggressiveness. They produce high-impact land processes such as soil erosion and nutrient losses. Economic and social consequences of these processes can be quite severe. However, hydrological extremes and their environmental implications are still poorly understood, particularly if analyzed in the context of climate change. Here, we analyze a 300 year long times series of historical rainfall patterns across the Mediterranean in the last three centuries and we investigate changes in the erosive forcing as related to climate changes. Our results show that the erosive forcing increased towards the end of the Little Ice Age (~1850) over western and central Mediterranean and that has been increasing in the recent warming period at low Mediterranean latitudes, due to a higher frequency of intensive storms. Such increased concentrated precipitation may lead to an intensification of land degradation processes triggered by soil erosion and transport across a range of scales from hillslopes to small catchments. [ABSTRACT FROM AUTHOR]

Summary: This paper presents and assesses the model MedREM, in which annual-based climate and rainstorm data are used to estimate the long-term (R)Universal Soil Loss Equations and its Revisions (USLE) annual rainfall erosivity over a large region. Two known models of rainfall erosivity (named after the first author of the original paper, Torri and Yang) were also assessed and compared with the MedREM. Yang and Torri models are both based on the annual precipitation models. MedREM also takes annual maximum daily precipitation data into account, and incorporates a longitude-dependent coefficient. The test area was a large region centered on the Mediterranean basin, in which 66 weather stations were available – 43 Italian and 23 out-of-Italy sites (12 countries, about 30–50° latitude North, and 10–50° longitude East) – with multi-year data of (R)USLE annual erosivity. The three models were calibrated against (R)USLE rainfall erosivity data from 55 stations and evaluated over a validation dataset from 11 Italian stations. On the validation dataset, the MedREM estimates generally compared well with the (R)USLE data according to Nush–Sutcliffe coefficient (0.87 against 0.76 and 0.73 with Torri and Yang model, respectively). Implications for erosivity modelling were discussed in the context of climatic features concluding that accurate estimations of site-specific annual erosivity for the Mediterranean region require process-based model with spatially-explicit parameterization. [Copyright &y& Elsevier]

Earth ecosystems are not static, and they respond to environmental changes, particularly climatic and anthropogenic. Precipitation varying in its extremeness, with shifts to greater or lesser intensity of individual storms and/or to change in the length and frequency of wet and dry periods, can adversely affect both urban and rural ecosystems. Here, the authors review long-term precipitation records of the central Mediterranean area and employ a Web geographical information system (GIS)-based analytical approach to compare current rainfall impact with historical data on different spatial and temporal scales. Autumn (September–November) was recognized as the most hazardous season that marks the evidence of a changing climate, with a shift toward more intense rainfalls in recent times. In the first decade of the third millennium, areas of peninsular and insular Italy have been especially affected by extreme rains. A focus was put on the island of Sicily, where extraordinary rain events occurred in September 2009, discussed in the context of upcoming trends and climate histories. An improved knowledge and understanding of the scale at which changes on extremes occur is essential for dealing with the forthcoming challenges regarding soil and water conservation practices. The characteristics of changes in natural rainfall, its role on terrestrial ecosystems, and its effect on surface water erosion dynamics are discussed. It is argued that understanding these issues are major priorities for future research to promote a better understanding of the Earth interaction with water resources and related hydrological issues. [ABSTRACT FROM AUTHOR]

Abstract: Sensitivity analysis studies how the variation in model outputs can be due to different sources of variation. This issue is addressed, in this study, as an application of sensitivity analysis techniques to a crop model in the Mediterranean region. In particular, an application of Morris and Sobol'' sensitivity analysis methods to the rice model WARM is presented. The output considered is aboveground biomass at maturity, simulated at five rice districts of different countries (France, Greece, Italy, Portugal, and Spain) for years characterized by low, intermediate, and high continentality. The total effect index of Sobol'' (that accounts for the total contribution to the output variation due a given parameter) and two Morris indices (mean μ and standard deviation σ of the ratios output changes/parameter variations) were used as sensitivity metrics. Radiation use efficiency (RUE), optimum temperature (T opt), and leaf area index at emergence (LAIini) ranked in most of the combinations site×year as first, second and third most relevant parameters. Exceptions were observed, depending on the sensitivity method (e.g. LAIini resulted not relevant by the Morris method), or site-continentality pattern (e.g. with intermediate continentality in Spain, LAIini and T opt were second and third ranked; with low continentality in Portugal, RUE was outranked by T opt). Low σ values associated with the most relevant parameters indicated limited parameter interactions. The importance of sensitivity analyses by exploring site×climate combinations is discussed as pre-requisite to evaluate either novel crop-modelling approaches or the application of known modelling solutions to conditions not explored previously. The need of developing tools for sensitivity analysis within the modelling environment is also emphasized. [Copyright &y& Elsevier]

The potential of mathematical models is widely acknowledged for examining components and interactions of natural systems, estimating the changes and uncertainties on outcomes, and fostering communication between scientists with different backgrounds and between scientists, managers and the community. For favourable reception of models, a systematic accrual of a good knowledge base is crucial for both science and decision-making. As the roles of models grow in importance, there is an increase in the need for appropriate methods with which to test their quality and performance. For biophysical models, the heterogeneity of data and the range of factors influencing usefulness of their outputs often make it difficult for full analysis and assessment. As a result, modelling studies in the domain of natural sciences often lack elements of good modelling practice related to model validation, that is correspondence of models to its intended purpose. Here we review validation issues and methods currently available for assessing the quality of biophysical models. The review covers issues of validation purpose, the robustness of model results, data quality, model prediction and model complexity. The importance of assessing input data quality and interpretation of phenomena is also addressed. Details are then provided on the range of measures commonly used for validation. Requirements for a methodology for assessment during the entire model-cycle are synthesised. Examples are used from a variety of modelling studies which mainly include agronomic modelling, e.g. crop growth and development, climatic modelling, e.g. climate scenarios, and hydrological modelling, e.g. soil hydrology, but the principles are essentially applicable to any area. It is shown that conducting detailed validation requires multi-faceted knowledge, and poses substantial scientific and technical challenges. Special emphasis is placed on using combined multiple statistics to expand our horizons in validation whilst also tailoring the validation requirements to the specific objectives of the application. [ABSTRACT FROM AUTHOR]

Background: The modular approach to analysis of genetically modified organisms (GMOs) relies on the independence of the modules combined (i.e. DNA extraction and GM quantification). The validity of this assumption has to be proved on the basis of specific performance criteria. Results: An experiment was conducted using, as a reference, the validated quantitative real-time polymerase chain reaction (PCR) module for detection of glyphosate-tolerant Roundup Ready® GM soybean (RRS). Different DNA extraction modules (CTAB, Wizard and Dellaporta), were used to extract DNA from different food/feed matrices (feed, biscuit and certified reference material [CRM 1%]) containing the target of the real-time PCR module used for validation. Purity and structural integrity (absence of inhibition) were used as basic criteria that a DNA extraction module must satisfy in order to provide suitable template DNA for quantitative real-time (RT) PCR-based GMO analysis. When performance criteria were applied (removal of non-compliant DNA extracts), the independence of GMO quantification from the extraction method and matrix was statistically proved, except in the case of Wizard applied to biscuit. A fuzzy logic-based procedure also confirmed the relatively poor performance of the Wizard/ biscuit combination. Conclusions: For RRS, this study recognises that modularity can be generally accepted, with the limitation of avoiding combining highly processed material (i.e. biscuit) with a magnetic-beads system (i.e. Wizard). [ABSTRACT FROM AUTHOR]

Abstract: Wind is a library providing a collection of stochastic approaches to generate wind speed data on daily and hourly time steps. Daily generation refers to as mean, maximum and minimum daily wind speed. The library is made available as software component, inclusive of hypertext help file, allowing the development of language-independent clients under Windows operating systems. The component includes advanced features for re-use in custom developed applications, and it allows independent extensions by third parties without requiring its re-compilation. Illustrative examples on how to extend and re-use the library are provided as C# code projects. [Copyright &y& Elsevier]

Summary: This work presents three empirical models (MMFI, Morais Modification of Fournier Index; GJRM, Grimm–Jones–Rusco–Montanarella; REMDB, Diodato–Bellocchi Rainfall–Erosivity Model) where monthly-based climate data are used to estimate long-term (R)USLE (Universal Soil Loss Equation and its Revisions) rainfall erosivity factor (R m, MJmmh−1 ha−1 month−1). The objective was to evaluate two known models (MMFI and GJRM), and compare the results with the novel model REMDB meant for complex terrains. MMFI and GJRM are both based on the precipitation amount, whilst REMDB takes site latitude, elevation and precipitation seasonality also into account. The test area was the Italian region, where 30 stations (altitudes from about sea level up to 1270m, over the latitudinal range 36–46°North) with sufficient data to calculate R m according to USLE were available. The three models were evaluated against USLE rainfall erosivity over a validation data set of 14 stations, using a range of performance statistics. The REMDB estimates generally compared well with the USLE estimates according to different statistics. For REMDB, the relative root mean square error was, in average, 48.58% against 71.49% for MMFI and 66.55% for GJRM. The average modelling efficiency of REMDB was 0.51 against −0.02 (MMFI) and 0.13 (GJRM). REMDB was also superior in preventing biased errors in time, as quantified by the average pattern index versus months: 17.65MJmmh−1 ha−1 month−1, against 58.54MJmmh−1 ha−1 month−1 (MMFI) and 57.76MJmm h−1 ha−1 month−1 (GJRM). Of the two simplified models, the MMFI was the worst performer while the GJRM model performed similarly to the REMDB at two mid-altitude sites of Central Italy. [Copyright &y& Elsevier]

Abstract: Meteorological stations placed in remote areas (such as mountainous sites) often record only precipitation and air temperature data, summarised into monthly tables. There is therefore a need for appropriate methods to estimate solar radiation data from monthly inputs. It is important to know the quality and characteristics of the estimates made in order to understand what impacts the modelled data may have on the use to which they are meant. This paper evaluates the behaviour of two empirical models based on the difference between maximum and minimum air temperatures and compares results with a newly developed model, for 24 stations (11 for model calibration, the rest for model validation) in Italian sites. The new model (SRMCT, solar radiation model for complex terrains) replaces the minimum air temperature with a monthly function and uses rainy days number to correct the air temperature range. Comparisons were made using a fuzzy-logic-based multiple-statistics assessment approach (Irad). An appraisal of the temporal distribution of mean errors over a year was also performed. The new model produced the best overall estimates. The two air temperature-based models can be an alternative approach when only air temperature data are available, but at many sites they introduced large inaccuracies and seasonal patterns in the error distribution. The study demonstrates the appropriateness of both air temperature range and rain correction factors for monthly based solar radiation estimates. At the same time, it shows the value and importance of using a range of assessment statistics, even aggregated into an overall indicator, to evaluate model estimates. [Copyright &y& Elsevier]

Abstract: Evapotranspiration (ET) is a cross-platform software component containing routines to estimate daily and hourly values of reference evapotranspiration (and related variables) according to alternative approaches. A standardized form of the FAO-56 implementation of the Penman–Monteith equation is used to estimate daily and hourly evapotranspiration for two reference surfaces (namely clipped grass and alfalfa). Other methods (implementations of Priestley–Taylor and Hargreaves equations) estimate daily evapotranspiration from limited sets of inputs. A multi-layer canopy, similar to a well-developed tomato crop within standard greenhouse conditions, is taken as a reference to estimate hourly evapotranspiration in greenhouse environment (Stanghellini approach). The component is released as .NET (C#) version, allowing the development of clients under Windows operating systems. The component has an extensive hypertext help file. The component design allows users developing client applications to extend the functionalities by adding further options for estimating reference ET. Illustrative examples of clients developed in C# are provided as source code; the component is made available as compiled version. Also, the component was used to activate a web service and a web application based on such a service; the relevant C# code is provided as example. [Copyright &y& Elsevier]

The software irene (Integrated Resources for Evaluating Numerical Estimates) is a data analysis tool designed to provide easy access to statistical techniques for use in model evaluation. Mostly, non-replicated model estimates (Ei) are compared against non-replicated measurements (Mi). The software also allows comparing individual estimates against replicated measurements (or vice versa) and replicated estimates against replicated measurements. The evaluation of model performance is essentially based on the difference Ei−Mi, or on the correlation–regression of Ei vs. Mi (or vice versa). In addition, model evaluation by probability distributions, pattern analysis, or fuzzy-based aggregation statistics is allowed. Graphics are included in most analytical tasks. The results are displayed in separate spreadsheets and can be exported into MS Excel workbooks. [Copyright &y& Elsevier]

RadEst3.00 estimates and evaluates daily global solar radiation values at given latitudes. Radiation is calculated as the product of the atmospheric transmissivity of radiation times the radiation outside the earth atmosphere. Four models estimate the atmospheric transmissivity, based on the daily temperature range. Model parameters can be fitted over one or more years of data by iterative procedures. Graphical and statistical evaluations of the estimates are presented. Reports of the analysis can be exported in a variety of formats. Penman–Monteith or Priestley–Taylor reference evapotranspiration is estimated, using both measured and estimated radiation. Utilities are provided to process numerous files, or correct possible constant biases in the data. Samples of data for tropical and temperate sites are supplied with the software. [Copyright &y& Elsevier]

Associated with livestock farming, grasslands with a high diversity of plant species are at the core of low-input fodder production worldwide. However, this Special Issue does not fully consider climate-induced biodiversity change and the extent to which population growth, urbanisation, land-use change, consumption lifestyles and resource exploitation threaten grassland biodiversity as key factors directly or indirectly associated with climate change. For instance, innovative systems at the interface of grassland and cereal production hold promise for reconciling production objectives with the preservation of biodiversity and soil-water-air resources, and pave the way for a multifunctional, sustainable and more climate-resilient agriculture. [Extracted from the article]

• Climate shift-erosion dynamics in shaping landscape evolution is emphasised. • Erosivity density and particulate sediment in Rhône prodelta coevolved over 1858–2022. • Rising rainfall erosivity unveils mounting erosive events in France after 1964. • These findings inform landscape management and conservation strategies. Understanding the historical context of rainfall erosivity enhances comprehension of climate and environmental changes, guiding strategies to address global environmental challenges. However, the understanding of the interplay between modern-era environmental hydrology, climate change, and storm evolution remains limited, despite the pivotal role of storm disasters in global environmental change, especially in inland-basin areas. To address this knowledge gap, we present the longest interannual rainfall erosivity history in France, with a specific focus on the area of Clermont-Ferrand, covering the period 1858–2022. Using the Integrated Rainfall Erosivity Model (IREM) approach, refined with process-based insights and expert knowledge, we analyzed the erosivity data and correlated them with environmental and climatic changes. Our findings indicate a rapid and abrupt change in rainfall erosivity around 1964, which we attribute to the negative phase of the North Atlantic Oscillation and a positive phase shift of the Dipole Mode Index. Moreover, the Mann-Kendal statistic demonstrates a significant positive trend in extreme rainfall erosivity (95th percentile) over the entire time-series. A notable finding is the strong coevolution observed between the erosivity density indicator and particulate organic carbon, modulated by multidecadal environmental hydrology in the Rhône River Basin (RRB). These results shed light on the complex interactions among storms, climate, and sediment organic matter, offering insights for environmental management and conservation strategies in the RRB and other regions with similar hydrological characteristics. [ABSTRACT FROM AUTHOR]

Snow cover duration is a crucial climate change indicator. However, measurements of days with snow cover on the ground (DSG) are limited, especially in complex terrains, and existing measurements are fragmentary and cover only relatively short time periods. Here, we provide observational and modelling evidence that it is possible to produce reliable time-series of DSG for Italy based on instrumental measurements, and historical documentary data derived from various sources, from a limited set of stations and areas in the central-southern Apennines (CSA) of Italy. The adopted modelling approach reveals that DSG estimates in most settings in Italy can be driven by climate factors occurring in the CSA. Taking into account spatial scale-dependence, a parsimonious model was developed by incorporating elevation, winter and spring temperatures, a large-scale circulation index (the Atlantic Multidecadal Variability, AMV) and a snow-severity index, with in situ DSG data, based on a core snow cover dataset covering 97 years (88% coverage in the 1907–2018 period and the rest, discontinuously from 1683 to 1895, from historical data of the Benevento station). The model was validated on the basis of the identification of contemporary snow cover patterns and historical evidence of summer snow cover in high massifs. Beyond the CSA, validation obtained across terrains of varying complexity in both the northern and southern sectors of the peninsula indicate that the model holds potential for applications in a broad range of geographical settings and climatic situations of Italy. This article advances the study of past, present and future DSG changes in the central Mediterranean region. [ABSTRACT FROM AUTHOR]

• Grassland model PaSim used to simulate a mown grassland system in central France. • Climate-dependent timeseries of optimal forage yield generated. • Remarkable increase in simulated forage yield (>29%) observed in the long term. • Annual forage yield values robustly correlated with teleconnection patterns. • Skillful seasonal forecasts of forage yield attained from climatic predictors. Climate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) was used to simulate, for the period 1959–2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim was to generate purely climate-dependent timeseries of optimal forage production, a variable that was maximized by brighter and warmer weather conditions at the grassland. A long-term increase was observed in simulated forage yield, with the 1995–2015 average being 29% higher than the 1959–1979 average. Such increase seems consistent with observed rising trends in temperature and CO 2 , and multi-decadal changes in incident solar radiation. At interannual timescales, sea surface temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation) and the North Atlantic Oscillation (NAO) index were found robustly correlated with annual forage yield values. Relying only on climatic predictors, we developed a stepwise statistical multi-regression model with leave-one-out cross-validation. Under specific management conditions (e.g., three annual cuts) and from one to five months in advance, the generated model successfully provided a p -value<0.01 in correlation (t -test), a root mean square error percentage (%RMSE) of 14.6% and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. This is the first modeling study on the possible role of large-scale oceanic–atmospheric teleconnections in driving forage production in Europe. As such, it provides a useful springboard to implement a grassland seasonal forecasting system in this continent. [ABSTRACT FROM AUTHOR]

• Geostatistics and GIS connected topo-climatic factors and forest productivity. • Spatial patterns showed the environmental control on forest productivity. • Probability maps differentiated high and low wood productivity rates. The prediction of forest productivity is essential for sustainable forest management, particularly in countries, like Italy, where forest is an important part of many protected areas. A spatial predictive probability model for forest productivity rates in Italy was developed over the period 1961–1990, based on 135 annually-resolved records of site productivity and auxiliary variables measured at 219 stations. Our analysis shows that the probability of finding high (>7.3 m3 ha−1 yr−1) and low (<5.8 m3 ha−1 yr−1) productivity rates changes across different regions of Italy. The generated spatial patterns contribute to a better understanding of the factors structuring the distribution of forest productivity in Italy because they reflect the dependence of temperature and water availability conditions on the latitudinal and altitudinal location of the study areas. We observed that the temperature control dominates forest productivity at high elevations and latitudes, whereas low-elevation sites in central and southern Italy are more sensitive to water availability. The proposed spatial probability modelling should be further assessed for its possible incorporation into forest management plans. [ABSTRACT FROM AUTHOR]

Knowledge on sustainable grassland management is available in the large body of literature. However, it is unclear where to look for it, and what is really relevant to the many interrelated challenges of sustainable grassland management. This special issue illustrates options to fill some of those gaps. This editorial introduces the Special Issue entitled "Grassland Management for Sustainable Agroecosystems". Two review articles deal with (i) concepts for monitoring grassland degradation (by Tiscornia et al. Agronomy2019, 9, 239) and (ii) impacts of alternative management practices and disturbances (by Wagle and Gowda et al. Agronomy2018, 8, 300). One paper (by Steiner et al. Agronomy2019, 9, 699). summarized a series of papers of the special issue. Other topics covered include four main aspects: (I) Landscape features (Ravetto Enri et al. Agronomy2019, 9, 333), two papers by Northup et al. Agronomy2019, 9, 329, Northup et al. Agronomy2019, 9, 281, and Ma et al. Agronomy2019, 9, 238; (II) climate (Zhou et al. Agronomy2019, 9, 219, Starks et al. Agronomy2019, 9, 235, and Moinet et al. Agronomy2019, 9, 124); (III) soil fertility (Franzluebbers et al. Agronomy2019, 9, 204, Poblete-Grant et al. Agronomy2019, 9, 191); and (IV) one on modeling (Puche et al. Agronomy2019, 9, 183). Two additional papers are from Andueza et al. Agronomy2019, 9, 273 (on the feed value of barn-dried hay) and Úbeda et al. Agronomy2019, 9, 340 (on the role of prescribed burns). [ABSTRACT FROM AUTHOR]

Hazardous hydrological events cause soil erosion and it is essential to anticipate the potential environmental impacts of prevailing erosion processes that occur at different time-scales. Here, we present the modelling of net soil erosion rates for the Bradano River Basin (southern Italy), based on rainfall erosivity, surface overland flow and transport sub-models. A semi-empirical framework was developed, upscaling point rainfall values based on the Foster-Thornes approach in order to give an insight into monthly and annual soil losses over the period 1950–1958 and 1961 (calibration) and over a longer time-frame (1950–2020: reconstruction). In the 2765-km2 study area, ~ 68% of the sediment mobilized within the basin reached the basin outlet (mean value for 1950–2020: ~ 366 Mg km−2 yr−1). A moderate declining trend in net erosion rates was observed after the 1980s, concurrent with the contraction of cropland in favour of natural vegetation and river channelization. Our results suggest that the parsimonious principle used here seems sufficiently robust to be suitable for applications in other Mediterranean landscapes. [ABSTRACT FROM AUTHOR]

Rainfall erosivity drives damaging hydrological events with significant environmental and socio-economic impacts. This study presents the world's hitherto longest time-series of annual rainfall erosivity (725–2019 CE), one from the Tiber River Basin (TRB), a fluvial valley in central Italy in which the city of Rome is located. A historical perspective of erosive floods in the TRB is provided employing a rainfall erosivity model based on documentary data, calibrated against a sample (1923–1964) of actual measurement data. Estimates show a notable rainfall erosivity, and increasing variability, during the Little Ice Age (here, ~ 1250–1849), especially after c. 1495. During the sixteenth century, erosive forcing peaked at > 3500 MJ mm hm–2 h–1 yr–1 in 1590, with values > 2500 MJ mm hm–2 h–1 yr–1 in 1519 and 1566. Rainfall erosivity continued into the Current Warm Period (since ~ 1850), reaching a maximum of ~ 3000 MJ mm hm–2 h–1 yr–1 in the 1940s. More recently, erosive forcing has attenuated, though remains critically high (e.g., 2087 and 2008 MJ mm hm–2 h–1 yr–1 in 1992 and 2005, respectively). Comparison of the results with sediment production (1934–1973) confirms the model's ability to predict geomorphological effects in the TRB, and reflects the role of North Atlantic circulation dynamics in central Italian river basins. [ABSTRACT FROM AUTHOR]

Climate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) is used to simulate, for the period 1959-2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim is to generate purely climate-dependent time series of optimal forage production, a variable that is found to maximize the years of brighter and warmer weather conditions at the grassland.In the long term, a remarkable increase in simulated forage yield (>29%) is obtained in all simulations between 1959-1979 and 1995-2015. Such increase seems consistent with observed rising trends in temperature and CO2 and multi-decadal changes in incident solar radiation. At interannual timescales, Sea Surface Temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation), and the North Atlantic Oscillation (NAO) index, appear robustly correlated with annual forage yield values. Relying on climatic predictors uniquely, a stepwise statistical multi-regression model with leave-one-out cross-validation is developed in this work. Under specific management conditions (e.g., 3 annual cuts) and from 1 to 5 months in advance, the generated model successfully provides a p-value<0.01 in correlation (t test), a root mean square error (RMSE) percentage of 14.6% (1.24 t DM ha-1) and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. These results may help in stimulating the debate about understanding the climate mechanisms at play in European grasslands and provide a useful springboard to implement a grassland seasonal forecasting system in France. [ABSTRACT FROM AUTHOR]

The emergence of decreasing trends in snowfall frequency and snow depth highlights the challenges arising from shifts in snow regimes. In particular, snow‐dependent southern Europe regions may be negatively impacted by such changes. Snow regimes strongly influence the water availability in reservoirs and groundwater. This study presents the world's longest series of monthly snowfall (fresh snow depth, snow days per year, days with snow on the ground) for the Parma Observatory, northern Italy (44°48′N and 10°19′E) between 1777 and 2018. These datasets were collected over the centuries using early rain gauges and recently, SIAP tipping bucket models. Sources of inhomogeneity were identified and analysed through the study of metadata and of nonparametric tests on datasets. Homogenized time series were obtained after correcting the observed snowfall data for the instruments response and localization characteristics, for the observing practices with the help of the standard normal homogeneity tests. The Buishand and the Mann–Kendall tests were further applied to check the correction and detect possible monotonic trends. Over the study period, days per year of snow decreased after the change point detected in 1897, in association with a rise in surface air temperature (including a distinct urban warming trend). In this study high numbers of snowy days per year (hereafter referred as snowfall frequency) and snow depth values during the latest phase of the LIA at Parma are consistent with a cycle of minimum solar activity, suggesting that enhanced, solar‐induced blocking activity dominated, with the arrival into the Mediterranean of large cold air masses developing over Siberia and northern Europe. Over the last century, snow appears to have resonance‐like characteristics as similar trends were observed over the Northern Hemisphere, where the extent of the snow cover has been reported to markedly decline in the transition spring and autumn seasons. [ABSTRACT FROM AUTHOR]

Rainfall erosivity and its derivative, erosivity density (ED, i.e., the erosivity per unit of rain), is a main driver of considerable environmental damages and economic losses worldwide. This study is the first to investigate the interannual variability, and return periods, of both rainfall erosivity and ED over the Mediterranean for the period 1680–2019. By capturing the relationship between seasonal rainfall, its variability, and recorded hydrological extremes in documentary data consistent with a sample (1981–2015) of detailed Revised Universal Soil Loss Erosion-based data, we show a noticeable decreasing trend of rainfall erosivity since about 1838. However, the 30-year return period of ED values indicates a positive long-term trend, in tandem with the resurgence of very wet days (> 95th percentile) and the erosive activity of rains during the past two decades. A possible fingerprint of recent warming is the occurrence of prolonged wet spells in apparently more erratic and unexpected ways. [ABSTRACT FROM AUTHOR]

The brevity of the instrumental record limits our understanding of snowfall variability and its directional patterns in the Mediterranean region. Here, we develop a 1,208‐year‐long (800–2017 CE) reconstruction of central Mediterranean snowfall variability based on documentary evidence from Italy. The record suggests that the recent reduction in Italian snowfall intensity is not unprecedented over the past millennium, since comparable patterns of low snowfall intensity also occurred during the Medieval Climate Anomaly. Increased snowfall during the Little Ice Age, however, was most likely associated with a shift of the Atlantic multi‐decadal variability towards negative values, and this overall cold phase further coincided with increased volcanic activity. Our findings on natural snowfall variability over the central Mediterranean in the past millennium provide a unique winter proxy for validating output from climate model simulations. Snow extent and accumulation are critical for water availability in many world areas. Climate variability and change could have important impacts on natural and human systems in snow‐dependent Mediterranean regions. Our 1,208‐year‐long (800–2017 CE) high‐resolution reconstruction of the snowfall events based on documentary evidence from across Italy (like the extended snowfall over Vesuvius occurred in 1776, http://tinyurl.com/lwe2wnj) shows that recent widespread increases in the occurrence of low‐snowfall years are likely not unprecedented in the long‐term record. [ABSTRACT FROM AUTHOR]

This study assessed two versions of the crop model CropSyst (i.e. EMS, existing; MMS, modified) for their ability to simulate maize (Zea mays L.) yield in South Africa. MMS algorithms explicitly account for the impact of extreme weather events (droughts, heat waves, cold shocks, frost) on leaf development and yield formation. The case study of this research was at an experimental station near Johannesburg where both versions of the model were calibrated and validated by using field data collected from 2004 to 2008. The comparison of EMS and MMS showed considerable difference between the two model versions during extreme drought and heat events. MMS improved grain-yield prediction by ~30% compared with EMS, demonstrating a better ability to capture the behaviour of stressed crops under a range of conditions. MMS also showed a greater variability in response when both versions were forced with scenarios of projected climate change, with increased severity of drought and increased temperature conditions at the horizons 2030 and 2050, which could drive decreased maize yield. Yield was even lower with MMS (8 v. 11 t ha-1 for EMS) at the horizon 2050, relative to the baseline scenario (~13 t ha-1 at the horizon 2000). Modelling solutions accounting for the impact of extreme weather events can be seen as a promising tool for supporting agricultural management strategies and policy decisions in South Africa and globally. [ABSTRACT FROM AUTHOR]

Abstract: Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2O emissions. Yield‐scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed. [ABSTRACT FROM AUTHOR]

There is interest in knowing historical spatio-temporal patterns of landslide activity. However, this is challenging to reconstruct because it is difficult to obtain detailed records for past landslide activity. Here, we deal with hydro-geomorphological signatures, such as storms, downpours, floods, snowmelt and mass movement, to detect annual slope instability events (ASIEs) over historical times. In order to obtain ASIEs for each year, a monthly Instability Density Index ( IDI ) was used and then monthly values were summed up to obtain a yearly value. Classes of monthly IDI varying between 0 (no instability) and 4 (highest instability) were determined from historical documents. We present an application for the Calore River Basin, Southern Italy, using data from a 313-year long series (1701–2013 CE). After 1880 CE the information becomes more valuable with directly observed landslide frequency. By this cataloguing, 129 ASIEs were obtained. Their evolution shows slight instability during the 18th century. Uneven and greater slope instability occurred instead across the 19th century, when an important phase of deforestation coincided with intensification of agricultural activities. Slope instability events continued during the 20th century but their causes are mainly related to anthropisation and the effects of recent climate change. It was determined that stormy autumns until the 19th century and successive winter-times with enhanced snowmelt, may have driven the reactivation of widespread instability events. We also found that mountainous and hilly terrains have an acute sensitivity to climate change. [ABSTRACT FROM AUTHOR]