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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]

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]

Numerous climate futures are now available from global climate models. Translation of climate data such as precipitation and temperatures into ecologically meaningful outputs for managers and planners is the next frontier. We describe a model-based open platform to assess vulnerabilities of agricultural systems to climate change on pixel-wise data. The platform includes a simulation modeling engine and is suited to work with NetCDF format of input and output files. In a case study covering a region (Auvergne) in the Massif Central of France, the platform is configured to characterize climate (occurrence of arid conditions in historical and projected climate records), soils and human management, and is then used to assess the vulnerability to climate change of grassland productivity (downscaled to a fine scale). We demonstrate how using climate time series, and process-based simulations vulnerabilities can be defined at fine spatial scales relevant to farmers and land managers, and can be incorporated into management frameworks. [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]

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]

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]

• 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]

The boreal forest of the northern hemisphere represents one of the world's largest ecozones and contains nearly one third of the world's intact forests and terrestrially stored carbon. Long-term variations in temperature and precipitation have been implied in altering carbon cycling in forest soils, including increased fluxes to receiving waters. In this study, we use a simple hydrologic model and a 40-year dataset (1971–2010) of dissolved organic carbon ( DOC ) from two pristine boreal lakes (ELA, Canada) to examine the interactions between precipitation and landscape-scale controls of DOC production and export from forest catchments to surface waters. Our results indicate that a simplified hydrologically-based conceptual model can enable the long-term temporal patterns of DOC fluxes to be captured within boreal landscapes. Reconstructed DOC exports from forested catchments in the period 1901–2012 follow largely a sinusoidal pattern, with a period of about 37 years and are tightly linked to multi-decadal patterns of precipitation. By combining our model with long-term precipitation estimates, we found no evidence of increasing DOC transport or in-lake concentrations through the 20th century. [ABSTRACT FROM AUTHOR]

Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensification of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a priority. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are applied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. Developing the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems related to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks. [ABSTRACT FROM AUTHOR]

Abstract: The optimal combination of yield and quality of hemp fibres from field grown crops is around flowering. Therefore prediction of flowering time would support in planning production and optimization of the cultivar choice for different agro-ecological zones. In the current paper the validation of a recently published model () is carried out for four varieties across a wide range of sites and thus of air temperature–photoperiod combinations. The model was evaluated by comparing its output to field observations of the duration between emergence and 50% flowering. The model output and observed times from emergence to 50% of flowering generally corresponded well, but some discrepancies were apparent. The biggest discrepancies between estimates and actual data were observed at extreme latitudes. The level of accuracy of the model predictions is satisfactory for strategic decision regarding sowing and harvesting time and cultivar choice, but tactical decisions (e.g. time of harvest based on flowering time) cannot be accurately supported. [Copyright &y& Elsevier]

Abstract: Multivariate spatial statistics techniques can be efficiently applied to generate fine spatial patterns of climate data in presence of an appropriate multivariate spatial structure over ungauged mountainous basins. However, they can become unsuitable when the data available over complex regions are sparse and affected by discordant spatial scales in primary and (colocated)-auxiliary variables. This is the case of actual evapotranspiration (AET). Combining GIS and geoindicators (e.g., topographical and vegetational indices), we proposed an upscaling procedure to overcome this problem, transforming a preliminary-smoothed macro-scale pattern (AET grid-data), into a local-scale pattern. The procedure was applied to a cropland test site at Mediterranean sub-regional basin scale (Tammaro, South Italy) to develop a climatological baseline estimation of AET refined at slope scale. After the upscaling, the most frequent estimated AET values were about 550mmyr−1 (with quasi-normal distribution), while underestimations were observed in the preliminary, smoothed map (positively skewed distribution with mean 460mmyr−1). The upscaling allowed the influence of the topographic factor to emerge, with a wider range of values (about 300–900mmyr−1) being estimated and substantially not visible in the smoothed pattern. A temporal climate pattern of soil water depletion in the growing season was also shown as reflected in the increase of AET flux in the period 1991–2008 in comparison to the precedent climate (1961–1990). [Copyright &y& Elsevier]

WARM (Water Accounting Rice Model) simulates paddy rice (Oryza sativa L.), based on temperature-driven development and radiation-driven crop growth. It also simulates: biomass partitioning, floodwater effect on temperature, spikelet sterility, floodwater and chemicals management, and soil hydrology. Biomass estimates from WARM were evaluated and compared with the ones from two generic crop models (CropSyst, WOFOST). The test-area was the Po Valley (Italy). Data collected at six sites from 1989 to 2004 from rice crops grown under flooded and non-limiting conditions were split into a calibration (to estimate some model parameters) and a validation set. For model evaluation, a fuzzy-logic based multiple-metrics indicator (MQI) was used: 0 (best)≤ MQI ≤1 (worst). WARM estimates compared well with the actual data (mean MQI =0.037 against 0.167 and 0.173 with CropSyst and WOFOST, respectively). On an average, the three models performed similarly for individual validation metrics such as modelling efficiency (EF >0.90) and correlation coefficient (R >0.98). WARM performed best in a weighed measure of the Akaike Information Criterion: (worst) (best), considering estimation accuracy and number of parameters required to achieve it (mean against 0.007 and ∼0.000 with CropSyst and WOFOST, respectively). WARM results were sensitive to 30% of the model parameters (ratio being lower with both CropSyst, <10%, and WOFOST, <20%), but appeared the easiest model to use because of the lowest number of crop parameters required (10 against 15 and 34 with CropSyst and WOFOST, respectively). This study provides a concrete example of the possibilities offered using a range of assessment metrics to evaluate model estimates, predictive capabilities, and complexity. [Copyright &y& Elsevier]

Abstract: Temperature and photoperiod can be used to simulate post-emergent hemp (Cannabis sativa L.) phenology. With reference to hemp in Italy, our main objective was to model field crops grown under a range of temperature and day length regimes. Dates of emergence and 50% of flowering were collected at Cadriano (Bologna) from serially sown field experiments (1996–1999, 2003–2005) on five cultivars: Carmagnola (late maturity), Felina 34 (medium maturity), Fibranova (late maturity), Futura (medium-late maturity), and Tiborszallasi (medium-late maturity). The database of phenological records was segregated into calibration and validation subsets. A phenology model was developed which utilises the beta function for response to hourly air temperature, and a switch-off function for response to day length. The life cycle of hemp from emergence to 50% of flowering was defined in terms of physiological development days (chronological days at the optimum photoperiod and temperature) and considered in three phases: juvenile phase (BVP), photo-sensitive phase (PIP), flower development phase (FDP). Critical temperatures (T b, base; T o, optimum, T c, ceiling), which did not vary widely across phases and cultivars, were estimated as common values: T b =1.9°C for BVP and 11.3°C for the other phases, T o =26.4°C and T c =40.0°C for all phases. Other parameters, i.e. day length of half-maximum development rate at PIP, and physiological development days for FDP, were also estimated as common values for all cultivars. Different genotypes were mainly characterised for the sensitivity to photoperiod (shape parameter n) and BVP length. With n ∼50, Felina 34 and Futura are regarded as low sensitive cultivars. Tiborszallasi was estimated as the highest sensitive cultivar (n close to 70), whereas Carmagnola and Fibranova showed an intermediate sensitivity (n ∼62). Felina 34 also differentiated for its relatively short BVP length at optimum conditions, i.e. ∼13 days; duration of about 20 days was the estimate for the other cultivars. Model performance against calibration dataset was good (percent relative root mean square in the range ∼6–20%), and comparison against independent data also confirmed the general applicability of this model. Owing to the importance of flowering date in hemp management techniques, these results can be used in decision support for hemp production though further evaluation of the model under a variety of latitudes is required. [Copyright &y& Elsevier]

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

The formalization of novel equations explicitly modelling the impact of extreme weather events into the crop model WOFOST (EMS: existing modelling solution; MMS: modified modelling solution) is proposed as a way to reduce the uncertainty in estimations of crop yield. A sensitivity analysis (SA) was performed to assess the effect of changing parameters values on the yield simulated by the model (both EMS and MMS) for different crops (winter and durum wheat, winter barley, maize, sunflower) grown under a variety of conditions (including future climate realisations) in Europe. A two-step SA was performed using global techniques: the Morris screening method for qualitative ranking of parameters was first used, followed by the eFAST variance-based method, which attributes portions of variance in the model output to each parameter. The results showed that the parameters related to the partitioning of assimilates to storage organs (FOTB) and to the conversion efficiency of photosynthates into storage organs (CVO) generally affected considerably the simulated yield (also underlying tight correlation with this output), whereas the parameters involved with respiration rate (Q10) or specific leaf area (SLA) became influential in case of unfavourable weather conditions. Major differences between EMS and MMS (which includes a component simulating the impact of extreme weather events) emerged in extreme cases of crop failure triggered by markedly negative minimum temperatures. With few exceptions, the two SA methods revealed the same parameter ranking. We argue that the SA performed in this study can be useful in the design of crop modelling studies and in the implementation of crop yield forecasting systems in Europe. [ABSTRACT FROM AUTHOR]

The impact of extreme events (such as prolonged droughts, heat waves, cold shocks and frost) is poorly represented by most of the existing yield forecasting systems. Two new model-based approaches that account for the impact of extreme weather events on crop production are presented as a way to improve yield forecasts, both based on the Crop Growth Monitoring System (CGMS) of the European Commission. A first approach includes simple relations – consistent with the degree of complexity of the most generic crop simulators – to explicitly model the impact of these events on leaf development and yield formation. A second approach is a hybrid system which adds selected agro-climatic indicators (accounting for drought and cold/heat stress) to the previous one. The new proposed methods, together with the CGMS-standard approach and a system exclusively based on selected agro-climatic indicators, were evaluated in a comparative fashion for their forecasting reliability. The four systems were assessed for the main micro- and macro-thermal cereal crops grown in highly productive European countries. The workflow included the statistical post-processing of model outputs aggregated at national level with historical series (1995–2013) of official yields, followed by a cross-validation for forecasting events triggered at flowering, maturity and at an intermediate stage. With the system based on agro-climatic indicators, satisfactory performances were limited to microthermal crops grown in Mediterranean environments (i.e. crop production systems mainly driven by rainfall distribution). Compared to CGMS-standard system, the newly proposed approaches increased the forecasting reliability in 94% of the combinations crop × country × forecasting moment. In particular, the explicit simulation of the impact of extreme events explained a large part of the inter-annual variability (up to +44% for spring barley in Poland), while the addition of agro-climatic indicators to the workflow mostly added accuracy to an already satisfactory forecasting system. [ABSTRACT FROM AUTHOR]

Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213 g C m −2 yr −1 , which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185 g C m −2 yr −1 on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006 g C m −2 yr −1 , respectively, in agreement with observed average GPP (1230 g C m −2 yr −1 ) and RECO (1046 g C m −2 yr −1 ). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was ∼5–16 g C week −1 across the study sites, while the goodness of fit (R 2 ) was ∼0.4–0.9. For evapotranspiration (ET), the average value of simulated ET (415 mm yr −1 ) for all sites and years is close to the average value of the observed ET (451 mm yr −1 ) by flux towers (on a weekly basis, RMSE∼2–8 mm week −1 ; R 2 = 0.3–0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system. [ABSTRACT FROM AUTHOR]

Highlights: [•] We run the Morris sensitivity-analysis method to the Pasture Simulation model. [•] Both uniform and normal distributions are employed to 28 vegetation parameters. [•] Sensitivity of nine outputs is examined at seven grassland sites in Europe. [•] Nine influential parameters are identified for model calibration. [•] We study the plasticity of the model with climate conditions. [Copyright &y& Elsevier]

Highlights: [•] Greenhouse soilless gerbera was grown in autumn and spring with fresh or moderately saline water. [•] The Penman–Monteith model and two regression equations, which responded to vapour pressure deficit and/or radiation, were used to predict the hourly transpiration rate. [•] Water salinity affected crop growth and production only in spring, but it influenced neither stomatal resistance nor model calibration. [•] The models explained 80–96% of the variability of measured transpiration. [ABSTRACT FROM AUTHOR]