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Soil suppressiveness which is the natural ability of soil to support optimal plant growth and health is the resultant of multiple soil microbial components; which implies many difficulties when estimating this soil condition. Microbial benefits for plant health from repeated digestate applications were assessed in three experimental sites surrounding anaerobic biogas plants in an intensively cultivated area of northern Italy. A 2-yr trial was performed in 2017 and 2018 by performing an in-pot plant growth assay, using soil samples taken from two fields for each experimental site, of which one had been repeatedly amended with anaerobic biogas digestate and the other had not. These fields were similar in management and crop sequences (maize was the recurrent crop) for the last 10 yr. Plant growth response in the bioassay was expressed as plant biomass production, root colonization frequency by soil-borne fungi were estimated to evaluate the impact of soil-borne pathogens on plant growth, abundance of Pseudomonas and actinomycetes populations in rhizosphere were estimated as beneficial soil microbial indicators. Repeated soil amendment with digestate increased significantly soil capacity to support plant biomass production as compared to unamended control in both the years. Findings supported evidence that this increase was principally attributable to a higher natural ability of digestate-amended soils to reduce root infection by saprophytic soil-borne pathogens whose inoculum was increased by the recurrent maize cultivation. Pseudomonas and actinomycetes were always more abundant in digestate-amended soils suggesting that both these large bacterial groups were involved in the increase of their natural capacity to control soil-borne pathogens (soil suppressiveness).

Crop simulation models are used to forecast the impacts of climate change on yield levels and to identify adaptation strategies. Nevertheless, crop quality has been almost neglected in available studies, despite its relevance on the economic and nutritional value of agricultural products. We present here a modeling study to evaluate the future trends of rice quality in the main European rice district, placed in Northern Italy. A rice growth model was coupled with a library of models of rice milling and cooking suitability, using current farmer management and baseline/future climatic scenarios as input for the simulations. Four general circulation models (NOResm, MIROC‐ESM, HadGEM2‐ES, and GISS‐ES) and two CO2 representative concentration pathways (2.6, 8.5) were used to generate 20‐year future climatic data centered on 2030 and 2070. Spatially distributed simulations were run at 2 × 2 km spatial resolution considering a Japonica (Loto) and a Tropical Japonica (Gladio) rice cultivar. The results depicted an overall decline in rice quality, especially for Loto (−5% of milling suitability considering GISS‐ES‐2.6 in all time frames; −8% in 2030 and −20% in 2070 under HadGEM2‐ES‐8.5). While the revenues of millers are expected to decrease of about 50 € t−1 in 2030 and 100 € t−1 in 2070 for Loto, minor changes are projected on Gladio milling and cooking suitability, except in the worst scenario in 2070 (−10 to −5% of cooking suitability, corresponding to −30 to −72 € t−1). Despite the need of reducing models uncertainty, this study provides variety‐specific indications on rice grain quality ready‐to‐use for crop specialists, farmers, and millers and that could raise the interest of different stakeholders of the agri‐food sector, including food scientists, geneticists, and policy makers.

Giant reed is a promising perennial grass providing ligno-cellulosic biomass suitable to be cultivated in marginal lands (MLs) and converted into several forms of renewable energy. This study investigates how much energy, in the form of biomethane, bioethanol, and combustible solid, can be obtained by the cultivation of this species in marginal land of two Italian regions, via the spatially explicit application of the Arungro crop model. Arungro was calibrated in both rainfed/well-irrigated systems, under non-limiting conditions for nutrient availability. The model was then linked to a georeferenced database, with data on (i) current/future climate, (ii) agro-management, (iii) soil physics/hydrology, (iv) land marginality, and (v) crop suitability to environment. Simulations were run at 500 × 500 m spatial resolution in MLs of Catania (CT, Southern Italy) and Bologna (BO, Northern Italy) provinces, characterized by contrasting pedo-climates. At field scale, Arungro explained 85% of the year-to-year variability of measured carbon accumulation in aerial biomass. At the provincial level, simulated energy yields progressively increased from bioethanol, to biomethane, and finally to combustible solid, with average values of 92-115-264 GJ ha−1 in BO and 105-133-304 GJ ha−1 in CT. Mean energy yields estimated for 2030 remained unchanged compared to the baseline, although showing large heterogeneity across the study area (changes between −6/+15% in BO and −16/+15% in CT). This study provides site-specific indications on giant reed current productions, energy yields, and natural water consumption, as well as on their future trends and stability, ready-to-use for multiple stakeholders of the agricultural sector involved in bioenergy planning.

A reliable forecast of the pre-harvest grain quality is requested by stakeholders in the rice sector, which is increasingly oriented to the achievement of superior standards to meet the market demand. Despite its economic importance, very few simulation models of the qualitative aspects of rice productions including the effects of weather conditions and farming practices are available. This paper presents GLORIFY, a forecasting system targeting the simulation of head rice yield (HRY), which represents the main determinant of rice market price at global level. A new HRY model was developed using experimental data collected in Northern Italy in 2006–2013 and referred to Loto (japonica) and Gladio (tropical japonica) cultivars, and it was coupled to the WARM rice simulator. Historical simulations were then performed in the period 1994–2013 to reproduce observed HRY variability, with model outputs and weather variables used as independent variables to build multi-regression models. At field level, model performances denoted a good agreement between observed and simulated HRY (R2 and modelling efficiency in the range 0.73–0.93). At province level, best results were obtained for Loto variety, as the regression model was able to explain 78% of the HRY variability, with a root mean square error (RMSE) of 0.77%. The model accuracy slightly decreased when leave-one-out cross-validation was applied (R2 = 0.61, RMSE = 1.04%). The present study lays the basis for a reliable estimation of HRY variability under different management and weather conditions.

Grain quality is crucial to meeting market demand and preserve the sustainability of the European rice sector. However the relationships between agro-meteorological conditions and major features of pre-harvest quality are not well understood. The evaluation of available models is needed to assess their suitability for predicting grain quality for different environmental conditions. This study presents a multi-site and multi-year evaluation of 26 models for the simulation of rice grain composition, milling quality and cooking quality, in the main European rice district (Northern Italy). The analysis was performed using data from 16 sites where the cultivars Loto (japonica) and Gladio (tropical japonica) were grown in 2011–2014. Model performances denoted models’ ability to reproduce grain quality variables, with increased modelling efficiencies (EF) from grain composition (−0.78

Growing food crops to meet global demand and the search for more sustainable cropping systems are increasing the need for new cultivars in key production areas. This study presents the identification of rice traits putatively producing the largest yield benefits in five areas that markedly differ in terms of environmental conditions in the Philippines, India, China, Japan and Italy. The ecophysiological model WARM and sensitivity analysis techniques were used to evaluate phenotypic traits involved with light interception, photosynthetic efficiency, tolerance to abiotic stressors, resistance to fungal pathogens and grain quality. The analysis involved only model parameters that have a close relationship with phenotypic traits breeders are working on, to increase the in vivo feasibility of selected ideotypes. Current climate and future projections were considered, in the light of the resources required by breeding programs and of the role of weather variables in the identification of promising traits. Results suggest that breeding for traits involved with disease resistance, and tolerance to cold- and heat-induced spikelet sterility could provide benefits similar to those obtained from the improvement of traits involved with canopy structure and photosynthetic efficiency. In contrast, potential benefits deriving from improved grain quality traits are restricted by weather variability and markedly affected by G × E interactions. For this reason, district-specific ideotypes were identified using a new index accounting for both their productivity and feasibility.

Supplementary file AJP Renal

Sunn hemp (Crotalaria juncea L.) is a fast growing, drought tolerant legume crop with potential as a biomass feedstock for advanced biofuels in Southern Europe, grown in either a single or double crop system. This study presents a new simulation model, SunnGro, which reproduces sunn hemp productivity, while providing a detailed description of leaf/branch size heterogeneity and its evolution during the vegetative season. The model was calibrated and validated using 20 field datasets collected from 2016 to 2018 in Greece, Spain, and Italy under non-limiting soil water conditions. High correlation between the simulated and measured values of branch number (R2 = 0.80), leaf number (R2 = 0.92), and biomass accumulation (0.67 < R2 < 0.82) demonstrated good model predictivity across sites, seasons, alternative sowing densities, dates, and harvest times. An uncertainty analysis was carried out under varying seasonal air temperatures and sowing times in five European locations to explore the capability of the model to identify the best agronomic practices for maximizing sunn hemp yield. Therefore, the current version of SunnGro is an effective tool for scenario analyses under varying management practices and changing climatic conditions.

Ecophysiological models can be successfully used to analyze genotype by environment interactions, thus supporting breeders in identifying key traits for specific growing conditions. This is especially true for traits involved with resistance/tolerance to biotic and abiotic stressors, which occurrence can vary greatly both in time and space. However, no modelling tools are available to be used directly by breeders, and this is one of the reasons that prevents an effective integration of modelling activities within breeding programs. ISIde is a software platform specifically designed for district-specific rice ideotyping targeting (i) resistance/tolerance traits and (ii) breeders as final users. Platform usability is guaranteed by a highly intuitive user interface and by exposing to users only settings involved with genetic improvement. Other information needed to run simulations (i.e., data on soil, climate, management) is automatically provided by the platform once the study area, the variety to improve and the climate scenario are selected. Ideotypes indeed can be defined and tested under current and climate change scenario, thus supporting the definition of strategies for breeding in the medium-long term. Comparing the performance of current and improved genotype, the platform provides an evaluation of the yield benefits exclusively due to the genetic improvement introduced. An example of the application of the ISIde platform in terms of functionalities and results that can be achieved is reported by means of a case study concerning the improvement of tolerance to heat stress around flowering in the Oristanese rice district (Italy). The platform is currently available for the six Italian rice districts. However, the software architecture allows its extension to other growing areas – or to additional genotypes – via dedicated tools available at the application page.

Crop models are used to estimate crop productivity under future climate projections, and modellers manage uncertainty by considering different scenarios and GCMs, using a range of crop simulators. Five crop models and 20 users were arranged in a randomized block design with four replicates. Parameters for maize (well studied by modellers) and rapeseed (almost ignored) were calibrated. While all models were accurate for maize (RRMSE from 16.5% to 25.9%), they were, to some extent, unsuitable for rapeseed. Although differences between biomass simulated by the models were generally significant for rapeseed, they were significant only in 30% of the cases for maize. This could suggest that in case of models well suited to a crop, user subjectivity (which explained 14% of total variance in maize outputs) can hide differences in model algorithms and, consequently, the uncertainty due to parameterization should be better investigated. Five crop models and 20 users were arranged in four randomized blocks.The significance of model factor for maize and rapeseed was evaluated.All models achieved good performance for maize and poor for rapeseed.Differences between models were significant only in 30% of the cases for maize.Parameterization uncertainty should be explicitly managed also in model ensembles.

Blast disease (Magnaporthe oryzae B. Couch) is one of the most important causes of rice yield losses worldwide. Although farmers implement countermeasures to limit its impacts, blast disease is still an important constraint to rice production in both tropical and temperate environments. This study presents the coupling of a generic disease model to the WARM rice simulator to quantify the pathogen impact on key physiological processes and thus on final yield. The impact of leaf blast was simulated by reducing the photosynthetic leaf area index and in turn radiation interception, as a function of disease progress rate. Panicle blast damage was reproduced by decreasing the percentage of photosynthates translocated to kernels. The modelling solution was calibrated and evaluated using field observations of blast impact at harvest, collected on 20 rice cultivars with different blast resistance and grown in five sites in Northern Italy in the period 1996–2012 (total 272 observations). Results showed a good correlation between simulated impacts (fraction of potential yield) and observations (0–5 scale used for the visual assessment, with 0 = no impact and 5 = complete crop failure), for both calibration (R2 = 0.57) and evaluation (R2 = 0.51) datasets. Model outputs were converted to the same scale used for the visual assessments to perform an in-depth evaluation of the modelling solution, which exactly matched the 46% of observed impact values, and presented an error of 1 class in 48.2% of the cases. This study demonstrated the soundness of the approach developed for crop-pathogen interactions and its suitability for the application in research—e.g., to explore the impacts of climate change on blast-related yield losses —and operational contexts—e.g., to test alternate fungicide strategies to optimize agricultural chemical applications.

We suggest that the unprecedented and unintended decrease of emissions of air pollutants during the COVID-19 lock-down in 2020 could lead to declining seasonal ozone concentrations and positive impacts on crop yields. An initial assessment of the potential effects of COVID-19 emission reductions was made using a set of six scenarios that variously assumed annual European and global emission reductions of 30% and 50% for the energy, industry, road transport and international shipping sectors, and 80% for the aviation sector. The greatest ozone reductions during the growing season reached up to 12 ppb over crop growing regions in Asia and up to 6 ppb in North America and Europe for the 50% global reduction scenario. In Europe, ozone responses are more sensitive to emission declines in other continents, international shipping and aviation than to emissions changes within Europe. We demonstrate that for wheat the overall magnitude of ozone precursor emission changes could lead to yield improvements between 2% and 8%. The expected magnitude of ozone precursor emission reductions during the Northern Hemisphere growing season in 2020 presents an opportunity to test and improve crop models and experimentally based exposure response relationships of ozone impacts on crops, under real-world conditions. This article is part of a discussion meeting issue ‘Air quality, past present and future’.

A simulation model was developed to study the accumulation dynamics of seed oil fatty acids (FAs) in response to environmental stimuli. The Petri Net (PN) formalism was used to encode a virtual analogue of the FA biosynthetic pathway, where the process is governed by rules targeting enzymatic regulatory mechanisms. Four alternative types of rules were conceived: i) constant enzymatic activity throughout seed development; ii) enzymatic activity regulated by temperature; iii) enzymatic activity regulated by time-dependent gene expression; iv) combination of temperature- and time-dependent regulatory mechanisms. A set of 10 PN-based candidate models were built upon these rules to be evaluated against real oil composition data gathered during seed development. Camelina sativa (L.) Crantz was chosen as a case-study crop to test model performance. Published experimental datasets from Italy, Spain and the USA were used to calibrate and validate the models through a cross-validation procedure. Models incorporating endogenous time-based regulation showed higher predictive power than those built upon temperature-based rules, but the best results were obtained when combinations of both types of rules were used. The highest scoring model used the beta function to model endogenously regulated time-dependency of enzymatic activity, and the response to temperature. The fitted model correctly reproduced time-course FA accumulation. In order to assess its behaviour under climate change, sample simulations were run under two artificial scenarios, one characterized by increased air temperature, and the other by imposed restriction to photosynthetic carbon supply to the seed. The latter scenario was intended to mimic drought stress in particular. At higher temperatures the model predicted an increase in saturated and monounsaturated FAs, with a concomitant decrease of the polyunsaturated fraction. Under simulated carbon restrictions the prevailing behaviour was a lowering of monounsaturated FAs whereas the polyunsaturated fraction markedly increased. In both case-studies the predicted responses were found to be consistent with previous literature reports.

We report the epidemiology of food-borne botulism in Puglia, Italy, between 1977-2017, using surveillance data and Experts' personal observations. As the disease is rare, the diagnosis is often missed or delayed, and cases are initially misdiagnosed. This was the case of a family outbreak of botulism in the 1970s.

Camelina (Camelina sativa L. Crantz) is an oilseed crop gaining interest due to its potential portfolio of derived bio-based products. The inclusion of camelina in traditional crop rotations is fostered by the possibility of growing it either as spring or autumn crop, the latter being of particular interest in the Mediterranean region. Here we present a process-based simulation model for camelina, CAMEL, and we evaluate its performances in predicting yield, oil production and principal fatty acid accumulation. CAMEL integrates a crop simulator with a model of soil water balance and with models to reproduce main seed qualitative traits. It was calibrated and validated using ten camelina field experiments performed in Northern Italy in non-limiting conditions for soil water availability and nitrogen fertilization during 2015–2017. The results for phenology (average error of 6.7 days), and biomass and yield accumulation (RRMSE = 23% for aboveground biomass and 9% for yield) denoted the ability of CAMEL to reproduce field observations of crop development and growth across growing seasons and sowing periods. The large correlation between simulated and measured oil fractions highlights the correct reproduction of the main camelina fatty acids. This work lays the basis for the use of CAMEL as a support tool to assess seed yield and quality in Northern Italy, besides further work is still needed to add the impact of management practices on yield and qualitative traits, before adopting CAMEL for in-season farmer support.

Crop models mathematically represent dynamic point-scale interactions between plant, weather, soil and management practices. They have been increasingly applied large scale (i.e. from farm-level to regional and global applications) to understand and quantify the trade-off between productivity, management and the sustainability of cropping systems, in terms of responsible use of resources (e.g. water and nitrogen) and of adaptation to or mitigation of climate change impacts. This contribution reviews the most recent information about spatializing crop models and provides a comprehensive overview of major assumptions and criticalities related to this methodological approach. The first paragraph focuses on the definition of crop models, presenting their historical evolution and main fields of application. A bibliometric analysis was carried out on 1017 scientific papers published between 1990 and 2018 in order to identify the most frequent scientific topics concerning the adoption of crop simulation modelling for sustainable agriculture. The second section describes the main sources of uncertainty in spatializing crop models, addressing two main aspects. Firstly, basic assumptions and validity domains of processes/phenomena represented may still not be valid when applied in a different spatial resolution. Secondly, reference input data needed to characterize the cropping system under study, to run models and test their performance at large scale can often be scarce and/or uncertain due to aggregation/disaggregation issues. The third section defines the minimum amount of data about environment (i.e. site, weather, soil), management (e.g. sowing and harvest date, cultivars and crop operations adopted) and crop type, needed to operate crop models at a given location under current/future climate scenarios. Necessary methodological indications for building a multi-layer georeferenced database facilitating coupling with biophysical models are also provided. Ways of integrating proxy variables (e.g. obtained from pedo-transfer functions and remote sensing data) and crop models have been reported. The last section presents two case studies dealing with the spatialized application of crop models to promote the sustainability of agriculture. A European case study is centred on the definition of farmer adaptation strategies to alleviate climate change impacts, while a regional case study evaluates the efficiency of water management and water footprint of tomato cultivation in Southern Italy.

This study aimed at evaluating the integrated measles and rubella surveillance system (IMRSS) in Apulia region, Italy, from its introduction in 2013 to 30 June 2016. Measles and rubella case reports were extracted from IMRSS. We estimated system sensitivity at the level of case reporting, using the capture–recapture method for three data sources. Data quality was described as the completeness of variables and timeliness of notification as the median-time interval from symptoms onset to initial alert. The proportion of suspected cases with laboratory investigation, the rate of discarded cases and the origin of infection were also computed. A total of 127 measles and four rubella suspected cases were reported to IMRSS and 82 were laboratory confirmed. Focusing our analysis on measles, IMRSS sensitivity was 82% (95% CI: 75–87). Completeness was >98% for mandatory variables and 57% for ‘genotyping’. The median-time interval from symptoms onset to initial alert was 4.5 days, with a timeliness of notification of 33% (41 cases reported ⩽48 h). The proportion of laboratory investigation was 87%. The rate of discarded cases was 0.1 per 100 000 inhabitants per year. The origin of infection was identified for 85% of cases. It is concluded that IMRSS provides good quality data and has good sensitivity; still efforts should be made to improve the completeness of laboratory-related variables, timeliness and to increase the rate of discarded cases.

Among non-food energy crops, giant reed ( Arundo donax L.) represents a promising opportunity to reduce the fossil fuel dependency of Mediterranean countries. Nevertheless, the response of this crop to future climate projections is an open issue despite the crucial implications for mid-term planning policies. In this study, we present an exploratory analysis of the climate change impact on giant reed productivity in the Lombardy plain (northern Italy), an area that is currently characterized by intensive fodder corn-based cropping systems, but where corn is expected to be negatively affected by projected changes in thermal and pluviometric regimes. A dedicated simulation environment was developed, by coupling Arungro, a process-based model specific to giant reed, to a database including information on the presence of biogas plants, land use, crop management and distribution, in addition to weather scenarios for current climate and future projections. The baseline climate (1975–1994) was obtained from the European Commission MARS database; the Hadley3 and NCAR realizations of the IPCC AR4 emission scenarios A1B and B1 were used to generate 20-year climate projections centred on 2020 and 2050. Spatially distributed simulations were run at a sub-regional scale in areas selected according to their attractiveness for investments and low risk of competition between feed and no-feed crop destinations. The results indicate that an increased local suitability of giant reed in future climate projections is expected in terms of biomass production (+20% in 2020 for all scenarios and +30% in 2050 for Hadley-A1B) and the economic and environmental sustainability of related cropping systems.

Using crop models as supporting tools for analyzing the interaction between genotype and environment represents an opportunity to identify priorities within breeding programs. This study represents the first attempt to use simulation models to define rice ideotypes improved for their resistance to biotic stressors (i.e., diseases); moreover, it extends approaches for evaluating the impact of changes in traits for tolerance to abiotic constraints (temperature shocks inducing sterility). The analysis—targeting the improvement of 34 varieties in six Italian rice districts—was focused on the impact of blast disease, and of pre-flowering cold- and heat-induced spikelet sterility. In silico ideotypes were tested at 5-km spatial resolution under current conditions and climate change scenarios centered on 2020, 2050, and 2085, derived according to the projections of two general circulation models–Hadley and NCAR–for two IPCC emission scenarios–A1B and B1. The study was performed using a dedicated simulation platform, i.e., ISIde, explicitly developed for ideotyping studies. The ideotypes improved for blast resistance obtained clear yield increases for all the combinations GCM × emission scenario × time horizon, i.e., 12.1 % average yield increase under current climate, although slightly decreasing for time windows approaching the end of the century and with a marked spatial heterogeneity in responses across districts. Concerning abiotic stressors, increasing tolerance to cold-induced sterility would lead to a substantial yield increase (+9.8 %) only for Indica-type varieties under current climate, whereas no increases are expected under future conditions and, in general, for Japonica-type varieties. Given the process-based logic behind the models used—supporting coherence of model responses under future scenarios—this study provides useful information for rice breeding programs to be realized in the medium-long term.

International audience; 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.

Plant height has a deep influence on the productivity of many crops, as it involves susceptibility to lodging, crop-weed competition, and the achievement of favorable harvest index. Nevertheless, modellers have practically ignored related ecophysiological processes, especially those modulated by management practices. The aim of this study was to analyze and model the processes involved with the effects of management on rice (Oryza sativa L.) elongation. Data were collected in two greenhouse experiments (2010-2011) where three factors (floodwater level, N fertilization, sowing density) were arranged in a split-plot design with three replicates. The model proposed demonstrated its suitability in reproducing both the dynamics involved with tissue elongation in the different phenological phases and the effects of submergence and N luxury consumption on elongation rates. Relative root mean square error (RRMSE) ranged between 4.23 and 12.41% for different treatments and years. The inclusion of algorithms for the impact of agronomic practices on plant height in cropping system models would increase their suitability for scenario analyses and for in silico ideotyping studies, owing to the great interest shown by geneticists in related traits. Moreover, this study-performed with students of a Cropping Systems MS course-demonstrated once more the power of modeling within educational activities. In this case, models were not the subject of the teaching but tools for analyzing processes and formalizing new knowledge. © Crop Science Society of America.

Crop models, like many representations of environmental processes, tend to be over-parameterised. A redesign of the SUCROS family of crop models, largely driven by sensitivity analysis, is presented here. In particular, two new versions of WOFOST, the most widespread model from this family, were developed. The first (WOFOST-GT) reduces model complexity through the definition of functions driven by few parameters with biological meaning. The other (WOFOST-GT2) improves canopy representation and senescence. Each version was evaluated for rice and winter wheat. Results highlighted a similar accuracy for the three versions: the original one achieved mean normalized RMSE of 13.75% and 10.75% for winter wheat and rice; corresponding values for the new versions were 14.42% and 10.79% (WOFOST-GT), and 14.38% and 10.85% (WOFOST-GT2). The new versions were considerably less complex, (60% less parameters). These improvements, increasing model usability without compromising its sophistication, can be transferred to other models from the same family.

Despite the availability of a variety of models to simulate crop growth and development, few operational approaches have been developed to assess pre-harvest quality of agricultural productions as a function of the conditions actually explored by the crop during the season. This represents a clear gap of knowledge researchers are trying to fill, in light of the evidences of a climate change-driven decline in the nutritional properties of important food crops. Rice represents the staple food for half of the world’s population, and this explains the noticeable interest in rice grain quality, because of the direct implications on the economic value of productions, on their market destination, and on food security issues. This paper presents a framework-independent .NET software library, i.e., UNIMI.CropQuality, implementing models to simulate various aspects of rice quality: amylose, protein, lipids and starch content, viscosity profile, chalkiness, cracking and head rice yield. Alternate approaches for the simulation of the same quality property are included, to allow users to select the most suitable for specific modelling studies. A case study is also presented where the library was linked to the WARM rice model and used to simulate head rice yield and the percentage incidence of cracked and milky white kernels (severely chalky) for two rice varieties in the main European rice district. RRMSE ranged between 4.33% and 6.47% for head rice yield, 21.88% and 32.18% for cracking percentage, 35.92% and 55.01% for milky white chalkiness; modelling efficiency were always positive. The component, developed according to the state-of-the-art of component-oriented software development, is released with a Software Development Kit containing help and code documentation files, as well as sample applications showing how to use the library with different crop simulators.

Wheat production in Morocco is crucial for economy and food security. However, wheat production is difficult because the semi-arid climate causes very variable wheat yields. To solve this issue, we need better prediction of the impact of drought on wheat yields to adapt cropping management to the semi-arid climate. Here, we adapted the models WOFOST and CropSyst to agro-climatic conditions in Morocco. Six soft and durum wheat varieties were grown during the 2011–2012 and 2012–2013 growing seasons in the experimental sites of Sidi El Aydi, Khemis Zemamra and Marchouch. Drip irrigation and rainfed treatments were arranged in a randomised-block design with three replicates. We determined the phenological stages of emergence, tillering, stem elongation, flowering and maturity. We measured aboveground biomass six times along the season. These data were used to adapt WOFOST and CropSyst to local conditions. Our results show that both models achieved good estimations, with R 2 always higher than 0.91, and positive values for Nash and Sutcliffe modelling efficiencies. Results of spatially distributed simulations were then analysed for the whole country in terms of different response to drought.

The main cultivation area of the Italian processing tomato is the Southern Capitanata plain. Here, the hardest agronomic challenge is the optimization of the irrigation water use, which is often inefficiently performed by farmers, who tend to over-irrigate. This could become unsustainable in the next years, given the negative impacts of climatic changes on groundwater availability and heat stress intensification. The aim of the study was to identify the most promising agronomic strategies to optimize tomato yield and water use in Capitanata, through a modeling study relying on an extensive dataset for model calibration and evaluation (22 data sets in 2005–2018). The TOMGRO simulation model was adapted to open-field growing conditions and was coupled with a soil model to reproduce the impact of water stress on yield and fruit quality. The new model, TomGro_field, was applied on the tomato cultivation area in Capitanata at 5 × 5 km spatial resolution using an ensemble of future climatic scenarios, resulting from the combination of four General Circulation Models, two extreme Representative Concentration Pathways and five 10-years time frames (2030–2070). Our results showed an overall negative impact of climate change on tomato yields (average decrease = 5–10%), which could be reversed by i) the implementation of deficit irrigation strategies based on the restitution of 60–70% of the crop evapotranspiration, ii) the adoption of varieties with longer cycle and iii) the anticipation of 1–2 weeks in transplanting dates. The corresponding irrigation amounts applied are around 360 mm, thus reinforcing that a rational water management could be realized. Our study provides agronomic indications to tomato growers and lays the basis for a bio-economic analysis to support policy makers in charge of promoting the sustainability of the tomato growing systems.

The increasing availability of high-quality sensors and computational power on low-cost mobile devices like smartphones and tablets is opening new possibilities for adopting this kind of technology for monitoring biophysical processes of interest for agronomic and environmental studies. A method for leaf area index (LAI) estimates based on gap fraction, derived from the segmentation of images acquired at 57° below the canopy, was recently proposed and implemented in the smartphone app PocketLAI ® , and successfully tested against commercial devices for paddy rice. In this study, PocketLAI was tested against the AccuPAR ceptometer on canopy structures (maize, row-seeded giant reed and natural grassland) that strongly deviate from the ideal assumption behind the simplified model for light transmittance into the canopy used in the app (i.e., random distribution of infinitely small leaves). The comparison between PocketLAI and AccuPAR showed overall good performances for the app, with root mean square error of 0.41, 0.49 and 0.96 m 2 m −2 for grassland, maize and giant reed respectively, and R 2 of 0.86, 0.92 and 0.88. A saturation effect was observed for PocketLAI for LAI values higher than 5 m 2 m −2 especially for giant reed, with the LAI values obtained with the app markedly underestimating those provided by AccuPAR. Although further studies are needed to better investigate the need for calibrating the app in case of low-quality devices, these results confirm the possible role of PocketLAI in providing a suitable alternative to the commercial tools available for indirect LAI estimates in contexts characterized by few economic resources or when a high portability is needed.

Leaf area index (LAI) is a crucial variable in agronomic and environmental studies, because of its importance for estimating the amount of radiation intercepted by the canopy and the crop water requirements. Direct methods for LAI estimation are destructive, labor and time consuming, and hardly applicable in case of forest ecosystems. This led to the development of different indirect methods, based on models for light transmission into the canopy and implemented into dedicated commercial instruments (e.g., LAI-2000 and different models of ceptometers). However, these instruments are usually expensive and characterized by a low portability, and could require long and expensive maintenance services in case of damages. In this study, we present an app for smartphone implementing two methods for LAI estimation, based on the use of sensors and processing power normally present in most of the modern mobile phones. The first method (App-L) is based on the estimation of the gap fraction at 57.5^o (to acquire values that are almost independent of leaf inclination) from luminance estimated above and below the canopy. The second method (App-G) estimates the gap fraction via automatic processing of images acquired below the canopy. The performances of the two methods implemented in the app were evaluated using data collected in a scatter-seeded rice field in northern Italy, and compared with those of the LAI-2000 and AccuPAR ceptometer, by determining the methods' accuracy (trueness and precision, the latter represented by repeatability and reproducibility) and linearity. The performances of App-G (mean repeatability limit=0.80m^2m^-^2; mean reproducibility limit=0.82m^2m^-^2; RMSE=1.04m^2m^-^2) were similar to those shown by LAI-2000 and AccuPAR, whereas App-L achieved the best trueness value (RMSE=0.37m^2m^-^2), although it resulted the less precise, requiring a large number of replicates to provide reliable estimations. Despite the satisfactory performances, the app proposed should be considered just as an alternative to the available commercial instruments, useful in contexts characterized by low economic resources or when the highest portability is needed.

The future of global rice productions in top producing countries is undermined by the impact of climate change threatening food security in the near future. In those European Mediterranean areas where rice is cultivated, this peculiar cropping system plays a crucial role in terms of sociocultural and ecological issues, and the climate change impact is still scarcely investigated. In this study, we explored the future trends of potential rice yields in the region considering the multiple sources of uncertainty associated with climate and yield predictions. Two rice crop models (STICS and WARM) were calibrated using 20 field experiments carried out in two main European rice producing areas − i.e., the Italian Lomellina and the French Camargue. These models were then applied under a range of climate change scenarios in 2030 and 2070 time frames, considering projections from the combination of four General Circulation Models and two extreme Representative CO2 Concentration Pathways (RCP 2.6 and 8.5). We compared the simulated yield levels with no adaptation, and designed adaptation strategies based on the anticipation of sowing date and the adoption of varieties with longer crop cycle. Our results showed that with no adaptation yields would decrease on average by 8% in 2030 and 12% in 2070 in Camargue and Lomellina. Future simulated yields in the two areas were lower than in the baseline in 67% (Camargue) and 84% (Lomellina) of the cases. The implementation of both adaptation strategies proved to be effective in reversing the situation, leading to an average yield increase of 28% and 25% in 2030 and 2070, respectively. The associated probability of lower yields than in current conditions was 24% in the two sites. Despite the uncertainty in predictions, mainly related to site, GCM and RCP, our findings indicate that the European rice sector has the potential to enhance current production levels in a changing climate, if longer cycle varieties will be grown in Mediterranean rice areas.

Highlights? Evaluating crop suitability is key to forecast crop distributions and productivity. ? Switching to crops more suitable under changed conditions is a form of adaptation. ? Suitability is a framework-independent software library of suitability approaches. The assessment of crop biophysical suitability to agro-environmental conditions is a valuable component of crop production studies, especially when evaluating productivity potential of new crops and areas, or for the assessment of potential cultivation shifts and crop adaptation needs under climate change scenarios. The software component Suitability presented herein implements several published approaches for computing crop suitability, based on available climate, soil and crop information. Users can access the Suitability software component via two application programming interfaces for single- and multi-cell estimations, the latter based on multiple regression methods. The component, extensible by third parties, is released as .NET 3.5 DLL, thus targeting the development of .NET clients. A case study on wheat suitability in Morocco is also presented.

Pest risk assessment studies are aimed at evaluating if weather conditions are suitable for the potential entry and establishment of an organism in a new environment. For fungal plant pathogens, the crucial aspect to be explored is the fulfillment of the infection process, that constitutes the first phase of the development of an epidemic as mainly driven by temperature and leaf wetness duration. This is of particular interest for climate change studies, because the modified pattern of temperature and moisture regimes could completely alter the known distribution and severity of plant disease epidemics. Biophysical process-based models could effectively be used in such studies, because they allow, within their applicability range, estimating organisms responses to climatic drivers in environmental conditions not yet experienced. One of the prerequisite of their adoption in operational contexts is a sensitivity analysis assessment aimed at understanding their ability (i) to differentiate the responses according to different parameterizations and (ii) to be sensitive to the variability of the input data. In this study, a generic potential fungal infection model simulating four pathogens chosen to provide a wide range in temperature and moisture requirements was analyzed. The model was run under diverse climatic conditions. The sensitivity of the model significantly changed according to the pathogen tested, and the relevance of its parameters in explaining model output resulted strongly linked to the environmental conditions tested, indicating its to be used in pest risk assessment studies.

BACKGROUND: Registry-based studies have identified risk factors for chronic kidney disease (CKD) and for progression to end-stage renal disease. However, usually, these studies do not incorporate sequential measurements of kidney function and provide little information on the prognosis of individual patients. The aim of this study is to identify which combinations of demographic and clinical characteristics are useful to discriminate patients with a differential annual decline in glomerular filtration rate (GFR). METHODS: This observational retrospective study includes patients enlisted in the registry of the Prevention of Progressive Renal Insufficiency Project of Emilia-Romagna region (Italy) from July 2004 to June 2010, with at least four serum creatinine measurements. Classification tree analysis (CTA) was used to identify subgroups of patients with a different annual GFR decline using demographic and laboratory data collected at study entry. RESULTS: The CTA procedure generated seven mutually exclusive groups. Among patients with proteinuria, those with a baseline estimated GFR (eGFR) of >33 mL/min/1.73 m2 exhibited the fastest illness progression in the study population (-3.655 mL/min/1.73 m2), followed by patients with a baseline eGFR of 4.3 mg/dL (-2.833 mL/min/1.73 m2). Among patients without proteinuria, those aged 67 years, females had on average a stable eGFR over time, with a large variability. CONCLUSIONS: It is possible to rely on a few variables typically accessible in routine clinical practice to stratify patients with a different CKD progression rate. Stratification can be used to guide decisions about the follow-up schedule, treatments to slow progression of kidney disease, prevent its complications and to begin planning for dialysis and transplantation.

The study purpose was to assess PSA velocity (PSAV) in healthy subjects in order to establish a reliable cutoff for the differential diagnosis of prostate cancer in a screening setting. We studied a series of 1666 healthy men aged 55 to 74 years undergoing two total PSA determinations at a four-year interval within a population-based randomized screening trial at the Centro per lo Studio e la Prevenzione Oncologica of Florence. First and second screening round PSA assays (PSA1 and PSA2) were carried out with the same method and by the same laboratory. PSAV (PSA1–PSA2/year) was determined in non-cancer subjects in the overall series or in specific age and PSA subgroups, and in subjects with cancer detected at the second screening round. Average PSAV in 1648 non-cancer subjects was 0.07 ng/mL/year (range −2.18+5.99, 95% CI 0.05–0.09); at least one third of subjects showed a decrease in PSA (negative PSAV), mostly of limited magnitude and in the low PSA range. Average PSAV in the 18 cancer patients was 1.16 ng/mL/year (range 0.10–5.6, 95% CI 0.56–1.77), which is significantly higher (p

The study offers a retrospective analysis of the positive predictive value (PPV) of several variables, i.e. digital rectal examination (DRE), transrectal ultrasonography (TRUS), PSA value, PSA density (PSAD), and free/total PSA ratio (F/T), for the histologic outcome of 179 prostate biopsies performed within a population-based screening trial. The ratio of spared benign biopsies to missed cancers (SBB/MC) if biopsy results had been decided on the basis of single variables was also evaluated. PPV was 82.9% for DRE, 56.3% for TRUS, 26.6% for PSA (cutoff ≥4 ng/mL), 47.4% for PSA (cutoff ≥10 ng/mL), 42.0% for PSAD (cutoff 0.15), 59.2% for PSAD (cutoff 0.20), 34.9% for F/T (cutoff 0.20) and 40.0% for F/T (cutoff 0.15). SBB/MC was 121/23 for DRE, 96/12 for TRUS, 11/10 for PSA (cutoff ≥4 ng/mL), 107/34 for PSA (cutoff ≥10 ng/mL), 87/23 for PSAD (cutoff 0.15), 109/26 for PSAD (cutoff 0.20), 45/8 for F/T (cutoff 0.20) and 70/14 for F/T (cutoff 0.15). Multivariate analysis of the association with biopsy outcome showed the highest odds ratio for TRUS (13.24, 95% CI=4.4–30.7), and considerably lower values for DRE (4.17, 95% CI=2.0–8–9), PSAD (cutoff 0.20: 3.24, 95% CI=–1.8–5.7) and F/T (cutoff 0.20 and F/T

The lack of standardized information on the evaluation of in vivo field methods is an important source of uncertainty in the interpretation of field data. The same words precision and accuracy can be frequently found in the agronomic and ecological literature, although often used without a real attempt to give these terms rigorous and shared meanings. On the contrary, standard protocols for determining accuracy and precision of analytical methods were successfully proposed in the last two decades and are now routinely used, especially within the chemical community. A first attempt to compile a standard guideline for in vivo field methods, derived by adapting the ISO 5725 protocol for the validation of analytical methods, is here presented. The concepts of levels, reference material, and inter-laboratory test derived from the protocol are redefined, and the underlying assumptions behind the adaptation of the ISO norm are introduced and discussed. Applicability and effectiveness of the proposed procedure are shown by means of a case study where the accuracy – i.e., trueness and precision, the latter composed by repeatability and reproducibility – of two diagnostic methods for indirect estimates of plant nitrogen nutritional status (chlorophyll meter and leaf color chart) was determined. The chlorophyll meter was more precise than leaf color chart, with precision value – expressed as relative standard deviations – lower than 6%. On the other hand, trueness indices showed better performances for leaf color chart, thus demonstrating the suitability of this method for supporting low-income farmers in managing topdressing fertilization, although at the price of performing a large number of reading replicates. However, these results are not aimed at drawing conclusions on techniques for supporting fertilization: the one presented is indeed just a case study used to assess the possibility of adopting the proposed procedure, as well as to highlight potential limits for its application. In this regard, the identification of reference values – needed for trueness quantification – is surely the most delicate issue, since the absence of conventional true values leads to the need of finding the most suitable solution according to the specific variable investigated and to the specific contexts in which the method under evaluation is applied. Hence, in light of both the encouraging results and the underlined limits, we just aim here at opening a discussion on the need for standardizing approaches and terminology for the evaluation of indirect field methods.

In an effort to better understand postharvest losses associated with low-cost tomato transport in West Africa we present a spatial-temporal simulation model that links the prevailing outside weather conditions, estimated using satellite meteorology, to the microclimate observed inside truck trailers (cryptoclimate) to determine the deterioration in tomato quality during transport. Tomatoes from Burkina Faso are transported under sub-optimal circumstances to important Ghanaian markets; during a number of these transports conditions for the tomato cargo inside trucks were measured while conditions outside the trucks were monitored by means of weather satellites. The presented tomato quality model analytically combines cryptoclimate, duration since harvest, and kinetic modelling to arrive at estimated firmness. Firmness of tomatoes in transport was monitored with a portable penetrometer in selected trucks, augmented with additional (acoustic firmness) data collected in a climate chamber. Half of these observations were used to calibrate a firmness loss model and the other half to validate the simulation results. Our results indicate that outside weather during transport can be reasonably well estimated using satellite meteorology. The model performance for the estimation of outside global radiation (Rg) and land-surface temperature (LST) were found to be satisfactory, with a RMSE=87.98Wm^-^2; bias=57.39Wm^-^2 and RMSE=2.95^oC; bias=0.91^oC, respectively. Results for the cryptoclimate estimation (conditions inside the trucks) for temperature, relative humidity, and light intensity were as follows: R^2=0.77, RMSE=4.18^oC (T"i"n"c"a"r"g"o); R^2=0.84, RMSE=19.59% (RH"i"n"c"a"r"g"o); and R^2=0.9, RMSE=137.31lx (LI"i"n"c"a"r"g"o). The postharvest loss model that relies on these estimates as its input explained on average 77% of the variance in observed tomatoes firmness, with total product losses ranging from 30% to 50% when integrated over the entire transportation period. With the accuracy of the model quantified and the causality of losses partially demonstrated, we argue that the simulation model can be useful as an economic resistor in transport optimization studies to investigate the cost-benefit of various measures to reduce postharvest losses. Such studies could help to illustrate what net gains can be expected if delays along the transportation route are reduced, cargo conditions are semi-controlled (e.g. pre-cooling treatment), or if a different transport schedule is adopted. The model may also be used to show the impact of different climate change scenarios on postharvest losses.

Summary Background Although tumor markers are frequently used in the follow-up of patients with breast cancer, two points are still being debated: 1) their cost/effectiveness has been neither demonstrated nor disproved; 2) the reliability of the currently used dichotomous division into a positive/negative cut-off should be definitely validated. Dynamic criteria of interpretation based on serial serum samples would probably be more effective for early detection of relapse. Patients and methods The aim of the present study was to compare the dichotomous cut-off based decision criteria to a dynamic serial sample based assessment of tumor markers. Since 1989, 794 patients have been followed in 11 institutions. CEA and CA15.3 were measured once a month for three months before every clinical examination. The present paper concerns the evaluation variability in 405 patients without evidence of disease in the first three institutions joining the study. Results In patients without evidence of disease, the coefficient of variation of all samples for every patient showed a median value of 19 for CEA and 21 for CA15.3. Variability was negatively associated with the antigen level and was most likely due to the analytical component. This was also confirmed by the significant difference in variability among the three institutions evaluated. The median value of the critical difference was 53% for CEA and 57% forCA15.3. Conclusions 1) Individually tailored dynamic decision criteria are applicable in about 50% of the cases. 2) The problem of improving the precision of tumor marker assays in the low dose range must be urgently addressed to the manufacturers of tumor markers by the scientific community in order to apply individually tailored decision criteria for patients in whom the serum level of biological markers is low.

Wheat is the staple food of 1.5 billion people worldwide and projected trends in global demand and productivity warn against food security risks over the next decades. Large-area crop monitoring and yield forecasting represent key issues to support agricultural policies, especially in developing countries. Among the existing monitoring systems, the most sophisticated are based on crop simulation models. Published reports of sensitivity analyses performed on different crop models show that parameters involved with leaf area expansion are often considered as the most relevant. Here we performed a multi-year spatially-distributed Monte Carlo-based sensitivity analysis of the models WOFOST and CropSyst for wheat simulation in Morocco. Due to the high number of sensitivity analyses to be performed, a 2-step procedure was adopted, with the Morris method used to identify parameters with a negligible effect and the Sobol' one applied on those remaining. Environmental and management information were derived from the European Commission MARS database. Our results show that parameters directly involved with photosynthesis played a major role: they explained more than 75% of the total output variance for CropSyst and more than 70% for WOFOST. Instead, parameters involved with the processes related to leaf area expansion resulted less relevant. The geographical patterns in terms of the relevance of parameters and processes shown by the same models under heterogeneous conditions could provide useful guidelines for driving breeders efforts towards specific plant traits, in the light of developing phenotypes suitable for specific conditions, e.g., varieties with a higher level of thermal adaptation in the Southern regions. This is the first time a multi-year spatially-distributed sensitivity analysis is carried out on two complex agro-ecological models., JRC.H.4-Monitoring Agricultural Resources

Although human immunodeficiency virus (HIV) infection has been a major global health problem for almost 3 decades, with the introduction of highly active antiretroviral therapy in 1996 and effective prophylaxis and management of opportunistic infections, mortality from acquired immunodeficiency syndrome has decreased markedly. In developed countries, this condition is now being treated as a chronic condition. As a result, rates of morbidity and mortality from other medical conditions leading to end-stage liver, kidney, and heart disease are steadily increasing in individuals with HIV. Because the definitive treatment for end-stage organ failure is transplantation, the demand for it has increased among HIV-infected patients. For these reasons, many transplant centers have eliminated HIV infection as a contraindication to transplantation, as a result of better patient management and demand.

The role of breast scintigraphy in the evaluation of 30 women with an uncertain diagnosis following triple assessment was investigated. Scintigraphy was positive in 7 of 13 cancers and 3 of 17 benign lesions and dubious in 1 of 13 cancers and 2 of 17 benign lesions. Sensitivity ranged from 61.5% to 53.8% depending whether dubious lesions were designated as positive or negative. This study shows that breast scintigraphy is not sufficiently sensitive or specific to use in the evaluation of lesions of uncertain diagnosis following triple assessment.

The significance of neuron specific enolase (NSE) was investigated in comparison with other tumor markers (CEA, CT, CA 15-3) used in the diagnosis and treatment monitoring of lung cancer. As previously described, the calcitonin assay proved to have very low sensitivity for small cell lung cancer (SCLC). The serum NSE assay was, however, shown to be a useful diagnostic aid for discrimination between histologically different lung cancers, and therefore this assay may be a valuable tool for treatment monitoring in SCLC patients. CA 15-3, also an unspecific marker, showed similar sensitivity to the NSE assay in SCLC patients, the sensitivity being higher than CEA in non small cell lung cancer (NSCLC)

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