Potential Response of Soil-Borne Fungal Pathogens Affecting Crops to a Scenario of Climate Change in Europe

A study was carried out on the potential response of soil-borne pathogens causing crop yield losses under a climate change scenario in Europe. A controlled chamber set of experiments was carried out to quantify pathogen response to temperature using pure colonies of three soil-borne fungi, representative of low (Fusarium nivale), medium-high (Athelia rolfsii) and high (Macrophomina phaseolina) temperature requirements. A generic model to simulate fungal growth response to temperature based on these experiments was developed and linked to a soil temperature model component, and to components to simulate soil water content accounting for crop water uptake of potential hosts. Pathogens relative growth was simulated over Europe using the IPCC A1B emission scenario as realization of the Hadley-CM3 global climate model, available from the European Commission and processed for use with biophysical models. The simulations resulting from using the time span centred on 2030 were compared to the baseline, centred on the year 2000, using a sample of 30 years of daily weather. The general trend of soil-borne pathogens response to the scenario of climate change is a relative increase in growth in colder areas of Europe, as a function of their temperature requirements. Projections of F. nivale in the future indicate a relative increase of this winter pathogen of wheat in Northern European countries. A. rolfsii and M. phaseolina, two soil-borne pathogens typical of warmer agricultural areas, could find more favourable conditions in areas of the Central Europe, but they differentiated in Southern Europe where A. rolfsii resulted affected by summer soil temperatures above optimum

Evolutionary trends and phylogenetic association of key morphological traits in the Italian rice varietal landscape

Efficient germplasm exploitation in crop breeding requires comprehensive knowledge of the available genetic diversity. Linking molecular data to phenotypic expression is fundamental for the profitable utilisation of genetic resources. Italian rice germplasm is an invaluable source of genes, being characterised by marked heterogeneity. A phenotypic characterisation is presented in this paper, with a focus on the evolutionary trends, and on the comparison with available molecular studies. A panel of 351 Italian rice varieties was analysed using seven key morphological traits, employing univariate and multivariate analyses. Considerable variability was found, with clear morphological trends towards reduced plant height, earliness, and spindle-shaped caryopses. Previous findings indicating that genetic diversity was maintained throughout time could not be confirmed, as small phenotypic variability was found in the most recent rice varieties. Consistency with phylogenetic data from previous studies was partial: one phylogenetic subgroup was phenotypically well distinct, while the others had overlapping characteristics and encompassed a wide range of phenotypic variation. Our study provides a quantitative ready-to-use set of information to support new breeding programs, as well as the basis to develop variety-specific calibrations of eco-physiological models, to identify promising traits in light of climate change conditions and alternative management scenarios

A multi-approach software library for estimating crop suitability to environment

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Use of remote sensing‑derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub‑field level

Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2 = 0.61; RMSE = 19.86 dry matter g m−2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2 = 0.47; RMSE = 75.52 dry matter g m−2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level

Analisi e modellizzazione dell'effetto di agrotecniche sull'altezza della pianta : il progetto MIATA

L\u2019altezza delle piante \ue8 importante per determinarne il potenziale produttivo e la suscettibilit\ue0 nei confronti di avversit\ue0 abio-tiche. Nonostante questo, i modelli disponibili la ignorano o la simulano utilizzando semplici funzioni logistiche indipendenti dai reali processi bioLsici in gioco e dalle modalit\ue0 di gestione. Il progetto MIATA, condotto da studenti del corso di Sistemi Colturali dell\u2019Universit\ue0 degli Studi di Milano, ha affrontato la problematica, fornendo soluzioni modellistiche utili sia a scopo previsionale che di supporto alla gestione

A set of software components for the simulation of plant airborne diseases

Models to evaluate the impact of plant diseases on crop production under current and future climatic conditions are increasingly requested by different stakeholders. This paper presents four software components - InoculumPressure, DiseaseProgress, ImpactsOnPlants, AgromanagementDisease - which implement models to simulate the dynamics of generic polycyclic fungal epidemics and interactions with crop physiological processes. The software architecture adopted allows extending the components with alternate approaches to reproduce specific pathosystems or compare predictive capabilities. As proofs of concept, (i) the components are coupled with two crop simulators to reproduce wheat brown rust and rice blast epidemics and their impacts on leaf area and yield formation; (ii) spatially distributed sensitivity analyses are performed for rice in China and wheat in Europe to investigate model behaviour; (iii) a preliminary evaluation against observations of rice blast severity is performed in Northern Italy. The components are explicitly targeted to the modelling of crop-pathogen interactions to perform scenario analysis

Datasets of harmonized risk assessment of grapevine downy mildew and phenological observations in eight Italian regions (2012-2017)

Phytosanitary bulletins released at weekly interval by eight Italian regional plant protection services in the growing seasons 2012-2017 were used to derive an harmonized dataset of grapevine downy mildew infection risk and phenological observations. The downy mildew infection risk (n = 8816) was classified using a 5-point Likert response item ranging from ‘very low’ (1) to ‘very high’ (5) by six independent evaluators with domain expertise in agronomy, phytopathology and agrometeorology. Common criteria were used in the risk assessment, considering i) the presence of disease symptoms in field surveys, ii) the host phenological susceptibility, iii) the weather forecasts in the next week from the bulletin release date, iv) the advice to apply a fungicide treatment and v) the outputs of epidemiological models. The phenological observations are provided as BBCH codes (n = 1689), which were either transcribed from the phytosanitary bulletins or derived from the narrative description of the visual observation. Phenological data refer to the main early and late grapevine varieties in the eight regions (NUTS-2 administrative unit). Each record is associated with the NUTS-2 and NUTS-3 (31 provinces) administrative unit of reference, to the growing season (2012-2017), and refers to the individual risk assessment by the six evaluators. The dataset is hosted by the Centre for Agriculture and Environment of the Italian Council for Agricultural Research and Economics. These data could be helpful to researchers who develop either grapevine phenological models or process-based epidemiological predictive algorithms in order to refine their calibration and evaluation, as well as being a valuable resource for stakeholders in charge of evaluating the effective implementation of Integrated Pest Management in the decision-making process of public plant protection services in Italy.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

An agro-physiological dataset on industrial tomatoes from nine years of field experiments conducted with alternative water-saving strategies in Mediterranean environments

This dataset has been collected during multi-year field trials conducted to assess the effect of alternative Deficit Irrigation (DI) strategies on industrial tomato. DI is a widely investigated farming strategy that reduces water applications below optimal crop requirements during the crop cycle without compromising production. The dataset comprises 100 experimental plots carried out in nine years and entails 32 DI treatments. The treatments include nine controls (well-watered), 15 constant DI treatments (i.e., same irrigation regime during the crop cycle), and eight variable DI treatments. The dataset is released as a Microsoft Excel file (extension .xlsx) organized in eight sheets corresponding to the main typologies of data, i.e., irrigation water scheduling (volume and timing), physical soil characteristics, daily weather data, and agro-physiological variables (i.e., phenology, stomatal conductance, crop temperature, crop yield, and quality). The dataset is presented in a Data In Brief article which is currently under review.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions

The evaluation of biophysical models is usually carried out by estimating the agreement between measured and simulated data and, more rarely, by using indices for other aspects, like model complexity and overparameterization. In spite of the importance of model robustness, especially for large area applications, no proposals for its quantification are available. In this paper, we would like to open a discussion on this issue, proposing a first approach for a quantification of robustness based on the variability of model error to variability of explored conditions ratio. We used modelling efficiency (EF) for quantifying error in model predictions and a normalized agrometeorological index (SAM) based on cumulated rainfall and reference evapotranspiration to characterize the conditions of application. Population standard deviations of EF and SAM were used to quantify their variability. The indicator was tested for models estimating meteorological variables and crop state variables. The values provided by the robustness indicator (IR) were discussed according to the models' features and to the typology and number of processes simulated. IR increased with the number of processes simulated and, within the same typology of model, with the degree of overparameterization. No correlation were found between IR and two of the most used indices of model error (RRMSE, EF). This supports its inclusion in integrated systems for model evaluation