New strategies to study and control plant diseases and their application to Kiwifruit Decline

openIn 2012, leaf scorches, wilting, sudden defoliation and dieback symptoms were observed for the first time on several kiwifruit plants in orchards located in Veneto (Northeast of Italy). Diseased plants were also characterised by a heavily compromised root system with none or very few feeding roots, rotting tissues on smaller roots and lack of cohesion between the external cylinder and the core. In relation to these symptoms the new disease was named Kiwifruit Decline (KD). KD rapidly spread in all the most important Italian growing areas and probably up to date is the most concerning phyto-pathological issue for kiwifruit growers. With the main aim to determine KD aetiology and to identify the epidemiological pattern of this disease outbreaks, canonical strategies and new technologies were integrated in an interdisciplinary approach. The work started with the definition of a conceptual framework on the symptoms observed in the field and with the reconstruction of the history of the disease based on the farmers’ experiences. These evidences were used as first-hand source of information and integrated with the experiences gathered by other Italian research groups to hypothesize the etiological causes most probably involved in the disease. From this analysis waterlogging and soil-borne pathogens emerged as the two most probable factors involved in the disease, although their role in the disease was still unknown. Therefore, the following step was the setup of a canonical experimental trial, where the effect of the two most probable etiological causes were compared under controlled conditions. The trial gave unequivocal results clearly stating the necessary interaction between waterlogging and soil borne pathogens to incite the disease. Furthermore, axenic isolation starting from plants that became diseased during this trial, allowed to have a first insight on soil-borne microorganisms potentially involved in the disease, suggesting that one or more pathogens (most probably Oomycetes) might be involved in the disease. Given these results a pathogenicity test was set up and confirmed that Phytopythium vexans was able to induce KD symptoms in both canopy and roots of kiwifruit plants. Once the role of a biotic factor was demonstrated, the studies moved back to the field focusing mostly on remote sensing technologies able to infer the physiological traits of the plants. Thermal and multispectral imagery acquired over a diseased field and classified with unsupervised clustering algorithms allowed to efficiently distinguish asymptomatic from symptomatic plants and to predict, one year in advance, the disease outbreak. Since the involvement of one or more potential soil-borne pathogens was proposed, a metabarcoding study was performed to have a first insight on fungal and oomycete communities associated with KD. Interestingly, Phytopythium vexans not only was found with a low relative abundance within diseased samples, but it was also recorded in healthy samples suggesting that the asymptomatic state of the plants is most probably linked to the environmental conditions averse to the development of the pathogens. Metabarcoding analysis also suggested Phytophthora sojae and Ilyonectria macrodidyma as new potential pathogen candidates. Results from this thesis provided several breakthroughs regarding the KD syndrome and defined the starting point for future studies. Indeed, not only the disease is now clearly associated to a combination of waterlogging conditions and soil-borne pathogens, but also a standardized protocol was setup to reproduce the disease. Moreover, new tools for in-field early disease detection are proposed and the first overview of fungal and oomycete community associated to KD is given for both root endosphere and rhizosphere compartments.Dottorato di ricerca in Scienze e biotecnologie agrarieopenSavian, Francesc

System simulation by SEMoLa

SEMoLa is a platform, developed at DISA since 1992, for system knowledge integration and modelling. It allows to create computer models for dynamic systems and to manage different types of information. It is formed by several parts, each dealing with different forms of knowledge, in an integrated way: a graphical user interface (GUI), a declarative language for modelling, a set of commands with a procedural scripting language, a specific editor with code highlighting (SemEdit), a visual modelling application (SemDraw), a data base management system (SemData), plotting data capabilities (SemPlot), a raster maps management system (SemGrid), a large library of random number generators for uncertainty analysis, support for fuzzy logic expert systems, a neural networks builder and various statistical tools (basic statistics, multiple and non-linear regression, moving statistics, etc.). The core part of the platform is the declarative modelling language (SEMoLa; simple, easy to use, modelling language). It relies on System Dynamics principles and uses an integrated view to represent dynamic systems through different modelling approaches (state/individual-based, continuous/discrete, deterministic/stochastic) without requiring specific programming skills. SEMoLa language is based on a ontology closer to human reasoning rather than computer logic and constitutes also a paradigm for knowledge management. SEMoLa platform permits to simplify the routinely tasks of creating, debugging, evaluating and deploying computer simulation models but also to create user libraries of script commands. It is able to communicate with other frameworks exchanging - with standard formats - data, modules and model components

Effect of meteorological and agronomic factors on maize grain contamination by fumonisin

Fumonisins are toxic secondary metabolites produced by fungi such as F.verticilloides. Maize is commonly colonized by several spoilage fungi both in pre- and post-harvest conditions. Field infection prevention is the best solution to contain contamination, using practices aimed at restricting plant stress and limiting the propagation of the disease. This work is focused on understanding the effect of environmental factors on the production of fumonisins in Friuli Venezia Giulia (NE Italy) on maize crops. The analysis has been performed on a dataset covering a period of 14 years (from 2000 to 2013), recording fumonisins contamination and daily meteorological data (air temperature, RH, Rain, Wind speed) for 13 different drying plants and for three different harvest times (early, medium and late). The drying plants collect grain production from an area of about 70.000-100.000 ha. Data were analyzed by full factorial ANOVA and a multiple regression approach was performed using STATA and SEMoLa software. ANOVA test pointed out a significant effect of factors \u201cyear\u201d and \u201charvest time\u201d (p<0.01) for fumonisin content. Instead, location had no significant effect. The best regression model (R2=0. 65, 2... observation) detected a significant correlation between fumonisin concentration and meteorological data in the period from 15th to 31st July. High fumonisin contents were positively correlated with daily thermal excursion, minimum temperature and wet conditions in this period. Silk drying and harvest time resulted as the key factors to contain and study fumonisins contamination in maize. Results will be used to implement a more complex dynamic model

A Metabarcoding Approach to Investigate Fungal and Oomycete Communities Associated with Kiwifruit Vine Decline Syndrome in Italy

Since 2012, kiwifruit vine decline syndrome (KVDS) has severely compromised all major kiwifruit-growing areas in Italy. Thus far, etiological studies were mainly focused on waterlogging effects or on the isolation of microorganisms from diseased plants; therefore, an all-encompassing picture of KVDS microbiota is still missing. This work aims to describe oomycete and fungal communities associated with KVDS and to identify key taxa potentially involved in the disease through a metabarcoding approach on root endosphere and rhizosphere samples. Two nearby fields with similar pedoclimatic conditions were identified based on KVDS spreading during a 4-year survey (2016 to 2019). In total, four sampling areas were selected, one from the control field with no sign of KVDS (asymptomatic site) and three from the KVDS-affected field (diseased site): (i) asymptomatic until the sampling date in 2018, (ii) symptomatic since 2018, and (iii) symptomatic since 2017. Total genomic DNA samples were subjected to a nested PCR approach separately targeting the internal transcribes spacer 2 regions of fungal and oomycete communities. The communities were compared in terms of α- and β-diversities, and key taxa were identified using univariate differential abundance tests. Major differences in taxa distribution were observed between samples from the different sites (asymptomatic and diseased) and were mostly linked to the oomycete community. Phytophthora sojae was the main taxa characterizing the diseased site and supposed to be involved in the disease and Phytopythium spp. were found related to the different plant health status. Finally, Dactylonectria macrodidyma, Phytopythium citrinum, and Thielaviopsis basicola were also proposed as new KVDS-related pathogens

Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season&rsquo;s first period of heat (July&ndash;August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017&ndash;2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit

On the Use of NDVI to Estimate LAI in Field Crops: Implementing a Conversion Equation Library

The leaf area index (LAI) is a direct indicator of vegetation activity, and its relationship with the normalized difference vegetation index (NDVI) has been investigated in many research studies. Remote sensing makes available NDVI data over large areas, and researchers developed specific equations to derive the LAI from the NDVI, using empirical relationships grounded in field data collection. We conducted a literature search using &ldquo;NDVI&rdquo; AND &ldquo;LAI&rdquo; AND &ldquo;crop&rdquo; as the search string, focusing on the period 2017&ndash;2021. We reviewed the available equations to convert the NDVI into the LAI, aiming at (i) exploring the fields of application of an NDVI-based LAI, (ii) characterizing the mathematical relationships between the NDVI and LAI in the available equations, (iii) creating a software library with the retrieved methods, and (iv) releasing a publicly available software as a service, implementing these equations to foster their reuse by third parties. The literature search yielded 92 articles since 2017, where 139 equations were proposed. We analyzed the mathematical form of both the single equations and ensembles of the NDVI to LAI conversion methods, specific for crop, sensor, and biome. The characterization of the functions highlighted two main constraints when developing an NDVI-LAI conversion function: environmental conditions (i.e., water and light resource, land cover, and climate) and the availability of recurring data during the growing season. We found that the trend of an NDVI-LAI function is usually driven by the ecosystem water availability for the crop rather than by the crop type itself, as well as by the data availability; the data should be adequate in terms of the sample size and temporal resolution for reliably representing the phenomenon under investigation. Our study demonstrated that the choice of the NDVI-LAI equation (or ensemble of equations) should be driven by the trade-off between the scale of the investigation and data availability. The implementation of an extensible and reusable software library publicly queryable via API represents a valid mean to assist researchers in choosing the most suitable equations to perform an NDVI-LAI conversion

On the Use of NDVI to Estimate LAI in Field Crops: Implementing a Conversion Equation Library

The leaf area index (LAI) is a direct indicator of vegetation activity, and its relationship with the normalized difference vegetation index (NDVI) has been investigated in many research studies. Remote sensing makes available NDVI data over large areas, and researchers developed specific equations to derive the LAI from the NDVI, using empirical relationships grounded in field data collection. We conducted a literature search using “NDVI” AND “LAI” AND “crop” as the search string, focusing on the period 2017–2021. We reviewed the available equations to convert the NDVI into the LAI, aiming at (i) exploring the fields of application of an NDVI-based LAI, (ii) characterizing the mathematical relationships between the NDVI and LAI in the available equations, (iii) creating a software library with the retrieved methods, and (iv) releasing a publicly available software as a service, implementing these equations to foster their reuse by third parties. The literature search yielded 92 articles since 2017, where 139 equations were proposed. We analyzed the mathematical form of both the single equations and ensembles of the NDVI to LAI conversion methods, specific for crop, sensor, and biome. The characterization of the functions highlighted two main constraints when developing an NDVI-LAI conversion function: environmental conditions (i.e., water and light resource, land cover, and climate) and the availability of recurring data during the growing season. We found that the trend of an NDVI-LAI function is usually driven by the ecosystem water availability for the crop rather than by the crop type itself, as well as by the data availability; the data should be adequate in terms of the sample size and temporal resolution for reliably representing the phenomenon under investigation. Our study demonstrated that the choice of the NDVI-LAI equation (or ensemble of equations) should be driven by the trade-off between the scale of the investigation and data availability. The implementation of an extensible and reusable software library publicly queryable via API represents a valid mean to assist researchers in choosing the most suitable equations to perform an NDVI-LAI conversion