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Aims In 2012, Italian kiwifruit orchards were hit by a serious root disease of unknown aetiology (kiwifruit decline, KD) that still causes extensive damage to the sector. While waterlogging was soon observed to be associated with its outbreak, the putative role of soil microbiota remains unknown. This work investigates the role of these two factors in the onset of the disease. Methods Historical rainfall data were analysed to identify changes that might explain KD outbreak and mimic the flooding conditions required to reproduce the disease in a controlled environment. A greenhouse experiment was thus designed, and vines were grown in either unsterilized (U) or sterilized (S) soil collected from KD-affected orchards, and subjected (F) or not (N) to artificial flooding. Treatments were compared in terms of mortality rate, growth, and tissue modifications. Results KD symptoms were only displayed by FU-treated vines, with an incidence of 90%. Ultrastructural observations detected tyloses and fibrils in the xylem vessels of all plants, irrespective of the treatment. Phytopythium vexans and Phytopythium chamaehyphon, isolated from roots of FU plants, emerged as the associated microorganisms. Conclusions We succeeded in reproducing KD under controlled conditions and confirmed its association with both waterlogging and soil-borne microorganism(s).

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The increasing demand for energy and expected shortage in the medium term, solicit innovative energy strategies to fulfill the increasing gap between demand-supply. For this purpose it is important to evaluate the potential supply of the energy crops and finding the areas of EU where it is most convenient. This paper proposes an agro-energy supply chain approach to planning the biofuel supply chain at a regional level. The proposed methodology is the result of an interdisciplinary team work and is aimed to evaluate the potential supply of land for the energy production and the efficiency of the processing plants considering simultaneously economic, energy and environmental targets. The crop simulation, on the basis of this approach, takes into account environmental and agricultural variables (soil, climate, crop, agronomic technique) that affect yields, energy and economic costs of the agricultural phase. The use of the Dijkstra's algorithm allows minimizing the biomass transport path from farm to collecting points and the processing plant, to reduce both the transport cost and the energy consumption. Finally, a global sustainability index (ACSI, Agro-energy Chain Sustainability Index) is computed combining economic, energy and environmental aspects to evaluate the sustainability of the Agroenergy supply chain (AESC) on the territory. The empirical part consists in a pilot study applied to the whole plain of Friuli Venezia Giulia (FVG) a region situated in the North-Eastern part of Italy covering about 161,300 ha. The simulation has been applied to the maize cultivation using three different technologies (different levels of irrigation and nitrogen fertilization: low, medium and high input). The higher input technologies allow to achieve higher crop yields, but affect negatively both the economic and energy balances. Low input levels provides, on the average, the most favourable energy and economic balances. ACSI indicates that low inputs levels ensure a more widespread sustainability of the agro-energy chain in the region. High ACSI values for high input levels are observed only for areas with very high yields or near the processing plant.

Crop yield forecasting activities are essential to support decision making of farmers, private companies and public entities. While standard systems use georeferenced agro-climatic data as input to process-based simulation models, new trends entail the application of machine learning for yield prediction. In this paper we present HADES (HAzelnut yielD forEcaSt), a hazelnut yield prediction system, in which process-based modeling and machine learning techniques are hybridized and applied in Turkey. Official yields in the top hazelnut producing municipalities in 2004–2019 are used as reference data, whereas ground observations of phenology and weather data represent the main HADES inputs. A statistical analysis allows inferring the occurrence and magnitude of biennial bearing in official yields and is used to aid the calibration of a process-based hazelnut simulation model. Then, a Random Forest algorithm is deployed in regression mode using the outputs of the process-based model as predictors, together with information on hazelnut varieties, the presence of alternate bearing in the yield series, and agro-meteorological indicators. HADES predictive ability in calibration and validation was balanced, with relative root mean square error below 20%, and R2 and Nash-Sutcliffe modeling efficiency above 0.7 considering all municipalities together. HADES paves the way for a next-generation yield prediction system, to deliver timely and robust information and enhance the sustainability of the hazelnut sector across the globe.

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