A harmonized dataset relating alternative farmer management practices to crop yield, soil organic carbon stock, nitrous oxide emissions, and nitrate leaching generated using IPCC methodologies and meta-analyses.

Farming practices such as soil tillage, organic/mineral fertilization, irrigation, crop selection and residues management influence multiple ecosystem services provided by agricultural systems. These practices exhibit complex, non-linear interrelationships that affect crop productivity, water quality, and non-carbon dioxide greenhouse gases (GHG) emissions, possibly offsetting their benefits regarding soil organic carbon (SOC) sequestration. Current methodologies from the Intergovernmental Panel on Climate Change (IPCC) for assessing the impacts of alternative farming practices on GHG emissions rely on global or country-specific coefficients.

Exploring the relationships between ground observations and remotely sensed hazelnut spring phenology.

Crop phenology is very important in regular crop monitoring. Generally, phenology is monitored through field observation surveys or satellite data. The relationships between ground observations and remotely sensed derived phenological data can enable near-real-time monitoring over large areas, which has never been attempted on hazelnuts.

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

The availability of field experimental data plays a pivotal role in advancing agricultural research, particularly in the Mediterranean, where farmers face significant challenges due to water scarcity and changing climatic conditions. We present a multi-year homogenized dataset of agro-physiological traits collected on industrial tomatoes and focused on the effect of deficit irrigation (DI). The dataset has been compiled over nine years and comprises 100 experimental plots, where 32 DI strategies have been tested.

Assessing the driving role of the anthropogenic landscape on the distribution of the Xylella fastidiosa-driven "olive quick decline syndrome" in Apulia (Italy).

The plant pathogen Xylella fastidiosa (Xf) is a significant threat to various economically important tree cash crops. Although previously found only in the Americas, the bacterium responsible for olive quick decline syndrome was detected in Apulia, Italy, in 2013. Since then, it has spread to approximately 54,000 ha of olive trees in the region, causing dramatic concern throughout the Mediterranean basin.

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 ( = 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 have been 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.

A public decision support system for the assessment of plant disease infection risk shared by Italian regions.

Integrated pest management (IPM) practices proved to be efficient in reducing pesticide use and ensuring economic farming sustainability. Digital decision support systems (DSS) to support the adoption of IPM practices from plant protection services are required by European legislation. Available DSSs used by Italian plant protection services are heterogeneous with regards to disease forecasting models, datasets for their calibration, and level of integration in operational decision-making.

Rotten Hazelnuts Prediction Simulation Modeling-A Case Study on the Turkish Hazelnut Sector.

The quality defects of hazelnut fruits comprise changes in morphology and taste, and their intensity mainly depends on seasonal environmental conditions. The strongest off-flavor of hazelnuts is known as rotten defect, whose candidate causal agents are a complex of fungal pathogens, with as the dominant genus. Timely indications on the expected incidence of rotten defect would be essential for buyers to identify areas where hazelnut quality will be superior, other than being useful for farmers to have the timely indications of the risk of pathogens infection.

Agronomic Strategies to Improve N Efficiency Indices in Organic Durum Wheat Grown in Mediterranean Area.

Organic farming systems are often constrained by limited soil nitrogen (N) availability. Here we evaluated the effect of foliar organic N and sulphur (S), and selenium (Se) application on durum wheat, considering N uptake, utilization efficiency (NUtE), grain yield, and protein concentration as target variables. Field trials were conducted in 2018 and 2019 on two old (Cappelli and old Saragolla) and two modern (Marco Aurelio and Nadif) Italian durum wheat varieties.

The HADES Yield Prediction System - A Case Study on the Turkish Hazelnut Sector.

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.

A process-based model to simulate sugarcane orange rust severity from weather data in Southern Brazil.

Forecasting the severity of plant diseases is an emerging need for farmers and companies to optimize management actions and to predict crop yields. Process-based models are viable tools for this purpose, thanks to their capability to reproduce pathogen epidemiological processes as a function of the variability of agro-environmental conditions. We formalized the key phases of the life cycle of Puccinia kuenhii (W.

Physiological Adaptation to Water Salinity in Six Wild Halophytes Suitable for Mediterranean Agriculture.

Owing to the high interspecific biodiversity, halophytes have been regarded as a tool for understanding salt tolerance mechanisms in plants in view of their adaptation to climate change. The present study addressed the physiological response to salinity of six halophyte species common in the Mediterranean area: , and . A 161-day pot experiment was conducted, watering the plants with solutions at increasing NaCl concentration (control, 100, 200, 300 and 600 mM).

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.

Causes of variation among rice models in yield response to CO examined with Free-Air CO Enrichment and growth chamber experiments.

The CO fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO] (E-[CO]) by comparison to free-air CO enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well.

Modelling the impacts of pests and diseases on agricultural systems.

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models.

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ].

Modelling soil borne fungal pathogens of arable crops under climate change.

Soil-borne fungal plant pathogens, agents of crown and root rot, are seldom considered in studies on climate change and agriculture due both to the complexity of the soil system and to the incomplete knowledge of their response to environmental drivers. A controlled chamber set of experiments was carried out to quantify the response of six soil-borne fungi to temperature, and a species-generic model to simulate their response was developed. The model was linked to a soil temperature model inclusive of components able to simulate soil water content also as resulting from crop water uptake.