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Understanding how climate change might influence the distribution and abundance of crop pests is fundamental for the development and the implementation of pest management strategies. Here we present and apply a modelling framework assessing the non-linear physiological responses of the life-history strategies of the Mediterranean fruit fly (Ceratitis capitata, Wiedemann) to temperature. The model is used to explore how climate change might influence the distribution and abundance of this pest in Europe. We estimated the change in the distribution, abundance and activity of this species under current (year 2020) and future (years 2030 and 2050) climatic scenarios. The effects of climate change on the distribution, abundance and activity of C. capitata are heterogeneous both in time and in space. A northward expansion of the species, an increase in the altitudinal limit marking the presence of the species, and an overall increase in population abundance is expected in areas that might become more suitable under a changing climate. On the contrary, stable or reduced population abundances can be expected in areas where climate change leads to equally suitable or less suitable conditions. This heterogeneity reflects the contribution of both spatial variability in the predicted climatic patterns and non-linearity in the responses of the species’ life-history strategies to temperature.

The rational control of harmful organisms for plants (pests) forms the basis of the integrated pest management (IPM), and is fundamental for ensuring agricultural productivity while maintaining economic and environmental sustainability. The high level of complexity of the decision processes linked to IPM requires careful evaluations, both economic and environmental, considering benefits and costs associated with a management action. Plant protection models and other decision tools (DTs) have assumed a key role in supporting decision-making process in pest management. The advantages of using DTs in IPM are linked to their capacity to process and analyze complex information and to provide outputs supporting the decision-making process. Nowadays, several DTs have been developed, tackling different issues, and have been applied in different climatic conditions and agricultural contexts. However, their use in crop management is restricted to only certain areas and/or to a limited group of users. In this paper, we review the current state-of-the-art related to DTs for IPM, investigate the main modelling approaches used, and the different fields of application. We also identify key drivers influencing their adoption and provide a set of critical success factors to guide the development and facilitate the adoption of DTs in crop protection.

Mastitis represents a significant challenge for dairy farming, resulting in economic losses and environmental impacts. This study assesses a model for the evaluation of the impact of mastitis on dairy productivity and Global Warming Potential (GWP) under diverse management scenarios. The model considers a range of factors, including bedding materials, milking systems, health surveillance, and overcrowding. The results of the simulation demonstrate that effective management, encompassing the utilization of sand bedding, and the presence of an annual herd health monitoring plan have the potential to reduce the prevalence of mastitis and enhance milk yield by up to 10% in milking parlors and 7% in automatic milking systems. At the herd level, the GWP ranged from 1.37 to 1.78 kg CO2eq/kg Fat- and Protein-Corrected Milk (FPCM), with the use of sand bedding resulting in a 14% reduction in GWP, while the utilization of non-composted manure-based materials led to an increase of 12%. The occurrence of overcrowding and a lack of adequate cleanliness in resting areas were found to have a markedly detrimental impact on both productivity and the environmental performance of cows. These findings illustrate the dual benefits of enhanced mastitis management, namely improved milk production and reduced environmental impact. They offer valuable insights for farmers and policymakers alike.

The dairy sector is facing increasing challenges in terms of its environmental impact. Methane (CH4) is a focal point of research due to its role in enteric emissions from livestock. This study investigates the effects of various feeding strategies on CH4 emissions from lactating Holstein cows fed total mixed ration (TMR) silage-based diets. Four different equations for estimating CH4 emissions were chosen according with accuracy and equation variables, and then compared checking whether diet composition has an effect on average emission levels. Only Mills equation detected differences between nutritional clusters. Considering this equation on average, the CH4 emissions were equal to 460.36 ± 46.95 g/d, 18.90 ± 1.57 g/kg DMI, 12.89 ± 2.83 g/kg FPCM, equal to a loss of 5.93% of gross energy intake. Clustering based on feed composition identified four distinct groups of diets, with no statistically significant difference in CH4 emissions. The highest emissions were found in the nutritional cluster with higher fibre and starch content, with methane production (MeP) reaching 485.85 g/d, 19.47 kg/kg DMI and 14.82 kg/kg FPCM. This indicates that diet nutrients profile significantly impacts CH4 emissions, underscoring the importance of adopting sustainable feeding strategies in dairy production. Notably, a positive correlation exists between MeP and milk productivity, while methane intensity negatively correlates with feed efficiency. The findings emphasise the necessity for context-specific emission factors and underscore the importance of implementing sustainable feeding practices to mitigate CH4 emissions enhancing the efficiency of dairy production systems.

The topic of sustainable water management has become of paramount importance at a global level, especially when considering the high-amount of water used in agriculture, which is a threat to water resource balance. Focused on 38 inventories of nonirrigated vineyard management in the Franciacorta wine-growing region in Italy, this study aims to understand how agronomic practices impact water resources. The integrated statistical approach, based on generalized linear models, reveals how context variables influence different water footprint indicators, such as water scarcity, acidification and freshwater ecotoxicity. Plant density and the presence of hillside vineyards are the primary influencing factors, while others, such as variety susceptibility, vineyard age and soil type may influence the shortage of water. The outcomes help to better understand the impact of management, thereby raising awareness in the wine sector about aspects often overlooked in traditional investigations. These findings offer specific insights for viticulturists, emphasizing the importance of a tailored management approach to minimize water footprint in viticultural practices and contribute to environmental sustainability.

This Guidance describes a two‐phase approach for a fit‐for‐purpose method for the assessment of plant pest risk in the territory of the EU. Phase one consists of pest categorisation to determine whether the pest has the characteristics of a quarantine pest or those of a regulated non‐quarantine pest for the area of the EU. Phase two consists of pest risk assessment, which may be requested by the risk managers following the pest categorisation results. This Guidance provides a template for pest categorisation and describes in detail the use of modelling and expert knowledge elicitation to conduct a pest risk assessment. The Guidance provides support and a framework for assessors to provide quantitative estimates, together with associated uncertainties, regarding the entry, establishment, spread and impact of plant pests in the EU. The Guidance allows the effectiveness of risk reducing options (RROs) to be quantitatively assessed as an integral part of the assessment framework. A list of RROs is provided. A two‐tiered approach is proposed for the use of expert knowledge elicitation and modelling. Depending on data and resources available and the needs of risk managers, pest entry, establishment, spread and impact steps may be assessed directly, using weight of evidence and quantitative expert judgement (first tier), or they may be elaborated in substeps using quantitative models (second tier). An example of an application of the first tier approach is provided. Guidance is provided on how to derive models of appropriate complexity to conduct a second tier assessment. Each assessment is operationalised using Monte Carlo simulations that can compare scenarios for relevant factors, e.g. with or without RROs. This document provides guidance on how to compare scenarios to draw conclusions on the magnitude of pest risks and the effectiveness of RROs and on how to communicate assessment results. This publication is linked to the following EFSA Supporting Publications article: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2018.EN-1440/full ‘This guidance supersedes: a) the entire Guidance on a harmonised framework for pest risk assessment and the identification and evaluation of pest risk management options by EFSA. https://doi.org/10.2903/j.efsa.2010.1495; b) Sections 1.8 and 1.9 of Guidance on methodology for evaluation of the effectiveness of options for reducing the risk of introduction and spread of organisms harmful to plant health in the EU territory. https://doi.org/10.2903/j.efsa.2012.2755’

An important challenge for livestock systems is the mitigation of environmental impacts while ensuring food security, and feed additives are considered as one of the most promising mitigation strategies. This study analyzed the innovation landscape of feed additives to reduce methane emissions in ruminants. The analysis is based on patent data to evaluate the development, scientific importance, and market-level impact of the innovations in this field. The results reveal that the EU is on the innovation frontier, with substantial and quality patent production. The innovation field is dominated by private players, characterized by high specificity in the R&D pipeline. Additives derived from plant or botanical extracts, together with 3-nitrooxypropanol (3-NOP), represent the emerging innovations, indicating a clear orientation toward more sustainable livestock systems. Despite the regulatory and semantic limitations related to the use of patent databases, data reveal a growing innovation activity at global level, which could lead to macroeconomic benefits for the entire livestock sector.

The Japanese beetle Popillia japonica is an invasive alien species recently introduced and established in Northern Italy. Adult beetles are very polyphagous and feed on vines, fruit trees, forest trees, crops, vegetables, ornamental and wild plant species. Eggs are usually laid by females in moist grassland in the summer, singly or in small clusters. Larvae feed on roots and may be transported in soil of plants for planting grown in containers. Restrictions on movement of plants grown in containers from infested to non-infested areas imposed by phytosanitary regulations have a significant economic impact on the nursery industry. An innovative approach was used to exclude beetle oviposition by weed mulching available for container-grown nursery stocks, and by testing larval survival to the application of chemical (cypermethrin) and organic (Heterorhabditis bacteriophora and Metarhizium brunneum) commercial pesticides registered for European nurseries. The high effectiveness of the method makes it a suitable component of a systems approach strategy for pest risk management, in order to achieve a safe production and trade of nursery plant material in areas infested by the Japanese beetle.

Spittlebugs are the vectors of the bacterium Xylella fastidiosa Wells in Europe, the causal agent of olive dieback epidemic in Apulia, Italy. Selection and distribution of different spittlebug species on host-plants were investigated during field surveys in 2016–2018 in four olive orchards of Apulia and Liguria Regions of Italy. The nymphal population in the herbaceous cover was estimated using quadrat samplings. Adults were collected by sweeping net on three different vegetational components: herbaceous cover, olive canopy, and wild woody plants. Three species of spittlebugs were collected: Philaenus spumarius L., Neophilaenus campestris (Fallén), and Aphrophora alni (L.) (Hemiptera: Aphrophoridae). Philaenus spumarius was the predominant species both in Apulia and Liguria olive groves. Nymphal stages are highly polyphagous, selecting preferentially Asteraceae Fabaceae plant families, in particular some genera, e.g., Picris, Crepis, Sonchus, Bellis, Cichorium, and Medicago. Host-plant preference of nymphs varies according to the Region and through time and nymphal instar. In the monitored sites, adults peak on olive trees earlier in Apulia (i.e., during inflorescence emergence) than in Liguria (i.e., during flowering and beginning of fruit development). Principal alternative woody hosts are Quercus spp. and Pistacia spp. Knowledge concerning plant selection and ecological traits of spittlebugs in different Mediterranean olive production areas is needed to design effective and precise control strategies against X. fastidiosa vectors in olive groves, such as ground cover modifications to reduce populations of spittlebug vectors.

Monitoring groundcover diversity in vineyards is a complex task, often limited by the time and expertise required for accurate botanical identification. Remote sensing technologies and AI-based tools are still underutilized in this context, particularly for classifying herbaceous vegetation in inter-row areas. In this study, we introduce a novel approach to classify the groundcover into one of nine categories, in order to simplify this task. Using UAV images to train a convolutional neural network through a deep learning methodology, this study evaluates the effectiveness of different backbone structures applied to a UNet network for the classification of pixels into nine classes of groundcover: vine canopy, bare soil, and seven distinct cover crop community types. Our results demonstrate that the UNet model, especially when using an EfficientNetB0 backbone, significantly improves classification performance, achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%. Although this study demonstrates the potential of integrating remote sensing and deep learning for vineyard biodiversity monitoring, its applicability is limited by the small image coverage, as data were collected from a single vineyard and only one drone flight. Future work will focus on expanding the model’s applicability to a broader range of vineyard systems, soil types, and geographic regions, as well as testing its performance on lower-resolution multispectral imagery to reduce data acquisition costs and time, enabling large-scale and cost-effective monitoring.