1,721,164 research outputs found
Supply Chains of Products of Animal Origin: a Complex Network Model for the Strategic Management
Full text linkA European skills strategy for the agri-food and forestry sectors – key challenges and prerequisites
Full text linkThe Erasmus+ FIELDS project aims to contribute to skill enhancement of workers in the agriculture, food industry and forestry sectors, to be able to make full use of the opportunities and comply with requirements of the ‘’Twin’’ Green and Digital transition. The FIELDS project focuses on the domains Digitalization, Sustainability, Bio-Economy and Management & Entrepreneurship. Skills include ‘’hard’’/ measurable and technology-based skills as well as ‘’soft’’ / transversal skills.This paper reports on key challenges and prerequisites for the development of a European Agri-Food and Forestry Skills Strategy. Starting with the results of a European agri-food-forestry trend analysis and focus groups discussion on skill and training needs in 10 European countries, this paper presents the results of a follow-up survey among key stakeholders of the European skills ecosystem, including the following topics: prerequisites for the development of training programs, harmonization challenges in the European agri-food and forestry skills ecosystem, and monitoring and key performance indicators the European agri-food and forestry skills ecosystem.The paper develops directions for a EU strategy on agri-food and forestry skills, including:- In the development of training programs in Europe special attention should be paid to management/entrepreneurship and soft skills, the position of training in practice, possibilities for online training, and attention to underprivileged groups- For the harmonization of the agri-food and forestry European skills ecosystem a common European catalogue and repository of training programs, linked to national systems, together with a system of micro credentials, is needed. This should be aligned with a harmonized certification system for VET courses/programs and VET providers. Agreement between public and private parties on the catalogue and certification system is essential.- A supra-national institute should be responsible for design and maintenance of a monitoring infrastructure for skills. The system to be designed should be smart, user friendly, upgradeable and interoperable. The newly established Agri-food Pact for Skills can play a central role in the establishment and governance of an ‘’Agri-food Skills Observatory’’
Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
Full text linkAccurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. To bridge this literature gap, an interpretable ML framework was developed for predicting lupin yield using Sentinel-2 remote sensing data integrated with georeferenced yield measurements. Data preprocessing involved computing vegetation indices, removing outliers, addressing multicollinearity, normalizing feature scales, and applying data augmentation techniques to correct target imbalance. Subsequently, six ML models were evaluated representing different algorithmic strategies. Among them, XGBoost showed the best performance ((Formula presented.) = 0.8756) and low error values across (Formula presented.), (Formula presented.), and (Formula presented.) metrics. To enhance model transparency, SHapley Additive exPlanations (SHAP) values were applied to interpret the feature contributions of the XGBoost model. The Enhanced Vegetation Index ((Formula presented.)) and Normalized Difference Vegetation Index ((Formula presented.)) were found to be key predictors of crop yield, both showing a positive correlation with higher values reflecting greater vegetation vigor and corresponding to increased yield. These were followed by (Formula presented.) (green) and (Formula presented.) (short-wave infrared), which captured key reflectance properties associated with chlorophyll activity and water content, respectively. Both of them substantially influence photosynthetic efficiency and plant health, ultimately affecting yield potential
Supply Chains of Products of Animal Origin: A Complex Network Model for Strategic Management
Full text linkDue to the dynamic evolution of food chains during the past years, currently available chain models are no longer able to meet the needs of operators. This article introduces a model for chain analysis which is used in an analysis of the supply chains of the entire Italian production of Animal Origin which constitute a complex network in which the actors of the agro-food system operate. The new model is innovative for two reasons: it represents many supply chains of products of animal origin from different productive species in a single model and, in addition, allows to represent the complexity of the chain network
Human–Robot Interaction through Dynamic Movement Recognition for Agricultural Environments
No full textIn open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety
Logistics and strategies evaluation of grain receiving operations using the system simulation approach
No full textThis research investigated elevator configurations and management strategies to improve the unloading operation at a large inland terminal elevator vs. 12 different combinations of grain receiving patterns, including the segregation of non-GM soybeans. The study was done by using the system simulation approach. The average service time (defined as the average amount of time a truck spent at the elevator between arrival and departure) was chosen as the index to evaluate the performance of the unloading operation. Additionally, network modeling was implemented to tie together in one system model the elevator and the harvest-transportation supply chain to quantify performance as a function of delivery from farmers\u27 fields (or on-farm handling facilities) and other elevators. The results showed that the performance of the unloading operation was heavily dependent on elevator configuration, management strategies and on incoming grain delivery patterns. The segregation of non-GM soybeans took less than 40 min.truck−1 during about 90% of the days in the harvest season. The facility could handle a non-GM soybeans flow of less than 2–3% of total soybeans deliveries without any significant disruption even on a busy day. Among the options tested, BATCH queue management (when trucks with the same grain type were served in batches rather than first-in, first-out [FIFO]) yielded time savings of about 27% when the unloading capacity used was above 72% of the theoretical one. For the enlargement of the pits, which allowed two hoppers of a semi truck trailer to be unloaded at the same time, the network model predicted up to a 17% increase in loads handled per day while reducing average service times by up to 34% compared to the actual elevator configuration. The traffic pattern change, which included the addition of an empty truck scale and the relocation of the probing station, yielded up to 23% in time savings versus the actual configuration. It was estimated that the traffic pattern change could be implemented for about one third of the costs of enlarging both existing receiving pits at this inland terminal elevator
Assessment Of Grain Elevator Receiving Operations Performance And Improvements By Means of Object Oriented Simulation
No full textAspetti tecnici, economici, energetici ed ambientali negli studi di logistica delle operazioni agricole
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Simulation of Logistic Effects and Contamination Levels During IP Handling of Quality Grains
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