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In the majority of nations, environmental sustainability is an integral aspect of government policies, highlighting the criticality of equipping upcoming engineers with the necessary environmental awareness and skills to tackle this complex challenge. Higher education institutions assume a pivotal role in fulfilling this mission as their engineering programs aspire to generate graduates who possess the necessary competencies to effectively operate as “sustainable” engineers. Novel pedagogical methodologies, such as service learning, are currently being utilized to foster the cultivation of sustainable development values. Service-Learning represents a highly congruent approach to achieving Sustainable Development Goal (SDG) 4, insofar as it seamlessly integrates educational curricula with relevant community issues. As a result, it effectively contributes to the advancement of education for a sustainable future. This investigation sought to assess the impact of service learning projects developed in a rural school community on the boosting of social skills and environmental values among engineering students. The empirical evidence suggests that Service-Learning initiatives centered on environmental issues are efficacious in promoting the acquisition of social and environmental values among students. This, in turn, results in a positive transformation of their skill sets. Significantly, a substantial proportion of environmental engineering students who previously demonstrated a low awareness of these skills acknowledged their acquisition upon completion of the Service-Learning projects. Moreover, no less than 75% of the participants endorsed the attainment of each of the 11 competencies evaluated.

Determining the residual value of tractors is imperative for comprehensive cost analyses within the agricultural machinery sector. Despite numerous studies offering various models and independent variables, the double square root regression approach, originally developed by Cross and Perry and adapted by ASABE for North American contexts, has been widely utilized. However, factors such as the complexity of OEM portfolios, steep price increases due to compliance with diesel emission regulations, and limited data availability in Europe and its market specificities necessitate improvements in predictive accuracy. This study evaluates different tractor cohort alternatives beyond engine horsepower to enhance predictive robustness. Incorporating brand and powertrain type alongside engine power significantly improved model performance and exhibited the strongest robustness, as evidenced by reduced the root mean square error (RMSE) and increased R-squared values. These findings contribute to the refinement of tractor residual value estimation models, offering valuable insights for stakeholders in the agricultural machinery industry.

Research investigating models for assessing new tractor pricing is notably scarce, despite its fundamental importance in conducting comprehensive cost analyses. This study aims to identify a model that is both user-friendly and robust, evaluating both parametric and Machine Learning-optimized non-parametric models. Among parametric models, the second-order polynomial model demonstrated superior performance in terms of R-squared (R2) of 0.97469 and a Root Mean Square Error (RMSE) of 15,633. Conversely, Machine Learning-optimized Gaussian Processes Regressions exhibited the most favorable overall R-squared (R2) of 0.99951 and a Root Mean Square Error (RMSE) of 2321. While the parametric polynomial model offers a solution with minimal mathematical and computational complexity, the non-parametric GPR model delivers highly robust outcomes, presenting stakeholders involved in new agriculture tractor transactions with superior data-driven decision-making capabilities.

The residual value of agricultural tractors plays a pivotal role in the financial viability of agribusiness enterprises. Nevertheless, there is a dearth of comprehensive studies concerning the prognostication of both retail and wholesale residual values specific to agricultural tractors within the context of Western Europe. This research introduces an innovative methodology for assessing the residual worth of agricultural tractors, with particular consideration given to the substantial pricing discrepancies between retail and wholesale transactions. Leveraging publicly available auction data, we develop a polynomial regression model aimed at forecasting the intricate relationship between retail and wholesale residual values. Notably, the model demonstrates an exceptional robustness, surpassing previous research endeavors, as evidenced by a remarkably low root-mean-square error (RMSE) of 0.0159 and a combined adjusted coefficient of determination (RSqAdj) of 0.9997. The findings of this study offer invaluable insights into a diverse array of stakeholders, empowering them to make well-informed decisions regarding machinery specifications, investment strategies, and asset disposal choices, thereby facilitating optimal financial performance.

Cold chain disruption and refrigeration failures are common issues in the logistics of perishable food products. In these cases, the use of pallet covers should be very useful, delaying the increase of product temperatures inside the pallets until cooling conditions are restored. However, there are no studies about the performance of pallet covers in these types of situations, which could persist for hours. This paper evaluates the performance of three different types of cold chain covers versus having no cover for three different types of vegetables (chard, cucumbers, and carrots). A refrigeration failure during the cold chain was simulated. The three covers presented an improvement in temperature loss compared to the no-cover situation, with the average time for the temperature to increase from 4 to 10 °C with a cover being a range of 214 to 506 min, while for no cover, from 162 to 211 min. Relative humidity (RH) always presented improved preservation with a cover than with no cover, except for one case. The correlation between the thermal images and sensor temperatures was also studied.

Harvesting is an integral component of the agricultural cycle, necessitating the use of high-performance grain harvester combines, which are utilized for a short period each year. Given the seasonality and significant cost involved, list prices ranging from a quarter to almost a million euros, a fact-based investment assessment decision-making process is essential. However, there is a paucity of research studies forecasting the remaining value of grain harvester combines in recent years. This study proposes a straightforward methodology based on public information that employs various parametric and non-parametric models to develop a robust and user-friendly model that can assist decision makers, such as farmers, contractors, sellers, and finance and insurance entities, in optimizing their harvesting operations. The model employs a power regression mode, with RMSE of 1.574 and RSqAdj of 0.8457 results, to provide accurate and reliable insights for informed decision-making. The robust model transparency enables us to easily create a mainstreamed spreadsheet-based dashboard tool.

The residual value of a tractor affects the cost of ownership. As there is not much transactional information available for used tractors, nor is there a history of new tractor prices, existing studies struggle to forecast the residual value of agricultural tractors. This is made even more challenging by the emission-regulation-related tractor price increase, low inflation in recent decades, and the complexity of the portfolio offerings from manufacturers. Using the new equivalent tractors, grouped by families of similar characteristics, bypasses these challenges and enables us to obtain larger data sets. These large data sets can be forecasted using transparent linear power regressions that offer the lowest root mean squared error (RMSE = 1.5574) and the highest combined, adjusted coefficient of determination (RSqAdj = 0.8457), outperforming all previously tested studies as well as the ensemble, Gaussian process regression, kernel, linear regression, neural network, support vector machine, and decision tree models. The accessibility of the public information required, as well as its processing using mainstream software through a model that is simple to use, yet robust, enables any stakeholder (manufacturers, sellers, financers, insurers, and, most of all, users) to reliably determine the residual value of an agricultural tractor, empowering them to make fact-based, cost-of-ownership-optimized decisions.

Abstract Global food trade has become an increasingly crucial element for feeding the world's population. Enhancing bilateral or multilateral trust in food safety in international food trade is not only important for promoting the sustainable development of trade but is also beneficial for cooperation when facing a global food crisis. However, highly credible traceability systems (TSs) for the cross‐border movement of food are still absent in many countries and regions. Blockchain is regarded as a promising technology that can help build trust for transparency and security issues. In this paper, an interconnected conceptual traceability framework based on blockchain is proposed in order to increase trust in food safety during food trade. Taking the food trade between China and the European Union as an example, a conceptual framework is designed in order to take full advantage of existing TSs in these two locations, and the features of logistical flow, data flow, and blockchain flow are analyzed. Considering the data capacity and data privacy level, a hybrid data storage method combining on‐chain and off‐chain is adopted. Smart contracts according to the features of cross‐border food trade—including the recording of exportation data, exporter inspection data, shipment data, importer inspection data, importation data, and tracing queries—are packaged and deployed to a blockchain network. An effective operation mechanism involving the distribution of related rights for different roles is presented. The blockchain‐based TS framework has the advantages of enhancing bilateral trust in cross‐border food trade, providing a flexible and intelligent technical framework, and having effective operability. Future challenges, such as data security, special smart contracts, and consensus mechanisms, and interoperability with other systems, are discussed.

Data science can help farmers when making a decision about tractor purchase. Buying a tractor represents a big investment for farmers, and price is one of their main concerns. This study presents the development of a web-based decision support tool (DST) that calculate the price of new and second-hand tractors, for the purpose of providing the decision-maker some information that will lead him to the final decision. The tool makes use of different algorithms based on predictive analytics methodologies. The dataset has information about 227 different observations of new tractors and 1003 of second-hand tractors, from different European countries. During the study, the prices of new and used tractor were modeled, testing parametric and non-parametric regression models with different segmentations and predictor variables. Non parametric models includes regression trees, support vector machines, ensembles of regression trees, Gaussian process, and neural networks. In both cases, for predicting the prices of new and second-hand tractors, adjusted R2 higher than 0.99 were achieved. The models developed were implemented in the DST which is fully operative, available in Internet, and free to use.

Urban areas are critical points that contribute to global warming and are also affected by climate change. One of the measures to move toward urban sustainability and to reduce the effects of climate change is the development of urban green infrastructures. Urban green infrastructures (UGIs) are being increasingly recognized as key providers of ecosystem services in cities, but there is still a lack of support from urban planners. We highlight the potential of urban green infrastructures for sustainable urban planning based on its capacity to mitigate climate change This paper studies the CO2 mitigation potential through a multi-intervention (agricultural and forestry) local case in the peri-urban surroundings of a big European city such as Madrid. We consider two inseparable aspects: the amount of atmospheric CO2-eq reduced through direct carbon uptake of the UGI and also the emission of greenhouse gases due to its implementation and maintenance. The analysis carried out has shown the benefits of urban green infrastructures and their contribution to the mitigation of climate change. The results demonstrate that the absorption capacity of the new urban green infrastructure is much greater than its ecological footprint. Therefore, it contributes to the mitigation of emissions from other urban activities, thus improving urban sustainability.

Every day, millions of tons of temperature-sensitive goods are produced, transported, stored or distributed worldwide, thus making their temperature and humidity control essential. Quality control and monitoring of goods during the cold chain is an increasing concern for producers, suppliers, logistic decision makers and consumers. In this paper we present the results of a combination of RFID and WSN devices in a set of studies performed in three commercial wholesale chambers of 1848 m3 with different set points and products. Up to 90 semi-passive RFID temperature loggers were installed simultaneously together with seven motes, during one week in each chamber. 3D temperature mapping charts were obtained and also the psychrometric data model from ASABE was implemented for the calculation of enthalpy changes and the absolute water content of air. Thus thank to the feedback of data, between RFID and WSN it is possible to estimate energy consumption in the cold room, water loss from the products and detect any condensation over the stored commodities.

Quality control and monitoring of perishable goods during transportation and delivery services is an increasing concern for producers, suppliers, transport decision makers and consumers. The major challenge is to ensure a continuous ‘cold chain’ from producer to consumer in order to guaranty prime condition of goods. In this framework, the suitability of ZigBee protocol for monitoring refrigerated transportation has been proposed by several authors. However, up to date there was not any experimental work performed under real conditions. Thus, the main objective of our experiment was to test wireless sensor motes based in the ZigBee/IEEE 802.15.4 protocol during a real shipment. The experiment was conducted in a refrigerated truck traveling through two countries (Spain and France) which means a journey of 1,051 kilometers. The paper illustrates the great potential of this type of motes, providing information about several parameters such as temperature, relative humidity, door openings and truck stops. Psychrometric charts have also been developed for improving the knowledge about water loss and condensation on the product during shipments.

The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.

Background: For more than 50 years, antibiotics have been used to maintain animal welfare and improve efficiency. Recently, antibiotics were found in the muscle, liver, and kidney of guinea pig carcasses put up for sale and human consumption, which is a public health issue. Probiotics are supplements of live microorganisms that, when administered in adequate doses, could replace growth-promoting antibiotics. Aim: This study analysed the effect of the administration of an oral probiotic mixture on the guinea pigs productive performance (Cavia porcellus). Methods: Fifty male guinea pigs, weaned at 14 days of age, were distributed in a completely randomised design of five treatments with 10 repetitions for each group. The treatments were: CONTROL group without probiotic; PROB 1 given 1 mL of probiotic; PROB 2 with 2 mL of probiotic; PROB 3 with 3 mL of probiotic; and AGP was given 300 ppm zinc bacitracin. The microorganisms used in the probiotic were Enterococcus hirae, Lactobacillus reuteri, L. frumenti, L. johnsoni, Streptococcus thoraltensis, and Bacillus pumilus. Productive parameters were evaluated from weaning to 70 days of age. Results: No statistically significant difference was found between the treatments on forage DMI, concentrate DMI, or total DMI (p>0.05). Similarly, no statistical difference was found between the treatments in terms of final weight or weight gain (p>0.05). Regarding the feed conversion ratio, there was a significant difference between treatments (p=0.045); the CONTROL group had the highest feed conversion ratio, followed by the AGP group, with the best feed conversion ratio observed in the PROB 3 group (p

ABSTRACT: This study evaluated the effect of the three inulin levels (0.1%, 0.2%, 0.4%) supplemented as a substitute for an antibiotic growth promoter (AGP, zinc bacitracin) and control in guinea pigs raised for human consumption. Fifty 14-day-old male guinea pigs were used. Productive parameters (weight gain, total dry matter intake, and feed conversion ratio (FCR)) and intestinal morphology of the duodenum, jejunum, and ileum at slaughter (70 days of age) were evaluated. An inverse relationship was observed between inulin levels and FCR (linear effect; P = 0.006). There was no statistically significant effect of the treatments on total dry matter intake and weight gain (P > 0.05). A linear effect of the inulin level on the villi’s length (VL), villi’s width (VW), and length/depth ratio (VL/DC) in the duodenum; VW in the jejunum; and VL in the ileum (P