Valorization of Agro-Wastes as Fillers in PLA-Based Biocomposites for Increasing Sustainability in Fused Deposition Modeling Additive Manufacturing.

The use of wheat middlings (WM) and rice husks (RH) as biofillers for mixing with poly(lactic acid) (PLA) matrix to produce new 3D-printable biocomposites was assessed. Filaments containing 10 and 20 wt.% agro-waste-derived biofillers were manufactured and, for the sake of comparison, filaments of neat PLA were also produced. The obtained filaments were characterized via thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), showing potential for further application in additive manufacturing processing. Three-dimensionally printed specimens were thus produced and characterized via: DSC, also evaluating the specific heat capacity (CP) of specific 3D-printed specimens; dynamic mechanical analysis (DMA), also applied for determining the coefficient of linear thermal expansion (CLTE) measured on 3D-printed specimens in two different directions (X and Y); and tensile tests. The latter testing campaign was carried out along three printing directions (X, Y, and Z axes) to test the intrinsic biocomposite features (X-printed samples) as well as interbead and interlayer adhesion (Y- and Z-printed specimens, respectively). All samples demonstrated acceptable properties. The inclusion of a cost-free natural material leads to a strong reduction of the whole material cost. Implementing this new class of composite material to an additive manufacturing technique can significantly reduce the environmental impact of 3D-printed products.

GIS-based land suitability analysis for the optimal location of integrated multi-trophic aquaponic systems.

Aquaponics has witnessed global proliferation and a notable enhancement in sustainability in recent years. Consequently, it assumes paramount importance to delineate optimal locations for its implementation, in fact, the success of an aquaponic facility also depends on its geographical placement, necessitating consideration of many variables encompassing natural resources, socioeconomic factors, infrastructural availability and environmental constraints, whether natural or artificial. This paper focuses on the definition and test in the Emilia-Romagna region (Italy) of a GIS-based multi-criteria land suitability assessment model aimed at allowing the diffusion and environmental integration of innovative integrated multi-trophic aquaponic systems. The process has been implemented with a Weighted Linear Combination (WLC) model, where decisions and criteria were selected via a participatory mechanism involving experts in various fields. The region has been subdivided into 50 × 50 m grid cells, with each grid cell being associated with a value ranging from 0 to 8. In this context, a rating of 0 means unsuitability, while a rating of 1 denotes minimal suitability, and the highest rating of 8 designates maximal suitability. Notably, a substantial portion of the surveyed territory has been found to be completely unsuitable for the establishment of aquaponic facilities. More than 86.4% of the remaining suitable areas were rated 6, 7, or 8, affirming the overall favourability of the Emilia-Romagna region for aquaponic installations. Finally, the veracity and robustness of the results have been tested through a one-at-a-time sensitivity analysis, that has proven the appropriateness of the proposed model.

Review of the Heat Stress-Induced Responses in Dairy Cattle.

In the dairy cattle sector, the evaluation of the effects induced by heat stress is still one of the most impactful and investigated aspects as it is strongly connected to both sustainability of the production and animal welfare. On the other hand, more recently, the possibility of collecting a large dataset made available by the increasing technology diffusion is paving the way for the application of advanced numerical techniques based on machine learning or big data approaches. In this scenario, driven by rapid change, there could be the risk of dispersing the relevant information represented by the physiological animal component, which should maintain the central role in the development of numerical models and tools. In light of this, the present literature review aims to consolidate and synthesize existing research on the physiological consequences of heat stress in dairy cattle. The present review provides, in a single document, an overview, as complete as possible, of the heat stress-induced responses in dairy cattle with the intent of filling the existing research gap for extracting the veterinary knowledge present in the literature and make it available for future applications also in different research fields.

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data.

Urbanization strongly correlates with land use land cover (LULC) dynamics, which further links to changes in land surface temperature (LST) & urban heat island (UHI) intensity. Each LULC type influences UHI differently with changing climate, therefore knowing this impact & connection is critical. To understand such relations, long temporal studies using remote sensing data play promising role by analysing the trend with continuity over vast area. Therefore, this study is aimed at machine learning centred spatio-temporal analysis of LST and land use indices to identify their intra-urban interaction during 1991-2021 (summer) in Imola city (specifically representing small urban environment) using Landsat-5/8 imageries. It was found that LST in 2021 increased by 38.36% from 1991, whereas average Normalised Difference Built-up Index (NDBI) increased by 43.75%, associating with increased thermal stress area evaluated using ecological evaluation index. Major LULC transformations included green area into agricultural arable-land and built-up. Finally, the modelled output shows that built-up & vegetation index have strongly impacted LST. This study, help to understand the relative impact of land-use dynamics on LST at intra-urban level specifically with respect to the small urban settings. Further assisting in designing and regenerating urban contexts with stable configuration, considering sustainability and liveable climate, for benefit of health of public and fragile population in particular.

Effects of LED supplemental lighting on the growth and metabolomic profile of Taxus baccata cultivated in a smart greenhouse.

Light emitting diode (LED) lamps are increasingly being studied in cultivation of horticultural, ornamental and medicinal plants as means to increase yield, quality, stress resistance, and bioactive compounds content. Enhancing the production of metabolites for medicinal or pharmaceutical use by regulating LED intensity and spectra is a challenging subject, where promising results have been achieved. Nevertheless, some species have been poorly investigated, despite their interest as a source of medicinally active substances, with particular reference to LED effects at the plant cultivation level. This study evaluates the effects of supplementary top-light LED treatments on Taxus baccata, one of the main sources of taxane precursors. Blue, red and mixed red-and-blue spectra were tested at 100 µM m-2 s-1. Moreover, 50 and 150 µM m-2 s-1 intensities were tested for the mixed spectrum. All treatments were set for 14 hours a day and were tested against natural light as control treatment, in a controlled environment, from 19 August to 9 December 2019, this latter date representing 112 days after treatment (DAT) began. A smart monitoring and control system powered by environmental and proximal sensors was implemented to assure homogeneity of temperature, humidity, and base natural light for all the treatments. It resulted in negligible deviations from expected values and reliable exclusion of confusing factors. Biometric measurements and 1H-NMR based metabolomic analysis were performed to investigate growth and phytochemical profile throughout the trial. One-way ANOVA showed that supplemental LED lighting increased plant height and number of sprouts. Considering the mixed red-and-blue spectrum, plant height increased almost proportionally from control to 100 µM m-2 s-1 (+20% at 112 DAT), with no further increase at higher intensity. The number of sprouts was strongly enhanced by LED treatments only in the early phase (48.9 vs. 7.5 sprouts in the averaged 50, 100 and 150 µM m-2 s-1 vs. the control at 28 DAT), with no differences related to intensity in the very early stage, and more persisting effects (up to 56 DAT) for higher intensities. After the very early growth stages (28 DAT), plant vigor showed a modest although significant increase over time compared to the control, with no differences related to light intensity (0.81 vs. 0.74 of NDVI in the averaged 50, 100 and 150 µM m-2 s-1 vs. the control, across 56, 84 and 112 DAT). The different spectra tested at 100 µM m-2 s-1 showed no significant differences in growth parameters, except for a slight beneficial influence of blue (alone or with red) compared to only red for sprouting. According to the metabolomic analysis, treated plants at 28 DAT were characterized by the highest content of sucrose and aromatic compounds. Signals of a putative taxane were detected in the 1H NMR profiles of plants, which were compared to the spectrum of baccatin III standard. However, the intensity of these spectral signals was not affected by the treatment, while they increased only slightly during time. Light at 150 µM m-2 s-1 induced the strongest variation in the metabolome. Conversely, light composition did not induce significant differences in the metabolome.

Assessing the environmental performance of plastic-based and felt-based green wall systems in a life-cycle perspective.

With the remarkable growth of cities and the increase of built-up areas, mitigation of urban heat island effects has become one of the most crucial challenges in social and environmental sustainability with significant impacts on public health. This has led to an increasing development of urban green infrastructure. Among those nature-based solutions, green wall systems have been receiving a growing attention, being a passive technology with their ability to reduce greenhouse gas emissions, adapt to climate change, improve air quality and reduce the heat island effect in urban environments. Despite that growing interest in studying the functions and features of such green systems, and the various types of living walls nowadays available, most studies evaluate their energy efficiency and performance only during the use phase. This study aimed to assess the overall environmental performances of two types of green walls in a life cycle perspective, considering the embodied energy, greenhouse gas emissions, materials and energy consumption, and embodied carbon. After collecting inventory data related to all components and processes of each system, a life cycle assessment with cradle to gate approach has been performed to compare the performances of a felt-based system without organic growth medium and a system based on plastic modules with organic growth medium. The main impacts have been detected in the production stage and materials used in systems structure. By comparing the results achieved in the 16 impact categories analyzed, the felt-based system showed the highest overall impact, with the use of fertilizers and aluminum components playing a crucial part. Polypropylene used to produce the panels, water used for plant irrigation and potting soil composition are the main environmental impact contributors in the plastic-based system. The results pointed out the importance of accurate choice of materials for the design and production of green walls.

Behavior and Welfare of Undocked Heavy Pigs Raised in Buildings with Different Ventilation Systems.

The present study aimed to evaluate animal welfare of pigs from the same farm, raised with two ventilation systems. The study involved 60 pens of fattening pigs, raised in two buildings: one naturally ventilated (NV) and the other mechanically ventilated (MV). Pigs were assessed on three observation days: at 40 kg (T1), 100 kg (T2), and 160 kg (T3) of live weight. Animal-based measures were used such as qualitative behavioral analysis (QBA), behavioral measures (BMs), and lesion and health measures (LHMs). Housing conditions (HCs) measured at each observation day were the number of pigs per pen, space allowance, temperature, light, and CO2. The association study was performed using a general linear model and analysis of variance. Ventilation effect was analyzed by performing computational fluid dynamics. Results showed that overall pigs raised in the MV were in a more positive affective state. Despite that, with hot temperatures, the higher occurrence of pig soiling indicated heat stress in pigs and consequent welfare impairment. The higher frequency of pigs showing dog sitting behavior at T2 and T3 suggest welfare worsening in the last phases of fattening. The study concludes that ventilation system influences animal behavior and overall animal welfare, especially during the warmer season.

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions.

Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016-2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production.

The agri-environmental footprint: A method for the identification and classification of peri-urban areas.

The aim of this research is to define and test a methodology for an articulated and systematic analysis of the countryside, which can lend support to urban and landscape planning processes in addition to improving knowledge of the landscape, and for the implementation of agricultural and rural development policies. We have conceived a multi-criteria and multilevel methodology that was integrated into a geographic information system (GIS) and is based on clustering and maximum likelihood classification algorithms. The proposed method focuses on various agri-environmental and socio-economic components, whose synthesis is performed by means of an interpretative key that was developed by the authors, the "Agri-Environmental Footprint", to quantify the impact of rural areas on urban systems. In particular, this paper presents the general framework of the methodology, a set of indexes that are defined for its first-level analyses, and the results of their implementation through a case study in the Emilia-Romagna Region (Italy). The method is based on the IsoCluster technique, which is associated with statistical analyses of criteria, such as the Principal Component Analysis and different data standardisation algorithms (min-max and z-score). The case study has allowed an iterative calibration of both the methodological framework and indexes.