Assessment of bioenergy plant locations using a GIS-MCDA approach based on spatio-temporal stability maps of agricultural and livestock byproducts: A case study.

Addressing the global challenge of energy sustainability and global directives on farming emissions, the United Nations, the European Union, and China have led with strict targets for clean energy, renewable share growth, and carbon neutrality, highlighting a commitment to collective sustainability. This work is situated within the ambit of the Sustainable Development Goals (SDGs), advocating for a transition towards renewable energy sources. With substantial and accessible bioenergy resources, notably in Hubei Province, China, biogas technology has emerged as an emission-cutting solution. This research, focused on the Jianghan Plain, employs an integrated approach combining spatial analyses with machine learning tools to evaluate crop yield stability over two decades, with the aim of maximising the biogas yield from agricultural byproducts, i.e., crop straw and livestock manure. Using Multi-Criteria Decision Analysis (MCDA), which is informed by grey-based DEMATEL, 9 constraints and 13 environmental, social, and economic criteria were assessed to identify optimal sites for biogas facilities. The findings underscore the significant bioenergy potential of agricultural byproducts from the plain of 6.3 × 1012 kJ/year at an 11.4 kJ/m2 density. Stability analyses revealed consistent biomass availability, with rice in Gongan and Shayang and wheat in Jiangling being the primary contributors. Through the MCDA, 45-66 optimal biogas plants were identified across 4 critical counties (Zhongxiang, Shangyang, Jingshan, and Yichen), balancing the energy supply and demand under various stable scenarios. Furthermore, this study demonstrated the criticality of moderate biomass stability for stakeholder consensus and identified areas of high stability essential for energy demand fulfilment. Theoretically, this study offers a practical model for bioenergy resource exploitation that aligns with global sustainability and carbon neutrality goals to address the urgent need for renewable energy solutions amidst the global energy crisis. Practically, this study sets a precedent for policy and planning in environmental, agricultural, and renewable sectors, signifying a step forwards in achieving environmental sustainability and an energy-efficient future.

On-Barn Forecasting Beef Cattle Production Based on Automated Non-Contact Body Measurement System.

The main task of selective breeding is to determine the early productivity of offspring. The sooner the economic value of an animal is determined, the more profitable the result will be, due to the proper estimation of high and low productive calves and distribution of the resources among them, accordingly. To predict productivity, we offer to use a systematic assessment of animals by using the main genetic parameters (correlation coefficients, heritability, and regression) based on data such as the measurement of morphological characteristics of animals, obtained using the automated non-contact body measurement system based on RGB-D image capture. The usefulness of the image capture system lies in significant time reduction that is spent on data collection and improvement in data collection accuracy due to the absence of subjective measurement errors. We used the RGB-D image capture system to measure the live weight of mother cows, as well as the live weight and body size of their calves (height at the withers, height in the sacrum, oblique length of the trunk, chest depth, chest girth, pastern girth). Cows and cattle of black-and-white and Holstein breeds (n = 561) were selected as the object of the study. Correlation analysis revealed the main indices for the forecast of meat productivity-live weight and measurements of animals at birth. Calculation of the selection effect is necessary for planning breeding work, since it can determine the value of economically beneficial traits in subsequent generations, which is very important for increasing the profitability of livestock production. This approach can be used in livestock farms for predicting the meat productivity of black-and-white cattle.

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras.

In beef cattle breeding, genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) arrays can reveal many loci of various production traits, such as growth, productivity, and meat quality. With the development of genome sequencing technologies, new opportunities are opening up for more accurate identification of areas associated with these traits. This article aims to develop a novel approach to the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features. The purpose of this study was to identify significant variants underlying differences in the qualitative characteristics of meat, using imputed data on the sequence of the entire genome. Samples of biomaterial of young Aberdeen-Angus breed cattle (n = 96) were the material for carrying out genome-wide SNP genotyping. Genotyping was performed using a high-density DNA chip Bovine GPU HD BeadChip (Illumina Inc., San Diego, CA, USA), containing ~150 thousand SNPs. The following indicators were selected as phenotypic features: chest width and chest girth retrieved by 3-D model and meat output on the bones. Correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals.

Environmental assessment of a two-stage high pressure anaerobic digestion process and biological upgrading as alternative processes for biomethane production.

Biomethane plays a key role in achieving decarbonization and sustainable development goals. According to the objectives that arise, choosing the most suitable production system allows optimization of production, thereby reducing CO2 emissions. In this study, three biomethane production scenario life cycle assessments were compared to determine which would maintain the lowest CO2 emissions. Conventional anaerobic digestion and an innovative process called two-stage high pressure anaerobic digestion were considered. These methods were combined with two upgrading processes: water scrubbing and biological upgrading. Cattle manure and sugar beets were used as substrates for the process. Emissions were 805.6 gCO2eq/m3CH4 for the traditional biogas production process combined with water scrubbing and 450.3 gCO2eq/m3CH4 for the two-stage anaerobic digestion process combined with biological upgrading. Furthermore, the analysis demonstrated that these values would be reduced by 29.5 % and 48.0 % if electrical energy were produced using only renewable energy sources.

A Comparative Study of Machine Learning Methods for Predicting Live Weight of Duroc, Landrace, and Yorkshire Pigs.

Live weight is an important indicator of livestock productivity and serves as an informative measure for the health, feeding, breeding, and selection of livestock. In this paper, the live weight of pig was estimated using six morphometric measurements, weight at birth, weight at weaning, and age at weaning. This study utilised a dataset including 340 pigs of the Duroc, Landrace, and Yorkshire breeds. In the present paper, we propose a comparative analysis of various machine learning methods using outlier detection, normalisation, hyperparameter optimisation, and stack generalisation to increase the accuracy of the predictions of the live weight of pigs. The performance of live weight prediction was assessed based on the evaluation criteria: the coefficient of determination, the root-mean-squared error, the mean absolute error, and the mean absolute percentage error. The performance measures in our experiments were also validated through 10-fold cross-validation to provide a robust model for predicting the pig live weight. The StackingRegressor model was found to provide the best results with an MAE of 4.331 and a MAPE of 4.296 on the test dataset.

A comparative environmental life cycle assessment of rice straw-based bioenergy projects in China.

Bioenergy is a promising solution for greenhouse gas (GHG) emissions mitigation. However, the emissions resulting from the different production stages must be quantified and evaluated. The life cycle assessment (LCA) method was used to compare and quantify the environmental burdens of three rice straw (RS) utilization scenarios for producing biogas, briquette fuel, and syngas. To our knowledge, this is the first study that applies the LCA approach to assess these three bioenergy scenarios in a single study where the main goal was to determine the most sustainable option. A total of 10 mid-point impact categories were investigated. The results indicated that the three scenarios achieved net positive energy and net negative GHG balances. The briquette fuel scenarios had the highest net energy balance (11,115 MJ/tonne dry RS), while the syngas scenario had the highest net GHG (-2,315 kg CO2-eq./tonne dry RS). Moreover, the syngas scenario was the most beneficial to the environment, achieving negative results in 9 out of the 10 impact categories; the largest was marine ecotoxicity (-853,897 kg 1,4-DB-eq./tonne dry RS). The biogas scenario achieved emission savings in 3 out of the 10 categories. Although the briquette fuel scenario had no negative values in the 10 categories, its overall contribution to environmental burdens was relatively low. Overall, the order of the three scenarios in terms of the most sustainable option is syngas > briquette fuel > biogas.

Challenges and Tendencies of Automatic Milking Systems (AMS): A 20-Years Systematic Review of Literature and Patents.

Over the last two decades, the dairy industry has adopted the use of Automatic Milking Systems (AMS). AMS have the potential to increase the effectiveness of the milking process and sustain animal welfare. This study assessed the state of the art of research activities on AMS through a systematic review of scientific and industrial research. The papers and patents of the last 20 years (2000-2019) were analysed to assess the research tendencies. The words appearing in title, abstract and keywords of a total of 802 documents were processed with the text mining tool. Four clusters were identified (Components, Technology, Process and Animal). For each cluster, the words frequency analysis enabled us to identify the research tendencies and gaps. The results showed that focuses of the scientific and industrial research areas complementary, with scientific papers mainly dealing with topics related to animal and process, and patents giving priority to technology and components. Both scientific and industrial research converged on some crucial objectives, such as animal welfare, process sustainability and technological development. Despite the increasing interest in animal welfare, this review highlighted that further progress is needed to meet the consumers' demand. Moreover, milk yield is still regarded as more valuable compared to milk quality. Therefore, additional effort is necessary on the latter. At the process level, some gaps have been found related to cleaning operations, necessary to improve milk quality and animal health. The use of farm data and their incorporation on herd decision support systems (DSS) appeared optimal. The results presented in this review may be used as an overall assessment useful to address future research.

Drivers of farmers' adoption and continuation of climate-smart agricultural practices. A study from northeastern Italy.

The EU rural development policy has addressed challenges related to climate change in agriculture by introducing public voluntary schemes, which financially support the adoption of climate-smart agricultural practices. Several factors, most of which are non-financial ones, drive adoption and continuation of these schemes by farmers. Despite the importance of these factors, only a few studies explore their role in the European context. This paper contributes to filling this gap from a twofold perspective. First, it investigates the role of the farming factors, technology accessibility, environmental features, policy design and social expertise at the territorial level on early adoption. Second, it sheds light on farmers' attitudes and motivations and on social pressure on their decision to continue or discontinue the practices, by surveying a sample of early adopters. Three schemes for the Veneto region rural development programme are considered: no-tillage, fertiliser reduction, and water and fertiliser reduction. The results highlight that non-financial factors should be considered in order to design more effective schemes to prompt farmers to adopt and continue such practices over the long run. The paper also stresses the need to complement financial support with proactive information-based instruments.

A sample of Italian vineyards: Landscape and management parameters dataset.

Agricultural land use plays a critical role in land planning sustainability. Employing a GIS-based decision-making protocol based on spatial and management data represents an appropriate tool for land planning. The Italian vineyards database presented here describes several spatial and management features of 3686 sample vineyards distributed throughout Italy. The dataset is presented as a centroid shapefile with the attribute table. The features were assessed with a GIS-based geospatial analysis. Parameters such as training system and shape of the vineyard block were attributed through visual assessment of Google Earth images. Row spacing, length-width ratio and headland size were determined using QGIS measuring tools. The mean and maximum slope was derived using a 20 m spatial resolution Digital Elevation Model (DEM). This database may help to establish planting criteria of new vineyards which comply with rational and sustainable requirements. Moreover, the dataset could be combined with other agricultural land use data for further analysis of land management. Furthermore, the database could be implemented to support global-scale vineyard management.

On-Barn Pig Weight Estimation Based on Body Measurements by Structure-from-Motion (SfM).

Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized.

A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns.

Frequent checks on livestock's body growth can help reducing problems related to cow infertility or other welfare implications, and recognizing health's anomalies. In the last ten years, optical methods have been proposed to extract information on various parameters while avoiding direct contact with animals' body, generally causes stress. This research aims to evaluate a new monitoring system, which is suitable to frequently check calves and cow's growth through a three-dimensional analysis of their bodies' portions. The innovative system is based on multiple acquisitions from a low cost Structured Light Depth-Camera (Microsoft Kinect™ v1). The metrological performance of the instrument is proved through an uncertainty analysis and a proper calibration procedure. The paper reports application of the depth camera for extraction of different body parameters. Expanded uncertainty ranging between 3 and 15 mm is reported in the case of ten repeated measurements. Coefficients of determination R² > 0.84 and deviations lower than 6% from manual measurements where in general detected in the case of head size, hips distance, withers to tail length, chest girth, hips, and withers height. Conversely, lower performances where recognized in the case of animal depth (R² = 0.74) and back slope (R² = 0.12).

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone.

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005-2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha(-1) applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha(-1) applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching.