Search Results (3,533)

Purpose: Cross-sectional analysis using open data has revealed an association between the traditional Japanese diet score (TJDS) and healthy life expectancy (HALE). This study aimed to clarify the association of the TJDS with the HALE and average life expectancy (LE) via a longitudinal analysis. Methods: Data regarding the food supply and total energy were extracted from the database of the Food and Agriculture Organization of the United Nations, and data regarding HALE and LE were obtained from the Global Burden of Disease Study 2019. The supply of items consumed frequently (rice, fish, soybeans, vegetables, and eggs) and less frequently (wheat, milk, and red meat) in the Japanese diet were scored (total: −8 to 8 points) and stratified into tertiles by country. The gross domestic product, aging rates, years of education, smoking rate, physical activity, and obesity rate were used as covariates. Longitudinal analyses were conducted for 143 countries, using the HALE and LE for each country from 2010 to 2019 as dependent variables and the 2010 TJDS as an independent variable. Results: The fixed effects (standard errors) were HALE 0.424 (0.102) and LE 0.521 (0.119), indicating significance (p < 0.001). Conclusion: The nine-year longitudinal analysis using international data suggests that the traditional Japanese diet based on rice may prolong the HALE and LE. Full article

The growing global demand for food and energy requires land-use strategies that support agricultural production and renewable energy generation. Agrivoltaic (APV) systems allow farmland to be used for both agriculture and solar power generation. The aim of this study is to critically synthesize the interactions between the key dimensions of APV implementation—technical, agronomic, legal, and economic—in order to create a multidimensional framework for designing an APV optimization model. The analysis covers APV system topologies, appropriate types of photovoltaic modules, installation geometry, shading conditions, and micro-environmental impacts. The paper categorizes quantitative indicators and critical thresholds that define trade-offs between energy production and crop yields, including a discussion of shade-tolerant crops (such as lettuce, clover, grapevines, and hops) that are most compatible with APV. Quantitative aspects were integrated in detail through a review of mathematical approaches used to predict yields (including exponential-linear, logistic, Gompertz, and GENECROP models). These models are key to quantitatively assessing the impact of photovoltaic modules on the light balance, thus enabling the simultaneous estimation of energy efficiency and yields. Technical solutions that enhance synthesis, such as dynamic tracking systems, which can increase energy production by up to 25–30% while optimizing light availability for crops, are also discussed. Additionally, the study examines regional legal frameworks and the economic factors influencing APV deployment, highlighting key challenges such as land use classification, grid connection limitations, investment costs and the absence of harmonised APV policies in many countries. It has been shown that APV systems can increase water retention, mitigate wind erosion, strengthen crop resilience to extreme weather conditions, and reduce the levelized cost of electricity (LCOE) compared to small rooftop PV systems. A key contribution of the work is the creation of a coherent analytical design framework that integrates technical, agronomic, legal and economic requirements as the most important input parameters for the APV system optimization model. This indicates that wider implementation of APV requires clear regulatory definitions, standardized design criteria, and dedicated support mechanisms. Full article

Soil compaction in grassland and agricultural soils reduces water infiltration, root growth and ecosystem services. Conventional deep tillage and coring can alleviate compaction but are energy intensive and strongly disturb the turf. This study proposes an earthworm-inspired subsurface robot as a low-disturbance loosening tool for compacted grassland soils. Design principles are abstracted from earthworm body segmentation, anchoring–propulsion peristaltic locomotion and corrugated body surface, and mapped onto a robotic body with anterior and posterior telescopic units, a flexible mid-body segment, a corrugated outer shell and a brace-wire steering mechanism. Kinematic simulations evaluate the peristaltic actuation mechanism and predict a forward displacement of approximately 15 mm/cycle. Using the finite element method and a Modified Cam–Clay soil model, different linkage layouts and outer-shell geometries are compared in terms of radial soil displacement and drag force in cohesive loam. The optimised corrugated outer shell combining circumferential and longitudinal waves lowers drag by up to 20.1% compared with a smooth cylinder. A 3D-printed prototype demonstrates peristaltic locomotion and steering in bench-top tests. The results indicate the potential of earthworm-inspired subsurface robots to provide low-disturbance loosening in conservation agriculture and grassland management, and highlight the need for field experiments to validate performance in real soils. Full article

Understanding how land tenure and socio-economic pressures shape landscape transformation is critical for sustainable management in biodiversity-rich regions. This study examines three decades (1987–2017) of land use and land cover (LU&LC) change in the Ngerenyi area of the Taita Hills, Kenya, by integrating multispectral Landsat analysis with household survey data. Harmonized pre-processing and supervised classification of four LU&LC classes, agriculture, built-up areas, high-canopy vegetation, and low-canopy vegetation, achieved overall accuracies above 80% and Kappa values exceeding 0.75. Transition modeling using the Minimum Information Loss Transition Estimation (MILTE) approach, combined with net-versus-swap metrics, revealed persistent decline and fragmentation of high-canopy vegetation, cyclical transitions between agriculture and low-canopy vegetation, and the near-irreversible expansion of built-up areas. Low-canopy vegetation exhibited the highest dynamism, reflecting both degradation from canopy loss and natural regeneration from fallowed cropland. Household surveys (n = 141) identified agricultural expansion, charcoal production, fuelwood extraction, and population growth as the dominant perceived drivers, with significant variation across tenure categories. The population in Taita Taveta County increased from 205,334 in 2009 to 340,671 in 2019, reinforcing documented pressures on land resources and woody biomass. As part of the Eastern Arc biodiversity hotspot, the landscape’s diminishing high-canopy patches underscore the importance of conserving undisturbed vegetation remnants as ecological baselines and biodiversity refuges. The findings highlight the need for tenure-sensitive, landscape-scale planning that integrates private landowners, regulates subdivision, promotes agroforestry and alternative energy options, and safeguards remaining high-canopy vegetation to enhance ecological resilience while supporting local livelihoods. Full article

Due to global increases in poultry meat and egg production, consumers request sustainable agricultural practices, requiring alternative solutions for future feeding. Global egg production increased by over 41% between 2000 and 2020, from 51 to 87 million tonnes, at an average increasing rate of 3%. Similarly, the production of poultry meat reached 145 million tonnes in 2023 and continues to increase, which amplifies the pressure on sustainable alternative feed solutions. Commercial poultry diets are typically based on a cereal (corn or wheat) as an energy source and a quality protein source, especially soybean meal (SBM), to provide essential amino acids. Soybean production is associated with deforesting and land use in several countries, sensitiveness to supply chains and price volatility. As a response to these challenges over the last decade, research and commercial innovation have intensively focused on alternative and novel feed resources that can be integrated into both broiler and layer diets. Some future candidate ingredients are insect meal, algae, agro-industrial by-products such as distiller’s dried grains with solubles (DDGS), brewery spent grains (BSG) and fermented feedstuffs (oilseed cakes/meals). Literature data showed that moderate inclusion of these alternative ingredients can be partly integrated in poultry diets, without compromising egg or meat quality. In some cases, studies showed improvements of productive performances and specific quality traits (yolk color, fatty acids and antioxidant compounds), offering potential to valorize waste streams, improve local circularity and provide functional ingredients for animals and humans. However, challenges still remain, especially in terms of nutrient variability, digestibility limitations, higher processing costs and still-evolving regulations which constrain mainstream adoption of some of these potential future alternatives. Full article

The construction sector accounts for 39% of GHG emissions, being the main contributor to embodied carbon emissions of building materials, and operational energy consumption for indoor thermal comfort. Cereal straw, an agricultural by-product, is emerging as a low-carbon alternative due to its thermal performance and negative embodied carbon. This paper aims to review recent advances of cereal straw as a building material for decarbonization of construction, analyzing its thermal properties, embodied carbon, and large-scale applications. A literature review focused on European-certified straw-based materials, grouped into four categories: straw bales, blown-in insulation, modular systems, and bio-composites. Twelve Product Environmental Declarations (EPDs) and technical specifications were examined to evaluate manufacturing processes, material properties, and Global Warming Potential (GWP) for cradle-to-gate stages (A1–A3), as well as their use in large-scale projects over the past five years. Thermal conductivity ranged from 0.043 to 0.068 W/m·K, while embodied carbon varied between –101.2 and –146.5 kg CO₂ eq/m³. Straw bales remain prevalent in small-scale housing, blown-in insulation supports retrofitting, and modular systems offer the most balanced performance, enabling high-rise or extensive built surfaces. The study concludes that straw products have the potential to decarbonize opaque elements of the envelope, reducing operational and embodied energy of buildings. Full article

This article analyzes how Austrian economic principles contribute to bioeconomic development in Ecuador, emphasizing key aspects such as property rights, spontaneous order, entrepreneurial innovation, institutional frameworks and decentralized knowledge. The relevance of incorporating an instrumental case was established, in which the scale, composition, and technology (SCT) effects model was applied to the comparative analysis of Ecuador, Chile, Argentina, and Brazil during the 2000–2023 period. This study was complemented by multiple linear regression, which was used to evaluate the relationship between economic growth, CO₂ emissions, the agricultural industry, foreign direct investment, and a composite bioeconomy index. The results showed complete decoupling between GDP and emissions in Ecuador, driven by technological improvements and transformations in key sectors such as agriculture and renewable energy. Chile and Brazil also showed paths of complete decoupling, although to a lesser extent for the latter, while Argentina exhibited relative decoupling, in which bioeconomic growth continues to be associated with an increase in emissions. The estimated models present an R² (between 0.81 and 0.91). This study shows that it is possible to move towards a sustainable bioeconomy. Full article

In the Republic of Moldova, orchard biomass represents an important resource for the production of densified solid biofuels, with peach having the highest sustainable energy potential (33.5 ± 6.54 GJ·ha⁻¹). However, the quality of solid biofuels derived from orchard biomass is often constrained by heterogeneity in moisture content, uneven particle size distribution, and inadequate drying or blending practices along the supply chain. Optimizing the solid biofuel supply chain is therefore essential to minimize feedstock variability, ensure consistent densification quality, and reduce production costs. The aim of this study was to improve the process of producing densified solid biofuels from orchard biomass. Specifically, the study investigated how raw material moisture and particle size influence briquette density and durability, and how ternary mixtures of peach biomass, wheat straw, and sunflower residues can be optimized for enhanced energy performance. All experimental determinations were performed using validated methods and calibrated equipment. The results showed that optimal performance is achieved by shredding the biomass with 4–8 mm sieves and maintaining the moisture content between 6 and 14%, resulting in briquettes with the density of 1.00–1.05 g·cm⁻³, ash content below 3–5%, and an energy yield of 18.4–19.2 MJ·kg⁻¹. Ternary diagrams confirmed the decisive role of peach lignocellulosic residues in achieving high density, low ash content, and increased energy yield, while wheat straw and sunflower residues can be used in controlled proportions to diversify resources and reduce costs. These findings provide quantitative insights into how mixture formulation and process parameters influence the briquette quality, contributing to the optimization of solid biofuel supply chains for orchard and agricultural residues. Overall, this study demonstrates that competitive solid biofuels can be produced through careful balancing of mixture composition and optimization of technological parameters, offering practical guidelines for sustainable bioenergy development in regions with abundant orchard residues. Full article

Precision agriculture will continue to heavily rely on data-driven models to enable more intensive crop monitoring and data-driven decisions. The available remote sensing techniques, particularly those based on multispectral Sentinel-2 data, still have major shortcomings due to cloud cover, low temporal resolution, and time lags in data availability. To address these shortcomings, this paper proposes a hybrid approach that combines Sentinel-2 satellite data with real-time data generated by low-cost ground-based single-pixel detectors (SPDs), such as the AS7263. This hybrid approach addresses key shortcomings in existing agricultural monitoring systems and offers a cost-effective, scalable solution for real-time monitoring and prediction of end-of-season yield, moisture, and plant height using simple PLRS models implemented directly in SPDs with an energy-efficient algorithm for deployment on the STM32G030 microcontroller. Full article

Soil salinization–alkalization is a critical abiotic constraint on global agriculture, threatening agroecosystem sustainability. Leymus chinensis, a high–quality perennial forage with strong stress resilience, is an ideal model for studying saline–alkali tolerance in graminaceous crops. We integrated physiological, transcriptomic, and metabolomic profiling to dissect its responses under moderate vs. severe carbonate stress, mimicking natural saline–alkali soils rather than single salt stress treatments. Multi–omics analysis revealed drastic reprogramming of energy metabolism, carbohydrate homeostasis, water transport, and secondary metabolism. Our novel finding reveals that L. chinensis uses stress–severity–dependent mechanisms, with flavonoid biosynthesis as a central “regulatory hub”: moderate saline–alkali stress acts as a stimulus for “Adaptive Activation” (energy + antioxidants), promoting growth, while severe stress exceeds tolerance thresholds, causing “systemic imbalance” (oxidative damage + metabolic disruption) and growth retardation. Via WGCNA and metabolome–transcriptome modeling, 22 transcription factors linked to key flavonoid metabolites were identified, functioning as molecular switches for stress tolerance. Our integrated approach provides novel insights into L. chinensis’ tolerance networks, and the flavonoid biosynthesis pathways and regulatory genes offer targets for precision molecular breeding to enhance forage stress resistance and mitigate yield losses from salinization–alkalization. Full article

The early detection of plant diseases is critical to improving agricultural productivity and ensuring food security. However, conventional centralized deep learning approaches are often unsuitable for large-scale agricultural deployments, as they rely on continuous data transmission to cloud servers and require high computational resources that are impractical for Internet of Things (IoT)-based field environments. In this article, we present a distributed deep learning framework based on Federated Learning (FL) for the diagnosis of plant diseases in IoT sensor networks. The proposed architecture integrates multiple IoT nodes and an edge computing node that collaboratively train an EfficientNet B0 model using the Federated Averaging (FedAvg) algorithm without transferring local data. Two training pipelines are evaluated: a standard single-model pipeline and a hierarchical pipeline that combines a crop classifier with crop-specific disease models. Experimental results on a multicrop leaf image dataset under realistic augmentation scenarios demonstrate that the hierarchical FL approach improves per-crop classification accuracy and robustness to environmental variations, while the standard pipeline offers lower latency and energy consumption. Full article

This study presents an optimization-driven design of a wireless communications network to continuously transmit environmental variables—temperature, humidity, weight, and water usage—in poultry farms. The reference site is a four-shed facility in Quito, Ecuador (each shed $120\mathrm{m}\times 12\mathrm{m}$) with a data center located $200\mathrm{m}$ from the sheds. Starting from a calibrated log-distance path-loss model, coverage is declared when the received power exceeds the receiver sensitivity of the selected technology. Gateway placement is cast as a mixed-integer optimization that minimizes deployment cost while meeting target coverage and per-gateway capacity; a capacity-aware greedy heuristic provides a robust fallback when exact solvers stall or instances become too large for interactive use. Sensing instruments are Tekon devices using the Tinymesh protocol (IEEE 802.15.4g), selected for low-power operation and suitability for elongated farm layouts. Model parameters and technology presets inform a pre-optimization sizing step—based on range and coverage probability—that seeds candidate gateway locations. The pipeline integrates MATLAB R2024b and LpSolve 5.5.2.0 for the optimization core, Radio Mobile for network-coverage simulations, and Wireshark for on-air packet analysis and verification. On the four-shed case, the algorithm identifies the number and positions of gateways that maximize coverage probability within capacity limits, reducing infrastructure while enabling continuous monitoring. The final layout derived from simulation was implemented onsite, and end-to-end tests confirmed correct operation and data delivery to the farm’s data center. By combining technology-aware modeling, optimization, and field validation, the work provides a practical blueprint to right-size wireless infrastructure for agricultural monitoring. Quantitatively, the optimization couples coverage with capacity and scales with the number of endpoints M and candidate sites N (binaries $M+N+MN$). On the four-shed case, the planner serves 72 environmental endpoints and 41 physical-variable endpoints while keeping the gateway count fixed and reducing the required link ports from 16 to 4 and from 16 to 6, respectively, corresponding to optimization gains of up to 82% and 70% versus dense baseline plans. Definitions and a measurement plan for packet delivery ratio (PDR), one-way latency, throughput, and energy per delivered sample are included; detailed long-term numerical results for these metrics are left for future work, since the present implementation was validated through short-term acceptance tests. Full article

This study presents an energy-efficient, room-temperature synthesis and characterization of methacrylated pullulan (Pull-MA) hydrogel developed for controlled nutrient delivery in agricultural applications. Fourier Transform Infrared Spectroscopy (FTIR) and Differential Scanning Calorimetry (DSC) analyses confirmed the successful functionalization of pullulan with methacrylate groups, accompanied by a decrease in thermal transition temperatures, indicative of increased polymer chain mobility. The synthesized Pull-MA hydrogel exhibited a high swelling capacity, reaching an equilibrium swelling ratio of 1068% within 5 h, demonstrating its suitability as a carrier matrix. The room-temperature synthesis approach enabled the in situ incorporation of microbial inoculant into the hydrogel network, preserving microbial viability and activity. SEM analysis performed under the different magnifications (1000, 2500, 5000, 10,000, 25,000×) has confirmed brittle nature of xerogels and increasing in structural irregularities with increasing in cultivation broth content.The biological performance of the fertilizer-loaded hydrogels was evaluated through seed germination assays using maize and pepper as model crops. The optimized formulation, T2 (Pull-MA: cultivation broth 1:5 w/w), significantly improved germination efficiency, as evidenced by increased relative seed germination (RSG), root growth rate (RRG), and germination index (GI) compared to both the control and the low-fertilizer formulation (T1, 1:2.5 w/w). These findings highlight the potential of Pull-MA hydrogels as bioactive seed-coating materials that enhance early seedling development through controlled nutrient release. The results lay a solid foundation for further optimization and future application of this system under real field conditions. Full article

As an integrated framework linking resource use and environmental sustainability, the WEF (Water–Energy–Food) system plays a vital role in achieving sustainable agricultural development. Focusing on the irrigation districts in the lower reaches of the Yellow River, this study constructed and applied a Super-Undesirable-SBM (super-efficiency undesirable slacks-based measure) model and a GTWR (geographically and temporally weighted regression) model from a WEF perspective to systematically analyze the spatiotemporal evolution and driving mechanisms of WEFSE (Water–Energy–Food Synergistic Efficiency) from 2000 to 2020. The overall WEFSE exhibited a continuous upward trend, with the spatial pattern gradually shifting from the southwest to the northeast and regional disparities becoming more pronounced. The efficiency demonstrated a significant positive spatial autocorrelation, indicating a stable clustering pattern of “high–high” and “low–low” efficiency areas. In terms of driving mechanisms, WEFSE evolved from being dominated by socio-economic drivers to a composite system jointly influenced by ecological and structural factors. Among these, PD (population density) and WP (proportion of water area) had increasingly positive effects, whereas PRE (precipitation) and NDVI (normalized difference vegetation index) imposed notable constraints. Meanwhile, PCL (proportion of cultivated land), GP (proportion of grassland), and AT (average temperature) exhibited significant spatial differentiation. This study highlights that the assessment of WEFSE and identification of its driving mechanisms using the Super-Undesirable-SBM and GTWR models can help to uncover the spatiotemporal dynamics of agricultural resource utilization, providing methodological support and decision-making insights for optimizing resource allocation and promoting sustainable development in the Yellow River irrigation districts and other complex agricultural systems. Full article

In recent years, the rapidly changing environment and climate have emphasized the need for sustainable development, particularly in the agricultural sector. Tractors are the most widely used machines in agriculture, making their energy efficiency crucial not only for environmental protection but also for reducing farming costs and enhancing economic sustainability. This study applies Yeo–Johnson data transformation to normalize the discretized data of 111 tractor models, enabling the classification of agricultural tractors based on energy efficiency. Tractors were categorized into five classes according to energy efficiency, and the upper limit of each class was used to quantify the rate of improvement in energy efficiency. Furthermore, a comparative analysis between the classification model from 2006 to 2010 and that from 2016 to 2020 demonstrated that the latter exhibits superior energy consumption efficiency. Specifically, the 2016–2020 model showed an improvement in energy efficiency ranging from approximately 20.57% to 54.86% across all power categories, with higher-rated power tractors achieving greater improvements. This comparison confirms that the energy efficiency of tractors in the latest classification model is further improved, reflecting the substantial technological advancements made over the past decade. Full article