Conference Agenda

The circadian clock is a fundamental biological oscillator regulating plant physiology and productivity. Because circadian oscillators exist in almost all plant cells and synchronize with neighboring cells, they collectively respond to environmental signals such as light and temperature. Based on this property, Chronoculture—an approach that optimizes plant production by controlling circadian rhythms—has recently attracted increasing attention in plant factory research. However, detecting ultra-weak environmental signals affecting circadian systems remains challenging because cellular oscillators are strongly influenced by internal noise.

In this study, we propose a method to detect ultra-weak environmental stimuli by utilizing the extreme desynchronization state of circadian clock cell populations. When oscillators are driven into a highly desynchronized condition, known as a singularity state, the collective system becomes highly sensitive to small perturbations. We developed a theoretical framework describing the response of desynchronized oscillator populations to weak signals and validated the concept through numerical simulations and biological experiments using Arabidopsis thaliana CCA1::LUC as a circadian reporter.

The results demonstrate that environmental fluctuations as small as one-tenth of the conventional stimulus intensity can be detected. This approach provides a new method for identifying subtle environmental fluctuations in plant factories and contributes to improved circadian-based plant production modeling.

In response to the global trend toward net-zero carbon emissions, Taiwan’s Agriculture and Food Agency (AFA) has focused on multiple cylinder diesel engine agricultural machinery with the highest diesel fuel usage as the main target for exhaust emission monitoring. In 2022, AFA set the opacity standard for black smoke emissions as 1.0 m⁻¹, and this standard was further tightened to 0.6 m⁻¹ starting in 2023. The Taiwan Agricultural Mechanization Research and Development Center, which was commissioned by the AFA, conducted exhaust emission tests on 67 tractors, and the posting in a noncompliance rate of 28.4% in 2022. In 2023, 114 tractors were tested, and the posting a noncompliance rate of 2.6% was observed. And from 2024 to 2025, 115 tractors were tested, and 1% didn’t meet the requirements, these average opacity of exhaust emissions is 0.16 m^-1. Furthermore, Comparative measurements of fuel consumption rates for two tractor models of the same brand and engine displacement but compliant with different European Union emission standards showed that EU stage Ⅳ tractors exhibited 15% higher fuel efficiency than EU stage Ⅲ tractors. Tractors designed and manufactured under more stringent emission standards demonstrate improved engine efficiency, thereby contributing to a reduction in annual CO₂e.

The Habitat Suitability Index (HSI) quantifies fish responses to environmental variables such as depth, temperature, and velocity, yet conventional estimation often relies on subjective curve selection, limiting reproducibility. We developed a Python- and Streamlit-based automated HSI estimation program using long-term national fish monitoring data (2011–2024) from the National Institute of Environmental Research (NIER). Only fish collected at depths ≤80 cm were included to ensure ecological consistency. The program integrates IFASG-based distribution normalization, automatic fitting of seven nonlinear models, and objective evaluation using R2, Pearson, and Spearman metrics. It also allows users to construct customized HSI curves interactively. Application to the Namhan and Bukhan River basins revealed clear inter-basin differences in habitat responses. The framework improves reproducibility and supports ecological flow assessment and basin-scale habitat management.

Fuel consumption during mechanised operations is a key indicator of agricultural machinery efficiency and is strongly influenced by the selected field operations and crop establishment itinerary. This study was conducted at the INIAV Elvas on a 4-hectare field using tractor telemetry to analyse fuel consumption, working speed, engine speed, and torque. These parameters were evaluated together with effective field capacity and field energy consumption for three technical itineraries for winter annual crops: no-till (NT), minimum tillage (MT), and conventional tillage (CT). A New Holland T5.120 tractor equipped with ISOBUS technology and automated guidance ensured consistent operation and eliminated overlaps across plots. Results demonstrated operational and energy advantage for NT. Effective field capacity reached 17.8 ha h⁻¹ during fertilisation and 8.2 ha h⁻¹ during spraying, while MT chisel operations were limited to 1.6 ha h⁻¹. NT improved operational timeliness by requiring fewer passes (three versus five in MT). Energy consumption and CO₂ emissions were lowest in NT (679.91 MJ ha⁻¹; 49.3 kg CO₂ ha⁻¹), followed by MT (1776.86 MJ ha⁻¹; 128.83 kg CO₂ ha⁻¹) and CT (3206.60 MJ ha⁻¹; 232.49 kg CO₂ ha⁻¹). Operational costs followed the same trend. These results support reduced tillage adoption to improve energy efficiency and sustainability.

Spraying is a critical technological operation for enhancing harvest and quality in orchards. Consequently, autonomous systems, such as Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs), are increasingly integrated into orchard management to optimize economic performance, reduce energy consumption, and mitigate environmental impacts. This study aims to evaluate the shifts in energy and environmental indicators across different spraying technologies.

Three different spraying systems were assessed: a conventional tractor-mounted air-blast sprayer, an autonomous UGV, and three UAV models. The energy and environmental indicators were analyzed using LCA methodology to quantify the Energy Input Flow and Global Warming Potential (GWP), with 1 ha of orchard treatment as the functional unit.

Energy assessment revealed the tractor sprayer as the most intensive (117 MJ/ha). The UGV reduced demand to 87 MJ/ha, while UAVs achieved a 4–6 fold reduction. Environmental assessment yielded unexpected results: the UGV and the conventional tractor-mounted sprayer had the highest GWP (≈10 kg CO₂ eq/ha). Conversely, UAV technology showed a significant advantage with GWP values about 4 kg CO₂ eq/ha.

Although all autonomous platforms reduce energy demand, UAVs provide the most significant environmental benefits. The results suggest that UAVs currently offer the highest efficiency for sustainable orchard spraying operations.

Climate change intensifies heat stress, posing significant risks to dairy systems. This study assesses its projected impacts on animal welfare and milk production in Goiás, Brazil, a key region for the national dairy industry. Downscaled climate projection data from the Eta model were used for short- (2021-2040), medium- (2041-2060), and long-term (2061-2080) horizons under RCP4.5 and RCP8.5 scenarios. Heat stress conditions were assessed using the Temperature-Humidity Index (THI). The corresponding impact on milk yield was calculated using the Milk Production Decline (MPD) index for both moderate-producing (25kg.cow−1.day−1) and high-producing (40kg.cow−1.day−1) cows. Future changes in dairy cattle welfare were evaluated by calculating the number of days with extreme THI (>78). The results indicate a significant increase in THI in all future scenarios. Heat stress intensification is most pronounced during spring and summer, with projections under RCP8.5 indicating near-continuous exposure to severe heat stress in some regions by the end of the century. These climatic changes translate into substantial milk production losses, particularly in high-producing herds, with projected declines reaching up to 10 kg·cow⁻¹·day⁻¹ by 2061–2080 under RCP8.5. These results reinforce the importance of assessing the impacts of climate change on animal welfare and milk production.

Climate change poses a major challenge to dairy production due to cattle susceptibility to heat stress, which is influenced by breed composition. Girolando cattle, developed in Brazil from Holstein (H) and Gir (G) crosses, are widely used in tropical systems for their balance of productivity and adaptability. This study assessed the impacts of climate change on heat stress for six Girolando compositions (1/4H, 3/8H, 1/2H, 5/8H, 3/4H, and 7/8H) and pure Holstein cattle in Goiás State, Brazil. Downscaled climate projections from the Eta regional model were analyzed for short-, medium-, and long-term periods under RCP4.5 and RCP8.5 scenarios. Heat stress was evaluated using the Temperature–Humidity Index (THI), and the annual number of days under severe heat stress (ndaysTHI) was calculated using breed-specific thresholds. Historical results showed the highest ndaysTHI for Holstein cattle and the lowest for Girolando 1/4H. Future projections indicate substantial increases in ndaysTHI across all scenarios. By the end of the century, Holstein cattle are expected to become unviable in parts of Goiás, while Girolando cattle with lower Holstein proportions consistently show greater thermal tolerance. These results highlight genetic improvement as a key adaptation strategy, although additional mitigation measures will be necessary under future climate conditions.

Agrivoltaics systems can modify microclimate and may benefit crops in Mediterranean environments more exposed to heat and water stress. This study quantified photovoltaic (PV)-induced microclimatic changes and assessed growth and yield of rosemary, sage, and thyme in central Italy, comparing plots beneath PV modules (test) with open-field plots (control). Microclimate was monitored for one year at 15-min intervals and aggregated into bimonthly windows aligned with growth surveys, with indicators evaluated during the most critical seasonal periods. Metrics included mean air and soil temperature, soil water content, degree-hours above 30 °C (heat load) and below 5 °C (cold exposure), and condensation hours. Compared with the control, the test area showed lower mean air temperature (−0.12 to −0.32 °C) and cooler soil (−0.49 to −2.41 °C). Summer heat load decreased in the warmest windows, and condensation hours were consistently lower. Plants under PV were more developed: plant volume increased from 0.1085 to 0.1750 m³ (Δ=+0.0665; T/C=1.61). At harvest, total fresh biomass increased by +3.11 kg (RD=0.47; RD=(T−C)/C), whereas dry biomass increased modestly (+0.26 kg; RD=0.075). Overall, PV-driven microclimate moderation promoted vegetative development and increased fresh biomass of aromatic species, supporting further investigation of seasonal drivers and product quality.

The objective was to apply multivariate analysis methods to identify factors influencing CO₂ emissions by tractors under three soil tillage systems. The experiment was conducted in Jaboticabal, SP (FCAV-UNESP), with three treatments (Conventional Tillage, Rotary Hoe, and Reduced Tillage with Chisel Plow) and 15 replications. The following variables were evaluated: tractor slippage; Soil Mechanical Resistance to Penetration (SMRP) in the 0–30 cm layers; fuel consumption (L h⁻¹) and subsequently converted to CO₂ emissions (kg h⁻¹). Data was analyzed using Principal Component Analysis (PCA) and path analysis. PCA results indicated in conventional tillage, CO₂ h⁻¹ was more associated with slippage; in chisel plowing, CO₂ h⁻¹ varied inversely with slippage; and in rotary hoe tillage, the multivariate association between CO₂ h⁻¹ and the studied variables was less evident. In path analysis, the total R² of CO₂ emis-sions (kg h⁻¹) was 0.464 (conventional), 0.426 (chisel plow), and 0.079 (ro-tary hoe). In the chisel plow treatment, the variable with the greatest direct influence on CO₂ h⁻¹ was slippage (R²=0.21). In conventional tillage, SMRP had the greatest direct contribution to CO₂ h⁻¹ (R²=0.38). In rotary hoe tillage, direct contributions were low, indicating that CO₂ h⁻¹ emissions were more dependent on other factors.

There are no standardized regulatory procedures for the use of urease inhibitors (UI) in livestock farming. In order for a realistic assessment of potential risks to be made, it is necessary to collect reliable exposure data that can be integrated into a risk assessment. Within this context, a method for recording dermal and inhalation exposure during the application of UI was developed and applied in an animal-specific approach. The method was first established in cattle farming and is based on fluorometry. Pyranine is applied in the same way as UI using the respective application technique. Dermal exposure is measured using Tyvek collectors on a life-size model animal, while inhalation exposure is measured using an aerosol collection pump that simulates the animal's specific breathing volume. The dosage quantity and application technique also vary between animal species, making it necessary to collect exposure data individually. Investigations in cattle farming have shown that the method is suitable for measuring exposure in a repeatable and reproducible way. The exposure data has already been integrated into a risk assessment that classifies the use of UI as safe. A method adaptation for pig farming has been carried out. The results also confirm good reproducibility.

Changes in snowfall patterns due to climate change directly affect the structural safety of agricultural greenhouses. The current Korean greenhouse design standard (KDS 41 12 00) applies a fixed snow density of 100 kg/m³ for snow depths less than 50 cm, failing to reflect actual meteorological variability. This study projects changes in snow depth, effective density, and snow load through the end of the 21st century using SSP climate scenarios, and evaluates the adequacy of the current design standard. Results show that the national average annual maximum snow depth decreases by 57% (SSP2-4.5) to 78% (SSP5-8.5) by the far future. However, effective density in the near future ranges from 119 to 132 kg/m³, exceeding the design standard by 19–32%, likely due to increased wet snow occurrence near 0°C. Although snow load decreases overall as depth reduction outweighs density increase, the persistent exceedance of the design density through mid-century suggests that the current depth-based load calculation underestimates density effects, necessitating a revision of snow density criteria to incorporate climate change projections.

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2025-25410054).

Composting dynamics, including temperature, oxygen, moisture, and gas emissions, vary significantly depending on feedstock characteristics and operational conditions. This study aimed to develop a composting pattern predictive model for composting process dynamics using a curated dataset compiled from literature sources, which were screened, digitized, normalized, and curated to construct representative multivariate composting patterns. The final reference pattern was subsequently compared with our pilot-scale composting experiments. The results showed strong pattern similarities for CO₂ (r = 0.878), moisture (r = 0.868), NH₃ (r = 0.864), and CH₄ (r = 0.770), indicating that the experimental gas-emission and moisture trajectories were broadly consistent with the literature-derived composting pattern. Further research will focus on improving these models through machine learning from data collected using a pilot-scale composting system with a working volume of 4 m³ equipped with real-time sensing of temperature, humidity, oxygen, and major gas emissions. The predicted results will be compared with measured data to evaluate model accuracy and applicability. Ultimately, this study seeks to establish the foundation for an intelligent composting management system that integrates real-time monitoring, predictive modeling, and operational control strategies for aeration and mixing to improve process stability and environmental performance.

This study evaluates the socio-economic and environmental impacts of the SPADE concept, a multi-drone platform for precision agriculture, forestry, and livestock management, and its contribution to the United Nations Sustainable Development Goals (SDGs). The objective is to assess whether SPADE provides measurable sustainability and economic advantages over conventional practices.

An integrated assessment framework combining Life Cycle Assessment (LCA), economic cost analysis, and multi-criteria evaluation was applied to three pilot scenarios: crop monitoring in Spain, forestry operations in Norway, and livestock grazing in Greece. Environmental impacts were assessed using cradle-to-grave LCA, focusing on greenhouse gas emissions, energy use, water consumption, and soil impact, while economic performance was evaluated through total cost of ownership comparisons.

Results show that SPADE operations exhibit low operational emissions, typically below 1–10 kg CO₂e per hectare per year, with negligible soil disturbance and no direct water use. Despite manufacturing dominating life-cycle impacts, SPADE demonstrates favourable environmental performance compared to fuel-based ground methods and competitive or lower total cost of ownership over a four-year lifespan.

The study concludes that SPADE offers clear environmental and economic benefits and represents a scalable, sustainable solution aligned with multiple SDGs.

Digital Agriculture can improve efficiency, production quality and environmental performance, but its use remains uneven especially for viticulture where structural and economic barriers hinder its implementation. In this context, the Winery Farming 4.0 project, funded by the Italian PRIN 2022 programme, aimed at supporting digital solutions in viticulture through integrated assessment and decision-support tools. The project combined surveys on farm digitalisation needs, screening of precision and smart technologies and the development of a modular Sustainability Assessment Tool integrating Life Cycle Assessment, Life Cycle Costing and Social Life Cycle Assessment within a DEXi framework validated on three variable-rate case studies: smartphone-supported fungicide application, tractor-mounted sensor-based fungicide application, and sensor-supported variable-rate fertilisation. The results showed that variable-rate precision viticulture technologies led to a reduction in environmental impacts compared to conventional uniform-rate management across all impact categories, where benefits are particularly significant in freshwater ecotoxicity that decreases by 33.87% in the smartphone-supported plant protection products applications, by 50.21% in the sensor-supported variable-rate fertilisation while human toxicity non-cancer shows a reduction of 42.22% in the tractor-mounted sensor-based plant protection product application. In the case of variable-rate fertilisation, a reduction in climate change impact of 49 kg CO₂ eq per ton of grapes is recorded.

While research on ISO 11783(ISOBUS) standardization concerning the interoperability of heterogeneous equipment in variable rate application (VRA) systems has advanced substantially, a technical lacuna persists in the seamless integration of these standards with practical prescription map generation and real-time monitoring. This paper provides a critical review of contemporary technical trends based on the data flow across the comprehensive lifecycle of spraying operations. Specifically, it initially analyzes the design and management of VRA prescription maps generated by Farm Management Information Systems (FMIS) within the ISO-XML data structure under the ISO 11783 framework. Subsequently, the study investigates the algorithmic mechanisms through which task data is transmitted to the implement controller for interpretation and execution as real-time operational commands. Finally, it synthesizes monitoring systems that leverage feedback data gathered via node communication during spraying operations, alongside frameworks for the quantitative assessment of application accuracy. This review identifies technical bottlenecks from standardized data generation to post-operational analysis, aiming to delineate a roadmap for next-generation, integrated tractor-implement monitoring frameworks.

Pollinator decline and labor shortages have increased the need for automated pollination technologies in apple production. This study proposes a vision-based approach for estimating three-dimensional (3D) flower directional information. Apple flowers are detected using a YOLOv8 segmentation model and categorized into front-view and side-view groups to simplify orientation analysis. An RGB-D camera is used to acquire depth information, and a 3D direction vector is generated using morphological and spatial features of the detected flowers. The generated direction vector represents the spatial orientation of each flower and can be utilized for generating robot approach directions and pollination targets in future robotic pollination systems. The proposed method was validated using apple trees in an outdoor environment under natural lighting conditions. Experimental observations demonstrated the feasibility of estimating directional information for both front-view and side-view flowers. The proposed framework generates 3D flower directional information using only simple front-view and side-view classification rather than complex multi-class orientation recognition. This approach provides a foundation for orientation-aware task planning in robotic pollination systems.

Plant-based protein value chains increasingly require carbon-footprint information that is generated from structured operational data rather than generic emission factors alone. Developed within VALPRO Path, this work presents an integrated digital workflow that connects lentil production data, greenhouse-gas analytics and QR-based traceability for downstream food applications. Commer-cial lentil fields in Italy were monitored through an FMIS environment integrat-ing field boundaries, crop calendars, machinery data, input records, transport ac-tivities, yield data and protein-related information. These heterogeneous data streams were synchronized and processed through a GHG calculation module based on agricultural carbon-accounting principles, enabling the estimation of emissions at field level and across various functional units including kg CO₂e kg⁻¹ protein. The module also preserved links between calculated indicators and their underlying operational records, supporting traceability, auditability and di-agnostic analysis of emission sources. The resulting outputs were connected to a QR-enabled digital interface for plant-based products packaging, allowing se-lected carbon-footprint information to be communicated at product level while maintaining privacy protection for sensitive farm data. The proposed workflow demonstrates how FMIS-based data integration, GHG analytics and digital trace-ability can support supplier-specific Scope 3 evidence and more transparent plant-based protein value chains.