6th Global Conference on Agriculture

Greenhouse production in hot-arid regions is constrained by the high energy and water demands of cooling, and by the limitations of the fan-and-pad evaporative system, which raises indoor humidity and produces uneven temperature distribution. This study presents a validated multi-scale computational fluid dynamics (CFD) model of a fan–chiller–tube-bank (FCTB) cooling system, in which air is cooled by sensible heat exchange with chilled water flowing over a dense array of plastic tubes, without adding moisture. A detailed tube-bank sub-model was solved in ANSYS Fluent using the standard k–ε turbulence model with conjugate heat transfer, and validated against field measurements; it reproduced the measured outlet air and water temperatures to within approximately 1°C. The validated outlet condition was then used as the inlet boundary for a full three-dimensional greenhouse model that incorporated solar heating via the Solar Load model. A mesh independence study confirmed convergence at 1.2 million cells, where the predicted outlet velocity changed by less than 1% relative to the finest mesh. The results show that the FCTB system maintains a nearly uniform air temperature of about 21°C in the cultivation zone, with a spatial variation of less than 2°C over the 6 m greenhouse length, while the polycarbonate cover reaches nearly 49°C under solar load. The air velocity in the occupied zone remains low (about 0.2 m s⁻¹), which is favourable for the crop. Psychrometric analysis confirms that the absolute humidity is almost unchanged between inlet and outlet (about 8.5 g of water per kg of dry air), demonstrating that the FCTB system delivers sensible cooling without humidification, a key advantage over the fan-and-pad system. The validated model provides a reliable basis for the optimisation and scale-up of low-humidity cooling for sustainable greenhouse production in hot-arid climates.

Reliable livestock records underpin farm efficiency, biosecurity traceability, and sustainability reporting. Such records are hard to maintain: grazing livestock are managed in mobs (herds and flocks), groups whose size and composition change continually. Stock entries and exits drive part of this change; management practices drive the rest, as limited paddock sizes force splitting and merging even when numbers are unchanged. We study a farm system of record whose source of truth is an event-sourced log of relative changes (deltas) punctuated by occasional absolute recounts; mob state is derived, not stored. Reconstructing a mob’s history from this log is required to answer basic questions such as how many animals it held on a given day or how many were treated before joining it. Reconstruction is difficult because records often arrive out of order and editing a past record may alter all following values. We present a small algebra of atomic operations that supports both relative and absolute changes and builds a queryable graph of a livestock population’s history one event at a time. Each operation maps a valid graph to a valid graph and has an exact inverse, so the graph stays correct and queryable after every event and every edit is reversible. Each operation also enforces two levels of correctness: structural integrity and head-count conservation. Inconsistent data therefore produces a specific, locatable rule violation instead of a silently wrong answer. Applied to commercial farm data, the algebra reconstructs mob histories and surfaces data-entry errors.

The objective of this study was to assess the performance of tall (traditional) and short-statured corn hybrids under a range of plant population densities and various N levels. Field trials were conducted in 2025 at 2 experimental locations in Virginia: Blacksburg (BB), and Mt. Holly (MH). Twelve treatments (combination of 4 plant population levels x 3 N rates) were replicated 4 times at each location. A4861 hybrid (TC) was evaluated at 25,000 (Low=LP), 28,000 (Medium=MP), 32,000 (High=HP), and 36,000 (Very High = VHP) plants per acre. PR116-20RIB hybrid (SC) – at 28,000 (LP), 32,000 (MP), 36,000 (HP), and 40,000 (VHP) plants per acre. Performance of both hybrids was assessed at 3 N levels: check (no additional N applied = Low (LN), 30 lb N/a = Medium (MN), and 60 lb N/a = High (HN). Nitrogen was side-dressed as liquid urea ammonium nitrate (UAN; 28-0-0). Corn yields ranged from 145 to 319 bu/a. Average yields were higher at BB (285 bu/a) compared to MH (211 bu/a). Yields for TC were higher compared to SC at both locations: on average at BB, TC yielded 23 bu/a more, and at MH – 48 bu/a more than SC. Lower yields were obtained with LN for both locations, except for SC at MH (lowest yield observed with HP+HN). At both locations, highest yields were observed for VHP+HN treatment, except for TC at BB (higher yield was obtained with LP+HN). Yields of TC were more stable across locations, growing conditions and treatments. Research will be continued over multiple locations and years to develop grower recommendations.

The objective of the study was to assess the potential benefits of bundling traditional N fertilizer with compost tea application for performance of wheat and corn under various N levels. Field trials were conducted in 2025 at 3 experimental locations in Virginia: Blacksburg (BB), Blackstone (BS), and Orange (OR). Nitrogen was split-applied: between preplant and top-dress as urea ammonium nitrate (UAN; 28-0-0) to achieve 50%, 75%, and 100% of recommended N. The effects of 75 and 100% N treatments were evaluated with and without compost tea - Super Compost Tea (Gardenersbud, Towanda, KS) foliar-applied at a rate of 5 gal/ac. Wheat yields ranged from 60 to 114 bu/a. No significant differences in wheat yield associated with treatments were observed at any of the locations. At BB, the highest N rate + compost tea resulted in the lowest wheat yield. At BS and OR, the medium N rate without compost tea treatment yielded the lowest. At all locations, the highest N rate without compost tea resulted in the highest wheat yields. No apparent wheat yield benefit was noted for bundling N with compost tea at any location. Corn yields ranged from 152 to 258 bu/a. Significant differences in corn yield were observed at BS, but not at BB or OR. The highest corn yields were obtained with the highest N rate + compost tea at both BS and OR. At BB, the highest corn yield was achieved with medium N rate without compost tea. No apparent corn yield benefit was noted for bundling N with compost tea at BB. Corn yields were improved numerically by bundling the highest N rate + compost tea. Analysis of multiple supplementary data is currently being carried out to deepen our understanding of treatment effects.

This paper presents AgroCraft 2040, an architecture for autonomous smart agriculture built around three ideas that rarely appear together in the same system: neuro-inspired edge inference, a decentralised decision layer based on partially-observable Markov models, and a mixed fleet of drones and autonomous ground vehicles (AGVs). Most existing proposals treat these as separate research threads. Here we put them into a single control loop, described as a Dec-POMDP, and we specify the loop with two algorithms: one that runs on each agent and covers perception through to action, and a second that handles task allocation over a shared map of stress events. To check whether the design is actually workable, we also built a small gridworld simulator and compared the proposed allocation against a random baseline for fleet sizes of one to eight agents. The coordinated policy finished every run, was between 13 and 18 times faster in steps-to-completion, and used roughly 15 times less cumulative movement at the larger fleet sizes. These numbers are preliminary but they do suggest the architecture is worth developing further, both in richer simulation and, eventually, in the field.

This study examines the impact of hosting IDPs on household food security outcomes in conflict-affected regions. To achieve this objective, Propensity Score Matching (PSM) was employed to estimate the causal effect of hosting IDPs on household food security. The results showed that after controlling for potential confounders, households hosting IDPs were significantly more likely to experience food insecurity compared to non-host households. On average, host households experienced severe food insecurity, which was 32.2% higher than non-host. Their reliance on negative coping strategies (CSI) was also 24% higher than compared to non-host households. To assess the robustness, Ordinary Least Squares (OLS) and binary logit models were estimated by using continuous and binary outcome measures, which conformed to a statistically significant treatment effect on household food security. Higher household income and livestock ownership were found to be protective against severe food insecurity, whereas demographic characteristics such as higher dependency ratios, older household heads, and female-headed households increased vulnerability. Overall, these findings suggest that there is a high prevalence of severe food insecurity observed among host households. Future research should explore the long run effect of hosting internal displacement on food security outcomes.

The physicochemical quality of sugar-sweetened carbonated beverages may deteriorate during storage, particularly when products are exposed to elevated temperatures throughout distribution and retailing. This study evaluated the effects of storage temperature and duration on the physicochemical stability of a commercial sugar-sweetened carbonated beverage, Pepsi Regular. Samples were stored at 4 °C, room temperature, and 45 °C for 35 days. Physicochemical analyses were performed periodically to determine pH, titratable acidity, dissolved carbon dioxide (CO₂), and total soluble solids expressed as °Brix. Data were evaluated using multifactor analysis of variance followed by Tukey’s post-hoc test, with statistical significance established at p < 0.05. Storage temperature and duration significantly affected the investigated parameters. The pH remained within 2.50 –2.72, although significant variations were observed among storage conditions. Titratable acidity showed the greatest deterioration at 45 °C, decreasing from 11.50 to 10.10 g/L. At room temperature, it declined from 11.90 to 11.00 g/L, whereas refrigerated samples showed only a limited reduction from 11.90 to 11.73 g/L. Dissolved CO₂ progressively decreased under all conditions, with the greatest loss at 45 °C and the lowest loss at 4 °C. °Brix exhibited comparatively limited variations, indicating greater stability of soluble solids than carbonation and acidity. These findings demonstrate that elevated temperature accelerates physicochemical deterioration, while refrigeration better preserves acidity, carbonation, and overall beverage quality.

Maize (Zea mays L.) is a representative C4 crop in which carbon assimilation through the Calvin cycle mainly occurs in bundle sheath cells (BSCs). Developing precise regulatory elements that enable cell-type-specific gene expression is essential for understanding C4 photosynthetic regulation and advancing crop genetic engineering. However, systematic identification and functional characterization of BSC-associated regulatory sequences remain limited. In this study, we established an integrated framework combining single-cell transcriptomics, cis-regulatory element analysis, and quantitative functional assays to identify and evaluate BSC-associated regulatory sequences. Ten upstream regulatory regions of reported BSC marker genes were cloned and characterized using dual-luciferase assays in maize protoplasts. The results revealed distinct transcriptional activities among candidate regions, with PCK1 and SHBP1 showing strong activation capacity, while GUS-based tissue analysis demonstrated that PRK2 exhibited highly specific expression in BSCs. Furthermore, integration of three published leaf single-cell transcriptomic datasets identified 81 conserved candidate genes. Developmental expression analysis and transient reporter assays further confirmed several candidates with strong and stable transcriptional activity. In addition, conserved cis-regulatory motifs and transcription factor binding patterns were analyzed to establish a weighted scoring model, enabling the prediction of high-confidence BSC-associated candidates. This study provides a comprehensive strategy for discovering cell-type-specific regulatory elements and offers valuable genetic resources for improving photosynthetic efficiency through plant synthetic biology approaches.

A fundamental element shaping biodiversity in agriculture is the crop structure on farms. A varied and even crop structure (a large number of different crops, covering similar areas) positively influences the diversity and abundance of fauna and flora species in a given area, in contrast to a simplified cropping structure, where, in extreme cases, only one (monoculture) or several species are grown in a given area (often for several years). Crop diversification involves maintaining an appropriate number and area of crops on arable land on a farm in a given year. The main goal of the research is to identify and assess trends in crop structure changes in Poland (in 2020-2025) and analyze their spatial diversification at the local (municipal) level. The analysis is based on detailed statistical data on 327 crop species. Advanced geographic information system (GIS) tools and spatial statistics methods were used, including local spatial autocorrelation indices (LISA) and diversity indices (Simpson). Areas of strong specialization (primarily in industrial and cereal crops) and "enclaves of diversity" related to horticulture and niche crops were identified. Particular attention was paid to the most important crops constituting the core of agricultural land in Poland (permanent grasslands, winter wheat, grain maize, winter rapeseed, and winter triticale). The high spatial resolution of the research, combined with the wide spectrum of species analyzed, allows for precise monitoring of agroecosystem transformation. The results are crucial for shaping local agricultural support policies, planning adaptation measures to climate change, and protecting agrobiodiversity in the context of the European Green Deal.