Plant Strategies Research Articles

Effects of Plant Extracts on Growth Promotion, Antioxidant Enzymes, and Secondary Metabolites in Rice (Oryza sativa) Plants

Plant extracts are widely used in sustainable agriculture practices to enhance crop production and reduce chemical usage in agriculture. This study employed several extraction solutions of various plant extracts to synthesize planting and spraying strategies, assess the persistence efficacy of rice, and investigate the influence of selected water extracts on secondary chemicals at different rice planting stages. Among 17 water extracts that were evaluated on rice seeds, 7 were enhanced to align with the lengths of rice roots 50–70% and shoots 40–50%. The analysis of extraction, spraying, and planting experiments revealed that water extracts, soil application, and transplanting were the most efficient methods for stimulating rice growth, especially 0.1 and 0.5% concentrations. The efficacy of the extracts remained intact also after 14 days of treatment. This study showed that photosynthesis and antioxidant activities may play crucial roles in plant growth. Rice growth stimulation has been linked to photosynthesis, flavonoid contents, and antioxidant enzymes, providing a balanced supply of nutrients for plant growth. Among all tested water extracts, Psidium guajava, Aloe vera, Allium sativum, and Medicago sativa extracts can be used to promote plant growth in organic farming.

Odor emission pattern of the waste storage workshop of kitchen waste treatment plant and control strategy study with CFD simulation.

Odor emission has become a great issue for kitchen waste management plants. Among all, unorganized emission source such as waste storage tank is the key cause. It is necessary to understand the odor emission characteristics and provide a proper control solution. In this study, a typical kitchen waste treatment plant located in Guangdong Province of China was selected to investigate the odor emission characteristics. According to the survey, the main complaint due to odor emission is on waste storage workshop. Hence, its odor emission has been investigated in this study. The gas samples were collected from the workshop in different season. According to the results, the odor emission during summer is the worst. In total, 105 odorous gases were detected from the waste storage workshop. The main odorous gases can be categorized into sulfur compounds, oxygen-containing organic compounds and terpenes. In specific, ethanol, acetic acid, methylmercaptan, α-pinene, methioether and limonene were the major odorous pollutants. Based on grey correlation, principal component analysis (PCA) and step-up regression analysis, methylmercaptan contributes the most to the odor concentration. It suggests that the odor emission control should pay more attention on methylmercaptan. The Computational Fluid Dynamics (CFD) stimulation was employed to investigate the odor distribution with applying air blowing as a curtain to separate the inside and outside atmosphere or suction to vacuum the inside air to prevent the odor emission. It was found that it could efficiently prevent odor emission by setting a 45° inclined air suction port at the top of the entrance gate. The study provides a theoretical basis on odor control for the waste storage workshop of kitchen waste management plants.

Frequency response coefficient optimal allocation strategy for multi-stacks alkaline water electrolyzer plant

Currently, power discrepancies between stacks are often overlooked when developing frequency response (FR) strategies for multi-stack alkaline water electrolyzer plants (AWEPs), which limits the full utilization of the plant’s FR potential. To address this issue, this paper introduces a stack-level FR coefficient allocation strategy aimed at minimizing post-contingency frequency deviation. Specifically, the quantitative relationship between the operating power of alkaline water electrolyzer (AWE) stacks and steady-state frequency deviation is first derived, followed by the formulation of an optimal FR problem. The solution to this problem is an FR strategy based on the principle of equal frequency deviation, which optimally allocates FR coefficients among AWE stacks according to their operating power, thereby maximizing the plant’s FR support capacity. Equivalent circuit model-based case study demonstrates that, compared to existing strategies, the proposed approach achieves a more balanced power distribution among the stacks during the FR process. This even power allocation not only reduces post-contingency frequency deviation but also enhances productivity and streamlines production management in the AWEP, thereby increasing the plant owner’s willingness to participate in FR. Specifically, the proposed strategy results in a 29.3% reduction in post-contingency frequency deviation, a 1.8% improvement in production efficiency under a 10 MW power imbalance, a 2.3% increase in hydrogen production rate under a 4 MW power imbalance, and more stable operation under a 16 MW power imbalance.

Why do flowers wilt?

Resources salvaged when flowers wilt on a perennial plant could promote reproduction by, in preference order, the same flowers (Hypothesis 1), adjacent flowers on the same plant (Hypothesis 2), or during the next flowering season by the same plant (Hypothesis 3). We tested the above hypotheses for Blandfordia grandiflora, a perennial species, where some plants included flowers that were allowed to wilt, while equivalent flowers on other plants were prevented from wilting. The abilities of these plants to produce seed were determined by liberally pollinating all flowers. To test Hypotheses 1 and 2, seed set per flower and per plant were compared between plants with and without wilting flowers. To specifically test Hypothesis 3, reproduction was prevented in all flowers. For each experiment, flowering was monitored in the same plants during the next flowering season, thus also enabling Hypothesis 3 to be tested. The results were consistent with Hypothesis 3, but not with Hypotheses 1 and 2. Hence, we verified, for the first time, that plants may benefit from salvaging resources from wilting flowers and re-using these resources for subsequent reproduction. However, contrary to expectations, plants re-used these resources to promote reproduction during subsequent flowering, and not during current flowering by either the same flowers or other flowers on the same plant. The plants must have transferred resources from wilting flowers to underground corms and roots, which provided resources necessary for subsequent flowering. This is likely part of a general plant strategy to salvage resources invested in reproduction during one flowering season and reuse these resources during subsequent flowering.

Invasion stage and competition intensity co-drive reproductive strategies of native and invasive saltmarsh plants: Evidence from field data

Biological invasion poses a significant threat to biodiversity conservation and also results in substantial economic loss including the excessive cost of management to control it. Still, its impact on plant sexual reproduction strategies remains underexplored in natural settings. We conducted a field experiment on native Phragmites australis and invasive Spartina alterniflora in Bohai Bay and assessed plant size (aboveground biomass and height) and sexual reproduction (ear biomass, reproductive allocation, etc.) in conjunction with water and soil properties. The results showed that during the early stage of invasion, the two species declined in size and sexual reproduction, with S. alterniflora showing a lesser decline than P. australis. However, in the late stage of invasion, S. alterniflora maintained its plant size by reducing its investment in sexual reproduction. Moreover, significant reproductive allometries were demonstrated by S. alterniflora under different competition intensities. P. australis displayed heightened sensitivity to water properties and soil non-resource conditions, while S. alterniflora adapted its inherent traits and environmental tolerance. S. alterniflora allocated more resources to thriving as an individual, while P. australis prioritized reproduction by increasing seed production. Overall, this study revealed the reproductive strategies that invasive and native species employ in response to competition and environmental factors, thereby offering crucial insights for conservation and management efforts.

Analysis of non-uniform grid-forming control techniques for the HVDC connection of renewable energy

Grid-forming control techniques are required to achieve a high penetration of power electronics-based renewable energy sources for decarbonized electric systems. However, different grid-forming control strategies can operate in the same grid, and grid stability shall be warranted. This paper analyses the use of different grid-forming control strategies for diode rectifier-based wind power plants. The analysed grid-forming controllers are the well-known droop control, an advanced droop control, and the virtual synchronous machine control. The controllers’ analysis validates the three grid-forming controllers’ interoperability, identifying each control parameter’s contribution to the stability of each system state variable. Furthermore, the analysis allows a better tuning of the control parameters. Additionally, a fault-ride-through strategy that improves the system restoration after faults is proposed and validated. The proposed fault-ride-through strategy achieves a soft restoration of the active power.

Impact of High-density Planting Spacing on Physiological Traits of Cocoa Grown under Coconut Trees

High-density planting (HDP) maximizes land productivity. Optimizing cocoa spacing under coconut trees enhances physiological traits and yield potential. Despite cocoa's integration into coconut agroforestry, spacing's impact on cocoa physiology is unclear. Studying this influence provides insights for better planting strategies, improved crop performance, and sustainable cocoa production in agroforestry settings. The experiment was conducted at the Coconut Farm of the Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore. Employing a Randomized Block Design (RBD) with eight treatments replicated three times, the study aimed to explore how different spacing levels influence physiological traits in cocoa cultivation. The treatments involved in the experiment included a double row of cocoa planted between two rows of coconut trees, with spacing configurations as follows: T1 (3m x 1.2m), T2 (3m x 2m), T3 (3m x 2.5m), and T4 (3m x 3m). Additionally, a single row of cocoa between two coconut rows was examined, with spacings represented by T5 (1.5m), T6 (2m), T7 (2.5m), and T8 (3m). Results indicated distinct patterns among spacing treatments, with significant differences observed in various physiological characteristics. Notably, T1 (3m x 1.2m) demonstrated the highest leaf area (462.71cm2) and leaf area index (4.85), while T8 (3m) exhibited the highest light interception (74.12%). Additionally, T3 (3m x 2.5m) showcased the highest chlorophyll index (40.52) in cocoa leaves. These findings underscore the importance of spacing configurations in influencing key physiological parameters in cocoa cultivation, providing valuable insights for optimizing planting practices.

Thermal priming of Saccharina latissima: a promising strategy to improve seaweed production and restoration in future climates

Saccharina latissima is a brown algal kelp species of ecological and economic importance. As the rise in sea surface temperature will threaten not only wild populations of S. latissima but also the productivity of kelp farms, crop enhancement techniques will become crucial to mitigate this threat. Priming is a common strategy in crop plants, in which seeds are pre-exposed to moderate stress to improve the performance and tolerance of plants when exposed to harsher conditions. We investigated the potential of thermal priming to improve growth and tolerance of S. latissima. Kelp gametophytes primed at 20°C for 2, 4 and 6 wk and then re-transferred to 5°C were compared to a naïve treatment maintained at 5°C. Gametophyte priming increased growth of subsequently formed sporophytes by up to 30% (for 4 wk priming) compared to the naïve treatment. Female gametophyte growth in the priming environment was positively correlated to offspring sporophyte growth, indicating a maternal effect. Sporophytes were exposed to heat stress of 20°, 22°, 23° and 24°C for 2 wk. Sporophytes from 4 and 6 wk primed gametophytes exhibited 11 d longer tolerance at 22°C, 7 d longer tolerance at 23°C and 1°C higher thermal tolerance over 7 d compared to naïve sporophytes and sporophytes from 2 wk priming. A priming time of 4 wk was optimal for both sporophyte growth and thermal tolerance. Our results suggest that priming is a promising crop enhancement technique that could improve yield for seaweed farmers and restoration of kelp forests threatened by warming climates.

Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants

Fossil fuel power plants are a significant contributor to global carbon dioxide (CO2) and nitrogen oxide (NOx) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the application of machine learning techniques in evaluating the emissions from fossil fuel power plants. This review first briefly introduces the continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems that are commonly used in power plants and highlights that machine learning models can significantly improve PEM systems through their capability to process and interpret large datasets intelligently to transform traditional emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency. Compared to previously published review articles, the key contribution and innovation in this present review is the discussion of machine learning models in CO2/NOx emissions according to the different algorithms used, including their advantages and disadvantages in a systematic way, which aims to help future researchers to develop more effective machine learning models. The most popular machine learning model includes reinforcement learning, a forward neural network, a long short-term memory neural network, and support vector regression. While each model method has its own advantages and disadvantages, we noted that training data quality, as well as the proper selection of model parameters, plays an important role. The challenges and research gaps, such as model transferability, a deep understanding of the physics of CO2/NOx emissions, and the availability of high-quality data for training machine learning models, are identified, and recommendations as well as potential future research directions to address these challenges are proposed and discussed.

Multi-criteria analysis of EU paper-producing companies concerning emissions trading and economic aspects

Industrial firms in the EU have had to adapt over the past decades to face the increasing pressure of becoming more environmentally responsible. The European Union Emissions Trading Scheme (EU ETS) has played a crucial role in this process, and its impact on participating firms is a matter of debate for researchers, corporate managers, and policy makers alike. Unfortunately, there are only a minimum of models that assess the impact of the EU ETS on individual firms, especially in the context of overall firm performance and dependencies on other (economic and social) factors. For the paper industry, such a study is completely lacking. The authors use the well-established framework of the CSR model and the DC-PROMETHEE method to explore the ranking of paper producing firms in the EU. DC-PROMETHEE considers the effect of the dependencies between criteria that can potentially distort the results. The study revealed that both the EU ETS itself and the links between the evaluating criteria have an impact on the final CSR ranking of the paper producing companies.

Evaluation of seed sett ages and stem segment position on sugarcane yield and yield related traits

Sugarcane (Saccharum officinarum L.) plays a vital role in tropical and sub-tropical economies due to its high sucrose content, bioenergy potential, and drought resistance. This study aimed to comprehensively investigate the effects of different seed sett (seed material) ages and variations in stem position of the seed setts on both yield and yield related traits of sugarcane cv. Khon Kaen3 (KK3). The experiment was laid out by 3×3 factorial in randomized complete block design (RCBD) with three replications. Factor A represented sugarcane seed sett ages (8, 9 and 10 months old), factor B represented different stem positions where seed setts segments were sectioned out (basal, mid and top part) and- used as planting material (clones). Agronomic management was administered at recommended rates and the growth, development and yield traits were observed. The results showed that seed sett ages influenced the number of tillers at specific growth stages, while different segments affected leaf area (LA) and stalk fresh weight. Seed sett age and position showed significant interactions for some traits. Notably, juice yield was significantly affected by both seed sett ages and segments, with the combination of 10-month-old seed setts and the middle segment yielding the highest juice. Brix value, a crucial trait for sugar production, remained unaffected by these factors. Dry matter accumulation was inconsistent, with seed sett ages impacting stalk dry weight. In conclusion, the results showed that most growth and development traits were not influenced by seed sett ages and stem position segments, but most importantly juice yield at maturity was influenced by seed sett age and position of stem segments. These findings enhance our understanding of sugarcane cultivation practices, offering valuable insights for optimizing planting strategies in the sugarcane industry.

Dynamic Pricing and Energy Management of Virtual Power Plant Based on Source-Charge Uncertainty

With the intermittent, random and volatile distributed renewable energy mass influx into the grid and power market, the power system will face greater challenges. As a small energy autonomous system that integrates distributed generation, energy storage and load, virtual power plant connects the power market and end users, and it is also an important subject of power market reform. Its operating efficiency directly determines the effectiveness of market reform. However, virtual power plant faces the market risk of double uncertainty of internal renewable energy output and load demand during operation, so it needs to optimize the market behavior to protect its own interests. Therefore, the paper takes virtual power plant as the research object, and discusses the dynamic pricing strategy of virtual power plant considering the double uncertainty of source and charge and real-time market linkage from the aspects of theoretical analysis, modeling construction and simulation analysis, to provide reference for decision pricing of virtual power plant and formulation of electricity market reform policy.

Linking fine‐root diameter across root orders with climatic, biological and edaphic factors in the Northern Hemisphere

The importance of fine‐root diameter for ecosystem functioning is increasingly recognized, yet much remains to be learned about the variation in fine‐root diameter at large scales. We conducted an analysis of fine‐root diameter for five root orders for 1163 plant species to detect patterns in relation to resource availability (e.g. carbon, nitrogen, water and net primary production (NPP)), stress intensity (e.g. plant/soil biodiversity and soil bulk density) and temperature. First‐ to fourth‐order root diameters showed non‐linear relationships with mean annual temperature (except for first‐order root diameter) and/or with latitude. The diameters of the five root orders decreased with increasing mean annual precipitation, but increased with greater NPP, which was the strongest determinant of fine‐root diameter. Increasing soil biodiversity was associated with decreasing diameters of fourth‐ and fifth‐order roots, while greater plant biodiversity was associated with decreasing diameters of first‐ to third‐order roots. Soil total nitrogen concentration had a positive effect on first‐order root diameter but a negative effect on fourth‐ and fifth‐order root diameters. The patterns reversed for soil total phosphorus concentration. First‐ to third‐order and fifth‐order root diameters increased with greater soil bulk density. Second‐ to fourth‐order root diameters increased with higher soil pH. Overall, the variables related to climatic, biological and edaphic factors explained 44–63% of the total variance in the diameters of the different root orders. The unique patterns of plasticity observed in fine‐root diameter across root orders in response to varying environmental conditions contributes to a diversification of plant strategies for nutrient/water acquisition and transport under climate change.

Specialists regulate microbial network and community assembly in subtropical seagrass sediments under differing land use conditions

Microorganisms in the sediment play a pivotal role in the functioning and stability of seagrass ecosystems and their dynamics are influenced by the nutrient acquisition strategies of host plants. While the distinct impacts of microbial generalists and specialists on community dynamics are recognized, their distribution patterns and ecological roles within seagrass ecosystems remain largely unexplored. To address this issue, we conducted an analysis of community assembly processes and co-occurrence relationships of both microbial generalists and specialists within sediment profiles (0–100 cm) from seagrass habitats subjected to differing land use conditions. The results revealed that seagrasses in Yifeng Estuary experienced the large proportion of cultivated land and exhibited higher organic carbon content in the 0–20 cm surface sediment layer. Nitrogen-cycling bacteria were predominantly associated with seagrasses from Yifeng Estuary, whereas Vibrio spp. was more prevalent in seagrasses from Liusha Bay. Notably, seagrass Halophia beccarii (YHB) in Yifeng Estuary harbored higher niche breadths for both microbial generalist and specialist compared to Halodule uninervis (LHU) and Halophia ovalis (LHO) from Liusha Bay. Stochastic processes were pivotal in shaping seagrass sediment microbial communities, with a higher immigration rate observed in YHB, suggesting greater microbial turnover in this area. Additionally, YHB sediment presented lower drift and higher dispersal limitation among generalists compared to LHU and LHO, whereas the pattern was reversed among specialists. Specialists were found to play a crucial role in shaping microbial interactions within YHB sediment, with genera Halioglobus identified as keystone species in the network. The specialists were further found to significantly influence microbial β-diversity in seagrass sediment directly. Overall, our findings illustrated how microbial generalists and specialists were distributed in seagrass sediments in response to land use changes and provided new insights into the potential roles of microbial regulation in degraded seagrass ecosystems.

Aggregation and Bidding Strategy of Virtual Power Plant

Aggregation and Bidding Strategy of Virtual Power Plant

Global patterns of plant functional traits and their relationships to climate

Plant functional traits (FTs) determine growth, reproduction and survival strategies of plants adapted to their growth environment. Exploring global geographic patterns of FTs, their covariation and their relationships to climate are necessary steps towards better-founded predictions of how global environmental change will affect ecosystem composition. We compile an extensive global dataset for 16 FTs and characterise trait-trait and trait-climate relationships separately within non-woody, woody deciduous and woody evergreen plant groups, using multivariate analysis and generalised additive models (GAMs). Among the six major FTs considered, two dominant trait dimensions—representing plant size and the leaf economics spectrum (LES) respectively—are identified within all three groups. Size traits (plant height, diaspore mass) however are generally higher in warmer climates, while LES traits (leaf mass and nitrogen per area) are higher in drier climates. Larger leaves are associated principally with warmer winters in woody evergreens, but with wetter climates in non-woody plants. GAM-simulated global patterns for all 16 FTs explain up to three-quarters of global trait variation. Global maps obtained by upscaling GAMs are broadly in agreement with iNaturalist citizen-science FT data. This analysis contributes to the foundations for global trait-based ecosystem modelling by demonstrating universal relationships between FTs and climate.

Daniel C. Laughlin: Plant Strategies—the demographic consequences of functional traits in changing environments

Daniel C. Laughlin: Plant Strategies—the demographic consequences of functional traits in changing environments

A Plant Strategy: Irrigation, Nitrogen Fertilization, and Climatic Conditions Regulated the Carbon Allocation and Yield of Oilseed Flax in Semi-Arid Area.

The injudicious use of water and fertilizer to maximize crop yield not only leads to environmental pollution, but also causes enormous economic losses. For this reason, we investigated the effect of nitrogen (N) (N0 (0), N60 (60 kg ha-1), and N120 (120 kg ha-1)) at different irrigation levels (I0 (0), I1200 (budding 600 m3 ha-1 + kernel 600 m3 ha-1), and I1800 (budding 900 m3 ha-1 + kernel 900 m3 ha-1)) on oilseed flax in the Loess Plateau of China in 2019 and 2020. The objective was to establish appropriate irrigation and fertilizer management strategies that enhance the grain yield (GY) of oilseed flax and maximize water and N productivity. The results demonstrated that irrigation and N application and their coupling effects promoted dry matter accumulation (DMA) and non-structural carbohydrate (NSC) synthesis, and increased the GY of oilseed flax. The contents of NSC in various organs of flax were closely related to grain yield and yield components. Higher NSC in stems was conducive to increased sink capacity (effective capsule number per plant (EC) and thousand kernel weight (TKW)), and the coupling of irrigation and N affected GY by promoting NSC synthesis. Higher GY was obtained by the interaction of irrigation and N fertilizer, with the increase rate ranging from 15.84% to 35.40%. Additionally, in the increased yield of oilseed flax, 39.70-78.06%, 14.49-54.11%, and -10.6-24.93% were contributed by the application of irrigation and nitrogen and the interaction of irrigation and nitrogen (I × N), respectively. Irrigation was the main factor for increasing the GY of oilseed flax. In addition, different climatic conditions changed the contribution of irrigation and N and their interaction to yield increase in oilseed flax. Drought and low temperature induced soluble sugar (SS) and starch (ST) synthesis to resist an unfavorable environment, respectively. The structural equation model showed that the key factors to increasing the GY of oilseed flax by irrigation and nitrogen fertilization were the differential increases in DMA, EC, and TKW. The increases in EC and TKW were attributed to the promotion of DMA and NSC synthesis in oilseed flax organs by irrigation, nitrogen fertilization, and their coupling effects. The I1200N60 treatment obtained higher water use efficiency (WUE) and N partial factor productivity (NPFP) due to lower actual evapotranspiration (ETa) and lower N application rate. Therefore, the strategy of 1200 m3 ha-1 irrigation and 60 kg ha-1 N application is recommended for oilseed flax in semi-arid and similar areas to achieve high grain yield and efficient use of resources.

The Size-Number Trade-Off and Resource Allocation in Saponaria officinalis Under Continuous Pollination

The size-number trade-off in plant reproduction indicates a necessary balance between producing a few large offspring versus many small ones, each with distinct survival and reproductive implications. This trade-off may be particularly important in species that reproduce continuously throughout the growing season, like Saponaria officinalis. Plants must dynamically adjust their resource allocation strategies to optimize reproductive success across multiple reproductive cycles. Although reproductive strategies in plants have been widely studied, little is known about how continuously reproducing species adjust their resource allocation in response to pollinator availability. This study seeks to clarify how Saponaria officinalis allocates resources under continuous pollination and whether this leads to a trade-off between seed number and seed size throughout the growing season. To explore this, my study used 60 plants that were located within a field of 1600 S. officinalis plants at Queen’s University Biological Station (QUBS). Plants were divided into control (30) and treatment (30) groups, with treatment plants receiving continuous pollination to simulate constant pollinator availability; and control plants receiving natural open-pollination. During my project, seed pods were categorized as empty or filled, and seeds were counted and weighed to assess reproductive output. I expected to find an increase in reproductive success in the treatment group, with steady production of larger seeds due to increased resource availability from continuous pollination. However, contrary to these expectations, there was no significant difference in seed weight between the treatment and control groups. This research contributes to a deeper understanding of the ecological and evolutionary mechanisms driving reproductive strategies in continuously reproducing species. The insights gained can inform broader ecological theories and have practical applications in conservation and agricultural management, where optimizing reproductive success under varying conditions is essential.

The Potential of Building Information Modeling in Agricultural Operations

Building Information Modeling (BIM) has been widely adopted in the construction industry, providing a virtual representation of physical objects and systems. In the context of agriculture, BIM can be applied to monitor and manage the physical and operational characteristics of farms and agricultural facilities using BIM digital twins. A BIM digital twin is a virtual representation of a physical object or system that is based on data from BIM models and other sources. The integration of sensors is essential in creating a BIM digital twin as they provide real-time data that can be used to update and maintain the virtual representation. The use of BIM digital twins in agriculture has the potential to improve the management of agricultural facilities, reduce environmental pollution, and promote the sustainability of agricultural land. Sensors can be used in agriculture to monitor various parameters such as soil moisture, temperature, and nutrient levels. BIM digital twins can also be used to monitor and manage sources of pollution, such as the use of pesticides and fertilizers, and to develop strategies to minimize their impact on the environment. BIM digital twins provide a comprehensive view of the condition of agricultural land and the factors that influence it, such as weather patterns, industry activities, and human activities. 4D BIM is an extension of traditional 3D BIM that adds a time dimension to the model. In the context of agriculture, 4D BIM can be used to visualize and simulate various processes related to time and soil management, such as planting and harvesting cycles, and to evaluate the impact of different planting strategies on crop yields. 4D BIM can also be used to model the impact of weather patterns on crop growth and to develop contingency plans for weather- related events. The use of BIM in agriculture has the potential to transform the industry by providing a new level of insight and control over agricultural operations. BIM models can be used to simulate and visualize different scenarios for agricultural facilities, such as the use of water, fertilizer, and energy, leading to improved efficiency and reduced waste. By using BIM to optimize operations, agricultural companies can increase their productivity and profitability. The integration of BIM digital twins and 4D BIM in agriculture has the potential to revolutionize the industry by providing new tools for monitoring and managing agricultural operations. The use of BIM digital twins and 4D BIM can help reduce environmental pollution, promote sustainability, and improve the efficiency and productivity of agricultural operations. Testing is needed to fully understand the potential benefits of BIM and BIM digital twins in agriculture and to develop best practices for their implementation.