Growth Simulation Model Research Articles

Applicability of Different Assimilation Algorithms in Crop Growth Model Simulation of Evapotranspiration

Applicability of Different Assimilation Algorithms in Crop Growth Model Simulation of Evapotranspiration

Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer.

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.

Phase-field modeling of lithium dendrite deposition process: When an internal short circuit occurs

Lithium dendrite growth is the main trigger for internal short circuits (ISC) of lithium-ion batteries. This paper investigates the lithium dendrite growth during the whole short circuit evolution process. In this paper, the lithium dendrite growth is divided into the initial deposition of the initial operation and the secondary deposition due to the internal short circuit. Firstly, a two-dimensional model based on the phase-field theory is developed and the growth in static electrolyte is investigated. Secondly, we summarize the whole evolution process and analyze the internal structural changes as well as the external features due to the internal short circuit. Based on this analysis, a model of secondary deposition in a flowing electrolyte is proposed. The model takes into account the effect of the flowing electrolyte on the dendrites. Finally, we investigate the growth of dendrites during secondary deposition. Due to an internal short circuit that damages the separator, a flowing electrolyte appears around the damaged region. This flowing electrolyte leads to uneven growth of dendrites, with “splitting” and “branch inhibition”. This phenomenon will accelerate the continued deposition of dendrites and induce a secondary internal short circuit. This work provides a quantitative simulation model for dendrite growth when an ISC occurs. Moreover, it also provides a reference for designing the structure of liquid batteries with high cycle times and safety.

Stress relief and crystal face transition process contribute to the stability of zinc anodes

Zn electrodes are suffering the dendrite growth owing to the enrichment of local space charge, distinct exposed face and residual stress. In this work, we investigated the crystal face properties and stress state of Zn foil through static energy calculations, dynamic crystal growth analysis and finite element simulation of stress states. Then thermal driven is deployed to modify the exposure face and residual stress of Zn foil, aiming for a dendrite-free electrode. The calculation of surface energies and simulation of crystal growth models for different crystal faces indicate that the (001) face can maintain stability during deposition. Inspired by this mechanism, the (101) exposed commercial Zn foil is modified by thermal processing. Firstly, the exposure level of the (001) face increases, though only the peak corresponding to the (002) crystal face is observed, due to the extinction effect of the densely packed plane (001) face. Further, the surface morphology becomes smooth and the stress is released with the progresses time. These stress relief and crystal face transition process strengthen the uniformity of ion distribution, and increase the interface stability during the crystal growth, which reduce the defect sites in the deposition. As a result, the Zn electrode exhibits tiny voltage hysteresis and outstanding cycle stability, which reveals improved electrochemical performance. Additionally, Li and Na can also be improved in exposed crystal faces and release strain energy through similar methods to enhance cycling stability.

A Harris Hawks optimization-based cellular automata model for urban growth simulation

A Harris Hawks optimization-based cellular automata model for urban growth simulation

Calibration, testing and application of the AquaCrop model for bean crop under irrigation regimes.

Crop growth simulation models relate the soil-water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 - non-irrigated, T1-fully irrigated, and T2-deficit irrigated) and sowing dates (S1-March 21, S2-April 24, and S3-August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769kgha-1) and water productivity (1,050 to 1,445kgm-3) with minimal application depths (< 150mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production.

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Yam Production-Related Agro-climatological Risks and Yam Yield Modeling in Côte d’Ivoire: A Review

In this paper, we present a review of the agro-climatological-related risk of yam production and models developed for yam yield prediction in C&amp;ocirc;te d&amp;rsquo;Ivoire. Four official national platforms (Ministry of Agriculture and Rural Development (MINADER), National Center for Agricultural Research (CNRA), National Agency for Rural Development Support (ANADER), Airport, Aeronautical and Meteorological Exploitation and Development Company (SODEXAM)) and six scientific search engines were investigated in this study including Theses.fr, African Journal Online, Science Direct, Google Scholar, WorldCat and Semantic Scholar. Using the boolean parameters &amp;ldquo;AND&amp;rdquo;, &amp;ldquo;OR&amp;rdquo; and &amp;ldquo;()&amp;rdquo; to facilitate and direct our search, we were able to define four key phrases comprising the topic words that were used in the search. Exclusion and inclusion criteria for the selection of documents were also defined in advance, as well as the criteria for reviewing and extracting information from selected documents. The results showed that no work in the field of agro-climatological risks related to yam production and yam yield modeling in C&amp;ocirc;te d&amp;rsquo;Ivoire was available on these online research platforms at the time of this literature review. However, other studies similar to the scope of this review on yam exist in several West African countries, particularly Ghana, Benin and Nigeria, and also in the Caribbean. These studies use simulation models such as the Approach for Land Use Sustainability (SALUS) model, the Environmental Policy Integrated Climate (EPIC) model and the Cropping Systems Simulation (CROPSYST) model for growth, yield modeling and the influence of climatic parameters on yam. In addition to these models, artificial intelligence through machine learning models was also seen in this review as an excellent tool for yield prediction for several crops including yams.

Strain-specific Growth Parameters are Important to Accurately Model Bacterial Growth on Baby Spinach in Simulation Models

Digital tools to predict produce shelf life have the potential to reduce food waste and improve consumer satisfaction. To address this need, we (i) performed an observational study on the microbial quality of baby spinach, (ii) completed growth experiments of bacteria that are representative of the baby spinach microbiota, and (iii) developed an initial simulation model of bacterial growth on baby spinach. Our observational data showed that the predominant genera found on baby spinach were Pseudomonas, Pantoea and Exiguobacterium. Rifampicin-resistant mutants (rifR mutants) of representative bacterial subtypes were subsequently generated to obtain strain-specific growth parameters on baby spinach. These experiments showed that: (i) it is difficult to select rifR mutants that do not have fitness costs affecting growth (9 of 15 rifR mutants showed substantial differences in growth, compared to their corresponding wild-type strain) and (ii) based on estimates from primary growth models, the mean (geometric) maximum population of rifR mutants on baby spinach (7.6 log10 CFU/g, at 6°C) appears lower than that of the spinach microbiota (9.6 log10 CFU/g, at 6°C), even if rifR mutants did not have substantial growth-related fitness costs. Thus, a simulation model, parameterized with the data obtained here as well as literature data on home refrigeration temperatures, underestimated bacterial growth on baby spinach. The root mean square error of the simulation’s output, compared against data from the observational study, was 1.11 log10 CFU/g. Sensitivity analysis was used to identify key parameters (e.g., strain maximum population) that impact the simulation model’s output, allowing for prioritization of future data collection to improve the simulation model. Overall, this study provides a roadmap for the development of models to predict bacterial growth on leafy vegetables with strain-specific parameters and suggests that additional data are required to improve these models.

Simulation of Space Objects And Future Perspective of Mosul's Space Expansion Until 2027 Under The Ca-Markov Model

Urban growth simulation models have proven their great effectiveness over years, as through these models a geographic information base is built in which presented to planners and specialists in urban planning, and in light of this, informed and correct decisions are made. Therefore, this research focused on predicting changes in the types of land cover in the city of Mosul from 2007 to 2017 to reach a prediction for the year 2027 using the simulation models available in the IDRISI SELVA program, and through the compatibility between geographic information systems and remote sensing programs. The CA-MARKOV model was employed to predict the size of expected changes in the types of land cover and obtain an objective map signifying the spread spatial organisms. The results showed that the city of Mosul is expected to expand spatially in the category of built-up areas to an area of up to (18) km2, where the constructed areas are expected to occupy an area of up to (143.5) km2, and by (79%) of the city region. It is expected that the city will be overcrowded with urbanization and what remains of barren areas and agricultural areas, are very limited and not commensurate with the increase of the city’s population, as it is expected to reach a number of (2,119,363) people in in the year 2027. Consequently, the research suggested expanding outside the city borders for a distance of 7 km2 in all directions by referring to experts in this field in order for the city to be built on a planned approach that does not distort its shape and grow more green spaces.

A new microlayer depletion model for numerical simulation of bubble growth during nucleate boiling

In this study, a new microlayer depletion model was developed to simulate bubble growth during the nucleate boiling of water and ethanol. By improving the coupled volume-of-fluid and level set model, the macro-two-phase flow was accurately tracked, eliminating velocity jumps at the interface. The proposed model was validated against pool boiling measurements in water and ethanol at atmospheric pressure. The results demonstrated that the microlayer contributed approximately 15 and 33.17 % to the growth of water and ethanol bubbles, respectively. Under the influence of the interfacial temperature, the mass accommodation coefficient of water could vary from 0.004 to 0.027. Considering the impact of the interfacial temperature on the mass accommodation coefficient could reduce the prediction error of the evaporative thermal resistance from 17.2 to 4.67 %. Furthermore, the study revealed that the evaporative thermal resistance of the water is comparable to the microlayer conductive thermal resistance, whereas the maximum evaporative thermal resistance of ethanol is less than 5 % of the microlayer conductive thermal resistance and can be neglected.

A bibliometric analysis of the literature on crop yield prediction: insights from previous findings and prospects for future research.

This research presents a bibliometric analysis of articles predicting crop yield using machine learning methods. While several systematic review articles exist, a comprehensive bibliometric analysis illustrating the knowledge structure and research trends, along with collaboration networks among authors, institutions, and countries in this field, has not been conducted. The study focused on 826 articles published over a 32-year period (1992 to 2023) and revealed a significant increase in publications, particularly in recent years. Zhang Zhao from China authored the majority of articles, while the highest number of citations was associated with articles by Zhu Yan and Lobell. Leading countries in article publications are the USA, China, India, Germany, Australia, and Canada, showing strong interconnections in citing each other's research. The Chinese Academy of Sciences and the US Department of Agriculture are the institutions with the highest number of articles and citations in this domain. The journals Agricultural and Forest Meteorology and Remote Sensing are recognized as top ranking journals in this field (Q1). Based on co-occurrence analysis, three main thematic domains were identified: weather and crop yield prediction, plant growth simulation models, and crop yield prediction using remote sensing data. The research suggests a focus on variables such as disease, pests, insects, and soil salinity when predicting yield. Additionally, greater attention should be given to discussions on food security and crop yield, especially in developing countries.

A method for calculating and simulating phenotype of soybean based on 3D reconstruction

In plant phenotypic research, accurate organ segmentation and crop simulation are crucial for optimizing crop planting and increasing yield. In this study, an efficient method for soybean organ segmentation and phenotypic growth simulation was explored based on 3D reconstruction technology. Taking Dongnong252 soybean as the research object, the soybean phenotype acquisition system based on Kinect sensor was used to achieve high-precision and non-destructive acquisition of soybean canopy images during the whole growth period. First, conditional filtering and statistical filtering were used to remove noise. Then, combined with the Intrinsic Shape Signatures-Coherent Point Drift (ISS-CPD) and the Iterative Closest Point (ICP) algorithms, the multi-view three-dimensional (3D) canopy structure of soybean was reconstructed, which provided a reliable basis for plant stem and leaf segmentation. On this basis, the average accuracy of plant stem and leaf segmentation was 79.99% by using the Distance-field-based segmentation pipeline (DFSP) algorithm. Furthermore, the accurate information was provided for extracting and calculating 3D phenotypic parameters such as plant height, crown width, stem thickness, leaf length and leaf width from three scales of whole plant, stem and leaf. The calculated values were highly consistent with the measured values, with an average coefficient of determination of 0.9654 and an average percentage error of 3.4862%. Meanwhile, by analyzing the quantitative relationship between phenotypic parameters and physiological development time, the data-driven Richards growth simulation model was introduced to accurately predict the growth process of soybean plants. The coefficient of determination R2 values of each phenotypic simulation model reached above 0.9357, which improved the goodness of fit of the model by 0.03 compared with the Logistic model, and its root mean square error (RMSE) ranged from 0.0020 to 0.1112. The research results indicated that this method had high accuracy and reliability in 3D reconstruction of soybean canopy, phenotype calculation, and growth simulation. It could provide quantitative indicators for soybean variety selection, planting, and management, and provide technical support and reference for high-throughput phenotype acquisition and analysis of field crops.

Examining limitations and future directions in climate change simulation models

Climate change refers to significant alterations in long-term climate conditions. If greenhouse gas emissions continue to rise, there is a high probability of exceeding the 1.5°C and 2° thresholds of global warming throughout the 21st century. This situation poses a serious threat to the agriculture sector and can lead to a decline in agricultural production and a reduction in product quality. Additionally, intensive farming practices can decrease the resilience of agriculture. This study aims to examine the effects of climate change on the agriculture sector, explain the concept of modeling and the parameters that can be measured, provide guidance on how modeling studies on alfalfa, and similar crops can be improved by identifying their shortcomings. The modeling method is used in many different fields by creating abstract representations of real-world objects or events via a mathematical equation, writing algorithm, or simulation. Parameters used in alfalfa modeling include yield, growth, carbon, water, nitrogen balance, climate effects, and other factors. However, these models have shortcomings such as the need for more comprehensive data collection and testing, the requirement for more parameter adjustments, the inability to address various crops and different growth cycles, the lack of simulation of crown and root roles in growth, sensitivity in measuring soil and input factors, limited testing and research, inaccuracies in automatic classification, the absence of growth and yield simulation models, and the lack of deep learning techniques. Addressing these shortcomings is crucial for achieving more reliable and effective results in the agricultural sector. Strengthening models and addressing these deficiencies have the potential to lead to more robust and sustainable solutions in agriculture.

Implications of incorrectly determining site index on stand-level management activities and financial returns in older generation loblolly pine plantations

Predicting future yields normally requires an estimate of site quality. A commonly used measure is site index (SI). SI is often incorrectly quantified operationally due to the ambiguity associated with selecting “site” trees. Plus, error in the measurement of height itself occurs. This study quantifies the impacts on the number and timing of thinnings, and the final harvest ages, as well as financial returns when incorrectly determining SI. Three values of SI (base age 25 years) were examined using two older generation loblolly pine plantation growth and yield simulation models from the Western Gulf, USA; 16.76 m, 21.34 m, and 25.91 m. Firstly, a particular SI was assumed to be the “true” value, growth and yield estimates were obtained, and financial assessments were conducted. The same process was then conducted again, but assuming that the SI was incorrectly determined by varying positively and negatively the SI by up to 1.22 m from the assumed “true” value. For these older generation plantations, incorrectly determining SI did impact the age of the first thinning by as much as 5 years. In some cases, errors of +/- 1.22 m in SI estimation had little impact on the estimated timing of the first thinning. Errors in SI of up to +/- 1.22 m had little impact on the number of thinnings across economic rotation ages. For both unthinned and twice-thinned stands, final harvest (clearcut) ages differed by as much as 4 years for SI errors up to +/- 1.22 m. These errors led to differences in Land Expectation Value (LEV) up to $406.50 ha-1. Across the three SI (16.76, 21.34, and 25.91 m), differences in LEV ranged from $237.49 to $406.50 ha-1. These differences in LEV could be enough to incorrectly not conduct, or incorrectly conduct, a silvicultural operation such as an herbicide treatment or a fertilization treatment across a rotation, among other treatments.

Influence of multiple spatiotemporal resolutions on the performance of urban growth simulation models

The study developed a framework to investigate the impact of multiple spatial and temporal resolutions on urban growth simulation. The research utilized the convolutional long short-term memory (ConvLSTM) model and three regular models and data from 2009 to 2017 to simulate the urban area of Liangjiang New District in 2018 and determine the optimal spatiotemporal resolution for urban expansion models. The results indicated that the ConvLSTM model has the best simulation result and the ideal temporal resolution for Liangjiang district is to include the previous two years of data, with an optimal spatial resolution of 90m and a spatiotemporal simulation zone within a two-year time step and 100×100 spatial information filter. At this combination, the kappa value of the ConvLSTM is 0.87 which is about 5% higher than others. Our findings revealed that the characteristics of input data can have a significant impact on simulation results and should be carefully considered during the simulation process.

Enhancing WOFOST crop model with unscented Kalman filter assimilation of leaf area index

ABSTRACT The challenging task of early yield prediction is an essential problem for present-day agriculture. It is commonly solved with a crop model along with relevant observation data: field scouting, in-situ sensors, satellite imagery data and information from previous growing seasons. Crop growth simulation models benefit greatly from application of these data; however, only a limited number of established data assimilation procedures receive notable application. Most studies focus on model parameter calibration, machine learning, ensemble Kalman filters (EnKF) or particle filters. These methods are powerful yet computationally expensive, which limits their extensive application. In this study, we bring into consideration a modern KF variant – the unscented Kalman filter (UKF). We implement the UKF data assimilation for leaf area index (LAI) within WOFOST PCSE model. To demonstrate its efficiency, we conduct simulations with EnKF and UKF assimilation of Sentinel-2 LAI data and compare the results to actual historical yield data of five crops on 2740 fields. Also, a field-level numerical experiment is set up to demonstrate the influence of LAI assimilation on the predicted yield. The results indicate the proposed approach performs consistently and significantly improves the accuracy of predicted yields.

Three-dimensional modeling of grain structure growth within ceramic tool material

Ceramic cutting tool materials possess specific advantages, including high hardness, excellent wear resistance, and high chemical stability, making them unparalleled in the machining of difficult-to-process materials compared to traditional cutting tools. The mechanical performance of these materials is primarily determined by their microstructure. Therefore, it is necessary to simulate and optimize the microstructure of ceramic cutting tool materials to provide theoretical guidance for further enhancing their fracture toughness. In this paper, a simulation model based on sintering densification theory was proposed, coupling crystal plasticity with a three-dimensional cellular automaton, to investigate the impact of nanoparticle content, sintering temperature, sintering pressure, and holding time on grain growth in composite ceramic materials using spark plasma sintering (SPS). Furthermore, an evolution model for grain growth of TiB2-TiC-SiC nanocomposite ceramic materials in cutting tools is established, and the optimal sintering process parameters are obtained. Finally, SPS experiments were conducted. Under the conditions of a sintering temperature of 1600 °C, holding time of 7 min, and sintering pressure of 40 MPa, optimal mechanical performance composite ceramic cutting tool materials were prepared. The experimental results, including a hardness of 25.6 GPa, fracture toughness of 7.1 MPa·m1/2, and flexural strength of 628.2 MPa, were consistent with the simulation outcomes. This indicates that the designed simulation model for nanocomposite ceramic grain growth reliably reflects the physical process of grain growth in the material and provides valuable guidance for further understanding the mechanisms underlying grain growth.

PSXII-20 Differences in Fat Accretion Rate of Different Depots of Angus, Brahman and Charolais Feedlot Steers

Abstract Understanding accretion rate of intramuscular and subcutaneous fat in feedlot steers is crucial to ensure that carcasses are meeting market specifications and therefore optimizing profitability. Intramuscular fat (marbling) and subcutaneous fat (P8 and rib) are key areas of assessment on the live animals and carcasses determining quality. The objective of the present study was to determine fat accretion rate in three popular breeds of cattle with contrasting accretion rate throughout the feedlotting period. We hypothesized that Angus would have the fastest fat accretion rate being early maturing. Thirty of each Angus, Brahman and Charolais steers were entered into a feedlot (344 ± 5.3 kg initial BW) and six of each breed randomly selected for slaughter at 50-day intervals for 200 days on feed (DOF). Ultrasound scanning and BW were measured at each slaughter point, measuring fat thickness at the rib and P8 site, intramuscular fat (IMF), kidney fat and the eye muscle area (EMA). Body weight, intramuscular fat (%), P8 and rib fat (mm) all had significant interactions between DOF and breed (P &amp;lt; 0.001). At the start of the feeding period, Charolais were lighter than Angus and Brahman (P &amp;gt; 0.001). However, Charolais (663.7 ± 9.31 kg) and Angus were the heavier (680.4 ± 8.68 kg) than Brahman (539.4 ± 8.68 kg) at 200 DOF (P &amp;lt; 0.05). The interaction for P8 and rib fat was due to all breeds having similar fat thickness at 0 DOF (P &amp;gt; 0.05) but Angus had greatest (21.38 ± 0.882 mm, 17.12 ± 0.549 mm), Brahman intermediate (16.73 ± 0.883 mm and 10.38 ± 0.550 mm) and Charolais thinnest (9.97 ± 0.966 mm, 7.23 ± 0.600 mm) P8 and rib fat, respectively, at 200 DOF (P &amp;lt; 0.05). Similarly, IMF was similar between breeds at 0 DOF (P &amp;gt; 0.05), but Angus had greater IMF (7.76 ± 0.287 %; P &amp;lt; 0.001) compared with Brahman (5.08 ± 0.287 %) and Charolais (5.18 ± 0.314 %), which were not different amongst them (P &amp;gt; 0.10). Results of the present study demonstrated the contrasting fat metabolisms of each genotype with Angus steers showing the fastest fat accretion rate for all fat depots, whereas Brahman steers showed intermediate and Charolais slowest subcutaneous fat accretion rate. However, both Brahman and Charolais steers showed similarly low IMF and kidney fat accretion rates. Fat accretion rate of each depot and whole body should be considered in mathematical simulation models of animal growth.

VS-Cambium-Developer: A New Predictive Model of Cambium Functioning under the Influence of Environmental Factors.

Climate changes influence seasonal tree-ring formation. The result is a specific cell structure dependent on internal processes and external environmental factors. One way to investigate and analyze these relationships is to apply diverse simulation models of tree-ring growth. Here, we have proposed a new version of the VS-Cambium-Developer model (VS-CD model), which simulates the cambial activity process in conifers. The VS-CD model does not require the manual year-to-year calibration of parameters over a long-term cell production reconstruction or forecast. Instead, it estimates cell production and simulates the dynamics of radial cell development within the growing seasons. Thus, a new software based on R programming technology, able to efficiently adapt to the VS model online platform, has been developed. The model was tested on indirect observations of the cambium functioning in Larix sibirica trees from southern Siberia, namely on the measured annual cell production from 1963 to 2011. The VS-CD model proves to simulate cell production accurately. The results highlighted the efficiency of the presented model and contributed to filling the gap in the simulations of cambial activity, which is critical to predicting the potential impacts of changing environmental conditions on tree growth.