Plant Growth Model Research Articles

GRC and pathway analysis: significance and applications in exploring RGR in plants

Utility of some modern methods (Growth Response Coefficient and Pathway Analysis) in analysing RGR and its components (NAR, SLA, LMF) has been depicted in the present study. Studies related to RGR are complex. Moreover, it is impractical for a researcher to study the entire spectrum of plant functional groups due to abundance and diversity of species in nature. RGR often varies between functional groups and in different environmental conditions as well. It is desirable to observe and identify characteristic patterns in plant growth models for better management practices. To achieve this goal, a more holistic approach is adopted which scrutinizes experimental data from number of independent studies in order to establish overall trends in RGR. The method is called meta-analysis. The, above mentioned statistical methods can further assist evolutionary biologists in tracing the evolutionary and phylogenetic characteristics of plants, the knowledge of which can be applied for modelling RGRs in future.

Is the Polylactic Acid Fiber in Green Compost a Risk for Lumbricus terrestris and Triticum aestivum?

Polylactic acid (PLA) bioplastic was introduced to the market as an environmentally friendly potential solution for plastic pollution. However, the effects of bioplastic debris mixed with composts on soil macroinvertebrates, plant growth and soil conditions are still unknown. Soil macroinvertebrates are soil health indicators. A reduction in their abundance is a sign of soil degradation. The objectives of this study were (i) to assess PLA debris in greenhouse composts, and (ii) to test the ecotoxicological effects of PLA debris mixed with compost on Lumbricus terrestris, a soil organism model, and on Triticum aestevium, a plant growth model. The study was comprised of three stages: (1) determine the PLA debris size distribution in composts; (2) assess the ecotoxicological effects of real-world concentrations (0% to 5%) of PLA mixed with compost on earthworm mortality and reproduction; and (3) assess the influence of compost mixed with real-world PLA concentrations on plant growth and physicochemical soil conditions. One percent of PLA debris was found in green composts, 40% of composted PLA debris measured between 1–10 mm, with a concentration of 82.8 ± 17.4 microplastics.gram−1 compost. A concentration of 1% PLA in composts resulted in significant mortality in earthworms. No significant effects of PLA mixed with composts were observed on plant growth or soil physicochemical conditions. Further studies are required in order to test the effect of this biopolymer on different earthworm and plant’ species.

Evaluation of weed impact on wheat biomass by combining visible imagery with a plant growth model: towards new non-destructive indicators for weed competition

To evaluate the impact of weeds on crops, precise identification and early prediction are required. This paper presents two new non-destructive indicators deduced from visible images: weed pressure (WP) and wheat growth status (WGS). They are based on the fractional vegetation cover (FVC) obtained from digital vegetation maps (crop vs. weeds) in a wheat field. FVC was determined for both plants with a Matthews Correlation Coefficient of 0.86 using machine learning classification [support vector machine-radial basis function (SVM-RBF)] combined with Bag of Visual Words technique. It was compared to destructive measurements of above-ground biomass (BM) and leaf area index (LAI). Since the coefficient of determination between FVC and BM is very good for wheat crop (r2 = 0.93), FVC is used to feed a growth model based on the Monteith equation. Replacing the standard approach by the image approach in the initialization of the model had no impact on the simulated BM values. WP characterized weed pressure, namely the FVCw/FVCc ratio and it quantified the crop–weed competition. The results show that up to the tillering stage, it could substitute for the BMw/BMc ratio resulting from a destructive approach. The second indicator, WGS assessed crop health through the monitoring of biomass production. It compared the theoretical wheat biomass simulated under non-stressed conditions, BMsimulated, to the actual biomass, BMobserved. The impact of weed on crop was evaluated by combining the results of these two indicators. This simple and fast method based on proximal detection data offers promising results in agroecological cropping systems, where high responsiveness is a major challenge for site-specific weed management.

Soybean production and yield in the context of global climatic changes

Abstract The global context of climate change predicts increases in the risk of important climatic factors that could directly influence plant survival and crop yields. Such projections are made using models of plant growth and development, climate, and possible future scenarios. However, the use of different models and methodologies, combined with different scenarios, produces an infinity of contrasting results, considering different combinations of temperature, water distribution, and CO 2 concentration in the atmosphere. This work makes projections of the possible climatic and environmental effects on the development and the yield of the soybean considering different scenarios. For maintenance and yield improvements, the needs and possibilities of using techniques related to the climate and the use and protection of soils and cultivars already adapted to different environments are emphasized. It is also expected that science will evolve to adapt plants to the expected stresses. Science should act to select genotypes that can respond to stresses by initiating processes that result in the activation of responses at the molecular, biochemical, and physiological level, in the fight to increase tolerance to abiotic stresses. Such advances lead us to believe that the exploration of the existing genetic variability will enable the selection of genotypes tolerant to drought, saline soils, and high temperatures. It is concluded that the set of knowledge that we have today, together with the scientific advances that are yet to come, allows humanity to continue having the hope of having a better future than those predicted in the most pessimistic scenarios.

Evaluation of the stability of vegetated slopes according to layout and temporal changes

Vegetation in slopes can effectively improve slope stability. However, it is difficult to estimate the effects of vegetation on slope stability because of variations in plant species and environmental conditions. Moreover, influences of plant growth on slope stability change with time, resulting in changes in the safety factor. This study was conducted to evaluate the stability of vegetated slopes with time and investigate the effects of different layouts of plant species on slope stability. Here, we used a plant growth model and slope stability analysis to build an evaluation model. To accomplish this, one species of tree, shrub and grass was chosen to set six layout patterns. A slope with no vegetation served as a control. The safety factors of the seven slopes were then calculated using the developed evaluation model and differences in the safety factors of slopes were compared and discussed. The slope vegetated with Platycladus orientalis reached the most stable state at the age of 60 years. Shrub slope (Vitex negundo) had the maximum safety factor after 20 years. Overall, the safety factor of vegetated slopes increased from 12.1% to 49.6% compared to the slope with no vegetation. When wind force was considered, the safety factor value of the slope changed from 3.5% to 43.5%. Vegetation mixtures of trees and grasses resulted in the best slope stability. Planting grasses on slopes can improve slope stability of trees to a greater degree than that of slopes with shrubs in the early stage of growth.

Modeling vegetative vigour in grapevine: unraveling underlying mechanisms

Mechanistic modeling constitutes a powerful tool to unravel complex biological phenomena. This study describes the construction of a mechanistic, dynamic model for grapevine plant growth and canopy biomass (vigor). To parametrize and validate the model, the progeny from a cross of Ramsey (Vitis champinii) × Riparia Gloire (V. riparia) was evaluated. Plants with different vigor were grown in a greenhouse during the summer of 2014 and 2015. One set of plants was grafted with Cabernet Sauvignon. Shoot growth rate (b), leaf area (LA), dry biomass, whole plant and root specific hydraulic conductance (kH and Lpr), stomatal conductance (gs), and water potential (Ψ) were measured. Partitioning indices and specific leaf area (SLA) were calculated. The model includes an empirical fit of a purported seasonal pattern of bioactive GAs based on published seasonal evolutionary levels and reference values. The model provided a good fit of the experimental data, with R = 0.85. Simulation of single trait variations defined the individual effect of each variable on vigor determination. The model predicts, with acceptable accuracy, the vigor of a young plant through the measurement of Lpr and SLA. The model also permits further understanding of the functional traits that govern vigor, and, ultimately, could be considered useful for growers, breeders and those studying climate change.

Photosynthetic Active Radiation, Solar Irradiance and the CIE Standard Sky Classification

Plant growth is directly related to levels of photosynthetic photon flux density, Qp. The improvement of plant-growth models therefore requires accurate estimations of the Qp parameter that is often indirectly calculated on the basis of its relationship with solar irradiation, RS, due to the scarcity of ground measurements of photosynthetic photon flux density. In this experimental campaign in Burgos, Spain, between April 2019 and January 2020, an average value of the Qp/Rs ratio is determined on the basis of measurements at ten-minute intervals. The most influential factor in the Qp/Rs ratio, over and above any daily or seasonal pattern, is the existence of overcast sky conditions. The CIE standard sky classification can be used to establish an unequivocal characterization of the cloudiness conditions of homogeneous skies. In this study, the relation between the CIE standard sky type and Qp/Rs is investigated. Its conclusions were that the Qp/Rs values, the average of which was 1.93±0.15 μmol·J−1, presented statistically significant differences for each CIE standard sky type. The overcast sky types presented the highest values of the ratio, while the clear sky categories presented the lowest and most dispersed values. During the experimental campaign, only two exceptions were noted for covered and partial covered sky-type categories, respectively, sky types 5 and 9. Their values were closer to those of categories classified as clear sky according to the CIE standard. Both categories presented high uniformity in terms of illumination.

An ecophysiological model of plant-pest interactions: the role of nutrient and water availability.

Empirical studies have shown that particular irrigation/fertilization regimes can reduce pest populations in agroecosystems. This appears to promise that the ecological concept of bottom-up control can be applied to pest management. However, a conceptual framework is necessary to develop a mechanistic basis for empirical evidence. Here, we couple a mechanistic plant growth model with a pest population model. We demonstrate its utility by applying it to the peach-green aphid system. Aphids are herbivores which feed on the plant phloem, deplete plants' resources and (potentially) transmit viral diseases. The model reproduces system properties observed in field studies and shows under which conditions the diametrically opposed plant vigour and plant stress hypotheses find support. We show that the effect of fertilization/irrigation on the pest population cannot be simply reduced as positive or negative. In fact, the magnitude and direction of any effect depend on the precise level of fertilization/irrigation and on the date of observation. We show that a new synthesis of experimental data can emerge by embedding a mechanistic plant growth model, widely studied in agronomy, in a consumer-resource modelling framework, widely studied in ecology. The future challenge is to use this insight to inform practical decision making by farmers and growers.

Performance of Long Short-Term Memory Networks for Modeling the Response of Plant Growth to Nutrient Solution Temperature in Hydroponic

This study examines the development of an approach for modeling the response of plant growth to nutrient solution temperature in hydroponic cultivation in a dynamic system. Nutrient solution temperature is one of the essential manipulating factors for plant growth in hydroponic cultivation. Determining the optimal control strategy of nutrient solution temperature during cultivation could lead to maximize the growth of the plant. By identifying the process using a dynamic system, the optimal control strategy can be determined. However, developing a dynamic model of plant growth to nutrient solution temperature is not easy due to physiological behavior between them are quite complex and uncertain. We propose the long short-term memory (LSTM) networks to identify and develop a model of dynamic characteristics of plant growth as affected by the nutrient solution temperature. Chili pepper plants were used to obtain time-series data of plant growth, with five different types of dynamic nutrient solution temperature patterns for system identification. The results showed that the proposed LSTM model provides promising performance in predicting the response of plant growth to nutrient solution temperature in hydroponic cultivation.

Two decades of research with the GreenLab model in agronomy.

With up to 200 published contributions, the GreenLab mathematical model of plant growth, developed since 2000 under Sino-French co-operation for agronomic applications, is descended from the structural models developed in the AMAP unit that characterize the development of plants and encompass them in a conceptual mathematical framework. The model also incorporates widely recognized crop model concepts (thermal time, light use efficiency and light interception), adapting them to the level of the individual plant. Such long-term research work calls for an overview at some point. That is the objective of this review paper, which retraces the main history of the model's development and its current status, highlighting three aspects. (1) What are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols? (3) What kind of applications can such a model address? This last question is answered using case studies as illustrations, and through the Discussion. The results obtained over several decades illustrate a key feature of the GreenLab model: owing to its concise mathematical formulation based on the factorization of plant structure, it comes along with dedicated methods and experimental protocols for its parameter estimation, in the deterministic or stochastic cases, at single-plant or population levels. Besides providing a reliable statistical framework, this intense and long-term research effort has provided new insights into the internal trophic regulations of many plant species and new guidelines for genetic improvement or optimization of crop systems.

Integrating dynamic plant growth models and microclimates for species distribution modelling

Climate is a major factor determining the distribution of plant species. Correlative models are frequently used to model the relationships between species distributions and climatic drivers but, increasingly, their use for prediction in novel scenarios such as climate change is being questioned. Mechanistic models, where processes limiting plant distribution are explicitly included, are regarded as preferable but more challenging.The availability of tools for simulating microclimates with high spatial and temporal definition has also opened new possibilities for simulating the limiting environmental stresses experienced by plants over their ontogeny. However, the field of mechanistic species distribution modelling is relatively new and the tools and theory for constructing these models are underdeveloped.In this paper we explore the potential for using a Dynamic Energy Budget model of organism growth integrated with microclimate and photosynthesis models. We model the interactions of plant growth and microclimatic stressors over the life stages of plant growth, and scale them up to demonstrate predictions of distribution at the continental scale. We develop the model using Julia, a new language for scientific computing, as a set of generic modelling packages. These have a modular, toolkit structure that has the potential to increase the efficiency and transparency of developing mechanistic SDMs.

Patterns, localized structures and fronts in a reduced model of clonal plant growth

A simplified model of clonal plant growth is formulated, motivated by observations of spatial structures in Posidonia oceanica meadows in the Mediterranean Sea. Two levels of approximation are considered for the scale-dependent feedback terms. Both take into account mortality and clonal, or vegetative, growth as well as competition and facilitation, but the first version is nonlocal in space while the second is local. Study of the two versions of the model in the one-dimensional case reveals that both cases exhibit qualitatively similar behavior (but quantitative differences) and describe the competition between three spatially extended states, the bare soil state, the populated state, and a pattern state, and the associated spatially localized structures. The latter are of two types, holes in the populated state and vegetation patches on bare ground, and are organized within distinct snaking bifurcations diagrams. Fronts between the three extended states are studied and a transition between pushed and pulled fronts identified. Numerical simulations in one spatial dimension are used to determine front speeds and confront the predictions from the marginal stability condition for pulled fronts.

Maize response to free air CO2 enrichment under ample and restricted water supply: field experimental data and output of a process-based hydrological plant growth model

This paper contains data from a two year FACE experiment with maize (Zea mays L., cv. ‘Romario’) investigating the interaction of two CO2 concentrations (378, 550 ppm) and two levels of water supply (sufficient: wet, limited: dry) on crop growth and plant composition. In the second year soil cover was also varied to test whether mitigation of evaporation by straw mulch increases the CO2 effect on water use efficiency. The datasets assembled herein contain data on weather, management, soil condition, soil moisture, phenology, dry weights and N concentrations of the plant (leaves, stems, cobs), green leaf area index, stem reserves, final yield and quality-related traits in the total plant and grains. Most of the experimental findings have already been published in scientific journals. Moreover, the data have been used in two crop modeling studies, and simulation results (on soil moisture, transpiration, evaporation and biomass) of one of these studies are also shown here.

Use of structurally-accurate 3D plant models for estimating light interception and photosynthesis of sweet pepper (Capsicum annuum) plants

Plant structure is a significant factor for influencing the light interception and photosynthesis of plants. The light interception on the plant surface can be analyzed by three-dimensional (3D) plant model and optical simulation, but its accuracy is directly affected by the structural accuracy of the 3D model. This study aims to analyze and compare the effect of the accuracy of 3D structural models on light interception and photosynthesis. 3D-scanned plant models with different structural accuracies were constructed, and the light interception and photosynthetic rate were analyzed at single leaf and whole-plant scales. When using a low accuracy model that lacked the fine structural details of the plant, it was overestimated in light interception and photosynthetic rate compared to the 3D-scanned model that has high structural accuracy. At the single leaf scale, the light interception was higher in the low-accuracy model than that in the 3D-scanned model due to self-shadings from higher curvature in the leaf surface. At the whole-plant scale, the light interception and the subsequent photosynthetic rate in the low-accuracy model were 18% and 45 to 58% higher than those in the 3D-scanned model at light intensities of 700–2000 μmol m−2 s−1 at the upper canopy. The 3D-scanned plant model could accurately estimate the light interception and photosynthetic rate of the plants through optical simulation. The presented methodology can contribute to accurate analyses of plant light environment, plant physiological response, and plant growth modelling.

Internal trophic pressure, a regulator of plant development? Insights from a stochastic functional-structural plant growth model applied to Coffea trees.

Using internal trophic pressure as a regulating variable to model the complex interaction loops between organogenesis, production of assimilates and partitioning in functional-structural models of plant growth has attracted increasing interest in recent years. However, this approach is hampered by the fact that internal trophic pressure is a non-measurable quantity that can be assessed only through model parametric estimation, for which the methodology is not straightforward, especially when the model is stochastic. A stochastic GreenLab model of plant growth (called 'GL4') is developed with a feedback effect of internal trophic competition, represented by the ratio of biomass supply to demand (Q/D), on organogenesis. A methodology for its parameter estimation is presented and applied to a dataset of 15 two-year-old Coffea canephora trees. Based on the fitting results, variations in Q/D are reconstructed and analysed in relation to the estimated variations in organogenesis parameters. Our stochastic retroactive model was able to simulate realistically the progressive set-up of young plant architecture and the branch pruning effect. Parameter estimation using real data for Coffea trees provided access to the internal trophic dynamics. These dynamics correlated with the organogenesis probabilities during the establishment phase. The model can satisfactorily reproduce the measured data, thus opening up promising avenues for further applying this original procedure to other experimental data. The framework developed can serve as a model-based toolkit to reconstruct the hidden internal trophic dynamics of plant growth.

Local control of resource allocation is sufficient to model optimal dynamics in syntrophic systems

Syntrophic systems are common in nature and include forms of obligate mutualisms in which each participating organism or component of an organism obtains from the other an essential nutrient or metabolic product that it cannot provide for itself. Models of how these complementary resources are allocated between partners often assume optimal behavior, but whether mechanisms enabling global control exist in syntrophic systems, and what form they might take, is unknown. Recognizing that growth of plant organs that supply complementary resources, like roots and shoots, can occur autonomously, we present a theory of plant growth in which root-shoot allocation is determined by purely local rules. Each organ uses as much as it can of its locally produced or acquired resource (inorganic nitrogen or photosynthate) and shares only the surplus. Subject to stoichiometric conditions that likely hold for most plants, purely local rules produce the same optimal allocation as would global control across a wide range of environmental scenarios, with sharing the surplus being the specific mechanism stabilizing syntrophic dynamics. Our local control model contributes a novel approach to plant growth modeling because it assumes a simple mechanism of root:shoot allocation that can be considered a higher-level physiological rule, from which the optimal growth outcome emerges from the system’s dynamics, rather than being built into the model. Moreover, our model is general, in that the mechanism of sharing the surplus can readily be adapted to many obligate syntrophic relationships.

On the mapping of especially valuable agricultural lands

Delineation of especially valuable agricultural lands (EVAL) is currently an important task, which will make it possible to preserve agricultural land for its direct use. There are currently no uniform approaches for delineation of EVAL, or they need to be upgraded. We have proposed a new approach based on GIS modeling and simulation of agricultural plant growth. It is proposed to delineate EVAL for each municipal district taking into account its existing specialization in agricultural production. The allocation of EVAL should be based on the assessment of potential productivity of soils and lands for cultivation of the main crops in the district. EVAL should also include pilot fields and areas used for scientific and educational purposes, regardless of potential soil productivity. The proposed approach has been successfully tested on the example of Yasnogorsk district of Tula region. It is shown that the EVAL map, based on the proposed approach, is more related to the actual land productivity and does not depend on the current land use within the area. It is possible to build an EVAL map for the whole country only by building such maps separately for all municipal districts of Russia.

Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize

Key messageHeritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve.Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

Light Interception and the Growth of Pastures under Ideal and Stressful Growing Conditions on the Allegheny Plateau.

Pasture-based livestock production is impacted by management and weather. In pastures, there is conflict between leaf retention for plant growth and leaf harvest for animal nutrition. Defoliated pastures with low light interception (LI) may have a low forage growth rate (FGR), while excessive growth shades leaves, reducing FGR and resulting in an S-shaped regrowth curve. To optimize production, it is best to keep FGR linear. Three studies were conducted to evaluate the impact of management and weather on FGR. Replicated pastures were used to measure FGR when grazed from 25 to 10 cm and allowed to regrow. The impact of alternative defoliation timings and intensities on FGR were studied using clipped treatments at three recovery intervals and two stubble heights. Variability in FGR was studied using a field validated plant growth model. Of the 24 growth periods studied, two displayed exponential, 12 linear and 10 linear-plateau growth. There was no effect of FM on growth curve form. In May and June, LI increased with canopy height, up to 0.93. Stubble height and days of growth impacted FGR with an interaction. There was no treatment impact on root density. Weather caused variation in FGR. A low FGR risk occurs at high elevations; greater risk occurs east of the plateau.

Turgor-driven plant growth applied in a soybean functional-structural plant model.

Turgor pressure within a plant cell represents the key to the mechanistical descriptiion of plant growth, combining the effects of both water and carbon availability. The high level of spatio-temporal variation and diurnal dynamics in turgor pressure within a single plant make it a challenge to model these on the fine spatial scale required for functional-structural plant models (FSPMs). A conceptual model for turgor-driven growth in FSPMs has been established previously, but its practical use has not yet been explored. A turgor-driven growth model was incorporated in a newly established FSPM for soybean. The FSPM simulates dynamics in photosynthesis, transpiration and turgor pressure in direct relation to plant growth. Comparisons of simulations with field data were used to evaluate the potential and shortcomings of the modelling approach. Model simulations revealed the need to include an initial seed carbon contribution, a more realistic sink function, an estimation of respiration, and the distinction between osmotic and structural sugars, in order to achieve a realistic model of plant growth. However, differences between simulations and observations remained in individual organ growth patterns and under different environmental conditions. This exposed the need to further investigate the assumptions of developmental and environmental (in)sensitivity of the parameters, which represent physiological and biophysical organ properties in the model, in future research. The model in its current form is primarily a diagnostic tool, to better understand and model the behaviour of water relations on the scale of individual plant organs throughout the plant life cycle. Potential future applications include its use as a phenotyping tool to capture differences in plant performance between genotypes and growing environments in terms of specific plant characteristics. Additionally, focused experiments can be used to further improve the model mechanisms to lead to better predictive FSPMs, including scenarios of water deficit.