The “general” ontogenetic growth model is inapplicable to crop growth

Effects of Light Quality and Photoperiod on Growth, Dry Matter Production and Yield of Ginger

We investigated the effects of light quality and photoperiod on the phenotypic characteristics, dry matter production, and yield of ginger under three light quality ratios (A1: blue light: white light = 1:4; A2: blue light: white light = 1:1; A3: pure white light) and two photoperiod conditions (B1: 12/12 h·d-1; B2: 16/8 h·d-1). The results demonstrated that blue light treatment significantly reduced plant height and the dry matter distribution ratio of stems and sheaths. In contrast, stem diameter, tiller number, leaf area, theoretical biomass (TBY), maximum accumulation rate (Vmax), average accumulation rate (Vaver), time point of maximum accumulation (Tmax), rapid growth period (DRGP), dry matter distribution ratio of leaves, roots, and rhizomes, number of rhizomes per plant, average rhizome weight, and yield all significantly increased with an increasing blue light ratio. Principal component analysis revealed distinct phenotypic traits, dry matter production characteristics, and yield-related traits under different blue light treatments. Blue light promoted tillering and increased stem thickness, which are key mechanisms for enhancing ginger yield. Additionally, prolonged photoperiods significantly increased plant height, stem diameter, branch number, leaf area, and biomass, while promoting the redistribution of photosynthetic products from leaves to rhizomes and increasing the proportion of dry matter allocated to rhizomes, thereby boosting ginger yield. These findings provide valuable insights into optimizing light conditions for ginger cultivation, highlighting the importance of a balanced blue-to-white light ratio and extended photoperiods in improving ginger growth and productivity.

The Modified Brière Equation and Its Applications

The Brière equation (BE) is widely used to describe the effect of temperature on the development rate of insects, and it can produce both symmetrical and asymmetrical bell-shaped curves. Because of its elasticity in curve fitting, the integrated form of BE has been recommended for use as a sigmoid growth equation to describe the increase in plant biomass with time. However, the start time of growth predicted by the sigmoid growth equation based on the BE is not completely comparable to empirical crop growth data. In the present study, we modified the BE by adding an additional parameter to further increase its elasticity for data fitting. We termed this new equation the modified Brière equation (MBE). Data for the actual height and biomass of 15 species of plants (with two cultivars for one species) were fit with the sigmoid growth equations based on both the BE and MBE assuming that the growth start time was zero for both. The goodness of fit of the BE and MBE sigmoid growth equations were compared based on their root-mean-square errors and the corresponding absolute percentage error between them when fit to these data. For most species, we found that the MBE sigmoid growth equation achieved a better goodness of fit than the BE sigmoid growth equation. This work provides a useful tool for quantifying the ontogenetic or population growth of plants.

A mechanistic model for nitrogen-limited plant growth

BACKGROUND AND AIMS

Nitrogen is often regarded as a limiting factor to plant growth in various ecosystems. Understanding how nitrogen drives plant growth has numerous theoretical and practical applications in agriculture and ecology. In 2004, Göran I. Ågren proposed a mechanistic model of plant growth from a biochemical perspective. However, neglecting respiration and assuming stable and balanced growth made the model unrealistic for plants growing in natural conditions. The aim of the present paper is to extend Ågren's model to overcome these limitations.

METHODS

We improved Ågren's model by incorporating the respiratory process and replacing the stable and balanced growth assumption with a three-parameter power function to describe the relationship between nitrogen concentration (Nc) and biomass. The new model was evaluated based on published data from three studies on corn (Zea mays) growth.

KEY RESULTS

Remarkably, the mechanistic growth model derived in this study is mathematically equivalent to the classical Richards model, which is the most widely used empirical growth model. The model agrees well with empirical plant growth data.

CONCLUSIONS

Our model provides a mechanistic interpretation of how nitrogen drives plant growth. It is very robust in predicting growth curves and the relationship between Nc and relative growth rate.

Standard Thermodynamic Properties, Biosynthesis Rates, and the Driving Force of Growth of Five Agricultural Plants

Elemental composition of Gossypium hirsutum L. (cotton), Oryza sativa L. (Asian rice), Phaseolus vulgaris L. (common bean), Saccharum spp. L. (sugarcane), and Zea mays L. (corn) was used to calculate their empirical formulas (unit carbon formulas) and growth stoichiometry. The empirical formulas were used to find standard enthalpy of formation, standard molar entropy, standard Gibbs energy of formation, and standard molar heat capacity. A comparison was made between thermodynamic properties of live matter of the analyzed plants and other unicellular and multicellular organisms. Moreover, the growth process was analyzed through standard enthalpy, entropy, and Gibbs energy of biosynthesis. The average standard Gibbs energy of biosynthesis was found to be +463.0 kJ/C-mol. Thus, photosynthesis provides energy and carbon for plant growth. The average intercepted photosynthetic energy was found to be 15.5 MJ/C-mol for the analyzed plants. However, due to inefficiency, a great fraction of the intercepted photosynthetic energy cannot be used by plants. The average usable photosynthetic energy was found to be -2.3 MJ/C-mol. The average thermodynamic driving force for growth is -1.9 MJ/C-mol. Driving forces of growth of C3 and C4 plants were compared. It was found that C4 plants have a greater driving force of growth than C3 plants, which reflects the greater efficiency of C4 photosynthesis. The relationship between the driving force and growth rates was analyzed by determining phenomenological L coefficients. The determined phenomenological coefficients span two orders of magnitude, depending on plant species and environmental conditions. The L coefficient of P. vulgaris was found to be lower than that of other plants, due to additional energy requirements of nitrogen fixation.

Understanding the Interactions between Biomass, Grain Production and Grain Protein Content in High and Low Protein Wheat Genotypes under Controlled Environments

Grain protein content (GPC) is a key quality attribute and an important marketing trait in wheat. In the current cropping systems worldwide, GPC is mostly determined by nitrogen (N) fertilizer application. The objectives of this study were to understand the differences in N response between high and low GPC wheat genotypes, and to assess the value of biomass growth analysis to assess the differences in N response. Six wheat genotypes from a range of high to low GPC were grown in low, medium and high N, under glasshouse conditions. This experiment was designed around non-destructive estimation of biomass using a high throughput image-based phenotyping system. Results showed that Spitfire and Mace had higher grain N% than Gazelle and QAL2000, and appeared to demand more N to grow their biomass. Moreover, at low N, Spitfire grew faster and achieved the maximum absolute growth rate earlier than high N-treated plants. High grain N% genotypes seem able to manage grain N reserves by compromising biomass production at low N. This study also indicated the importance of biomass growth analysis to show the differences in the N responsiveness of high and low GPC wheat.

A New Flexible Sigmoidal Growth Model

Biological growth is driven by numerous functions, such as hormones and mineral nutrients, and is also involved in various ecological processes. Therefore, it is necessary to accurately capture the growth trajectory of various species in ecosystems. A new sigmoidal growth (NSG) model is presented here for describing the growth of animals and plants when the assumption is that the growth rate curve is asymmetric. The NSG model was compared with four classic sigmoidal growth models, including the logistic equation, Richards, Gompertz, and ontogenetic growth models. Results indicated that all models fit well with the empirical growth data of 12 species, except the ontogenetic growth model, which only captures the growth of animals. The estimated maximum asymptotic biomass wmax of plants from the ontogenetic growth model was not reliable. The experiment result shows that the NSG model can more precisely estimate the value and time of reaching maximum biomass when growth rate becomes close to zero near the end of growth. The NSG model contains three other parameters besides the value and time of reaching maximum biomass, and thereby, it can be difficult to assign initial values for parameterization using local optimization methods (e.g., using Gauss–Newton or Levenberg–Marquardt methods). We demonstrate the use of a differential evolution algorithm for resolving this issue efficiently. As such, the NSG model can be applied to describing the growth patterns of a variety of species and estimating the value and time of achieving maximum biomass simultaneously.

Simulation of crop growth, time to maturity and yield by an improved sigmoidal model

Models that accurately estimate maximum crop biomass to obtain a reliable forecast of yield are useful in crop improvement programs and aiding establishment of government policies, including those addressing issues of food security. Here, we present a new sigmoidal growth model (NSG) and compare its performance with the beta sigmoidal growth model (BSG) for capturing the growth trajectories of eight crop species. Results indicated that both the NSG and the BSG fitted all the growth datasets well (R² > 0.98). However, the NSG performed better than the BSG based on the calculated value of Akaike’s information criterion (AIC). The NSG provided a consistent estimate for when maximum biomass occurred; this suggests that the parameters of the BSG may have less biological importance as compared to those in the NSG. In summary, the new sigmoidal growth model is superior to the beta sigmoidal growth model, which can be applied to capture the growth trajectory of various plant species regardless of the initial biomass values at the beginning of a growth period. Findings of this study will be helpful to understand the growth trajectory of different plant species regardless of their initial biomass values at the beginning of a growth period.

Constrained growth flips the direction of optimal phenological responses among annual plants

Phenological changes among plants due to climate change are well documented, but often hard to interpret. In order to assess the adaptive value of observed changes, we study how annual plants with and without growth constraints should optimize their flowering time when productivity and season length changes. We consider growth constraints that depend on the plant's vegetative mass: self-shading, costs for nonphotosynthetic structural tissue and sibling competition. We derive the optimal flowering time from a dynamic energy allocation model using optimal control theory. We prove that an immediate switch (bang-bang control) from vegetative to reproductive growth is optimal with constrained growth and constant mortality. Increasing mean productivity, while keeping season length constant and growth unconstrained, delayed the optimal flowering time. When growth was constrained and productivity was relatively high, the optimal flowering time advanced instead. When the growth season was extended equally at both ends, the optimal flowering time was advanced under constrained growth and delayed under unconstrained growth. Our results suggests that growth constraints are key factors to consider when interpreting phenological flowering responses. It can help to explain phenological patterns along productivity gradients, and links empirical observations made on calendar scales with life-history theory.

Comparison of dwarf bamboos (Indocalamus sp.) leaf parameters to determine relationship between spatial density of plants and total leaf area per plant

The relationship between spatial density and size of plants is an important topic in plant ecology. The self-thinning rule suggests a -3/2 power between average biomass and density or a -1/2 power between stand yield and density. However, the self-thinning rule based on total leaf area per plant and density of plants has been neglected presumably because of the lack of a method that can accurately estimate the total leaf area per plant. We aimed to find the relationship between spatial density of plants and total leaf area per plant. We also attempted to provide a novel model for accurately describing the leaf shape of bamboos. We proposed a simplified Gielis equation with only two parameters to describe the leaf shape of bamboos one model parameter represented the overall ratio of leaf width to leaf length. Using this method, we compared some leaf parameters (leaf shape, number of leaves per plant, ratio of total leaf weight to aboveground weight per plant, and total leaf area per plant) of four bamboo species of genus Indocalamus Nakai (I. pedalis (Keng) P.C. Keng, I. pumilus Q.H. Dai and C.F. Keng, I. barbatus McClure, and I. victorialis P.C. Keng). We also explored the possible correlation between spatial density and total leaf area per plant using log-linear regression. We found that the simplified Gielis equation fit the leaf shape of four bamboo species very well. Although all these four species belonged to the same genus, there were still significant differences in leaf shape. Significant differences also existed in leaf area per plant, ratio of leaf weight to aboveground weight per plant, and leaf length. In addition, we found that the total leaf area per plant decreased with increased spatial density. Therefore, we directly demonstrated the self-thinning rule to improve light interception.

An optimal proportion of mixing broad-leaved forest for enhancing the effective productivity of moso bamboo

Moso bamboos (Phyllostachys edulis) are important forestry plants in southern China, with substantial roles to play in regional economic and ecological systems. Mixing broad-leaved forests and moso bamboos is a common management practice in China, and it is fundamental to elucidate the interactions between broad-leaved trees and moso bamboos for ensuring the sustainable provision of ecosystem services. We examine how the proportion of broad-leaved forest in a mixed managed zone, topology, and soil profile affects the effective productivity of moso bamboos (i.e., those with significant economic value), using linear regression and generalized additive models. Bamboo's diameter at breast height follows a Weibull distribution. The importance of these variables to bamboo productivity is, respectively, slope (25.9%), the proportion of broad-leaved forest (24.8%), elevation (23.3%), gravel content by volume (16.6%), slope location (8.3%), and soil layer thickness (1.2%). Highest productivity is found on the 25° slope, with a 600-m elevation, and 30% broad-leaved forest. As such, broad-leaved forest in the upper slope can have a strong influence on the effective productivity of moso bamboo, ranking only after slope and before elevation. These factors can be considered in future management practice.

Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time

The use of high-throughput phenotyping systems and non-destructive imaging is widely regarded as a key technology allowing scientists and breeders to develop crops with the ability to perform well under diverse environmental conditions. However, many of these phenotyping studies have been optimized using the model plant Arabidopsis thaliana. In this study, The Plant Accelerator(®) at The University of Adelaide, Australia, was used to investigate the growth and phenotypic response of the important cereal crop, Sorghum bicolor L. Moench and related hybrids to water-limited conditions and different levels of fertilizer. Imaging in different spectral ranges was used to monitor plant composition, chlorophyll, and moisture content. Phenotypic image analysis accurately measured plant biomass. The data set obtained enabled the responses of the different sorghum varieties to the experimental treatments to be differentiated and modelled. Plant architectural instead of architecture elements were determined using imaging and found to correlate with an improved tolerance to stress, for example diurnal leaf curling and leaf area index. Analysis of colour images revealed that leaf 'greenness' correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content. It is shown that imaging sorghum using a high-throughput system can accurately identify and differentiate between growth and specific phenotypic traits. R scripts for robust, parsimonious models are provided to allow other users of phenomic imaging systems to extract useful data readily, and thus relieve a bottleneck in phenotypic screening of multiple genotypes of key crop plants.

Capturing spiral radial growth of conifers using the superellipse to model tree-ring geometric shape

Tree-rings are often assumed to approximate a circular shape when estimating forest productivity and carbon dynamics. However, tree rings are rarely, if ever, circular, thereby possibly resulting in under- or over-estimation in forest productivity and carbon sequestration. Given the crucial role played by tree ring data in assessing forest productivity and carbon storage within a context of global change, it is particularly important that mathematical models adequately render cross-sectional area increment derived from tree rings. We modeled the geometric shape of tree rings using the superellipse equation and checked its validation based on the theoretical simulation and six actual cross sections collected from three conifers. We found that the superellipse better describes the geometric shape of tree rings than the circle commonly used. We showed that a spiral growth trend exists on the radial section over time, which might be closely related to spiral grain along the longitudinal axis. The superellipse generally had higher accuracy than the circle in predicting the basal area increment, resulting in an improved estimate for the basal area. The superellipse may allow better assessing forest productivity and carbon storage in terrestrial forest ecosystems.

Cascade effects of crop species richness on the diversity of pest insects and their natural enemies

Understanding how plant species richness influences the diversity of herbivorous and predatory/parasitic arthropods is central to community ecology. We explore the effects of crop species richness on the diversity of pest insects and their natural enemies. Using data from a four-year experiment with five levels of crop species richness, we found that crop species richness significantly affected the pest species richness, but there were no significant effects on richness of the pests' natural enemies. In contrast, the species richness of pest insects significantly affected their natural enemies. These findings suggest a cascade effect where trophic interactions are strong between adjacent trophic levels, while the interactions between connected but nonadjacent trophic levels are weakened by the intermediate trophic level. High crop species richness resulted in a more stable arthropod community compared with communities in monoculture crops. Our results highlight the complicated cross-trophic interactions and the crucial role of crop diversity in the food webs of agro-ecosystems.