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Xinyou Yin

Publications and source records attributed to Xinyou Yin.

8 recordsLinked to original sources
PubMed2006

Mathematical review of literature to assess alternative electron transports and interphotosystem excitation partitioning of steady-state C3 photosynthesis under limiting light.

Linear whole-chain electron (e(-)) transport plays a dominant role in generating NADPH and ATP required for carbon fixation in chloroplasts. However, other e(-) pathways may be present to contribute to the flexibility of e(-) transport in meeting demands by various downstream metabolic processes. The estimation of the fluxes of these alternative pathways in vivo is difficult, as they are not amenable to direct experimental measurement. A recently developed model based on the generalized stoichiometry for the chloroplast e(-) transport pathways makes it possible to indirectly but quantitatively assess the fractions of e(-) that follow the alternative pathways. This model approach is used to review data from the literature on concurrent measurements of gas exchange and chlorophyll (Chl) fluorescence under steady-state, limiting light, non-photorespiratory conditions. The review suggests possible in vivo occurrence of cyclic e(-) transport (CET) under such conditions. About 10% of e(-) from the reduced ferredoxin follow the pseudocyclic mode, notably in support of nitrate reduction. The estimated fraction of e(-) from the reduced plastoquinone that follows the Q-cycle ( f(Q)) depends on the number of protons required per ATP synthesis. Our model approach also allows the excitation partitioning to photosystem II (PSII) to be assessed quantitatively. Most important, the model helps assess the limit value to uncertain physiological parameters and answer the 'what-if' question with regard to the effect of non-measured processes or measurement uncertainties on the estimations of alternative e(-) transports.

Electron Transport↗
PubMed2005

Complex quality traits: now time to model.

A recently published dynamic peach model by Françoise Lescourret and Michel Génard simulates changes in quality during final stages of fruit growth. It predicts quality, emerging from complex processes and influenced by environment and management, remarkably well and describes complex emerging behaviour. The model could prove an excellent tool to integrate novel insight from metabolic profiling and pathways of taste-related compounds, ultimately enabling the analysis of gene networks responsible for fruit maturation processes.

Crops, Agricultural↗
PubMed2005

QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley.

Combining ecophysiological modelling and genetic mapping has increasingly received attention from researchers who wish to predict complex plant or crop traits under diverse environmental conditions. The potential for using this combined approach to predict flowering time of individual genotypes in a recombinant inbred line (RIL) population of spring barley (Hordeum vulgare L.) was examined. An ecophysiological phenology model predicts preflowering duration as affected by temperature and photoperiod, based on the following four input traits: f(o) (the minimum number of days to flowering at the optimum temperature and photoperiod), theta1 and theta2 (the development stages for the start and the end of the photoperiod-sensitive phase, respectively), and delta (the photoperiod sensitivity). The model-input trait values were obtained from a photoperiod-controlled greenhouse experiment. Assuming additivity of QTL effects, a multiple QTL model was fitted for the model-input traits using composite interval mapping. Four to seven QTL were identified for each trait. Each trait had at least one QTL specific to that trait alone. Other QTL were shared by two or all traits. Values of the model-input traits predicted for the RILs from the QTL model were fed back into the ecophysiological model. This QTL-based ecophysiological model was subsequently used to predict preflowering duration (d) for eight field trial environments. The model accounted for 72% of the observed variation among 94 RILs and 94% of the variation among the two parents across the eight environments, when observations in different environments were pooled. However, due to the low percentage (34-41%) of phenotypic variation accounted for by the identified QTL for three model-input traits (theta1, theta2 and delta), the QTL-based model accounted for somewhat less variation among the RILs than the model using original phenotypic input trait values. Nevertheless, days to flowering as predicted from the QTL-based ecophysiological model were highly correlated with days to flowering as predicted from QTL-models per environment for days to flowering per se. The ecophysiological phenology model was thus capable of extrapolating (QTL) information from one environment to another.

Chromosome Mapping↗
PubMed2005

Model analysis of flowering phenology in recombinant inbred lines of barley.

A generic model for flowering phenology as a function of daily temperature and photoperiod was applied to predict differences of flowering times among 96 individuals (including the two parents) of a recombinant inbred line population in barley (Hordeum vulgare L.). Because of the large number of individuals to study, there is a need for simple ways to derive model parameters for each genotype. Therefore the number of genotype-specific parameters was reduced to four, namely f(o) (the minimum number of days to flowering at the optimum temperature and photoperiod), (1) and (2) (the development stages for the start and the end of the photoperiod-sensitive phase, respectively), and delta (the photoperiod sensitivity). Values of these parameters were estimated using a newly described methodological framework based on data from a photoperiod-controlled experiment where plants were mutually transferred between long-day and short-day environments at regular intervals. This modelling approach was tested in eight independent field environments of different sowing dates in two growing seasons. The four-parameter model predicted 37-67% of observed phenotypic variation in an environment, 76% of variation in across-environment mean days to flowering among the genotypes, and 96% of variation in across-genotype mean among the eight environments. When all the observations of the 96 genotypes across the eight environments were pooled, the model explained 81% of the total variation. Sensitivity analysis showed that all four model parameters were important for predicting differences in flowering time among the genotypes; but their relative importance differed and the ranking was in the order of f(o), delta, theta1, and theta2. This study highlighted the potential of using ecophysiological models to assist the genetic analysis of quantitative crop traits whose phenotype is often environment-dependent.

Flowers↗
PubMed2005

[Relationships between climate change and rice development and its yield formation: a simulation study].

With the application of mechanistic model (RICAM 1.3, RIce growth Calendar Model), this paper simulated the rice development and its yield formation under different climatic conditions at multi-locations of Asia. A three-stage Beta model (3s-Beta) was developed to predict the flowering stage of rice and to describe its three successive phases of photo-thermal response, i.e., basic vegetative phase, photoperiod-sensitive phase, and post photoperiod-sensitive phase. The 1980-1989 multi-location data of Morioka (Japan, 39 degrees 43'N), Nanchang (China, 28 degrees 36'N) and Los Banos (Philippines, 14 degrees 11'N) were used to verify the suitability of the model in studying ecosystem change. Comparisons of simulated results with observed data showed that this model could generally predict the eco-physiological processes of rice, and performed very well over a wide range of environments.

Biomass↗
PubMed2003

Some quantitative relationships between leaf area index and canopy nitrogen content and distribution.

In a previous study (Yin et al. 2000. Annals of Botany 85: 579-585), a generic logarithmic equation for leaf area index (L) in relation to canopy nitrogen content (N) was developed: L=(1/ktn)1n(1+ktnN/nb). The equation has two parameters: the minimum leaf nitrogen required to support photosynthesis (nb), and the leaf nitrogen extinction coefficient (ktn). Relative to nb, there is less information in the literature regarding the variation of ktn. We therefore derived an equation to theoretically estimate the value of ktn. The predicted profile of leaf nitrogen in a canopy using this theoretically estimated value of ktn is slightly more uniform than the profile predicted by the optimum nitrogen distribution that maximizes canopy photosynthesis. Relative to the optimum profile, the predicted profile is somewhat closer to the observed one. Based on the L-N logarithmic equation and the theoretical ktn value, we further quantified early leaf area development of a canopy in relation to nitrogen using simulation analysis. In general, there are two types of relations between L and N, which hold for canopies at different developmental phases. For a fully developed canopy where the lowest leaves are senescing due to nitrogen shortage, the relationship between L and N is described well by the logarithmic model above. For a young, unclosed canopy (i.e. L < 1.0), the relation between L and N is nearly linear. This linearity is virtually the special case of the logarithmic model when applied to a young canopy where its total nitrogen content approaches zero and the amount of nitrogen in its lowest leaves is well above nb. The expected patterns of the L-N relationship are discussed for the phase of transition from young to fully developed canopies.

Algorithms↗
PubMed2003

A flexible sigmoid function of determinate growth.

A new empirical equation for the sigmoid pattern of determinate growth, 'the beta growth function', is presented. It calculates weight (w) in dependence of time, using the following three parameters: t(m), the time at which the maximum growth rate is obtained; t(e), the time at the end of growth; and w(max), the maximal value for w, which is achieved at t(e). The beta growth function was compared with four classical (logistic, Richards, Gompertz and Weibull) growth equations, and two expolinear equations. All equations described successfully the sigmoid dynamics of seed filling, plant growth and crop biomass production. However, differences were found in estimating w(max). Features of the beta function are: (1) like the Richards equation it is flexible in describing various asymmetrical sigmoid patterns (its symmetrical form is a cubic polynomial); (2) like the logistic and the Gompertz equations its parameters are numerically stable in statistical estimation; (3) like the Weibull function it predicts zero mass at time zero, but its extension to deal with various initial conditions can be easily obtained; (4) relative to the truncated expolinear equation it provides more reasonable estimates of final quantity and duration of a growth process. In addition, the new function predicts a zero growth rate at both the start and end of a precisely defined growth period. Therefore, it is unique for dealing with determinate growth, and is more suitable than other functions for embedding in process-based crop simulation models to describe the dynamics of organs as sinks to absorb assimilates. Because its parameters correspond to growth traits of interest to crop scientists, the beta growth function is suitable for characterization of environmental and genotypic influences on growth processes. However, it is not suitable for estimating maximum relative growth rate to characterize early growth that is expected to be close to exponential.

Mathematics↗