Xinyou Yin

Using combined measurements of gas exchange and chlorophyll fluorescence to estimate parameters of a biochemical C3 photosynthesis model: a critical appraisal and a new integrated approach applied to leaves in a wheat (Triticum aestivum) canopy

We appraised the literature and described an approach to estimate the parameters of the Farquhar, von Caemmerer and Berry model using measured CO(2) assimilation rate (A) and photosystem II (PSII) electron transport efficiency (Phi(2)). The approach uses curve fitting to data of A and Phi(2) at various levels of incident irradiance (I(inc)), intercellular CO(2) (C(i)) and O(2). Estimated parameters include day respiration (R(d)), conversion efficiency of I(inc) into linear electron transport of PSII under limiting light [kappa(2(LL))], electron transport capacity (J(max)), curvature factor (theta) for the non-rectangular hyperbolic response of electron flux to I(inc), ribulose 1.5-bisphosphate carboxylase/oxygenase (Rubisco) CO(2)/O(2) specificity (S(c/o)), Rubisco carboxylation capacity (V(cmax)), rate of triose phosphate utilization (T(p)) and mesophyll conductance (g(m)). The method is used to analyse combined gas exchange and chlorophyll fluorescence measurements on leaves of various ages and positions in wheat plants grown at two nitrogen levels. Estimated S(c/o) (25 degrees C) was 3.13 mbar microbar(-1); R(d) was lower than respiration in the dark; J(max) was lower and theta was higher at 2% than at 21% O(2); kappa(2(LL)), V(cmax), J(max) and T(p) correlated to leaf nitrogen content; and g(m) decreased with increasing C(i) and with decreasing I(inc). Based on the parameter estimates, we surmised that there was some alternative electron transport.

Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models

To study the performance of genotypes under different growing conditions, plant breeders evaluate their germplasm in multi-environment trials. These trials produce genotype × environment data. We present statistical models for the analysis of such data that differ in the extent to which additional genetic, physiological, and environmental information is incorporated into the model formulation. The simplest model in our exposition is the additive 2-way analysis of variance model, without genotype × environment interaction, and with parameters whose interpretation depends strongly on the set of included genotypes and environments. The most complicated model is a synthesis of a multiple quantitative trait locus (QTL) model and an eco-physiological model to describe a collection of genotypic response curves. Between those extremes, we discuss linear-bilinear models, whose parameters can only indirectly be related to genetic and physiological information, and factorial regression models that allow direct incorporation of explicit genetic, physiological, and environmental covariables on the levels of the genotypic and environmental factors. Factorial regression models are also very suitable for the modelling of QTL main effects and QTL × environment interaction. Our conclusion is that statistical and physiological models can be fruitfully combined for the study of genotype × environment interaction.

The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley

Crop modelling has so far contributed little to the genetic analysis of a quantitative trait. This study illustrates how a simple model for crop phenological development, which assumes that crop development rate is affected by daily effective temperature, can assist the identification of Quantitative Trait Loci (QTLs), using specific leaf area (SLA) in barley as an example. The SLA was measured in a field experiment six times during the growing season of 94 recombinant inbred lines (RILs) derived from a cross between cultivars Prisma and Apex. Of the six measurements, one was conducted at the same physiological age for all RILs (at flowering), four were undertaken at specific chronological days prior to flowering, and the last one was taken at 14 days after flowering. When the measured SLA was directly used as the quantitative trait, one to three QTLs were detected for SLA at each measurement time. The major dwarfing gene denso segregating in the population was found to affect SLA strongly at all measurement times except at flowering. If SLA of the different RILs was corrected for differences in physiological age at the time of measurement, by the use of the crop development model, QTLs were detected for SLA at only three stages. Furthermore, the effect of the denso gene was no longer significant during the preflowering stages. The effect of the denso gene detected in the first instance was therefore the consequence of its direct effect on the duration of the preflowering period. This demonstrates the important role that crop development models can play in QTL analysis of a trait that varies with developmental stage. Potential uses of ecophysiological crop growth models in QTL analysis are briefly discussed.

Simulation of wheat growth and development based on organ-level photosynthesis and assimilate allocation

Intimate relationships exist between form and function of plants, determining many processes governing their growth and development. However, in most crop simulation models that have been created to simulate plant growth and, for example, predict biomass production, plant structure has been neglected. In this study, a detailed simulation model of growth and development of spring wheat (Triticum aestivum) is presented, which integrates degree of tillering and canopy architecture with organ-level light interception, photosynthesis, and dry-matter partitioning. An existing spatially explicit 3D architectural model of wheat development was extended with routines for organ-level microclimate, photosynthesis, assimilate distribution within the plant structure according to organ demands, and organ growth and development. Outgrowth of tiller buds was made dependent on the ratio between assimilate supply and demand of the plants. Organ-level photosynthesis, biomass production, and bud outgrowth were simulated satisfactorily. However, to improve crop simulation results more efforts are needed mechanistically to model other major plant physiological processes such as nitrogen uptake and distribution, tiller death, and leaf senescence. Nevertheless, the work presented here is a significant step forwards towards a mechanistic functional-structural plant model, which integrates plant architecture with key plant processes.

Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics

Functional genomics has been driven greatly by emerging experimental technologies. Its development as a scientific discipline will be enhanced by systems biology, which generates novel, quantitative hypotheses via modelling. However, in order to better assist crop improvement, the impact of developing functional genomics needs to be assessed at the crop level, given a projected diminishing effect of genetic alteration on phenotypes from the molecule to crop levels. This review illustrates a recently proposed research field, crop systems biology, which is located at the crossroads of crop physiology and functional genomics, and intends to promote communications between the two. Past experiences with modelling whole-crop physiology indicate that the layered structure of biological systems should be taken into account. Moreover, modelling not only plays a role in data synthesis and quantitative prediction, but certainly also in heuristics and system design. These roles of modelling can be applied to crop systems biology to enhance its contribution to our understanding of complex crop phenotypes and subsequently to crop improvement. The success of crop systems biology needs commitments from scientists along the entire knowledge chain of plant biology, from molecule or gene to crop and agro-ecosystem.

Evaluating a new method to estimate the rate of leaf respiration in the light by analysis of combined gas exchange and chlorophyll fluorescence measurements

Day respiration (Rd) is an important parameter in leaf ecophysiology. It is difficult to measure directly and is indirectly estimated from gas exchange (GE) measurements of the net photosynthetic rate (A), commonly using the Laisk method or the Kok method. Recently a new method was proposed to estimate Rd indirectly from combined GE and chlorophyll fluorescence (CF) measurements across a range of low irradiances. Here this method is tested for estimating Rd in five C3 and one C4 crop species. Values estimated by this new method agreed with those by the Laisk method for the C3 species. The Laisk method, however, is only valid for C3 species and requires measurements at very low CO2 levels. In contrast, the new method can be applied to both C3 and C4 plants and at any CO2 level. The Rd estimates by the new method were consistently somewhat higher than those by the Kok method, because using CF data corrects for errors due to any non-linearity between A and irradiance of the used data range. Like the Kok and Laisk methods, the new method is based on the assumption that Rd varies little with light intensity, which is still subject to debate. Theoretically, the new method, like the Kok method, works best for non-photorespiratory conditions. As CF information is required, data for the new method are usually collected using a small leaf chamber, whereas the Kok and Laisk methods use only GE data, allowing the use of a larger chamber to reduce the noise-to-signal ratio of GE measurements.

Physiological basis of genetic variation in leaf photosynthesis among rice (Oryza sativa L.) introgression lines under drought and well-watered conditions.

To understand the physiological basis of genetic variation and resulting quantitative trait loci (QTLs) for photosynthesis in a rice (Oryza sativa L.) introgression line population, 13 lines were studied under drought and well-watered conditions, at flowering and grain filling. Simultaneous gas exchange and chlorophyll fluorescence measurements were conducted at various levels of incident irradiance and ambient CO2 to estimate parameters of a model that dissects photosynthesis into stomatal conductance (g s), mesophyll conductance (g m), electron transport capacity (J max), and Rubisco carboxylation capacity (V cmax). Significant genetic variation in these parameters was found, although drought and leaf age accounted for larger proportions of the total variation. Genetic variation in light-saturated photosynthesis and transpiration efficiency (TE) were mainly associated with variation in g s and g m. One previously mapped major QTL of photosynthesis was associated with variation in g s and g m, but also in J max and V cmax at flowering. Thus, g s and g m, which were demonstrated in the literature to be responsible for environmental variation in photosynthesis, were found also to be associated with genetic variation in photosynthesis. Furthermore, relationships between these parameters and leaf nitrogen or dry matter per unit area, which were previously found across environmental treatments, were shown to be valid for variation across genotypes. Finally, the extent to which photosynthesis rate and TE can be improved was evaluated. Virtual ideotypes were estimated to have 17.0% higher photosynthesis and 25.1% higher TE compared with the best genotype investigated. This analysis using introgression lines highlights possibilities of improving both photosynthesis and TE within the same genetic background.

Using chromosome introgression lines to map quantitative trait loci for photosynthesis parameters in rice (Oryza sativa L.) leaves under drought and well-watered field conditions

Photosynthesis is fundamental to biomass production, but sensitive to drought. To understand the genetics of leaf photosynthesis, especially under drought, upland rice cv. Haogelao, lowland rice cv. Shennong265, and 94 of their introgression lines (ILs) were studied at flowering and grain filling under drought and well-watered field conditions. Gas exchange and chlorophyll fluorescence measurements were conducted to evaluate eight photosynthetic traits. Since these traits are very sensitive to fluctuations in microclimate during measurements under field conditions, observations were adjusted for microclimatic differences through both a statistical covariant model and a physiological approach. Both approaches identified leaf-to-air vapour pressure difference as the variable influencing the traits most. Using the simple sequence repeat (SSR) linkage map for the IL population, 1–3 quantitative trait loci (QTLs) were detected per trait–stage–treatment combination, which explained between 7.0% and 30.4% of the phenotypic variance of each trait. The clustered QTLs near marker RM410 (the interval from 57.3 cM to 68.4 cM on chromosome 9) were consistent over both development stages and both drought and well-watered conditions. This QTL consistency was verified by a greenhouse experiment under a controlled environment. The alleles from the upland rice at this interval had positive effects on net photosynthetic rate, stomatal conductance, transpiration rate, quantum yield of photosystem II (PSII), and the maximum efficiency of light-adapted open PSII. However, the allele of another main QTL from upland rice was associated with increased drought sensitivity of photosynthesis. These results could potentially be used in breeding programmes through marker-assisted selection to improve drought tolerance and photosynthesis simultaneously.

A model for photothermal responses of flowering in rice. I. Model description and parameterization.

Most models that predict crop development based on temperature and photoperiod ignore critical changes in photothermal responses during crop ontogeny. A new, detailed model was developed to predict development to flowering in rice (Oryza sativa L.), based on the Beta function which is commonly used for skewed probability density functions in statistics. As the model accounts for different photothermal responses of three successive phases during preflowering ontogeny, it was referred to as the three-stage Beta model. The model was parameterized for 17 rice cultivars using data of two controlled-environment experiments. Model parameters which do not vary strongly among cultivars and parameters which can be estimated from values of other parameters were identified. This analysis reduced the number of parameters to be estimated to five. The parameters from the controlled-environment experiments were used to predict rice development as observed from an independent three-location field experiment with 12 cultivars. The model accurately predicted varietal and locational variation in rice flowering date.

Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity

BACKGROUND Process-based ecophysiological crop models are pivotal in assessing responses of crop productivity and designing strategies of adaptation to climate change. Most existing crop models generally over-estimate the effect of elevated atmospheric [CO2], despite decades of experimental research on crop growth response to [CO2]. ANALYSIS A review of the literature indicates that the quantitative relationships for a number of traits, once expressed as a function of internal plant nitrogen status, are altered little by the elevated [CO2]. A model incorporating these nitrogen-based functional relationships and mechanisms simulated photosynthetic acclimation to elevated [CO2], thereby reducing the chance of over-estimating crop response to [CO2]. Robust crop models to have small parameterization requirements and yet generate phenotypic plasticity under changing environmental conditions need to capture the carbon-nitrogen interactions during crop growth. CONCLUSIONS The performance of the improved models depends little on the type of the experimental facilities used to obtain data for parameterization, and allows accurate projections of the impact of elevated [CO2] and other climatic variables on crop productivity.