X Yin

Wageningen University, Wageningen, Gelderland, Netherlands

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Publications (30)26.31 Total impact

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    ABSTRACT: Decreasing water availability for rice based systems resulted in the introduction of water saving production systems such as aerobic rice and alternate wetting–drying technology. To further improve resource use efficiency in these systems, water management should be attuned to critical growth stages, requiring accurate prediction of crop phenology. Photoperiod-sensitivity of aerobic rice genotypes complicates the estimation of the parameters characterising phenological development and hence impairs predictions. To overcome this complication, we followed a two-step approach: (1) the photoperiod response was determined in growth chambers, through a reciprocal transfer experiment with variable day length, conducted at a fixed temperature, and consecutively, (2) the temperature response was studied by combining the obtained photoperiod parameters with data from field experiments. All four aerobic rice genotypes tested exhibited strong photoperiod-sensitivity. Durations of basic vegetative phase (BVP) i.e. when plants are still insensitive to photoperiod, photoperiod-sensitive phase (PSP), and post-PSP (PPP) varied among genotypes. The temperature response of the genotypes was explored by combining phenological observations in the reciprocal transfer experiment with observations in two field experiments. The temperature range in the field experiments was too narrow to obtain convergence to a unique set of temperature response parameters, regardless whether a bilinear or a beta model was used. Sensitivity analysis however provided clear arguments in support of the recent doubts on the validity of a commonly used set of cardinal temperatures for rice phenology. Using standard cardinal temperatures, the rate of development at temperatures below 31 °C was overestimated. This finding stresses the need for experiments on rice phenology under a wider range of temperatures.
    Field Crops Research 01/2014; 155:14–22. DOI:10.1016/j.fcr.2013.09.027 · 2.61 Impact Factor
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    ABSTRACT: To improve our understanding of fruit growth responses to temperature, it is important to analyze temperature effects on underlying fruit cellular processes. This study aimed at analyzing the response of tomato (Solanum lycopersicum) fruit size to heating as affected by changes in cell number and cell expansion in different directions. Individual trusses were enclosed into cuvettes and heating was applied either only during the first 7 days after anthesis (DAA), from 7 DAA until fruit maturity (breaker stage), or both. Fruit size and histological characteristics in the pericarp were measured. Heating fruit shortened fruit growth period and reduced final fruit size. Reduction in final fruit size of early-heated fruit was mainly associated with reduction in final pericarp cell volume. Early heating increased the number of cell layers in the pericarp but did not affect the total number of pericarp cells. These results indicate that in the tomato pericarp, periclinal cell divisions respond differently to temperature than anticlinal or randomly oriented cell divisions. Late heating only decreased pericarp thickness significantly. Continuously heating fruit reduced anticlinal cell expansion (direction perpendicular to fruit skin) more than periclinal cell expansion (direction parallel to fruit skin). This study emphasizes the need to measure cell expansion in more than one dimension in histological studies of fruit
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    ABSTRACT: Given the need for parallel increases in food and energy production from crops in the context of global change, crop simulation models and data sets to feed these models with photosynthesis and respiration parameters are increasingly important. This study provides information on photosynthesis and respiration for three energy crops (sunflower, kenaf, and cynara), reviews relevant information for five other crops (wheat, barley, cotton, tobacco, and grape), and assesses how conserved photosynthesis parameters are among crops. Using large data sets and optimization techniques, the C(3) leaf photosynthesis model of Farquhar, von Caemmerer, and Berry (FvCB) and an empirical night respiration model for tested energy crops accounting for effects of temperature and leaf nitrogen were parameterized. Instead of the common approach of using information on net photosynthesis response to CO(2) at the stomatal cavity (A(n)-C(i)), the model was parameterized by analysing the photosynthesis response to incident light intensity (A(n)-I(inc)). Convincing evidence is provided that the maximum Rubisco carboxylation rate or the maximum electron transport rate was very similar whether derived from A(n)-C(i) or from A(n)-I(inc) data sets. Parameters characterizing Rubisco limitation, electron transport limitation, the degree to which light inhibits leaf respiration, night respiration, and the minimum leaf nitrogen required for photosynthesis were then determined. Model predictions were validated against independent sets. Only a few FvCB parameters were conserved among crop species, thus species-specific FvCB model parameters are needed for crop modelling. Therefore, information from readily available but underexplored A(n)-I(inc) data should be re-analysed, thereby expanding the potential of combining classical photosynthetic data and the biochemical model.
    Journal of Experimental Botany 01/2012; 63(2):895-911. DOI:10.1093/jxb/err321 · 5.79 Impact Factor
  • X. Yin · P. C. Struik
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    ABSTRACT: Classical crop models have long been established to understand crop responses to environmental factors, by integrating quantitative functional relationships for various physiological processes. In view of the potential added value of robust crop modelling to classical quantitative genetics, model-input parameters or traits are increasingly considered to represent ‘genetic coefficients’. A number of case studies, in which the effects of quantitative trait loci or genes have been incorporated into existing ecophysiological models to replace model-input traits, have shown promise of using models in analyzing genotype-phenotype relationships of more complex crop traits. Studies of functional genomics will increasingly enable the elucidation of the molecular genetic basis of these model-input traits. To fulfil the great expectations from this integrated modelling, crop models should be upgraded based on understandings at lower organizational levels. The recently proposed ‘crop systems biology’, which combines modern genomics, traditional physiology and biochemistry, and advanced modelling, is believed ultimately to realize the expected roles of in silico modelling in narrowing genotype-phenotype gaps. We will summarise recent research activities and express our opinions on perspectives for modelling genotype-by-environment interactions at crop level.
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  • M.S. Khan · P.C. Struik · Putten · P.E.L · X Yin · Eck · H.J · M. Malosetti · Eeuwijk · F.A
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    ABSTRACT: An ecophysiological model was designed to quantify variation in potato (Solanum tuberosum L.) canopy dynamics. Model algorithms describe the build- up phase, maximum cover phase, and decline phase based on five parameters: t m1 (transition from accelerating to diminishing growth during the build-up phase, t 1 (end of the build-up phase when canopy cover attains its maximum level v max ), t 2 (end of the phase of maximum cover), and t e (end of the crop cycle). Parameter values were estimated for 5 cultivars and 100 individuals of an F1 population using data collected in 6 field experiments. The model successfully described differences in canopy dynamics among individual genotypes across environments. Model parameters were used to derive several secondary variables: P 1 , P 2 and P 3 (length of the three phases) and A sum (area under the canopy cover curve reflecting the crop ’ s capacity to intercept incoming radiation). P 1 was relatively conservative, but P 2 and P 3 varied greatly. There were negative correlations among P 1 , P 2 , and P 3 , suggesting that genotypes with slow canopy build-up had a relatively short P 2 , but a relatively long P 3 . Furthermore, P 2 was short when v max was below 100%, whereas it could be (much) longer when v max reached 100%. Later genotypes had a higher A sum because they had longer P 2 and P 3 . Total biomass production depends on the absorbed photosynthetically active radiation, which is proportional to A sum . Potato models predicting yield could use our approach to improve estimates of canopy light interception under diverse environmental conditions. Our approach also allows identifying potato canopy characteristics relevant for breeding.
    European Potato Journal 09/2010; 53:393 – 419. DOI:10.1007/s11540-010-9180-z · 0.84 Impact Factor
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    ABSTRACT: We investigated the potential of a model-based approach to assist in the genetic analysis of the environment-sensitive, quantitative crop trait canopy cover in potato (Solanum tuberosum L.). We used a model based on beta functions to analyze the genotype×environment interactions related to the dynamics of canopy cover. The model equations describe three phases of canopy growth: build-up phase (P1), maximum cover phase (P2), and decline phase (P3). The model has five parameters: t m1 indicates the transition from accelerating to diminishing growth during P1, t 1 marks the end of P1 when canopy cover attains its maximum level v max , t 2 marks the end of P2 when canopy cover starts to decline, and t e represents the end of the crop cycle when canopy cover has declined to nil. Values of these parameters were estimated for 100 individuals of an F1 population, their parents, and five standard cultivars differing in maturity type, using data collected in six field experiments. The model successfully described differences in canopy dynamics among individual genotypes across environments. Model parameters were used to derive several secondary variables: DP2 (duration of P2), DP3 (duration of P3), and A sum (area under the canopy cover curve reflecting the crop ’ s capacity to intercept incoming radiation). The length of P1 (i.e. t1 ) was relatively conservative, but D P2, and DP3 varied greatly. Later genotypes had higher A sum because they had longer DP2, and DP3 . Genotypic and phenotypic variance components of the F1 population were estimated for all traits across environments and almost all of them proved significant (P<0.01). For most traits, genetic variability and heritability were high. There are opportunities, therefore, for future potato breeding programmes to exploit the genetic variability available in the F1 segregating population and to select for highly heritable traits in order to improve radiation interception efficiency.
    European Potato Journal 06/2010; 53:199 – 252. DOI:10.1007/s11540-010-9157-y · 0.84 Impact Factor
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    ABSTRACT: Cynara (Cynara cardunculus) is a perennial C3 herb that has its potential as bioenergy crop. This paper aims (a) to derive empirical relationships to predict cynara seed yield per head and per unit area, avoiding laborious extraction of seeds from the complex structure of its inflorescences; (b) to determine the head-weight distribution per unit area, the seed composition and the oil profile of cynara seeds; and (c) to estimate the range of cynara biomass, seed and oil yield in representative parts of Greece. We analyzed 16 field experiments, varying in crop age and environmental conditions in Greece. Seed yield per head (SYhead) can be accurately predicted as a linear function of dry head weight (Hw): SYhead=0.429·Hw−2.9 (r2=0.96; n=617). Based on this relationship, we developed a simple two-parameter equation to predict seed yield per unit area (SY): SY=HN·(0.429·μ−2.9), where μ is the mean head weight (g head−1) and HN is the total number of heads per unit area, respectively. The models were tested against current and published data (n=180 for head-level; n=35 for unit area-level models), and proved to be valid under diverse management and environmental conditions. Attainable cynara seed yields ranged from 190 to 480 g m−2 yr−1, on dry soils and on aquic soils (shallow ground water level). This variation in seed yield was sufficiently explained by the analyses of head-weight distribution per unit area (small, medium and large heads) and variability of seed/head weight ratio at head level. Seed oil concentration (average: 23%) and crude protein concentration (average: 18.7%) were rather invariant across different seed sizes (range: 26–56 mg seed−1) and growing environments.
    GCB Bioenergy 06/2010; 2. DOI:10.1111/j.1757-1707.2010.01045.x · 4.25 Impact Factor
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    R. Xu · J. Dai · W. Luo · X. Yin · Y. Li · X. Tai · L. Han · Y. Chen · L. Lin · G. Li · C. Zou · W. Du · M. Diao
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    ABSTRACT: Leaf area index (LAI) is an important variable for modelling canopy photosynthesis and crop water use. In many crop simulation models, prediction of LAI is very sensitive to errors in the value of parameter “specific leaf area” (SLA), which often relies on destructive measurements to determine. In this study, we present a model for predicting LAI of greenhouse crops based on the quantification of easily measured morphological traits as affected by temperature and radiation. Our model predicts LAI based on canopy light interception as a function of node development rate along with specific leaf size and elongation rates characteristics defined on a leaf number basis. Growth studies with five greenhouse crops (cucumber, sweet pepper, chrysanthemum, tulip and lilium) were conducted in different greenhouses and different sites during 2003 to 2009. The model was evaluated, in comparison with two commonly used methods for predicting LAI – the growing degree days (GDD) based model and SLA based model, using independent data from other experiments. The coefficient of determination (r2) and the root mean squared error (RMSE) between the predicted and measured values using our photothermal method are 0.99 and 0.95 (r2, RMSE) for leaf number, 0.98 and 0.01 m for specific leaf length, and 0.98 and 0.13 m2 m−2 for canopy LAI. For the GDD-based model, the r2 and RMSE are 0.93 and 4.23, 0.82 and 0.04 m, 0.87 and 0.48 m2 m−2 for the three traits, respectively. For the SLA-based model, the r2 and RMSE for canopy LAI is 0.81 and 1.24 m2 m−2 when using the estimated SLA data as input or 0.94 and 0.25 m2 m−2 when using the measured SLA data as input. So, our model better predicts LAI for greenhouse crops at different latitudes and a range of planting densities and pruning systems. Although calibrations for specific light regime, pruning practices and cultivars are needed, the fact that production conditions in commercial greenhouse production are often well controlled and production practices are often rather standardized implies a general applicability of our model.
    Agricultural and Forest Meteorology 04/2010; 150(4-150):541-552. DOI:10.1016/j.agrformet.2010.01.019 · 3.89 Impact Factor
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    ABSTRACT: 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.
    Journal of Experimental Botany 03/2010; 61(8):2203-16. DOI:10.1093/jxb/erq025 · 5.79 Impact Factor
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    NJAS: wageningen journal of life sciences 12/2009; 57(1). DOI:10.1016/j.njas.2009.12.001 · 1.14 Impact Factor
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    X Yin · P.C. Struik
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    ABSTRACT: Nearly three decades ago Farquhar, von Caemmerer and Berry published a biochemical model for C3 photosynthetic rates (the FvCB model). The model predicts net photosynthesis (A) as the minimum of the Rubisco-limited rate of CO2 assimilation (Ac) and the electron transport-limited rate of CO2 assimilation (Aj). Given its simplicity and the growing availability of the required enzyme kinetic constants, the FvCB model has been used for a wide range of studies, from analysing underlying C3 leaf biochemistry to predicting photosynthetic fluxes of ecosystems in response to global warming. However, surprisingly, this model has seen limited use in existing crop growth models. Here we highlight the elegance, simplicity, and robustness of this model. In the light of some uncertainties with photosynthetic electron transport pathways, a recently extended FvCB model to calculate Aj is summarized.
    NJAS: wageningen journal of life sciences 12/2009; 57(1). DOI:10.1016/j.njas.2009.07.001 · 1.14 Impact Factor
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    P.C. Struik · X Yin
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    X Yin · P.C. Struik
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  • P.C. Struik · X Yin
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    X Yin · P.C. Struik
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    ABSTRACT: The response of the whole crop to environmental conditions is a critical factor in agriculture. It can only be understood if the organization of the crop system is taken into account. A popular view in modern science is that genomics (and other `omics¿) will provide knowledge and tools to allow the characteristics of the crop to be altered for improved actual and potential crop yields, increased resource use efficiency and enhanced crop system health. As a result of this view, (molecular) plant systems biology has been considered as an approach to assist crop improvement for increased production, via modelling `how things work¿ in (sub-)cellular units. However, phenotypes at the crop level, for example, as expressed in grain yields, are extremely complex, and not only achieved by molecular pathways but also through multiple intermediate metabolic and physiological processes. These processes are controlled by numerous genes whose effects and expression are highly dependent on environmental perturbations. Current prevailing initiatives for (molecular) plant systems biology so far have put little emphasis on bringing the `omics¿ information to the crop level. Here, crop systems biology is presented as a complementary modelling approach to assist plant-breeding programmes to improve the yield and related resource use efficiencies of major crops. This crop systems biology approach honours the combined role of modern functional genomics and traditional sciences (such as crop physiology and biochemistry) in understanding and manipulating crop phenotypes relevant to agriculture. A stepwise routine for the development of crop systems biology models is proposed. Ultimately, these models should enable in silico assessment of crop response to genetic fine-tuning under defined environmental scenarios.
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    ABSTRACT: To study the performance of genotypes under different growing conditions, plant breeders evaluate their germplasm in multi-environment trials. These trials produce genotype by environment data. We present various 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 two-way analysis of variance model, without genotype by 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 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 factor. Factorial regression models are also very suitable for the modeling of QTL main effects and QTL by environment interaction. Our conclusion is that statistical and physiological models can fruitfully be combined for the study of genotype by environment interaction