Phenotyping field-state wheat root system architecture for root foraging traits in response to environment×management interactions.

An important aspect of below-ground crop physiology is its root foraging performance, which is inherently related to root system architecture (RSA). A 2-yr field experiment was conducted and the field-state wheat RSA was phenotyped for root foraging trait (RFT). Four RSA-derived traits, i.e. Root horizontal angle (RHA), axial root expansion volume (AREV), RSA convex hull volume (CHV) and effective volume per unit root length (EVURL), were analyzed for RFTs in response to environment × management interactions. Results showed a dynamical RHA process but without statistical difference both within crop seasons and tillage treatments. AREV increased with root developmental stages, revealing an overall better root performance in the first year. However, tillage treatments did not induce observed difference within both crop seasons. CHV varied drastically from year to year and between tillage treatments, correlating well to the root length, but not with RHA. EVURL was both sensitive to tillage treatments and crop seasons, being a potential indicator for RFT. Above all, tillage effect on RFT was statistically far less than that induced by crop seasons. Pro/E assisted modeling can be used as an effective means for phenotyping integrated, RSA-derived, RFTs for root foraging response to induced environment × management interactions.

Phenotyping for the dynamics of field wheat root system architecture.

We investigated a method to quantify field-state wheat RSA in a phenotyping way, depicting the 3D topology of wheat RSA in 14d periods. The phenotyping procedure, proposed for understanding the spatio-temporal variations of root-soil interaction and the RSA dynamics in the field, is realized with a set of indices of mm scale precision, illustrating the gradients of both wheat root angle and elongation rate along soil depth, as well as the foraging potential along the side directions. The 70d was identified as the shifting point distinguishing the linear root length elongation from power-law development. Root vertical angle in the 40 mm surface soil layer was the largest, but steadily decreased along the soil depth. After 98d, larger root vertical angle appeared in the deep soil layers. PAC revealed a stable root foraging potential in the 0-70d period, which increased rapidly afterwards (70-112d). Root foraging potential, explained by MaxW/MaxD ratio, revealed an enhanced gravitropism in 14d period. No-till post-paddy wheat RLD decreased exponentially in both depth and circular directions, with 90% roots concentrated within the top 20 cm soil layer. RER along soil depth was either positive or negative, depending on specific soil layers and the sampling time.

A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model.

Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. However, it is difficult to simultaneously extract panicle branch and spikelet/grain information from images at the same resolution due to the different scales of these traits. To use a lower resolution and meet the accuracy requirement, we proposed an interdisciplinary method that integrated image analysis and a 5-point calibration model to rapidly estimate SNPP. First, a linear relationship model between the total length of the primary branch (TLPB) and the SNPP was established based on the physiological characteristics of the panicle. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. Finally, a 5-point calibration method was adopted to improve the universality of the model. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. The estimation accuracy was consistent with the accuracy determined using manual measurements. The proposed method uses available concepts and techniques for automated estimations of rice yield information.

[Dimensional fractal of post-paddy wheat root architecture].

To evaluate whether crop rooting system was directionally dependent, a field digitizer was used to measure post-paddy wheat root architectures. The acquired data was transferred to Pro-E, in which virtual root architecture was reconstructed and projected to a series of planes each separated in 10° apart. Fractal dimension and fractal abundance of root projections in all the 18 planes were calculated, revealing a distinctive architectural distribution of wheat root in each direction. This strongly proved that post-paddy wheat root architecture was directionally dependent. From seedling to turning green stage, fractal dimension of the 18 projections fluctuated significantly, illustrating a dynamical root developing process in the period. At the jointing stage, however, fractal indices of wheat root architecture resumed its regularity in each dimension. This wheat root architecture recovered its dimensional distinctness. The proposed method was applicable for precision modeling field state root distribution in soil.

[Testing of germination rate of hybrid rice seeds based on near-infrared reflectance spectroscopy].

Germination rate of rice seeds was measured according to technical stipulation of germination testing for agricultural crop seeds at present. There existed many faults for this technical stipulation such as long experimental period, more costing and higher professional requirement. A rapid and non-invasive method was put forward to measure the germination rate of hybrid rice seeds based on near-infrared reflectance spectroscopy. Two varieties of hybrid rice seeds were aged artificially at temperature 45 degrees C and humidity 100% condition for 0, 24, 48, 72, 96, 120 and 144 h. Spectral data of 280 samples for 2 varieties of hybrid rice seeds with different aging time were acquired individually by near-infrared spectra analyzer. Spectral data of 280 samples for 2 varieties of hybrid rice seeds were randomly divided into calibration set (168 samples) and prediction set (112 samples). Gormination rate of rice seed with different aging time was tested. Regression model was established by using partial least squares (PLS). The effect of the different spectral bands on the accuracy of models was analyzed and the effect of the different spectral preprocessing methods on the accuracy of models was also compared. Optimal model was achieved under the whole bands and by using standardization and orthogonal signal correction (OSC) preprocessing algorithms with CM2000 software for spectral data of 2 varieties of hybrid rice seeds, the coefficient of determination of the calibration set (Rc) and that of the prediction set (Rp) were 0.965 and 0.931 individually, standard error of calibration set (SEC) and that of prediction set (SEP) were 1.929 and 2.899 respectively. Relative error between tested value and predicted value for prediction set of rice seeds is below 4.2%. The experimental results show that it is feasible that rice germination rate is detected rapidly and nondestructively by using the near-infrared spectroscopy analysis technology.

Related physicochemical properties to microstructure of hard and soft wheat grains with different kernel thickness and specific density.

Physicochemical properties of wheat grains with largest kernel thickness always was lowest than the other sections, examination of microstructure of wheat grains can help us understand this phenomena. Two varieties of wheat, soft white winter wheat Yangmai 11 and hard white winter wheat Zhengmai 9023, were fractionated into five sections by kernel thickness. Then the fractionated wheat grains in 2.7-3.0 mm section were separated into three fractions by kernel specific density sequentially. Microstructure of the fractured surface were evaluated at different scale level to two varieties wheat with different kernel thickness and specific density by using environmental scanning electron microscopy. Compactness and size of endosperm cell tended to decrease with decreasing wheat kernel thickness and specific density. Protein matrix tended to increase with decreasing wheat kernel thickness and specific density. Size of starch granules and proportion for different type starch granules also varied with different wheat kernel thickness and specific density. Those microstructure properties of the fractured surface, formation of endosperm cells, protein matrix and starch granules were close related to rheological properties and pasting properties of wheat grains.