Indigenous chicken products are increasingly favored by consumers due to their unique meat and egg quality.  However, the relatively poor egg-laying performance largely impacts the economic benefits and hinders sustainable development of the local chicken industry.  Thus, excavating key genes and effective molecular markers associated with egg-laying performance is necessary to improve egg production via genetic selection in indigenous breeds.  In the present study, comparative hypothalamic transcriptome between pre-laying (15 weeks old) and peak-laying (30 weeks old) Lushi blue-shelled-egg (LBS) chicken was performed.  A total of 518 differentially expressed genes (DEGs) were identified.  Among the DEGs, 64 genes were enriched in 10 Gene Ontology (GO) terms associated with reproductive regulation via GO analysis and considered as potential candidate genes regulating egg-laying performance.  Of the 64 genes, 16 showed high connectivity (degree≥12) by protein–protein interaction (PPI) network analysis and were considered as potential core candidate genes (PCCGs).  To further look for key candidate genes from the PCCGs, firstly, the expression patterns of the 16 genes were examined in the hypothalamus of two indigenous breeds (LBS and Gushi (GS) chickens) between the pre-laying and peak-laying stages using quantitative real-time PCR (qRT-PCR).  Eleven out of the 16 genes showed significantly differential expression (P<0.05) with the same changing trends in the two breeds.  Then, correlations between the expression levels of the above 11 genes and egg numbers and reproductive hormone concentrations in serum were investigated in high-yielding and low-yielding GS chickens.  Of the 11 genes, eight showed significant correlations (P<0.05) between their expression levels and egg numbers, and between expression levels and reproductive hormone concentration in serum.  Furthermore, an association study on single nucleotide polymorphisms (SNPs) identified in these eight genes and egg production traits was carried out in 640 GS hens, and a significant association (P<0.05) between the SNPs and egg numbers was confirmed.  In conclusion, the eight genes, including CNR1, AP2M1, NRXN1, ANXA5, PENK, SLC1A2, SNAP25 and TRH, were demonstrated as key genes regulating egg production in indigenous chickens, and the SNPs sites within the genes might be served as markers to provide a guide for indigenous chicken breeding.  These findings provide a novel insight for further understanding the regulatory mechanisms of egg-laying performance and developing molecular markers to improve egg production of indigenous breeds.

Leaf area index (LAI) is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen (N) status of crops.  This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice.  Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014.  Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice.  LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period.  Group and individual plant characters (e.g., tiller number (TN) and plant height (H)) were investigated simultaneously.  Two N indicators of plant N accumulation (NA) and N nutrition index (NNI) were measured as well.  A calibration equation (LAI=1.7787LAI₂₀₀₀–0.8816, R²=0.870**) was developed for LAI-2000.  The linear regression analysis showed a significant relationship between NA and actual LAI (R²=0.863**).  For the NNI, the relative LAI (R2=0.808**) was a relatively unbiased variable in the regression than the LAI (R²=0.33**).  The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice (NA=29.778LAI–5.9397; NNI=0.7705RLAI+0.2764).  Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H (LAI=–0.3375(TH×H×0.01)²+3.665(TH×H×0.01)–1.8249, R²=0.875**).  With these models, the N status of rice can be diagnosed conveniently in the field.

The objective of this work was to develop a dynamic model for describing leaf curves and a detailed spatial geometry model of the rice leaf (including sub-models for unexpanded leaf blades, expanded leaf blades, and leaf sheaths), and to realize three-dimensional (3D) dynamic visualization of rice leaves by combining relevant models.  Based on the experimental data of different cultivars and nitrogen (N) rates, the time-course spatial data of leaf curves on the main stem were collected during the rice development stage, then a dynamic model of the rice leaf curve was developed using quantitative modeling technology.  Further, a detailed 3D geometric model of rice leaves was built based on the spatial geometry technique and the non-uniform rational B-spline (NURBS) method.  Validating the rice leaf curve model with independent field experiment data showed that the average distances between observed and predicted curves were less than 0.89 and 1.20 cm at the tilling and jointing stages, respectively.  The proposed leaf curve model and leaf spatial geometry model together with the relevant previous models were used to simulate the spatial morphology and the color dynamics of a single leaf and of leaves on the rice plant after different growing days by 3D visualization technology.  The validation of the leaf curve model and the results of leaf 3D visualization indicated that our leaf curve model and leaf spatial geometry model could efficiently predict the dynamics of rice leaf spatial morphology during leaf development stages.  These results provide a technical support for related research on virtual rice.

This paper was to develop a model for simulating the leaf color changes in rice (Oryza sativa L.) based on RGB (red, green, and blue) values. Based on rice experiment data with different cultivars and nitrogen (N) rates, the time-course RGB values of each leaf on main stem were collected during the growth period in rice, and a model for simulating the dynamics of leaf color in rice was then developed using quantitative modeling technology. The results showed that the RGB values of leaf color gradually decreased from the initial values (light green) to the steady values (green) during the first stage, remained the steady values (green) during the second stage, then gradually increased to the final values (from green to yellow) during the third stage. The decreasing linear functions, constant functions and increasing linear functions were used to simulate the changes in RGB values of leaf color at the first, second and third stages with growing degree days (GDD), respectively; two cultivar parameters, MatRGB (leaf color matrix) and AR (a vector composed of the ratio of the cumulative GDD of each stage during color change process of leaf n to that during leaf n drawn under adequate N status), were introduced to quantify the genetic characters in RGB values of leaf color and in durations of different stages during leaf color change, respectively; FN (N impact factor) was used to quantify the effects of N levels on RGB values of leaf color and on durations of different stages during leaf color change; linear functions were applied to simulate the changes in leaf color along the leaf midvein direction during leaf development process. Validation of the models with the independent experiment dataset exhibited that the root mean square errors (RMSE) between the observed and simulated RGB values were among 8 to 13, the relative RMSE (RRMSE) were among 8 to 10%, the mean absolute differences (da) were among 3.85 to 6.90, and the ratio of da to the mean observation values (dap) were among 3.04 to 4.90%. In addition, the leaf color model was used to render the leaf color change over growth progress using the technology of visualization, with a good performance on predicting dynamic changes in rice leaf color. These results would provide a technical support for further developing virtual plant during rice growth and development.

The accurate assessment of the spatiotemporal changes in soil nutrients influenced by agricultural production provides the basis for development of management strategies to maintain soil fertility and balance soil nutrients. In this paper, we combined spatial measurements from 2 157 soil samples and geostatistical analysis to assess the spatiotemporal changes in soil organic carbon (SOC), total nitrogen (TN), available phosphorus (AP) and available potassium content (AK) from the first soil survey (in the 1980s) to the second soil survey (in the 2000s) in the Taihu region of Jiangsu Province in China. The results showed that average soil nutrients in three soil types all exhibited the increased levels in the 2000s (except for AK in the yellow brown soil). The standard deviation of soil nutrient contents increased (except for TN in the paddy soil). Agricultural production in the 20 years led to increases in SOC, TN, AP and AK by 74, 82, 89 and 65%, respectively, of the Taihu areas analyzed. From the 1980s to 2000s all the nugget/sill ratios of soil nutrients indices were between 25 and 75% (except for AK in the yellow brown soil in the 2000s), indicating moderate spatial dependence. The ratio of AP in the yellow brown soil in the 2000s was 88.74%, showing weak spatial dependence. The spatial correlation range values for SOC, TN, AP and AK in the 2000s all decreased. The main areas showing declines in SOC, TN and AP were in the northwest. For AK, the main region with declining levels was in the east and middle of western areas. Apparently, the increase in soil nutrients in the Taihu region can be mainly attributed to the large increase in fertilizer inputs, change in crop systems and enhanced residues management since the 1980s. Future emphasis should be placed on avoiding excess fertilizer inputs and balancing the effects of the fertilizers in soils.

Plant nitrogen (N) uptake is a good indicator of crop N status. In this study, a new method was designed to determine the central wavelength, optimal bandwidth and vegetation indices for predicting plant N uptake (g N m-2) in winter wheat (Triticum aestivum L.). The data were collected from the ground-based hyperspectral reflectance measurements in eight field experiments on winter wheat of different years, eco-sites, varieties, N rates, sowing dates, and densities. The plant N uptake index (PNUI) based on NDVI of 807 nm combined with 736 nm was selected as the optimal vegetation index, and a linear model was developed with R2 of 0.870 and RMSE of 1.546 g N m-2 for calibration, and R2 of 0.834, RMSE of 1.316 g N m-2, slope of 0.934, and intercept of 0.001 for validation. Then, the effect of the bandwidth of central wavelengths on model performance was determined based on the interaction between central wavelength and bandwidth expansion. The results indicated that the optimal bandwidth varies with the changes of the central wavelength and with the interaction between the two bands in one vegetation index. These findings are important for prediction and diagnosis of plant N uptake more precise and accurate in crop management.

The vast area and marked variation of China make it difficult to predict the impact of climate changes on rice productivity in different regions. Therefore, analyzing the spatial and temporal characteristics of rice potential productivity and predicting the possible yield increment in main rice production regions of China is important for guiding rice production and ensuring food security. Using meteorological data of main rice production regions from 1961 to 1970 (the 1960s) and from 1996 to 2005 (the 2000s) provided by 333 stations, the potential photosynthetic, photo-thermal and climatic productivities in rice crop of the 1960s and 2000s in main rice production regions of China were predicted, and differences in the spatial and temporal distribution characteristics between two decades were analyzed. Additionally, the potential yield increment based on the high yield target and actual yield of rice in the 2000s were predicted. Compared with the 1960s, the potential photosynthetic productivity of the 2000s was seen to have decreased by 5.40%, with rates in northeastern and southwestern China found to be lower than those in central and southern China. The potential photo-thermal productivity was generally seen to decrease (2.56%) throughout main rice production regions, decreasing most in central and southern China. However, an increase was seen in northeastern and southwestern China. The potential climatic productivity was observed to be lower (7.44%) in the 2000s compared to the 1960s, but increased in parts of central and southern China. The potential yield increment from the actual yield to high yield target in the 2000s were no more than 6×103 kg ha-1 and ranged from 6×103 to 12×103 kg ha-1 in most of the single- and double-cropping rice growing regions, respectively. The yield increasing potential from the high yield target to the potential photo-thermal productivity in 2000s were less than 10×103 kg ha-1 and ranged from 10×103 to 30×103 kg ha-1 in most of the single- and double-cropping rice growing regions, respectively. The potential yield increment contributed by irrigation was between 5×103 and 20×103 kg ha-1, and between 20×103 and 40×103 kg ha-1 in most of the single- and double-cropping rice growing regions, respectively. These findings suggested that the high yield could be optimized by making full use of climatic resources and through a reasonable management plan in rice crop.

Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspectral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822,R738) during the early-mid period (from jointing to booting), and it was RVI (R822,R738) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.