Sugarcane has a large, complex, polyploid genome that has hindered the progress of genomic research and molecular marker-assisted selection.  The user-friendly SSR markers have attracted considerable attention owing to their ideal genetic attributes.  However, these markers were not characterized and developed at the genome-wide scale due to the previously lacking high-quality chromosome-level assembled sugarcane genomes.  In this present study, 744 305 and 361 638 candidate SSRs were identified from the genomes of S. officinarum and S. spontaneum, respectively.  We verified the reliability of the predicted SSRs by using 1 200 interspecific SSR primer pairs to detect polymorphisms among 11 representative accessions of Saccharum, including S. spontaneum, S. officinarum, S. robustum, and modern sugarcane hybrid.  The results showed that 660 SSR markers displayed interspecific polymorphisms among these accessions.  Furthermore, 100 SSRs were randomly selected to detect the genetic diversity for 39 representative Saccharum accessions.  A total of 320 alleles were generated using 100 polymorphic primers, with each marker ranging from two to seven alleles.  The genetic diversity analysis revealed that these accessions were distributed in four main groups, including group I (14 S. spontaneum accessions), group II (two S. officinarum accessions), group III (18 modern sugarcane hybrid accessions), and group IV (five S. robustum accessions).  Experimental verification supported the reliability of the SSR markers based on genome-wide predictions.  The development of a large number of SSR markers based on wet experiments is valuable for genetic studies, including genetic linkage maps, comparative genome analysis, genome-wide association studies, and marker-assisted selection in Saccharum.

Alginate oligosaccharides (AOS), belonging to the class of functional marine oligosaccharides, are low-molecular polymers linked by β-1,4-mannuronic acid (M) and α-1,4-guluronic acid (G), which could be classically obtained by enzymatic hydrolysis of alginate. With low viscosity and good water solubility, as well as anti-oxidant, immune regulation, anti-bacterial and anti-inflammatory activities, AOS have been widely used in medical science and functional food, green agriculture and other fields. As new bio-feed additives, AOS have broad potential applications in animal husbandry. In this review, the sources of alginate, chemical structure and preparation methods of AOS, and their biological activities and application in livestock and poultry are summarized. We expect this review could contribute to lay a foundation of application and further research for AOS in livestock and poultry.

Visualization of simulated crop growth and development is of significant interest to crop research and production.  This study aims to address the phenomenon of organs cross-drawing by developing a method of collision detection for improving vivid 3D visualizations of virtual wheat crops.  First, the triangular data of leaves are generated with the tessellation of non-uniform rational B-splines surfaces.  Second, the bounding volumes (BVs) and bounding volume hierarchies (BVHs) of leaves are constructed based on the leaf morphological characteristics and the collision detection of two leaves are performed using the Separating Axis Theorem.  Third, the detecting effect of the above method is compared with the methods of traditional BVHs, Axis-Aligned Bounding Box (AABB) tree, and Oriented Bounding Box (OBB) tree.  Finally, the BVs of other organs (ear, stem, and leaf sheath) in virtual wheat plant are constructed based on their geometric morphology, and the collision detections are conducted at the organ, individual and population scales.  The results indicate that the collision detection method developed in this study can accurately detect collisions between organs, especially at the plant canopy level with high collision frequency.  This collision detection-based virtual crop visualization method could reduce the phenomenon of organs cross-drawing effectively and enhance the reality of visualizations.

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.

Despite the improvement in cultivar characters and management practices, large gaps between the attainable and potential yields still exist in winter wheat of China.  Quantifying the crop potential yield is essential for estimating the food production capacity and improving agricultural policies to ensure food security.  Gradually descending models and geographic information system (GIS) technology were employed to characterize the spatial variability of potential yields and yield gaps in winter wheat across the main production region of China.  The results showed that during 2000–2010, the average potential yield limited by thermal resource (YG_T) was 23.2 Mg ha^–1, with larger value in the northern area relative to the southern area.  The potential yield limited by the water supply (YG_W) generally decreased from north to south, with an average value of 1.9 Mg ha^–1 across the entire study region.  The highest YG_W in the north sub-region (NS) implied that the irrigation and drainage conditions in this sub-region must be improved.  The averaged yield loss of winter wheat from nutrient deficiency (YG_N) varied between 2.1 and 3.1 Mg ha^–1 in the study area, which was greater than the yield loss caused by water limitation.  The potential decrease in yield from photo-thermal-water-nutrient-limited production to actual yield (YG_O) was over 6.0 Mg ha^–1, ranging from 4.9 to 8.3 Mg ha^–1 across the entire study region, and it was more obvious in the southern area than in the northern area.  These findings suggest that across the main winter wheat production region, the highest yield gap was induced by thermal resources, followed by other factors, such as the level of farming technology, social policy and economic feasibility.  Furthermore, there are opportunities to narrow the yield gaps by making full use of climatic resources and developing a reasonable production plan for winter wheat crops.  Thus, meeting the challenges of food security and sustainability in the coming decades is possible but will require considerable changes in water and nutrient management and socio-economic policies.

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.