Wenshan Guo

Assessment of the AquaCrop model for use in simulation of irrigated winter wheat canopy cover, biomass, and grain yield in the North China Plain.

Improving winter wheat water use efficiency in the North China Plain (NCP), China is essential in light of current irrigation water shortages. In this study, the AquaCrop model was used to calibrate, and validate winter wheat crop performance under various planting dates and irrigation application rates. All experiments were conducted at the Xiaotangshan experimental site in Beijing, China, during seasons of 2008/2009, 2009/2010, 2010/2011 and 2011/2012. This model was first calibrated using data from 2008/2009 and 2009/2010, and subsequently validated using data from 2010/2011 and 2011/2012. The results showed that the simulated canopy cover (CC), biomass yield (BY) and grain yield (GY) were consistent with the measured CC, BY and GY, with corresponding coefficients of determination (R2) of 0.93, 0.91 and 0.93, respectively. In addition, relationships between BY, GY and transpiration (T), (R2 = 0.57 and 0.71, respectively) was observed. These results suggest that frequent irrigation with a small amount of water significantly improved BY and GY. Collectively, these results indicate that the AquaCrop model can be used in the evaluation of various winter wheat irrigation strategies. The AquaCrop model predicted winter wheat CC, BY and GY with acceptable accuracy. Therefore, we concluded that AquaCrop is a useful decision-making tool for use in efforts to optimize wheat winter planting dates, and irrigation strategies.

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).

Effect of nitrogen levels and nitrogen ratios on lodging resistance and yield potential of winter wheat (Triticum aestivum L.).

Lodging is one of the constraints that limit wheat yields and quality due to the unexpected bending or breaking stems on wheat (Triticum aestivum L.) production worldwide. In addition to choosing lodging resistance varieties, husbandry practices also have a significant effect on lodging. Nitrogen management is one of the most common and efficient methods. A field experiment with Yangmai 20 as research material (a widely-used variety) was conducted to study the effects of different nitrogen levels and ratios on culm morphological, anatomical characters and chemical components and to explore the nitrogen application techniques for lodging tolerance and high yield. Results showed that some index of basal internodes, such as stem wall thickness, filling degree, lignin content, cellulose content, water-soluble carbohydrate (WSC) and WSC/N ratio, were positively and significantly correlated with culm lodging-resistant index (CLRI). As the increase of nitrogen level and basal nitrogen ratio, the basal internodes became slender and fragile with the thick stem wall, while filling degree, chemical components and the strength of the stem decreased gradually, which significantly increased the lodging risk. The response of grain yield to nitrogen doses was quadratic and grain yield reached the highest at the nitrogen ratio of 50%:10%:20%:20% (the ratio of nitrogen amount applied before sowing, at tillering stage, jointing stage and booting stage respectively, abbreviated as 5:1:2:2). These results suggested that for Yangmai 20, the planting density of 180×104ha-1, nitrogen level of 225 kg ha-1, and the ratio of 5: 1: 2: 2 effectively increased lodging resistance and grain yield. This combination of planting density and nitrogen level and ratio could effectively relieve the contradiction between high-yielding and anti-lodging.

Rice and wheat grain counting method and software development based on Android system

We proposed a fast rice and wheat grain counting methods based on mobile phones.A new formula on image feature points and the number of grains was put forward.The counting time of this study is generally less than conventional methods. Thousand grain weight is the main component of rice and wheat yields, and an important indicator for variety breeding and cultivation management. Grain counting is an essential step for thousand grain weight measurement. Among several current counting methods, manual counting is laborious and time-consuming; electronic counting devices are expensive; the counting accuracy based on image segmentation processing is not high; and their uses are inconvenient. This study attempts to develop an application program (APP) for fast rice and wheat grain counting based on Android mobile phones for convenient use. The study identifies the relationship between image feature points and the number of grains, explores the measurement method of image feature points, and compares it with existing counting methods in terms of similarities and differences. The study also formulates grain counting calculations and develops an application program that is easy to operate. The high accuracy of this counting method has been demonstrated by tests of different varieties. The error ratio are below 2%. The program has a short running time. The counting time is generally less than one second (1s) for no more than 400 seeds. The program is convenient and easy to operate. The counting and batch-processing operations are simple. In summary, the grain counting method built in this study can be used as an effective rice and wheat grain counting tool. This study also provides a reference for the development of application programs for grain counting of other kinds of crops.

Grain Yield, Starch Content and Activities of Key Enzymes of Waxy and Non-waxy Wheat (Triticum aestivum L.)

Waxy wheat has unique end-use properties; however, its production is limited due mainly to its low grain yield compared with non-waxy wheat. In order to increase its grain yield, it is critical to understand the eco-physiological differences in grain filling between the waxy and non-waxy wheat. In this study, two waxy wheat and two non-waxy wheat cultivars were used to investigate the differences in starch-associated enzymes processes, sucrose and starch dynamics, yield components, and the final grain yield. The results indicated that the mean total grain starch and amylose content, the average 1000-kernel weight and grain yield of the waxy wheat were lower than those of the non-waxy wheat at maturity. The amylose content was significantly and positively correlated with the activity of GBSS (r = 0.80, p < 0.01). Significant positive correlation also exists among activities of AGPase, SSS, GBSS, and SBE, except for GBSS-SBE. In summary, our study has revealed that the reduced conversion of sucrose to starch in the late grain filling stage is the main cause for the low kernel weight and total starch accumulation of the waxy wheat. The reduced conversion also appears to be a factor contributing to the lower grain yield of the waxy wheat.

Evaluation of Seed Emergence Uniformity of Mechanically Sown Wheat with UAV RGB Imagery

The uniformity of wheat seed emergence is an important characteristic used to evaluate cultivars, cultivation mode and field management. Currently, researchers typically investigated the uniformity of seed emergence by manual measurement, a time-consuming and laborious process. This study employed field RGB images from unmanned aerial vehicles (UAVs) to obtain information related to the uniformity of wheat seed emergence and missing seedlings. The calculation of the length of areas with missing seedlings in both drill and broadcast sowing can be achieved by using an area localization algorithm, which facilitated the comprehensive evaluation of uniformity of seed emergence. Through a comparison between UAV images and the results of manual surveys used to gather data on the uniformity of seed emergence, the root-mean-square error (RMSE) was 0.44 for broadcast sowing and 0.64 for drill sowing. The RMSEs of the numbers of missing seedling regions for broadcast and drill sowing were 1.39 and 3.99, respectively. The RMSEs of the lengths of the missing seedling regions were 12.39 cm for drill sowing and 0.20 cm2 for broadcast sowing. The UAV image-based method provided a new and greatly improved method for efficiently measuring the uniformity of wheat seed emergence. The proposed method could provide a guideline for the intelligent evaluation of the uniformity of wheat seed emergence.

Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data

Abstract The appropriate spectral vegetation indices can be used to rapidly and non-destructively estimate the leaf nitrogen concentration (LNC) in wheat for on-farm wheat management. However, the accuracy of estimation should be further improved. Previous studies focused on developing vegetation indices, but research about modeling algorithms were limited. In this study, multiple-kernel support vector regression (MK-SVR) was used to assess the LNC in wheat based on satellite remote sensing data. The objectives of this study were to (1) investigate the applicability of the MK-SVR algorithm for remotely estimating the LNC in wheat, (2) test the performance of the MK-SVR regression model, and (3) compare the performance of the MK-SVR algorithm with multiple linear regression (MLR), partial least squares (PLS), artificial neural networks (ANNs), and single-kernel SVR (SK-SVR) algorithms for wheat LNC estimation. In-situ LNC data over four years at different sites in Jiangsu Province of China were measured during the jointing, booting, and anthesis stages; one HJ-CCD image of wheat was obtained during each stage. Vegetation indices were calculated based on these images, and correlations between vegetation indices and LNC data were measured. Finally, a MK-SVR model whose inputs were vegetation indices was established to estimate the LNC during each stage. The results showed that the MK-SVR model performed well in estimating LNC. The coefficients of determination (R2) of the estimated-versus-measured LNC values for the three stages were respectively 0.73, 0.82, and 0.75, meanwhile, the corresponding root mean square errors (RMSE) and the relative RMSE were respectively 0.13 and 6.6%, 0.21 and 7.7%, and 0.20 and 6.5%. Thus, the MK-SVR algorithm provides an effective way to improve the prediction accuracy of LNC in wheat on a large scale.

Author Correction: Grain Yield, Starch Content and Activities of Key Enzymes of Waxy and Non-waxy Wheat (Triticum aestivum L.)

A correction to this article has been published and is linked from the HTML version of this paper. The error has not been fixed in the paper.

Does cyclic water stress damage wheat yield more than a single stress

The occurrence of water stress during wheat growth is more frequent due to climate change. Three experiments (cyclic drought, cyclic waterlogging, and cyclic drought plus waterlogging) were conducted to investigate the effects of mild and severe cyclic/single water stress at elongation and heading stages on winter wheat (Triticum aestivum L.) yield. The effect of either mild drought at elongation or mild waterlogging at heading on wheat yield was not significant; however, significance did occur under other single water stresses. As the stress becomes more severe, the yield loss significantly increases. Extreme drought/waterlogging treatment at elongation caused a greater yield penalty than stress at heading stage. Except the combination of mild drought and mild waterlogging treatment, cyclic water stress significantly decreased wheat yields. The decrease in wheat yield under cyclic severe drought and waterlogging was significantly higher than any other treatment, with percentage decreases of 71.52 and 73.51%, respectively. In general, a yield reduction from mild cyclic water stress did not indicate more severe damage than single treatments; in contrast, grain yield suffered more when water stress occurred again after severe drought and waterlogging. Drought during elongation significantly decreased kernel number, whereas drought at heading/waterlogging during elongation and heading decreased the spike weight, which might be the main reason for the yield penalty. Furthermore, water stress caused variation in the decrease of total biomass and/or harvest index. The present study indicates comprehensive understanding of the types, degree, and stages of water stress are essential for assessing the impact of multiple water stresses on wheat yield.

Estimating the responses of winter wheat yields to moisture variations in the past 35 years in Jiangsu Province of China

Jiangsu is an important agricultural province in China. Winter wheat, as the second major grain crop in the province, is greatly affected by moisture variations. The objective of this study was to investigate whether there were significant trends in changes in the moisture conditions during wheat growing seasons over the past decades and how the wheat yields responded to different moisture levels by means of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The study started with a trend analysis and quantification of the moisture conditions with the Mann-Kendall test and Sen’s Slope method, respectively. Then, correlation analysis was carried out to determine the relationship between de-trended wheat yields and multi-scalar SPEI. Finally, a multivariate panel regression model was established to reveal the quantitative yield responses to moisture variations. The results showed that the moisture conditions in Jiangsu were generally at a normal level, but this century appeared slightly drier in because of the relatively high temperatures. There was a significant correlation between short time scale SPEI values and wheat yields. Among the three critical stages of wheat development, the SPEI values in the late growth stage (April-June) had a closer linkage to the yields than in the seedling stage (October-November) and the over-wintering stage (December-February). Moreover, the yield responses displayed an asymmetric characteristic, namely, moisture excess led to higher yield losses compared to moisture deficit in this region. The maximum yield increment could be obtained under the moisture level of slight drought according to the 3-month SPEI at the late growth stage, while extreme wetting resulted in the most severe yield losses. The moisture conditions in the first 15 years of the 21st century were more favorable than in the last 20 years of the 20th century for wheat production in Jiangsu.