Effects of waterlogging at different stages and durations on maize growth and grain yields

2022 ◽  
Vol 261 ◽  
pp. 107334
Author(s):  
Chao Huang ◽  
Yang Gao ◽  
Anzhen Qin ◽  
Zugui Liu ◽  
Ben Zhao ◽  
...  
Keyword(s):  
Grain Yields ◽  
Maize Growth

scholarly journals Evaluation of In-Season Nitrogen Management for Summer Maize in North Central China

ISRN Agronomy ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
S.-C. Zhao ◽  
P. He ◽  
Z.-M. Sha ◽  
S.-L. Xing ◽  
K.-J. Li
Keyword(s):  
Field Experiments ◽  
Early Summer ◽  
Central China ◽  
Summer Maize ◽  
North Central ◽  
Grain Yields ◽  
Maize Growth ◽  
N Supply ◽  
N Management ◽  
N Rates

We conducted field experiments in which nitrogen (N) was applied to summer maize at different rates and different basal/topdressing ratios. The experiments were carried out in 2009 in Hengshui and Xinji, Hebei province, China. The results showed that basal application of N was necessary for maize growth in early summer and for high grain yields. For the Hengshui and Xinji sites, 30 and 57 kg N ha−1, respectively, would meet the N demands of maize before 7-leaf stage. The total rates of 120 and 180 kg N ha−1, respectively, would maximize grain yields, and in-season N management based on crop N demands and soil N supply could reduce N inputs by more than 50% in Hengshui and 25% in Xinji, respectively, in one maize growth season, compared with farmers' practice, but the sustainability of the optimum N rates for maximum grain yield of next seasons crop needs to be further studied. Optimum N management should take into account the existing nutrient conditions at each site, soil fertility and texture, and crop demands.

QUALITATIVE STABILITY OF WINTER WHEAT VARIETIES AT DIFFERENT LOCATIONS

2000 ◽  
Vol 48 (2) ◽  
pp. 191-195
Author(s):  
C. Kondora ◽  
M. Szabó ◽  
A. Máté ◽  
G. Szabó
Keyword(s):  
Quality Control ◽  
Winter Wheat ◽  
Evaluation Method ◽  
Wheat Varieties ◽  
Local Conditions ◽  
Grain Yields ◽  
Baking Quality ◽  
Small Plot ◽  
High Yielding Varieties ◽  
Winter Wheat Varieties

Owing to the significant differences in the adaptability of state-registered varieties, those which can adapt well to the local conditions should be given preference. There are several high-yielding varieties available in Hungary with excellent agronomic properties, good adaptability and satisfactory baking quality. This study was conducted to analyse the adaptability of 34 state-registered winter wheat varieties tested in the small plot trials of the National Institute for Agricultural Quality Control (NIAQC) at 5–9 locations between 1994 and 1997 based on their gluten quantity and farinographic index. For the comparison of the varieties the evaluation method of Eberhart and Russell (1966) was applied as modified by Bedő and Balla (1977). The qualitative stability and adaptability values of the varieties differ from the adaptability and stability values calculated from the grain yields. Some winter wheat varieties have good qualitative adaptability and stability, while others have special adaptability and poor qualitative stability, but the majority of the varieties do not belong to these groups.

Effects of continuous straw return to soil on maize growth and soil chemical and physical characteristics

2012 ◽  
Vol 20 (3) ◽  
pp. 291-296 ◽  
Author(s):  
Ping MU ◽  
En-He ZHANG ◽  
Han-Ning WANG ◽  
Yong-Feng FANG
Keyword(s):  
Physical Characteristics ◽  
Maize Growth ◽  
Straw Return

Maize Growth and Yield Following Late Summer Hail

Crop Science ◽  
1994 ◽  
Vol 34 (5) ◽  
pp. 1400-1403 ◽  
Author(s):  
L. M. Dwyer ◽  
D. W. Stewart ◽  
L. Evenson ◽  
B. L. Ma
Keyword(s):  
Late Summer ◽  
Growth And Yield ◽  
Maize Growth

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals Identifying new variation at the J locus, previously identified as e6, in long juvenile ‘Paranagoiana’ soybean

2021 ◽  
Author(s):  
Nour Nissan ◽  
Elroy R. Cober ◽  
Michael Sadowski ◽  
Martin Charette ◽  
Ashkan Golshani ◽  
...  
Keyword(s):  
Single Gene ◽  
Pcr Amplification ◽  
Gene Control ◽  
Grain Yields ◽  
Short Day ◽  
Exon 4 ◽  
Short Days ◽  
Kasp Markers

Abstract Key message A previously identified soybean maturity locus, E6, is discovered to be J, with the long juvenile allele in Paranagoiana now deemed j−x. Abstract Soybean grown at latitudes of ~20° or lower can produce lower grain yields due to the short days. This limitation can be overcome by using the long juvenile trait (LJ) which delays flowering under short day conditions. Two LJ loci have been mapped to the same location on Gm04, J and E6. The objective of this research was to investigate the e6 allele in ‘Paranagoiana’ and determine if E6 and J are the same locus or linked loci. KASP markers showed that e6 lines did not have the j−1 allele of LJ PI 159925. A population fixed for E1 but segregating for E6, with e6 introgressed from Paranagoiana, showed single gene control for flowering and maturity under short days. Sequencing Glyma.04G050200, the J gene, with long amplification Taq found that the e6 line ‘Paranagoiana’ contains a Ty1-copia retrotransposon of ~10,000 bp, inserted within exon 4. PCR amplification of the cDNA of Glyma.04G050200 also showed differences between the mRNA sequences (presence of insertion in j−x). Hence, we conclude that the loci E6 and J are one locus and deem this new variation found in Paranagoiana as j−x.

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