scholarly journals Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes

2021 ◽  
Author(s):  
Lucas Costa ◽  
Jordan McBreen ◽  
Yiannis Ampatzidis ◽  
Jia Guo ◽  
Mostafa Reisi Gahrooei ◽  
...  
Keyword(s):  
Heat Stress ◽  
Grain Yield ◽  
Hyperspectral Imaging ◽  
Complex Traits ◽  
Small Sample ◽  
Data Availability ◽  
Physiological Traits ◽  
Functional Regression ◽  
Yield Prediction ◽  
Genetic Gains

AbstractQuantifying certain physiological traits under heat-stress is crucial for maximizing genetic gain for wheat yield and yield-related components. In-season estimation of different physiological traits related to heat stress tolerance can ensure the finding of germplasm, which could help in making effective genetic gains in yield. However, estimation of those complex traits is time- and labor-intensive. Unmanned aerial vehicle (UAV) based hyperspectral imaging could be a powerful tool to estimate indirectly in-season genetic variation for different complex physiological traits in plant breeding that could improve genetic gains for different important economic traits, like grain yield. This study aims to predict in-season genetic variations for cellular membrane thermostability (CMT), yield and yield related traits based on spectral data collected from UAVs; particularly, in cases where there is a small sample size to collect data from and a large range of features collected per sample. In these cases, traditional methods of yield-prediction modeling become less robust. To handle this, a functional regression approach was employed that addresses limitations of previous techniques to create a model for predicting CMT, grain yield and other traits in wheat under heat stress environmental conditions and when data availability is constrained. The results preliminarily indicate that the overall models of each trait studied presented a good accuracy compared to their data’s standard deviation. The yield prediction model presented an average error of 13.42%, showing the function-on-function algorithm chosen for the model as reliable for small datasets with high dimensionality.

scholarly journals Morpho-physiological traits associated with heat stress tolerance in tropical maize (Zea mays L.) at reproductive stage

2019 ◽  
Vol 13 ((04) 2019) ◽  
pp. 536-545 ◽  
Author(s):  
Jewel Jameeta Noor ◽  
M.T. Vinayan ◽  
Shahid Umar ◽  
Pooja Devi ◽  
Muhammad Iqbal ◽  
...  
Keyword(s):  
Heat Stress ◽  
Grain Yield ◽  
Stress Tolerance ◽  
Inbred Lines ◽  
Physiological Traits ◽  
Optimal Temperature ◽  
Tropical Maize ◽  
Important Trait ◽  
Heat Stress Tolerance ◽  
Secondary Traits

Heat stress resilience has emerged as an important trait in maize hybrids targeted for post–monsoon spring cultivation in large parts of South Asia and many other parts of the tropics. Selection based on grain yield alone under heat stress is often misleading, and therefore an approach involving stress-adaptive secondary traits along with grain yield could help in the development of improved, stable heat stress tolerant cultivars. We attempted to identify reliable and effective secondary traits associated with heat stress tolerance in tropical maize and sources of heat stress tolerant germplasm. A panel of 99 elite maize inbred lines representing the wider genetic diversity of tropical maize and a set of 58 elite hybrids were phenotyped under natural heat stress and optimal temperature for grain yield and 15 secondary traits including 10 morpho-physiological traits and 5 yield attributes. Evaluation under natural heat stress was done during the spring season by adjusting the planting date so that the complete reproductive stage (from tassel emergence to late grain filling) was exposed to heat stress. The optimal temperature trial was planted during the monsoon season with no exposure to heat stress at any crop stage. Heat stress significantly affected most of the observed traits. Among the traits studied two yield attributing traits, i.e.- ears per plant (EPP) and kernel per row (KPR), and three morpho-physiological traits, i.e.- chlorophyll content (CC), leaf firing (LF) and tassel blast (TB) were found to be the key secondary traits associated with grain yield under heat stress. In addition, low anthesis-silking internal (ASI) is an important trait that needs to be added in the index selection for heat stress tolerance. The study identified nine promising heat stress tolerant maize inbred lines with desirable secondary traits and grain yield under severe heat stress, which could be used as source germplasm in heat stress tolerance maize breeding program.

scholarly journals Variance components of combining ability for different morpho-physiological traits for heat tolerance in bread wheat

2017 ◽  
Vol 9 (3) ◽  
pp. 1338-1342
Author(s):  
Amarjeet Kumar ◽  
Swati Swati ◽  
N. K. Singh ◽  
Birendra Prasad ◽  
Anil Kumar
Keyword(s):  
Heat Stress ◽  
Grain Yield ◽  
Chlorophyll Content ◽  
Bread Wheat ◽  
Heat Tolerance ◽  
Climate Scenario ◽  
Kernel Weight ◽  
Grain Weight ◽  
Physiological Traits ◽  
Physiological Characters

To estimate the level of heat tolerance for different genotypes of bread wheat with respect to morphological characters under studied grains/ spike, grain weight/spike, grain filling duration (duration between the anthesis stage and the physiological maturity), 1000-kernel weight and grain yield/plant for yield. Physiological traits like relative injury (RI %), chlorophyll content, canopy temperature depression (CTD), were used in present investigation to contribute toward capability of plants to tolerate heat stress of the yield contributing traits during heat stress.The findings of present investigation had clearly explained that influences of environments on morpho physiological characters i.e. grain yield per plant (14886.15) and its attributing traits i.e. spike length (459.7), tillers per plant (622.34), spikelets per spike (278.1), 1000 kernel weight (13262.39), grain weight per spike (177.89) and number of grains per spike (2898.44) in wheat were highly significant and positive. Among the parent and their crosses had handsome amount of variations across the environment. The results of interaction for environments with parents, lines, testers and their crosses with respect to morpho physiological characters in wheat was found significant for some characters while variation was absent for other characters studied. Physiological traits like relative injury per cent, chlorophyll content and CTD were vital parameters to quantify the degree of heat stress to develop tolerant genotypes which is urgent and present need under changing climate scenario.

Genetic Gains in Grain Yield and Physiological Traits of Winter Wheat in Shandong Province, China, from 1969 to 2006

Crop Science ◽  
2012 ◽  
Vol 52 (1) ◽  
pp. 44-56 ◽  
Author(s):  
Y. G. Xiao ◽  
Z. G. Qian ◽  
K. Wu ◽  
J. J. Liu ◽  
X. C. Xia ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Shandong Province ◽  
Physiological Traits ◽  
Genetic Gains

Genetic gains in grain yield and physiological traits of winter wheat in Hebei Province of China, from 1964 to 2007

2019 ◽  
Vol 239 ◽  
pp. 114-123 ◽  
Author(s):  
Yanrong Yao ◽  
Lihua Lv ◽  
Lihua Zhang ◽  
Haipo Yao ◽  
Zhiqiang Dong ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Hebei Province ◽  
Physiological Traits ◽  
Genetic Gains

scholarly journals Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

2021 ◽  
Vol 13 (12) ◽  
pp. 2338
Author(s):  
Shuaipeng Fei ◽  
Muhammad Adeel Hassan ◽  
Zhonghu He ◽  
Zhen Chen ◽  
Meiyan Shu ◽  
...  
Keyword(s):  
Grain Yield ◽  
Ensemble Learning ◽  
Complex Traits ◽  
Multispectral Imaging ◽  
Grain Filling ◽  
Support Vector ◽  
Growth Stages ◽  
Yield Prediction ◽  
Multispectral Data ◽  
Irrigation Treatments

Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield between for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat.

Photosynthetic organs contributions to grain yield genetic gains in Chilean winter wheat

2021 ◽  
Author(s):  
Abdelhalim Elazab ◽  
Felipe Moraga ◽  
Alejandro del Pozo
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Genetic Gains

scholarly journals Epistasis for Three Grain Yield Components in Rice (Oryxa sativa L.)

Genetics ◽  
1997 ◽  
Vol 145 (2) ◽  
pp. 453-465 ◽  
Author(s):  
Zhikang Li ◽  
Shannon R M Pinson ◽  
William D Park ◽  
Andrew H Paterson ◽  
James W Stansel
Keyword(s):  
Grain Yield ◽  
Complex Traits ◽  
Yield Components ◽  
Genetic Basis ◽  
Kernel Weight ◽  
Progeny Testing ◽  
Grain Yield Components ◽  
Main Effects ◽  
Grain Number Per Panicle

The genetic basis for three grain yield components of rice, 1000 kernel weight (KW), grain number per panicle (GN), and grain weight per panicle (GWP), was investigated using restriction fragment length polymorphism markers and F4 progeny testing from a cross between rice subspecies japonica (cultivar Lemont from USA) and indica (cv. Teqing from China). Following identification of 19 QTL affecting these traits, we investigated the role of epistasis in genetic control of these phenotypes. Among 63 markers distributed throughout the genome that appeared to be involved in 79 highly significant (P < 0.001) interactions, most (46 or 73%) did not appear to have “main” effects on the relevant traits, but influenced the trait(s) predominantly through interactions. These results indicate that epistasis is an important genetic basis for complex traits such as yield components, especially traits of low heritability such as GN and GWP. The identification of epistatic loci is an important step toward resolution of discrepancies between quantitative trait loci mapping and classical genetic dogma, contributes to better understanding of the persistence of quantitative genetic variation in populations, and impels reconsideration of optimal mapping methodology and marker-assisted breeding strategies for improvement of complex traits.

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