The Ideotype for Seed Size: A Model Examining the Relationship between Seed Size and Actual Yield in Pea

Smitchger, Jamin A.; Weeden, N. F.

doi:10.1155/2018/9658707

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…Seed is the start and end points of plant life, and the important determinants of growth and development. Seed size is a key agronomic characteristic of evolutionary fitness in plants during domestication and breeding in many crops with seeds as the main product organ, and a key factor affecting seed yield, eating quality, tolerances to environmental stresses (Song et al, 2007;Bai et al, 2010;Gu et al, 2017;Smitchger and Weeden, 2018). In general, large seeds have more advantages than small ones in crop production because large seeds have bigger endosperm or cotyledons that can provide more nutrients for seedling establishment (Chen et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Trait Loci for Seed Size Variation in Cucurbits – A Review

et al. 2020

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Cucurbits (Cucurbitaceae family) include many economically important fruit vegetable crops such as watermelon, pumpkin/squash, cucumber, and melon. Seed size (SS) is an important trait in cucurbits breeding, which is controlled by quantitative trait loci (QTL). Recent advances have deciphered several signaling pathways underlying seed size variation in model plants such as Arabidopsis and rice, but little is known on the genetic basis of SS variation in cucurbits. Here we conducted literature review on seed size QTL identified in watermelon, pumpkin/squash, cucumber and melon, and inferred 14, 9 and 13 consensus SS QTL based on their physical positions in respective draft genomes. Among them, four from watermelon (ClSS2.2, ClSS6.1, ClSS6.2, and ClSS8.2), two from cucumber (CsSS4.1 and CsSS5.1), and one from melon (CmSS11.1) were majoreffect, stable QTL for seed size and weight. Whole genome sequence alignment revealed that these major-effect QTL were located in syntenic regions across different genomes suggesting possible structural and functional conservation of some important genes for seed size control in cucurbit crops. Annotation of genes in the four watermelon consensus SS QTL regions identified genes that are known to play important roles in seed size control including members of the zinc finger protein and the E3 ubiquitinprotein ligase families. The present work highlights the utility of comparative analysis in understanding the genetic basis of seed size variation, which may help future mapping and cloning of seed size QTL in cucurbits.

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Section: Introductionmentioning

confidence: 99%

Quantitative Trait Loci for Seed Size Variation in Cucurbits – A Review

et al. 2020

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“…They found that the normalized pixel area of the rice kernel predicts the single kernel weight with 0.95% accuracy. Smitchger and Weeden (2018) found the ideal seed size for maximum seed yield in pea plants and reported that the seeds with the maximum size had the highest seed weight and harvest index. However, in all these cases, the main focus was on in-field plant traits to predict the soybean seed weight.…”

Section: Resultsmentioning

confidence: 99%

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean

Duc,

Ramlal,

Rajendran

et al. 2023

Front. Plant Sci.

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Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs

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“…This finding suggested that compared with the intolerant group, the tolerant group had more photosynthates to consume to form a higher number of seeds. The increase in the number of seeds in the tolerant group was closely related to photosynthate production (Smitchger and Weeden 2018).…”

Section: Yield and Yield Components Numbers Of Pods Seeds And Seed We...mentioning

confidence: 91%

Morphophysiological and yield traits of soybean varieties tolerant of intercropping with maize

PERMANASARI,

SULISTYANINGSIH,

KURNIASIH

et al. 2023

Biodiversitas

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Abstract. Permanasari I, Sulistyaningsih E, Kurniasih B, Indradewa D. 2023. Morphophysiological and yield traits of soybean varieties tolerant of intercropping with maize. Biodiversitas 24: 3872-3880. Not all soybean varieties grow optimally when intercropped with maize. Previously, we identified seven varieties (i.e., "Demas 1", Dena 1", "Dena 2", "Derap 1", "Devon 1", "Devon 2", and "Wilis") and two (i.e., "Dega 1" and "Mahameru") as tolerant and intolerant of intercropping based on selection indices and yield. This research aims to determine the morphophysiological and yield traits of tolerant and intolerant soybean groups intercropped with maize. The study used a randomized complete block design with three blocks as replications, and conducted from October 2019 to February 2020. Results showed that light intensity in the intercropping system had decreased by 19.91% relative to that in the monoculture system at 7 weeks after planting. The tolerant group displayed significant gains in seed weight per plant (143.66%), total biomass (50.04%), harvest index (53.33%), total number of pods and seeds per plant (119.39% and 128.86%), leaf nitrogen uptake and content (20.83% and 6.44%), chlorophyll-a:b ratio (7.20%), and stem diameter (7.65%) than the intolerant group. Correlation analysis revealed that leaf nitrogen content showed the highest correlation and significantly contributed to seed weight per plant in the tolerant and intolerant groups. Seed weight in the tolerant group was mainly affected by morphological (total biomass and harvest index), physiological (leaf nitrogen content), and yield components (number of pods), whereas that in the intolerant group was influenced only by physiological parameters (nitrogen content). Nitrogen content was considered suitable as a selection indicator for determining the seed yield of intercropped soybean varieties. Furthermore, using soybean tolerant groups for intercropping may be a solution to increase soybean production in Indonesia.

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The Ideotype for Seed Size: A Model Examining the Relationship between Seed Size and Actual Yield in Pea

Cited by 10 publications

References 14 publications

Quantitative Trait Loci for Seed Size Variation in Cucurbits – A Review

Quantitative Trait Loci for Seed Size Variation in Cucurbits – A Review

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean

Morphophysiological and yield traits of soybean varieties tolerant of intercropping with maize

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