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The yield of mechanically harvested rapeseed (Brassica napus L.) can be increased by optimum plant density and row spacing.

ABSTRACT: To determine the effects of plant density and row spacing on the mechanical harvesting of rapeseed (Brassica napus L.), field experiments were conducted. Higher plant density produced fewer pods and reduced the yield per plant. Wider row spacing at higher plant densities increased seeds per pod and the 1000-seed weight, resulting in a higher yield per plant. The highest yields were achieved at a density of 45 × 10(4) plants ha(-1) (D45) in combination with 15 cm row spacing (R15) because mortality associated with competition increased as both the plant density and row spacing increased. The leaf area index (LAI) and pod area index (PAI) showed similar relations to the yield per hectare, and they were positively correlated with the percentage of intercepted light, whereas the radiation use efficiency (RUE) was positively correlated with population biomass. Reduced plant height and increased root/shoot ratios led to a decreased culm lodging index. Improved resistance to pod shattering was also observed as plant density and row spacing increased. The angle of the lowest 5 branches decreased as row spacing increased under D30 and D45. All of these structural changes influenced the mechanical harvesting operations, resulting in the highest yield of mechanically harvesting rapeseed under D45R15.

SUBMITTER: Kuai J

PROVIDER: S-EPMC4685391 | biostudies-other | 2015 Dec

REPOSITORIES: biostudies-other

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Project description:Genetic improvement of seed yield per plant (SY) is one of the major objectives in Brassica napus breeding programme. SY, being a complex quantitative trait is directly and indirectly influenced by yield-component traits such as siliqua length (SL), number of seeds per siliqua (NSS), and thousand seed weight (TSW). Therefore, concurrent improvement in SL, NSS and TSW can lead to higher SY in B. napus. This study was conducted to identify significant SNPs and putative candidate genes governing SY and its component traits (SL, NSS, TSW). All these traits were evaluated in a diverse set of 200 genotypes representing diversity from wide geographical locations. Of these, a set of 125 genotypes were chosen based on pedigree diversity and multi-location trait variation for genotyping by sequencing (GBS). Best linear unbiased predictors (BLUPs) of all the traits were used for genome-wide association study (GWAS) with 85,126 SNPs obtained from GBS. A total of 16, 18, 27 and 18 SNPs were found to be significantly associated for SL, NSS, TSW and SY respectively. Based on linkage disequilibrium decay analysis, 150 kb genomic region flanking the SNP was used for the identification of underlying candidate genes for each test trait. Important candidate genes involved in phytohormone signaling (WAT1, OSR1, ARR8, CKX1, REM7, REM9, BG1) and seed storage proteins (Cruciferin) were found to have significant influence on seed weight and yield. Genes involved in sexual reproduction and fertilization (PERK7, PERK13, PRK3, GATA15, NFD6) were found to determine the number of seeds per siliqua. Several genes found in this study namely ATS3A, CKX1, SPL2, SPL6, SPL9, WAT1 showed pleiotropic effect with yield component traits. Significant SNPs and putative candidate genes identified for SL, NSS, TSW and SY could be used in marker-assisted breeding for improvement of crop yield in B. napus. Genotypes identified with high SL, NSS, TSW and SY could serve as donors in crop improvement programs in B. napus.Supplementary informationThe online version contains supplementary material available at 10.1007/s12298-021-01060-9.

Project description:BackgroundStudying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY.ResultsTo achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019-2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2) were used to evaluate the performance of the algorithms. The best performance with all fifteen measured traits as inputs was achieved by the Nu-support vector regression algorithm with quadratic polynomial kernel function (R2 = 0.860, RMSE = 0.266, MAE = 0.210). The multilayer perceptron neural network algorithm with identity activation function (MLPNN-Identity) using three traits obtained from stepwise and backward selection methods appeared to be the most efficient combination of algorithms and feature selection methods (R2 = 0.843, RMSE = 0.283, MAE = 0.224). Feature selection suggested that the set of pods per plant and days to physiological maturity along with plant height or first pod height from the ground are the most influential traits in predicting rapeseed SY.ConclusionThe results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed.

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The yield of mechanically harvested rapeseed (Brassica napus L.) can be increased by optimum plant density and row spacing.

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