The Effect of Sowing Date and Plant Density on Cotton Yield

Bozbek, Taner; Sezener, Volkan; Ünay, Aydın

doi:10.3923/ja.2006.122.125

Cited by 29 publications

(15 citation statements)

References 3 publications

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“…Variation for means and range of LY and its contributing traits in the same sowing environments of 2 years was at par as compared with different sowing environments of the same year. A similar pattern of variation has been reported in previous studies for upland cotton ( Bozbek et al, 2006 ; Khan et al, 2017 ). Estimation of phenotypic correlation among the recorded traits showed that seed cotton yield has a significant and highly positive correlation with BW and NB irrespective of the environment.…”

Section: Discussionsupporting

confidence: 89%

Identification of Novel Marker–Trait Associations for Lint Yield Contributing Traits in Upland Cotton (Gossypium hirsutum L.) Using SSRs

et al. 2021

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Improving the yield of lint is the main objective for most of the cotton crop improvement programs throughout the world as it meets the demand of fiber for textile industries. In the current study, 96 genotypes of Gossypium hirsutum were used to find novel simple sequence repeat marker-based associations for lint yield contributing traits by linkage disequilibrium. Extensive phenotyping of 96 genotypes for various agronomic traits was done for two consecutive years (2018 and 2019) in early, normal, and late sown environments. Out of 168 SSR markers screened over the 96 genotypes, a total of 97 polymorphic markers containing 293 alleles were used for analysis. Three different models, i.e., mixed linear model (MLM), compressed mixed linear model (CMLM), and multiple locus mixed linear model (MLMM), were used to detect the significant marker–trait associations for six different environments separately. A total of 38 significant marker–trait associations that were common to at least two environments were considered as promising associations and detailed annotation of the significant markers has been carried out. Twenty-two marker–trait associations were found to be novel in the current study. These results will be very useful for crop improvement programs using marker-assisted cotton breeding.

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Section: Discussionsupporting

confidence: 89%

Identification of Novel Marker–Trait Associations for Lint Yield Contributing Traits in Upland Cotton (Gossypium hirsutum L.) Using SSRs

et al. 2021

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“…Remote identification of crop type is widely studied for many agricultural applications. In most cases, this is done retrospectively after the growing season [1], however, in-season mapping is required for better decisions for policymakers and others in the agribusiness sector [2]. For example, knowing the type of crop and the hectares of a particular crop can help in making crucial management, marketing, and logistical decisions [3].…”

Section: Introductionmentioning

confidence: 99%

Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

et al. 2020

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A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

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“…This effect was most prominent for fibre length which also showed considerable variation over the years (1981–2010) due to the effects of changing weather conditions. Later sowing negatively impacts cotton yield ( Cathey and Meredith 1988 ; Bozbek et al 2006 ), but does not influence boll numbers ( Barradas and Lopez-Bellido 2009 ). Therefore, breeding strategies that provide low-temperature tolerance might increase flexibility for planting times especially in cooler regions ( Tan et al 2011 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting the effects of environment and management on cotton fibre growth and quality: a functional-structural plant modelling approach

et al. 2014

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Fruit quality and more specifically quality of the fiber in the fruit of cotton, depends on interactions between fruit position in the plant architecture, temperature and agronomical practices, such as sowing time, mulching with plastic film, and topping of the plant's main stem and branches. A functional and structural cotton model CottonXL for fiber quality (strength, length and micronaire) was implemented at the level of each individual fruit in relation to thermal time for optimizing cotton fiber quality by matching cotton management to the environment. The model may be used to address climate and land use change scenarios.

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The Effect of Sowing Date and Plant Density on Cotton Yield

Cited by 29 publications

References 3 publications

Identification of Novel Marker–Trait Associations for Lint Yield Contributing Traits in Upland Cotton (Gossypium hirsutum L.) Using SSRs

Identification of Novel Marker–Trait Associations for Lint Yield Contributing Traits in Upland Cotton (Gossypium hirsutum L.) Using SSRs

Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

Predicting the effects of environment and management on cotton fibre growth and quality: a functional-structural plant modelling approach

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