Plant population density in cotton: Addressing knowledge gaps in stand uniformity and lint quality under dryland and irrigated conditions

Sapkota, Bala Ram; Adams, Curtis B.; Kelly, Brendan; Rajan, Nithya; Ale, Srinivasulu

doi:10.1016/j.fcr.2022.108762

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…These factors influence reflectance values and relative index effectiveness ( Wientjes et al., 2017 ; Lozada et al., 2020 ; Herr et al., 2023 ). Vegetation indices have many applications in capturing routine trait estimates like plot quality, biotic, and abiotic stress ( Sankaran et al., 2015a ; Guo et al., 2021 ; Sarkar et al., 2022 ; Sapkota et al., 2023 ), as well as previously infeasible traits like chlorophyll content and nitrogen content ( Xie and Yang, 2020 ; Yin et al., 2022 ). Unlike vegetation indices, water indices such as Normalized Water Index (NWI) use infrared range reflectance to evaluate stomatal conductance and overall photosynthetic efficiency ( Babar et al., 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications

Herr,

Carter

2023

Front. Plant Sci.

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In an era of climate change and increased environmental variability, breeders are looking for tools to maintain and increase genetic gain and overall efficiency. In recent years the field of high throughput phenotyping (HTP) has received increased attention as an option to meet this need. There are many platform options in HTP, but ground-based handheld and remote aerial systems are two popular options. While many HTP setups have similar specifications, it is not always clear if data from different systems can be treated interchangeably. In this research, we evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor. Each handheld radiometer was used for two years simultaneously with the unoccupied aircraft systems (UAS) in collecting winter wheat breeding trials between 2018-2021. Spectral reflectance indices (SRI) were calculated for each system. SRI heritability and correlation were analyzed in evaluating the platform and SRI usability for breeding applications. Correlations of SRIs were low against UAS SRI and grain yield while using the Cropscan system in 2018 and 2019. Dissimilarly, the SVC system in 2020 and 2021 produced moderate correlations across UAS SRI and grain yield. UAS SRI were consistently more heritable, with broad-sense heritability ranging from 0.58 to 0.80. Data standardization and collection windows are important to consider in ensuring reliable data. Furthermore, practical aspects and best practices for these HTP platforms, relative to applied breeding applications, are highlighted and discussed. The findings of this study can be a framework to build upon when considering the implementation of HTP technology in an applied breeding program.

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Section: Introductionmentioning

confidence: 99%

Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications

Herr,

Carter

2023

Front. Plant Sci.

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Large‐scale breeding applications of unoccupied aircraft systems enabled genomic prediction

Herr,

Schmuker,

Carter

2024

The Plant Phenome Journal

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Breeding for improved, reliable cultivars despite growing environmental irregularity can be challenging. Unoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology that has been shown to help interpret the mechanisms associated with crop productivity and environmental response, creating potential for improved breeding strategies. Spectral reflectance indices (SRIs), encompassing both vegetation and water indices like normalized difference vegetation index (NDVI), normalized difference red‐edge index, and normalized water index, were employed to assess 4094 winter wheat genotypes across 11,593 breeding plots at Washington State University from 2019 through 2022. SRIs were then used with genomic data in univariate models as covariates and multivariate models as secondary response variables for predictions of grain yield. The prediction accuracy of models was evaluated using a leave‐one‐year‐out validation strategy against a base genomic prediction method. Including SRI data as fixed effects in univariate genomic prediction models can improve prediction accuracy over the control but is unreliable across years. When used in multivariate models, SRIs improve prediction performance across years but require high‐performance computational resources that could limit feasibility. In univariate models, when test year NDVI data were available and used to calculate breeding values, prediction performance was at least 16% better than the control, ranging in prediction accuracy from 0.54 in 2019 to 0.93 in 2020. This study highlights the limited reliability of SRI use in genomic prediction of untested environments and locations. However, a significant application for the technology can be found in early‐season UAS data collection to aid accurate predictions in late season, a helpful tool in tight turnaround times commonly experienced in winter crop breeding programs.

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Long-term assessments of cotton fiber quality in response to plant population density: Reconciling fiber quality and its temporal stability

Zhang

Han

Wang

et al. 2023

Industrial Crops and Products

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Plant population density in cotton: Addressing knowledge gaps in stand uniformity and lint quality under dryland and irrigated conditions

Cited by 3 publications

References 32 publications

Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications

Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications

Large‐scale breeding applications of unoccupied aircraft systems enabled genomic prediction

Long-term assessments of cotton fiber quality in response to plant population density: Reconciling fiber quality and its temporal stability

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