Latin America: A Development Pole for Phenomics

Camargo, Anyela; Lobos, Germán

doi:10.3389/fpls.2016.01729

Cited by 19 publications

(25 citation statements)

References 30 publications

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“…One strategy for improving and expediting the selection of these elite genotypes is the acquisition of high-dimensional phenotypic data (high-throughput phenotyping) (Bowman et al, 2015; Camargo and Lobos, 2016; Crain et al, 2016). …”

Section: Introductionmentioning

confidence: 99%

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

et al. 2017

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Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.

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Section: Introductionmentioning

confidence: 99%

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

et al. 2017

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“…New multi-processor/multi-core technologies [49] gave way to the construction of more powerful phenotyping technologies to better understand the genotype-environment relationship [50]. Such phenotyping technologies can track the formation and development of static and dynamic traits at the cellular and plant levels respectively [51,52]. For example, Camargo et al [53] phenotyped a cohort of the wheat MAGIC population across their life cycle and under controlled conditions.…”

Section: Advances In Plant Phenotypingmentioning

confidence: 99%

Emerging Advanced Technologies to Mitigate the Impact of Climate Change in Africa

Ribeiro

Rodriguez

2020

Plants

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Agriculture remains critical to Africa’s socioeconomic development, employing 65% of the work force and contributing 32% of GDP (Gross Domestic Product). Low productivity, which characterises food production in many Africa countries, remains a major concern. Compounded by the effects of climate change and lack of technical expertise, recent reports suggest that the impacts of climate change on agriculture and food systems in African countries may have further-reaching consequences than previously anticipated. Thus, it has become imperative that African scientists and farmers adopt new technologies which facilitate their research and provide smart agricultural solutions to mitigating current and future climate change-related challenges. Advanced technologies have been developed across the globe to facilitate adaptation to climate change in the agriculture sector. Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9), synthetic biology, and genomic selection, among others, constitute examples of some of these technologies. In this work, emerging advanced technologies with the potential to effectively mitigate climate change in Africa are reviewed. The authors show how these technologies can be utilised to enhance knowledge discovery for increased production in a climate change-impacted environment. We conclude that the application of these technologies could empower African scientists to explore agricultural strategies more resilient to the effects of climate change. Additionally, we conclude that support for African scientists from the international community in various forms is necessary to help Africans avoid the full undesirable effects of climate change.

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“…To the extent that breeders can consider useful additional information (e.g., plant physiology) to plan each season's crosses and perform early selection of material suitable for each breeding program's aims, it should be possible to improve both efficiency (cost and time) and productivity (higher proportion of new cultivars carrying advantageous characteristics) [3,4].…”

Section: Introductionmentioning

confidence: 99%

Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

et al. 2019

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To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear Regression (MLR) and Partial Least Squares (PLS)] with or without (W/O) prior wavelength selection (multicollinearity, genetic algorithms, or in combination) were considered. PLS generated better estimates than MLR, although prior wavelength selection improved MLR predictions. When data from the environments were combined, PLS W/O gave the best assessment for most of the traits, while in individual environments, the results varied according to the trait and methodology considered. The highest validation predictions were obtained for chlorophyll a/b (R2Val ≤ 0.87), maximum electron transport rate (R2Val ≤ 0.60), and the irradiance at which the electron transport rate is saturated (R2Val ≤ 0.59). The results of this study, the first to model modulated chlorophyll fluorescence by reflectance, confirming the potential for implementing this tool in blueberry breeding programs, at least for the estimation of a number of important physiological traits. Additionally, the differential effects of the environment on the spectral signature of each cultivar shows this tool could be directly used to assess their tolerance to specific environments.

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Latin America: A Development Pole for Phenomics

Cited by 19 publications

References 30 publications

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

Emerging Advanced Technologies to Mitigate the Impact of Climate Change in Africa

Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

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