Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding.

Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = -0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = -0.80), and b* (r = -0.75) were highly correlated with LLS visual scores. NDVI (r = -0.72), v* (r = -0.71), b* (r = -0.64), and GA (r = -0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H 2 > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.

RGB-image method enables indirect selection for leaf spot resistance and yield estimation in a groundnut breeding program in Western Africa.

Early Leaf Spot (ELS) caused by the fungus Passalora arachidicola and Late Leaf Spot (LLS) also caused by the fungus Nothopassalora personata, are the two major groundnut (Arachis hypogaea L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73-33, Gha-GAF 1723, Zam-ICGV-SM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa.

Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut (Arachis hypogaea L.) germplasm.

Early leaf spot (ELS) and late leaf spot (LLS) diseases are the two most destructive groundnut diseases in Ghana resulting in ≤ 70% yield losses which is controlled largely by chemical method. To develop leaf spot resistant varieties, the present study was undertaken to identify single nucleotide polymorphism (SNP) markers and putative candidate genes underlying both ELS and LLS. In this study, six multi-locus models of genome-wide association study were conducted with the best linear unbiased predictor obtained from 294 African groundnut germplasm screened for ELS and LLS as well as image-based indices of leaf spot diseases severity in 2020 and 2021 and 8,772 high-quality SNPs from a 48 K SNP array Axiom platform. Ninety-seven SNPs associated with ELS, LLS and five image-based indices across the chromosomes in the 2 two sub-genomes. From these, twenty-nine unique SNPs were detected by at least two models for one or more traits across 16 chromosomes with explained phenotypic variation ranging from 0.01 - 62.76%, with exception of chromosome (Chr) 08 (Chr08), Chr10, Chr11, and Chr19. Seventeen potential candidate genes were predicted at ± 300 kbp of the stable/prominent SNP positions (12 and 5, down- and upstream, respectively). The results from this study provide a basis for understanding the genetic architecture of ELS and LLS diseases in African groundnut germplasm, and the associated SNPs and predicted candidate genes would be valuable for breeding leaf spot diseases resistant varieties upon further validation.

Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models.

Peanut (Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a∗, b∗, u∗, v∗, green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a∗, u∗, GA, GGA, and CSI were significantly (p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting.

Corrigendum: Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models.

[This corrects the article DOI: 10.3389/fpls.2021.658621.].

Aerial high-throughput phenotyping of peanut leaf area index and lateral growth.

Leaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models' suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.

Identification of quantitative trait loci associated with nitrogen use efficiency in winter wheat.

Maintaining winter wheat (Triticum aestivum L.) productivity with more efficient nitrogen (N) management will enable growers to increase profitability and reduce the negative environmental impacts associated with nitrogen loss. Wheat breeders would therefore benefit greatly from the identification and application of genetic markers associated with nitrogen use efficiency (NUE). To investigate the genetics underlying N response, two bi-parental mapping populations were developed and grown in four site-seasons under low and high N rates. The populations were derived from a cross between previously identified high NUE parents (VA05W-151 and VA09W-52) and a shared common low NUE parent, 'Yorktown.' The Yorktown × VA05W-151 population was comprised of 136 recombinant inbred lines while the Yorktown × VA09W-52 population was comprised of 138 doubled haploids. Phenotypic data was collected on parental lines and their progeny for 11 N-related traits and genotypes were sequenced using a genotyping-by-sequencing platform to detect more than 3,100 high quality single nucleotide polymorphisms in each population. A total of 130 quantitative trait loci (QTL) were detected on 20 chromosomes, six of which were associated with NUE and N-related traits in multiple testing environments. Two of the six QTL for NUE were associated with known photoperiod (Ppd-D1 on chromosome 2D) and disease resistance (FHB-4A) genes, two were reported in previous investigations, and one QTL, QNue.151-1D, was novel. The NUE QTL on 1D, 6A, 7A, and 7D had LOD scores ranging from 2.63 to 8.33 and explained up to 18.1% of the phenotypic variation. The QTL identified in this study have potential for marker-assisted breeding for NUE traits in soft red winter wheat.

Quantitative trait locus mapping of the transpiration ratio related to preflowering drought tolerance in sorghum (Sorghum bicolor).

To meet future food needs, grain production must increase despite reduced water availability, so waterproductivity must rise. One way to do this is to raise the ratio of biomass produced to water transpired, which is controlled by the ratio of CO2 assimilation (A) to transpiration (E) (i.e. the transpiration ratio, A : E divided by vapour pressure deficit) or anything affecting stomatal movement.. We describe the genetic variation and basis of A, E and A : E among 70 recombinant inbred lines (RILs) of sorghum (Sorghum bicolor (L.) Moench), using greenhouse experiments. Experiment 1 used 40% and 80% of field capacity (FC) as water regimes; Experiment 2 used 80% FC. Genotype had a significant effect on A, E and A : E. In Experiment 1, mean values for A : E were 1.2-4.4 mmol CO2 mol-1 H2O kPa-1 and 1.6-3.1 mmol CO2 mol-1 H2O kPa-1 under 40% and 80% FC, respectively. In Experiment 2, values were 5.6-9.8 mmol CO2 mol-1 H2O kPa-1. Pooled data for A : E and A : E VPD-1 from Experiment 1 indicate that A : E fell quickly at temperatures >32.3°C. A : E distributions were skewed. Mean heritabilities for A : E were 0.9 (40% FC) and 0.8 (80% FC). Three significant quantitative trait loci (QTLs) associated with A:E, two on SBI-09 and one on SBI-10, accounted for 17-21% of the phenotypic variation. Subsequent experiments identified 38 QTLs controlling variation in height, flowering, biomass, leaf area, greenness and stomatal density. Colocalisation of A : E QTLs with agronomic traits indicated that these QTLs can be used for improving sorghum performance through marker assisted selection (MAS) under preflowering drought stress.

Overexpression of AtLOV1 in Switchgrass alters plant architecture, lignin content, and flowering time.

BACKGROUND: Switchgrass (Panicum virgatum L.) is a prime candidate crop for biofuel feedstock production in the United States. As it is a self-incompatible polyploid perennial species, breeding elite and stable switchgrass cultivars with traditional breeding methods is very challenging. Translational genomics may contribute significantly to the genetic improvement of switchgrass, especially for the incorporation of elite traits that are absent in natural switchgrass populations. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we constitutively expressed an Arabidopsis NAC transcriptional factor gene, LONG VEGETATIVE PHASE ONE (AtLOV1), in switchgrass. Overexpression of AtLOV1 in switchgrass caused the plants to have a smaller leaf angle by changing the morphology and organization of epidermal cells in the leaf collar region. Also, overexpression of AtLOV1 altered the lignin content and the monolignol composition of cell walls, and caused delayed flowering time. Global gene-expression analysis of the transgenic plants revealed an array of responding genes with predicted functions in plant development, cell wall biosynthesis, and flowering. CONCLUSIONS/SIGNIFICANCE: To our knowledge, this is the first report of a single ectopically expressed transcription factor altering the leaf angle, cell wall composition, and flowering time of switchgrass, therefore demonstrating the potential advantage of translational genomics for the genetic improvement of this crop.

ORAChromatography and total phenolics content of peanut root extracts.

A large number of compounds have been reported in peanut plants. Many of these compounds are phytoalexins, which are produced by plants experiencing environmental stress and often exhibit antioxidant activity. It is difficult to determine which of the many compounds has the greatest impact on total antioxidant capacity in a mixture. The objectives of this research were to examine the oxygen-radical absorbing capacity (ORAC) value and total phenolic contents of peanut root extracts and peanut root extract fractions collected via HPLC. Peanut roots were extracted from four different cultivars (Brantley, NC-12, Phillips, and Wilson) with 70% aqueous ethanol with ultrasonic assistance. Each cultivar was sampled in duplicate. The extracts were fractionated into 18 3-min fractions by HPLC using a C-18 column. Fractions and crude extracts were freeze dried. ORAC values and total phenolic content were then determined for all fractions and crude extracts. Fractions had a significant effect on the µM TE/mg gallic acid equivalents (GAE). ORAC values ranged from -46.89 µM TE to 185 µM TE in HPLC fractions. ORAChromatography can be used to focus on antioxidants in complex samples.