Maria Balota

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.

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.

Transpiration Response to Vapor Pressure Deficit in Field Grown Peanut

Abstract Water deficit, i.e., rainfall amounts and distribution, is the most common abiotic stress that limits peanut production worldwide. Even though extensive research efforts have been made to ...

Heat Stress Related Physiological and Metabolic Traits in Peanut Seedlings

ABSTRACT To maintain high yields under an increasingly hotter climate, high temperature resilient peanut cultivars would have to be developed. Therefore, the mechanisms of plant response to heat need to be understood. The objective of this study was to explore the physiological and metabolic mechanisms developed by virginia-type peanut at early growth stages in response to high temperature stress. Peanut seedlings were exposed to 40/35 C (heat) and 30/25 C (optimum temperature) in a growth chamber. Membrane injury (MI), the Fv/Fm ratio, and several metabolites were evaluated in eight genotypes at four time-points (day 1, 2, 4, and 7) after the heat stress treatment initiation. Even though we were able to highlight some metabolites, e.g., hydroxyproline, galactinol, and unsaturated fatty acid, explaining specific differential physiological (MI) responses in peanut seedlings, overall our data suggested general stress responses rather than adaptive mechanisms to heat. Rather than individual metabolites, a co...

Comparison of Virginia and Runner-Type Peanut Cultivars for Development, Disease, Yield Potential, and Grade Factors in Eastern Virginia

ABSTRACT Peanut (Arachis hypogaea L.) is an important crop in the Virginia-Carolina (VC) region. Virginia-type cultivars are preferred vs. other peanut types because of the in-shell and gourmet mar...

Distinguishing plant population and variety with UAV-derived vegetation indices

Variety selection and seeding rate are two important choice that a peanut grower must make. High yielding varieties can increase profit with no additional input costs, while seeding rate often determines input cost a grower will incur from seed costs. The overall purpose of this study was to examine the effect that seeding rate has on different peanut varieties. With the advent of new UAV technology, we now have the possibility to use indices collected with the UAV to measure emergence, seeding rate, growth rate, and perhaps make yield predictions. This information could enable growers to make management decisions early in the season based on low plant populations due to poor emergence, and could be a useful tool for growers to use to estimate plant population and growth rate in order to help achieve desired crop stands. Red-Green-Blue (RGB) and near-infrared (NIR) images were collected from a UAV platform starting two weeks after planting and continued weekly for the next six weeks. Ground NDVI was also collected each time aerial images were collected. Vegetation indices were derived from both the RGB and NIR images. Greener area (GGA- the proportion of green pixels with a hue angle from 80° to 120°) and a* (the average red/green color of the image) were derived from the RGB images while Normalized Differential Vegetative Index (NDVI) was derived from NIR images. Aerial indices were successful in distinguishing seeding rates and determining emergence during the first few weeks after planting, but not later in the season. Meanwhile, these aerial indices are not an adequate predictor of yield in peanut at this point.

UAV remote sensing for phenotyping drought tolerance in peanuts

Farmers can benefit from growing drought tolerant peanut (Arachis hypogaea L.) cultivars with improved yield when rainfall is sporadic. In the Virginia-Carolina (VC) region, drought is magnified by hot summers and usually occurs in July and Aug when pod and seed growth are intense. At these growth stages, weekly supply of 50 to 75 mm of water is needed to ensure profitability. Irrigation can supplement crop water needs, but only 10% of the peanut farms are irrigated. In this frame, drought tolerant varieties can be profitable, but breeding for cultivars with improved drought tolerance requires fast yet accurate phenotyping. Our objective was to evaluate the potential of UAV remote sensing technologies for drought tolerance selection in peanut. In this study, we examined the effect of drought on leaf wilting, pod yield, grading characteristics, and crop value of 23 peanut cultivars (Virginia, Runner, and Valencia type). These varieties were arranged in a factorial design, with four replications drought stressed and two replications well-watered. Drought was imposed by covering the drought stressed plots with rainout shelters on July 19; they remained covered until August 29 and only received 38 mm irrigation in mid Aug. The well-watered plots continued to receive rain and supplemental irrigation as needed. During this time, Canopy Temperature Depression (CT) and Normalized Differential Vegetative Index (NDVI) were collected from the ground on all plots at weekly intervals. After the shelters were removed, these measurements were collected daily for approximately 2 weeks. At the same time, Red-Green-Blue (RGB), near-infrared (NIR), and infrared (IR) images taken from an UAV platform were also collected. Vegetation indices derived from the ground and aerial data were compared with leaf wilting, pod yield and crop value. Wilting, which is a common water stress symptom, was best estimated by NDVI and RGB, and least by CT; but CT was best in estimating yield, SMK and crop value in particular when taken on the ground at 15 days water stress imposition. Interestingly, CT predicted well plant wilting even before it occurred, i.e., correlation coefficients were negative and over 0.750 when CT was measured on July 19 and 20 even though wilting was visible only after two weeks. The data, yet preliminary, show promising potential for remote sensing technologies, at the ground and aerial, for peanut variety selection for improved drought tolerance.

Exploratory use of a UAV platform for variety selection in peanut

Variety choice is the most important production decision farmers make because high yielding varieties can increase profit with no additional production costs. Therefore, yield improvement has been the major objective for peanut (Arachis hypogaea L.) breeding programs worldwide, but the current breeding approach (selecting for yield under optimal production conditions) is slow and inconsistent with the needs derived from population demand and climate change. To improve the rate of genetic gain, breeders have used target physiological traits such as leaf chlorophyll content using SPAD chlorophyll meter, Normalized Difference Vegetation Index (NDVI) from canopy reflectance in visible and near infra-red (NIR) wavelength bands, and canopy temperature (CT) manually measured with infra-red (IR) thermometers at the canopy level; but its use for routine selection was hampered by the time required to walk hundreds of plots. Recent developments in remote sensing-based high throughput phenotyping platforms using unmanned aerial vehicles (UAV) have shown good potential for future breeding advancements. Recently, we initiated a study for the evaluation of suitability of digital imagery, NDVI, and CT taken from an UAV platform for peanut variety differentiation. Peanut is unique for setting its yield underground and resilience to drought and heat, for which yield is difficult to pre-harvest estimate; although the need for early yield estimation within the breeding programs exists. Twenty-six peanut cultivars and breeding lines were grown in replicated plots either optimally or deficiently irrigated under rain exclusion shelters at Suffolk, Virginia. At the beginning maturity growth stage, approximately a month before digging, NDVI and CT were taken with ground-based sensors at the same time with red, blue, green (RGB) images from a Sony camera mounted on an UAV platform. Disease ratings were also taken pre-harvest. Ground and UAV derived vegetation indices were analyzed for disease and yield prediction and further presented in this paper.

Erratum to: Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat

Canopy spectral reflectance (CSR) is a cost-effective, rapid, and non-destructive remote sensing and selection tool that can be employed in high throughput plant phenotypic studies. The objectives of the current study were to evaluate the predictive potential of vegetative indices as a high-throughput phenotyping tool for nitrogen use efficiency in soft red winter wheat (SRWW) (Triticum aestivum L.) and determine the optimum growth stage for employing CSR. A panel of 281 regionally developed SRWW genotypes was screened under low and normal N regimes in two crop seasons for grain yield, N uptake, nitrogen use efficiency for yield (NUEY) and nitrogen use efficiency for protein (NUEP). Vegetative indices were calculated from CSR and the data were analyzed by year and over the 2 years. Multiple regression and Pearson’s correlation were used to obtain the best predictive models and vegetative indices. The chosen models explained 84 and 83 % of total variation in grain yield and N uptake respectively, over two crop seasons. Models further accounted for 85 and 77 % of total variation in NUEY, and 85, and 81 % of total variation in NUEP under low and normal N conditions, respectively. In general, yield, NUEY and NUEP had greater than 0.6 R values in 2011–2012 but not in 2012–2013. Differences between years are likely a result of saturation of CSR indices due to high biomass and crop canopy coverage in 2012–2013. Heading was found to be the most appropriate crop growth stage to sense SRWW CSR data for predicting grain yield, N uptake, NUEY, and NUEP. K. Pavuluri B. K. Chim C. A. Griffey W. E. Thomason (&) Department of Crop and Soil Environmental Sciences, Virginia Tech University, 330 Smyth Hall, Blacksburg, VA 24061, USA e-mail: wthomaso@vt.edu M. S. Reiter Virginia Tech, Eastern Shore Agriculture Research and Extension Center, 33446 Research Drive, Painter, VA 23420-2827, USA M. Balota Virginia Tech, Tidewater Agriculture Research and Extension Center, 6321 Holland Road, Suffolk, VA 23437, USA 123 Precision Agric (2015) 16:405–424 DOI 10.1007/s11119-014-9385-2

Comparison of Three Transgenic Peanut Lines with Their Parents for Agronomic and Physiological Characteristics

ABSTRACT Peanut (Arachis hypogea L.) is an important crop in the Virginia-Carolina (VC) region, but cool and wet falls may result in significant yield reductions due to Sclerotinia blight, caused by Sclerotinia minor (Jagger), a major disease in the region. Transgenic lines expressing a barley oxalate oxidase were previously shown to confer improved resistance to the disease. This research compared three blight resistant transgenic lines for oxalate oxidase, N70, P39, and W73 with their non-transgenic parents, NC 7, Perry, and Wilson, and high yielding check cultivars Bailey and CHAMPS. The objective was to ensure that the agronomic and physiological characteristics of the transformed lines were not negatively impacted by the transformation with oxalate oxidase before making recommendations for production. In 2009 and 2010, experimental plots were grown in two distinct fields for soil type and available water capacity for a total of four environments. The transgenic lines were statistically comparable wit...