Performing Department
(N/A)
Non Technical Summary
Barley production is concentrated in the northern Great Plains of the US where ambient temperature is suitable for small grains. Montana has the largest acreage dedicated to barley production than any other states, mostly planted under dryland with low rainfall. The yield and malting quality of barley are negatively affected by drought, especially under extreme conditions of drought due to climate change. The understanding of plant root characteristics of barley stay-green trait gene and the development of varieties that are drought tolerant will be a new toolbox for the producers under dryland and irrigated conditions. This will enhance the utility of stored moisture and improve water productivity from rainfall and applied supplemental irrigation. This is important for barley produced to meet malt quality specification, including a high percentage of plump kernels with low protein. Availability of barley stay-green trait and their optimal management strategy to meet industry standards for malting will enhance sustainability and farmers' income. Sets of pair of genetic lines (same barley background but with confirmed presence and absence of the stay-green trait) were identified and reproduced and will be studied in Montana and in Canada for detailed root characteristics via root imaging system. We will identify and reproduce the best barley lines with desirable root characteristics that provide better yield and malting qualities under rainfed conditions. The barley performance (phenotype) will be documented and will be described and associated with multispectral sensors to improve our ability to characterize stay-green trait performance including a study on varied water regime performance. The documentation of the mechanism of growth (belowground and aboveground) of stay-green trait will strengthen our capacity to develop varieties that are drought tolerant while achieving malting quality. Availability of these climate-smart genetic lines and the eventual development of varieties that can withstand drought or limited supplemental irrigation will improve farmer profit, improve availability of desirable malt barley quality in the industry, and attain sustainability via improvement of profitability under rainfed systems and also through the reduction of supplemental irrigation application amount and cost in irrigated systems.
Animal Health Component
0%
Research Effort Categories
Basic
20%
Applied
60%
Developmental
20%
Goals / Objectives
This proposal is dependent on previous research projects: 1) Research in many crops including barley that relate stay-green trait to improved performance, 2) Our work (Williams et al., 2022) relating stay-green to change in root development, 3) Our work genetically mapping the stay-green trait, relating it to root traits and quality performance, and identifying five related quantitative trait loci (QTL), and 4) Our development of near-isogenic lines (NILs), where pairs of lines are essentially genetically identical except for the locus of interest, e.g., the genetic region controlling stay-green and or related to root development. In deploying NILs, we will 1) evaluate stay-green root and shoot phenotypes throughout plant development 2) compare the amount of water required to meet yield and quality goals in stay-green versus non-stay-green NILs, and then 3) relate those phenotypes to vegetative reflectance data that fast track breeding selections. The long-term goal is to reduce water use by improving water utilization of the soil profile to support more sustainable dryland barley production. The advantage of NILs is that they allow the evaluation of single gene regions, removing confounding effects of other genes. Lines in a population could vary for many traits besides stay-green, for example, plant height or tiller number that would impact water usage, potentially confounding the stay-green phenotype. Thus, these targeted NILs allow the effects of specific genetics to be studied using far fewer lines. Once the impacts of individual loci under specific conditions are understood, then different combinations of loci can be created to best fit specific environments.This proposal is designed to answer the questions: 1) Does barley with the stay-green trait respond to abiotic stress and maintain yield and quality homeostasis better than non-stay-green barley? 2) What belowground and aboveground mechanisms are involved in drought stress tolerance in stay-green barley genotypes? 3) How is sustainable production of barley best managed? and 4) Can we select for belowground traits with high-throughput methods?The objectives of the proposed research are to:understand the dynamic mechanisms of stay-green QTLs on roots and shoots by observing a set of NILs with contrasting alleles in field trials with mini-rhizotrons under different growing conditions,test different irrigation management practices comparing stay-green and non stay-green NILs to determine the optimal water requirement for each, andinvestigate thermal and multispectral imagery in developing a high-throughput methodology that relates to rooting patterns facilitating breeding selections for stay-green with improved water use.
Project Methods
Objective 1. Understand the dynamic mechanism of stay-green QTLs on roots by observing a set of NILS with contrasting alleles in situ with mini-rhizotrons under different growing conditions.Near-isogenic line (NIL) pairs (same barley background but with confirmed presence and absence of the stay-green trait) that vary for the stay-green alleles at QTLs on 2H, 6H, and 7H identified previously through QTL mapping, have been developed. The first site is at the Montana State University Post Farm. The second site is at the Kernen Crop Research Farm of the University of Saskatchewan, Saskatoon, Canada. A randomized complete block design will be used with three blocks at each location. It will be planted at 258 plants/m2 (24 plants/ft2). Immediately after planting, transparent acrylic tubes will be installed using a mini-rhizotron coring probe at 30o off the soil surface on mid-row of each plot. Root scanning will be done at flowering and physiological maturity along the entirety of each tube to coincide with biomass sampling. Rootsnap! software will be used for root tracing. Percent deep root length and percent change in root length will be determined as described in Williams et al. (2022).Detailed biomass sampling will be conducted at flowering and physiological maturity (PM). At flowering, heads, leaves, and stems will be separated, weighed, and dried. Prior to drying the leaves, their total leaf area will be measured using an LI-3100C area meter. The biomass collected at PM will be quantified for the number of plants and heads. Five representative plants will be subsampled and ground for total carbon and total nitrogen determinations. These tests will verify our assumption that a better stay-green root system will also be efficient in nitrogen uptake for aboveground biomass and grain quality. Also, the total carbon will provide information if stay-green trait aboveground carbon assimilation is distinctive relative to the non-stay-green. This will allow us to interpret the performance mechanism of the stay-green trait. Ten heads will also be subsampled to count the number of spikelets (groups of seeds in the head) and seeds per head. Flight missions will be scheduled as specified in Objective 3. Agronomic measures include heading date, maturity, plant height, yield, test weight, and percent grain protein. Dr. Sherman's malt quality lab will also malt and measure standard malt quality traits including starch extract, soluble protein, free amino nitrogen, soluble total protein, alpha-amylase, diastatic power, and beta-glucan as described in Turner et al. (2019) following ASBC protocols. Analysis of variance will be employed using lines (and year) as fixed effects and blocks as random effects. We will also add a contrast analysis between the stay-green QTL allele and the corresponding non-stay-green allele of each NIL pair.Objective 2. Test different irrigation management practices comparing stay-green and non stay-green NILs to determine optimal water requirement for each.Ten genotypes from objective1 will be evaluated in the field for stress tolerance and adaptation in the northwestern and southcentral regions of Montana. This study will be carried out in randomized complete block over three water regime environments: a) 100% evapotranspiration (100ET) as a check, b) 66% ET deficit irrigation, and c) rainfed. The irrigation will be delivered via overhead sprinklers on center pivot/lateral move sprinklers. We will install soil moisture sensors to each water treatment for the selected NIL pairs complementing the soil water balance approach using the daily reference ET, crop coefficient, rainfall, and the soil water holding capacity (Allen et al., 1998).To understand the actual plant adaptation to stress, the following will be recorded: heading, physiological maturity (PM) scores. Flagleaf senescence scores at the soft dough stage will be scored as any noticeable green pigmentation of the plant tissues past PM approaching to harvest maturity. Yield components will be measured such as biomass, harvest index, number of productive tillers, number of spikelets, number of seeds per head, and seed size. A subsample of biomass will be ground for total N and C determinations. Flight missions will be scheduled as specified in objective 3. A Ph.D. student will measure these detailed in-season measurements in northwestern and southcentral Montana. Effective plot yields and quality will be measured for each plot and tested for test weight, protein, moisture, plump, and malting quality as described on objective 1.Data will be analyzed using SAS mixed procedure PROC GLIMMIX. This procedure allows us to quantitatively and qualitatively depict the source of various levels of interactions. Interactions between or among factors of water regimes x genetics x environment x year will allow careful examination of the stay-green performance to limited water, if it is consistent over time and space, or if the stay-green trait achieves yield stability in controlled stress in relation to the non-stressed treatment.Objective 3. Investigate thermal and multispectral imagery in developing a high-throughput methodology that relates to rooting patterns facilitating breeding selections for stay-green with improved water use.Flight missions will be conducted using UAVs with an Altum PT with visible, infrared, and thermal sensors. Both a fixed wing eBee and DJI Matrice UAVs are available in existing equipment, along with two Altum PT Micasense sensors. The data acquisition commences as barley awns begin to show (boot stage) and will be done weekly through physiological maturity. As physiological maturity progresses (near soft dough stage), imaging continues twice per week to ensure the capture of the extension of green pigmentation in stay-green barley that will be used to associate with the actual field plot senescence scores. This will strengthen our documentation for the expected extended grainfill duration or photosynthetic activity of the stay-green trait compared with its genetic control. This will be done across three project sites in Montana and the site in Saskatoon with the assigned graduate student and P. Nugent (Co-PD) and the respective PD and Co-PDs at each location. We will identify georeferenced surfaces (i.e., ground control panels detailed below) and measure their actual surface temperature using a handheld thermometer (Agri-Therm III Everest Intersci.). This will provide empirical relationships between the known georeferenced surface temperatures and the thermal data. Representative plot surface temperatures of the various treatments will be recorded using the handheld thermometer to determine the effectiveness of the thermal data collected from the UAV. The early vigor analysis will be carried out using a normalized difference vegetation index approach. Then later on, when there are reference plots that achieve 100% canopy closure on the ground, we can use the known canopy closure as a reference to calculate the percent canopy closure of the various treatments using the methods specified in Torrion et al., 2014b.Thermal camera calibration, including adjustment for field of view angle, and temperature stability corrections will be employed. Within this project, the relation of the stay-green trait to other traits such as senescence rate, physiological maturity occurrences, biomass (ground cover), yield inferences, and cooling off will be explored. These parameters are continuous value parameters rather than classification problems. The previously mentioned machine learning tools can be applied to the prediction of continuous traits. To do this, rather than a softmax or sigmoid layer, a linear regression layer is appended to the headless network, and the network can then be trained to fit a continuous parameter.