Performing Department
N FL Research & Education Ctr
Non Technical Summary
Drought stress is responsible for more crop loss than any other abiotic or biotic stress, thus making it a critical goal in many breeding programs. But progress has been slow, primarily due to both the complexity of conditions that have been categorized under the general term "drought", and because of the complexity and breadth of individual plant processes and traits which contribute to "tolerance" to water scarcity. In addition, the concept of successful drought tolerance in crop species diverges from many established paradigms regarding plant drought tolerance in general, because crops must do more than survive - they must continue to yield under drought. In addition, the two conditions of survival and performance under drought depend on different physiological and genetic mechanisms. All these factors have led to the lack of true breakthroughs in solving the issue of breeding for crop drought tolerance.From an experimental view, the current "drought tolerance" breeding paradigm utilizes a focus on isolated trait approaches, usually aimed at scoring survival under severe drought scenarios. This selection of survivors under severe water deficit actually tends to select for maladaptive phenotypes when placed in real-world production scenarios. For example, genotypes selected for tight stomatal control over transpiration would show high survivability in the typical research drought selection scenario, but would likely experience yield loss under most production scenarios due to their loss of photosynthetic capability. In addition, if selection was aimed at deep roots, a trait now considered to fit the current "drought tolerant" paradigm, this may select for phenotypes that are maladapted to current irrigated production scenarios where energy spent on deep roots would not typically be rewarded under mild or moderate drought conditions. Therefore, adaptability highly depends on the set of conditions under which selection occurs, leading to conclusions that "drought tolerance" can be claimed for a myriad or traits or genes if tested under the appropriate conditions, or the "right drought scenario".
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
Water deficit stress is responsible for more crop loss than any other abiotic or biotic stress. Since 1964, losses in national average cereal production due to drought were approximately 10%, and trends indicate that losses increased during the period from 1985 until 2007. Water scarcity will continue to threaten crop production in the future as a growing human population will undoubtedly demand more freshwater to meet food supply and residential and municipal water consumption. As this occurs, stored freshwater sources (surficial and aquiferious) will likely be depleted over time, and crop water demand will further rely on precipitation. This will undoubtedly pose a threat to regions with low precipitation and/or soils with low water holding capacity. One solution to mitigate yield losses associated with water deficit stress is breeding for traits that provide increased resilience to water scarcity. Enhancing water stress resistance requires systematic phenotyping approaches that quantify relevant crop traits in large breeding populations. A major limitation to past phenotyping systems is the expense of equipment and labor needed to evaluate even a small population. Breeding programs must screen thousands of genotypes to identify superior individuals, especially for quantitative traits such as water stress resilience. An additional challenge to improve has been the inability to quantify the correct suite of traits needed to provide agronomic water resilience. Approaches have largely attempted to identify traits which provide general adaptability to all water deficit scenarios. This is an unlikely solution due to the heterogeneity in regional hydrologic conditions. To address these limitations/challenges, our multi-disciplinary team proposes a novel approach. We will use peanut as a model legume crop and integrate recent sensor advances, the fundamentals of crop water use, and recently developed modeling approaches to estimate genotypic water stress resilience and heritability. This will enhance the specific adaptability of peanut germplasm to regional hydrologic scenarios. Our team envisions several impacts this project would have on U.S. sustainability and crop improvement. The long-term goal is to utilize the information obtained in this proposed research to develop peanut cultivars whose traits effectively utilize soil water resources. This goal will result in cultivars which have the potential to mitigate yield loss under water stress conditions, and have greater yield potential in high yielding production scenarios where water limitation is less severe. This goal would have impacts across the entire peanut farming community and U.S. peanut production. Additionally, our approach can be modified to apply to any crop commodity. We anticipate that the results of this experimental approach would be adopted by agricultural scientists and would result in its application to other major crop commodities grown in the U.S.Objectives Several primary objectives of this proposed research have been formulated to identify germplasm whose traits can be used for enhancing cultivar specific adaptability to common soil hydrological conditions found across peanut producing regions. Objective 1: Screen a large population of peanut genotypes for traits which may be preferential to various soil hydrologic conditions for further assessment in the field. This screening will be conducted under controlled greenhouse environments. Specific adaptability will be determined based on stomatal responses to soil water conditions and root system architecture. Objective 2: Further assess phenotypic traits of peanut genotypes to various soil water conditions present at multiple field sites. Thermal imaging will be used to estimate transpiration and root system architecture will be evaluated using mini-rhizotrons. These measurements will be in addition to examining genotypic soil water depletion zones, harvest index, and pod yield utilizing methodologies our team has utilized successfully in previous research. Objective 3: Model genotypic heritability of the trait information collected in both greenhouse and field trials. An index which encompasses all the measured above and below ground traits will be developed to determine the heritability of the suite of traits which maximizes soil water acquisition and utilization.
Project Methods
Task 1: High-throughput greenhouse screenings assessing genotypic stomatal limitation and root system architecture under varying soil water scenarios.Proposed Project Activities:We will construct cylindrical mesocosms appropriate for greenhouse root evaluations at the University of Florida's Center for Stress Resilient Agriculture (CSRA) located at the PSREU. Mesocosm dimensions will be approximately 0.10 m diameter x 1.0 m length; each will be fitted with a plastic liner allowing for the entire soil section to be removed intact. The mesocosms are designed to fit into slots on a permanent housing structure. The screening process will consist of three separate assays occurring in the first year of the project. The experimental arrangement in each screening will be a completely randomized design with four replications. Each screening will consist of approximately 25 peanut genotypes and two soil water conditions consisting of a well-watered (WW) and regulated water deficit irrigation (DI) treatment beginning at 50 DAP. Once a moderate level of soil water deficit is reached (50% depletion of soil available water capacity at 30 cm), mesocosms will be maintained at this level using ETc replenishment until 70 DAP. The water deficit stress at mid-season will correspond to the phenological development when peanut is most sensitive to yield reduction resulting from water deficit stress.Data Analysis and InterpretationAll the data collected in these greenhouse screenings will be integrated in novel analyses to determine the coordination of above and below ground traits, leading to more specific defined CAPs that can be utilized for field screening. During this task, root parameters, root and canopy biomass, and canopy temperature values will be collected and jointly analyzed to aid in screening. Previous studies (Tron et al. 2015) have indicated that root architecture depth, density and growth velocity when clustered using an unsupervised hierarchical clustering technique have resulting clusters that align in a meaningful way with clusters estimated using functional root parameters. Furthermore, these cluster associations are environmentally dependent. To build upon these previous studies, soft co-clustering approaches will be applied to simultaneously determine the most informative traits and cluster the measured data. In particular, we will initially apply the simultaneous clustering and attribute discrimination algorithm, SCAD, (Frigui and Nasraoui, 2000) to the collected data.Task 2: Use phenotypic information from the greenhouse screening to test genotypic adaptability to field sites with varying irrigation strategies and soil water holding capacities. Proposed Project Activities:Phenotypic field screenings of selected CSAs will begin in year two and continue into year three of the proposed project (2019 and 2020). Field trials will be implemented at the PSREU and the North Florida Research and Education Center (NFREC) in Marianna, FL. The NFREC has a loamy sand soil type with moderate water holding capacity representing a peanut production environment which could be irrigated or dryland. The PSREU site is situated on deep sand with little water holding capacity representing a peanut production system where irrigation would be most needed. Each site location will have a unique set of genotypes which were chosen based on the phenotypic data collected and CSA analysis from the results of the greenhouse screenings. Automated rainout shelters will be available to exclude rainfall from experimental units at the PSREU. The capability to exclude rainfall makes the site ideal to examine phenotypic suitability to soil water deficit conditions during critical phenological development stages. The experimental design at the PSREU will be a split-plot arrangement in a randomized complete block design (RCBD) with four replications. The whole-plots will consist of a well-watered treatment (WW) meeting crop demand, and a moderate mid-season water deficit treatment (40-80 DAP), both controlled via drip irrigation. Sub-plots would consist of 15 peanut genotypes. At the NFREC site, we will examine phenotypic responses of peanut genotypes under natural rain fed conditions as well as irrigated conditions. This will provide field screening environments reflective of the local production systems. The experimental design at the NFREC will also be a split-plot arrangement in a randomized complete block design (RCBD) with four replications. The whole-plots will consist of two water treatments, irrigated to meet crop demand and non-irrigated rainfed conditions, controlled using overhead irrigation applied through a center pivot. Sub-plots will consist of 15 peanut genotypes.Data Analysis and Interpretation: In order to examine the relationship between phenotypic responses in the greenhouse versus the field, the multi-variate cluster analysis approach described in Task 1 will be applied to the field trial traits as well. The resulting clusters will be compared to those identified using the greenhouse data. This analysis will be repeated with both the same set of measurements used in the greenhouse studies as well as the expanded set of measurements that incorporate soil water depletion in the soil profile, harvest index, and pod yields.Task 3: Model genotypic heritability of the trait information collected in both the greenhouse and field trails.Proposed Project Activities Throughout the project, an approach will be developed and implemented for modeling root trait parameters to differentiate genotypes in terms of their stress tolerance under varying environmental conditions, as well as to estimate the heritability of measured phenotypic traits. The complex relationship between measured root traits and environmental conditions will be modeled using a hierarchical Bayesian model. The developed model will be an extension of prior work introduced by Wasson et al. (2017). Wasson et al. (2017) proposed a hierarchical Bayesian model that incorporates both genotypic and environment specific parameters to model root count across depth. In their approach, root count parameters measured at discrete depth bins and, due to the discrete nature of root counts, assumed to be distributed according to a Poission distributionthat is governed according to a Gamma distribution with genotypic and environmentally specific hyperparameters. In this project, the concept of this model will be extended such that continuous-valued traits (e.g., root length and diameter) across soil depth can be modeled. Furthermore, we will investigate the use of a multi-variate approach in which multiple root parameters are simultaneously modeled under varying environmental conditions. These extensions will be accomplished through the investigation of the use of a Gamma distribution that will be governed by with genotypic and environmentally specific hyperparameters governed by tied genotypic and environmentally specific parameters to model each continuous valued trait. The traits that will be modeled will be determined from the attribute/trait discrimination results found through the multi-variate cluster analysis under Tasks 1 and 2. d. Data Analysis and Interpretation: After development and application of the proposed model, the estimated parameters will be examined to determine heritability. Heritability will be predicted in an approach similar to that of Wasson et al. (2017) in which the ratio of the variation of genotypic-specific hyperparameters to the variation of both genotypic and environment specific hyperparameters. Namely, each genotypic-specific hyperparameter will be evaluated in terms of heritability and related to observed phenotypic variation.