Positive effects of public breeding on US rice yields under future climate scenarios.

In this study, we model and predict rice yields by integrating molecular marker variation, varietal productivity, and climate, focusing on the Southern U.S. rice-growing region. This region spans the states of Arkansas, Louisiana, Texas, Mississippi, and Missouri and accounts for 85% of total U.S. rice production. By digitizing and combining four decades of county-level variety acreage data (1970 to 2015) with varietal information from genotyping-by-sequencing data, we estimate annual historical county-level allele frequencies. These allele frequencies are used together with county-level weather and yield data to develop ten machine learning models for yield prediction. A two-layer meta-learner ensemble model that combines all ten methods is externally evaluated against observations from historical Uniform Regional Rice Nursery trials (1980 to 2018) conducted in the same states. Finally, the ensemble model is used with forecasted weather from the Coupled Model Intercomparison Project across the 110 rice-growing counties to predict production in the coming decades for Composite Variety Groups assembled based on year of release, breeding program, and several breeding trends. Results indicate positive effects over time of public breeding on rice resilience to future climates, and potential reasons are discussed.

AgTC and AgETL: open-source tools to enhance data collection and management for plant science research.

Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an Extract-Transform-Load (ETL) data integration process where data are extracted from heterogeneously formatted files, transformed to meet standard criteria, and loaded into a database. There, data are stored and can be accessed for data analysis-related processes, including dynamic data visualization through web-based tools. Both AgTC and AgETL are flexible for application across plant science experiments without programming knowledge on the part of the domain scientist, and their functions are executed on Jupyter Notebook, a browser-based interactive development environment. Additionally, all parameters are easily customized from central configuration files written in the human-readable YAML format. Using three experiments from research laboratories in university and non-government organization (NGO) settings as test cases, we demonstrate the utility of AgTC and AgETL to streamline critical steps from data collection to analysis in the plant sciences.

Quantifying physiological trait variation with automated hyperspectral imaging in rice.

Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.

Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models.

Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.

Where the wild things are: genetic associations of environmental adaptation in the Oryza rufipogon species complex.

Crop wild relatives host unique adaptation strategies that enable them to thrive across a wide range of habitats. As pressures from a changing climate mount, a more complete understanding of the genetic variation that underlies this adaptation could enable broader utilization of wild materials for crop improvement. Here, we carry out environmental association analyses (EAA) in the Oryza rufipogon species complex (ORSC), the wild progenitor of cultivated Asian rice, to identify genomic regions associated with environmental adaptation characterized by variation in bioclimatic and soil variables. We further examine regions for colocalizations with phenotypic associations within the same collection. EAA results indicate that significant regions tend to associate with single environmental variables, although 2 significant loci on chromosomes 3 and 5 are detected as common across multiple variable types (i.e. precipitation, temperature, and/or soil). Distributions of allele frequencies at significant loci across subpopulations of cultivated Oryza sativa indicate that, in some cases, adaptive variation may already be present among cultivars, although evaluation in cultivated populations is needed to empirically test this. This work has implications for the potential utility of wild genetic resources in pre-breeding efforts for rice improvement.

State spaces for agriculture: A meta-systematic design automation framework.

Agriculture is a designed system with the largest areal footprint of any human activity. In some cases, the designs within agriculture emerged over thousands of years, such as the use of rows for the spatial organization of crops. In other cases, designs were deliberately chosen and implemented over decades, as during the Green Revolution. Currently, much work in the agricultural sciences focuses on evaluating designs that could improve agriculture's sustainability. However, approaches to agricultural system design are diverse and fragmented, relying on individual intuition and discipline-specific methods to meet stakeholders' often semi-incompatible goals. This ad-hoc approach presents the risk that agricultural science will overlook nonobvious designs with large societal benefits. Here, we introduce a state space framework, a common approach from computer science, to address the problem of proposing and evaluating agricultural designs computationally. This approach overcomes limitations of current agricultural system design methods by enabling a general set of computational abstractions to explore and select from a very large agricultural design space, which can then be empirically tested.

Regulator of Awn Elongation 3, an E3 ubiquitin ligase, is responsible for loss of awns during African rice domestication.

Two species of rice have been independently domesticated from different ancestral wild species in Asia and Africa. Comparison of mutations that underlie phenotypic and physiological alterations associated with domestication traits in these species gives insights into the domestication history of rice in both regions. Asian cultivated rice, Oryza sativa, and African cultivated rice, Oryza glaberrima, have been modified and improved for common traits beneficial for humans, including erect plant architecture, nonshattering seeds, nonpigmented pericarp, and lack of awns. Independent mutations in orthologous genes associated with these traits have been documented in the two cultivated species. Contrary to this prevailing model, selection for awnlessness targeted different genes in O. sativa and O. glaberrima. We identify Regulator of Awn Elongation 3 (RAE3) a gene that encodes an E3 ubiquitin ligase and is responsible for the awnless phenotype only in O. glaberrima. A 48-bp deletion may disrupt the substrate recognition domain in RAE3 and diminish awn elongation. Sequencing analysis demonstrated low nucleotide diversity in a ~600-kb region around the derived rae3 allele on chromosome 6 in O. glaberrima compared with its wild progenitor. Identification of RAE3 sheds light on the molecular mechanism underlying awn development and provides an example of how selection on different genes can confer the same domestication phenotype in Asian and African rice.

Physiological trait networks enhance understanding of crop growth and water use in contrasting environments.

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential ("risky" stomatal regulation), high xylem hydraulic conductivity and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance and low stomatal conductance at low leaf water potential ("conservative" stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits, soil characteristics and their interactions (i.e., networks), has potential to improve our understanding of crop performance in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.

A transposon-mediated reciprocal translocation promotes environmental adaptation but compromises domesticability of wild soybeans.

Large structural variations frequently occur in higher plants; however, the impact of such variations on plant diversification, adaptation and domestication remains elusive. Here, we mapped and characterised a reciprocal chromosomal translocation in soybeans and assessed its effects on diversification and adaptation of wild (Glycine soja) and semiwild (Glycine gracilis) soybeans, and domestication of cultivated soybean (Glycine max), by tracing the distribution of the translocation in the USDA Soybean Germplasm Collection and population genetics analysis. We demonstrate that the translocation occurred through CACTA transposon-mediated chromosomal breakage in wild soybean c. 0.34 Ma and is responsible for semisterility in translocation heterozygotes and reduces their reproductive fitness. The translocation has differentiated Continental (i.e. China and Russia) populations from Maritime (i.e. Korea and Japan) populations of G. soja and predominately adapted to cold and dry climates. Further analysis revealed that the divergence of G. max from G. soja predates the translocation event and that G. gracilis is an evolutionary intermediate between G. soja and G. max. Our results highlight the effects of a chromosome rearrangement on the processes leading to plant divergence and adaptation, and provides evidence that suggests G. gracilis, rather than G. soja, as the ancestor of cultivated soybean.

A Coordinated Suite of Wild-Introgression Lines in Indica and Japonica Elite Backgrounds.

Rice, Oryza sativa L., is a cultivated, inbreeding species that serves as the staple food for the largest number of people on earth. It has two strongly diverged varietal groups, Indica and Japonica, which result from a combination of natural and human selection. The genetic divergence of these groups reflects the underlying population structure of their wild ancestors, and suggests that a pre-breeding strategy designed to take advantage of existing genetic, geographic and ecological substructure may provide a rational approach to the utilization of crop wild ancestors in plant improvement. Here we describe the coordinated development of six introgression libraries (n = 63 to 81 lines per library) in both Indica (cv. IR64) and Japonica (cv. Cybonnet) backgrounds using three bio-geographically diverse wild donors representing the Oryza rufipogon Species Complex from China, Laos and Indonesia. The final libraries were genotyped using an Infinium 7K rice SNP array (C7AIR) and analyzed under greenhouse conditions for several simply inherited (Mendelian) traits. These six interspecific populations can be used as individual Chromosome Segment Substitution Line libraries and, when considered together, serve as a powerful genetic resource for systematic genetic dissection of agronomic, physiological and developmental traits in rice.

Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought.

Understanding the genetic and physiological basis of abiotic stress tolerance under field conditions is key to varietal crop improvement in the face of climate variability. Here, we investigate dynamic physiological responses to water stress in silico and their relationships to genotypic variation in hydraulic traits of cotton (Gossypium hirsutum), an economically important species for renewable textile fiber production. In conjunction with an ecophysiological process-based model, heterogeneous data (plant hydraulic traits, spatially-distributed soil texture, soil water content and canopy temperature) were used to examine hydraulic characteristics of cotton, evaluate their consequences on whole plant performance under drought, and explore potential genotype × environment effects. Cotton was found to have R-shaped hydraulic vulnerability curves (VCs), which were consistent under drought stress initiated at flowering. Stem VCs, expressed as percent loss of conductivity, differed across genotypes, whereas root VCs did not. Simulation results demonstrated how plant physiological stress can depend on the interaction between soil properties and irrigation management, which in turn affect genotypic rankings of transpiration in a time-dependent manner. Our study shows how a process-based modeling framework can be used to link genotypic variation in hydraulic traits to differential acclimating behaviors under drought.

An improved 7K SNP array, the C7AIR, provides a wealth of validated SNP markers for rice breeding and genetics studies.

Single nucleotide polymorphisms (SNPs) are highly abundant, amendable to high-throughput genotyping, and useful for a number of breeding and genetics applications in crops. SNP frequencies vary depending on the species and populations under study, and therefore target SNPs need to be carefully selected to be informative for each application. While multiple SNP genotyping systems are available for rice (Oryza sativa L. and its relatives), they vary in their informativeness, cost, marker density, speed, flexibility, and data quality. In this study, we report the development and performance of the Cornell-IR LD Rice Array (C7AIR), a second-generation SNP array containing 7,098 markers that improves upon the previously released C6AIR. The C7AIR is designed to detect genome-wide polymorphisms within and between subpopulations of O. sativa, as well as O. glaberrima, O. rufipogon and O. nivara. The C7AIR combines top-performing SNPs from several previous rice arrays, including 4,007 SNPs from the C6AIR, 2,056 SNPs from the High Density Rice Array (HDRA), 910 SNPs from the 384-SNP GoldenGate sets, 189 SNPs from the 44K array selected to add information content for elite U.S. tropical japonica rice varieties, and 8 trait-specific SNPs. To demonstrate its utility, we carried out a genome-wide association analysis for plant height, employing the C7AIR across a diversity panel of 189 rice accessions and identified 20 QTLs contributing to plant height. The C7AIR SNP chip has so far been used for genotyping >10,000 rice samples. It successfully differentiates the five subpopulations of Oryza sativa, identifies introgressions from wild and exotic relatives, and is useful for quantitative trait loci (QTL) and association mapping in diverse materials. Moreover, data from the C7AIR provides valuable information that can be used to select informative and reliable SNP markers for conversion to lower-cost genotyping platforms for genomic selection and other downstream applications in breeding.

Bridging old and new: diversity and evaluation of high iron-associated stress response of rice cultivated in West Africa.

Adoption of rice varieties that perform well under high iron-associated (HIA) stress environments can enhance rice production in West Africa. This study reports the genetic characterization of 323 rice accessions and breeding lines cultivated in West Africa using genotyping-by-sequencing and their phenotypic response to HIA treatments in hydroponic solution (1500 mg l-1 FeSO4·7H2O) and hot-spot fields. The germplasm consisted of four genetic subpopulations: Oryza glaberrima (14%), O. sativa-japonica (7%), O. sativa-indica Group 1 (45%), and O. sativa-indica Group 2 (25%). Severe versus mild stress in the field was associated with a reduced SPAD value (12%), biomass (56%), and grain yield (57%), with leaf bronzing explaining 30% and 21% of the variation for biomass and grain yield, respectively. Association mapping using 175 indica genotypes identified 23 significant single nucleotide polymorphism (SNP) markers that mapped to 14 genomic regions. Genome-wide association study (GWAS) signals associated with leaf bronzing, a routinely used indicator of HIA stress, differed in hydroponic compared with field conditions. Contrastingly, six significant SNPs on chromosomes 8 and 9 were associated with the SPAD value under HIA stress in both field and hydroponic experiments, and a candidate potassium transporter gene mapped under the peak on chromosome 8. This study helps define criteria for assessing rice performance under HIA environments.

Conifers depend on established roots during drought: results from a coupled model of carbon allocation and hydraulics.

Trees may survive prolonged droughts by shifting water uptake to reliable water sources, but it is unknown if the dominant mechanism involves activating existing roots or growing new roots during drought, or some combination of the two. To gain mechanistic insights on this unknown, a dynamic root-hydraulic modeling framework was developed that set up a feedback between hydraulic controls over carbon allocation and the role of root growth on soil-plant hydraulics. The new model was tested using a 5 yr drought/heat field experiment on an established piñon-juniper stand with root access to bedrock groundwater. Owing to the high carbon cost per unit root area, modeled trees initialized without adequate bedrock groundwater access experienced potentially lethal declines in water potential, while all of the experimental trees maintained nonlethal water potentials. Simulated trees were unable to grow roots rapidly enough to mediate the hydraulic stress, particularly during warm droughts. Alternatively, modeled trees initiated with root access to bedrock groundwater matched the hydraulics of the experimental trees by increasing their water uptake from bedrock groundwater when soil layers dried out. Therefore, the modeling framework identified a critical mechanism for drought response that required trees to shift water uptake among existing roots rather than growing new roots.

Corrigendum: A Coordinated Suite of Wild-Introgression Lines in Indica and Japonica Elite Backgrounds.

[This corrects the article DOI: 10.3389/fpls.2020.564824.].

A framework for genomics-informed ecophysiological modeling in plants.

Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first principles-based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L. to set the stage for future large-scale process-based modeling of intraspecific variation. We develop a new canopy growth submodel for B. rapa within the process-based model Terrestrial Regional Ecosystem Exchange Simulator (TREES), test input parameters for feasibility of direct estimation with observed phenotypes across cultivated morphotypes and indirect estimation using genomic prediction on a recombinant inbred line population, and explore model performance on an in silico population under non-stressed and mild water-stressed conditions. We find evidence that the updated whole-plant model has the capacity to distill genotype by environment interaction (G×E) into tractable components. The framework presented offers a means to link genetic variation with environment-modulated plant response and serves as a stepping stone towards large-scale prediction of unphenotyped, genetically related individuals under untested environmental scenarios.

Haplotype Characterization of the sd1 Semidwarf Gene in United States Rice.

CORE IDEAS: Genomic data from diverse germplasm used for application in targeted breeding germplasm. Six SNPs identified that can characterize all haplotypes present at SD1 locus in diverse rice. Three alleles of the SD1 gene identified in US rice germplasm including two semidwarf alleles. Two SNPs identified and validated that differentiate the SD1 allele present in US germplasm. KASP assays designed for both SNPs for use in high-throughput breeding applications. Plant height is an important target in US rice (Oryza sativa L.) breeding programs and the large effect of the sd1 semidwarf gene makes it a suitable target for marker-assisted selection. Although the deletion underlying the semidwarf allele is known and a gel-based DNA marker is available, this marker is not ideal for applied breeding because of throughput and cost constraints. The objectives of this study were to characterize the haplotype diversity at the SD1 locus within US rice germplasm and develop a single nucleotide polymorphism (SNP) assay for breeding applications. The International Rice Research Institute (IRRI) SNP-Seek database was used to characterize the haplotype diversity present at the SD1 locus across a set of rice accessions and seven haplotypes were identified. The US rice germplasm was not well represented in the IRRI database, so a set of six SNPs was identified that could differentiate all detected haplotypes. These SNPs were designed into Kompetitive allele specific polymerase chain reaction (KASP) assays and screened across 359 elite US genotypes. Of the seven haplotypes, two were present within the US germplasm, one of which was the semidwarf deletion allele. A third haplotype was observed within the US medium-grain germplasm and demonstrated to be a semidwarf allele derived from the induced mutation in the 'Calrose76'. Two SNPs were identified that distinguish the three SD1 haplotypes present in the US germplasm. These SNPs were validated across the US germplasm and two biparental populations.

An imputation platform to enhance integration of rice genetic resources.

As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of integrating resources for downstream applications. While a number of imputation software packages are available, the limitations to utilization for the rice community include high computational demand and lack of a reference panel. To address these challenges, we develop the Rice Imputation Server, a publicly available web application leveraging genetic information from a globally diverse rice reference panel assembled here. This resource allows researchers to benefit from increased marker density without needing to perform imputation on their own machines. We demonstrate improvements that imputed data provide to rice genome-wide association (GWA) results of grain amylose content and show that the major functional nucleotide polymorphism is tagged only in the imputed data set.

Ethylene-gibberellin signaling underlies adaptation of rice to periodic flooding.

Most plants do poorly when flooded. Certain rice varieties, known as deepwater rice, survive periodic flooding and consequent oxygen deficiency by activating internode growth of stems to keep above the water. Here, we identify the gibberellin biosynthesis gene, SD1 (SEMIDWARF1), whose loss-of-function allele catapulted the rice Green Revolution, as being responsible for submergence-induced internode elongation. When submerged, plants carrying the deepwater rice-specific SD1 haplotype amplify a signaling relay in which the SD1 gene is transcriptionally activated by an ethylene-responsive transcription factor, OsEIL1a. The SD1 protein directs increased synthesis of gibberellins, largely GA4, which promote internode elongation. Evolutionary analysis shows that the deepwater rice-specific haplotype was derived from standing variation in wild rice and selected for deepwater rice cultivation in Bangladesh.