The Land Institute

Wheat (Triticum aestivum L.) is one of the major crops and a staple food for approximately 35% of the world population. Wheat production is constantly threatened by stripe rust, one of the most devastating wheat diseases, which is caused by Puccinia striiformis f. sp. tritici (Pst). Improving the disease resistance of wheat cultivars is the most efficient and sustainable way to control stripe rust. In this study, we aimed to explore the novel resistance resources in a collection of 180 cultivated emmer wheat accessions (T. turgidum ssp. dicoccum) collected from 32 countries on five continents. The cultivated emmer wheat accessions were genotyped by wheat Illumina iSelect 9 K single-nucleotide polymorphism (SNP) array, and phenotypes were evaluated in a greenhouse using four races and in five field environments. Conducting a genome-wide association study, we identified 14 quantitative trait loci (QTLs) associated with stripe rust resistance. Two QTLs, QYr.emmer-4A.2 and QYr.emmer-6A, confer all-stage resistance, and 12 QTLs, located on chromosomes 1A, 2A, 2B, 3A, 3B, 4A, 5A, 5B, 7A, and 7B, confer adult plant resistance (APR) to stripe rust. Compared with the known stripe rust resistance QTLs, most of the QTLs identified in this study are very likely novel QTLs. The APR QTL, QYr.emmer-7A, was consistently detected among multiple data sets from different environments and was validated using a biparental mapping population. The SNPs, simple sequence repeat markers and the genomic loci identified in this study will provide very useful information for QTL mapping and resistance breeding programs.

Thinopyrum intermedium (c.n. intermediate wheatgrass), marketed under the trade name Kernza, is a promising species for perennial grain production based on seed size, ease of threshing, resistance to shattering, and grain quality. Although numerous generations of breeding for seed yield have been completed, the impact of selection on non-target traits is unknown. Here, we evaluated structural and functional changes brought about by selection for seed yield over a sequence of nine selection cycles (C0 to C9). In two experiments under semi-controlled environmental conditions, we compared gas exchange (A, E, gs, and A/Ci curves), leaf and root morphology, and the structure of seedlings from 10 generations. We found that the selection for yield throughout cycles indirectly changed the leaf structure (leaf size, leaf thickness, and leaf anatomy) and physiology (carbon acquisition and transpiration per unit area), with later cycles showing larger leaves with higher rates of CO2 assimilation and transpiration. Changes in root structure followed similar trends: selection resulted in longer, more branched, and finer roots. These changes in non-target traits are linked to resource-use strategies and to ecosystem services provided by Kernza. Understanding how the domestication of perennial grains impacts non-target traits will aid in the design of integrated breeding programs for Kernza and other perennial grain crops.

Genomic selection (GS) can accelerate plant breeding gains by reducing breeding cycle times, reducing phenotyping costs, or improving selection accuracy. GS is especially promising for perennial crops such as intermediate wheatgrass (IWG, Thinopyrum intermedium) that may require multiple years of evaluation under phenotypic recurrent selection. A major obstacle in implementing GS is the need for an affordable, high-density, genetic marker system that is scalable to thousands or tens-of-thousands of samples in breeding programs, especially in emerging or minor crop species. As sequencing costs continue to decrease, low-coverage whole genome skim-sequencing (skim-seq) has become an attractive method for GS. Using commercial laboratory products and open-source software, we implemented whole genome prediction at breeding program scale using ultra-low coverage (0.01x- 0.05x, 100-125 million reads per sample) whole genome skim-seq. Using STITCH (Sequencing to Imputation Through Constructing Haplotypes) imputation software, we evaluated optimization of imputation parameters including sequence coverage and number of assumed ancestral haplotypes. Finally, we evaluated whole genome prediction cross-validation accuracies using reduced representation genotyping-by-sequencing (GBS) versus skim-seq data for IWG, an outcrossing, heterozygous, large genome (12.7 Gb), polyploid perennial species. Our results indicate correlations between cross-validation accuracies across five traits in IWG using skim-seq data (r=0.29-0.61) can be used as effectively as GBS (r=0.29-0.55) while generating low-coverage archival sequence data that will be robust to technological advances. These methods will be applicable to a wide range of crops and scale to breeding program size, allowing for more tractable implementation of GS within breeding programs.

Societal Impact Statement Soils are globally degraded due in part to conventional agriculture and wildland conversion. To address the global challenge of soil degradation, we formed an interdisciplinary, cross‐institutional collaborative research team, New Roots for Restoration, to understand how perennial plant root and shoot traits relate to one another, and how they influence and are influenced by plant communities and the soil. We highlight how coordinated research projects, expertise cores, and cross‐institution training activities simultaneously advance scientific understanding and workforce development. We share approaches and tools, as well as challenges and lessons learned. This institute highlights how multi‐institution teams integrating diverse biological subdisciplines can advance major scientific and training goals. Summary The challenge of restoring degraded soils must be met with integrated knowledge and practice of distinct biological subdisciplines. Here we share a model of scientific collaboration, an interdisciplinary cross‐institution team: New Roots for Restoration Biology Integration Institute (NRR). NRR focuses on how plant organismal systems (roots and shoots) relate to one another, and how those relationships influence and are influenced by plant communities and the soil ecosphere. Our aim is to enhance the ability to predict belowground functional traits from aboveground phenotypes to identify ecologically and functionally appropriate materials to restore natural ecosystems and to develop sustainable agricultural systems of the future. Plants provide the ecological and structural foundation of terrestrial landscapes, building soils and supporting productivity in natural and agricultural ecosystems. Plants connect aboveground components through their root systems in terrestrial ecosystems to the soil ecosphere. However, the field lacks a basic understanding of how plant traits, from roots to shoots, govern these connections. This is especially true for perennial plants, which are inherently difficult to study. NRR seeks to elucidate how attributes of the plant organismal system (plant traits, genetics, genetics × environment interactions), community ecology, and the soil ecosphere contribute to variation in aboveground and belowground plant structures through 10 integrated projects supported by expertise cores. Our efforts demonstrate how the integration of biological subdisciplines can advance understanding of the impacts of deep‐rooted plant systems in both restored grasslands and perennial agroecosystems.

Biological nitrogen fixation (BNF) is the largest natural source of new nitrogen (N) that supports terrestrial productivity1,2, yet estimates of global terrestrial BNF remain highly uncertain3,4. Here we show that this uncertainty is partly because of sampling bias, asfield BNF measurements in natural terrestrial ecosystems occur where N fixers are 17 times more prevalent than their mean abundances worldwide. To correct this bias, we develop new estimates of global terrestrial BNF by upscaling field BNF measurements using spatially explicit abundances of all major biogeochemical N-fixing niches. We find that natural biomes sustain lower BNF, 65 (52-77) Tg N yr-1, than previous empirical bottom-up estimates3,4, with most BNF occurring in tropical forests and drylands. We also find high agricultural BNF in croplands and cultivated pastures, 56 (54-58) Tg N yr-1. Agricultural BNF has increased terrestrial BNF by 64% and total terrestrial N inputs from all sources by 60% over pre-industrial levels. Our results indicate that BNF may impose stronger constraints on the carbon sink in natural terrestrial biomes and represent a larger source of agricultural N than is generally considered in analyses of the global N cycle5,6, with implications for proposed safe operating limits for N use7,8.

SummaryPerennial crops are promising candidates for building climate resilient agricultural systems because they can sustain production in challenging environments and their deep roots mitigate soil erosion, nutrient leaching and preserve soil carbon. However, these plants are challenging to breed with classical approaches due to self-incompatibility, polyploidy, and large complex genomes. These factors have been a barrier to cultivar development, but with the advances in genomic technologies, this can change. The possibility of assembling genomes and generating molecular markers in a cost-effective way means that cultivars can be developed faster with marker-assisted selection similarly to major crops. Here, we describe a pathway to accelerate domestication, starting from the establishment of a diversity collection, assembly of a reference genome, the development of breeding populations, and use of sequencing to identify relevant alleles. We provide recommendations to successfully use genomics to accelerate domestication given an entirely new era of DNA sequencing technologies.

To realize the potential of sainfoins to contribute to sustainable agriculture and expand on demonstrated uses and benefits, de novo domestication is occurring to develop perennial Baki™ bean, the trade name used by The Land Institute for pulses (i.e., grain legumes) derived from sainfoins. The objective of this study was to characterize amino acid and fatty acid profiles of depodded seeds from commercial sainfoin (Onobrychis viciifolia) seed lots, and compare these results with data published in the Global Food Composition Database for Pulses. The fatty acid profile consisted primarily of polyunsaturated fatty acids (56.8%), compared to monounsaturated (29.0%) and saturated fatty acids (14.2%), and n-3 fatty acids (39.5%), compared to n-9 (28.4%) and n-6 (17.6%) fatty acids. The essential fatty acid linolenic acid (18,3 n-3) was the most abundant fatty acid (39.2%), followed by oleic acid (18,1 cis-9) (27.8%), and the essential fatty acid linoleic acid (18,2 n-6) (17.3%). The amino acid profile consisted primarily of the nonessential amino acids glutamic acid (18.3%), arginine (11.6%), and aspartic acid (10.8%), followed by the essential amino acids leucine (6.8%), and lysine (5.8%). Essential amino acid content met adult daily requirements for each amino acid. This indicates that sainfoin seeds may be a complete plant protein source. However, further research is necessary to better understand protein quality, defined by protein digestibility in addition to the amino acid profile. By demonstrating favorable fatty acid and amino acid profiles to human health, these results contribute to a growing body of evidence supporting the potential benefits of perennial Baki™ bean, a novel, perennial pulse derived from sainfoins.

Selection for increased yield changed structure, physiology and overall resource-use strategy from conservative towards acquisitive leaves. Alternative criteria can be considered, to increase yield with less potentially negative traits. We compared the morphology, anatomy and physiology of wild and semi-domesticated (SD) accessions of Silphium integrifolium (Asteraceae), in multi-year experiments. We hypothesized that several cycles of selection for seed-yield would result in acquisitive leaves, including changes predicted by the leaf economic spectrum. Early-selection indirectly resulted in leaf structural and functional changes. Leaf anatomy changed, increasing mesophyll conductance and the size of xylem vessels and mesophyll cells increased. Leaves of SD plants were larger, heavier, with lower stomatal conductance, lower internal CO2 concentration, and lower resin concentration than those of wild types. Despite increased water use efficiency, SD plants transpired 25% more because their increase in leaf area. Unintended and undesired changes in functional plant traits could quickly become fixed during domestication, shortening the lifespan and increasing resource consumption of the crop as well as having consequences in the provision and regulation of ecosystem services.

Abstract Ploidy manipulation has been used in many crop improvement programs to develop plant species with wider adaptability and desirable traits. The objectives of this study were to create stable tetraploid centipedegrass (Eremochloa ophiuroides [Munro] Hack.) lines and evaluate them for beneficial traits. To generate polyploid lines, “TifBlair” (2n = 2x = 18) seeds were exposed to gamma radiation and callus was generated using tissue culture, exposed to glyphosate and regenerated into plantlets. After 5 years of continuous propagation, two lines, Hongliang Wang (HW)16 and HW123, were found to be tetraploid, and one line, HW61, was found to be a mixoploid using flow cytometry and chromosome counts. Nuclear DNA contents for diploid and tetraploid lines ranged from 1.97 to 2.10 and 4.14 to 4.30 pg 2C−1, respectively. Ten centipedegrass lines were genotyped using seven simple sequence repeat markers and clustered by origin (derived from TifBlair or the University of Georgia breeding program). Six lines were evaluated for morphological and physiological traits under greenhouse conditions. Trait evaluation showed that tetraploid lines had larger stomata and leaf width and reduced stomatal density. For all other traits evaluated under greenhouse conditions, although significant differences were observed between lines, no consistent differences separated diploid and tetraploid lines. Between the tetraploid lines, HW16 had higher evapotranspiration, percentage green cover, and relative water content than HW123, whereas HW123 had a higher average clipping yield and root width than HW16. In conclusion, irradiation and tissue culture are valuable techniques for the generation of stable polyploid lines, and an increased ploidy level led to larger vegetative structures in centipedegrass.

A common requirement of plant breeding programs across the country is companion planting – growing different species of plants in close proximity so they can mutually benefit each other. However, the determination of companion plants requires meticulous monitoring of plant growth. The technique of ocular monitoring is often laborious and error prone. The availability of image processing techniques can be used to address the challenge of plant growth monitoring and provide robust solutions that assist plant scientists to identify companion plants. This paper presents a new image processing algorithm to determine the amount of vegetation cover present in a given area, called fractional vegetation cover. The proposed technique draws inspiration from the trusted Daubenmire method for vegetation cover estimation and expands upon it. Briefly, the idea is to estimate vegetation cover from images containing multiple rows of plant species growing in close proximity separated by a multi-segment PVC frame of known size. The proposed algorithm applies a Hough Transform and Simple Linear Iterative Clustering (SLIC) to estimate the amount of vegetation cover within each segment of the PVC frame. When applied as a longitudinal study on a 177 field image dataset, this analysis provides crucial insights into plant growth. As a means of comparison, the proposed algorithm is compared with SamplePoint and Canopeo, two trusted applications used for vegetation cover estimation. The comparison shows a 99% similarity with both SamplePoint and Canopeo demonstrating the accuracy and feasibility of the algorithm for fractional vegetation cover estimation.