Seth C. Murray

Increased food and ecosystem security via perennial grains

Perennial grains hold promise, especially for marginal landscapes or with limited resources where annual versions struggle. Despite doubling of yields of major grain crops since the 1950s, more than one in seven people suffer from malnutrition (1). Global population is growing; demand for food, especially meat, is increasing; much land most suitable for annual crops is already in use; and production of nonfood goods (e.g., biofuels) increasingly competes with food production for land (2). The best lands have soils at low or moderate risk of degradation under annual grain production but make up only 12.6% of global land area (16.5 million km2) (3). Supporting more than 50% of world population is another 43.7 million km2 of marginal lands (33.5% of global land area), at high risk of degradation under annual grain production but otherwise capable of producing crops (3). Global food security depends on annual grains—cereals, oilseeds, and legumes—planted on almost 70% of croplands, which combined supply a similar portion of human calories (4, 5). Annual grain production, though, often compromises essential ecosystem services, pushing some beyond sustainable boundaries (5). To ensure food and ecosystem security, farmers need more options to produce grains under different, generally less favorable circumstances than those under which increases in food security were achieved this past century. Development of perennial versions of important grain crops could expand options.

Sweet Sorghum Genetic Diversity and Association Mapping for Brix and Height

Sweet sorghum [Sorghum bicolor (L.) Moench], like its close relative, sugarcane (Saccharum spp.), has been selected to accumulate high levels of edible sugars in the stem. Sweet sorghums are tall and produce high biomass in addition to sugar. Little has been documented about the genetic relationships and diversity within sweet sorghums and how sweet sorghums relate to grain sorghum racial types. In this study, a diverse panel of 125 sorghums (mostly sweet) was successfully genotyped with 47 simple sequence repeats (SSRs) and 322 single nucleotide polymorphisms (SNPs). Using both distance‐based and model‐based methods, we identified three main genetic groupings of sweet sorghums. Based on observed phenotypes and known origins we classified the three groups as historical and modern syrup, modern sugar/energy types, and amber types. Using SSR markers also scored in an available large grain sorghum germplasm panel, we found that these three sweet groupings clustered with kafir/bicolor, caudatum, and bicolor types, respectively. Using the information on population structure and relatedness, association mapping was performed for height and stem sugar (brix) traits. Three significant associations for height were detected. Two of these, on chromosomes 9 and 6, support published QTL studies. One significant association for brix, on chromosome 1, 12kb from a glucose‐6‐phosphate isomerase homolog, was detected.

Genetic diversity and population structure analysis of accessions in the US historic sweet sorghum collection

Sweet sorghum has the potential to become a versatile feedstock for large-scale bioenergy production given its sugar from stem juice, cellulose/hemicellulose from stalks, and starch from grain. However, for researchers to maximize its feedstock potential a first step includes additional evaluations of the 2,180 accessions with varied origins in the US historic sweet sorghum collection. To assess genetic diversity of this collection for bioenergy breeding and population structure for association mapping, we selected 96 accessions and genotyped them with 95 simple sequence repeat markers. Subsequent genetic diversity and population structure analysis methods identified four subpopulations in this panel, which correlated well with the geographic locations where these accessions originated or were collected. Model comparisons for three quantitative traits revealed different levels of population structure effects on flowering time, plant height, and brix. Our results suggest that diverse germplasm accessions curated from different geographical regions should be considered for plant breeding programs to develop sweet sorghum cultivars or hybrids, and that this sweet sorghum panel can be further explored for association mapping.

Challenges of Detecting Directional Selection After a Bottleneck: Lessons From Sorghum bicolor

Multilocus surveys of sequence variation can be used to identify targets of directional selection, which are expected to have reduced levels of variation. Following a population bottleneck, the signal of directional selection may be hard to detect because many loci may have low variation by chance and the frequency spectrum of variation may be perturbed in ways that resemble the effects of selection. Cultivated Sorghum bicolor contains a subset of the genetic diversity found in its wild ancestor(s) due to the combined effects of a domestication bottleneck and human selection on traits associated with agriculture. As a framework for distinguishing between the effects of demography and selection, we sequenced 204 loci in a diverse panel of 17 cultivated S. bicolor accessions. Genomewide patterns of diversity depart strongly from equilibrium expectations with regard to the variance of the number of segregating sites, the site frequency spectrum, and haplotype configuration. Furthermore, gene genealogies of most loci with an excess of low frequency variants and/or an excess of segregating sites do not show the characteristic signatures of directional and diversifying selection, respectively. A simple bottleneck model provides an improved but inadequate fit to the data, suggesting the action of other population-level factors, such as population structure and migration. Despite a known history of recent selection, we find little evidence for directional selection, likely due to low statistical power and lack of an appropriate null model.

Plant breeding for harmony between agriculture and the environment

Plant breeding programs primarily focus on improving a crops environmental adaptability and biotic stress tolerance in order to increase yield. Crop improvements made since the 1950s – coupled with inexpensive agronomic inputs, such as fertilizers, pesticides, and water – have allowed agricultural production to keep pace with human population growth. Plant breeders, particularly those at public institutions, have an interest in reducing agricultures negative impacts and improving the natural environment to provide or maintain ecosystem services (eg clean soil, water, and air; carbon sequestration), and in creating new agricultural paradigms (eg perennial polycultures). Here, we discuss recent developments in, as well as the goals of, plant breeding, and explain how these may be connected to the specific interests of ecologists and naturalists. Plant breeding can be a powerful tool to bring “harmony” between agriculture and the environment, but partnerships between plant breeders, ecologists, urban plann...

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Selection Mapping of Loci for Quantitative Disease Resistance in a Diverse Maize Population

The selection response of a complex maize population improved primarily for quantitative disease resistance to northern leaf blight (NLB) and secondarily for common rust resistance and agronomic phenotypes was investigated at the molecular genetic level. A tiered marker analysis with 151 simple sequence repeat (SSR) markers in 90 individuals of the population indicated that on average six alleles per locus were available for selection. An improved test statistic for selection mapping was developed, in which quantitative trait loci (QTL) are identified through the analysis of allele-frequency shifts at mapped multiallelic loci over generations of selection. After correcting for the multiple tests performed, 25 SSR loci showed evidence of selection. Many of the putatively selected loci were unlinked and dispersed across the genome, which was consistent with the diffuse distribution of previously published QTL for NLB resistance. Compelling evidence for selection was found on maize chromosome 8, where several putatively selected loci colocalized with published NLB QTL and a race-specific resistance gene. Analysis of F2 populations derived from the selection mapping population suggested that multiple linked loci in this chromosomal segment were, in part, responsible for the selection response for quantitative resistance to NLB.

Feasibility of surface-enhanced Raman spectroscopy for rapid detection of aflatoxins in maize.

Rapid and sensitive surface-enhanced Raman spectroscopy (SERS) for aflatoxin detection was employed for development of the models to classify and quantify aflatoxin levels in maize at concentrations of 0 to 1,206 μg/kg. Highly effective SERS substrate (Ag nanosphere) was prepared and mixed with a sample extract for SERS measurement. Strong Raman bands associated with aflatoxins and changes in maize kernels induced by aflatoxin contamination were observed in different SERS spectroscopic regions. The k-nearest neighbors (KNN) classification model yielded high classification accuracy and lower prediction error with no misclassification of contaminated samples as aflatoxin negative. The multiple linear regression (MLR) models showed a higher predictive accuracy with stronger correlation coefficients (r = 0.939-0.967) and a higher sensitivity with lower limits of detection (13-36 μg/kg) and quantitation (44-121 μg/kg) over other quantification models. Paired sample t test exhibited no statistically significant difference between the reference values and the predicted values of SERS in most chemometric models. The proposed SERS method would be a more effective and efficient analytical tool with a higher accuracy and lower constraints for aflatoxin analysis in maize compared to other existing spectroscopic methods and conventional Raman spectroscopy.

Genome Wide Association Study for Drought, Aflatoxin Resistance, and Important Agronomic Traits of Maize Hybrids in the Sub-Tropics

The primary maize (Zea mays L.) production areas are in temperate regions throughout the world and this is where most maize breeding is focused. Important but lower yielding maize growing regions such as the sub-tropics experience unique challenges, the greatest of which are drought stress and aflatoxin contamination. Here we used a diversity panel consisting of 346 maize inbred lines originating in temperate, sub-tropical and tropical areas testcrossed to stiff-stalk line Tx714 to investigate these traits. Testcross hybrids were evaluated under irrigated and non-irrigated trials for yield, plant height, ear height, days to anthesis, days to silking and other agronomic traits. Irrigated trials were also inoculated with Aspergillus flavus and evaluated for aflatoxin content. Diverse maize testcrosses out-yielded commercial checks in most trials, which indicated the potential for genetic diversity to improve sub-tropical breeding programs. To identify genomic regions associated with yield, aflatoxin resistance and other important agronomic traits, a genome wide association analysis was performed. Using 60,000 SNPs, this study found 10 quantitative trait variants for grain yield, plant and ear height, and flowering time after stringent multiple test corrections, and after fitting different models. Three of these variants explained 5–10% of the variation in grain yield under both water conditions. Multiple identified SNPs co-localized with previously reported QTL, which narrows the possible location of causal polymorphisms. Novel significant SNPs were also identified. This study demonstrated the potential to use genome wide association studies to identify major variants of quantitative and complex traits such as yield under drought that are still segregating between elite inbred lines.

Influence of Genetic Background on Anthocyanin and Copigment Composition and Behavior during Thermoalkaline Processing of Maize.

Visual color is a primary quality factor for foods purchase; identifying factors that influence in situ color quality of pigmented maize during processing is important. Twenty-four genetically distinct pigmented maize hybrids (red/blue, blue, red, and purple) were used to investigate the effect of pigment and copigment composition on color stability during nixtamalization and tortilla chip processing. The red/blue and blue samples generally contained higher proportions of acylated anthocyanins (mainly cyanidin-3-(6″-malonylglucoside)) than the red and purple color classes. Phenolic amides were the major extractable copigments in all samples (450-764 μg/g), with red samples containing the most putrescines and blue samples containing the most spermidines. Even though samples with higher proportions of acylated anthocyanins retained more pigments during processing, this did not relate to final product color quality. In general, the red/blue samples retained their color quality the best and thus are good candidates for genetic improvement for direct processing into alkalized products.