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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Safety Analysis of Soybean Processing for Advanced Life Support

Soybeans (cv. Hoyt) is one of the crops planned for food production within the Advanced Life Support System Integration Testbed (ALSSIT), a proposed habitat simulation for long duration lunar/Mars missions. Soybeans may be processed into a variety of food products, including soymilk, tofu, and tempeh. Due to the closed environmental system and importance of crew health maintenance, food safety is a primary concern on long duration space missions. Identification of the food safety hazards and critical control points associated with the closed ALSSIT system is essential for the development of safe food processing techniques and equipment. A Hazard Analysis Critical Control Point (HACCP) model was developed to reflect proposed production and processing protocols for ALSSIT soybeans. Soybean processing was placed in the type III risk category. During the processing of ALSSIT-grown soybeans, critical control points were identified to control microbiological hazards, particularly mycotoxins, and chemical hazards from antinutrients. Critical limits were suggested at each CCP. Food safety recommendations regarding the hazards and risks associated with growing, harvesting, and processing soybeans; biomass management; and use of multifunctional equipment were made in consideration of the limitations and restraints of the closed ALSSIT.

Hentges, Dawn L.↗
NASA NTRS1990

Photomorphogenesis and photoassimilation in soybean and sorghum grown under broad spectrum or blue-deficient light sources

The role of blue light in plant growth and development was investigated in soybean (Glycine max [L.] Merr. cv Williams) and sorghum (Sorghum bicolor [L.] Moench. cv Rio) grown under equal photosynthetic photon fluxes (approximately 500 micromoles per square meter per second) from broad spectrum daylight fluorescent or blue-deficient, narrow-band (589 nanometers) low pressure sodium (LPS) lamps. Between 14 and 18 days after sowing, it was possible to relate adaptations in photosynthesis and leaf growth to dry matter accumulation. Soybean development under LPS light was similar in several respects to that of shaded plants, consistent with an important role for blue light photoreceptors in regulation of growth response to irradiance. Thus, soybeans from LPS conditions partitioned relatively more growth to leaves and maintained higher average leaf area ratios (mean LAR) that compensated lower net assimilation rates (mean NAR). Relative growth rates were therefore comparable to plants from daylight fluorescent lamps. Reductions in mean NAR were matched by lower rates of net photosynthesis (A) on an area basis in the major photosynthetic source (first trifoliolate) leaf. Lower A in soybean resulted from reduced leaf dry matter per unit leaf area, but lower A under LPS conditions in sorghum correlated with leaf chlorosis and reduced total nitrogen (not observed in soybean). In spite of a lower A, mean NAR was larger in sorghum from LPS conditions, resulting in significantly greater relative growth rates (mean LAR was approximately equal for both light conditions). Leaf starch accumulation rate was higher for both species and starch content at the end of the dark period was elevated two- and three-fold for sorghum and soybean, respectively, under LPS conditions. Possible relations between starch accumulation, leaf export, and plant growth in response to spectral quality were considered.

NASA Discipline Life Support Systems↗
NASA NTRS2022

Are Soybean Models Ready for Climate Change Food Impact Assessments?

An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO 2 concentration [CO 2 ] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO 2 ]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO 2 ] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO 2 ] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO 2 ] and temperature is needed to reduce the uncertainty from the individual models.

Agricultural Model Inter-comparison and Improvemen↗
DOE OSTI2022

Uptake of individual and mixed per- and polyfluoroalkyl substances (PFAS) by soybean and their effects on functional genes related to nitrification, denitrification, and nitrogen fixation

Here in this study, we set up a soil-microbe-soybean system spiked with PFOA, PFOS, or a PFAS mixture of eight PFAS and investigated the distribution of PFAS in the system and impacts on the abundance and expression level of genes involved in the nitrogen (N) cycle. When soybean was exposed to the PFAS mixtures, synergistic uptake by shoots was detected. PFAS exhibited remarkable impacts on abundance of nitrification and denitrification genes in both bulk soil and rhizosphere as well as expression of N fixation gene in soybean nodules. The abundance of nitrification genes AOA and AOB amoA and denitrification gene nirK was significantly reduced (p < 0.05) in almost all treatments in bulk soil, except PFOA at 10 μg/kg. The abundance of other functional genes, such as nirS and norZ was affected differently depending on PFAS concentrations and sample location, either bulk soil or the rhizosphere. Interestingly, the N fixation gene nifH in soybean nodules was overexpressed by a PFAS mixture at 100 μg/kg. Hence, this work provided in-depth knowledge regarding the distribution of PFAS and their impacts on the N cycle for the studied system. Results from this study provide insights on assessing risks posed by individual or mixed PFAS to soybean.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unlocking soybean meal pectin recalcitrance using a multi-enzyme cocktail approach

Pectin is a complex plant heteropolysaccharide whose structure and function differ depending on its source. In animal feed, breaking down pectin is essential, as its presence increases feed viscosity and reduces nutrient absorption. Soybean meal, a protein-rich poultry feed ingredient, contains significant amounts of pectin, the structure of which remains unclear. Consequently, the enzyme activities required to degrade soybean meal pectin and how they interact are still open questions. In this study, we produced 15 recombinant fungal carbohydrate-active enzymes (CAZymes) identified from fungal secretomes acting on pectin. After observing that these enzymes were not active on soybean meal pectin when used alone, we developed a semi-miniaturized method to evaluate their effect as multi-activity cocktails. We designed and tested 12 enzyme pools, containing up to 15 different CAZymes, using several hydrolysis markers. Thanks to our multiactivity enzymatic approach combined with a Pearson correlation matrix, we identified 10 fungal CAZymes efficient on soybean meal pectin, 9 of which originate from Talaromyces versatilis. Based on enzyme specificity and linkage analysis, we propose a structural model for soybean meal pectin. Our findings underscore the importance of combining CAZymes to improve the degradation of agricultural co-products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean ( Glycine max ) nested association mapping population

We report photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500–2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R 2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Transfer learning-based soybean LAI estimations by integrating PROSAIL, UAV, and PlanetScope imagery

Accurate Leaf Area Index (LAI) estimations at the soybean plot scale is achievable using high-resolution Unmanned Aerial Vehicle (UAV) imagery and field measurement samples. However, the limited coverage of UAV flights restricts large-scale remote sensing monitoring in expansive soybean fields. This study leverages the broad coverage and 3-m resolution of PlanetScope satellite imagery to extend LAI prediction from UAV to satellite scales through transfer learning, using UAV-scale LAI estimates as a benchmark to validate cross-scale consistency. To address this challenge, this study proposed the LAI-TransNet, a two-stage transfer learning framework designed for precise and scalable soybean LAI prediction across large areas, demonstrating its effectiveness in cross-scale monitoring. In Stage 1, a UAV-scale benchmark is established using PROSAIL-simulated UAV reflectance data (UAV-Sim) and field-measured soybean LAI. Traditional machine learning, deep learning, and transfer learning models are trained on a hybrid UAV-Sim and field-measured dataset (UAV-Sim_Measured), with the transfer learning model CNN-TL, fine-tuned using pre-trained weights derived from UAV-Sim, achieving the highest accuracy (R 2 = 0.81, RMSE = 0.64 m 2 /m 2 , rRMSE = 11.5 %). In Stage 2, LAI-TransNet is developed by fine-tuning the CNN-TL model on PlanetScope simulated data (PS-Sim), preprocessed via cross-domain mapping to align UAV and satellite spectral features. Real PlanetScope imagery is corrected for reflectance consistency with reference to UAV imagery spectral profiles. LAI-TransNet outperforms other deep learning models trained directly on PS-Sim (R 2 = 0.69 vs. 0.60–0.63), ensuring robust cross-scale consistency. In conclusion, by bridging UAV and satellite scales, LAI-TransNet enables large-scale soybean LAI monitoring, enhancing precision agriculture management through improved monitoring with the PlanetScope imagery.

Leaf area index (LAI)↗
DOE OSTI2022

Impacts of corn intercropping with soybean, peanut and millet through different planting patterns on population dynamics and community diversity of insects under fertilizer reduction

Corn is one of the key grain crops in China and the excessive use of chemical fertilizers and pesticides seriously damages the ecological environment in fields. To explore a more scientific and reasonable way to plant corn and simultaneously reduce the overuse of chemical fertilizers and pesticides, the impact of corn intercropping with soybean, peanut, and millet, respectively, through five planting patterns, including three intercropping patterns (2 corn rows to 2, 3 and 4 rows of soybean/peanut or 2, 4 and 6 millet rows, respectively) and two monoculture patterns of corn and soybean, peanut or millet under normal (600 kg/ha) and reduced (375 kg/ha) levels of NPK (N:P 2 O 5 :K 2 O = 15:15:15) fertilization on the population abundance and community diversity of insects, leaf nutrients, and induced plant hormones jasmonic acid (JA) and salicylic acid (SA) was studied in 2018 and 2019. The results showed that the insect community indexes of the species number ( S ), the diversity index ( H ), and the uniformity index ( E ) generally increased under intercropping and were significantly higher than those under corn monoculture. The prevalence of Asian corn borer ( Ostrinia furnacalis ) on the intercropping corn plants decreased by based on the average of seven surveys per year for each treatment 2.9 to 17 heads per 30 plants compared with that on the monoculture corn plants. The number of natural enemy insect species on corn plants under intercropping was significantly higher than that under corn monoculture. That is, intercropping may decrease the population of Asian corn borers by increasing S , H , E , and natural enemy insect species (NEI). Moreover, intercropping type and fertilizer level significantly affected corn leaf nutrient contents. Compared with the normal fertilizer level, fertilizer reduction significantly reduced the foliar contents of amino acids, soluble protein, and soluble sugar in corn plants. In addition, corn–soybean and corn–peanut intercropping significantly increased the three nutrient contents in corn leaves compared with corn monoculture. In terms of corn nutrients, intercropping could compensate for the effects of fertilizer reduction. The foliar content of JA in corn–soybean intercropping was significantly higher than in corn monoculture. Under corn–soybean and corn–peanut intercropping, SA was significantly lower than under corn monoculture. Overall, intercropping, not fertilizer reduction, can significantly increase insect community diversity while reducing the population abundances of the key insect pest species on corn plants. Intercropping reduced the SA content, increased amino acids and thus reduced the susceptibility of corn to the pest insects.

Li, Likun↗
DOE OSTI2016

Isolation of three B-box zinc finger proteins that interact with STF1 and COP1 defines a HY5/COP1 interaction network involved in light control of development in soybean

LONG HYPOCOTYL5 (HY5) and STF1 (Soybean TGACG-motif binding Factor 1) are two related bZIP transcription factors that play a positive role in photomorphogenesis and hormonal signaling. In this study, we compared full length STF1 and truncated STF1 overexpression lines and found that the C-terminal 133 amino acids (194–306) possess all the HY5-like function in Arabidopsis. The STF1-DC1 mutant (1–306), with a 20 amino acid deletion at the carboxy terminus, failed to complement the hy5 mutant phenotype, which suggests an intact C-terminus is required for STF1 function. To understand the role of the C-terminal domain in photomorphogenesis we used a yeast two-hybrid screen to isolate proteins that bind to the STF1 C-terminus. We isolated three soybean cDNAs encoding the zinc-finger proteins GmSTO, GmSTH, and GmSTH2, which interact with STF1. These proteins belong to a family of B-box zinc finger proteins that include Arabidopsis SALT TOLERANCE (STO) and STO HOMOLOG (STH) and STH2, which play a role in light-dependent development and gene expression. The C-terminal 63 amino acids of STF1, containing a leucine zipper and the two N-terminal B-boxes, contains the domain involved in interactions between STF1 and GmSTO. In addition, we identified an interaction between soybean COP1 (GmCOP1) and GmSTO and GmSTH, as well as STF1, which strongly suggests the presence of a similar regulatory circuit for light signaling in soybean as in Arabidopsis. This study shows that photomorphogenic control requires complex molecular interactions among several different classes of transcription factors such as bZIP, B-box factors, and COP1, a ubiquitin ligase. - Highlights: • STF1 interact with GmSTO, GmSTH and GmSTH2. • The bZIP transcription factor STF1 requires an intact C-terminal domain for STF1 function. • STF1 and GmSTO are nuclear proteins.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Higher than expected N 2 O emissions from soybean crops in the Pampas Region of Argentina: Estimates from DayCent simulations and field measurements

In developing countries, agriculture generally represents a large fraction of GHG emissions reported in National Inventories, and emissions are typically estimated using Tier 1 IPCC guidelines. However, field data and locally adapted simulation models can improve the accuracy of IPCC estimations. In this report we aimed to quantify anthropogenic N2O emissions from croplands of Argentina through field measurements, model simulations and IPCC guidelines. Here we measured N 2 O emissions and their controlling factors in 62 plots of the Pampas Region with corn, soybean and wheat/soybean crops and in unmanaged grasslands. We accounted for gross emissions from crops and background emissions from unmanaged grasslands to calculate net anthropogenic emissions from crops as the difference between them. We calibrated and evaluated the DayCent model and then simulated different weather and management scenarios. Finally, we applied IPCC guidelines to estimate anthropogenic N 2 O emissions at the same plots. The DayCent model accurately simulated annual N 2 O emission for all crops as compared to measured data (RMSE = 1.4 g N ha -1 day -1 ). Measured and simulated emissions in soybean crops were higher than in corn and wheat/soybean crops. Gross N 2 O emissions ranged from 1.4 to 5.1 kg N ha -1 yr -1 for current environmental (soil and weather) and management (crops and fertilizer doses) conditions. Background emissions ranged between 1.1 and 1.3 kg N ha -1 yr -1 , and therefore net anthropogenic emissions ranged from 0.3 to 4.0 kg N ha-1 yr -1 . IPCC Tier 1 emission factors underestimated N2O releases from soybean, that were on average 4.87 times greater when estimated with DayCent and observations (0.53 vs 2.47 and 2.69 kg N ha -1 yr -1 , respectively). On the contrary, IPCC estimates for corn and wheat/soybean crops were similar to modeled and measured values. Our results suggest that N 2 O emissions from the vast 15 million ha of soybean croplands in the Pampas Region may be substantially underestimated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO 2

Heat and drought stresses are two emerging climatic threats to the US maize and soybean production, yet their impacts on yields are collectively determined by the magnitude of climate change and rising atmospheric CO2 concentration. Here we present a study that quantified the current and future yield responses of US rainfed maize and soybean to climate extremes, and for the first time characterized spatial shifts in the relative importance of temperature, heat and drought stress. Crop yields are simulated using the Agricultural Production Systems sIMulator (APSIM), driven by the high-resolution (12 km) Weather Research and Forecasting (WRF) Model downscaled future climate scenarios at two time slices (1995-2005 and 2085-2094). Our results show that climatic yield gaps and interannual variability are greater in the core production area than in the remaining US by the late 21st century under both Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios, and the magnitude of change is highly dependent on the current climate sensitivity and vulnerability. Elevated CO2 partially offsets the climatic yield gaps and reduces interannual yield variability, and effect is more prominent in soybean than in maize. We demonstrate that drought will continue to be the largest threat to US rainfed maize and soybean production, although its dominant role gradually gives way to other impacts of heat extremes. We also reveal that shifts in the geographic distributions of dominant stressors are characterized by increases in the concurrent stress, especially for the US Midwest. These findings imply the importance of considering drought and extreme heat simultaneously for future agronomic adaptation and mitigation strategies, particularly for breeding programs and crop management.

APSIM↗
DOE OSTI2019

Construction and comparison of three reference-quality genome assemblies for soybean

Here, we report reference-quality genome assemblies and annotations for two accessions of soybean (Glycine max) and for one accession of Glycine soja, the closest wild relative of G. max. The G. max assemblies provided are for widely used US cultivars: the northern line Williams 82 (Wm82) and the southern line Lee. The Wm82 assembly improves the prior published assembly, and the Lee and G. soja assemblies are new for these accessions. Comparisons among the three accessions show generally high structural conservation, but nucleotide difference of 1.7 single-nucleotide polymorphisms (snps) per kb between Wm82 and Lee, and 4.7 snps per kb between these lines and G. soja. snp distributions and comparisons with genotypes of the Lee and Wm82 parents highlight patterns of introgression and haplotype structure. Comparisons against the US germplasm collection show placement of the sequenced accessions relative to global soybean diversity. Analysis of a pan-gene collection shows generally high conservation, with variation occurring primarily in genomically clustered gene families. We found approximately 40-42 inversions per chromosome between either Lee or Wm82v4 and G. soja, and approximately 32 inversions per chromosome between Wm82 and Lee. We also investigated five domestication loci. For each locus, we found two different alleles with functional differences between G. soja and the two domesticated accessions. The genome assemblies for multiple cultivated accessions and for the closest wild ancestor of soybean provides a valuable set of resources for identifying causal variants that underlie traits for the domestication and improvement of soybean, serving as a basis for future research and crop improvement efforts for this important crop species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Genetic variation and genetic complexity of nodule occupancy in soybean inoculated with USDA110 and USDA123 rhizobium strains

Abstract Background Symbiotic nitrogen fixation differs among Bradyrhizobium japonicum strains. Soybean inoculated with USDA123 has a lower yield than strains known to have high nitrogen fixation efficiency, such as USDA110. In the main soybean-producing area in the Midwest of the United States, USDA123 has a high nodule incidence in field-grown soybean and is competitive but inefficient in nitrogen fixation. In this study, a high-throughput system was developed to characterize nodule number among 1,321 Glycine max and 69 Glycine soja accessions single inoculated with USDA110 and USDA123. Results Seventy-three G. max accessions with significantly different nodule number of USDA110 and USDA123 were identified. After double inoculating 35 of the 73 accessions, it was observed that PI189939, PI317335, PI324187B, PI548461, PI562373, and PI628961 were occupied by USDA110 and double-strain nodules but not by USDA123 nodules alone. PI567624 was only occupied by USDA110 nodules, and PI507429 restricted all strains. Analysis showed that 35 loci were associated with nodule number in G. max when inoculated with strain USDA110 and 35 loci with USDA123. Twenty-three loci were identified in G. soja when inoculated with strain USDA110 and 34 with USDA123. Only four loci were common across two treatments, and each locus could only explain 0.8 to 1.5% of phenotypic variation. Conclusions High-throughput phenotyping systems to characterize nodule number and occupancy were developed, and soybean germplasm restricting rhizobium strain USDA123 but preferring USDA110 was identified. The larger number of minor effects and a small few common loci controlling the nodule number indicated trait genetic complexity and strain-dependent nodulation restriction. The information from the present study will add to the development of cultivars that limit USDA123, thereby increasing nitrogen fixation efficiency and productivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Genome-wide characterization of the soybean DOMAIN OF UNKNOWN FUNCTION 679 membrane protein gene family highlights their potential involvement in growth and stress response

The DMP (DUF679 membrane proteins) family is a plant-specific gene family that encodes membrane proteins. The DMP family genes are suggested to be involved in various programmed cell death processes and gamete fusion during double fertilization in Arabidopsis. However, their functional relevance in other crops remains unknown. This study identified 14 genes from the DMP family in soybean (Glycine max) and characterized their physiochemical properties, subcellular location, gene structure, and promoter regions using bioinformatics tools. Additionally, their tissue-specific and stress-responsive expressions were analyzed using publicly available transcriptome data. Phylogenetic analysis of 198 DMPs from monocots and dicots revealed six clades, with clade-I encoding senescence-related AtDMP1/2 orthologues and clade-II including pollen-specific AtDMP8/9 orthologues. The largest clade, clade-III, predominantly included monocot DMPs, while monocot- and dicot-specific DMPs were assembled in clade-IV and clade-VI, respectively. Evolutionary analysis suggests that soybean GmDMPs underwent purifying selection during evolution. Using 68 transcriptome datasets, expression profiling revealed expression in diverse tissues and distinct responses to abiotic and biotic stresses. The genes Glyma.09G237500 and Glyma.18G098300 showed pistil-abundant expression by qPCR, suggesting they could be potential targets for female organ-mediated haploid induction. Furthermore, cis-acting regulatory elements primarily related to stress-, hormone-, and light-induced pathways regulate GmDMPs, which is consistent with their divergent expression and suggests involvement in growth and stress responses. Overall, our study provides a comprehensive report on the soybean GmDMP family and a framework for further biological functional analysis of DMP genes in soybean or other crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Reformulation of dicamba herbicide: Impacts on offsite transport and soybean damage

Abstract The herbicide dicamba (3,6‐dichloro‐2‐methoxybenzoic acid) is commonly used to control broadleaf weeds in soybeans. Dicamba, however, is susceptible to volatilization and drift, thereby causing significant plant damage to nontarget crops downwind. Dicamba was reformulated to reduce volatility and off‐target movement. The effectiveness of the dicamba reformulation was assessed by quantifying dicamba emissions following spray application and investigated how meteorological factors influenced the off‐target movement. The experiments were conducted at the University of Minnesota Agricultural Experiment Station (UMORE Park) during the growing season of 2018, 2019, 2021, and 2022. Multiple high‐flow polyurethane foam air samplers were used to measure dicamba concentrations downwind from a 4‐ha soybean field sprayed with dicamba. Dicamba emissions were estimated using backward Lagrangian modeling constrained by the air sample observations. The results indicate that dicamba emissions and downwind transport were significant for several days following application. Further, non‐traited soybeans located within 15–45 m showed substantial dicamba‐related damage. In warmer, drier seasons, increased dicamba emissions caused more severe damage to downwind soybeans, likely worsened by drought stress preventing recovery. Favorable atmospheric conditions that reduced potential drift can be difficult to achieve in terms of the typical weather experienced over agricultural sites in the Upper Midwest. These results indicate that the dicamba reformulation has not adequately prevented significant post‐spray volatilization losses and downwind transport.

Agriculture↗
DOE OSTI2022

Effects of soybean 7S protein on the quality and digestibility of dry rice noodles under twin‐screw extrusion process

Summary Starch and protein are important components of food, the mixed system formed between them (complexes, gels, mixtures, etc.) can improve the physical and chemical properties of starch and protein, and its potential application in the food industry has also attracted widespread attention. In this study, indica rice flour and soybean 7S protein (β‐Conglycinin) were used as the experimental materials to explore the effect of soybean 7S protein addition on the quality and digestibility of rice noodles. Aims to develop rice noodles with high protein and low GI, so that people can take care of their health while satisfying their appetites. The results showed that with the increase of soybean 7S protein addition, the starch digestibility decreased, resulting in the estimated glycaemic index (eGI) and glycaemic load (GL) decreasing. However, the cooking loss rate and breakage rate of rice noodles gradually increased, the water absorption first increased and then decreased, and the taste of rice noodles is acceptable when the 7S protein addition amount is within 9%. The interaction between starch and soybean 7S protein was mediated by weak interactions such as hydrogen bonds and hydrophobic forces indicated by Fourier transform infrared spectroscopy (FTIR) and X‐ray diffraction (XRD).

Qiao, Fan↗
DOE OSTI2019

Multiple GmWRI1s are redundantly involved in seed filling and nodulation by regulating plastidic glycolysis, lipid biosynthesis and hormone signalling in soybean ( Glycine max )

It has been reported that lipid biosynthesis in plant host root cells plays critical roles in legumefungal or -rhizobial symbioses, but little is known about its regulatory mechanism in legume– rhizobia interaction. Soybean WRINKLED1 (WRI1) a and b, with their alternative splicing (AS) products a’ and b’, are highly expressed in developing seeds and nodules, but their functions in soybean nodulation are not known. GmWRI1a and b differently promoted triacylglycerol (TAG) accumulation in both Arabidopsis wild-type and wri1 mutant seeds and when they ectopically expressed in the soybean hairy roots. Transcriptome analysis revealed that 15 genes containing AW boxes in their promoters were targeted by GmWRI1s, including genes involved in glycolysis, fatty acid (FA) and TAG biosynthesis. GmWRI1a, GmWRI1b and b’ differentially transactivated most targeted genes. Overexpression of GmWRI1s affected phospholipid and galactolipid synthesis, soluble sugar and starch contents and led to increased nodule numbers, whereas GmWRI1 knockdown hairy roots interfered root glycolysis and lipid biosynthesis and resulted in fewer nodules. These phenomena in GmWRI1 mutants coincided with the altered expression of nodulation genes. Thus, GmWRI1-regulated starch degradation, glycolysis and lipid biosynthesis were critical for nodulation. GmWRI1 mutants also altered auxin and other hormone-related biosynthesis and hormone-related genes, by which GmWRI1s may affect nodule development. The study expands the views for pleiotropic effects of WRI1s in regulating soybean seed filling and root nodulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The soybean β‐expansin gene GmINS1 contributes to nodule development in response to phosphate starvation

Abstract Legume biological nitrogen fixation (BNF) is the most important N source in agricultural ecosystems. Nodule organogenesis from the primordia to the development of mature nodules with the ability to fix N 2 largely determines BNF capacity. However, nodule growth is often limited by low phosphorus (P) availability, while the mechanisms underlying nodule development responses to P deficiency remain largely unknown. In this study, we found that nodule enlargement is severely inhibited by P deficiency, as reflected by the smaller individual nodule size from a soybean core collection in the field. Wide‐ranging natural diversity in nodule size was further identified in soybeans reared in low P soils, with the FC‐1 genotype outperforming FC‐2 in assessments of nodulation under low P conditions. Among β‐expansin members, GmINS1 expression is most abundantly enhanced by P deficiency in FC‐1 nodules, and its transcript level is further displayed to be tightly associated with nodule enlargement. Four single nucleotide polymorphisms discovered in the GmINS1 promoter distinguished the FC‐1 and FC‐2 genotypes and accounted for the differential expression levels of GmINS1 responses to P deficiency. GmINS1 overexpression led to increases in nodule size, infected cell abundance, and N 2 fixation capacity and subsequently promoted increases in N and P content, soybean biomass, and yield. Our findings provide a candidate gene for optimizing BNF capacity responses to low P stress in soybean molecular breeding programs.

Yang, Zhaojun↗