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Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images. [ABSTRACT FROM AUTHOR]

SUMMARY: Global warming, climate change, and industrial pollution are altering our environment subjecting plants, microbiomes, and ecosystems to an increasing number and complexity of abiotic stress conditions, concurrently or sequentially. These conditions, termed, "multifactorial stress combination" (MFSC), can cause a significant decline in plant growth and survival. However, the impacts of MFSC on reproductive tissues and yield of major crop plants are largely unknown. We subjected soybean (Glycine max) plants to a MFSC of up to five different stresses (water deficit, salinity, low phosphate, acidity, and cadmium), in an increasing level of complexity, and conducted integrative transcriptomic‐phenotypic analysis of their reproductive and vegetative tissues. We reveal that MFSC has a negative cumulative effect on soybean yield, that each set of MFSC condition elicits a unique transcriptomic response (that is different between flowers and leaves), and that selected genes expressed in leaves or flowers of soybean are linked to the effects of MFSC on different vegetative, physiological, and/or reproductive parameters. Our study identified networks and pathways associated with reactive oxygen species, ascorbic acid and aldarate, and iron/copper signaling/metabolism as promising targets for future biotechnological efforts to augment the resilience of reproductive tissues of major crop plants to MFSC. In addition, we provide unique phenotypic and transcriptomic datasets for dissecting the mechanistic effects of MFSC on the vegetative, physiological, and reproductive processes of a crop plant. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max) production is greatly affected by persistent and/or intermittent droughts in rainfed soybean-growing regions worldwide. Symbiotic N2 fixation (SNF) in soybean can also be significantly hampered even under moderate drought stress. The objective of this study was to identify genomic regions associated with shoot carbon isotope ratio (δ13C) as a surrogate measure for water use efficiency (WUE), nitrogen isotope ratio (δ15N) to assess relative SNF, N concentration ([N]), and carbon/nitrogen ratio (C/N). Genome-wide association mapping was performed with 105 genotypes and approximately 4 million single-nucleotide polymorphism markers derived from whole-genome resequencing information. A total of 11, 21, 22, and 22 genomic loci associated with δ13C, δ15N, [N], and C/N, respectively, were identified in two environments. Nine of these 76 loci were stable across environments, as they were detected in both environments. In addition to the 62 novel loci identified, 14 loci aligned with previously reported quantitative trait loci for different C and N traits related to drought, WUE, and N2 fixation in soybean. A total of 58 Glyma gene models encoding for different genes related to the four traits were identified in the vicinity of the genomic loci. [ABSTRACT FROM AUTHOR]

SUMMARY: Global warming and climate change are driving an alarming increase in the frequency and intensity of extreme climate events, such as droughts, heat waves, and their combination, inflicting heavy losses to agricultural production. Recent studies revealed that the transcriptomic responses of different crops to water deficit (WD) or heat stress (HS) are very different from that to a combination of WD + HS. In addition, it was found that the effects of WD, HS, and WD + HS are significantly more devastating when these stresses occur during the reproductive growth phase of crops, compared to vegetative growth. As the molecular responses of different reproductive and vegetative tissues of plants to WD, HS, or WD + HS could be different from each other and these differences could impact many current and future attempts to enhance the resilience of crops to climate change through breeding and/or engineering, we conducted a transcriptomic analysis of different soybean (Glycine max) tissues to WD, HS, and WD + HS. Here we present a reference transcriptomic dataset that includes the response of soybean leaf, pod, anther, stigma, ovary, and sepal to WD, HS, and WD + HS conditions. Mining this dataset for the expression pattern of different stress response transcripts revealed that each tissue had a unique transcriptomic response to each of the different stress conditions. This finding is important as it suggests that enhancing the overall resilience of crops to climate change could require a coordinated approach that simultaneously alters the expression of different groups of transcripts in different tissues in a stress‐specific manner. [ABSTRACT FROM AUTHOR]

Planting date and cultivar maturity group (MG) are major management factors affecting soybean [Glycine max (L.) Merr.] yield, but their effect on seed oil and protein concentration, and in particular meal protein concentration, is less understood. We quantified changes in seed oil and protein, and estimated meal protein concentration, and total oil and protein yield in response to planting date and cultivar MG ranging from 3 to 6 and across locations comprising a 8.3° range in latitude in the U.S. Midsouth. Our results show that delayed planting date and later cultivar maturity reduced oil concentration, and this was partially associated with a decrease in temperature during the seed fill phase. Thus, optimum cultivar MG recommendations to maximize total oil yield (in kg ha-1) for planting dates in May and June required relatively earlier cultivar MGs than those recommended to maximize seed yield. For planting dates in April, short-season MG 3 cultivars did not increase oil yield compared to full-season MG 4 or 5 cultivars due to a quadratic yield response to planting date at most locations. Planting date and cultivar maturity effects on seed protein concentration were not always consistent with the effects on estimated meal protein concentration after oil extraction. Meal protein concentration decreased with lower temperatures during seed fill, and when the start of seed fill occurred after August 15, but relatively short-season cultivar MGs reduced the risk of low meal protein concentration. Meal protein concentration is a trait of interest for the feed industry that would be beneficial to report in future studies evaluating genetic, management, and environmental effects on seed protein concentration. [ABSTRACT FROM AUTHOR]

Summary: Heat waves occurring during droughts can have a devastating impact on yield, especially if they happen during the flowering and seed set stages of the crop cycle. Global warming and climate change are driving an alarming increase in the frequency and intensity of combined drought and heat stress episodes, critically threatening global food security.Because high temperature is detrimental to reproductive processes, essential for plant yield, we measured the inner temperature, transpiration, sepal stomatal aperture, hormone concentrations and transcriptomic response of closed soybean flowers developing on plants subjected to a combination of drought and heat stress.Here, we report that, during a combination of drought and heat stress, soybean plants prioritize transpiration through flowers over transpiration through leaves by opening their flower stomata, while keeping their leaf stomata closed. This acclimation strategy, termed 'differential transpiration', lowers flower inner temperature by about 2–3°C, protecting reproductive processes at the expense of vegetative tissues.Manipulating stomatal regulation, stomatal size and/or stomatal density of flowers could serve as a viable strategy to enhance the yield of different crops and mitigate some of the current and future impacts of global warming and climate change on agriculture. [ABSTRACT FROM AUTHOR]

Drought causes significant soybean [ Glycine max (L.) Merr.] yield losses each year in rain-fed production systems of many regions. Genetic improvement of soybean for drought tolerance is a cost-effective approach to stabilize yield under rain-fed management. The objectives of this study were to confirm previously reported soybean loci and to identify novel loci associated with canopy wilting (CW) using a panel of 200 diverse maturity group (MG) IV accessions. These 200 accessions along with six checks were planted at six site-years using an augmented incomplete block design with three replications under irrigated and rain-fed treatments. Association mapping, using 34,680 single nucleotide polymorphisms (SNPs), identified 188 significant SNPs associated with CW that likely tagged 152 loci. This includes 87 SNPs coincident with previous studies that likely tagged 68 loci and 101 novel SNPs that likely tagged 84 loci. We also determined the ability of genomic estimated breeding values (GEBVs) from previous research studies to predict CW in different genotypes and environments. A positive relationship (P ≤ 0.05;0.37 ≤ r ≤ 0.5) was found between observed CW and GEBVs. In the vicinity of 188 significant SNPs, 183 candidate genes were identified for both coincident SNPs and novel SNPs. Among these 183 candidate genes, 57 SNPs were present within genes coding for proteins with biological functions involved in plant stress responses. These genes may be directly or indirectly associated with transpiration or water conservation. The confirmed genomic regions may be an important resource for pyramiding favorable alleles and, as candidates for genomic selection, enhancing soybean drought tolerance. [ABSTRACT FROM AUTHOR]

A combination of drought and heat stress, occurring at the vegetative or reproductive growth phase of many different crops can have a devastating impact on yield. In soybean (Glycine max), a considerable effort has been made to develop genotypes with enhanced yield production under conditions of drought or heat stress. However, how these genotypes perform in terms of growth, physiological responses, and most importantly seed production, under conditions of drought and heat combination is mostly unknown. Here, we studied the impact of water deficit and heat stress combination on the physiology, seed production, and yield per plant of two soybean genotypes, Magellan and Plant Introduction (PI) 548313, that differ in their reproductive responses to heat stress. Our findings reveal that although PI 548313 produced more seeds than Magellan under conditions of heat stress, under conditions of water deficit, and heat stress combination its seed production decreased. Because the number of flowers and pollen germination of PI 548313 remained high under heat or water deficit and heat combination, the reduced seed production exhibited by PI 548313 under the stress combination could be a result of processes that occur at the stigma, ovaries and/or other parts of the flower following pollen germination. [ABSTRACT FROM AUTHOR]

Water and nutrient acquisition is a critical function of plant root systems. Root system architecture (RSA) traits are often complex and controlled by many genes. This is the first genome-wide association study reporting genetic loci for RSA traits for field-grown soybean (Glycine max). A collection of 289 soybean genotypes was grown in three environments, root crowns were excavated, and 12 RSA traits assessed. The first two components of a principal component analysis of these 12 traits were used as additional aggregate traits for a total of 14 traits. Marker–trait association for RSA traits were identified using 31,807 single-nucleotide polymorphisms (SNPs) by a genome-wide association analysis. In total, 283 (non-unique) SNPs were significantly associated with one or more of the 14 root traits. Of these, 246 were unique SNPs and 215 SNPs were associated with a single root trait, while 26, four, and one SNPs were associated with two, three, and four root traits, respectively. The 246 SNPs marked 67 loci associated with at least one of the 14 root traits. Seventeen loci on 13 chromosomes were identified by SNPs associated with more than one root trait. Several genes with annotation related to processes that could affect root architecture were identified near these 67 loci. Additional follow-up studies will be needed to confirm the markers and candidate genes identified for RSA traits and to examine the importance of the different root characteristics for soybean productivity under a range of soil and environmental conditions. [ABSTRACT FROM AUTHOR]

Key message: QTL analysis identified 16 QTLs, grouped in eight loci on seven soybean chromosomes that were associated with carbon isotope ratio (δ13C) in a biparental recombinant inbred population. Drought is a major limitation to soybean yield, and the frequency of drought stress is likely to increase under future climatic scenarios. Water use efficiency (WUE) is associated with drought tolerance, and carbon isotope ratio (δ13C) is positively correlated with WUE. In this study, 196 F6-derived recombinant inbred lines from a cross of PI 416997 (high WUE) × PI 567201D (low WUE) were evaluated in four environments to identify genomic regions associated with δ13C. There were positive correlations of δ13C values between different environments (0.67 ≤ r ≤ 0.78). Genotype, environment, and genotype × environment interactions had significant effects on δ13C. Narrow sense heritability of δ13C was 90% when estimated across environments. There was a total of 16 QTLs on seven chromosomes with individual QTLs explaining between 2.5 and 29.9% of the phenotypic variation and with additive effects ranging from 0.07 to 0.22‰. These 16 QTLs likely identified eight loci based on their overlapping confidence intervals. Of these eight loci, two loci on chromosome 20 (Gm20) were detected in at least three environments and were considered as stable QTLs. Additive QTLs on Gm20 showed epistatic interactions with 10 QTLs present across nine chromosomes. Five QTLs were identified across environments and showed significant QTL × environment interactions. These findings demonstrate that additive QTLs and QTL × QTL interactions play significant roles in genetic control of the δ13C trait. Markers flanking identified QTLs may facilitate marker-assisted selection to accumulate desirable QTLs to improve WUE and drought tolerance in soybean. [ABSTRACT FROM AUTHOR]

Heat stress negatively affects photosynthesis in crop plants. Chlorophyll fluorescence provides information about the efficiency of the light-dependent reactions of photosynthesis and can be measured non-destructively and rapidly. Four soybean (Glycine max) genotypes were grown in controlled environments at 28/20°C (control), followed by imposition of control, 38/28°C, and 45/28°C day/night temperature regimes for 7 days. Coordinated chlorophyll fluorescence, gas exchange, and chloroplast ultrastructure measurements were conducted over the course of the 7-day temperature treatments and revealed contrasting responses among the different genotypes. Although generally similar, the extent of the impact of elevated temperatures on net photosynthesis differed among genotypes. Despite dramatic effects on photosynthetic light reactions, net photosynthetic rates were not reduced by exposure to 45°C on the 1st day of treatment imposition. Temporal dynamics of light reaction characteristics over the course of the 7-day heat-wave simulation revealed distinct responses among the genotypes. Similarly, chloroplast ultrastructure examination identified contrasting responses of DT97-4290 and PI603166, particularly with respect to starch characteristics. These changes were positively associated with differences in the percent area of chloroplasts that were occupied by starch grains. Elevated temperature increased number and size of starch grains on the 1st day of DT97-4290 which was coordinated with increased minimum chlorophyll fluorescence (F0) and reduced leaf net CO2 assimilation (A). Whereas on the 7th day the elevated temperature treatment showed reduced numbers and sizes of starch grains in chloroplasts and was coordinated with similar levels of F0 and A to the control treatment. Unlike starch dynamics of PI603166 which elevated temperature had little effect on. The genotypic differences in photosynthetic and chloroplast ultrastructure responses to elevated temperatures identified here are of interest for the development of more tolerant soybean cultivars and to facilitate the dissection of molecular mechanisms underpinning heat stress tolerance of soybean photosynthesis. [ABSTRACT FROM AUTHOR]

Background: Photosynthesis is able to convert solar energy into chemical energy in the form of biomass, but the efficiency of photosynthetic solar energy conversion is low. Chlorophyll fluorescence measurements are rapid, nondestructive, and can provide a wealth of information about the efficiencies of the photosynthetic light reaction processes. Efforts aimed at assessing genetic variation and/or mapping of genetic loci associated with chlorophyll fluorescence phenotypes have been rather limited. Results: Evaluation of SoySNP50K iSelect SNP Beadchip data from the 189 genotypes phenotyped in this analysis identified 32,453 SNPs with a minor allele frequency (MAF) ≥ 5%. A total of 288 (non-unique) SNPs were significantly associated with one or more of the 21 chlorophyll fluorescence phenotypes. Of these, 155 were unique SNPs and 100 SNPs were only associated with a single fluorescence phenotype, while 28, 11, 2, and 14 SNPs, were associated with two, three, four and five or more fluorescence phenotypes, respectively. The 288 non-unique SNPs represent 155 unique SNPs thatmark 53 loci. The 155 unique SNPs included 27 that were associated with three or more phenotypes, and thus were called multi-phenotype SNPs. These 27 multi-phenotype SNPs marked 13 multi-phenotype loci (MPL) identified by individual SNPs associated with multiple chlorophyll fluorescence phenotypes or by more than one SNP located within 0. 5 MB of othermulti-phenotype SNPs. Conclusion: A search in the genomic regions highlighted by these 13 MPL identified genes with annotations indicating involvement in photosynthetic light dependent reactions. These, as well as loci associated with only one or two chlorophyll fluorescence traits, should be useful to develop a better understanding of the genetic basis of photosynthetic light dependent reactions as a whole as well as of specific components of the electron transport chain in soybean. Accordingly, additional genetic and physiological analyses are necessary to determine the relevance and effectiveness of the identified loci for crop improvement efforts. [ABSTRACT FROM AUTHOR]

Water deficit stress is a major factor limiting soybean [Glycine max (L.) Merr.] yield. High water use efficiency (WUE) offers a means to potentially ameliorate drought impact, but increased WUE is often associated with a reduction in transpiration (T) and an accompanied reduction in photosynthesis. This interdependence of T and photosynthesis is a major constraint in selection for high WUE by breeding programs. Measurement of genetic variability in WUE and T through carbon isotope ratio (δ13C) and oxygen isotope ratio (δ18O), respectively, could be important in identifying genotypes with high WUE that also have relatively high T and thus higher rates of biomass production. This study's objective was to identify genomic regions associated with δ13C and δ18O. A diverse collection of 373 soybean genotypes was grown in four field environments and whole-plant samples collected at early reproductive growth were characterized for δ13C and δ18O. After quality assessment, 31,260 polymorphic single-nucleotide polymorphism (SNP) markers with a minor allele frequency ⩾5% were used for association analysis. Genome-wide association analysis identified 54 environment-specific SNPs associated with δ13C and 47 SNPs associated with δ18O. These SNP markers tagged 46 putative loci for δ13C and 21 putative loci for δ18O and may represent an important resource for pyramiding favorable alleles for drought tolerance and identifying genotypes with high WUE. [ABSTRACT FROM AUTHOR]

While soybean [Glycine max (L.) Merr.] production risk is typically managed by planting a range of maturity groups (MGs) across a few different planting dates (PDs), there have been no reports that have quantified changes in risk and profitability using this diversification strategy. Three years of field-trial data from eight locations in six states were analyzed to determine risk-return tradeoff s across MG and PD. Producer revenue expectations were adjusted by soybean harvest date, assessing oil and meal premiums or discounts, and differential irrigation requirements by MG and PD, whereas costs for seed, fuel, fertilizer, equipment, and chemicals were held constant. Using portfolio theory, an efficient frontier-maximizing net returns for a given level of risk or minimizing risk for a given level of net return-was estimated by location. Cultivars from MG III and MG IV had higher expected net returns than MG V and VI at all locations. Early-season planting combinations were found to be riskier than the three successive planting dates but led to oil, protein, and seasonal sale price premiums. Across different environments, selecting two to six MG'PD combinations was sufficient to lower risk by 29 to 40% when compared to the single, profit-maximizing M' PD choice. Depending on location, this risk reduction decreased net returns between 2 and 22% when compared to the profit-maximizing MG'PD choice. [ABSTRACT FROM AUTHOR]

Background: Chlorophyll is a major component of chloroplasts and a better understanding of the genetic basis of chlorophyll in soybean [Glycine max (L.) Merr.] might contribute to improving photosynthetic capacity and yield in regions with adverse environmental conditions. A collection of 332 diverse soybean genotypes were grown in 2 years (2009 and 2010) and chlorophyll a (eChl_A), chlorophyll b (eChl_B), and total chlorophyll (eChl_T) content as well as chlorophyll a/b ratio (eChl_R) in leaf tissues were determined by extraction and spectrometric determination. Total chlorophyll was also derived from canopy spectral reflectance measurements using a model of wavelet transformed spectra (tChl_T) as well as with a spectral reflectance index (iChl_T). Results: A genome-wide associating mapping approach was employed using 31,253 single nucleotide polymorphisms (SNPs) to identify loci associated with the extract based eChl_A, eChl_B, eChl_R and eChl_T measurements and the two canopy spectral reflectance-based methods (tChl_T and iChl_T). A total of 23 (14 loci), 15 (7 loci) and 14 SNPs (10 loci) showed significant association with eChl_A, eChl_B and eChl_R respectively. A total of 52 unique SNPs were significantly associated with total chlorophyll content based on at least one of the three approaches (eChl_T, tChl_T and iChl_T) and likely tagged 27 putative loci for total chlorophyll content, four of which were indicated by all three approaches. Conclusions: Results presented here show that markers for chlorophyll traits can be identified in soybean using both extract-based and canopy spectral reflectance-based phenotypes, and confirm that high-throughput phenotyping-amenable canopy spectral reflectance measurements can be used for association mapping. [ABSTRACT FROM AUTHOR]

Carotenoids are organic pigments that are produced predominantly by photosynthetic organisms and provide antioxidant activity to a wide variety of plants, animals, bacteria, and fungi. The carotenoid biosynthetic pathway is highly conserved in plants and occurs mostly in chromoplasts and chloroplasts. Leaf carotenoids play important photoprotective roles and targeted selection for leaf carotenoids may offer avenues to improve abiotic stress tolerance. A collection of 332 soybean [Glycine max (L.) Merr.] genotypes was grown in two years and total leaf carotenoid content was determined using three different methods. The first method was based on extraction and spectrophotometric determination of carotenoid content (eCaro) in leaf tissue, whereas the other two methods were derived from high-throughput canopy spectral reflectance measurements using wavelet transformed reflectance spectra (tCaro) and a spectral reflectance index (iCaro). An association mapping approach was employed using 31,253 single nucleotide polymorphisms (SNPs) to identify SNPs associated with total carotenoid content using a mixed linear model based on data from two growing seasons. A total of 28 SNPs showed a significant association with total carotenoid content in at least one of the three approaches. These 28 SNPs likely tagged 14 putative loci for carotenoid content. Six putative loci were identified using eCaro, five loci with tCaro, and nine loci with iCaro. Three of these putative loci were detected by all three carotenoid determination methods. All but four putative loci were located near a known carotenoid-related gene. These results showed that carotenoid markers can be identified in soybean using extract-based as well as by high-throughput canopy spectral reflectance-based approaches, demonstrating the utility of field-based canopy spectral reflectance phenotypes for association mapping. [ABSTRACT FROM AUTHOR]

Much research has been conducted on the changes in gene expression of the model plant Arabidopsis to low-oxygen stress. Flooding results in a low oxygen environment in the root zone. However, there is ample evidence that tolerance to soil flooding is more than tolerance to low oxygen alone. In this study, we investigated the physiological response and differential expression of root-related transcription factors (TFs) associated with the tolerance of soybean plants to soil flooding. Differential responses of PI408105A and S99-2281 plants to ten days of soil flooding were evaluated at physiological, morphological and anatomical levels. Gene expression underlying the tolerance response was investigated using qRT-PCR of root-related TFs, known anaerobic genes, and housekeeping genes. Biomass of flood-sensitive S99-2281 roots remained unchanged during the entire 10 days of flooding. Flood-tolerant PI408105A plants exhibited recovery of root growth after 3 days of flooding. Flooding induced the development of aerenchyma and adventitious roots more rapidly in the flood-tolerant than the flood-sensitive genotype. Roots of tolerant plants also contained more ATP than roots of sensitive plants at the 7th and 10th days of flooding. Quantitative transcript analysis identified 132 genes differentially expressed between the two genotypes at one or more time points of flooding. Expression of genes related to the ethylene biosynthesis pathway and formation of adventitious roots was induced earlier and to higher levels in roots of the flood-tolerant genotype. Three potential flood-tolerance TFs which were differentially expressed between the two genotypes during the entire 10-day flooding duration were identified. This study confirmed the expression of anaerobic genes in response to soil flooding. Additionally, the differential expression of TFs associated with soil flooding tolerance was not qualitative but quantitative and temporal. Functional analyses of these genes will be necessary to reveal their potential to enhance flooding tolerance of soybean cultivars. [ABSTRACT FROM AUTHOR]

Abstract: An inverse relationship between soybean [Glycine max (L.) Merr.] seed protein and oil concentration is well documented in the literature. A negative correlation between protein and yield is also often reported. The objective of this study was to determine the effect of high rates of N applied at planting on seed protein and oil. Nitrogen was surface-applied at soybean emergence at rates of 290kgha−1 in 2002, 310kgha−1 in 2003, and 360kgha−1 in 2004. Eight cultivars ranging from Maturity Group II–IV were evaluated under the Early Soybean Production System (ESPS). However, not all cultivars were evaluated in all 3 years. Glyphosate herbicide was used in all 3 years and a non-glyphosate herbicide treatment was applied in 2002. Cultivars grown in 2003 were also evaluated under an application of 21.3kgha−1 of Mn. All cultivar, herbicide, and Mn treatments were evaluated in irrigated and non-irrigated environments with fertilizer N (PlusN treatment) or without fertilizer N (ZeroN treatment). When analyzed over all management practices (years, cultivars, herbicide, and Mn treatments), the PlusN treatment resulted in a significant decrease in protein concentration (2.7 and 1.9%), an increase in oil concentration (2.2 and 2.7%), and a decrease in the protein/oil ratio (4.7 and 4.6%) for the irrigated and non-irrigated environments, respectively. However, the overall protein and oil yield increased with the application of fertilizer N at planting (protein: 5.0% irrigated, 12.7% non-irrigated and oil: 9.9% irrigated and 18.9% non-irrigated). These increases were due to the increase in seed yield with the application of large amounts of fertilizer at planting. Additionally, a significant correlation (r =0.45, P =0.0001) was found between seed protein concentration and seed yield. No significant correlation was found between seed oil concentration and seed yield. The data demonstrate the inverse relationship between protein and oil and indicate that large amounts of N applied at planting do not change this relationship. [Copyright &y& Elsevier]

Nitrogen supplied by N2 fixation to soybean [Glycine max (L.) Merr.] may not be sufficient to maximize yield. Field studies were conducted in 2002, 2003, and 2004 on Sharkey clay soil (very-fine, smectitic, thermic Chromic Epiaquert) at Stoneville, MS (33°26′ N lat). The objective was to determine the effect of high rates of N applied as a replacement for N2 fixation in nonirrigated and irrigated environments. Eight cultivars ranging from Maturity Group II to IV were planted on 17 Apr. 2002, 2 Apr. 2003, and 25 Mar. 2004. Not all cultivars were evaluated in all 3 yr. Glyphosate herbicide was used in all 3 yr and a non-glyphosate herbicide treatment was applied in 2002. Cultivars grown in 2003 were also evaluated under an application of 21.3 kg ha-1 of Mn. All cultivar, herbicide, and Mn treatments were evaluated in irrigated and nonirrigated environments with fertilizer N (PlusN treatment) or without fertilizer N (ZeroN treatment). In the PlusN treatment, granular NH4NO3 was surface applied at soybean emergence at rates of 290 kg ha-1 in 2002, 310 kg ha-1 in 2003, and 360 kg ha-1 in 2004. When analyzed over all management practices (years, cultivars, herbicide, and Mn treatments), the PlusN treatment resulted in significantly decreased ureide concentration (57.2 and 53.5% reduction) and significantly increased biomass accumulation (14.1 and 16.7%), N accumulation (12.8 and 28.1%), and seed yield (7.7 and 15.5%) for the irrigated and nonirrigated environments, respectively. The majority of the yield increase in each environment resulted from increased number of seed (9.5% irrigated and 16.2% nonirrigated). These results confirm the sensitivity of N2 fixation to drought and indicate that N2 fixation may limit yield of soybean grown in both irrigated and nonirrigated environments of the midsouthern USA, and that N2 fixation deficiencies occur before the beginning of processes that determine number of seed. [ABSTRACT FROM AUTHOR]

Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring. [ABSTRACT FROM AUTHOR]

Lutein has significant nutritional benefits for human health. Therefore, enhancing soybean lutein concentrations is an important breeding objective. However, selection for soybeans with high and environmentally stable lutein concentrations has been limited. The objectives of this study were to select soybeans with high seed lutein concentrations and to determine the stability of lutein concentrations across environments. A total of 314 genotypes were screened and 18 genotypes with high lutein concentrations and five genotypes with low lutein concentrations were selected for further examination. These 23 genotypes and two check varieties were evaluated under six environments (two planting dates for 2 years at one location and two planting dates for 1 year at another location). Lutein concentrations were influenced by genotype, environment and genotype × environment interactions. Genotypes with late maturity and low lutein concentrations were more stable than those with early maturity and high concentrations. Early (May) planting resulted in greater lutein concentrations than late (June) planting. Among the genotypes evaluated, PI603423B (7.7 μg/g) and PI89772 (5.8 μg/g) had the greatest mean lutein concentrations and exhibited medium and high stability across the six environments, respectively. Thus, these genotypes may be useful for breeding soybeans with high and stable seed lutein concentrations. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max [L.] Merr.) grain composition determines its uses, nutritional quality, value, and the amount of nutrients removed from agricultural ecosystems. Yet few studies have evaluated the effects of annual farming practices on composition. This study was conducted to determine whether soybean grain composition is altered by farming practices and how these practices impact yield and nutrient removal. The effects of long-term cropping systems (continuous soybean or soybean in rotation with corn (Zea mays L.), both tilled and no-till) on yield and concentrations of grain protein, oil, and nine mineral components, after 20 and 22 years of continuous management, were determined. Grain yield, protein, K, Zn, B, and Cu concentrations differed among cropping systems in both years while Mn concentration differed in one of two years. Oil, P, Ca, Mg, and Fe concentrations did not differ among cropping systems in either year, but across both years mean Fe concentration was affected. Differences in K, Fe, and Zn concentrations were mostly attributed to tillage each year, while those in protein and Cu concentrations were mostly attributed to rotation each year. Distinct B concentrations were attributed to tillage while distinct Mn concentrations were attributed to rotation. Grain yield differences were attributable to both rotation and tillage each year. In most instances, oil and protein yield, and mineral removal were dominated by grain yield differences and not concentration. Nonetheless, long-term tillage and rotation greatly affected soybean grain composition. Thus, rotation and tillage should be accounted for when assessing seed quality and seed composition. [ABSTRACT FROM AUTHOR]

Crop biomass estimation with high accuracy at low-cost is valuable for precision agriculture and high-throughput phenotyping. Recent technological advances in Unmanned Aerial Systems (UAS) significantly facilitate data acquisition at low-cost along with high spatial, spectral, and temporal resolution. The objective of this study was to explore the potential of UAS RGB imagery-derived spectral, structural, and volumetric information, as well as a proposed vegetation index weighted canopy volume model (CVM VI) for soybean [ Glycine max (L.) Merr.] aboveground biomass (AGB) estimation. RGB images were collected from low-cost UAS throughout the growing season at a field site near Columbia, Missouri, USA. High-density point clouds were produced using the structure from motion (SfM) technique through a photogrammetric workflow based on UAS stereo images. Two-dimensional (2D) canopy structure metrics such as canopy height (CH) and canopy projected basal area (BA), as well as three-dimensional (3D) volumetric metrics such as canopy volume model (CVM) were derived from photogrammetric point clouds. A variety of vegetation indices (VIs) were also extracted from RGB orthomosaics. Then, CVM VI , which combines canopy spectral and volumetric information, was proposed. Commonly used regression models were established based on the UAS-derived information and field- measured AGB with a leave-one-out cross-validation. The results show that: (1) In general, canopy 2D structural metrics CH and BA yielded higher correlation with AGB than VIs. (2) Three-dimensional metrics, such as CVM, that encompass both horizontal and vertical properties of canopy provided better estimates for AGB compared to 2D structural metrics (R2 = 0.849; RRMSE = 18.7%; MPSE = 20.8%). (3) Optimized CVM VI , which incorporates both canopy spectral and 3D volumetric information outperformed the other indices and metrics, and was a better predictor for AGB estimation (R2 = 0.893; RRMSE = 16.3%; MPSE = 19.5%). In addition, CVM VI showed equal prediction power for different genotypes, which indicates its potential for high-throughput soybean biomass estimation. Moreover, a CVM VI based univariate regression model yielded AGB predicting capability comparable to multivariate complex regression models such as stepwise multilinear regression (SMR) and partial least squares regression (PLSR) that incorporate multiple canopy spectral indices and structural metrics. Overall, this study reveals the potential of canopy spectral, structural and volumetric information, and their combination (i.e., CVM VI) for estimations of soybean AGB. CVM VI was shown to be simple but effective in estimating AGB, and could be applied for high-throughput phenotyping and precision agro-ecological applications and management. [ABSTRACT FROM AUTHOR]

Ureides are the N-rich products of N-fixation that are transported from soybean nodules to the shoot. Ureides are known to accumulate in leaves in response to water-deficit stress, and this has been used to identify genotypes with reduced N-fixation sensitivity to drought. Our objectives in this research were to determine shoot ureide concentrations in 374 Maturity Group IV soybean accessions and to identify genomic regions associated with shoot ureide concentration. The accessions were grown at two locations (Columbia, MO, and Stuttgart, AR) in 2 yr (2009 and 2010) and characterized for ureide concentration at beginning flowering to full bloom. Average shoot ureide concentrations across all four environments (two locations and two years) and 374 accessions ranged from 12.4 to 33.1 µmol g-1 and were comparable to previously reported values. SNP-ureide associations within and across the four environments were assessed using 33,957 SNPs with a MAF ≥0.03. In total, 53 putative loci on 18 chromosomes were identified as associated with ureide concentration. Two of the putative loci were located near previously reported QTL associated with ureide concentration and 30 loci were located near genes associated with ureide metabolism. The remaining putative loci were not near chromosomal regions previously associated with shoot ureide concentration and may mark new genes involved in ureide metabolism. Ultimately, confirmation of these putative loci will provide new sources of variation for use in soybean breeding programs. [ABSTRACT FROM AUTHOR]

• Long-term soybean breeding for yield has increased plant N demands. • Total shoot N and N from BNF increased with cultivar year of release. • Sensitivity of BNF to soil mineral N has not changed over years of soybean breeding. Long-term soybean [ Glycine max (L) Merr.] breeding for yield has increased plant nitrogen (N) demands. On one hand, because N fertilizer application in soybean production systems continues to be insignificant, increased plant N demands over time may have been satisfied from greater biological N fixation (BNF). On the other hand, increased soil residual N over time may have affected the sensitivity of nodulation and BNF. To understand the impact of breeding for greater yield and the effect of soil residual N on nodulation and BNF, two field and a greenhouse study were conducted. Field studies were conducted with maturity group IV soybean cultivars released from 1930 to 2005 and included experiments in four environments. Total shoot N and N from BNF increased with cultivar year of release in two of the four environments. Simulation of different levels of residual soil mineral N by application of 0, 34, 67, and 101 kg N ha−1 shortly after planting resulted in linear increases in shoot N content and δ15N, and linear decreases in nodule number and nodule dry matter in the field. Consistent with these results, fertigation of greenhouse-grown soybean cultivars with different levels of NH 4 NO 3 led to a reduction in nodule number, dry matter, and size. Overall, results from these studies indicate that increases in seed yields with cultivar year of release were associated with greater amounts of N derived from BNF as well as greater total shoot N accumulation, but the relative contribution of BNF to total shoot N did not change over time. Analysis also suggest that the sensitivity of nodulation and BNF to soil mineral N has not been altered over the course of soybean breeding. [ABSTRACT FROM AUTHOR]

• Breeding for high WUE will not limit yield response to elevated CO 2. • The high WUE cultivar showed lower leaf level photosynthetic capacity than low WUE cultivar. • The high WUE cultivar compensate the lower photosynthetic activity with a bigger leaf area. • High yield response to elevated CO2 was also associated with high HI. The predicted increase in atmospheric CO 2 concentration [CO 2 ] is expected to enhance photosynthesis and seed yield in crops such as soybean [ Glycine max (L.) Merr.]. However, future breeding for high water use efficiency (WUE) could interfere with the amount of carbon (C) fixed by leaves and seed mineral composition under elevated [CO 2 ] due to lower stomatal conductance (g s). In the present study, two genotypes with contrasting WUE were grown in open top chambers (OTC) under ambient (410 ppm; a[CO 2 ]) and elevated (610 ppm; e[CO 2 ]). In order to test performance of both cultivars to changing CO 2 conditions, growth, photosynthetic performance (leaf and canopy level) and seed mineral composition were analyzed. The low WUE genotype had a greater response to e[CO 2 ] in terms of leaf daily photosynthetic C gain due to greater g s , which was compensated in the high WUE genotype by an increase in leaf area (LA). However, in the low WUE genotype, improved daily photosynthetic C gain did not translate into greater biomass or seed yield [CO 2 ] response compared to the high WUE genotype, suggesting better assimilate partitioning by the high WUE genotype. In terms of seed composition, the high WUE genotype generally had lower mineral concentrations at e[CO 2 ] compared to a[CO 2 ], but greater total amounts of nutrient (due to higher seed yield) under e[CO 2 ] compared to the low WUE genotype. Findings presented here highlight importance of genetic variation in soybean response to future atmospheric [CO 2 ] which should be considered when breeding for future climates. [ABSTRACT FROM AUTHOR]

Preharvest crop yield prediction is critical for grain policy making and food security. Early estimation of yield at field or plot scale also contributes to high-throughput plant phenotyping and precision agriculture. New developments in Unmanned Aerial Vehicle (UAV) platforms and sensor technology facilitate cost-effective data collection through simultaneous multi-sensor/multimodal data collection at very high spatial and spectral resolutions. The objective of this study is to evaluate the power of UAV-based multimodal data fusion using RGB, multispectral and thermal sensors to estimate soybean (Glycine max) grain yield within the framework of Deep Neural Network (DNN). RGB, multispectral, and thermal images were collected using a low-cost multi-sensory UAV from a test site in Columbia, Missouri, USA. Multimodal information, such as canopy spectral, structure, thermal and texture features, was extracted and combined to predict crop grain yield using Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Support Vector Regression (SVR), input-level feature fusion based DNN (DNN-F1) and intermediate-level feature fusion based DNN (DNN-F2). The results can be summarized in three messages: (1) multimodal data fusion improves the yield prediction accuracy and is more adaptable to spatial variations; (2) DNN-based models improve yield prediction model accuracy: the highest accuracy was obtained by DNN-F2 with an R2 of 0.720 and a relative root mean square error (RMSE%) of 15.9%; (3) DNN-based models were less prone to saturation effects, and exhibited more adaptive performance in predicting grain yields across the Dwight, Pana and AG3432 soybean genotypes in our study. Furthermore, DNN-based models demonstrated consistent performance over space with less spatial dependency and variations. This study indicates that multimodal data fusion using low-cost UAV within a DNN framework can provide a relatively accurate and robust estimation of crop yield, and deliver valuable insight for high-throughput phenotyping and crop field management with high spatial precision. • A low-cost multi-sensor UAV for crop monitoring & phenotyping was developed. • Canopy structure, temperature and texture are important features for yield model. • Multimodal data fusion showed effectiveness in yield prediction. • DNN provided promising results in yield prediction across genotypes and over space. [ABSTRACT FROM AUTHOR]