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Maize (Zea mays L.) grain yield is considered to be highly associated with carbohydrate dynamics in leaves and developing ears during the critical period bracketing silking. Carbohydrate changes are sensitive to variation in nitrogen (N) availability, yet a comprehensive analysis of the N effect on various carbohydrate concentrations around silking remains elusive. A 2-yr field study was conducted to investigate grain yield, N uptake, ear dry matter and carbohydrate concentrations in ear-leaves and whole ears (prior to silking) and kernels (after silking) of maize grown with 0, 150, and 300 kg N ha-1. Greater N availability increased maize shoot dry matter and N content at silking and physiological maturity, as well as grain yield. While N had little effect on ear-leaf glucose concentration, sucrose concentration increased but starch concentration decreased with increasing N, regardless of sampling time. Prior to silking, glucose and fructose concentrations in the developing ear responded positively to increasing N availability, but sucrose and starch concentrations declined. In growing kernels shortly after silking, glucose and fructose concentrations in N fertilized treatments were significantly lower than those in the zero-N treatment. In contrast, a significant increase in kernel starch concentration was found in response to 300 kg N ha-1. These observations point to an important role of the carbohydrate composition of unpollinated ears prior to silking with regard to kernel set and post-silking kernel starch accumulation, and thus final crop yield. [ABSTRACT FROM AUTHOR]

Integrating information on nitrogen (N) mineralization potentials into a fertilization plan could lead to improved N use efficiency. A controlled incubation mineralization study examined microbial biomass dynamics and N mineralization rates for two soils receiving 56 and 168 kg N ha−1in a Panoche clay loam (Typic Haplocambid) and a Wasco sandy loam (Typic Torriorthent), incubated with and without cotton(Gossypium hirsutumL.)residues at 10 and 25°C for 203 days. Microbial biomass activity determined from mineralized carbon dioxide (CO2) was higher in the sandy loam than in clay loam independent of incubation temperature, cotton residue addition and N treatment. In the absence of added cotton residue, N mineralization rates were higher in the sandy loam. Residue additions increased N immobilization in both soils, but were greater in clay loam. Microbial biomass and mineralization were significantly affected by soil type, residue addition and temperature but not by N level. [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]

Growing conditions in the U.S. Midsouth allow for large soybean [Glycine max L. (Merr.)] yields under irrigation, but there is limited information on planting dates (PD) and maturity group (MG) choices to aid in cultivar selection. Analysis of variance across eight (2012) and 10 (2013) locations, four PD, and 16 cultivars (MG 3-6), revealed that the genotype by environment (GxE) interaction accounted for 38 to 22% of the total yield variability. Stability-analysis techniques and probability of low yields were used to investigate this interaction. Planting dates were grouped within early- and late-planting systems. Results showed that MG 4 and 5 cultivars in early-planting systems had the largest average yields, whereas for late-planting systems, late MG 3 to late MG 4 cultivars had the largest yields. Least square means by MG within planting systems at each environment showed that MG 4 cultivars had the greatest yields or were not significantly different from the MG with the greatest yields in 100% of the environments for both early- and late-planting systems. Yields of MG 5 cultivars were similar to those of MG 4 in 100% of the environments with an early planting but only in 20% of the environments with a late planting. The MG 3 cultivars were the best second choice for late plantings, with similar yields to MG 4 cultivars in 55 to 75% of the environments. These results have profound implications for MG recommendations in irrigated soybean in the U.S. Midsouth and indicate the need to reconsider common MG recommendations. [ABSTRACT FROM AUTHOR]

Nitrogen (N) limitation reduces leaf growth and photosynthetic rates of maize ( Zea mays), and constrains photosynthate translocation to developing ears. Additionally, the period from about 1 week before to 2 weeks after silking is critical for establishing the reproductive sink capacity necessary to attain maximum yield. To investigate the influence of carbohydrate availability in plants of differing N status, a greenhouse study was performed in which exogenous sucrose (Suc) was infused around the time of silking into maize stems grown under different N regimes. N deficiency significantly reduced leaf area, leaf longevity, leaf chlorophyll content and photosynthetic rate. High N-delayed leaf senescence, particularly of the six uppermost leaves, compared to the other two N treatments. While N application increased ear leaf soluble protein concentration, it did not influence glucose and suc concentrations. Interestingly, ear leaf starch concentration decreased with increasing N application. Infusion of exogenous suc tended to increase non-structural carbohydrate concentrations in the developing ears of all N treatments at silking and 6 days after silking. However, leaf photosynthetic rates were not affected by suc infusion, and suc infusion failed to increase grain yield in any N treatment. The lack of an effect of suc infusion on ear growth and the high ear leaf starch concentration of N-deficient maize, suggest that yield reduction under N deficiency may not be due to insufficient photosynthate availability to the developing ear during silking, and that yield reduction under N deficiency may be determined at an earlier growth stage. [ABSTRACT FROM AUTHOR]

The xylem-limited bacterium Xylella fastidiosa causes Pierce's disease (PD), whose disease symptoms are primarily the result of xylem vessel blockage in susceptible grapevines. Stem internode and petiole tissues from infected and uninfected control plants of four grape genotypes (Vitis vinifera, V. rufotomentosa, V. smalliana, and V. arizonica/candicans) differing in PD susceptibility were examined using scanning electron microscopy (SEM). Tyloses, fibrillar networks, and gum plugs were observed in lumens of tracheary elements in petioles and internodes of both water-inoculated control plants and X. fasfidiosa-inoculated plants of all genotypes. Bacteria were not observed in control plants. In both petiole and internode tissues, the greatest number of occluded xylem vessels were observed in V. vinifera and the smallest number in V. arizonica/candicans. The number of xylem vessels infested with X. fastidiosa was greatest in V. vinifera and did not differ among the other three genotypes. Systemic infection was found in all genotypes. The frequency with which X. fastidiosa infested vessels were observed using SEM corresponded well with bacterial levels estimated by enzyme-linked immunosorbent assay. Among infected plants, tylose formation in internodes was lowest in V. arizonica/candicans and did not differ among the other three genotypes. Infection with X. fastidiosa strongly induced tylose formation in V vinifera and V. smalliana but not in V. arizonica/candicans. Analysis across tissues and genotypes indicated an induction of fibrillar networks and gum occlusions in response to X. fastidiosa infection, whereas treatment comparisons within genotypes were not significant except for V vinifera petioles. Limiting the spread of X. fastidiosa infection by xylem conduit occlusions does not appear to be the mechanism conferring PD resistance or tolerance to V. arizonica/candicans, V. smalliana, or V. rufotomentosa. In contrast, the strong induction of tyloses may be detrimental rather than beneficial for V. vinifera survival after X. fastidiosa infection. [ABSTRACT FROM AUTHOR]

Breeders successfully increased US soybean [Glycine max (L.) Merr.] yields over the past nearly 100 years and altered various plant characteristics underpinning the yield gains. However, the impact of breeding on plant‐level water‐use efficiency (WUEp) has not been examined yet. This study, conducted across eight environments using maturity group IV cultivars released between 1930 and 2005, aimed to (1) determine if soybean WUEp, assessed using C isotope composition (δ13C) measurements on shoot biomass sampled at early seed filling (R5), has changed with cultivar year of release (YoR), and (2) assess how canopy temperature (CT) and WUEp relate to each other and to seed yield. Across environments and cultivars, δ13C ranged from −27.52‰ to −28.24‰ and the correlation between cultivar YoR and WUEp was positive in four individual environments (p ≤ 0.07), as well as across the eight environments (p = 0.0083), with an average increase of shoot δ13C of 0.004‰ per year of soybean breeding. Lower average δ13C values for specific environments were associated with higher precipitation prior to biomass sampling, which is consistent with a lower WUEp when more water was available. Interestingly, across environments, midday CT at early seed filling was negatively correlated with YoR and WUEp, suggesting that intrinsic WUE of more recently released cultivars was lower during high demand periods. Further studies are needed to understand the mechanisms underlying these relationships between WUEp and CT and to identify physiological mechanisms that can be targeted for breeding high‐yielding cultivars while increasing or maintaining WUEp. [ABSTRACT FROM AUTHOR]

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multisite and multitrait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set (cross-validation 1: CV1), a subset of the sites (cross-validation 2: CV2), and/or with 2 yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north–south distribution of sites. The genetic correlations between the yield surrogates and the biomass yield were generally positive (mean height r = 0.85; mean flowering time r = 0.45) and did not vary due to subpopulation or growing region (North, Middle, or South). Genomic prediction models had CV predictive abilities of −0.02 for individuals using only genetic data (CV1), but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from 1, 2, 3, 4, and 5 sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with the following: 1 site observation of flowering time (0.39); 1 site observation of flowering time and fall height (0.51); 1 site observation of fall height (0.52); 1 site observation of biomass (0.55); and 5 site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging, but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance. [ABSTRACT FROM AUTHOR]

Key Message: Soybean seed oil and meal composition traits can be combined without interference to provide additional value to the crop. Soybean [Glycine max (L.) Merr.] is an important crop worldwide; its overall value comes from seed oil and high protein meal. The development of soybean varieties with allele combinations for improved oil and meal quality is expected to provide a compositional value bundle for soybean. The high oleic and low linolenic acid seed oil trait (HOLL; > 70% oleic and < 3% linolenic acid) is targeted to optimize the health and functional properties of soybean oil. For soybean meal, metabolizable energy is improved by altering the carbohydrate profile with increased sucrose and decreased anti-nutritional factors, raffinose family of oligosaccharides (RFOs). Previous research identified four variant alleles of fatty acid desaturase (FAD) genes and two raffinose synthase (RS) genes necessary for the HOLL trait in soybean oil and Low or Ultra-Low (UL) RFO traits in soybean meal, respectively. We employed a molecular marker-assisted breeding approach to combine six alleles conferring the desired soybean oil and meal value traits. Eight environment field trials were conducted with twenty-four soybean lines to evaluate phenotypic interactions among the variant alleles of FAD and RS genes. The results indicated that the four FAD gene alleles conditioned the HOLL fatty acid profile of the seed oil regardless of the allele status of the RS genes. Independent of the allele combination of the FAD genes, soybean with two variant alleles of the RS genes had the desired RFO trait in the seeds. The results confirm the feasibility of soybean variety development with this unique combination of oil and meal traits. [ABSTRACT FROM AUTHOR]

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]

Objective: Soybean seed development is negatively impacted by elevated temperatures during seed fill, which can decrease seed quality and economic value. Prior germplasm screens identified an exotic landrace able to maintain ~ 95% seed germination under stress conditions that reduce germination dramatically (> 50%) for typical soybean seeds. Seed transcriptomic analysis was performed for two soybean lines (a heat-tolerant landrace and a typical high-yielding adapted line) for dry, mature seed, 6-h imbibed seed and germinated seed. Seeds were produced in two environments: a typical Midwestern field and a heat stressed field located in the Midsouth soybean production region. Results: Transcriptomic analysis revealed 23–30K expressed genes in each seed tissue sample, and differentially expressed genes (DEGs) with ≥ twofold gene expression differences (at q-value < 0.05) comprised ~ 5–44% of expressed genes. Gene ontology (GO) enrichment analysis on DEGs revealed enrichment in heat-tolerant seeds for genes annotated for general and temperature-specific stress, as well as protein-refolding. DEGs were also clustered in modules using weighted co-expressed gene network analysis, which were examined for enrichment of GO biological process terms. Collectively, our results provide new and valuable insights into this unique form of genetic abiotic stress tolerance and to soybean seed physiological responses to elevated temperatures. [ABSTRACT FROM AUTHOR]

Key message: The genetic architecture of symbiotic N fixation and related traits was investigated in the field. QTLs were identified for percent N derived from the atmosphere, shoot [N] and C to N ratio. Soybean [Glycine max (L.) Merr.] is cultivated worldwide and is the most abundant source of plant-based protein. Symbiotic N2 fixation (SNF) in legumes such as soybean is of great importance; however, yields may still be limited by N in both high yielding and stressful environments. To better understand the genetic architecture of SNF and facilitate the development of high yielding cultivars and sustainable soybean production in stressful environments, a recombinant inbred line population consisting of 190 lines, developed from a cross between PI 442012A and PI 404199, was evaluated for N derived from the atmosphere (Ndfa), N concentration ([N]), and C to N ratio (C/N) in three environments. Significant genotype, environment and genotype × environment effects were observed for all three traits. A linkage map was constructed containing 3309 single nucleotide polymorphism (SNP) markers. QTL analysis was performed for additive effects of QTLs, QTL × environment interactions, and QTL × QTL interactions. Ten unique additive QTLs were identified across all traits and environments. Of these, two QTLs were detected for Ndfa and eight for C/N. Of the eight QTLs for C/N, four were also detected for [N]. Using QTL × environment analysis, six QTLs were detected, of which five were also identified in the additive QTL analysis. The QTL × QTL analysis identified four unique epistatic interactions. The results of this study may be used for genomic selection and introgression of favorable alleles for increased SNF, [N], and C/N via marker-assisted selection. [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]

The complexity of environmental factors affecting crops in the field is gradually increasing due to climate change-associated weather events, such as droughts or floods combined with heat waves, coupled with the accumulation of different environmental and agricultural pollutants. The impact of multiple stress conditions on plants was recently termed "multifactorial stress combination" (MFSC) and defined as the occurrence of 3 or more stressors that impact plants simultaneously or sequentially. We recently reported that with the increased number and complexity of different MFSC stressors, the growth and survival of Arabidopsis (Arabidopsis thaliana) seedlings declines, even if the level of each individual stress is low enough to have no significant effect on plants. However, whether MFSC would impact commercial crop cultivars is largely unknown. Here, we reveal that a MFSC of 5 different low-level abiotic stresses (salinity, heat, the herbicide paraquat, phosphorus deficiency, and the heavy metal cadmium), applied in an increasing level of complexity, has a significant negative impact on the growth and biomass of a commercial rice (Oryza sativa) cultivar and a maize (Zea mays) hybrid. Proteomics, element content, and mixOmics analyses of MFSC in rice identified proteins that correlate with the impact of MFSC on rice seedlings, and analysis of 42 different rice genotypes subjected to MFSC revealed substantial genetic variability in responses to this unique state of stress combination. Taken together, our findings reveal that the impacts of MFSC on 2 different crop species are severe and that MFSC may substantially affect agricultural productivity. [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]

[Display omitted] Soybean is a pivotal agricultural commodity around the world, primarily because of its high seed protein and oil concentration. Therefore, farmers, breeders and end-users are highly interested in understanding and predicting the soybean seed composition traits from the individual field level or agroecosystem. Seed composition traits are the proportions of different chemical and physical makeup of soybean seeds. Frequent daily coverage of PlanetScope (PS) satellite provides a unique opportunity of estimating seed composition due to its ability to track crop growth and development with its unique combination of high spatial and temporal resolution. We aim to predict six different soybean seed composition traits (i.e., protein, oil, sucrose, fiber, ash, starch) using PS imagery of standing soybean crops and machine learning algorithms. We developed multi-stream deep neural network which is based on two types of recurrent neural networks, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) that utilize temporal phenology observed from PS. Four statistical machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVR) were used for comparison. Our results show that GRU worked well for protein (R2 0.36, NRMSE 3.62%) and oil (R2 0.53, NRMSE 4.78%), SVR showed the best results for sucrose (R2 0.74, NRMSE 8.34%), fiber (R2 0.21, NRMSE 4.20%), and starch (R2 0.15, NRMSE 16.84%), and PLSR provided the best result for ash (R2 0.60, NRMSE 1.70%). Among the features, vegetation indices at later reproductive stages were found as the most important variables compared to texture features. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. [ABSTRACT FROM AUTHOR]

Emerging biofuel feedstock systems are well suited for use on less productive marginal soils in the Midwestern USA. The systems could replace commodity crop agriculture that may not be economically feasible on these soils with current input and output prices, and meet a growing renewable energy demand. Three annual bioenergy crops, maize ( Zea mays L.), sweet sorghum ( Sorghum bicolor (L.) Moench), and high biomass sorghum (HBS) were grown in rotation with soybean ( Glycine max L.) for five years on marginal soils at two locations. Maize aboveground dry matter (DM) yield and grain yield, sweet sorghum aboveground DM yield, and juice yield and Brix, and HBS DM yield were evaluated and used to calculate theoretical ethanol yields. Intermittent drought occurred at both sites during three of the five years notably reducing yield; a terminal drought in 2011 reduced sorghum yields and inhibited maize grain development at both sites. Theoretical ethanol yields averaged across years from sweet sorghum and HBS were greater than from maize at both locations, and indicate that sweet sorghum has the greatest yield potential, but HBS yield was the most stable. The central Missouri site maintained greater dry matter yield, and theoretical ethanol yield than the southwestern Missouri site. Due to the occurrence of drought during the study, the findings have relevance for evaluating marginal land management for annual bioenergy crops in differing rainfall patterns with climate change. [ABSTRACT FROM AUTHOR]

Climate change is causing an increase in the frequency and intensity of droughts, heat waves, and their combinations, diminishing agricultural productivity and destabilizing societies worldwide. We recently reported that during a combination of water deficit (WD) and heat stress (HS), stomata on leaves of soybean (Glycine max) plants are closed, while stomata on flowers are open. This unique stomatal response was accompanied by differential transpiration (higher in flowers, while lower in leaves) that cooled flowers during a combination of WD + HS. Here, we reveal that developing pods of soybean plants subjected to a combination of WD + HS use a similar acclimation strategy of differential transpiration to reduce internal pod temperature by approximately 4 °C. We further show that enhanced expression of transcripts involved in abscisic acid degradation accompanies this response and that preventing pod transpiration by sealing stomata causes a significant increase in internal pod temperature. Using an RNA-Seq analysis of pods developing on plants subjected to WD + HS, we also show that the response of pods to WD, HS, or WD + HS is distinct from that of leaves or flowers. Interestingly, we report that although the number of flowers, pods, and seeds per plant decreases under conditions of WD + HS, the seed mass of plants subjected to WD + HS increases compared to plants subjected to HS, and the number of seeds with suppressed/aborted development is lower in WD + HS compared to HS. Taken together, our findings reveal that differential transpiration occurs in pods of soybean plants subjected to WD + HS and that this process limits heat-induced damage to seed production. [ABSTRACT FROM AUTHOR]

The technologically adapted agricultural procedures convert conventional farming practices and introduce smart farming or smart agriculture. Manual interventions in farming are unavoidable, however, it was reduced due to the Internet of Things (IoT). Sensors are used to monitor the farms which reduce the manpower requirements as well the cost. In this research work, a smart monitoring and prediction system was developed using IoT along with Fog computing. The physical data from farms are collected through IoT sensors and processed using a novel correlation-based ensemble classifier. Fog computing is adopted in the proposed work to reduce the data transmission delay and computation complexities. Simulation analysis using benchmark datasets demonstrates the proposed model performance in terms of precision, recall, F1-score, and accuracy. Comparative analysis with conventional techniques like neural networks, extreme learning machine, and hybrid particle swarm optimization algorithm, validates the superior performance of the proposed model. With maximum accuracy of 96.67% proposed model outperforms conventional approaches. [ABSTRACT FROM AUTHOR]

Switchgrass, a potential biofuel crop, is a genetically diverse species with phenotypic plasticity enabling it to grow in a range of environments. Two primary divergent ecotypes, uplands and lowlands, exhibit trait combinations representative of acquisitive and conservative growth allocation strategies, respectively. Whether these ecotypes respond differently to various types of environmental drivers remains unclear but is crucial to understanding how switchgrass varieties will respond to climate change. We grew two upland, two lowland, and two intermediate/hybrid cultivars of switchgrass at three sites along a latitudinal gradient in the central United States. Over a 4‐year period, we measured plant functional traits and biomass yields and evaluated genotype‐by‐environment (G × E) interaction effects by analyzing switchgrass responses to soil and climate variables. We found substantial evidence of G × E interactions on biomass yield, primarily due to deviations in the response of the southern lowland cultivar Alamo, which produced more biomass in hotter and drier environments relative to other cultivars. While lowland cultivars had the highest potential for yield, their yields were more variable year‐to‐year compared to other cultivars, suggesting greater sensitivity to environmental perturbations. Models comparing soil and climate principal components as explanatory variables revealed soil properties, especially nutrients, to be most effective at predicting switchgrass biomass yield. Also, positive correlations between biomass yield and conservative plant traits, such as high stem mass and tiller height, became stronger at lower latitudes where the climate is hotter and drier, regardless of ecotype. Lowland cultivars, however, showed a greater predisposition to exhibit these conservative traits. These results suggest switchgrass trait allocation trade‐offs that prioritize aboveground biomass production are more tightly associated in hot, dry environments and that lowland cultivars may exhibit a more specialized strategy relative to other cultivars. Altogether, this research provides essential knowledge for improving the viability of switchgrass as a biofuel crop. [ABSTRACT FROM AUTHOR]

Certain cultivars of maize show increased tolerance to water deficit conditions by maintenance of root growth. To better understand the molecular mechanisms related to this adaptation, nodal root growth zone samples were collected from the reference inbred line B73 and inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water deficits. Plants were grown under various water stress levels in both field and controlled environment settings. FR697-specific RNA-Seq datasets were generated and used for a de novo transcriptome assembly to characterize any genotype-specific genetic features. The assembly was aided by an Iso-Seq library of transcripts generated from various FR697 plant tissue samples. The Necklace pipeline was used to combine a Trinity de novo assembly along with a reference guided assembly and the Viridiplantae proteome to generate an annotated consensus "SuperTranscriptome" assembly of 47,915 transcripts with a N50 of 3152 bp in length. The results were compared by Blastn to maize reference genes, a Benchmarking Universal Single-Copy Orthologs (BUSCO) genome completeness report and compared with three maize reference genomes. The resultant 'SuperTranscriptome' was demonstrated to be of high-quality and will serve as an important reference for analysis of the maize nodal root transcriptomic response to environmental perturbations. [ABSTRACT FROM AUTHOR]

Artificial intelligence technology and image recognition technology are playing an increasingly important role in information warfare, while battlefield image recognition and information processing are at the heart of information processing in warfare. This research will use deep learning image recognition technology and QT development platform, combined with target damage tree analysis and Bayesian network inference method, to research and develop the design of large-scale surface warships damage assessment system. A large-scale surface warships damage assessment system was designed. The system can quickly identify the target large-scale surface warships type with an accuracy rate of over 91%. On this basis, damage assessment is carried out in terms of target vulnerability, combatant power analysis, and bullet-eye rendezvous. A new damage classification is established. The system can improve the efficiency of large-scale surface warships damage assessment, can be well combined with the front-line information collection pictures to assess, and overcome the traditional large-scale surface warships damage assessment and problems of slow and inaccurate manual processing of raw data. It provides a new way of thinking for large-scale surface warships damage assessment research. [ABSTRACT FROM AUTHOR]

Corn ( Zea mays L.) grain composition is important for human and livestock nutrition, when used as seed, and for ethanol production. However, few studies have evaluated the effects of common cultural practices on corn grain composition. This study was conducted to determine whether corn grain elemental composition is affected by tillage practices (tillage or no-tillage), and whether tillage affects grain, protein, and oil yield, and removal of elements from the field in grain. The concentration of protein, oil, P, K, Ca, Mg, Mn, Fe, Zn, Cu, and B, and grain yield were determined in years 20 and 22 of long-term tillage and no-tillage treatments. Tillage treatment did not affect any grain component across both years of sampling, but Cu concentrations were greater under no-tillage in one year. Grain, oil, and protein yield was not affected by tillage treatments across years, but was greater one year under tillage and one year under no-till. The removal of P and Fe was greater under tillage in 2010, and that of Ca and Mn was greater under no-till in 2012. Removal of Cu was greater one year under tillage and one year under no-tillage. Correlation and principle components analysis suggests that there are some differences in the relationships among the grain components between tillage treatments. However, results indicate that tillage is not a dominant factor affecting corn grain composition and removals of nutrients are dominated by grain yield and not the concentration in the grain. [ABSTRACT FROM AUTHOR]

The pre-harvest estimation of seed composition from standing crops is imperative for field management practices and plant phenotyping. This paper presents for the first time the potential of Unmanned Aerial Vehicles (UAV)-based high-resolution hyperspectral and LiDAR data acquired from in-season stand crops for estimating seed protein and oil compositions of soybean and corn using multisensory data fusion and automated machine learning. UAV-based hyperspectral and LiDAR data was collected during the growing season (reproductive stage five (R5)) of 2020 over a soybean test site near Columbia, Missouri and a cornfield at Urbana, Illinois, USA. Canopy spectral and texture features were extracted from hyperspectral imagery, and canopy structure features were derived from LiDAR point clouds. The extracted features were then used as input variables for automated machine-learning methods available with the H2O Automated Machine-Learning framework (H2O-AutoML). The results presented that: (1) UAV hyperspectral imagery can successfully predict both the protein and oil of soybean and corn with moderate accuracies; (2) canopy structure features derived from LiDAR point clouds yielded slightly poorer estimates of crop-seed composition compared to the hyperspectral data; (3) regardless of machine-learning methods, the combination of hyperspectral and LiDAR data outperformed the predictions using a single sensor alone, with an R2 of 0.79 and 0.67 for corn protein and oil and R2 of 0.64 and 0.56 for soybean protein and oil; and (4) the H2O-AutoML framework was found to be an efficient strategy for machine-learning-based data-driven model building. Among the specific regression methods evaluated in this study, the Gradient Boosting Machine (GBM) and Deep Neural Network (NN) exhibited superior performance to other methods. This study reveals opportunities and limitations for multisensory UAV data fusion and automated machine learning in estimating crop-seed composition. [ABSTRACT FROM AUTHOR]

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

Key message: We investigate the genetic basis of panicle architecture in switchgrass in two mapping populations across a latitudinal gradient, and find many stable, repeatable genetic effects and limited genetic interactions with the environment. Grass species exhibit large diversity in panicle architecture influenced by genes, the environment, and their interaction. The genetic study of panicle architecture in perennial grasses is limited. In this study, we evaluate the genetic basis of panicle architecture including panicle length, primary branching number, and secondary branching number in an outcrossed switchgrass QTL population grown across ten field sites in the central USA through multi-environment mixed QTL analysis. We also evaluate genetic effects in a diversity panel of switchgrass grown at three of the ten field sites using genome-wide association (GWAS) and multivariate adaptive shrinkage. Furthermore, we search for candidate genes underlying panicle traits in both of these independent mapping populations. Overall, 18 QTL were detected in the QTL mapping population for the three panicle traits, and 146 unlinked genomic regions in the diversity panel affected one or more panicle trait. Twelve of the QTL exhibited consistent effects (i.e., no QTL by environment interactions or no QTL × E), and most (four of six) of the effects with QTL × E exhibited site-specific effects. Most (59.3%) significant partially linked diversity panel SNPs had significant effects in all panicle traits and all field sites and showed pervasive pleiotropy and limited environment interactions. Panicle QTL co-localized with significant SNPs found using GWAS, providing additional power to distinguish between true and false associations in the diversity panel. [ABSTRACT FROM AUTHOR]