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Polyploidy results from whole-genome duplication and is a unique form of heritable variation with pronounced evolutionary implications. Different ploidy levels, or cytotypes, can exist within a single species, and such systems provide an opportunity to assess how ploidy variation alters phenotypic novelty, adaptability, and fitness, which can, in turn, drive the development of unique ecological niches that promote the coexistence of multiple cytotypes. Switchgrass, Panicum virgatum, is a widespread, perennial C4 grass in North America with multiple naturally occurring cytotypes, primarily tetraploids (4×) and octoploids (8×). Using a combination of genomic, quantitative genetic, landscape, and niche modeling approaches, we detect divergent levels of genetic admixture, evidence of niche differentiation, and differential environmental sensitivity between switchgrass cytotypes. Taken together, these findings support a generalist (8×)–specialist (4×) trade-off. Our results indicate that the 8× represent a unique combination of genetic variation that has allowed the expansion of switchgrass’ ecological niche and thus putatively represents a valuable breeding resource.

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]

Both carbon limitation and developmentally driven kernel failure occur in the apical region of maize (Zea mays L.) ears. Failed kernel development in the basal and middle regions of the ear often is neglected because their spaces usually are occupied by adjacent ovaries at harvest. We tested the spatial distribution of kernel losses and potential underlying reasons, from perspectives of silk elongation and carbohydrate dynamics, when maize experienced water deficit during silk elongation. Kernel loss was distributed along the length of the ear regardless of water availability, with the highest kernel set in the middle region and a gradual reduction toward the apical and basal ends. Water deficit limited silk elongation in a manner inverse to the temporal pattern of silk initiation, more strongly in the apical and basal regions of the ear than in the middle region. The limited recovery of silk elongation, especially at the apical and basal regions following rescue irrigation was probably due to water potentials below the threshold for elongation and lower growth rates of the associated ovaries. While sugar concentrations increased or did not respond to water deficit in ovaries and silks, the calculated sugar flux into the developing ovaries was impaired and diverged among ovaries at different positions under water deficit. Water deficit resulted in 58% kernel loss, 68% of which was attributable to arrested silks within husks caused by lower water potentials and 32% to ovaries with emerged silks possibly due to impaired carbohydrate metabolism. [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]

Construction economics of plant roots exhibit predictable relationships with root growth, death, and nutrient uptake strategies. Plant taxa with inexpensively constructed roots tend to more precisely explore nutrient hotspots than do those with costly constructed roots but at the price of more frequent tissue turnover. This trade-off underlies an acquisitive to conservative continuum in resource investment, described as the "root economics spectrum (RES)." Yet the adaptive role and genetic basis of RES remain largely unclear. Different ecotypes of switchgrass (Panicum virgatum) display root features exemplifying the RES, with costly constructed roots in southern lowland and inexpensively constructed roots in northern upland ecotypes. We used an outbred genetic mapping population derived from lowland and upland switchgrass ecotypes to examine the genetic architecture of the RES. We found that absorptive roots (distal first and second orders) were often "deciduous" in winter. The percentage of overwintering absorptive roots was decreased by northern upland alleles compared with southern lowland alleles, suggesting a locally-adapted conservative strategy in warmer and acquisitive strategy in colder regions. Relative turnover of absorptive roots was genetically negatively correlated with their biomass investment per unit root length, suggesting that the key trade-off in framing RES is genetically facilitated. We also detected strong genetic correlations among root morphology, root productivity, and shoot size. Overall, our results reveal the genetic architecture of multiple traits that likely impacts the evolution of RES and plant aboveground-belowground organization. In practice, we provide genetic evidence that increasing switchgrass yield for bioenergy does not directly conflict with enhancing its root-derived carbon sequestration. [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]

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]

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]

The adoption of precision agriculture involves a demand for equipment and solutions to create an accurate diagnostic of the spatial variability to be managed at the field level. Sugarcane has faced some challenges due to the limited solutions adapted to the crop, which develops throughout the year and involving a large-scale harvest. LiDAR (Light Detection and Ranging) technology is a high-resolution tool that permits the measurement of vegetative growth in a non-destructive way, assisting, for example, in harvest planning. The objective was to describe the three-dimensional (3D) data processing to characterize the spatial variability of sugarcane fields in the pre-harvest period. An aerial platform was used for data acquisition 10 days before and after harvesting. The digital models of surface, of terrain, and the canopy height model (CHM) were generated to spatialize plants height based on point cloud. The LiDAR-derived metrics extracted were percentiles (P50th; P90th–P99th), with the highest value of the coefficient of variation observed for the P50th (59%), indicating that there is high spatial variability in plant height. The RMSE (Root Mean Squared Error) among field measurements and sugarcane stalk height from CHM was 0.47 m. This study demonstrates that 3D sensing data can provide relevant information for the assessment of the crop height and, potentially, to consider it as an indicator of the field regions with distinct levels of production. [ABSTRACT FROM AUTHOR]

• A criterion for grading lodging risk levels based on probability was established. • The wind speed risks in three main quinoa production areas of China were analyzed. • Optimizing plant height to counter lodging risks under high-yielding conditions. • The spatial distribution of recommended plant height in study areas was drawn. Stem lodging is a major restriction for further quinoa yield improvement under high-yielding conditions. Plant height is one of the most important factors to affect stem lodging resistance, but how to determine the optimal plant height to meet specific lodging resistance requirements in different areas is rarely discussed. This study analyzed the long-term wind speed data in three main quinoa production regions of China (Inner Mongolia, Qinghai, and Gansu), and calculated the wind speed under different risk conditions (probability = 0.1, 0.3 and 0.5). Then, three high yielding conditions were selected based on a field experiment, and their optimal quinoa plant heights were calculated to ensure adequate lodging resistance in every specific area by using the generalized crop lodging model. Results showed that during the susceptible lodging period, 70–97% of the study regions experienced a maximum daily wind speed of 4–6 m s–1 with a probability of 0.3–0.5. As the probability decreased to 0.1, a higher maximum daily wind speed of 6–8 m s–1 would prevail in 69–75% of the study regions. Besides, central Inner Mongolia, western Qinghai, and northern Gansu experienced higher maximum daily wind speeds than other regions during the susceptible lodging period. To achieve adequate lodging resistance under a probability of 0.3–0.5, quinoa height should be decreased to be lower than 1.2–1.6 m in 72–99% of the study regions. Furthermore, under a probability of 0.1, quinoa height needed to be lower by at least another 0.2 m more in 86–98% of the study regions. [ABSTRACT FROM AUTHOR]