The application of biochar amendments to soil has been proposed as a strategy for mitigating global carbon (C) emissions and soil organic carbon (SOC) loss. Biochar can provide additional agronomic benefits to cropping systems, including improved crop yield, soil water holding capacity, seed germination, cation exchange capacity (CEC), and soil pH. To maximize the beneficial effects of biochar amendments towards the inventory, increase, and management of SOC pools, nondestructive analytical methods such as ground penetrating radar (GPR) are needed to identify and quantify belowground C. The use of GPR has been well characterized across geological, archaeological, engineering, and military applications. While GPR has been predominantly utilized to detect relatively large objects such as rocks, tree roots, land mines, and peat soils, the objective of this study was to quantify comparatively smaller, particulate sources of SOC. This research used three materials as C sources: biochar, graphite, and activated C. The C sources were mixed with sand—12 treatments in total—and scanned under three moisture levels: 0%, 10%, and 20% to simulate different soil conditions. GPR attribute analyses and Naïve Bayes predictive models were utilized in lieu of visualization methods because of the minute size of the C particles. Significant correlations between GPR attributes and both C content and moisture levels were detected. The accuracy of two predictive models using a Naïve Bayes classifier for C content was trivial but the accuracy for C structure was 56%. The analyses confirmed the ability of GPR to identify differences in both C content and C structure. Beneficial future applications could focus on applying GPR across more diverse soil conditions.

Sorghum species provide a unique opportunity to develop perennial cropping systems due to their interspecific hybridization compatibility and phenotypic plasticity from annual to perennial life cycle. Sorghum bicolor (L.) Moench is a diploid (2n = 2x = 20) annual species that does not produce rhizomes, whereas Sorghum propinquum (Kunth) Hitchc. is a diploid (2n = 2x = 20) perennial, rhizomatous species native to Asia and is considered a wild relative of S. bicolor. Because of their relatively close taxonomic relationship, S. bicolor × S. propinquum hybrids offer both valuable insights between annuals and perennials, as well as hybridization opportunities for the introgression of perennialism into a major cereal crop. The objective of this study was to characterize a novel S. bicolor × S. propinquum F₂ population for height, tillering capacity, days to flowering, and overwintering capacity. Our research identified F₂ hybrids exhibiting transgressive segregation for height, whereas hybrids were intermediate between the parental extremes for days to mid‐anthesis and tillering capacity. Fortuitous harsh winter conditions in 2018 applied strong natural selection pressure for overwintering capacity, allowing for the identification of F₂ hybrids with greater overwintering capacity than S. propinquum. The present results provide both novel perennial sorghum germplasm resources and insight towards developing effective breeding programs for perennial cereal cropping systems.

Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel- and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.] Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment.

This study investigates the deployment of artificial intelligence (AI) knowledge-base assistants using open educational resources (OER) for computer science education, demonstrating the feasibility of providing AI-assisted technical support in resource-constrained environments. Our methodology combines structured knowledge extraction from 82 open-licensed Markdown documents, systematic benchmarking of Qwen-7B and DeepSeek-MoE on consumer-grade GPUs (NVIDIA RTX 3060, 12 GB VRAM), and a multi-criteria evaluation framework incorporating response accuracy, computational efficiency, retrieval-augmented generation (RAG)-specific metrics, and energy sustainability. Key results show that Qwen-7B (FP16) achieves 71.5% overall accuracy with 1.4 s average latency, while DeepSeek-MoE (FP16) achieves 79.8% overall accuracy and 82.3% accuracy on multi-hop reasoning tasks. NF4 4-bit quantization reduces VRAM usage by 38.7% for Qwen-7B (6.2 GB → 3.8 GB) and 37.9% for DeepSeek-MoE (5.8 GB → 3.6 GB). Our most energy-efficient configuration consumes 1.8 mWh per query (1.8 Wh per 1,000 queries), measured at the GPU device rail with idle power subtracted. This work makes four contributions: (1) systematic multi-dimensional benchmarks for on-premise, OER-aligned AI knowledge-base assistants in introductory computer science education; (2) hardware-specific deployment guidelines for consumer-grade GPUs without cloud dependency; (3) quantitative evidence that NF4 4-bit quantization-aware fine-tuning preserves educational response utility within 2 percentage points of accuracy degradation while reducing VRAM by 38.7%; and (4) a reproducible evaluation protocol serving as a reference design for future research. The system is characterized as a knowledge-base assistant rather than a validated intelligent tutor; comprehensive pedagogical validation with controlled learner studies remains a priority for future work.