Hemp (Cannabis sativa L.) is an emerging high-value specialty crop that can be cultivated for either fiber, seed, or cannabidiol (CBD). The demand for hemp and its products has been consistently on the rise in the 21st century. The United States of America (USA) has reintroduced hemp and legalized its production as an agricultural commodity through the 2018 Federal Farm Bill. Although there is a renewed interest in the adoption of hemp due to the emerging market, its production in the United States (US) remains limited partly because of unclear agronomic guidance and fertilization recommendations. This review article provides information on the current agronomic management practices that are available in the literature and identifies the future research needs for cultivating this multipurpose crop to address the growing market demands. Hemp production could be beneficial if managed properly. Hemp fertilizer requirements vary in accordance with the type of hemp grown (seed, fiber, or CBD), soil, environmental conditions and requires a wide range of macro- and micronutrients. Integrating management practices in hemp cultivation intended to build soil health is promising since the hemp cropping system is suitable for crop rotation, cover cropping, and livestock integration through animal waste applications. Hemp also has significant environmental benefits since it has the potential to remediate contaminated soils through phytoremediation, convert high amounts of atmospheric CO₂ to biomass through bio-sequestration, and hemp biomass for bioenergy production. This review identifies that most of the agronomic research in the past has been limited to hemp fiber and, to some extent, hemp seed but not CBD hemp. With the increase in the global markets for hemp products, more research needs to be conducted to provide agronomic guidelines for sustainable hemp production.

This review evaluates the use of unmanned aerial vehicles (UAVs) in detecting and managing water stress in specialty crops through thermal, multispectral, and hyperspectral imaging. Based on 104 scholarly articles from 2012 to 2024, the review highlights the advantages, limitations, and evolution of these imaging systems. Vineyards are the most studied crops for precision irrigation compared to other crops. The paper traces the shift from standalone imaging to multi-sensor fusion approaches, integrating vegetation indices and machine learning models for improved accuracy, resolution, and real-time stress assessment. It also addresses knowledge gaps such as scalability, payload constraints, and computational demands. Issues like flight altitude, sensor angle, and lighting conditions can lead to data inconsistencies, affecting water stress detection and decision-making. Emerging technologies like LiDAR, AI, and machine learning are proposed to enhance UAV data processing and stress detection. Future research should focus on developing automated data correction, multi-sensor fusion, and AI-driven real-time analysis to address sensor calibration and environmental factors. The review also advocates for integrating UAV data with satellite and ground sensors into smart irrigation systems to create a multi-scale monitoring framework, thereby advancing precision agriculture and water resource management.

Identifying the importance of soil biology in different land use systems is critical to assess the present conditions of declining soil (C) and global land degradation while regulating soil health and biogeochemical nutrient cycling. A study was undertaken in a mixed watershed comprising of different land use systems (agricultural, grassland, agroforestry, and eroded); situated in the Shiwalik region in the foot hills of the lower Himalayas in India, a fragile ecosystem susceptible to land degradation. Soil samples from 0–15 and 15–30 cm depths were collected from these land use systems and analyzed for a suite of different soil health indicators, including physio-chemical soil properties, aggregate stability, soil microflora, and the enzymatic activities that are critical for nutrient cycling. Principal component analysis was used to group different land uses and understand their association with soil microflora, enzyme activities, and soil physio-chemical properties. We found that a greater number of soil microflora and enzymatic activities were associated with grassland and agroforestry land use systems. Aggregate-associated soil C correlated well with the soil microflora under different land use systems studied. The biplots revealed that the fungal:bacterial ratio (2 × 10³–0.1 × 10³) was a robust indicator of C accumulation and soil health, and was in greater association with the agroforestry land use system. Random forest, a non-parametric statistical test, on average explained that 68% to 92% of the variability in soil microbial population was due to land use and other soil health properties. Overall, the biological soil health indicators used in this study demonstrated the fact that land use management systems that employ constant crop cover with minimal disturbance have the potential to improve soil sustainability and ecological functioning.

Information about evapotranspiration (ET) and its components, that is, evaporation and transpiration, is crucial for a wide range of water and ecosystem management applications. However, partitioning ET into its two components is often challenging because of their spatiotemporal variabilities and lack of process understanding. This study developed a machine learning (ML) framework to shed light on ET processes and assess the relative importance of different drivers by incorporating hydrometeorology and biomass productivity variables. The Shapley Additive Explanations (SHAP) approach was applied to enhance explainability and rank the importance of ET drivers and their components. A total of 62 variables covering hydrometeorological and biomass productivity dimensions were considered from the Reynolds Creek Critical Zone Observatory (CZO) station in Idaho. The variable importance assessment identified the leading drivers individually for evaporation, transpiration and ET (soil water content for evaporation, vapour pressure deficit for transpiration and soil water content for ET). The results further highlighted the value of combining hydrometeorological and biomass productivity variables to achieve better predictability of ET processes.

Amaranth (Amaranthus spp.) is an increasingly high-valued niche vegetable crop among small organic growers in North Carolina, due to its increasing demand among diverse immigrant groups. Production is however hampered by insect pests such as the flea beetle (FB), Disonycha glabrata (Coleoptera: Chrysomelidae), that cause significant yield reduction. Chemical insecticides are generally applied for pest control despite their known risks to health and the environment. Integrated pest management (IPM), which is a cost effective and environmentally friendly approach is still under-exploited in vegetable production by small growers. We studied IPM approaches, suitable for organic production of amaranth by screening nine amaranth varieties for resistance to the flea beetle (FB), D. glabrata, grown with, and without, mulch. D. glabrata population was 60% higher in plots with mulch compared to plots without. The amaranth varieties Molten fire and Green Callaloo recorded the lowest and the highest beetle population commensurate with low, and high leaf damage, respectively. Conversely, leaf yields in the mulched plots were 50% less than recorded in the zero-mulch counterpart, with Green Callaloo variety recording the lowest. These findings will serve as building blocks for a sustainable pest management plan that is appropriate for organic production of Amaranthus spp. in North Carolina.