Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass in oats, Austrian winter peas (AWP), turnips, and a combination of all three crops across six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining the normalized difference red-edge (NDRE) index and CHM provided the best biomass model before termination (R² = 0.84). For bitemporal images, green reflectance, CHM, and the ratio of near-infrared (NIR) to red achieved the best performance (R² = 0.85). Cover crop species also influenced the model performance. Oats were best modeled using the enhanced vegetation index (EVI) (R² = 0.86), AWP with red-edge reflectance (R² = 0.71), turnips with NIR, GNDVI, and CHM (R² = 0.95), and mixed species with NIR and blue band reflectance (R² = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture.

Early detection of nitrogen deficiency in cotton requires timely identification of stress symptoms like leaf chlorosis (yellowing) and canopy stunting. Chlorosis initially appears in older, lower-canopy leaves, which are often not visible in conventional nadir-looking imaging. This study investigates oblique ground-based multispectral imaging to estimate plant height and capture spectral details from the upper (UC) and lower (LC) cotton canopy layers. Images were collected from four camera pitch and height configurations: set 1 (30°, 2 m), set 2 (55°, 2 m), set 3 (68°, 3 m), and set 4 (70°, 1.5 m). A pre-trained monocular depth estimation model (MiDaS) was used to estimate plant height from aligned RGB images and an empirically derived tangential model corrected for perspective distortion. Further, the lower and upper vertical halves of the plants were categorized as LC and UC, with vegetation indices (CI_green, CI_rededge) calculated for each. The aligned images in set 1 had the best sharpness and quality. The plant height estimates from set 1 had the highest correlation (r = 0.64) and lowest root mean squared error (RMSE = 0.13 m). As the images became more oblique, alignment and monocular depth/height accuracy decreased. Also, the effects of perspective and object-scale ambiguity in monocular depth estimation were prominent in the high oblique and relatively low altitude images. The spectral vegetation indices (VIs) were affected by band misalignment and shadows. VIs from the different canopy layers demonstrated moderate correlation with leaf nitrogen concentration, and sets 2 and 3 specifically showed high and low differences in VIs from the UC and LC layers for the no and high-nitrogen treatments, respectively. However, improvements in the multispectral alignment process, extensive data collection, and ground-truthing are needed to conclude whether the LC spectra are useful for early nitrogen stress detection in field cotton.

Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study assesses unmanned aerial vehicle-mounted multispectral sensors to estimate biomass across oats, Austrian winter peas (AWP), turnips, and a mix of all three replicated in six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining normalized difference red-edge (NDRE), vegetation index (NDVI), and CHM provided the best biomass model before termination (R2 = 0.90). For bitemporal images, green reflectance, CHM, near-infrared (NIR)/red ratio, and green normalized difference vegetation index (GNDVI) achieved optimal performance (R2 = 0.86). Cover crop species influenced the model performance. Oats were best modeled with enhanced vegetation index (EVI) (R2 = 0.86), AWP with red-edge reflectance (R2 = 0.71), turnips with NIR, GNDVI, and CHM (R2 = 0.95), and mixed species with NIR and blue band reflectance (R2 = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture.

Maize (Zea mays L.) production requires substantial nitrogen (N) during the grain-filling reproductive stages. Therefore, splitting N fertilizer applications between planting and the early reproductive stage (R1) may improve maize growth and yield while reducing environmental N losses. This study evaluated the effects of split N applications on maize growth, yield, N use efficiency (NUE), and residual soil N over two contrasting seasons. Eight different split N applications, ranging from 100:0 to 50:50 (planting: R1), and two reduced rates (50% and 25% of the full rate) were tested for 2 years. In 2021, split applications did not impact aboveground biomass or grain yield, although delaying 50% of N to R1 reduced leaf area index (LAI) and chlorophyll content. However, upon the second application, all split-N treatments recovered with rapid N uptake during early reproductive growth stages. In 2022, under severe drought, overall maize growth declined. Moreover, reducing N application to 50% did not affect biomass or grain yield compared to full rates, but improved N uptake and agronomic efficiency while reducing residual soil nitrate. Further reduction of the N fertilizer rate to 25% significantly reduced maize growth and yield. These findings suggest that applying 50% of N at planting allows flexibility to adjust remaining applications based on water stress. The study showed that adaptive N strategies can maintain productivity, enhance NUE, and reduce environmental risk in dryland maize systems under variable climatic conditions.

Nitrous oxide (N ₂ O) emissions from croplands are a distinct environmental challenge because N ₂ O is both a potent greenhouse gas and a major contributor to stratospheric ozone depletion. Emissions arise largely from agricultural nitrogen (N) inputs and microbially mediated nitrification and denitrification as N pools are transformed under fluctuating soil oxygen and moisture conditions. Yet N inputs are central to maintaining yield stability, ensuring an adequate food protein supply, and sustaining farm profitability. Therefore, mitigation strategies that rely predominantly on reducing N use are difficult to scale without compromising agricultural productivity. When expressed as chlorofluorocarbon-11–equivalent ozone-depleting emissions, N ₂ O (179.0 kt yr ⁻¹ ) exceeds the combined emissions of legacy ozone-depleting substances and transitional hydrochlorofluorocarbons (145.7 kt yr ⁻¹ ), strengthening the case for prioritizing agricultural N ₂ O within climate and ozone-protection frameworks. In this review, we synthesize current understanding of nitrification- and denitrification-derived pathways of N ₂ O formation and the biological sink of N ₂ O reduction to dinitrogen (N ₂ ), emphasizing how fertilizer-driven shifts in N pools interact with soil to regulate pathway dominance and the N ₂ O:N ₂ product ratio. We then place these mechanisms in the context of 21st-century fertilizer-use trajectories and rising atmospheric N ₂ O and summarize the stratospheric reactions that couple agricultural N ₂ O to ozone loss. Building on these mechanistic foundations, we evaluate direct nitrification-targeted mitigation by contrasting synthetic nitrification inhibitors with emerging biological nitrification-inhibition traits and their potential integration into the cropping system. Finally, we highlight how recent Conference of the Parties negotiations and the 2024 Global N ₂ O Assessment are elevating N ₂ O as a priority on the climate and ozone protection agenda. We argue that durable scaling of mitigation will require outcome-based quantification, including models and measurement strategies that deliver credible seasonal-to-annual estimates with transparent uncertainty, so that management changes translate into verifiable N ₂ O reductions and eligible incentives.