Search Results (268)

Maize, as a critical crop for China’s food security, is constantly challenged by weed infestations and environmental risks associated with herbicide overuse. Improving herbicide utilization efficiency through equipment optimization and intelligent control during spraying has become an essential strategy for weed management in Chinese maize fields. However, most current sprayers fail to achieve coordinated control of spray volume and nozzle parameters, and their performance is typically evaluated using single indices, such as the coefficient of variation (CV) for spray uniformity and deposition density. In this study, a split-split-plot experiment was conducted in 2022–2023 to assess the feasibility of herbicide reduction using intelligent variable-rate boom sprayers in summer maize fields on the North China Plain (NCP). The key variables included spray volume (225 vs. 180 L/ha), nozzle type (AI11003VS/LECHLER11003 in 2022; TTI11004/LECHLER11004 in 2023), and herbicide dose (recommended, −15%, and −30% reduction). Results showed that the coefficients of variation for droplet coverage and density remained below 12% for all treatments (n = 4), indicating stable spray performance. A higher spray volume (225 L/ha) significantly improved deposition uniformity (p < 0.01). In 2022, herbicide input could be reduced by 15–30% while maintaining efficacy above 90% when applied at the 3–4 leaf stage of dominant weeds. However, in 2023, efficacy dropped to 72.67% when the herbicide was applied at a 30% reduced dose with 180 L/ha and when dominant weeds had reached the 5–6 leaf stage or higher, indicating an agronomic risk. Reduced herbicide input decreased maize injury by 47–53%. Only the 30% reduced-dose treatment significantly increased maize yield by 3.05% in 2022 and 2.62% in 2023 compared to the control (both p < 0.05). Spray volume significantly influenced droplet deposition and weed control efficacy; thus, caution is warranted regarding herbicide reduction for later weed growth stages. This study demonstrates that real-time variable-rate boom sprayers, optimized for spray volume and nozzle type, can reduce herbicide use without compromising weed control efficacy or maize yield, providing both theoretical support and practical guidance for sustainable herbicide management in summer maize fields on the NCP. Full article

Oat is a dual-purpose crop valued for both grain and forage. The stay-green (SG) trait, which delays leaf senescence and prolongs photosynthesis, has been shown to increase yield and quality in several crop species, yet its performance across diverse environments in oats remains underexplored. In this study, multi-location field trials were conducted in Ledu, Huangzhong and Haiyan, Qinghai Province, China, to comprehensively evaluate the performance of stay-green oat lines. The traits evaluated included grain yield components, nutritional quality, and seedling establishment traits. A TOPSIS (technique for order preference by similarity to an ideal solution) model, coefficient of variation (CV) and G × E (genotype × environment) visualization were used to assess adaptability, stability, and genotype × environment interactions. On average, the stay-green lines exhibited an 16.00% increase in plot yield and a 22.93% increase in thousand-grain weight compared to controls. Notable improvements were also observed in the starch (7.58% LN_SG in HZ and HY) and protein (3.58%, QY5_SG all the sites) contents, as well as multiple seedling establishment indices, with the seedling vigor indices increasing by more than 50%. Stability analysis further showed that the stay-green lines were stable in spike length, thousand-grain weight, water-soluble carbohydrates, and seed and seedling vigor. TOPSIS analysis identified ‘LN_SG’ as the top-performing and most adaptable genotype across all environments. Overall, stay-green oat lines demonstrated superior performance in grain yield, nutritional quality, and seedling establishment. These findings highlight their potential for field application and their value as parental materials in oat breeding programs enhancing environmental adaptability and stability. Full article

Integrated rice–crayfish (Oryza sativa–Procambarus clarkii) co-culture (RC) systems have gained prominence due to their economic benefits and ecological sustainability; however, the interactions between soil properties and microbial communities in such systems remain poorly understood. This study evaluated the effects of the RC systems on soil physicochemical characteristics and microbial dynamics in paddy fields of southern Henan Province, China, over the 2023 growing season and subsequent fallow period. Using a randomized complete design, rice monoculture (RM, as the control) and RC treatments were compared across replicated plots. Soil and water samples were collected post-harvest and pre-transplanting to assess soil properties, extracellular enzyme activity, and microbial community structure. Results showed that RC significantly enhanced soil moisture by up to 30.2%, increased soil porosity by 9.6%, and nearly tripled soil organic carbon compared to RM. The RC system consistently elevated nitrogen (N), phosphorus (P), and potassium (K) throughout both the rice growth and fallow stages, indicating improved nutrient availability and retention. Elevated extracellular enzyme activities linked to carbon, N, and P cycling were observed under RC, with enzymatic stoichiometry revealing increased microbial nutrient limitation intensity and a shift toward P limitation. Microbial community composition was significantly altered under RC, showing increased biomass, a higher fungi-to-bacteria ratio, and greater relative abundance of Gram-positive bacteria, reflecting enhanced soil biodiversity and ecosystem resilience. Further analyses using the Mantel test and Random Forest identified extracellular enzyme activities, PLFAs, soil moisture, and bulk density as major factors shaping microbial communities. Redundancy analysis (RDA) confirmed that total potassium (TK), vector length (VL), soil pH, and total nitrogen (TN) were the strongest environmental predictors of microbial variation, jointly explaining 74.57% of the total variation. Our findings indicated that RC improves soil physicochemical conditions and microbial function, thereby supporting sustainable nutrient cycling and offering a promising, environmentally sound strategy for enhancing productivity and soil health in rice-based agro-ecosystems. Full article

Mangrove forests are widely recognized for their effectiveness as carbon sinks and serve as critical ecosystems for mitigating the effects of climate change. Current research lacks comprehensive, large-scale carbon storage datasets for wetland ecosystems, particularly across Mexico and other understudied regions worldwide. Therefore, the objective of this study was to develop a high spatial resolution map of carbon stocks, encompassing both aboveground and belowground components, within the Marismas Nacionales system, which is the largest mangrove complex in northeastern Pacific Mexico. Our approach integrates primary field data collected during 2023–2024 and incorporates some historical plot measurements (2011–present) to enhance spatial coverage. These were combined with contemporary remote sensing data, including Sentinel-1, Sentinel-2, and LiDAR, analyzed using Random Forest algorithms. Our spatial models achieved strong predictive accuracy (R² = 0.94–0.95), effectively resolving fine-scale variations driven by canopy structure, hydrologic regime, and spectral heterogeneity. The application of Local Indicators of Spatial Association (LISA) revealed the presence of carbon “hotspots,” which encompass 33% of the total area but contribute to 46% of the overall carbon stocks, amounting to 21.5 Tg C. Notably, elevated concentrations of carbon stocks are observed in the central regions, including the Agua Brava Lagoon and at the southern portion of the study area, where pristine mangrove stands thrive. Also, our analysis reveals that 74.6% of these carbon hotspots fall within existing protected areas, demonstrating relatively effective—though incomplete—conservation coverage across the Marismas Nacionales wetlands. We further identified important cold spots and ecotones that represent priority areas for rehabilitation and adaptive management. These findings establish a transferable framework for enhancing national carbon accounting while advancing nature-based solutions that support both climate mitigation and adaptation goals. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Annular flow is a common flow configuration encountered in fields such as food engineering, energy and power engineering, and petroleum engineering. The annular space formed by the inner and outer pipes exhibits unique characteristics, with the distinct curvatures of the inner and outer pipes rendering the annulus fundamentally different from a circular pipe. The complexity of the annular structure complicates the rapid calculation of turbulent statistics in engineering practice, as modeling these statistics necessitates a comprehensive understanding of their peak characteristics. However, current research lacks a thorough understanding of the peak characteristics of turbulent flows in annuli with varying diameter ratios (the ratio of the inner tube’s diameter to the outer tube’s diameter) between the inner and outer pipes. To gain a deeper insight into the turbulent peak characteristics within annular flows, this study employs numerical simulation methods to investigate the first- and second-order turbulent statistics under different diameter ratios resulting from varying curvatures of the inner and outer pipes. These statistics encompass velocity distribution, the position and magnitude of maximum velocity, turbulence intensity, turbulent kinetic energy, and Reynolds stress. The research findings indicate that the contour plots of velocity, turbulence intensity, and turbulent kinetic energy distributions under different diameter ratio conditions exhibit central symmetry. The peaks of the first-order statistical quantities are located in the mainstream region of the annulus, and their positions gradually shift closer to the center of the annulus as the diameter ratio increases. For the second-order statistical quantities, peaks are observed near both the inner and outer walls, and their positions move closer to the walls as the diameter ratio rises. The peak values of turbulent characteristics show significant variations across different diameter ratios. Both the inner and outer wall surfaces exhibit peaks in their second-order statistical quantities. For instance, the maximum value of Reynolds stress near the inner tube is 101.4% of that near the outer tube, and the distance from the wall where the maximum Reynolds stress occurs near the inner tube is 97.2% of the corresponding distance near the outer tube. This study is of great significance for optimizing the diameter combination of the inner and outer pipes in annular configurations and for evaluating turbulent statistics. Full article

Accurate and timely wheat yield prediction is valuable globally for enhancing agricultural planning, optimizing resource use, and supporting trade strategies. Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Aerial Vehicle (UAV) multispectral (MS) and Red-Green-Blue (RGB) imagery. Research analyzes five European wheat cultivars across 400 experimental plots created by combining 20 nitrogen, phosphorus, and potassium (NPK) fertilizer treatments. Yield variations from 1.41 to 6.42 t/ha strengthen model robustness with diverse data. The ML approach is automated using PyCaret, which optimized and evaluated 25 regression models based on 65 vegetation indices and yield data, resulting in 66 feature variables across 400 observations. The dataset, split into training (70%) and testing sets (30%), was used to predict yields at three growth stages: 9 May, 20 May, and 6 June 2022. Key models achieved high accuracy, with the Support Vector Regression (SVR) model reaching R² = 0.95 on 9 May and R² = 0.91 on 6 June, and the Multi-Layer Perceptron (MLP) Regressor attaining R² = 0.94 on 20 May. The findings underscore the effectiveness of precisely measured MS indices and a rigorous experimental approach in achieving high-accuracy yield predictions. This study demonstrates how a precise experimental setup, large-scale field data, and AutoML can harness UAV and machine learning’s potential to enhance wheat yield predictions. The main limitations of this study lie in its focus on experimental fields under specific conditions; future research could explore adaptability to diverse environments and wheat varieties for broader applicability. Full article

Monsoon evergreen broad-leaved forests (MEBFs) represent one of the most species-rich and structurally complex vegetation types, and one of the most widely distributed forests in Yunnan Province, Southwest China. However, they have yet to undergo a comprehensive analysis on their community diversity, spatial differentiation patterns, and underlying drivers across Yunnan. Based on extensive field surveys during 2021–2024 with 548 MEBF plots, this study employed the Unweighted Pair Group Method for forest community classification and Non-metric Multidimensional Scaling for ordination and interpretation of community–environment association. A total of 3517 vascular plant species were recorded in the plots, including 1137 tree species, 1161 shrubs, and 1219 herbs. Numerical classification divided the plots into 3 alliance groups and 24 alliances: (1) Castanopsis–Schima (Lithocarpus) Forest Alliance Group (16 alliances), predominantly distributed west of 102°E in central-south and southwest Yunnan; (2) Castanopsis–Machilus (Beilschmiedia) Forest Alliance Group (6 alliances), concentrated east of 101°E in southeast Yunnan with limited latitudinal range; (3) Castanopsis–Camellia Forest Alliance Group (2 alliances), restricted to higher-elevation mountainous areas within 103–104° E and 22.5–23° N. Climatic variation accounted for 81.1% of the species compositional variation among alliance groups, with contributions of 83.5%, 57.6%, and 62.1% to alliance-level differentiation within alliance groups 1, 2, and 3, respectively. Precipitation days in the driest quarter (PDDQ) and precipitation seasonality (PS) emerged as the strongest predictors of community differentiation at both alliance group and alliance levels. Topography and soil features significantly influenced alliance differentiation in Groups 2 and 3. Collectively, the interaction between the monsoon climate and topography dominate the spatial differentiation of MEBF communities in Yunnan. Full article

This study addressed a significant and relevant issue, specifically the production of high-quality fruit planting material linked to an economically viable nursery operation. The process considered both the pedo-climatic conditions of the region where the fruit planting material was cultivated and the technological elements utilized. The objective of this research was to gather information regarding the necessity and effectiveness of implementing localized irrigation for plum trees in the nursery in the context of various fertilization treatments. It also aimed to investigate the variations in leaf area among Cacanska Lepotica and Stanley plum cultivars subjected to various irrigation (non-irrigated control, 10 mm, 20 mm, and 30 mm) and fertilization (unfertilized control, N₈P₈K₈, N₁₆P₁₆K₁₆, and N₂₄P₂₄K₂₄) methods. The study was conducted within a private nursery situated in the northwest region of Romania using a 4 × 2 × 4 split-split-plot design with five replications. This research took place in the summer of 2024, in the second field of the nursery during the growth stage of grafted trees. The implementation of various NPK fertilization methods (8%, 16%, and 24%) led to enhancements in leaf surface developments (increased by 6.53–16.14% compared to the control). The application of fertilization ranging from 8 to 16% and subsequently from 16 to 24% was effectively absorbed by the plum trees, resulting in a substantial growth of 180–226 cm². Irrigation with 30 mm generated significant increases in the leaf area of 4.42–14.27% compared to the control. To obtain optimal yields of grafted trees, it is advisable to utilize a combination of irrigation and NPK fertilization. To promote the appropriate growth and development of the trees, it is essential to monitor the soil moisture levels and to implement irrigation during times of water shortage when the trees exhibit heightened water usage. The research findings indicated that both cultivars experienced similar advantages from 24% NPK fertilization and 30 mm of irrigation; therefore, the implementation of the aforementioned technological elements is strongly recommended. Full article

Ocean ecosystem services provisioning is driven by phytoplankton, which form the base of the ocean food chain in aquatic ecosystems and play a critical role as the Earth‘s carbon sink. Phytoplankton is highly sensitive to temperature, making it vulnerable to the effects of temperature variations. The aim of this research was to develop and test a workflow analysis to monitor the impact of sea surface temperature (SST) on phytoplankton biomass and primary production by combining field and remote sensing data of Chl-a and net primary production (NPP) (as proxies of phytoplankton biomass). The tropical zone was used as a case study to test the procedure. Firstly, machine learning algorithms were applied to the field data of SST, Chl-a and NPP, showing that the Random Forest was the most effective in capturing the dataset’s patterns. Secondly, the Random Forest algorithm was applied to MODIS SST images to build Chl-a and NPP time series. The time series analysis showed a significant increase in SST which corresponded to a significant negative trend in Chl-a concentrations and NPP variation. The recurrence plot of the time series revealed significant disruptions in Chl-a and NPP evolutions, potentially linked to El Niño–Southern Oscillation (ENSO) events. Therefore, the analysis can help to highlight the effects of temperature variation on Chl-a and NPP, such as the long-term evolution of the trend and short perturbation events. The methodology, starting from local studies, can support broader spatial–temporal-scale studies and provide insights into future scenarios. Full article

In this paper, we study the geomagnetic storm that occurred on 23–24 April 2023. We present variations in the values of interplanetary magnetic field (IMF-Bz), solar wind parameters (Vsw, Nsw, Tsw, and Psw), geomagnetic index (SYM-H), and vertical total electron content (VTEC) obtained from 18 GPS-TEC stations situated in equatorial, mid-latitude, and high-latitude regions. We analyze the variations in total electron content (TEC) before, during, and after the storm using VTEC plots, dTEC% plots, and global ionospheric maps for each GNSS receiver station, all referenced to universal time (UT). Our results indicate that GNSS receiver stations located at high latitudes detected an increase in ionospheric density during the main phase and a decrease during the recovery phase. In contrast, stations in equatorial and mid-latitude regions detected a decrease in ionospheric density during the main phase and an increase during the recovery phase. Large dTEC% values ranging from −80 to 190 TECU were observed a few hours before and during the storm period (23–24 April 2023); these can be compared to values ranging from −10 to 20 TECU on the day before (22 April 2023) and the day after (25 April 2023). Notably, higher dTEC% values were observed at stations in high and middle latitudes compared to those in the equatorial region. As the storm progressed, the TEC intensification observed on global ionospheric maps appeared to shift from east to west. A detailed analysis of these maps showed that equatorial and low-latitude regions experienced larger spatial and temporal TEC variations during the storm period compared to higher-latitude regions. Full article

Real-time monitoring of soil health parameters is crucial for efficient use of resources, improving agricultural productivity, and sustainability. Traditional soil analysis methods, although accurate, are time-consuming and lack the spatial and temporal resolution necessary for dynamic agricultural environments. Recent advancements in sensor technology offer promising alternatives, enabling real-time, in situ monitoring of key soil health indicators. This study details the deployment and validation of novel Sensor-in-Field probes at the Donald Danforth Plant Science Center Farm in Missouri, U.S., in a winter wheat plot. Three Sensor-in-Field probes were evaluated for their ability to measure nitrate (NO₃), ammonium (NH₄), soil organic matter (SOM), carbonaceous soil minerals (CSMs), soil volumetric density (SVD), soil hydration state (SHS), and total soil carbon (TSC) over a 28-day period. The probes’ coefficients of variation were well within acceptable limits (<20%) for all parameters. The measured metrics averaged 0.05% ± 0.001 and 1.92% ± 0.02 for CSMs and SOM, respectively, while TSC was 1.18% ± 0.15. For the nutrients, the measured NO₃ and NH₄ values were 4.44 ppm ± 0.37 and 2.78 ppm ± 0.22, respectively. The accuracy of the soil probes was validated at a certified traditional soil analysis laboratory. Three samples were collected at three different time points and analyzed. Bland–Altman analysis showed <$\pm$ 10% difference between the soil probes and traditional lab analysis for CSMs, SOM, and TSC, while t-test analysis reported p-values > 0.005 for NO₃, NH₄, and SHS/SVD, indicating non-significant differences between the probes and traditional soil analysis methods. Full article

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach. Full article

Antecedent soil moisture is a critical driver of hydrological and erosive processes, directly affecting runoff generation and soil loss. An accurate assessment of soil water content (SWC) variability is therefore essential for sustainable land and water management, particularly in arid and semiarid regions. This study explores the use of two emerging nuclear techniques, cosmic ray neutron sensors (CRNS) and proximal gamma ray spectroscopy (PGRS), to monitor SWC at the field scale in a semiarid agricultural field in NE Spain. Changes in soil moisture induced by a 16 mm rainfall event were monitored to evaluate the sensitivity and response of both techniques under dry and wet conditions. A stationary CRNS, located in the centre of the study field, recorded neutron counts at hourly intervals over a two-week period. Complementary PGRS surveys were conducted before and after the rainfall event, including (i) stationary measurements at the four corners of a 20 × 20 m plot, and (ii) mobile stop-and-go measurements along ten transects across the plot, with a spatial resolution of one metre. The results captured clear temporal dynamics in SWC, inferred from neutron count variations, as well as significant differences in ⁴⁰K (cps) measurements, between dry and wet conditions. These differences were observed when comparing the data from both stationary and mobile surveys conducted before and after the event. The integration of CRNS and PGRS offers complementary insights into scale, temporal dynamics and spatial variability, validating and highlighting the potential of these sensors for soil moisture monitoring. Both techniques demonstrated high sensitivity to variations in soil water content, and their complementary capabilities offer a robust, multi-scale approach with clear applications for precision agriculture and soil conservation. Full article

Enhancing nitrogen use efficiency (NUE) through optimized application methods can benefit agronomic productivity and environmental sustainability. This study examined three nitrogen application strategies, flat rate, soil-based sensing, and remote sensing-based prescription maps, for corn in southeast South Dakota, USA. Soil-based sensing utilized an electrical conductivity (EC) sensor while the normalized difference vegetation index (NDVI) was extracted from remote sensing data using Sentinel-2 images to create different zones. In the flat-rate method, nitrogen is applied uniformly at all plots, regardless of field variations. On the other hand, the sensor-based methods recommended variable rates of nitrogen applications to address field variations. The results of the present study showed that remote sensing-based methods significantly identify field variations as different zones (p < 0.05). The remote sensing-based method improved NUE compared to the flat-rate method, with increases of 2.21, 29.24, 29.6, and 82.09% in zones 1, 2, 3, and 4, respectively. However, adjusting the spatial and temporal nitrogen requirement rates using a soil-based sensor was difficult. The findings suggest remote sensing-based method can offer nitrogen optimization by incorporating in-season environmental variability, enhancing agronomic efficiency and sustainability. Full article