Search Results (346)

Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs between high-yielding ponded and aerobic rice, (ii) validate the feasibility of using the squared simplified canopy chlorophyll content index (SCCCI²) for N uptake estimates, and (iii) explore the SCCCI² and similar chlorophyll-sensitive indices for grain quality monitoring. Multispectral images were collected from an unmanned aerial vehicle during both rice-growing seasons. Above-ground biomass and nitrogen (N) uptake were measured at panicle initiation (PI). The performance of single-vegetation-index models in estimating rice N uptake, as previously published, was assessed. Yield and grain quality were determined at harvest. Results showed that canopy reflectance in the visible and near-infrared regions differed between aerobic and ponded rice early in the growing season. Chlorophyll-sensitive indices showed lower values in aerobic rice than in the ponded rice at PI, despite having similar yields at harvest. The SCCCI² model (RMSE = 20.52, Bias = −6.21 Kg N ha⁻¹, and MAPE = 11.95%) outperformed other models assessed. The SCCCI², squared normalized difference red edge index, and chlorophyll green index correlated at PI with the percentage of cracked grain, immature grain, and quality score, suggesting that grain milling quality parameters could be associated with N uptake at PI. This study highlights canopy reflectance differences between high-yielding aerobic (averaging 15 Mg ha⁻¹) and ponded rice at key phenological stages and confirms the validity of a single-vegetation-index model based on the SCCCI² for N uptake estimates in ponded and non-ponded rice crops. Full article

Evapotranspiration (ET) is an important part of agricultural water consumption, yet little is known about nocturnal evapotranspiration (ETN) patterns. An eddy covariance system was used to observe ET over five consecutive years (2020–2024) during the growing season in a dry farming area of the Loess Plateau. Daytime and nocturnal evapotranspiration were partitioned using the photosynthetically active radiation threshold to reveal the changing characteristics of ETN at multiple time scales and its control variables. The results showed the following: (1) In contrast to the non-significant trend in ETN on the diurnal and daily scales, monthly ETN dynamics exhibited two peak fluctuations during the growing season. (2) The contribution of ETN to ET exhibited seasonal characteristics, being relatively low in summer, with interannual variations ranging from 10.9% to 14.3% and an annual average of 12.8%. (3) The half-hourly ETN, determined by machine learning methods, was driven by a combination of factors. The main driving factors were the difference between surface temperature and air temperature (Ts-Ta) and net radiation (Rn), which have almost equivalent contributions. Regression analysis results suggested that Ta was the main factor influencing ETN/ET at the monthly scale. This study focuses on the nighttime water loss process in dry farming fields in Northwest China, and the results provide a basis for rational allocation and efficient utilization of agricultural water resources in arid regions. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. Full article

Fungi are a diverse and essential group that play crucial ecological roles. However, they remain understudied in tropical countries like Ecuador in terms of their forest or protected areas, particularly across diverse ecosystem zones such as seasonal forests and salt flats. This study aimed to inventory fungal diversity in two specific zones: the dry forest (DF) and the salt flat (SF) within the Reserva Ecologica Arenillas (REAR), located in El Oro, Ecuador. The results recorded 162 specimens representing 47 species belonging to 34 genera, identified morphologically. Although statistically significant, the difference in species richness and abundance between the dry forest and the salt flat was minimal, with the dry forest showing slightly higher values. Nonetheless, certain species were prevalent in both ecosystems, such as Cerrena hydnoides, Pycnoporus sanguineus, Hexagonia tenuis, and Chondrostereum sp., alongside four species with resupinate habit, all of them growing on decayed wood. The Shannon and Simpson indices were calculated to assess alpha diversity, revealing higher diversity in the DF. To evaluate differences in community composition between habitats, non-metric multidimensional scaling (NMDS) and permutational analysis of variance (PERMANOVA) were applied, indicating greater species turnover and dominance of specific taxa in the DF compared to the SF. These findings highlight the importance of the fungal diversity found in the REAR while also pointing to the need for more exhaustive monitoring and comparative studies with other wild or protected areas to fully understand and conserve this biodiversity. Full article

The production of winter wines in Southeastern Brazil represents a relatively recent but expanding viticultural approach, with increasing adoption across diverse wine-growing regions. This system relies on the double-pruning technique, which allows for the harvest of grapes during the dry and cooler winter season, favoring a greater accumulation of sugars, acids, and phenolic compounds. This study aimed to characterize the phenological stages, thermal requirements, yield, and fruit quality of the fine wine grape cultivars ‘Sauvignon Blanc’, ‘Merlot’, ‘Tannat’, ‘Pinot Noir’, ‘Malbec’, and ‘Cabernet Sauvignon’ under double-pruning management in a subtropical climate. The vineyard was established in 2020, and two production cycles were evaluated (2022/2023 and 2023/2024). Significant differences in the duration of phenological stages were observed among cultivars, ranging from 146 to 172 days from pruning to harvest. The accumulated thermal demand was higher in the first cycle, with a mean of 1476.9 growing degree days (GDD) across cultivars. The results demonstrate the potential of Vitis vinifera L. cultivars managed with double pruning for high-quality wine production under subtropical conditions, supporting the viability of expanding viticulture in the state of São Paulo. ‘Cabernet Sauvignon’ and ‘Sauvignon Blanc’ showed the highest yields, reaching 3.03 and 2.75 kg per plant, respectively, with productivity values of up to 10.8 t ha⁻¹. ‘Tannat’ stood out for its high sugar accumulation (23.4 °Brix), while ‘Merlot’ exhibited the highest phenolic (234.9 mg 100 g⁻¹) and flavonoid (15.3 mg 100 g⁻¹) contents. These results highlight the enological potential of the evaluated cultivars and confirm the efficiency of the double-pruning system in improving grape composition and wine quality in non-traditional viticultural regions. Full article

Sea level rise is an escalating threat to saltmarsh ecosystems as increased inundation can lead to decreased biomass, lowered productivity, and plant death. Another potential stressor is elevated nitrogen often brought into coastal regions via freshwater diversions. Nitrogen has a controversial impact on belowground biomass, potentially affecting saltmarsh stability. In this study, we examined the effects of inundation and nitrogen on common saltmarsh plants (Spartina alterniflora and Spartina patens) placed within two marsh organs (a collection of PVC pipes at different levels, the varied elevation levels expose the plants to different inundation amounts) located in the Pascagoula River, Mississippi, USA, with six rows and eight replicates in each row. We randomly fertilized four replicates in each row with 25 g/m² of NH4⁺-N every two-three weeks during the growing season in 2021 and 2022. We concurrently collected vegetative traits such as plant height and leaf count to better understand strategies saltmarshes utilize to maximize survival or growth. We harvested half of the vegetation in Year 1 and the remaining in Year 2 to evaluate the impact of inundation and nitrogen on above- and belowground biomass at different temporal scales. We developed Bayesian models that show inundation had a largely positive impact on S. alterniflora and a mostly negative impact S. patens, suggesting that S. alterniflora will adapt better to increasing inundation than S. patens. Additionally, fertilized plants from both species had higher aboveground biomass than non-fertilized plants for both years, with nitrogen addition only showing impact on belowground biomass in the long term. Our results highlight the importance of long-term study to facilitate more-informed restoration and conservation efforts in coastal wetlands while accounting for climate change and sea level rise. Full article

Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article

The long-term nature of and heterogeneity in industrialization has led to high formaldehyde (HCHO) concentrations with seasonal and regional variation in North China, and this is highly influenced by changes in meteorological and population conditions. Here, we analyzed the spatial and temporal distribution characteristics of tropospheric HCHO VCD (vertical column density) and their key drivers in North China from 2019 to 2023 based on the HCHO daily dataset from TROPOMI. The results showed that the spatial distribution of tropospheric HCHO VCD in North China presented similar variation characteristics in the past 5 years, with the highest in the center, followed by the east and the lowest in the west. Seasonal variations were characterized, with the highest tropospheric HCHO VCD concentrations in summer and the lowest ones in spring. In addition, the effects of meteorological elements on HCHO VCD were analyzed based on the ERA5 dataset, and the correlation of HCHO VCD with temperature and wind was strong. In contrast, the correlation with precipitation and surface solar radiation was low, and the effects were different between the growing and non-growing seasons (the growing season, i.e., March–November, is defined as the period when the plant or a part of it actually grows and produces new tissues, while the non-growing season refers to December–the following February). Population density is directly proportional to tropospheric HCHO VCD. In this study, a higher-resolution spatial and temporal distribution model of tropospheric HCHO VCD in North China is obtained based on TROPOMI, which effectively characterizes the driving factors of HCHO VCD. Our study provides an important reference for developing of air pollution control measures in North China. Full article

Cupressus funebris forests grow relatively fast and have a strong natural regeneration ability, showing great potential in carbon sequestration. Global warming has already had a significant impact on its distribution pattern. This study used the Maximum Entropy Model (MaxEnt) and the distribution data of Cupressus funebris communities to explore the contraction and expansion of the adaptive distribution of Cupressus funebris. The research results are as follows: The contemporary adaptive distribution area of Cupressus funebris is mainly located in the southern region of China, and the area of the adaptive distribution accounts for approximately 7.15% of the total land area. The main driving variables affecting the distribution of Cupressus funebris are annual precipitation, the minimum temperature of the coldest month, isothermality, temperature seasonality, carbonate content, and altitude. Among them, climate plays a dominant role in the distribution of this community. Under different carbon emission scenarios in the future, the adaptive distribution areas show an expansion trend, but most of the highly adaptive areas are shrinking and the changes are relatively significant. In the high emission pathway, the distribution area continues to expand in the north while gradually contracting in the southern regions. The community distribution shows a trend of migrating to higher latitudes and altitudes in northern regions, and there are significant non-linear characteristics in altitude migration under the scenario of intensified carbon emissions. This study provides theoretical guidance for the protection and management of Cupressus funebris forests and helps to improve the carbon sequestration capacity of the communities in the context of carbon neutrality. Full article

Forest aboveground biomass (AGB) serves as a crucial quantitative indicator that reflects the carbon sequestration capacity of forests, and accurately mapping AGB is pivotal for assessing forest ecosystem stability. However, mapping AGB in subtropical evergreen broadleaf forests in southern China presents challenges due to their complex canopy structure, stand heterogeneity, and spectral signal saturation. The phenological features reflecting seasonal vegetation dynamics are conducive to over-coming these challenges. By analyzing differential spectral reflectance patterns during the non-growing (Jan–Mar, Nov–Dec) versus growing (Apr–Oct) seasons, this study established a phenological feature-based methodology for improving AGB estimation in subtropical evergreen broadleaf forests. Subsequently, four time series vegetation indices (VI), namely NDVI, EVI2, NDPI, and IRECI were employed to extract phenological features (PFs) for mapping forest AGB using a multiple linear regression model (MLR), K-nearest neighbor model (KNN), support vector machine model (SVM), and random forest model (RF). The results demonstrated significant differences in Sentinel-2 spectral reflectance (740–1610 nm bands) between the growing and non-growing seasons. The PFs demonstrated the highest distance correlation coefficient (0.57), significantly outperforming other baseline feature types (0.44). Furthermore, seasonal changes in NDVI and NDPI were found to better reflect AGB accumulation in evergreen broadleaf forests compared to EVI2 and IRECI. Incorporating diverse PFs derived from all four VI significantly enhanced the accuracy of AGB mapping by yielding rRMSE values ranging from 21.01% to 25.06% and R² values ranging from 0.40 to 0.58. The results inferred that PFs can be considered a key factor for alleviating spectral signal saturation problems while effectively improving the accuracy of AGB estimation. Full article

The current available direct and indirect forage biomass estimation methods are prohibitive for producers because they are labor-intensive and time-consuming. Current literature states that (i) machine learning algorithms are promising in agriculture, and (ii) proximity and multispectral sensors can be employed to predict biomass. This research aimed to develop bermudagrass [Cynodon dactylon (L.) Pers.] biomass prediction models using the Random Forest regressor with laser, ultrasonic, multispectral sensors, precipitation, and N fertilization as input features. The prediction models—cultivar-specific and non-cultivar-specific—were developed using six bermudagrass cultivars, managed with four N rates, at four different locations, collecting data at 2, 4, and 6 weeks of bermudagrass regrowth (WOR) at two consecutive growing seasons (2018 and 2019). The 4 WOR, all-features, all-cultivars model had the highest performance when evaluating the model using ten-fold cross-validation (R² = 0.75, MAPE = 26.79%, RMSE = 1.0 Mg ha⁻¹), with the laser having the highest feature importance score (65.5%). However, the Greenfield cultivar-specific model benefited from removing the laser and ultrasonic readings from the training dataset, achieving R² = 0.68, MAPE = 29.95%, RMSE = 0.82 Mg ha⁻¹. Overall, the Random Forest regressor, proximity, and multispectral sensors proved to be efficient tools for developing effortless and efficient models to accurately predict bermudagrass biomass yield in Oklahoma. Full article

Pyroligneous acid (PA) or wood vinegar, a co-product of biomass pyrolysis, is thought to be beneficial for plant productivity and soils, with the potential to reduce otherwise harmful agrochemicals. Here, we review the evidence for the use of PA on plant growth and soil health parameters. The analysis includes 65 peer-reviewed studies with 171 (yield) and 123 (plant biomass) data sets, covering 33 different crops belonging to 6 plant groups. Significant positive, non-linear relationships between PA concentration, yield, and plant biomass were found at concentrations as low as 0.1%, with the optimum at around 0.5–1% and overall positive effects up to 6–11% (depending on the application type), but yield declines above these concentrations, suggesting herbicidal effects. Across the whole data set, yield and biomass increase by an average of 21% and 25%, respectively, and by an average of 31% at the optimum rate. The positive effect of PA is most pronounced for plant growth under sub-optimal conditions (salt, drought, and pathogens), while responses did not differ between plant groups. Soil organic matter content shows a small but significant positive response to PA application, but the amount of data is very small compared to the plant parameters. The major shortcomings identified include inconsistent measures of applied PA (amount and composition) and the short duration of experiments of typically only 1–2 growing seasons, which prevents analysis of long-term PA effects. Overall, the results of this review encourage further research on PA for sustainable agriculture. Full article

The global demand for transportation systems is growing due to the rise in passenger and cargo traffic, predicted to reach twice the current level by 2050. Although this growth signifies social and economic progress, its impact on energy consumption and greenhouse gas emissions cannot be overlooked. Developments in the transportation industry must align with advancements in emerging energy production systems. In this regards, UNSDG 7 advocates for “affordable and clean energy”, leading to a global shift towards the electrification of transport systems, sourcing energy from a mix of renewable and non-renewable resources. This paper proposes an integrated hybrid renewable energy system with grid connectivity to meet the electrical and thermal loads of a tourist complex, including an electric vehicle charging station. The analysis was carried on in nine locations with different weather conditions, with various components such as wind turbines, photovoltaic systems, diesel generators, boilers, converters, thermal load controllers, and battery energy storage systems. The proposed model also considers the effects of seasonal variations on electricity generation and charging connectivity. Sensitivity analysis has been carried on investigating the impact of variables on the techno-economic parameters of the hybrid system. The obtained results led to interesting conclusions. Full article

Water use efficiency (WUE) connects two key processes in terrestrial ecosystems: the carbon and water cycles. Thus, it is important to evaluate temporal and spatial changes in WUE over a prolonged period. The spatiotemporal variation characteristics of the WUE in the Mongolian Plateau from 1982 to 2018 were analyzed based on the net primary productivity (NPP), evapotranspiration (ET), temperature, precipitation, and soil moisture. In this study, we used remote sensing data and various statistical methods to evaluate the spatiotemporal patterns of water use efficiency and their potential influencing factors on the Mongolian Plateau from 1982 to 2018. In total, 27.02% of the region witnessed a significant decline in the annual WUE over the 37 years. Two abnormal surges in the WUE_Season (April–October) were detected, from 1997 to 1998 and from 2007 to 2009. The trend in the annual WUE in some broadleaf forest areas in the middle and northeast of the Mongolian Plateau reversed from the original decreasing trend to an increasing trend. WUE has shown strong resilience in previous analytical studies, whereas the WUE in the artificial vegetation area in the middle of the Mongolian Plateau showed weak resilience. WUE had a significant positive correlation with precipitation, soil moisture, and the drought severity index (DSI) but a weak correlation with temperature. WUE had strong resistance to abnormal water disturbances; however, its resistance to the effects of temperature and DSI anomalies was weak. The degree of interpretation of vegetation changes for WUE was higher than that for meteorological factors, and WUE showed weak resistance to normalized difference vegetation index (NDVI) disturbances. Delaying the start of the vegetation growing season had an increasing effect on WUE, and the interaction between phenological and meteorological vegetation factors had a non-linear enhancing effect on WUE. Human activities have contributed significantly to the increase in WUE in the eastern, central, and southern regions of the Mongolian Plateau. These results provide a reference for the study of the carbon–water cycle in the Mongolian Plateau. Full article