Search Results (2,142)

The forests in China’s temperate semi-arid region play a significant role in water conservation, carbon storage, and biodiversity protection. An accurate estimation of their aboveground biomass (AGB) is crucial for assessing key ecological characteristics, such as forest carbon storage capacity, biodiversity, and ecological productivity. This provides a scientific basis for forest resource management and ecological conservation in this region. In this study, we extract 17 features related to the dominant species (Larix gmelinii and Betula platyphylla), including 7 vegetation indices derived from remote sensing data, 14 indices from 7 satellite bands, and 3 forest site characteristics. We then analyze the correlations between the AGB and these features. We compare the performance of AGB estimation models using linear regression (LR), polynomial regression (PR), ridge regression (RR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and random forest regression (RFR). The results show that for Larix gmelinii, the Landsat 8 bands TM4 and TM7 have a greater degree of correlation with the AGB than the other features, while for Betula platyphylla, bands TM3 and TM4 show a greater degree of correlation with the AGB, and elevation has a weaker correlation with the AGB. Although the linear regression (LR) demonstrates certain advantages for AGB estimation, particularly when the AGB values range from 40 to 70 t/ha, the RFR outperforms in overall performance, with estimation accuracies reaching 85% for Betula platyphylla and 89% for Larix gmelinii. This study reveals that both the species and environmental characteristics may significantly influence the selection of the remote sensing features for AGB estimation, and the choice of algorithm for model optimization is critical. This study innovatively extracts the features related to the dominant species in temperate forests, analyses their relationships with environmental factors, and optimizes the AGB estimation model using advanced regression techniques, offering a method that can be applied to other forest regions as well. Full article

Sewage sludge biochar (SSB) is an innovative environmental material with remediation capabilities and significant potential for soil enhancement. This study aimed to accurately assess the dual regulatory effects of SSB on plant growth and soil quality. We conducted potting experiments with ryegrass and cosmos to analyze the impacts of SSB on plant growth, soil quality, and microbial communities. The partial least squares path model (PLS-PM) analysis was employed to elucidate the intrinsic relationships between SSB application and soil environmental factors, microbial communities, and plant growth. The results indicated that the application of SSB significantly enhanced the growth of ryegrass and cosmos, improved the soil quality, and increased the quantity of soil beneficial bacteria in the inter-root soil microbial communities. The addition of 9% and 3% (w w⁻¹) SSB resulted in the most substantial growth of ryegrass and cosmos, with aboveground biomass increasing 68.97% and 68.12%, respectively, and root biomass increasing by 49.87% and 45.14%. PLS path analysis revealed that SSB had a significant effect on the number of bacteria, which also played an important role in soil environmental factors such as pH and conductivity. This study provides a scientific basis for the utilization of sludge resources, green agriculture, and soil improvement. Additionally, it offers technical support for optimizing the application strategy of sludge biochar. Full article

Alfalfa (Medicago sativa), an important leguminous forage crop, is valued for its high nutritional content, substantial yield, palatability, and broad adaptability. Drought is among the most significant environmental constraints on alfalfa growth, particularly in the karst regions of southwestern China. In this study, we conducted pot experiments to investigate the growth and physiological responses of seven alfalfa varieties introduced into the karst region of Guizhou under drought conditions. The results revealed that drought stress markedly reduced both plant height and aboveground biomass accumulation. Moreover, under drought stress, these alfalfa varieties exhibited increased root length, root surface area, and root tip number; elevated protective enzyme activities; and decreased levels of hydrogen peroxide (H₂O₂) and malondialdehyde (MDA), thereby maintaining relatively higher water content. Each of the seven varieties displayed distinct growth and physiological adaptation mechanisms under drought stress. Integrating principal component analysis and membership function analysis, we ranked the drought resistance of these alfalfa varieties from highest to lowest as follows: Crown > WL525 > Colosseo > Victoria > PANGO > Giant 801 > Dimitra. These findings provide valuable insights for introducing drought-resistant alfalfa varieties into karst regions of southwestern China and offer guidance for breeding and cultivation strategies across various environmental conditions. Full article

The forest root-shoot ratio (R/S) is an important indicator of the structure and function of forest ecosystems. It reflects the adaptive strategies of plants to environmental changes, and its pattern of change along the latitudinal gradient is of great significance for understanding the response of forest ecosystems to environmental changes. Although numerous studies have addressed the relationship between climate, soil conditions, and the ratio of below-ground biomass to above-ground biomass (R/S) at the local scale, the pattern of R/S variations along the latitudinal gradient in different types of forests, as well as the dominant factors, remain unclear. This study, based on field surveys and literature collected from 2005 to 2020 on 384 planted forests and 541 natural forests in China, investigates the patterns of forest root-shoot ratio variation along latitudinal gradients in planted and natural forests. The study demonstrated a positive correlation between forest R/S ratio and increasing latitudinal gradients across different forest types, including planted and natural forests (p < 0.001). The results demonstrated a negative correlation between R/S in both planted and natural forests and mean annual temperature, annual precipitation and soil phosphorus content. Conversely, a positive correlation was observed between R/S and soil nitrogen content and soil pH. It can be observed that plantation forests are more susceptible to alterations in forest factors than natural forests. Latitudinal patterns can not only directly affect the R/S of planted and natural forests, but also affect forest R/S by influencing climate and forest factors and the interactions of the factors together. Our study distinguishes the pattern of R/S changes along the latitudinal gradient in planted and natural forests and its influencing factors. These findings are important for understanding the pattern changes in different forest ecosystems and provide a theoretical basis for efficiently guiding forest management. Full article

Chinese milk vetch (CMV), as a typical green manure in southern China, plays an important role in improving soil quality and partially substituting nitrogen chemical fertilizers for rice production. Accurately estimating the aboveground biomass (AGB) of CMV is crucial for quantifying the biological nitrogen fixation amount (BNFA) and assessing its viability as a nitrogen fertilizer alternative. However, the traditional estimation methods have low efficiency in field-scale evaluations. Recently, unmanned aerial vehicle (UAV) remote sensing technology has been widely adopted for AGB estimation. This study utilized UAV-based multispectral and RGB imagery to extract spectral (Sp), textural (Tex), and structural features (Str), comparing various feature combinations in AGB estimation for CMV. The results indicated that the fusion of spectral, textural, and structural features indicated optimal estimation performance across all feature combinations, resulting in R² values of 0.89 and 0.83 for model cross-validation and spatial transferability validation, respectively. The inclusion of textural and spectral features notably improved AGB estimation, indicated an increase of 0.15 and 0.14 in R² values for model cross-validation and spatial transferability validation, respectively, compared with relying on spectral features only. Estimation based exclusively on structural features resulted in R² values of 0.65 and 0.52 for model cross-validation and spatial transferability validation, respectively. The present study establishes a rapid and extensive approach to evaluate the BNFA of CMV at the full blooming stage utilizing the optimal AGB estimation model, which will provide an effective calculation method for chemical fertilizer reduction. Full article

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements of Diameter at Breast Height with height (DBH and H), and optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity in AGB estimation and are labor-intensive. Recent advancements in remote sensing technologies, predominantly Light Detection and Ranging (LiDAR), offer potential improvements in accurate AGB estimation and ecological monitoring. Nonetheless, there is limited research on the combined use of UAS (Uncrewed Aerial System) and Backpack-LiDAR technologies for detailed forest biomass. Thus, our study aimed to estimate AGB at the plot level for Picea crassifolia forests in eastern Qinghai, China, by integrating UAS-LiDAR and Backpack-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed to segment the point clouds from the Backpack-LiDAR, detect seed points and calculate the DBH of individual trees. After that, using these initial seed point files, we segmented the individual trees from the UAS-LiDAR data by employing the Point Cloud Segmentation (PCS) method and measured individual tree heights, which enabled the calculation of the observed/measured AGB across three specific areas. Furthermore, advanced regression models, such as Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Regression (SVR), are used to estimate AGB using integrated data from both sources (UAS and Backpack-LiDAR). Our results show that: (1) Backpack-LiDAR extracted DBH compared to field extracted DBH shows about (R² = 0.88, RMSE = 0.04 m) whereas UAS-LiDAR extracted height achieved the accuracy (R² = 0.91, RMSE = 1.68 m), which verifies the reliability of the abstracted DBH and height obtained from the LiDAR data. (2) Individual Tree Segmentation (ITS) using a seed file of X and Y coordinates from Backpack to UAS-LiDAR, attaining a total accuracy F-score of 0.96. (3) Using the allometric equation, we obtained AGB ranges from 9.95–409 (Mg/ha). (4) The RF model demonstrated superior accuracy with a coefficient of determination (R²) of 89%, a relative Root Mean Square Error (rRMSE) of 29.34%, and a Root Mean Square Error (RMSE) of 33.92 Mg/ha compared to the MLR and SVR models in AGB prediction. (5) The combination of Backpack-LiDAR and UAS-LiDAR enhanced the ITS accuracy for the AGB estimation of forests. This work highlights the potential of integrating LiDAR technologies to advance ecological monitoring, which can be very important for climate change mitigation and sustainable environmental management in forest monitoring practices. Full article

Intercropping of arable crops might provide yield benefits over monocrops. To assess the effect of sowing date and sowing ratio on biomass formation and competition over time, a two-year field experiment with wheat and pea plants was performed in Eastern Austria with two sowing times (autumn vs. spring) and with the following substitutive wheat–pea sowing ratios (%:%): 100:0, 75:25, 50:50, 25:75, 12.5:87.5 and 0:100. For both crops, facultative cultivars were used. Compared to spring-sowing, autumn-sowing resulted in earlier development of plants, taller plants, faster soil coverage, a higher crop growth rate up to mid-May in the first year and early June in the second year, more above-ground dry matter production and in the second year also in a higher land equivalent ratio (LER) of intercrops. Sowing ratios affected absolute and relative growth rates of wheat and pea plants. Wheat, which was generally the stronger partner in the intercrops, out-competed pea plants in all intercrops in the first year due to a higher early crop growth rate and in the second year, when the monocrop biomass of wheat was lower than that of pea plants, even in the intercrops with lower wheat and higher pea share. All intercrops resulted in a yield advantage compared to the monocrops as indicated by the LER. At final harvest, this yield advantage was over both sowing times and all four intercropping ratios 14% in the first and 10% in the second year. The competitive abilities of individual crops in mixtures, as indicated by the partial LER, were not affected by the sowing time. Full article

Airborne LiDAR (ALS) data have been extensively utilized for aboveground biomass (AGB) estimation; however, the high acquisition costs make it challenging to attain wall-to-wall estimation across large regions. Some studies have leveraged ALS data as intermediate variables to amplify sample sizes, thereby reducing costs and enhancing sample representativeness and model accuracy, but the cost issue remains in larger-scale estimations. Satellite LiDAR data, offering a broader dataset that can be acquired quickly with lower costs, can serve as an alternative intermediate variable for sample expansion. In this study, we employed a three-stage up-scaling approach to estimate forest AGB and introduced a method for quantifying estimation uncertainty. Based on the established three-stage general-hierarchical-model-based estimation inference (3sGHMB), an RK-3sGHMB inference method is proposed to make use of the regression-kriging (RK) method, and then it is compared with conventional model-based inference (CMB), general hierarchical model-based inference (GHMB), and improved general hierarchical model-based inference (RK-GHMB) to estimate forest AGB and uncertainty at both the pixel and forest farm levels. This study was carried out by integrating plot data, sampled ALS data, wall-to-wall Sentinel-2A data, and airborne P-SAR data. The results show that the accuracy of CMB ($R_{adj}^2$ = 0.37, RMSE = 33.95 t/ha, EA = 63.28%) is lower than that of GHMB ($R_{adj}^2$ = 0.38, RMSE = 33.72 t/ha, EA = 63.53%), while it is higher than that of 3sGHMB ($R_{adj}^2$ = 0.27, RMSE = 36.58 t/ha, EA = 60.43%). Notably, RK-GHMB ($R_{adj}^2$ = 0.60, RMSE= 27.07 t/ha, EA = 70.72%) and RK-3sGHMB ($R_{adj}^2$ = 0.55, RMSE = 28.55 t/ha, EA = 69.13%) demonstrate significant accuracy enhancements compared to GHMB and 3sGHMB. For population AGB estimation, the precision of the proposed RK-3sGHMB (p = 94.44%) is the highest, providing that there are sufficient sample sizes in the third stage, followed by RK-GHMB (p = 93.32%) with sufficient sample sizes in the second stage, GHMB (p = 90.88%), 3sGHMB (p = 88.91%), and CMB (p = 87.96%). Further analysis reveals that the three-stage model, considering spatial correlation at the third stage, can improve estimation accuracy, but the prerequisite is that the sample size in the third stage must be sufficient. For large-scale estimation, the RK-3sGHMB model proposed herein offers certain advantages. Full article

Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring the global carbon cycle. However, accurate estimation of forest AGB remains a significant challenge, especially when integrating multi-source remote sensing data, and the effects of different feature combinations for AGB estimation results are unclear. In this study, we proposed a method for estimating forest AGB by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), and the Advanced Land Observing Satellite digital elevation model (ALOS DEM), and field survey data. The continuous tree height (TH) feature was derived using GF-7 stereo imagery and the ALOS DEM. Spectral features were extracted from S1 and S2, and topographic features were extracted from the ALOS DEM. Using these features, 15 feature combinations were constructed. The recursive feature elimination (RFE) method was used to optimize each feature combination, which was then input into the extreme gradient boosting (XGBoost) model for AGB estimation. Different combinations of features used to estimate forest AGB were compared. The best model was selected for mapping AGB distribution at 30 m resolution. The outcomes showed that the forest AGB model was composed of 13 features, including TH, topographic, and spectral features extracted from S1 and S2 data. This model achieved the best prediction performance, with a determination coefficient (R²) of 0.71 and a root mean square error (RMSE) of 18.11 Mg/ha. TH was found to be the most important predictive feature, followed by S2 optical features, topographic features, and S1 radar features. Full article

The timely and precise estimation of crop aboveground biomass (AGB) is crucial for evaluating crop development and forecasting yields. The objective is to examine the differences, advantages, and limitations between time series parameters and single-time-phase indicators derived from various vegetation indices in AGB estimation. Moreover, we aim to quantitatively investigate and elucidate the impact of the topographic and geographic conditions of the study region on the estimation of highland barley AGB. Results indicate that AGB simulations utilizing time series parameters from vegetation index time series (VI-TS) curves yield satisfactory results for all three VIs, with the exception of the Normalized Difference Vegetation Index (NDVI), which encounters saturation issues. The performance metrics are as follows: the Enhanced Vegetation Index (EVI) (R² = 0.73, RMSE = 20.24 g/m²), the Soil-Adjusted Vegetation Index (SAVI) (R² = 0.67, RMSE = 20.97 g/m²), and the Normalized Difference Mountain Vegetation Index (NDMVI) (R² = 0.54, RMSE = 24.92 g/m²). The inclusion of our quantitative terrain factor improves the simulation accuracies of NDVI, SAVI, and NDMVI. Overall, the terrain factor has a beneficial impact on the highland barley AGB simulation outcomes. This study establishes a foundational framework for the timely and precise estimation of highland barley biomass, crucial for monitoring agricultural production in plateau mountainous regions. Full article

Consistent and accurate data on forest biomass and carbon dynamics are essential for optimizing carbon sequestration, advancing sustainable management, and developing natural climate solutions in various forest ecosystems. This study quantifies the forest biomass in designated forests based on GEDI LiDAR datasets with a unique compartment-level monitoring of unexplored hilly areas of Mansehra. The integration of multisource explanatory variables, employing machine learning models, adds further innovation to the study of reliable above ground biomass (AGB) estimation. Integrating Landsat-9 vegetation indices with ancillary datasets improved forest biomass estimation, with the random forest algorithm yielding the best performance (R² = 0.86, RMSE = 28.03 Mg/ha, and MAE = 19.54 Mg/ha). Validation with field data on a point-to-point basis estimated a mean above-ground biomass (AGB) of 224.61 Mg/ha, closely aligning with the mean ground measurement of 208.13 Mg/ha (R² = 0.71). The overall mean AGB model estimated a forest biomass of 189.42 Mg/ha in the designated moist temperate forests of the study area. A critical deficit in the carbon sequestration potential was analysed, with the estimated AGB in 2022, at 19.94 thousand tons, with a deficit of 0.83 thousand tons to nullify CO₂ emissions (20.77 thousand tons). This study proposes improved AGB estimation reliability and offers insights into the CO₂ sequestration potential, suggesting a policy shift for sustainable decision-making and climate change mitigation policies. Full article

Polymer-coated controlled-release fertilizers’ (CRFs) unique nutrient release mechanism has the potential to mitigate the leaching of mobile soil nutrients, such as nitrate-nitrogen (NO₃-N). The study aimed to evaluate the capacity of a polymer-coated CRFs to maintain maize (Zea mays L.) crop growth/health indicators and production goals, while reducing NO₃-N leaching risks compared to conventional (CONV) fertilizers in North Florida. Four CRF rates (168, 224, 280, 336 kg N ha⁻¹) were assessed against a no nitrogen (N) application and the current University of Florida Institute for Food and Agricultural Sciences (UF/IFAS) recommended CONV (269 kg N ha⁻¹) fertilizer rate. All CRF treatments, even the lowest CRF rate (168 kg N ha⁻¹), produced yields, leaf tissue N concentrations, plant heights, aboveground biomasses (AGB), and leaf area index (LAI) significantly (p < 0.05) greater than or similar to the CONV fertilizer treatment. Additionally, in 2022, the CONV fertilizer treatment resulted in increases in late-season movement of soil NO₃-N into highly leachable areas of the soil profile (60–120 cm), while none of the CRF treatments did. However, back-to-back leaching rainfall (>76.2 mm over three days) events in the 2023 growing season masked any trends as NO₃-N was likely completely flushed from the system. The results of this two-year study suggest that polymer-coated CRFs can achieve desirable crop growth, crop health, and production goals, while also having the potential to reduce the late-season leaching potential of NO₃-N; however, more research is needed to fully capture and quantify the movement of NO₃-N through the soil profile. Correlation and Principal Component Analysis (PCA) revealed that CRF performance was significantly influenced by environmental factors such as rainfall and temperature. In 2022, temperature-driven nitrogen release aligned with crop uptake, supporting higher yields and minimizing NO₃-N movement. In 2023, however, rainfall-driven variability led to an increase in NO₃-N leaching and masked the benefits of CRF treatments. These analyses provided critical insights into the relationships between environmental factors and CRF performance, emphasizing the importance of adaptive fertilizer management under varying climatic conditions. Full article

Nitrogen (N) and phosphorus (P) are key limiting factors for carbon (C) fluxes in artificial grasslands. The impact of their management on ecosystem C fluxes, including net ecosystem productivity (NEP), ecosystem respiration (ER), and gross ecosystem productivity (GEP) in the Loess Plateau is unclear. An experiment was conducted to study changes in these C fluxes with varying N (0, 5, 10, 15, and 20 g N m⁻²) and P (0 and 10 g P m⁻²) additions from 2022 to 2023 in a lucerne plantation. Results showed that N addition positively influenced NEP and GEP in the first year after planting with N addition at the rate of 10 g N m⁻² was optimal for C assimilation, but it had negligible effect on ER in both two years in the studied lucerne (Medicago sativa ssp. sativa) plantation. Phosphorus addition significantly increased ER and stimulated GEP, resulting in an increasing effect on NEP only at the early stage after planting. The addition of N and P enhanced soil N and P availability and further improved the leaf chemical stoichiometry characteristics, leading to changes in biomass distribution. The more belowground biomass under N addition and more aboveground production under P addition resulted in different responses of ecosystem C fluxes to N and P addition. The results suggest that the effects of N and P fertilization management on the ecosystem C cycle may be largely dependent on the distribution of above- and belowground plant biomass in the artificial grassland ecosystem. Full article

A novel time delay Lotka–Volterra (TDLV) model was developed by extending the concept of time delay from integer order to fractional order. The TDLV model was constructed to simulate the dynamics of aboveground biomass per individual of three dominant herbaceous plant species (Leymus chinensis, Agropyron cristatum, and Stipa grandis) in the typical grasslands of Inner Mongolia. Comparative analysis indicated that the TDLV model outperforms candidate models, such as Logistic, GM(1,1), GM(1,N), DGM(2,1), and Lotka–Volterra model, in terms of all fitting criteria. The results demonstrate that interspecies competition exhibits clear feedback and suppression effects, with Leymus chinensis playing a central role in regulating community dynamics. The system is locally stable and eventually converges to an equilibrium point, though Stipa grandis maintains relatively low biomass, requiring further monitoring. Time delays are prevalent in the system, influencing dynamic processes and causing damping oscillations as populations approach equilibrium. Full article

A two-year field trial (2021–2023) was conducted to evaluate the impacts of cover crop (CC) inclusion (cereal rye, crimson clover, mixtures of cereal rye and crimson clover, and fallow control) and nitrogen (N) fertilization (0, 22, 45, 90, 135, and 180 kg N ha⁻¹) in cotton production in sandy soils. Cover crops were planted in October and terminated two weeks before cotton planting in May. The N was applied in split applications. Cover crop aboveground biomass was collected, oven dried, and weighed, and then used for C and N analyses. Soils were sampled at CC termination and analyzed for biogeochemical properties. Cotton lint yields and agronomic nutrient use efficiency (aNUE) were estimated. The CC mixtures provided higher organic C and N inputs as residue returns than individual species. Integrating CCs had limited impacts on measured soil properties. Integrating CCs resulted in positive, neutral, and adverse effects on lint yield and aNUE depending on species and growing seasons. Applying N at 22 kg ha⁻¹ increased lint yields in 2022, while higher rates did not improve the yields further. Similar patterns of impacts were observed at the N rate of 45 kg ha⁻¹ in 2023. The results indicated that integrating CC mixtures can favor long-term C and N sequestration in sandy soils. However, optimal management is essential to realize their benefits. Relevant research to better understand the decomposition of their residues would be beneficial in improving the management of desirable outcomes. Full article