Aboveground Biomass Dynamics Research Articles

Trends in planted native tree biomass established in a tropical Andes city water basins

Studying tree growth processes helps us evaluate the success of restoration efforts in recovering ecosystem processes such as carbon accumulation. Here, we study aboveground biomass dynamics in four common‐endemic tree species from the tropical Andes (Quercus humboldtii, Retrophyllum rospigliosii, Citharexylum subflavescens, and Croton magdalenensis), established in the main water supply basins of Medellín, Colombia. We use measurements from 19 permanent plots from five censuses between 2010 and 2023 to develop biomass equations as a function of t for each species (B = f[t]) by combining tree growth models of diameter (D) as a function of time (t) and pre‐existing allometric equations of biomass (B) developed in the same site as a function of D and wood density (ρ). We found that, because of initially higher growth rates characteristic of species belonging to the fast‐growing end of the fast‐slow growth economic spectrum, C. magdalenensis and C. subflavescens were the species with higher biomass accumulation at 13 years (142 and 132 kg, respectively). However, the potential of the slow‐growing species (Q. humboldtii and R. rospigliosii) for carbon sequestration initiatives in the long term was highlighted. To our knowledge, this is the first study in the region examining the dynamics of aboveground biomass in Andean restoration programs. It combines growth models and allometric equations, which can be useful for measuring the success of the implementation of ecological restoration programs and evaluating the need to apply adaptive management strategies.

Active Remote Sensing Assessment of Biomass Productivity and Canopy Structure of Short-Rotation Coppice American Sycamore (Platanus occidentalis L.)

The short-rotation coppice (SRC) culture of trees provides a sustainable form of renewable biomass energy, while simultaneously sequestering carbon and contributing to the regional carbon feedstock balance. To understand the role of SRC in carbon feedstock balances, field inventories with selective destructive tree sampling are commonly used to estimate aboveground biomass (AGB) and canopy structure dynamics. However, these methods are resource intensive and spatially limited. To address these constraints, we examined the utility of publicly available airborne Light Detection and Ranging (LiDAR) data and easily accessible imagery from Unmanned Aerial Systems (UASs) to estimate the AGB and canopy structure of an American sycamore SRC in the piedmont region of North Carolina, USA. We compared LiDAR-derived AGB estimates to field estimates from 2015, and UAS-derived AGB estimates to field estimates from 2022 across four planting densities (10,000, 5000, 2500, and 1250 trees per hectare (tph)). The results showed significant effects of planting density treatments on LIDAR- and UAS-derived canopy metrics and significant relationships between these canopy metrics and AGB. In the 10,000 tph, the field-estimated AGB in 2015 (7.00 ± 1.56 Mg ha−1) and LiDAR-derived AGB (7.19 ± 0.13 Mg ha−1) were comparable. On the other hand, the UAS-derived AGB was overestimated in the 10,000 tph planting density and underestimated in the 1250 tph compared to the 2022 field-estimated AGB. This study demonstrates that the remote sensing-derived estimates are within an acceptable level of error for biomass estimation when compared to precise field estimates, thereby showing the potential for increasing the use of accessible remote-sensing technology to estimate AGB of SRC plantations.

Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data

Aboveground biomass (AGB) of plants is an agroecological indicator that can be used to monitor crop growth status and quantify biomass carbon stock in agricultural ecosystems. Although satellite remote sensing data and crop models are widely employed to estimate AGB dynamics, their application in small-scale research plots is often constrained by the unavailability of freely and timely high-resolution satellite imageries and the challenge of acquiring reliable model inputs like initial soil water and nitrogen content. This study integrated a crop growth model (APSIM-Wheat) and radiative transfer model (PROSAIL) to estimate AGB dynamics at a small plot scale (ca. 5 to 20 m2) within variety trials (ca. 1 to 2 ha) by assimilating trial-level remote sensing data into the hybrid APSIM-PROSAIL model. The APSIM-PROSAIL integration, developed by deriving PROSAIL input parameters from APSIM-Wheat output variables, was verified by evaluating its capacity to reproduce trial-level Sentinel-2 (S2) NDVI dynamics across 30 variety trials in 2020. The comparison of the simulated and observed trial-level S2 NDVI dynamics yielded an overall RRMSE of 18.2% (n = 646 observations), suggesting the potential of using observed S2 NDVI dynamics to constrain APSIM-PROSAIL and estimate environment variables in cropping systems. This constraint was subsequently assessed in 28 variety trials in 2021, where APSIM-PROSAIL inversely estimated unknown trial-specific variables including initial soil water and nitrogen content across the trials. Using the estimated initial soil status and the plot information including genotypes and management as sown, we demonstrated that this method could also facilitate accurate retrievals of plot-level AGB dynamics with an overall RRMSE of 20.9% (n = 976 measurements). The proposed APSIM-PROSAIL demonstrated the capability to accurately retrieve plot-level AGB dynamics and reproduce trial-level S2 NDVI dynamics across a broad and diverse range of variety trials in the Australian Wheatbelt, indicating its potential utility to facilitate biomass carbon stock assessment. It holds the potential as a tool to explore the relationships between canopy spectral information and crop traits in response to genotype-environment-management interactions in agricultural ecosystems. The method enables the examinations of within-field variabilities for crop traits through retrieving them at small spatial scales from coarse remote sensing data.

Capturing woody aboveground biomass historical change and potential under climate change using Landsat time-series for afforestation in dryland of China

Capturing long-term dynamics and the potential under climate change of woody aboveground biomass (AGB) is imperative for calculating and raising carbon sequestration of afforestation in dryland. It is always been a great challenge to accurately capture AGB dynamics of sparse woody vegetation mixed with grassland using only Landsat time-series, resulting in changing trajectory of woody AGB estimates cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was firstly selected and interannual time-series of composited SR was smoothed using S-G filter for each pixel, and then optimal machine learning algorithm was selected to estimate woody AGB time-series. Pixels that have reached AGB potential were detected based on the AGB changing trajectory, and the potential was spatial-temporal extended using random forest model combining environmental variables under current climate condition and CMIP6 climate models. Results show that: 1) minimum value composite based on NIRv during Jul.-Sep. is more capable of explaining woody AGB variation in dryland (R = 0.87, p < 0.01), and Random Forest (RF) model has the best performance in estimating woody AGB (R2 = 0.75, RMSE = 4.74 t·ha−1) among sis commonly used machine learning models. 2) Annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R2 = 0.97, p < 0.001) indicating explicit growth regularity of woody vegetation, which provides physiological foundation for determining woody AGB potential. 3) Woody AGB potential can be accurately simulated by RF combining environmental variables (R2 = 0.95, RMSE = 2.89 t·ha−1), and current woody AGB still has a potential of small increase, whereas the overall losses of woody AGB potential were observed in 2030, 2040 and 2050 under CMIP6 SSP-RCP scenarios.

How do short-term and long-term factors impact the aboveground biomass of grassland in Northern China?

The aboveground biomass (AGB) of grassland, a crucial indicator of productivity, is anticipated to widespread changes in key ecosystem attributes, functions and dynamics. Variations in grassland AGB have been extensively documented across various spatial and temporal scales. However, a precise method to disentangle long-term effects from short-term effects on grassland AGB and assess the attribution of explanatory factors for AGB change remains elusive. This study aimed to quantify the impact of key climatic factors, soil properties, and grazing intensity on grassland AGB changes, utilizing data spanning the 1980s and the 2000s in Northern China. The Co-regression model was explored to separate the long-term effects and short-term effects of grassland AGB, while the Generalized Linear Model (GLM) was utilized to analyze the contributions of key variables to AGB. This approach effectively avoids issues related to regression to the mean and mathematical coupling. The results revealed that the influence of climatic variables, soil texture and grazing intensity on grassland AGB changes could be decomposed into long-term, short-term and random effects. Long-term effects explained 73.6% of AGB variation, whereas short-term effect only accounted for 5.9% of AGB change. Additionally, the short-term effect was divided into direct and indirect effects, with the direct effect explaining 1.3% of AGB variation, and the indirect effect explained 4.6% of AGB dynamics. The relative importance of key variables in grassland AGB was assessed, identifying soil parameters and precipitation as the main driving factors in the study area. This study introduces a robust methodology to enhance model performance in distinguishing long-term and short-term effects on grassland AGB, contributing to the sustainable development of grassland ecology in similar regions.

Interannual precipitation variability dominates the growth of alpine grassland above-ground biomass at high elevations on the Tibetan Plateau

The impact of global climate change on mountainous regions with significant elevational gaps is complex and often unpredictable. In particular, alpine grassland ecosystems, are experiencing changes in their spatial patterns along elevational gradients, which increases their vulnerability to degradation. Therefore, a more detailed understanding of spatiotemporal changes in alpine grassland productivity along elevational gradients and an elevation-dependent characterization of the effects of climatic variables on grassland productivity dynamics are essential. Thus, we conducted a study in the Tibetan Plateau, where we collected 2251 above-ground biomass (AGB) observations collected from 1986 to 2020. Mean annual temperature (TMP), annual precipitation (PRE), interannual precipitation variability (CVP), and snowmelt (SNMM) were chosen as influential variables. Using the Random Forest algorithm, we generated an AGB raster dataset covering the period 1989–2020 based on earth observation data at 30 m resolution to examine the dynamics of alpine grasslands and their response to climate change with respect to elevation. The results showed that the AGB of alpine grassland on the Tibetan Plateau was 49.17 g/m2. We observed an increasing trend in grassland AGB at high elevations, with a growth rate of about 0.28 g/m2 per year within the interval of 3100–4800 m. However, above the elevation of approximately 4400–4600 m, we observed a decoupling trend between grassland AGB and TMP. Moreover, at most elevations, the proportion of maximum partial correlation coefficients for CVP, PRE, and SNMM surpassed that of TMP. We found the dominant role of precipitation variability on grassland AGB dynamics, with 22.80 % and 18.86 % for CVP+ and CVP-, respectively. The proportion of CVP+ did not vary much at different elevations, whereas the proportion of CVP- increased with elevation, varying between 12.85 and 30.25 %. In the future, precipitation on the Tibetan plateau is expected to increase, potentially reversing its original positive impact.

Spatio‐Temporal Dynamics of Aboveground Biomass in China's Oasis Grasslands Between 1989 and 2021

AbstractGrassland provides multiple ecosystem services and plays a key role in preventing desert encroachment and maintaining oasis stability. In China, the area of cropland in oases has expanded significantly in recent decades, which results in a rapid increase in agricultural water demand and encroachment on grassland subsistence space. However, our knowledge about how the expansion of cropland affects oasis grasslands remains limited. We used machine learning, temporal segmentation of spectral trajectories, and maximum covariance analysis to generate an annual oasis grassland aboveground biomass (AGB) data set at 30‐m resolution from 1989 to 2021 based on multiple remotely sensed and ground observation data sets, and investigated the dynamics of grassland AGB under cropland expansion in oases. We found that overall oasis grassland AGB increased significantly (0.3 gm−2 yr−1, P &lt; 0.01) during 1989–2021, but trends in AGB were not consistent across basins. In the Yellow River, Turpan Hami, Qaidam, and southern Altai Mountains River Basins, AGB was dominated by a significant increase. Conversely, in all basins in southern Xinjiang, AGB showed a decreasing trend due to the rapid cropland expansion. Spatially, cropland expansion and AGB dynamics were strongly coupled. In regions characterized by agricultural concentration, grassland AGB benefitted from resource spillover via edge effects. However, in downstream areas or those with a low proportion of cropland, where most grasslands are distributed, the relationship between the two shifted to a trade‐off. Our study provides a scientific basis for identifying priority areas for ecological restoration and for science‐based planning of the scale of cropland in oases.

The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection.

Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach

Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote sensing technologies offers a promising means of monitoring aboveground biomass (AGB) in wetland environments. However, the scarcity of field data poses a significant challenge to the utilization of spaceborne data for accurate estimation of AGB in coastal wetlands. To address this limitation, this study presents a novel multi-scale approach that integrates field data, aerial imaging, and satellite platforms to generate high-quality biomass maps across varying scales. At the fine scale level, the AVIRIS-NG hyperspectral data were employed to develop a model for estimating AGB with an exceptional spatial resolution of 5 m. Subsequently, at a broader scale, large-scale and multitemporal models were constructed using spaceborne Sentinel-1 and Sentinel-2 data collected in 2021. The Random Forest (RF) algorithm was utilized to train spring, fall and multi-temporal models using 70% of the available reference data. Using the remaining 30% of untouched data for model validation, Root Mean Square Errors (RMSE) of 0.97, 0.98, and 1.61 Mg ha−1 was achieved for the spring, fall, and multi-temporal models, respectively. The highest R-squared value of 0.65 was achieved for the multi-temporal model. Additionally, the analysis highlighted the importance of various features in biomass estimation, indicating the contribution of different bands and indices. By leveraging the wetland inventory classification map, a comprehensive temporal analysis was conducted to examine the average and total AGB dynamics across various wetland classes. This analysis elucidated the patterns and fluctuations in AGB over time, providing valuable insights into the temporal dynamics of these wetland ecosystems.

Height–diameter allometry for a dominant palm to improve understanding of carbon and forest dynamics in forests of Puerto Rico

AbstractTropical forests play a major role in the global carbon cycle but their diversity and structural complexity challenge our ability to accurately estimate carbon stocks and dynamics. Palms, in particular, are prominent components of many tropical forests that have unique anatomical, physiological, and allometric differences from dicot trees, which impede accurate estimates of their aboveground biomass (AGB) and population dynamics. We focused on improving height estimates and, ultimately, AGB estimates for a highly abundant palm in Puerto Rico, Prestoea acuminata. Based on field measurements of 1003 individuals, we found a strong relationship between stem height and diameter. We also found some evidence that height–diameter allometry of P. acuminata is mediated by various sources of environmental heterogeneity including slope and neighborhood crowding. We then examined variability in AGB estimates derived from three models developed to estimate palm AGB. Finally, we applied our novel height:diameter allometric model to hindcast dynamics of P. acuminata in the Luquillo Forest Dynamics Plot during a 27‐year period (1989–2016) of post‐hurricane recovery. Overall, our study provides improved estimates of AGB in wet forests of Puerto Rico and will facilitate novel insights to the dynamics of palms in tropical forests.Abstract in Spanish is available with online material.

Inter- and intra-year forest change detection and monitoring of aboveground biomass dynamics using Sentinel-2 and Landsat

National aboveground forest biomass products enable monitoring of biomass dynamics in a consistent and repeatable manner to inform carbon accounting and sustainable forest management activities. The availability of images of the Earth's surface from a combination of Landsat and Sentinel-2A and -2B provides an opportunity and capability to track intra-year biomass dynamics availing upon a twice weekly acquisition opportunity. We developed an algorithm that leverages these spatially and spectrally compatible data sources, called Tracking Intra- and Inter-year Change (TIIC), to monitor forest change across Canada's forested ecozones (>650Mha) in near-real time. Combining the stand-replacing change information from TIIC with spatially explicit maps of aboveground biomass (AGB), we demonstrate how intra-annual and inter-annual AGB dynamics, including losses due to stand-replacing disturbances and gains from vegetation growth, can be quantified temporally and spatially. Using independent validation data, our results for the focus year 2019 indicate that TIIC, by analysing images from May 30 to September 1, can accurately detect stand-replacing disturbances across Canada's forested ecozones with an overall accuracy of 99% and correctly attribute the type of change (i.e., wildfire or mechanical removal, the latter of which includes timber harvesting) with an overall accuracy of 99%. From an initial producer's accuracy of 23% for the changed class, accuracy increases incrementally as additional images are added throughout the growing season. attaining a producer's accuracy of 98% by the end of the analysis period. Intra-year change information complements information on long-term trends derived from annual time series monitoring over several decades. Our analysis of AGB dynamics indicated that in every ecozone, widespread small positive AGB increments yielded overall AGB gains that were greater than the sum of the large, punctual AGB losses resulting from stand-replacing disturbances. Overall in 2019, forest AGB increased across Canada's forested ecozones by 2.54%. Temporally, 80% of AGB losses stemming from mechanical removal occurred over the winter and are as such concentrated at the beginning of the growing season. In contrast, AGB losses linked to fires happen more stochastically throughout the growing season and occupy a greater area. By way of disturbance type, 36% of 2019 AGB loss was attributed to mechanical removal, and 64% was attributed to wildfire. Following the approach demonstrated herein, AGB changes can be tracked at a temporal frequency informative of forest management practices and ecological processes on the landscape, thereby refining our understanding of AGB dynamics.

Monitoring Spatiotemporal Variation of Individual Tree Biomass Using Multitemporal LiDAR Data

Assessing the spatiotemporal changes in forest aboveground biomass (AGB) provides crucial insights for effective forest carbon stock management, an accurate estimation of forest carbon uptake and release balance, and a deeper understanding of forest dynamics and climate responses. However, existing research in this field often lacks a comprehensive methodology for capturing tree-level AGB dynamics using multitemporal remote sensing techniques. In this study, we quantitatively characterized spatiotemporal variations of tree-level AGB in boreal natural secondary forests in the Greater Khingan Mountains region using multitemporal light detection and ranging (LiDAR) data acquired in 2012, 2016, and 2022. Our methodology emphasized improving the accuracy of individual tree segmentation algorithms by taking advantage of canopy structure heterogeneity. We introduced a novel three-dimensional metric, similar to crown width, integrated with tree height to calculate tree-level AGB. Moreover, we address the challenge of underestimating tree-level metrics resulting from low pulse density, ensuring accurate monitoring of AGB changes for every two acquisitions. The results showed that the LiDAR-based ΔAGB explained 62% to 70% of the variance of field-measured ΔAGB at the tree level. Furthermore, when aggregating the tree-level AGB estimates to the plot level, the results also exhibited robust and reasonable accuracy. We identified the average annual change in tree-level AGB and tree height across the study region, quantifying them at 2.23 kg and 0.25 m, respectively. Furthermore, we highlighted the importance of the Gini coefficient, which represents canopy structure heterogeneity, as a key environmental factor that explains relative AGB change rates at the plot level. Our contribution lies in proposing a comprehensive framework for analyzing tree-level AGB dynamics using multitemporal LiDAR data, paving the way for a nuanced understanding of fine-scale forest dynamics. We argue that LiDAR technology is becoming increasingly valuable in monitoring tree dynamics, enabling the application of high-resolution ecosystem dynamics products to elucidate ecological issues and address environmental challenges.

Drought diminishes aboveground biomass accumulation rate during secondary succession in a tropical forest on Hainan Island, China

With rising temperatures and altered annual precipitation patterns, climatic factors are likely to play an increasingly important role in controlling the biomass accumulation rate of tropical secondary forests. However, the relative importance of biotic and abiotic factors and demographic progresses (growth, mortality, and recruitment) driving biomass dynamics in these ecosystems remain largely unclear, especially in tropical Asia. We explored aboveground biomass (AGB) dynamics in old-growth and secondary tropical forests on Hainan Island, China, across two consecutive five-year intervals (2010–2020). Drought frequency was higher in the second vegetation census interval. We studied the relationships of AGB and its three demographic components with drought frequency, tree diversity, soil nutrient and microorganismal abundance, and successional stage. We found that AGB accumulation rate decreased as AGB increased gradually during secondary succession. The different drought frequency between the two intervals was the dominant factor predicting the net change of AGB. With high-frequency droughts, the loss of AGB intensified and the increment rate of AGB decreased, especially in old-growth forests during second interval (2015–2020). Considering the impact of the three demographic progresses on net change in AGB, the relative influence of mortality was the largest (61.1%), followed by growth (38.3%) and recruitment (0.6%). Successional stage was another major factor affecting AGB and its dynamics, showing a positive relationship with AGB stock and a negative relationship with net AGB change. Species richness and fungal richness had a significant positive association with AGB, while soil available nutrient had a significant negative relationship. Overall, our results show that drought frequency had a strong impact on AGB dynamics, and that the resilience of AGB accumulation to drought decreased in later successional stages. Thus, with the intensification of global climate change, the effects of high-frequency drought events should be taken into account when studying the factors affecting AGB recovery in tropical secondary forests.

Selective logging and shifting agriculture may help maintain forest biomass on the Yucatán peninsula

AbstractEvaluation and monitoring of forest biomass are important to help combat global warming and conserve biodiversity. Above ground biomass (AGB) mapping has been effectively used to assess forest loss, degradation, and recovery in the tropics. In this study, temporal (2007, 2010, and 2015) AGB maps were developed for the Mayan Zone of the Yucatan Peninsula, México integrating vegetation data from the National Forest Inventory (NFI) with synthetic aperture radar (SAR) image data (ALOS PALSAR). We assess if degradation could be attributed to the land use categories present in the study area: selectively logged forest, shifting agriculture, permanent commercial cultivation, and conservation forest. Spatial autoregressive models are applied to determine differences in AGB dynamics between land use categories, compared to baselines of mature conserved forest. We find that forest biomass remains stable in the study area. AGB does not differ in selectively logged areas compared to conserved mature forests. Biomass losses are observed due to deforestation for commercial cropping and pasture. AGB in shifting agriculture areas, however, fluctuates and shows a slight gain from 2007 to 2015. AGB mapping using NFI data and SAR imagery has the potential for monitoring forest loss and degradation on the Yucatan Peninsula.

Stand structural diversity and elevation rather than functional diversity drive aboveground biomass in historically disturbed semiarid oak forests

Forests provide biodiversity reservoirs and multiples ecosystem services such as global aboveground biomass dynamics and stock. Ecosystem functions varied according to biotic and abiotic factors, however, the effects of abiotic factors (e.g. elevation) on this relationship was rarely investigated, especially in areas impacted by human activities. We explored the relationships between aboveground biomass (AGB), a key proxy of ecosystem functioning, and plant species diversity in semiarid forests of western Iran submitted to a long history of past disturbances. Forty forest patches were sampled according to an elevation gradient (1700–2100 m) and to two types of forest cover: open cover indicating an intense past use or closed cover for more preserved forests. For each patch, the composition of woody species was recorded and woody species diversity indices (richness, Simpson, Shannon and Evenness) were calculated. We also estimated AGB and computed a wide set of community-weighted mean or functional divergence of ecological traits for woody species. We found that AGB sharply decreased with elevation (from 105.68 ± 10.29 ton/ha at 1700 m to 36.18 ± 6.43ton/ha at 2100 m) for the closed cover but variations were much less pronounced for the open cover (16.32 ± 2.97 ton/ha to 30.83 ± 1.96 ton/ha, respectively). Diversity indices (richness, Simpson, Shannon) increased with elevation for the closed cover and the other traits exhibited strong variations with elevation (either positive or negative) whereas no clear trend was visible in the open cover. Similar results were found when computing the linear relationships between AGB and the different diversity indices which were highly significant in the closed cover only. Surprisingly, the relationships were negative between AGB and most diversity indices (e.g., richness, Shannon, Simpson, Evenness). The dominant Persian oak was in fact the main contributor to AGB which was reduced in stands with a higher tree mixture, a result in line with the mass ratio hypothesis. Our results also illustrate the long-lasting effects of past conservation management on the forest cover and in shaping the relationships between forest ecosystem functions (AGB production) and biodiversity in semiarid areas.

The Interaction of Fungicide and Nitrogen for Aboveground Biomass from Flag Leaf Emergence and Grain Yield Generation under Tan Spot Infection in Wheat

Pyrenophora tritici-repentis (Died.) Drechs., the causal agent of tan spot, is one of the most serious biotic diseases affecting wheat worldwide (Triticum aestivum L.). Studying the interaction between different fungicide mixtures and nitrogen (N) rates under tan spot outbreaks is of key importance for reducing aboveground biomass and grain yield losses. Taking this into account, our study took a mechanistic approach to estimating the combined effect of different fungicides and N fertilization schemes on the severity of tan spot, green leaf area index, SPAD index, aboveground biomass dynamics, and yield in a wheat crop affected at the reproductive stage. Our results indicated that reductions in green leaf area, healthy area duration (HAD), and the chlorophyll concentration (SPAD index) due to increases in the percentage of damage led to decreases in biomass production (-19.2%) and grain yield (-48.1%). Fungicides containing triazole + strobilurin + carboxamides (TSC) or triazole + strobilurin (TS) combined with high N doses showed the most efficient disease control. The positive physiological effects of TSC fungicides, such as extending the green leaf area, are probably responsible for the greater production of aboveground biomass (+29.3%), as well as the positive effects on grain yield (+15.8%) with respect to TS. Both fungicide treatments increased grains per spike, kernel weight, spikes m-2, grains m-2, and grain yield. The increase in biomass in the TSC tended to cause slighter non-significant increases in grains per spike, 1000-kernel weight and grain yield compared with TS. The linear regression revealed positive associations among the extension of HAD and biomass (+5.88 g.m-2.HAD-1.day-1), grain yield (+38 kg.ha.HAD-1.day-1), and grain number (100.7 grains m2.HAD-1.day-1), explained by the interactions of high N doses and fungicides. Our study is the first report of the positive effect of TSC fungicides with high N doses on grain yield related-traits under tan spot infections in wheat.

The ability of crop models to predict soil organic carbon changes in a maize cropping system under contrasting fertilization and residues management: Evidence from a long-term experiment

This study assesses the ability of an ensemble of crop models (MME) to predict the impacts of fertilization and crop residue management on soil organic carbon (SOC) and aboveground biomass (AGB) in a long-term experiment (LTE) based on continuous maize cropping systems. Data from a LTE in Northern Italy were used. Treatments included continuous grain (MG) or silage (MS) maize, fertilized with mineral, cattle slurry, and farmyard manure. The MME median resulted the best predictor of the observed values. Models performance was better when simulating MG than MS, and for crops treated with mineral compared to organic fertilizers. The ability to predict the dynamics of SOC was affected by the model used and by the year × residues management and year × fertilizer interactions. The model and the residue × fertilizer interaction affected the ability to simulate AGB dynamics. Results showed that a MME can effectively predict the long-term dynamics of SOC and maize crop production under contrasting fertilization and crop residue management, and thus their potential for climate change mitigation. The uncertainty in the simulation of SOC is related to the model routines simulating SOC partitioning and to the complexity of the interactions between management factors over time. Highlights - A crop model ensemble was compiled to simulate soil organic carbon and maize aboveground biomass dynamics in a long-term experiment. - The performances of stand-alone models and their ensemble were assessed under contrasting fertilization and crop residue management. - The multi-model ensemble using the median value of simulation was the best predictor of the variables observed in the long-term experiment. - Improved performances in simulations were observed when crop residues were incorporated into the soil, regardless of the fertilization management. - The uncertainty in SOC simulation increased over time for cropping systems with silage maize and organic fertilization.

Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat

Understanding changes to aboveground biomass (AGB) in forests undergoing degradation is crucial for accurately and completely quantifying carbon emissions from forest loss and for environmental monitoring in the context of climate change. Monitoring forest degradation as compared to deforestation presents technical challenges because degradation involves widespread, low-intensity AGB removal under varying temporal dynamics. Charcoal production is a key driver for forest degradation in Africa and is projected to increase in the future years. In Sub-Saharan Africa (SSA), where charcoal production drives widespread ABG removal, the utility of optical remote sensing for degradation quantification is challenged by the large inter-seasonal variation and high complexities in ecosystem structure. Limited field measurements on tree structure and aboveground biomass density (AGBD) in many parts of the SSA also impose constraints. In this study, we present a novel data fusion approach combining 3D forest structure from NASA's GEDI Lidar with optical time-series data from Landsat to quantify biomass losses associated with charcoal-related forest degradation over a 10-year time period. We used machine learning models with Landsat spectral indices from the time period of limited hydric stress (LHS) as predictor variables. By applying the best performing Random Forest (RF) model to LandTrendr-stabilized annual LHS Landsat composites, we produced annual forest AGBD maps from 2007 to 2019 over the Mabalane district in southern Mozambique where the dry forest ecosystem was under active charcoal-related degradation since 2008. The RF model achieved an RMSE value of 7.05 Mg/ha (RMSE% = 42%) and R2 value of 0.64 using a 10-fold cross-validation dataset. We quantified a total AGB loss of 2.12 ± 0.06 Megatons (Mt) over the 10-year period, which is only 6.35 ± 2.56% less than the total loss estimated using field-based data as previously published for the same area and time. In addition to quantifying biomass loss, we constructed annual AGBD maps that enabled the characterization of disturbance and recovery. Our framework demonstrates that fusing GEDI and Landsat data through predictive modeling can be used to quantify past forest AGBD dynamics in low biomass forests. This approach provides a satellite-based method to support REDD+ monitoring and evaluation activities in areas where field data is limited and has the potential to be extended to investigate a variety of different disturbance events.

Aboveground Biomass of Wetland Vegetation Under Climate Change in the Western Songnen Plain.

Understanding the spatiotemporal dynamics of aboveground biomass (AGB) is crucial for investigating the wetland ecosystem carbon cycle. In this paper, we explored the spatiotemporal change of aboveground biomass and its response to climate change in a marsh wetland of western Songen Plain by using field measured AGB data and vegetation index derived from MODIS datasets. The results showed that the AGB could be established by the power function between measured AGB density and the annual maximum NDVI (NDVImax) of marsh: Y = 302.06 × NDVImax1.9817. The averaged AGB of marshes showed a significant increase of 2.04 g⋅C/m2/a, with an average AGB value of about 111.01 g⋅C/m2 over the entire western Songnen Plain. For the influence of precipitation and temperature, we found that the annual mean temperature had a smaller effect on the distribution of marsh AGB than that of the total precipitation in the western Songnen Plain. Increased precipitation in summer and autumn would increase AGB by promoting marshes’ vegetation growth. In addition, we found that the minimum temperature (Tmin) and maximum temperatures (Tmax) have an asymmetric effect on marsh AGB on the western Songnen Plain: warming Tmax has a significant impact on AGB of marsh vegetation, while warming at night can non-significantly increase the AGB of marsh wetland. This research is expected to provide theoretical guidance for the restoration, protection, and adaptive management of wetland vegetation in the western Songnen Plain.

Robinia pseudoacacia decline and fine root dynamics in a plantation chronosequence in the Yellow River Delta, China

Abstract Fine roots (&amp;lt;2 mm in diameter) play a significant role in tree growth and stand productivity. However, knowledge of fine root dynamics in saline alkali soil remains limited. Using sequential soil core and ingrowth core methods, we assessed the dynamics of aboveground biomass (AGB) and production (AGP), fine root biomass (FRB), production (FRP) and turnover (FRT), and soil properties (water content [SWC], salt content [SSC], pH and nutrients) in 2-, 18-, 32- and 42-year-old black locust (Robinia pseudoacacia L.) stands in the Yellow River Delta, China. Corresponding to the unimodal growth pattern in AGP, the AGB of black locust rapidly increased until age 32 and then slowed down. In contrast, across all depths, FRB and FRP took a unimodal pattern with a decrease in growth around age 32, and FRT continually decreased with stand age. SWC and soil nutrients increased with stand age, whereas SSC and pH decreased with stand age until age 32 and then increased. Based on the correlation analysis, we may presume that in addition to the age effect, the decline of black locust in AGP is most likely caused by SWC, SSC, and pH stresses through the dysfunction in nutrient absorption by fine roots.