Strong correlations between aboveground net primary productivity (ANPP) of grasslands and mean annual temperature or precipitation have been widely reported across regional or continental scales; however, inter-annual variation in these climate factors correlates poorly with site-specific ANPP. We hypothesize that the reason for these weak correlations is that the impacts of climatic variation on grassland productivity depend on the timing and intensity of variation in temperature and precipitation. In this study, long-term records of grassland productivity on the Loess Plateau in China were related with daily temperature and precipitation during 1992–2011 using Partial Least Squares (PLS) regression to test the above-mentioned hypothesis. Our results suggested that temperature increases during the early stage of the growing season (April–May) were positively correlated with ANPP. However, these effects were canceled out when this phase was followed by a hot and dry summer (June–July). Impacts of drought and heat in August on productivity were negligible. Increased temperature and precipitation during the senescence period (September–October) and a warmer dormancy phase (November–March) were negatively correlated with productivity in the following year, while precipitation during the dormancy period had no detectable effects. Climatic variability in summer has thus far been the dominant driver of temporal variation in grassland productivity. Warming during winter and spring currently play minor roles, but it seems likely that the importance of these secondary impacts may increase as warming trends continue. This evaluation of climate variability impacts on ecosystem function (e.g. grassland productivity) implies that not only the magnitude but also the timing of changes in temperature and precipitation determines how the impacts of climate changes on ecosystems will unfold. © 2016 Elsevier B.V.
Tag: Aboveground biomass
Optimal measurement strategies for aboveground tree biomass in agricultural landscapes
Robust and cost effective methods for estimating aboveground biomass of trees on farms are necessary to understand the significance of this carbon pool, identify climate change mitigation opportunities and support smallholder farmers’ ability to capitalize on the emerging green economy. Using a dataset of 72 destructively sampled trees and 855 non-destructively sampled trees, we identified methodologically and economically efficient strategies to construct allometric equations and measure on-farm tree biomass. We found that robust biomass estimates can be obtained from measuring diameter at breast height (DBH) alone. Inclusion of tree height, specific wood density, and/or crown area in the allometric equation changed the biomass estimates by only 1.3 %, though these additional variables improved precision by reducing the error from 7.8 % to between 4.8 and 7.0 %. Research accuracy-to-cost trade-offs can be optimized by building equations based on destructive measurements of trees that span stem diameters found in the landscape; equations based on only small or large diameter trees result in poor estimates. Given (1) the resources required to measure additional dendrometric parameters in the field (2) the potential to introduce measurement errors that can propagate through estimates at farm and landscape scales, and (3) the need to quickly increase the amount of data available at low cost, we recommend that allometric equations for trees on farms be based solely on DBH and that the sampling strategy capture the range of tree sizes found in the landscape and future indirect quantification should focus on diameter at breast height as a predictor of biomass to save resources.
Growth and Biomass Yield of Grey Sedge (Lepironia articulata Retz. Domin) under Different Shoot-Cutting Intervals in a Tropical Peatland
Grey sedge (Lepironia articulata Retz. Domin) is a plant endemic to tropical peatlands and is widely used as a handicraft and biodegradable product that brings income to local farmers. However, its habitat has been decreasing due to peatland degradation, which has forced local farmers to harvest L. articulata repeatedly in the same habitat. To examine the effects of repeated shoot cutting at different time intervals on L. articulata growth and biomass yield, a mesocosm experiment was conducted from June 2019 to March 2020 in a tropical peatland in Perigi village, Ogan Ilir District, South Sumatra, Indonesia, using a randomized block design with four treatments and three replicates. The treatments were as follows: P1 (cutting every 1 month), P2 (cutting every 2 months), P3 (cutting every 3 months), and P4 (cutting at 6-months). The results showed that P1 significantly reduced monthly shoot height, shoot diameter, shoot number, dry biomass, cumulative shoot number, and cumulative dry biomass. In contrast, considering L. articulata‘s regenerative growth, the growth and cumulative biomass yield of P3 (1453.5 ± 518.4 g m−2) were as good as those of P4. These results indicate that the harvesting interval should be longer than 3 months for the sustainable use of L. articulata in tropical peatlands without damaging its regenerative ability.
Assessment of the efficiency of improved cooking stoves and their impact in reducing forest degradation and contaminant emissions in Eastern Rwanda
Biomass is a major source of cooking energy in Rwanda. This high dependence on biomass strongly contributes to forest degradation, biodiversity loss, and atmospheric pollution. Shifting from the traditional three-stone stove (TSS) to improved cooking stoves (ICS) is being promoted to reduce wood consumption and air pollutant emissions. This study assessed the emission reduction of adopted ICS and their impact on forest conservation through a combined laboratory and kitchen performance test (KPT). Out of 940 households surveyed in Eastern Province (EP) of Rwanda, 317 used ICS. Among these users, 180 were selected and revisited for an in-depth interview about the adoption process. The four most popular ICSs were selected for evaluating thermal efficiency and emissions of contaminating gases using laboratory test protocol ISO 19867-1. In addition, the avoided wood consumption of improved cooking stoves compared to the TSS was assessed using a KPT in 100 households. The survey findings indicate that 33.7 % of households use only ICS, 23.1 % only TSS, and 56.4 % ICS and TSS combined. The laboratory results show that the Save80 stove had the highest thermal efficiency (45.8 %) followed by Igisubizo (32.8 %), EcoZoom (32.6 %), and Songa (29.6 %). The emission results showed a significant difference in CO2 (carbon dioxide) emissions (p = 0.001) between the ICS models. However, the emissions of PM2.5 (fine particulate matter) and CO (carbon monoxide) of ICS were high compared to the WHO (World Health Organization) guidelines. The KPT results show that using ICS would result in an estimated annual saving of 149,313 tons of firewood corresponding to 0.7 m3 of wood, leading to an annual equivalent reduction of 0.02 ha of forest per household and 7167 ha of forest in the EP. The KPT results show that using ICS reduced CO2 emissions by 0.372 tons per household per year and by 111,088 tons of CO2 in the EP. ICSs have been shown to enhance fuelwood efficiency, leading to reduced forest degradation.
Survival and Growth Performance of Calophyllum inophyllum L. Seedlings in Peat Soil and at Different Levels of Groundwater
Selecting proper tree species for revegetation is important for restoring tropical peatland. Tamanu (Calophyllum inophyllum L.) has been suggested one of the promising native species in Indonesia for the revegetation of tropical peatland; however, there is a lack of understanding of its growth in peat soil with different groundwater levels. We investigated the survival rate, plant height, number of leaves, root collar diameter, and dry biomass of tamanu seedlings grown in peat soil with different groundwater levels using a randomized block design in a water tank. The study involved five groundwater level treatments and 12 tree-level replicates each: M0 (non-groundwater level, control); M1 (−15 cm below the soil surface); M2 (−10 cm); M3 (−5 cm); and M4 (0 cm, soil surface level). The results showed that all tamanu seedlings survived in every treatment. However, their plant height, number of leaves, and root collar diameter growth were significantly inhibited in the highest groundwater level condition (M4, 0 cm) at 81 days after sowing, while their growth in −15 cm of groundwater level (M1) was as good as the growth in the fully drained conditions in M0 (control). This result indicates that the groundwater level should not be higher than −15 cm for the growth of tamanu. Considering that peatlands in Indonesia are assessed to be degraded when the groundwater level is lower than −40 cm, this study suggests that maintaining groundwater levels between −40 cm and −15 cm in peatland is the best condition for rapid growth of early tamanu seedlings while protecting peatlands. Further studies are needed to extend the range of water levels and water treatment duration.
Central African biomass carbon losses and gains during 2010–2019
Disturbance, vegetation productivity, and recovery are crucial for aboveground biomass carbon (AGC) dynamics. Here, we use multiple satellite-based datasets to analyze the drivers of AGC dynamics in Central Africa. During 2010–2019, deforestation induced a gross AGC loss of 102.2 ± 17.1 Tg C year−1, which was counterbalanced by an AGC increase of 116.9 ± 41.1 Tg C year−1, leading to a net gain of 14.6 ± 3.8 Tg C year−1. Compared to anthropogenic and soil factors, changes in climate-related factors (e.g., radiation) are more important for the non-deforestation AGC changes. A large AGC increase was found in the northern savannas. In moist forests, strong biomass recovery and growth largely compensated the carbon loss from deforestation and degradation. Considering the increasing resource demand due to rapid population growth, reconciling natural conservation and economic development in Central Africa remains challenging and depends on climate changes and country-specific social-economic conditions.
Aligning international financial flows and the COMIFAC Convergence Plan
Central African forests, including those in the Congo Basin, play an essential role at the global level. They help to regulate the global climate, in part through the exchange of gases, which has led to them being designated the planet’s ‘second tropical forest lung’. The carbon stock held in their biomass and peatlands is estimated at 80 billion tons, equivalent to almost 10 years of global carbon dioxide emissions. Central Africa’s undisturbed forests now sequester more carbon than those in the Amazon (Dalimier et al. unpublished) and South-East Asia, and currently constitute the world’s largest tropical carbon sink. According to recent estimates by Global Forest Watch (GFW) researchers, forests in the Congo Basin sequester 600 million tons of CO2 more than they emit annually. On average, these forests emit 530 million tons of carbon each year – a figure that has remained stable – and remove 1.1 billion tons.
Growth, Productivity, Biomass and Carbon Stock in Eucalyptus saligna and Grevillea robusta Plantations in North Kivu, Democratic Republic of the Congo
Initiated by the World Wildlife Fund (WWF) more than a decade ago in North Kivu, single-species plantations of Eucalyptus saligna and Grevillea robusta constitute, with other village plantations, the current legal source of wood-energy for the communities bordering the Virunga National Park (PNVi). This study assesses the growth and productivity of these plantations in two sites with different soil and climatic conditions to predict their production over time. The study also assesses the carbon stock and long-term CO2 fixation in the biomass of the studied plantations to deduce their contribution to climate change mitigation. Non-destructive inventories were carried out during three consecutive years in 20 E. saligna and 12 G. robusta plantations in Sake and Kirumba. Analysis of the data revealed that both species have similar diametric growth while height growth and productivity were significantly higher in the E. saligna plantations. The productivity of E. saligna was also higher in Kirumba than in Sake, while that of G. robusta was higher in Sake than in Kirumba. The differences observed were mainly related to species, silviculture, altitude and concentration of bioavailable elements in the soils. The analysis of productivity evolution over time allowed us to determine optimal rotations at 8 and 12 years, respectively, for E. saligna and G. robusta plantations. The relationships between biomass or carbon stock and tree diameter were not different between the studied species but were significantly different at the stand level. If silviculture was standardized and plantations carefully monitored, carbon stock and long-term CO2 fixation would be higher in G. robusta plantations than in E. saligna plantations. These results indicate that while for productivity reasons E. saligna is the favoured species in wood-energy plantations to quickly meet the demand of the growing and disadvantaged population living in the vicinity of PNVi, carefully monitored G. robusta plantations could be more interesting in terms of carbon credits. To simultaneously optimise wood-energy production and carbon storage in the plantations initiated in North Kivu, E. saligna and G. robusta should be planted in mixture. In addition, species and site characteristics adapted silvicultural management practices must be applied to these plantations, which are very important for the region, its population and its park. Finally, the economic profitability as well as the sustainability of the plantations should be assessed in the longer term in North Kivu.
Estimation of Aboveground Biomass in Agroforestry Systems over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data
Agroforestry systems (AFS) offer viable solutions for climate change because of the aboveground biomass (AGB) that is maintained by the tree component. Therefore, spatially explicit estimation of their AGB is crucial for reporting emission reduction efforts, which can be enabled using remote sensing (RS) data and methods. However, multiple factors including the spatial distributions within the AFS, their structure, their composition, and their variable extents hinder an accurate RS-assisted estimation of the AGB across AFS. The aim of this study is to (i) evaluate the potential of spaceborne optical, SAR and LiDAR data for AGB estimations in AFS and (ii) estimate the AGB of different AFS in various climatic regions. The study was carried out in three climatic regions covering Côte d’Ivoire and Burkina Faso. Two AGB reference data sources were assessed: (i) AGB estimations derived from field measurements using allometric equations and (ii) AGB predictions from the GEDI level 4A (L4A) product. Vegetation indices and texture parameters were generated from optical (Sentinel-2) and SAR data (Sentinel-1 and ALOS-2) respectively and were used as predictors. Machine learning regression models were trained and evaluated by means of the coefficient of determination (R2) and the RMSE. It was found that the prediction error was reduced by 31.2% after the stratification based on the climatic conditions. For the AGB prediction, the combination of random forest algorithm and Sentinel-1 and -2 data returned the best score. The GEDI L4A product was applicable only in the Guineo-Congolian region, but the prediction error was approx. nine times higher than the ground truth. Moreover, the AGB level varied across AFS including cocoa (7.51 ± 0.6 Mg ha−1) and rubber (7.33 ± 0.33 Mg ha−1) in the Guineo-Congolian region, cashew (13.78 ± 0.98 Mg ha−1) and mango (12.82 ± 0.65 Mg ha−1) in the Guinean region. The AFS farms in the Sudanian region showed the highest AGB level (6.59 to 82.11 Mg ha−1). AGB in an AFS was mainly determined by the diameter (R2 = 0.45), the height (R2 = 0.13) and the tree density (R2 = 0.10). Nevertheless, RS-based estimation of AGB remain challenging because of the spectral similarities between AFS. Therefore, spatial assessment of the prediction uncertainties should complement AGB maps in AFS.
Declining Amazon biomass due to deforestation and subsequent degradation losses exceeding gains
In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011–2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom-up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon.