Financial compensation for biodiversity conservation in Ba Be National Park of Northern Vietnam

Terrestrial biodiversity is seriously threatened by an increasing deforestation rate. Financial incentives can stimulate local people to participate in biodiversity conservation. A common approach to derive financial compensation is estimating the opportunity costs arising from the adoption of conservation-friendly land-use practices. Assuming that farmers are risk averse, we used a decision-making under uncertainty approach to derive financial compensation for local people living in and around Ba Be National Park, Vietnam. Moderately risk-averse farmers need US$ 228 ha⁻¹ year⁻¹ financial compensation for the protection of natural forests in the Ecological Rehabilitation and Buffer Zones, whereas the required compensation reaches US$ 334 ha⁻¹ year⁻¹ in the Strict Protection Zone. These compensation payments were derived by comparing financial returns from natural forests with those of the most profitable land-use (maize). Compensation amounts decrease to US$ 130 ha⁻¹ year⁻¹ in the Ecological Rehabilitation and Buffer Zones and to US$ 218 ha⁻¹ year⁻¹ in the Strict Protection Zone, when considering diversification strategy. Derived financial compensations assume that farms comprise 20% croplands and 80% forest. We conclude that compensation amounts vary with farmers’ risk preferences, whether or not diversification strategy is considered, and the level of protection of a given area.     

Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido,Japan

Trees are recognized as a carbon reservoir, and precise and convenient methods for forest biomass estimation are required for adequate carbon management. Airborne light detection and ranging (LiDAR) is considered to be one of the solutions for large-scale forest biomass evaluation. To clarify the relationship between mean canopy height determined by airborne LiDAR and forest timber volume and biomass of cool-temperate forests in northern Hokkaido, Japan, we conducted LiDAR observations covering the total area of the Teshio Experimental Forest (225 km²) of Hokkaido University and compared the results with ground surveys and previous studies. Timber volume and aboveground tree carbon content of the studied forest stands ranged from 101.43 to 480.40 m³ ha^–1 and from 30.78 to 180.54 MgC ha^–1, respectively. The LiDAR mean canopy height explained the variation among stands well (volume: r² = 0.80, RMSE = 55.04 m³ ha^–1; aboveground tree carbon content: r² = 0.78, RMSE = 19.10 MgC ha^–1) when one simple linear regression equation was used for all types (hardwood, coniferous, and mixed) of forest stands. The determination of a regression equation for each forest type did not improve the prediction power for hardwood (volume: r² = 0.84, RMSE = 62.66 m³ ha^–1; aboveground tree carbon content: r² = 0.76, RMSE = 27.05 MgC ha^–1) or coniferous forests (volume: r² = 0.75, RMSE = 51.07 m³ ha^–1; aboveground tree carbon content: r² = 0.58, RMSE = 19.00 MgC ha^–1). Thus, the combined regression equation that includes three forest types appears to be adequate for practical application to large-scale forest biomass estimation.     

Forest degradation assessment in the upper catchment of the river Tons using remote sensing and GIS

The present study investigates the status of forest degradation in the upper catchment of the river Tons in the Uttarakhand state of India, including Govind Wildlife Sanctuary and the National Park by the same name using remote sensing and the geographic information system (GIS). The study revealed that more than 50% of the study area is covered with snow and the alpine grasslands while 8.1% area is under agriculture. Degraded forest covers maximum area (53 km²), followed by moderately (30.4 km²) and severely degraded (26.8 km²) forests. The lower and middle slopes showed higher degradation than upper slopes due to multiple uses for agriculture, horticulture, agroforestry and grazing by the local people. Over time, the degradation and deforestation, without adequate protection, have led to severe soil erosion, biodiversity and the habitat depletion for a large number of rare and endemic species including loss of livelihood to the local people.     

Carbon profiles of typical forest types across Europe assessed with CO2FIX

This paper presents for 16 typical forest types across Europe a standard carbon sequestration profile. The study was carried out with the model CO2FIX which was parameterised with local yield table data and additional required parameters. CO2FIX quantifies the carbon of the forest ecosystem–soil–wood products chain at the stand level. To avoid misleading results annual net sequestration rates are not presented here, because these strongly fluctuate in time. Therefore, only its advancing mean is presented as a more reliable indicator. This avoids a great deal of uncertainty for policy makers. The variation between forest types is large, but mean sequestration rates mostly peak after some 38 years (with a net source lasting up to 15 years after afforestation) at an average value of 2.98 Mg C ha⁻¹ per year (ranging between forest types from 4.1 to 1.15). After 200 years, the net sequestration rate saturates to a value of 0.8 Mg C ha⁻¹ per year (ranging from 1.4 to 0.13). The long-term mean carbon stock in tree biomass and products amounts on average to 114 Mg C ha⁻¹ (ranging from 52 to 196).     

Forest cover dynamics analysis and prediction modeling using logistic regression model

Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (β = −26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2 km² of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC = 87%).     

Estimating biomass and carbon mitigation of temperate coniferous forests using spectral modeling and field inventory data

Realizing the importance of forest carbon monitoring and reporting in climate change, the present study was conducted to derive spectrally modeled aboveground biomass and mitigation using Landsat data in combination with sampled field inventory data in the coniferous forests of Western Himalaya. After conducting preliminary survey in 2009, 90 quadrats (45 each for calibration and validation) of 0.1 ha were laid in six forest types for recording field inventory data viz. diameter at breast height, height, slope and aspect. Biomass carbon (Mg ha^− 1) was worked out for different forest types and crown density classes (open with 10–40% crown density and closed with > 40% crown density) using recommended volume equations, ratios and factors. Biomass carbon map (aboveground + belowground) was generated for the entire region using geospatial techniques. Normalized difference vegetation index (NDVI) was generated and spectral values were extracted to establish relation (R² = 0.72, p < 0.01) with the field inventory data. The model developed was validated (R² = 0.73, p < 0.01) with 45 sample observations not used earlier for predicting and generating biomass carbon map (2009) for the entire region. The data from field based inventory indicates highest total biomass carbon (171.40, σ ± 23.19) Mg ha^− 1 for Fir–Spruce (closed) which has relatively more mature girth classes and low tree density. This value was found to be significantly higher than other forest types. Lowest biomass carbon was observed for Blue Pine (open) (37.15, σ ± 11.82) Mg ha^− 1. The NDVI values for the entire region ranged from 0 to 0.62 and consequently the spectrally derived aboveground biomass carbon varied from 0 to 600 Mg ha^− 1. The study demonstrates the application of mapping, spectral responses and sampled field inventory for type wise assessment of carbon mitigation in temperate coniferous forests of Himalayas.     

Scale matters: Indicators of ecological health along the urban–rural interface near Columbus,Georgia

Ecological data obtained from field plots can provide detailed information about ecosystem structure and function. However, this information typically reflects processes that occur over small spatial areas. Accordingly, it is difficult to extrapolate these data to patterns and processes that take place at regional scales. Satellite imagery can provide a means to explore environmental variables over a larger area. Therefore, our main objective was to examine the utility of a regional ecological assessment tool using landscape indicators of ecosystem health in a rapidly developing area of West Georgia near the city of Columbus. Indicator variables included in the assessment were: population density and change, road density, percent forest land-cover, forest patch density, landscape Shannon's Diversity Index, proportion of all streams with roads within 30 m, proportion of area that has agriculture on slopes >3%, proportion of all streams with adjacent agriculture, and proportion of all streams with adjacent forest cover. Cluster analysis was used to combine these variables into different groups, and resulting cluster means were used to rank regional areas according to degree of environmental impact. To assess the spatial accuracy of this tool results were compared to those obtained from a separate plot-level field-based forest condition study. Results derived using the landscape ecological assessment tool suggest that rural areas were the least environmentally impacted (or most healthy) of all areas in West Georgia, and support the findings from the field study. Results for developing areas were mixed between the two different studies and may be attributed to differences in scale. Overall, it appears that this tool is useful for broad generalizations about a given landscape, but is not detailed enough for site-specific management goals due to its inherent coarse spatial resolution (30 m × 30 m). However, these site-specific goals may be achieved using higher resolution (1 m × 1 m) satellite imagery and warrants further research. In any case, this tool is a useful asset for anyone needing a rapid diagnosis of ecosystem health in an inexpensive and timely manner.     

Soil nitrate accumulation explains the nonlinear responses of soil CO2 and CH4 fluxes to nitrogen addition in a temperate needle-broadleaved mixed forest

The responses of soil-atmosphere carbon (C) exchange fluxes to growing atmospheric nitrogen (N) deposition are controversial, leading to large uncertainty in the estimated C sink of global forest ecosystems experiencing substantial N inputs. However, it is challenging to quantify critical load of N input for the alteration of the soil C fluxes, and what factors controlled the changes in soil CO₂ and CH₄ fluxes under N enrichment. Nine levels of urea addition experiment (0, 10, 20, 40, 60, 80, 100, 120, 140 kg N ha⁻¹ yr⁻¹) were conducted in the needle-broadleaved mixed forest in Changbai Mountain, Northeast China. Soil CO₂ and CH₄ fluxes were monitored weekly using the static chamber and gas chromatograph technique. Environmental variables (soil temperature and moisture in the 0–10 cm depth) and dissolved N (NH₄⁺-N, NO₃⁻-N, total dissolved N (TDN), and dissolved organic N (DON)) in the organic layer and the 0–10 cm mineral soil layer were simultaneously measured. High rates of N addition (≥60 kg N ha⁻¹ yr⁻¹) significantly increased soil NO₃⁻-N contents in the organic layer and the mineral layer by 120%-180% and 56.4%-84.6%, respectively. However, N application did not lead to a significant accumulation of soil NH₄⁺-N contents in the two soil layers except for a few treatments. N addition at a low rate of 10 kg N ha⁻¹ yr⁻¹ significantly stimulated, whereas high rate of N addition (140 kg N ha⁻¹ yr⁻¹) significantly inhibited soil CO₂ emission and CH₄ uptake. Significant negative relationships were observed between changes in soil CO₂ emission and CH₄ uptake and changes in soil NO₃⁻-N and moisture contents under N enrichment. These results suggest that soil nitrification and NO₃⁻-N accumulation could be important regulators of soil CO₂ emission and CH₄ uptake in the temperate needle-broadleaved mixed forest. The nonlinear responses to exogenous N inputs and the critical level of N in terms of soil C fluxes should be considered in the ecological process models and ecosystem management.     

Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques

Forests are living dynamic systems and these unique ecosystems are essential for life on earth. Forest fires are one of the major environmental concerns, economic, and social in the worldwide. The aim of current research is to identify general indicators influencing on forest fire and compare forest fire susceptibility maps based on the boosted regression tree (BRT), generalized additive model (GAM), and random forest (RF) data mining models in the Minudasht Township, Golestan Province, Iran. According to expert opinion and literature review, fifteen condition factors on forest fire have been selected in the study area. These are slope degree, slope aspect, elevation, topographic wetness index (TWI), topographic position index (TPI), plan curvature, wind effect, annual temperature and rainfall, soil texture, distance to roads, rivers, and villages, normalized difference vegetation index (NDVI), and land use. Forest fire locations were identified using MODIS images, historical records, and extensive field checking. 106 (≈70%) locations, out of 151 forest fires identified, were used for models building/training, while the remaining 45 (≈30%) cases were used for the models validation.BRT, GAM, and RF data mining models were used to distinguish between presence and absence of forest fires and its mapping. These algorithms were used to perform feature selection in order to reveal the variables that contribute more to forest fire occurrence. Finally, for validation of models, the area under the curve (AUC) for forest fire susceptibility maps was calculated. The validation of results showed that AUC for three mentioned models varies from 0.7279 to 0.8770 (AUC_BRT = 80.84%, AUC_GAM = 87.70%, and AUC_RF = 72.79%,). Results indicated that the main drivers of forest fire occurrence were annual rainfall, distance to roads, and land use factors. The results can be applied to primary warning, fire suppression resource planning, and allocation work.     

Ecosystem functions as indicators for heathland responses to nitrogen fertilisation

Anthropogenic deposition of reactive nitrogen (N) has increased during the 20th century, and is considered an important driver of shifts in ecosystem functions and biodiversity loss. The objective of the present study was to identify those ecosystem functions that best evidence a target ecosystem’s sensitivity to N deposition, taking coastal heathlands as an example. We conducted a three-year field experiment in heathlands of the island Fehmarn (Baltic Sea, North Germany), which currently are subject to a background deposition of 9 kg N ha⁻¹ yr⁻¹. We experimentally applied six levels of N fertilisation (application of 0, 2.5, 5, 10, 20, and 50 kg N ha⁻¹ yr⁻¹), and quantified the growth responses of different plant species of different life forms (dwarf shrubs, graminoids, bryophytes, lichens) as well as shifts in the C:N ratios of plant tissue and humus horizons. For an applicability of the experimental findings (in terms of heathland management and critical load assessment) fertilisation effects on response variables were visualised by calculating the treatment ‘effect sizes’. The current year’s shoot increment of the dominant dwarf shrub Calluna vulgaris proved to be the most sensitive indicator to N fertilisation. Shoot increment significantly responded to additions of ≥ 5 kg N ha⁻¹ yr⁻¹ already in the first year, whereas flower formation of Calluna vulgaris increased only in the high-N treatments. Similarly, tissue C:N ratios of vascular plants (Calluna vulgaris and the graminoids Carex arenaria and Festuca ovina agg.) only decreased in the highest N treatments (50 and 20 kg N ha⁻¹ yr⁻¹, respectively). In contrast, tissue C:N ratios of cryptogams responded more quickly and sensitively than vascular plants. For example, Cladonia spp. tissue C:N ratios responded to N additions ≥ 5 kg N ha⁻¹ yr⁻¹ in the second study year. After three years we observed an increase in cover of graminoids and a corresponding decrease of cryptogams at N fertilisation rates of ≥ 10 kg N ha⁻¹ yr⁻¹. Soil C:N ratios proved to be an inappropriate indicator for N fertilisation at least within our three-year study period. Although current critical N loads for heathlands (10−20 kg N ha⁻¹ yr⁻¹) were confirmed in our experiment, the immediate and highly sensitive response of the current year’s shoots of Calluna vulgaris suggests that at least some ecosystem functions (e.g. dwarf shrub growth) also might respond to low (i.e. < 10 kg N ha⁻¹ yr⁻¹) but chronic inputs of N.     

Extinction thresholds and negative responses of Afrotropical ant-following birds to forest cover loss in oil palm and agroforestry landscapes

Afrotropical ant-following birds are vulnerable to forest loss and disturbance, but critical habitat thresholds regarding their abundance and species richness in human-dominated landscapes, including industrial oil palm plantations, have never been assessed. We measured forest cover through Landsat imagery and recorded species richness and relative abundance of 20 ant-following birds in 48 plots of 1-km², covering three landscapes of Southwest Cameroon: Korup National Park, smallholder agroforestry areas (with farms embedded in forest), and an industrial oil palm plantation. We evaluated differences in encounter frequency and species richness among landscapes, and the presence of critical thresholds through enhanced adaptive regression through hinges. All species were detected in Korup National Park and the agroforestry landscape, which had similar forest cover (>85%). Only nine species were found in the oil palm plantation (forest cover = 10.3 ± 3.3%). At the 1-km² scale, the number of species and bird encounters were comparable in agroforests and the protected area: mean species richness ranged from 12.2 ± 0.6 in the park and 12.2 ± 0.6 in the agroforestry matrix to 1.0 ± 0.4 in the industrial oil palm plantation; whereas encounters decreased from 34.4 ± 3.2 to 26.1 ± 2.9 and 1.3 ± 0.4, respectively. Bird encounters decreased linearly with decreasing forest cover, down to an extinction threshold identified at 24% forest cover. Species richness declined linearly by ca. one species per 7.4% forest cover lost. We identified an extinction threshold at 52% forest cover for the most sensitive species (Criniger chloronotus, Dicrurus atripennis, and Neocossyphus poensis). Our results show that substantial proportions of forests are required to sustain complete ant-following bird assemblages in Afrotropical landscapes and confirm the high sensitivity of this bird guild to deforestation after industrial oil palm development. Securing both forest biodiversity and food production in an Afrotropical production landscape may be best attained through a combination of protected areas and wildlife-friendly agroforestry.     

Energy and carbon accounting to compare bioenergy crops

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Highlights► Comparing biofuel crops is exceedingly difficult using conventional measurement methods. ► The PETRO approach quantitatively compares biological matter in terms of energy and carbon. ► Tracking carbon flux from air to liquid fuel compares all biofuel crop yields on a common basis. ► Reporting energy yields (GJ ha⁻¹ y⁻¹) and carbon flux (Mg_C ha⁻¹ y⁻¹) enables regional comparison.     

Energy balance and greenhouse gas emissions in organic and conventional avocado orchards in Mexico

There is a worldwide growing awareness of the negative impacts of the increasing fossil fuel reliance and greenhouse gas (GHG) emissions from agriculture, in particular for intensive crop systems. We analyze the energy balances and greenhouse gas emissions from export-oriented avocado orchards in Mexico. Avocado is a very important export crop and one of the main drivers of land-use change in the country. We compared 12 avocado orchards under organic and conventional management during two production cycles (2010 and 2011) in a representative region of Central Mexico. Our analysis shows no significant differences in energy consumption and GHG emissions between organic and conventional systems with 55 and 56 GJ ha⁻¹, and 3.30 t CO₂ equiv. ha⁻¹ and 3.57 t CO₂ equiv. ha⁻¹, respectively.Organic systems show three times more use of renewable energy than their conventional counterparts. However both systems depend heavily on fossil fuel inputs, machinery and N-fertilizers (synthetic or organic). Also, there is a high heterogeneity in management practices and input application within both systems, which is reflected in a large variation of their energy-related parameters. Given that avocado production is rapidly expanding in Mexico, a move toward organic production without systematically changing toward less fossil fuel dependent agricultural practices would not be sufficient to ensure a sustainable production.     

Diversity and spatial clustering of shade trees affect cacao yield and pathogen pressure in Costa Rican agroforests

The importance of the spatial organisation of individuals in explaining species coexistence within a community is widely recognised. However, few analyses of spatial structure have been performed on tropical agroforests.The main objective of this study was to highlight the links between spatial organisation of shade trees on the one hand, and shade tree species richness and cacao yield on the other, using data from 29 cacao agroforests in Costa Rica.A method of spatial statistics, Ripley's K-function, was used to analyse the spatial organisation of shade and cacao trees in the study plots. For each stand, the X and Y coordinates of ≥2.5-m-tall trees were recorded. In each plot we also assessed shade tree species richness and cacao yield (with total number of pods = number of pods damaged by frosty pod rot + number of healthy pods).Three types of stands were identified: the first was characterised by significant clustering of shade trees, the highest shade tree species richness (S = 6), and the highest number of damaged pods (139 pods ha^?1 year^?1). The second type was characterised by random spatial organisation of shade trees. The third type showed a trend towards regular organisation. Species richness of shade trees did not differ significantly between the last two types (S = 4 for both), nor did the number of damaged pods (56 pods ha^?1 year^?1 and 67 pods ha^?1 year^?1 respectively).Although the trends were not statistically significant for all the variables in our data set, the clustered spatial structure appears to favour a synergy between environmental (tree species richness), and provisioning (cacao production) services.     

A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

Soil organic carbon (SOC) is a key indicator of ecosystem health, with a great potential to affect climate change. This study aimed to develop, evaluate, and compare the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping SOC stocks in the Eastern Mau Forest Reserve, Kenya. Auxiliary data, including soil sampling, climatic, topographic, and remotely-sensed data were used for model calibration. The calibrated models were applied to create prediction maps of SOC stocks that were validated using independent testing data. The results showed that the models overestimated SOC stocks. Random forest model with a mean error (ME) of −6.5 Mg C ha⁻¹ had the highest tendency for overestimation, while SVR model with an ME of −4.4 Mg C ha⁻¹ had the lowest tendency. Support vector regression model also had the lowest root mean squared error (RMSE) and the highest R² values (14.9 Mg C ha⁻¹ and 0.6, respectively); hence, it was the best method to predict SOC stocks. Artificial neural network predictions followed closely with RMSE, ME, and R² values of 15.5, −4.7, and 0.6, respectively. The three prediction maps broadly depicted similar spatial patterns of SOC stocks, with an increasing gradient of SOC stocks from east to west. The highest stocks were on the forest-dominated western and north-western parts, while the lowest stocks were on the cropland-dominated eastern part. The most important variable for explaining the observed spatial patterns of SOC stocks was total nitrogen concentration. Based on the close performance of SVR and ANN models, we proposed that both models should be calibrated, and then the best result applied for spatial prediction of target soil properties in other contexts.     

Nitrogen balance in and export from agricultural fields associated with controlled drainage systems and denitrifying bioreactors

Nitrate loss from drainage tiles across the cornbelt of the upper midwestern US is a result of intensive agriculture with limited crop diversity, extensive periods of fallow soil, and the need for high fertilizer applications to corn, all located on a hydrologically modified landscape. Two methods proposed to reduce tile nitrate export are managed or controlled drainage to limit tile flow and bioreactors to enhance denitrification. Nitrogen budgets and tile flow monitoring were conducted over two- to three-year periods between 2006 and 2009. We estimated N budgets in a seed corn-soybean rotation farming system near DeLand, east-central Illinois, USA, with free (FD) and controlled drainage (CD) patterned tile systems. In addition, wood chip filled trenches (bioreactors) were installed below the CD structures, one lined with plastic and one unlined. We measured daily tile flow and nitrate-N (NO₃-N) concentrations and calculated cumulative N loss from the tile water at both FD and CD areas for a period of three cropping years. We also monitored the tile flow and nitrate concentration in inlet and outlet of the bioreactor associated with a CD system and evaluated the efficiency of the bioreactor for two cropping years. Most components of the N balance were unaffected by CD (yields and therefore N harvested, surface soil denitrification), and there was a negative N balance in the soybean cropping year (?165 and ?163 kg N ha^?1 at FD and CD areas, respectively), whereas seed corn cropping in the following year resulted in positive N balances (29 and 34 kg N ha^?1 at FD and CD areas, respectively). For two years, the overall N balances were ?136 and ?129 kg N ha^?1 at FD and CD areas, respectively, consistent with other recent corn belt studies showing a small net depletion of soil organic N. Controlled drainage greatly reduced tile N export, with a three-year average loss of 57.2 kg N ha^?1 yr^?1 from FD compared to 17 kg N ha^?1 yr^?1 for CD. There was high uncertainty in denitrification measurements and thus the fate of missing N in the CD system remained unknown. Nitrate reduction efficiency of the bioreactor varied greatly, with periods where nearly 100% of the nitrate was denitrified. The overall efficiency of the bioreactor associated with the CD system in reducing the tile N load was 33%. When nitrate was non-limiting, the nitrate removal rate of the bioreactor was 6.4 g N m^?3 d^?1. Little N₂O emission was found from the bioreactor bed and is not thought to be a problem with these systems. Both the tile bioreactor and controlled drainage greatly reduced tile nitrate export in this leaky seed corn and soybean agricultural field.     

Toward consistent measurements of carbon accumulation: A multi-site assessment of biomass and basal area increment across Europe

The use of tree-ring data in carbon cycle research has so far been limited because traditional study designs are not geared toward quantifying forest carbon accumulation. Existing studies that assessed biomass increment from tree rings were often confined to individual sites and used inconsistent sampling schemes. We applied a consistent biomass-oriented sampling design at five managed forest sites located in different climate zones to assess the annual carbon accumulation in above-ground woody tissues (i.e. stems and branches) and its climate response. Radial growth and biometric measurements were combined to reconstruct the annual biomass increment in individual trees and upscaled to the site level. In addition to this, we estimated that 32–60 trees are required at these five sites to robustly quantify carbon accumulation rates. Tree dimensions and growth rates varied considerably among sites as a function of differing stand density, climatic limitations, and management interventions. Accordingly, mean site-level carbon accumulation rates between 65 g C m⁻² y⁻¹ and 225 g C m⁻² y⁻¹ were reconstructed for the 1970–2009 period. A comparison of biomass increment with the widely used basal area increment (BAI) revealed very similar growth trends but emphasized the merits of biomass assessments due to species-specific BAI/biomass relationship. Our study illustrates the benefits and challenges of combining tree-ring data with biometric measurements and promotes the consistent application of a standardized sampling protocol across large spatial scales. It is thus viewed as a conceptual basis for future use of tree-ring data to approach research questions related to forest productivity and the terrestrial carbon balance.     

Management of white mold in processing tomatoes by Trichoderma spp. and chemical fungicides applied by drip irrigation

Field trials were carried out to evaluate six treatments combining biological agents and chemical fungicides applied via chemigation against white mold (Sclerotinia sclerotiorum) on processing tomatoes. The experiment was performed in Goiânia, Brazil, with tomato hybrid Heinz 7155 in 2009 and 2010 in a field previously infested with S. sclerotiorum sclerotia. Treatments were arranged in a randomized complete block design in a 2 × 3 factorial structure (with and without Trichoderma spp. 1.0 × 10⁹ viable conidia mL⁻¹ ha⁻¹) × fluazinam (1.0 L ha⁻¹), procimidone (1.5 L ha⁻¹) and control, applied by drip irrigation. Treatments were applied three times 10 days apart, starting one month after transplanting. Each treatment consisted of plots with three 72-meter rows with four plants m⁻¹ and 1.5 m spacing between rows, with three replications. Based on disease incidence evaluated weekly, the area under the disease progress curve (AUDPC) was obtained. Yield and its components were evaluated in addition to fruit pH and °Brix. Results were subjected to ANOVA, Scott-Knott (5%), and regression analysis. Biocontrol using Trichoderma spp. via chemigation singly or in combination with synthetic fungicides fluazinam and procimidone reduced AUDPC and increased fruit yield up to 25 t ha⁻¹. The best treatment for controlling white mold also increased pulp yield around 1.0 and 7.0 t ha⁻¹ in 2009 and 2010, respectively. The present work demonstrated the advantages of white mold biological control in processing tomato crops, where drip irrigation favored Trichoderma spp. delivery close to the plants and to the inoculum source.     

Nutrient uptake and release in ponds under long-term and short-term lotus (Nelumbo nucifera) cultivation: Influence of compost application

Uptake and release of nutrients from ponds used for lotus cultivation were measured in ponds under short-term (1 yr) cultivation with compost application (pond I) and under long-term (20 yr) cultivation without compost application (pond II). Total inflow loads of TN (irrigation water, rainfall and compost) during lotus cultivation period in ponds I and II were 72.3 and 34.3 kg ha^?1 182 day^?1, respectively. TN removal rates in ponds I and II were 77.3 and 49.8% of total inflow load, respectively. Major removal mechanisms of TN were attributed to microbial processes and uptake by lotus. The total outflow loads (infiltration and runoff) of TN during the lotus cultivation period were 13.9 kg ha^?1 182 day^?1 (19.2% of total inflow TN load) for pond I, and 11.3 kg ha^?1 182 day^?1 (32.9% of total inflow TN load) for pond II. For TP the total inflow loads (irrigation water, rainfall and compost) during lotus cultivation in ponds I and II were 80.8 and 1.9 kg ha^?1 182 day^?1, respectively. TP removal rates in ponds I and II were 84.9 and ?274.1% of total input, respectively. Phosphorus removal was attributed to lotus uptake and soil adsorption. The total outflow loads (infiltration and runoff) of TP during lotus cultivation period were 10.1 kg ha^?1 182 day^?1 (12.5% of total inflow TP load) for pond I, and 6.6 kg ha^?1 182 day^?1 (355.6% of total inflow TP load) for pond II. TN and TP in runoff from pond I (with compost) was higher than that in pond II (without compost), showing that TN and TP in runoff were strongly influenced by compost addition. Therefore, in order to satisfy established water-quality standards, the amount of compost used in lotus cultivation should be evaluated.     

Increasing nitrogen depositions can reduce lichen viability and limit winter food for an endangered Chinese monkey

Increasing economic growth and industrial development in China is starting to impact even remote areas such as the Shennongjia nature reserve, where nitrogen pollution is becoming a major environmental threat. The epiphytic lichen flora is particularly rich in this area and is one of the components of this habitat most sensitive to nitrogen pollution. Since lichens represent an important food resource for the endangered monkey species Rhinopithecus roxellana, a reduction in lichen availability would have harmful consequences for the conservation of its habitat in the Shennongjia Mountains. To investigate the effects of increased nitrogen availability on the local lichen communities, so far scarcely considered, we conducted a one-year field experiment measuring growth, survival, and phosphomonoesterase activity of the widespread species Usnea luridorufa in response to nitrogen (up to 50 kg N ha⁻¹ year⁻¹ deposition) and phosphorus supply. Growth and survival of thalli and propagules of U. luridorufa decreased when treated with N deposition >12.05 kg N ha⁻¹ year⁻¹ and >2.14 kg N ha⁻¹ year⁻¹, respectively. The important role of phosphorus availability in relation to nitrogen supply was demonstrated by the increase in phosphomonoesterase activity with increasing nitrogen availability until a nitrogen toxicity threshold was reached. However, the high concentration of phosphorus in rainwater showed that phosphorus is not a limiting nutrient in the area.The results make a contribution to the knowledge of the negative effects of increased N deposition in the Shennongjia forest ecosystem.