Regulation of different light conditions for efficient biomass production and protein accumulation of Spirulina platensis

Light plays an important role in the photosynthesis and metabolic process of microalgae.However,how different light conditions regulate the biomass production and protein accumulation of microalgae is mostly unknown.In this study,the influence of different light conditions,including light colors,densities,and light:dark cycles on the cell growth and biochemical composition of Spirulina platensis was symmetrically characterized.Under different colored lights,S.platensis all shows an increase trend within the increased light intensity ranges;however,each showing different optimal light intensities.At the same light intensity,different colored lights show different growth rate of S.platensis following the sequence of red>white>green>yellow>blue.The maximum growth rate and protein accumulation were determined as 21.88 and 5.10 mg/(L·d)when illuminated under red LED.The energy efficiency of different light sources was calculated and ranked as red>white>blue≈green>yellow.Transcriptomic analysis suggests that red light can promote cell growth and protein accumulation by upregulating genes related to photosynthesis,carbon fixation,and C-N metabolism pathways.This study provides a conducive and efficient way to promote biomass production and protein accumulation of S.platensis by regulating light conditions.

Biomass and dominance of conservative species drive above-ground biomass productivity in a mediterranean-type forest of Chile

Background: Forest productivity has a pivotal role in human well-being. Vegetation quantity, niche complementarity, mass-ratio, and soil resources are alternative/complementary ecological mechanisms driving productivity. One challenge in current forest management depends on identifying and manipulating these mechanisms to enhance productivity. This study assessed the extent to which these mechanisms control aboveground biomass productivity(AGBP) of a Chilean mediterranean-type matorral. AGBP measured as tree aboveground biomass changes over a 7-years period, was estimated for twelve 25 m × 25 m plots across a wide range of matorral compositions and structures. Variables related to canopy structure, species and functional diversity, species and functional dominance, soil texture, soil water and soil nitrogen content were measured as surrogates of the four mechanisms proposed. Linear regression models were used to test the hypotheses. A multimodel inference based on the Akaike’s information criterion was used to select the best models explaining AGBP and for identifying the relative importance of each mechanism.Results: Vegetation quantity(tree density) and mass-ratio(relative biomass of Cryptocarya alba, a conservative species) were the strongest drivers increasing AGBP, while niche complementarity(richness species) and soil resources(sand, %) had a smaller effect either decreasing or increasing AGBP, respectively. This study provides the first assessment of alternative mechanisms driving AGBP in mediterranean forests of Chile. There is strong evidence suggesting that the vegetation quantity and mass-ratio mechanisms are key drivers of AGBP, such as in other tropical and temperate forests. However, in contrast with other studies from mediterranean-type forests, our results show a negative effect of species diversity and a small effect of soil resources on AGBP.Conclusion: AGBP in the Chilean matorral depends mainly on the vegetation quantity and mass-ratio mechanisms.The findings of this study have implications for matorral restoration and management for the production of timber and non-timber products and carbon sequestration.

Dynamics of Above-and Below-ground Biomass and C,N,P Accumulation in the Alpine Steppe of Northern Tibet

The temporal dynamics of the biomass, as well as the carbon （C）, nitrogen （N）, phosphorus （P） concentrations and accumulation contents, in above- and below-ground vegetation components were determined in the alpine steppe vegetation of Northern Tibet during the growing season of 2OLO. The highest levels of total biomass （311.68 g m-2）, total C （115.95 g m-2）, total N （2.60 g m-2）, and total P （0.90 g m-2） accumulation contents were obtained in August in 2010. Further, biomass and nutrient stocks in the below-ground components were higher than those of the above-ground components. The dominant species viz., Stipa purpurea and Carex moorcrofli had lower biomass and C, N, P accumulations than the companion species which including Oxytropis. spp., Artemisia capillaris Thunb., Aster tataricus L., and SO on.

Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests

Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.

Nickel accumulation and its effect on biomass,protein content and antioxidative enzymes in roots and leaves of watercress(Nasturtium officinale R.Br.)

In order to understand its response towards nickel stress, watercress （Nasturtium officinale R. Br.） was exposed to nickel （1-25 mg/L） for 1, 3, 5 and 7 days. The accumulation and translocation of nickel were determined and the influence of nickel on biomass, protein content and enzymatic antioxidants was examined for both roots and leaves. It was determined that N. o fficinale could accumulate appreciable amounts of Ni in both roots and leaves. Nickel accumulated particularly in the roots of plants. Biomass increased at low nickel concentrations but certain measurable change was not found at high concentrations. Under stress conditions the antioxidant enzymes were up-regulated compared to control. An increase in protein content and enzyme activities was observed at moderate exposure conditions followed by a decline at both roots and leaves. The maximum enzyme activities were observed at different exposure conditions. Our results showed that N. officinale had the capacity to overcome nickel-induced stress especially at moderate nickel exposure. Therefore, N. officinale may be used as a phytoremediator in moderately polluted aquatic ecosystems.

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.

Estimation of the Above-ground Biomass of Tamarix Species along a Transect at the Tarim River's Middle Reaches

[Objective] The purpose was to research the distribution characteristics of Tamarix species above-ground biomass of Tarim River＇s middle reaches and to find out best-fit linear-regression model of Tamarix species in this area.[Method] By dint of the most common sampling method PCQ,five samples in the middle reaches of Tarim River were collected.The best-fit linear-regression model of Tamarix species of this area was set up,based on the fieldwork and the model of Evangelista and obtained the distribution rules of Tamarix species of Tarim River＇s middle reaches.[Result] The result indicated that this model fitted for the estimation of aboveground biomass of the study area.According to the distribution rules of aboveground biomass,it was clear that underground water was the major element which decided the distribution of aboveground biomass.[Conclusion] The study provided theoretical basis for the calculation of biomass of Tamarix.

Plant cover as an estimator of above-ground biomass in semi-arid woody vegetation in Northeast Patagonia,Argentina

The quantification of carbon storage in vegetation biomass is a crucial factor in the estimation and mitigation of CO2 emissions.Globally,arid and semi-arid regions are considered an important carbon sink.However,they have received limited attention and,therefore,it should be a priority to develop tools to quantify biomass at the local and regional scales.Individual plant variables,such as stem diameter and crown area,were reported to be good predictors of individual plant weight.Stand-level variables,such as plant cover and mean height,are also easy-to-measure estimators of above-ground biomass(AGB)in dry regions.In this study,we estimated the AGB in semi-arid woody vegetation in Northeast Patagonia,Argentina.We evaluated whether the AGB at the stand level can be estimated based on plant cover and to what extent the estimation accuracy can be improved by the inclusion of other field-measured structure variables.We also evaluated whether remote sensing technologies can be used to reliably estimate and map the regional mean biomass.For this purpose,we analyzed the relationships between field-measured woody vegetation structure variables and AGB as well as LANDSAT TM-derived variables.We obtained a model-based ratio estimate of regional mean AGB and its standard error.Total plant cover allowed us to obtain a reliable estimation of local AGB,and no better fit was attained by the inclusion of other structure variables.The stand-level plant cover ranged between 18.7%and 95.2%and AGB between about 2.0 and 70.8 Mg/hm^(2).AGB based on total plant cover was well estimated from LANDSAT TM bands 2 and 3,which facilitated a model-based ratio estimate of the regional mean AGB(approximately 12.0 Mg/hm^(2))and its sampling error(about 30.0%).The mean AGB of woody vegetation can greatly contribute to carbon storage in semi-arid lands.Thus,plant cover estimation by remote sensing images could be used to obtain regional estimates and map biomass,as well as to assess and monitor the impact of land-use change on the carbon balance,for arid and semi-arid regions.

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.

Assessment of tree diversity and above-ground biomass in coffee agroforest dominated tropical landscape of India's Central Western Ghats

The purpose of this study was to quantify the changes in tree diversity and above-ground biomass associated with six land-use types in Kodagu district of India's Western Ghats. We collected data on species richness,composition and above-ground biomass(AGB) of trees,shrubs and herbs from 96 sample plots of 0.1 ha. Totals of83 species from 26 families were recorded across the landuses. Tree species richness, diversity and composition were significantly higher in evergreen forest(EGF) than in other land-uses. Similarly, stem density and basal area were greater in EGF compared to other land-uses. Detrended correspondence analysis(DCA) yielded three distinct groups along the land-use intensities and rainfall gradient on the first and second axes, respectively. The first DCA axis accounted for 45% and second axis for 35% of the total variation in species composition. Together the first two axes accounted for over 2/3 of the variation in species composition across land-use types. Across the land-uses,AGB ranged from 58.6 Mg ha-1 in rubber plantation to327.3 Mg ha-1 in evergreen forest. Our results showed that species diversity and AGB were negatively impacted bythe land-use changes. We found that coffee agroforests resembled natural forest and mixed species plantation in terms of tree diversity and biomass production, suggesting that traditional coffee farms can help to protect tree species, sustain smallholder production and offer opportunities for conservation of biodiversity and climate change mitigation.

Biomass Accumulation and Nutrient Uptake of Jerusalem Artichoke (<i>Helianthus tuberosus</i>L.)

The dynamics of biomass accumulation during the growing period, the yield of leafy stalks and tubers, and the nutrient concentration and nutrient uptake of the yield were investigated for two Jerusalem artichoke varieties (Tápiói Korai and Tápiói Sima) in a field experiment involving mineral fertilisation. Considerable differences were observed between the dynamics of leafy stalk and tuber development in Tápiói Korai which has a short vegetation period and Tápiói Sima where the vegetation period is long. The maximum dry matter ratio between the tuber yield and the leafy stalk yield was 1:1 for Tápiói Korai and 1:4.5 for Tápiói Sima. During the period when the maximum aboveground biomass developed in Tápiói Korai, 100 kg.ha-1 N and P fertiliser resulted in the highest leafy stalk yield (38.34 t.ha-1), while for Tápiói Sima, which developed a much greater leafy stalk mass, the highest aboveground biomass yield (78-80 t.ha-1) was given in response to 200 kg.ha-1 N supplemented by P and K fertiliser. Both artichoke varieties produced the great-est tuber yield at a N rate of 200 kg.ha-1, supplemented with P and K fertiliser. The nutrient concentration in the leafy stalks was highest on the 85th day of the vegetation period, prior to intensive dry matter accumulation in the leafy stalks and before tuber formation began. In both varieties the maximum nutrient uptake was recorded on the 155th day. Great differences were observed between the varieties in terms of specific nutrient uptake. For a tuber yield of 10 t, together with the corresponding leafy stalk yield, the specific nutrient uptake of the Tápiói Korai variety amounted to 48 kg N, 10 kg P, 83 kg K, 30 kg Ca and 10 kg Mg, while for Tápiói Sima these figures were 162 kg N, 30 kg P, 300 kg K, 84 kg Ca and 45 kg Mg.

Allometric prediction of above-ground biomass of eleven woody tree species in the Sudanian savanna-woodland of West Africa

Allometric models are necessary for estimating biomass in terrestrial ecosystems. Generalized allometric relationship exists for many tropical trees, but species- and region-specific models are often lacking. We developed species-specific allometric models to predict aboveground biomass for 11 native tree species of the Sudanian savanna- woodlands. Diameters at the base and at breast height, with species means ranging respectively from 11 to 28 cm and 9 to 19 cm, and the height of the trees were used as predictor variables. Sampled trees spanned a wide range of sizes including the largest sizes these species can reach. As a response variable, the biomass of the trees was obtained through destructive sampling of 4 754 trees during wood harvesting. We used a stepwise multiple regression analysis with backward elimination procedure to develop models separately predicting, total biomass of the trees, stem biomass, and biomass of branches and twigs. All species- specific regression models relating biomass with measured tree dimen- sions were highly significant （p 〈 0.001）. The biomass of branches and twigs was less predictable compared to stem biomass and total biomass, although their models required fewer predictors and predictor interac- tions. The best-fit equations for total above-ground biomass and stem biomass bad R2 〉 0.70, except for the Acacia species; for branches including twig biomass, R2-values varied from 0.749 for Anogeissus leiocarpa to 0.183 for Acacia macrostachya. The use of these equations in estimating available biomass will avoid destructive sampling, and aid in planning for sustainable use of these species.

Land use and above-ground biomass changes in a mountain ecosystem,northern Thailand

The conversion of forests into agricultural lands is a major cause of deforestation,particularly in the mountain ecosystems of northern Thailand.It results in a rapid loss of biological diversity of both flora and fauna.In addition,the above-ground biomass(AGB),which can be a major source of carbon storage,is also decreased.This study aimed to predict the AGB in Doi Suthep-Pui National Park,Chiang Mai province,based on land-use/land cover(LULC)changes from 2000 to 2030.Landsat-5 TM(2000)and Landsat-8 TM(2015)satellite images were analyzed to predict LULC changes to 2030.Temporary plots(30 m 930 m)were established in each LULC type for AGB analysis;trees with diameters at breast height≥4.5 cm were identified and measured.AGB of all LULC types were analyzed based on specific allometric equations of each type.The results show that area of forest and nonforested areas fluctuated during the study period.Through the first 15 years(2000–2015),5%(2.9 km^2)of forest changed to either agriculture or urban lands,especially mixed deciduous forest and lower montane forest.There was a similar trend in the 2030 prediction,showing the effect of forest fragmentation and the resultant high number of patches.Total AGB tended to decrease over the 30-year period from 12.5 to 10.6 t ha^-1 in the first and second periods,respectively.Deforestation was the main factor influencing the loss of AGB(30.6 t ha^-1)related to LULC changes.Furthermore,habitat loss would be expected to result in decreased biological diversity.Consequently,a management plan should be developed to avoid unsustainable land use changes,which may adversely affect human well-being.

Quantification of Above-Ground Biomass and Carbon Sequestration Potential of Roadside Trees in the Plateau Department of Benin Republic

Roadside trees are effective natural solutions for mitigating climate change. Despite the usefulness of trees to carbon sequestration, there is a dearth of information on the estimation of biomass and carbon stock for roadside trees in the study area. This study aimed to estimate the carbon stock and carbon dioxide equivalent of roadside trees. A complete enumeration of trees was carried out in Kétou, Pobè and Sakété within the communes of the Plateau Department, Bénin Republic. Total height and diameter at breast height were measured from trees along the roads while individual wood density value was obtained from wood density database. The allometric method of biomass estimation was adopted for the research. The results showed that the total estimations for above-ground biomass, carbon stock and carbon equivalent from all the enumerated roadside trees were 154.53 mt, 72.63 mt and 266.55 mt, respectively. The results imply that the roadside trees contain a substantial amount of carbon stock that can contribute to climate change mitigation through carbon sequestration.

Biomass Fraction of Phosphate-Accumulating Organisms Grown in Anoxic and Aerobic Stages under Optimum Nitrate Concentration

The effects of nitrate concentration on the capability of phosphorus uptake in the main anoxic stage were investigated.Meanwhile, the biomass fractions — heterotrophs, phosphateaccumulating organisms( PAOs),and nitrifying organisms in a pilot-scale enhanced biological phosphorus removal( EBPR) system— were both experimentally and theoretically evaluated( from the mass balance calculations of organic matter, nitrogen and phosphorus),under optimum nitrate concentration in the main anoxic stage,in which the influent chemical oxygen demand( COD)concentration was stabilized at( 290 ± 10) mg·L- 1and the influent total phosphorus( TP) concentration was stabilized at( 7. 0 ± 0. 5)mg · L- 1. In long term operations,the process exhibited high performance in removing organic matter, nitrogen, and phosphorus. Approximately 46. 41% of organic matter,57. 21% of nitrogen,and 48. 14% of phosphorus were removed from the influent in the form of carbon dioxide,nitrogen gas,and polyphosphate,respectively. XH( heterotrophs),XPAO( PAOs),and XAUT( autotrophs) were regarded as the major organisms responsible for biomass production. The yield fractions of XHgrowth in the first anoxic,the second anoxic,and the aerobic stages were 10. 24%,19. 11%,and 19. 71%,respectively; the yield fractions of XPAO growth in the second anoxic and the aerobic stages were 24. 34% and19. 86%,respectively; the yield fraction of XAUTgrowth in the aerobic stage was 6. 74%. These results showed that XHand XPAOformed the major community. Moreover,a higher amount of XPAOgrowth on stored poly-hydroxyalkanoates( PHAs) under the anoxic condition was seen in this EBPR system for municipal wastewater treatment.

Tree Allometry in Tropical Forest of Congo for Carbon Stocks Estimation in Above-Ground Biomass

The research was aimed to estimate the carbon stocks of above-ground biomass (AGB) in Lesiolouna forest in Republic of Congo. The methodology of Allometric equations was used to measure the carbon stock of Lesio-louna tropical rainforest. The research was done with six circular plots each 40 m of diameter, with a distance of 100 m between each plot, depending on the topography of the site of the installation of these plots. The six studied plots are divided in two sites, which are: Iboubikro and Ngambali. Thus, in the six plots, there are three plots in Iboubikro site and three plots in Ngambali site. The results of this study show that the average carbon stock of aboveground biomass (AGB) in six plots was 170.673 t C ha-1. So, the average of carbon stock of aboveground biomass (ABG) in Iboubikro site was 204.693 t C ha-1 and in the Ngambali site was 136.652 t C ha-1. In this forest ecosystem, the high stock of carbon was obtained in Plot 3, which was in Iboubikro site. Plot 3 contains 20 trees and an average DBH of 24.56 cm. However, the lowest carbon stock was obtained in Plot 4, which was in Ngambali site. Also, Plot 4 contains 11 trees and an average DBH of 31.86 cm. The results of this research indicate that, the forests in the study area are an important carbon reservoir, and they can also play a key role in mitigation of climate change.

Estimation of biomass in wheat using random forest regression algorithm and remote sensing data

Wheat biomass can be estimated using appropriate spectral vegetation indices.However,the accuracy of estimation should be further improved for on-farm crop management.Previous studies focused on developing vegetation indices,however limited research exists on modeling algorithms.The emerging Random Forest(RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling.The objectives of this study were to(1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass,(2) test the performance of the RF regression model,and(3) compare the performance of the RF algorithm with support vector regression(SVR) and artificial neural network(ANN) machine-learning algorithms for wheat biomass estimation.Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing,booting,and anthesis stages of growth.Fifteen vegetation indices were calculated based on these images.In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition.The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage,and its robustness is as good as SVR but better than ANN.The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China.

Planting density affected biomass and grain yield of maize for seed production in an arid region of Northwest China

Field experiments were conducted from 2012 to 2015 in an arid region of Northwest China to investigate the effects of planting density on plant growth, yield, and water use efficiency（WUE） of maize for seed production. Five planting densities of 6.75, 8.25, 9.75, 11.25 and 12.75 plants/m^2 were conducted in 2012, and a planting density of 14.25 plants/m^2 was added from 2013 to 2015. Through comparison with the Aqua Crop yield model, a modified model was developed to estimate the biomass accumulation and yield under different planting densities using adjustment coefficient for normalized biomass water productivity and harvest index. It was found that the modified yield model had a better performance and could generate results with higher determination coefficient and lower error. The results indicated that higher planting density increased the leaf area index and biomass accumulation, but decreased the biomass accumulation per plant. The total yield increased rapidly as planting density increased to 11.25 plants/m^2, but only a slight increase was observed when the density was greater than 11.25 plants/m^2. The WUE also reached the maximum when planting density was 11.25 plants/m^2, which was the recommended planting density of maize for seed production in Northwest China.

Ecological biomass allocation strategies in plant species with different life forms in a cold desert, China

Biomass allocation patterns among plant species are related to their adaptive ecological strategies. Ephemeral, ephemeroid and annual plant life forms represent three typical growth strategies of plants that grow in autumn and early spring in the cold deserts of China. These plants play an important role in reducing wind velocity in the desert areas. However, despite numerous studies, the strategies of biomass allocation among plant species with these three life forms remain contentious. In this study, we conducted a preliminary quadrat study during 2014–2016 in the southern part of the Gurbantunggut Desert, China, to investigate the allocation patterns of above-ground biomass(AGB) and below-ground biomass(BGB) at the individual level in 17 ephemeral, 3 ephemeroid and 4 annual plant species. Since ephemeral plants can germinate in autumn, we also compared biomass allocation patterns between plants that germinated in autumn 2015 and spring 2016 for 4 common ephemeral species. The healthy mature individual plants of each species were sampled and the AGB, BGB, total biomass(TB), leaf mass ratio(LMR) and root/shoot ratio(R/S) were calculated for 201 sample quadrats in the study area. We also studied the relationships between AGB and BGB of plants with the three different life forms(ephemeral, ephemeroid and annual). The mean AGB values of ephemeral, ephemeroid and annual plants were 0.806, 3.759 and 1.546 g/plant, respectively, and the mean BGB values were 0.106, 4.996 and 0.166 g/plant, respectively. The mean R/S value was significantly higher in ephemeroid plants(1.675) than in ephemeral(0.154) and annual(0.147) plants. The mean LMR was the highest in annual plants, followed by ephemeroid plants and ephemeral plants, reflecting the fact that annual plants allocate more biomass to leaves, associated with their longer life span. Biomass of ephemeral plants that germinated in autumn was significantly higher than those of corresponding plants that germinated in spring in terms of AGB, BGB and TB. However, the R/S value was similar in plants that germinated in autumn and spring. The slope of regression relationship between AGB and BGB differed significantly among the three plant life forms. These results support different biomass allocation hypotheses. Specifically, at the individual level, the AGB and BGB partitioning supports the allometric hypothesis for ephemeroid and annual plants and the isometric hypothesis for ephemeral plants.

Biomass Components and Environmental Controls in Ningxia Grasslands

Grassland plays an important role in the global carbon cycle and climate regulation. However, there are still large uncertainties in grassland carbon pool and also its role in global carbon cycle due to the lack of measured grassland biomass at regional scale or global scale with a unified survey method, particular for below-ground biomass. The present study, based on a total of 44 grassland sampling plots with 220 quadrats across Ningxia, investigated the characteristics of above-ground biomass （AGB）, below-ground biomass （BGB）, litter biomass （LB）, total biomass （TB） and root：shoot ratios （R：S） for six predominantly grassland types, and their relationships with climatic factors. AGB, BGB, LB and TB varied markedly across different grassland types, the median value ranging from 28.2-692.6 g m-2 for AGB, 130.4-2 036.6 g m-： for BGB, 9.2-82.3 g m2 for LB, and 168.0-2 681.3 g m-： for TB. R：S showed less variation with median values from 3.2 to 5.3 （excluding marshy meadow）. The different grassland types showed similar patterns of biomass allocation, with more than 70% BGB for all types. There is evidence of strong positive effects associated with mean annual precipitation （MAP） and negative effects associated with mean annual temperature （MAT） on AGB, BGB, and LB, although both factors have the opposite effect on R：S.