Biomass Categories Research Articles

Comparative computational fluid dynamics analysis of fast pyrolysis of agricultural feedstocks across different biomass categories

This article presents a comprehensive Computational Fluid Dynamics (CFD) assessment of fast pyrolysis of three different agricultural feedstocks representing three different categories: Red oak (hardwood), Switchgrass (herbaceous biomass), and Picea glauca (softwood). The study aimed to evaluate the performance of each feedstock based on particle density, temperature distribution, and volume fraction distribution. The comparison of the simulation results with experimental data showed an average error factor below 13%. The study discovered that Red oak had the highest bio-oil production yield, followed by Switchgrass, which had a yield 10% lower, and Picea glauca, which had the weakest performance having a yield 20% lower than Red oak. Interestingly, optimal operating conditions for the production of bio-oil across all three feedstocks comprised a temperature of 500 °C and particle size of approximately 400 μm. However, increasing the pyrolysis temperature resulted in an increase in gas yield, whereas increasing particle size led to a rise in char production yield. Furthermore, the findings suggest that herbaceous biomass tends to perform better in fast pyrolysis for bio-oil production compared to softwood. This is attributed to the higher hemicellulose content in herbaceous feedstock which has a significant impact on bio-oil yield compared to cellulose and lignin. Moreover, hardwood and herbaceous samples, which have similar levels of cellulose and hemicellulose, demonstrated superior performance compared to softwood, which has lower hemicellulose content. Nonetheless, herbaceous biomass had about 10% extractable content, which made it perform weaker than hardwood.

Selective formation of fuel BXT compounds from catalytic hydrodeoxygenation of waste biomass over Ni-decorated beta-zeolite

Upgrading of bio-oil extracted from the various components of biomass into transportation fuels is typically attained via surface-assisted hydrodeoxygenation (HDO) reactions. Date palm (Phoenix dactylifera) pits constitute an important category of waste biomass in many parts of the world. Herein, we report a comprehensive integrated experimental-simulation approach to investigate HDO process of vapor fraction generated from pyrolysis of date pits over the metal-supported catalyst Ni/H-Beta zeolite. The catalyst was synthesized and characterized using various techniques including XRD, FTIR, SEM-EDX, and TPR. Catalytic tests were carried out in a 2-stage catalytic reactor where pyrolysates from the first reactor flows over the catalyst placed in the second reactor. Selective formation of toluene (and other alkylated benzenes) indicates a profound selectivity towards the formation of benzene-toluene-xylene (BXT) fractions especially in the temperature range 200–400 °C. As the temperature increases, fragmentation and bimolecular reactions involving toluene leads to synthesis of unwanted PAHs, most notably azulene. Considering 3,4-altrosan as a model compound for the lignin content in date pits, governing mechanisms of HDOs, was mapped out using density functional theory (DFT) calculations. Results reported herein are expected to assist in the optimizing operational conditions that leads to the production of the valuable BXT fraction from pyrolysis of date pits.

Kinetic parameters for H abstraction from the serine amino acid molecule

The Serine compound signifies an important model entity of polar amino acids species in various categories of biomass. The presence of adjacent amino and hydroxyl groups is expected to afford this molecule distinct fragmentation pathways. Comprehending the decomposition chemistry of serine (and other amino acids) is of a crucial important in the pursuit to minimize emission of notorious nitrogen-bearing species during thermal treatment of biomass. This contribution provides thermo-kinetic parameters for H abstraction from the five distinct sites in the serine molecule; namely amine’s, secondary’s, hydroxyl’s, and α CH hydrogen atoms by the abundant radical species in the pyrolytic/oxidative environments (H, CH3, NH2, OH, and HO2). For all considered radicals, it is illustrated that reactions of these five radicals predominantly ensue through abstraction of the α CH’s hydrogen atoms. Nonetheless, computed reaction rate constants also disclose that abstraction from the secondary’s H site becomes important as the temperature increases. Outcomes provided herein could be deployed in formulating robust kinetic models that account for the nitrogen transformation chemistry in energy recovery of biomass.

Formation of value-added products from the pyrolysis of date pits: A combined experimental–DFT approach

In arid and semiarid environments, bio-oil extraction from environmentally favorable and waste biomass is presently used as a mainstream strategy for the development of sustainable sources of energy. In keeping with this, we provide a thorough analysis of the pyrolysis of date palm pits here, focusing particularly on relevant thermal degradation profiles and pyrolytic products over a wide temperature range. Pyrolysis experiments were performed using a single-stage reactor at several temperature intervals. The temperature ranges were chosen following mass loss profiles of date pit biomass obtained by thermogravimetric analyzer (TGA) analysis. Density functional theory (DFT) calculations map out reaction pathways that dictate the decomposition of two model compounds that represented oxygen and nitrogen content in the collected condensed and non-condensed fractions. In-depth temperature-dependent profiles of pyrolytic products are thoroughly presented and analyzed. Analysis of products indicates the generation of a wide spectra of green diesel's ingredients of (i.e., long aliphatic compounds) and transportation fuels additives (alkylated benzenes). Aromatic compounds attain the highest fractions in the collected bio-oil at investigated temperature windows. Glucose derivatives appear in appreciable loads in the non-condensable fractions. The obtained mass loss curves reflect degradation curves for the most prominent biomass structural components; namely lignocellulosic, hemicellulose, and lignin. Findings presented herein shall be helpful to develop pyrolysis technologies that are suited for niche categories of biomass found in arid and semiarid regions where cultivation of standard energy crops is impractical.

Coupling biomass gasification and inline co-steam reforming: Synergistic effect on promotion of hydrogen production and tar removal

Biomass to green H2 is a promising strategy to achieve clean energy. This study proposed a novel concept of enhancing H2 production and tar removal via biomass gasification coupled with inline co-steam reforming (BGCSR) process. Varying gasification/pyrolysis biochar materials (G/PCMs) were applied assisted-reforming agents and co-reactants at the steam reforming stage in the BGCSR process. First, four representative biomass categories (wheat straw, microalgae, sewage sludge, and cow manure) were subjected to BGCSR process. Microalgae presented the more positive synergistic effect on H2 production and tar reduction. It was noticed that microalgae subjected to BGCSR in the presence of G/PCMs showed the comparable H2 concentration (over 56 vol%). The highest cumulative gas yield (104.17 mmol/g) and H2 yield (59.55 mmol/g) were manifested in the presence of C[F900], with the highest synergistic effect of 16.02% and 19.74%, respectively. C[F900] also presented the most significant synergistic effect (13.81%) on tar elimination. There outcomes were in relation to the properties of G/PCMs, e.g., elemental compositions, inherent alkali and alkaline earth metals (AAEMs), and surface functional groups. In addition, reaction mechanism regarding BGCSR was elucidated. Overall, this study provided a novel and effective way to valorize biomass into H2-enriched gas whilst minimizing tar formation.

Earthworm community structure and population dynamics at Minkong forest of Mokokchung, Nagaland, India

Aim: The present study was undertaken to assess the seasonal variations of density, biomass and community characteristics of earthworms in two different types of forest at Minkong area, Mokokchung. Methodology: The survey was conducted during the period from January 2019-February 2020 in two areas viz., mixed forest and plantation. Standard methods were followed to determine the ecological categories of earthworms, density and biomass, relative abundance and species richness as well as for determination of different soil parameters viz., pH, moisture, bulk density, organic carbon, total and available nitrogen, available phosphorus and potassium. Results: 12 number earthworm species viz., Amynthas corticis (Kinberg), Amynthus gracilis (Kinberg), Drawida assamensis (Gates), Drawida hodgarti (Stephenson), Drawida nepalensis (Michaelsen), Drawida sp., Eutyphoeus assamensis (Stephenson), Eutyphoeus festivus (Gates) Metaphire houlleti (Perrier), Perionyx excavatus (Perrier), Perionyx simlaensis (Michaelsen) and Pontoscolex corethurus (Müller) were recorded. Dominant earthworm species in both the areas were D. nepalensis, P. excavatus, Drawida sp. and E. festivus. The Shannon diversity, Simpson diversity and Pielou’s species evenness were 2.17, 0.87 and 0.87 in mixed forest and 2.05, 0.86, 0.86 in plantation. The earthworm density and biomass were greater in plantation (276.74 ind m-2 and 338.47g m-2) compared to mixed forest (230.52 ind m-2 and 254.55 g m-2). Interpretation: As earthworm community characteristics differ depending on the type of forest, therefore, earthworm’ sdiversity and its indices can be used as an ecological indicator of anthropogenic disturbances and can help designing conservation programs for reclaiming the depleted areas. Key words: Biomass, Earthworm diversity, Minkong, Relative abundance, Subtropical ecosystem

Nth-plant scenario for forest resources and short rotation woody crops: Biorefineries and depots in the contiguous US

• Nationwide depots & biorefineries to meet target costs, quality & quantity from woody resources. • Forest residues: trees, tops & limbs. Short rotation crops: poplar, willow, pine, & eucalyptus. • Maximum accessible forest residues at $79.07/dt & $85.51/dt was 103 and 106 M dt at any ash target. • Short rotation woody crops increased total to 153 & 195 M dt at 1% & 1.75% ash & $85.51/dt cost targets. • Woody resources could address 55% of EPA’s goals, 195 M dt/year at $85.51/dt cost & 1.75% ash target. Estimating the US potential of woody material is of vital importance to ensure cost-effective supply logistics and develop a sustainable bioenergy and bioproducts industry. We analyzed a mature conversion technology for woody resources for the contiguous US that takes advantage of economies of scale: the nth-plant. We developed a database to quantify the total accessible woody biomass within a distributed network of preprocessing depots and biorefineries considering both quality specifications for conversion and a target cost to compete with fossil fuels. We considered two categories of woody biomass: 1) forest residues from trees, tops and limbs produced from conventional thinning and timber harvesting operations as well as non-timber tree removal; and 2) short rotation woody crops such as poplar, willow, pine, and eucalyptus. A mixed integer linear programming model was developed to analyze scenarios with woody feedstock blends at variable biomass ash contents and cost targets at the biorefinery. When considering a target cost of $85.51/dry ton (2016$) at the biorefinery, the maximum accessible biomass from forest residues in 2040 remained constant at 106 million dry tons regardless of ash targets. Including short rotation woody crops as part of the blend increased the total accessible biomass to 153 and 195 million dry tons at ash targets of 1% and 1.75%, respectively. We concluded from our analysis that woody resources could address about 55% of EPA’s (Environmental Protection Agency) target of 16 billion gallons of cellulosic biofuel.

Carbon Pools in a 77 Year-Old Oak Forest under Conversion from Coppice to High Forest

Recent model projections and many research results across the world suggest that forests could be significant carbon sinks or sources in the future, contributing in a such a way to global warming mitigation. Conversion of coppice forest to high forest may play an important role towards this direction. This study deals with the estimation of biomass, carbon pool and accumulation rates in all IPCC biomass categories of a 77 year-old oak ecosystem, which has been subjected to conversion from coppice to high forest through repeated tending measures. The research includes a plethora of field tree measurements, destructive sampling of representative oak trees and a systematic sampling of dead wood (standing and fallen), litter and soil. Furthermore, for the estimation of above ground tree living biomass at the stand level, we developed and tested appropriate allometric biomass equations based on the relationships between various independent tree variables (morphological characteristics) and the different tree biomass compartments or leaf biomass. Data analysis shows that coppice conversion results in large accumulation of carbon in all ecosystem pools, with an average annual carbon rate accumulation of 1.97 Mg ha–1 in living above and below ground tree biomass and small amounts to dead wood and litter. The developed allometric equations indicate that above ground tree living biomass can be reliable and precisely predicted by the simple measurement of tree diameter.

Enhancing Smallholder Wheat Yield Prediction through Sensor Fusion and Phenology with Machine Learning and Deep Learning Methods

Field-scale prediction methods that use remote sensing are significant in many global projects; however, the existing methods have several limitations. In particular, the characteristics of smallholder systems pose a unique challenge in the development of reliable prediction methods. Therefore, in this study, a fast and reproducible new approach to wheat prediction is developed by combining predictors derived from optical (Sentinel-2) and radar (Sentinel-1) sensors using a diverse set of machine learning and deep learning methods under a small dataset domain. This study takes place in the wheat belt region of Ethiopia and evaluates forty-two predictors that represent the major vegetation index categories of green, water, chlorophyll, dry biomass, and VH polarization SAR indices. The study also applies field-collected agronomic data from 165 farm fields for training and validation. According to results, compared to other methods, a combined automated machine learning (AutoML) approach with a generalized linear model (GLM) showed higher performance. AutoML, which reduces training time, delivered ten influential parameters. For the combined approach, the mean RMSE of wheat yield was from 0.84 to 0.98 ton/ha using ten predictors from the test dataset, achieving a 99% confidence interval. It also showed a correlation coefficient as high as 0.69 between the estimated yield and measured yield, and it was less sensitive to the small datasets used for model training and validation. A deep neural network with three hidden layers using the ten influential parameters was the second model. For this model, the mean RMSE of wheat yield was between 1.31 and 1.36 ton/ha on the test dataset, achieving a 99% confidence interval. This model used 55 neurons with respective values of 0.1, 0.5, and 1 × 10−4 for the hidden dropout ratio, input dropout ratio, and l2 regularization. The approaches implemented in this study are fast and reproducible and beneficial to predict yield at scale. These approaches could be adapted to predict grain yields of other cereal crops grown under smallholder systems in similar global production systems.

Comparison of two multiple plankton samplers: MOCNESS and Multinet Mammoth

AbstractTo ensure an optimal continuation of a long time series of zooplankton monitoring surveys, two types of equipment for depth‐stratified mesozooplankton sampling were compared. The Institute of Marine Research (Norway) has applied the MOCNESS with good results since 1985, but recent events have made it necessary to change to the Multinet Mammoth. During a cruise in March 2019, both sampling devices were calibrated before 17 paired deployments of the 2 gears were undertaken. During each deployment, three nets and depth‐strata covering ~ 425–200, 200–100, and 100–0 m were sampled. All samples were size‐fractionated or taxonomically fractionated into 10 different biomass categories. The results revealed no significant differences between the two gears when comparing total depth‐integrated biomass (2.46 ± 0.36 vs. 2.61 ± 0.59 gDW m−2) or depth‐integrated biomass of any specific biomass category. Running paired t‐tests separately for all combinations of biomass categories and nets, the differences were only significant for zooplankton biomasses in the 180–1000 μm size fraction and only for Net 2. Possible reasons for this result are discussed in the paper. Gears produced similar catches whether sampling during day or night. We conclude that the MOCNESS and Multinet Mammoth in this study provided comparable results regarding abundances of various zooplankton categories.

Influence of food quality on larval growth of Atlantic bluefin tuna (Thunnus thynnus) in the Gulf of Mexico

AbstractLarval abundances of Atlantic bluefin tuna (ABT) in the Gulf of Mexico are currently utilized to inform future recruitment by providing a proxy for the spawning potential of western ABT stock. Inclusion of interannual variations in larval growth is a key advance needed to translate larval abundance to recruitment success. However, little is known about the drivers of growth variations during the first weeks of life. We sampled patches of western ABT larvae in 3–4 day Lagrangian experiments in May 2017 and 2018, and assessed age and growth rates from sagittal otoliths relative to size categories of zooplankton biomass and larval feeding behaviors from stomach contents. Growth rates were similar, on average, between patches (0.37 versus 0.39 mm d−1) but differed significantly through ontogeny and were correlated with a food limitation index, highlighting the importance of prey availability. Otolith increment widths were larger for postflexion stages in 2018, coincident with high feeding on preferred prey (mainly cladocerans) and presumably higher biomass of more favorable prey type. Faster growth reflected in the otolith microstructures may improve survival during the highly vulnerable larval stages of ABT, with direct implications for recruitment processes.

Machine learning approach for categorical biomass higher heating value prediction based on proximate analysis

ABSTRACT The present study attempts a new approach for optimizing higher heating value (HHV) of agricultural biomass from only proximate analysis data, thereby requiring nominal specificity compared to elemental composition-based models. This objective was achieved by classifying the largest extracted 1140 data into five categories and by adopting five machine learning (ML) models namely: Multi Layer Perceptron, Random Forest, Support Vector Machine, Linear Regression and Artificial Neural Network. Further, relative correlation and contribution of different input parameters on target variable was performed to forecast the impact level and interaction. It showed higher relevancy factor for FC and VM as 0.82 and 0.91 respectively. Compared to other models, Artificial neural network and Random Forest model provided better accuracy with higher correlation coefficient values (0.991 and 0.989 respectively) and lower root mean square error 0.215 and 0.283 respectively. The validity and applicability of the database and ML modeling were verified using Box plots, in which more than 96% of the data for each category of biomass were located in the valid region. The values of statistical indicators proved better prediction accuracy of our proposed models in comparison to other reported literature models for all predefined categories of biomass.

Recent progress in biomass-derived carbonaceous composites for enhanced microwave absorption

Carbonaceous microwave absorbing materials are in vital demand due to the extensive electromagnetic pollution in 5G network era and urgent requirements for stealth technology in national defense domain. Rather than the complicated vapor deposition method, a simple biomass-derived approach sheds light on the mass production of carbon materials for its ubiquitous, environmental-friendly, cost-off, and sustainable advantages. Herein, a concise review of recent advances in designing carbonaceous materials for EM attention is provided with particular stress on the biomass categories and the synthetic method. The three dimensional (3D) interconnected network of carbon materials are highlighted in analysis regarding the biomass selection, functional process, pore-forming strategy and the microwave absorption performance of the corresponding composites. Nature fiber-derived carbon materials, possessing high-aspect ratio fiber structure, are also discussed due to their potential in weaving manufacture and diverse application for flexible cloaking fabric. In the end, the current challenge and the directional perspective for utilizing biomass-derived carbon absorbing materials with effective EM properties are outlined.

Development of a non-linear model for prediction of higher heating value from the proximate composition of lignocellulosic biomass

ABSTRACT The determination of calorific value (in terms of higher heating value or HHV) is of much significance for the assessment of the energy recovery potential of biomass. The literature reported that HHV of the biomass can be computed using the proximate composition of the biomass. The prediction of HHV from the proximate composition of biomass is of great interest as it can save expensive and time-consuming laboratory trials. The present work evaluates the available prediction models for the estimation of HHV of the biomass from the literature. A thorough review showed that most literature reported models are limited in terms of applicability across different biomass categories. The present study proposed a non-linear prediction model that can be universally used to predict calorific value across biomass categories. Accordingly, the proposed model was validated using the proximate composition of various biomass specimens extracted from the literature to confirm the universal applicability of the model. The accuracy and precision of model prediction were assessed in terms of statistical indicators. The values of the statistical indicators showed that the proposed model had better prediction efficiency (in terms of accuracy and precision) and less bias compared to the other literature reported models for all biomass categories. The model predicted maximum energy recovery potential for biomass having high fixed carbon, high volatile matter, and low ash. The maximum energy recovery of 19.148 MJ/kg predicted by the model on validation was found to be associated with less than 2% (1.19%) percent error.

Allometric models for estimating aboveground biomass of selected homestead tree species in the plain land Narsingdi district of Bangladesh

Globally allometric equations based biomass estimation is a popular non-destructive method for estimating biomass and sequestered carbon. However, the destruction process is involved in the development of allometric models. This study developed allometric models of aboveground biomass (AGB) for five frequently planted homestead tree species in Bangladesh. The tree species were Aphanamixis polystachya, Ficus hispida, Mangifera indica, Melia azedarach, and Swietenia mahagoni. Fifteen individuals from each species were randomly selected and felled. The felled trees were cut into manageable sections and processed for calculation of fresh and dry biomass. The considered explanatory variables were diameter at breast height (DBH), total height (TH), DBH × TH, and DBH2 × TH. For each category of fresh and dry biomass of the five tree species, the study developed a total of 40 models. However, based on the goodness-of-fit statistics, the study primarily selected 17 models for fresh biomass and 22 models for dry biomass. Finally, the best-fit model for each species was selected based on the Akaike Information Criterion (AIC) and Residual Standard Error (RSE). The whole study shows that a combination of Diameter at Breast Height (DBH) at 1.3 m above the ground and the total height of the trees are the essential explanatory variable for most of the species. The relationship between the AGB and the selected dendrometric variables were significant at p < 0.05 levels. The allometric models developed by this study will be useful for better estimation of biomass and sequestered carbon in the plain land homestead forests of Bangladesh.

Technical, economic and environmental assessement of bioethanol biorefinery from waste biomass

This study presents a sequential three-steps methodology for the technical, economic and environmental assessment (TEEA) of bioethanol production from waste biomass. In EU the most abundant waste biomasses produced in 2018 could be ascribed to three main categories: lignocellulosic (329.41 Mt), starch (160 Mt) and sugar-based (58.56 Mt). The technical assessment compiled an inventory of the waste biomasses and subsequently designed their biological conversion into ethanol through integrated biorefinery processes by means of material flow analysis (MFA); the economic assessment was aimed at the definition of the cut-off size of the biorefinery plant necessary to achieve profitability; the environmental assessment was based on Life Cycle Analysis (LCA) and energy balance (i.e. energy input consumption). For each of the three waste biomass categories, at least one that was significant as available amount and representative in terms of physico-chemical characteristics, was evaluated: sugarcane for sugar-based, potatoes for starch-based and rice straw, cattle manure and organic fraction of municipal solid waste (OFMSW) for lignocellulosic biomasses. The technical assessment of the biorefinery routes lead to the following yields (kg of bioethanol per kg of biomass): 0.16, 0.17, 0.22, 0.19 and 0.14 respectively. The economic profitability was reached by all biorefineries and Net Present Value (M€) were: 0.85 for sugarcane, 0.11 for potatoes, 0.09 for rice straw, 0.11 for cattle manure and 0.39 for OFMSW. From the environmental perspective, cattle manure reached the highest reduction of climate change and acidification impacts compared to other biomasses, while sugarcane achieved the lowest energy input consumption (around 64%).

Potential for energy and biofuel from biomass in India

This study is dedicated to the quantification of biomass potential in India. Therefore, the estimation of quantity of biomass and its potential for energy and biofuel production is needed to calculate. Two categories of biomass i.e., energy crops and agroforestry residues are considered in this study. Only marginal land is taken under consideration for cultivation of energy crops. The bottom-up approach is followed for estimation of marginal land. RPR methodology is used for calculation of the quantity of agroforestry residues. The result of the study shows significant biomass potential in India from both categories of biomass. This study suggests that the policy, which supports creation of demand, investors protection through risk cover and subsidies, would be prove helpful for harvesting India’s biomass potential.

Soil exposed to silver nanoparticles reveals significant changes in community structure and altered microbial transcriptional profiles

Anthropogenic activities can disrupt soil ecosystems, normally resulting in reduced soil microbial health. Regulatory agencies need to determine the effects of uncharacterized substances on soil microbial health to establish the safety of these chemicals if they end up in the environment. Previous work has focused on measuring traditional ecotoxicologial endpoints within the categories of microbial biomass, activity, and community structure/diversity. Because these tests can be labor intensive, lengthy to conduct, and cannot measure changes in individual gene functions, we wanted to establish whether metatranscriptomics could be used as a more sensitive endpoint and provide a perspective on community function that is more informative than taxonomic identification of microbes alone. We spiked a freshly collected sandy loam soil (Vulcan, Alberta, Canada) with 0, 60, 145, 347, 833, and 2000 mg kg−1 of silver nanoparticles (AgNPs), a known antagonist of microorganisms due to its propensity for dissolution of toxic silver ions. Assessments performed in our previous work using traditional tests demonstrated the toxicity of AgNPs on soil microbial processes. We expanded this analysis with genomics-based tests by measuring changes in community taxonomic structure and function using 16S rDNA profiling and metatranscriptomics. In addition to identifying bacterial taxa affected by AgNPs, we found that genes involved in heavy metal resistance (e.g., the CzcA efflux pump) and other toxicity response pathways were highly upregulated in the presence of silver. Dose-response analysis using BMDExpress2 software successfully modeled many physiologically relevant genes responding to low concentrations of AgNPs. We found that the transcriptomic point of departure (BMD50) was lower than the IC50s calculated using the traditional tests in our previous work. These results suggest that dose-response modeling of metatranscriptomic gene expression is a useful tool in soil microbial health assessment. SummaryGenomics-based endpoints for the assessment of soil microbial health can be used to perform quantitative dose-response modeling, and soil-based RNAseq adds functional insights.

GIS BASED ANALYSIS OF SPATIAL DISTRIBUTION OF NDVI FOR AGRICULTURAL APPLICATIONS IN SALEM DISTRICT – TAMIL NADU

Abstract. Remote sensing satellites in recent years have emerged as a vital tool for generating the biophysical information, which further helps to evolve the optimal land use plan for sustainable development of an area. The natural resources are to be categorized to obtain the area best suitable for crop production so that they could be better utilized in agricultural planning. The Normalized Difference Vegetation Index (NDVI) has been widely used to monitor moisture-related vegetation condition. The 8-day composite and spatial resolution of 250 m for the years 2002–2012 have obtained from the Moderate Resolution Imaging Spectro-radiometer (MODIS) Surface Reflectance (MOD09A) used for grouping biomass. The MOD09A product was selected because it consisted of both visible and infrared bands, which is requisite for deriving NDVI. The NDVI was used to determine the biomass categorization had four classes B1 (NDVI of 0.06–0.10), B2 (0.1 to 0.2), B3 (0.2–0.4) and B4 (&gt; 0.4) which were rated as poor, moderate, good and excellent, respectively. Here, excellent biomass category was found to cover more area compared to other biomass categories. The per cent area covered under excellent category was (88.7 %) in Salem district. This showed that the agriculture area in this district is largely suitable for crop growth. The categorization of biomass as good to excellent in Salem might be due to the good seasonal (both monsoon) rainfall. It could pave way for better agricultural management and transfer of technology.

Assessment of the Energy Potential of Chicken Manure in Poland

Animal waste, including chicken manure, is a category of biomass considered for application in the energy industry. Poland is leading poultry producer in Europe, with a chicken population assessed at over 176 million animals. This paper aims to determine the theoretical and technical energy potential of chicken manure in Poland. The volume of chicken manure was assessed as 4.49 million tons per year considering three particular poultry rearing systems. The physicochemical properties of examined manure specimens indicate considerable conformity with the data reported in the literature. The results of proximate and ultimate analyses confirm a considerable effect of the rearing system on the energy parameters of the manure. The heating value of the chicken manure was calculated for the high moisture material in the condition as received from the farms. The value of annual theoretical energy potential in Poland was found to be equal to around 40.38 PJ. Annual technical potential of chicken biomass determined for four different energy conversion paths occurred significantly smaller then theoretical and has the value from 9.01 PJ to 27.3 PJ. The bigger energy degradation was found for heat and electricity production via anaerobic digestion path, while fluidized bed combustion occurred the most efficient scenario.