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Students are expected to display complete integrity in their academic work. In particular, activities such as plagiarism and cheating or any activity designed to defeat the purpose of assessment are breaches of academic integrity.

Plagiarism is the act of taking and using another person’s work as one’s own. For the purpose of these rules any of the following acts constitute plagiarism, unless the work is appropriately acknowledged:

 copying the work of another student;
 directly copying any part of another person’s work;
 summarizing the work of another person;
 using or developing an idea or thesis derived from another person’s work;
 using results obtained by another person;
 incitement by a student of another to plagiarise.

Research Aims

Biomass is termed as a resource which is gathered by the burning of woods and various other organic matters. Although the burning of fuels releases carbon dioxide emissions, the utilization of biomass is termed as renewable resource. The utilization of renewable resources has been adopted in various places in the United Kingdom as well. The utilization of biomass can be adopted for converting into bio-energy which in turn will generate electricity (Marshall & Thenkabail, 2015). This is termed as a renewable energy generation. These types of energy are also used in place of other conventional energy sources. The utilization of the biomass resource can also be used to generate heat for various reasons and can also be used in the form of bio-fuel. The constituents of plant based biomass are the lignin or cellulose while wastage and animal wastes can also be used for obtaining energy from it.

It has been stated that 50 percent of the power generated in the United Kingdom comes from various amounts of low-carbon based sources. Although the utilization of biomass resources for burning processes leads to the formation of a large amount of gaseous emissions, the government of UK thinks of it as a renewable source of energy (Tilly et al., 2016). A major power station of the United Kingdom has switched their requirements from the utilization of coal to biomass. Many other power station of The United Kingdom has reinstated their business operations from the utilization of coal based power plant to biomass based.

This section of the paper discusses about the research aims and objectives.

Research aims

The aims of this research are to calculate the amount of biomass from five different crops. These crops are to be considered from the country UK. This research also aims to identify the efficiency of the biomass resources.

Research objectives

  • To select five different crops from the country of UK
  • To estimate the biomass count from these five crops
  • To successfully evaluate the insights obtained

This section presents various research based questions and hypothesis to be followed by the study.

Research questions

  1. What are the different crops found in UK to be used for this research?
  2. What type of data can be collected from these crops?
  3. What insights are obtained from such data collection methods?

Research hypothesis

This paper utilizes the involvement of statistical data that will be utilized in making the analysis. Moreover, the crops involved will be selected from the United Kingdom.

By conducting such research analysis, the researcher tries to evaluate the biomass count from plant resources. The research method will utilize the collection of data from five different crops. After collecting the information, this paper will make an analysis of the data involved.

Thus, it can be summarized that this section of the research paper is mainly involved in analyzing the biomass collection from five different crops. These crops will be selected as they are to be found from the United Kingdom.

Research Objectives

This section of the paper is concerned with the discussion of the associated literature which is related to the topic. The various aspects to be considered for this research is also depicted in this section of the paper.

For the utilization of biomass energy from the plant based resources, the adoption of energy crops are a pre-requisite. There are three different types of energy crops which are broadly adopted for the type of resources obtained from them. The first type of crop is the agricultural energy crops which include oil crops, sugar crops and starch crops (Kross et al., 2015). These comprises of wheat, barley, oat, potato and sugar beet. The second type of crop is the non-weedy crops which mainly includes hemp and rye. The third type of crop is the short rotation based energy crops which consists of eucalyptus, sycamore and poplar.

There are many other types of crops which are grown in the United Kingdom but these crops are considered mainly due to their energy harvesting capabilities. These crops can remain in situ conservation for a range of 7 to 25 years. In addition, these crops have much deeper rooting characteristics. Moreover, their growth rate is very rapid which in turn produces much high amount of yield (Bendig et al., 2015). Furthermore, the fertilizer input and the pesticide input is also very low which helps in better productivity of the crops. The harvesting of these crops takes place in the winter season and in the early spring season. These crops are also supposed to reach greater heights and form dense plantations.

For the research involved in this paper, the utilization of five different crops is required. These five crops are wheat, barley, oat, potato and sugar beet. These crops will be collected and the biomass content will be calculated. These crops will be stored in plastic bags which can be re-sealed whenever required (Dong et al., 2016). These will then be kept in a cool condition and the weighing of the crops will be done which will then be dried. If the sample plant is not being weighed for measuring fresh weights, they will be transported in wet ice and will be weighed within the next 24 hours.

There are various methods which can be utilized in the context of estimating biomass content of crops. These are mainly used to find out the amount of resources that can be obtained for obtaining the necessary requirements. The vegetation will be measured by considering the quadrate frame of measurement. In this case, the delineation of the areas are required using the help of these types of frames (Ledo et al., 2018). The measurement of multiple areas from such quadrates is to be considered which will help in collecting the samples as required from the whole plantation involved.

Research Questions

Although the need for calculating the biomass is a necessary consideration in case of any plantation, the most common method of calculating the biomass is by using the harvest based method. This involves removing the biomass and then drying it in the oven. This is used to remove the water content from the plants involved which is then weighed. The water content of plants can vary depending on the time of estimation (Fu et al., 2014). The water content varies during the day and during the night. In addition, the seasonal basis is also responsible for changing the water weight. This is the reason for drying the samples for collecting weights. Another consideration to b adopted is the presence of the plants with respect to ground level.

For considering the biomass concentration, some plants have higher density above the ground level while some others have low density in that region (Eitel et al., 2014). For various processes involved in researches, the utilization of above ground measurements are more preferred than the below ground measurements. This is mainly because there are various problems associated in collecting information and data associated from below ground measurements.

The use of various equations will be adopted for calculating the biomass of the crops involved. With reference to the larger plantations, the calculation of biomass is based on the various sections of them. These sections are mainly calculated by considering the stem, branch, foliage and fruits (Jannoura et al., 2015). In each of the plantation involved, there are difference calculation based metrics which are used for determining the biomass present.

In case of these above equations, the DBH is termed as the diameter of the breast height which is used to denote the main diameter involved. The H symbol stands for height which denotes the height of the concerned sample. Using these equations, the calculations of biomass is made.

The estimation of the biomass and its assessment processes is comprised of two different stages. The first stage is the power plant design stage and the other is the power plant re-commissioning stage. The methods involved in both the stages of biomass estimation are same but the objectives involved in them are totally different.

In the power plant design stage, the method involves a detailed analysis and assessment at the village level and the regional level. The regional level comprises of both the block and the district involved. There are four main objectives of this design stage. The first objective is to make a successful forecasting of its availability for considering its supply for a span of five years. This step is followed by adopting the estimation of the pricings of the biomass resources for the next five year period. The third objective is to apply for streamlining the process involved which will help in providing biomass as a resource for the next time period of five years (Bendig et al., 2014). The last step involved is validating the gasification technology which is based on the use of biomass as a resource. This validation is considered in terms of calculating type of technology to be used and the capacity of the storage services. For example, in case of a 30Kw gasifier which is running daily for 10 hours, the calculation is done by considering the number of days available. In case the number of days being 350, the total number of biomass to be generated is 210 MT (30 * 10 * 350 * 2= 2, 10,000 Kg= 210 MT).

Research Hypothesis

In this stage of re-commissioning power plant, the analysis and evaluation of the biomass content is considered. This is done by considering both the level of villages and regional aspects so that an effective evaluation can be carried out. These evaluations are done on a detailed basis such that all the objectives and steps involved are met. The first step involved is to estimate the availability of the supply of biomass based resources. The next step is similar to the above method which involves streamlining the process of biomass supply chain system which helps in meeting the continuous demands of the associated parameters present in the power plant (Hiloidhari, Das&Baruah, 2014). The next step involved is to estimate the final price of the biomass resource which is based on its availability and the variations of the seasons involved in the region. The last option involved in this process is to adopt various alternative solutions which will help in facilitating biomass supplies by considering community participation based alternatives. These types of alternatives include waste land based energy plantations and integrations of energy plantations in association to the forest department.

This section of the paper discusses about the various methods involved in doing the research. Moreover, the processes involved in data collection and analysis is also discussed in this section of the paper.

This section of the research paper is basically involved in the collection of the biomass from five different crops. The data pertaining to it are shown in a very logical manner. This paper has also utilized the involvement of the positivism philosophy where the involvement of scientific resources for the conduction of research is adopted. Moreover, for analyzing the various data and information collected, the use of the deductive approach is utilized.

The first step involved is the collection of moisture content. This is done by following the equation listed below. The final values involved in a mass/area consideration are assessed according to the above-ground based considerations.

The next step involves reporting the O horizons on the basis of the mass/volume or mass/area basis. This process is then followed by calculating the kg/ha content by calculating the grams plant material/250 cm^3. This O horizons mass is converted to soil bulk density which is calculated by. This is then converted to area with kg/ha units. When considering the root biomass, only the above ground resource is to be considered without the litter.

Associated Literature with the Topic

The various equipment needed for sampling the plant crops are clippers, stakes, plastic bags, ice chest, weighing scale, oven for drying, desiccant and desiccators, documentation papers, labels and spring scale. A calcium chloride-based pellet will be used as a desiccant which will then be placed in a desiccators jar for absorbing moisture from it.

The area to be sampled will be considered in a 50*50 cm notes. This will be followed by clipping vegetation to the surface of the soil. The next step involved is the separation of the crops on the basis of species and live or dead factions. These separations will then be weighed and then inserted into the oven and re-weighed for measuring the gravimetric parameters. The surface litters and O horizons are measured separately.

The plant tissues will be placed in aluminum dishes or the paper bags which varies according to the size of the sample. This will then be set in an oven with temperatures of 80 degrees. The specimen will then be dried for a span of 24 to 48 hours which will then be placed in desiccators jar and then re-weighed for dry weight. In addition, the sample specimen will be weighed for each interval of 4 to 8 hours to validate that no further water weight is lost. This process must be done in a very careful manner so as to not char the material. In case of limited availabilities of the oven space, the other specimens from the crops will be refrigerated up to a week. The samples must not be allowed to decay which will result in a halt on the research process. Furthermore, the lesser amount of succulent specimens can be kept at room temperature to prevent them from decaying.


1st-2nd week

3rd-4th week

5th-6th week

7th-8th week

8th-9th week

10th-11th week

12th-13th week

Selecting the research topic

Getting approval for topic from the respected authorities

Performing secondary data collections in an initial basis

Training of the final draft for the research proposal

Submission for the research proposal

Studying the in-depth literature of the  secondary data

Preparing the literature review associated

Identification of the participants for conducting primary research

Conducting interview of the participants

Arrangement of ideas in data collection

Data analysis and evaluation

Interpreting and discussing about the data

Developing the conclusion

Final revision of the paper

Submitting the research paper


Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., &Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6(11), 10395-10412.

Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., ...&Bareth, G. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79-87.

Dong, T., Liu, J., Qian, B., Zhao, T., Jing, Q., Geng, X., ...& Shang, J. (2016). Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 49, 63-74.

Estimating Biomass Content of Crops

Eitel, J. U., Magney, T. S., Vierling, L. A., Brown, T. T., & Huggins, D. R. (2014). LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Research, 159, 21-32.

Fu, Y., Yang, G., Wang, J., Song, X., & Feng, H. (2014). Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Computers and Electronics in Agriculture, 100, 51-59.

Greaves, H. E., Vierling, L. A., Eitel, J. U., Boelman, N. T., Magney, T. S., Prager, C. M., & Griffin, K. L. (2015). Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sensing of Environment, 164, 26-35.

Hiloidhari, M., Das, D., &Baruah, D. C. (2014). Bioenergy potential from crop residue biomass in India. Renewable and sustainable energy reviews, 32, 504-512.

Jannoura, R., Brinkmann, K., Uteau, D., Bruns, C., &Joergensen, R. G. (2015). Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter. Biosystems Engineering, 129, 341-351.

Jin, X., Yang, G., Xu, X., Yang, H., Feng, H., Li, Z., ...& Zhao, C. (2015). Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAR-2 data. Remote Sensing, 7(10), 13251-13272.

Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34, 235-248.

Ledo, A., Heathcote, R., Hastings, A., Smith, P., & Hillier, J. (2018). Perennial-GHG: A new generic allometric model to estimate biomass accumulation and greenhouse gas emissions in perennial food and bioenergy crops. Environmental Modelling & Software, 102, 292-305.

Li, W., Niu, Z., Chen, H., Li, D., Wu, M., & Zhao, W. (2016). Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological indicators, 67, 637-648.

Li, W., Niu, Z., Huang, N., Wang, C., Gao, S., & Wu, C. (2015). Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China. Ecological indicators, 57, 486-496.

Marshall, M., &Thenkabail, P. (2015). Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 205-218.

Tilly, N., Aasen, H., &Bareth, G. (2015). Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sensing, 7(9), 11449-11480.

Tilly, N., Hoffmeister, D., Cao, Q., Huang, S., Lenz-Wiedemann, V., Miao, Y., &Bareth, G. (2014). Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. Journal of Applied Remote Sensing, 8(1), 083671.

Zhang, T., Wooster, M. J., Green, D. C., & Main, B. (2015). New field-based agricultural biomass burning trace gas, PM2. 5, and black carbon emission ratios and factors measured in situ at crop residue fires in Eastern China. Atmospheric Environment, 121, 22-34.

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