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N-Budgeting For Crops
N-Budgeting For Crops:
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August 19, 2013
PART 1
Using David Herridge’s Nbudget to estimate amount of N to be applied
N-Budget is an Excel-Based, simple-to-use calculator developed to assist farmers as well their advisers within northern grains of Australia to estimate water levels and soil nitrate during sowing and the N requirement for oilseeds and cereals. This tool also gives estimates of how much of N fixed by legume grain crops (David, 2011).
Calculation Using NBudget
This section offers calculations of how NBudget tool were used to estimate the amount of N applied for Dubbo (paddock) during winter and summer cropping, and the timing is within the sowing month.
Dubbo was selected from the site details on the dropdown list. The names of the paddock and farm were typed or (inserted) as shown is fig 1. The paddock is portrayed to be of medium fertility, for instance medium use of fertilizer N and pulses. The type of soil is red-brown.
Two seasons earlier
From the dropdown list, I chose the crop cultivated in the paddock the season prior to the last.Two seasons ago, the crop within the paddock was N-fertilized canola (obtained from the drop-down list). The post-fallow soil N estimate, for example at the beginning of the previous season, is found to be 62 kg N/ha. Had the paddock would have been cultivated; the value could have been about 20% more.
Previous Season
Nbudget then gives soil nitrate estimates of past fallow, for instance soil nitrate at the moment the advisor or farmer is making decision regarding fertilizer N inputs for the next cropping season. From the dropdown list I selected the crop that was cultivated in the paddock, then inserts protein, (if it is cereal crop), yield and amount of N fertilizer applied. The results are; Fertilized with 70 kg N/ha, wheat yielding is 2.1 t/ha and 13.0% of grain protein.
Present Summer Fallow- The estimates of the Nbudget is 67 kg N/ha soil nitrate at sowing. At sowing, soil water is estimated at 75 mm derived from the selected fallow rainfall of 243 mm.
Targeting proteins and grain yields, Requirements of fertilizer N
The estimated grain yields for the next season are automatically calculated using default values of WUE (Water Use Efficiency). The protein default values are 9.5%, 10%, 13, 11.5% for sorghum, barley, durum and bread wheat respectively. Grain proteins for fababean, chickpea, and canola are at default values, which do not need to be changed.
The N requirement for oilseed crops and cereals and the N amounts fixed by these legumes are then worked out together with the post fallow (residual) nitrate levels.
Fig 1 Calculations Using NBuget
The N-tool wheat yield projections were 2.4, 3.1 and 3.7 t/ha for the 30 percentile (poor), 50 percentile (average) and 70 percentile (good seasons) requiring 78 kg N/ha. The project grain proteins ranged from 12.7% to 10.7% during poor and good seasons respectively. The projected yields of durum were nearly the same, with 50% higher N requirements at 117 kg N/ha. On the other hand, Barley was projected to yield approximately 30% more but significantly less N fertilizer i.e. (35 kg N/ha). The projected yields of canola was 1.2-1.9t/ha, and requiring approximately 100 kg N/ha. The tool predicted that fababean and chickpea could yield about 3.0t/ha and fix nearly 155 kg H/ha, based on the season and pulse.
The nitrate levels for the post fallow, for instance soil nitrates in the next 12 months, were also projected for all the six crops for various seasonal cases. The values were ranging from 3 kg N/ha following high yielding (good season) barely to over 80 kg N/ha following fababeans and water stressed (low yielding) durum crops and canola.
PART 2
Factors that Affects N dynamics and Estimation of N requirements
De-nitrification
According Bonde, Christensen & Cerri (2002), denitrification is a process through which nitrate is changed into gases (nitrous oxide or dinitrogen) and hence, are released into the atmosphere. This process can be an essential means for N loss on fine and medium textured soils. Some of environmental elements that determine whether denitrification happens and to what degree include;
Nitrate: It must be available for denitrification to happen. If it is in low levels or absent, the denitrification losses would be less.
Aeration and Soil water level: Denitrification happens in wet soils of low concentration of oxygen and it increases with duration of time that the soil is saturated. Lodged water would cause more nitrate percentage to be denitrified.
Soil Organic matter (SOM): Soils that have low soluble OM have less likelihood for denitrification as opposed to soils with greater soluble OM. Hence nitrate, which resides below 12 inches deep into the soil profile where there is minimal OM will have a minimal or greatly reduced denitrification probability.
Effect of Soil Organic Matter on the rate of mineralization
Soil OM decomposition and consequent release of inorganic N happens through the activity of fungi and bacteria (Molina, Clapp, Larson, 2005). Soil mineralogy and environmental elements influence the actions and players of microflora, which subsequently determine the N mineralization rate within the soil and hence the mineralized amount over time. Climate reacts with soil properties, comprising the soil’s microflora to affect the chemical nature and size of the soils N pool. Moisture content and soil temperature have major effect over N mineralization activities. Optimal N mineralization happens at 30-350C of soil temperature. Dry soils have low N mineralization due to low microbial activity. Lack of O2 in saturated soils reduces N mineralization.
Significance of Taking sufficient cores while soil sampling
According to Gilmour, Cogger, Jacobs, Evanylo, (2010) taking enough cores helps one to have composite soil samples, (samples which include as opposed to excluding) and hence the fertilizer band will have the minimal variation or deviation from the “actual”/true values.
References
Bonde, T.A., Christensen, T & Cerri, C . (2002). Dynamics of soil organic matter as
reflected by natural 13C abundance in particle size fractions of forested and cultivated
Oxisols. Soil Biol. Biochem. 24:275-277.
Gilmour, J. T., Cogger, C. G., Jacobs, L. W., Evanylo, g. K., Sullivan, D. M. (2010).
Decomposition and plant available nitrogen in biosolids: laboratory studies, field studies,
and computer simulation.” J. Environ. Qual. 32: 1498-1507.
Molina, J.A.E., Clapp, C.E., Larson, W.E., (2005). Potentially mineralizable nitrogen in soil: the
simple exponential model does not apply to the first 12 weeks of incubation. Soil Science
Society of America Journal 44, 442–443.
David H, (2011), NBudget’ – a nitrogen management tool for cropping systems retrieved from
HYPERLINK “http://www.grdc.com.au/Resources/Bookshop/2011/10/Managing-Legume-and-” http://www.grdc.com.au/Resources/Bookshop/2011/10/Managing-Legume-and-
Fertiliser-N-for-Northern-Grains-Cropping
Use Of Theories In Research
Use Of Theories In Research
Contents
TOC o “1-3” h z u Introduction PAGEREF _Toc381193032 h 1Presentation of theories in research articles PAGEREF _Toc381193033 h 1Theory development PAGEREF _Toc381193034 h 2Role played by theories in research articles PAGEREF _Toc381193035 h 3Conclusion PAGEREF _Toc381193036 h 4
IntroductionThe development of theories is probably the main and most important thing when it comes to the creation of a business research project that is to be successful .this is due to the fact that it pushes the author towards taking stock of other knowledge which has been produced previously and is relevant.it also assists authors to make identification of gaps, strengths, deliver constructs which are clear and at the same time ensure that there is the channeling of theoretical developments that will lead to the production of new knowledge which is valuable within the field. They also help in providing a comprehensive and complex conceptual understanding of certain things which can not be pinned down .theories therefore give a new lens to researchers through which they can be able to look into problems that are complicated thus shifting their focus on the different aspects of the data thus providing a framework through which analysis can be conducted. The generation of theories is done through deductive means through an act of creativity that has been empirically formed .therefore we can say that theories are created from a process of deduction and induction.
Presentation of theories in research articles
It has been an observation that development of theories is not something that is not anything that is unique.as a matter of fact it comes from the universal need in humans to explain and order their personal experiences. A theory is a system of knowledge which is organized which is applicable in various circumstances in order to explain a particular set of phenomena. Theoretical models on the other hand involve constructs which act as a representation of some physical, social or biological processes that comprise of a set of variables and logical and quantitative relationships found between them. The construction of the models is done so that it is possible for reasoning to be done within a particular framework which is idealized.
A dilemma that always presents itself is whether focusing on making attempts towards the production of ground breaking work which violently breaks away from what is convection or otherwise continue theoretical work that has been started by others who worked on a similar thing within their field. The most common form of theory development is that of the incremental improvement of the middle range theories that exist. Though most scholars have the dream of the creation of a theoretical perspective that is full-blown, wholly new and broad-gauged only a few of them end up with the realization of the dream. Instead theory development done by most of the researchers comprises of the improvement on extant explanations for what can already be observed through a process that involves empirical, logical or practical tests. When it comes to business research there is the anticipation of new conceptualizations of leadership, motivation, strategy or group dynamics hence this form of theorizing should not be termed as primary domain of theory development which is a scholarly craft.
Theory developmentAt the beginning of writing a business research, writers should ensure that their main focus is the development of the theories which are related to their specific area of expertise where they can easily express a practical understanding that they have gained as they have studied or have been working. Theory can be termed as practical since it is quite useful when it comes to guiding practice.at the same time it is only a good theory that can be practical but bad theories often end up being quite dysfunctional and they might even turn out to be harmful. The effective development of practices leads to the production of theories which end up lending themselves for more development. Once there has been a general familiarization with bodies of work that have been peer reviewed in a specific subject matter then the process of theorizing starts off through activities such as abstracting ,relating ,generalizing, selecting, synthesizing ,explaining and idealizing. All these activities involve an intermittent spin out of reference lists, diagrams, lists of variables and hypothesis. The products that emerge give a summary of the progress, give directions and act as place makers. They comprise of vestiges of theories but in the real sense they are not theories on their own.
The analysis of theory development done by researchers that is done through the use of graphical methods is among the most effective ways of making a quick comparison on various frameworks that have been peer-reviewed. The use of graphical models is also simple and compelling since it guides the process of theory development since its features for representing and constituting theories are meant to exemplify the actual qualities of the ideal theory. The graphical model is a natural way of lending itself towards the development of conceptualizations which are both systematic and complete. The production of a literature review is something that many researchers do not do right. Researchers can not produce good research if they are not able to understand the work that has been previously done in the same field. Lack of this prior understanding makes the researcher be disadvantaged. Therefore, doing literature review requires a good theory development since it is going to be based on work that has been done before.
Role played by theories in research articlesA good theoretical model in research articles has various characteristics. Theories have to give a meaning, they should also enable people understand and make an interpretation of data. The theories give a specification of the important variables and give a reason for this. They also give an explanation and description of relationships which give a link to the variables. They also identify the existing boundary conditions under which variables are supposed to or not supposed to be related. Theories also help in the identification and definition of problems, give a prescription of solving the problem and lead to the facilitation of the problem responses. They also allow the generalization that goes beyond the sample that is immediate and at the same time make it possible to make predictions. Any research article that is written without theory is termed as being trivial a technical feat which is likely to lead to confusion and boredom.in contracts, research articles that are guided by theories or involve theory development lead to the generation of excitement and understanding.
Good theory guides both readers and researchers into something new which they would not be bale to learn from anywhere else. When it comes to reading, good theory leads to the development of a sense of illumination and discovery. Good theory leads to creation of clear and coherent world processes through the simplification and structuring their understanding. Basically in research articles theories can be used in designing of research questions, give a guide for the selection of data which is relevant ,interpretation of data and finally give propositions to explanations of causes which that are underlying or have an influence on the phenomena that has been observed.
ConclusionTheory development is very important in any research article .this means that any research that is done should always encompass the development of theory if researchers want to successfully carry out the research. This should hence be embraced by all researchers and be part and parcel of every research article.
NBA TEAM PERFORMANCE AND SALARY
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DateTitle: NBA TEAM PERFORMANCE AND SALARY
Introduction
This chapter outlines the research design which offers an explanation into what type of research this study is. It also defines the population of the study and the specific sampling technique used. The method of data collection is carefully detailed and the method of data analysis explained.
Research design
Research design is considered as a “blueprint” for research, dealing with at least four problems: which questions to study, which data are relevant, what data to collect, and how to analyze the results. The best design depends on the research question as well as the orientation of the researcher. The study will adopt descriptive survey as a method of collecting information by using a standardized form of interview schedule and administering a questionnaire to a sample of individuals. Both the secondary and primary data will be used in the analysis.
The design is also suitable because it gives an in-depth description of the phenomena in their existing setting. This fits well when undertaking a game level analysis of team total salary dispersion and team performance in the National Basketball Association. Descriptive survey is also preferred because it is economical in collecting data from over a large sample with high data turn over.
The model
In order to undertake a game level analysis of team total salary dispersion and team performance in the National Basketball Association, the study use the following model:
Yij=β0+ β1coaeij + β2coawij+ β3salij + Eij
Where: Yij=yi/yj is the ratio of the point scored by team I to that by team j in the game where j is at home against I.
Salij=sali/salj= is the ratio of the total team salary of team I and total salary of team j.
Coaeij=coaei/coaej is the coach experience ratio by team I and tam j
Coawij=coawi/cowj is the coach winning ratio by team I and team j
Coawi=thw/thp for team I coach winning ratio is the total game he won divided by the total game he played.
Estimation method
Estimation of the model above involves several econometric analyses. Yij is clearly endogenous variable because it depends on the number of game-minute played and hence is determined jointly with the game outcome. The measures of salary dispersion employed to the study are strictly exogenous. When coaching variables are included in the estimation, they are treated as exogenous and entered into the model as conventional instruments. Using these instruments sets, we can efficiently estimate the coefficients β1, β2, β3.
Data
The data collected covers five NBA seasons from 2006 to 2011. The set of data comprise of information on player who participated in games for those seasons. During the study, the variables are calculated from a reduced dataset that comprise the first game of each season for match-up of teams. The game level panel is a combination of 4176 unique games which is a reflection of 69% of total games played throughout the season. The sources of data for the salaries are: basketball-reference.com, USAToday.com and the independent statistician Patricia Bender. All the players had at least one salary figure quoted for each season. The box scores of the regular NBA seasons provided the game-level statistics for the period 2006-2011 sourced from basketball-reference.com.
Data Analysis Methods
The data collected will be analyzed using both descriptive and inferential statistics. Qualitative data will be analyzed using grounded theory methods. Open coding was used to initially name and categorize the data, and selective coding was used to develop a more general framework. The study will use frequency count, charts, bar graphs and percentage in data analysis. Because the study is quantitative in nature, it will be appropriate to use frequency count, mean, standard deviation, minimum and maximum values of variables
Quantitative data analysis will be done to generate frequencies percentages. The resulting quantitative data will be then interpreted using simple statistical method. The questionnaire will be coded and analyzed using Statistical Package for Social Sciences (SPSS) version 21. The data collected through questionnaire will be classified on the basis of common attributes then tallied to obtain statistical frequencies, tabulated and finally analyzed using descriptive statistics. This helps to collapse large volume of quantitative data in numerical form for ease of statistical interpretation. Frequency and distribution tables, graphs and diagrams will be integral during the process of data analysis. Comparative analyses will be used to outline the characteristics of the leading attributes within the variables under discussion.
The hypotheses formulated for this study will be tested using logistic regression model. This has been used to examine the relationship between dependent and independent variables. Logistic regression is multiple regression but with an outcome variable that is a categorical dichotomy and predictor variables that are continuous or categorical.
References
Dobson, Stephen, and John A. Goddard. The Economics of Football. Cambridge: Cambridge University Press, 2011. Print.
Fort, Rodney D. International Sports Economics Comparisons. Westport, Conn. [u.a.: Praeger, 2004. Print.
Rosner, Scott, and Kenneth L. Shropshire. The Business of Sports. Sudbury, Mass: Jones and Bartlett Publishers, 2004. Print.
Wise, Aaron N, and Bruce S. Meyer. International Sports Law and Business. The Hague: Kluwer Law International, 1997. Print.
Bonner, John T. The Social Amoebae: The Biology of Cellular Slime Molds. Princeton: Princeton University Press, 2009. Print.