Development of Soil Nutrient Sensors

Development of Soil Nutrient Sensors The rising demand for food crops and the growing concern for environment have made it necessary to shift from traditional agricultural practices towards modern agricultural practices. Traditional agricultural practices are labor intensive, time consuming, expensive and also a cause of environment pollution. To achieve sustainable agriculture, it is necessary that the precision agriculture technologies and practices are integrated with the traditional practices, which will also help to deal with the spatial heterogeneity of the soil [1]. The biggest hurdle in the proper implementation of precision agriculture is the inability to generate information related to a particular site rapidly and at an acceptable cost using laboratory analysis and soil sampling methods. The nutrients required for the healthy growth of a crop are obtained from the soil. The quality of crop yield depends on the quality of soil in which it grows. Therefore, soil testing is an important aspect of precision agriculture. The proposed research work is an effort towards the design and development of a soil monitoring system that can be used to estimate the urea content in soil. The system makes use of Partial Least Squares Regression Technique (PLSR) for the estimation of urea. The system can be made portable, smart, low cost and user friendly through the use of embedded systems. With some modifications the system can be designed to estimate more than one soil component. The thesis is organized in the following chapters as described below. Chapter I (Introduction) â€Å"Agriculture not only gives riches to a nation, but the only riches she can call her own†[2]. The growth in the demand for food, feed and fiber globally is anticipated to grow by 70 percent. The demand for crops for industrial use and in the production of bio-energy is also expected to rise simultaneously. The increasing demand for agricultural goods will put huge pressure on the limited resources available. The increase in urban settlement areas will force agriculture to compete for land and water. Agriculture will therefore have to adapt itself to newer conditions and at the same time will have to address issues related to climate change, maintenance of biodiversity and preservation of natural habitats [3]. To meet these demands, farmers therefore need to equip themselves with new technologies so as to increase productivity with limited number of resources. Sustainable resource management is the need of the hour. Conservation of soil quality is crucial to sustainability in agriculture. This has led to a shift from the use of traditional agricultural practices to modern agricultural practices so that the available resources are utilized in a sustainable manner. The modern technique of farming known as precision farming is based on the concept of site specific crop management. This method takes into consideration variability exhibited by the soil and accordingly inputs are applied based on the local requirements within a field. Soil sensing plays an important role in precision farming. Large numbers of soil sensors are being developed all around the world to measure different soil properties. Some of which are still in the research and development stage and some of which are commercially available. Based on their principle of working these soil sensors can be classified as follows: Electrical and Electromagnetic sensors: Depending on the composition of soil under test, electrical capacitance or inductance, resistivity or conductivity of the soil is measured. The response time of these sensors is very fast, they have high durability and are of low cost. These sensors are commercially available. Optical and Radiometric sensors: These sensors, through the use of electromagnetic waves, measure the level of energy that is either absorbed or reflected by the soil particles depending on the soil composition. The properties of the soil are measured using visible and near-infrared wavelengths [4]. They can be used for the estimation of CEC, soil texture, moisture and other soil parameters with the help of appropriate data analysis techniques. Mechanical sensors: these sensors measure soil resistance with the help of a tool used in the soil. The measure of resistance offered by the soil has a relation with the compaction of the soil which is a spatially varying property of soil. Acoustic sensors and Pneumatic sensors: Though these are a class of mechanical sensors, they can be used as an alternative means for the differentiation of physical and mechanical characteristics of soil. Measurements taken using pneumatic and acoustic sensor have been used to correlate soil texture and compaction. The application of acoustic sensors in characterizing the physical state of soil is not very clear and requires more research work. Electrochemical sensors: These sensors produce an output voltage through the use of ion selective membranes, depending on the activity of ions under study such as H+, K+, NO3 −, Na+, etc. Soil pH sensors using this technique are already commercially available. The extraction of ions such as potassium in real time is still not possible even though the concept appears to be simple. There is a need to develop fast, real time and portable soil sensors which can generate soil report instantly. Thus, the problem of designing and developing a smart soil monitoring system was formulated using a reconfigurable embedded system platform. Chapter II (Literature Survey and Objectives) The conventional laboratory methods of soil testing have a number of limitations such as they are expensive, labor intensive and time consuming. As such new methods of soil testing are being developed across the globe. A number of soil nutrient sensing techniques are in the stage of development and testing. These sensors can be broadly classified into two types depending on the techniques of measurement being used. 1. Optical sensing uses reflectance spectroscopy technique wherein the light that is absorbed/reflected by soil particles is measured. Since optical sensing techniques have the advantage of being non-destructive they are more widely used as compared to electrochemical sensing techniques [5], [6]. Soil color analysis can be used for estimating soil organic matter content through the use of optical sensors [7]. The visual and near-infrared spectral reflectance in optical sensing can be used for estimating soil texture, moisture, CEC etc. [8]. 2. Electrochemical sensing is based on the measurement of current or voltage generated between the sensing electrode and the reference electrode. The amount of voltage or current measured is related to the concentration of the selected ions such as H+, K+, NO3-, etc. [8]. Ion selective electrodes made of glass or polymer membrane, or ion-selective field effect transistors are used for the measurement of soil fertility. Ion-selective membrane sensors have a huge potential in the development of on-the-go soil nutrient(s) and pH sensors [9]. Currently, the accuracy of the results using these sensors is low as compared to those using laboratory tests, but this can be taken care of by increasing the sampling density. Use of Spectroscopic techniques in the estimation of soil properties has been demonstrated since 1970’s [10]. Various methods using spectral analysis have been proposed for the measurement of the soil properties. Methods that are based on the physical and analytical characteristics of the signal and chemometric based empirical methods provide good effective predictability. Therefore, the relation between soil properties and soil absorption can be used to develop regressions using field and laboratory data for calibration. Spectroscopic techniques are found to be faster, can provide real time measurements and are of low cost, as compared to conventional methods and hence are found to be more suitable when there are more samples and analysis to be done. Also, unlike laboratory testing methods which require sample pre-processing and the use of chemical extractants, spectroscopic techniques can be used directly, thus saving on cost and time [11]. Thus, the problem of developing a soil nutrient sensor using RF spectroscopy based on the dielectric principle was formulated. The thesis emphasizes on the design and development of the sensor and the use of embedded platform to make it portable, real time and user friendly system through the use of DSP algorithms. Objectives: In order to meet the global requirements of increased crop productivity and sustainable agriculture, there is an urgent need of developing soil sensors which are fast, accurate and portable. Also, the problem was formulated keeping in mind the conditions of Indian farmers. Indian farmers are mainly small farmers who are poor, technically unfit and cannot afford modern tools. This research work is being undertaken with the main objective of developing a fast, portable, cost effective and user friendly soil monitoring system to analyze the fertility status of the soil. The objectives of the research work are the design of a dielectric cell to measure absorption loss at RF frequencies for various soil nutrients and to use this RF data to develop a FPGA based smart soil monitoring system for accurate prediction of soil content using PLSR technique. The system shall be user friendly as well as reprogrammable for changed environmental conditions. Chapter III (System Design for Soil Monitoring System) The block diagram of proposed design for Soil Monitoring System is as shown in Figure 1. The design consists of RF data obtained from Scalar Network Analyzer fed as input to Altera DE2 board with target as NIOS II FPGA. The RF data is obtained from the soil sensor connected between a tracking generator and a spectrum analyzer. A soil sensor based on the dielectric loss technique is designed and constructed to measure the RF responses of various soil nutrients. The cell is rectangular in shape with outer dimensions 13cmx2cmx2.5cm and is made up of PMMA sheets. The inside surface of the cell is lined with gold foil and the same is connected to the outer shield of the feed connectors so as to provide the necessary shielding effect. The outer surface of the cell is covered with a copper foil and is also provided with the necessary shielding effect. A wire made of gold is connected from the input feed connectors to the output feed connector at centre of the cell. The RF spectrum of a sample is measured by placing it in the cell. A tracking generator is used for injecting an RF signal into the sample through the central gold wire. Thus, a dielectric cell consisting of the central wire, the outer copper shield and the sample is formed. The signal strength starts reducing as it propagates through the central wire from the input end to the output end of the cell, due to the dielectric loss associated with the sample solution. Thus, an output signal proportional to the absorption loss of the sample solution is captured by the RF spectrum analyzer connected at the receiver end of the cell. Signal Hound USB-TG44A tracking generator and Signal Hound USB-SA44B spectrum analyzer are used with both the instruments working in the frequency range of 1Hz-4.4GHz. Figure 1: Block diagram of the Soil Monitoring System Figure 2 shows the RF spectra for urea in the range 10MHz to 4.4GHz. Figure 2: RF Spectra of urea. Figure 3: Section of urea spectra with varying concentrations Samples for obtaining the RF responses of various soil components are prepared by dissolving the required component in distilled water. The amount of the component to be added to water was calculated from the data obtained from agricultural department. This amount was taken as the normal concentration of a particular component found in the soil. Samples of varying concentrations of the soil components are prepared and denoted as 1 for normal, 0.5 for half the normal, 2 for twice and 3 for thrice. The soil components considered for the study are urea, potash, phosphate, calcium carbonate and sodium chloride. The frequency range of 10 MHz-4.4GHz is divided into smaller frequency ranges based on the unique frquencies at which the variation in the attenuation is found as per the change in the concentration of the soil component. A set of recorded spectra for various combinations of the five soil components with concentrations ranging from 0.5 to 1.5 are used in the calibration file. In order to predict the unknown concentration of urea in a sample, the detected spectra containing the urea signature along with the other components is passed through signal conditioning stage. The output from Spectrum Analyzer is stored in the computer. This data is then fed to a CYCLONE II device with Altera Nios II processor running on it. The recorded spectra are then passed through SIMPLS algorithm running on NIOS II processor. The algorithm predicts the concentration of unknown sample (Urea) and displays the result on LCD or a computer screen. The SNR of the detected spectra must be sufficiently high so a s to provide reliable urea specific information and therefore data processing is needed to identify spectral features of urea from the combination spectra originating from interfering matrix components like potash, phosphate, sodium chloride and calcium carbonate. We can extend the use of this system for the analysis of other soil components by modifying the processing algorithms required to analyze that component without changing the hardware. Chapter IV (Multivariate Data Analysis) It is a statistical analysis technique used in the case of data consisting of multiple variables. Due to the advancements in the field of information technology there is a huge amount of data being generated in various fields. Though the magnitude of data available is huge, it is still a challenge to derive useful information and knowledge from this data. Multivariate Analysis can be used to derive meaningful information for the improvement of process performance and product quality. Over the last decade, multivariate analysis is being successfully used to monitor and model chemical/biological processes [12]. Techniques using multivariate data analysis are widely used in the analysis of spectral data both quantitatively and qualitatively. Quick analysis of complex samples from their spectral signatures can be done using standard tools like Partial Least Squares (PLS), Principal Component Regression (PCR), Principal Component Analysis (PCA), Multivariate Curve Resolution (MCR) and discriminant analysis based on chemometric techniques [13]. Partial least squares (PLS) isone of the recent multivariate data analysis technique particularly useful in situations where there is a large set of independent variables (i.e., predictors). A set of dependent variables can be predicted from this set of independent variables by using PLS. Partial Least Squares (PLS) can be an effective tool for the analysis of data as it has minimum constraints on scales of measurement, size of sample, and residual distributions. It consists of methods for regression and classification, and techniques for reducing dimens ion and tools for modeling. The basic assumption on which the PLS methods work is that a small number of latent variables that are not directly observed or measured are used to drive the observed data from a process or a system. The technique of PLS for projecting the observed data to its latent structure was developed by Herman Wold and coworkers. PLS is now being used as a standard tool in the analysis of a wide spectrum of chemical data problems in chemometrics. The successful data analysis of PLS in chemometrics has led to its increase use in other scientific fields such as bioinformatics, food research, medicine, pharmacology, social sciences, physiology etc. PLS is a multivariate technique that transforms the input-output data onto a smaller latent space, by extracting a small number of principal factors having an orthogonal structure. The extraction of the factors is done in such a way that it provides maximum correlation with the dependent variable [14]. To model linear relations between multivariate measurements, PLS is used as a standard tool. Multivariate Calibration Model for Soil Monitoring System: Multivariate spectroscopic data can be analyzed using the PLSR model. PLSR is one of the techniques of multiple linear regressions and is probably the least restrictive of the various multivariate techniques used in multiple linear regression models. This feature of PLSR makes it possible to be used in situations when there are limitations on the use of other multivariate methods, for example, when the predictor variables are many as compared to number of observations.PLSR can be used as an elementary analysis tool for the selection of suitable predictor variables and in the identification of outliers. PLSR model based on SIMPLS algorithm using C language is developed and ported on NIOS II platform to estimate the urea concentration. The PLSR model is validated for the following cases: Case 1: Changing urea concentrations from below normal to above normal i.e. from 0.5 to 2 and keeping other components at their normal concentration value i.e. 1. Case 2: Changing the concentration of each of the other soil component from 0.5 to 2 and keeping urea constant in all the cases. Chapter V (Design of FPGA Soft Cores for Soil Monitoring System) DSP functions can be implemented using two different types of programming platforms: digital signal processors (DSP) and field programmable gate arrays (FPGAs). Digital signal processors are microprocessors specifically designed for handling DSP tasks, while FPGAs are reconfigurable signal processors. The factors that make FPGAs more suitable, particularly for high performance computing applications are: (i) Huge potential for implementation of parallelism (ii) The control logic is embedded (iii) On-board memory in FPGA helps to overcome the limitation set by number of I/O pins on processor logics memory access bandwidth and hence results into significant performance benefits (iv) A higher capacity FPGA can be used on the same board as an older device, to support performance upgrades. DSP Implementation on Embedded system The implementation of DSP algorithms is done on Altera platform. A Nios II system is designed to measure the concentration of urea in soil. The Nios II system is the heart of the instrument which controls the various modules of the system like interacting with 12 bit ADC and performing the SIMPLS algorithms on the spectral data to estimate the concentration of urea. The whole interface and algorithms are implemented using 32-bit NIOS II soft-core ported on CYCLONE II FPGA. Chapter VI (Analysis, Results and Conclusion) The thesis covers the design and development of soil sensor based on the dielectric technique. The technique proposed the use of RF signals in the range of 10MHz-4.4GHz and analyzing the detected spectra in the soil sample for urea signature. In this thesis a novel Soil Monitoring System is developed using RF spectroscopy based on embedded technology. An Altera DE2 board based on NIOS II soft-core platform and having target as CYCLONE II (EP2C6) is used to estimate the urea content in soil in the RF range of 10MHz-4.4GHz. SIMPLS algorithm for PLSR model is developed using C language and embedded on the NIOS II platform for the estimation of urea concentration. The designed sensor was tested for its precision by recording the spectra of a particular component over a number of times. The PLSR model was validated by calculating percentage error under various conditions. It was found that the predicted urea values showed percentage error which was within the acceptable levels required fo r device development.

An Analysis of William Gibsons Idoru Essay -- Gibson Idoru Essays

  Ã‚  Ã‚  Ã‚   William Gibson's Idoru is a novel thick with implications and extrapolations related to the oncoming and (present) age of electronic para-reality. Stylistically, it is far from perfect, but in theme it has a firm grasp on the concept of the simulacra as it mimics, masks and replaces reality.    Gibson's characters are rarely paintings of great depth. While I would strongly disagree with the assertion that they are archetypes cut out from a mold, I would still note that they are not particularly rich or personal. This probably derives from the author's style of writing which is the radical end of the spectrum of "showing, not telling," so that we are shown the characters' pasts, physical status, and present situations, and as readers we are to intuit the logical psychological conditions associated with those factors. Gibson has rich situations, not rich characters.    That's why I find it so strange that the New York Times Book Review wrote, "Chia is one of [Gibson's] most winning creations." I fail to understand the logic. It's as though, by making her young and in a strange situation, we're to develop an instant affinity for her. Now obviously, Gibson himself is not the one to decree that his characters are strong or weak. So it is not a flaw on the part of his writing when a reader attributes an archetype to one of his characters, but I would tend to think that, by design or simple lack of skill, Gibson writes his characters a little flat. (Which, in the context of a discussion of simulacra, makes it all the more amusingly ironic that book reviewers would attribute what they would call a "hidden" level to the quality of the writing not otherwise apparent.)    Another stylistic tool Gibson employed wa... ...and eventually defines reality? It was a simply computer, just like Idoru was simply a novel. Yet the seashells in the make of that case serve to create a fantasy as readily and importantly as the words on paper serve to create a reality (and, paradoxically, the reality in which those seashells existed.) Simply because each is not real does not disrupt the validity of their creations, for if that were true, then the seashells would never have existed in the first place, even in our minds.    Gibson understands this closely, and Idoru does an excellent job of illustrating it. While not technically perfect, it is effective, and creates an image which is useful for us to learn from.    Works Cited and Consulted:    Gibson, William. Neuromancer. (Ace Books: New York 1984)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   _____, Idoru. (Berkeley Books: New York 1996)   

Attentional Blink

INTRODUCTION The Attentional Blink Experiment aims to determine the capability of an individual to recognize both the targets given that he or she is subjected to rapidly changing stimuli. Moreover, the theory states that after the detection of the first target in a rapid stream of visual stimuli, the second target is missed (Niewenstain, Potter, & Theeuwes, 2009). Hence, the experiment means to prove whether attentional blink is present in the experiment and if the theory is correct.Furthermore, the suggested hypothesis for this experiment is that the higher separation of the two targets with each other will increase the probability of discriminating and reporting the second target with respect to the first. In addition, the experiment was conducted inside the ergonomics laboratory at the Science and Technology research building on February 5, 2013 using the Wadsworth Coglab program application. It was done at only one site to ensure the consistency of the environment. Also, each te st consisted of 100 trials. I. PROBLEM STATEMENT Attentional blink is present between targets of short separation.II. OBJECTIVES 1. Aims to confirm the presence of attention blink in the different subjects. 2. Aims to show that the percentage reported for the 2nd target increases as the separation of the two targets increases through the use of statistical analysis. 3. To identify improvements for the report of the second target in the stimulus presentation, assuming the theory is correct. III. METHODOLOGY A. Selection of Subjects The minimum required subjects was fifteen subjects which consists of the students of the present Ergcog2 laboratory class, and they were asked to answer the attention blink experiment honestly.The group decided to add additional of 10 subjects outside of DLSU with the same conditions given to the first fifteen subjects in the class. This was done for the reason that more data leads to more consistent and less biased results. There was no particular reason nor criteria used in choosing the subjects. They were chosen out of convenience. Apparently, the subjects chosen were composed of both male and female and all subjects were in between the ages of 18-22 years old. B. Experiment Proper 1. Fifteen subjects (from the class) and ten subjects (outside DLSU) were chosen to answer the experiment on attention blink.They were chosen using convenience sampling distribution. 2. There are two trials in this experiment and the group considered this factor. Trial 1: Subjects took the experiment without being distracted. Trial 2: Subjects took the experiment while being disturbed during the whole experimental period. Subjects were having simultaneous conversation during the whole experiment. 3. The software is activated. Pressing the spacebar indicates the start of the first trial where a sequence of letters appears. Each letter in the sequence is only flashed for 100 milliseconds. 4.The task of the subjects is to determine if letter J, letter K, o r both letters were flashed in each sequence. 5. The subject presses the â€Å"J† and â€Å"K† keys to indicate that the letters â€Å"J† and â€Å"K† were flashed in the sequence respectively. The subject can also press both â€Å"J† and â€Å"K† keys if he/she believes that both letters were flashed. 6. The keys that were pressed by the subjects are flashed immediately in the screen for the subjects to be able to check whether the software was able to receive the information correctly or not. 7. Space bar is pressed by the subject to proceed to the next trial. . After the 100 trials, a window appears which shows the graphical result of the test that was done by the subject. The graph shows the rate of how the subjects were able to detect the targets due to how the targets were separated. 9. The results were analysed and conclusions and recommendations were made at the end of the experiment. C. Tools Used * Computers with CogLab Software ar e used to run the trials in which data are gathered. D. Possible Causes of Error (Factors) Fatigue of the subjects is a possible cause of error in the experiment.One run is composed of 100 trials, which can be very tiring for the eyes. As a result, the subject’s ability to detect targets may deteriorate at the latter trials of the experiment. Environmental factors can also be a possible cause of error like having noise in the background or having a conversation while doing the test. This is to test whether this kind of factor has a significant effect on the ability of the subject to detect targets. The subjects not taking the attention blink test seriously may also be a possible source of error in the experiment.Some subjects may have just rushed the test. How the subjects would take the experiment is solely dependent on their level of seriousness. IV. RESULTS & DISCUSSION Table 1. Summary of the Mean and Std. Deviation Response on 1st target | Separation target| | 0| 2| 4| 6 | 8| Mean (percent)| 56| 54. 5| 58| 54. 5| 58| Std. Deviation| 11. 7| 13. 4| 17. 2| 15. 5| 19. 6| Figure 1. Percent Response Vs Target Separation for 1st target Table 1 shows that for the first target the average responses for the 5 separation target are near to each other.The results for each target separation might be varied for the subjects as seen in the deviations which are at the range of 11. 7 to 19. 6, but comparing the 5 mean would only result to a standard deviation of 1. 75. This means that the results are almost constant and has minimal deviation. Figure 1 also shows this trend that the % responses for each target separation are near each other. Looking at the results it can also be seen that the subjects can only see 54. 5% to 58% of the 1st target, since fluctuations in the graph is within these range. Table 2. Summary of the Mean and Std.Deviation Response on 2nd target | Separation target| | 0| 2| 4| 6| 8| Mean (percent)| 5. 0| 39. 0| 42. 5| 58. 5| 60. 5| Std. Deviat ion| 6. 2| 16. 6| 11. 4| 11. 6| 15. 7| Figure 2. Percent Response Vs Target Separation for 2nd target Table 2 shows that the percent mean of the responses increases as the target separation increases. Again the results for each target separation also varied for the subjects since the deviation ranges from 6. 2 to 16. 6. But the deviation for the zero separation is not that big compared to the others, since most of the respondents here cannot detect the 2nd target.The deviation for each target separation might be big but the data and Figure 2 would show a linear relationship with between the % response and the target separation of the 2nd target. This means that the respondents are able to detect the 2nd target more as the separation between the two target increases. The % response of the respondents for the 2nd target is from 5% to 60. 5%. Figure 3. Percent Response Vs Target Separation for 1st and 2nd target Figure 3 would show a clearer relationship between the 1st target and the 2nd target.The line for 1st target (blue) would show an almost straight line pattern while the line for the 2nd target (red) would show a line that increases as target separation increases. The graph also shows that for target separation 0 to 4, the 1st target has a higher % response. But when the separation became 6 and 8 the 2nd target is seen more by the respondents. We could also see that the deviation between the 1st and 2nd target decreases as the target separation increases. For the 0 target separation the difference between the two targets are 51% for the 2 sec target separation it became 15. % and the difference becomes smaller as target separation increases. The best result is seen in the 8 sec target separation since 1st target has a 58% response and the 2nd is 60% response the difference between the two is only 2%. In addition, in order to identify the targets better the subject only focuses on the letters â€Å"J† and â€Å"K† and disregards the other lett ers in the series. In fact, this selective nature of perception would lessen the overloading of information. According to Reed (2004), selectivity is defined as the focusing of aspects of attention, wherein the subject pays attention to some aspects while ignores the others.To prove that the attentional blink theory is correct in stating that the first target is unaffected by the separation of the signals. And the second target, on the other hand, shows that the longer the separation period of the first signal to the second, the higher the response (Mackewn & Goldthwaithe, 2004). Regression technique is used to see the relationship between the target separation and % response of the 1st and 2nd target. This would show how the target separation (independent) affects the detection of the target for the 1st and 2nd target (dependent). Table 3.Regression summary for 1st target. N= 50| Beta| Std. Err. of Beta| B| Std. Err. of B| t(48)| p-level| Intercept|   |   | 55. 4| 3. 75| 14. 77 | 0. 00| Separation| 0. 04| 0. 14| 0. 20| 0. 77| 0. 26| 0. 80| The regression summary would show that the separation of the target is not related with the percent response of the 1st target since the p-level of the regression is 0. 80 meaning it is not significant in identifying the value for the 1st target. Table 4. Regression summary for 2nd target. N= 50| Beta| Std. Err. of Beta| B| Std. Err. of B| t(48)| p-level| Intercept|   |   | 15| 3. 9| 4. 18| 0. 00| Separation| 0. 79| 0. 09| 6. 53| 0. 73| 8. 92| 0. 00| The regression summary shows that for the 2nd target the target separation is significant since a p-level of 0. 00 is shown. Therefore, this means that target separation affects the % response for the 2nd target. On the other hand the beta value of 6. 53 shows that as the target separation increases the % response for the 2nd target also increases. The Attentional Capacity Theory Duncan et al. have proposed that T1 occupies attentional capacity to the detriment of a trai ling T2 target.This theory suggests that the duration for which T1 continues to occupy attentional capacity is related directly to the T2 processing difficult (Rochester Institute of Technology). This explains why the 2nd target increases as the separation time increases. It is because the theory states that every person has their own attentional capacity and if separation time is bigger the information processing do not overlap and the two targets are seen by the respondent. This also why the first letter is first seen since it is the one that occupies the person’s attentional capacity first and is first processed by the person.Outside Factors In the conduction of the experiment, although the distraction may have a small effect on the signal detection of the subjects, the results as shown in the graphs make it clear that attentional blink is not affected by the amount of external distraction since it is an internal issue. As mentioned, in trial 2 the subjects were distracted by assigning someone close enough to generate a conversation with them through the whole duration of the experiment. There is no significant difference found in the detection of the targets between being distracted and not.This is because the subjects were observed to say â€Å"ha? † more often than not during the conversation. Having their attention focused on the experiment applies the theory of selective attention wherein one tries to pay attention to one input in the presence of others (Glass & Holyoak, 2004). Visual dominance is another concept that can be seen in the experiment. It can be observed that visual targets dominate over auditory targets (Glass & Holyoak, 2004). This also explains why the subject is more inclined in doing the experiment rather than chatting with the distracter. V. CONCLUSION & RECOMMENDATIONSBased on the results of the experiment, it can be concluded that the theory of attentional blink is correct. The hypothesis made at the beginning could b e verified by the results obtained. These results show that separation does not have a large effect on the probability that the first target would not be detected by the respondents, since the average percentage reported for the first target by the respondent is relatively unaffected by separation. The values were close to each other. On the other hand, average percentage reported for the second target increased as the separation increased.This shows that the hypothesis that the longer the separation between the targets, the higher the chance of getting the targets right is correct. There are less chances of an attentional blink when more letters are in between, since the second letter is delayed. This gives the user a chance to have more accurate results. Although the program was effective in proving that the Attentional Blink Theory is correct, there could still be some improvements that could be done. Since the program has a black background and white letters for the stimuli, a w ay of making the second target easier to detect is to add color or change the background color.According to (Pashler, 1999), the second target could be easier to detect when there is color discrimination. When provided, it seems to cause the blink to virtually disappear because there is a different dimension. Sizes of the letters were the same for all. Biased attention may occur when the contrast and sizes of the targets differ (Proulx & Egeth, 2006). In the research conducted by Proulx and Egeth (2006), objects with better luminance contrast are processed rapidly and precisely compared to lower contrast items. It also shows that larger objects can influence visual performance.In order for the subject to identify the second target, the size of the signals or targets can be made bigger. A sample for this is illustrated below. Based from observation, the respondents made mistakes on entering what letter they saw. The program did not allow the respondent to change his or her answer. A recommendation for the enhancement of the program could be having the function to let the respondent change his or her answer, so that the respondents’ probability of getting the correct answer would increase. This in turn can improve the respondents’ data.

Alpine Plant Climate - Free Essay Example

Sample details Pages: 17 Words: 5029 Downloads: 10 Date added: 2017/06/26 Category Statistics Essay Did you like this example? Alpine plant biodiversity in the Central Himalayan region: Perspective of global climate change Summary Don’t waste time! Our writers will create an original "Alpine Plant Climate" essay for you Create order Increase in surface temperature at global scale has already affected a diverse set of physical and biological systems in many parts of the world and if it increases at this rapid rate then the condition would be worst one could have ever thought off. Garhwal Himalaya, major part of the great Himalayan mountainous system is also much sensitive and vulnerable to the local, regional and global changing climate. Due to strong altitudinal gradient, varied climatic conditions and diverse set of floral and faunal composition, the impact of climate change seems to be much higher. This paper highlights some important features of the changing pattern of vegetational composition, distribution and impact of climate change on the phenological aspect of major alpine plant species present in the Garhwal Himalayan region. It also shows cumulative changes, which operate at local level but are globally pervasive. These cumulative changes include change in the land cover/ land use and other anthropogen ic activities, which are related to the climate change. Overall biodiversity in the Himalayan region has been depleted as the consequences of complex and multitude pressure of climate change. The depleted biodiversity has indirectly affected the socio-economic development of the local communities on which their sustenance depends and is inherently critical to the consideration and management of natural resource. Introduction Plant diversity and Status The varied altitudinal, climatic and topographical conditions in the Himalaya results in different types of microhabitats. Geographic isolation, glaciations, evolution and migration of the species in the past all together have contributed to the high level of biodiversity in this mountain system. As per genetic, species and ecosystem level resources, Himalaya is one of the hotspots of biodiversity in the world, which represents about one-tenth of the worlds known species of high altitude plant and animal species. Some parts in the Himalayan region are center for origin of many crops and fruit species and are important source of gene for their wild relatives. The floral diversity of this region shows assemblage of many endemic and exotic species of plants from the adjoining regions. A large number of western Himalayan flora in the Garhwal Kumaon region seems to have been invaded from Tibet, western China and adjoining north-east Asia (Rau, 1975). In the present scenario biodiversity seems to have been depleted in these regions due to land degradation, habitat fragmentation, increasing population pressure, over exploitation of bio-resources and finally due to the changing pattern of the climate. Nearly 10% of flowering plants are listed under various categories of threatened species. Red Data Book of Indian plants listed about 620 threatened species, of which, 28 are presumed extinct, 124 endangered, 81 vulnerable, 160 rare and 34 insufficiently known (Nayar and Sastry, 1987, 1988), however, Red list of threatened plants indicates 19 species as extinct. Among others, 1236 species are listed as threatened, of which, 41 taxa are possibly extinct, 152 endangered, 102 vulnerable, 251 rare and 690 of indeterminate status (IUCN, 1997). From the Himalayan region the important plant species included in threatened categories are mostly the valuable medicinal and aromatic plants, which, support the economic condition and health care sys tem of the local communities. It is well known that, in the context of the present scenario of climate change especially due to global warming many of the high-elevated ecosystems are severely sensitive and vulnerable. Their fragility may accelerate the changes occurring in their composition and structure to the slight variations in climatic factors. These regions include glacier, alpine pasture/ meadows and timber line ecosystem, which are the important source of the seasonal runoff, freshwater, valuable medicinal and aromatic plants, grazing land, source of timber and wild edibles for the mankind. Future scenario of climate change: According to the Third Assessment Report of Intergovernmental Panel on Climate Change (IPCC) 2001, average global temperature close to the earths surface has increased by 0.6 C 0.2 C since 19th century mainly due to the emission of CO2. If human beings do not act to reduce the present level of CO2 there will be additional increment in temperature of 1.4 C to 5.8 C in the next 40 100 year. Current information available on the pattern of future climate change through General Circulation Models (GCMs) suggested that the annual mean warming would increase about 3C in the decade of 2050s and about 5C in decade of the 2080s over the land region of Asia. Precipitation would increase annually about 7% and 11% in decades of 2050s and 2080s respectively. There would be a decline in the summer precipitation that seems likely to be over the central part of arid and semi-arid Asia. GCM also showed high uncertainty in future projection of winter and summer precipitation over south Asia, because much of tropical Asian climate is noticeably associated with the annual monsoon cycle. In Central Himalayan region, through the assessment of people perception it is interpreted that, climate change resulted in the increase in warming, decline in rainfall during March- May, high rainfall during Aug- Sept instead of normal peak in July- Aug, decline in the snowfall intensity and winter precipitation in Jan-Feb instead of Dec-Jan (Saxena et al., 2004). This scenario can hardly trigger to think about the changing pattern of climate or its negative and positive impacts at local, regional and global level. Although assessment of future climate change scenario through some of scientific models needs a better infrastructure and high technological inputs, specific impact of climate change on different ecosystems can be discerned by comprehensive studies on long term monitoring of the different aspects of ecosystem which is lacking in the Indian context especially in the Garhwal Himalayan region due to poor infrastructure and management practices. So, as per as need concern in these remote areas the assessment of impact on the natural resources in future climate changes can be done through the site-specific sensitivity analysis and it can be related to the traditional knowledges of the peoples living in this particular region of the Himalaya. Sensitivity analysis would help to assess what will be happen if various climatic variables changed, and analysis also evaluates the positive or negative impacts of changing climate on the natural resources. This assessment would help us to make the l ocal communities realize the importance of conservation and management practice so that the endangered and threatened species could be saved from becoming extinct. Assessment of vulnerability and adaptive capacity of the various ecosystems and to develop indigenous knowledge based coping mechanism are important to determine the impact of climate change. This also links the ecological processes to the social processes and appreciates the relationship between the biodiversity and ecosystem functioning. Climate change: Impact on different vegetation zone Natural ecosystems at high elevations are much more sensitive to the climatic variations (Ramakrishnan et al., 2003) or global warming then the managed systems. Their sensitivity is prominently attributed to their limited productivity during snow-free growing season (Price et al., 2000), low dispersal capability, geographically localized, genetically impoverished, highly specialized and slow reproducing ability of the high altitude plants (McNeely, 1990; WWF, 2003). As a consequence of global warming the present distribution of species in high altitude ecosystems projected to shift higher as results of upward altitudinal movement of the vegetation belts. Although the rate of vegetation change is expected to be slow and colonization success would depend on the ability of adaptation and interaction of the plant species with the climate and other associated species, weeds, exotic and invasive species. Their success also depends on their ecological niche width and their role in the ecosy stem functioning. Increase in the temperature would result competition between such species and new arrivals. As the result, species which have wide ecological tolerance have an advantage to adapt and those which are at the edge of range, genetically impoverished, poor dispersal ability and reproducer are under the threshold of extinction. A likely impact of climate change is also observed over the phenological aspect of vegetation in the alpine, sub alpine and timberline zone. Changes in the pattern of snowfall and snowmelt in these mountain regions and increase in mean annual surface temperature has pronounce impact on the date and time of the flowering and other phenophases of certain valuable, keystone species of plants. Earlier snowmelt simulate early flowering in some early growing plants and possibly increase in surface temperature may extend the growing period and productivity of certain grass species in the cooler climatic region. There is a gradual decrease in the growing period from timberline to the snow line, Rawat and Pangtey, (1987) reported about 20 weeks growing period near timberline and barely 4-6 weeks above 5000 m asl. Thus, increase in the average temperature due to global warming the growing period of the vegetation would be seems to extend at high altitudes. Evidences of climate change through p eople perception in Garhwal Himalaya reveals that increase in the warming results decline in the yield of apple fruits and shortening the maturity period of winter crops, whereas, the production of cash crops like potato, peas and kidney beans under warm condition increases. Change in rainfall pattern, snowfall intensity will increase large-scale mortality and damage to the crops, which are close to the maturity on the other hand, Barley and wheat crop production is severely affected due to winter precipitation in months of Jan- Feb (Saxena et al., 2004). Vulnerability of different vegetation belts in the Garhwal Himalaya. Dominant tree species in the low and mid altitude zone have a wider range of distribution. Shorea robusta the climax species of lower elevation is distributed over moist to dry deciduous bio-climates in central India where temperature is much higher while rainfall is quite low. Quercus spp. the climax species at mid elevation is also distributed over a wide range (1100- 1800m) The mid altitude which is dominated by broad leaves and coniferous forest (Rao, 1994) mainly species of Quercus spp. and Pinus spp. on response to the warming may be replaced by the species like Shorea robusta and Terminalia spp. Warming also increases the chance of greater fire risk in dry or moist deciduous forests, these impacts on the forest can directly influence the local livelihood based on fuel and fodder (Ramakrishnan et al. 2003). Rhododendron arboreum is a very prominent forest species because of its red flowers covering almost the whole canopy. At higher elevations this species used to attain peak flowering stage in February / March but now due to warming flowering time in this species seems to shift in the months of January/February. The phenological calendar at lower altitude has thus shifted to the higher altitudes. Exact times of leaf fall, flushing, flowering and fruiting may vary depending upon the elevation indicating sensitivity of phenophases to temperature and moisture stress regime. Flowering and fruiting start earlier about a month with increase in elevation by 600 m (increase in temperature by 2.4 degree C) in Rhododendron arboreum, Prunus cerasoides, Myrica esculenta, Pyrus Pashia and Reinwardtia indica in Central Himalaya. Leafless period in deciduous species like Aesculus indica and Alnus nepalensis is longer at higher altitude as compared to lower altitude. At higher elevation (1500-3300m) i n Central Himalaya, evergreen and winter deciduous species occur equally across the elevation/temperature gradient. All across the elevation / temperature gradient, majority of tree species show vernal flowering. Species showing vernal flowering (before 15 June) increased in frequency and those with aestival flowering (between 15 June 15 September) decreased with increase in annual temperature drown based on the elevation gradient. Thus, change in the temperature would affect flowering and fruiting time of different species or also induce change in species composition. Vegetation of the timberline in different parts of world not only differs in terms of species composition but also exhibit different types of species (Crawford, 1989). In some regions the timberline represents exclusively evergreen conifers while in some it represents totally deciduous broad-leaved trees (Purohit, 2003). In the central Himalaya the Betula utilis, Abies pindrow and Rhododendron campanulatum, are the native species of timberline (Rawal and Pangtey, 1993), and have a complex, spatial habitat and reservoir of large number of medicinal and aromatic plants and wild edibles. During recent past, timberline, the most prominent ecological boundary in the Himalaya where the sub-alpine forests terminates, has been identified as sensitive zone to environmental change and could be effectively modeled / monitored for future climate change processes. The species from tree-line have a narrow range of distribution, as temperature optima for most of these species is higher than the temperature in their natural habitats, warming will be expected to promote their growth but they may be threatened if they fail to compete with the changing climatic conditions (Saxena et al., 2004). Due to the over exploitation and changing global climatic condition many of the medicinal and aromatic plants in and around the timberline shrunk in size and distribution from their natural habitats and some of them are listed rare, threatened and endangered. Besides, the herbs some tree species of the timberline across the western Himalaya viz. Taxus baccata, Betula utilis etc. are also facing sever threats of depletion (Purohit, 2003). Most of the species valued by local communities have a poor soil seed bank, there could be large-scale local extinction of these species if seed production on a landscape scale decline (Saxena et al., 2004). Swan (1967) identified two parts of the alpine region i.e. above timberline (Lower alpine zone; 300 -4000 masl) and higher alpine zone (4000 masl snowline). Grasses and sedges are dominating members of alpine vegetation at lower altitude but they are characteristically replaced by non- grassy dwarf plant species at higher altitude near snowline. The area immediate above timberline and zone of stunted trees shrubs marks the alpine scrub. The vegetation of the lower alpine zone consists of dwarf shrubs, cushionoid herbs, grasses and sedges, Salix, Rosa, Lonicera, Ribes, Cotoneaster and Berberis etc. form the major shrub species at lower alpine zone (Kala et. al., 1998). The herbaceous flora of this zone represent spectacular array of multicolored flowers and include many short period growing cycle plant species. The major herbs of this zone are Potentilla, Geranium, Fritillaria, Lilium, Corydalis, Cyananthus, Anemone, Ranunculus, and Impatiens etc. The vegetation of the higher alpine zone is rather sparse, dotted with moraines, boulders and rocky slopes forming suitable habitat for the patches of shrubs e.g. Rhododendron lepidotum, Juniperus spp. Betula utilis and many species of colourful flowering plants, grasses and sedge etc. In the alpine with the onset of summer, the physical condition of the every patches of ground undergoes constant change, this is the root cause for the instability and succession of plants. Another feature of alpine plant distribution is that in the same habitat one could see the growth of several related or unrelated species and only one species dominate in the entire habitat almost to the exclusion of the other species. This difference may be due to the Physico- chemical properties of the soil. Initiation of growing season depends on the intensity of snowfall in the proceeding season and start of the melting of snow during spring (April May). In alpine region flowering is started during the month of May in some species, but in most of the species flowering occurs during June to late July and it goes up to early August (Nautiyal et al., 2001). Jennifer A. Dunne et al. (2003) reported that in experimental condition, increasing 2C average soil temperature during the growing season for every two weeks of earlier snowmelt flowering time is advanced by 11 day in the sub-alpine region. Senescence at community level was gradually starts from July to September depending on the growth cycle of the plant species in Central Himalaya (Nautiyal et al., 2001). However in a study conducted by Zhang and Welker (1996) in Tibetan Tundra alpine the community senescence, which actually starts in September was postponed until October under warmer condition and stimulates the growth of grasses. It indicates that the warmer condition as result of increase CO2 enrichment extend the growing period and increase in the grass productivity and distrib ution may suppress the growth of forbs, shrubs (Zhang and Welker, 1996), similarly the valuable medicinal plants also affected (Ramakrishnan et al., 2003). It is possible that timber productivity in the high altitudes/ longitudes could increase as result of climate change, but it could take decades to occur and the newly form forests habitats are likely to retain lower level of native biodiversity due to loss of species that are unable to cope and some species will become more abundant and widely distributed (Alward et. al., 1999) Biotic invasion is another important cause of change in the geographical distribution of the plant species, which is derived or accelerated by the global change. Elevated CO2 might enhance the long-term success and dominance of exotic grasses and their shift in species composition mainly driven by global change has potential to accelerate fire cycle and may reduce biodiversity (Smith et al, 2000). The water use efficiency due to increase atmospheric CO2 can allow increase in potential distribution of Acacia nilotica spp. indica in Australia and increase temperature favour its reproductive life cycle (Kriticos et al, 2003). As the glaciers are receding at a fast rate the newly formed moraine belt is an excellent area to study the invasion of plants from the adjacent mountains and pastures.In recent several land uses and land covers of the high altitude is eroded due to the glacier melting, avalanches and land slides, which favour to extend the distribution of Polygonum polystachyum, a fast growing herb, is mostly found on freshly eroded slopes, past camping sites, river banks and avalanche tracks (Kala et. al., 1998). The other successful invaders found in these habitats are species of Lonicera and Berberis followed by Rosa and Ephedra. Increase temperature may results higher pathogen survival rate and most of the plant species will be severely threatened due to insect, pest and fungal disease. To the changing climate, plants can respond following possible ways firstly no change in their species composition but change in productivity and biogeochemical cycle. Secondly, evolutionary adaptation to the new climatic condition either through plasticity (i.e. shift in phenology) or through genetic response. Followed by emigration to the new areas, as warming observed in the alpine has been associated with upward movement of some plant taxa by 1-4 meter per decade on mountain tops and loss of some taxa that formally were restricted to higher altitude (Grabherr et.al., 1994). Ultimately, they may undergo extinction (Bawa and Dayanandan 1998, Ramakrishnan et al.2003). Most of the plant species changes over time through the process of succession, with pioneer species preparing the way for others, identifying the species present, the physical forms plant takes and the area they occupied are the way for observing change. All the changes involve dynamic and that are difficult or impossi ble to predict, natural ecosystems in this regard serve as a kind of natural laboratory, where natural mechanisms of change such as change in climatic condition and change in the feature of physical and biological systems observe practically. Appropriate management strategies need to developed in such a way that it may have to find a new balance between traditional conservation and maintenance of biodiversity and other ecosystem functioning. Effect on the vegetation: Upward movement of the vegetation belt. It result change in the pattern of structure and distribution of many valuable plant species, Reduction in the area of severely sensitive ecosystem like high altitude pastures, snow cover peaks and important glaciers. Changes in the phenology of some plant species, which include change in time of flowering and seed formation. Changes in the habitat, which is favourable for new alien weedy and invasive species. Increases fire risk in the sub-temperate and temperate dry deciduous and pine forests. Increases productivity of some grass species from the high altitude regions. Adverse impact on the timber production of forest. Effect on the agro-system: Changes the pattern and time of cropping. Shortening the maturity period of some winter crops, which are traditionally important constituent of mountain agriculture. Increase in the pathogen survival rate and crops are more susceptible to pest, insect and fungal diseases. Decline in the yield productivity of some traditional crops; whereas increasing temperature may also be favour the productivity crops like wheat. Decline in the yield of some horticultural fruits which needs chilling effect for their fruit development as seen in case of Apple fruit production. Uncertain high precipitation leads to destruction of crop productivity during flowering, seed formation and maturation time. Effect on Physical system: Accelerate intensity of glacier melting. Reduces area under snow cover and changes the time of snowmelt and snowfall at high-elevated ecosystems. Adverse impact on the seasonal runoff, freshwater availability. Increases the incident of landslides in mountains, drought condition and sever flood condition at lowland regions. Soil properties and process like organic matter decomposition, leaching and soil-water relation were influenced by increase temperature. Socio-economic conditions of the humankind severely affected: Reduction in the area of pasture adversely affect the local pastoral economy, as most of the local livestock of the transhumant and adjoining lowland peoples depends on the high altitude pastures in Garhwal in the summer season. Impact on the timber, medicinal plants and agriculture in the high altitude region in some extent gives negative results to the related industries. Economy through the hydropower generation is affected. Change in the social culture of the peoples living at high altitude regions, i.e. the time of the migration of the transhumant in Garhwal in recent affected due to the adverse climatic conditions. Which also affect their source of economy like agriculture, wool based occupation etc. Changes were also seen in the health conditions of the people living in high altitude, peoples of these regions now more worried about the heat stresses, vector borne diseases, respiratory, eye disorder etc. Status of many endangered wildlife fauna in the Himalayan region affected, and changes in the behavioural and seasonal migration of the animal species can be possible. Table: Distribution of some major plant species at different altitudinal belt of Garhwal Himalaya. Altitude (m asl) Plant species 500- 1400 Shrubs: Zizyphus xylopyrus, Woodfordia fructicosa, Trees: Rhododendron arboreum, Shorea robusta, Dalbergia sisso, Acacia catechu, Adina cardifolia, Terminalia, Cassia fistula, Mallotus philippensis, Bombax ceiba.Agele, 1500-2400 Herbs: Clematis montana, Anemone rivularis, A. obturiloba, Ranunculus hirtellus, Thalictrum chelidonii,Barbarea vulgaris, Silene indica, Malvia verticillata, Geraanium nepalense, Fragaria indica, Potentilla fulgens Epilobium pulustre,Bupleurum falcatum, Aster peduncularis, A. thomsonii, , Gentiana aprica etc. Shrubs: Prunus cornuta, Rosa macrophylla, Zizyphus xylopyrus, Woodfordia fructicosa Trees: Rhododendron arboreum, Shorea robusta, Dalbergia sisso, Acacia catechu, Pinus roxburghii,P. wallichiana, Quercus leucotricophora, Q. semecarpifolia, Adina cardifolia, 2500- 3400 Herbs: Anemone rivularis, A. obturiloba, Ranunculus hirtellus, Thalictrum chelidonii, T. minus, T. elegans, Aquilegiaa pubiflora, Caltha palustris Clematis montana, Clematis barbellata, Delphinium vestitum, Podophyllum hexandrum, Corydalis cornuta, Arabis nova, Viola canescens, Silene edgeworthii, S. Indica, Stellaria monosperma, Geranium collinum, G. himalayense, Trigonella emodi, Geum roylei, Potentilla fruticosa, P. fulgens, P. gelida, P. leuconota, P. polyphylla etc. Grasse Sedge: Carex cruciata, Agrostis pilosula,Poa supina, P. alpina, Danthonia. Shrubs: Cotoneaster macrophylla, Cotoneaster acuminatus, Lonicera, Salix, Rubus foliolosus, Spiraea bella, Berberis glaucocarpa, Myricaria bracteata, Skimmia laaureola, Astragallus candolleanus, Rosa macrophylla. Ribes himalense, Trees: Betula utilis, Taxus baccata, Rhododendron campanulatum, Alnus nitida, A. nepalensis, Abies pindrow, Cedrus deodara, Pinus wallichiana, Acer ceasium, Junipers 3500-4400 Herbs: Cypridium elegans*, C. himalaicum, Epipogium aphyllum, Dactylorrhiza hatagirea, Listera tenuis, Neottianthe secundiflora, Aconitum balfouri, A. falconeri, A. heterophyllum, A. violaceum, Ranunculus pulchellus, Thalictrum alpinum, Podophyllum hexandrum, Acer caesium*, Meconopsis aculeate, Corydalis sikkimensis, Megacarpaea polyandra, Astragallus himalayanus, Nardostachys graandiflora*, Picrorhiza kurrooa*, Pleurospermum angelicoides, Saussurea costus*, S. obvallata, Angelica glauca, Ribes griffithii, Lonicera asperifolia, Waldhemia tomentosa, Primula glomerata, Arnebia benthamii, Geranium pratense, Impatiens thomsonii, I. racemosa, Dioscorea deltoidea*, Allium humile, A. stracheyi*, A. wallichi, Clintonia udensis, Thamnocalamus falconeri, Orobanche alba, Sedum ewersii, S. heterodontum,Pimpnella diversifolia, Morina longifolia Grasse Sedge: Elymus thomsonii, Agrostis munroana, Calamagrostis emodensis, Danthonia cachemyriana, Festuca polycolea, Poa pagophila, Stipa roylei, Carex infuscate, C. nivalis, Kobresia royleana, K. duthei etc. Shrubs: Cotoneaster duthiana, Cotoneaster acuminatus Hippophae tibetana, Rosa sericea, Sorbus macrophylla, S. ursine, Rhododendron anthopogon, Trees: Sorbus aucuparia, Cedrus deodara, Betulla utilis, 4500- above Herbs: Oxygraphis glacialis, Ranunculus pulchellus,Corydalis bowerii, Alyssum canescens,Draba altaica, Silene gonosperma, Potentilla sericea, Sedum bouverii, Saussurea obvallata, S. simpsoniana, Christolea himalayensis Literature cited Rau, M. A. (1975). High altitude flowering plants of west Himalaya. BSI, Howrah, India, pp.214. Singh, D. K. and Hajra, P. K., in Changing Perspectives of Biodiversity Status in the Himalaya (eds Gujral, G. S. and Sharma, V.), British Council Division, British High Commission, Publ. Wildlife Youth Services, New Delhi, 1996, pp. 23-38. Dunne, J.A., Harte, J. and Taylor, K. (2003). Sub alpine Meadow Flowering Phenology Responses To Climate Change: Integrating Experimental And Gradient Methods, Ecological Monographs 73 (1), pp. 69-86. IPCC (2001). Climate Change-2001: Impacts, Adaptation and Vulnerability, contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Kriticos, D.J., Sutherst, R.W., Brown, J.K., Adkings, S.W. and Maywald, G.F. (2003) Climate Change and The Potential Distribution of an Invasive Alien Plant: Acacia nilotica ssp.indica in Australia, Journal of Applied Ecology, 40; 111-124. Nautiyal, B.P., Prakash, V and Nautiyal, M.C. (2000). Structure And Diversity Pattern Along An Altitudinal Gradient In An Alpine Meadow Of Madhyamaheshwer, Garhwal Himalaya, India. Indian Journal of Environmental Science 4(I). 39- 48. Nautiyal, M.C., Nautiyal, B.P. and Prakash, V. (2001). Phenology And Growth Form Distribution In An Alpine Pasture At Tungnath, Garhwal Himalaya. Mountain Research and Development, Vol. 21, No. 2, 177-183. Price, M.V. and Waser, N.M. (2000). Responses of sub alpine meadow vegetation to four year of experimental warming. Ecological Application 10: 811-823. Purohit, A.N. (2003). Studies on Structural and Functional Aspects of Timberline Vegetation in Nanda Devi Biosphere Reserve Garhwal Himalaya, Ph.D Thesis, Deptt. Of Botany, H.N.B.Garhwal University, Srinagar Garhwal. Saxena K.G., Ramakrishnan, P.S, Maikhuri, R.K, Rao, K.S, Patnaik, S. (2004). Assessment Of Vulnerability Of Forests, Meadows And Mountain Ecosystems Due To Climate Change. Com-4E: Winrock Agriculture, Forestry Ch-14 pm6 2nd Proof 20-01-04. Argiculture Forestry And Natural Ecosystem.pp.163-168. Zhang, Y. and Welker, J.M. (1996). Tibetan Alpine Tundra Responses To Simulated Changes In Climate: Aboveground Biomass And Community Responses, Arctic And Alpine Research, Vol. 28, No. 2, pp. 203 209. Smith, S.D., Huxman, T.E., Zitzer, S.F., Charlet, T.N., Housman, D.C., Coleman, J.S., Fenstermaker, L.K., Seemann, J.R., Nowak, R.S. (2000). Elevated CO2 increases productivity and invasive species success in an arid ecosystem, Nature, Vol. 408, pp.79-81. Naithani, B.D. (1984). Flora of Chamoli, Vol. 1, Published by Director Botanical Survey of India, Hawrah, pp X XI. Table 2 Plant species Flowering and fruiting time (Kala et al., 1998) Flowering and fruiting time (Naithani, 1984) Herbs Cypridium elegans* June June C. himalaicum July- August September Dactylorrhiza hatagirea June August June Oct Listera tenuis August- Sept Aug Sept Aconitum balfouri August- Sept Sept Oct Aconitum heterophyllum July Sept Sept Oct Aconitum violaceum July- Aug Sept Oct Ranunculus pulchellus May July October Corydalis cornuta June- Aug May Oct Corydalis cashemeiam June- July May Aug Podophyllum hexandrum April- June April- Oct Potentilla fruticosa July- Oct July- Oct Artemisia spp. June-July Sept- Oct Aster diplostephioides Aug-Oct Aug- Oct Geum roylei June-July May- Oct Pimpinella diversifolia June- Oct June -Oct Meconopsis aculeate July- Sept June- Oct Corydalis sikkimensis June- Aug July Megacarpaea polyandra June- July May June Astragallus himalayanus Aug- Sept June-Oct Nardostachys grandiflora* July Aug Aug- Oct Picrorhiza kurrooa* June June- July Pleurospermum angelicoides Aug Oct Aug- Oct Saussurea costus* August S. obvallata Aug Sept Aug- Oct Angelica glauca Aug Oct Aug- Oct Rheum emodi July- Aug Lonicera asperifolia June Sept Waldhemia tomentosa August Sept- Oct Primula macrophylla June -July May- June Arnebia benthamii July Aug May- Aug Dioscorea deltoidea* May- Oct Allium humile June July June- Aug Allium stracheyi* August Sept- Oct Allium wallichi August July- Oct Clintonia udensis June May- Sept Orobanche alba July- Aug Aug- Oct Morina longifolia July Sept Stellaria monosperma Aug- Oct Aug- Oct Geranium collinum July-Aug August Geranium pretense Aug-Sept Aug- Sept Anemone rivularis June- Aug May- Aug Caltha palustris May- July May- June Clematis Montana May- June April- June C. roylei Oct- March Ranunculus hirtellus June- Aug May- Oct Thalictrum elegans July- Aug June- Oct T. alpinum June- Aug June- Aug Delphinium cashmerianum Aug -Sept August Impatiens glandulifera July- Aug July- Aug Urtica dioica Aug- Sept Feb-August Silene duthei July- Aug July- Aug Christolea himalayensis Aug- Sept Grasses Sedges Elymus thomsonii August Aug- Sept Agrostis Aug- Sept Aug- Sept Bromus ramosus September Sept- Oct Calamagrostis spp. Aug- Sept Aug- Sept Danthonia spp. July Sept Sept- Oct Festuca spp.F. valesiaca July- Sept April- June, Aug- Oct Poa alpine June- July Sept- Oct Stipa roylei July Sept Sept- Oct Carex spp. Aug- Sept Aug- Sept Kobresia spp. K. laxa July- Aug Sept- Oct, June- Oct Shrubs Hippophae tibetana Berberis chitria, B. aristata May June May- Nov, April- Nov Myricaria bracteata June -July Ribes himalense June July May- Oct Rosa macrophylla June May- Oct Cotoneaster microphylla April- May April- Oct Lonicera parviflora June -Sept Rhododendron anthopogon June April- Oct (3500-4000masl) Salix fruticulosa May- June May Oct Astragallus candolleanus June-Aug Ephedra gerardiana July- Aug Skimmia laureola May- June April- Nov Juniperus indica June- Oct Betula utilis May- June May, Oct- Nov Rhododendron arboreum April- May April- Oct (1500-2700masl) Acer caesium* March May April- Nov Rhododendron campanulatum June-July May- Oct (3000-3500masl) Shorea robusta Acacia catechu March- July Dalbergia sisso Feb- May Adina cardifolia Terminalia Feb- Oct Cassia fistula April- June Mallotus philippensis Bombax ceiba Pinus roxburghii Jan- June P. wallichiana April- June, Sept- Nov Quercus leucotricophora March-May, Dec- Feb Q. semecarpifolia May- August Cedrus deodara Aug- Nov Taxus baccata April Sept- Oct Alnus nepalensis Oct- Dec Abies pindrow May Sept- Oct Picea smithianaa April Oct, Nov Albizia labbeck March- May Cupressus torulosa April- Sept Celtis australis March-April, Sept-Oct Aegle marmelos May- June Table 1: A comparison of western Himalayan plant diversity with the plant diversity of India and Himalayan region. Plant Categories India Western Himalaya Himalaya (Singh and Hajra, 1996) Angiosperms (Nayar, 1996) 17,672 8000 Gymnosperms (Singh and Mudgal, 1997) 48 23 44 Pteridophytes (Ghosh and Ghosh, 1997) 1,135 321 600 Bryophytes (Mosses) (Vohra and Aziz,1997, Singh 1997) 2,850 751 1737 (Liverworts)(D.K. Singh, 2001) 235 Lichens (Singh and Sinha, 1997) 2,021 550 1159 Algae (Rao and Gupta, 1997) 6,500 Fungi (Sharma, 1997) 14,500 6900