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Insect-Resistant GM Rice in Farmers' Fields: Assessing Productivity and Health Effects in China Jikun Huang, et al. Science 308, 688 (2005); DOI: 10.1126/science.1108972 The following resources related to this article are available online at (this information is current as of January 8, 2009 ):
Updated information and services, including high-resolution figures, can be found in the online version of this article at: Downloaded from on January 8, 2009 Supporting Online Material can be found at: A list of selected additional articles on the Science Web sites related to this article can be found at: This article cites 5 articles, 1 of which can be accessed for free: This article has been cited by 47 article(s) on the ISI Web of Science. This article has been cited by 9 articles hosted by HighWire Press; see: This article appears in the following subject collections: Botany Information about obtaining reprints of this article or about obtaining permission to reproduce this article in whole or in part can be found at:

Science (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. Copyright 2005 by the American Association for the Advancement of Science; all rights reserved. The title Science is a registered trademark of AAAS.

REPORTS eterization of diapycnal mixing continues to be a major uncertainty in assessing the ocean_s ability to sequester heat, pollutants, and carbon dioxide.
References and Notes
1. M. E. Stern, Tellus 12, 172 (1960). 2. R. B. Lambert, W. Sturges, Deep-Sea Res. 24, 211 (1977). 3. R. W. Schmitt, J. Phys. Oceanogr. 11, 1015 (1981). 4. B. Ruddick, J. Phys. Oceanogr. 22, 1274 (1992). 5. R. W. Schmitt, H. Perkins, J. D. Boyd, M. C. Stalcup, Deep-Sea Res. 34, 1697 (1987). 6. M. Tsuchiya, L. D. Talley, J. Geophys. Res. 103, 12,899 (1998). 7. A. P. S. Wong, G. C. Johnson, J. Phys. Oceanogr. 33, 1493 (2003). 8. J. R. Ledwell, A. J. Watson, C. S. Law, Nature 364, 701 (1993). 9. J. R. Ledwell, A. J. Watson, C. S. Law, J. Geophys. Res. 103, 21,499 (1998). 10. K. L. Polzin, J. M. Toole, J. R. Ledwell, R. W. Schmitt, Science 276, 93 (1997). 11. J. R. Ledwell, E. T. Montgomery, K. L. Polzin, R. W. Schmitt, J. M. Toole, Nature 403, 179 (2000). 12. J. D. Boyd, H. Perkins, Deep-Sea Res. 34, 337 (1987). 13. R. W. Schmitt, Annu. Rev. Fluid Mech. 26, 255 (1994). 14. R. W. Schmitt, J. M. Toole, R. L. Koehler, E. C. Mellinger, K. W. Doherty, J. Atmos. Ocean. Tech. 5, 484 (1988). 15. R. W. Schmitt, Deep-Sea Res. 26A, 23 (1979). 16. R. Lueck, Deep-Sea Res. 34, 1677 (1987). 17. G. Marmorino, W. K. Brown, W. D. Morris, Deep-Sea Res. 34, 1667 (1987). 18. A. E. Gargett, R. W. Schmitt, J. Geophys. Res. 87, 8017 (1982). 19. T. J. McDougall, Deep-Sea Res. 38, 367 (1991). 20. T. R. Osborn, J. Phys. Oceanogr. 10, 83 (1980). 21. T. Osborn, C. Cox, Geophys. Fluid Dyn. 3, 321 (1972). 22. T. J. McDougall, in Small-Scale Turbulence and Mixing in the Ocean, J. N. a. B. Jamart, Ed. (Elsevier Oceanography Ser., vol. 46, Elsevier, New York, 1988), pp. 21–36. 23. L. St. Laurent, R. W. Schmitt, J. Phys. Oceanogr. 29, 1404 (1999). 24. The 95% confidence limits for the diffusivities were calculated using a ‘‘bootstrap’’ method with the statistics of 1000 random subsamples of half of the 165 stations for each of the measured variables. This treats each station as independent but does not account for possible systematic errors in the flux ratio or flux Richardson number. 25. M. C. Gregg, T. Sanford, Deep-Sea Res. 34, 1689 (1987). 26. M. E. Stern, Deep-Sea Res. 14, 747 (1967). 27. J. M. Toole, D. T. Georgi, Prog. Oceanogr. 10, 123 (1981). 28. C. J. Garrett, J. Phys. Oceanogr. 12, 952 (1982). 29. 30. 31. 32. 33. 34. 35. W. J. Merryfield, J. Phys. Oceanogr. 30, 1046 (2000). T. M. Joyce, J. Phys. Oceanogr. 7, 626 (1977). R. Ferrari, K. Polzin, J. Phys. Oceanogr. (in press). M. C. Gregg, J. Geophys. Res. 94, 9686 (1989). K. Polzin, J. M. Toole, R. W. Schmitt, J. Phys. Oceanogr. 25, 306 (1995). E. Kunze, in Double-Diffusive Convection, A. Brandt and J. Fernando, Ed. Geophys. Monogr. 94, 313 (1995). R. W. Schmitt, in Small-Scale Turbulence and Mixing in the Ocean, J. N. a. B. Jamart, Ed. (Elsevier Oceanography Ser., vol. 46, Elsevier, New York, 1988), pp. 435–452. W. J. Schmitz, J. R. Luyten, R. W. Schmitt, Bull. Mar. Sci. 53, 1048 (1993). We wish to thank the captains and crews of the R/Vs Oceanus and Seward Johnson for consistently fine work throughout our cruises. We also thank D. Wellwood, T. Bolmer, T. Farrar, T. Donoghue, B. Guest, C. Sellers, S. Birdwhistell, S. Sutherland, A. deBoer, L. Houghton, S. Ledwell, A. Benitez, G. Hernandez, and R. Brathwaite for scientific assistance during the cruises. This work was supported by the National Science Foundation under grants OCE-0081502 and OCE0350743. This is Contribution Number 11284 of the Woods Hole Oceanographic Institution.

36. 37.

14 December 2004; accepted 16 March 2005 10.1126/science.1108678

Insect-Resistant GM Rice in Farmers’ Fields: Assessing Productivity and Health Effects in China
Jikun Huang,1* Ruifa Hu,1 Scott Rozelle,2 Carl Pray3
Although no country to date has released a major genetically modified (GM) food grain crop, China is on the threshold of commercializing GM rice. This paper studies two of the four GM varieties that are now in farm-level preproduction trials, the last step before commercialization. Farm surveys of randomly selected farm households that are cultivating the insect-resistant GM rice varieties, without the aid of experimental station technicians, demonstrate that when compared with households cultivating non-GM rice, small and poor farm households benefit from adopting GM rice by both higher crop yields and reduced use of pesticides, which also contribute to improved health. Despite promises that GM crops could make a contribution to the reduction of hunger throughout the world, GM varieties are primarily used for industrial crops, such as cotton, and feed crops for animals (1–3). The difficulties of commercializing GM rice (and other food crops) appear to be causing declines in the amount and direction of public and private biotechnology research (4). Consequently, GM rice has not been commercialized anywhere in the world, and little is in the pipeline in most countries. Even China, a country
1 Center for Chinese Agricultural Policy, Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Jia 11, Datun Road, Beijing 100101, China. 2Department of Agricultural and Resource Economics, University of California, 1 Shields Avenue, Davis, CA 95616, USA. 3Department of Agricultural, Food, and Resource Economics, Rutgers University, 55 Dudley Road, New Brunswick, NJ 08901– 8520, USA.

*To whom correspondence should be addressed. E-mail:

that aggressively commercialized Bt cotton and invested heavily into research on GM food crops, has not commercialized any major food crops. One reason that commercialization may not have proceeded is that there has been little independent evidence on whether GM food crops would really improve farmer welfare. This study_s objective is to report on the results of an economic analysis that uses data from eight rice preproduction trial sites in China. We attempt to answer three questions: Does GM rice help reduce pesticide use in the fields of farmers? Do the new varieties of GM rice increase the yields for farmers? Are there any identifiable health effects on the farmers that adopt GM rice strains? China_s biotechnology research program has generated a wide array of new technologies, including several GM rice varieties (5). A number of GM rice varieties have entered and passed field and environmental release trials, and four varieties are in preproduction VOL 308 SCIENCE

trials in farmers_ fields. Two of the varieties— the two in which the scientists that developed the varieties gave our study team permission to undertake economic analysis—are the focus of this study (5). One variety, GM Xianyou 63, was created to be resistant to rice stem borer and leaf roller by insertion of a Chinesecreated Bacillus thuringiensis (Bt) gene (5, 6). The other variety, GM II–Youming 86, also was created to be resistant to rice stem borers, but in this case, the resistance was created by introducing a modifed cowpea trypsin inhibitor (CpTI) gene into rice (5–8). The insectresistant GM varieties entered preproduction trials in 2001. The nature of China_s preproduction trial system has facilitated the analysis of the effect of insect-resistant GM rice on farm households before commercialization. The preproduction trials of GM Xianyou 63 are being conducted by farmers in seven villages in five counties in Hubei province. The trials for GM II–Youming 86 are being conducted in one village in Fujian province. In the preproduction villages, households were randomly selected to participate in the study. All of the farmers that were randomly selected did participate (i.e., there were no drop-outs), and so all farmers in the sample villages can be divided into two groups— adopters and nonadopters. Each adopter was provided with a fixed amount of insect-resistant GM rice seed. For households with limited land size, the seed was enough to cover all of their plots (henceforth, full adopters). Others received only enough to cover part of their plots (partial adopters). Except for being provided insect-resistant GM rice seed (at the same price as they would have paid for nonGM varieties), there were no subsidies, and adopters cultivated the insect-resistant GM rice without the assistance of technicians. Because farmers use their own periodic, in-field


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REPORTS observations on the severity of pest infestation to decide whether or not to apply pesticides on both the insect-resistant GM and non-GM rice (that is, they are not following a prescribed dosage), the study can provide an estimate of the amount of farm-level pesticide reduction that can come from the adoption of the insectresistant GM rice. Our analysis presented here is based on surveys of a randomly selected subsample of households in the preproduction villages. During the first year of the study (2002), in six of the eight sample villages, there were only a limited number of adopters, and so all of them were chosen (some were full; the rest were partial adopters). A similar number were randomly chosen from all adopters in the other two villages. In total, 40 adopters (28 partial and 12 full) were chosen in 2002. In addition, 37 nonadopters (about one for each adopter) were chosen randomly from the pool of nonadopters in each village. In total, 77 households were surveyed in 2002. During 2003, a similar strategy was used, but because more insect-resistant GM seed was distributed, more adopters were added to the survey. Overall, 101 were interviewed in 2003 (32 nonadopters, 53 partial adopters, and 16 full adopters). There were 69 households that were interviewed in both years. The enumerators, using producer-recall interviewing techniques, collected information on inputs and outputs for all of the plots on which the farmers produced rice, including detailed information on pesticide use and the variety of rice grown. Farmers also recounted the prices paid for pesticides and whether or not the plot was adversely affected by a weather shock. In total, the survey obtained data from 347 rice production plots: 123 plots planted with the insect-resistant GM rice varieties and 224 plots planted with non-GM rice. Data from the surveys demonstrate that the characteristics of rice producers using the insect-resistant GM rice and non-GM rice are nearly identical and that the main difference between the households is in the level of pesticide use (9). For example, there is no statistical difference between the size of the farm or the plot or plots, the share of rice in the household_s cropping pattern, or the household head_s age or education. In contrast, there is a large difference in the use of pesticides (Table 1) (10). GM rice farmers apply the same types of pesticides but apply them less than once per season (0.5 times) compared with 3.7 times per season by non-GM rice farmers. The difference in the levels of pesticide use on insect-resistant GM and non-GM rice is statistically significant. On a per hectare basis, the quantity of and expenditure on pesticides of non-GM rice production is 8 to 10 times as high, respectively, as those for insect-resistant GM rice. Insect-resistant GM rice adopters spend only 31 yuan per season per hectare on only 2.0 kg of pesticide for spraying for pests, whereas nonadopters spend 243 yuan for 21.2 kg. Because other factors might affect pesticide use when comparing insect-resistant GM rice and non-GM rice, multiple regression can determine the net impact of the adoption of insect-resistant GM varieties on pesticide use. To estimate a use function for pesticide by China_s rice farmers in the sample areas, the following model is used: Pesticide use 0 fðGM rice varieties; pesticide price; weather effects; year effects; producer and farm characteristicsÞ ð1Þ Equation (1) is similar to models that have been used elsewhere in the literature (11, 12). To empirically estimate Eq. (1), the data from the survey are used to create variables that are based on standard definitions (13). The dependent variable for the analysis is the quantity of pesticides used per season (although substantively identical results are generated from either the number of sprayings per season or the value of pesticide use). The independent variable of interest, the use of the insect-resistant GM rice varieties, is measured by including a single dummy variable (GM rice, both varieties) which equals 1 if the farmer used either GM Xianyou 63 or GM II– Youming 86. In an alternative specification, the use of GM rice is measured by including two GM variety–specific dummy variables (GM Xianyou 63 and GM II–Youming 86) and two non-GM variety dummy variables. A set of household 0 to 1 indicator variables (108 of them—one for each sample household minus 1) is included to isolate the effect of GM varieties on pesticide use from observed and unobserved producer characteristics. The regression analysis illustrates the importance of insect-resistant GM rice varieties in reducing pesticide use (Table 2, rows 2 to 6). The significant, negative coefficient on the BGM rice, both variety[ variable means that GM rice use allows farmers to reduce pesticide use by 16.77 kg/ha, a reduction of nearly 80% (when compared with pesticide use of farmers using non-GM varieties— Table 1, row 3). The negative and significant coefficients on the GM Xianyou 63 and GM II–Youming 86 variables also demonstrate that each variety significantly reduces pesticides. Although the magnitudes of the coefficients differ, tests show that there is no statistical difference between the actual effects of the two insect-resistant GM varieties on pesticide use (Table 2, rows 3 and 4) (14). The data also show that there is a difference, albeit narrower, between yields of insect-resistant GM and non-GM varieties. SCIENCE VOL 308 According to the descriptive data in Table 1, the mean of insect-resistant GM rice yields (6364 kg/ha) is higher than those of non-GM varieties (6151), although only by 3.5%. A box plot also shows that the median of insectresistant GM rice yields is marginally higher than those of non-GM rice (fig. S1). ANOVA tests that differentiate among year, village, and GM versus non-GM effects demonstrate that the effect is statistically significant (15). Multiple regression analysis largely supports the descriptive results (Table 2). Holding all household-level effects, plot-specific inputs, and certain other plot characteristics constant, the yields of insect-resistant GM varieties are 6% higher than those of non-GM varieties. When examining the effects of specific varieties (compared with other conventional varieties—the base category), the yields of GM Xianyou 63 are shown to be 9% higher (at the 10% level of significance) than other conventional varieties. Although the yields of GM II–Youming 86 are not found to be significantly different from conventional non-GM varieties, this result in part may be due to the fact that there are relatively few observations (because preproduction trials of GM II–Youming 86 are from one village only, and there are relatively few farm households that were partial adopters). Therefore, according to the descriptive and multiple regression analyses, although the evidence on effect of the insect-resistant GM rice varieties on increasing yields is not as overwhelming as that which examines the relationship between the GM rice varieties and pesticides, the GM Xianyou 63 rice variety does appear to increase yields (between 6 and 9%) (16). The high incidence of pesticide-related illness in households in developing countries, including China, created an interest in tracking the health effects of insect-resistant GM rice adoption (11, 12, 17). To assess the effects in this study_s sample, enumerators asked
Table 1. Pesticide use and yields of insect-resistant GM rice adopters and nonadopters in preproduction trials in China, 2002–2003 (means T SD). Insect-resistant GM rice includes two varieties, GM Xianyou 63 and GM II–Youming 86. Data are from the authors’ survey. Parameter Adopters Nonadopters

Pesticide spray 0.50 T 0.81 3.70 T 1.91 (times) Expenditure on 31 T 49 243 T 185 pesticide (yuan/ha) Pesticide use 2.0 T 2.8 21.2 T 15.6 (kg/ha) Pesticide spray 0.73 T 1.50 9.10 T 7.73 labor (days/ha) Rice yield (kg/ha) 6364 T 1294 6151 T 1517 No. of observations 123 224 (plots)

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REPORTS households about how the use of pesticides affected their health during, or immediately after, the time that they applied pesticides (18). Specifically, the questionnaire asked the farmers, BDuring or after spraying for pesticides on your farm, did you suffer from any of the following symptoms: headaches, nausea, skin irritation, digestive discomfort, or other problems?[ If the respondent answered Byes,[ a follow-up question was asked: BAfter beginning to feel poorly, did you take any one of the following actions: 1) visit a doctor; 2) go home and recover at home; 3) take some other explicit action to mitigate the symptoms?[ If the respondent answered Byes[ to both of the questions, it was recorded as a case of pesticide-induced illness. In the same way that research on Bt cotton adoption showed that the productivity effects of Bt cotton were supplemented by positive health effects (3), according to the analysis based on the survey data, similar effects occur within the sample rice-growing households. Among the sample farmers, there were no full adopters that reported being affected adversely by pesticide use in either 2002 or 2003 (Table 3). Of those that cultivated both insect-resistant GM and non-GM plots, 7.7% of households in 2002 and 10.9% of households in 2003 reported that their health was affected adversely by pesticide use; none, however, reported being affected after working on the sample GM plot. Of those that used only non-GM varieties, the health of 8.3% of households in 2002 and 3% in 2003 was affected adversely. This study provides evidence that there are positive impacts of the insect-resistant GM rice on productivity and farmer health. Insectresistant GM rice yields were 6 to 9% higher than conventional varieties, with an 80% reduction in pesticide usage and a reduction in their adverse health effects. Such high potential benefits suggest that products from China_s plant biotechnology industry could be an effective way to increase both competitiveness internationally and rural incomes domestically. The benefits are only magnified if the health effects are added. The implications of the commercialization of GM rice in China also could far exceed the productivity and health effects on its own producers. Paarlberg suggests that if China were to commercialize a major crop, such as rice, it is possible that it would influence the decisions about the commercialization of GM crops in the rest of the world (4).

Table 2. Estimated parameters using a household fixed-effects model for estimating the effect of insectresistant GM rice varieties on farmers’ pesticide application and the yields of households in preproduction trials in China. The coefficients from the multiple regression model represent the net effect of insect-resistant GM rice varieties on pesticide use and yield, with the other plot-varying variables in the model held constant. For rice variety dummies, the base value is other non-GM varieties. Model 1 has both varieties as one variable; model 2 has treated the two varieties separately. The use of household fixed effects is accomplished by including 108 household dummy variables (equals 1 for the household and 0 otherwise), which allows for the control for all unobserved non–time-varying producer and farm characteristics. Values are means T SD. The symbols *, ., and - denote significance at 1, 5, and 10%, respectively. Data are from the authors’ survey. Pesticide use (kg/ha) Model 1 Intercept Variety dummies GM rice, both varieties Variety-specific dummy variables GM Xianyou 63 GM II–Youming 86 Non-GM Xianyou 63 Non–GM II–Youming 86 Control variables Pesticide price (yuan/kg) Natural disaster dummy (affected 0 1) 2003 year dummy Labor (log) Fertilizer (log) Machine (log) Other inputs (log) Pesticides (log) Household dummy variables No. of observations 19.93 T 1.17* –16.77 T 1.28* –17.15 –25.33 1.04 –1.25 –0.02 T 0.03 8.56 T 2.65* –0.17 T 1.20 T T T T 2.60* 5.48* 2.61 3.82 –0.51 T 0.05* Model 2 19.78 T 1.32* Yields (kg/ha) in log Model 1 7.55 T 0.50* 0.06 T 0.030.09 0.02 –0.03 0.07 T T T T 0.050.10 0.05 0.07 Model 2 7.61 T 0.51*


–0.02 T 0.03 8.65 T 2.65* –0.01 T 1.24

–0.51 T 0.05* –0.05 0.17 0.03 0.00 0.02 0.00 T T T T T T 0.02. 0.07. 0.06 0.01 0.04 0.00


–0.05 T 0.02. 0.17 T 0.07. 0.04 T 0.06 0.00 T 0.01 0.03 T 0.04 0.00 T 0.00 Included but not reported 347 347


Table 3. The effect of insect-resistant GM rice use on the health effects of farmers in sample preproduction village sites in China, 2002–2003. Full adopters planted insect-resistant GM rice only; partial adopters planted both GM and non-GM rice; and nonadopters planted non-GM rice only. The numbers are the percentage of sample households that were adversely affected by pesticides. Data are from the authors’ survey. Adverse health effects reported and year 2002 2003 Partial adopters GM plot 0.0 0.0 0.0 0.0 Non-GM plot 7.7 10.9 8.3 3.0

1. M. Qaim, D. Zilberman, Science 299, 900 (2001). 2. It should be noted that despite there being no genetically modified major food grains being used anywhere in the world, there are more minor food crops, such as papaya and squash. 3. J. Huang, S. D. Rozelle, C. E. Pray, Q. Wang, Science 295, 674 (2002). 4. R. Paarlberg, Issues Sci. Technol. Online (Spring 2003); available at 5. See Supporting Online Material (SOM), section 1 on Science Online. 6. J. Tu et al., Nat. Biotechnol. 18, 1101 (2000). 7. C. Deng, G. Song, J. Xu, Z. Zhu, Acta Bot. Sin. 45, 1084 (2003). 8. This paper discusses the line and the gene construct for CpTI and transfer of the gene into tobacco. Information on the transfer of the gene into rice is in preparation for publication (J. Huang, R. Hu, S. Rozelle, C. Pray). 9. See SOM, section 2. Insect-resistant GM rice strains are produced only to deal with pests and not to increase flavor or alter nutrition. As a consequence, there is no difference in prices between the insectresistant GM rice and non-GM rice. 10. See SOM, section 7. 11. J. M. Antle, P. L. Pingali, Am. J. Agric. Econ. 76, 418 (1994). 12. J. Huang, F. Qiao, L. Zhang, S. Rozelle, ‘‘Farm pesticide, rice production, and the environment’’ [Economy and Environment Program for Southeast Asia (EEPSEA) Res, Rep. 2001-RR3, International Development Research Center (IDRC), Singapore, 2001]. 13. See SOM, section 3. 14. See SOM, section 4. 15. See SOM, section 5. 16. See SOM, section 6. 17. J. Huang, R. Hu, C. Pray, F. Qiao, S. Rozelle, Agric. Econ. 29, 55 (2003). 18. See SOM, Section 8, for original and translation of questions. 19. We are grateful to Q. Zhang and Z. Zhu and their colleagues who developed GM rice in China for technical inputs and to P. J. Hines and three anonymous referees for helpful comments. The authors acknowledge the support of the National Natural Science Foundation of China (grants 70021001, 70333001, and 70325003) and the Chinese Academy of Science (KZCX3-SW-419). Supporting Online Material DC1 Materials and Methods SOM Text Fig. S1 Tables S1 to S6 References and Notes 21 December 2004; accepted 9 February 2005 10.1126/science.1108972

Full adopters



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References and Notes…...

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...A Beautiful Place     I think we all have a beautiful place in our mind. I have a wonderful place that made me happy a lot of times, many years ago. But sometimes I think that I am the only person who likes this place and I'm asking myself if this place will be as beautiful as I thought when I will go back to visit it again. Perhaps I made it beautiful in my mind.     This place is meaningful to me because it is part of the county I loved, is part of the county where I grew up and is part of my childhood. This place is in the country in an old region named Appalachia, a small piece of the Appalachian Mountains, in a town named Pikeville.     Pikeville is a polluted town because of the coal industry. People live in apartment or condominium buildings because of its little space available. I grew up in one of the many buildings in Pikeville admiring from my bedroom window the beauty of the mountains, always exploring with my eyes the forest or the meadows, looking for a clean and quiet place. And, I found one on a hill in the back of the town. It is about 100 feet square, it has seven old trees, wild flowers and a lot of bugs and ants during summer time.     I used to go there to sit down on a rock and watch the town and my trees. There was a very old tree, a maple tree, with a huge trunk. The others were smaller, three in the back, three on my left side and the old maple tree on my right. There were flowers, many kinds, white, yellow, purple and blue. It...

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...companies as far as brand promotion is concerned. The primary objective of advertisements is not to sell the product/service but to make the potential customer base aware of its existence and its uniqueness. But with changing times it becomes necessary for the companies to modify their approaches they use in their advertisements to reach out to the masses. Thousands of campaigns go live every year but only few stay in our mind. Ever wondered what made those ads stay in our minds forever and how companies change their marketing campaigns over years and more over why they do so? Your task is to do a comparative analysis between the ads (any one of the following pairs) and make a presentation of max 5 slides (excluding the cover slide). 1. ‘Oh Yes Abhi’ Pepsi ( V/s ‘Yeh dil maange more’ Pepsi ( Presents 2. ‘Mehendi’ Cadbury ( V/s ‘Mithaas jo banae dosti ko khaas’ Cadbury ( 3. ‘Stay by my side’ Vodafone ( V/s ‘Zoo Zoo’ Vodafone ( 4. ‘Go back to mountain’ Bisleri ( V/s ‘Kiss to drink’ Bisleri ( 5. ‘Dhak Dhak’ Hero Honda ( V/s ‘Hum mein hain Hero’ Hero Honda......

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Saffola Ad Campaigns

...a person with poor health or worse. * Saffola Gold: Oil Kam, Pachhtava Kam Campaign acknowledges the rising awareness among people with respect to physiological health concerns and steers attention towards the latest Saffola Gold that has been formulated using superior Losorb technology, allowing it to be absorbed less in the food and hence protecting the health of the people. * Saffola Gold: Healthy Khao, Healthy Khao Campaign roped in celebrity chef Sanjeev Kapoor to promote Saffola Gold. The concept here being that the audience would have a face to associate with the product, specifically the face of a well know chef and trusted TV personality. He propagated the need for people to eat healthier. * Saffola Gold: Abhi Toh Yeh Jawaan Hai Campaign was targeted at adults, in particular those with stressful jobs taking a toll on their health. The concept being that Saffola Gold is a healthy oil and also helps in lowering cholesterol. * Saffola Tasty: Aaj Ke Zamaane Mein Dil Ki Hifazat Campaign covered the unique selling point of the product i.e. Saffola Tasty is an oil that makes food more appetizing without adversely affecting the body. * Saffola NutriBlend: Aapke Parivaar Ki Suraksha Ke Liye Campaign covers the story of a mother and her son, with a harried mother chasing after her son to protect him from the various dangers in life. The concept states that she can’t go on like this and she must switch to Saffola oil to make him......

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...Modulo Arithmetic Problem: If we line up the students in this class in 2 rows, there is one student left. If we line up them in 3 rows, there are two left. If we line up them in 5 rows, there are 3 left. How many number of students in this class? Definition. Given a, b are integers and n is a positive integer, a[pic]b (mod n) , or a and b are congruent modulo n, if n|(a-b). Example. 3 [pic]0 (mod 3), 4[pic] 1 (mod 3), 5 [pic]2 (mod 3), 6[pic] 0 (mod 3), -1[pic] 2 (mod 3), -2[pic] 1 (mod 3), … Now, we can collect all integers that are congruent in the same set, called the congruent class, as defined in the following: Definition. The set of integers congruent to r (mod n) is called the congruence class of r (mod n), is the set [r]={ r + kn | k[pic] Z} The collection of [0], [1], .., [n-1] is denoted by Zn. Definition. The Integers modulo n, or Zn , is defined as Zn ={[0], [1], [2], …, [n-1]} Theorem. The following are equivalent: i) a[pic]b (mod n) ii) [a]=[b] iii) There are exactly n congruence classes (mod n), i.e., [0], [1], [2], …, [n-1] The modulo arithmetic + and * do not depend on the representation, as in the following: Theorem. If a, b, c, and d are integers, a[pic]b (mod n) and c[pic]d (mod n) i) a +c[pic]b +d (mod n) ii) ac[pic]bd (mod n) Definition. If [r] and [s] are congruence classes (mod n), then [r]+[s]=[r+s] [r][s]=[rs] Example. In Z4 ={[0], [1], [2], [3]} [1]+[1]=[2], [1]+[2]=[3],......

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...Itne tension me kyu ho? Yogesh: Vaat lag gayi hai….vaat! Pichle campaigns me kuch zyaada hi kharcha ho gaya. Panauti lagi hai saala jab se company join ki hai, roz roz baj rahi hai. 2 din diye hai boss ne. Wife: Baby aaj apne apni neelam wali angoothi nahi pehni? Bol rahi hu mai. Mihir ne bhi ek episode me nahi pehni thi. Baby director ne use poore 1 saal ke liye gayab kar diya. Baby white colour mujhpe suit bhi nahi karta. Don't you worry baby. Parvati ne bhi apne pati ke liye ek baba ko bulaya tha. Me Parvati ko abhi Whatsapp karti hu. Yogesh: Dekho tumhara whatscrap baadme karna. Mujhe abhi dua ki nahi, dawa ki jarurat hai. Sar fataa jaa raha hai, me sone jaa raha hu. Wife: Are khana to kha lo…..(Sad puzzled look) (Yogesh leaves the scene.) *Black out* Scene 4: Bedroom: It's already around noon. Yogesh is still sleeping. His wife enters the room. Wife: Aree…abhi tak so rahe hai yeh. (tries to wake him up) Utho baby dopehar hoo gayi hai..abhi to utho….maine kal kaha tha na ek baba ke baare me..wo aate hi honge….(bhopu in background) lagta hai aa gaye…..(pallu on forehead…) Yogesh wakes up, startled at the cacophony of the bugles. Both of them walk out to the living room. The door is already open. 6 chelas walk in. They assume their position for a 3-a-side guard of honour. Anviksha walks in. Anviksha: Param gyaani…..Vidvaan…..………(Kuch punches daalo)………………….Baba Guerilla padhaar rahe hai……! *Bugles* Naveen walks in the scene. Anviksha: Tulsi, Parvati......

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...Mera naam Khalid Alnufaie hey. Main Saudi Arabia mein janam liya tha. Mere sheher ka naam jahan maine janam liya uska naam Taif hai. Main abhi 26 saal ka hun. Mere 3 bhai hain aur 1 bhen hey. Main apne sare bhai bhen main sabse bada hun. Maine high school ki padhai Saudi Arabia se 2002 mein kahtam ki. Fir mein Saudi Arabia mein college ki padhai ki 7 saal tak. Fir mein 2010 USA agaya aur padhai karne ke liye aur abhi main eek saal ki padhai khatam karli hai. Mujhe safar karna bhot pasand hey. Mein bhot sare desh ghum chukka hun jaise ki England, Egypt, Italy aur France. Inme mujhe sabse zada England mein maza aya. England bhot sundar desh hai lekin Englad thoda mhenga desh hai.Mujhe safar karne main bhot maza ata hai kyonki mujhe kafi aram milta hai. Main USA isiliye aya kyonki mujhe ghumna kafi pasand hai aur main naye log aur yahanki riti riwaz ko janna chahata hun. My name is Khalid Alnufaie. I was born in Saudi Arabia. The name of the city where I was born is Taif. I am 26 years old right now. I have 3 brothers and 1 sister. Among them I am the eldest one. I finished my high school in Saudi Arabia in the year 2002 and then I continued studying for the college for another 7 years. I came to USA in the year 2010 for further studies and so far I have finished 1 year of studying. I love travelling a lot. I have been to several countries like England, Egypt, Italy and France. Among these, I really loved England because it was very beautiful but a little expensive. The......

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...Dear Friend. I humbly request your consent and co-operation so as I can present you as an heir/next-of-kin of a deceased account proceeds value 5,508,609.00 INR (Five Crore five Lakh eight thousand Six Hundred and Nine India rupees only) I am writing this letter in confidence believing that it is the wish of God to meet with you since we have not met before, I got your e-mail address from AOL- Local Yellow Page with regard to your profile, after searching all the job site, Hindu, matrimony and religious site looking for a trust worthy person and I decided to contact you. I am Mrs. Kavita Mazumdar of Auditing and accounting section staff in Royal bank of Scotland Barakhamba road branch New Delhi, during my investigation and auditing I came across many inactive accounts but there is a particular account that has not been noticed by the previous/past auditors the account has been dormant without any claim of the fund. I write to solicit for your support and assistance to carry out this deal in my bank that will benefit us. Lying in one of the many inactive accounts is the sum of 5,508,609.00 INR (Five Crore fifty Lakhs eight thousand Six Hundred and Nine India rupees only) belonging to a foreign customer (Mr.Chowalert Jitjamnong) who was a gas consultant here in India, he happened to be deceased during a vacation trip with his wife (Mrs .Siriphut Jitjamnong) and the only child (Chawit Jitjamnong) on board One-Two-Go Orient-Thai Airlines flight OG269 Phuket......

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...prevent misgivings. However, the individual is less likely to do so and would spend at his own with regardless to what has happened or what may even happen. As mentioned earlier, the Government may obtain external loans from the World Bank, IMF etc. The individual on the other hand is not as fortunate as such. Generally, developed countries lend a helping hand through grants and foreign aid e.g. for countries like Bangladesh, etc Abhi mujh mein kahin Baaqi thodi si hai zindagi Jagi dhadkan nayi Jaana zinda hoon main toh abhi Kuch aisi lagan iss lamhe mein hai Ye lamha kahaan tha mera Ab hai saamne Issey chhoo loon zaraa Mar jaaoon ya jee loon zaraa Khushiyaan choom loon Yaa ro loo'n :'( zaraa Mar jaaoon ya jee loon zaraa Now there is very little life left in me, a new heart bit has arrived in me….which tells me that still I m alive……there is such attachement in this moment which seems whr was dis moment till now………. Now the moment is here…sud I touch it a bit Sud I die or live for a bit……. Sud I except the happiness or cry …………. Ho o.. abhi mujh mein kahin Baaqi thodi si hai zindagi Ho.. dhoop mein jalte huey tann ko, chhaya perh ki mill gayee Roothe bachche ki hansi jaise, phuslaane se phir khill gayee Kuchh aisa hi abb mehsoos dil ko ho rahaa hai Barso'n ke puraane zakhm pe marham laga saa hai Kuch ehsaar hai, iss lamhe mein hai Ye lamha kahaan tha mera Ab hai saamne Issey chhoo loon zara Mar jaaoon ya jee loon zara Khushiyaan choom loon Yaa......

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Abhi networking sites should be permanently banned, i am with this topic? I'll suggest a couple of possible reasons for arguing that social networking websites should be banned: Social networking sites bring privacy into the public doman. There are things abouteach of us that are best kept private and for good reason. We would generally reveal ourselves in the ways that we choose to those that we choose, except when it comes to the online world. It has been said that you can almost build up an entire profile of someone by their online habits. People are often naive about the dangers of identity theft, they stick to the same username from one site to the next, they post little snippets of information in various places without realising that each is like a piece of a jigsaw. If you find one common thread, such as a username, then it is relatively simple to find several pieces of one jigsaw, put them together, and you start to establish a comprehensive profile of that person. You might find out what they do for a living, the area in which they live, the names of their loved ones, their pets, their email address, their likes and dislikes, the list is endless. More and more people are becoming the victims of identity theft, if not for financial gain then for malicious purposes. Celebrities have had people pretend to be them on websites such as Twitter and Facebook, causing them a great deal of embarrassment. Everyday folk have been slandered, cyber bullied and impersonated...

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