Breeding Rice to Revolutionize the World

Breeding Rice to Revolutionize the World

Rice is well-known for being a staple in Asia, however it is lesser known that it is also prevalent in Italy when concerning all of Europe. Rice originally came from China and found its way to Italy through Alexander the Great’s expedition through Asia.  Italy is the largest producer of rice in Europe, producing more than 50% of the continent’s rice, but that amount isn’t sizeable because they aren’t ranked in the top ten countries of rice production worldwide. They may not use much rice in their cuisine, but they have changed how rice is prepared to suit their own needs and wants. Rice in Italy diverged, whether intentional or unintentional, from rice in China due to the differences in environment, tastes, and culture. By knowing how & why they diverge and what the end product is, new divergences can be made to create new dishes and new variants of ingredients that have never been seen.

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Rice comes from the seed of Oryza sativa and Oryza glaberrima. Oryza sativa is the rice we commonly know as Asian rice, it contains two major subspecies, long-grain indica rice(e.g. Basmati Rice and Jasmine Rice) and short-grain japonica rice(e.g. Arborio rice and Vialone Nano). Oryza glaberrima is commonly known as African rice, its production has declined because of the higher yielding Asian rice and does not have the same recognition as Asian rice. Asian rice was domesticated from Oryza rufipogon and African rice was domesticated from Oryza barthii. In comparison, African rice is hardy, nutty, filling, low-labour, and pest resistant. Rice is an annual crop and is normally grown in countries with low labor costs and high rainfall. Rice can be grown in many different loactions because of water-controlled terrace systems, however it cannot be grown varying environments. The fact that rice as we know it originated from all the way in Asia and parts of Africas, causes the long history of rice’s journey throughout the world to be that much more significant.

As the agricultural commodity with the third-highest worldwide production of 741 million tonnes in 2016(after sugarcane and maize), China and India produced 50% of it. Although rice is commonly known throughout the world, rice isn’t consumed that much outside of Asia. Asia produces and consumes approximately 90% of the worlds rice. Because of the amount of rice China consumes, they are only the 6th ranking country in terms of exports. Rice production in developing countries has many flaws and can easily be fixed to boost income. “A developing country (or a low and middle income country(LMIC), less developed country, less economically developed country (LEDC), or underdeveloped country) is a country with a less developed industrial base and a low Human Development Index(HDI) relative to other countries”(O’Sullivan, 461). The developing countries lose around $89 billion(USD) from post-harvest losses from the lack of proper storage, retail and poor transport. At the same time, rice is labor intensive and benefits from cheap labor costs, which can be easily found in developing countries. Although it is not certain how it would turn out, if this loss was fixed, many more people could be fed and the economy and development of those countries would most likely improve. One might say that the possibility still stands that the labor costs would outweigh the profits, but when people earn more, they spend more; thus encouraging the economic development of said countries. Many factors, true or false, may weigh in to the fact that this would not occur. For instance, the fact that developed countries do not produce a large amount of rice on a global scale and that rice may be a poor man’s food because of its lack of versatility, low price, and plain flavours; may lead one to believe that developed countries do not consume and produce rice for a reason. Italy is one of the outliers of the world, producing the most rice outside of Asia.

Of Italy’s most produced crops, rice resides in the top ten. This goes to show that rice is quite impactful to their country’s economy and culture. Italy exports approximately 50% of their rice generating an annual revenue of US $614.1 million of their US $40.719 billion total agriculture GDP. In 2011 they exported around 722.14(000t) out of a total production of 1490.15(000t). When considering an Italian’s diet, rice does not play that large a role. In 2011 rice was 1% of an Italian’s protein intake and 2% of their protein intake in one day. Compared to wheat, this amount is very underwhelming; wheat comprises of 30% and 33% of an Italian’s protein and calorie intake, respectively. In 2017/2018, the USDA estimated Italy’s milled rice production at 1.1 million metric tons(mmt) and their wheat production at 7.2 million metric tons. Although they consume around thirty times the amount of wheat compared to rice, they only produce seven times the amount.

Rice is a totally different story when considering China. Rice plays a large part of a Chinese diet comprising of 16% and 27% of their protein and calorie intake, respectively. However, in the northern region of China wheat is more dominant in their lifestyle. This is most likely due to how cold the north is versus the other regions of China. Wheat is much more cold resistant than rice. It can survive down to -10 °F to -28 °F(-23.3 °C to -33.33 °F) during the winter, versus rice’s 50°F to 55.4 °F(10°C to 13°C) during its early stages of development.

The threshold for the crops changes in stages:

Rice Stages:

Germination: 50°F to 59°F(10°C to 15°C)

Vegetative: 42.8°F to 53.6°F(6°C to 12°C)

Reproductive: 53.6°F to 62.6°F(12°C to 17°C)

Wheat stages:

Rice requires temperate climate for proper growth which both Italy and China possess. Italy has the Po River Valley & China has the Yangtze River Valley. These regions are most suitable for rice because they possess a temperate climate, a fresh water supply(rainfall or water from a river), and fertile riverine alluvial soil(of relating to a river and eroded/reshaped by water). Rice requires approximately four to five months of growth to reach maturity and thus be ready for harvest. Due to the nature of the crop, it can avoid the colder months in the winter. Italy and China primarily plants rice within April through May and harvests between September and October. They share similar weather during that time period, deviating by ~7.5°F with China having a hotter climate(75°F). These numbers were calculated by averaging out the minimum temperatures and maximum temperatures of a city within the region. The differences of average rainfall once again shows that China has a more suitable climate for rice, with Yangtze having 43 to 60 inches(1,100 to 1600 mm) per year and Po having 25 to 40 inches(650 to 1,000 mm) per year. As expected, China is better suited to produce rice, which clarifies why it produces and consumes so much more. As a result of the differences in climate, different characteristics are sought after and bred into the Italian rice strains.

In Asia, rice is used as a food item that offers little flavour in order to bring out the flavours for the dish. For example, a curry’s flavour may be too strong, and would thus need to be diluted and cleansed with a plain item, rice. Although it is eaten with many different foods, it is not “paired” because they are only eaten together, not made together. However, Italian cuisine uses a lot of dairy product which is paired with their rice dishes. In one of their common dishes, risotto, rice is made with wine, butter, and cheese. It is made to be creamy and flavourful. Originally a Chinese crop and dish, Italians adapted it to suit their tastes as well as different grains of rice to better suit their needs in cooking. The prefer starchy, creamy, and rice with a bite to it. By gaining more knowledge on the adaptation process and the many different varieties of rice, it can be improved upon to better everyone’s way of life.

“The Puzzle of Italian Rice Origin and Evolution: Determining Genetic Divergence and Affinity of Rice Germplasm from Italy and Asia”, a peer reviewed academic article, was written by many authors who contributed equally: Xingxing Cai, Jing Fan, Zhuxi Jiang , Barbara Basso, Francesco Sala, Alberto Spada, Fabrizio Grassi, and Bao-Rong Lu. This article was written to benefit rice breeding programs, by targeting the appropriate germplasm from a specific region to transfer its genetic traits based on its evolution and genetic diversity. They researched and sampled 348 varieties of rice from Italy and Asian countries in order to identify the genetic divergence of cultivated and wild rice from different regions. As groups of rice, the data showed a close genetic relationship between the two. The uniqueness of Chinese rice varieties are due to the high number of private alleles found in their DNA. “Alleles are pairs or series of genes on a chromosome that determine the hereditary characteristics”(yourdictionary.com). Since it is a private allele, they show different qualities because of the differences in the rice’s DNA. Microsatellite or Simple Sequence Repeat(SSR) fingerprinting sets of rice in Italy and China show that they share a close relationship and suggest that Italian rice possibly originated from northern China. SSR fingerprinting essentially allows for the analysis of the composition of an organism, it reads the DNA or “code” of an organism in order to determine the genetic similarities. By reading the hereditary(able to pass down to the next generation) genetic similarities and differences, scientists are able to breed rice strains to better suit their wants. Whether it is in order to improve taste, texture, nutrition, growth efficiency, cost efficiency, weather resistance or more, this knowledge of reading DNA and breeding allows for many possibilities in the future of food and humanity. The purpose of this work is to benefit rice breeding programs, in order to target the appropriate germplasm from a specific region to transfer its genetic traits based on its evolution and genetic diversity. This supports my argument on how Italian rice has diverged from Chinese rice and the differences between the two genetically.

All in all, we can breed new strains of rice to better suit our wants. But that’s it? Not even close. There are so many possibilities that open up because of breakthroughs in genetical analysis and modification. Human thirst for new flavours and foods that align with their preferences can bring about great change in our way of life. Not just in a culinary sense. This can bring about economical, nutritional, agricultural, environmental, political, medical, and technological change to benefit humanity. Genetically improving rice, even in the slightest bit, can have a large impact on the entire world. Creating just a bit of efficiency, maybe making it one cent cheaper per pound, would allow for US $16.34 billion of worldwide saving. The grains can also be made more effective/nutritious which would in turn cause consumers to spend less on supplements where they are lacking and more on entertainment. Improving agriculturally by building resistances & allowing for growth in non-native regions would allow for more variety in fresh, locally-sourced ingredients. Improving the environment by modifying how plants intake carbon dioxide and output oxygen. Impacting politics by being an important part of a government’s attention and resources. Even genetically modifying humans, not for major things yet, but problems that can and should be fixed such as hereditary diseases: heart disease, arthritis, diabetes, and cancer. As well as technological changes to support the advancing developing countries. One small change in one small part of people’s lives can skyrocket into advancements in many different aspects of humanity.

Works Cited

“Asia.” Ricepedia, CGIAR: Research Program on Rice Agri-Food Systems, ricepedia.org/rice-around-the-world/asia.

“Climate – China.” Climates to Travel: World Climate Guide, www.climatestotravel.com/climate/china#temperate.

“Climate – Italy.” Climates to Travel: World Climate Guide, www.climatestotravel.com/climate/italy#po_valley.

“Crop Production in Greece and Italy.” Foreign Agricultural Service, United States Department of Agriculture, 18 Aug. 2017, ipad.fas.usda.gov/highlights/2017/08/greeceitaly/index.htm.

“Crops.” FAOSTAT, Food and Agriculture Organization of the United Nations, www.fao.org/faostat/en/#data/QC.

Cruz, Renata Pereira da, and Raul Antonio Sperotto. “Avoiding Damage and Achieving Cold Tolerance.” Food and Energy Security, vol. 2, no. 2, 19 June 2013, doi:https://www.onlinelibrary.wiley.com/doi/full/10.1002/fes3.25.

“Cultivated Rice Species.” Ricepedia, CGIAR: Research Program on Rice Agri-Food Systems, ricepedia.org/rice-as-a-plant/rice-species/cultivated-rice-species.

“Developing Countries 2019.” World Population Review, 2019, worldpopulationreview.com/countries/developing-countries/.

Gooii. “Rice – Indica.” Rice – Indica – Food Types – Cookit!, cookit.e2bn.org/cooking2/foodtypes-462-rice-indica.html.

“Italy Is the Largest Rice Producer in the EU-28.” ItalianFOOD.net, 10 Apr. 2017, news.italianfood.net/2016/01/12/italy-is-the-largest-rice-producer-in-the-eu-28/.

“Italy.” Ricepedia, CGIAR: Research Program on Rice Agri-Food Systems, ricepedia.org/italy.

Klein, Robert. “Wheat Resistance to Freeze Injury.” Winter Wheat Injury and Cold Temperatures, Institute of Agriculture and Natural Resources, cropwatch.unl.edu/winter-wheat-injury-and-cold-temperatures.

Klien, Robert. “Winter Wheat Injury and Cold Temperatures.” CropWatch, University of Nebraska-Lincoln, 14 Aug. 2018, cropwatch.unl.edu/winter-wheat-injury-and-cold-temperatures.

“Rice Around the World: Italy.” IYR 2004: All about Rice: Italy, Food and Agriculture Organization of the United Nations, www.fao.org/rice2004/en/p7.htm.

Rickman, Joseph, and Sam Mohanty. Rice Almanac. vol. 4, CGIAR: Research Program on Rice, 2013, Archive.org, archive.org/details/RiceAlmanac.

Sebastian, Ahnert E, and Yong-Yeol Ahn. “Flavour Network and the Principles of Food Pairing.” Scientific Reports, vol. 1, 15 Dec. 2011, doi:https://doi.org/10.1038/srep00196.

“The World Factbook: Italy.” Central Intelligence Agency, Central Intelligence Agency, 17 July 2019, www.cia.gov/library/publications/the-world-factbook/geos/it.html.

“What the World Eats.” What the World Eats, National Geographic, 2014, www.nationalgeographic.com/what-the-world-eats/.

 

Fruit Fly Cross Breeding Experiment

Introduction:
The experiment conducted used drosophila or fruit flies to test certain crosses such as a sepia female drosophila x wild male drosophila, a white female drosophila x wild male drosophila, and red/vestigial female drosophila x sepia/normal male drosophila. They are easy and inexpensive to maintain and can also be easily examined (“Drosophila melanogaster”). In addition, drosophila has the most rapid reproductive rate of any dried-fruit insect (“Fruit fly (Drosophila melanogaster)”). Furthermore, drosophila are diploid organisms which means that their chromosomes are arranged in homologous pairs and for a simple phenotypic trait, there will be two copies of the gene (one on each chromosome). Pairs of genes are called alleles and for each autosomal non sex-linked gene or trait, there are two alleles. Identical alleles in an organism signify that the organism is homozygous for that gene. In this experiment, we did a monohybrid cross, dihybrid cross, and a sex-linked cross. According the basic genetic law, in our experiment, the monohybrid cross should yield 50% red females and 50% red males in the F1 generation and 37.5% Red Females, 12.5% Sepia Females, 37.5% Red Males, and 12.5% Sepia Males in the F2 generation. The X-linked cross should yield 50% red eyed females and 50% white eyed males in the F1 generation and 25% Red Females, 25% White Females, 25% Red Males, and 25% White Males in the F2 generation.
Finally, the dihybrid cross should yield 50% red normal males and 50% red normal female in the F1 generation and 28.125% red normal males, 28.125% red normal females, 9.375% red vestigial males, 9.375% red vestigial females, 9.375% sepia normal males, 9.375% sepia normal females, 3.125% sepia vestigial males, and 3.125% sepia vestigial females in the F2 generation.
Hypotheses:
Cross I
Null: The distribution of the F1 and F2 offspring of the sepia female drosophila x wild male drosophila in cross1 will be 50% red females and 50% red males (or 100% red) in the F1 generation and 37.5% Red Females, 12.5% Sepia Females, 37.5% Red Males, and 12.5% Sepia Males (or 75% Red and 25% Sepia) in the F2 generation.
Cross 2
Null: The distribution of the F1 and F2 offspring of the white female drosophila x wild male drosophila in cross 2 will be 50% red eyed females and 50% white eyed males in the F1 generation and 25% Red Females, 25% White Females, 25% Red Males, and 25% White Males (or 50% red and 50% white) in the F2 generation.
Cross 3
Null: The distribution of the F1 and F2 offspring of the red/vestigial female drosophila x sepia/normal male drosophila in cross 3 will be 50% red normal males and 50% red normal female in the F1 generation and 28.125% red normal males, 28.125% red normal females, 9.375% red vestigial males, 9.375% red vestigial females, 9.375% sepia normal males, 9.375% sepia normal females, 3.125% sepia vestigial males, and 3.125% sepia vestigial females (or 56.25% Red Normal, 18.75% Red Vestigial, 18.75% Sepia Normal, and 6.25% Sepia Vestigial) in the F2 generation.
Methodology:
Materials- vials, fruit flies, fruit fly food, plugs, FlyNap (anesthetic), nets, microscopes, paint brushes, probes, freezer.
Procedure- To begin this lab, we must make a container that can sustain life for the fruit flies and their offspring. Since we are observing three crosses, there will be three vials needed. We added fruit fly food to each of the containers and netting inside the vials. We then added the parent generation flies needed for all three vials and sealed it with the plugs. Their traits have been checked to make sure they are the proper parent generation flies that we were supposed to observe. After we finished setting it up, we left the vials alone to let the flies breed and produce larvae which will be the F1 generation. After a few days, we placed the vials in the freezer in order to slow down to flies for removal. The parents are removed in order to prevent interference with the data The F1 flies are taken out and placed into a separate vial with FlyNap to complete incapacitate them for observation. Our group then took those flies and placed them under the microscope to see what traits they display. Between all of the crosses, we observed the sex, eye color, and in cross 3, the wing type. After counting up the flies, we removed them and left the original housing vials alone for the F2 generation to spawn. After a few days, we repeated the counting process with this generation of flies. When we finished all of the counting for our data, we released the remaining flies and cleaned out the vials at the conclusion of this lab.
Results:
Cross 1 – Sepia Female Drosophila x Wild Male Drosophila
Monohybrid Cross – Red is Autosomal Dominant
R = Red ; r = Sepia

rr crossed with RR (F1 Generation)

 

r

r

R

Rr

Rr

R

Rr

Rr

The expected phenotype for the F1 generation of cross 1 is all red eyed drosophila, 50% red females and 50% red males

Rr crossed with Rr (F2 Generation)

 

R

r

R

RR

Rr

r

Rr

rr

The expected phenotype ratio for the F2 generation of cross 1 is 3:1, 75% red drosophila and 25% sepia drosophila. In terms of sex, 37.5% Red Females, 12.5% Sepia Females, 37.5% Red Males, and 12.5% Sepia Males
Cross 2 – White Female x Wild Male
X – Linked Trait – White is mutant

XwXw crossed with Xw+Y (F1 Generations)

 

Xw

Xw

Xw+

Xw+Xw

Xw+Xw

Y

XwY

XwY

The expected flies in the F1 generation of cross 2 are 50% red eyed females and 50% white eyed males

Xw+Xw crossed with XwY (F2 Generations)

 

Xw+

Xw

Xw

Xw+Xw

XwXw

Y

Xw+Y

XwY

The expected flies in the F2 generation of cross 2 are 25% Red Females, 25% White Females, 25% Red Males, and 25% White Males
Cross 3 – Red/Vestigial Female x Sepia/Normal Male
Dihybrid Cross

RRvv crossed with rrVV (F1 Generation)

 

Rv

Rv

Rv

Rv

rV

RrVv

RrVv

RrVv

RrVv

rV

RrVv

RrVv

RrVv

RrVv

rV

RrVv

RrVv

RrVv

RrVv

rV

RrVv

RrVv

RrVv

RrVv

The expected flies in the F1 generation of cross 3 are all red normal flies; 50% red normal males and 50% red normal female.

RrVv crossed with RrVv (F2 Generation)

 

RV

Rv

rV

rv

RV

RRVV

RrVv

RrVV

RrVv

Rv

RRVv

RRvv

RrVv

Rrvv

rV

RrVV

RrVv

rrVV

rrVv

rv

RrVv

Rrvv

rrVv

rrvv

The expected flies in the F2 generation of cross 3 are 9 red normal flies, 3 red vestigial flies, 3 sepia normal flies, and 1 sepia vestigial fly; 28.125% red normal males, 28.125% red normal females, 9.375% red vestigial males, 9.375% red vestigial females, 9.375% sepia normal males, 9.375% sepia normal females, 3.125% sepia vestigial males, and 3.125% sepia vestigial females.
Data:

Cross 1 – Sepia Female Drosophila x Wild Male Drosophila

F1 Generation

 
 
 
 
 

Traits

Sex

Day 1

Day 2

Day 3

Totals

Red

Male

3

3

55

61

Red

Female

4

2

58

64

Sepia

Male

0

0

6

6

Sepia

Female

0

0

4

4

Cross 1 – Sepia Female Drosophila x Wild Male Drosophila

F2 Generation

 
 
 
 
 

Traits

Sex

Day 1

Day 2

Day 3

Totals

Red

Male

10

18

7

35

Red

Female

9

20

9

38

Sepia

Male

4

5

3

12

Sepia

Female

9

6

1

16

Cross 2 – White Female x Wild Male

F1 Generation

 
 
 
 
 

Traits

Sex

Day 1

Day 2

Day 3

Totals

Red

Male

0

0

15

15

Red

Female

24

2

32

58

Sepia

Male

24

1

10

35

Sepia

Female

0

0

13

13

Cross 2 – White Female x Wild Male

F2 Generation

 
 
 
 
 

Traits

Sex

Day 1

Day 2

Day 3

Totals

Red

Male

2

0

0

2

Red

Female

2

1

2

5

Sepia

Male

4

0

2

6

Sepia

Female

2

2

1

5

Cross 3 – Red/Vestigial Female x Sepia/Normal Male

F1 Generation

 
 
 
 
 

Traits

Sex

Day 1

Day 2

Day 3

Totals

Red/Normal

Male

33

2

 

35

Red/Normal

Female

18

2

 

20

Red/Vestigial

Male

0

0

 

0

Red/Vestigial

Female

0

0

 

0

Sepia/Normal

Male

0

0

 

0

Sepia/Normal

Female

0

0

 

0

Sepia/Vestigial

Male

0

0

 

0

Sepia/Vestigial

Female

0

0

 

0

Cross 3 – Red/Vestigial Female x Sepia/Normal Male

F2 Generation

 
 
 
 
 

Traits

Sex

Day 1

Day 2

Day 3

Totals

Red/Normal

Male

14

10

6

30

Red/Normal

Female

16

13

5

34

Red/Vestigial

Male

6

2

3

11

Red/Vestigial

Female

4

2

3

9

Sepia/Normal

Male

8

2

2

12

Sepia/Normal

Female

6

6

2

14

Sepia/Vestigial

Male

2

1

0

3

Sepia/Vestigial

Female

2

0

0

2

Chi-Square Test:

Cross 1 F2 Generation

Trait (Eye Color)

Observed (o)

Expected (e)

((o – e)2)/e

Red

73

75.75

0.0998

Sepia

28

25.25

0.2995

Total

101

101

 

Chi-Square Test = X2 =

0.3993

Cross 1 F2 Generation (AP Biology 2015 Class Data)

Trait (Eye Color)

Observed (o)

Expected (e)

((o – e)2)/e

Red

570

543

1.3425

Sepia

154

181

4.0276

Total

724

724

 

Chi-Square Test = X2 =

5.3701

Cross 2 F2 Generation

Trait (Eye Color)

Observed (o)

Expected (e)

((o – e)2)/e

Red

7

9

0.4444

White

11

9

0.4444

Total

18

18

 

Chi-Square Test = X2 =

0.8888

Cross 2 F2 Generation (AP Biology 2015 Class Data)

Trait (Eye Color)

Observed (o)

Expected (e)

((o – e)2)/e

Red

442

390

6.9333

White

338

390

6.9333

Total

780

780

 

Chi-Square Test = X2 =

13.8666

Cross 3 F2 Generation

Trait (Eye Color & Wings)

Observed (o)

Expected (e)

((o – e)2)/e

Red/Normal

64

64.69

0.0074

Red/Vestigial

20

21.56

0.1129

Sepia/Normal

26

21.56

0.9144

Sepia/Vestigial

5

7.19

0.6671

Total

115

115

 

Chi-Square Test = X2 =

1.7018

Cross 3 F2 Generation (AP Biology 2015 Class Data)

Trait (Eye Color & Wings)

Observed (o)

Expected (e)

((o – e)2)/e

Red/Normal

448

458.44

0.2377

Red/Vestigial

187

152.81

7.6497

Sepia/Normal

134

152.81

2.3154

Sepia/Vestigial

46

50.94

0.4791

Total

815

815

 

Chi-Square Test = X2 =

10.6819

Discussion of Results and Conclusions:
The null hypothesis for Cross 1 was supported by F2 generations’ results. The margin of error for the chi-square in both generations is small, therefore, acceptable. However, the F1 generation was supposed to yield 100% red in fly eyes. Our results contained a few sepia flies, which ultimately leads us to reject the null hypothesis. The null hypothesis for Cross 2 is rejected for the same reasons as Cross 1. Cross 2’s F1 generation should have contained only 50% red-eyed females and 50% white-eyed males. There were flies with traits that weren’t supposed to spawn in the F1 generation (white females and red males). As for Cross 3, the null hypothesis was that there were going to be 50% red/normal males and 50% red/normal females for F1. Our results matched the expected. On top of that, the F2 results had a low margin of error as indicated by the chi-square, which means we can accept the null hypothesis.

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The F1 data for Cross 1 didn’t completely meet our expectations. There were supposed to be only red-eyed flies, regardless of sex (expected 50-50 between males and females). We ended up with a few sepia flies on the day 3 fly count. The deviation could have been set up by error. Knowing how fast flies mature and breed, one of the earlier larvae to reach adult form could have gotten enough extra time between the days to breed. Those two F1 flies could bring in F2 flies during the F1 count through sexual reproduction. It is also possible that a few of the F1 flies stayed in the vials instead of being taken out for counting. Those remaining F1 flies could have extra time to breed since they are more mature than the remain larvae and pupae. Cross 2 experienced a similar phenomenon in F1 where flies have traits they should not have. The expected results of F1 for Cross 2 should yield 50% white males and 50% red females. There should not have been any other flies with different traits than that. However like Cross 1, Cross 2’s observed results could have experienced the same scenarios of error that were mentioned earlier. Cross 3’s F1 generation experience no deviation and only yielded flies with the expected traits.
Cross 1’s F2 punnett squares estimated that the expected phenotype ratio for the F2 generation of cross 1 is 3:1, 75% red drosophila and 25% sepia drosophila. In terms of sex, that yields 37.5% Red Females, 12.5% Sepia Females, 37.5% Red Males, and 12.5% Sepia Males. The chi-square results indicate a low margin of error as the expected and observed are close without any major deviations. Cross 2’s F2 punnett squares estimated that the expected flies are 25% Red Females, 25% White Females, 25% Red Males, and 25% White Males. In terms of eye color, there were supposed to be about 50-50 whites and reds. The chi-square results of the class data show that there are were slightly more red-eyed flies spawned than white-eyed flies. The margin of error was rather significant in that case. Cross 3’s punnett squares has estimates of 28.125% red normal males, 28.125% red normal females, 9.375% red vestigial males, 9.375% red vestigial females, 9.375% sepia normal males, 9.375% sepia normal females, 3.125% sepia vestigial males, and 3.125% sepia vestigial females. Our chi square results of Cross 3 indicate precision and a low margin of error between the results and the expected percentages. Deviation in any of the crosses could have happened as a result of several scenarios. Our group did encounter difficulty extracting flies for the counting process. We tried freezing them, but given the amount of time, we had to shorten the tenure in the cold for the flies. That left some flies still active enough to accidentally escape from the lab. Even when we did manage to slow down the flies, some of them were hopelessly stuck inside the vials. Occasionally, the food paste in the vials shifted enough where it basically plasters any fly in its way. That took away several flies from our data as they could not be extracted for counting.
We seemed to be vulnerable the error in this lab and several different things could have gone wrong. As I discussed earlier, the difficulty in the removal of the flies played a major factor in skewing our data. Whether they are stuck to the paste or escaped the lab, that gives us less flies to count. Cross 2’s vial experienced the food paste shifting during our lab. A plague in our results was the appearance of flies with traits that are supposed to be absent from F1 actually appearing in our F1 data. This occurred in Cross 1 and 2. This could have been because of how fast a fruit fly matures and the amount of time given between the days we counted the flies. That could have given the F1 flies enough time to breed and spawn F2 during the days we counted for the F1 flies only. A simple issue we faced was time. The lack of time in a class period was detrimental with the incapacitation step for the counting process. We put the vials in the freezer in order to slow down the flies for transfer, but given the time, we rushed to get the counting done despite the fact that some of the flies were very active.
Works Cited
“Drosophila melanogaster.” World of Genetics. Gale, 2007. Science in Context. Web. 31 May 2015.
“Fruit fly (Drosophila melanogaster).” World of Scientific Discovery. Gale, 2007. Science in Context. Web. 31 May 2015.
Digital image. Fruit Fly Head HD Desktop Wallpaper: High Definition: Mobile. N.p., n.d. Web. 07 June 2015.
Digital image. The Conversation. N.p., n.d. Web. 7 June 2015.