Proso Millet

It’s been long, I wanted to write something on the crop I am working on.

It is Proso millet.

Like all other grass family members, it is from Poaceae family. Genus is Panicum. Scientific name is Panicum miliaceum.

It is is mostly cultivated in USA, China, India, Niger, Nepal, Africa, Russia, Ukraine, Belarus, Middle East, Turkey, Poland, Germany, Switzerland, and Romania. Radioactive dating showed its origin was around 10,000 BP in China. It was introduced in the USA by German-Russian Immigrant in 1875.

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Fig. 1 Countries growing proso millet around the world

Proso millet is the best suited crop in the semi-arid region of the world. where annual rainfall is pretty low. Here in the western Nebraska and the Central Great Plains where annual rainfall is < 100mm, proso millet stand out as an important rotational crop with winter wheat. High water use efficiency (WUE) with shallow root system (~90 – 92 cm root) helps it thrive and adapt only using the minimal water which is available through the spring rain. It also helps in conservation of subsurface water for the deep-rooted crop like corn, wheat, sunflower. Cultivating proso in the summer fallow also helps in controlling the annual grass weeds. Short growing season of proso millet also makes it ideal and helps it fit perfectly in the winter wheat – proso millet rotation.

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Fig. 2 Map of Central Great Plains (Map Source: https://bit.ly/2EC6UXc)

Proso millet is also a miracle grain with multitudinous health benefits.

· It is gluten free,

· It has low glycemic index,

· It increases blood HDL level

· It has high antioxidant

· It has high mineral content

· Essential amino acid index is higher than wheat

Quinoa and Proso millet

Though its abundant health and environmental benefits, it is mostly used as bird seeds in USA. However, due to recent upsurge in health consciousness among people and rapid wave of ancient grains in market proso millet did get some attentions. However still far behind the imported ancient grain like “quinoa”. Though if you compare the nutritional profile of proso millet and quinoa, both are in neck to neck fight with similar carbohydrate, protein, vitamin, amino acids and mineral quantities. Both quinoa and proso millets are gluten free. Moreover, proso millet is locally grown seeds and increased market share can improve the local farm economy. While rapid increase in the export of quinoa has created imbalance in local economy and also impacting the ecosystem of the importing country due to conversion of forest lands to farmland to meet the demands. But, despite all the facts, proso millet is still struggling to get into the human food market where quinoa has established itself as a golden seed of health benefits.

New hybrid varieties with high nutritional value and extension regarding different prospects and perspectives of proso millet is the need of time to expand and develop new market for proso millet.

So, What we do ?

we are trying to develop new varieties of proso millet which will be more marketable and test the developed varieties in multi location for the environment and genotype interaction and try to select the best varieties which gives significant yield in different environment and locations.

proso millet

For Further Readings: Beyond Bird Feed: Proso Millet for Human Health and Environment

https://www.mdpi.com/2077-0472/9/3/64/htm

Odds Ratio

What is odd ratio ?

  • Odds ratio (OR) is statistical quantifier which measures the association between an exposure and an outcome. OR represents the odds of an outcome in present of particular exposure with comparison to odds in the absence of that exposure. In a simple definition you can how likely or probability of occurrence of an event in presence or absence of a particular exposure.
  • OR are mostly used in case control, cross sectional and cohort studies.

Where and When to use it?

  • OR is used to measure the relative odds of occurrence of an outcome especially disease or disorder in a given exposure. For an example, how likely the chance of occurrence of cancer in presence and absence of smoking as a factor.
  • OR can also be used to measure the risk factor for a particular outcome.

OR = 1 Exposure doesn’t affect the odds of outcome.

OR >1 Exposure associated with higher odds of outcome

OR <1 Exposure associated with lower odds of outcome

in rare outcomes OR = RR (relative risk) where the incidence of disease is < 10%

Then What is Confidence Interval (CI), which is always calculated with OR ?

  • 95% CI is used to estimate the precision of OR. A large CI indicates low level of precision for OR while a small CI indicates high precision of OR.
  • Remember, CI is not measure of statistical significance.
  • Its give you the range where your prediction will be in 95% of the time when you are estimating a population based on your sample. (CI is calculated with bootstrapping)

Now lets see, how you can calculate OR and CI using R :

Lets Say, There is a particular disease “X” and I want to check does it have any relationship with Demographic factor like Rural and Urban or simply does livelihood somehow effect the occurrence of the disease. So, I did sampling and collected information from both rural and urban population and it was like:

Positive Negative
Rural 65 55
Urban 46 34

So, all total I collected 200 people’s information, where 120 people were from rural area where 65 were found positive for the disease and 55 were negative. From urban I collected 80 samples and I found 46 were positive and 34 were negative for the disease. So, now lets do the OR test.

So, the question will be like what is the odds of having the disease for someone living in rural area and urban area ?

For this, I am going to use “epiR” package.

>install.packages(“epiR”)
>library(epiR)

#Lets create a matrix of 2 by 2 and give the dataset a name of X
X <- matrix(c(65, 55, 46, 34), nrow = 2, byrow = TRUE)
X

     [,1] [,2]
[1,]   65   55
[2,]   46   34

#Lets chane the row names and colnames as according to the example
rownames(X) <- c(“Positive”, “Negative”)
colnames(X) <- c(“Rural”, “Urban”)
X

Rural Urban
Positive    65    55
Negative    46    34

#OR test
epi.2by2(X, method = “cohort.count”) (I used cohort count, here you can use Wald test or Fischer test accordingly)

             Outcome +    Outcome -      Total        Inc risk *        Odds
Exposed +           65           55              120              54.2                 1.18
Exposed -           46           34                80              57.5                 1.35
Total                    111           89               200             55.5                 1.25

Point estimates and 95 % CIs:
-------------------------------------------------------------------
Inc risk ratio                                            0.94 (0.73, 1.21)
Odds ratio                                                 0.87 (0.49, 1.55)
Attrib risk *                                              -3.33 (-17.36, 10.70)
Attrib risk in population *                    -2.00 (-14.84, 10.84)
Attrib fraction in exposed (%)              -6.15 (-36.33, 17.34)
Attrib fraction in population (%)          -3.60 (-19.96, 10.52)
-------------------------------------------------------------------
 X2 test statistic: 0.216 p-value: 0.642
 Wald confidence limits
 * Outcomes per 100 population units

interpretation : so the odds ratio tells us that the odds are 0.87 times great that someone living in rural areas will have X disease compare to urban areas. However, p value is above o.05 which indicate the results are not statistically significant.

You can even calculate the same in classical way by dividing odds of each case lets say:

chances or odds of having someone the disease living in rural area will be = 65/55 = 1.18

and similarly for someone living in urban area will be = 46/34 = 1.35

so the odds ratio will be = 1.18 / 1.35 = 0.87

For further Read: You can checkout,

Szumilas, M. (2010). Explaining odds ratios. Journal of the Canadian academy of child and adolescent psychiatry, 19(3), 227. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938757/