Small coding stuffs and rants on Rookie site

Using panelsets

Sat, 02 Jan 2021 00:00:00 +0000

Courtesy of panelset.js by Garrick Aden-Buie, from his xaringanExtra package: https://pkg.garrickadenbuie.com/xaringanExtra/#/panelset

For example, this panelset:

Hello! 👋

hello

Goodbye 💨

goodbye

Was created by combining this theme’s panelset and panel shortcodes:

{{< panelset class="greetings" >}}
{{< panel name="Hello! :wave:" >}}
  hello
{{< /panel >}}
{{< panel name="Goodbye :dash:" >}}
  goodbye
{{< /panel >}}
{{< /panelset >}}

You could also revert to HTML as well. For example, this panelset:

Question

Which came first: the :chicken: or the :egg:?

Simulating genetic drift

Sun, 08 Nov 2020 00:00:00 +0000

Genetic drift is the result of bernouli process on survival of individuals (given some probability for each of them) of a population over a number of independent trials (Generation).

Apparently there are two techniques of seeing such process – one individual level, other the population level. Both solutions are illustrated below. Let us suppose population of N individuals remains fixed from generation to generation, likewise, Fitness probability of “A” allele ($p(A)$) and “a” allele ($p(a)$) both starts off equal. Now we can generate incremental population survival probability for each individual for given population size:

Missing negative from the normal

Sat, 07 Nov 2020 00:00:00 +0000

A normal function isn’t so normal

The normal density function is:

$$ \large f(x) = \frac{1}{\sqrt{2 \pi} \sigma} \exp^{-\frac{(x - \mu)^2}{(2 \sigma^2)}} $$

It doesn’t make sense to calculate the probability for a single value in a continuous probability function, it is by definition zero, but you can calculate relative likelihoods (heights). dnorm simply gives the value of the function for a given x, not the area under the curve for that x (which is basically nothing for a single value). To find the density (height) for a single x value on the normal distribution, use dnorm() in the following way (here each x value is treated as separate and vectorized over),

Relating quantile distribution and selection intensity

Sat, 07 Nov 2020 00:00:00 +0000

What has wikipedia to say about ?

In probability and statistics, the quantile function, associated with a probability distribution of a random variable, specifies the value of the random variable such that the probability of the variable being less than or equal to that value equals the given probability. It is also called the percent-point function or inverse cumulative distribution function.

For example, the cumulative distribution function of exponential ($\lambda$) (i.e. intensity $\lambda$ and expected value (mean) $\frac{1}{\lambda}$) is:

Internals of Mixed Models

Thu, 29 Oct 2020 00:00:00 +0000

Linear mixed models are widely used in Agriculture and Plant Breeding, as of recent. With access to genotype data high resolution phenotype data, it has become more of a requirement to use this family of model.

Mixed models allow for experimental (design or outcome) variables’ parameter estimates to have probabilistic distributions – most commonly normal – with opportunity to specify different variance-covariance components among the levels of those variables. In this post, I wish to discuss on some of the popular mixed modeling tools and techniques in the R community with links and discussion of the concepts surrounding variations of modeling techniques.

The nature of code: Why is it

Wed, 23 Sep 2020 00:00:00 +0000

Many find genetics, as a field of science on its own, charming. Many more are excited to learn about the science that fits seamlessly into complexity driven life of organisms, providing explanation for natural phenomena at both micro-evolutionary and macro-evolutionary scales. But only few find fascination with its deep running concepts, going down to more fundamental physical theories. This article tries to at least expose, if not laid satisfying argument, to some of fundamental questions in genetics concerning nature of code (mostly chemical behaviour). In particular, following are some of the questions I plan to touch upon (Credit goes to a student of mine who posed these questions one evening and left me pondering on details):

Color formatting of correlation table

Sat, 19 Sep 2020 00:00:00 +0000

Correlation

Correlation is a bivariate summary statistic. It basically talks of direction and magnitidue of association of two variables. Besides formatting with significance stars, color coding correlation coefficient table might be helpful to pick patterns out in a quick glimpse.

Table 1 presents correlation matrix of yield and yield component traits (a blue $\rightarrow$ red color profile represents increasing magnitude of positive correlation between traits). Following code is helpful if somebody provides a correlation table with stars in it and tells you to prettify it. Note that lower or upper halves only cannot be used to determine the discrete color values so full column is required.

Serpentine design and sorting

Sat, 19 Sep 2020 00:00:00 +0000

Take a grid and serpentine it row-wise or column-wise

This fn joins two matrices alternately columnwise, which is why this is the source of inspiration for generating serpentine design.

alternate.cols <- function(m1, m2) {
  cbind(m1, m2)[, order(c(seq(ncol(m1)), seq(ncol(m2))))]
}

A custom function to create a serpentine design in whatever fashion specified:

serpentine <- function(x, columnwise=TRUE){
  if (columnwise) {
    odd <- x[, seq(1, by=2, length.out = ncol(x)/2)] # odd x
    rev_even <- x[, seq(from = 2, 
                        by=2, 
                        length.out = (ifelse((ncol(x)%%2 != 0), 
                                             ((ncol(x)/2)-1), 
                                             (ncol(x)/2))))][seq(dim(x)[1],1),] # or, even[rev(1:nrow(x)),] # reversed even x
    alternate_cbind <-  cbind(odd, rev_even)[, order(c(seq(ncol(odd)), 
                                                       seq(ncol(rev_even))))]
    return(alternate_cbind)}
  else {
    odd <- x[seq(1, by=2, length.out = nrow(x)/2),] # odd x
    rev_even <- x[seq(from = 2, by=2, length.out = (ifelse((nrow(x)%%2 != 0), 
                                                           ((nrow(x)/2)-1), 
                                                           (nrow(x)/2)))), ][, seq(dim(x)[2],1)] # or, even[, rev(1:ncol(x))] # reversed even x
    alternate_rbind <-  rbind(odd, rev_even)[order(c(seq(nrow(odd)), 
                                                     seq(nrow(rev_even)))), ]
    return(alternate_rbind)
  }
}

Let’s see the function in action

Cluster dendrogram: An introduction and showcase

Mon, 07 Sep 2020 00:00:00 +0000

A cluster analysis is a classification problem. It is dealt in several ways, one of which is hierarchial agglomeration. The method allows for easy presentation of high dimensional data, more of so when the number of observations is readily fitted into a visualization.

Here’s I deal with a case of clustering typically seen in agriculture and field research where a researcher tests typically a large number of genotypes and seeks to see them organized into distinguishable clusters using dendrogram. Data concerns observations on disease incidence in rice genotypes of various stages – germinating seed to maturity nearing crop. Following provides a descriptive summary of the observation variables.

Disease epidemiology: A simulation scenario of infectious viral disease (COVID-19)

Fri, 07 Aug 2020 00:00:00 +0000

SIR model of COVID-19 epidemiology

## # A tibble: 5,555 × 6
##    beta_id  time       S      I      R beta_value
##    <chr>   <dbl>   <dbl>  <dbl>  <dbl>      <dbl>
##  1 1         0   0.7     0.02   0.01          3.2
##  2 1         0.5 0.662   0.0541 0.0134        3.2
##  3 1         1   0.575   0.133  0.0223        3.2
##  4 1         1.5 0.420   0.268  0.0420        3.2
##  5 1         2   0.242   0.411  0.0763        3.2
##  6 1         2.5 0.117   0.491  0.122         3.2
##  7 1         3   0.0521  0.506  0.172         3.2
##  8 1         3.5 0.0235  0.484  0.222         3.2
##  9 1         4   0.0111  0.450  0.269         3.2
## 10 1         4.5 0.00559 0.413  0.312         3.2
## # ℹ 5,545 more rows

Linear model fitting for regression: Basics and Variation

Fri, 07 Aug 2020 00:00:00 +0000

Linear model (simple forms) fitting

I use mtcars dataset to construct some basic regression models and fit those.

# convert available data to use in fitting
mtcars_reg_df <- mtcars %>% 
  rownames_to_column("carnames") %>% 
  as_tibble() %>% 
  mutate_at(c("gear", "am", "vs", "cyl"), as.factor)

We will be comparing difference between cylinder means for mpg.

# # intercept only lm tidiying and visualization
mpg_model1 <- mtcars_reg_df %>%
  group_by(cyl) %>%
  nest() %>%
  group_by(cyl) %>%
  mutate(mpg_model = map(data, ~lm(`mpg` ~ 1, .x))) %>%
  mutate(
    # rsqrd = map_dbl(mpg_model, ~summary(.x)[['r.squared']]), # this is '0' of intercept only model
    intercept_pvalue = map_dbl(mpg_model, ~summary(.x)[['coefficients']][1, 4]),
    intercept_se = map_dbl(mpg_model, ~summary(.x)[['coefficients']][1, 2]),
    intercept_coef = map_dbl(mpg_model, ~summary(.x)[['coefficients']][1, 1])
  ) %>%
  select(cyl, mpg_model, contains("intercept"), data)

Intercept-only models (each group fitted a different one) are plotted to reflect variation in estimated parameters. Two methods can be used to obtain same result. In the first, standard error obtained from model summary can be directly used; in the other SE can be manually computed.

Logistic Regression: Part I - Fundamentals

Fri, 07 Aug 2020 00:00:00 +0000

Likelihood theory

Probit models were the first of those being used to analyze non-normal data using non-linear models. In an early example of probit regression, Bliss(1934) describes an experiment in which nicotine is applied to aphids and the proportion killed is recorded. As an appendix to a paper Bliss wrote a year later (Bliss, 1935), Fisher (1935) outlines the use of maximum likelihood to obtain estimates of the probit model.

Logistic Regression: Part II - Varietal adoption dataset

Fri, 07 Aug 2020 00:00:00 +0000

Binary classifier using categorical predictor

Let’s say we have two variable – survey response of farmer to willingness to adopt improved rice variety (in YES/NO) and them having been trained earlier about agricultural input management (in trained/untrained).

Read in the data and notice the summary.

rice_data <- readxl::read_xlsx(here::here("content", "blog", "data", "rice_variety_adoption.xlsx")) %>% 
  mutate_if(.predicate = is.character, as.factor)

rice_variety_adoption <- readxl::read_xlsx(here::here("content", "blog", "data", "rice_variety_adoption.xlsx")) %>%
  select(improved_variety_adoption, training) %>% 
  # convert data to suitable factor type for analysis.
  mutate_if(is.character, as.factor)

head(rice_variety_adoption) # now we have data

## # A tibble: 6 × 2
##   improved_variety_adoption training
##   <fct>                     <fct>   
## 1 No                        No      
## 2 Yes                       No      
## 3 No                        No      
## 4 Yes                       No      
## 5 Yes                       No      
## 6 No                        No

As a basic descriptive, contruct one way and two way cross tabulation summary, showing count of each categories. This is because logistic regression uses count data, much like in a non-parametric model.

Paste together multiple columns

Fri, 07 Aug 2020 00:00:00 +0000

# # paste together dataframe columns by column index
# take the following df

df <- data.frame(my_number = letters[1:5], 
                 column_odd1 = rnorm(5), 
                 column_even1 = rnorm(5), 
                 column_odd2 = rnorm(5), 
                 column_even2 = rnorm(5), 
                 column_odd3 = rnorm(5), 
                 column_even3 = rnorm(5))

df %>% 
  select(1) %>% 
  bind_cols(data.frame(setNames(lapply(list(c(2,3), c(4, 5), c(6, 7)), function(i) 
    do.call(sprintf, c(fmt = "%0.3f (%0.3f)", # round at third place after decimal. use %s if columns were character type
                       df[i]))), c("new_column1", "new_column2", "new_column3"))))

##   my_number     new_column1     new_column2     new_column3
## 1         a -0.682 (-1.828) -1.304 (-0.113)  1.121 (-0.586)
## 2         b -1.178 (-0.809)   0.534 (0.524) -1.330 (-0.709)
## 3         c -0.432 (-2.787) -1.586 (-0.105)  1.449 (-0.533)
## 4         d  -1.258 (0.455) -0.071 (-0.786) -0.326 (-0.607)
## 5         e   0.329 (0.525)   0.011 (1.141)  0.043 (-0.228)

Not so fun with Nepalese flag

Thu, 06 Aug 2020 00:00:00 +0000

Anatomy of flag

The anatomy of flag is well described in tipsntricks/readresort section but the quirks of constructing it are not. I will be updating attending to complete the graphics as soon as I learn more.

So far, only this ugly shape is what I have here to show;

Piecewise Linear Function: An Introduction

Thu, 06 Aug 2020 00:00:00 +0000

Definition and meaning

Wikipedia defines a piecewise linear function as:

“a function defined on a (possibly unbounded) interval of real numbers, such that there is a collection of intervals on each of which the function is an affine function”

Example

# piecewise linear functions using if statements

# piecewise linear function defined using if for cases.
pw.function <- function(t) {
  if(t < 0.5) {
    0.7 * t/0.5
  } else {
    0.7 + 0.3*(t-0.5)/0.5
  }
}

pw.function <- Vectorize(pw.function)

tibble(t = seq(0.05:0.99, by = 0.05)) %>% 
  mutate(pfun_out = pw.function(t)) %>%
  ggplot() +
  geom_line(aes(x = t, y = pfun_out), group = "combined_grp")

Application

In segmentation and curve fitting. For a larger discourse refer to segmented package in cran.

The birthday problem: Non analytical solution

Thu, 06 Aug 2020 00:00:00 +0000

# Birthday problem

crossing(n = 2:100, 
         x = 2:4) %>% 
  mutate(probability = map2_dbl(n, x, ~pbirthday(.x, coincident = .y))) %>% 
  ggplot(aes(n, probability, color = factor(x))) +
  geom_line() +
  labs(x = "People in room", 
       y = "Probability X people share a birthday", 
       color = "X")

# Approximating birthday paradox with Poisson distribution

crossing(n = 2:250, 
         x = 2:4) %>% 
  mutate(combinations = choose(n, x), 
         probability_each = (1/365)^(x-1), 
         poisson = 1-dpois(0, combinations * probability_each), 
         pbirthday_x = map2_dbl(n, x, ~pbirthday(.x, coincident = .y))) %>% 
  gather(type, probability, pbirthday_x, poisson) %>% 
  ggplot(aes(n, probability, color = factor(x), lty = type)) +
  geom_line() +
  labs(x = "People in room", 
       y = "Probability X people share a birthday", 
       color = "X", 
       lty = "")

# the reason is because events are no longer weakly dependent-every pair makes triplets
# more likely.

# Analytical solution to birthday problem (Mikhail Papov; bearlogic.github.io)

# Suppose, we are interested in the probability that, in a set of n randomly chosen people, some pair of them will have the same
# birthday (which we refer to as event A).

# Using Kolmogorov axionms and conditional probability, we can derive an analytical solution for P(A):

# P(A) = 1-\frac{n!.\binom{365}{n}}{365^n}

# This can be solved in `R` as:

pa <- function(n){
  1 - (factorial(n) * choose(365, n))/(365^n)
}

map_dfr(.x = list(probability = 1:50), .f = pa) %>% 
  mutate(x = seq_along(probability)) %>% 
  ggplot(aes(x = x, y = probability)) +
  geom_line()

Making Summary Tables in R

Tue, 04 Aug 2020 00:00:00 +0000

Background

Table output of R is one of the richest and satisfying to use feature. Rmarkdown format provides loads of package support to create, format, and present tables beautifully. This is on one aspect extremely useful while on the other end it could very well be daunting as to choose between various package options to use while formating your table. I have a bunch of suggestions and enlistments here to help get off that dilemma.

Tidytuesday: Claremont Run, X-men Characters

Tue, 04 Aug 2020 00:00:00 +0000

X men characters

Data dictionary explore

Table: Table 1: Data summary


Name	Piped data
Number of rows	308
Number of columns	9
_______________________
Column type frequency:
character	8
numeric	1
________________________
Group variables	None

Variable type: character

Array operation: Outer product

Fri, 31 Jul 2020 00:00:00 +0000

Incidentally, I ran into outer product function outer today. It is extremely powerful function, in that it computes all combinations of product of two objects. One simplest and obvious demonstration is the multiplication table of numbers. 😄

We can show multiplication table of numbers 1 through 12 each multiplied 1 through 10.

outer(1:10, 1:12, "*")

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
##  [1,]    1    2    3    4    5    6    7    8    9    10    11    12
##  [2,]    2    4    6    8   10   12   14   16   18    20    22    24
##  [3,]    3    6    9   12   15   18   21   24   27    30    33    36
##  [4,]    4    8   12   16   20   24   28   32   36    40    44    48
##  [5,]    5   10   15   20   25   30   35   40   45    50    55    60
##  [6,]    6   12   18   24   30   36   42   48   54    60    66    72
##  [7,]    7   14   21   28   35   42   49   56   63    70    77    84
##  [8,]    8   16   24   32   40   48   56   64   72    80    88    96
##  [9,]    9   18   27   36   45   54   63   72   81    90    99   108
## [10,]   10   20   30   40   50   60   70   80   90   100   110   120

Another immediate use can be seen in design and combinatorics. For instance,

Time Series: Cointegration Analysis

Fri, 31 Jul 2020 00:00:00 +0000

Cointegration

Although not a new topic in finance and portfolio management, as some of the tweets below suggest, it’s application in field of Agricultural Economics and commodity market analysis is not often highlighted. A tweet relating S2F and bitcoin (BTC) trading was posted recently.

In this post, I demonstrate what are the basic ingredients of the cointegration analysis. In particular, I provide some context as to how it came to being and what it’s roots are in theory. While catching along the core applications, I will use example dataset and present its features, in synology.

Time Series: Basic Analysis

Thu, 04 Jun 2020 00:00:00 +0000

Background

This post is the first in a series of upcoming blog that tries to describe application of a lesser used technique in econometrics – time series analysis. I make extensive use of datasets available in several R packages – mostly the tsibbledata package. Furthermore, an external package hosted in github.com/FinYang/tsdl repo will be used.

Long way on return

Thu, 28 May 2020 00:00:00 +0000

Some new announcement

There is supposed to be new div tip here (this tip is defined in a custom file called divtips.scss)

:::ohshit My content goes in here and it is due to ohshit div. :::

CSS Grid Scaffold

Tue, 02 Jul 2019 00:00:00 +0000

“Grid is the very first CSS module created specifically to solve the layout problems we’ve all been hacking our way around for as long as we’ve been making websites.”

— Chris House, A Complete Guide to CSS Grid Layout ¹

Overview

Since I began building websites in Y2K, I’ve lost count how many times the phrase “…there’s got to be a better way to do this” has passed my lips. Most times, while fighting with floats and widths of content and sidebars or just basically trying to get something beside something else without using a stupid TABLE.

Built-in Contact Form

Mon, 01 Jul 2019 00:00:00 +0000

Formspree makes it easy to receive submissions from HTML forms on your static website.

Functional Form

This theme has a form-to-email feature built in, thanks to the simple Formspree integration. All you need to activate the form is a valid recipient email address saved in the front matter of the form (/content/forms/contact.md). Of course, the example shown below (your@email.here) must not be used. Please use your actual email address.

Correlation and pathway analysis with path diagrams

Thu, 21 Mar 2019 00:00:00 +0000

Background

Correlation study is one of the most extensively yet not fully appreciated topic. It forms the backbone of several other inferential studies. Path analysis, on a similar note, is a derived technique that explains directed dependencies among a set of variables. It is almost exactly a century old now and still finds uses in several fields of causal inference.

In oder to understand the process of causal inference (thought to be successor of path analysis), it is important to understand the basics about categories of variables. Below I have pointed out some of the concepts.

Valentine wheat

Sat, 09 Feb 2019 00:00:00 +0000

!Wheat flower

Private lives of wheat

Wheat are romantics! Hopeless romantics – not hapless by any means, though. 😄

It is the sesaon of dancing Wheat. Beginning with contacting the soil first this November and since then, wheat has been challenging. Challenging the sod early on, micromanging the cosm surrounding the biosphere, mobilizing all it could get from pretty much listless ground. After establishing itself too good, given the prolificacy of it’s tillering and the degree of sophisticated root development, it exhibits unmatched potential to tame the other forces of nature – those comprise water, light and air. Neither an atom nor a quanta is wasted. Wheat feasts upon it all to become ever stronger, ever more lushier. It casts it’s influence furthermore by suppressing any growth forms competing it. Wheat is so assertive that none get chance to outgrow once it’s foothold is established. And now, the season is to blame for what it has transformed into – a romantic. Henceforth, i will mostly be divulging about this species on what was observable today at various scales in production plots.

Seed growth rate of major cultivated crops

Sun, 03 Feb 2019 00:00:00 +0000

Context

(\#tab:smr-vegetables)Seed growth rate of common cultivated crop species
Crop	Number of cultivars	Seed growth rate_mean (mg per seed per day)	Seed growth rate_range (mg per seed per day)	Effective filling period_mean (day)	Effective filling period_range (day)	Maximum size_mean (mg per seed)	Maximum size_range (mg per seed)
Cereals:
Wheat (Triticum aestivum L.)	26	1.4	2.1-1.0	29	45-19	41	55-23
Barley (Horedum vulgare L.)	13	1.6	2.4-0.6	25	43-18	38	50-22
Rice (Oryza sativa L.)	12	1.2	2.0-0.9	24	36-12	28	50-20
Sorghum (Sorghum bicolor (L.) Moench.)	9	0.9	1.9-0.4	23	42-20	28	37-19
Maize (Zea mays L.) (inbreds)	22	7.4	9.7-3.6	31	39-23	228	322-86
Maize (Zea mays L.) (hybrids)	10	8.8	10.4-7.0	35	41-23	302	410-229
Legumes:
Soybean (Glycine max (L.) Merr.)	21	6.8	14.7-3.6	29	46-13	202	484-84
Bean (Phaseolus vulgaris L.)	20	18.9	33.1-10.2	18	24-14	345	540-190
Pea (Pisum sativum L.)	5	10.5	14.3-5.6	22	35-12	195	224-150
Field pea (P. arvense)	2	9.5	13.0-6.0	25	32-18	211	232-190
Broad bean (Vicia faba L.)	3	36.9	55.0-20.0	31	57-16	1104	2017-414
Cowpea (Vigna unguiculata L. Walp)	3	8.4	12.2-4.4	8	9.0-7.0	73	122-32
Groundnut (Arachis hypogaea)	2	12.8	14.0-11.6	44	45-43	563	626-500
Oil seeds:
Flax (Linum usitatissimum L.)	2	0.2	0.3-0.2	31	35-27	8	8.0-7.0
Sunflower (Helianthus annuus L.)	7	1.6	2.0-1.2	34	48-30	54	75-39

References

Page 46, Seed Biology and Yield of Grain Crops, 2nd Edition

World production and grain composition of major cultivated species

Sun, 03 Feb 2019 00:00:00 +0000

Context

Out of crops raised for their seed/grains (listed under 35 species, by FAO; FAOSTAT, 2014), only 22 species are produced in substantial amounts. Species of graminae and leguminosae families alone account for about 85 percent of the total grain production. As presented here in Table 1.

Production

(\#tab:world-production)Global production of major cultivated crops
Crop	Crop species	World production^[Average of 2011 to 2014, FAOSTAT (2016)] (1000 t)
Poaceae
Maize	Zea mays L.	950394
Rice	Oryza sativa L.	733424
Wheat	Triticum spp.	700828
Barley	Hordeum vulgare L.	138252
Sorghum	Sorghum bicolor (L.) Moench	58647
Millet^[May include members of other genera such as Pennisetum, Papspalm, Setoria and Echinochla]	Panicum miliaceum L.	26528
Oat	Avena sativa L.	22639
Rye	Secale cereale L.	14906
Triticale	X Triticosecale Wittm ex A. Camus	14653
Fabaceae
Soybean	Glycine max (L.) Merrill	272426
Groundnut^[In the shell]	Arachis hypogaea L.	41366
Bean^[Also includes other species of Phaseolus and, in some countries, Vigna species.]	Phaseolus vulgaris L.	23898
Chickpea	Cicer arietinum L.	12735
Pea, dry^[May include P. arvense (field pea).]	Pisum sativum L.	11013
Cowpea	Vigna unguiculata (L.) Walp.	6661
Lentil	Lens culinaris Medikus	4831
Broad bean	Vicia faba L.	4332
Pigeon pea	Cajanus cajan L. Millsp.	4454
Others^[Rapeseed is in the Brassicaceae, sunflower and safflower are in the Asteraceae, and sesame is in Pedaliaceae.]
Rapeseed^[May include industrial and edible (canola) types, data from some countries includes mustard (Brassica juncea (L.) Czern, et Coss)]	Brassica napus L., B campestris L.	67789
Sunflower	Helianthus annuus L.	40931
Sesame	Sesamum indicum L.	4738
Safflower	Carthamus tinctoris L.	776

Grain composition

(\#tab:grain-comp)Global production of major cultivated crops
Crop	Crop species	Harvested unit	Seed carbohydrate (g_per_kg)	Seed oil (g_per_kg)	Seed protein (g_per_kg)
Poaceae
Maize	Zea mays L.	Caryopsis	800	50	100
Rice	Oryza sativa L.	Caryopsis	880	20	80
Wheat	Triticum spp.	Caryopsis	750	20	120
Barley	Hordeum vulgare L.	Caryopsis^[Harvested grain usually includes the lemma and palea]	760	30	120
Sorghum	Sorghum bicolor (L.) Moench	Caryopsis	820	40	120
Millet^[May include members of other genera such as Pennisetum, Papspalm, Setoria and Echinochla]	Panicum miliaceum L.	Caryopsis	690	50	110
Oat	Avena sativa L.	Caryopsis^[Harvested grain usually includes the lemma and palea]	660	80	130
Rye	Secale cereale L.	Caryopsis	760	20	120
Triticale	X Triticosecale Wittm ex A. Camus	Caryopsis	594	18	131
Fabaceae
Soybean	Glycine max (L.) Merrill	Non-endospermic seed	260	170	370
Groundnut^[In the shell]	Arachis hypogaea L.	Non-endospermic seed	120	480	310
Bean^[Also includes other species of Phaseolus and, in some countries, Vigna species.]	Phaseolus vulgaris L.	Non-endospermic seed	620	20	240
Chickpea	Cicer arietinum L.	Non-endospermic seed	680	50	230
Pea, dry^[May include P. arvense (field pea).]	Pisum sativum L.	Non-endospermic seed	520	60	250
Cowpea	Vigna unguiculata (L.) Walp.	Non-endospermic seed	570	10	250
Lentil	Lens culinaris Medikus	Non-endospermic seed	670	10	280
Broad bean	Vicia faba L.	Non-endospermic seed	560	10	230
Pigeon pea	Cajanus cajan L. Millsp.	Non-endospermic seed	560	20	250
Others^[Rapeseed is in the Brassicaceae, sunflower and safflower are in the Asteraceae, and sesame is in Pedaliaceae.]
Rapeseed^[May include industrial and edible (canola) types, data from some countries includes mustard (Brassica juncea (L.) Czern, et Coss)]	Brassica napus L., B campestris L.	Non-endospermic seed	190	480	210
Sunflower	Helianthus annuus L.	Cypsela	480	290	200
Sesame	Sesamum indicum L.	Non-endospermic seed	190	540	200
Safflower	Carthamus tinctoris L.	Cypsela	500	330	140

References

Page 3 and 4, Seed Biology and Yield of Grain Crops, 2nd Edition

Color charts: An introductory review on applications to qualitative crop phenotyping

Wed, 23 Jan 2019 00:00:00 +0000

Background

Colorimetry is a fascinating topic to discuss. In conjunction with the patterns of a natural world (See this awesome video about fibonacci numbers and plants), colors could have mesmerizing feels. In this post and the follow-up article, we will discuss in details about colorimetric features of a universe made of plants, in particular, which are cultivated/adopted and have edible human values – the agricultural crops. Then again, there are quite a large number of agricultural species to deal with. So, we will be making a touch down on some common crop species, i.e. Pea (Pisum sativum, wild counterpart of the famous Lathyrus pea studied by Mendel) and Wheat (Triticum aestivum).

Maize seed value chain

Thu, 17 Jan 2019 00:00:00 +0000

Introduction

There are a great deal of influencers along the chain of generating finished seed material from a produce offered by farmers. As we discussed earlier in this post, the process takes on a fairly complex pathway while certification and processing requirements are being met. Carrying out the process requires resources, a large amount of them. This post tries to quantitatively explain a general expense scheme of a typical company in Maize seed business (Note, however, that the concepts generalize well to both Hybrid and OP seeds). So why approach to business management through the cost concept? I’ll just bullet point some of the direct benefits of thinking quantitatively.

Seed multiplication ratio and its significance in production planning

Sun, 13 Jan 2019 00:00:00 +0000

Context

SMR of Cereal crops

SMR of vegetable crops

(\#tab:smr-vegetables)Seed multiplication ratio of common vegetable crops
SN	Crop	Seed rate (per ha)	Seed yield	Seed Multiplication Ratio
1	Broad leaf mustard	600	600	1000.0
2	Bottle gourd	5000	160	32.0
3	Bitter gourd	5000	120	24.0
4	Broccoli	600	600	1000.0
5	Carrot	5000	600	120.0
6	Cabbage (Drum head)	600	800	1333.3
7	Cabbage (Golden acre)	600	700	1166.7
8	Cauliflower (Kathmandu local)	500	300	600.0
9	Cauliflower (Snowball)	500	240	480.0
10	Stringy beans	20000	600	30.0
11	Hot pepper	1000	160	160.0
12	Chenopodium	10000	700	70.0
13	Cucumber	3000	100	33.3
14	Pole bean	30000	800	26.7
15	Bush bean	80000	800	10.0
16	Knolkhol	1000	800	800.0
17	Onion	10000	500	50.0
18	Pea	100000	1000	10.0
19	Pumpkin	5000	160	32.0
20	Radish (White neck)	6000	800	133.3
21	Radish (Minnow early)	6000	500	83.3
22	Capsicum	1000	100	100.0
23	Squash	10000	200	20.0
24	Sponge gourd	5000	200	40.0
25	Swisschard	20000	800	40.0
26	Spinach	15000	500	33.3
27	Tomato	500	100	200.0
28	Turnip	5000	800	160.0
29	Watermelon	4000	100	25.0
30	Onion (For western mid hills)	10000	800	80.0

References

Adapted from Nepali writing of document on Seed production technology of major vegetable crops cultivated in Nepal (Basanta Chalise and Dr. Tul Bahadur Pun). Authors cite: FAO, 1984 for their tabulation.

Seed Business: Process Management Guide

Sat, 12 Jan 2019 00:00:00 +0000

The problem

Seed business is a multifaceted undertake. Although most businesses suffer strong feedback effects, seed business are more markedly left with those than most others. Let us refer to a simple case description to recapitulate just how pronounced it can be, and we are talking about the success in a long term venture.

Bean: Description of Agromorphology

Mon, 07 Jan 2019 00:00:00 +0000

Introduction

Agromorphology constitutes what’s observable and that which is economic. Because agriculture has important connection to economy, this connection is at best rung everywhere though what agriculture reveres and, and when talked modestly, relies on: Crops.

Unsurprisingly, finer details that agriculture touches upon to make ends met (processes and resources involved along the Production-Consumption chain) are convoluted. We just cannot discourse enough. This post, however, tries to make a connection between the economy and botany, however through generalization and prioritization.

String tip: complex pattern recognition

Sat, 22 Dec 2018 00:00:00 +0000

Background

This post is all about examples and use cases. So…Let’s break a leg.

Extract all words except last one using anchors and look arounds

nasty_char <- c("I love playing wildly") # remove the last word "wildly"

stringr::str_extract(nasty_char, ".*(?=\\s[:alpha:]*$)")

## [1] "I love playing"

String tip: vectorized pattern replacement

Sat, 22 Dec 2018 00:00:00 +0000

Example case

Suppose you have a bunch of really filthy names, which makes you puke… You can go about fixing those with the help of stringi and stringr

Lets say following character vector hosts those filthy names.

filthy <- c("Grains %", "Moisture (gm/kg)", "Plant height (cm)", "White   spaces", "White space  (filth%)")
filthy

## [1] "Grains %"              "Moisture (gm/kg)"      "Plant height (cm)"    
## [4] "White   spaces"        "White space  (filth%)"

Now to get rid of the filth use string manipulation.

Variance component based parameter estimation of incomplete block designs

Wed, 12 Dec 2018 00:00:00 +0000

Introduction

Variance component models are also suited for analysis of incomplete block designs, besides complete block designs. This post aims to demonstrate exactly that. Using a dataset generated from alpha lattice design, I show how the design can be properly modeled and fit using OLS regression having various fixed model components. This system of model fitting is analogous to classical ANOVA based technique of estimating parameters.

Linear mixed model formulation

Sun, 09 Dec 2018 00:00:00 +0000

Introduction

Mixed models are quite tricky, in that, while being very powerful extensions of linear models, they are somewhat difficult to conceptualize and otherwise to specify. Mixed models have, in addition to usual fixed effect combination of factors, random effects structure. These structure need to be specified in the model formula in R. While formula specification of a model is unique in it’s own respect, the formuala expression too leads to an object with differnt properties than a regular R object. Although, the complexity of formula syntax can arbitrary (constrained by classess and methods working on that), a general guideline is applicable for most of the mixed modeling utilities. These include: lme4, nlme, glmmADMB and glmmTMB.

Rice landraces of Nepal: A reflection of past

Mon, 03 Dec 2018 00:00:00 +0000

Background

This is really a brash article meant to convey exactly what title hints at. Much of the contents are translated/transliterated from “Rice Science and Technology in Nepal: A historical, socio-cultural and technical compendium”, which was published in 2017. As of now, primary authors are left uncredited, but as entries become widely accepted, original writers and publishers will be duely recognized.

Design and analysis of spit plot experiments

Tue, 27 Nov 2018 00:00:00 +0000

Split plot design

Design and fieldbook template

In a field experiment to test for effects of fungicide on crop, treatment of fungicides may be distinguised into multiple factors – based on chemical constituent, based on formulation, based on the mode of spray, etc. In a general case scenario where two former factors could be controlled, factor combinations may be organized in several different ways. When fully crossed implementation is not possible, split plot design comes to the rescue.

Layout and visualization of experimental design

Sun, 25 Nov 2018 00:00:00 +0000

Functional approach to creating and combing multiple plots

This approach highlights features of gridExtra package that allows combining multiple grob plots using function calls. We explicitly use lapply/split or similar class of purrr functions to really scale the graphics.

We load a Hybrid maize trial dataset, with fieldbook generated using agricolae::design.rcbd(). The dataset looks as shown in Table 1, after type conversion and cleaning.

(\#tab:rcbd-maize-fieldbook)Intermediate maturing hybrids with 50 entries each in 3 replicated blocks
Rep	Block	Plot	Entry	col	row	moisture1	moisture2	Ear count	Plant height
1	1	1	1	1	1	3.0	3.5	35	270
1	1	2	3	1	2	3.0	3.5	25	266
1	1	3	18	1	3	3.5	4.0	30	261
1	1	4	32	1	4	4.0	4.5	26	224
1	1	5	37	1	5	4.0	4.5	30	268
1	2	6	27	1	6	4.0	4.5	20	268
1	2	7	21	1	7	4.0	4.5	25	277
1	2	8	13	1	8	3.5	4.0	25	264

For the given dataset, we can draw on the information that Rep variable was used as field level blocking factor (Although separate, Block, variable exists, it was nested inside the Rep.) Therefore, to begin with, we ignore other spatial grouping variable. Now, since the grid graphics only requires two way represenation of plotting data, we have row and col information feeding for that.

Design and analysis of balanced randomized complete block (RCBD) experiment

Wed, 14 Nov 2018 00:00:00 +0000

Introduction

Balanced block designs are a class of randomized experimental design that contain equal number of records for a particular level of categorical variable across all blocks.

Example 1

Let us generate some data from random process mimicking a single factor process consisting of 3 levels.

Agriculture development chronology in Nepal

Sun, 04 Nov 2018 00:00:00 +0000

Toolbox for working with Unicode and Nepali fonts

Here’s a collection of excellent python scripts to convert back and forth among unicode and preeti font. To convert unicode text to preeti font:

https://github.com/globalpolicy/UnicodeToPreeti

To convert preeti font to unicode text:

https://github.com/globalpolicy/PreetiToUnicode

Developing flowcharts: an illustration of wheat breeding scheme

Sun, 04 Nov 2018 00:00:00 +0000

Flow diagrams are jam-packed with information. They normally describe a process and actors that are involved in making that happen.

With r package diagram, which uses r’s basic plotting capabilities, constructing flowcharts is as easy as drawing any other graphics.

This post expands on creating simple flowdiagrams using example scenario of a wheat breeding program. The information for this graph was, most notably, deduced from those provided by senior wheat breeder of Nepal, Mr. Madan Raj Bhatta.

Expressing timestamp data in calendar

Sun, 04 Nov 2018 00:00:00 +0000

Unlike composing a text memos and keeping tracks of those, calendar graphics is a highly effective visual aid to taking notes and summarizing them. Well, we all have used calendar, one way or the other, in our lifetimes.

Calendar based graphics enables an accurate catch at the very first glance; For example, it is very easy relating one activity of a period to another when they are laid linearly with precise graduations. Calendar graphics does exactly that – some features (usually tiles) provide graduation, representing fixed interval of time (e.g., a day). This when combined with text allows unlimited freedom to provide narration for specific intervals.

Image and video editing hacks using ffmpeg

Sat, 03 Nov 2018 00:00:00 +0000

Compose a slideshow

This is beginner’s tutorial for starting up with open source ffmpeg library.

In the beginning, it’s shown how we can convert an image gallery to a video slideshow. For example we have a directory called images filled with photos that we want to knit together to obtain a slideshow. A video, however, has an additional time/length attribute besides the attribute that controls which order the images are sawn together in. Along with these, several other features underlie a video composition, some of which will be discussed in this tutorial.

Varietal database of lentil in Nepal

Fri, 26 Oct 2018 00:00:00 +0000

This one is my effort to compose an updated database on the current situation of lentil germplasm in Nepal. I’ve managed to list out the varieties that have been made available so far (either through release or registration process). Although, a lot of other popular genotypes are trending in cultivation as of now. I consider NARC’s varietal catalogs to be the most authentic, so have borrowed most of the information from these published documents.

Grade X result

Fri, 19 Oct 2018 00:00:00 +0000

Total Marks and Final Grades {-}

roll number	English Total Marks	Nepali Total Marks	C. Maths Total Marks	Science Total Marks	Extension Total Marks	Soil Total Marks	Plant protection Total Marks	Fruit Total Marks	Agronomy Total Marks	Computer Total Marks	O. Maths Total Marks	English Total Marks Agg grade	Nepali Total Marks Agg grade	C. Maths Total Marks Agg grade	Science Total Marks Agg grade	Extension Total Marks Agg grade	Soil Total Marks Agg grade	Plant protection Total Marks Agg grade	Fruit Total Marks Agg grade	Agronomy Total Marks Agg grade	Computer Total Marks Agg grade	O. Maths Total Marks Agg grade
1	70.2	57.2	NA	72.4	77.3	84.9	81.9	74.8	88.2	88.3	NA	B+	C+	NA	B+	B+	A	A	B+	A	A	NA
2	76.8	46.2	NA	63.2	72.1	82	77	71.7	81.2	74.7	NA	B+	C	NA	B	B+	A	B+	B+	A	B+	NA
3	82.8	57	NA	56	89.8	82.5	82.1	85.3	89.7	79	NA	A	C+	NA	C+	A	A	A	A	A	B+	NA
4	42.4	19.5	NA	20.6	48.8	58.2	50.1	49.8	54.8	67	NA	C	E	NA	D	C	C+	C+	C	C+	B	NA
5	55.2	34.3	NA	33.2	49.6	64.2	59.8	57.1	61	73.3	NA	C+	D	NA	D	C	B	C+	C+	B	B+	NA
6	49.5	43.3	NA	24.8	51.7	55.5	52.9	46.4	55.4	65.5	NA	C	C	NA	D	C+	C+	C+	C	C+	B	NA
7	71.4	50.4	NA	54.8	75.7	71.6	76.8	61.7	80.6	77.2	NA	B+	C+	NA	C+	B+	B+	B+	B	A	B+	NA
8	73.8	45.3	NA	62	83.9	81.7	78.1	83.6	88.2	79.4	NA	B+	C	NA	B	A	A	B+	A	A	B+	NA
9	51.9	39.9	NA	26.8	56.9	56	62	49.6	57.2	64.7	NA	C+	D	NA	D	C+	C+	B	C	C+	B	NA
10	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
11	42	20.6	NA	22.1	49.2	54.2	53.8	44.5	51.2	59.3	NA	C	D	NA	D	C	C+	C+	C	C+	C+	NA
12	52.6	22	NA	36.9	57.9	69.6	67.1	61.2	65.8	68.2	NA	C+	D	NA	D	C+	B	B	B	B	B	NA
13	61.8	30.8	NA	27.9	59.5	65.8	63.3	53.8	62.4	70.7	NA	B	D	NA	D	C+	B	B	C+	B	B+	NA
14	58.8	32.6	NA	19	51.8	54.7	63.3	48.4	60.1	68	NA	C+	D	NA	E	C+	C+	B	C	B	B	NA
15	69.3	34.6	NA	35.1	55.3	62.3	63.5	63.8	66.2	80.6	NA	B	D	NA	D	C+	B	B	B	B	A	NA
16	54.9	23.9	NA	29	48.7	56.6	54.4	53.4	55.7	59.6	NA	C+	D	NA	D	C	C+	C+	C+	C+	C+	NA
17	58.2	37.2	NA	25.7	66.9	66.6	66.3	62.8	70.1	64.4	NA	C+	D	NA	D	B	B	B	B	B+	B	NA
18	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
19	48.7	40.9	NA	27.4	77.1	72.8	70	66.5	75.2	62.6	NA	C	C	NA	D	B+	B+	B+	B	B+	B	NA
20	48.1	26.5	NA	22.9	56.7	64.6	54	51.2	64.6	61.9	NA	C	D	NA	D	C+	B	C+	C+	B	B	NA
21	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
22	70.5	42.8	NA	55	70.5	74.4	60.8	73.2	81.5	73.2	NA	B+	C	NA	C+	B+	B+	B	B+	A	B+	NA
23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
24	32.5	31.2	NA	18.7	46.2	47.4	49.2	48.5	46.7	55.4	NA	D	D	NA	E	C	C	C	C	C	C+	NA
25	46.7	43.3	NA	29.6	55.2	59.9	59.7	55.5	60.3	61.7	NA	C	C	NA	D	C+	C+	C+	C+	B	B	NA
26	58.5	51.6	NA	36.6	75.3	70.6	75.8	67.4	80.4	68.3	NA	C+	C+	NA	D	B+	B+	B+	B	A	B	NA
27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
28	66.3	45.9	NA	58.6	77.9	80.8	79	63	79.1	81.1	NA	B	C	NA	C+	B+	A	B+	B	B+	A	NA
29	43.8	42.2	NA	30.7	55.9	64.4	57.5	51.9	56.1	64.1	NA	C	C	NA	D	C+	B	C+	C+	C+	B	NA
30	55.2	41.2	NA	29	55.8	68	61.8	57.6	65.5	64.3	NA	C+	C	NA	D	C+	B	B	C+	B	B	NA
31	67.8	51.8	NA	51.8	80.3	74.9	79.3	73.4	82.6	72.1	NA	B	C+	NA	C+	A	B+	B+	B+	A	B+	NA
32	61.8	41.4	NA	52.4	71.9	76.4	76	74.7	77	66.3	NA	B	C	NA	C+	B+	B+	B+	B+	B+	B	NA
33	54.9	45.1	NA	30.5	65.2	78.9	76.8	64.2	78.1	72.8	NA	C+	C	NA	D	B	B+	B+	B	B+	B+	NA
34	52	37.6	NA	25.9	51.4	60.2	60.6	55.5	60.5	61.8	NA	C+	D	NA	D	C+	B	B	C+	B	B	NA
35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
36	54.6	40.9	NA	32.3	51.8	62.6	58.9	58.5	55.2	65.7	NA	C+	C	NA	D	C+	B	C+	C+	C+	B	NA
37	18.9	14.7	NA	17.4	5.7	24	18	18	10.1	25.7	NA	E	E	NA	E	E	D	E	E	E	D	NA
38	61.5	42.7	NA	59.4	80.8	83.2	72.8	74.4	84	70.8	NA	B	C	NA	C+	A	A	B+	B+	A	B+	NA
39	74.4	40.2	NA	49.8	62.1	60.9	60.9	59.1	66.6	77.1	NA	B+	C	NA	C	B	B	B	C+	B	B+	NA

NA indicates the absence of student

Tidyverse and tidbits

Thu, 18 Oct 2018 00:00:00 +0000

Ideas surrounding tidy evaluation

R code is a tree

Every expression in R can be broken down to a form represented by a tree. For instance, on top of the tree there is “a function call” followed by it’s branches: first child = function name itself, other children = function arguments. Complex calls have multiple levels of branching.
The code tree could be captured by quoting

expr() quotes your(function developer) expression

Flowering phenology of Rice genotypes in irrigated tropical condition of Nepal

Wed, 17 Oct 2018 00:00:00 +0000

Dhangadhi of Kailali, Nepal remains relatively hotter during summer season with respect to average condition of terai agro-ecology. A regime of high day temperature with bright sunshine and cool night temperature along with plenty of seasonal rain during larger part of rice growing season favors good growth of rice crop in the region.

Based on a range of cultivation sowings carried out in the summer/rainy season of 2018, flowering dates were recorded for each plot of varieties, and the duration since planting to flowering was determined. Here, flowering time is ascribed to the period when approximately 50% of the anthers in each plots could be promptly seen as extruding. I summarize the resulting days to flowering period in the bar chart shown in Figure 1.

Layout and planning of individual varietal demonstrations in small areas

Wed, 17 Oct 2018 00:00:00 +0000

Design and layout plan

Figure 1: Grid layout design of varietal demonstration for small number of plots

Memorize the date? Don't need to, use lubridate

Wed, 17 Oct 2018 00:00:00 +0000

My sufferance

Time and again, I’ve suffered due to my humanistic limitations of memorizing things promptly. I suck at remembering stuffs, dates particularly. So, In this blog trip (Oh! this is a trip btw, because I don’t forsee myself surpassing my memory limitations any sooner than death), I will be stating if not rambing on some lifesaving tricks of picking up pieces of your faulty brain.

The balm

I’m getting into the details of using base R’s date() and date related functions. At this time, It’s might seem relevent to have some understanding of “POSIXlt” and “POSIXct” object classes. But most often these never interfere unless you have a good – not expecting perfect – conscience of how you recorded your dates and what you eventually intent to achieve from it. Anyway, for a quick reference, here I’ve quoted the R’s documentation on ?DateTimeClasses:

Diallel cross: A case study of mating designs

Sat, 15 Sep 2018 00:00:00 +0000

Mating designs

Introduction

Mating designs allow for partitioning of phenotypic effects – as due to genotype, environment or interacting effects among genes and alleles. Using one or more of these mating schemes, identification of heterotic groups, estimation of general and specific combining abilities and testing of environmental interactions could be done. Progenies resulting from a well designed mating are used for the dissection of trait genetics.

Writing a Reference/Recommendation Letter

Fri, 24 Aug 2018 00:00:00 +0000

Draft a LoR for yourself

Stability analysis: how to guide

Tue, 14 Aug 2018 00:00:00 +0000

Meaning of stability

Comparison of treatments may also imply cross comparison of their stability across multiple environments, especially when a study constitutes a series of trials that are each conducted at different locations and/or at different periods in time (henceforth referred to as MET; Multi-Environment Trial). Several situations exist where only mean based performance analysis are regarded inconclusive.

For example, in varietal release process the authorizing body seeks record of consistent trait performace of certain crop genotype. The imperative is: a variety needs to be stably exhibit it’s characters in the proposed domain of cultivation, which generally is a wide area, throughout a long duration of cultivation cycles. This pre-condition of stable character inheritance is more relevant to crops constituting a homogenous and homozygous population. Either of the location, time period or combination of both, more commonly framed as year in field researches, could be assumed to present an unique environment that treatment entries are tested in. Thus, for results to be widely applicable, performance measures across environments should be more or less stable. To the contrary, the concept of utilizing differential character expression across different environments is often explored when interaction between genotypes and environments result in more desirable character.

Resource optimization

Sun, 12 Aug 2018 00:00:00 +0000

library(lpSolve)
library(tidyverse)

Issue

A farmer has 600 katthas of land under his authority. Each of his katthas of land will either be sown with Rice or with Maize during the current season. Each kattha planted with Maize will yield Rs 1000, requires 2 workers and 20 kg of fertilizer. Each kattha planted with Rice will yield Rs 2000, requires 4 workers and 25 kg of fertilizers. There are currently 1200 workers and 11000 kg of fertilizer available.

Hybrid Maize: History, Prospect and Practice

Sun, 05 Aug 2018 00:00:00 +0000

Questions about maize

To when does maize retrace back in history of Nepal?
- To the dawn of agriculture
How could have maize had found place in Nepalese lifestyle?
- Settlers and travellers
What are the reasons behind popularity of Maize in Nepal?
- Major stape diet
- Rich in protein content
- Convenient eat-as-you-go food
- Makes excellent animal feed and fodder
Does maize have a substitute?
- No
What will maize mean for Nepal?
- A supporting cane in a long winding walk to food soverignity
Will maize rule?
- Unfortunately no.

Answers on maize

First off, It is necessary to be clear about what constitutes Maize. Gleaning from the fact that, most if not all, present day commercial varieties belong to Zea genera, one might unwittingly gather a narrow sense for belongingness of Maize. Actually, Maize covers a whole deal of species of grass family placed under tribe Maydeae (pronounced “may-day” if one wishes to lay a bit more emphasis that maize definitely deserves). Diverse tribe maydae covers seven major identified genuses, one of which is the popular genus Zea.

Thanking the farmers

Tue, 30 May 2017 00:00:00 +0000

Everybody has their own secret mantra which they find useful and will eventually apply for their unique set of problems. As a rummaging wanna-be R programmer, almost daily I shovel through rich contents of web in lookout for gurus. It’s a bit puzzling to know that most of them come faceless, nevertheless amazing are the quality of the contents the offer.

Either of the creating, editing or deploying a website has never been easy, to me especially. In fact, It’s just been a few weeks, not more than a month that I deployed my first website. I can’t thank enough the blogdown project by Yihui Xie that, at the first place, inspired me to make this personalized website of my own, showing how seemlessly can the technicalities be overcome. Before this I haven’t for once attempted to write an html code for a website as such.

Dealing with factors

Thu, 18 May 2017 00:00:00 +0000

A `factor` is an headache

I have a dataset, cleaning which has been a pain lately. I’m going to use 20 observations of the imported dataset in this post to demonstrate how pathetically have I been advancing with it.

plot	jan_23_2017	jan_26_2017	jan_29_2017	feb_02_2017
1	0	0	b	am
2	f	s	1p	10p
3	b	a	sm	3p
4	b	b	ap	2
5	0	b	bp	s
6	b	a	sp	3

Providing it a context, the columns represent multiple observations of same variable at different dates, as apparent from the column names.

An encounter with blogdown

Mon, 01 May 2017 00:00:00 +0000

An encounter with web content related to R, although for me it is usually random, is in general welcoming. The link to this amazing feature building on the fact that using a markdown language is rather a pleasant way to write a blog post in a fully costumizable website all by yourself is enticing, to me at least. I started weaving thoughts right at the moment.

Rep	Block	Plot	Entry	col	row	moisture1	moisture2	Ear count	Plant height
1	1	1	1	1	1	3.0	3.5	35	270
1	1	2	3	1	2	3.0	3.5	25	266
1	1	3	18	1	3	3.5	4.0	30	261
1	1	4	32	1	4	4.0	4.5	26	224
1	1	5	37	1	5	4.0	4.5	30	268
1	2	6	27	1	6	4.0	4.5	20	268
1	2	7	21	1	7	4.0	4.5	25	277
1	2	8	13	1	8	3.5	4.0	25	264

Rep	Block	Plot	Entry	col	row	moisture1	moisture2	Ear count	Plant height
1	1	1	1	1	1	3.0	3.5	35	270
1	1	2	3	1	2	3.0	3.5	25	266
1	1	3	18	1	3	3.5	4.0	30	261
1	1	4	32	1	4	4.0	4.5	26	224
1	1	5	37	1	5	4.0	4.5	30	268
1	2	6	27	1	6	4.0	4.5	20	268
1	2	7	21	1	7	4.0	4.5	25	277
1	2	8	13	1	8	3.5	4.0	25	264

Rep	Block	Plot	Entry	col	row	moisture1	moisture2	Ear count	Plant height
1	1	1	1	1	1	3.0	3.5	35	270
1	1	2	3	1	2	3.0	3.5	25	266
1	1	3	18	1	3	3.5	4.0	30	261
1	1	4	32	1	4	4.0	4.5	26	224
1	1	5	37	1	5	4.0	4.5	30	268
1	2	6	27	1	6	4.0	4.5	20	268
1	2	7	21	1	7	4.0	4.5	25	277
1	2	8	13	1	8	3.5	4.0	25	264