The nature of code: Why is it

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):

World production and grain composition of major cultivated species

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

Grain composition

References

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

Seed growth rate of major cultivated crops

Context

References

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

Seed Business: Process Management Guide

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.

Linear mixed model formulation

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.

Design and analysis of spit plot experiments

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

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	tillering	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.

Developing flowcharts: an illustration of wheat breeding scheme

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.

Layout and planning of individual varietal demonstrations in small areas

Design and layout plan

Flowering phenology of Rice genotypes in irrigated tropical condition of Nepal

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.

Stability analysis: how to guide

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.