eChapters

The application of back-cross breeding first proposed by H. V. Harlan and M. N. Pope in 1922 provides a precise way of improving varieties that excel in a large number of attributes but are deficient in a few key characteristics. F. N. Briggs too started an extensive back-crossing programme to develop bunt resistant varieties of wheat in 1922. As the name implies, the back-cross method of breeding makes use of a series of back-crosses to the variety to be improved during which the character or characters in which improvement is sought is maintained by selection. At the end of back-crossing, the gene (or genes) being transferred will be heterozygous. Selfing at the end of last back- cross produces homozygosity for this gene pair and coupled with selection will result in a variety similar to the well adapted and recurrent parent but superior for the particular characteristic for which improvement programme was initiated.

In back-cross method of breeding, the adapted and highly desirable parent is called the recurrent parent and the source of desirable character being transferred is called as donor parent. It is most effective and easy to apply when the missing trait is qualitatively inherited, dominant and produces a phenotype that is readily observed in a hybrid plant. The procedure for transferring a recessive trait is similar to that for dominant trait but involves an additional step of selfing. Back-crossing is also used to transfer entire sets of chromosomes in a foreign cytoplasm to create a cytoplasmic male sterile (CMS) genotype as has been practiced in maize, onion and wheat, etc. This is done by crossing the donor (of chromosomes) as male until all donor chromosome are recovered in the cytoplasm of the recurrent parent. Back-cross breeding has also been used to develop isogenic lines (lines that differ only in alleles at a specific locus) for traits like disease resistance and plant height, etc.

Mangelsdorf (1951) has opined that hybrid corn has been the most far-reaching development in applied biology. Hybrid cultivars have contributed enormously to world’s food and feed resources. Although dramatically successful at first in corn, hybrids have spread to several other crops both in cross-pollinated and self-pollinated species. Their use is likely to spread more in time to come. Hybrid varieties are those in which F1 populations are used to produce commercial crop. Parents of F1 may be inbred lines, clones, varieties, or other populations. Hybrid breeding is historically rooted in development of hybrids in corn and therefore most of the issues covered in this section relate to hybrid breeding in maize.

Genetics and Gene

Genetics is defined as the branch of biology dealing with heredity and variation. The hereditary units which are transmitted from one generation to the next are called genes. Genes have been defined in various ways. These are units of inheritance located in linear order on chromosomes. The genes are considered as hereditary determinants of a specific biological function located in a fixed place on the chromosome. Chemically, gene is a segment of DNA (deoxyribonucleic acid) encoding one polypeptide and defined operationally by the cis-trans or complementation test. Gene is the functional unit of inheritance (a sequence of DNA) which is located on a certain position (locus, plural loci) on a chromosome. The locus is sometimes used interchangeably for gene. DNA, in conjunction with a protein matrix, forms nucleoprotein and becomes organized into structures with distinctive staining properties which are known as chromosomes. The chromosomes are located in the nucleus of the cell. Since genes are located on the chromosomes, in many ways behavior of genes is parallel to the behavior of the chromosomes. Two or more alternative forms of a gene are called as alleles. All genes on a chromosome are said to be linked and they all belong to the same linkage group.

The artificial intelligence (AI) world is quickly growing as it reaches several different industries. You currently will see AI used for many different purposes from agriculture to automotive; with the passage of time you will see even more of it. Agriculture is one of the most important branches of the IT industry. Agriculture is an important industry and is a big part of our economy’s base. In terms of annual sales, the agricultural industry contributes to our economy almost 17%.AI is becoming a technical advancement that boost and protects crop yield, as climate change and populations rise. Here in this chapter basics of Artificial Intelligence have been discussed how it can be related to agriculture.

1. Introduction

Artificial Intelligence has become most prominent in current generation. Every field is utilizing Artificial Intelligence for their better results and which also result in saving a lot of time. As Artificial intelligence is boosting different sectors to increase the productivity and efficiency and its utilization across different sectors like in the field of CIVIL Artificial Intelligence can be used to analyze the structures of buildings, dams etc., and Artificial Intelligence solutions are assisting in so many ways to overcome the traditional challenges in every field.

I. What is Artificial Intelligence

Artificial Intelligence is one of the most prominent field of Computer Science Engineering which attempts to redefine the tasks which are carried out by humans in their daily life with better results and maximum accuracy. Artificial Intelligence is actually meant to ease the work of human where the word artificial states the work done without any human interference and intelligence states the capabilities like human brain to perform tasks. The field artificial Intelligence has given so many definitions from time to time which can be typically stated as

Growing more than one crop in sequence or simultaneously as a mixed crop is an ancient practice. In Indian Vedas, there is a mention about double cropping. In early periods, the objective to adopt sequential cropping practices was to reduce the risk of total crop failure due to uncertain monsoon and to have a variety of produce to meet food requirement of farmer’s family, fodder to animals and to meet his cash requirements. Other benefits of yield increase was due to complementary effects of the component crops in improving the soil fertility, spreading the labour requirement throughout over a period of time and to reduce damage caused by weeds, pests and diseases were only incidental.

Multiple cropping in simple terms refers to the practices that increase production in a unit area of land through two or more growing crops simultaneously. The importance of intensive cropping in sustainable agriculture has been well understood by the agricultural scientists of late and there has been surge of interest in the research community to explore these complex systems in detail and to see what can be learnt from the farmers in our quest to make cropping system more sustainable progress made in the past through intensive research carried by the various agricultural universities, ICAR, research institutes/ coordinated research projects and international research institutes such as IRRI, ICRISAT, IITA and CIMMYT, it has shown that and more efficient utilization of the natural resources is possible by suitably modifying the present cropping systems in the various regions of the world.

The present cropping intensity in India is 136 per cent. There is a great prospect for increasing the cropping intensity both in irrigated and rainfed areas. In many irrigated areas, cropping intensity can be increased to 200-300 per cent. There is good scope to increase cropping intensity to 180-200 per cent with proper policies and technology options in rainfed conditions also.

This chapter describes fundamental terminology and lays the foundational framework for the course. It offers a general understanding of statistics, its purpose, and its role in addressing diverse problems across various fields of study. Additionally, it explains the rationale behind choosing R software as a powerful tool for data analysis.

Basic Concepts and Terminology

Statistics is an essential tool in agriculture, helping researchers to collect, analyze, interpret, and present numerical data related to plant traits, and agricultural practices. The fundamental goal is to make informed decisions and enhance crop yields, quality, and resilience.

To illustrate, researchers choose to work with a sample due to practical considerations, such as time and cost constraints. For instance, in a field with 10,000 crop plants, a researcher aims to determine the average plant height in the field. To conserve time and resources, they randomly select 200 plants from various locations within the field. These 200 plants constitute a sample from the entire population of 10,000 plants. By analyzing the height observed in the sample, the researcher can conclude the average height of the entire crop field.

The stability of yield is an important characteristic to be considered when judging the value of a cropping systems when compared to others cropping systems. Here, we focus on multiple cropping and how this agroecological practice can be used to increase the sustainability of crop production. Strip, mixed and relay intercropping can be used to increase crop yields through resource partitioning and facilitation. In addition to achieving greater productivity, diversifying cropping systems through the use of strategic cropping systems will increase yield stability, reduce weeds, pests, disease and improve soil health. Several multiple cropping systems are already implemented in industrialized agricultural landscapes, including mixed intercropping with perennial grasses and legumes as forage and relay intercropping with winter wheat and red clover. Because multiple cropping systems will provide numerous benefits, researchers should be clear about their objectives and use appropriate methods so as not to draw spurious conclusions when studying multiple cropping systems. In order to advance the practice, experiments that test the effects of multiple cropping systems should use standardized methodology and researchers should report a set of common criteria to facilitate cross-study comparisons. Multiple cropping systems with two or more crops appears to be less common with annuals than perennials, which is likely due to differences in the mechanisms responsible for complementarity. One area where intercropping with annuals in industrialized agricultural landscapes has advanced is with cover crops, where private, public and governmental organizations have harmonized efforts to increase the adoption of cover crop mixtures.

Abstract

The knowledge of programming language(s) is necessary to solve biocomputational problems and also understand the data pattern. Computer programming is used to develop software to extract information from data which is very crucial while practicing bioinformatics. Programming language is a man-made, computer-understandable, artificial language, that is designed to instruct a machine, particularly a computer to get the desired output. This chapter briefly narrates the concept of algorithms, programming languages, and the historical perspective of the same.

Abstract

Sequence alignment is the positioning of DNA, RNA, or protein sequences with each other to identify the regions of identity, which may be the result of functional, structural, or evolutionary relationships between the sequences. Therefore, sequence alignment is the process of aligning two sequences to achieve the maximum levels of identity and conservation. This chapter deals with the basic concept of sequence alignment and the tools associated with multiple sequence alignment. Further, it describes various matrices used in sequence alignment.

Initiatives for Promotion of Bioinputs presents government, institutional, state-level, NGO, and private-sector efforts supporting natural and sustainable farming. Central initiatives discussed include Paramparagat Krishi Vikas Yojana, Bharatiya Prakritik Krishi Paddhati, National Mission on Natural Farming, ICAR initiatives, National Project on Organic Farming, Bio-Input Resource Centres, farmer incentives, CISS, MOVCDNER, and PM-PRANAM. State examples include natural-farming initiatives in Andhra Pradesh, Karnataka, Himachal Pradesh, Gujarat, Madhya Pradesh, and Tamil Nadu. The chapter also considers NGOs, voluntary organizations, farmer networks, startups, and private-sector participation. Together, these initiatives illustrate institutional pathways for scaling bioinputs and strengthening natural farming adoption. for adoption by farming communities.

This chapter begins by providing an essential overview of different data transformation methods and their importance. It primarily concentrates on exploring three crucial transformation techniques: the square root transformation, log transformation, and arc sine transformation. Additionally, this chapter explores the practical application and analysis of these transformations using the R programming language.

Data Transformation

Data transformation is often needed in statistical analysis and data processing for several key reasons:

• Assumption of homogeneity of variances: ANOVA requires that the variances within each group being compared are equal. When this assumption is violated, i.e., when variances are significantly different across groups, data transformation can help stabilize these variances.

• Normality assumption: ANOVA assumes that the residuals (the differences between observed and predicted values) are normally distributed. However, real-world data often deviates from this normal distribution. Data transformation, such as log or square root transformations, can help in reshaping the distribution of data.

• Handling skewed data: In situations where the data is highly skewed, the results of ANOVA can be misleading. Transformations can reduce skewness, making the distribution more symmetric and suitable for ANOVA.

• Proportional or percentage data: When dealing with data in the form of proportions or percentages, certain transformations (like the arc sine transformation) are necessary. This is because proportions have a bounded scale (0–1 or 0–100%) and their variances are not constant across the range, which can violate ANOVA assumptions

In this chapter, we explore Latin square design (LSD), a specialized form of experimental design renowned for its efficiency in controlling variation. This chapter thoroughly discovers the foundational principles, structure, and application of LSD in experimental research. It details mathematical models for LSD and guides readers through the interpretation of ANOVA tables. Furthermore, the chapter provides a systematic approach to conducting LSD analysis using R software.

Latin Square Design

The LSD is a statistical design used extensively in experiments, especially when specific conditions are met:

• Controlled variation in two directions: LSD is particularly suited for experiments where variation can be controlled along two different factors. This design structures the experimental units into a square matrix, allowing two sources of variability to be accounted for.

• Adherence to experimental design principles: Like other designs, LSD follows the key principles of randomization, replication, and local control. Randomization in LSD is applied within the rows and columns of the square matrix, ensuring unbiased treatment comparisons.

• Efficient use of resources: LSD allows for an effective utilization of resources by controlling two variables simultaneously. This makes it a more efficient design compared to RBD or CRD when there are two sources of nuisance variables.

•    A corn plant requires a certain number of growing degree units (GDUs) to reach maturity, regardless of the number of calendar days it takes to accumulate.
•    The relationship of GDU accumulation and corn development, along with utilizing the estimated number of days to reach a certain growth stage, can help predict when important growth stages will occur.
•    Growth stages can be used to help growers make timely applications of herbicides and fungicides. The following information is on corn growth and development from emergence to R6 growth stage.
•    Corn growth stage development can vary according to corn maturity. An early maturing product may produce fewer leaves or develop through growth stages faster than a slower paced late maturing product. This is important to remember when staging corn in relation to accumulated GDUs.

Abstract

Omics arise from ome which means complete or totality. It refers to the collection of technologies used to investigate the roles, relationships, and actions of the various types of molecules that make up an organism’s cells. Omics technologies provide the tools required to investigate differences in DNA, RNA, proteins, and other cellular molecules between species and individuals within a species. These technologies comprise, genomics, transcriptomics, proteomics, metabolomics, ionomics, glycomics, lipomics, cultureomics, pharmacogenomics, metagenomics, foodomics, eigenomics, phenomics, etc. The inclusion of all omics domain is known as multiomics. In this chapter we describe from basic, type structure of omics to all the recent cutting edge technologies in multiomics.

Principle

The principle is to extract plasmid DNA from bacterial cell suspensions and is based on the alkaline lysis procedure developed by Birnboim and Doly (Nucleic Acids Research 7:1513, 1979). It is a known fact that plasmids are much smaller super coiled DNA molecules than the much larger and less super coiled bacterial chromosomal DNA. This difference in topology enables selective precipitation of the chromosomal DNA and cellular proteins and separates them from plasmids and RNA molecules. The cells are lysed under alkaline conditions, which denature both nucleic acids and proteins, and when the solution is neutralized by the addition of Potassium Acetate, chromosomal DNA and proteins precipitate because it is impossible for them to renature correctly (they are so large). Plasmids renature correctly and stay in solution, effectively separating them from chromosomal DNA and proteins.

Terminology of experiments
    
Evaluation trials are important in plant breeding where the selected lines are compared with standard varieties. In most cases, it will be necessary to make comparable yield trials under field conditions. In such case, the field becomes the experimental unit. In these cases, the field becomes the experimental unit. It requires the use of small plots and adequately replicated.

Abstract

Drug design is a complex computational process that involves studying the structural and physicochemical properties of biological moieties in order to identify new chemical or biochemical substances that can be used as drugs with specific pharmacological properties. The drug discovery pipeline is a crucial aspect of drug design that requires a detailed understanding of the various phases involved. In this chapter, we discuss the fundamental principles and phases of drug design. Additionally, we delve into the concept of docking, a critical step in the drug discovery process that involves predicting the orientation and affinity of a ligand binding to a protein target. The use of computational tools and techniques in drug design has revolutionized the field and is essential for the development of safe and effective drugs.

Objectives

• To study working principles of compound microscope
• Study different parts of compound microscope

Introduction

A compound microscope is an optical instrument consisting of two short focal-length convex lenses that are used to observe the highly magnified images of small objects by naked eyes. The compound microscope is capable of magnifying up to 1000 the image of a small object by complex system of lens arrangement. The working distance varies from 0.14 to 4 mm. Compound microscopes generally require the sample to be seen to be sufficiently thin, or translucent, to allow the light to pass. Such microscopes provide high magnification, but the view is two-dimensional.

Introduction
Cytoplasmic male sterility (CMS), a condition under which a plant is unable to produce functional pollen, is widespread among higher plants. CMS systems represent a valuable tool in the production of hybrid seed in self-pollinating crop species, including maize, rice, cotton, and a number of vegetable crops. Hybrids often exhibit heterosis, more commonly known as hybrid vigor, whereby hybrid progeny exhibit superior growth characteristics relative to either of the parental lines. CMS systems can be of considerable value in facilitating efficient hybrid seed production.
Male-sterility-inducing cytoplasms were recognized in the 1900s. Bateson and Gairdner reported male sterility in flax in 1921, was inherited from the female parent, although genes passed from both the male and female parents affected its expression. Chittenden and Pellow (1927) recognized that male sterility in flax was due to an interaction between the cytoplasm and nucleus, with the male sterility conditioned by the homozygous recessive nuclear genotype in combination with the sterility-inducing cytoplasm.

The first step in applying the correct rate of fertilizer is calculating crop nutrient requirements. A soil test is the only way to measure how much P₂ O₅ and K₂ O are available in soils, and soil tests are available through several private and public laboratories.

Fertilizer Calculation

To compute the equivalent amount of fertilizer necessary to supply the corresponding amounts of nutrients, the following formula is used: