eChapters

Introduction

In the last two decades, there has been a significant change in the Indian society, not only in the terms of economics following the post 1991 reforms but also in the dietary and health sectors. There is an increasing demand for livestock products, as a result the farming systems are becoming more and more intensive. With the high input intensive agricultural practices on the one hand, the limitations in terms of environmental degradation, loss of biodiversity, health hazards to consumers, etc. are also on the rise which are acting as limiting factors to continue on the same path of intensification. To achieve sustainable intensification of agriculture, mixed crop-livestock systems, combining livestock and cash crops at farm level, are considered to be a good alternative (Russelle et al, 2007; Wilkins, 2008; Ryschawy et al., 2012). These practices benefit the environment by improving nutrient cycling (Hendrickson et al., 2008). In the agriculture based states of India, about 50% of the income is from the crop-livestock systems (Deshingkar et al., 2008). Mixed crop-livestock systems also generate higher economic efficiency in saving production costs through complementaries between crop and livestock (Wilkins, 2008). Apart from direct income by diversifying production, there is an added advantage of reducing risks with regard to market fluctuations (Russelle et al., 2007).

Introduction

Food security is defined as "a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life" (The State of Food Insecurity, 2001) (see http://www.who.int/trade/glossary/story028/en/). As of 2015, the concept of food security has mostly focused on food calories rather than the quality and nutrition of food. Thus, food security usually emphasizes food quantity and therefore "nutrition security" is used for the quality dimension. The concept of food security has four dimensions: availability, accessibility, utilization and vulnerability. The interactions among them determine how food secure a country is or how adequate, steady and risk-free food consumption is at the household and individual levels (Kumar and Ayyappan, 2014). Though substantial progress has been achieved in reducing food insecurity, maintaining food security at the national and household level is a major challenge for India (Singh, 2014). The present article briefs about global and Indian scenarios on food security aspects, and the key role of livestock in achieving zero hunger by means of livestock-derived foods such as milk, egg and meat products.

20.1 Unit Testing in Python Programming

The process of testing wheather a particular unit is working properly or not is called unit testing. A unit test checks small component in your application

Example

def testsum1():

assert sum([1,2,3])==6 def testsum2():

assert sum([1,1,1])==6 #assertion error

testsum1()

testsum2()

20.1.1 Step Involved in Unit Testing

(1) Import unittest from library.

(2) Create a class for test and inherit (unittest.TestCase).

(3) Write the test function in the class.

(4) Call unittest.main()

19.1 Python Turtle Graphics

Turtle is a special feathers of Python. Using Turtle, we can easily draw in a drawing board.

First we import the turtle module. Then create a window, next we create turtle object and using turtle method we can draw in the drawing board.

19.2 Some Turtle Method

Example

from turtle import *

t = Turtle() #object in python

wn=Screen()

wn.title(?my first graphic”)

wn.bgcolor(?yellow”) #color filling in window

t.shape(?turtle”) #turtle shape given

t.color(?red”, “green”)

18.1 Library Introduction

This library contains modules for various types of functionalities. Some commonly use module of python standard library are:

(i) math module: which provides mathematical function to support difference types of calaculations.

(ii) cmath modules: which provides mathematical function for complex number.

(iii) random module: which provides function generation random number.

(iv) staticstics module: which provides mathematical satical function.

(v) urllib module: which provides URL handling function so that you can access websites from within your program.

1. NmPy library, provides some advance math functionalities along with tools to create and manipulate numeric arrays.

2. Scipy library, provides algorithmic and mathematical tools for scientific calculations.

17.1 Modules

• A module is a file containing python definitions and statements.

• A python module is (.py file) containing variables, class definitions, statement and function related to a particular task. The major feature of having module is that its content can be reused in other programs, without having to rewrite or recreate them.

• They are executed only the first time the module name is encounter in an import statement.

Type of modules:

• User defined modules

• Built-in modules

Ex.-array, math, numpy, sys, etc.

16.1 OOP in python

Object-oriented programming (OOP) is a programming language model which organized object and data.

16.1.1 What is Encapsulation

If same variable is reapet in two different palce then which problem is solved using encapsulation it means one variable is making private otherwise program generate error.

16.1.2 Abstraction

Abstraction in Python is the process of hiding the real implementation of an application from the user and emphasizing only on usage of it. For example, consider you have bought a new electronic gadget. Along with the gadget, you get a user guide, instructing how to use the application, but this user guide has no info regarding the internal working of the gadget.

Another example is, when you use TV remote, you do not know how pressing a key in the remote changes the channel internally on the TV. You just know that pressing + volume key will increase the volume.

15.1 Data Visualization

Data visualization is the graphical representation of information and data. By using different visual elements like charts, graphs and maps. Data visualization tool provides easy way to see the understand the data and related trend/pattern.

1. There are multiple tools available for data visualization using which we can perform data visualization.

2. In python we can use a library called “matplot” for data visualization.

3. Python based plotting library “matplotlib” enables us to produce 2D as well as 3D graphics i.e. charts/graphs.

4. Pyplot interface of matplotlib is used for representing 2D graphs.

14.1 Basic Plotting: plot

This functionality on Series and DataFrame is just a simple wrapper around the matplotlib libraries plot() method.

Example

import pandas as pd

import numpy as np

df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range(‘1/1/2000’, periods=10), columns=list(‘ABCD’))

df.plot()

Its output is as follows:

If the index consists of dates, it calls gct().autofmt_xdate() to format the x-axis as shown in the above illustration.

13.1 Statistical Functions

Statistical methods help in the understanding and analyzing the behavior of data. We will now learn a few statistical functions, which we can apply on Pandas objects.

Percent_change

Series, DatFrames and Panel, all have the function pct_change(). This function compares every element with its prior element and computes the change percentage.

Example

import pandas as pd

import numpy as np

s = pd.Series([1,2,3,4,5,4])

print s.pct_change()

df = pd.DataFrame(np.random.randn(5, 2))

print(df.pct_change())

12.1 Introduction Basic Functionality

By now, we learnt about the three Pandas DataStructures and how to create them. We will majorly focus on the DataFrame objects because of its importance in the real time data processing and also discuss a few other DataStructures.

12.2 Series Basic Functionality

Let us now create a Series and see all the above tabulated attributes operation

Example

import pandas as pd

import numpy as np

#Create a series with 100 random numbers

s = pd.Series(np.random.randn(4))

print(s)

Its output is as follows:

0 0.967853

1 -0.148368

2 -1.395906

3 -1.758394 dtype: float64

11.1 Introduction

Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures. The name Pandas is derived from the word Panel Data - an Econometrics from Multidimensional data.

In 2008, developer Wes McKinney started developing pandas when in need of high performance, flexible tool for analysis of data.

Prior to Pandas, Python was majorly used for data munging and preparation. It had very little contribution towards data analysis. Pandas solved this problem. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data - load, prepare, manipulate, model, and analyze.

Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.

10.1 Lambda Function

Lambda Function or anonymous function in python programming

A lambda function can take any number of arguments, but can only have one expression.

Syntax:

Lambda arguments: expression only single line

The expression is executed and the result is returned.

Example

x=lambda a:a+10

Print(x(5))

9.1 Introduction Numpy

Numpy: is apython package which stands for “Numerical python”.

This was created in 2005 by Travis Oliphant.

Using numpy we can perform the following functionalities:

1. Mathematical and logical calculation on array.

2. Fourier transforms and routines for shape manipulation.

3. It has built in functions for linear algebra random number generation.

4. Used for scientific calculations.

5. It is faster than python list.

6. It is fast because it is associated with c programming.

NumPy is often called as an alternate for MatLab (a programming platform designed specifically for engineers and scientists for data analysis, developing algorithms, create models and application etc.)

8.1 Introduction to Dictionary in Python

A dictionary is a collection which is unordered changeable and index. In python dictionaries are written within curly brackets, and they have keys and values. Means the dictionary contains two things first is the key and the second is the value.

8.1.1 Create and Print a Dictionary

Example

dict1={‘brand’:‘Suzuki’,‘model’:‘dzire’,‘year’:2020}

print(dict1)

Accessing items: you can access the items of a dictionary by referring to its key name, inside square brackets.

Example

Accessing items: there is also a method called get() for the same task.

dict1={‘brand’:‘Suzuki’,‘model’:‘dzire’,‘year’:2020}

print(dict1)

y=dict1.get(?model”)

print(y)

7.1 Tuple Introduction

A tuple is a collection which is order and unchangeable. In python tuples are written with round brackets. Tuples in python are very similar to list which contains different types of elements with the following major differences.

It is declared as

tuple1=(1,2,3,“ram”,“shyam”)

Program implementation import timeit

listtime=timeit.timeit(stmt=“[1,2,3,4,5,6,7,8,9]”,number=1000000)

tupletime=timeit.timeit(stmt=“(1,2,3,4,5,6,7,8,9)”,number=1000000)

print(?List takes time:”, listtime)

print(?Tuple takes time:”, tupletime)

6.1 List Introduction

1. List is a collection of different values or different types of items.

2. Unlike array in c/c++/java a list is capable of storing different types of values under one roof.

3. The items in the list are separated with the comma (,) and enclosed with the square brackets [].

4. List provides us the facility to store multiple types of in a single unit.

Example:

a=[“ram”,1,“shyam”,12.5]

b=[1,2,3,4]

a[0]=ram

a=[“ram”,1,“shyam”,12.5]

print(a)

print(a[0])

ram

5.1 String Statement

String in python is stored as individual characters i.e. indexed wise. The index by default starts with 0.

So, here P will be represented by str[0] or str[-6], y will be represented by str[1] or str[-5]

Note: strings are immutable and hence you cannot change the individual letters of string using assignment operator.

Example:

name=“shyam”

Name[0]=“A”

Result error

4.1 Introduction to Function in Python

Python Functions is a block of related statements designed to perform a computational, logical, or evaluative task. The idea is to put some commonly or repeatedly done tasks together and make a function so that instead of writing the same code again and again for different inputs, we can do the function calls to reuse code contained in it over and over again. Functions can be both built-in or user-defined.

Function means sub-program

1. Function definition

2. Function call

Example:

def message: #function definition

print(?hello python”)

message() #function call

3.1 Control statement in python

Python statements are executed one by one in the same order as they appear in the program. But there may be some requirement when we want to execute the statements based upon a condition. Even there may be some situation where we want to execute a single statement or multiple statement (block of statement) “n” times till the condition is true. So in order to control the flow of execution of the program python provides us control statements.

There are two types of control statements:

(a) Selection statement

(i) if

(b) Looping condition

(i) While (ii) for