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Indian Spices: These are dried, fragrant, aromatic or pungent vegetables or plant substances. They can be whole, broken or in ground forms. Primary function in food is seasoning rather than nutrition and which contributes to the flavour and piquancy in foods and beverages. These have been used as food additives widely since ancient times [http://www.imedpub.com/articles/herbal-medicines-possible-risks-andbenefits.pdf,https://www.ncbinlm.nih.gov/pmc/articles/PMC3170500/pdf/nihms307509.pdf). They also exert digestive stimulant action of food enhancing the taste. A few medicinal properties of spices are as being a tonic, carminative, stomachic, diuretic and antispasmodic (Aboaba et al., 2011; Achi, 2006 and Adefegha et al., 2012). This has led for pharmacological applications in the indigenous systems of medicine as digestive stimulants and to relieve digestive disorders and makes the food more palatable. In many countries around the world these are grown and reared for the purpose of trade and distribution. In the ancient times the spice merchants have been the most popular traders. India is well known for spices with a wide variety of recipes that are laced with them.

To look into the nutritional security around the globe, FAO and WHO, have undertaken the analysis of policies. They have covered many issues which include; World Summit on Food Security, pillars of food security and availability, access, utilization, stability, effects of food insecurity, stunting and chronic nutritional deficiencies, depression, anxiety and sleep disorders. There are many challenges to achieve food security. These include global water crisis, land degradation, climate change, agricultural diseases, food versus fuel, politics, food sovereignty, food wastes, risks to food security, population growth, fossil fuel dependence, homogeneity in the global food supply, price setting, land use change, global catastrophic risks, agricultural subsidies and role towards children.

We get energy from food. We need energy for all the activities which we get by eating. The body structures as muscles, organs and bones are composed of the nutrients contained in food which are absorbed by the body.

Nutritional requirement of the major nutrients

Nutrients are the substances which are found in food and drive biological activity. They are essential for the human body. These are categorized into proteins, fats, carbohydrates (sugars, dietary fiber), vitamins and minerals (Sugishima, 2019). They perform the vital functions like building all parts of the body as muscle, bone, teeth and blood. Out of the 20 different kinds of amino acids that make up proteins nine are not synthesized in the body and are therefore called essential amino acids. These essential amino acids have to be supplemented through food. High-protein, low-carbohydrate diets have been found to be effective.Protein is an important component of every cell in the body. Large part of hair and nails are made up of proteins. The human body uses protein to build and repair the tissues, helps in making enzymes hormones and other body chemicals. Protein is an important building block of bones, muscles, cartilage, skin and blood.

We all eat food because it provides the nutrients that are needed for the various functions of the body. Energy is required for growth and repair of tissues, to maintain body temperature and for physical activity. Energy comes from foods which are rich in carbohydrates, proteins and fat. This provides energy to the body from other nutrients and helps to keep the skin and nervous system healthy. They help towards the various biological activity in the human body. These nutrients are grouped into carbohydrates, proteins, lipids (fat), water, vitamins and minerals. These are used for growth, maintenance, repair and other needs of the body. Protective foods are which contain adequate amounts of vitamins, minerals and high-quality proteins which protect against the development of a deficiency disease. There are various chemical reactions taking place within each cell of a living organism. These provide energy for vital processes and for synthesizing new organic materials. This process is known as Metabolism. Living organisms get energy from the environment and use it for movement, growth, development and reproduction.

According to Indian Philosophy (Bhat, 2012) the physical body is called Annamaya Kosha.A body, when well nourished grows well by absorbing the energies from the food consumed. Fasting is another aspect of traditional food system. It is meant to purify the mind and the body.

The human body consists of trillions of cells and to be healthy it needs variety of foods and nutrients. The food around the globe includes a variety of spices which have many health benefits. It has been found that spices and vegetables in the diet are very important for living healthy. The traditional foods, nuts, seeds, wild fruits and vegetables which are used in the diet are grown in specific areas. Traditional food system plays a significant role towards maintaining the well-being and the health of people in any specific region.

The branch of statistics which describes the data is called descriptive statistics. After collection, the data are presented in form of tables, diagrams or graphs. This facilitates the further analysis of data. The most basic form of data analysis is descriptive analysis of data collected and presented for the study. The descriptive statistics summarises the data and helps to find the representative value of the entire data set and the variation of the other values in the data set around the representative value. The descriptive statistics includes the measures of central tendency or averages, measures of dispersion or scatteredness, measures of skewness and the measures of kurtosis. Before going to discuss the R codes for various measures of descriptive analysis, we would first discuss in brief about these measure. This will help in better understanding of the R codes.

Measures of central tendency or averages: They are the representative values of the data series. They give an idea about the concentration of the values in the central part of the distribution of the data. There are five types of averages. They are 1. Arithmetic Mean (A.M.) 2. Geometric mean (G.M.) 3. Harmonic Mean (H.M.) 4. Median 5. Mode.

Diagrams and graphs are the effective and attractive ways of presenting data. Widely used diagrams for data presentation are pi chart and bar diagrams. Bar diagrams are also of various types; (i) Simple bar diagram (ii) Component or sub-divided bar diagram (iii) Multiple bar diagram (iv) Percentage bar diagram. The widely used graphical presentation are histograms, frequency polygon and frequency curve.

There are numerous libraries to create charts and graphs in R programming language.

Pie-chart- A pie-chart is a representation of values of variable as sectors of a circle with different colours/shadings. The colours/shadings used for different sectors are indexed at the side of the graph. pie() function is used to create pie chrt in R.

Let us create an exel file names as ‘exp_sports’ which contains the expenditure made (in Rs.) under various sports in a sports club. The name of the sports are cricket, hockey, tennis and football. The names of sports are placed under the column head ‘sports’. The corresponding expenditure (in Rs.) are 7200, 3600, 1800, 1800 and they are placed under the column head ‘expenditure’.

The basic syntax for creating a pie-chart using the R is.

Frequency Distribution is the arrangement of various values of a variable in order of magnitude either individually or in groups alongwith their corresponding frequencies or class frequencies. Frequency distribution is necessary for data condensation and revealing pattern within the data. Frequency distribution can be (i) Ungrouped Frequency Distribution (ii) Grouped frequency distribution.

In case ofungrouped frequency distribution, the individual values ofthe variable are arranged in ascending order alongwith their frequencies. Frequency is the number of times a particular observation is repeated.

In case of grouped frequency distribution, the values of variable are grouped into classes alongwith their corresponding class frequencies. Class frequency of a class is the number of observations lying within the upper class limit (maximum value) and lower class limit (minimum value) of the class.

Let us first illustrate the preparation of ungrouped frequency distribution. Consider 50 petridishes each containing 6 no.s of seeds for germination. Let the variable x_i denote the no. of seeds germinated in a petridish.

Matrix is a rectangular array of numbers arranged in rows and columns. The order of a matrix is written as r x c, where r is the no. of rows and c is the no. of columns. The numbers included in a matrix are called elements of the matrix. The name of the matrix is denoted by an uppercase English alphabet.

The most important use of R programming lies in importing of data files which serves as input for the analysis work. After the analysis is over by using R codes, operators and functions, the output result must also be exported in form of files. The files are imported in form of csv files (comma separated variable), text files or excel files.

Before going to the actual content of the chapter we have to have an idea about csv files. CSV stands for Comma Separated value. It is used to store tabular data, such as a spreadsheet or database. CSV is delimited text file in which the values are separated by commas. CSV files occupy much less space than excel files and also gets imported in a program at a much faster rate than Excel files. CSV Files also does not manipulate data and stores as it is.

Let us consider that an excel file named as ‘plant’ which stores three variables, named as ph, pd and yield is created inside the folder named as ‘My Data’ which is placed in the desktop. Thus the path of the file is “C:\\Desktop\\My Data\\plant”

To import excel file the package will be readxl and the command will be library(readxl)

In R there is facility of reshaping the data. Reshaping of data includes joining of two or more vectors to create a data frame. Also two or more data frames can be merged. Appropriate functions are available for these jobs. While merging the data frames, we can join columns or rows.

Illustration of joining two vectors by using cbind() function.

R packages include functions, codes and sample data. The functions used in R are contained in packages. A package in R contains several functions which are related to some particular type of job. A package conatins at least one function and there is no maximum limit to the no. of functions in a package. The set of packages in R which are set by default in the software is called base package. It contains a lot of commonly used functions. The packages in R are stored under a directory called “library” in the R environment. The packages get installed by default while installing R in the computer. Also more packages can be added later on after installation, when required for some specific purposes. The base packages are available by default when we start using R. But the other packages which are already installed while installing R have to be loaded while using R. These packages can be viewed in the package window by clicking on package. To install the package we have to click on install menu in the package window and then write the name of the package.

The packages can also be installed by using the function install.packages(“abc”) where abc is the name of the package to be installed.

Once a package is installed, it remains in the hard disk of the PC/laptop. But the package will have to be called for use in the current R session. The package once installed can be loaded for use in the current R session by checking the box near the name of the package displayed after clicking on the install menu in the Files/ Plots/Packages/Help window. The package can also be loaded by using library() function. The command will be library(“abc”) where abc is the name of the package. The loaded package remains in the memory for the current R session. Once we quit the R session, the installed package has to loaded again if needed.

This chapter deals with the objects used in R which contains the data in various formats. The data types used in R has also been mentioned in this chapter. The chapter also includes the functions used in R as the codes for accomplishing several jobs related to data analysis.

Unlike C language and Java, the variables in R are not declared as some data type. The variables are assigned with R object and the data type of R object becomes the data type of the variable. The commonly used R objects in R are vectors, lists, matrices, arrays, factors and data frames. The vector object is the simplest one and other R-objects are built upon the vectors. The different data types of these vectors are logical, numeric, integer, complex, character and raw. The objects in R are stored by the name of a variable and the data type of the object is determined by its content. To know the data type of a variable the class() function is used. The various data types are described as below:

R software is a programming language used for statistical analysis, graphical representation and reporting, and is currently developed by the R Development Core Team. R software is available freely under the GNU General Public License, and the binary versions are provided for the Windows, Linux and Mac operating systems. To develop R, effort from all over the world is made in a collaborative manner.

What is GNU?

GNU stands for GNU’s Not Unix. It is a Unix like computer operating system, but different from Unix. GNU consists of GNU packages which are program specific and released by GNU project. Free software means that the users of the software have freedom to run and use the software and also can copy, change, improve and distribute the software freely.

Development of R

R software was initially written by Robert Gentleman and Ross Ihaka also known as “R & R” of the statistics department of the University of Auckland, New Zealand. R appeared first in 1993. Since then various contribution to R has been made by a large group of talented individuals by sending code and bug reports. A core group, known as the “R Core Team” modify the R source code archive since 1997. Thus R is a collaborative project with many contributors.

Strategies To Improve The Productivity Of Drylands

With renewed interest in rainfed agriculture during 1970s, both the national (AICRPDA and CRIDA) and international institutions (ICRISAT) have studied several aspects of rainwater management. Recommendations have been made after their through testing both at the research stations and on farmer’s fields. Initially these technologies were accepted by the farmers because of the pursuasion by the Scientists and the incentives, but many of them are forgotten once the support is withdrawn. It is now time for us to examine whether the technologies themselves were weak or certain components were missing. Malfunctioning of the software may be due to a bug right from the beginning or it entered into the system at a later stage. It is also possible that we neglected certain critical inputs which became major constraints making the system defunct. Whatever the reason it should be identified and set right if the technology has to continue as tool for tackling the challenges of rainfed agriculture.

Watershed is defined as a natural hydrological unit that covers a specific land surface area from which runoff passes through a common outlet or area with common drainage or outlet. In simple terms, it implies a catchments or drainage basin from which water drains towards a single channel. It may extend over a few acres only or may cover thousands of acres.

Watershed management is the integration of technology within the natural boundaries of a drainage area for optimum development of land, water and plant resources to meet the basic minimum needs of the people in a sustained manner.

It is also defined as the development and management of watershed resources in such a manner as to achieve optimum production without deterioration of resources base or disturbing the ecological balance. It is also termed as “Resource centered technology” since it helps in assessment, augmentation and optimal utilization of all natural resources viz., land, water and vegetation, it prevents deterioration of resources and at the same time ensures sustained productivity of land to meet basic needs of people.

Climate change is looming large on the globe. Food security is one of the key issues that inevitably needs to be resolved under the specters of climate change, particularly in the fragile ecosystems. Never before in the history has Indian agriculture been as vulnerable and uncertainty ridden as it is today. A glimpse of the dynamics of Indian agriculture reveals that it has systematically deviated away from its base environment, the prop that nourishes all biological resources. Today’s agriculture is valued against the prices it fetches from the market, especially the global market. Its contribution to human health and welfare, ecological integrity, resilience of nature etc., are grossly neglected.

Agriculture in developing countries must undergo a significant transformation in order to meet the related challenges of achieving food security and responding to climate change. Projections based on population growth and food consumption patterns indicate that agricultural production will need to increase by at least 70 per cent to meet demands by 2050. Most estimates also indicate that climate change is likely to reduce agricultural productivity, production stability and incomes in some areas that already have high levels of food insecurity. Developing climate–smart agriculture is thus crucial to achieving future food security and climate change goals.

The production, processing and marketing of agricultural goods are central to food security and economic growth. Products derived from plants and animals include foods (such as cereals, vegetables, fruits, fish and meat), fibers (such as cotton, wool, hemp and silk), fuels (such as dung, charcoal and biofuels from crops and residues) and other raw materials (including medicines, building materials, resins, etc.,). Production has been achieved through a number of production systems which range from smallholder mixed cropping and livestock systems to intensive farming practices such as large monocultures and intensive livestock rearing. The sustainable intensification of production, especially in developing countries, can ensure food security and contribute to mitigating climate change by reducing deforestation and the encroachment of agriculture into natural ecosystems