eChapters

5.1. INTRODUCTION

Mean and standard deviation summarizes the salient features of a data set. But they do not describe adequately the full characteristics of the data. For an adequate description of the characteristics of a data set, a convenient method is to plot a frequency distribution curve. Such curve is constructed by first drawing a frequency polygon, which may then be smoothened to a frequency curve, indicating the distribution. The frequency curve or distribution of specific biological variables generally has a specific form and plays a very important role in statistical theory and practice. It is customary to describe a curve in terms of its symmetry or assymmetry and its flatness or peakedness.

The following types of distribution are commonly seen in the biological world:

4.1. INTRODUCTION

Dispersion is the extent to which values in a distribution differ from the average of the distribution. To quantify the extent of the variation, there are certain measures namely:

1. Range
2. Quartile Deviation
3. Mean Deviation
4. Standard Deviation

Range and Quartile deviation measure the dispersion by calculating the spread within which the values lie. Mean Deviation and Standard Deviation calculate the extent to which the values differ from the average.

3.1. INTRODUCTION

In this chapter, we will study the measures of central tendency which is a numerical method to explain the data in brief. We can see examples of summarizing a large set of data in day to day life like average marks obtained by students of a class in a test, average rainfall in an area, average production in a factory, average income of persons living in a locality or working in a firm etc.

One of the most important objectives of statistical analysis is to get one single value that describes the characteristic of the entire mass of data. Such a value is called the central value or an ‘average’ or the expected value of the variable. This single value is the point of location around which individual values cluster and, therefore, called the measure of location. Since this single value has a tendency to be somewhere at the center and within the range of all values it is also known as the measure of central tendency. The purpose of computing an average value for a set of observations is to obtain a single value which is representative of all the items.

2.1. INTRODUCTION

Many biological variables do not meet the assumptions of parametric statistical tests as they are not normally distributed, the variances are not homogeneous, or both. Objects in nature commonly exhibit one of three distributions:

1.1. INTRODUCTION

In this chapter, we will know how to classify raw data. The purpose of classifying raw data is to bring order in them so that they can be statistically analysed easily. Classification of objects or things saves our valuable time and effort. It is done in a logical manner and similar things are arranged into a group or class.

The Indian Society for Protected Cultivation (ISPC) has organized a National Seminar on ‘Advances in Protected Cultivation’ at the NASC Complex, DPS Marg, Pusa, New Delhi, on 21 March 2013, with an objective of collection and collation of scientific advances made in protected cultivation in India and dissemination of these among policy-planners, farmers, students, scientists, industry and other stakeholders. In spite of the fact that protected cultivation is in its state of infancy in the country in terms of scientific engagements, more than 120 delegates from distant parts of the country registered for the seminar besides good number of policy-planners, recognized names in agriculture and higher officials from industry particularly corporate associate life members of the society.

In the inaugural session, Dr Brahma Singh, President, ISPC and Chairman, Organizing Committee welcomed the Chief Guest Padama Bhushan, Dr R S Paroda, formerly Secretary and Director-General, Indian Council of Agricultural Research (ICAR), New Delhi and Chairman Haryana Kisan Ayog, Chaudhary Charan Singh Haryana Agricultural University , Hisar ; Guests of Honour Dr M M Pandey, Deputy Director-General (Agricultural Engineering), ICAR and Dr H S Gupta, Director, Indian Agricultural Research Institute, New Delhi, other dignitaries on and off the dais, delegates, press and media. He also highlighted that this seminar intended to review the advances made in protected cultivation in India with a purpose to accelerate the same. He emphasized that protected cultivation offers opportunities to earn more from the limited land, water and other resources. It also enables production of quality products which fetch more prices and pride, besides being a potential practice for provisioning disease-free quality planting material for horticultural crops.

Global demand for cut flowers is increasing steadily at around 10 per cent per annum. The share of India in this flourishing trade estimate at over US$ 40 billion per annum is negligible at 0.4 per cent. Floriculture include cut flowers, pot plants, cut foliage, seeds, bulbs, tubers, rooted cuttings and dried flowers or leaves. At international cut flower trade, flowers such as rose, carnation, Chrysanthemum, gerberas, gladiolus, gypsophilla, liatris, nerine, orchids, anthurium, tulips and lilies are preferred. Roses, gerberas and carnation are grown in greenhouses. The open field crops are Chrysanthemum, roses, gaillardia, lily, marigold, aster, tuberose etc. In India, floriculture is emerging as an important economic activity, earning foreign exchange. It has become a potential farm income enterprise. The domestic markets for high-quality flowers have been growing at 10- 20 per cent per year for the last few years. India’s flower business is estimated to be worth $ 400 million at retail, and about $ 100 million in international trade. Flower exports are about $ 30 million, constituting mainly of roses and dried flowers. Both Central and State Governments have been more than proactive and have made large investments in this industry in assisting production and marketing.

A large variety of floricultural crops can be produced in protected environment as economic cash crops for the benefit of farmers. Problems associated with floriculture in hills are small, scattered land holdings and sparsely located population. There is a lack of irrigation facilities due to scarce availability of water resources; widespread natural vagaries like drought, hail storms, frost etc. There is a serious gaps in application of advanced floricultural technologies for improving quality and productivity; high pressure on land use for different purposes like cereal crop production, fodder production, etc. due to low per capita land availability; difficulty in technology dissemination due to difficult terrain, hostile climate, poor communication facilities and sparsely located population. Consuming markets within the state are very less which result in dependence upon distant markets of the county. There is a high post-harvest losses due to lack of modern post-harvest management system; very high temperature differentials in production areas and consumer markets; perishable nature of flowers, resulting in high post-harvest losses during transportation; and distant location of production areas from main roads. High cost of marketing, lack of organised system of marketing, lack of bargaining power with individual growers due to small production levels; inadequate availability of market intelligence to farmers resulting in imbalances in distribution of produce in different markets.

Fusarium wilt of carnation caused by Fusarium oxysporum f.spp dianthi causes severe yield losses under protected cultivation. For the management of Fusarium wilt, 27 isolates of Bacillus strains were isolated from the rhizosphere of different crops including carnation. Among various species of Bacillus, Bacillus amyloliquefaciens (BSC7) was identified as a most virulent antagonistic bacterial isolate. Studies on antagonism under Environmentally Controlled Scanning Electron Microscopy (ESEM), depicted the presence of rod shaped bacterial cells with biofilm formation at 44847x. Detection for the presence of antibiotic biosynthetic genes reflected the presence of surfactin, iturin, bacillomycin and fengycin genes. The 24 isolates of Bacillus spp. showed positive reaction for surfactin gene. Nineteen isolates were amplified for Iturin A gene except BS5, BS9, BSC1, BSC2, BSC11, BSD1 and BSD2. Strains of B. subtilis BS1, BS2, BS5, BS6 and BS8 expressed Bacillomycin D gene. The genomic DNA of strains BS1, BS2, BS4, BS6, BS7 BS8, BS11 and BS14 showed positive for Fengycin D gene. The isolate of B. subtilis BS2 showed positive reaction for four genes namely surfactin, iturin, bacillomycin and fengycin.

In vitro screening of Bacillus, B. amyloliquefaciens (BSC7), P. aeruginosa (P1) and T. viride (TV1) showed highest inhibition against F.oxysporum f.sp.dianthi to an extent of 55.95, 55.29 and 81.61 per cent over the control respectively. The antifungal activity of antagonistic B. amyloliquefaciens BSC7 and T. viride TV1 against F.oxysporum f.sp. dianthi was confirmed under ESEM by observing disintegration, lysis and structural abnormalities of the hyphae of the pathogen at the zone of interaction.

H orticultural crops are most important ones incurring huge amount of pesticides in our country. The intensity of esticides on them is highest per unit area. However research on integrated pest management has not matched the progress and problems of plant protection at large and protected cultivation in particular. The USA, UK, Israel and Russia, pioneers in protected cultivation, are able to harvest productivity of up to 500 tonnes/ha of tomato and 200 tonnes/ha each of cucumber and capsicum, while in India up to 300 tonnes/ha of tomato and 100 tonnes/ha each of cucumber and capsicum are obtained, has compared to normal open field where their yield ranges from 40 to 60 and 10 to 20 tonnes/ha in India, respectively.

The sustainability of an agro-ecosystem is represented by its ability to maintain a given level of productivity over time and a given quantitative-qualitative level of environmental resources (Loucks, 1977). The greenhouse is also a form of agro-ecosystem, where unlike other agro-ecosystems, environment has been adapted to crop in order to maximize its productivity. The greenhouse system has to take maximum advantage of natural benefits offered by location in certain latitudes while minimizing the disadvantages (Castilla, 2002). Higher productivity under such protected cultivation is attainable mainly on account of reduced competition, healthy environment, better nutrient management, increased surface area and longer crop duration. However, ideal and stable environment with warm, humid and abundant food under protected conditions provides an excellent platform for pest development often more than open field crops, leading to severe pest situations in greenhouses, as revealed by extensive surveys.

The beginning of protected cultivation in the country has paved the way for export of horticultural produce, especially of flowers to earn valuable foreign exchange. The technology also became handy for quality production of flowers and F₁ hybrid seeds for domestic and export markets. In view of the globalization of international market and tremendous boost that is being given for export of flowers, there has been a spurt in the demand for protected cultivation technology. Low-cost polyhouses are quite popular in the North-East and in the northern plains, flowers are being raised in polyhouses either for capturing the early markets or to improve the quality of seedlings. Several commercial floriculture ventures are coming up in Uttrakhand, Maharashtra, Karnataka and Tamil Nadu to meet the demands of both domestic and export markets. There has been a substantial increase in area under protected cultivation of different flower crops. Though there were initial hitches owing to the cost sharing, farmers of some states have taken maximum benefits of the existing schemes and adopted the technology. Protected cultivation technology is one of the most useful technology inputs which hold promise to transform Indian floriculture and bring in the “Colour Revolution”.

Nearly, 40 countries are involved in protected cultivation of flower and vegetable crops. New production centers are being added constantly, mostly from developing nations. Today, majority of production is from developing countries like Zimbabwe, Morocco, Kenya, Ecuador, Costa Rica, Mexico, Singapore, Malaysia and India. In developed world, USA has an area of about 6,000 ha under greenhouses which are mostly used for floriculture. The Netherland is traditional exporter of greenhouse grown flowers all over the world. Israel, the largest exporter of cut flowers, has wide range of crops under greenhouses. About 1,000 ha area is under protected cultivation. Approximately, 98% of flowers are grown under open field cultivation and hardly 2% under the greenhouse cultivation.

Orchids are known for their exquisite beauty and trade in global floriculture business. The Netherlands is pioneer in roduction and ex ort of tem erate orchid while Thailand and Hawaii are the world leader in tropical orchids, respectively. Recently, Malaysia, Singapore, Thailand and Sri Lanka are catching the international market. The orchid industry is expanding in terms of magnitude in some of the major orchid-producing nations. The global orchid market has passed $ 1 Billion mark in 2010. The leading orchid producing countries in 2010 are:

Country Production ($ million) Japan 280 USA 170 Taiwan 130 China 70 Brazil 60 UK 45 Thailand 35 Italy 30 New Zealand 20 Source: Orchid News The world consumption of orchids (potted plants) is valued more than $ 500 million (2000). The Dominican Republic, Costa Rica and India have recently begun to produce orchids for export. Commercial production of orchids has increased dramatically through the world in the past quarter century. The most common genera sold in Japan from 1993 to 2002 were Phalaenopsis (30%), Nobile type Dendrobium (20%), Oncidium (20%), Cymbidium (15%) and Cattleya (10%). The potted orchids sold through Dutch flower auctions increased from 50, 0000 to 18 million pots.

India is the second largest producer of vegetable crops with a total production of 146.5 million tonnes (2010-11). More than 60 vegetable crops belonging to, Solanacious, Cucurbitaceous, Leguminous, Cruciferous (cole crops), root crops and leafy vegetables are grown in tropical, subtropical and temperate regions. The demand of vegetables will be 225 million tonnes by 2020 and 350 million tonnes by 2030. A larger quantity of quality seed of vegetable crops will be required in order to achieve the demands of vegetables. The limiting factors which reduce seed yield under open field condition are biotic and abiotic stresses, requirement of isolation distance and generation system of seed multiplication etc. These problems can be effectively managed when we go for seed production under protected structures.

Protected cultivation was introduced in India during 1985 but its progress is slow during the last 25 years due to lack of suitable varieties and hybrids for protected cultivation. Presently, area under polyhouse in India is 2000 ha, therefore, knowledge of polyhouse vegetable production technology of tomato and cucumber with intervention of drip irrigation and fertigation along with suitable varieties will be quite effective for enhancing their productivity. Therefore, it is imperative to develop hybrid seed and production of suitable varieties/hybrids at affordable prices to small and marginal farmers to achieve the targeted production potential of vegetable crops. Keeping in view some genotypes were isolated and pure line and hybrid breeding programme were adopted for improvement of these genotypes. After three years of multilocational testing at tarai, midhills and higher hills under polyhouse condition, four genotypes were identified and released. They are: Pant Parthenocarpic Cucumber 2 (1,755 q/ha) and Pant Parthenocarpic Cucumber 3 (1,605 q/ha) of cucumber and Pant Polyhouse Hybrid Tomato 1 (1,616 q/ha) with average fruit weight of 130-140 g and Pant Polyhouse Tomato 2 (1,291 q/ha) of tomato with single fruit weight 100-105 g. These genotypes were released by Uttarakhand State Variety Release Committee during May 2011 for commercial cultivation.

Kerala has a tropical humid weather characterised by high rainfall and humidity. The intense rainfall during June-September is a limiting factor for vegetable cultivation. High cost of labour, rapid urbanisation, small size of holdings and quality conscious consumers are some of the factors favouring protected cultivation in Kerala. Rain shelters and modified naturally-ventilated polyhouses are recommended structures for protected cultivation of vegetables. The important vegetables grown are cucumber, yard long bean, Capsicum, okra, tomato, cabbage, cauliflower and leafy vegetables. The conventional saw-toothed structure has been modified to suit Kerala weather by making variation in height and improving the roof and side ventilation so that temperature variation inside the structure is only 1°C compared to the ambient. The Kerala Agricultural University has also developed an ad-hoc package for protected cultivation of vegetables, considering the soil and weather parameters and is providing necessary technical support to growers and extension officers. The technology for large-scale production of virus-free vegetable transplants in pro-trays has also been standardized. There is a growing demand for quality, disease-free seedlings and technology can be scaled up into a profitable agri-entreprenurial model. Open field precision farming through fertigation and mulching is profitable compared to conventional system of cultivation. Besides maximising water-and nutrient-use efficiency of the crop, this technology offers scope for mechanisation in vegetable cultivation saving human labour requirement by almost 50%. Rain shelters are useful for year-round production of vegetables in homestead for small growers.

Floriculture industry comprises of cultivation and trade of cut and loose flowers, potted plants, foliage and bedding plants, and dried flowers under open field as well as protected conditions. Globally, more than 145 countries are involved in cultivation of floricultural crops. Though the art of growing flowers is not new to India, protected cultivation is relatively new in India. Enormous genetic diversity, varied agroclimatic conditions, versatile human resources offer India a unique scope for diversification into new avenues which have not been explored to a greater extent. India also has a strong dry flower industry, which contributes a major share to the overall trade. Other segments like fillers, potted plants, seeds and planting material, turf grass industry and value-added products also contribute a share in overall growth of floriculture industry.

Cut flower production is a growing business in most of the Asian countries. Technology for production is well-known and it has been utilized to the maximum in many countries. As a consequence, cut flowers are commercially produced in larger quantities in these countries, resulting in surplus quantity during normal periods of blooming. On the other hand, such prices are much higher during the periods of needs such as certain special occasions, or off-season.

In spite of considerable progress achieved during the last four decades in vegetable production in India, average productivity of vegetable crops is still low as compared to national and world level, indicating the scope to enhance their yield potential of quality produce. This is because of an appealing gap between potential yield and actual yield. In many vegetables, yield realized is even lesser than 50 % of potential yield. Sometime, farmers growing vegetable crops during main season do not get back their cost of cultivation. The prices of the same vegetable produce is very high during off-season. Several biotic and abiotic stresses are major factors responsible for low productivity and poor quality in different vegetable crops under open field cultivation, especifically in rainy and post-rainy seasons. The increasing demand of off-season high quality vegetables in various markets of big cities has called the attention of vegetable growers for diversification from traditional techniques of vegetable cultivation to modern systems of cultivation.

Protected cultivation of vegetable crops offers best choice for diversification. Production of crops under protected conditions has great potential in augmenting production and quality of vegetables, in main and off-season, maximizing water and nutrient-use efficiency under varied agroclimatic conditions. This technology has great potential especially in peri-urban areas, since it can be profitably used for growing high- value vegetables like tomato, cheery tomato, coloured Capsicum and parthenocarpic cucumbers, and healthy and virus-free seedlings in agri-entrepreneurial models.

Tomato varieties produced 50% in plastic house cover more than what they produce in open air (Diez et al., 1985). Nevertheless this production increase is not, by itself the reason for the use of protected cultivation which requires higher expenses. The key factor is the cost increase of final product obtained during off-season, which created an interesting margin in spite of high production costs. The effect of cost of seed in total cost is negligible, which permitted the use of hybrid seed. The new hybrids have brought about production increases which can be calculated to be over 50% more than what the greenhouse varieties produced with the cost of hybrid seed giving an effect on total production costs of only 6-7%. So that under these circumstances, production increase associated with hybrids is certainly very advantageous, apart form other benefits to be derived from hybrids.

R aising of healthy vegetable seedlings is most important aspect in vegetable production, because, up to a large extent it determines the productivity and profitability. Nursery raising under open field conditions suffers by a number of biotic and abiotic factors like, continuous rains, too low and too high temperatures, disease and insect-pest etc. For the early establishment in the field and to reduce the cost of production, vegetable seedlings are being raised in plug trays under controlled conditions round the year. Under this system, each transplant grows in an individual cell to reduce the competition among transplants and maintain the uniformity. Plug transplants establish better in field because roots are not damaged in pulling, transplants often result in better stands, and their growth can be controlled more easily through fertility and water management. With the passage of time, a lot of advances have been made in this direction and a number machines have been developed like media sterilizer, media mixer, plug trays filling, seed sowing machine, growth chamber etc. by which the raising of seedlings under controlled environment becomes easier and labour intensive.

The growth is insulated to allow for heating or cooling according to specific germination needs of the seed. This technique reduces fluctuations in temperature and moisture that can occur in an open house. The growth chambers are usually an insulated room in which temperature and relative humidity can be maintained at a precise level. Air circulation is important to ensure uniform temperature and humidity throughout the chamber. Grafting in vegetables is a recent practice to control soil-borne diseases and nematodes, for both field and greenhouse grown crops. In addition, grafted vegetables can produce higher yields and have improved tolerance to environmental stresses, soil salinity and low soil temperatures which is only possible under protected conditions. Thus under present scenario, raising of seedlings under controlled environment offers opportunity to go for commercial nursery with different machines.

In India, risk-prone, high altitude area constitutes the easternmost trans-Himalayan part of Ladakh and Zanskar range (Jammu and Kashmir) and some parts of Lahoul Spiti (Himachal Pradesh). Truly described as cold desert with low population density, Ladakh constitutes the easternmost trans-Himalayan part of Jammu and Kashmir in India, comprising two districts, Leh and Kargil, bordering Pakistan and China (Fig. 1).

Greenhouse essentially functions based on solar energy to control environment for protecting cultivation from adverse condition. Computer simulation and mathematical modeling are important tools for determining the energy efficient design as well as predicting the overall system performance. The greenhouse climate may be used for crop drying, distillation, biogas plant heating, space conditioning, and aquaculture. Thus, any required configuration of greenhouse can be thermally modeled for predicting and optimizing the performance. The solar energy based greenhouse modeling are useful in evaluation of various applications like greenhouse heating, greenhouse cooling, greenhouse drying and aquaculture greenhouse. Greenhouse clubbed with ground air collector increase in plant and ambient temperature of 5–7°C during night in winter. Whereas, using evaporative cooling pad with greenhouse reduces the ambient temperature of 4–5°C during midday summer. A greenhouse can be very well used for large-scale crop drying, while not in use for cultivation. Some other applications such as covered crop drying and aquaculture are also discussed.

P rotected cultivation technology envisages immense promise, especially for high-value crops against various biotic and abiotic stresses. The climatic parameters stress conditions like unusually low and high temperature, humidity, solar radiation, wind velocity comes under abiotic stresses. The present climate change scenario dealing with various biotic and abiotic stress conditions poses serious threat to agricultural production throughout the world. It is estimated that due to one-degree rise in temperature during rabi season, wheat production declines by 4-5 million tonnes in India. Similarly due to prevailing low temperature and frost like conditions, especially in Northern India, several million tonnes of horticultural crops like tomato, brinjal etc. got wasted every year. Protected cultivation technology deals with following types of protected structures :

Growing population, rapid urbanization and changing climate have created a global challenge to meet the food and nutritional needs. In India, rapid urbanization is not only taking place in larger cities, but satellite towns are competing for food, water, transportation and above all adding to pollution of the environment. As warming of the climate gathers alarming pace, posing a danger to agriculture, greenhouse cultivation is the only answer to save crops of flowers and vegetables. The yield under polyhouse cultivation can be achieved to the level of 5-8 times as compared to open field cultivation.

PROTECTED STRUCTURES

Protected cultivation is unique and specialized form of agriculture. Protected or greenhouse cultivation of vegetables is one of the means for clean vegetable production in peri-urban areas. The protected cultivation facilitates year-round production of high- quality produce with minimum labour and irrigation water, and almost wards off pest problem. In peri-urban regions, where climate prevents or does not favour year-round production of crops in open field, vegetable production can be taken up in protected environment. Use of protected structure for cultivation of crops as a commercial venture is a recent phenomenon, especially in the tropical areas.

Greenhouse refers to a structure which provide relatively better environment for plant growth. The goal is to create congenial temperature and relative humidity in desired range. The greenhouse covered with polythene sheet, also called as polyhouse, has polythene sheet (UV stabilized 200 micron LDPE sheet) as covering over a relatively rigid frame (made of GI pipes, angle iron, boomboo or wooden) which entraps the solar energy to increase the temperature and provide light for plant propagation. A normal greenhouse has four basic components: 1) frame made from rigid material like GI pipes, wood or bamboo; 2) glazing material, which is transparent to provide enough light for plant propagation; 3) climate control system, which may be natural ventilation or forced ventilation using fans with or without cooling pads, shading nets, pigment painting, etc. and 4) plant growing medium (soil or artificial media like cocopeats) with needful arrangement for supplying inputs (water, fertilizers, pesticides, etc.). The design of greenhouse involves structural design of frame with enough climate control mechanism. It mainly depends on shape, size, material of frame, local climatic conditions, wind pressure, and desired height from crop production point of view. Fabrication of greenhouse should be such that it provides enough structural stability with wind pressure and other factors, perfect joints, anchoring of polythene, and minimum hindrances in crop production practices.

Protected structures such as cloches, low tunnels, greenhouses, livestock housing, aquaculture facilities and phytotrones provide varying degrees of climate control for agricultural production. While cloches and low tunnels provide only passive modification of the growing environment, all other protected structures have the possibility of active and precise growing environment control.

GREENHOUSE TECHNOLOGY

Greenhouse technology permits the management of both biotic and abiotic stresses related to plant growth to maximize the productivity and quality. Resultantly, profitability per unit area per unit time increases by two to three orders of magnitude as compared to open field cultivation. Introduction of greenhouse technology in India for commercial production is only about three decades old, whereas it is more than two centuries old in USA, UK, Holland, France, Italy, etc.

A greenhouse is a framed or inflated structure covered with a transparent or translucent material where plant environment could be, at least partially controlled and which is large enough to permit a person to enter and carry out cultural operations. A greenhouse is called a glasshouse, when covered with glass, and plastic house when plastic is the glazing material. Materials of construction and cropping activities are also used to identify greenhouses. In addition to the growing facility, a commercial greenhouse complex has a head house, an input storage and a sales area. All these components are so arranged that unwanted movements of man and material are obviated.The greenhouse complex should have adequate wind breaks on the upstream side of the wind direction to provide protection against heavy winds.

Protected cultivation of vegetable crops refers to creation of favourable environmental conditions around vegetable plants, offsetting or minimizing the detrimental effects of prevailing or expected to prevail abiotic and biotic factors. In open fields, vegetables face several abiotic and biotic stresses, resulting in lower or poor or no yield. This condition is altered by using different protected structures for cultivation and/or nursery production. Vegetables which are transplanted using seedlings should be produced by raising seedlings under protected conditions. Protected conditions ensure production of healthy seedlings. The use of healthy seedlings ensures better and higher production of vegetables both under protected conditions and open field conditions. Nursery grown in nursery trays can be transported long distances and can wait till field is ready or human resources is available for transplanting. Structures are ideal for production of plug plants/transplants in different sizes of portable trays.

Since ancient times, agriculture is an outdoor or open field production of crops. Open field production is climate and weather dependent. In fact, growth and development of crops under a particular set of climate parameters defines geographical location, productivity and production period of different crops. The magnitude of impact of climate and weather on agricultural productivity and quality of produce is appreciated by farmers and the scientific community, including horticulturists. Abiotic and biotic factors govern crop production potential and quality of products. Among the major constraints in production of horticultural crops are temperature (hot or cold), sunlight duration and quality, water deficiencies or excesses, atmospheric moisture (relative humidity), weeds, deficiency of nutrients, heavy winds, carbon dioxide and host of diseases and insect pests. There are ecological optima for obtaining production potential of each of the crops. Deviation from these conditions results in yield losses partially and sometimes totally. However, near optimal climatic conditions could be created by controlling the climate with the help of greenhouse using

The article is the Inaugural address delivered by Dr. R.S. Paroda at Indian Society for Protected Cultivation (ISPC-registered in March 2012), New Delhi on 21 March 2013 in a seminar ‘‘Advances on Protected Cultivation’’ at NASC, Complex, Pusa, New Delhi different protected structures/methods/devices and such cultivation under controlled environmental conditions is termed as as protected cultivation.

Introduction

Globally, the SOC pool of 1,550 Pg (1 Pg = 1015 g) to 1-m depth is about three times that of biotic and twice that of atmospheric pools (Post et al. 1982, Watson et al. 2000). In arid and semi-arid ecosystems, SOC contents show high degree of spatial variability due to patchiness of vegetation (Wiesmeier et al. 2009). The arid ecosystem might play a key role in mitigation of climate change effects through sequestering carbon in soils (United Nations 2011). Therefore, a suitable land use system having high C sequestration potential for arid ecosystem needs to be identified and promoted. However, the prevailing conditions of arid ecosystems normally does not support dense vegetation. Therefore, low carbon stock is expected in both above-ground vegetation and below-ground soil. Fruit trees are an integral part of arid farming system and played a significant role to improve the nutritional security and employment opportunity of farmers but also have potential role to increase carbon storage both in biomass and soil. To quickly assess the soil carbon restoration programmes, it is important to know how much soil carbon is stored in an agricultural farm through adoption of different land management practices. Therefore, reliable estimates of SOC pools are essential to establish the soil carbon sequestration programmes at different landscape scales.

Introduction

Soil organic carbon (SOC) content plays a key role in maintaining soil fertility in agricultural farm. At present, there has been great interest to reduce the atmospheric CO₂ level through a chain of increasing vegetation carbon pools first and then to store them as soil carbon pool. To quickly assess the soil carbon restoration programmes, it is important to know how much soil carbon is stored in an agricultural farm through adoption of different land management practices. Average SOC content is considered in most soil carbon pool calculations in spite of its large spatial variation in landscape, and thus leads to inaccurate estimate of SOC stock for an area. Therefore, reliable estimates of SOC pools and their spatial variability are essential to establish the soil carbon sequestration programmes at different landscape scales. Here, a case example of assessing the spatial variation at a typical agricultural farm is discussed. The experimental field was located at Badoda village of Jaisalmer district, Rajasthan covering an area of about 76.12 ha. The farm lies between 265^o2′30″-26°53′30″ N and 71°12′15″-71°13′15″ E (Fig. 24.1) and 20 km away from Jaisalmer city in eastward direction. Elevation of the farm ranged from 232 m to 248 m above mean sea level with an overall slope from south to north. Among total area of the farm, 85.38% area was under cultivation in four main blocks: block A, B, C, and D whereas rest area was under grassland.

Introduction

High degree of spatial variability in soil properties is observed at different scales. This variability is generally originated from the combined effect of physical, chemical or biological processes operating with different intensities at different scales (Goovaerts 1998). Knowledge of this spatial variationof soil properties is important in several land management applications e.g. variable rate application of nutrient and water in agricultural production system, land suitability classifications, modeling water balance components at landscape scales, precision farming etc. The concept of ‘management zone’ was also evolved in response to this large variability with the main purpose in efficient utilization of agricultural inputs with respect to spatial variation of soils and its properties (Franzluebbers et al. 1996, Atherton et al. 1999, Malhi et al. 2001). Therefore, an appropriate understanding of spatial variation of soil properties is essential. The most important way to gather knowledge in this aspect is to prepare soil maps through spatial interpolation of point-based measurements of soil properties. Here, a case study is discussed where soil water retention at two critical levels e.g. field capacity (FC) and permanent wilting point (PWP) was spatially characterized through geostatistical approaches. Because these two soil moisture contents guide farmers to apply right amount of irrigation water at right time. The study was carried out at the experimental farm of ICAR-Indian Agricultural Research Institute, New Delhi, India (28°37′-28°39′ N, 77°8′ 30″-77°10′30″ E, 217-241 m above mean sea level). A detailed discussion of the study is available in Santra et al. (2008)

Introduction

Microorganisms generally present in all the environments are the most abundant and diverse living organisms present on earth. Microorganisms have great impact on the biosphere due to their inbuilt ability to transform the key elements of life into forms usable by other organisms and plants. Because of heterogeneity in nature of soils from variations in physical, chemical and biological properties the various natural habitat are microbially most diverse environments on the earth maintaining the stability between complex microbial communities and their function (Daniel 2005). One gram of soil contains about 10 billion microorganisms of possibly thousands of different species but microbial diversity in general, and particularly in extreme environments, and its role in ecology is poorly understood (Rossello-Mora and Amann 2001).

Deserts present a unique environment with very little precipitation and large variations in day and night temperatures, making it inhospitable environment for living organisms. Still a number of microbial species do survive in those harsh environments. The populations of aerobic bacteria in deserts across the world are reported to vary from < 10 in Atacama desert to 1.6 × 10⁷ cfu g^–1 in soils of Nevada (Skujnis 1984), and India is not exception to that with the bacterial population in the range of 1.0 × 10³ to 8.35 × 10⁶ cfu g^–1 sediment or ml^–1 water in the samples collected from cold deserts of north western Himalayas (Yadav et al. 2015).

Introduction

The life support systems of a country and socioeconomic development of its people largely depend on soils. The soils are the most valuable natural resources for the society. It caters the basic needs of mankind by producing food, fibre and timber. It is therefore essential to know the distribution and extent of different soils and their qualities, potentialities and constraints for sustainable utilization of this valuable natural resource (Shyampura and Sehgal 1995). Information on distribution, potential and constraints of major forest soils are essential to design most appropriate soil management systems in order to increase productivity of forests as forests also offer excellent potential for poverty reduction and rural economic growth in India. However, unlike agriculture lands forest soils usually draw less attention and are ignored in spite of the fact that they nourish the lungs of the Earth. Soil quality is the most important factor in forest management decisions. Soils will determine productivity of a particular forest and management strategy. Knowing about forest soils can serve as a basis for forest management decisions, including land acquisition, species selection for planting, site preparation requirements, watershed development, fertilization prescriptions, stand density/ composition, and harvest timing, as well as decisions affecting land ownership and use.

Introduction

The natural resources of arid regions particularly soil and water are limited and is often in a delicate environmental balance. Desert encroachment due to lack of conservation planning, and the dangers of destroying or depleting beyond recovery these productive resources, are evident at present time and may be disastrous if development is based on short term expediency rather than long term stability.

The arid zone of India is spread over 38.7 million ha area, out of which 31.7 m ha in under hot arid region and 7 m ha under cold region. The hot arid region occupies major part of north-western India (28.57 m ha) and occurs in small pockets (3.13 m ha) in south India. The north-western arid region occurs between 22o30' and 32o05' N latitudes and from 68o05' to 75o45' E longitudes, covering western part of Rajasthan (19.6 m ha, 69%), north-western Gujarat (6.22 m ha, 21%) and 2.75 m ha (10%) in south-western part of Haryana and Punjab (Faroda et al. 1999). Rainfall distribution is highly uneven over space and time (CV>60%). The region receives low rainfall (<100 mm to 500 mm), has high evapotranspiration and high temperature regime (Rao and Singh 1998). Groundwater is deep and often brackish. The western-central area is devoid of drainage system and surface water resources are meager (Fig. 20.1). Due to low and erratic rainfall, replenishment of water resources is also very poor. The entire Rajasthan state is being categorized as the driest state and water scarce (having per capita water availability below 1000 m3 year-1) since 1991 in the country. Increasing pollution by industrial units, big and small, unregulated mining and even over-extraction of water from deep wells also add to the water quality problem in number of districts. Rapid urbanization and industrialization make such existing differences even more

Introduction

Soil is a dynamic natural body, characterized by high degree of spatial variability due to combined effect of physical, chemical or biological processes that operate with different intensities at different scales (Goovaerts 1998). Reports have shown that there is large variability in soil properties, crop yield, disease, weed etc., not only in large-sized fields (Godwin and Miller 2003,Vrindts et al. 2005), but also in small-sized fields (Bhattacharya et al. 2008). Therefore, knowledge of hydro-physical properties of soils is crucial for management practices for all kinds of agricultural practices for efficient utilization of input resources. Plant growth and development is highly related to soil hydro-physical properties, which not only depend on soil intrinsic textural and chemical characteristics (Hamza and Anderson 2005) but also on soil management.

Soil physical properties which change with the variation in soil structure include bulk density, total porosity, pore geometry (size distribution, shape, continuity and tortuosity), penetration resistance and aggregate stability, consequently affecting soil hydraulic properties such as water retention characteristics, plant available water capacity, infiltration capacity and saturated hydraulic conductivity. These soil properties are dependent on seasonal climatic conditions, management practices, crop development and biological activity (Reynolds et al. 2007). The hydro-physical properties of soils, i.e., water retention and water permeability in both saturated and unsaturated zones not only shape soil water balance but also decide the conditions for plant growth, development and yield. They also determine water availability for the plant root system and the transfer of water with chemical compounds dissolved in it into deeper layers.

Land resources units or major land resources units are the suitable segmentations of land, based on available natural resources, their potential use and management. Irrespective of terms used by different organizations or institutes, these units have spatial dimension and represent a composite unit for interstate, regional, and national level planning. A typical meaning of these land units can be related to terms like land system. Christian and Stewart (1953) of the Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia devised the ‘Land system’ as the composite mapping-unit which has been defined as “an area or group of areas, throughout which there is a recurring pattern of topography, soils and vegetation”. This system is based on the premise that the land system is expressed on aerial photographs by a distinctive pattern and, as such, the land system can be directly mapped from aerial photographs (Christian and Stewart 1953, Mabbutt and McAlpine 1965). It is a fact that during those period, aerial photographs were more frequently used and studies related to land systems used technique of aerial photogrammetry. As per USDA (United States Developmental Agency), the typical segmentations are; (1) Land Resource Region (LRR), which is the highest level in the hierarchy (2) Major Land Resource Area (MLRA), the second highest level and generally representing broad landforms or a geologic region at a small scale and (3) the Land Resource Unit (LRU).

Concept and Definition

Desertification is a major problem in the drylands of India, affecting the way of life for its inhabitants. The problem is more severe in the arid lands in the northwestern part of the country. There have been a few numbers of definitions of the word “Desertification”. However for simplicity of its origin and meaning we would refer to two of the frequently used ones; (1) proposed in 1991 by UNEP: “Land degradation in arid, semi-arid and dry sub-humid areas resulting mainly from adverse human impact and (2) prepared for the Earth Summit at Rio, held in June 1992, for inclusion in Agenda 21, as “Land degradation in arid, semi-arid and dry sub-humid areas resulting from climatic variations and human activities. (Anonymous 1992).

Comparing two of these initial definitions, it would show that first one indicated desertification as a state of natural resources like soil and vegetation, mainly resulting from human activity while the second one included climate as a major cause. However, it reduced the emphasis of human activities as being the primary responsible factor and tried to link the possible impact of climate change on desertification. But the simplicity of the 1992 definition of desertification helped researchers to bring in sharp focus on the special environmental and socio-economic problems of the drylands.

So over the years, the key words that explains the term “desertification” has been changing or are being amended for specific purposes. This would at least mean that the problem of desertification is still debated world-wide, and its impact is not limited to some nations/regions as was presumed and discussed in first of UNCCD platforms. Therefore, as a precaution and combating this process, few catchy words like non-sustainable management, productivity, over-exploitation of natural resources, dryland livelihood, droughts are found to surround the word desertification. Earlier, some, people equated desertification with advancing boundaries of the existing desert and in India, examples of expansion / contraction of geographical boundary of Thar desert was a public debate among various researchers and planners. In the same time, some of the definitions tried to broaden its geographical scope so that greater populations suffering from adverse effects of land degradation would benefit out of various remedial measures.

Introduction

In India, hot arid ecosystem covering an area of 31.7 million ha lies at north western part of the country and 62% of this area falls in western Rajasthan covering 12 districts i.e. Jodhpur, Jaisalmer, Jhunjhunu, Sikar, Hanumangarh, Sri Ganganagar, Bikaner, Barmer, Pali and Sirohi. The major landforms in arid western Rajasthan are dune and inter-dune plains having sandy to sandy-loam textured soils. Temperature remains high for most part of the year reaching up to 42-45°C during prolonged summer months (April to July), whereas annual rainfall is very low and highly sporadic (100-400 mm) mainly occurs during August and September. Annual potential evapotranspiration ranges from 1500-2000 mm. Loss of fertile top soil from denuded soil surface through wind erosion is frequent during summer months. The region is subjected to frequent occurrence of droughts, once in every three years, which further intensifies the process of desertification (Samra et al. 2006). Shorter growing period, sandy soils, very low available water capacity, inadequate nitrogen and phosphorus availability are the major constraints. Soils are prone to severe wind erosion, terrain deformation and nutrient depletion. The region is mostly mono cropped and double cropping is being practiced extensively in canal and tubes well command area. Extensive fallowing in the past even in very good rainfall years because of uneven undulating topography and severe windstorm are the cause of concern. Ground water is deep and is usually brackish, declining at alarming rate due to excessive withdrawal for irrigation and other purposes. The region, however, sustains on perennial vegetation of Prosopis cineraria, Tecomella undulata, arid shrubs and grasses, which are rich resources of fodder for animal because cropping is affected by vagaries of monsoon. During last two to three decades, a 2 to 3-fold increase in irrigated areas with cultivation of both kharif and rabi crops has been recorded and most of these increments are resulted through conversions from rainfed areas, grazing lands and sand dunes into irrigated areas.

Introduction

Over the past few decades, soil resources have witnessed many changes driven by demographic pressure and climate change. It is necessary to have up to date soil information to monitor the state of soils health with time. Soil resource inventory provides information about the potentialities and limitation of soils for its proper utilization. Soil survey offers an accurate and scientific inventory of different soils, their properties and extent. An in-depth knowledge of the type of soils and their spatial distribution is a prerequisite for developing rational land use planning. Remote sensing is the science of obtaining information about the earth’s surface without actually being in contact with it. On the other hand, proximal sensing refers to remotely sensed measurements that are taken at the field or laboratory level. Both these processes involve an interaction between incident radiation and the targets of interest. Fundamentally, remote sensing data products are a representation of the functional response of objects to energy sources. These are typically spectra, which are acquired as a discrete series of reflectance measurements taken at different wavelength intervals, or bands. In the shorter wavelengths of the electromagnetic spectrum (visible part), features can be detected by virtue of reflected solar energy, while in the longer wavelengths (microwave, thermal parts), sensing of emitted energy works. Conventional soil sampling, and subsequent laboratory analysis are generally time consuming and costly. In this context, remote sensing plays a crucial role for studying soil resources. The term remote sensing is generally used for airborne and space borne acquisitions, whereas proximal sensing refers to ground-based laboratory and field measurements. Advances in the quantitative disciplines like remote sensing and proximal sensing have laid the foundations for a spatial exploration of soil-system dynamics within a landscape context (Pennock and Veldkamp 2006).

Introduction

Soil is one of the vital resource, and is the basis of the existence of mankind. Management of this resource including its conservation and utilization is of crucial importance. It is a base on which all life depends. In the recent past, with burgeoning populations and the national goals of seeking self-sufficiency in food and fibre production, this resource base is slowly being stripped. While natural systems often adapt to stress in a remarkable fashion, some relationships - once destroyed - can never be restored. Soil resources can be assessed and monitored through its spectral signatures gathered through remote sensing.

The term ‘remote sensing’ is commonly restricted to methods that employ electromagnetic energy (such as lights, heat, micro wave) as means of detecting and measuring target characteristics. Sensors acquire data as various earth surface features reflect or e mit electromagnetic energy (EM energy). Remote sensing is the technology that is now the principal method (tool) by which the earth’s surface and atmosphere (as targets or objects of surveillance) are being observed, measured, and interpreted from vantage points. Remote sensing of the earth traditionally has used reflected energy in the visible and infrared, and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor.

Introduction

Accuracy and uncertainty analysis is an important step in preparation of surface map through geostatistical techniques or digital soil mapping approach. It is because that we rely on a map based on how much accurate it is and the degree of uncertainty associated with the estimates while the map was prepared. Any stakeholder for a soil map especially policy makers is interested in accuracy and uncertainty of the map before taking suitable land management decisions involving soil map as an input. Again the level of accuracy and uncertainty depends on the properties for which the map has been developed. If the property of interest is sensitive to environment or human consumption, then the accuracy level should be very high while the uncertainty level should be very low e.g. heavy metal concentration in soil, quality parameters of water etc.

Introduction

GIS (Geographical Information System) based mapping takes cogence of three major geometrical dimensions, point, line and polygons and in GIS these three features correlate their relationship through a concept called Topology. Among the various applications where covariate processing requires specific attention, the landuse/ land cover and Digital Elevation Models/surfaces or terrains are the major area of interest. Use and processing of data for these covariates carries importance as accuracy of both these aspects would influence the derivatives extracted out of them further. For example, if landuse is improperly defined, the site for which planning is proposed may not sustain, similarly, point data/elevation data in case of DEMs would decide the exact topography of any landforms for their further utilization. For their use in GIS, while DEMs depend upon point data, the landuse/ land cover will be taken care by polygons. For the purpose of the study, we are going to discuss about these two covariates in the following paragraphs.

Generally, Covariate (covariable) (koh-vair-iãt) in statistics is defined as a continuous variable that is not part of the main experimental manipulation but has an effect on the dependent variable. In other words, as per ANCOVA (Analysis of covariance) definition: a covariate is a control variable.

Introduction

Quantum Geographical Information System (QGIS) is an open source GIS environment. The software package is available freely in web https://www.qgis.org/en/site/forusers/download.html) and can be installed in a variety of computer platform e.g. Windows, Linux, Mac OS X, Android etc. Latest information on QGIS is available inhttp://www.qgis.org/en/site/. Basically, the QGIS GUI is divided into five areas: (i) Menu Bar, (ii) Tool Bar (iii) Map Legend, (iv) Map View and (v) Status Bar. QGIS allows users to define a global and project-wise coordinate reference system (CRS) for layers without a pre-defined CRS. It also allows the user to define custom coordinate reference systems and supports on-the-fly (OTF) projection of vector and raster layers. All of these features allow the user to display layers with different CRSs and have them overlay properly. QGIS has support for approximately 2,700 known CRSs. Definitions for each CRS are stored in a SQLite database that is installed with QGIS. The CRSs available in QGIS are based on those defined by the European Petroleum Search Group (EPSG) and the Institute Geographique National de France (IGNF) and are largely abstracted from the spatial reference tables used in Geospatial Data Abstraction Library (GDAL). For Post GIS layers, QGIS use the spatial reference identifier that was specified when the layer was created. For data supported by OGR, QGIS relies on the presence of a recognized means of specifying the CRS. In the following sections, few basic GIS analysis using example spatial data from arid western India is discussed. However, readers are advised to go through different tools and functions available in QGIS after installation of the system in personal computers.

Introduction

Spatial datasets always exhibit two common features (i) the occurrence of a few very large concentrations (hot-spots) and (ii) the presence of data below the detection limit (black spots). Extreme values can strongly affect the characterization of spatial patterns, and subsequently the prediction. Several approaches exist to handle strongly positively skewed histograms (Saito and Goovaerts 2000). One common approach is to first transform the data (e.g. normal, Box Cox or lognormal), perform the analysis in the transformed space, and back-transform the resulting estimates. Such transform, however, does not solve problems created by the presence of numerous black spot data, since either it yields a spike of similar transformed values or, in the case of the normal-score transform, it requires a necessarily subjective ordering of all equally-valued observations. Moreover, except for the normal score transform (Deutsch and Journel 1998), it does not guarantee the normality of the transformed histogram, which is required to compute confidence intervals for the estimates. Last, the back-transform of estimated moments is not straightforward and can introduce bias if not done properly (Saito and Goovaerts 2000); for example, lognormal kriging estimates cannot simply be exponentiated. Another way to attenuate the impact of extreme values is to use more robust statistics and estimators. The non-parametric approach of indicator kriging (IK) falls within that category (Journel 1983, Goovaerts 2001). The basic idea is to discretize the range of variation of the environmental attribute by a set of thresholds (e.g. deciles of sample histogram, detection limit, regulatory threshold) and to transform each observation into a vector of indicators of non-exceedence of each threshold. Kriging is then applied to the set of indicators and estimated values are assembled to form a conditional cumulative distribution function (ccdf). The mean or median of the probability distribution can be used as an estimate of the concentration of the material in question (Barabas et al. 2001, Cattle et al. 2002, Goovaerts et al. 2005).

Introduction

In geographical location, the observations taken in proximity tend to be more alike than observations made at points that are far apart-that is, the values tend to be correlated. An area of study that takes this sort of spatial consideration into account is called geostatistics. A primary motivation for sampling is to make meaningful estimates of values at nearby positions in space and time. When values are assumed independent, the best estimate for an unmeasured point will be the mean. However, for correlated values nearby values are estimated by interpolation. The simplest interpolation scheme is a nearest neighbor estimate for which the unknown values are based on the closest measured location. Inverse distance is another sort of interpolation technique where weights are estimated based on the inverse distance squared. Kriging is a geostatistical interpolation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas.

It makes the best use of existing knowledge by taking account of the way that a property varies in space through the variogram model. In its original formulation a kriged estimate at a place was simply a linear sum or weighted average of the data in its neighbourhood. Weights are based on the distance between the measured points, the prediction locations, and the overall spatial arrangement among the measured points. Since then kriging has been elaborated to tackle increasingly complex problems in mining, petroleum engineering, pollution control and abatement, and public health. The term is now generic, embracing several distinct kinds of kriging, both linear and non-linear.

Introduction

Spatial variation structure of soil properties is a key input for digital soil mapping and generally determined through geostatistical techniques (Webster and Oliver 2007). In geostatistics, spatial variation is expressed by semivariogram ?(h) , which measures the average dissimilarity between data separated by a vector h. It is generally computed as half of the average squared difference between the components of data pairs:

Introduction

The first step of geostatistical analysis in R is the creation of dataset which is acceptable in R environment by using the data collected from field survey or laboratory experiments. Different type of data is collected during experiments e.g. sand content, silt content, organic carbon content, geographical location of data points, soil type, land use type etc. Here, we will discuss how these data are converted into a acceptable dataset format for R environment and how we manage these data. In R this task is performed in two steps. First, a data structure is created to hold the data and second is the entering or exporting the data into data structure.

Introduction

Spatial data is different from a non-spatial data in the sense that it always has a spatial reference system which is nothing but a pair of coordinate values and a system of reference for these coordinates. For example, the locations of benchmark soil series of India can be defined as pairs of longitude and latitude in decimal degree values with respect to the prime meridian at Greenwich and zero latitude at the equator. The World Geodetic System (WGS84) is a frequently used system of reference on the Earth. The different types of spatial data are: (i) point, which is a single point location, such as a GPS reading or a geocoded address (ii) line, which is defined as a set of ordered points, connected by straight line segments (iii) polygon, an area, marked by one or more enclosing lines, possibly containing holes and (iv) grid, a collection of points or rectangular cells, organised in a regular lattice. Among the above said spatial data, the first three are vector data models and represent entities as exactly as possible, while the fourth data model is a raster data model, representing continuous surfaces by using a regular tessellation. In this chapter, we discuss how a spatial data can be handled in R. There are several packages available in R in which spatial data analysis can be handled e.g. sp, rgdal, raster etc. All the contributed packages for spatial data analysis in R address two broad areas: moving spatial data into and out of R, and analysing spatial data in R. In the following section, the sp package for spatial data handling is briefly discussed.

Introduction

R is one of the most popular platforms for data analysis and visualization currently available. It is free, open-source software, with versions for Windows, Mac OS X, and Linux operating systems. R is a language and environment for statistical computing and graphics, similar to the S language originally developed at Bell Labs. It’s an open source solution to data analysis that’s supported by a large and active worldwide research community. But there are many popular statistical and graphing packages available e.g. Microsoft Excel, SAS, IBM SPSS, Stata, and Minitab etc., however R has many interesting features so why it is mostly followed by researchers and academicians. Few of the major features of R are given below:

Soil sampling is an integral and essential component of soil fertility evaluation and nutrient management research. The effectiveness of soil sampling is a prerequisite for soil testing to achieve its goals for efficient nutrient management and plant nutrition for improved yield and quality. The underlying basis for soil sampling is that a soil sample taken represents the “population” which may be a plot, field or a watershed. It further implies that nutrient status of the representative soil sample(s) determined in a laboratory would reflect nutrient status of a plot, field or watershed and is of interest for correcting nutrient disorders in the field or watershed.

The most important factor that influences the effectiveness of soil sampling is soil heterogeneity. However, in a relatively homogenous group of fields or plots, a small number of samples may be sufficient to represent the population to a more heterogenous group of fields that would require more number of samples to represent the soil population. Whenever, soil samples to be taken in field following points needs to be kept in mind.

The arid region in India is spread over in 38.7 million hectare land. Out of the total 31.7 m hectare lies in hot arid region and remaining 7 m hectare comes under cold arid region. The hot arid region occupies major part of northwestern India (28.7 m ha) and a small pocket (3.13 mha) in southern India. The north western arid region occurs between 22°30’ and 32°05’ N latitude and 68°05’ to 75°45’ E, covering western part of Rajasthan, north western Gujarat and south western parts of Haryana and Punjab. About 62% area of arid region falls in western Rajasthan and 20% in Gujarat. Haryana and Punjab together constitute 7% area of arid region. The state wise distribution of arid region is shown in Table 3.1. Further our discussion is based on the majority area of arid region, belonging to Rajasthan, Gujarat, Punjab and Haryana.

Introduction

A complex spatial soil properties pattern is directly controlled by soil internal factors and anthropogenic impacts.Hartemink et al. (2001) elaborate a strong decline in mineralogy, morphology and genesis of soil research than strong increase in pedometrics applications during 1967 to 2001. Hartemink and McBratney (2008) identified this change as “soil science renaissance”. Basically, the soil science deeply rooted in agriculture, geology, and chemistry but the paradigm has been shifted from classification and inventorization to quantification of soil patterns in spatial as well as temporal patterns and their impact on the ecosystem health and hydrological cycle. The environmental keen approach explains that soil is in the center of the ecosystem and highly interacting with the biotic and abiotic factors of the ecosystem and a complex pattern and process has been co-evolved with the time.