eChapters

Introduction

Food supply is a major concern in the near future as the human population touches 9 billion in 2050 and therefore, more food has to be produced. This has to be achieved in the face of climate change amounting to constant pressure of biotic and abiotic stresses. However, Swaminathan (2007) opined that science and technology can play a very important role in stimulating and sustaining an evergreen revolution leading to long-term increases in productivity without associated ecological harm. Rice is considered as global grain since it is consumed by more than half of the world population. In India rice supplies calorie requirement to more than 70 per cent of its population. This is evident from the fact that rice is cultivated in all the 30 states and 5 union territories of the Indian subcontinent of diverse ecologies ranging from below sea level to mountainous regions. Though India tops in rice acreage (43.61 mha) its productivity of 3.19 t/ha (rough rice) is far below world average of 4.12 t/ha and that of Asia’s average of 4.19 t/ha (Shobharani et al., 2010). This low productivity is mainly due to unfavourable ecologies of the varied ecosystems which, rice encounters during critical stages of its growth. Salinity and submergence are major abiotic stresses, next to drought, limiting rice production in South East Asia including India.

In India nearly 8 mha are salt affected of which 2 mha are coastal saline and 3.4 mha are sodic (Singh, 1994). Rice is predominant crop in these areas especially in vast stretches of eastern and western coasts of India. The presence of salts, in excess to plant growth, in the soil solution makes the soil detrimental to crop growth. The source of the excess salt may be from soil or irrigation water. The content and composition of salts (ions) in tandem with types of soil and weather conditions prevailing during the crop period decides the severity of salt stress. Similarly, f lash floods or short-term submergence regularly affect rice growing areas in South and Southeast Asia. Even more favorable irrigated areas experience flooding problems during the monsoon season. It is also prevalent in rainfed lowlands where most of the present day rice varieties do not survive complete submergence for more than a week. In India 30% of the rice growing area is prone to such menace resulting in severe yield losses (Neeraja et al., 2007).

Introduction

Pulses belong to the taxonomic family Fabaceae, containing over 18,000 species divided into the three sub-families Mimosoideae, Caesalpinoideae and Papilionoideae. Pulses are commonly cultivated for several decades in all over the world because of the nutritional value of their seeds. Among different pulses, soybean, chickpea, common bean, cowpea, green gram, black gram and pigeon pea contribute significantly to serve the major protein source for the diets of large numbers of people living in Asia, Africa, and South America.

Production and Productivity of Pulses in india

In India, the production of pulses has not been able to keep pace with their domestic demand, resulting in import of 4-5 million tonnes of pulses per annum, especially from the countries like Canada, Myanmar and Australia to meet its domestic requirement (Kumar et al., 2018). India is the largest producer (around 25% of global production), however it consumes 27% and imports around 14% of its pulses requirements. The yield of pulses in India is quite low at 781 kg/ha which might be due to various factors. Pulse crops does not show any significant increase in area and production during 1950-51 to 2009-10, however, significant growth in area and production has been recorded during the last five years (i.e., 2010-2011 to 2016-17), with the adoption of high yielding varieties, increased usage of agricultural inputs like fertilizers and manures etc. The pulses under irrigation are cultivated in about 37% of the area while 63% of pulses are grown under rainfed conditions.

The productivity of pulses has increased about 77% at 779 kg/ha during 2016-17 from the level of 441 kg/ha during 1950-51. It is imperative to mention that the New Agriculture Technology (NAT) introduced during mid-sixties has increased the production of food-grains from 50.82 million tonnes during 1950-51 to 275.68 million tonnes during 2016-17 with the increase in area from 97.32 million hectares to 128 million hectares. In order to achieve self- sufficiency in pulses, the projected requirement by the year 2025 is estimated at 27.5 MT and to meet this requirement, the productivity needs to be enhanced to 1000 kg/ha.

Introduction

Rice is known as the grain of life, and is synonymous with food for Asians. In addition to being a staple food and an integral part of social rites, rituals, and festivals in all Asian countries, it has a medicinal value too, which was clearly recognized by the medicine systems of the region. Ancient Ayurvedic treatises laud the Raktashali red rice as a nutritive food and medicine. The medicinal value of other rices such as Sashtika, Sali, and parched rice have been documented in the Charaka Samhita (c. 700 BC) and the Susruta Samhita (c. 400 BC), in the treatment of various ailments such as diarrhea, vomiting, fever, hemorrhage, chest pain, wounds, and burns.

According to late Dr. Richaria, the well known rice scientist, 4, 00,000 varieties of rice existed in India during the Vedic period. According to his estimates, even today 2, 00,000 varieties of rice exist in India - a truly phenomenal number. Farmers in every part of country have a deep knowledge of their own rice varieties, This has enabled them to harvest a crop even under the most severe stress situations. Traditional paddy varieties are location specific and has its own advantages viz. saline tolerant, drought tolerant, pest tolerant, used for specific purpose such as roofing material for houses etc.

Plants are frequently subjected to environmental stresses such as water deficit, freezing, heat and salt stress. Soil salinity is a major problem in arid and semi- arid regions, where rainfall is insufficient to leach salt and excess of sodium ion down and out of root zone. In Asia, around 21.5 million ha of cultivable land was affected by salinity. India had 8.6 million ha of salinity affected cultivable land. It is observed that the area of salinity has been increasing in India. In Tamil Nadu, the area affected by salinity is around 6 lakh ha. Salinity affected areas are Chengalpattu, Salem, Thanjavur, Trichy, Tirunelveli, Dharmapuri and Ramanathapuram. Salinity is the most serious threats to the agriculture especially in the arid and semi-arid region. The presence of salts in the soil and their effects on the plant physiological mechanisms was major restrictive factor for agricultural productivity. Investigations on such soils have revealed that they were charged with high and abnormal soluble salt concentrations that lowered agricultural yield, limited crop distribution and even lead to extinction of certain species.

Salinity adversely affects plant growth and development, hindering seed germination, seedling growth, enzyme activity, DNA, RNA, protein synthesis and mitosis. There has been a variation in the response of plants to salinity with its growth stages according to quantity and period of exposure to salt. Salinity by competing in nutrition element absorption causes growth reduction. Primary salt injuries include metabolic disturbance and inhibition of growth and development. Secondary salt effects include nutrient deficiency and osmotic dehydration. Excessive amounts of salts, especially sodium chloride (NaCl), in the soil induce osmotic effects, leading to changes in plant metabolism. General symptoms of damage by salt stress are growth inhibition, accelerated development and senescence and death during prolonged exposure. Salt stress causes the reduction of rice yield and severe salt stress may even threaten survival.

Life on Earth is dependent on microorganisms for many essential services. Microbes are intimate partners in global agriculture by way of mobilization of nutrients, antagonism against pathogens and exploitation of plant- microbe interactions and optimization of plant microbiome allows farmers to apply less chemical fertilizers and pesticides, and improving plant growth and yield for sustainable development.

India has 2.4% of land area of world and 8.1% global species diversity. India has 7500 km long coast line in nine states and three union territories. About 14% of Indian population (17 crores) lives in coastal area. In 10.78 million ha of coastal ecosystem, rice is the major coastal crop. Coastal rice accounts for 15% of total area and production of India. Coconut, cashew, areca nut, spices, plantation and fruit crops are the other important coastal crops. The east coast in four states (Tamil nadu, Andhra Pradesh, Orissa, West Bengal) and one union territory (Pondicherry) has sandy coast and plains with medium to high rainfall (100-250 cm), and hot humid climate. It has marshy lands and mangroves. The west coast in five states (Kerala, Karnataka, Goa, Maharashtra, Gujarat) and two union territories (Diu and Daman, Dadar and Nagarhaeli) has fertile sandy plains with high rainfall (250-300 cm) and hot humid climate suitable for horticulture and plantation crops. Western Ghats near west coast is a hot spot with high biodiversity. ICAR established the Central Coastal Agriculture Research Institute (CCARI) at Goa in 2015 to carry out research on coastal agricultural crops.

Soil salinity in coastal regions is not only a soil type character, but also due to low quality irrigation water. The plant associated microorganisms “Plant microbiome” play a significant role in conferring resistance to soil salinity. The endophytic bacteria- rhizobium, rhizobacteria- azospirillum, azotobacter, phosphobacteria, PGPR bacteria, cyanobacteria, symbiotic fungi- ecto and endo mycorrhiza, alleviate the impact of soil salinity. Minimal microbiome for plants is the key determinant of plant health and productivity. The plant- microbe interactions are of three types: (i) Positive: Beneficial organisms- nitrogen fixers, phosphobacteria, mycorrhiza; (ii) Negative: Harmful organisms - plant pathogens; (iii) Neutral: Transient organisms. Millions of microbes inhabit plants, form complex ecological communities to improve plant growth, crop quality and productivity.

Introduction

The coastal tract of peninsular India extends from Rann of Kutch in Gujarat to Malabar Coast in Kerala on the western coast and from Coromandal coast in Tamil Nadu up to Sunderban delta in West Bengal on the eastern coast. The eastern coast is generally characterized by a wider coast line when compared to its western counterpart. Coastal ecosystem in India occupies an area of about10.78 million hectares (1,07,833 km²) extending in nine states. It also occupies considerable area underLakshadweep and Andaman and Nicobar group of Islands. The extent and distribution of coastalarea have been shown in Table 1.

Agriculture is dependent directly on weather and indirectly on the consequences of climate change. Changes in temperature, precipitation, incoming solar radiation and CO2 concentration are expected to impact crop growth to a greater extent. The overall impact of climate change on worldwide food production is considered to be low to moderate only when there is successful adaptation strategies towards climate change (IPCC, 1998). Global agricultural production could be benefited by the doubling CO2 concentration. All sectors including agriculture will be impacted by the adverse effect of climate change imposed on water resources (Gautam and Kumar, 2007; Gautam, 2009). India is expected to face more seasonal variation in temperature with more warming in the winters than summers (Christensen et al., 2007; Cruz et al., 2007). The frequency of drought occurrence is increasing due to variations in the monsoonal rainfall. Climate change is posing a great threat to agriculture and food security. Water is the most critical agricultural input in India, as 55% of the total cultivated areas do not have irrigation facilities.

Even with the variations in climate conditions, food accessibility for the present generation is assured, but as far as the future is concerned there is still the uncertainity of agricultural production. All climate models predict that there will be more extreme weather conditions, with more droughts, heavy rainfall and storms in agricultural production regions. Such extreme weather events will inf luence pest and disease occurance, imposing severe risks of crop failure. In India, climate change could be a potential threat towards food security due to population growth, industrialization and economic development. India’s climate could become warmer under conditions of increased atmospheric carbon dioxide.

Global warming has become a worldwide concern after feeling the rise of temperature much more clearly after 1990. Global warming has negatively affected agricultural production throughout the world attributed by the climate change parameters. Effects of climate change on horticultural crops reported nowadays include problems of biotic and abiotic stress, yield loss, quality loss, etc. To understand the impact of climate change problems in horticulture, it is prerequisite to comprehend the issues related to global carbon cycle, climate change phenomenon, relationship between climate change variables and direct as well as indirect climate change effects.

Introduction

Vetiver, commonly known as Khus grass is a perennial grass of Indian origin. Vetiver roots contain fragrant essential oil, which is a perfume by itself. Aroma chemicals such as vetriverol, vetriverone and vetriveryl acetate are prepared from this volatile oil. In India, it is mainly used in perfumes, cosmetics, and aromatherapy, food and f lavouring industries. Since the plant has extensive finely structured fibrous roots, it is useful in both soil and water conservation and the plant itself is drought tolerant. The world production of vetiver oil is around 300 tons per annum of which India contributes about 20-25 tons only. The world major producers are Haiti, India, Java and Reunion. In India, it is cultivated in the states of Rajasthan, Uttar Pradesh, Karnataka, Tamil Nadu, Kerala, Andhra Pradesh and Telangana, with an annual production of about 20 tons of oil. The present consumption of vetiver oil in India is about 100 tons and 80% of the domestic consumption is met by export only. As the internal demand for vetiver oil is very high, concerns are risingover the improved production and quality of raw materials used. Vetiver is a miracle crop and a big boon for the farmers to increase incomes. This would increase farmer’s income by three to four times. This crop has demand not only in India but also across the world. India is gaining big ground in cultivation of vetiver.

Introduction

Global warming is a phenomenon of climate change characterized by a general increase in average temperatures of the Earth, which modifies the weather balances and ecosystems for a long time. In fact, the averages temperature of the planet has increased by 0.8° Celsius (33.4°F) compared to the end of the 19th century. Each of the last three decades has been warmer than all previous decades since the beginning of the statistical surveys in 1850. At the pace of current CO₂ emissions, scientists expect an increase of between 1.5° and 5.3°C (34.7° to 41.5°F) in average temperature by 2100 (Solarimpulse foundation, 2018). Most of the several dozens of predictive models indicate that average temperature can increase by 1.7-5.3°C, as a result of doubling CO₂ concentration within next 60-100 years. 2.3°C is a value most often mentioned what means an increase by 0.3°C a decade.

Climate change has emerged as the most pressing global challenge of the 21st century and which is one of the largest and most complex problems, the developed community has ever faced. The impacts of higher temperatures, variable precipitation, and extreme weather events have already begun to impact the economic performance of countries and the lives and livelihoods of millions of poor people. India is among the countries most vulnerable to climate change. It has one of the highest densities of economic activity in the world, and very large numbers of poor people who rely on the natural resource base for their livelihoods, with a high dependence on rainfall. By 2020, pressure on India’s water, air, soil, and forests is expected to become the highest in the world.

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation and climate change threatens the global food security.

Coastal ecosystems of india

India has a long coastline of 7,517 km that span 13 maritime states and union territories, surrounded by the Indian Ocean, Arabian Sea and Bay of Bengal. It has an Exclusive Economic Zone of 2.02 million square kilometers. The country has a diverse range of habitats: continental area and offshore islands and a variety of coastal ecosystems such as estuaries, lagoons, coral reefs, mangroves, salt marshes, sea grass beds, backwaters, rocky coasts, mudf lats, sand dunes, and coastal beaches. Coasts and islands form 2.8% of India’s geographical area. The coastal ecosystem consists of 43,230 km² of coastal wetlands with 97 major estuaries and 34 major lagoons; 4921 km² of mangroves with 31 mangrove areas, and 3062.97 km² of coral reefs with 5 coral reef areas (MoEFCC, 2018). A network of 14 major, 44 medium and numerous minor rivers together with their tributaries with a total length of over 40,000 km, contribute to the coastal sea directly or indirectly. By area, tidal/mudf lats are predominant and by length, more than half of the Indian coastline is sandy. The states of Gujarat, Tamil Nadu and Andhra Pradesh have extensive coastline (Fig. 1).

Introduction

India contains around 7517 km of coast line, which is surrounded by the Arabian Sea on the west, the Bay of Bengal on the east, and the Indian Ocean to its south. The Indian Coastal ecosystem, where land and water join to create an environment with a distinct structure, diversity, and f low of energy. They include salt marshes, mangroves, wetlands, estuaries, and bays and are home to many different types of plants and animals. (Marale. 2013). The geographic and land use details of Indian coastal zone is presented in Table 1.

Introduction

One of the major problems being faced by cities and towns relate to management of municipal solid waste (MSW). Waste quantities are increasing day by day due to the increasing population and the municipal authorities are not able to upgrade or scale up the facilities required for proper management of such wastes. In many cities and towns, garbage is littered on roads and foot-paths. Citizens are also not accustomed to use the available storage facilities (dust bins) set up by the authorities. At large, lack of organized system of house- to-house collection of waste has created the littering habits. By and large, hardly we can see any city/town complying with the Municipal Solid Wastes (Management and Handling) Rules, 2000 in ‘totality”. As per information provided by Central Pollution Control Board (CPCB), 1,27,486 TPD (Tons per day) municipal solid waste was generated in the country during 2011-12. Out of which, 89,334 TPD (70%) of MSW is collected and 15,881 TPD (12.45%) is processed or treated.

In spite of various measures taken towards the proper waste collection, segregation and disposal, proper waste processing units were established as per the following details as per CPCB. The waste processing technologies reported in the country are; composting, vermi composting, biogas plant, RDF- pelletisation and others. Some of the sepelletization plants are associated with power plants for generation of electricity. However, mechanical composting and vermi composting are more popular in the country.

1.1 R and R studio

R has become the world-wide language for statistics, predictive analytics, and data visualization. R is not limited to statistics; it has a wide range of tools for spatial analysis and digital image analysis and can handle any process needed for image analysis flexibly. It offers a wide range of methodologies for digital image processing, from the most basic to the most complex. As an open source project, it’s freely available for a range of platforms, including Windows, Mac OS X, and Linux. An integrated development environment (IDE) for R, R Studio, allows interactive execution of R functions with user interface. RStudio is available in two formats - RStudio Desktop and RStudio Server - both available in free and fee-based (commercial) editions.

1.1.1. Download and install R

The R software setup files can be downloaded from any of the widely distributed Comprehensive R Archive Network (CRAN). To download R, please choose your preferred CRAN mirror, a location close to you. Follow the following steps:

Vector data consists of points, lines and polygons with x, y coordinates known as vertices representing the geographic location i.e. latitude and longitude. Vector data depicts the shape of the spatial feature. Points consist of single x, y locations. Examples of points are tower location, sampling location, etc. Lines represent 1 – dimensional features like roads, streams, etc., and are composed of many or at least two connected points or vertices. Polygons denote 2 – dimensional features like boundaries with three or more vertices that are connected and closed. The vector data are represented in the following figure (fig. 5.1).

Coordinate Reference System (CRS) is a framework of coordinate based local, regional or global reference system used to represent the locations of geographic features. Coordinate Reference Systems (CRS) provide a standardized way to define the real-world geographic locations which further enable us to use common locations of geographic datasets for integration. The datasets from different sources and CRS are transformed to a common CRS in order to perform various integrated analytical operations like overlaying of data layers. Coordinate Reference Systems are mainly of two types:

1. Geographic Coordinate System: This is global or spherical coordinate system in which locations of features on earth are defined in terms of latitude and longitude values. Latitude and longitude are angles based on a point at the centre of the earth. In this system, locations are defined by degrees, therefore, distances cannot be measured accurately. The most common geographic coordinate system is the World Geodetic System 84 (WGS84).
2. Projected Coordinate System: In this system, locations of features are defined using cartesian x, y coordinates on a flat, two – dimensional surface. This system is based on sphere or ellipsoid projected on flat surface. Projected Coordinate System are generally referred as projec- tion. It is required for preparing maps on paper or computer screen. Un- like Geographic Coordinate System, projected coordinate system has ac- curate measurements of distance, angles and areas across the two dimen- sions. However, there is alwaystrade-offs between area, direction, shape, and distance in this system due to distortions created while representing ellipsoid or spheroid on a flat map. So, the projection type is selected based on its application. Most common projections are Universal Trans- verse Mercator (UTM), Lambert Conformal Conic and Albers Equal Area.

Raster sub-setting is done to select the desired area or bands of the raster image on which the user wants to perform analysis. Raster sub-setting is done in two ways:

Spectral subset: Spectral sub-setting is done to extract few desired bands from the multiband raster image which are required for analysis.

Spatial subset: Spatial sub-setting is done to minimize the area or extent of raster image to the desired area or extent for further analysis.

Raster sub-setting in R is done by functions available in the ‘raster’ package. The following packages are required in this chapter:

Vector data analysis is performed to convert raw input vector data to output that provides new and meaningful information which ultimately supports decision making and disclose hidden patterns. There are various techniques to analyse vector data like buffering, intersect, union, and so on. Some of the most commonly used techniques with their R-code examples are explained in this chapter.

First of all, you need to import all the packages which are required for performing this exercise. These packages include rgdal and sp for importing and re-projecting vector data as well as raster and rgeos for performing vector analysis operations.

Mosaic is the combination of two or more raster images into single image. Sometimes, single image captured by remote sensing satellite doesn’t completely covers the desired study area mostly. Therefore, users resort for mosaicking two or more images captured by satellite sensors from different paths so that the whole study area is merged into a single raster image to facilitate the image processing and analysis. The pixel size of the raster images should be same for mosaicking.

In most of the cases, there is spatial overlapping among the raster images from which we want to create mosaic. These overlapping areas are handled in multiple ways which is being discussed further using R – code example. For mosaicking raster images in R, mosaic function of the raster package is used.

Many times, users have to work with raster datasets of different spatial and radiometric resolutions. So, there is requirement of bringing their resolutions to the same resolution in order to facilitate their processing. Resampling is the process of deriving pixel values from the existing image to the new raster image of different pixel size. Resampling is most common pre-processing step in remote sensing. It is done both at spatial and radiometric scale. At spatial scale, resampling is done either to increase the spatial resolution i.e. decrease pixel size known as Upsampling or to decrease the spatial resolution i.e. increase pixel size known as Downsampling. Radiometric resampling is performed to increase or decrease the radiometric resolution of the image. In R, resampling of raster image is performed by raster package. The details of the resampling methods and their respective R – code with examples are discussed in this chapter.

First of all, we need to import the raster image on which we wish to perform resampling operations by using following R – code. In R, resampling is feasible on one band only. So, we will import single band raster image.