eChapters

As we look to the future, climate crisis is looming large and it is essential to address several aspects of climate-resilient agriculture. One of the critical areas that require attention is scaling up climate-resilient agricultural practices. This involves promoting and supporting the adoption of climate-resilient agricultural practices, such as conservation agriculture, agroforestry, and climate-smart agriculture, among small-scale farmers and rural communities. There is vast scope for upgrading traditional technologies making best use of modern technologies. To achieve this, there is a need:
• To increase funding and investment in climate-resilient agriculture. This can be achieved through a combination of government funding, private sector investment, and international aid. The funding should be used to support research and development, technology transfer, and capacity building, as well as to provide financial incentives to farmers to adopt climate-resilient agricultural practices.
• Strengthening international cooperation is also critical for promoting climate-resilient agriculture. This involves sharing knowledge, expertise, and resources among countries, as well as collaborating on research and development, technology transfer, and capacity building. International cooperation can also help to promote policy and institutional reforms that support climate-resilient agriculture.
• Promoting climate information and early warning systems. This involves providing farmers and rural communities with accurate and reliable climate information, as well as early warning systems that can help them prepare for and respond to climate-related disasters. Climate information and early warning systems can be provided through a range of channels, including mobile phones, radio, and television.
• Supporting climate-resilient agricultural research and development is also critical for promoting climate-resilient agriculture. This involves investing in research and development that focuses on improving crop varieties and animal breeds, developing new technologies and practices, and improving our understanding of the impacts of climate change on agriculture. Research and development can also help to identify and promote climate-resilient agricultural practices that are suitable for different regions and contexts.
• Promoting policy and institutional reforms is also essential for supporting climate-resilient agriculture. This involves promoting policies and

This theory belongs to the ‘market area’ or ‘profit maximisation’ approach and has focused on spatial variations in scales potential. August Losch was a German economist and he proposed his theory in 1939 in a book entitled Die taumliches Ordnung Derwirt’s Chaff.

He disregarded spatial variations in production costs by holding them constant, and instead depicted optimal location as occurring where the largest possible market area is monopolised – that is, where sales potential and total revenue potential are maximised. Losch sought to explain the size and shape of market areas within which a location would command the largest revenue.

Farmers’ data collection can be a valuable input for the development of agrometeorological advisories because it provides a unique perspective on the local weather and climate conditions that affect their crops and farming practices. Farmers have a deep understanding of the local environment and are often able to observe subtle changes in weather patterns and soil conditions that may not be captured by traditional meteorological instruments. By collecting data on weather patterns, soil moisture, crop growth, and other factors, farmers can provide valuable insights into the complex relationships between weather, climate, and agriculture. This data can be used to improve the accuracy and relevance of agrometeorological advisories, which are critical for helping farmers make informed decisions about planting, irrigation, pest management, and other aspects of crop management. By incorporating farmers’ data into the development of these advisories, researchers and policymakers can create more effective and targeted recommendations that take into account the specific needs and conditions of local farming communities. Furthermore, the collection of farmers’ data can also help to identify gaps in existing meteorological data and provide insights into the impacts of climate change on local agriculture. This information can be used to inform the development of more effective climate adaptation strategies and to support the creation of more resilient and sustainable agricultural systems.
In addition, the involvement of farmers in data collection can also help to build trust and promote the adoption of agrometeorological advisories. By working closely with farmers and incorporating their knowledge and expertise into the development of these advisories, researchers and policymakers can create more effective and sustainable solutions that meet the needs of local farming communities. Overall, the collection of farmers’ data can provide a valuable input for the development of agrometeorological advisories, helping to create more effective, targeted, and sustainable solutions that support the needs of local farming.
5.1 Low Cost Instrumentation
The use of low-cost instrumentation in agriculture is essential for enabling farmers to collect accurate and reliable data on various parameters that affect crop growth, soil health, and farm management. In many developing countries, smallholder farmers lack access to expensive and sophisticated equipment, making it difficult for them to make informed decisions about their farming practices

In this chapter of machine learning algorithms we will cover the basic algorithms of machine learning with their implementation using dataset and discuss their pro’s and cons of algorithms for better understanding and how to use them in any project or with any dataset to get an correct result which can useful for research or study.

Key Component

We defined a data set composed of audio, images and binary labels that provided an understanding of how we could train a model from snippets to classifications. This sort of problem is one of many kinds of machine learning problems when we try and predict a specified unknown label given known inputs, provided that a dataset of examples for which labels are known is called supervised learning. Let’s discus what are the major key components we have to get there

Agriculture 4.0, otherwise called “Digital Agricultural Revolution”, addresses a change in outlook in agriculture, utilizing state of the art innovations to improve different parts of cultivating tasks. These innovations include the Internet of Things (IoT), Artificial brainpower (Artificial intelligence), Huge Information, distributed computing, Cloud computing, Decision Support System (DSS), high level detecting innovation, and independent robots. Sensors and mechanical technology assume a urgent part in gathering fundamental field information, which is then sent to a neighborhood or cloud server by means of IoT innovation for capacity, handling, and examination. Large information and Artificial intelligence-based strategies can be utilized to change over this information into significant experiences. To work with client connection and informed independent direction, a DSS outfits clients with the fundamental devices to improve the agricultural framework and embrace proper activities.
AI (ML), a subset of Artificial intelligence, has shown extraordinary potential in upgrading different parts of agriculture 4.0. It very well may be characterized as a PC program or framework that can learn explicit errands without being expressly modified to do so. An interaction includes the utilization of a PC to go with choices in light of various information inputs. For this situation, information means a bunch of models. Marked information is frequently utilized for managed learning undertakings (where the model gains from named models), and unlabeled information may be utilized for solo learning assignments (where the model tracks down examples and designs in the information).
ML for sure advantage from a lot of information to accomplish significant exactness in their errands. With regards to farming, acquiring immense and various information can be at times testing yet crucial for the outcome of ML models. IoT sensors are instrumental in gathering a different scope of farming information as they can be decisively sent across fields to catch pertinent data with respect to, for example, soil conditions, Climate factors, crop wellbeing, and animals’ measurements. The inescapable reception of IoT innovation works with constant and continuous information procurement, empowering

As we know there are different branches of artificial Intelligence and machine Learning of one of them which has become a most advance and widely used branch of artificial Intelligence in every field of this generation whether it may be either electronics, civil, mechanics, Bioengineering, medical or agriculture etc., In this chapter we will study about machine learning and its types with the most common algorithms of machine learning which are being mostly used in different scenarios and their implementation using python language has been discussed in the implementation part of this book. Here we will also see where machine learning can be used.

i. Introduction

Machine learning is a subset of Artificial Intelligence and one of the most important part of artificial Intelligence. It is a field of Computer science which is totally differs from traditional computational approaches. In traditional approaches, a set of instruction is explicitly provided to the algorithm to solve any problem. Whereas in Machine Learning is a domain of an AI which uses a statistical analysis and can enables a system to always learn from its data without any external inputs in order to give an essential output. Due to this machine learning makes computers in advance to make model which provide decision based on input data.

Machine Learning involves a lot of algorithms which usually based on their requirements and functionalities to use where and when. To make a machine learning model we always require the data on which model can be made. Machine learning uses the algorithm which continuously learns from the data available as training data. A machine learning model is generated after the training of algorithm from the data. After the training completes, when trained model is provided with other real-life data then you can get an essential outcome. For example, in figure 3.1 Machine learning Model we use a predictive algorithm and make a model by providing it with some data, and later on we can receive prediction’s based on real data as input.

AIR POLLUTION FROM AUTO EXHAUST

Motor vehicles are the major source of a number of pollutants, in particular carbon monoxide (CO),nitrogen oxides (NOx), unburnt hydrocarbons (HC),ozone (O3) and other photo chemical oxidants and lead (Pb) and, in smaller proportions Total Suspended Particulate Matter (TSPM), sulphur dioxide (SO2) and volatile organic compounds.

Thomas Robert Malthus enunciated his views about population in his famous book, Essay on the Principle of Population as it affects the Future Improvement of Society, published in 1798. Malthus revolted against the prevailing optimism shared by his father and Godwin that a perfect state could be attained if human restraints could be removed.

Malthus’ objection was that the pressure of increasing population on the food supply would destroy perfection and there would be misery in the world. Malthus was severely criticised for his pessimistic views which led him to travel on the continent of Europe to gather data in support of his thesis. He incorporated his researches in the second edition of his Essay published in 1803.

The Malthusian theory of population is the most well-known theory on population in economics. Malthus pointed out that an accelerated increase in population would outweigh the increase in food production. This would have an adverse impact on the development of an economy. The Malthusian theory explains the relationship between the growth in food supply and in population. It states that population increase faster than food supply and if unchecked leads to vice or misery. This theory is explained in the following propositions

In his classic book ‘The Ascent of Man’, Bronowski has written, “When I was a young man, we all thought that mastery came from man’s domination of his physical environment. Now we have learned that real mastery comes from understanding and moulding the living environment.” Our Biosphere, the living environment that supports us all, has problems and we humans are mainly responsible. We do not live ‘gently’ on the earth. The seven billion of us require air, water, food, energy, housing and amenities and we have developed the earth to provide them and have also created problems. That the problems exist are serious is the bad news. That we are recognizing them and that man has shown ability to organize and grow in ascending order from time to time is the good news.

ABSTRACT

The purpose of this paper is to map the diversity of Formicoidea, richness and habitat preferences with respective to the different forest cover, classified based on the NDVI from LANDSAT TM imagery with the help of remote sensing and GIS. The study is carried out in Sirumalai hills region of Dindigul District, Tamil Nadu state, India recorded a total of 26 species with 15 genera belonging to four sub-families by a taxonomical basis. The NDVI based forest classification results that the area has disturbed dry deciduous forest (SA), undisturbed dry deciduous forest (SB), disturbed mixed deciduous forest (SC), undisturbed semi evergreen forest (SD), undisturbed grassland (SE) and undisturbed dry evergreen forest (SF) types of forest covers. The results of diversity studies, statistical analyses and field study reveals that the tropical semi evergreen forest and tropical dry evergreen forest have yielded more number of species, genera than the mixed deciduous forest and dry deciduous forest which are most disturbed by human beings in establishment of estates, management strategies by the government and land use by the local farmers.

ABSTRACT

The Mumbai region of Maharashtra has been witnessing very rapid urbanization development in terms of industrial, commercial and residential establishments. This has resulted in reclamation of tidal flats and estuarine regions. The ‘marine clay', characteristic of this environment has high swelling potential and consequently affects the performance of the structures. This will have a serious negative impact on the urbanization expansion. In this study, an attempt is made to map the spatial distribution of these expansive clays using the state of art Hyperion data. Reflectance spectra of representative samples collected from the field is used to map marine clays using Spectral Angle Mapper (SAM) technique. Laboratory analyses of these foundation soils (reflectance spectroscopy, mineralogy and Atterberg limits) indicate that they have significant amount of montmorrilonite and has good swelling potential (A_C>1.4). The derived expansive clay abundance map will be of use while planning appropriate remedial measure and future development.

Key Words : Swelling clay, Spectral characters, Geotechnical properties

The resources on the land have started showing sign of faster depletion owing to the population explosion, over exploitation of mineral and water resources and also the improper exploitation and the related wasting of the natural resources. Hence a time has come to search for more and more newer natural resources amongst which the ocean resources gain supreme importance as this treasure is yet to be hunted due to the non - availability of suitable technology for the same.

Such ocean resources mainly include the mineralogical and biological resources. The ocean biological resources, their birth, growth, population and migration greatly depend as chlorophyll content, total dissolved oxygen, total suspended solids, salinity etc. These parameters which constitute the marine environment drastically change in a quick span of time owing to the dynamically changing marine forces.

Abstract

GIS in marine fisheries has taken off much later than its application in land. But the strides made in this field since 1990’s has been quite significant, as many serious mapping studies and modeling initiatives have been recorded. With the third dimension added to the 2D spatial data, the marine GIS have all the ingredients of making it a fertile domain for research for decades to come. Most of the studies and products pertaining to GIS in fishery management had been revolving around mapping of habitats, geo-referencing of catch and effort data and modeling linking oceanographic and biogeochemical factors with living resources. Satellite remotely sensed information like ocean colour can act as a surrogate to fish biomass. Further remotely sensed SST, wind and other parameters can be effectively modeled to predict fish abundance, their habitat and related aspects. This manuscript pours into the various products and outputs based on GIS in marine fisheries around the world and the Indian initiatives.

Key words: GIS, marine resources, oceanography, primary productivity and climate change

1.0 Introduction

Geographic Information Systems (GIS) have been one of the most prolific scientific enhancements in recent past, which by virtue of out of box expansion of traditional database concept have opened up an entirely new vista of knowledge and opportunities. It’s probably one such unique application where information collation and analysis happen hand in hand resulting in palpable mining of data in perspective. A formal introduction to GIS would define it as a collection of computer hardware, software, data and personnel designed to collect, store, update, manipulate, analyze and display geographically referenced information. By virtue of its conceptualizations, there are no single GIS; systems can be assembled in an infinite number of ways. This is one such field, which probably always seeks the frontiers of innovations in the fields of ICT and modeling.

Introduction
Marine Pollution
The pollution of the sea substances that humans have directly or indirectly introducedinto the marine environment, which can have negative impacts on the ecosystem, including health risks to people, obstructions to marine activities, and a decline in the quality of seawater.
It happens when invading species or chemicals or particles or garbage from industry, agriculture, or residential sources find their way into the ocean and can have detrimental impacts.
The majority of maritime pollution originates on land. Nonpoint causes of pollution include wind-blown debris and agricultural runoff.
A great deal of potentially harmful substances stick to small particles, which plankton and benthos animals (most of which are deposit or filter feeders) then consume.
Pesticides swiftly find their way into marine food webs after entering the marine ecology. These pesticides can induce diseases and mutations once they enter food webs, which can be dangerous for both humans and the entire food chain.
Marine food webs may also be exposed to toxic metals. These have the power to alter biochemistry, behavior, reproduction, tissue composition, and growth inhibition in marine organisms. Additionally, a lot of animal diets contain a lot of fish meal or fish hydrolysate. Toxins from the sea can thus spread to land animals and then show up in meat and dairy products.

Microbial inoculants in agriculture, horticulture and forestry can be defined as preparations containing strains of microorganisms which can augment the microbiological process such as nitrogen fixation, phosphate mobilization, excretion of growth promoting substances, decomposition of organic wastes, biocontrol of insect pests and diseases etc. Thus they become an important component of sustainable agriculture. Microbial inoculants are based on renewable sources available in soil and nature and are low cost input and eco-friendly. These inoculants carrying the microorganisms when applied to soil/plant promote plant growth and productivity.

The research methodology constitutes a comprehensive and well-structured plan of action for conducting the investigation and serves as a blueprint for the research process. Its purpose is to gain an understanding of the underlying concepts, methods, and techniques employed to define the study, gather relevant information, analyse data, and interpret the findings, thereby revealing concrete evidence and formulating theories.

In this chapter, an in-depth exploration of the research methodology is undertaken to comprehend the concepts, methods, and techniques employed for designing the study, gathering data, conducting data analysis, and interpreting the results. To facilitate clarity and ease of comprehension, the chapter has been divided into the following sub-headings:

1. Research Design

2. Locale of research

3. Pilot study

4. Sampling Design

5. Variables and their Empirical measurement 6. Methods of Data collection

7. Preparation of Interview Schedule

8. Item analysis and Expert’s rating for devising scoring technique for variables

9. Pre-testing of Interview Schedule

10. Data Processing and Analysis for FCM

11. Statistical Tools used for Data Analysis

12. Software used for Data Analysis

7.1 Introduction

This chapter describes the development of data logger of agricultural field parameter monitoring system with MATLAB. The system comprises of Arduino, water level sensor, humidity sensor, light sensor, soil moisture sensor, temperature sensor, smoke detector, power supply, PC with MATLAB. The system is designed to monitor the environment and agriculture field parameters. The field device is designed to collect all physical parameters and same is communicated serially to MATLAB with I/O package. Fig.7.1 shows block diagram of the system. Table.7.1 shows the component list required to develop the system.

Temperature influences most plant processes, including photosynthesis, transpiration, respiration, germination, flowering, adaptation and distribution of crops. As temperature increases (up to a point), photosynthesis, transpiration, and respiration increase. When combined with day-length, temperature also affects the change from vegetative (leafy) to reproductive (flowering) growth. Depending on the situation and the specific plant, the effect of temperature can either speed up or slow down this transition.

Temperature is the measure of mean kinetic energy per molecule of the molecules in an object, while the heat is the measure of total kinetic energy of all the molecules of that object. A large object may  have a much lower temperature than a small object and may still have greater heat content by virtue of larger number of molecules in it. Thus heat and temperature are not the same.