eChapters

Water colour is consequences of the plankton, humus, metal ions (iron and manganese), weeds and peat materials, presence in water. Iron oxides cause reddish color whereas Manganese oxides causes brown or brackish colour in water. Water colour of an aquatic body also indicates the density of planktons. Brownish colouration without any foul smell is an indication of good growth of zooplankton. Phytoplankton or algae are usually green in colour and these green alage in large quantities turn the green color of water. Water color suggests plankton or other aquatic life growth, clear and transparent colour of water indicates low and poor growth of plankton. Brownish green to greenish brown colour of water indicates well for aquaculture.
Principle
Natural waters colour comprising yellow-brownish in appearance. It has been observed that potassium chloroplatinate (K2PtCl2) tinted with small amounts of CoCl2 yield colours that are very much like the natural colours of water. 1 mg/L of Pt (K2PtCl6) produces a standard 1 unit of colour. The intensity of colour increases as pH rises. For this reason recording pH together with colour is advisable.

18.1 Introduction

The commercially available water quality simulation models with various dimensions (1D, 2D and 3D) and database software designed to quantify movement and concentration of contaminants in lakes, streams, estuaries, and marine environments. The following water quality models were described below (Table 18.1).

18.2 Environmental Fluid Dynamics Code

The Environmental Fluid Dynamics Code (EFDC) is a multifunctional surface water modeling system, which includes hydrodynamic, sediment-contaminant, and eutrophication components. It has been applied to over 100 water bodies including rivers, lakes, reservoirs, wetlands, estuaries, and coastal ocean regions in support of environmental assessment and management and regulatory requirements. IT is a state-of-the-art hydrodynamic model that can be used to simulate aquatic systems in one, two, and three dimensions. It has evolved over the past two decades to become one of the most widely used and technically defensible hydrodynamic models in the world. It uses stretched or sigma vertical coordinates and Cartesian or curvilinear, orthogonal horizontal coordinates to represent the physical characteristics of a waterbody. It solves three-dimensional, vertically hydrostatic, free surface, turbulent averaged equations of motion for a variable-density fluid. Dynamically-coupled transport equations for turbulent kinetic energy, turbulent length scale, salinity and temperature are also solved. The model allows for drying and wetting in shallow areas by a mass conservation scheme.

Vegetation is a key component in hydrologic modeling. Transpiration, a component in hydrologic cycle, is contributed by vegetation. Most of the hydrologic model thus includes vegetation as a parameter. For accurate and appropriate model output, first requirement is good quality data. Most of the time, the data on vegetation is confusing, as in literature various researcher have used different values for common vegetation parameters. Here some common parameters of some important crops, which yield good quality results, are given.

Kharif crops are generally sown during last week of May and first week of June, while rabi crop are sown during last week of November and first week of December. But this period will vary from region to region as well as depends on variety of crop.

Abstract

Rice blast disease (Pyricularia grisea cavara) which is very much weather sensitive, continues to be an enigmatic problem of West Siang District of Arunachal Pradesh, owing to its geographical location and climate. There is an urgent need of a forewarning system which can provide advance information for outbreak of the diseases attack so that protection measures can be implemented before the actual economic damage of the crop occurs. Two most important statistical techniques for disease forecasting viz. regression analysis and artificial neural network were chosen and forecasting models has been developed for the area and analyzed with local weather data to find best fit for the area and also compared with the Support Vector Machine (SVM) based model RB-Pred developed by Institute of Microbial Technology, Chandigarh. The model based on artificial neural network was found to be best with accuracy of 75-80%, with the present data and algorithm. The accuracy can be improved with long archive records and proper implementation and algorithm selection of the hidden layer, of both ANN and SVM based (being special case of ANN) models, for site-specific forecasting.

Soils vary greatly in their organic matter content. For all practical purposes the organic matter content of the soil parallels the soil N content. The C/N ratio of soil organic matter generally falls in the range of 10 to 12. Because of the ease with which Kjeldahl determination can be made, N is often used as an index of soil organic matter content.

3.1. Composition of Organic Matter in Soil

Organic matter constitutes 1 to 6% of the topsoil weight of most upland soils. In a typical grassland soil, 5 to 6% of organic matter may be present in the top 15 cm. Top soils with less than 1% organic matter are mostly limited to desert regions. On the contrary, the organic matter content of soils in low, wet areas may be as high as 90% or even more. Soils with more than 12 - 18% organic carbon (approximately 20 - 30% organic matter) are called organic soils.

Heat units also called growing degree-days (GDD), effective heat units, or growing degree units, are a heuristic tool in phenology. GDD is a simple means of relating plant growth, development and maturity to air temperature. The concept is widely accepted as a basis for building phenology and population dynamic models. Degree day units are often used in Agrometeorology, Agronomy, Entomology and Pathology essentially to estimate or predict the lengths of the different phases of development in crop plants, insects, pathogens etc.

Introduction

A solar collector, the special energy exchanger, converts solar irradiation energy either to the thermal energy of the working fluid in solar thermal applications, or to the electric energy directly in Photovoltaic (PV) applications. For solar thermal applications, solar irradiation is absorbed by a solar collector as heat which is then transferred to its working fluid (air, water or oil). The heat carried by the working fluid can be used to either provide domestic hot water/heating, or to charge a thermal energy storage tank from which the heat can be drawn for use later (at night or cloudy days). Solar collectors are usually classified into two categories according to concentration ratios: non-concentrating collectors and concentrating collectors. A non-concentrating collector has the same intercepting area as its absorbing area, whilst a sun-tracking concentrating solar collector usually has concave reflecting surfaces to intercept and focus the solar irradiation to a much smaller receiving area, resulting in an increased heat flux so that the thermodynamic cycle can achieve higher Carnot efficiency when working under higher temperatures.

India’s economic security continues to be predicated upon the agriculture sector, and the situation is not likely to change in the foreseeable future. Even now, agriculture supports 58% of the Indian population, as against about 75% at the time of independence. In the identical period, the contribution of agriculture and allied sector to the Gross Domestic Product (GDP) has fallen from 61 to 13%. As of today, India supports 16.8% of world’s population on 4.2% of world’s water resources and 2.3% of global land resources. Also per caput availability of resources is about 4 to 6 times less than that of world average. This will further decrease due to increasing demographic pressure and consequent diversion of the land for non-agricultural uses. Around 51% of India’s geographical area is already under cultivation as compared to 11% of the world average. The present cropping intensity is 136% and it has been registered an increase of only 25% since independence. Further, rainfed and dry-lands area constitute approximately 65% of the total net sown area. There is also an unprecedented degradation of land (107 million ha) and groundwater resource, and also fall in the rate of growth of total factor productivity. This deceleration needs to be arrested and agricultural productivity has to be doubled to meet growing demands of the population by 2050. Efficiency-mediated improvement in productivity is the most viable option to raise production of various commodities including horticulture.

Soil pH

 Soil reaction is measured by pH (Puissance de Hydrogen) of a suspension of

soil in water. The word “pH” has originated from the French word “pouvoir

hydrogen” which means powers of hydrogen. The acidity, neutrality or

alkalinity of a soil is measured in terms of hydrogen ion activity (active

concentration) of the soil water system. Most of the agricultural soils have

a pH varying from 4.5 to 8.5. The active concentration of hydrogen ion is

termed as ‘active acidity’ which is 1/50,000 or 1/1,00,000 times lesser than the

exchange acidity or exchangeable hydrogen.

 Soil pH is a measure of the activity of ionised H (H+) in the soil solution. The

pH of a solution can be defined as the negative logarithm to base 10 of the H+

ion activity or the logarithm of the reciprocal of the H+ ion activity.

pH = -log10

 a[H

 +] = log10

 1/[H

 +]

 There is difference between activity and concentration. Activity and

concentration are related by the mathematical expression

 a

 c

 F

 = where a = activity of a species (moles L-1)

 c = concentration of that species (moles L-1), f = activity coefficient

 a = c for dilute solutions, f = 1

 Activity coefficient of an ionic species is the degrees of freedom of that species

to participate in an equilibrium reaction or to maintain its charge under an

ionic environment.

There is widespread belief now that forests can be used to reduce the costs of slowing climate change. While the role of forests in the global carbon cycle has long been acknowledged, recent discussions within the context of the United Nations Framework Convention on Climate Change, as well as efforts to write climate change legislation in the United States, have emphasized the role forests might play. The most recent policy efforts have focused on near-term actions to reduce deforestation in tropical countries. The rationale for considering forests at all in the policy mix stems partly from the physical components of the issue. The world’s forest estate is exceedingly large:

Since the Green Revolution of 1960s, Indian agriculture have been highly dependent on chemical fertilizers, exploitation of ground water for irrigation, intensive tillage etc. to raise the food grain production of the country towards ensuring food security for all. The consequences of these practices resulted in declined and degraded soil health, soil erosion and dried up water capillaries - ultimately posing threat to the sustainability of the future production. This invites the need of affordable changes in the agricultural practices without hampering the production. Conservation Agriculture, as put forward by FAO, is a set of sustainable agricultural practices, aims to conserve, improve and make efficient use of natural resources through management of soil, water and biological resources in aid with external inputs. The three basic principles of Conservation Agriculture include - minimum soil disturbances, permanent soil cover and crop rotation or crop diversification.

7.1. Summary

The present research had been carried out to study the social ecology of arsenic contamination in groundwater; factors, perception and impact on life and livelihood of the selected village of West Bengal and in the end the comparison is made. The study had been conducted at village Mitrapur in the Haringhata block of West Bengal. A pilot study was conducted to understand the area, its people, institution, communication and extension system of these states.

An exhaustive list of respondents was prepared critically with the help of some villagers. From the list, 70 respondents from the village were selected for the study through a non-random snowball sampling method. The primary data were collected with the help of a structured interview schedule by following the personal interview method. The secondary data were collected from our departmental library, internet, Department of Agriculture, West Bengal, Bidhan Chandra Krishi Viswavidyalaya etc. for establishing the conceptual framework of the study.

Three types of Issues are Covered in this Study

• The influence of social, cultural, and/or economic variables on how drinking water poisoned with arsenic affects people’s life, notably their health (for example socio-economic patterns of illness and social),

• Social factors influencing people’s response: especially in relation to other priorities, perceptions of arsenic, and social roles and relationships.

The Asia-Pacific region, particularly the rural areas within CMCs, faces severe climate change impacts, including frequent natural disasters, rising temperatures, and sea-level rise, which threaten agriculture, livelihoods, and food security. Climate- resilient rural development (CRRD) is crucial for mitigating these vulnerabilities, addressing challenges such as agricultural dependency, limited infrastructure, governance gaps, financial constraints, and socio-cultural barriers. Key approaches like climate-smart agriculture, strengthened infrastructure, innovative start-up ecosystems, enhanced access to climate information, and inclusive policies empower rural communities to adapt and thrive. Stakeholders—governments, international organizations, the private sector, and local communities—play complementary roles in driving resilience through policy, funding, technology, and grassroots innovation. Sensitising all stakeholders across countries to mainstream climate change aspects and its impact on livelihoods, among all departments and the information dissemination by these departments to the public and relevant stakeholders regarding the climate change effects needs to be a continuous process. Success stories from Bangladesh

Introduction

There is a widespread degradation of natural resource, e.g., soil erosion, nutrient loss, water-logging, salinity, alkalinity, acidity, decline in soil organic carbon (SOC) content, ground water depletion and micronutrient depletion. The major causes are washing away of topsoil and organic matter due to water erosion, intensive deep tillage and inversion tillage, dismally low levels of fertilizer application, mining and other commercial activities, faulty agricultural practices, no or low use of organics, indiscriminate use of herbicides, pesticides, fungicides etc. Other cause is loss of organic matter with higher temperatures, which increase rate of microbial decomposition of organic matter due to adverse effect of climate change. Climate change due to global warming as a result of increased concentration of GHGs in the atmosphere is a well-established fact. Impacts of climate change are experienced throughout the world. Increases in global mean temperature, rise in sea-level, and occurrence of extreme events are the consequences of climate change. These changes may deplete the SOC pool and soil structural stability, increase soil’s susceptibility to water runoff and erosion, disrupt cycles of water, carbon, nitrogen, phosphorus, sulphur and other elements, and cause adverse impacts on biomass productivity, biodiversity and the environment. Climate change is likely to have a variety of impacts on soil quality. In order to restore soil quality, enhance productivity, it is of utmost importance to focus on conservation agriculture practices on long-term basis. Conservation Agriculture (CA) is gaining importance as an alternative to conventional agriculture.

Introduction

The Indo-Gangetic Plain is one of the world’s major foodgrain producing regions. The Indian states falling under this region, viz. Punjab, Haryana, Uttar Pradesh, Himachal Pradesh, Bihar and West Bengal, are also the major rice-wheat growing states spread over 10.5 million hectares in the country. During the past 30 years, agricultural production growth in this region has been able to keep pace with population demand for food in the country mainly due to adoption of green revolution technologies inducing yield growth, followed by area expansion. But this opportunity is ceasing very fast due to limited scope for increasing the availability of arable land and natural resources. The other issue is the conservation of the basic resources of land and water for sustainability of agriculture in the Indo-Gangetic Plain. It is generally believed that the rice-wheat system has strained the natural resources in this region and more inputs are required to attain the same yield levels (Swarup and Singh, 1989; Kumar and Yadav, 1993; Lal et al., 2004). It is, therefore, imperative now to promote alternative technologies that would help conserve the much needed but gradually depleting natural resources while boosting productivity growth in the long-run by maintaining soil health and production environment. As a part of this strategy, conservation agriculture and resource conserving technologies play a major role in sustaining and enhancing the productivity of the rice-wheat system at a lower cost of production.

Introduction

Intensive soil tillage, burning of crop residues and over use of fertilizer and irrigation water under current agricultural practices has accelerated the pace of degradation in Indian agriculture. Intensive soil tillage increases soil erosion and nutrient runoff into nearby waterways. Crop residue burning resulting in loss of plant nutrients and release of greenhouse gases (GHGs) in the atmosphere. Imbalanced use of chemical fertilizer leads to soil compaction, slows down the fertilizer utilization rate and contaminates the local environment. Increasing demand of irrigation water causes water shortage and harms the environment in several ways including increased salinity, nutrient pollution, and the degradation of flood plains and wetlands. In the face of these management and environmental challenges, there is a need to formulate such agricultural practices which improve the productivity of natural resources as well as of external inputs and help to prevent soil degradation. Conservation agriculture (CA) practices such as reduced tillage, residue retention and proper crop rotations offer such solutions. CA also helps in making agricultural systems more resilient to climate change and safeguard ecosystem services.

Introduction

India agriculture is now at turning point as we have travelled a long way after the advent of green revolution. Over the past four to five decades our strategies, policies and actions were guided by goals of ‘self-sufficiency’ in foodgrains production via green revolution. Indian agriculture has been successful in achieving increased food grains production albeit at a low level of satisfaction. While the mission of increasing foodgrains production stands somehow achieved, these gains were accompanied by widespread problems of resource degradation and high factor productivity, which now pose a serious challenge to the continued ability to meet the demand of an increasing population and lifting our people above the poverty line (Tan et al., 2010). Indian agriculture has reached a point where it must seek new directions – those by way of strategies, policies and actions which must be adopted to move forward addressing sustainable intensifications. The past strategies to increase food grains production, however, have resulted in massive exploitation of natural resources, contributing to unsustainable growth; there is need to change this approach in the future. This will call for strategies, which are different than the ones we adopted in the ‘green revolution’ era. High levels of fertilizer use and decreasing resource use-efficiency are increasingly contributing to groundwater pollution and increased emissions of green-house gases (GHGs) (Gupta, 2004). High level uses of pesticides in many areas have become a major health hazard. Thus, with continuously deteriorating resources, widespread problem of soil and water contamination and eroding ecological foundation, sustainability of agriculture is becoming highly questionable (Conway and Barbier, 1990). There is need to seriously debate on the strategies required to ensure that agriculture continues to play a critical role in the overall development.

Introduction

The Indian agriculture is very complex and carrying out multi-functionalities of providing food, nutrition and ecological security besides employment and livelihood for over 700 million people. Indeed, India has made a marvelous achievement in attaining self-sufficiency in food grain production after the induction of Green Revolution which eventually resulted in maintaining all-time high buffer stock in warehouses of our country. Such rosy picture in production trends turned to be bleak in the past few years. There are various reasons behind this. Shrinking resources of prime lands, deforestation and accelerated erosion, deterioration of soil physical environment, increasing waterlogging and salinity in canal irrigated areas, declining water table in well-irrigated areas, poor management of rainwater, lower efficiency of inputs such as water, fertilizers and agrochemicals, rapid industrialization coupled with pollution and environmental degradation and hazards have aggravated the problem. At the same time, increase in atmospheric concentrations of greenhouse gasses (GHG), of which the most common is carbon dioxide (CO₂), is the primary cause of global warming and the Intergovernmental Panel on Climate change (IPCC) estimates that the current greenhouse gases (GHGs) concentrations are 30% more than the pre-industrial level. C is accumulating in the atmosphere at a rate of 3.5 Pg (Pg = 1015 g or billion tons) per annum, the largest proportion of which resulting from the burning of fossil fuels and the conversion of tropical forests to agricultural production. Under such circumstance, there is an imperative need to produce more from less arable land and water through meticulous management of basic agricultural resources such as soil, water and biological inputs.

Introduction

The green revolution is exemplified by too much adoption of high soil disturbance, modern varieties, energy exhaustive technologies, high amount of capital investment and simplification of farming systems (Meeus, 1993), resulting in uniform and featureless landscapes (Nassauer and Westmacott, 1987) in the most intensively farmed arable areas. In post-green revolution epoch, we had experienced a period of remarkable escalation in food productivity, despite rising land values and increasing land scarcity. Farmers from both developing and developed countries get exposed to several production constraints for achieving a sustainable, cost-effective and assured return from their field. Depending upon the circumstances and the scale of farm activities, farmers face varying degrees of climate risk, biotic invasion, and economic uncertainty. Thus, from the context of economical ambiguities, volatile food prices, social pressure to buffer ups and downs of agricultural production and achieving food security for ever growing population and curtailing poverty, farmers must be equipped with novel cultural practices to achieve a sustainable and assured cost-effective grain production. Research experiments throughout the globe found that the principal indicators for unsustainability of agricultural systems are: (1) intensive tillage induced decrement of soil organic matter (OM), structural degradation of soil, accelerated water and wind erosion, reduced water infiltration rates, surface sealing and crusting, soil compaction, (2) insufficient return of organic materials into soil, and (3) monocropping.

Introduction

Persistent use of conventional farming practices based on extensive tillage, especially when combined with removal or in-situ burning of crop residues, has magnified soil erosion losses and swiftly degraded soil resource base. One of the glaring examples for the aforementioned statement is the ‘Dust Bowl’ in the U.S during 1930s in Great plains, where 91 M ha of land was degraded by severe soil erosion (Hobbs, 2007).

One of the primary challenges of our time is to feed growing and more demanding world population with reduced external inputs and minimal environmental impacts (nature paper). Conservation Agriculture (CA) is a set of management practices for sustainable agricultural production without excessively disturbing the soils, while protecting it from the processes of soil degradation like erosion, compaction, aggregate breakdown, loss of organic matter, leaching of nutrients, and processes that are accentuating due to anthropogenic interactions in the presence of extremes of weather and management practices. The organic materials conserved through this practice are decomposed slowly, and much of materials are incorporated into the surface soil layer, thus reducing the liberation rate of carbon as CO₂ into the atmosphere. In the total balance, carbon is sequestered in the soil, and turns the soil into a net sink of carbon. So, CA enhance soil health by improving soil aggregation, reducing compaction through promotion of biological tillage, increasing surface soil organic matter and carbon content, and moderating soil temperature and weed suppression. CA reduces cost of cultivation, saves time, increases yield through timelier seeding/planting, reduces pest and diseases through stimulation of biological diversity, and reduces green house gas emissions.

Machinery development, refinement and adoption for a range of soil and cropping situations will be fundamental in any success to promote conservation agriculture practices. Agricultural machinery or tools, which support conservation agriculture generally refer to the cultivation systems with minimum or zero tillage and in-situ management of crop residues. Minimum tillage is aimed at reducing tillage to the minimum necessary that would facilitate favorable seedbed condition for satisfactory establishment of crop. Zero tillage is however an extreme form of minimum tillage. With the development of direct drilling machines, almost all research work was attempted to define the responses of direct-drilled seeds in relation to soil micro-environments. Different designs of direct drilling machines viz. zero till drill, no till plant drill, strip till drill, and rotary slit no till drill have been developed with controlled traffic measures for energy efficient and cost-effective seeding of crops without tillage. In this paper various issues, challenges and prospects of conservation agriculture in India has been discussed.

Introduction

Future agricultural production potential is in danger due to the existing cropping system, characterized by irresponsible chemical utilization, extensive tillage, monoculture, and soil health degradation. Consequently, a significant challenge for modern agriculture lies in ensuring adequate nourishment for the entire world’s population while also safeguarding the environment. The intensification of agricultural practices negatively impacts vital natural resources like soil, water, air, and biodiversity throughout various developmental stages. While the conventional cropping system have enhanced crop productivity, its consequences including land degradation, resource depletion, reduced organic matter insoil, draught, soil salinity, erosion, and climate change pose significant global threats (Tomar et al. 2014; Yadav et al. 2014; Mandal et al. 2020; Rani et al. 2021).

Introduction
The richness of a country can be measured by its biological diversity. It is essential to both human existence and financial security. The most basic concept of biodiversity is expressed by the diversity of creatures. Biodiversity is represented by the supplies of food, clothing, shelter, and medicines provided by plants; by the sources of food, assistance, wild life, and human resource efficiency provided by animals; and by the patterns of variation seen in the microbiological world. More than 20,000 kinds of ants, 3,000,000 species of beetles, 28,000 species of fish, and almost 20,000 species of orchids would be hard for the average person to believe.
Definitions
• The collection and interplay of genes, species, and ecological diversity in a specific location and time is known as biodiversity. Different genes, species, habitats, and their relative abundance are all included in the phrase biodiversity.
• The portion of nature known as biodiversity comprises the genetic variations between individuals of the same species as well as the variety and richness of all plant and animal species at various spatial dimensions, such as a region, a nation, and the entire planet.
• “The diversity of life, which encompasses ecosystem or community diversity, species diversity, and genetic diversity, and includes the entire range of variety and variability within and among living organisms and the ecological complexes in which they occur,” is another definition for it.

Continental drift is the theory that continents move slowly about the earth’s surface, changing their positions relative to one another and to the poles of the earth. It includes the idea that former continents may have broken apart and that the fragments have recombined in different patterns (Fig. 15.1). Some authorities have considered that this happened only once, beginning within the past 200 million years; others believe that it has long been a continuous process, so that all existing continents are mosaics. Those who accept the theory believe that the rates of motion have been only a few inches a year. Earlier, it was considered that the continents moved like ships through a slightly plastic ocean floor. Today some consider that the continents are frozen into the earth’s surface like rafts in ice, and that the motions are the result of growth of the sea floor along certain midocean ridges and reabsorption of the sea floor elsewhere beneath deep ocean trenches and young mountains. The continents are carried passively about embedded in moving plates of the surface. Some authorities  now  agree  that  there  is  a  deformable  layer,  called  the asthenosphere, at depths of from about 50 to 400 kilometers within the earth. It is white hot, and slightly viscous, and many believe that the surface can break into plates which slide over it.

This chapter introduces continuous cover forestry as an approach to managing forest ecosystems while maintaining relatively continuous canopy cover and ecological processes. It examines the concept and evolution of continuous cover forestry, its ecological foundations, and advanced silvicultural practices. The chapter considers its ecological, economic, and social benefits and discusses its relevance to climate change adaptation and sustainable forest management. Comparative perspectives with conventional forestry, global case studies, challenges, limitations, and future prospects are also addressed. The approach emphasizes maintaining forest structure, regeneration, biodiversity, and ecosystem functions while allowing appropriate harvesting and resource utilization.

Abstract

Amongst various oilseed crops grown across the world, soybean (Glycine max L. Merrill) is globally important as a high source of protein and oil for human consumption and is also being used as potential feed for animals. Soybean is grown under rain-fed conditions in the tropics and subtropics in the marginal lands with less amount of external application of chemical fertilizers. The frequent climate variations have created a number of biotic and abiotic stresses, drought being a major abiotic stress which adversely affects the soybean productivity. Thus, to enhance the productivity of soybean, besides managing the nutrients, stress management is of utmost importance. There is a great opportunity of application of microbes especially arbuscular mycorrhizal fungi (AMF) where its application alone or in combination with plant growth promoting rhizobacteria (PGPR) can help in nutrient mobilization and remediation of plant stresses.

Crop disease forecasting is a crucial component of integrated crop management, enabling farmers and agricultural practitioners to anticipate and prepare for potential disease outbreaks. The importance of crop disease forecasting cannot be overstated, as it allows for the timely implementation of disease management strategies, reducing the risk of yield losses and minimizing the use of chemical pesticides. Conventional methods of crop disease forecasting have been widely used for decades, relying on observations of weather patterns, crop phenology, and disease symptoms to predict the likelihood of disease outbreaks. These methods have been refined over time, incorporating new technologies and data sources to improve their accuracy and reliability (Fig. 10.1). Despite the advent of modern technologies, such as machine learning and remote sensing, conventional methods of crop disease forecasting remain a vital tool for many farmers and agricultural practitioners. These methods are often simple, cost-effective, and accessible, making them particularly suitable for smallscale farmers and resource-constrained agricultural systems. This chapter will provide an overview of conventional methods of crop disease forecasting, highlighting their principles, advantages, and limitations. The chapter will also discuss the importance of crop disease forecasting in modern agriculture, emphasizing its role in promoting sustainable crop production and reducing the environmental impact of agricultural practices.
10.1 Conventional methods of Forecasting
There are several conventional methods of crop disease forecasting that have been widely used for decades. Some of these methods include:
10.1.1 Weather-based forecasting
This method uses weather data, such as temperature, rainfall, and humidity, to predict the likelihood of disease outbreaks. Weather-based forecasting models use statistical relationships between weather variables and disease incidence to make predictions (Fig. 10.2).

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.

Correlation and regression are essential statistical tools for examining relationships among climate variables and agricultural responses. Correlation measures the strength and direction of association, while regression develops mathematical relationships for explanation and prediction. This chapter discusses their application to rainfall, temperature, humidity, evapotranspiration, crop growth and yield. It explains simple and multiple relationships, assumptions, interpretation, model fitting, predictive power and potential errors. These methods support crop-weather modelling, yield forecasting, climate-impact assessment and agricultural decision-making. By identifying how climatic variables influence biological and production processes, correlation and regression analysis help researchers generate reliable predictions and design more resilient and productive agricultural management strategies under changing environmental conditions.

Introduction

Modern intensive agriculture systems based on the principles of yield maximization have been plagued with multiple constraints such as declining factor productivity, decreasing resource use efficiency (fertilizers, water, labour etc.), soil organic matter decline, salinization, soil structure degradation, water and wind erosion, reduced water infiltration rates, soil compaction due to surface sealing and crusting, declining ground water table, pest and disease outbreak, weed resistance and weed shifts (Paustian et al., 2016). To address these overwhelming issue, conservation agriculture (CA) has been defined by FAO as a concept of resource saving agricultural crop production which is based on enriching the natural and biological processes above and below ground. The major purpose for its advancement is to reduce the production cost, save resources, enhance yields and utilize resources like nutrient and water efficiently. Conservation tillage denotes soil management system that follows the principle of at least 30% of the soil surface coverage with crop residues after seeding of the crop (Jarecki and Lal, 2003). CA, a resource-efficient agriculture helps to conserve, improve and utilize natural resources efficiently through integrated management of available soil, water and biological resourwces combined with external inputs and thus contributes to environmental conservation as well as enhanced and sustained agricultural production in the long term. The practice and wider extension of conservation agriculture thus requires a deeper understanding of its ecological underpinnings for managing its various elements for sustainable intensification.

This chapter provides an in-depth exploration of advanced techniques and conceptual frameworks—here termed “Weather Intelligence”—and their potential applications in precision agriculture. Although the term is not yet standardized, it serves as an exploratory framework for integrating advanced forecasting, diverse datasets, and data analytics to capture both natural and anthropogenic influences on weather patterns. The discussion spans crop and livestock production as well as allied sectors such as fisheries, aquaculture, plantations, horticulture, and specialized areas like flower farming. By integrating real-time data from sensors, remote sensing platforms, and historical records, these exploratory systems aim to empower farmers and food producers to optimize irrigation, planting, harvesting, and overall resource management. Agriculture has always depended on weather, yet the rapid pace of climate change and the need for sustainable practices have driven a technological revolution in farm management. In today’s digital age, advanced forecasting techniques and data analytics are converging into a new conceptual framework we call “Weather Intelligence.” Although this term is exploratory and not yet widely adopted, it encapsulates the vision of integrating high-resolution satellite imagery, IoT devices, and machine learning algorithms to forecast local microclimatic conditions with unprecedented accuracy. These forecasts enable proactive adjustments to critical operations—such as irrigation scheduling, planting, and harvesting, thereby mitigating weather-induced risks and enhancing productivity. Moreover, it is increasingly evident that weather patterns are not only shaped by natural phenomena but also by human-induced risks such as wars, conflicts, and environmental abuse. Factors like landfills, water and sea pollution, toxic dumping, and unsustainable industrial practices are altering microclimates around the world. This complex interplay of natural and anthropogenic forces calls for the incorporation of a broader array of variables and larger datasets into forecasting systems. In this context, embracing Weather Intelligence as a dedicated field of study and application in agricultural and allied sciences becomes not only desirable but essential. This chapter aims to detail the evolution and current state of these integrated approaches, discuss their potential applications across various agricultural sectors, and outline strategies for precision decision making. While much of the content is exploratory in nature, it provides a conceptual framework for future research

Introduction

Crop discrimination is the process of using remote-sensing technologies to identify and characterize different types of crops based on their electromagnetic behavior at different wavelengths. Each crop has a unique spectral signature that reflects its response to the incident radiation. However, the spectral signature can also vary depending on the season, the angle of the sensor, crop’s features, illumination intensity, weather phenomenon, and topography among other external factors (Parida et al., 2023).

Crop disease dynamics is a critical aspect of plant pathology that deals with the study of the interactions between the disease-causing agent, the host plant, and the environment. It involves understanding the complex relationships between these components and how they influence the development and spread of crop diseases. Crop disease dynamics is a dynamic and constantly changing process that is influenced by a multitude of factors, including the type and virulence of the disease-causing agent, the susceptibility and resistance of the host plant, and the environmental conditions such as temperature, humidity, and rainfall. Understanding these dynamics is essential for developing effective disease management strategies that can mitigate the impact of crop diseases on agricultural productivity and food security. The study of crop disease dynamics involves examining the various components of the disease triangle, which includes the disease-causing agent, the host plant, and the environment. Each of these components plays a critical role in the development and spread of crop diseases, and understanding their interactions is essential for developing effective disease management strategies. The disease-causing agent, whether it is a fungus, bacterium, virus, or nematode, plays a critical role in the development and spread of crop diseases. Understanding the biology and ecology of the disease-causing agent, including its life cycle, mode of transmission, and survival strategies, is essential for developing effective disease management strategies. The host plant is also a critical component of the disease triangle, and its susceptibility or resistance to the disease-causing agent plays a major role in determining the outcome of the disease. Understanding the genetic and molecular basis of host plant resistance is essential for developing crop varieties that are resistant to specific diseases. The environment also plays a critical role in the development and spread of crop diseases, and factors such as temperature, humidity, and rainfall can influence the survival and transmission of the disease-causing agent. Understanding the environmental factors that influence crop disease dynamics is essential for developing effective disease management strategies that take into account the specific environmental conditions of a given region. In this chapter, we will explore the complex dynamics of crop diseases, including the interactions between the disease-causing agent, the host plant, and the environment. We will examine the various components of the disease triangle

Crop diseases are a significant threat to agricultural production worldwide, posing a major challenge to food security, economic stability, and environmental sustainability. These diseases, caused by a variety of pathogens including fungi, bacteria, viruses, and nematodes, can have devastating effects on crop yields, quality, and overall plant health. The causes of crop diseases are complex and multifaceted, involving a combination of factors such as environmental conditions, plant susceptibility, and pathogen characteristics. Climate change, soil degradation, and water scarcity can all contribute to an increased risk of crop disease outbreaks. Additionally, the widespread use of monoculture farming practices, which involve the cultivation of a single crop species over large areas, can lead to a loss of genetic diversity and increased vulnerability to disease. The consequences of crop diseases are far-reaching and can have significant impacts on agricultural production, food security, and the environment. According to the Food and Agriculture Organization (FAO) of the United Nations, an estimated 20-30% of global crop production is lost to pests and diseases each year, with crop diseases accounting for a significant proportion of these losses. This translates to billions of dollars in economic losses and millions of tonnes of food lost, exacerbating food insecurity and poverty, particularly in developing countries. In addition to the economic and food security impacts, crop diseases can also have significant environmental consequences. The use of chemical pesticides and fungicides to control disease outbreaks can pollute soil, air, and water, harming beneficial organisms and ecosystems. Furthermore, the development of pesticideresistant pathogens can lead to the use of even more toxic chemicals, perpetuating a cycle of environmental degradation. In this chapter, we will explore the causes and consequences of crop diseases in greater detail, examining the complex interplay of factors that contribute to disease outbreaks and the impacts of these outbreaks on agricultural production, food security, and the environment. We will also discuss strategies for managing and controlling crop diseases, including the use of resistant crop varieties, integrated disease management (IDM) practices, and sustainable agriculture approaches.
3.1 Symptoms of Crop Disease
Crop disease refers to any abnormal condition or disorder that affects the health and productivity of crops, including plants, trees, and other vegetation.

Introduction

Conservation Agriculture (CA) is a set of soil management practices that minimize the disruption of the soil’s structure, composition and natural biodiversity. These include: i) maintenance of permanent or semi-permanent soil cover (using either a previous crop residue or specifically growing a cover crop for this purpose); ii) minimum soil disturbance through tillage (just enough to get the seed into the ground); iii) regular crop rotations to help combat the various biotic constraints. CA also uses or promotes where possible or needed various management practices listed below: i) utilization of green manures/ cover crops (GMCC’s) to produce the residue cover; ii) no burning of crop residues; iii) integrated disease and pest management; iv) controlled/limited human and mechanical traffic over agricultural soils. When these CA practices are used by farmers one of the major environmental benefits is reduction in fossil fuel use and greenhouse gas (GHG) emissions.

In India, efforts to develop, refine and disseminate conservation-based agricultural technologies have been underway for nearly two decades and made significant progress since then even though there are several constraints that affect adoption of CA. Particularly, tremendous efforts have been made on no-till in wheat under a rice-wheat rotation in the Indo-Gangetic plains. The technologies of CA provide opportunities to reduce the cost of production, save water and nutrients, increase yields, increase crop diversification, improve efficient use of resources, and benefit the environment. However, there are still constraints for promotion of CA technologies, such as lack of appropriate seeders especially for small and medium scale farmers, competition of crop residues between CA use and livestock feeding, burning of crop residues, availability of skilled and scientific manpower and overcoming the bias or mindset about tillage. The need to develop the policy frame and strategies is urgent to promote CA in the region. (Bhan and Behera, 2014).

The green revolution enhanced the food grain production by increasing  productivity of the arable crops as the only means of expanding output. The impact of this ‘revolution’ was first realized especially in the north Indian states of Punjab, Haryana, and western Uttar Pradesh. Since the new technology was based on use of seeds of high yielding varieties, increased use of irrigation and chemical fertilizers, pesticides and farm mechanization; the incremental gains in productivity of late began to end and food production started stagnating, and in many places it even acquired a decreasing trend. It is well accepted fact that ‘success always has its costs and green revolution has been no exception’. Critics of green revolution point to the environmental and health hazards due to increased use of agro-chemicals and the vulnerabilities that is there due to the spread of monocultures in which indigenous land races have been ignored and least emphasis was given on cropping systems based on crop diversification.

Abstract

This paper portray the generation of spectral signature library for semi arid area crops using hyperspectral data and the scope of the application of the generated digital library for the classification of the satellite imagery of similar regions.

Hypersprctral data have narrow bands which help to provide additional information related to observed features. In case of this agriculture study crop related information are gathered using hyperspectral sensors The variation in the observations are due to physic-chemical characteristics such as biochemical composition, physical structure, water content and plant ecophysical status. Using the hand held spectroradiometer the reflectance is captured for the bajra, moong, moth crops from the Rajasthan, India’s semi arid region. The observations have been taken for different dates considering the phenology of the crops. Hyperion data from EO-1 satellite have been used to implement the classification operation based on the spectral signature library of bajra. It is observed that the image with the crops can be classified according to the phenology difference.

The subject on micrometeorology deals with small scale meteorology of near ground surface with its related elements of both physical and biological processes. When this micrometeorology is integrated for crop production purposes, then it becomes crop micrometeorology, which is very important in the science of agricultural climatology. This crop micrometeorology gets varied with the architecture of the crops grown, available soil moisture, agronomical practices followed, soil texture, topography of the area, the nature of climate and weather that prevails etc. The soil surface heat, exerts a profound frictional effect on wind movements and hence it is different from the air layers at higher levels. These special features constitute the microclimate, which is modified compared to a flat bare surface by factors like topography, nature of soil surface, presence of vegetation and type of vegetative cover. The presence of crop canopy leads to distinct differences in the thermal and humidity regimes of the soil, the air layers immediately above it and the distribution of radiation and wind profiles inside the canopy. The study on the physical exchanges of the transfer of heat, momentum and moisture in the microclimatic region is called as micrometeorology (Venkataraman and Krishnan, 1992).

Radha Krishna Murthy (2002) reported that the micrometeorology is the micro scale of both the physical and meteorological factors that are taking place within the ecosphere. The ecosphere means the area between just above the crop canopy and just below the root zone of a crop. Further he was on the opinion that the microclimate of valley is important as compared to the entire climate of the mountain for the bio-life of valley. For the crops purpose, the microclimate is the climate of the ground surface to top of the crop canopy or otherwise it can be called as crop’s climate, a modified one from the Stevenson’s Screen climate/weather observation.

Introduction 
The preceding chapter introduced the foundational concept of the systems approach, emphasizing the interconnectedness of components within complex entities and the power of simulation to understand their dynamic behavior. Building upon this understanding, This chapter delves into one of the most vital applications of system simulation in agriculture: crop models. These sophisticated computational tools stand as the cornerstone for quantitatively representing the intricate interplay between the soil, the plant, and the atmosphere, providing a dynamic lens through which to examine agricultural productivity and resource use efficiency. Crop models are essentially mathematical descriptions that mimic the growth, development, and yield of agricultural crops under various environmental conditions and management practices. They encapsulate our scientific understanding of how plants absorb light, water, and nutrients, how they convert these resources into biomass, and how they respond to stresses like drought, heat, or nutrient deficiency. Far from being mere statistical regressions, these are often process-based models, meaning they attempt to simulate the underlying biological, physical, and chemical mechanisms that govern crop performance. By integrating knowledge from disciplines such as plant physiology, soil science, meteorology, and agronomy, crop models provide a holistic and dynamic representation of the entire agricultural system. The evolution of crop modeling has mirrored advancements in computational power and scientific understanding. From early empirical models that relied heavily on statistical relationships between yield and a few environmental factors, the field has progressed to complex mechanistic models that simulate detailed physiological processes on a daily or even hourly basis. This evolution has transformed crop models from purely research-oriented tools into powerful instruments for real-world decision-making. Their ability to conduct virtual experiments—simulating hundreds or thousands of growing seasons

Introduction 
The preceding chapter elucidated the concepts of potential and achievable crop production, highlighting the significant gap that often exists between a crop‘s theoretical maximum yield and what is realized in the field. While genetic potential and optimal climatic conditions define the upper limits, the vast majority of agricultural systems globally operate under less-than-ideal circumstances. Among the most pervasive and impactful constraints on crop productivity are moisture (water) and nutrient limitations. These two factors, either individually or in complex interaction, are frequently the primary drivers of the yield gap, preventing crops from expressing their full genetic potential and significantly impacting farm profitability and regional food security. Water, as the universal solvent and a direct participant in photosynthesis and countless metabolic processes, is indispensable for plant life. However, its availability in agricultural systems is highly variable and often insufficient. From the rainfed fields dependent on increasingly erratic rainfall patterns to irrigated lands facing dwindling water resources, moisture stress – encompassing both drought and, paradoxically, waterlogging – poses an immense challenge. Drought alone accounts for a substantial portion of global crop losses, directly affecting plant physiological functions, growth, and ultimately, yield. Conversely, excessive moisture can lead to anaerobic soil conditions, nutrient leaching, and root damage, equally debilitating for crop performance. Parallel to moisture, the availability and judicious management of essential plant nutrients are foundational to productive agriculture. Plants require a suite of macro- and micronutrients for healthy growth, enzymatic activity, and the synthesis of biomass. Soils, over time and with continuous cropping, often become depleted of these vital elements, necessitating external application through fertilizers or the enhancement of natural nutrient cycling processes. However, nutrient management in the field is rarely perfect. Deficiencies of

Introduction

India is bestowed with diverse agro-climatic conditions which favour the production of a variety of fruit crops. At present India is second largest producer of fruits in the world after China. Fruit culture can be highly profitable by increasing employment opportunities and commercialization of the rural sector. During early sixties, priority was to achieving self sufficiency in food grain production in our country, however, over the years, fruit culture has emerged as an indispensable part of agriculture, offering a wide range of choices to the farmers for crop diversification. It also provides ample opportunities for sustaining large number of agro-industries to generate substantial employment opportunities (Bardhan, 2016). Amongst various tropical and sub-tropical fruit crops grown in India, guava, pomegranate, acid lime, sweet orange and mandarins, if left on its own, give several light crops of the variable quantities and qualities from the various flowering flushes throughout the year. Under natural conditions, these crops produce flowers thrice in a year i.e. February-March (Ambe Bahar), June-July (Mrig Bahar) and October-November (Hasth Bahar) with the corresponding harvest during rainy, winter and spring season, respectively (Boora et al., 2016, Lal et al., 2017). Owing to this, potential yield is not obtained during the desired period. Therefore, to obtain higher fruit yield during a particular period, these fruit crops are given a resting period with artificial means so that the natural flowering tendency of the trees is altered. It can be achieved through root exposure and its pruning (Boora et al., 2016), shoot pruning (Tiwari et al., 1992, Thakre et al., 2016)

Large quantities of crop residues are produced and if properly used, they can provide cost effective means for supplying organic matter, plant nutrients and improved soil health. The highly lignocellulosic nature of crop residues indicate their potential role in maintaining soil organic matter and enriching the soils with N-rich microbial biomass resulting from decompositions. However, their indiscriminate handling often burning leads to decline in soil health, air pollution and overall loss. Since most crop residues find use as feed and fuel, efficient recycling of the remaining amount can have very significant and positive effect.

5.1 Introduction

A Crop Simulation Model is a mathematical model which describes crop growth and development processes as a function of environment, soil conditions, and crop management. Simulation model is a schematic representation which works on set of underlying assumptions, equations and algorithms, closely replicating the behaviour of the agricultural systems. A crop growth simulation model predicts the harvestable yield. Apart from this final state of crop production, the model also provides a host of quantitative information about major processes involved in the various stages of crop development. The information covers each component of the crop such as leaves, stems, roots and harvestable products as they change over time, including the changes in soil moisture and nutrient status.

An increase in the population, demands an increase in agricultural production with available resources. Efficient management of available resources under varying weather conditions is important for increasing agricultural productivity. Besides that, the emphasis of agricultural production changes from quantity to quality and sustainability (Aggarwal et al., 2000). Solution of these new challenges lies in understanding the interaction of the numerous elements affecting the plant growth and generating a crop growth model. This extremely complicated information needs to be processed and interpreted on a spatial scale. Information technology which has unleashed new frontiers in data analysis and automation is the driving force behind this model generation. These powerful computer programmes simulate the crop growth or yield of crops under various management regimes and provide near real time information to help farmers to make strategic decision in the management of their crops.

The susceptibility and vulnerability of crops to diseases are critical factors that determine the impact of disease outbreaks on agricultural productivity and food security. Crop diseases can have devastating effects on crop yields, quality, and marketability, resulting in significant economic losses for farmers and the agricultural industry as a whole. The susceptibility of crops to diseases refers to the inherent characteristics of the crop that make it more or less prone to infection by a particular diseasecausing agent. This can include factors such as the crop’s genetic makeup, its physiological state, and its environmental conditions. For example, some crop varieties may have a natural resistance to certain diseases, while others may be more susceptible due to their genetic characteristics. Vulnerability, on the other hand, refers to the degree to which a crop is exposed to disease-causing agents and is unable to withstand or recover from disease outbreaks. This can include factors such as the crop’s location, climate, soil type, and farming practices. For example, crops grown in areas with high disease pressure, inadequate soil moisture, or poor farming practices may be more vulnerable to disease outbreaks. Understanding the susceptibility and vulnerability of crops to diseases is essential for developing effective disease management strategies. By identifying the factors that contribute to a crop’s susceptibility and vulnerability, farmers and agricultural experts can take proactive measures to prevent or mitigate disease outbreaks. This can include using disease-resistant crop varieties, implementing integrated pest management practices, and adopting sustainable agricultural practices that promote soil health and biodiversity. In this chapter, we will explore the concept of susceptibility and vulnerability of crops to diseases in more detail, including the factors that contribute to these phenomena and the strategies that can be used to manage and mitigate disease outbreaks. By understanding the complex interplay between crops, diseases, and environmental factors, we can develop more effective and sustainable approaches to disease management that promote agricultural productivity and food security.
4.1 Factors Determining Susceptibility
The susceptibility of crops to diseases is determined by a complex interplay of factors, including genetic, physiological, and environmental factors. Genetic factors play a crucial role in determining the susceptibility of crops to diseases. Some crop varieties may have a natural resistance to certain diseases, while others

The Crop Weather Calendar (CWC) is arguably one of the most practical and indispensable tools developed within agricultural meteorology. It transcends simple weather data by synthesizing comprehensive information about a specific crop’s entire life cycle with the historical climate patterns and recommended agricultural practices for a particular geographical area, typically an agro-climatic zone or even a district. More than just a timetable, it serves as a strategic blueprint that guides farmers and agricultural professionals toward optimized, resilient, and sustainable farming (Fig. 4.1).Imagine it as a detailed annual planner for a specific crop, where every entry is backed by decades of climate data and agronomic research. 4.2 Crop Weather Calendar: A Deeper Dive