eChapters

8.1 Introduction

After deciding the optimum size of farm pond and its dimensions, the next step begins the construction of farm pond. Further the site selection and location of the pond in the selected site to be finished before the construction of pond starts. The selection of site and location of farm pond areas have been discussed in Chapter 3. In this chapter, discussion on methodologies for construction of farm pond including spillways, inlet and outlet structures, design of grassed waterways and embankment have been presented. As a guiding principle, the construction of farm pond involves the following steps:

8.2 Site Clearing

The area where the pond is to be constructed has to be inspected first. The site where pond has to be dug out should be cleared to an extent of about 10 m from all sides of top dimensions of the pond. All bushes, shrubs, stumps and other unwanted materials should be dug out and removed. If there are roots of plants, they should be uprooted. The land may be ploughed and harrowed, if necessary, to get a more or less even topography.

7.1 General

The major portion of water in a crop field gets lost through evaporation and transpiration. Evaporation is the process by which water is changed from the liquid state to vapor state. It takes place from the adjacent soil, water surface or from the surfaces of leaves of the plants. Transpiration is the removal of water vapour through plant body. Evapotranspiration(ET) is the sum of evaporation and transpiration. The process of evaporation and transpiration goes on simultaneously and difficult to separate them in a crop field.

The term consumptive use is used to refer evapotranspiration together with the water used in the metabolic activities of the crop plants. The actual amount of water used in metabolic activities is insignificant (less than 1% to ET). Therefore, the consumptive use and evapotranspiration are often used as synonymous term. It includes the water consumed by the plants

6.1 Introduction

In order to increase the production and productivity of crops, provision of irrigation system is highly essential. A project that makes use of weir or a barrage for providing irrigation to fields is called as direct irrigation system. On the other hand, a storage irrigation system makes use of storage reservoir by construction of dams. In order to provide irrigation by both the schemes, it is necessary to have a network of canal systems. A canal is an artificial channel, generally trapezoidal in shape constructed on the ground to carry water to the fields. The canal systems that provide water to the fields include main canals, branch canals, distributaries, minors and watercourses. It is imperative to design the canal systems properly for a certain realistic value of peak discharge that must pass through them so as to provide sufficient irrigation to the command areas. The flow irrigation that provides irrigation water to the fields may be of two types i.e. Inundation irrigation and Perennial irrigation.

9.1 Introduction

In order to increase the production and productivity of crops, provision of irrigation system is highly essential. A project that makes use of weir or a barrage for providing irrigation to fields is called as direct irrigation system. On the other hand, a storage irrigation system makes use of storage reservoir by construction of dams. In order to provide irrigation by both the schemes, it is necessary to have a network of canal systems. A canal is an artificial channel, generally trapezoidal in shape constructed on the ground to carry water to the fields. The canal systems that provide water to the fields include main canals, branch canals, distributaries, minors and watercourses. It is imperative to design the canal systems properly for a certain realistic value of peak discharge that must pass through them so as to provide sufficient irrigation to the command areas. The flow irrigation that provides irrigation water to the fields may be of two types i.e. Inundation irrigation and Perennial irrigation.

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.

Cow Based Formulations describes traditional agricultural preparations made from cow-derived materials and other locally available ingredients for soil fertility, pest management, disease management, crop growth, and stress management. The chapter covers Ghana Jeevamrutham, Jeevamrutha, Amruthajalam, Amudham solutions, buttermilk-based preparations, and microbial formulations for improving soil fertility. It also presents Neemastram, Agniastram, Brahmastram, Dashparni Kashayam, and related preparations for pest management, followed by BeejAmrutham, Beejaraksha, herbal teas, and other disease-management inputs. Panchagavya, fish amino acid, egg amino acid, vermiwash, and cow dung–asafoetida solution broaden the discussion toward growth promotion and stress management in natural farming. using locally accessible agricultural resources today.

2.1 Introduction
As time passes, the customers are used to the practice of having short life for the products, which they buy and use. This is because of the globalization of business and growing global competition all over the world. The companies engaged in manufacturing and selling the products which are used at large volume by households and partly by industries, introduce new models to attract customers and to have more market share. This in turn has impacts on customer buying and retention decisions of those products. Though the companies introduce new models, they are expected to keep the prices of the products at affordable ranges so that there is always a larger customer segment to buy those products. This is like egg or chicken situation, because companies introduce many new models to out beat competition and in turn customers expect improved versions of those models in shorter time intervals. This forces companies to continuously engage themselves to come out with new models or improved models which have convergence of technologies to give maximal customer satisfaction. So, creativity in product design becomes an essential requirement of the product development teams of the companies.
2.2 Product Design
Product design aims to include the following tasks to solve the problems of the users or address expected needs of customers in a given market. These tasks are human intensive.
i. Understanding end-users’ requirements
ii. Imagining alternate product ideas to address their needs

Drought is a recurrent feature of climate or climatic anomaly or weather related natural disaster. It occurs when the precipitation remains deficient over an extended period of time, usually for a season or more and affects water shortage in water bodies, streams, soil moisture, ground water etc. resulting into low crop production and economic performance of large regions or entire country. Drought occurs in virtually all regions varying its severity, low and high from regions to regions.

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).

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.

Crop water requirement is the water required by the plants for its survival, growth, development and to produce economic parts. This requirement is applied either naturally by precipitation or artificially by irrigation. Hence the crop water requirement includes all losses like:

• Transpiration loss through leaves (T)

• Evaporation loss through soil surface in cropped area (E)

• Amount of weather used by plants (WP) for its metabolic activities which is estimated as less than 1% of the total water absorption. These three components cannot be separated so easily. Hence the ET loss is taken as crop water use or crop water consumptive use.

• Other application losses are conveyance loss, percolation loss, runoff loss, etc., (WL).

• The water required for special purposes (WSP) like puddling operation, ploughing operation, land preparation, leaching, requirement, for the purpose of weeding, for dissolving fertilizer and chemical, etc.

• Hence the water requirement is symbolically represented as:

Rainfed farming has a distinct place in the Indian agriculture, occupying 67 % of cultivated area contributing 44 % of food grains to our national food basket, supporting 40% of human and 65% of livestock population. The system is characterized by resource poor farmers, poor infrastructure and low investment in technology inputs. Even when full irrigation potential of the country is realized, 50% of the net sown area is continued to be rainfed. The important problems encountered in rainfed areas are unfavorable weather, limited choice of crops and varieties, low cropping intensity and unstable productivity. The research work done by the net work of AICRPDA (All India Co-ordinate Research Project for Dryland areas) centres and SAUs (State Agricultutal Universities) indicated that the production in rainfed environment could be enhanced by 200 % compared to the productivity of farmers’ field by adopting improved cropping systems and agro techniques.

Crowdsourcing is a concept that involves obtaining information, ideas, or services from a large group of people, often through an online platform. This approach has become increasingly popular in recent years, particularly with the rise of social media, mobile devices, and other digital technologies. In the context of agriculture, crowdsourcing can be used to collect data on various aspects of farming, such as crop yields, soil conditions, and weather patterns. This data can be used to provide farmers with valuable insights and advice on best practices for farm management. One of the key benefits of crowdsourcing in agriculture is that it allows farmers to share knowledge and expertise with each other. By pooling their collective knowledge and experience, farmers can gain a better understanding of the challenges and opportunities facing their industry, and develop more effective strategies for overcoming them (Fig.7.1).
For example, a crowdsourcing platform might allow farmers to share data on crop yields and soil conditions. This data could then be used to create detailed maps of soil types and conditions, which could be used to provide farmers with advice on optimal planting and harvesting schedules.
Another example of crowdsourcing in agriculture is the use of mobile apps to collect data on weather patterns. By collecting data from a large number of

Applied agroclimatology integrates climatology with agronomy to understand and manage interactions between weather, climate, crops, soils and agricultural resources. It converts climatic observations and forecasts into practical information for sowing, irrigation, fertilizer application, pest management and harvesting. This chapter introduces agro-meteorological processes, crop-environment relationships, resource management and climate-smart decision-making. It explains how increasing temperatures, altered rainfall, droughts, floods and extreme weather threaten agricultural productivity and farmer livelihoods. Agroclimatic zoning, crop-calendar adjustment, suitable variety selection, early-warning systems and weather-based insurance are discussed as important approaches for strengthening food security, managing climate risks and developing resilient agricultural systems.

2.1. Open source satellite data

The satellite images have been invaluable information sources for Natural Resource Management. They provide synoptic view of the Earth periodically helping in inventory, surveying, and monitoring in agriculture, hydrology, geology, mineralogy, land cover, land use and environment. Free global satellite image data of spatial resolutions of 1m to 1 km and spectral resolution of single band to hyperspectral are available from an increasing number of sources (Table 2.1).

This chapter deals with data importing, exporting and data manipulations in R. R can import almost all kinds of data format. The basic data format of .txt and .csv and data from other softwares like MS office .xls and .xlsx, spss ……, stata ……., etc. however, we will discuss in this chapter to read and write the basic formats and the .xlsx data which we use the most. Further, we will also discuss the basic data manipulations. For a detailed reading on data manipulation, readers can refer to the first book of the series namely “Data Handling and Manipulation in R”.
2.1. Primary Spectral Data Processing
The original spectra consisting of relative reflectance values of 2151 points (at 1 nm interval) between 350 and 2500 nm were averaged at every tenth-nanometer wavelength interval from 360 to 2490 nm by integration technique. Thus, the new spectra contained reflectance values at 360, 370, 380, 2490 nm, are inclusive. For example, the new reflectance value for 360 nm represented an integrated value of the original reflectance values from 356 to 365 nm, inclusive. Similarly, the reflectance at 400 nm was an integrated reflectance values from 396 to 405 nm, while the reflectance at 2490 nm was an integrated value of reflectance from 2486 to 2495 nm. This was done to reduce the volume of data for analysis and to match it more closely to the spectral resolution of the instrument (3 to 10 nm). This procedure resulted in a new dataset, with reflectance values specified for 214 wavelengths for all samples, which were further processed for noise removal.
2.2. Importing Data in R
We can rerad many format of tabular data in R such as excel, text, csv, etc. In thid book we will be using the csv format. Before reading the data, arrange data in excel/CSV sheet so that spectral data in column is followed by chemical data/ lab data. This will ensure that if any soil property or properties added in next column there is no need to change code for selection of spectral data in R environment.

Abstract

Carrying out effective and sustainable agriculture has become an important issue in recent years. A key to this is the usage of modern technologies for precision agriculture. Recent technological developments allowed envisioning sensor devices with distributed ambient sensory network, which could be a potential technology for monitoring various natural phenomena (weather parameters, soil moisture, etc.) at micro level. As more and more agricultural data are virtually being harvested along with the crops and are being collected/ stored in databases, the same can be used in productive decision making if appropriate data mining techniques are developed/applied. As a case study for this, an experiment was conducted with four consecutive agricultural (Kharif and Rabi) seasons in a semi-arid region of India to understand the crop-weather-environment-pest/diseases relations using wireless sensory and field-level surveillance data on closely related and interdependent pest/disease dynamics of groundnut crop. Association rule and multivariate regression mining techniques/algorithms were propose developed /tailor-made to turn the data into useful information/ knowledge/relations/trends to know crop-weather- environment-pest/disease continuum. These findings have been used for development of prediction models (cumulative and non-cumulative) followed by a web based pest/disease decision support system.

Keywords : Data Mining, Pest/Disease Management, Precession Protection, Wireless Sensor Networks