eChapters

In many situations, experiments may not known in advance which contrasts they wish to compare, or they may be interested in more than (m – 1) possible comparisons.  In many exploratory experiments, the comparisons of interest are fixed only after preliminary examinations of the data.
 
13.1 Scheffe’s Method for Comparing All Contrasts

Scheffe (1953) has proposed a method for comparing any all possible contrasts between treatment means.  In the Scheffe method, the type I error is at most a for any of the possible comparisons.

Abstract

In recent years, significant studies have been carried out on Plant growth promoting rizhobacteria (PGPR) to replace the usage of pesticides and fertilizers to improve the plant health. Plant growth promoting rizhobacteria (PGPR) is a group of rhizosphere bacteria, which are symbionts to plants and influence their growth by providing plant growth promoting substances. Different bacterial genera act as plant growth promoting rizhobacteria (PGPR): Pseudomonas sp., Rhizobium sp., Azotobacter sp., Bacillus sp. Pseudomonads are gram negative rods, aerobic, fluorescent, motile and are able to produce siderophores which influence plant growth. There are multiple potential traits offered to the plants by plant growth promoting (PGP) Pseudomonas sp. are (1) Biocontrol agent against plant pathogens (2) Induced systemic resistance, which is a defense mechanisms in plants preceding to pathogenic attack (3) Influence of plant growth promoting (PGP) Pseudomonas sp.on agricultural crops (4) Siderophores, which are low molecular, high affinity iron chelating compounds which benefits the plants to acquire iron molecules (5) Nitrogen fixation (6) Phytoremediation of heavy metals (7) Solubilization of inorganic phosphates (8) Secretion of plant hormones (9) Bacteriocins(10) Antifungal factors (11) Antibiosis traits and (12) Endophytic PGP Pseudomonas sp.

ABSTRACT

Multiple shoot induction from the cotyledonary node explants of Leucaena leucocephala was studied in presence of various concentrations of TDZ. With the increase in concentration of TDZ from 0.045 μM to 0.227 μM the number of regenerated shoots increased. However, the length of individual shoot decreased. Further increase in TDZ concentration upto 22.71 μM resulted in the formation shoot clusters. Presence of TDZ in the medium inhibited shoot elongation. Transfer of the regenerated shoots to ½ x MS medium supplemented with 0.54 μM NAA ensured elongation of the shoots, which also developed roots. Rooted plantlets were transferred to coco peat for further development and hardening. This highly efficient regeneration protocol may find application in the genetic transformation of this leguminous tree species.

Introduction

The term multipurpose trees (MPT) refers to all woody perennials that are grown to provide more than one significant function (shelter shade, land sustainability) of the land use system they implement. More emphasis is given to trees which are indigenous in nature and have the capability to fix atmospheric nitrogen. All trees are said to be multipurpose. The term “multipurpose” as applied to trees for agroforestry refers to their use for more than one service or production function in an agroforestry system (Burley and Wood, 1991).

Multipurpose trees species (MPT) are grown to provide more than one significant crop of function or form. These may include fuel wood, fiber, fodder, flower, fruit, fertilizer (6F) timber, industrial purpose, nitrogen f ixing, soil conservation, soil protection and rehabilitation, shade and nurse trees, medicine, windbreak, shelterbelts, live fences, aesthetic or recreational purpose, shown in Table 16.1.

Trees are an integral and indispensable constituents of our natural surroundings. The trees keep the environment clean, make the soil healthy and provide food, fibre and shelter to mankind and wild animals. Trees help in stabilizing the soil, a vital natural resource, prevention of soil and water erosion, control run off water in the catchment areas, acts as a shelter belt against wind erosion in arid zone, act as a wind breaker to protect the coastal region during storm and cyclone besides contributing substantially to economic development. The National Commission on Agriculture stress the need for afforestation of about 40 million ha of land presently lying barren because of salinity, sodicity, waterloggings, ravines, coastal sandy areas, stony and gravelly lands, high altitude and slopping lands. Several agencies in various parts of the country are engaged in afforestation of these degraded lands.

Farmers have been growing trees for different purposes for thousands of years. Tree species that are grown to provide more than one significant function are called multipurpose trees. These functions may be productive such as producing fuelwood, timber, fibre, fodder, food, medicine, etc. and/or protective such as soil conservation, shade, shelterbelt, microclimate amelioration, land sustainability, biodiversity preservation, etc. All trees are multipurpose; some, however, are more multipurpose than others. Tree species can be multipurpose in two ways.

1. A single tree can provide more than one function. For example, Gliricidia sepium is grown as living fences that provide fuel, fodder and green manure for agricultural crops - all at the same time.
2. Trees of the same species, when managed differently, can provide different functions. For example, Leucaena leucocephala is managed so that some trees will mainly yield wood while others mainly produce leaf fodder.

Farmers can grow multipurpose trees (MPT) in various combinations with other crops, as in agroforestry, in block plantations of trees or in naturally regenerating tree farms. In certain cases, multipurpose trees are grown and managed for only one purpose. For example, Gliricida sepium is grown only to provide shade in coffee plantations. The same species may be planted in some other places and is managed differently for a very different use.

Introduction
Agroforestry, social forestry, community forestry, village forestry, and farm forestry are all practices that involve tree cultivation primarily outside designated forest reserves. Agroforestry, in particular, is a land-use approach that integrates trees with agricultural crops, shrubs, pastures, or livestock. This combination of trees and shrubs within land management systems can be structured in a spatial arrangement or occur in a sequential manner over time. The term ‘woody perennials’ is sometimes used to describe these trees and shrubs, referring to all long-lived plants that persist for more than a year, including bamboos and palms. In agroforestry systems, trees may not always be deliberately planted; natural regeneration may be encouraged by protecting emerging seedlings, or mature trees may be intentionally retained in fields and pastures. These woody perennials are often categorized as “multipurpose trees” (MPTs) or “multipurpose trees and shrubs” (MPTS) due to their diverse uses. While most trees can be considered multipurpose, this distinction highlights the varied benefits trees offer in agroforestry systems, as opposed to singlepurpose timber plantations focused solely on wood production.
Conversely, tree cultivation in designated forest areas is often directed toward industrial needs, commonly referred to as industrial forestry. The promotion of MPTs in agroforestry holds great promise for addressing land-use challenges and improving livelihoods by enhancing environmental sustainability and economic opportunities for local communities. Agroforestry research has now advanced to a stage where suitable tree-crop combinations, their management practices, and genetically improved planting materials can be recommended for specific conditions. Furthermore, continuous advancements in research by international and national agroforestry organizations are contributing to the benefit of farmers. The availability of information on multipurpose tree species (MPTs) for many species has facilitated the adoption of agroforestry as a viable land-use system. However, for certain MPTs, a lack of fundamental data on growth rates, breeding patterns, resilience to environmental stress,

In sub-tropical agro-climatic conditions of India, a variety of insect-pest, weeds and diseases hamper the economic growth of field crops. For effective control of these pests, a number of pesticides including insecticides, fungicides, herbicides and nematicides are to be applied. Estimation technique for single pesticideonly can not be followed for detection of residues of all categories of pesticides. Hence, this constantly expanding use of pesticides on food crops accentuates the need for rapid, precise and sensitive method for determination of pesticide residues of all the major groups of pesticides. In such situation, multi-residue analytical technique can be efficiently followed for detection and estimation of multiresidues of intra and inter class xenobiotics.

The purpose of multiresidue analysesistodeterminetheresidues ofas manypesticides as possible withina short period of time even if the recoveries of somecompounds are low. In multiresidue methods the recoveries up to 70% are accepted.The recoveriesless than70%have tobe mentionedspecifically. Multiresidue analysis is relativelymore sensitiveand requires extreme vigilance on the part of the analyst during processingof samples. Additionally, instrument, GLC,need to beequipped withcapillary columnsforhigh resolutionand sophisticated detectorslikeECD, NPDand MSD for detectingmulti-residues of the pesticides. Multiresidue analytical methods/techniques havebeen developed for determination of general pesticide residues for some environmental componentsand differentfood commodities.Criteria formulti-pesticides screening as per California Department of Food and Agriculture (CDFA) are:

7.1 Introduction
In chapter 5, the cluster sampling has been discussed in which clusters were considered as sampling units and all the elements in the selected clusters were enumerated completely. It has been stated there that the cluster sampling is economical under certain circumstance but the method restricts the spread of sample over the population which results generally in increased sampling variance of the estimator under consideration. It is, therefore, logical to expect that the efficiency of the estimator will be increased by distributing the elements over a large numbers of clusters. Hence instead of observing all the units in the selected clusters, one may like to observe only few elements of the selected clusters. This will involve the sampling work to be done also within the selected clusters. If we do so, the process of selection will involve sampling work to be done at two stage, one for selecting the clusters where sampling units are the clusters and the second for selecting the units (elements) within the selected clusters where sampling units are the elements.
The process of sampling can be done at more than two stages. In this case the first stage unit will be larger than the second stage units and the second stage units will be larger than the third stage units and so on. Due to process of sampling in stages, such sampling is called “Multistage sampling.”

Q.1 Cluster sampling restricts the spread of sample over the population which results generally in increased sampling variance of estimators under considerataion.

Q.2 In adopting cluster sampling, few selected clusters are enumerated partially.

7.1 Introduction

In chapter 5, the cluster sampling has been discussed in which clusters were considered as sampling units and all the elements in the selected clusters were enumerated completely. It has been stated there that the cluster sampling is economical under certain circumstance but the method restricts the spread of sample over the population which results generally in increased sampling variance of the estimator under consideration. It is, therefore, logical to expect that the efficiency of the estimator will be increased by distributing the elements over a large numbers of clusters. Hence instead of observing all the units in the selected clusters, one may like to observe only few elements of the selected clusters. This will involve the sampling work to be done also within the selected clusters. If we do so, the process of selection will involve sampling work to be done at two stage, one for selecting the clusters where sampling units are the clusters and the second for selecting the units (elements) within the selected clusters where sampling units are the elements.

ABSTRACT

India is the second largest producer of vegetables in the world after China with an annual production of 162.19 million tonnes from an area of 9.2 m ha. The requirement of vegetables in the country has increased a lot to meet the nutritional requirement of an estimated 1200 million population expected by 2020-21. Although the productivity levels of vegetables have increased manifold, it would not be sufficient to feed the ever increasing population. Production per unit area of land, time and inputs can be increased by improving efficiency of the rate and extent to which solar energy is harvested for conversion to economic produce. In multiple cropping systems, the possibility of more efficient use of resources like sunlight, nutrient and water per unit area is higher leading to higher production stability. It can be very much successful in high imput requiring crops like vegetables. Intensive cropping involving multi-tiered vegetable crops like pointed gourd + elephant foot yam + cowpea grown on the single piece of land at the same time can generate profits up to Rs 1,30,000 per acre. India ranks first in the youth population. If the energy of these dynamic youth is properly utilized in the field of better agricultural practices, especially vegetable cultivation, it will help improve the national productivity of vegetable crops and generate employment as a whole. Thus, intercropping involving multi-storied/ multi-tier cropping system can serve as well-established practice for sustained supply of vegetables throughout the year through which rural youth can generate high income for sustainable livelihoods and increased per capita income.

Abstract

A study was conducted to evaluate the nature and magnitude of genetic divergence and also to identify divergent parents in one hundred eight sorghum germplasm collected from different agro eco-geographical areas in India. Mahalanobis D² statistics revealed considerable genetic diversity among the 108 genotypes of sorghum. The genotypes grouped into 11 clusters.

Multivariate analysis is essentially the statistical process of simultaneously analyzing multiple independent variables with multiple dependent (outcome or criterion) variables using matrix algebra. During the last two or three decades, multivariate statistical analysis has become increasingly popular. The theory has made great progress, and with the rapid advances in computer technology, routine applications of multivariate statistical methods are implemented in several statistical software packages. The traditional approach to the teaching of multivariate statistical analysis, as exemplified by Anderson (1958), relies heavily on advanced matrix mathematics. On the other hand, Hury and Riedwyl (1988) suggest that it is possible to understand most of the basic ideas underlying multivariate statistical analysis without a mastery of such mathematics, provided that these are conveyed with the help of real data sets. In this chapter we propose a non-mathematical data-driven approach for teaching multivariate statistical methods to students. Despite this, we are mindful of the need for students to know some basic linear algebra and univariate statistical concepts. Such basic knowledge provides students with the foundation necessary for the application of the appropriate multivariate statistical procedures and for the interpretation of results.

Introduction

Time series data is data that has been collected in a specific pattern with time stamps. (Nayak and Ghosh, 2013). This type of time-oriented data is collected at regular intervals, such as hourly, daily, monthly, or weekly. Time series data can be used to make accurate predictions in a variety of fields and domains, such as foreign currency rates, stock market trends, energy consumption estimates, and climate change. Machine learning and data mining techniques can be used to uncover hidden patterns in historical data and forecast future trends (Aftab et al. 2018a, 2018b, Mishra et al. 2017).

Weather forecasting based on historical data is a complex but very beneficial task that comes with several issues that must be resolved to achieve optimal results (Wu and Chau, 2013). Weather data includes attributes or features such as temperature, pressure, humidity, and wind speed. Machine learning techniques are used to forecast future weather conditions by identifying hidden patterns and relationships in historical weather data (Aftab et al. 2018b). Prediction of precipitation is an important part of the weather forecasting process. The use of machine learning and artificial intelligence techniques is regarded as a critical component in farmer services and policy formulation. Farmers require weather forecasting to plan their activities following the predicted weather. Accurate and timely rainfall prediction in farming, in particular, can be extremely beneficial in planning and security measures for operations, agricultural tasks, water reservoir systems, and construction and transportation activities (Chau and Wu, 2010). Forecasting rainfall will become increasingly important in the future.

It is an acute specific infection of the salivary glands especially of the parotids sometimes submandibular and sublingual glands also; occasionally the latter glands are affected first.

The most affected are the children or young adults of both sexes often living in a school or a barrack and the disease spreads rapidly from one person to another in close contact by droplets. This virus reaches the salivary glands by bloody or lymphatic because mumps is a systemic disease and may cause in addition, inflammation of the meninges, brain, pancreas and the gonads. The disease is more prevalent in winter and spring months. The incubation period is about 3 weeks.

The affected parotid glands and the surrounding tissues become hyperaemic and oedematous, the acinar cells disintegrate with round cell infiltration but later on, heal completely without any scarring.

Testes when involved have much oedema, perivascular infiltration with lymphocytes, localized haemorrhage, destruction of the germinal epithelia and blocking of the tubules with cellular debris. Only certain localized areas are affected, other parts remain unaffected.