eChapters

Experimentation is fundamental to scientific investigation because it provides a systematic basis for studying natural and controlled systems and drawing conclusions from observations. In forestry and horticulture, experiments are often affected by developmental, environmental, genetic, sampling, and experimental variations. This chapter introduces the principles required for planning, conducting, analysing, and interpreting such experiments. It explains comparative experiments, the need for experimentation, experimental units, sampling units, plots, blocks, treatments, factors, levels, and sources of variation. Particular emphasis is given to the basic principles of experimental design—randomisation, replication, and local control—which help improve validity, precision, and coverage of experimental results.

Hypothesis testing provides a statistical framework for making inferences about populations from sample observations obtained through random experiments. This chapter introduces the scientific logic underlying statistical hypotheses and explains the formulation of null and alternative hypotheses. It discusses one-tailed and two-tailed tests, levels of significance, Type I and Type II errors, sampling distributions, test statistics, critical regions, p-values, and decision-making. The chapter also explains statistical significance and the interpretation of test results. Applications of Student’s t-test and related procedures are illustrated through forestry and biological examples, enabling researchers to determine whether observed differences between means can reasonably be attributed to treatments rather than random sampling variation.

Analysis of Variance (ANOVA) is an important statistical technique for separating variation attributable to different sources in an experiment. The chapter explains how total variation can be partitioned into controlled and uncontrolled components and how experimental error is used to assess treatment differences. The F-test provides the basis for testing the homogeneity of several treatment means simultaneously. The chapter discusses the assumptions underlying ANOVA, including independence, normality, additivity, and homogeneity of variance. It also introduces fixed- and random-effects models and explains their relevance to experimental analysis. Forestry and agricultural examples demonstrate the application of ANOVA for interpreting treatment and experimental effects.

Transformation of data is useful when the assumptions required for analysis of variance are not adequately satisfied. Forestry experiments may produce observations showing skewness, unequal variances, non-normality, or departures from additivity. This chapter explains the need for transforming observations to an appropriate scale before statistical analysis. It discusses the characteristics of an ideal transformation, including stabilisation of variance, improvement of normality, efficient estimation of means, and achievement of linear and additive effects. Various transformation techniques used in forestry experimental analysis are introduced with suitable examples. The chapter emphasizes that appropriate transformation can improve the validity, sensitivity, and interpretability of statistical tests.

When ANOVA indicates significant differences among treatment means, further analysis is required to determine which particular means differ significantly. This chapter deals with post-ANOVA procedures used for detailed comparison of treatment means. It introduces methods such as critical difference, least significant difference, and multiple comparison procedures. Particular attention is given to Scheffé’s method, Newman–Keuls method, and Duncan’s New Multiple Range Test. The chapter explains the principles, calculations, and interpretation of these techniques using forestry examples, including comparisons among tree species and provenances. It also discusses considerations for selecting an appropriate multiple-range or comparison method according to the objectives and number of comparisons involved.

Orthogonal designs provide an efficient framework for arranging treatments and sources of variation so that their effects can be estimated independently. This chapter introduces contrasts, orthogonal contrasts, and the concept of orthogonality in experimental design. Orthogonality helps prevent the effects of different factors from becoming entangled and facilitates systematic statistical analysis. The chapter discusses three basic experimental designs: Completely Randomized Design, Randomized Complete Block Design, and Latin Square Design. Their principles, applications, and suitability for forestry and agricultural experiments are explained. Examples involving seedlings, tree species, fertilizers, chemicals, wood pieces, and seeds demonstrate how appropriate design selection can reduce heterogeneity and improve experimental precision.

Factorial experiments provide an efficient method for studying two or more factors simultaneously at different levels. This chapter introduces the concept and notation of factorial arrangements and explains treatment combinations in experiments such as 2 × 2 and higher-order factorials. It distinguishes among simple effects, main effects, and interaction effects, emphasizing the importance of interactions in interpreting biological responses. Factorial experiments allow researchers to examine the effects of individual factors as well as their combined influence. The chapter also explains that factorial treatments may be arranged using different basic experimental designs, such as Completely Randomized, Randomized Block, or Latin Square designs, according to experimental objectives and conditions.

Loss of experimental observations is a common practical problem in forestry and agricultural experiments because seeds, seedlings, plants, and trees may be damaged by natural events, animals, anthropogenic activities, or other causes. This chapter presents the missing plot technique for estimating unavailable observations so that an experiment can continue to be analysed. It explains why missing observations can reduce the information and precision of an experiment and discusses alternative approaches for handling incomplete data. The chapter focuses particularly on the method of least squares and the iteration method. Applications include estimation of missing values in Randomized Block Designs and other experimental layouts used in forestry research.

Analysis of Covariance (ANCOVA) combines features of analysis of variance and regression analysis to account for the influence of a concomitant or covariate variable. In forestry and horticultural experiments, initial measurements or other associated characteristics may influence the response variable and contribute to experimental variation. This chapter explains how treatment effects can be evaluated after adjusting for the linear relationship with a covariate. It presents the ANCOVA framework, calculation of sums of squares, regression coefficients, adjusted error and treatment variation, F-tests, and adjusted treatment means. Examples involving seedling growth and physiological responses demonstrate how covariance adjustment can provide more precise comparisons among treatments.

Split-plot design is particularly useful when certain biological treatments cannot conveniently be applied to small experimental units. This chapter explains how large experimental plots can be divided into smaller sub-plots to accommodate subsidiary treatments. Main-plot treatments are randomly allocated to larger units, while sub-plot treatments are independently allocated within them. The design allows researchers to study main effects, sub-plot effects, and interactions, although greater precision is generally obtained for sub-plot comparisons than for main-plot comparisons. Examples include wood preservation and nursery experiments involving sowing methods and seasons. The chapter also explains the two error terms required for appropriate analysis of split-plot experiments.

Series of experiments are conducted at different locations, times, or combinations of locations and years to evaluate the spatial and temporal performance of treatments. Such experiments are especially important in forestry and agricultural research when conclusions are intended to apply across wider geographical or environmental conditions. This chapter explains combined analysis of a series of experiments after individual experiments have first been analysed according to their respective designs. Particular attention is given to testing the homogeneity of error variances, including Bartlett’s test. When variances are homogeneous, pooled analysis can assess treatment effects, location or year effects, and treatment-by-location or treatment-by-year interactions, thereby evaluating consistency of treatment responses.

Augmented designs are useful when a large number of standard varieties or treatments are available for replication but new or promising materials are available only in limited quantities. This chapter introduces augmented designs as modifications of standard experimental designs in which additional, usually unreplicated, entries are incorporated alongside replicated standard treatments. Applications include testing new varieties, seedlings, medicinal plants, growth regulators, seedling carriers, and progeny under forestry and agricultural conditions. The chapter explains the principles of augmented designs and their applications in experimental research. It also discusses augmented Completely Randomized Design, including its layout, analysis of variance, and comparison of replicated varieties and unreplicated seedlings.

Botanical Name: Cocos nucifera L. Family: Arecaceae / Palmae Chromosome No: 32 Place of Origin: Malaysia Common Name: Coconut 2.1 Introduction Coconut (Cocos nucifera L.), is widely known as ‘Kalpavriksha’ (i.e., tree of heaven) as each and every part of the palm is useful in one way or the other and is an important perennial oil yielding crop of humid tropics. It provides food security and livelihood opportunities to 20 million people around the globe and 10 million people in India (Chowdappa et al., 2017). Even though India is the largest coconut growing country in the world, its contribution to the export market is not significant. Coconut as an edible oil and food crop, is now slowly transforming into raw material for various pharmaceutical, nutraceutical and cosmetic products. Coconut is rich in fibre, vitamins and minerals. Because of its health benefits beyond its nutritional content, it has been considered as “functional food”. Hence, there exists a huge scope for coconut-based agribusiness in India, which will increase the present 8% level of value addition to 25%. The coconut-based value-added products are desiccated coconut powder, coconut milk, coconut cream, coconut milk powder, coconut flour, coconut water concentrate, coconut jelly (Nata-de-coco), coconut vinegar, virgin coconut oil, coconut chips, coconut pickle, coconut ice cream, neera and neera based value added products such as syrup, squash, honey, jaggery, candy and sugar (Chowdappa et al., 2017).

Botanical Name: Areca catechu L. Family: Arecaceae / Palmae Chromosome No: 32 Place of Origin: Philippines, Malay, Indonesia, Cochin, China and Malaya Peninsula Common Name: Betel nut 3.1 Introduction Arecanut (Areca catechu L.), commonly known as Supari or Betel palm or Arecanut palm, is cultivated primarily for its kernel obtained from the fruit which is chewed in its tender, ripe or processed form. It is an important cash crop of India. The economic produce is the fruit called ‘betel nut’ and used mainly for masticatory purposes. Its cultivation is restricted in South Western and North Eastern regions of the country. The chromosome number of Areca catechu L. was first determined as 2n=32 (Venkatasubban, 1945) and it belongs to family Arecaceae. It is one of the important commercial crops grown in parts of Karnataka, Kerala, Assam, Meghalaya, West Bengal, and Andaman and Nicobar Islands. The cultivation of arecanut has also been extended to other states like Tamil Nadu, Andhra Pradesh and Maharashtra. India is the largest producer and consumer of arecanut in the world holding 62% of the area and 60% of the production. The other major arecanut growing countries are Indonesia, Bangladesh, China, Myanmar and Thailand. Arecanut being a profitable plantation crop, it is important to understand the package of practices and seasonal operations to be followed in the nursery, in young gardens and in old plantations to get maximum returns. To get additional income in the adverse periods, concept of cropping system is very important. The exact origin of areca nut is not known till date but, arecanut is a palm which grows in much of the tropical Pacific, Asia, and parts of East Africa (Kumar, 2013).

Botanical Name: Elaeis guineensis Jacq.

Family: Arecaceae / Palmae

Chromosome No: 32

Place of Origin: Guinea Coast of West Africa

Common Name: Oil Palm

4.1 Introduction

The cultivated oil palm Elaeis guineensis Jacq. is a native of West Africa and exists in wild, semi- wild and cultivated forms. It was introduced as an ornamental crop in 1860 and planted at National Botanical Garden, Kolkata. Research on oil palm in India started during 1960 with the establishment of a research station at Thodupuzha by the Department of Agriculture, Kerala with Dura and Tenera germplasm imported from Malaysia and Nigeria. National Research Centre (NRC) for oil palm is situated at Elur, Andhra Pradesh. Oil palm crop is one of the highest oil yielding crops among all perennial crops. It gives 4 to 6 tonnes of edible oil/ha from 3 to 25 years of its life span compared to less than one tonne of oil per ha from other cultivated oil yielding crops. This oil palm is considered as golden palm due to its high yielding capacity. Among the 10 major oilseeds, oil palm is accounted for 5.5% of global land use for cultivation, but produced 32.0% of global oils and fats output.

Botanical Name: Borassus flabellifer L.

Family: Arecaceae

Chromosome No: 36

Place of Origin: Tropical region of Africa, Asia and New Guinea

5.1 Introduction

Palmyrah is a monocotyledon and perennial crop. The word “Borassus” was derived from a Greek word and it means the leathery covering of the fruit and “flabellifer”, means fan-bearer. The distinguishing characters of palm in this genus are their palmate, fanlike leaves and dioecious character, i.e., male and female flowers are borne on separate trees (sex ratio 1:1), allotetraploid (X= 8 or 9, n = 18), the male tree has a heteromorphic XY pair of chromosomes while the female has an XX pair (Sankaralingam et al., 1999). Next to coconut, palmyrah is the most abundant palm found in the world. Palmyrah thrives in arid conditions and is grown extensively in Tamil Nadu. Palakkad district of Kerala is popularly known as land of palmyra trees. Palmyra trees are known as the icon of this district and have a vast cultural, heritage and literary association.

Botanical Name: Theobroma cacao L.

Family: Sterculiaceae

Chromosome No: 20

Place of Origin: Tropical South America

Common Name: Cacao, cacao bean, cocoa, cocoa bean

6.1 Introduction

Cocoa (Theobroma cacao L.) is a perennial plant and is used as beverage crop. The generic name Theobroma means the “Food of the God”. The word cacao refers to the tree, while the word cocoa refers to the drink made from its seeds. The roasted product of the dried beans is called as Cacao nibs that are used for the manufacture of various products. When cacao nibs are ground, the resulting product is called chocolate liquor or mass, which contains around 55% fat. The fat that is pressed from chocolate liquor is termed as cacao butter. It is mainly used for the manufacture of chocolates, in pharmaceutical preparations and soap making. After pressing out the fat the resultant product with reduced fat content is called cacao powder. For preparation of chocolates cacao powder is mixed with cacao butter and sugar with a definite ratio. Milk chocolates are prepared by adding milk to the above mixture. The cocoa powder contains 22- 25% fat, 20.4% protein and 35% carbohydrate with caloric value of 452 cal. Rind or shell of pod is used as a cattle feed.

Botanical Name: Anacardium occidentale L.

Family: Anacardiaceae

Chromosome No: 42

Place of Origin: Brazil

Common Name: Cashew, cashew tree, caju

7.1 Introduction

Cashewnut (Anacardium occidentale L.) is an important tropical tree crop which leads the edible nuts in international trade with 29% market share only after hazelnuts (35%) and followed by walnuts (21%) and almonds (16%). It is one of the major exports earning crops which accounts nearly Rs. 2515 crores contributing about 1.5% of the total Indian exports. It was only during the past two decades it gained the status of horticulture crop. India is the world’s largest producer of cashewnut. The cashew cultivation area in India during 2018-19 was 10.89 lakh hectares with an annual production of 743 thousand metric tons of fruits. Maharashtra is a much-advanced state for horticulture which occupies large area (1.9 lakh hectares) under this crop (Directorate of Cashew & Cocoa Development, Cochin, Kerala). Cashew exports amounted to over 566 million U.S. dollars from India in fiscal year 2020. This included cashew kernels and cashew nut shell liquid. India’s main export markets for this commodity included South Korea, China, Spain, United States and Belgium (Department of Agriculture, Cooperation & Farmers Welfare).

Botanical Name: Coffea arabica, Coffea canephora

Family: Rubiaceae

Chromosome No: 44, 22

Place of Origin: Africa

Common Name: Coffee

8.1 Introduction

Coffee is widespread throughout the tropics with more than 70 species. All cultivated species originate from Africa. Economically important today is Coffea arabica (Arabica, 64 % of world production) and Coffea canephora, (Robusta, 35 %). It is cultivated worldwide on approximately 10.3 million hectares and represents the sole economic income for more than 25 million families. The crop is produced and exported by more than 60 nations and ranks as one of the top cash crops in developing countries with 3.19 lakh MT production per annum in India in 2018-19 from more than 459 thousand hectares crop area. The coffee plant is a fast-growing tropical bush tree, with two types of shoots: upright growing orthotropic main shoots (stem), and horizontally growing plagiotropic shoots (branches). Traditionally, coffee trees are cultivated only for the berries, which are processed using dry or wet techniques directly in the growing areas to the final raw product, green coffee; this serves as the basis for various coffee products. Both the coffee species tolerate shade and share quite similar growth requirements with various forest crops and trees, thus predisposing them to be grown in agro-forestry ecosystems.

Botanical Name: Camellia sinensis L.

Family: Theaceae

Chromosome No: 30

Place of Origin: China

Common Name: Tea

9.1 Introduction

Tea (Camellia sinensis L.) of family Camelliaceae or Theaceae is the oldest and second most widely consumed beverage in the world following water. Tea is an evergreen plant that mainly grows in tropical and subtropical climates. India is world’s 2nd largest tea producer after China with production of 1.25 million tonnes in 2020. In India it is grown in an area of 6 lakh hectares, contributing one fourth of world total tea production annually. Interestingly, India is also the world’s largest consumer of the beverage and using nearly 30 percent of the world’s tea output. India stands third in terms of tea export after Kenya (including neighbouring African countries) and China. The total tea export was US$ 501.16 million in April 2020 to November 2020 and for November 2020 it was US$ 71.61 million.

Botanical Name: Phoenix dactylifera L.

Family: Arecaceae

Chromosome No: 2n = 36

Place of Origin: Countries around Persian Gulf

10.1 Introduction

Date palm is a nutritive fruit rich in sugar and iron and predominantly seen in desert oasis. It is the main fruit crop in arid and semiarid regions, particularly in the arid regions of western Asia and North Africa. The palm tree is well adapted to desert environments that are characterized by extreme temperatures and water shortage. Beyond the arid climates, date palm can also be grown in many other countries for food or as an ornamental plant including the continents of Americas, Southern Europe, Asia, Africa and Australia. The majority of date palm growing areas are located in developing or underdeveloped countries where dates are considered the primary food crop, thus playing a major role in the nutritional status of these communities. By-products from date palm are used in building structures, animal feed, and also in several items such as baskets and ropes. The date palm tree has been in cultivation since 2400 BC. It is one of the oldest cultivated fruit trees on earth. India is the leading importer of dates. According to FAO statistics, in 2011 India was the largest world importer of dates (256,295 mt) and its neighbour Pakistan, in 2012, was the world’s sixth largest producer (600,000 mt). The major date palm growing areas in India are Kutch (Gujarat), Rajasthan and certain parts of the Punjab, as well as Tamil Nadu state to some extent. In Kutch, there are more than 2 million date palms, the majority of them grown from seeds and offshoots, providing a huge biodiversity for experimentation and improvement of products. Efforts by the Indian government in the last decade have resulted in an increase of date palm cultivation from 8,973 to 16,000 ha and establishment of a prominent effort in Rajasthan (Shah, 2014).

Botanical Name: Hevea brasiliensis

Family: Euphorbiaceae

Chromosome No: 2n =36

Place of Origin: Amazon basin in Brazil

Common Name: Rubber tree, Rubber plant, Para rubber tree

11.1 Introduction

Rubber is a fast-growing tropical tree and mainly grown for the production of latex. In 1495, Columbus was the first person to report about latex. The latex was used for rub out pencil marks and this gave the product name as rubber. The latex is used for production of various rubber products. The most important application of latex rubber was initially accomplished in the 1880s when the rubber was used for making motor cars tyres. When rubber is heated with sulphur, it retains the physical properties.

Soil testing identifies various constraints in soil that limit crop production. Fertilizer recommendation and soil management practices to be adopted are based on the soil test report. Soil testing starts with collection of soil samples from the field
and ends with interpretation of soil test results leading to necessary recommendations for increasing crop production.

Aim of the Experiment: The composite soil samples, collected, are air-dried and processed before analysis in the laboratory.

Soil texture is the relative proportion of sand, silt and clay in soil by weight. As per the classification of the inorganic soil particle by the International Society of Soil Science, diameters of the sand, silt and clay particles vary from 2 to 0.02 mm, 0.02 to 0.002 mm and less than 0.002 mm, respectively. There are 3 broad textural groups i.e sand, loam and clay and each broad textural group is subdivided into different textural classes depending upon the proportion of sand, silt and clay.

Pore space or porosity : Porosity is an index of the relative pore volume in the soil or volume fraction of pores. Its value generally lies in the range of 0.3-0.36 (30-60%). Coarse textured soils tend to be less porus than fine textured soil, though the mean size of individual pores is greater in the former than in the latter. The total porosity, in any case, reveals nothing about the pore size distribution which itself is an important property.

Primary particles tend to adhere under natural condition so as to form secondary unit called aggregates. It is very difficult to determine the diameter of the individual aggregated particle. So the mean weight diameter is calculated as a single value constant, which indicate the wetted average value for a group of aggregates.

The soil water plays an important role in the physical, chemical and biological activities taken place in the soil which directly or indirectly affects the crop growth. The water content of the soil can be measured by different methods. These methods are gravimetric method, neutron scattering method and electrical resistance method.

Field capacity may be defined as the amount of water held in soil after excess water has drained away and the rate of downward movement of water was materially decreased which usually taken place within two to three days after heavy rain or irrigation. In this condition water was moved out of the macropores and air has moved into take its place. 

Infiltration is the entry of a fluid from one medium into another. Soil water plays an important role in nature. The entry of rain or irrigation water into soil is called infiltration. Infiltration rate is defined as the amount of water entering into a soil per unit area per unit time under one atmospheric pressure. The infiltration rate depends upon initial moisture content of soil, condition of soil surface and permeability of various layers.

The maximum water holding capacity is the amount of water, expressed as percentage on oven dry basis held by a soil when it is completely saturated with water. At this moisture content, all the pore spaces are completely filled with water.

Determination of soil texture mainly depends on the complete dispersion of soil aggregates into their individual primary particles without triturating the particles themselves, accurate fractionation of the sample into various separates, sedimentation of different particles in the viscous medium as well as measurement of individual particle by the hydrometer.

Atomic weight of an element is defined as the relative average weight of its atom as compared to the weight of an atom of C12 (stable isotope) taken as 12 on the a.m.u. (atomic mass unit) scale.

Electrical conductivity of the soil is the quantity of electricity in coulombs that passes per second per cm3 of the soil solution under an applied potential difference of 1 volt. It is written as EC in abbreviated form and is expressed in millimohs cm-1 or dS m-1 (deciSiemen m-1). Current flows in the soil solution due to presence of soluble salts like Cl- , SO2- 4 and NO3 - of Na+, K+ Ca++ and Mg++. These salts fully dissociate in water to their constituent cations and anions and the current is carried by those cations anions in the soil solution.

In the detection of soil organic carbon a known weight of soil is heated with an excess volume of standard K2 Cr2 O7 in the presence of Con. H2 SO4 . The soil is slowly digested at the low temperature by the heat of dilution of H2 SO4 and the organic carbon in the soil is thus oxidized to CO2 . The highest temperature attained by the heat of dilution reaction produced with the addition of Con. H2 SO4 is approximately 12o C which is sufficient to oxidize the active forms of the soil organic carbon but not the more inert forms of carbon that may be present.

The lime requirement (LR) of an acid soil is the amount of lime needed to neutralize the acidity from an initial condition to some target soil pH, like 7.0. Different methods are used to calculate a LR for specific soils. Lime requirement tests will always recommend adding enough lime to neutralize all the acidity associated with soil pH, much of the salt-replaceable acidity, and some of the residual acidity.

The statement is still valid as long as it is understood that in an aqueous solution the substances involved are subject to dissociation, which changes the ionization state of the substances. 

The Kjeldahl method is mainly divided into three main steps. This method needs to be carried out in proper sequence. 

In Bray’s method a mixture of ammonium fluoride and hydrochloric acid (NH4F + HCl) act as the extracting agent. It works in two ways, firstly, the hydrogen ions from HCl have a solubilising effect, especially on Ca-P, which makes phosphate get extracted. Secondly, the fluoride ions (from ammonium fluoride) complex with the Al3+ and Fe3+ ions, thereby releasing the Al and Fe bound phosphorous into the extract.

Olsen P were found to bear a quadratic relationship, with Olsen’s extractant underestimating the content in phytoavailable P of soils with high Olsen P contents relatively to soils with low contents. The ‘‘change point’’ at which phytoavailable P began to increase rapidly per unit change in Olsen P was 53 mg Olsen P kg–1 soil. For the acid soils, a significant quadratic relationship was found between the amount of P desorbed to water and Olsen P at the three soil:solution ratios studied.

Potassium is extracted from the soil with the help of suitable extractant CH3COONH4 by shacking followed by filtration or centrifugation & is determined in the extract using flame photometer. The analysis photometer is based on the measurement of the intensity of characteristic lime emission given by the element to be determined .

Reagents

• Buffer solution : Dissolve 67.5 g of Ammo. Chloride in 200 ml distilled water. Add 570 ml Ammonium solution. The volume make up to 1 ltr.

• EBT : 0.2 g in 25 ml methanol.

Soil is shaken with 0.15 % CaCl2 solution. Chloride ions displace adsorbed sulphate during extraction. Calcium ions suppress the extraction of soil organic matter and hence eliminate the contamination caused by extractable organic S.

Soil is shaken with 0.15 % CaCl2 solution. Chloride ions displace adsorbed sulphate during extraction. Calcium ions suppress the extraction of soil organic matter and hence eliminate the contamination caused by extractable organic S.

Diethylene tetramine penta acetic acid (DTPA) being a chelating agent is used in the determination of available Fe, Mn, Cu and Zn. When the soil is shaken with a solution of DTPA, it combines with metal ions in the solution and form soluble complexes of Fe++, Mn++, Cu++ and Zn++. The CaCl2 and triethanolamine (TEA) solution slightly raise and buffer the soil pH and mitigates the effect of Ca++ and Mg++.

Procedure

• Take 1 g soil in a 50 ml beaker

• Add 25 ml 1M KCL solution