eChapters

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Plant physiological processes determine growth, development, productivity and adaptation to changing environmental conditions. This chapter examines how artificial intelligence can improve understanding of plant physiology and stress responses. It considers physiological processes including photosynthesis, transpiration, nutrient dynamics and hormonal regulation and explores how AI can analyze complex biological, environmental and phenotypic datasets. Machine learning, deep learning, computer vision and predictive analytics support interpretation of information obtained through sensors, imaging, remote sensing and omics technologies. The chapter emphasizes early detection and prediction of stresses such as drought, salinity, temperature extremes, nutrient imbalance and pathogen pressure. It also discusses explainable AI, digital twins and federated learning as emerging approaches for plant stress research.

Predictive modelling of plant growth can help farmers and researchers improve crop management, resource utilization and production planning. This chapter examines machine learning approaches for predicting important plant growth characteristics. It introduces plant growth fundamentals and the environmental and management factors that influence development. The chapter covers the machine learning workflow, including data acquisition, preprocessing, feature engineering, model development and evaluation. Supervised learning, unsupervised learning and deep learning approaches are considered for predicting plant height, biomass, canopy development, leaf area and yield-related characteristics. Data from IoT devices, drones and satellite imagery can strengthen prediction models. The chapter also discusses precision agriculture applications, limitations and emerging technologies such as explainable AI, digital twins and edge AI.

Early identification of crop diseases enables timely management interventions and can reduce losses in agricultural production. This chapter investigates the use of artificial intelligence and sensor data for detecting crop diseases at an early stage. It introduces the fundamentals of crop disease detection and reviews AI techniques applicable to agricultural monitoring. Sensor technologies provide continuous information about crop and environmental conditions, while AI models analyze these data to identify disease-related patterns. The chapter presents an AI- and sensor-based framework, including implementation and experimental procedures, followed by performance analysis. Challenges and limitations are examined, together with future research directions aimed at improving accuracy, reliability and real-time disease monitoring.

Early and accurate detection of plant diseases is essential for protecting crop productivity and reducing agricultural losses. This chapter explores artificial intelligence-based plant disease detection using image processing and deep learning. It introduces major plant diseases and the fundamentals of image processing before examining deep learning approaches for identifying disease symptoms from plant images. AI models can automate disease recognition and support timely interventions in smart agricultural systems. The chapter presents a proposed methodology and discusses results and analysis, practical applications and limitations. It also considers challenges associated with data quality, environmental conditions and model performance, followed by future directions for developing more accurate, scalable and intelligent plant disease diagnostic systems

The integration of Internet of Things technologies with artificial intelligence is creating increasingly connected and data-driven agricultural systems. This chapter examines how IoT and AI can support smart farming through continuous data collection, analysis and decision-making. It discusses technological infrastructure for advanced agriculture and improvements in field operations enabled by connected sensors and intelligent systems. Applications in precision farming, including orchard and vineyard management, are considered alongside emerging technologies and high-throughput phenotyping. IoT devices can provide information about environmental and crop conditions, while AI methods transform these data into useful management insights. The chapter concludes by examining future directions and research challenges associated with integrated smart farming systems.

Intelligent irrigation requires systems capable of responding dynamically to crop, soil and environmental conditions. This chapter explores fusion-driven reinforcement learning for precision irrigation, with particular attention to rice alternate wetting and drying and drip farming systems. It introduces the overall framework and methodology used to combine information from different sources for irrigation decision-making. Kalman filter fusion is considered for integrating relevant data, while reinforcement learning provides an adaptive environment in which irrigation strategies can be optimized. The chapter presents results and discussions associated with the proposed approach and considers its potential contribution to efficient water management. Future research directions focus on improving intelligent irrigation systems and their agricultural applicability.

The adoption of artificial intelligence is transforming agricultural production, management and value chains while creating significant economic and social implications. This chapter examines the socio-economic dimensions of AI adoption in agriculture. It considers economic impacts, changes in labour and employment, rural livelihoods and the accessibility of digital technologies. Particular attention is given to the digital divide and barriers that may limit adoption among different agricultural communities. The chapter also addresses policy, governance and ethical considerations surrounding AI deployment. AI applications across agricultural value chains and regional and global perspectives are discussed. Finally, the chapter considers future socio-economic transformations, research gaps and the requirements for responsible and inclusive agricultural digitalization.

Remote sensing provides large-scale information about crops and agricultural environments through satellite, aerial and other sensing technologies. This chapter examines the integration of remote sensing and artificial intelligence for crop monitoring and yield prediction. It introduces the fundamentals of agricultural remote sensing, important sensors and data types, vegetation indices and crop health assessment. Data acquisition and preprocessing are followed by applications of AI models for monitoring crop conditions and predicting yields. Remote sensing datasets can be combined with machine learning and other AI techniques to identify patterns associated with crop performance. The chapter also discusses case studies, limitations, emerging technologies and future research opportunities in intelligent crop monitoring and yield forecasting