Jesse Omondi Owino; Alice Adongo Onyango; Esther Njenga Mugure; Peter Murithi Angaine

doi:10.19080/ECOA.2025.04.555648

Mini Review

Towards Morphometry and the Guiding Need for Development of Better Seed

Jesse Omondi Owino*, Alice Adongo Onyango, Esther Njenga Mugure and Peter Murithi Angaine

Rift Valley Ecoregion Research Program, Kenya Forestry Research Institute (KEFRI), Kenya

Submission: February 14, 2025;Published: March 18, 2025

*Corresponding author: Jesse Omondi Owino, Rift Valley Ecoregion Research Program, Kenya Forestry Research Institute (KEFRI), P.O. Box 382- 20203, Londiani, Kenya

How to cite this article: Jesse Omondi O, Alice Adongo O, Esther Njenga M, Peter Murithi A. Towards Morphometry and the Guiding Need for Development of Better Seed. Ecol Conserv Sci. 2025; 4(5): 555648. DOI:10.19080/ECOA.2025.04.555648

Abstract

African forestry is at a critical juncture where sustainable development must align with biodiversity conservation while delivering economic benefits. Morphometric methods play a pivotal role in improving seed quality, directly influencing tree survival rates and adaptability across diverse African climates. The interplay between morphometry and African forestry encompasses various scientific aspects, providing insight into distinct tree species and their ecosystems. Quantitative analysis using morphometric techniques enables researchers to identify growth variability patterns and spatial distributions, essential for effective forest management and conservation strategies. Given the ecological impacts of deforestation and climate change, morphometric evaluations have become crucial tools for ecologists addressing these challenges. Morphometric techniques significantly enhance seed selection by identifying optimal characteristics for improved timber productivity and survival rates in local environmental conditions. Understanding morphological traits aids professionals in assessing biodiversity and ecological relationships impacting forest health. Advanced morphometric approaches, including digital imaging and predictive modelling, improve the selection process, ensuring high-quality seeds for reforestation efforts. Predictive applications such as near-infrared spectroscopy and machine learning techniques like support vector machines and convolutional neural networks facilitate seed quality assessment and selection. Despite these advancements, challenges such as climate change variability, resource limitations, and the need for supportive policies hinder the widespread application of morphometry in African forestry. Addressing these challenges requires collaboration among researchers, policymakers, and forestry practitioners. Future research should focus on scaling up morphometric applications and fostering interdisciplinary approaches to optimize germplasm selection, thereby enhancing forest productivity and resilience against environmental threats.

Keywords: Morphometry; African forestry; Biodiversity conservation; Climate change; Environmental conditions

Abbreviations: DBH: Diameter at Breast Height; UAV: Unmanned Aerial Vehicles; SVM: Support Vector Machine; NIR: Near-Infrared

Introduction

African forestry is at a critical juncture where sustainable development, biodiversity conservation, and economic benefits must be balanced. Morphometry provides a valuable tool for improving seed quality, ensuring better tree survival and adaptability. Given Africa’s diverse climatic conditions, morphometric studies can significantly contribute to the selection of high-yield, resilient tree species. The intricate relationship between morphometry and African forestry is a critical area of study that encompasses various scientific and ecological dimensions. Morphometry, the quantitative analysis of form and structure, plays a pivotal role in understanding the diverse tree species and their respective ecosystems across the African continent. This approach allows researchers to quantify growth patterns, shape variations, and spatial distributions, which are essential for effective forest management and conservation strategies. Given the significant ecological and economic implications of forestry in Africa, incorporating morphometric data enhances our comprehension of biodiversity and ecosystem functionality. As environmental challenges such as deforestation, climate change, and habitat degradation escalate, morphometric analysis becomes an invaluable tool for ecologists and forest managers alike. Thus, exploring the role of morphometry in African forestry not only informs scientific inquiry but also supports sustainable practices vital for preserving this rich biological heritage.

Role of Morphometry in African Forestry

Morphometric techniques enable the identification of superior seed traits, enhancing tree growth, survival, and productivity. Studies in African forestry science have highlighted the benefits of using morphometry to select trees with better adaptation capabilities to local environmental conditions. Morphometry, the quantitative analysis of form and structure in organisms, holds significant importance in the field of forestry, particularly within the context of African ecosystems. Understanding the morphological characteristics of tree species enables researchers and practitioners to assess biodiversity, ecological interactions, and the health of forested areas. In African forestry, where diverse climatic and geographical conditions prevail, morphometric studies can inform conservation strategies and sustainable management practices. Such assessments are vital for determining species resilience against environmental changes, thus ensuring the protection of unique genetic resources. For instance, similar to the conservation efforts directed at the Tankwa goats that have been identified as a distinct genetic population, morphometric analyses can help highlight the need for targeted preservation of endemic tree species.

This approach ultimately contributes to maintaining ecological balance and enhancing productivity within these vital ecosystems Ngcauzele et al. [1], Society WG [2]. The integration of morphometric techniques in forest management plays a pivotal role in enhancing the understanding of tree growth dynamics and ecological health within tropical forests. For instance, studies on species such as Pterocarpus angolensis and Bobgunnia madagascariensis in Miombo woodlands demonstrate the significance of crown dimensions as they relate to the trees photosynthetic capacity, which directly impacts overall growth and biomass productivity Costa et al. [3]. The application of morphometric measurements, including diameter at breast height (DBH) and crown size, allows forest managers to make informed decisions regarding silvicultural practices tailored to specific species characteristics. Additionally, analysing watershed morphology and land use patterns, as seen in the Raya Watershed, underscores the importance of incorporating detailed assessments of topography and hydrological dynamics to develop effective management strategies that protect biodiversity and enhance ecosystem stability Kurzah et al. [4]. Thus, morphometric techniques are indispensable tools in the sustainable management of African forestry.

The application of morphometry in assessing tree growth and health is pivotal in African forestry, where understanding tree dynamics can significantly impact conservation and management practices. Morphometric techniques enable researchers to quantify tree dimensions-such as height, diameter, and crown width-providing critical insights into growth patterns and vitality. These parameters can reflect environmental stressors and the trees overall resilience, especially in diverse ecological settings. For instance, studies have shown that certain morphometric indicators correlate with specific environmental and biological variables, helping identify trees that may be under duress from factors such as drought or disease Kozak et al. [5]. Furthermore, advancements in remote sensing technologies, including unmanned aerial vehicles (UAVs), have enhanced the precision of morphometric assessments, allowing for more comprehensive monitoring of forest ecosystems Bogawski et al. [6]. Such approaches are not only vital for sustainable forestry practices but also contribute to the broader goals of biodiversity conservation and ecological health in the region.

Predictive Applications in Seed Selection

Research in African forestry has demonstrated that morphometric traits can serve as reliable predictors of germination success and seedling vigor. Such studies underscore the importance of integrating morphometric analysis into seed orchard management Research in African forestry has demonstrated that morphometric traits can serve as reliable predictors of germination success and seedling vigor. Such studies underscore the importance of integrating morphometric analysis into seed orchard management. When it comes to seed quality attributes that indicate a likelihood of seed germination and seed viability are crucial, and a rapid and efficient technique to assess the viability and germination state of seeds before they are cultivated, sold, and planted is greatly needed Xia et al. [7]. The basic concept of near-infrared (NIR) spectroscopy is the absorption of electromagnetic radiation with wavelengths between 780 and 2500 nm, When assessing the quality of natural resources, including fruits, vegetables, crops, trees, and their seeds, NIR spectroscopy has proven to be an effective tool Xia et al. [7]. One of the key goals of the breeding programmes is increasing the seed yield, Nonetheless, assessing Seed yield in extensive breeding populations containing thousands of genotypes is difficult and requires a significant amount of time Shahsavari et al. [8]. Support vector machine (SVM) is a widely used and sophisticated machine learning algorithm capable of identifying both linear and nonlinear patterns in data. The benefits of using SVMs include a high quantity of hidden units and improved formulation of learning problems. Creation of prediction models for predicting seed quality such as neural networks (CNNs), makes it easier to choose seeds with the best chance of germination and production. (Prasuna et al 2024).

Comprehensive experimentation according Mahoney et al. [9] is also another predictive application that can be conducted to investigate the effects of widely used seed set selection techniques in active learning, particularly in a predictive coding context. This includes assessing various active learning strategies against established continuous active learning approaches to identify effective training methods for swiftly and accurately classifying large groups. Techniques for feature engineering and selection that lower complexity but retain predictive power are used to identify the key elements influencing germination quality. (Prasuna et al 2024).

Implications for Germplasm Improvement

The establishment of seed orchards with genetically superior stock can significantly boost reforestation efforts across the continent. Morphometric techniques facilitate the selection of high-quality seeds, reducing genetic variability that may hinder tree development [10-14]. The establishment of seed orchards with genetically superior stock can significantly boost reforestation efforts across the continent. Morphometric techniques facilitate the selection of high-quality seeds, reducing genetic variability that may hinder tree development. Progenies from seed orchards produce about 25% more than those from unimproved seed lots, making them an economical way to boost seed output and attain sustainable harvests from forest plantations Prescher ,2007. Using morphometric approaches, which statistically measure seed size and shape which are indicative of genetic, physiological, and ecological factors that ultimately affect yield, quality, and market price helps pick high-quality seeds Cervantes et al 2016. Morphometric investigation is further improved by digital imaging and computer tools, which make it possible to calculate shape indices such roundness Yu et al 2013.

Since the mid-1900s, when forest tree breeding began, the use of improved forest regeneration material has become a crucial component of forestry in many nations Ruotsalainen,2014. The goal of forest tree breeding is to ensure that genetically enhanced materials for reforestation are available. This is through controlled crossings, field testing, and selection, to improve the quality and quantity of wood-based raw materials Ruotsalainen,2014. Getting a superior genotype into a plantation is quicker when using the clonal option Matheson and Lindgren,1985. A robust sexually based breeding effort is necessary to provide improved genotypes for any clonal propagation scheme to succeed Matheson and Lindgren,1985. The connection between tree breeding and silvicultural practices is represented by seed orchards. Their genetic efficiency is crucial because it dictates how much genetic variety and gain future forest tree plantings will experience Funda, et al,2012. Advancement in technologies like genotyping-bysequencing (GBS) and chip-based genotyping, facilitate breeding efforts such as Marker-Assisted Selection (MAS) by providing a comprehensive map of genetic variants Rasheed et al 2024.

Challenges and Considerations

Climate change impacts on forestry have shown increasing variability in climate conditions posing a challenge to seed selection processes, necessitating adaptive strategies in forestry management. Existing resource limitations in many African nations resulting to challenges related to funding, infrastructure, and expertise in advanced morphometric studies. There is a need for policy integration. Policies supporting research and development in morphometric applications for seed improvement must be prioritized to ensure sustainability.

Conclusion

Morphometry presents an invaluable approach to enhancing tree seed quality in African forestry. By integrating predictive studies into tree improvement programs, forestry practitioners can optimize germplasm selection, leading to improved forest productivity and resilience. Future research should focus on scaling up morphometric applications and fostering collaboration between research institutions and policymakers to address resource limitations and climate-related challenges.

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