Influence of Clay Minerals on Some Soil Fertility Attributes: A Review

Abstract

Clay minerals constitute an important component of the soil system and knowledge of their role in soil fertility is imperative for sustainable soil management and productivity. The aim of this work is to overview the influence of clay minerals on some major soil fertility attributes. The rationale for carrying out this work is that most soil fertility studies rarely incorporate soil mineralogy. Clay minerals, through their physical and chemical properties, affect soil fertility by controlling nutrient supplies and availability, through the sequestration and stabilization of soil organic matter, by controlling soil physical properties through microaggregate formation, by influencing soil acidity and controlling soil microbial population and activity. The main processes involved in these relationships are dissolution-precipitation and adsorption-desorption processes, alongside mechanisms involving the formation of short-range-ordered phases. Although the determination of soil mineralogical properties is very costly and time-consuming, information about a soil's mineralogy is imperative for a holistic understanding and proper management of soil fertility. Therefore, the development of rapid, low-cost, reliable and efficient techniques of soil mineralogical analysis, directly applicable to soil fertility investigations, constitutes a major challenge. Also, future research should investigate the relationships between clay minerals and soil nitrogen vis-à-vis sequestration and stabilization. Lastly, clay minerals should be considered in studies dealing with soil quality assessment, especially in the choice of soil quality indicators.

Douglas fir and Norway spruce admixtures to beech forests along in Northern Germany – Are soil nutrient conditions affected?

“…The annual mean precipitation ranged from 1030 mm (HZ) to 670 mm (GD) and, the soil texture ranged from clay soils (21% of clay at HZ region) to sandy soils (3% of clay at GD region). It is well known that both abiotic factors directly influence the nutrient stocks in the mineral soil and the pH-value, regardless of species composition (Kome et al,2019). The Figure 5.…”

“…high base cation stocks were observed at HZ and SL, the Southern regions while comparably low values were found at UL and GD, the Northern sites. Fine particles (clay and fine silt) are characterized by a high specific surface area which can contribute to high ion exchange capacity (Kome et al,2019).…”

Species and Phenotypic Distribution Models Reveal Population Differentiation in Ethiopian Indigenous Chickens

“…Clay content is a proxy for soil fertility and has impacts on feed availability for poultry. Through their physical and chemical properties, clay minerals can be expected to have more nutrient reserves in the tropics (Landon, 2014;Kome et al, 2019). All the nine environmental variables selected for their association with habitat suitability by SDMs had significant effects on differentiation of quantitative traits.…”

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

“…Soil texture is one of the most critical physical properties of soil and is classified based on the relative proportions of sand, silt, and clay. These individual particle-size fractions 2 of 23 (PSFs) are a type of compositional soil data and have important impacts on the behavior of other physical, chemical, and biological properties of soil; furthermore, it is a major control of soil fertility [1][2][3][4]. For example, the buffering capacity of soils [5], fertilizer application rates [6,7], soil carbon maintenances [8,9], and taxonomic-based diagnostic horizon definitions [10] are greatly dependent on soil PSFs and textural classes.…”

“…Within the DSM literature, the use of these optimization techniques is limited, and a comprehensive comparison has yet to be carried out on individual soil properties-in particular, compositional soil data, such as PSFs. Hence, the main objectives of this study were: (1) to develop and test the performance of metaheuristic, optimization algorithms that were hybridized with ANNs for predicting soil PSFs using existing soil survey; (2) to rank the explanatory power of individual environmental covariates for predicting the distribution of soil PSFs for a case study in northern Iran; and (3) to map the spatial distribution of soil PSFs and soil textural classes at a provincial scale. This study was carried out in the Mazandaran Province of northern Iran, which is an agriculturally significant region with intensive crop production and diverse soil types.…”