Soil Texture Research Articles

Random forest approach to estimate soil thermal diffusivity: Evaluation and comparison with traditional pedotransfer functions

Pedotransfer functions (PTFs) are widely used in science and engineering to calculate soil thermal diffusivity (κ) as a function of soil moisture content and other soil physical properties. However, available data on κ are rather scarce and already developed PTFs suffer from significant uncertainties in applicability and performance. Few studies attempted to systematically review and evaluate these PTFs with large dataset of κ. The objectives of this study were to (1) review and collate currently available PTFs for κ; (2) compile a κ dataset to evaluate performance of these PTFs, (3) build a machine learning (ML) (i.e., random forest-RF) model with three classes of soil physical properties to determine soil physical properties most appropriate to use as predictors for calculating κ, and (4) compare the RF modelling with the traditional PTFs. The correlation coefficient (r), centered root-mean-square (E'), and standard deviation (SD) were used to evaluate the performance of the above PTFs. A total of nine PTFs were synthesized and assessed with a compiled dataset consisting of 998 measurements from 106 soils with a wide range of textures. In general, the PTFs of Lukiashchenko and Arkhangelskaya (2018) (LA2018–1 and LA2018–2) perform the best. While the quadratic equation of Mady and Shein (2018) (MS2018–1 and MS2018–2) showed better performance for coarse and medium textured soil. However, the overall performance of the nine PTFs for predicting κ was not as accurate as RF. RF can satisfactorily calculate κ taking into account sand, silt, clay contents, and soil moisture content as predictors.

Soil micronutrients (Zn and Fe) fractions and response of rice (Oryza Sativa) in different soil of Haryana under rice-wheat cropping system

The significance of zinc and iron in crop production, particularly in rice-wheat cropping systems, has been increasingly recognized. The present study aimed to determine the effect of different soil samples collected from the rice-wheat cropping system of Haryana with different soil texture on rice grain yield and micronutrient content using a greenhouse trial with four levels of Fe (0, 25, 50 mg kg−1 as soil and 0.5% foliar spray of FeSO4 at 45 DAS) and four levels of Zn (0, 5, 10 mg kg−1 as soil and 0.5% foliar spray of ZnSO4 at 35 DAS). This study discovered that rice grain yield showed a positive correlation with soil Zn and Fe concentrations in soil before sowing and a negative correlation with soil Fe and Zn concentrations in soil after crop harvesting. In most of the soils studied, foliar spray alone Fe @ 0.5% foliar (45 DAS) and Zn @ 0.5% foliar (35 DAS) improved Fe and Zn concentrations in rice significantly more than soil application of 25 mg Fe kg−1 and 5 mg Zn kg−1, respectively. The rice grain yield in clay soil was 14-42% higher than in sandy soil with different doses of Zn and Fe application. The order of preponderance of different Fe and Zn fractions were CA-Fe < OM-Fe < EX-Fe < FeMnOX-Fe < Res-Fe and Res-Zn > FeMnOX-Zn > OM-Zn > CA-Zn > Ex-Zn in the soils, respectively. Conclusively, concomitant consideration of grain yield and grain Zn and Fe concentrations of rice are the sustainable approach toward food targets achievement.

Effect of soil management on carbon stock and soil aggregation in an area of natural regeneration and surrounding systems in the Atlantic Forest biome

This study aimed to quantify total organic carbon (TOC), carbon of humic substances (HS), and their stocks and evaluate the soil structural stability of areas with different uses under sandy loam soil texture. Soil samples were collected from managed areas and a reference area: Permanent Pasture (PP), No-Till (NT), Private Natural Heritage Reserve in the process of natural regeneration (PNHR) and Native Forest (FN). Dry mass analysis, carbon stock quantification, chemical fractionation of soil organic matter and soil aggregation were carried out. The NF area had the highest deposition of litter mass (ML). The PP and NT areas had the highest bulk density (Bd). TOC and Stock-C contents were higher in PNHR, followed by NF, and stratification index (STRATI) was also higher in the regeneration area. The NT, PNHR, and NF areas had a higher proportion of carbon fulvic acid fraction (C-FA) than carbon humic acid fraction (C-HA), but the fraction with the highest representation in all areas was carbon humin fraction (C-HUM). The PP, PNHR, and NF areas obtained the best aggregate stability indicators, as well as a higher proportion of macroaggregates, with the NT area having low aggregate stability. Recovery of C contents was observed in recent years in the area of PNHR, leading to a greater storage of C, which shows a quantitative recovery of C in the soil in this area after four years of natural regeneration. Furthermore, the PP and NT areas present a lower capacity for C sequestration, mainly due to the management conditions. Keywords: aggregate stability, climate change, crop production, soil quality.

Combining Laser-Induced Breakdown Spectroscopy and Visible Near-Infrared Spectroscopy for Predicting Soil Organic Carbon and Texture: A Danish National-Scale Study.

Laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (vis-NIRS) are spectroscopic techniques that offer promising alternatives to traditional laboratory methods for the rapid and cost-effective determination of soil properties on a large scale. Despite their individual limitations, combining LIBS and vis-NIRS has been shown to enhance the prediction accuracy for the determination of soil properties compared to single-sensor approaches. In this study, we used a comprehensive Danish national-scale soil dataset encompassing mostly sandy soils collected from various land uses and soil depths to evaluate the performance of LIBS and vis-NIRS, as well as their combined spectra, in predicting soil organic carbon (SOC) and texture. Firstly, partial least squares regression (PLSR) models were developed to correlate both LIBS and vis-NIRS spectra with the reference data. Subsequently, we merged LIBS and vis-NIRS data and developed PLSR models for the combined spectra. Finally, interval partial least squares regression (iPLSR) models were applied to assess the impact of variable selection on prediction accuracy for both LIBS and vis-NIRS. Despite being fundamentally different techniques, LIBS and vis-NIRS displayed comparable prediction performance for the investigated soil properties. LIBS achieved a root mean square error of prediction (RMSEP) of <7% for texture and 0.5% for SOC, while vis-NIRS achieved an RMSEP of <8% for texture and 0.5% for SOC. Combining LIBS and vis-NIRS spectra improved the prediction accuracy by 16% for clay, 6% for silt and sand, and 2% for SOC compared to single-sensor LIBS predictions. On the other hand, vis-NIRS single-sensor predictions were improved by 10% for clay, 17% for silt, 16% for sand, and 4% for SOC. Furthermore, applying iPLSR for variable selection improved prediction accuracy for both LIBS and vis-NIRS. Compared to LIBS PLSR predictions, iPLSR achieved reductions of 27% and 17% in RMSEP for clay and sand prediction, respectively, and an 8% reduction for silt and SOC prediction. Similarly, vis-NIRS iPLSR models demonstrated reductions of 6% and 4% in RMSEP for clay and SOC, respectively, and a 3% reduction for silt and sand. Interestingly, LIBS iPLSR models outperformed combined LIBS-vis-NIRS models in terms of prediction accuracy. Although combining LIBS and vis-NIRS improved the prediction accuracy of texture and SOC, LIBS coupled with variable selection had a greater benefit in terms of prediction accuracy. Future studies should investigate the influence of reference method uncertainty on prediction accuracy.

Projecting the impact of climate change on honey bee plant habitat distribution in Northern Ethiopia

Climate change significantly affects the diversity, growth, and survival of indigenous plant species thereby influencing the nutrition, health and productivity of honey bees (Apis mellifera). Hypoestes forskaolii (Vahl) is one of the major honey bee plant species in Ethiopia’s Tigray region. It is rich in pollen and nectar that typically provides white honey, which fetches a premium price in both local and inter-national markets. Despite its socio-economic and apicultural significance, the distribution of H. forskaolii has been declining, raising concerns regarding its conservation efforts. However, there is limited knowledge on how environmental and climatic factors affect its current distribution and response to future climate change. The study investigates the current and projected (the 2030s, 2050s, 2070s, and 2090s) habitat distributions of H. forskaolii under three future climate change scenarios (ssp126, ssp245, and ssp585) using the Maximum Entropy Model (MaxEnt). The results show that land use (50.1%), agro-ecology (28%), precipitation during the Driest Quarter (11.2%) and soil texture (6.1%) predominantly influence the distribution of H. forskaolii, collectively explaining 95.4% of the model's predictive power. Habitats rich in evergreen trees and mosaic herbaceous with good vegetation cover are identified as the most suitable for H. forskaolii. The spatial distribution of H. forskaolii is concentrated in the highlands and mid-highlands of the eastern and southern parts of Tigray, characterized by a colder temperature. Across the three climate change scenarios, the size of suitable habitat for H. forskaolii is projected to decrease over the four time periods studied. Predictions under the ssp585 scenario reveal alarming results, indicating a substantial decrease in the suitable habitat for H. forskaolii from 4.26% in the 2030s to 19.09% in the 2090s. Therefore, given the challenges posed by climate change, research efforts should focus on identifying and evaluating new technologies that can help the H. forskaolii species in adapting and mitigating the effects of climate change.

Landslide susceptibility assessment in Addi Arkay, Ethiopia using GIS, remote sensing, and AHP

Landslides account for the breakdown of natural topographies, impacting many mountainous areas and leading to loss of lives and damaged infrastructure. This research aims to generate a reliable landslide susceptibility zonation map employing geospatial and Analytical Hierarchy Processes (AHP) in Addi Arkay Woreda, North Gondar Zone, Amhara Regional State, northern Ethiopia. The present study uses remote sensing data, geographic information system (GIS) tools, AHP, and weighted linear combination (WLC) models to analyze multiple environmental variables, including slope, aspect, curvature, lithology, soil texture, topographic wetness index (TWI), and rainfall. As per the results, around 186.12 km2 (13.26%) of the total study area is under very high landslide susceptibility and 140.85 km2 (10.05%) under very low susceptibility. Using Google Earth images for inaccessible areas, 121 landslide inventories were identified through fieldwork. Of these inventories, 85 were used to train the model and 36 for testing. The performance of the AHP model was validated by the Receiver Operating Characteristics (ROC) curve (0.75), which indicates good predictive accuracy for identifying landslide-prone areas. These findings are essential to regional land use planning, hazard mitigation, and landslide prevention efforts. Additionally, this study contributes to the scientific understanding of landslide dynamics in the Northwestern highlands of Ethiopia and offers a methodological framework that can be applied to other regions with similar geological and climatic conditions.

Impact of Land Use Changes on Soil Erodibility in the Outer Himalayas

Land use change hurts soil characteristics such as permeability, soil texture and aggregate stability, soil erodibility etc. which make soils susceptible to erosion and degradation. The outer Himalayan region is prone to large-scale soil erosion by water owing to the vast disparity in the slope gradient, undulating relief, largely mountainous joined with high-intensity rainfall and lithological characteristics of these rocks. Soil erodibility, one of the key factors affecting the rate of erosion can be estimated by using various soil erodibility indices. In examining erodibility indices across various land use changes, it was evident that both the clay ratio (CR) and modified clay ratio (MCR) escalated as land use shifts from forest to more disturbed environments. As we transit from forest to agriculture, these ratios increased and were even more marked in the transition from forest to wasteland. The erosional behaviour as per CR and MCR in agriculture land use transitions could be arranged in the order that forest &lt; agriculture &lt;builtup&lt; wastelands. Among wasteland use transitions, the high CR and MCR indicated potential challenges in soil management, while the lower CR and MCR in transitions to agriculture and forest suggested more favourable soil conditions for these specific uses. The CR and MCR ratios indicated that the lower the ratio, the more would be the clay accumulation and lesser the erosion. This information is crucial for understanding the implications of land use changes on soil erosion and health and for guiding effective land management practices in diverse ecological settings.

Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field

Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers but do not account for future soil moisture loss. Predicting soil moisture loss is challenging because it is influenced by numerous factors, including soil texture, weather conditions, and plant characteristics. This article proposes a solution to improve irrigation efficiency, which is called DRLIC (deep reinforcement learning for irrigation control). DRLIC is a sophisticated irrigation system that uses deep reinforcement learning (DRL) to optimize its performance. The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss. We introduce an irrigation reward function that enables our control agent to learn from previous experiences. However, there may be instances in which the output of our DRL control agent is unsafe, such as irrigating too much or too little. To avoid damaging the health of the plants, we implement a safety mechanism that employs a soil moisture predictor to estimate the performance of each action. If the predicted outcome is deemed unsafe, we perform a relatively conservative action instead. To demonstrate the real-world application of our approach, we develop an irrigation system that comprises sprinklers, sensing and control nodes, and a wireless network. We evaluate the performance of DRLIC by deploying it in a testbed consisting of six almond trees. During a 15-day in-field experiment, we compare the water consumption of DRLIC with a widely used irrigation scheme. Our results indicate that DRLIC outperforms the traditional irrigation method by achieving water savings of up to 9.52%.

Relationships among soil moisture at various depths under diverse climate, land cover and soil texture

Soil moisture is an important component of the hydrological cycle and a key mediator between land surface and atmospheric interactions. Although substantial progress has been made in remote sensing of soil moisture at different spatial scales, the shallow penetration depth of remote sensors greatly limits their utility for applications in meteorological modelling and hydrological studies where the critical variable of interest is the root-zone soil moisture content. Therefore, this study assesses the relationship between soil moisture at the surface (10 cm) and in lower soil layers (20, 40, 60, 80, 100, and 120 cm) under varying climates, soils, and crop types. Cross-correlation analysis is applied to daily in-situ soil moisture measurements from 4712 locations in agricultural lands across the contiguous United States. Our analysis demonstrates that zero-day lag always produced the highest correlation between 10 cm soil moisture and soil moisture in the lower layers. In addition, a positive and strong relationship between 10 and 20 cm soil moisture (r = 0.84) was observed, while the relationships between 10 and 40 cm soil moisture were moderate (r = 0.52). The decline in cross-correlation continued to the deeper soil layers, which indicated that, on a daily timescale, the surface soil moisture gradually becomes decoupled with soil moisture at greater depths. Therefore, our research suggests that the estimation of soil moisture in the soil layers up to 40 cm based on surface soil moisture is most promising. However, the influence of climate, crop type, and soil texture on the strength of relationships between surface and lower layers makes the prediction difficult. The comparatively weak relationship between precipitation and soil moisture (0.09–0.32), as well as the relationship between reference evapotranspiration (ETo) and soil moisture (−0.19–0.18), in this study can be attributed to scale mismatching from different data sources.

Insight into the variation of soil hydraulic properties under beech and spruce forest—A case study in the forest of Tharandt, NE Germany

AbstractBackgroundThe increasing vulnerability of forests in the temperate zone due to climate change has led to modification in the forest structure to secure woody raw materials and ecosystem benefits. Such changes will influence hydrological processes both at the stand and catchment scale. Soil hydraulic properties (SHP) play an important role in assessing the water cycle in these ecosystems. Yet, knowledge regarding the effect of forest‐ and site‐specific conditions on SHP in temperate climates is scarce.AimsThis work addresses this research gap by assessing the variation of SHP under two common European forest stands, Fagus sylvatica and Picea abies (1) with comparable site conditions, and (2) across differing site conditions.MethodsWe determined the soil water retention curve (WRC) and the hydraulic conductivity curve (HCC) in several plots with the bimodal Kosugi–Mualem's hydraulic model. These functions were determined using combined field and laboratory measurements, including hydraulic conductivity and water content from soil samples.Results(1) We observed distinct variations in SHP between beech and spruce forest stands with comparable site conditions; however, no clear pattern in the variation was discernible. (2) A noticeable effect of the site‐specific characteristics on the SHP was detected. Moreover, SHP in each analysed forest type presented individual variations.ConclusionsThis study demonstrates that SHP present a wide range of variations in terms of both forest‐ and site‐specific conditions. Hence, due to its heterogeneity, we emphasise the need for more research to better characterise SHP in temperate zone forests. Moreover, this study underlines the urgent use of a minimum set of parameters in studies when addressing SHP (e.g., tree age, soil texture).

Environmental factors determining weed species distribution in organic manure and inorganic fertiliser smallholder maize (Zea mays L.) production

AbstractWeeds are a major concern in crop production, and their environmental plasticity hinders successful control. A phytosociological study assessed weed distribution patterns in smallholder maize (Zea mays L.) production under different climatic and soil factors in the 2020/2021 and 2021/2022 cropping seasons. Thirty‐six maize fields from the Highveld (Mankayane) and the Middleveld (Luve) of Eswatini, were investigated with three fertiliser regimes applied (cattle manure only, cattle manure plus inorganic fertiliser and inorganic fertiliser only). Phytosociological attributes of weed species including density were collected using a 25 m transect placed horizontally at the centre of each field. Climatic factors and soil physiochemical properties considered included altitude, rainfall, temperature, soil texture, soil pH, nitrogen, phosphorus, potassium, calcium, magnesium, copper, iron, zinc and manganese. Fifty‐six weed species belonging to 16 families were recorded. Distance‐based multivariate multiple regression (DistLM) analysis was used to determine the relationship between weed species composition as the dependent variable from the fertiliser regimes and the environmental factors as the independent variable. Eight environmental variables explained 25% variation in species distribution namely; altitude, temperature, nitrogen, phosphorus, potassium, copper, magnesium and percentage silt. At Luve, temperature, copper and magnesium significantly explained weed distribution in all three fertiliser regimes, while phosphorus influenced weed distribution in the manure only regime. At Mankayane, nitrogen affected weed distribution in the manure plus inorganic fertiliser and inorganic fertiliser regimes whereas altitude, phosphorus, potassium, and %silt influenced weed distribution in the manure only and manure plus inorganic fertiliser regimes. The observations suggest that some weed species coexist between fertiliser regimes and study areas while others occur in specific environments only.

Dataset of soil hydraulic parameters in the Yellow River Basin based on in situ deep sampling

Soil hydraulic parameters are vital for precisely characterizing soil hydrological processes, which are critical indicators for regulating climate change effects on terrestrial ecosystems and governing feedbacks between water, energy, and carbon–nitrogen cycles. Although many studies have integrated comprehensive soil datasets, data quality and cost challenges result in data completeness deficiencies, especially for deep soil information. These gaps not only impede methodological endeavours but also constrain soil parameter-based ecosystem process studies spanning from local profiles to global earth system models. We established a soil dataset across the entire Yellow River Basin (YRB) (795,000 km2) using high-density in situ field sampling. This observation-based dataset contains records of soil texture (2924), bulk density (2798), saturated hydraulic conductivity (2782), and water retention curve parameters (1035) down to a maximum depth of 5 m. This dataset, which extends the recorded data range for deep soil hydraulic parameters, is valuable as a direct data resource for environmental, agronomical and hydrological studies in the YRB and regions with similar pedological and geological backgrounds around the world.

Geospatial insights into Alphonso mango cultivation: a comprehensive land suitability study in the coastal belt of Maharashtra, India.

Land suitability assessment is integral to the advancement of precision agriculture. This inquiry is focused on identifying optimal regions for cultivating Alphonso mango in the coastal belt of Maharashtra, spanning across Palghar, Raigad, Thane, Ratnagiri, and Sindhudurg districts. Employing a GIS-based Analytic Hierarchy Process (AHP) methodology, 10 crucial parameters have been considered, encompassing climatic, physical, and chemical soil characteristics: cation exchange capacity, organic carbon, slope, rainfall, soil pH, soil texture, mean annual soil temperature, base saturation, soil drainage, and soil depth. Weights are assigned to these parameters based on expert opinions and existing literature to determine their significance in developing a soil suitability map. The study reveals distinct land suitability zones for Alphonso mango cultivation. The land suitability map designates 25.78% of the study area as highly suitable, while 9.18% is considered unsuitable for Alphonso mango cultivation. To validate the study, the Receiver Operating Characteristic (ROC) curve has been employed, indicating an 83% approval rate for the reliability and performance of the soil suitability. The results categorise soil suitability classes, providing valuable insights for farmers and agricultural planners to make informed decisions regarding Alphonso mango cultivation in similar geoenvironmental regions.

The soil sample conservation method and its potential impact on ammonium, nitrate and total mineral nitrogen measurements

Scientific literature for mineral soil nitrogen content (ammonium and nitrate) measurements usually differs regarding the chosen soil sample conservation method (fresh, frozen or air-dry) before soil extraction and analysis. In addition, most of the commonly used methodologies focus on the definition of the analysis processes, regardless of the previous sample conservation methodology. With the aim to fill this gap, we performed an analysis of the conservation method effect on the nitrate, and ammonium content in frozen or air-dried samples, comparing the results with direct fresh extraction. Moreover, the effects of additional soil parameters, such as soil texture, organic matter content or mineral nitrogen content range were also studied. The results showed that both, frozen and air drying soil samples, were capable of reliably preserving the N content of soil samples in most cases. However, some ammonium losses may occur in frozen samples when a high N content (>30 mg kg−1) is present. It was also observed that air-dried soil samples can reduce the soil nitrate and increase ammonium content in samples with a high N content. It was also observed that significant amounts of organic matter in soil can alter the mineral N measured depending on the conservation method chosen. On the other hand, the soil texture presented small effects on the mineral N measurements. In any case, a broader range of soils conditions (including i.e. pH or different natural organic matter content) should be further tested to confirm our findings.

Radon Dynamics in Granite and Calcareous Soils: Long-Term Experiments in a Semi-Arid Context

Radon in soil poses a significant health risk when it accumulates inside dwellings. The estimation of radon potential is a difficult task due to the complex dynamics of radon within soil and its relations with the weather. This research focuses on the variability of radon activity, driven by environmental changes, assessed in two loam soils (loamy sand–granite soil and silty clay loam-calcareous soil) with different radium contents. We conducted an experiment with teow soil columns in a semi-controlled outdoor laboratory, in a warm semi-arid climate. We also examined the consequences of abundant rainfall on radon activity through artificial soil water content (SWC) experiment conditions. Statistical analyses reveal that SWC is the most significant parameter influencing radon activity in these experiments. Radon is proportional to SWC and inversely proportional to temperature, evapotranspiration, and pressure in both soils, while wind is negatively related only in the loamy sand soil. Based on our findings, we modelled radon potential considering different soils and climatic contexts. SWC influences radon potential by changing radon emanation, activity, and permeability, depending on the local soil texture and radium concentration.

Pertumbuhan dan Produksi Jagung pada Berbagai Varietas dan Dosis Trichokompos

The productivity of maize as a staple food needs continuous enhancement through various methods, one of which is fertilization. The exploration of diverse fertilizers is expected to help reducing the reliance on increasingly expensive synthetic fertilizers. Fertilization is a crucial practice to improve soil fertility on marginal lands. It refers to land with productivity limitations due to factors such as low soil fertility, poor soil texture, or environmental conditions that do not support optimal plant growth. By providing additional nutrients through trichocompost fertilizer, plants are expected to grow better even under suboptimal soil conditions. This study aims to investigate the effect of trichocompost fertilizer dosage on various maize varieties grown on marginal land. The research was conducted using an experimental design with plot sizes of 3 x 5 meters. The method employed was a factorial Randomized Block Design (RBD) with two factors. The first factor was the dosage of trichocompost (control, 3 tons/ha, and 6 tons/ha), and the second factor was maize varieties (Sinhas1, Nasa29, JH36, Bisi18, and Pioneer27), with each treatment repeated three times, resulting in a total of 45 experimental plots. The parameters tested included plant height, number of leaves, ear height, days to flowering, and fresh ear weight. The results showed that trichocompost had a significant effect on two parameters: the increase in plant height and the number of leaves at 49 days after planting (DAP), as well as the fresh ear weight. The maize variety significantly affected almost all tested parameters.

Edaphic Characteristics of &lt;i&gt;Rafflesia&lt;/i&gt; Habitats in Indonesia: Implications for Conservation and Propagation

Rafflesia, a holoparasitic and endophytic plant, depends on its host, Tetrastigma spp., for survival, thus highlighting the critical interdependence between these species. Given the endangered status of Rafflesia due to anthropogenic pressures and narrow distribution, comprehensive conservation efforts are crucial. Ecological data on edaphic conditions, particularly the presence of the host, are important for effective conservation strategies. This study assessed soil properties across Rafflesia habitats on Sumatera, Borneo, and Java islands, revealing similarities in pH, carbon, nitrogen, cation exchange capacity (CEC), while the soil texture varied. These findings contribute valuable insights for informed conservation initiatives, both in-situ and ex-situ.

Nitrogen fertilizers affecting the efficiency of mineral and organic phosphorus fertilizers in tropical soils cultivated with maize

Most conventional phosphorous (P) fertilizers are highly soluble, which favors P retention in the solid phase of the soil, and this condition hinders P availability to plants. Alternative P fertilizers, such as organominerals and struvite, have a slow release of nutrients into the soil, which in turn favors P uptake by plants; nevertheless, soil acidification caused by nitrogen (N) fertilizers can reduce P availability in the soil. In his study, we assessed the effect of N fertilizers on P availability for maize (Zea mays) grown in tropical soils with alternative P fertilizers. An experiment was set up in columns under greenhouse conditions with maize in two cultivation cycles in two soils with contrasting textures (medium and clayey textures), two N fertilizers (calcium nitrate - CN - and ammonium sulfate - AS), three P fertilizers (triple superphosphate - TSP -, organomineral - OM - and struvite - ST), and a control treatment (without addition of P and N fertilizers). At 45 days after emergence, we measured the biomass production and the N and P contents in plants to calculate fertilization efficiency as well as the soil P content. AS promoted the highest aboveground N accumulation, whereas the highest efficiency was obtained with CN. The alternative P sources did not differ in terms of P availability when combined with both N sources. Soil textures were sensitive to fertilizer combination. Further studies are needed to assess struvite interaction with other elements in the soil and the P release kinetics to plants.

Open geospatial data can predict the early field performance of Scots pine, Norway spruce and silver birch seedlings in Nordic boreal forests

ABSTRACT Accurate knowledge of site conditions and their effects on regeneration establishment is important for selecting the most appropriate tree species and regeneration methods for a given regeneration site. This study examined the response of the first-year field performance of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and silver birch (Betula pendula Roth.) seedlings in boreal forests to variables available in open forest and natural resources datasets. Survival, height increment and damage of planted tree seedlings and the success of direct seeding of pine were analysed on a total of 284 plots (1000 m2) in 18 regeneration experiments established in 2020–2022 in southern and central Finland. The height increment of silver birch was higher than that of conifers, while the lowest mortality rate was found for spruce. In the generalised linear mixed models, topographic wetness index, soil texture, site type and growing stock at clearcut explained the species-specific survival and height increment of planted seedlings and the success of pine seeding. Low-cost, open geospatial data effectively provide useful details on the site conditions suitable for diversifying tree species composition in boreal forests instead of monocultures.

Texture and clay mineralogy as main drivers of the priming effect in temperate forest soils

Abstract Background and aims This work aimed to determine how the soil parameters affect the magnitude and direction of priming effect (accelerated or decreased decomposition of native SOM under addition of new organic substrates, PE) in temperate acidic forest soils. Methods Thirteen topsoil samples were incubated for 163 days with the addition of maize residues. Soil respiration was measured and natural isotope labelling was used in order to separate the respiration sources (SOM, maize and PE). The effect of soil parameters on PE was studied using linear regression and structural equation modelling (SEM). Results Soils with high C/N ratio showed the lowest magnitude of cumulative PE (R2 = 0.321, p &lt; 0.05) and the longest negative PE period. A positive relationship was found between PE and the pH (R2 = 0.511, p &lt; 0.05). SEM analysis showed that pH and C/N ratio has direct (β = 0.50) and indirect (β = 0.20, via modifying soil texture and mineralogy) effect on PE. Soil texture and mineralogy had a significant effect on PE: texture affects the proportions of soil respiration sources and PE was reduced by the dithionite–citrate–bicarbonate–extractable Al (AlDCB, R2 = 0.454, p &lt; 0.05), silt + clay (R2 = 0.421, p &lt; 0.05), non-swelling clay mineral (R2 = 0.575, p &lt; 0.05) and illite (R2 = 0.522, p &lt; 0.05) contents. SEM analysis also highlighted that the AlDCB, illite and silt + clay contents has a great effect (β=−0.59) on the PE. Conclusion The silt + clay content and mineral composition of the soil, including the Al oxide and illite contents may thus significantly inhibit the magnitude of PE, and consequently the decomposition of SOM under acidic conditions.