Micro-near-infrared (Micro-NIR) spectroscopy has emerged as a promising technique for accurate and cost-effective estimation of soil attributes compared to traditional wet chemistry methods and ...conventional NIR spectroscopy. Despite its potential, the full extent of its capabilities and applications under different soil conditions remained unexplored. This study has evaluated the potential of a low-cost micro-NIR sensor for predicting soil organic carbon (SOC) and clay contents in agricultural soils under dry and fresh conditions. Two sets of soil samples i.e., A (92 samples, Netherlands) and B (92 samples, Belgium, France, and Germany) were collected and analysed for SOC and clay contents using standard laboratory methods. A micro-NIR of 2000 ∼ 2450 nm spectral range with 18–28 nm resolution (NIRONE D2.5, Spectral Engine, Germany) was used to scan both fresh and air-dried soil samples. Partial least squares regression (PLSR) models were developed for each dataset separately with and without feature selection using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and slime mould algorithm (SMA). Micro-NIR sensor performed differently for the two datasets, predicting SOC and clay contents accurately for the dataset A while failing for the dataset B. The best accuracy achieved for SOC in the dataset A-fresh (coefficient of determination in prediction (RP2) = 0.76 and root mean square errors in prediction (RMSEP) = 0.27 %) was improved with the dry samples (RP2 = 0.81 and RMSEP = 0.27 %). The prediction of clay content was rather poor (RP2 = 0.48 and RMSEP = 5 %). Feature selection by SMA and CARS methods improved SOC prediction for the dataset A of fresh (Rp2 = 0.79 and RMSEP = 0.25 %) and dry soils (Rp2 = 0.84 and RMSEP = 0.25 %), respectively. Using CARS improved the results of clay prediction for the dataset A of dry soil (Rp2 = 0.56; RMSEP = 4.62 %). Poor performance for the dataset B could be attributed to the non-normal data distribution (SOCskewness = 4.30 and clayskewness = 3.05) and the larger soil variability encountered including soil types. Therefore, this study indicates that a micro-NIR sensor is a potential innovation and can predict SOC accurately and clay with rather moderate accuracy for a normally distributed dataset.
•Micro-NIR sensor can accurately predict soil organic carbon.•Micro-NIR sensor can predict soil clay content to a certain degree of accuracy.•Feature analysis can enhance the accuracy of micro-NIR for SOC and clay content predictions.•Sample’s attributes distribution has influence on the prediction using micro-NIR.
Air-drying and wetting of air-dried soil samples with water (i.e., rewetting) are widely used sample treatments in soil analyses. It is recognized that both air-drying and rewetting of soil samples ...affect the characteristics of organic matter (OM), but systematic evaluations are scarce. In this review, we synthesize what is known in the scientific literature concerning the types and magnitudes of effects resulting from air-drying and rewetting with respect to i) characteristics of aggregate-associated and water-extractable OM, ii) soil microbiota, and iii) decomposition of OM. Air-drying of soil samples results in the formation of new and/or stronger OM-mineral interactions as well as increased hydrophobicity and mineral surface acidity. The formation of new and enhancement of existing OM-mineral interactions may lead to an increase in perceived aggregate stability, potentially affecting estimates of amount and persistence of OM associated with soil aggregates. Compared to field moist samples, air-dried samples had 8–41% higher relative dry mass proportions in the 2–0.25mm aggregate size fraction. Pronounced changes in the amount and composition of the water-extractable OM and soil microbiota are also detected during the course of air-drying and rewetting with the potential to affect the conclusions derived from OM decomposition experiments. Air-dried soil samples were found to have 2–10 times higher amounts of water extractable organic carbon and a decrease between 3% and 69% in the microbial biomass carbon (using the substrate-induced respiration technique) compared to field moist samples. The magnitude of air-drying and rewetting derived effects on sample characteristics appears to be site and soil type specific.
•Air drying results in new or stronger organic matter–mineral interactions.•Air-drying can lead to an increase in hydrophobicity and mineral-surface acidity.•Air-drying and rewetting affect aggregate-associated and water-extractable OM.•Air-drying and rewetting impact microbiota and results of decomposition experiments.•The magnitude of effects from air-drying and rewetting are soil type specific.
Sugar beet fertilization is a very complex agrotechnical measure for farmers. The main reason is that technological quality is equally important as sugar beet yield, but the increment of the root ...yield does not follow the root quality. Technological quality implies the concentration of sucrose in the root and the possibility of its extraction in the production of white table sugar. The great variability of agroecological factors that directly affect root yield and quality are possible good agrotechnics, primarily by minimizing fertilization. It should be considered that for sugar beet, the status of a single plant available nutrient in the soil is more important than the total amounts of nutrients in the soil. Soil analysis will show us the amount of free nutrients, the degree of soil acidity and the status of individual elements in the soil so that farmers can make a compensation plan. An estimate of the mineralizing ability of the soil, the N min, is very important in determining the amount of mineral nitrogen that the plant can absorb for high root yield and good technological quality. The amount of N needed by the sugar beet crop to be grown is an important factor, and it will always will be in the focus for the producers, especially from the aspect of trying to reduce the N input in agricultural production to preserve soils and their biodiversity but also to establish high yields and quality.
Understanding farmer perceptions of soil fertility is necessary for the development of appropriate assessment methods for sustainable agro‐ecosystems. This study investigated farmer perceptions of ...soil fertility and management in four villages of eastern South Africa. A questionnaire was administered to 50 farmers from each village to obtain a general overview of local soil knowledge as well as soil fertility perceptions and assessment. Ten farmers were then chosen from each village for in‐field walks and to gather in‐depth knowledge of local soil fertility concepts, soil‐crop associations and soil management. During in‐field walks, farmers were asked to identify fertile, moderately fertile and low soil fertility plots in their fields from which topsoil samples were taken for laboratory fertility analysis. Local soil fertility descriptors included crop performance and yield, soil texture, stoniness and consistence. Using these descriptors, farmers have developed specific soil use and management practices. There was generally poor agreement between farmer qualitative assessment and measured chemical fertility parameters. However, the study revealed that the generally ignored local qualitative soil knowledge of farmers could be linked to crop performance and potentially supports laboratory soil analysis in terms of its implication for smallholder agriculture in remote areas. The descriptors identified and overall assessment used by farmers in this study reflected considerable soil knowledge employed in daily decision‐making. Action learning and research that links local soil fertility descriptors to soil acidity and specific soil nutrient levels is thus recommended for effective identification of yield‐limiting factors for sustainable crop production in low‐input agriculture.
Appropriate soil conditions are important for the success of culturing tomatoes. In fact, there are mineral elements that are essential for the good and healthy development of tomatoes, namely, ...nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, and zinc. Additionally, organic matter and pH play important parts in the process. In this context, this study aimed to characterize a soil destined to produce an industrial tomato variety in South Portugal. As such, mineral elements content, pH, electrical conductivity, humidity, organic matter, and color (without humidity and without humidity and organic matter) were analyzed in 16 soil samples before any type of soil preparation was carried out. Through principal components analysis (PCA), it was possible to observe that electrical conductivity and humidity are more correlated with each other than pH and organic matter. However, the pH of soil varied between 6.9 (minimum) and 7.3 (maximum): in accordance with the ideal range values for tomato production. Additionally, regarding quantification of mineral elements, Fe showed a higher content, followed by K, Ca, P, Mg, S, Zn, and As. However, regarding the color of the soil without humidity and without humidity and organic matter, there were significant differences between CieLab parameters (L, Chroma, and Hue). Nevertheless, soil conditions of the field presented good requirements for tomato production, despite the higher levels of Fe in the soil and the presence of As.
Soil is considered a highly complex ecosystem, providing food and maintaining crop and animal productivities. Soil variability can affect plant production. Accordingly, this study aimed to compare ...soil chemical characteristics from two different locations in the same region of western Portugal (Lourinhã), intended for potato production. Soil was collected and analyzed for soil chemical properties (pH, electric conductivity, organic matter, and mineral element content). Through a principal components analysis (PCA), it was possible to identify that the interrelations among the mineral elements were explained in the projections of components one and two for both fields. Regarding Field A, Ca, K, Fe, P, S, Mg, As, Pb, and Zn are more correlated with each other than the other mineral element (Cd). On the other hand, in Field B, all the mineral elements correlate differently compared to Field A (except Cd), and show that K, As, Mg, Ca, Zn, Fe, and Pb are the most correlated with each other. Additionally, Fe and S are more correlated in Field A; however, in Field B, Fe and Zn are the ones that are more correlated with each other. Additionally, although both soils have the same pH (slightly basic soil—ideal for agriculture), they show a significantly different content of organic matter and conductivity, where Field B presents higher contents of both parameters. The obtained data are discussed, concluding that the soils, despite being geographically close, have different relationships between elements and different contents of organic matter and electrical conductivity, which may lead to differences in potato production.
Soils provide plants both with a physical home and all the essential nutrients and support they crave to thrive. Such circumstances pave the way for a close analysis of the level of viability of ...different types of soils, and hence the need to assess the suitability of the experimental field in which to implement an agronomic biofortification itinerary. Thus, soil samples were collected from different sites of a wheat field. A rectangular grid was applied. Afterwards, pH and electrical conductivity were determined with a potentiometer; the mineral quantification was measured using an XRF analyzer and color analysis were performed with a Minolta CR 400 colorimeter. Moisture and organic matter content analyses were also carried out. No significant differences were found when considering the moisture content, pH, electrical conductivity, and the mineral values of Fe and Mn. As opposed to this, slight differences were observed in organic matter content, color parameters, and in Ca, K, S, Cu, and Zn. Concerning the macroelements, the most prevalent mineral was Ca, followed by K and S. As for the microelements, Zn was the least dominant mineral, as opposed to Cu, Mn, and Fe. Data showed that this experimental field has proven to be eligible to implement an agronomic biofortification workflow due to the slightly acid pH and the lower amount of organic matter content.
•Mineral analysis of soils interfacing archaeology and analytical chemistry was conducted.•The methods used in archaeological soil analysis were discussed.•There is an urgent need for an in-depth ...evaluation of the methods used to achieve archaeological goals.
In the last decade, different soil types have been analysed to evaluate the effect of human activities from an archaeological point of view. In particular, in the last few years, tremendous advances have been made in sample preparation and analytical methods used in archaeological soil analyses. However, there is still a need to set standardized protocols to achieve different archaeological goals. Therefore, in this study, the analytical methods available to study archaeological soils have been reviewed together with a critical discussion on the challenging archaeological questions, which could be answered by determining their mineral composition. Data on trace elements and rare earth elements, which could provide a fingerprint of the human activities, have been evaluated in different studies and discussed here.
ABSTRACT The X-ray fluorescence (XRF) is an analytical technique for determination of elemental composition of different materials. In soils, the XRF has many pedological, environmental and agronomic ...applications, mainly after the emergence of portable equipments (pXRF). This technique has been recently adopted and successfully used for soil characterization worldwide, but very rare works have been carried out in soils of developing countries. The soil characterization includes the complete elemental composition determination (nutrients, trace and rare-earth elements) and allows estimating some soil physical and chemical properties. In Brazil, this technique is still incipient, mainly the use of pXRF, however, it can greatly contribute to soil characterization in-field or in-lab conditions and also replacing methods of soil analyses considered non-environmentally friendly. This review summarizes the XRF technique including principles and the main applications of pXRF in soils highlighting its potential for tropical Soil Science.
RESUMO Fluorescência de raios-X (FRX) é uma técnica analítica para determinação da composição elementar de diferentes materiais. Em solos, a FRX apresenta muitas aplicações pedológicas, ambientais e agronômicas, principalmente após a emergência de equipamentos portáteis (pXRF). Essa técnica tem sido utilizada com sucessso no mundo todo para caracterização do solo, entretanto, são raros os trabalhos em solos de países em desenvolvimento. A caracterização do solo inclui a determinação completa da composição elementar (nutrientes, elementos-traço e terras-raras) e permite a estimativa de atributos químicos e físicos do solo. No Brasil, a FRX é ainda incipiente, principalmente o uso do pXRF, entretanto, essa técnica pode contribuir grandemente para a caracterização do solo no campo, em condições laboratoriais e, também, substituindo alguns métodos de análise do solo considerados não prejudicial ao ambiente. Esta revisão sumariza a técnica de FRX incluindo princípios e as principais aplicações do pXRF, destacando seu potencial de uso na Ciência do Solo tropical.
An analysis is carried out for two hydrologically contrasting but thermodynamically similar areas on the Tibetan Plateau, to evaluate soil moisture analysis based on the European Centre for ...Medium‐Range Weather Forecasts (ECMWF) previous optimum interpolation scheme and the current point‐wise extended Kalman filter scheme. To implement the analysis, this study used two regional soil moisture and soil temperature networks (i.e., Naqu and Maqu) on the Tibetan Plateau. For the cold‐semiarid Naqu area, both ECMWF soil moisture analyses significantly overestimate the regional soil moisture in the monsoon seasons. For the cold‐humid Maqu network area, the ECMWF products have comparable accuracy as reported by previous studies in the humid monsoon period. The comparisons were made among the liquid soil moisture analysis from ECMWF, the ground station's measurements and the satellite estimates from the Advanced Scatterometer sensor. The results show reasonable performances of the ECMWF soil moisture analyses (i.e., both optimum interpolation and extended Kalman filter products) and the Advanced Scatterometer level 2 products, when compared to the in situ measurements.
Key Points
ECMWF's land analyses overestimate soil moisture in cold‐semiarid area
In cold‐humid area, ECMWF's soil moisture analyses are reasonable
The liquid only SM analysis outperforms total SM analysis
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