There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Portable NIR spectroscopy: the route to green analytical chemistry

Since the Green Revolution, world agriculture has relied heavily on agrochemical inputs (synthetic fertilizers and pesticides) in cereal- and oilseed-dominated monoculture systems, which has had long-lasting effects on soil health. Significant efforts have been undertaken to suppress these adverse impacts and resolve the conflict between producing sufficient agri-food to address world hunger and maintaining/enhancing soil health for the long run. In the context of agricultural land use and crop/soil managements impacting soil health, this comprehensive review explores the differences in soil fertility, soil quality, and soil health via delving into the complex assemblage of organisms – the home of the multi-dimensional interactive ecosystem. Through analyzing numerous studies across the major agricultural regions in the world, we provide feasible strategies to improve soil health, such as promoting the conversion of atmospheric N2 to plant-available N to enrich soil nutrients; diversifying crop rotations using alternative, cover, and green crops to increase systems’ resilience; adopting no-till with straw retention and organic/inorganic mulches to increase infiltration, stimulate enzyme activities, and enhance soil microbiome functions; and promoting C–N–water cycling to nourish soil microenvironments. These strategies, through coordinated management of the interactive nexus of plant–soil–microbe–anthropogenic activities, will help improve soil health. 

Strategies to improve soil health by optimizing the plant–soil–microbe–anthropogenic activity nexus

Abstract Purpose Wildfire is one of the most important natural disturbances in forest and multi-vegetation ecosystems, directly or indirectly affecting the structural processes and functions of forest ecosystems with varying degrees. Wildfire releases vast amounts of carbon dioxide and other substances by destroying vegetation, making itself an important topic for the study of global change and environmental impacts. Therefore, a deeper understanding of this topic is particularly crucial for managing forest ecosystems. Methods This paper was based on a literature search of the Web of Science database for international forest wildfire research, utilizing bibliometric and quantity statistical analysis methods. Results The results show that forest wildfire research has been rapidly growing over the last 20 years, with the number of relevant articles generally increasing yearly at an average annual growth rate of about 22.45%. The US tops the list in terms of total and independent publications, with a total of 3111 articles (49.88%). The key journals publishing on this topic include 12 journals, Stephens S.L., Bergeron Y., and Lindenmayer D.B. are the key contributing authors to the field, and research institutions are primarily concentrated in the US Forest Service. Keyword co-occurrence analysis shows that current forest wildfire research is focused on seven main areas. This paper systematically reviewed the progress and hotspots of international forest wildfire research in recent decades, mainly focusing on occurrences, severity, management, and warning techniques for wildfires, as well as the impact of climate change and human activities on wildfires. Conclusions The study concludes that research trends in this field have undergone a significant evolution in recent decades. The future forest wildfire research moves towards a combination of typical mechanisms and large-scale effects across spatial and temporal scales, deep integration of aerospace and earth observations and precise simulations, discipline fusion, and couplings research. We believe that this study provides a comprehensive and systematic overview for future forest wildfire observation, prediction, management, and investigation of ecological effects. 

/pdf/advances-in-the-study-of-global-forest-wildfires-hzg8ah34.pdf

Advances in the study of global forest wildfires

The recent technologies employed for rapid, cost-effective, and non-destructive prediction of soil particle size distribution (clay, sand, and silt) are becoming increasingly interesting among soil scientists. Our aims were to explore the effect of surface, profile wall, and surface + profile wall on prediction accuracy using individual and combined both soil spectra (Vis-NIR and pXRF) with machine learning algorithms for sand, silt, and clay. In total, 191 soil samples were collected from the soil surface (0–30 cm) and profile wall (1 m × 1 m) from cultivated fields in Eskisehir, Central Anatolia of Türkiye. The pXRF (0–45 keV) and Vis-NIR (350–2500 nm) spectroradiometers were used to obtain soil spectra from sieved soil samples. The prediction accuracy of each soil particle size was evaluated by 54 models to explore the predictive performance. The five machine learning algorithms (elastic net, lasso, random forest, ridge, and support vector machine-linear) were applied with calibration (70% soil samples) and validation (30% soil samples) data set for each soil particle size.Results showed the dominant clay mineral in the A and C horizons is chlorite. Moderate and high prediction accuracy for sand (R2 = 0.56–0.84) and clay (R2 = 0.61–0.80), whereas only moderate prediction accuracy for silt (R2 = 0.47–0.55) using both soil spectra in the surface, profile wall, and surface + profile wall. The highest prediction accuracy for each soil particle size was achieved in the soil profile wall using Vis-NIR spectra with elastic net, which outperformed other samplings such as individual pXRF, combined both soil spectra, and other machine learning algorithms. In addition, the prediction accuracy of clay was more affected by sampling strategies compared to sand and silt. We concluded that individual Vis-NIR spectroradiometer can be utilized to achieve the highest prediction accuracy for sand, silt, and clay ratio in semiarid ecosystems for soil surveys and land use studies. 

Soil particle size prediction using Vis-NIR and pXRF spectra in a semiarid agricultural ecosystem in Central Anatolia of Türkiye

Polyethylene (PE) microplastics are emerging pollutants that pose a significant threat to the environment and human health. However, little is known about the effects of PEs on soil‒plant interactions, especially in heavy metal (HM)-contaminated soil. In this study, the effects of PE on rhizosphere soil enzyme activities, microbial interactions and nutrient cycling processes were analyzed from ecological network and functional gene perspectives for the first time. The results indicated that PE-MP addition significantly reduced the biomass of Bidens pilosa L. In addition, the partial increase in carbon, nitrogen, and phosphorus enzyme activities suggested that the effects of PE as a carbon source on microbial functions in HM-contaminated soil should not be ignored. The average path length of bacterial network nodes was found to be higher than that of fungal network nodes, demonstrating that the bacterial ecological network in PE-MP and HM cocontaminated environments has good buffering capacity against changes in external environmental conditions. Furthermore, structural equation modeling demonstrated that particle size and dosage affect soil nutrient cycling processes and that cycling processes are acutely aware of changes in any factor, such as soil moisture, soil pH and soil nitrogen nutrients. Hence, PE-MP addition in HM-contaminated soil has the potential to alter soil ecological functions and nutrient cycles. 

Effects of polyethylene microplastics and heavy metals on soil-plant microbial dynamics.

Simple Summary Minimizing the effects of climate change by reducing GHG emissions is crucial and can be accomplished by truly understanding the carbon footprint phenomenon. This study aims to improve the understanding of carbon footprint alteration due to agricultural management and fertility practices. It provides a detailed review of carbon footprint management under the impacts of environmental factors, land use, and agricultural practices. The results show that healthy soils have numerous benefits for the general public and especially farmers. These benefits include being stable and resilient, resistant to erosion, easily workable in cultivated systems, good habitat for soil micro-organisms, fertile and good structure, large carbon sinks, and hence lower carbon footprint. Intensive tillage is harmful to soil structure by oxidizing carbon and causing GHG emissions. If possible, no-till; if not, minimum tillage frequency and depth of tillage, and optimum moisture are recommended. The soil should be at an appropriate level of moisture when tillage takes place. Diverse cropping systems are better for the soil than monocultures. Minimizing machinery operations can help to avoid soil compaction. Building soil organic carbon in the most stable form is the most efficient practice of sustainable crop production. Abstract Global attention to climate change issues, especially air temperature changes, has drastically increased over the last half-century. Along with population growth, greater surface temperature, and higher greenhouse gas (GHG) emissions, there are growing concerns for ecosystem sustainability and other human existence on earth. The contribution of agriculture to GHG emissions indicates a level of 18% of total GHGs, mainly from carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Thus, minimizing the effects of climate change by reducing GHG emissions is crucial and can be accomplished by truly understanding the carbon footprint (CF) phenomenon. Therefore, the purposes of this study were to improve understanding of CF alteration due to agricultural management and fertility practices. CF is a popular concept in agro-environmental sciences due to its role in the environmental impact assessments related to alternative solutions and global climate change. Soil moisture content, soil temperature, porosity, and water-filled pore space are some of the soil properties directly related to GHG emissions. These properties raise the role of soil structure and soil health in the CF approach. These properties and GHG emissions are also affected by different land-use changes, soil types, and agricultural management practices. Soil management practices globally have the potential to alter atmospheric GHG emissions. Therefore, the relations between photosynthesis and GHG emissions as impacted by agricultural management practices, especially focusing on soil and related systems, must be considered. We conclude that environmental factors, land use, and agricultural practices should be considered in the management of CF when maximizing crop productivity.

/pdf/carbon-footprint-management-by-agricultural-practices-2m5poo82.pdf

Carbon Footprint Management by Agricultural Practices

• Soil profile had a considerable variation of soil properties and SQI with depth and within horizons. • Cubist model can be used to predict SQI. • The grid soil sampling can be used to characterize soil properties and SQI in soil profile wall. • Combined Vis-NIR and pXRF spectra had better prediction performance than individual Vis-NIR and pXRF spectra. A soil quality index (SQI) can be used to manage the soil for potential uses and remediate limitations of soil for sustainable agriculture and ecology. The objectives of this study were to (i) characterize vertical and horizontal variations of some soil properties and SQI in a Fluventic Haploxerepts profile in Eskişehir, Turkey, (ii) compare interpolation methods for mapping the distribution of soil properties and SQI in the profile wall, and (iii) assess cubist model for predicting soil properties and SQI using individual and combined Vis-NIR and pXRF spectra in the profile. The soil profile wall (1 m deep × 1 m wide) was divided into a raster of 10 × 10 cm intervals for collecting a total of 100 soil samples. The soil samples were analyzed for soil physical, chemical properties, and elemental concentrations and scanned using Vis-NIR (350–2500 nm) and pXRF (0–8 and 0–45 kV) spectrometers. Standard scoring function was used for standardization of indicators. Weighted linear combination method was used to calculate SQI and F-AHP for assigning weights of indicators. The 100 samples were split into calibration (70%) and validation (30%) datasets. Individual or combined Vis-NIR and pXRF spectra were used in cubist model to evaluate the feasibility for predicting soil properties and SQI in the Inceptisol. Results suggested that the soil profile had a considerable variation of soil properties and SQI with depth and within horizons. The A horizon had the highest SQI. In addition, Vis-NIR had better prediction performances for pH, SOM, Fe, and Mn (R 2 = 0.42–0.88), whereas pXRF had better prediction performances for EC, CaCO 3 , P, and Zn (R 2 = 0.18–0.90). In addition, combining Vis-NIR, pXRF8, and pXRF45 had better prediction performance for sand, silt, clay, K, Cu, and SQI (R 2 = 0.81–0.97). We concluded that Vis-NIR and pXRF spectra can be successfully used to predict SQI in Fluventic Haploxerepts. Combined Vis-NIR and pXRF spectra had better prediction performance for SQI. 

A soil quality index using Vis-NIR and pXRF spectra of a soil profile

Using pXRF and vis-NIR spectra for predicting properties of soils developed in loess

Assessing the effect of soil to water ratios and sampling strategies on the prediction of EC and pH using pXRF and Vis-NIR spectra

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