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Understanding soil fertility, influenced by macronutrients such as nitrogen, phosphorus, and potassium, is essential for the implementation of adaptive agriculture based on various soil conditions. Near-infrared spectroscopy technology provides non-destructive, rapid soil property measurements without chemicals, and is applicable both in the field and in the laboratory. However, the wide NIR spectrum range and neural network complexities can hinder Artificial Neural Network (ANN) training and inference, leading to time and resource inefficiency, particularly without sophisticated computing devices. This study examined data reduction methods to enhance ANN performance in predicting soil macronutrients using NIR spectra. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were applied to select wavelengths from the 1000–2500 nm for ANN input, comparing their performance. Approximately 237 NIR reflectance data points from paddy soils were transformed into absorbance data. MLR used forward selection to identify wavelengths with correlations higher than 0.9, whereas PCA selected wavelengths corresponding to the loading factor peaks for each principal component. These selected wavelengths served as inputs for the ANN model. The ANN performance was assessed using the correlation and determination coefficients, RMSE, RPD, and model consistency. For nitrogen, the PCA+ANN model with reflectance spectra performed better (RPD 2.4-4.8) than the MLR+ANN model (RPD 2.2-2.6) using fewer wavelengths (5-9 for PCA+ANN vs. 9-12 for MLR+ANN). For phosphorus estimation, the PCA+ANN model also excelled (RPD 2.3-7.0 vs. 2.3-2.4) with fewer wavelengths (4-7 vs. 7). The PCA+ANN model showed superior performance (RPD 4.3-9.5 vs. 4.2-4.4), using the same number of wavelengths (4-8 vs. 4-6). [ABSTRACT FROM AUTHOR]

Inadequate waste management and illegal trash dumping continue to be the leading causes of severe environmental pollution. Human exposure to harmful heavy metals has emerged as a serious health concern on the continent. Some people in Alice, a small town, grow their food in home gardens. They use animal manure and compost derived from soil obtained from landfills to enhance the fertility of the garden soil. Heavy metal heaps in garbage disposals are constantly present, releasing dangerous amounts of metal into the environment. The harmful effects of heavy metals on plants lead to unsanitary conditions and environmental problems. Animals and people who consume these vegetables may also be at risk for health problems. Assessing the soil's enzyme activity can potentially lessen the negative effects of the accumulated pollutants and improve the soil's overall health and quality. Soil enzymes are biologically active components that have a catalytic impact and are released from root exudates, crop residues, and animal remains. The activity of enzymes serves as an excellent bioindicator of soil cleanliness and quality because they are sensitive to heavy metals. X-ray diffraction (XRD) was used to quantify the mineral elements in soil using 40 kV parallel beam optics, 30 mA, and CuKα radiation. Meanwhile, the activity of the enzyme was essayed in different coupled substrates. Thirteen (13) clay minerals were found, including Talc 2M, Kaolinite 2M, and Chlorite Lawsonite Muscovite 2M1. The detected trace elements have high concentration levels that exceed the World Health Organization's (WHO) allowed levels. The identified elements affected the enzyme activity at different levels. The Mn, Al, Si, V, Ti, and Ca negatively affect soil enzyme activity, specifically invertase (INV). However, the amount of Mg, K, Fe, and Zn showed a slightly positive effect on the same enzyme (INV). According to this view, these elements come from several sources, each with a particular impact on soil contamination and enzyme activity. High levels of heavy metals in this study may be due to improper waste disposal, limited recycling opportunities, lack of public awareness, and inadequate enforcement of waste management regulations. It is essential to employ Fourth Industrial Revolution (4IR) technologies, correct disposal techniques, suitable agricultural methods, preventive regulations, and efficient waste management to mitigate the negative effects of heavy metals on the environment. [ABSTRACT FROM AUTHOR]

This article presents the findings of a field experiment conducted in West Bengal to study the growth, yield, and water use efficiency of grass pea under different nutrient management practices. The results showed that fertilizing the grass pea plot with the recommended dose of fertilizer along with foliar application of Borax resulted in the highest plant height, number of branches, dry matter accumulation, number of pods per plant, pod length, number of seeds per pod, and 100-seed weight. The plot treated with RDF+ Borax also recorded the highest seed yield, stover yield, and water use efficiency. The study concludes that the combined use of RDF with Borax is the best option for better production of grass pea in terms of quality and quantity in the New Alluvial Zone of West Bengal. [Extracted from the article]

The development of suitability species models look for the availability to growth in a study area. These models can be used for different targets. In this research, a suitability model of Eucalyptus has been developed to soils contaminated by trace elements management. Guadiamar Green Corridor has been selected due to the huge data available regarding trace elements, forestry species and so on. Logistic regression (LR) and Random Forest (RF), as popular machine learning model, were applied in a geodatabase from Guadiamar Green Corridor with more of 20 years of data. This database is composed by soil physical and chemical variables, climate (temperature min and max, annual precipitation), forestry species. The results show the poor performance of LR and RF applied directly over the unbalanced training set. However, when Upsampling or SMOTE are applied, both procedures improve its sensitivity, however, RF show more improve that LR. The methodology applied can help to determine the potential distribution of Eucalyptus in similar Mediterranean areas and extended to different areas according to Soil, Climate and Trace Elements data. Finally, the models developed under this research work can be used to reduce human and environmental health by trace elements taking into account local conditions but also climate change scenarios. [ABSTRACT FROM AUTHOR]

Urban areas are the hot-spots of soil pollution, as a result of increase in urban populations, vehicular transport increase and industrial growth. Due to the rapid growth of urbanization, urban soil contamination by potentially toxic trace elements (PTEs) has become one of the major public health and environmental concern. This research assessed PTEs (As, Cd, Pb, Cr, Cu, Co, Mn, Zn, and Ni) concentrations in urban soils from the Kermanshah urban areas in western Iran. Thirty soil samples were collected from residential urban parks, as from depth of 0––20 cm. Concentrations of PTEs in the soil were determined by ICP-OES. Multivariate analysis method was applied to distinguish the anthropogenic and natural sources of PTEs. Also geostatistics and geographical information system (GIS) were applied to produce spatial distribution patterns. The contamination degree of PTEs was assessed on the basis of geoaccumulation index (Igeo), the Enrichment factor (EF), single pollution index (Pi) and Nemerow pollution index (PINemerow). Applying principal component analysis (PCA), three components were extracted. The first principal component with a high loading for Mn, Co, Cd, Cu, and As, the second principal component, with high contribution of Cr, Ni and Pb and the third component having strong loadings of Zn. Mn, Co, Cd, Cu, and As may be defined as an anthropogenic, Cr, Ni and Pb reflect the contribution of natural and anthropogenic sources and Zn was mostly controlled by natural sources. The results of Pi were consistent with PCA. Spatial distributions showed that that the higher Pb concentration was concentrated mostly in the central and south parts of the city and higher Cd in the east parts of the study area. The map of As indicated maximum concentration in the north and west parts of the study area. Similar trends of spatial distributions between Cu and Zn, between Cr, Ni, and between Co and Mn were observed. The metal concentrations indicated that the Kermanshah city is threatened by PTEs associated with anthropogenic activities specially traffic. [ABSTRACT FROM AUTHOR]