Solid Waste in Agricultural Soils: An Approach Based on Environmental Principles, Human Health, and Food Security

In recent decades, projections involving population growth, changes in consumption patterns, modifications of the wastes produced, and a significant increase in resource extraction have caused concern in the scientific world, in treatment companies, and in environmental and governmental agencies throughout the world, regarding the destination of the large volume of solid wastes generated, the relatively high contents of potentially toxic, carcinogenic and mutagenic substances and pathogenic microorganisms. Waste management has become very important to ensure elementary resources such as water, phosphorus, and food in the future. The recycling policy thus requires that wastes be classified in terms of their appropriateness for new uses and also based on their origins and hazardousness of handling. These classifications are essential in order to allow a minimum of rationality in their new destinations. Currently, several studies have been performed to use solid wastes from human activities as soil conditioners and/or fertilizers for increasing crop productivity. Therefore, studies that monitor organic waste effects on agricultural soils deserve the attention of the international scientific community, as it enables increases in the productivity of agricultural crops, fiber, and biomass energy combined to reduce risks to human, plant, and animal health and environment

Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil

Soil chemical and physical analyses are the major sources of data for agriculture. However, traditional soil analyses are time-consuming, not cost-efficient, and not environmentally friendly. An alternative to traditional soil analyses is soil spectroscopy. This technique is a low-cost and quick analytical method, which can be implemented in a laboratory and/or in-situ. Nevertheless, some spectrometers are expensive and do not contemplate the entire spectrum. Despite this limitation, the main objective of the study was to create a soil spectral library of the Piauí State using only the 1000–2500 nm range. In this sense, it was evaluated and standardized the soil spectral library by accessing the combination of smoothing, standard normal variate, continuum removal, and Savitzky-Golay derivative spectral preprocessing procedures with partial least squares, random forest, and cubist machine learning algorithms. It was collected 262 geo-referenced soil samples at the layer of 0.00–0.20 m across the entire Piauí State, representing most of its soil variability. The soil properties evaluated were pH(H 2 O), sand, clay, and soil organic carbon (SOC) contents. This study demonstrated that the Standard Normal Variate was one of the most promising preprocessing procedures to improve model predictions for pH(H 2 O), sand, and clay. For SOC and pH, the best overall results were without preprocessing the soil spectra. Moreover, the cubist model was the most accurate in predicting soil properties. Finally, our study showed evidence of the potential and feasibility of using this soil spectral library to estimate soil properties such as pH(H 2 O), sand, clay, and SOC

Quality reference values for rare earth elements in soils from one of the last agricultural frontiers in Brazil

Environmental impacts caused by the addition of rare earth elements (REEs) to agricultural soils are a growing concern. The sedimentary basin of the Gurguéia River is located in one of the last agricultural frontiers in Brazil; nevertheless, data regarding quality reference values (QRVs) for REEs in soils are still scarce. The objective of this study was therefore to determine the natural concentration and establish the QRVs of REEs in soils of Gurguéia watershed, Brazil. Fifty-five composite soil samples were collected at sites under no or minimal anthropic interference. The average REE natural concentrations in soils from the Gurguéia watershed were lower than those found in other regions of Brazil and worldwide, following the order (mg kg–1): Ce (14.01) > Nd (6.19) > La (5.52) > Pr (2.51) > Sm (1.45) > Gd (0.93) > Dy (0.63) > Er (0.42) > Yb (0.39) > Tb (0.28) > Eu (0.26) > Lu (0.20). The parent material was the main factor that controlled the distribution of REEs in soils. The QRVs in soils followed the order (mg kg–1): Ce (18.8) > Nd (7.92) > La (6.32) > Pr (3.3) > Sm (1.97) > Gd (1.35) > Dy (0.85) > Er (0.55) > Yb (0.47) > Tb (0.37) > Lu (0.25). These values serve as a basis to assist the development of legislation, including REE thresholds for Brazilian soils