Proximal sensing provides clean, fast, and accurate quality control of organic and mineral fertilizers.

Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R2 = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint.

A Proximal Sensor-Based Approach for Clean, Fast, and Accurate Assessment of the Eucalyptus spp. Nutritional Status and Differentiation of Clones.

Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R2). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops.

Clean quality control of agricultural and non-agricultural lime by rapid and accurate assessment of calcium and magnesium contents via proximal sensors.

Ca and Mg are the most important chemical elements in lime. Properly measuring Ca and Mg contents is essential to assess the quality of lime products. Quality control guarantees the adequate use of lime in industrial processes, in soils, and helps avoiding adulteration. Proximal sensors can aid in this process by determining Ca and Mg contents easily, rapidly and without producing chemical waste. The objective of this study was to evaluate the use an environmentally-friendly method of analyzing the quality of lime. We studied 1) the use of portable X-ray fluorescence (pXRF) to predict concentrations of Ca and Mg in lime, 2) tested if NixPro™ sensor can improve prediction accuracy and 3) tested if sample preparation methods (grinding) affect analyses. 74 samples of lime were analyzed by two different laboratories (lab. 1 = 38, lab. 2 = 36). All samples submitted to pXRF and NixPro™ analyses. Sensor analyses were done in whole (CP) and ground (AQ) samples to test the effect of sample preparation in prediction performance. High correlation was found between Ca and Mg contents measured via pXRF and laboratory analyses. Mg-CP presented the highest correlation coefficient (r = 0.81); Mg-AQ, the lowest (0.57). Predictions presented good performance (R2 > 0.68); Mg had the best results (0.86). Separating models per laboratory showed that some datasets are harder to model, probably due to variability in the source material (limestone). The addition of NixPro™ data contributed to improve prediction accuracy, although slightly. Predictions using CP samples presented the best results, especially for Mg, indicating that grinding is not necessary. This pioneer study demonstrated that fused proximal sensors can be used to rapidly and easily determine contents of Ca and Mg in soil amendments without producing chemical waste.

Potentially toxic elements in iron mine tailings: Effects of reducing soil pH on available concentrations of toxic elements.

Tailings from iron mining are characterized by high concentrations of iron and manganese oxides, as well as high pH values. With these characteristics, most of the potentially toxic elements (PTE) contained in the tailings are somewhat unavailable. The aim of the present study was to evaluate how a reduction in the pH of iron mine tailings may affect PTE availabilities. The tailings were collected on the banks of the Gualaxo do Norte River (Mariana, MG, Brazil), one of the main areas impacted by the rupture of the Fundão Dam (Barragem de Fundão). A completely randomized experimental design was used, including five pH values (6.4, 5.4, 4.3, 3.7, and 3.4) and five replications. The concentrations of the PTE (Ba, Cr, Cd, Co, Cu, Fe, Mn, Pb, Ni, and Zn) were determined after extraction following different methodologies: USEPA 3051A, DTPA, Mehlich-1, Mehlich-3, and distilled water. A comparison of the available concentrations of the elements in the tailings with those in a soil not impacted by tailings shows that Cr, Cd, Cu, Fe, Mn, Ni, Ba, and Co were higher in the soil impacted by the tailings. The different methods used for evaluating the availability of PTE in the tailings at various pH exhibited the following decreasing order in relation to the quantity extracted: Mehlich-3 > Mehlich-1 > DTPA > distilled water. However, regarding sensitivity to change in pH, the order was DTPA > water > Mehlich-1 > Mehlich-3. The increases in the concentrations of PTE due to the reduction in the pH of the tailings did not lead to concentrations that exceed the limits of Brazilian regulations. The DTPA extractant exhibited higher coefficients of correlation between the PTE concentrations and the pH of the tailings, proving to be suitable for use in areas affected by the deposition of iron mine tailings.

X-ray fluorescence spectrometry applied to digital mapping of soil fertility attributes in tropical region with elevated spatial variability.

Portable X-ray fluorescence (pXRF) spectrometry offers valuable information for prediction models of soil fertility attributes spatial variation, although this approach is yet scarce in tropical regions. This study aims to predict and build spatial variability maps of soil pH, remaining phosphorus (P-Rem), soil organic matter (SOM) and sum of bases (SB) using pXRF results through stepwise multiple linear regression (SMLR) and Random Forest (RF) in a highly variable tropical area. Composite samples from soil A horizon were collected at 90 points throughout the campus of the Federal University of Lavras, Minas Gerais, Brazil, for pH, P-Rem, SOM, SB and pXRF analyses. RF predictions showed the highest accuracies, especially for P-Rem and SB (R² values of 0.66 and 0.55, respectively). Attributes that showed higher R² in punctual predictions also exhibited higher R² in spatial predictions. Data obtained from pXRF in tandem with RF can be used to assist prediction models for soil fertility attributes, consequently enabling the digital mapping of such attributes and helping to improve the knowledge about the spatial variability of such attributes in soils of tropical climate. This technique can therefore assist in the identification and orientation of adequate management practices in tropical agricultural practices.

Copper and Zn distribution in humic substances of soil after 10 years of pig manure application in south of Santa Catarina, Brazil.

This study aims to evaluate available Cu and Zn levels in soil and related in soil organic matter (SOM) fractions (fulvic acids-FA, humic acids-HA, and humins-HU) after 10 years of application of pig slurry (PS) and pig deep litter (PL). Soil samples were collected from an experiment with black oat/corn succession under no-tillage in southern Brazil. The treatments consisted of fertilization of 90 and 180 kg N ha-1 applied as PS and PL from 2002 to 2012 and a control treatment without any fertilization. SOM chemical fractionation was performed in air-dried samples. Copper and Zn concentrations were analyzed in soil (total, EDTA- and CaCl2-extracted) and in SOM fractions. The amount of Cu and Zn (in mol) related to each fraction of SOM (Cu/C and Zn/C molar ratios) was established. The applications of PS and PL promoted the accumulation of total and available Cu and Zn, especially in the PL180 treatment. The highest amount of Zn was found with HU, while for Cu both HA and HU were important retention compartments. The highest Cu/CFA, Cu/CHA and Cu/CHU ratios were found with the addition of PL. Increases in Zn/C ratio were found mainly in FA fraction. The high levels of Cu and Zn obtained in the HCl-extracted SOM fraction suggest that a considerable part is bound to SOM and clay minerals with low energy. However, the SOM is an important source of metal adsorption in soils with swine manure application.