2.1.

Chemical and Mineralogical Characterization of the Inputs

The materials used in this study, which are here called inputs or coproducts, were collected from a depth of 0 to 15 m in a limestone-mining area in Tietê, São Paulo, Brazil. These inputs, which are located at different depths, were named ritmite (from Irati formation), bituminous shale (BS) (from Irati formation), and Corumbataí formation (CF). The contents of CaO, MgO, ${\mathrm{K}}_2\mathrm{O}$, and S of the inputs used in this study are shown in Table 1.

Table 1

Total contents of CaO, MgO, K2O, and S found in the inputs used in the experiment.

Mineralogy of the inputs was determined through x-ray diffraction with Shimadzu XRD-6000 equipment, in a fraction smaller than 0.074 mm, using $\mathrm{CuK}\alpha$ radiation in staircase sweep, at an interval of $2\theta$ of 5° to 70°, with angular step of 0.02° in Bragg configuration.¹⁸ Afterward, the results were interpreted by comparison with the reference standards contained in the powder diffraction file.¹⁹

2.2.

Chemical and Particle-Size Characterization of the Soils

To carry out the experiment, we used soil samples from the surface layer (0 to 20 cm) of a Ferralsol and an Arenosol.²⁰ They were collected from a native forest area in Mandaguari and Paranavaí, PR, Brazil, respectively. After collection, the samples were ground, air-dried, and sieved with a 1-cm sieve. For chemical and clay content characterization, the samples were dried in an oven with forced-air circulation at 50°C until we obtained the constant mass. Then, the samples were ground and sieved with a 2-mm sieve. Determination of P and ${\mathrm{K}}^{+}$ (Mehlich-1), ${\mathrm{Ca}}^{2+}$, ${\mathrm{Mg}}^{2+}$, ${{\mathrm{SO}}_4}^{2-}$, pH (water 2:1), and C organic followed the methodologies proposed by Teixeira et al.,²¹ whereas Si (soluble) followed the methodology described by Korndörfer.²² The contents of H + Al were evaluated by the pH exchange of the sample against the SMP buffer.²³ After determining the elements, we calculated the sum of bases ($\mathrm{SB}={\mathrm{Ca}}^{2+}+{\mathrm{Mg}}^{2+}+{\mathrm{K}}^{+}$) and the potential capacity of cations exchange ($\mathrm{CEC}=\mathrm{SB}+{\mathrm{H}}^{+}+{\mathrm{Al}}^{3+}$), as described in Table 2.

Table 2

Clay content and chemical attributes of the topsoil layer (0 to 20 cm) of the Ferralsol and the Arenosol used in the study.

2.3.

Delineation and Experimental Procedure

The study was conducted in a greenhouse at the State University of Maringá. The treatments were arranged within triple factorial scheme ($6\times 2\times 2+2$), randomly designed with five repetitions, and placed into pots ($n=130$) in a greenhouse. The factors taken into account were type of input, input particle size, and soil class. The types of input used were coproduct 1 (50% CF + 50% ritmite), coproduct 2 (100% CF), coproduct 3 (100% ritmite), coproduct 4 (100% BS), coproduct 5 (30% CF + 15% BS + 55% ritmite), and coproduct 6 (55% CF + 15% BS + 30% ritmite). The two additional controls consisted of treatments in which there was no input application, one for each soil class evaluated. The doses of coproducts 1, 3, and 5 were calculated in accordance with the Soil Chemistry and Fertility Commission of Rio Grande do Sul and Santa Catarina (SCFC-RS/SC),²⁴ according to the contents of CaO of the inputs and the ${\mathrm{Ca}}^{2+}$ of both soil classes since they are mainly characterized as sources of such nutrient. As for coproducts 2, 4, and 6, the doses were calculated in accordance with SCFC-RS/SC²⁴ based on the contents of ${\mathrm{K}}_2\mathrm{O}$ of these inputs and the ${\mathrm{K}}^{+}$ of the two soil classes, for they are a material with potential to be used as sources of that nutrient for the soil. The input particle sizes evaluated were filler (100% smaller than 0.30 mm) and powder ($100\%<2.00\mathrm{mm}$, $70\%<0.84\mathrm{mm}$, $50\%<0.30\mathrm{mm}$). That gives them a theoretical reactivity of 100% and 68%, respectively. The soil classes used were Arenosol and Ferralsol.

The treatments with the applied amounts of ${\mathrm{K}}_2\mathrm{O}$, CaO, MgO, and S are described in Table 3.

Table 3

Description of the treatments with the applied amounts of K2O, CaO, MgO, and S.

The coproducts doses were applied into pots containing 3.0 kg of dry soil (Ferralsol and Arenosol). Humidity control aimed at keeping the values close to the field capacity of both soil classes used. The pots were placed inside a greenhouse for incubation during 140 days.

After incubation, we did the soil sampling with an auger for determining the macronutrients ${\mathrm{K}}^{+}$ and P (Mehlich-1). Afterward, fertilization recommendations were followed in order to increase the contents of P and ${\mathrm{K}}^{+}$ of the soil to the critical content of soybean (Glycine max L.) in accordance with what is recommended by SCFC-RS/SC.²⁴ For correcting P and ${\mathrm{K}}^{+}$, we used triple superphosphate and coproduct 2 (CF), respectively, both applied during sowing. We also inoculated soybean with Bradyrhizobium spp. for nitrogen fixation.

Right after application of the inputs that are sources of P and K, and after inoculation, we sowed 10 soybean seeds (NIDERA NA5909) per vase, and when the plant reached the V1 stage, we did the thinning, which left 4 plants per pot. A few moments before collecting the soybean leaves, we acquired the radiometric data (reflectance) from the adaxial part of the leaves ($n=130$) with the aid of an ASD leaf clip (next item). Leaves were collected 42 days after sowing, at the V6 stage. After that, the leaves were dried in an oven with forced-air circulation at 60°C, until constant mass. Then, we evaluated the production of LDM, as well as the contents of N, P, K, Ca, Mg, S, Cu, Fe, Mn, and Zn. For N, at first, the tissue samples were subjected to sulfuric digestion and, after that, the N was determined through distillation in a semi-micro Kjeldahl device and titration with sulphuric acid ($0.025\mathrm{mol}{\mathrm{l}}^{-1}$). As for S, first came nitric-perchloric digestion, followed by extract precipitation with ${\mathrm{BaCl}}_2$ and determination through a UV–Vis spectrophotometer in transmittance form, in accordance with Da Silva.²⁵ The other nutrients were determined using an inductively coupled plasma atomic emission spectrometer.

2.4.

Radiometric Data (Reflectance) of the Soybean

The radiometric scanning of the soybean leaves was done with an ASD FieldSpec 3 spectroradiometer operating at 350 to 2500 nm, with spectral resolution of 3 nm at 350 to 1400 nm and 30 nm at 1400 to 2500 nm [short-wave infrared (SWIR)]. Using the leaf clip allowed for collecting the leaf reflectance spectra of living vegetation directly in the greenhouse (Fig. 1). For spectra data collection, initially the spectroradiometer was optimized to adjust the sensitivity of the instrument’s detectors according to the specific illumination conditions at the time of measurement with 20 scans per white reference (Spectralon standard). Then, to achieve the relative reflectance measurements, the white reference was collected before scanning the leaves samples until a nice, clean, 100% reference line was obtained according to the specifications provided by the Reflection and Calibration Laboratory.²⁶ For each plant individual, three leaves were measured in reflectance mode. The mean was then calculated according to recommendations of Nanni and Demattê.²⁷

Fig. 1

Scheme of conducting the experiment and collecting spectral data on soybean.

2.5.

Data Processing and Statistical Analysis

The results of both LDM and contents of macro- and micronutrients of the leaves were initially subjected to descriptive statistical analysis with the Excel software—XLSTAT.²⁸ We used The Unscrambler 10.4 software to smoothing the reflectance (soybean leaves) data through the multiplicative signal correction (MSC) method in order to reduce the additive scattering effect.²⁹ The rationale behind MSC is to compensate for these additive effects. In addition, the preprocessing technique MSC was selected because it has been shown to improve the performance of spectroscopy models. The results were centered on the mean and subjected to partial least squares regression (PLSR)³⁰ with the iterative algorithm NIPALS³¹ in order to estimate of LDM and macro- and micronutrients contents of soybean leaves.

To generate the prediction models from the reflectance spectrum (Vis–NIR), we first removed the wavelengths of marginal (350 to 379 nm), considered to be of greater noise³² and only the wavelengths in the range between 380 and 1000 nm (Vis–NIR) were used since this range is often employed to probe the properties of living plant leaves.⁶ Subsequently, spectral scanning’s of the 130 samples were divided into two groups. The first represented 91 samples used for generating the model (calibration), and the second represented 39 samples used for validating (prediction) the adjusted PLSR model. These samples were randomly selected from the data set, thus avoiding bias that could influence the model quality. The leave-one-out cross-validation (cv) method was used as a preliminary form of predicting the soybean attributes.³³ In the prediction (p) process, parameters such as determination coefficient ($R^2$), root-mean-square error (RMSE), ratio of performance to deviation (RPD), and bias were used to evaluate quality and accuracy of the model (Fig. 2). Rossel et al.³⁴ and Saeys et al.³⁵ state that the values of $R^2$ are classified as: $R^2<0.50$ (models with poor predictions only capable of distinguishing high and low values); $R^2$ between 0.50 and 0.65 (models with moderate predictions that may be used for evaluation and correlation); $R^2$ between 0.65 and 0.80 (good prediction models that enable quantitative predictions; $R^2$ between 0.80 and 0.90 (models of very good quantitative predictions); and, finally, $R^2\ge 0.90$ (excellent prediction models). Chang et al.³⁶ and Dunn et al.³⁷ have suggested that models with RPD values lower than 1.5 should be considered insufficient for the majority applications, whereas models with RDP values between 1.5 and 2.0 are considered useful in relation to the accuracy of their predictions, and models with values greater than 2.0 should be considered excellent.

Fig. 2

Flowchart of the methodology used in the PLSR.

3.1.

Mineralogical Characterization of the Coproducts

Figure 3 shows the peaks of x-ray diffractometry of the coproducts used in the study, in compliance with Rodrigues et al.³⁰ For ritmite [Fig. 3(a)], we verified peaks related to quartz, smectite, calcite, dolomite, and pyrite. As for CF [Fig. 3(b)], the peaks identified referred to smectite, illite, quartz, feldspar, and mica. Regarding BS [Fig. 3(c)], the peaks occurred due to quartz, albite, mica, and pyrite. These results, in most of the cases, corroborate the available literature on their mineralogy.^38–40

Fig. 3

X-ray diffractometry of the materials produced in limestone-mining: (a) ritmite, (b) CF, (c) BS. ca/do = calcite and dolomite, sm = smectite, qz = quartz, fe = feldspar, py = pyrite, mi = mica, and ab = albite.³⁰

3.2.

Descriptive Analysis of the LDM and Nutrients Contents of Soybean

Table 4 shows the results of the descriptive analysis of LDM and the nutrients contents found in the soybean leaves after cultivation in soil incubated with mining coproducts. The coefficient of variation values (CV) ranged from 9.97 % for Ca and 91.02 % for Mn.

Table 4

Descriptive analysis of the LDM production and foliar nutrients contents of soybean subjected to the application of limestone-mining coproducts.

3.3.

Estimation of LDM and Nutrients Contents of Soybean Using Vis–NIR Spectroscopy

It can be noted in Fig. 4 that there was a separation between treatments evaluated when compared to their reflectance, which allows to infer about the possibility to use the spectral curves in the differentiation of treatments, as well as in the prediction of plants attributes (LDM and nutrients content). In addition, absorption valleys close to 497 and 690 nm can be noted due to chlorophyll⁴¹ and in the range of 700 to 770 nm, the reflectance sharply increases due to the fact that there is a cavity with a large reflecting surface in the sponge tissue structure in the mesophyll.⁴²

Fig. 4

Vis–NIR reflectance spectra raw of soybean (a) and after the preprocessing technique (MSC) (b) for all treatments. Chl = chlorophyll.

3.3.2.

Prediction (p)

To evaluate the capability of the PLSR models adjusted to predict LDM, as well as the nutrients contents in the leaf, these models were tested with a set of spectral data, different from those used for calibration and cross-validation. The scatter plot together with the results of the multivariate statistics is present in Fig. 5 ($n=39$).

Fig. 5

Validation results for reference values versus predicted values for LDM and macro- and micronutrients contents of soybean ($n=39$). Dotted and solid lines are for regression and 1:1, respectively.

The ${R^2}_{\mathrm{p}}$ values found decreased in relation to the previous phases (c) and (cv). For variables N, Ca, Cu, Fe, and Mn, the models generated according to ${R^2}_{\mathrm{p}}$ were considered poor ($<0.24$). For variables S and Zn, the models were considered moderate due to the ${R^2}_{\mathrm{p}}$ found (0.55 and 0.58, respectively). For LDM, P, and Mg, the model was considered good (${R^2}_{\mathrm{p}}$ 0.65 – 0.70), and for K, the model generated was considered very good (${R^2}_{\mathrm{p}}$ $0.80$). Regarding the ${\mathrm{RMSE}}_{\mathrm{p}}$, the values obtained ranged from $0.1584\mathrm{g}{\text{plant}}^{-1}$ (LDM) to $80.5424\mathrm{mg}{\mathrm{kg}}^{-1}$ (Mn). For bias, with the exception of nutrients Fe and Mn, the values found were close to zero.

As for the RPD values, it is noted that the models presented results considered insufficient ($<1.42$) for the variables N, Ca, and Fe. As for the variable LDM and the other nutrients evaluated, the models found, according to the RPD, varied between useful (1.5 to 2.0) and excellent ($>2.0$).

The following are the correlation graphs between laboratory results (reference) and those obtained via Vis–NIR spectroscopy (predicted) (Fig. 5). As already mentioned, the variables LDM, K, Mg, Zn, S, and P were the ones with less dispersion and, consequently, better prediction capacity. On the other hand, the nutrients N, Ca, Cu, Fe, and Mn were the most dispersed and difficult to estimate.

3.4.

Interpretation of Regression Coefficients (RC)

Figure 6 shows the regression coefficients of the PLSR model. We can observe regions of valleys and peaks where the RCs had a higher weight for the model construction. In general, the important wavelengths for the variables were close to 380 to 400 nm, 500 to 530 nm, 600 to 690 nm, and 700 to 750 nm.

Fig. 6

Coefficient of the PLSR regression model for the variables (a) LDM, (b) N, (c) P, (d) K, (e) Ca, (f) Mg, (g) S, (h) Cu, (i) Fe, (j) Mn, and (k) Zn of soybean.

For the LDM variable, we can see peaks and valleys, resulting from higher RC, in the regions near 400, 500, and 530 nm. For N, peaks and valleys are seen in regions near 600 and 750 nm [Fig. 6(b)]. The variables P and K presented higher RC in wavelengths close to 400, 500, and 700 nm [Figs. 6(c) and 6(d)]. The important wavelengths selected for Ca prediction are found in four regions, around 550 and 720 nm, [Fig. 6(e)]. For Mg, higher RC is close to 400, 500, and 680 nm [Fig. 6(f)]. For S, the highest RC occurred at 400 and 700 nm [Fig. 6(g)].

Regarding Cu, Fe, and Mn [Figs. 6(h)–6(j)], despite several wavelengths with higher weights for the model construction, due to the low $R^2$ values found for these nutrients (Fig. 5), the RC found proved to be of little use. Finally, for Zn [Fig. 6(k)], higher RC was observed in regions near 400 and 620 nm.

Table 6 describes the summary of the most responsive wavelengths according to the coefficient of the PLSR regression (Fig. 6) obtained for the estimation of the variables LDM production and nutrients contents of soybean.

Table 6

Most responsive wavelengths selected according to regression coefficients for variables LDM and nutrients contents of soybean.

4.1.

Descriptive Analysis of the LDM and Nutrients Contents of Soybean

The results obtained for the nutrients contents (plant) N, P, K, S, and Mn according to SCFC-RS-SC²⁴ were considered insufficient for the development of the crop. For Cu, Fe, and Zn, the contents varied between insufficient and sufficient for the crop. Moreover, according to the Pimentel-Gomes⁴³ classification, the coefficients of variation for the evaluated variables were classified as low for Ca, medium for LDM and N, high for S, and very high for P, K, Mg, Cu, Fe, Mn, and Zn. These results occurred because of the sources of nutrients used, such as mining coproducts. Due to the diversified mineralogy (Fig. 3), these coproducts provided varied input of elements, which contributed to the high range of minimum and maximum values of the nutrient contents. Furthermore, the use of inputs with different input particle size and solubility rates also favoured the heterogeneity in the coefficient of variation values found.

4.2.

Estimation of LDM and Nutrients Contents of Soybean Using Vis–NIR Spectroscopy

Although the plant spectrum does not present characteristic inflections of the nutrients present in the plants, the peaks and dips caused by chlorophyll (Fig. 4) and the high reflectance of the NIR region typical of plants with the well-structured mesophyll⁴⁴ are signs of satisfactory nutritional conditions.^45,46 This allowed the construction of models, such as PLSR, which have the potential to make indirect predictions of nutrients. In addition, the production of LDM can also be directly related to the chlorophyll and the leaves’ mesophyll,⁶ which allows its estimation, as discussed below.

4.3.

Interpretation of Regression Coefficients (RC)

As for the RCs (Fig. 6), their interpretation is necessary to avoid possible accidental correlations.⁶³ Thus, one can better understand how each variable (wavelength) contributes to the significant variation of the LDM estimate model and soybean nutrient contents. For the LDM variable, high RC close to 500 nm may be related to chlorophyll⁴¹ since studies have shown that wavelengths close to this region correlate with chlorophyll and plant nutrient concentration,⁶⁴ which allows greater production of LDM.

Thus, the concentration of chlorophyll and nutrient contents are important for leaf mesophyll development, which may have caused high RC near 750 nm. As for the N contents, the RC obtained in this study is supported by other studies that aimed at estimating nitrogen by the Vis–NIR spectroscopy in other crops, such as pepper⁶⁵ and sugarcane.³² Concerning the variables P and K, the results of the RC found in this study are corroborated by the work of Zhang et al.,⁴⁹ who studied the use of image Vis–NIR spectroscopy to predict P and K in rapeseed crop. Oliveira and Santana,⁶⁶ when evaluating the use of Vis–NIR spectroscopy for the prediction of nutrients in Eucalyptus, also found high RC values in the region of 400 to 900 nm for P and N, as in this work. Both nutrients are closely related to photosynthetic traits in plants,⁶⁷ which is directly related to high RC in the Vis–NIR reflectance. Pinstein et al.⁵¹ found, in the most part of the spectrum Vis–NIR, a correlation pattern between K and N, suggesting some cross-correlation involving these two elements at the leaf level, which allows the creation of vegetation indices close to these regions. In addition, other studies report the importance of wavelengths of the green⁶⁸ and red⁶⁹ regions to estimate the concentration of P and K in plants.

The important wavelengths selected for Ca prediction were found in two regions (around 550 and 720 nm), according to previous research studies such as Oliveira and Santana⁶⁶ studying the Ca content in Eucalyptus. The reflectance in wavelengths near from 500 nm is associated with active photosynthetic radiation,⁴⁹ especially the role of Ca as a cofactor in the photosystem II.⁷⁰ The NIR region also presented higher absolute RC values for Ca prediction. Among the functions of Ca in higher plants are cell wall synthesis and cell membrane integrity.⁷¹ In this sense, there is a relationship between the structural components (mesophyll) and the energy in the NIR region.⁴²

For Mg, the largest RC near 680 nm can be related to the chlorophyll molecule since Mg integrates it, besides being an activator of some enzymes in plants.⁷¹ Regarding S, the high values of RC at near 700 nm are supported by the work by Cozzolino and Moron,⁵⁴ who found, near this region, high correlations with S. Furthermore, wavelengths in this region have been used in the management of S in wheat (Triticum aestivum L.).⁷² Finally, for Zn, the high values of RC around 400 and 620 nm can be related to chlorophyll,⁴¹ which, in turn, can be correlated with nutrient concentrations.⁶⁴

4.4.

Applications of Vis–NIR Spectroscopy as a Technique for Estimating LDM and Nutrients Contents in Soybean

Soybean plants require significant amounts of mineral nutrients to achieve adequate growth and productivity, and this need depends on the fertility and class of the soil. Although the mineral nutrition of soybeans and the conventional techniques for assessing the nutritional status are much studied, there is limited research on the use of alternative techniques to routine analysis, such as the use of the Vis–NIR spectroscopy in predicting LDM and leaf nutrient contents.

Soybean leaves are a complex mixture of chemical compounds such as water, pigments, carbohydrates, proteins, and others. All these compounds contribute substantially to generate a characteristic spectrum of the target (leaves). The nutritional and physical state of the plant (thickness and roughness) also helps to form a differentiated spectrum. In this study, the application of mining coproducts (alternative inputs) in the soil, different in input particle size and solubility, allowed a wide diversity of nutritional conditions, varying between better-nourished plants and others with deficiencies (Table 4). Thus, there was a high CV for the evaluated variables, which affected, in part, the creation of models with better ${R^2}_{\mathrm{p}}$ and ${\mathrm{RPD}}_{\mathrm{p}}$ for certain variables.⁷³

Despite its limiting conditions, this study demonstrates potential use of the Vis–NIR spectroscopy to predict variables that presented ${R^2}_{\mathrm{p}}$ and ${\mathrm{RPD}}_{\mathrm{p}}$ above 0.50 and 1.50, respectively, as it was the case of variables LDM, P, K, Mg, S, and Zn, with emphasis on the first four, which presented ${R^2}_{\mathrm{p}}$ above 0.65.

The results of this study demonstrated that a system based on a portable equipment, such as the Vis–NIR spectrometer, can provide a better knowledge of the nutritional condition of certain nutrients as well as estimate LDM of soybean grown after application of mining coproducts. Because it is a fast, low-cost, and non-destructive method, Vis–NIR spectroscopy can be applied in areas cultivated with annual crops such as soybean. Due to the ease of acquisition of this equipment and the processing of the data, it can be used in areas with precision agriculture, considering that this type of agriculture requires a large number of samples.⁷⁴ Although there are many studies reporting the use of the Vis–NIR–SWIR range as a technique for predicting nutrients in plants, this work demonstrated that the use of the Vis–NIR (380 to 1000 nm) range has the potential to generate satisfactory prediction models for several attributes of soybean plants. This allows for the acquisition of cheaper spectroradiometers, which do not necessarily need to operate in the SWIR range.

Finally, the positive results obtained, in this study, indicate that it may also be feasible to use of the Vis–NIR spectroscopy to evaluate other chemical compounds present in soybean that are important in commercialization.