In this study, we characterized Zn seed coats adhered on soybean seed surface [Glycine max (L.) Merrill] and then investigated the effects of such coatings on germination and finally monitored the transference of Zn from the coatings to the seedlings. For that, seeds were coated with ZnSO₄, 40 nm ZnO, and 5 µm ZnO at 4 mg of Zn per kg of seed. The shape and homogeneity of Zn deposits on the seed surface depended on the type of Zn source. X-ray spectroscopy unraveled that before the germination, Zn neither crossed the seed coat nor was biotransformed. Regardless of the particle size, ZnO-based treatments increased germination ratio, seedlings root, and shoot development, whereas ZnSO₄ suppressed them. After sowing, most of Zn on the seed coat was transferred to soil, while less than 13 wt% was assimilated by the seedling 14 days after sowing. Either this minor fraction of Zn was taken up during the imbibition process or absorbed by during seedling growth. Zinc oxide-based treatments represent a viable alternative to Zn supply in order to boost soybean germination and seedling development.

Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder ( 2.5. Although XRF analysis of pressed pellets allowed a slight gain in performance over loose powder samples for the prediction of ex-K and ex-Ca, satisfactory performances were also obtained with loose powders, which require minimal sample preparation. The prediction models with local samples showed promising results and encourage more detailed investigations for the application of pXRF in tropical soils.

Several factors have contributed to bring pressure on agriculture. In this context, nanomaterial properties can be explored to design more efficient fertilizers and therefore increase productivity. In the present study, the roots of Phaseolus vulgaris were immersed in several nano ZnO dispersions for 48 h. The absorption and transport phenomena were in vivo monitored by X-ray fluorescence spectroscopy (XRF) and X-ray absorption spectroscopy (XAS). The nanoparticle size, concentration and coating with surfactants affected the rate of Zn release and therefore its uptake. In vivo X-ray absorption spectroscopy showed that Phaseolus vulgaris takes up Zn bound to both citrate and malate, while entire nanoparticles were only absorbed when roots were injured. X-ray fluorescence microanalysis unravelled that besides xylem bundles, root to shoot Zn transport can take place through the cortex.

The difference in the matrix present in soil samples from different areas limits the performance of nutrient analysis via XRF sensors, and only a few strategies to mitigate this effect to ensure an accurate analysis have been proposed so far. In this context, this research aimed to compare the performance of different predictive models, including a simple linear regression (RS), multiple linear regression (MLR), partial least-squares regression (PLS), and random forest (RF) models for the prediction of Ca and K in agricultural soils. RS models were evaluated on XRF data without (RS1) and with (RS2) Compton normalization. In addition, it was assessed whether using soil texture information and/or vis–NIR spectra as auxiliary variables would optimize the predictive performance of the models. The results showed that all strategies allowed the mitigation of the matrix effect to some degree, enabling the determination of their Ca and K contents with excellent predictive performance (R² ≥ 0.84). The best performance was obtained using RS2 for the Ca prediction (R² = 0.92, RSME = 48.25 mg kg⁻¹ and relative improvement (RI) of 52.3% compared to RS1) and using an RF for the K prediction (R² = 0.84, RSME = 17.43 mg kg⁻¹ and RI of 24.3% compared to RS1). The results indicated that sophisticated models did not always perform better than linear models. Furthermore, using texture data and vis–NIR spectra as auxiliary data was promising only for the K prediction, which showed an error reduction in the order of 10%, contrasting with the Ca prediction, which did not reduce the prediction error by more than 1%. The best modeling approach in our study proved to be attribute-specific. These results give further insight into the development of intelligence modeling strategies for sensor-based soil analysis.