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Watersuspended on chilled superhydrophobic surfaces exhibits delayedfreezing; however, the interdrop growth of frost through subcooledcondensate forming on the surface seems unavoidable in humid environments.It is therefore of great practical importance to determine whetherfacile defrosting is possible on superhydrophobic surfaces. Here,we report that nanostructured superhydrophobic surfaces promote thegrowth of frost in a suspended Cassie state, enabling its dynamicremoval upon partial melting at low tilt angles (<15°). Thedynamic removal of the melting frost occurred in two stages: spontaneousdewetting followed by gravitational mobilization. This dynamic defrostingphenomenon is drivenby the low contact angle hysteresis of the defrosted meltwater relativeto frost on microstructured superhydrophobic surfaces, which formsin the impaled Wenzel state. Dynamic defrosting on nanostructuredsuperhydrophobic surfaces minimizes the time, heat, and gravitationalenergy required to remove frost from the surface, and is of interestfor a variety of systems in cold and humid environments. [ABSTRACT FROM AUTHOR]

We demonstrate a controllable surface-coordinated linear polymerization of long-chain poly(phenylacetylenyl)s that are self-organized into a "circuit-board" pattern on a Cu(100) surface. Scanning tunneling microscopy/spectroscopy (STM/S) corroborated by ab initio calculations, reveals the atomistic details of the molecular structure, and provides a clear signature of electronic and vibrational properties of the poly(phenylacetylene)s chains. Notably, the polymerization reaction is confined epitaxially to the copper lattice, despite a large strain along the polymerized chain that subsequently renders it metallic. Polymerization and depolymerization reactions can be controlled locally at the nanoscale by using a charged metal tip. This control demonstrates the possibility of precisely accessing and controlling conjugated chain-growth polymerization at low temperature. This finding may lead to the bottom-up design and realization of sophisticated architectures for molecular nano-devices. [ABSTRACT FROM AUTHOR]

The advanced third-stage larvae (AdvL) of Gnathostoma lamothei was obtained from experimental hosts. Frogs Lithobates heckscheri and snakes Nerodia fasciata pictiventris were compatible hosts allowing optimal larval development. AdvL are 4,487.94 μm long, have two lateral cervical papillae between rows 10 and 16 and an excretory pore at row 23. The average counts of the cephalic bulb hooklets from the four rows are 39.3, 43.3, 44.2, and 47.3. Larvae show an esophagus that represents 40 % of the body width. These findings indicate that amphibians and reptiles could be involved as G. lamothei natural hosts; nevertheless, their role as etiological agents of human gnathostomiasis is uncertain. This paper reports for the first time the taxonomic description of G. lamothei AdvL obtained from experimental hosts and contributes to the understanding of its life cycle. [ABSTRACT FROM AUTHOR]

The influence of different sorption sites of isoreticular metal-organic frameworks (IRMOFs) on interactions with explosive molecules is investigated. Different connector effects are taken into account by choosing IRMOF-1(Be) (IRMOF-1 with Zn replaced by Be), and two high explosive molecules: 1,3,5-trinitro-s-triazine (RDX) and triacetone triperoxide (TATP). The key interaction features (structural, electronic and energetic) of selected contaminants were analyzed by means of density functional calculations. The interaction of RDX and TATP with different IRMOF-1(Be) fragments is studied. The results show that physisorption is favored and occurs due to hydrogen bonding, which involves the C-H groups of both molecules and the carbonyl oxygen atoms of IRMOF-1(Be). Additional stabilization of RDX and TATP arises from weak electrostatic interactions. Interaction with IRMOF-1(Be) fragments leads to polarization of the target molecules. Of the molecular configurations we have studied, the Be-O-C cluster connected with six benzene linkers (1,4-benzenedicarboxylate, BDC), possesses the highest binding energy for the studied explosives (-16.4 kcal mol for RDX and -12.9 kcal mol for TATP). The main difference was discovered to be in the preferable adsorption site for adsorbates (RDX above the small and TATP placed above the big cage). Based on these results, IRMOF-1 can be suggested as an effective material for storage and also for separation of similar explosives. Hydration destabilizes most of the studied adsorption systems by 1-3 kcal mol but it leads to the same trend in the binding strength as found for the non-hydrated complexes. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Synthesis of nucleobases in nonaqueous environments is an alternative way for the emergence of terrestrial life, which could solve the fundamental problem connected to the hydrolytic instability of nucleic acid components in an aqueous environment. In this contribution, we present a plausible reaction route for the prebiotic synthesis of nucleobases in formamide, which does not require participation of the formamide trimer and aminoimidazole-carbonitrile intermediates. The computed activation energy of the proposed pathway is noticeably higher than that of the HCN-based synthetic route, but it is still feasible under the experimental conditions of the Saladino synthesis. We show that, albeit both the pyrimidine and purine ring formation utilizes the undissociated form of formamide, the dehydration product of formamide, HCN, may also play a key role in the mechanism. The rate-determining step of the entire reaction path is the cyclization of the diaza-pentanimine precursor. The subsequent formation of the imidazole ring proceeds with a moderate activation energy. Our calculations thus demonstrate that the experimentally suggested reaction path without the involvement of aminoimidazole-carbonitrile intermediates is also a viable alternative for the nonaqueous synthesis of nucleobases. [ABSTRACT FROM AUTHOR]

Among the distinct strategies proposed to expand the genetic alphabet, size-expanded nucleobases are promising for the development of modified DNA duplexes with improved biotechnological properties. In particular, duplexes built up by replacing canonical bases with the corresponding benzo-fused counterparts could be valuable as molecular nanowires. In this context, this study reports the results of classical molecular dynamics simulations carried out to examine the structural and dynamical features of size-expanded DNAs, including both hybrid duplexes containing mixed pairs of natural and benzo-fused bases (xDNA) and pure size-expanded (xxDNA) duplexes. Furthermore, the electronic structure of both natural and size-expanded duplexes is examined by means of density functional computations. The results confirm that the structural and flexibility properties of the canonical DNA are globally little affected by the presence of benzo-fused bases. The most relevant differences are found in the enhanced size of the grooves, and the reduction in the twist. However, the analysis also reveals subtle structural effects related to the nature and sequence of benzo-fused bases in the duplex. On the other hand, electronic structure calculations performed for xxDNAs confirm the reduction in the HOMO--LUMO gap predicted from the analysis of the natural bases and their size-expanded counterparts, which suggests that pure size-expanded DNAs can be good conductors. A more complex situation is found for xDNAs, where fluctuations in the electrostatic interaction between base pairs exerts a decisive influence on the modulation of the energy gap. [ABSTRACT FROM AUTHOR]

Abstract: Classical molecular dynamics (MD) and Grand Canonical Monte Carlo (GCMC) simulations were used to generate self-diffusivities, adsorption isotherms and density distributions for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in five isoreticular metal-organic frameworks (IRMOFs), which varied in the cage size and in the presence and location of amine groups. These simulations were performed at room temperature (300K) and low pressures (up to 1ppm RDX). The atomic charges required for MD and GCMC simulations were calculated from quantum mechanical (QM) calculations using two different charge generation methods—Löwdin Population Analysis and Natural Bond Orbital Analysis. Both charge sets show that the presence of amine groups increases the amount of RDX adsorbed. The cage size and the location of amine groups also affect the loading of RDX. The amount of RDX adsorbed is correlated with the energy of adsorption. The activation energy for diffusion of RDX is not positively correlated with the energy of adsorption. The density distributions identify the location of the adsorption sites of RDX-exclusively in the big cage around the metal complex vertices and between benzene rings. In the absence of amine groups on the framework, one of nitro groups on RDX interacts closely with the metal complex. In the IRMOFs functionalized with amine groups, a second nitro group of the RDX interacts with an amine group, enhancing adsorption. With regard to the application as a smart nanoporous preconcentrator, these IRMOFs are found to concentrate RDX up to 3000 times compared to the gas phase, on a volumetric basis. From a simple Langmuir estimation, the selectivity of RDX over butane is up to 5000. The diffusion of RDX is sufficiently high for real time sensor applications. These results indicate IRMOFs can be tailored with functional groups to be highly selective nanoporous preconcentrators. [Copyright &y& Elsevier]

We have demonstrated in our earlier work that a few natural and synthetic estrogens can be effectively transformed through reactions mediated by lignin peroxidase (LiP). The behaviors of such reactions are variously influenced by the presence of natural organic matter (NOM) and/or veratryl alcohol (VA). Certain white rot fungi, e.g. Phanerochaete chrysosporium, produce VA as a secondary metabolite along with LiP in nature where NOM is ubiquitously present. Herein, we report a study on the products resulting from LiP-mediated oxidative coupling reactions of a representative estrogen, 17β-estradiol (E2), and how the presence of NOM and/or VA impacts the formation and distribution of the products. A total of six products were found, and the major products appeared to be oligomers resulting from E2 coupling. Our experiments revealed that these products likely formed colloidal solids in water that can be removed via ultrafiltration or settled during ultracentrifugation. Such a colloidal nature of the products could have important implications in their treatability and environmental transport. In the presence of VA, the products tended to shift toward higher-degree of oligomers. When NOM was included in the reaction system, cross-coupling between E2 and NOM appeared to occur. Data obtained from E-SCREEN test confirmed that the estrogenicity of E2 can be effectively eliminated following sequential reactions mediated by LiP. [ABSTRACT FROM AUTHOR]

In order to test the feasibility of using metal-organic frameworks (MOFs) to pre-concentrate explosive molecules for detection, molecular simulations of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) within IRMOF-1 were performed. Grand canonical Monte Carlo (GCMC) simulations were used to generate adsorption isotherms for pure RDX, RDX in dry air, and RDX in wet air. In addition to the isotherms, the GCMC simulations provide adsorption energies and density distributions of the adsorbates within the MOF. Molecular dynamics simulations calculate diffusivities and provide a detailed understanding of the change in conformation of the RDX molecule upon adsorption. The presence of dry air has little influence on the amount of RDX that adsorbs. The presence of wet air increases the amount of RDX that adsorbs due to favourable interactions between RDX and water. We found a Henry's law constant of 21.2 mol/kg/bar for both pure RDX and RDX in dry air. The RDX adsorption sites are located (i) in big cages, (ii) near a vertex, and (iii) between benzene rings. The energy of adsorption of RDX at infinite dilution was found to be - 9.2 kcal/mol. The distributions of bond lengths, bond angles and torsion angles in RDX are uniformly slightly broader in the gas phase than in the adsorbed phase, but not markedly so. The self-diffusivity of RDX in IRMOF-1 is a strong function of temperature, with an activation energy of 6.0 kcal/mol. [ABSTRACT FROM AUTHOR]

A reliable laboratory index of N availability would be useful for making N recommendations, but no single approach has received broad acceptance across a wide range of soils. We compared several indices over a range of soil conditions to test the possibility of combining indices for predicting potentially mineralizable N (N0). Soils (0-5 and 5-15 cm) from nine tillage studies across the southern USA were used in the evaluations. Long-term incubation data were fit to a first-order exponential equation to determine N0, k (mineralization rate), and N0 (N0 estimated with a fixed k equal to 0.054 wk-1). Out of 13 indices, five [total C (TC), total N (TN), N mineralized by hot KCI (Hot N), anaerobic N (Ana_N), and N mineralized in 24 d (Nmin_24)] were strongly correlated to N0 (r > 0.85) and had linear regressions with r2 > 0.60. None of the indices were good predictors of k. Correlations between indices and N0 improved compared with N0, ranging from r = 0.90 to 0.95. Total N and flush of CO2 determined after 3 d (FLCO2) produced the best multiple regression for predicting N0 (R2 = 0.85) while the best combination for predicting N0 (R2 = 0.94) included TN, Fl_CO2 Cold_N, and NaOH_N. Combining indices appears promising for predicting potentially mineralizable N, and because TN and Fl_C02 are rapid and simple, this approach could be easily adopted by soil testing laboratories. [ABSTRACT FROM AUTHOR]

Thirteen metric tons of poultry litter are produced annually by poultry producers in the U.S. Poultry litter contains the sex hormones estradiol and testosterone, endocrine disruptors that have been detected in surface waters. The objective of this study was to evaluate the potential impact of poultry litter applications on estradiol and testosterone concentrations in subsurface drainage and surface runoff in irrigated crop land under no-till and conventional-till management. We conducted an irrigation study in fall of 2001 and spring of 2002. Four treatments, no-till plus poultry litter, conventional-till plus poultry litter, no-till plus conventional fertilizer, and conventional-till plus conventional fertilizer, were evaluated. Flow-weighted concentration and load ha¿1 of the two hormones were measured in drainage and runoff. Soil concentrations of estradiol and testosterone were measured. Based on comparisons to the conventional fertilizer (and control) treatments, poultry litter did not add to the flow-weighted concentration or load ha¿1 of either estradiol or testosterone in subsurface drainage or surface runoff. Significant differences were, however, observed between tillage treatments: flow-weighted concentrations of estradiol were greater for no-till than conventional-till plots of the June irrigation; and runoff loads of both estradiol and testosterone were less from no-till than conventional-till plots for the November irrigation. Although the differences between no-till and conventional-tillage appeared to affect the hydrologic transport of both hormones, the differences appeared to have inconsequential environmental impact. [Copyright &y& Elsevier]

Poultry litter (PL) can supply N, P, K, and other plant nutrients; however, excessive application may cause environmental problems, depending on management and crop nutrient demand. Changes in soil test (ST) nutrient content in a Cecil soil (a fine, kaolinitic, thermic Typic Kanhapludult) during a 10-yr period of PL use was evaluated at the USDA-ARSJ. Phil Campbell, Sr., Natural Resource Conservation Center, Watkinsville, GA. During the cotton (Gossypium hirsutum L.) cropping phase (1995-2000), 4.4 Mg PL ha-1 yr-1 resulted in small changes in ST nutrient content in the surface 15 cm. Differences were observed between tillage treatments, with less accumulation of Ca, Mg, and Mn and greater accumulation of Zn for no-till (NT) than conventional tillage (CT). During the corn (Zea mays L.) cropping phase (2001-2005), average annual PL inputs (11.2 Mg hat) increased P and Zn contents, with changes being similar for CT and NT After 10 yr, ST nutrient contents in the surface 15 cm reflected 25, 4, 45, 26, 17, and 97% of the input from PL for P. K, Ca, Mg, Mn, and Zn, respectively. Changes in soil profile nutrient content (to a depth of 60 cm) from 1997 to 2005 were predominantly at 0 to 15 cm, where P and Zn increased >200%. Accumulation of Ca, K, P, and Zn at lower depths was also observed. Strategies for increasing nutrient removal following repeated long-term application of PL should be considered to avoid excessive levels of nutrients. [ABSTRACT FROM AUTHOR]

The general aim of this study was to evaluate physicochemical properties, prebiotic activity and anticancer potential of jackfruit (Artocarpus heterophyllus) seed flour. The drying processes of jackfruit seeds were performed at 50, 60 and 70 °C in order to choose the optimal temperature for obtaining the flour based on drying time, polyphenol content and antioxidant capacity. The experimental values of the moisture ratio during jackfruit seed drying at different temperatures were obtained using Page's equation to establish the drying time for the required moisture between 5 and 7% in the flour. The temperature of 60 °C was considered adequate for obtaining good flour and for performing its characterization. The chemical composition, total dietary fiber, functional properties and antioxidant capacity were then examined in the flour. The seed flour contains carbohydrates (73.87 g/100 g), dietary fiber (31 g/100 g), protein (14 g/100 g) and lipids (1 g/100 g). The lipid profile showed that the flour contained monounsaturated (4 g/100 g) and polyunsaturated (46 g/100 g) fatty acids. Sucrose, glucose, and fructose were found to be the predominant soluble sugars, and non-digestible oligosaccharides like 1-kestose were also found. The total polyphenol content was 2.42 mg of gallic acid/g of the sample; furthermore, the antioxidant capacity obtained by ferric reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) was 901.45 µmol Trolox/100 g and 1607.87 µmol Trolox/100 g, respectively. The obtained flour exhibited good functional properties, such as water and oil absorption capacity, swelling power and emulsifier capacity. Additionally, this flour had a protective and preventive effect which is associated with the potential prebiotic activity in Lactobacillus casei and Bifidobacterium longum. These results demonstrate that jackfruit seed flour has good nutritional value and antioxidant and prebiotic activity, as well as potential protective effects and functional properties, making it an attractive food or ingredient in developing innovative functional products. [ABSTRACT FROM AUTHOR]

Abstract: Batch distillation is a traditional and widely-used technique to produce Pisco brandy, a young spirit made from Muscat wine. It is necessary to track a given ethanol composition in the distillate in order to obtain a reproducible spirit with a desired aromatic profile. The use of multiple ethanol sensors represents a considerable cost, which prevents many distilleries from adopting this technology. Aiming to provide practical and affordable industrial-scale distillation control technology, we developed a soft-sensor to estimate distillate ethanol concentration on-line based on four temperature measurements in the still. The soft-sensor, calibrated with laboratory and industrial experimental data, consisted of an Artificial Neural Network and involved simple data pre-processing procedures. Simplicity and good performance were the metrics adopted for testing different algorithms and network structures. Returning mean prediction errors of ±0.6% v/v with laboratory scale distillations and ±1.6% v/v in industrial trials, the resulting accuracy of the soft-sensor is sufficient to improve standard practice and reproducibility. [Copyright &y& Elsevier]

Local effective microorganism (LEM) is an inoculant produced using leaf litter collected from forest floors near the location where it is to be utilized. While this locally-sourced inoculant is used around the world, more research is needed to fully understand the potential benefits or drawbacks of its use in agricultural systems. The objectives of this study were to observe the effects of combining LEM with composted broiler litter to fertilize edamame (Glycine max L.) on plant-available nitrogen, nematode trophic group communities, and soybean productivity. The study was carried out in a randomized, complete block design on piedmont soils in the southeastern United States comparing broiler litter composted with LEM, False-LEM or water (Control) treatments which were applied at the beginning of each growing season (June 2015, 2016, 2017). In the first year of the study, soil (0–10 cm) that received the LEM treatment mineralized greater amounts of N and mineralized N faster than CONT soils (P = 0.0665 and P = 0.0717), respectively, during one week of incubation. In year 2 (2016) plots experienced drought stress, with soil moistures as low as 2%. In LEM plots soil samples taken during the drought contained significantly greater populations of all nematodes, excluding Mononchidae when compared to the other treatments. When calculated per unit of soil N, measured after application of treatments, no differences in edamame soybean yield were observed between treatments. Combining LEM with composted broiler litter jump-started N mineralization early in growing seasons and maintained abundance of multiple nematode trophic groups during drought. This signifies LEM's potential to strengthen a soil's food web resistance to drought stress – providing more security for a functional agroecosystem under uncertain climate conditions. • Nitrogen mineralization is jump-started by addition of locally effect microorganisms. • Edamame nodulation was not influenced by bio-inoculum. • Bio-inoculant maintained free-living nematode communities under drought conditions. [ABSTRACT FROM AUTHOR]

The state diagram, which is defined as a stability map of different states and phases of a food as a function of the solid content and temperature, is regarded as fundamental approach in the design and optimization of processes or storage procedures of food in the low-, intermediate-, and high-moisture domains. Therefore, in this study, the effects of maltodextrin addition on the freezing points ( T m ′ , T m ) and glass transition temperatures ( T g ′ , T g ) required for the construction of state diagrams of fruit juice model systems by using differential scanning calorimetry methods was investigated. A D-optimal experimental design was used to prepare a total of 25 anhydrous model food systems at various dry mass fractions of fructose, glucose, sucrose, pectin, citric acid, and maltodextrin, in which this last component varied between 0 and 0.8. It was found that maltodextrin mass fractions higher than 0.4 are required to induce significant increases of T g ′ , T m ′ , T g , and T m curves. From this perspective, maltodextrin is a good alternative as a cryoprotectant and as a carrier agent in the food industry. Furthermore, solute-composition-based mathematical models were developed to evaluate the influence of the chemical composition on the thermal transitions and to predict the state diagrams of fruit juices at different maltodextrin mass fractions. [ABSTRACT FROM AUTHOR]

The behavior of a 50 nm Cu-Ni alloy film on Si in a process of reactive solid-state dewetting is presented. The films were annealed at a range of temperatures (300-700 °C) in 1% H2 99% N2 reducing atmosphere. The resulting alloy and silicide particles formed by film dewetting and film reaction with the substrate were distinguished by selective wet etching and examined by scanning electron microscopy and spectroscopy. After potassium hydroxide etch, regions that etch slower than silicon substrate have distribution statistics similar to the alloy and silicide particles prior to their removal, indicating strong coupling between mass transport across the interface and along the surface. [ABSTRACT FROM AUTHOR]

Classical molecular dynamics (MD) simulations were used to investigate how free surfaces, as well as supporting substrates, affect phase separation in a NiAg alloy. Bulk samples, droplets, and droplets deposited on a graphene substrate were investigated at temperatures that spanned regions of interest in the bulk NiAg phase diagram, i.e., miscible and immiscible liquid, liquid-crystal, and crystal-crystal regions. Using MD simulations to cool down a bulk sample from 3000 K to 800 K, it was found that phase separation below 2400 K takes place in agreement with the phase diagram. When free surface effects were introduced, phase separation was accompanied by a core-shell transformation: spherical droplets created from the bulk samples became core-shell nanoparticles with a shell made mostly of Ag atoms and a core made of Ni atoms. When such droplets were deposited on a graphene substrate, the phase separation was accompanied by Ni layering at the graphene interface and Ag at the vacuum interface. Thus, it should be possible to create NiAg core-shell and layer-like nanostructures by quenching liquid NiAg samples on tailored substrates. Furthermore, interesting bimetallic nanoparticle morphologies might be tuned via control of the surface and interface energies and chemical instabilities of the system. [ABSTRACT FROM AUTHOR]

Solar flares cause a rapid increase in ionization in the ionosphere owing to significant enhancement of ionizing solar radiation in the X-ray and extreme ultraviolet (EUV) spectral ranges. The change of electron densities in the ionosphere influences the propagation of radio waves. The ionospheric response to solar flares is investigated for three selected examples recorded during the maximum and decreasing phase of the solar cycle 24 with time resolution of several seconds by continuous Doppler sounding systems installed in the Czech Republic (50N, 14E), Taiwan (24N, 121E) and Northern Argentina (27S, 65W). The reflection heights of sounding signals are derived from nearby ionospheric sounders. The measured Doppler shifts are compared with EUV and X-ray data from the GOES-15 satellite. It is shown that the largest Doppler shifts are observed at times when the time derivatives of EUV fluxes are maximal, while the Doppler shifts are around zero at times when the EUV fluxes reach maxima. This means that loss processes balance the ionization when the EUV fluxes maximize. The attenuation of Doppler signal caused by enhanced electron density in the D and E layer was well correlated with the cosmic noise absorption measured by riometer. For large ionizing fluxes, the attenuation leads to very low signal-to-noise ratio, loss of the received signal, and inability to process both Doppler shift spectrograms and ionograms. [ABSTRACT FROM AUTHOR]

A theoretical and experimental characterization of N(1)‐(2′,3′‐dihydroxypropil)thymine (DHPT), a potential prebiotic nucleoside analogue of 5‐methyluridine, is performed. A proposed methodology based on a solvation method was used to study conformational transformations of the different low‐energy conformers of DHPT according to time‐dependent IR spectroscopy. NMR and CD spectroscopy provides additional evidence of these transformations. The conformational transformations appear to be due to solvent and DHPT interactions. This highlights the importance of experimental conditions on conformer ratio equilibrium, in particular, the interpretation of experimental conditions used for determining the stereoisomers' absolute configuration. Nucleoside analogues are molecules that differ from the canonical ones in that they are comprised of a noncanonical DNA base, sugar, or both. Interactions between nucleoside analogues and solvent molecules are proposed as the main responsible for their conformational transformations, which were studied both experimentally and computationally. The evidence of these transformations is provided by time‐dependent infrared spectra and made apparent by the variation of intensity related to a specific stereoisomer. [ABSTRACT FROM AUTHOR]

The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict field-grown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphic-climatic variables. We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d + Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. The C-PF, Smin-7d + Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk = 0.038*Nan + 0.106*SOC + 0.74; Ra2 = 0.87). The Smin-10wk, C-PF, and Smin-7d + Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from C-PF (Smin-app (modified) = 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified) = 3.0*Smin-10wk + 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions. [ABSTRACT FROM AUTHOR]

In an effort to enhance our knowledge on how to control the movement of metallic nanodroplets, here we have used classical molecular dynamics simulations to investigate whether Cu nanostructures deposited on nanopillared substrates can be made to jump at desired angles. We find that such control is possible, especially for Cu nanostructures that are symmetric; for asymmetric nanostructures, however, control is more uncertain. The work presented here borrows ideas from two seemingly different fields, metallic droplets and water droplets in the dynamic Leidenfrost regime. Despite the differences in the respective systems, we find common ground in their behavior on nanostructured surfaces. Due to this, we suggest that the ongoing research in Leidenfrost droplets is a fertile area for scientists working on metallic nanodroplets. [ABSTRACT FROM AUTHOR]