Proceedings - Soil Science Society of America最新文献

Accurate measurement of metal concentrations in soil and water is vital for healthy crop production and decision making for environmental surveys. While there are a multitude of laboratory-based soil analysis methods, such as inductively coupled plasma-optical emission spectroscopy (ICP-OES), flame emission spectrometry, and atomic absorption spectroscopy, most are time and resource intensive. Additionally, there is a lack of information for rapid analysis of elements for aqueous soil extractions. The goal of this research is to establish elemental correlations between portable X-ray fluorescence (pXRF) measurements of Mehlich III soil extractions and traditional elemental measurements via ICP-OES. We hypothesize that certain metals can be accurately measured in aqueous soil extraction solutions by pXRF to the same degree as they are measured by ICP-OES. To test this hypothesis, Mehlich III and 2% nitric acid solutions with known elemental concentrations were analyzed via ICP-OES and pXRF. Soil samples extracted using Mehlich III were compared between ICP-OES and pXRF to verify correlations. High correlations were found for As, Ca, Cd, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Se, V, and Zn in both the Mehlich III and 2% nitric acid solutions at concentrations between 5 and 85 mg L⁻¹. P, S, and Si did not show high correlations at concentrations <100 mg L⁻¹. These results indicate that between 5 and 85 mg L⁻¹, pXRF analysis of aqueous solutions and soil extractions is a reliable technique; however, at low concentrations (i.e., <5 mg L⁻¹ for metals and <100 mg L⁻¹ for P and S), pXRF is not well suited.

Data transformation of the reference soil organic matter (SOM) decomposition rates (k_ref), often derived as turnover times or in alternative formats, is commonly used to develop ecological models for projecting the persistence of SOM. However, the effects of reciprocal or logarithmic transformation of k_ref on model performance and edaphic-climatic patterns remain uncertain. Here, we convert published k_ref values into reciprocal or logarithmic formats and establish machine learning models between the transformed k_ref and edaphic-climatic predictors. We show that models trained with the transformed k_ref exhibit a 11.6%−68.4% reduction in performance upon re-conversion to k_ref compared to those trained with the original k_ref. The variable importance analysis identifies distinct key predictors governing the original k_ref and its transformed counterparts. This suggests that data transformation alters the relative significance of predictors without necessarily improving k_ref prediction performance. Consequently, our study underscores the importance of directly focusing on the original values rather than alternative representations when dissecting a given variable's patterns and mechanisms in ecological modeling.

Arrington, K. E., Ordóñez, R. A., Rivera-Ocasio, Z., Luthard, M., Tierney, S., Spargo, J., Finney, D., Kaye, J., & White, C. (2024). Improving a nitrogen mineralization model for predicting unfertilized corn yield. Soil Science Society of America Journal, 88, 905–920. https://doi.org/10.1002/saj2.20665

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Many soybean [Glycine max (L.) Merr.] growers in the US Midwest rely on soil test values for evaluating the crop's fertilizer needs. However, threshold values for Illinois were calibrated to soybean yield in the 1960s when the production systems and yield potential were much different than today. The objective of this study was to determine which and how well soil test values predict yield of unfertilized soybean. Preplant soil samples, collected from 133 trials across Illinois from 2014 to 2021, were analyzed for 14 chemical attributes and compared to unfertilized soybean grain yields from those same studies. Pearson correlation coefficients (r), principal factor analysis, and latent variable regression models were used to determine those soil attributes most closely associated with grain yield and yield components. The association of planting date and yield (r = −0.56) led to dividing the data set into five planting date groups. Soil fertility levels resulted in a strong correlation with yield for Late or Very-late planting groups, but not for the Early or Very-early groups. A factor analysis of soil attributes largely resulted in retention of two factors, identified as Soil Organic Charge and Soil Fertility, across the planting-date groups. Regression of these factors with yield confirmed that soil fertility had a greater influence on grain yield for late-planted soybeans than early-planted and that these differences were associated with average seed weight. Therefore, positioning late-planted soybeans in higher fertility fields and early-planted soybeans in lower-fertility fields could reduce the need for supplemental fertilization.

The two most important abiotic plant stressors that impact plant development and crop yields are water stress and salinity stress. These issues are particularly important in arid and semi-arid regions. According to a 2019 research paper, “thirty crop species provide 90% of our food, most of which display severe yield losses under moderate salinity.” Moderate salinity is defined as extracted pore-water salinity in the range of 4–8 dS m⁻¹. Currently, commercially available soil moisture and bulk soil electrical conductivity sensors can estimate in situ soil pore-water electrical conductivity with suitably calibrated soil moisture and electrical conductivity models for a wide range of soil types and growing media. With knowledge of the pore-water electrical conductivity it is possible to estimate osmotic tension. Furthermore, there are commercially available dielectric tensiometers that provide soil water tension measurements from the water content of a porous matrix component that is in equilibrium with the water capillary forces in the surrounding soil or growing media. Combining soil moisture and soil water tension measurements enables water retention curves and the hydraulic properties of a soil to be determined. However, the overall ability of a plant to extract water from a soil or substrate is typically dominated by water tension and osmotic tension. Currently, while the technology blocks exist in different commercial offerings, the combination of a water tension and osmotic tension in an integrated sensor does not exist. A key benefit of the porous matrix in a dielectric tensiometer is that electrical measurements include a component of extracted water from the soil or growing media. With the appropriate dielectric characterization of the porous matrix, there should be no need for soil-specific calibrations. The aim of the paper is to outline the measurement processing that could be implemented into an integrated water tension and osmotic tension sensor.

Soil microbial extracellular enzymes play crucial roles in soil carbon and nutrient cycling by catalyzing soil biochemical processes. However, the activity and stoichiometry of enzymes involved in the carbon, nitrogen, and phosphorus cycles in the environment with the highest density of wild giant pandas on the southern slopes of the Qinling Mountains is unknown. We have established eight research areas at an elevation of 1090–2621 m. The β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG), and acid phosphatase (APase) in soil samples were measured. We used redundancy analysis and structural equation model to evaluate the driving factors of metabolic restriction of soil microorganisms along the elevational gradient, and four models were used to cross-evaluate the nutrient restriction status of soil microorganisms. The results showed that most soil physiochemical properties, soil microbial biomass, and microbial extracellular enzymes exhibited a hump-shaped trend with increasing elevation. Elevation indirectly affected soil enzyme activity and stoichiometry by C, N, and P status. Microorganisms are limited by C at lower and higher elevations but limited by N at medium elevations. These results could help strengthen the conservation and management of the wild panda's natural habitat.

The extent to which cover crops (CCs) accumulate soil organic carbon (SOC) in the entire soil profile is still unclear. We measured SOC, permanganate oxidizable C (POX-C), and particulate organic matter (POM) concentrations down to 60-cm soil depth in early [2–3 week before corn (Zea mays L.) planting]- and late-terminated (at corn planting) winter rye (Secale cereale L.) CCs in rainfed and irrigated no-till continuous corn systems in the U.S. Corn Belt after 10 years. CCs increased SOC stock and SOC, POX-C, and POM concentrations but only in the irrigated system in the upper 5-cm depth. Late-terminated CC increased SOC concentration by 4.710 ± 3.501 g kg⁻¹ and accumulated SOC at 0.207 ± 0.145 Mg C ha⁻¹ year⁻¹. It increased POX-C and POM concentrations, on average, by 1.194 times. CCs likely increased SOC in the irrigated system by producing more biomass (2.247 ± 0.370 Mg ha⁻¹) than in the rainfed system (0.949 ± 0.338 Mg ha⁻¹). At least 2 Mg ha⁻¹ of CC biomass may be needed to increase SOC. Because winter CCs often produce <1 Mg ha⁻¹ of biomass when typically planted late and terminated early, extending the CC growing window by terminating CCs at or after crop planting (planting green) may boost CC biomass and SOC accumulation, although high-C soils or Mollisols, such as our study soils (>22 g C kg⁻¹), may limit SOC gains. We submit CCs would sequester more SOC in low-C, eroded, and low-fertility soils. Overall, winter rye CCs minimally alter soil C in the soil profile in no-till continuous corn systems after 10 years.

Wildfire is a disturbance expected to increase in frequency and severity, changes that may impact carbon (C) dynamics in the soil ecosystem. Fire changes the types of C sources available to soil microbes, increasing pyrogenic C and coarse downed wood, and if there is substantial tree mortality, decreasing C from root exudates and leaf litter. To investigate the impact of this shift in the composition of C resources on microbial processes driving C cycling, we examined microbial activity in soil sampled from an Oregon burn 1 year after fire from sites spanning a range in soil burn severity from unburned to highly burned. We found evidence that postfire rhizosphere priming loss may reduce soil C loss after fire. We measured the potential activity of C-acquiring and nitrogen (N)-acquiring extracellular enzymes and contextualized the microbial resource demand using measurements of mineralizable C and N. Subsurface mineralizable C and N were unaltered by fire and negatively correlated with hydrolytic extracellular enzyme activity (EEA) in unburned, but not burned sites. EEA was lower in burned sites by up to 46%, but only at depths below 5 cm, and with greater decreases in sites with high soil burn severity. These results are consistent with a subsurface mechanism driven by tree mortality. We infer that in sites with high tree mortality, subsurface EEAs decreased due to loss of rhizosphere priming and that inputs of dead roots contributed to mineralizable C stabilization.

Allophane and ferrihydrite are the main hosts of phosphate in allophanic Andosols, which are vital soil resources that support high human population densities. However, the sorption mechanism of phosphate on allophane has not been elucidated, unlike that of ferrihydrite. In particular, the effects of residence time on phosphate sorbed on allophane remain unclear. Therefore, the objectives of this study were to (1) understand the effect of residence time on the stability of phosphate sorbed on allophanic Andosol and allophane by desorption experiments using arsenate and (2) elucidate the sorption mechanism of phosphate on allophane using solid-state ³¹P nuclear magnetic resonance (NMR). Consequently, the slow sorption of phosphate onto allophanic Andosol, allophane, and ferrihydrite continued for approximately 150 days. The ratio of total desorbable phosphate to phosphate sorbed onto the allophanic Andosol and allophane decreased with increasing residence time. In other words, phosphate sorption on allophanic Andosol and allophane was more irreversible with increasing residence time. The NMR spectra and X-ray diffraction patterns showed that the molecular environment of phosphate sorbed onto allophane and ferrihydrite did not change at any residence time. Therefore, the slow sorption and irreversibility of phosphate were caused not by surface precipitation but by internal diffusion. In addition, the NMR spectra showed that most of the phosphate sorbed on allophane was present as inner-sphere complexes.