Inclusion Of Soil Properties Research Articles

Identifying Controlling Factors of Bioaccumulation of Selected Metal(loid)s in Various Soil–Cereal Crop Systems within Cultivated Fluvisols

The paired Fluvisol and cereal samples in both the field screening and controlled experiments are reported to elucidate the soil–crop relationship for As, Cd, and Pb in relation to changing contamination levels. Significant varietal differences in plant uptake were observed for crop type (barley, triticale) and the harvested part of the crop (oat shoots and grain). When parametrizing the stepwise regression models, the inclusion of soil properties often improved the performance of soil–crop models but diverse critical soil parameters were retained in the model for individual metal(loid)s. The pH value was often a statistically significant variable for Cd uptake. For As and Pb, the more successful model fit was achieved using the indicators of quantity or quality of soil organic matter, but always with lower inherent model reliability compared to Cd. Further, a single correlation analysis was used to investigate the relationship between extractable metal concentrations in soil solution and their crop accumulation. For Cd, there were strong intercorrelations among single extractions, the NH4NO3 extraction stood out with perfect correlation with plant uptake in both experiments. For As and Pb, the CaCl2 and Na2EDTA solutions outperformed other single extractions and were the better choice for the assessment of depositional fluvial substrates.

Identification of the most limiting factor for rice yield using soil data collected before planting and during the reproductive stage

AbstractSoil parameters, measured before crop planting, are typically used to quantify the relationship between soil properties and crop yield and to identify factors limiting crop yield. However, soil properties during sensitive stages of crop growth may have a greater effect on crop yield than the initial values. We determined if inclusion of soil properties, measured during the reproductive stage, could improve the accuracy of crop yield prediction. Classification and regression trees were used to determine the explanatory power of different soil and rice variables for predicting rice yield in alkaline salt‐affected paddy fields in northeast China. The traditional method explained 77.5% of the rice yield variation and identified soil CO32− in the 0‐ to 10‐cm soil layer, with single explanatory power of 53.4%, as the most important predictor. The whole explanatory power of the methods including soil variables during the reproductive stage and yield components, with/without soil variables before planting, increased to 81.3%. The residual sodium carbonate, measured in the 0‐ to 10‐cm soil layer during the reproductive stage, was identified as the most limiting factor due to its single maximum explanatory power of 60.5%. We conclude that inclusion of soil properties, measured during the reproductive stage, has potential for improving the rice yield prediction accuracy by enhancing the explanatory power in identification of the most limiting factor. These results encourage further investigation of the role of soil properties during sensitive stages of crop growth in crop yield prediction under different soil and climatic conditions.

Soil properties are useful to examine denitrification function development in created mitigation wetlands

We investigated structural soil attributes, the development of denitrification potential (DP), and their relations in created and natural non-tidal freshwater wetlands in Virginia. Soil attributes included soil organic matter (SOM), total organic carbon (TOC), total nitrogen (TN), pH, gravimetric soil moisture (GSM), and bulk density (Db). A subset of soil attributes were analyzed across the sites, using Euclidean cluster analysis, resulting in three soil condition (SC) groups of increasing wetland soil development (i.e., SC1<SC2<SC3 less to more developed) as measured by accumulation of TOC and TN, the increase of GSM, and the decrease of Db. Denitrification enzyme activity (DEA) was measured for DP. DEA rates were somewhat different by wetland site, but with no age-based trajectory. No significant difference was seen in DEA rates by sampling period (p=0.06). However, DEA rates were clearly differentiated by SC groups, with the highest rates in SC3 followed by SC1 and lowest in SC2. The lowest DEA rates in SC2 seemed associated with a higher soil pH (∼6.6) in the group than that (∼5.3) of the other SC groups, but it needs a further investigation. The principal component analysis (PCA) of soil physicochemical properties and average DP showed the association between the development of denitrification function and the maturation of soil conditions in wetlands. The outcome of the study suggests that the use of a suite of simple soil properties may be useful to examine the development of denitrification function in created mitigation wetlands. The inclusion of soil properties in post-construction monitoring should be required to enhance our understanding and prediction of the functional development of created mitigation wetlands.

Evaluation of Mehlich 3 for Extraction of Copper and Zinc from Irish Grassland Soils and for Prediction of Herbage Content

: The objective of this study was to determine how the extractant Mehlich 3 (M3) compared with other methods currently used in Ireland for determination of copper (Cu) and zinc (Zn) in soils. Samples from eight different mineral soil types, four of sandstone/shale and four of limestone origin, were analyzed for copper and zinc using M3 and conventional extractants. Herbage samples were taken from the soils and analyzed for Cu and Zn. Mehlich 3 results showed good correlation with ethylenediamine‐tetraacetic acid (EDTA)– and diethylenetriamine‐pentaacetic acid (DTPA)–extractable Cu and Zn. Inclusion of soil properties in multiple regression models improved the coefficients of determination. All extractants were equal in their ability to predict Cu and Zn herbage content. Differences between sandstone/shale and limestone soils in relation to herbage content were also found, with the better relationship found in sandstone/shale soils.

Comparison of EDTA and sequential extraction tests for phytoavailability prediction of manganese and zinc in agricultural alkaline soils

Twenty-eight soil samples with a wide range of chemical and physical properties were collected from typical uncontaminated soils of Central Spain, characterised in general by their medium or high calcium carbonate contents. The extractability and solid-phase fractionation of manganese (Mn) and zinc (Zn) in the soils was determined by the single extraction procedure proposed by the BCR for calcareous soils (EDTA extractability) and two different sequential extraction procedures: a modification of the five-step Tessier method (procedure A) and the three-step BCR method (procedure B). The EDTA extractable Mn levels were relatively high (on average 30%), but they were less in the case of Zn (on average 3.7%). Manganese was mainly released in the residual fraction when using procedure A (47%) and in the reducible fraction when using procedure B (47%). Zinc in these soils was dominantly associated with mineral lattices (procedure A: 79%; procedure B: 91%). The fractionation methods for the prediction of phytoavailability of Mn and Zn for barley in a greenhouse experiment were not significantly better than EDTA plus soil properties. Inclusion of soil properties in the best-fitting multivariate regression models improved the relationship for metal concentration in plants. Values for R 2 of the models ranged from 0.37 to 0.95.

Predicting Zn and Cu status in cereals – potential for a multiple regression model using soil parameters

Prediction of micronutrient deficiency normally involves soil extraction tests and correlations between soil extractable micronutrients and their concentration in plant tissues. The present study reports that the inclusion of soil properties in addition to soil extraction data represents a powerful tool for improving models for a simple determination of phytoavailable Zn and Cu. Soil and plant samples were collected in spring 2000 from 19 cereal fields in Central and Southeast Norway at sites with known micronutrient deficiency. Based on the sample information, the model approach used stepwise regression, applying actual plant Zn and Cu concentration as the response, and soil extractable Zn/Cu and physico-chemical properties of the soil as model predictors. A model involving citric acid extractable Zn coupled with selected soil properties could predict the concentration of Zn in plant tissues of barley and oats grown under field conditions with a significantly high precision (R2=0·96, P&lt;0·001). The soil parameters included in the model, and their importance for phytoavailability of Zn, were in the order: citric acid extractable Zn&gt;clay content&gt;total Zn&gt;pH&gt;total C&gt;total N. Inclusion of DTPA extractable Zn in the model, rather than citric acid, resulted in a slightly lower precision in the prediction of phytoavailable Zn (R2=0·86, P&lt;0·001). A corresponding model for phytoavailable Cu, determined by DTPA and soil parameters, gave less precise prediction (R2=0·59, P&lt;0·01). DTPA was more suitable for predicting phytoavailable Cu than citric acid. Plant Mn concentration could not be correlated to any of the extracting agents.

Soil Factors Affecting Plant Concentrations of Cadmium, Copper, and Zinc on Sludge‐Amended Soils

Establishment of maximum cumulative metal loading rates of sludge require metals in soils be related to metal concentrations in plants grown on those soils. The relationship between plant concentrations of Cd, Cu, and Zn and soil properties on sites of long-term municipal sludge application were evaluated. Swiss chard (Beta vulgaris L.) and lettuce (Lactuca sativa L.) were planted on five fields (Typic Hapludolts) with known sludge disposal history. After harvest, tobacco (Nicotina tabacum L.) following swiss chard and peanut (Arachis hypogea L.) following lettuce were planted in the same plots. Extractable Cd, Cu, and Zn (Mehlich-3, 0.05 M DTPA, 0.05 M EDTA), clay, humic matter, organic C, and pH were determined on a composite 20-cm depth soil sample and Mehlich-3 extractable metals were determined by 15-cm increments on a 60-cm depth sample from each plot. Tissue concentrations of Cd, Cu, and Zn were measured near harvest maturity (swiss chard and lettuce) or shortly after anthesis (tobacco and peanut). Simple linear regressions were poor between plant metal concentration and soil-extractable metal for all extractants. Inclusion of soil properties in the best-fit multivariate regression models improved the relationship for metal concentration in plants. Values for R2 ranged from 0.30 to 0.96 with the poorest correlations obtained for Cu in lettuce and peanut. Best-fit models for all other crop-extractant-metal combinations had R2 > 0.83. Metals extracted by the three extractants were correlated with each other so their inclusion in models describing plant concentrations of heavy metal in this study gave similar results.