Including Soil Variables Research Articles

Management zone delineation based on soil properties measured during the reproductive stage of rice in the field

AbstractIdentification of key soil properties is an essential prerequisite to delineating the management zone (MZ), which is traditionally done by selecting soil parameters before crop planting. However, soil properties during sensitive stages of crop growth may have a greater effect on the MZ delineation than those before planting. This study is aimed to determine whether inclusion of soil properties measured in surface layers during the reproductive stage of rice could enhance the appropriateness of MZ delineation based on sodic/saline‐sodic paddy fields. When including soil variables during the reproductive stage, with/without soil variables before planting, a similar MZ pattern (MZ pattern 3) was generated. Furthermore, when including soil variables during the reproductive stage and rice yield components, with/without soil variables before planting, another similar MZ pattern (MZ Pattern 4) was generated. Compared with other patterns, the MZ pattern 4 showed higher relative variances for rice yield and the mean of its most important yield‐influencing factors, with lower coefficient of variation (CV) in all MZs compared with the whole‐paddock CV. Likewise, this pattern showed improvement in discriminating soil and rice variables among MZs. These findings confirm that inclusion of soil properties during the reproductive stage of rice has potential for enhancing the appropriateness of MZ delineation. This study also necessitates inclusion of carbonate‐derived variables when delineating MZs in sodic/saline‐sodic paddy fields. Our results encourage further investigation of the role of soil properties in surface soil layers at critical crop growth stages in MZ delineation under multiple soil and climatic conditions.

Exploring the Niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae), Using Multiple Species Distribution Model Approaches.

The American dog tick, Dermacentor variabilis (Say) (Acari: Ixodidae), is a vector for several human disease-causing pathogens such as tularemia, Rocky Mountain spotted fever, and the understudied spotted fever group rickettsiae (SFGR) infection caused by Rickettsia montanensis. It is important for public health planning and intervention to understand the distribution of this tick and pathogen encounter risk. Risk is often described in terms of vector distribution, but greatest risk may be concentrated where more vectors are positive for a given pathogen. When assessing species distributions, the choice of modeling framework and spatial layers used to make predictions are important. We first updated the modeled distribution of D. variabilis and R. montanensis using maximum entropy (MaxEnt), refining bioclimatic data inputs, and including soil variables. We then compared geospatial predictions from five species distribution modeling frameworks. In contrast to previous work, we additionally assessed whether the R. montanensis positive D. variabilis distribution is nested within a larger overall D. variabilis distribution, representing a fitness cost hypothesis. We found that 1) adding soil layers improved the accuracy of the MaxEnt model; 2) the predicted ‘infected niche’ was smaller than the overall predicted niche across all models; and 3) each model predicted different sizes of suitable niche, at different levels of probability. Importantly, the models were not directly comparable in output style, which could create confusion in interpretation when developing planning tools. The random forest (RF) model had the best measured validity and fit, suggesting it may be most appropriate to these data.

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.

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

BackgroundShrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent.MethodsThis study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only.ResultsThe predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables.ConclusionsOur study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants.

A spatial epidemiological analysis of nontuberculous mycobacterial infections in Queensland, Australia.

BackgroundThe epidemiology of infections with nontuberculous mycobacteria (NTM) has been changing and the incidence has been increasing in some settings. The main route of transmission to humans is considered to be from the environment. We aimed to describe spatial clusters of cases of NTM infections and to identify associated climatic, environmental and socio-economic variables.MethodsNTM data were obtained from the Queensland Mycobacterial Reference Laboratory for the period 2001–2011. A Bayesian spatial conditional autoregressive model was constructed at the postcode level, with covariates including soil variables, maximum, mean and minimum rainfall and temperature, income (proportion of population earning < $32,000 and < $52,000) and land use category.ResultsSignificant clusters of NTM infection were identified in the central Queensland region overlying the Surat sub-division of the Great Artesian Basin, as well as in the lower North Queensland Local Government Area known as the Whitsunday region. Our models estimated an expected increase of 21% per percentage increase of population earning < $52,000 (95% CI 9–34%) and an expected decrease of 13% for every metre increase of average topsoil depth for risk of Mycobacterium intracellulare infection (95% CI -3 – -22%). There was an estimated increase of 79% per mg/m3 increase of soil bulk density (95% CI 26–156%) and 19% decrease for every percentage increase in population earning < $32,000 for risk of M. kansasii infection (95% CI -3 – -49%).ConclusionsThere were distinct spatial clusters of M. kansasii, M. intracellulare and M. abscessus infections in Queensland, and a number of socio-ecological, economic and environmental factors were found to be associated with NTM infection risk.

Better environmental data may reverse conclusions about niche‐ and dispersal‐based processes in community assembly

Variation partitioning of species composition into components explained by environmental and spatial variables is often used to identify a signature of niche- and dispersal-based processes in community assembly. Such interpretation, however, strongly depends on the quality of the environmental data available. In recent studies conducted in forest dynamics plots, the environment was represented only by readily available topographical variables. Using data from a subtropical broad-leaved dynamics plot in Taiwan, we focus on the question of how would the conclusion about importance of niche- and dispersal-based processes change if soil variables are also included in the analysis. To gain further insight, we introduced multiscale decomposition of a pure spatial component [c] in variation partitioning. Our results indicate that, if only topography is included, dispersal-based processes prevail, while including soil variables reverses this conclusion in favor of niche-based processes. Multiscale decomposition of [c] shows that if only topography was included, broad-scaled spatial variation prevails in [c], indicating that other as yet unmeasured environmental variables can be important. However, after also including soil variables this pattern disappears, increasing importance of meso- and fine-scaled spatial patterns indicative of dispersal processes.

Soil carbon stocks and soil solution chemistry in Quercus ilex stands in Mainland Spain

The quantification of terrestrial carbon pools is important for the modeling of carbon fluxes in forest ecosystems. As a contribution to the understanding of the factors that influence the carbon sequestration capacity of Mediterranean forest soils, the present study focuses on the quantification of the superficial soil carbon stocks in evergreen oak stands (Quercus ilex L.) representative of its distribution area in Mainland Spain and to analyze the influence of site factors (climate and topography) and the soil chemical properties in the topsoil carbon storage capacity. For that purpose, 103 Quercus ilex stands were studied grouped in four main formations: 40 wooded grassland ecosystems with scattered oak trees (dehesas), 14 open Holm oak stands, 28 mixed Holm oak forests and 21 dense Holm oak forests. The soil organic carbon stocks (SOCS) in the upper organo-mineral soil layer ranged between 1.4–15.6 kg m−2 and total soil carbon stocks (TSCS) between 1.4–17.9 kg m−2. Tree density was a significant factor for SOC storage in the soil. The wooded grassland dehesas presented the minimum superficial SOC stocks (3.6 kg m−2), while dense Holm oak forests reached the maximum average values (7.6 kg m−2). Maximum SOCS (>10 kg m−2) were measured in the mixed and dense Holm oak forests over soils with calcareous substrates. Summer mean temperature (R = −0.46; P < 0.001) was the climatic variable that most influenced the SOCS. Soil properties had stronger positive correlations with SOCS than site factors: nitrogen concentrations (R = 0.70; P < 0.001), clay content (R = 0.62; P < 0.001), soluble calcium (R = 0.60; P < 0.001) and magnesium (R = 0.42; P < 0.001). Climatic and topographic variables together explained 30 % of the SOCS variability. An increase up to 63 % was obtained by including soil variables. Under Mediterranean climate conditions, the soil properties that enhance the organic matter protection achieve a notable relevance. The soil carbon storage is favored by large organic matter inputs, high soil clay contents, a calcium-saturated soil matrix and reduced summer aridity.

Use of land facets to design linkages for climate change

Least-cost modeling for focal species is the most widely used method for designing conservation corridors and linkages. However, these linkages have been based on current species' distributions and land cover, both of which will change with large-scale climate change. One method to develop corridors that facilitate species' shifting distributions is to incorporate climate models into their design. But this approach is enormously complex and prone to error propagation. It also produces outputs at a grain size (km2) coarser than the grain at which conservation decisions are made. One way to avoid these problems is to design linkages for the continuity and interspersion of land facets, or recurring landscape units of relatively uniform topography and soils. This coarse-filter approach aims to conserve the arenas of biological activity rather than the temporary occupants of those arenas. In this paper, we demonstrate how land facets can be defined in a rule-based and adaptable way, and how they can be used for linkage design in the face of climate change. We used fuzzy c-means cluster analysis to define land facets with respect to four topographic variables (elevation, slope angle, solar insolation, and topographic position), and least-cost analysis to design linkages that include one corridor per land facet. To demonstrate the flexibility of our procedures, we designed linkages using land facets in three topographically diverse landscapes in Arizona, USA. Our procedures can use other variables, including soil variables, to define land facets. We advocate using land facets to complement, rather than replace, existing focal species approaches to linkage design. This approach can be used even in regions lacking land cover maps and is not affected by the bias and patchiness common in species occurrence data.