Phosphorus (P) is an essential macronutrient for plant growth and is mainly present in agricultural soil in unavailable forms. Phosphate-solubilizing microorganisms (PSMs) increase soil P availability. The objective of the present study was to assess the population and type of PSMs and their relationships with soil characteristics in the agricultural soil of Manokwari. Twenty-one composite soil samples (0–20 cm) were collected at the rhizospheres of plants in the Prafi and Masni Districts. A dilution technique and plate count method on Pikovskayas agar medium were used to examine the PSM population, phosphate-solubilizing index (PSI), and various soil properties. The results obtained showed that the total population of phosphate-solubilizing bacteria ranged between 25×10 3 and 550×10 3 CFU g –1 of soil, while that of phosphate-solubilizing fungi was between 2.0×10 3 and 5.0×10 3 CFU g –1 of soil at all locations. The PSI of the isolates ranged between 1.1 to 3.6 mm, with the most efficient and highest PSI being obtained for Bacillus sp. (strain 8) and the lowest for Pseudomonas sp. (strain 15). Six isolates found at all locations were identified at the genus level: Chromobacterium sp., Pseudomonas sp., Bacillus sp., Micrococcus sp., Caulobacter sp., and Aspergillus sp. A correlation was observed between the number of PSMs and the level of soil P availability and moisture content, indicating an increase in soil P availability with a greater abundance of PSMs in soil.
Most developing areal for peanut crop (Arachis hoypogeae) is upland, that is dominated by parent soil and has acid characteristis. Main constraints for this soil are pH and low soil productivity. Dolomite plus is a dolomite ameliorant, with phosphate nutrient. The dolomite plus beside as the ameliorant and a source of magnecium and calcium nutrients, also as a source of phosphate nutrient. The objective of the research was to study effectivity of dolomite plus on peanut growth in Inceptisols soil. This research was conducted in the greenhouse using a randomize completely designed with 8 treatments and 5 replications. The treatments were control, NPK, and combinations of NPK with six dolomite plus levels. Relative Agronomic Effectiveness (RAE) analyses was used determine to the effectivity of dolomite plus. The result showed that application of dolomite plus 1,600 kg ha -1 with NPK fertilizer increased dry weight of grain yield untill 27% (11.53 to 14.65 g plant -1 ) compared to NPK fertilizer application alone, that was showed by RAE > 100% or among 171-251%. Application of dolomite plus with NPK increased soil pH, soil available P (Bray 1), Ca and Mg exchangeable, and CEC as 1.9 unit; 6.2 mg kg -1 ; 15.87 cmol(+) kg -1 ; 14.27 cmol(+) kg -1 ; and 17.29 cmol(+) kg -1 respectively. Maximum rate of dolomite plus was 2,500 kg ha -1 with the yield was 14.2 g plant -1 grain dry weight. The rate of dolomite plus that was higher than 2,500 kg ha -1 could decrease the yield.
Djuuna IAF, Abbott LK, Van Niel K (2010) Predicting infectivity of Arbuscular Mycorrhizal fungi from soil variables using Generalized Additive . The objective of this study was to predict the infectivity of arbuscular mycorrhizal fungi (AM fungi), from field soil based on soil properties and land use history using generalized additive models (GAMs) and generalized linear models (GLMs). A total of 291 soil samples from a farm in Western Australia near Wickepin were collected and used in this study. Nine soil properties, including elevation, pH, EC, total C, total N, P, K, microbial biomass carbon, and soil texture, and land use history of the farm were used as independent variables, while the percentage of root length colonized (%RLC) was used as the dependent variable. GAMs parameterized for the percent of root length colonized suggested skewed quadratic responses to soil pH and microbial biomass carbon; cubic responses to elevation and soil K; and linear responses to soil P, EC and total C. The strength of the relationship between percent root length colonized by AM fungi and environmental variables showed that only elevation, total C and microbial biomass carbon had strong relationships. In general, GAMs and GLMs models confirmed the strong relationship between infectivity of AM fungi (assessed in a glasshouse bioassay for soil collected in summer prior to the first rain of the season) and soil properties.
Soil chemical, physical and biological analyses are a crucial but often expensive and time-consuming step in the characterization of soils. Rapid and accurate predictions and relatively simple methods are ideally needed for soil analysis. The objective of this study was to predict some soil properties (e.g. pH, EC, total C, total N,C/N, NH4-N, NO3-N, P, K, clay, silt, and sand and soil microbial biomass carbon) across the Wickepin farm during summer season using a Mid-Infra Red - Partial Least Square (MIR–PLS) method. The 291 soil samples were analyzed bothwith soil extraction procedure and MIR Spectrometer. Calibrations were developed between MIR spectral data and the results of soil extraction procedures. Results using the PLS-MIR showed that MIR-predicted values were almost as highly correlated to the measured value obtained by the soil extraction method of total carbon, total nitrogen and soil pH. Values for EC, NH4-N, NO3-N, C/N, P, K, clay, silt, sand, and soil microbial biomass carbon were not successfully predicted by the MIR – PLS technique. There was a tendency for these factors to correlate with the MIR predicted value, but the correlation values were very low. This study has confirmed that the MIR-PLS method can be used to predict some soil properties based on calibrations of MIR values.Keywords: MIR-Partial Least Square, MIR-Spectroscopy, soil properties
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.