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Comparison of soil quality index using three methods.

ABSTRACT: Assessment of management-induced changes in soil quality is important to sustaining high crop yield. A large diversity of cultivated soils necessitate identification development of an appropriate soil quality index (SQI) based on relative soil properties and crop yield. Whereas numerous attempts have been made to estimate SQI for major soils across the World, there is no standard method established and thus, a strong need exists for developing a user-friendly and credible SQI through comparison of various available methods. Therefore, the objective of this article is to compare three widely used methods to estimate SQI using the data collected from 72 soil samples from three on-farm study sites in Ohio. Additionally, challenge lies in establishing a correlation between crop yield versus SQI calculated either depth wise or in combination of soil layers as standard methodology is not yet available and was not given much attention to date. Predominant soils of the study included one organic (Mc), and two mineral (CrB, Ko) soils. Three methods used to estimate SQI were: (i) simple additive SQI (SQI-1), (ii) weighted additive SQI (SQI-2), and (iii) statistically modeled SQI (SQI-3) based on principal component analysis (PCA). The SQI varied between treatments and soil types and ranged between 0-0.9 (1 being the maximum SQI). In general, SQIs did not significantly differ at depths under any method suggesting that soil quality did not significantly differ for different depths at the studied sites. Additionally, data indicate that SQI-3 was most strongly correlated with crop yield, the correlation coefficient ranged between 0.74-0.78. All three SQIs were significantly correlated (r = 0.92-0.97) to each other and with crop yield (r = 0.65-0.79). Separate analyses by crop variety revealed that correlation was low indicating that some key aspects of soil quality related to crop response are important requirements for estimating SQI.

SUBMITTER: Mukherjee A

PROVIDER: S-EPMC4141864 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Although a number of different methods have been used to quantify soil bacteria, identifying the optimal method(s) for soil bacterial abundance is still in question. No single method exists for undertaking an absolute microbial count using culture-dependent methods (CDMs) or even culture-independent methods (CIMs). This study investigated soil storage and pretreatment methods for optimal bacterial counts. Appropriate storage temperature (4°C) and optimal pretreatment methods (sonication time for 3 min and centrifugation at 1400 g) were necessary to preserve bacterial cell viability and eliminate interference from soil particles. To better estimate soil bacterial numbers under various cellular state and respiration, this study also evaluated three CDMs (i.e., colony forming unit, spotting, and most probable number (MPN) and three CIMs (i.e., flow cytometry (FCM), epifluorescence microscopy (EM) count, and DNA quantitation). Each counting method was tested using 72 soil samples collected from a local arable farm site at three different depths (i.e., 10-20, 90-100, and 180-190 cm). Among all CDMs, MPN was found to be rapid, simple, and reliable. However, the number of bacteria quantified by MPN was 1-2 orders lower than that quantified by CIMs, likely due to the inability of MPN to count anaerobic bacteria. The DNA quantitation method appeared to overestimate soil bacterial numbers, which may be attributed to DNA from dead bacteria and free DNA in the soil matrix. FCM was found to be ineffective in counting soil bacteria as it was difficult to separate the bacterial cells from the soil particles. Dyes used in FCM stained the bacterial DNA and clay particles. The EM count was deemed a highly effective method as it provided information on soil mineral particles, live bacteria, and dead bacteria; however, it was a time-consuming and labor-intensive process. Combining both types of methods was considered the best approach to acquire better information on the characteristics of indigenous soil microorganisms (aerobic versus anaerobic, live versus dead).

Project description:Sleep quality decreases with aging and thus sleep complaints are prevalent in older adults, particularly for those with cognitive impairment and dementia. For older adults, emerging evidence suggests poor sleep quality increases risk of developing cognitive impairment and dementia. Given the aging population-and the impending economic burden associated with increasing numbers of dementia patients-there is pressing need to improve sleep quality among older adults. As such, research efforts have increased focus on investigating the association between age-related sleep changes and cognitive decline in older adults. Sleep quality is a complex construct to evaluate empirically, and yet the Pittsburg Sleep Quality Index (PSQI) is commonly used in studies as their only measure of sleep quality. Furthermore, the PSQI may not be the best sleep quality measure for older adults, due to its reliance on the cognitive capacity to reflect on the past month. Further study is needed to determine the PSQI's validity among older adults. Thus, the current study examined sleep quality for 78 community dwelling adults 55+ to determine the PSQI's predictive validity for objective sleep quality (as measured by actigraphy). We compared two subjective measures of sleep quality-the PSQI and Consensus Sleep Diary (CSD)-with actigraphy (MotionWatch 8©; camntech). Our results suggest perceived sleep quality is quite different from objective reality, at least for adults 55+. Importantly, we show this difference is unrelated to age, gender, education, or cognitive status (assessed using standard screens). Previous studies have shown the PSQI to be a valuable tool for assessing subjective sleep quality; however, our findings indicate for older adults the PSQI should not be used as a substitute for actigraphy, or vice versa. Hence, we conclude best practice is to include both subjective and objective measures when examining sleep quality in older adults (i.e., the PSQI, CSD, and actigraphy).

Project description:BackgroundAssessing RNA quality is essential for gene expression analysis, as the inclusion of degraded samples may influence the interpretation of expression levels in relation to biological and/or clinical parameters. RNA quality can be analyzed by agarose gel electrophoresis, UV spectrophotometer, or microcapillary electrophoresis traces, and can furthermore be evaluated using different methods. No generally accepted recommendations exist for which technique or evaluation method is the best choice. The aim of the present study was to use microcapillary electrophoresis traces from the Bioanalyzer to compare three methods for evaluating RNA quality in 24 fresh frozen invasive breast cancer tissues: 1) Manual method = subjective evaluation of the electropherogram, 2) Ratio Method = the ratio between the 28S and 18S peaks, and 3) RNA integrity number (RIN) method = objective evaluation of the electropherogram. The results were also related to gene expression profiling analyses using 27K oligonucleotide microarrays, unsupervised hierarchical clustering analysis and ontological mapping.ResultsComparing the methods pair-wise, Manual vs. Ratio showed concordance (good vs. degraded RNA) in 20/24, Manual vs. RIN in 23/24, and Ratio vs. RIN in 21/24 samples. All three methods were concordant in 20/24 samples. The comparison between RNA quality and gene expression analysis showed that pieces from the same tumor and with good RNA quality clustered together in most cases, whereas those with poor quality often clustered apart. The number of samples clustering in an unexpected manner was lower for the Manual (n = 1) and RIN methods (n = 2) as compared to the Ratio method (n = 5). Assigning the data into two groups, RIN > or = 6 or RIN < 6, all but one of the top ten differentially expressed genes showed decreased expression in the latter group; i.e. when the RNA became degraded. Ontological mapping using GoMiner (p < or = 0.05; > or = 3 genes changed) revealed deoxyribonuclease activity, collagen, regulation of cell adhesion, cytosolic ribosome, and NADH dehydrogenase activity, to be the five categories most affected by RNA quality.ConclusionThe results indicate that the Manual and RIN methods are superior to the Ratio method for evaluating RNA quality in fresh frozen breast cancer tissues. The objective measurement when using the RIN method is an advantage. Furthermore, the inclusion of samples with degraded RNA may profoundly affect gene expression levels.

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Comparison of soil quality index using three methods.

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