Minimum Data Set Indicators Research Articles

Comparative Analysis of Soil Quality Assessment and Its Perception by Rice Farmers

The present study was conducted in three villages of district Budgam in the union territory of Jammu and Kashmir, to find out how farmers differentiate the quality of soils and to determine the level of concurrence between farmers perception and scientific assessment of soil quality. Five fields in each village were selected and ranked on the basis of soil quality indices computed from the minimum data set of indicators, including plant available nutrients N, P, K, Ca, Mg, S, OC, BD, WHC (water holding capacity), CEC (cation exchange capacity) as well as microbial count. The respondents ranked the same 5 selected fields on the bases of their experience and perceptions of soil quality. The study reveals that 58% of farmers ranked the best soils correctly whereas, the percentage of farmers who ranked 2nd, 3rd, 4th and 5th soils correctly was 40, 30, 40, and 45%, respectively. The study found that a greater number of farmers from the remotest village Dalwash were able to judge the soils properly, thereby indicating more profound knowledge and better cognitive abilities to understand soils in the local context. The results divulged by the current study highlight the remarkable local soil knowledge of the farmers and therefore, linking this knowledge system with scientific concepts would prove valuable for sustained land-use management.

Flood effects on soil quality in the southern and central foothills of Pathanamthitta district of Kerala

A study was undertaken to assess the quality of post-flood soils in AEU 12 of Pathanamthitta district and develop GIS maps depicting the spatial distribution of soil quality. A minimum data set of indicators for assessing soil quality was set up from seventeen selected physical, chemical and biological parameters analysed using principal component analysis. Six principal components with eigen value greater than 1 were selected and nine parameters with higher factor loadings were retained for the minimum data set viz., soil pH, available Mg, sand per cent, available K, available P, available B, acid phosphatase activity and silt per cent. A weighted soil quality index was formulated after assigning appropriate weights and scores to the selected indicators. Mean soil quality was found to be higher in areas with sediment deposition after the floods in the Pampa basin. Soil quality was observed to be medium for majority of the samples (54.7%) analysed.

Implementation and scale-up of physical activity and behavioural nutrition interventions: an evaluation roadmap

BackgroundInterventions that work must be effectively delivered at scale to achieve population level benefits. Researchers must choose among a vast array of implementation frameworks (> 60) that guide design and evaluation of implementation and scale-up processes. Therefore, we sought to recommend conceptual frameworks that can be used to design, inform, and evaluate implementation of physical activity (PA) and nutrition interventions at different stages of the program life cycle. We also sought to recommend a minimum data set of implementation outcome and determinant variables (indicators) as well as measures and tools deemed most relevant for PA and nutrition researchers.MethodsWe adopted a five-round modified Delphi methodology. For rounds 1, 2, and 3 we administered online surveys to PA and nutrition implementation scientists to generate a rank order list of most commonly used; i) implementation and scale-up frameworks, ii) implementation indicators, and iii) implementation and scale-up measures and tools. Measures and tools were excluded after round 2 as input from participants was very limited. For rounds 4 and 5, we conducted two in-person meetings with an expert group to create a shortlist of implementation and scale-up frameworks, identify a minimum data set of indicators and to discuss application and relevance of frameworks and indicators to the field of PA and nutrition.ResultsThe two most commonly referenced implementation frameworks were the Framework for Effective Implementation and the Consolidated Framework for Implementation Research. We provide the 25 most highly ranked implementation indicators reported by those who participated in rounds 1–3 of the survey. From these, the expert group created a recommended minimum data set of implementation determinants (n = 10) and implementation outcomes (n = 5) and reconciled differences in commonly used terms and definitions.ConclusionsResearchers are confronted with myriad options when conducting implementation and scale-up evaluations. Thus, we identified and prioritized a list of frameworks and a minimum data set of indicators that have potential to improve the quality and consistency of evaluating implementation and scale-up of PA and nutrition interventions. Advancing our science is predicated upon increased efforts to develop a common ‘language’ and adaptable measures and tools.

Soil quality index applied to Cucurbit production (Chaco, Argentina)

Soil quality (SQ) assessment is an important tool for monitoring the sustainability of production systems. The National Institute of Agricultural Technology (INTA) selected a minimum dataset of indicators (MDS) with the objective of generating a tool for diagnosis and monitoring of soils subject to land use changes. The MDS is composed of: total organic carbon, particulate organic carbon, total nitrogen, pH, bulk density and the wind erodible fraction. The objective of this work was to use the MDS and the Soil Quality Index (SQI) derived from it to describe the state of the soils intended for Cucurbit production in Chaco. The MDS was sufficiently sensitive to describe the state of the soils. The low and moderate SQ highlighted the need to incorporate soil conservation practices, given the low levels of organic matter and physical degradation. These practices would also contribute to the control of wind erosion, a process that is poorly managed in the area.<strong></strong>

A comparison of soil quality evaluation methods for Fluvisol along the lower Yellow River

Soil quality evaluation as a decision-making tool to improve understanding of soil quality is essential for grading croplands and adopting proper agricultural practices. Various methods of soil quality evaluation have been developed, which have occasionally generated inconsistent evaluation results between differing soil types. The applicability of these techniques is seldom tested before implementing an evaluation method on a specific soil region. Fluvisol is an important soil resource for agriculture in China, especially for irrigation districts along the lower Yellow River. In the present study, the soil quality of two typical agricultural counties (Yucheng and Kenli) along the lower Yellow River was evaluated using four commonly utilized methods. In the two counties, the overall spatial patterns of soil quality derived from the four methods were similar, with differences in details existing among these methods. The soil quality in Yucheng, ranging from moderate to high, is superior to that observed in Kenli, where salinity is the primary limiting factor. In addition, the applicability of soil quality evaluation methods on the Fluvisol was investigated. It was found that the integrated quality indexing-linear scoring (IQI–LS) and the Nemoro indexing-linear scoring (NQI–LS) methods were the most accurate and practical of the four methods studied. These methods, which are based on the total data set of indicators, show better performance for soil quality evaluation on a Fluvisol. Further, different evaluation methods based on the minimum data set of indicators were compared, considering both the accuracy of the evaluation and the economic cost of obtaining the soil data. The results from the present study indicate that the IQI–LS method based on the minimum data set of indictors is recommended for large-scale soil quality evaluations.

Evaluation of Soil Physical Quality Index S for Some Tropical and Temperate Medium-Textured Soils

The soil physical quality (SPQ) index S can provide inconsistent designations of SPQ and has a lack of consistency with other physical indicators for some soils. The aim of this study was to compare the suitability of S in identifying SPQ against 12 SPQ indicators, including water-release-related indicators, physical properties, and visual examinations. This study was conducted on medium-textured soil samples taken from tropical and temperate soils. Comparisons of SPQ class and relationships between indicators were used to judge the S SPQ designation. For the studied soils, S classified SPQ in the same way as other indicators when the condition of the soil was optimal or degraded but not when it was intermediate. This demonstrates that the proposed critical limits for S are not generally valid and do not apply for all soil conditions. Porosity parameters from the water release curve were more consistent indicators of SPQ than S. Our work also demonstrates that scores from visual examinations have at least similar resolution (P > 0.05) to the other indicators of SPQ evaluated. The use of S as an indicator to be considered as part of a minimum data set of indicators of SPQ assessment is less viable when other indicators such as bulk density, porosity, and visual examination are much more easily determined and more consistent than S. Therefore, it is too ambitious to consider that a unique indicator such as the S index could be used to evaluate SPQ as such.

Soil quality index of a crosby silt loam in central Ohio

• We assessed soil quality index using the scoring function analysis. • The saturated hydraulic conductivity was the key soil physical indicator for SQI. • The soil organic carbon was the key soil chemical indicator for SQI. • The SQI was significantly correlated with corn yield. Soil quality index (SQI) assessment is an important tool for evaluating land use and soil management practices in agroecosystems. Thus, the objectives of this research were to assess SQI using the scoring function analysis for different agricultural management practices, and to evaluate the effects of tillage and drainage systems on SQI of a crosby silt loam in central Ohio. Treatments included two tillage: no-till (NT), chisel tillage (CT), and two drainage factors: drainage ( D ) and no-drainage (ND). Three main steps were followed for the SQI assessment: (1) identification of the minimum data set of indicators, (2) indicator interpretation, and (3) integration of all indicator scores into one SQI value. The data showed that saturated hydraulic conductivity ( K sat ) and soil organic carbon (SOC) concentration were the most discriminating and key indicator for SQI assessment. The SQI was not significantly affected by subsurface drainage treatment ( D = 0.69 and ND = 0.70, P = 0.763). The results imply that long-term NT and subsurface drainage may not significantly alter the SQI because of the effects of weather condition, crop rotation, and weed-control. The SQI was significantly correlated with corn yield ( R = 0.62, P < 0.05; n = 12), suggesting that the SQI is an effective and useful tool for assessing the agronomic productivity. The SQI computed by the method described is a useful tool to synthesize the soil and agronomic information.

Pedotransfer functions to estimate soil bulk density for Northern Africa: Tunisia case

Countries should provide regularly national inventories of greenhouse gas emissions and sinks and, and for the agriculture and forestry sectors this comprise national estimates of soil organic carbon (C) stocks. Estimation of soil C stock requires soil bulk density (Db) values. However, direct measurement of Db is often lacking mainly for soils in arid and semi-arid conditions. Much effort has been made in finding alternative solution to predict Db, either improving in situ determinations, either improving estimation procedures based on other soil properties. Regression models or pedotransfer functions (PTFs) based on easily measured soil properties constitute an adequate tool to assess Db, since it needs a minimum data set of indicators. A forward stepwise multiple linear regression routine was used to predict Db from physico-chemical soil properties. In this study, a soil database was organised from published and unpublished data from Tunisia. The database consisted of 238 soil profiles corresponding to 707 soil horizons from Tunisia. A general regression model fitted with all the data showed that OC, Clay, coarse-Sand and pH were the principal contributors to Db prediction (R2 = 0.55, standard error of prediction = 0.14). Additional models based on different set of variables are also provided providing alternative solutions for different levels of soil information. Predictions of the models were often improved when the data were partitioned into groups by soil depth (0–40 and 40–100 cm) and soil orders. This study also showed that CaCO3 might be an important predictor for deeper soil horizon. The proposed PTFs for Tunisia might be useful for a larger range of soil from arid and sub arid regions.