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The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of crop types. Recent advances in remote-sensing technologies and data mining approaches of...

Management practices influence soil properties in both the seasonal short-term and multi-year long-term. Seasonal changes in soil compaction were assessed at the Ohio Triplett-Van Doren No-Tillage Experimental Plots, which have been operational for more than 55 years. Soil penetration resistance (PR), carbon, and nitrogen were measured from plots under plow tillage (PT), minimum tillage (MT), and no-tillage (NT) management in Hoytville and Wooster, Ohio. Water retention curves (WRC) and pore size distribution (PSD) were evaluated at the Wooster site only. Significant tillage treatment effects on PR were observed at both sites throughout the soil profile, but varied by sampling time. In the silt loam, NT systems showed higher PR than plow treatments until below the plow depth of 20 cm, where the presence of a plow pan was seen under the PT treatment. During the summer and fall these subsurface treatment effects were no longer present. The tillage effects were more pronounced in the silty clay loam soil, where NT plots were more compacted than MT and PT treatments throughout the profile. This was observed in all the spring, summer and fall sampling surveys. There were significant tillage treatment effects (p

Despite the truth that the agricultural land use planning exercises have so far covered small and fragmented part of the African continent, the involvement of farmers who are among the direct beneficiaries of the outputs have been limited. This work demonstrates the contributions of farmers on the land use planning process for rice production in Kilombero Valley, Tanzania. Analytic hierarchic process (AHP) was used to assign scores of comparative importance of attributes for a suitable land for rice production. Scoring was done by three groups: farmers, extension staff, and joint group comprising both farmers and extension staff. Joint group scores were considered more refined as they were generated by discussions and consensus between the two groups. Results showed that the three groups sequentially ranked the attributes the same. However, the attributes actual scores were different. The farmers’ scores were consistently close to the joint group’s scores compared to the extension staff group. The closeness suggests superiority and consistence of farmers’ perceptions of importance of the identified attributes used for this land use planning exercise. Thus, this study recommends more involvement of farmers in agricultural land use planning process for better and sustainable land use planning outputs. Key words: Kilombero Valley, rice, land use planning, Tanzania, Analytic Hierarchic Process.

Inadequacy of spatial soil information is one of the limiting factors to making evidence-based decisions to improve food security and land management in the developing countries. Various digital soil mapping (DSM) techniques have been applied in many parts of the world to improve availability and usability of soil data, but less has been done in Africa, particularly in Tanzania and at the scale necessary to make farm management decisions. The Kilombero Valley has been identified for intensified rice production. However the valley lacks detailed and up-to-date soil information for decision-making. The overall objective of this study was to develop a predictive soil map of a portion of Kilombero Valley using DSM techniques. Two widely used decision tree algorithms and three sources of Digital Elevation Models (DEMs) were evaluated for their predictive ability. Firstly, a numerical classification was performed on the collected soil profile data to arrive at soil taxa. Secondly, the derived taxa were spatially predicted and mapped following SCORPAN framework using Random Forest (RF) and J48 machine learning algorithms. Datasets to train the model were derived from legacy soil map, RapidEye satellite image and three DEMs: 1 arc SRTM, 30 m ASTER, and 12 m WorldDEM. Separate predictive models were built using each DEM source. Mapping showed that RF was less sensitive to the training set sampling intensity. Results also showed that predictions of soil taxa using 1 arc SRTM and 12 m WordDEM were identical. We suggest the use of RF algorithm and the freely available SRTM DEM combination for mapping the soils for the whole Kilombero Valley. This combination can be tested and applied in other areas which have relatively flat terrain like the Kilombero Valley.

International audience; Digital Soil Mapping and Assessment (DSMA) has progressed from challenging traditional soil science paradigms, through small scale prototyping, to large-scale implementation capturing quantitative measures of soil attributes and functions. This paper considers the future for DSMA in the context of a highly uncertain world where high-quality knowledge of soil dynamics will be important for responding to the challenges of sustainability. Irrespective of whether the need is for survival, increased productivity or broadening the services provided from land management, or simply securing the soil itself, we see DSMA as a fundamental approach and essential tool. With a broadening need and a strong foundation in the practice of DSMA now in place, the theory, tools and technology of DSMA will grow significantly. We explore expected changes in covariate data, the modelling process, the nature of base data generation and product delivery that will lead to tracking and forecasting a much wider range of soil attributes and functions at finer spatial and temporal resolutions over larger areas, particularly globally. Equally importantly, we expect the application and impact of DSMA to broaden and be used, directly and collaterally, in the analysis of land management issues in coming decades. It has the capacity to provide the background to a soil and landscape ‘digital twin’ and the consequent transformation in monitoring and forecasting the impacts of land management practices. We envision the continued growth of DSMA skills amongst soil scientists and a much broader community of practice involved in developing and utilizing DSMA products and tools. Consequently, there will be a widening and deepening role of public-private partnerships in this development and application.

As urbanization expands, interest in understanding soil properties and functions in urban environments has also increased. Urban soils play an important role in the global carbon (C) cycle. Due to natural and human influences on various forms of C, quantifying soil inorganic carbon (SIC) becomes critical in urban areas. In this study, the suitability of diffuse reflectance mid-infrared Fourier-transform spectroscopy (mid-DRIFTS) to rapidly estimate calcite and dolomite concentrations was tested. Also, the effectiveness of HCl acid pre-treatment to remove carbonates to enable proper estimation of soil organic carbon (SOC) in urban soils was evaluated. Specific mid-infrared frequencies known to be associated with carbonates, i.e. dolomite and calcite, were identified, and specific peak areas were measured and calibrated with results obtained with the volumetric calcimeter method. Multiple linear regression was used to estimate total carbonates with peaks areas at 2930 cm−1, 2515 cm−1 and 730 cm−1 resulting in a 3.72% root mean squared error of an independent validation set (RMSEV). Greater prediction accuracy of calcite (875 cm−1) and dolomite (875 cm−1 and 730 cm−1) were achieved using single peaks in bivariate relationships resulting in RMSEV of 1.27% and 4.22%, respectively. Soil samples were treated with 1 M HCl for 24 h to remove carbonates, and total carbonate, dolomite, and calcite contents were estimated again using both volumetric calcimeter and mid-DRIFTS. The reduction in carbonate concentrations post-acid treatment was considerable (97% for dolomite, and 96% for calcite and total carbonates), but was not complete. Thus, this could compromise quantification of other soil carbon fractions such as total SOC. Using mid-DRIFTS peak areas of CO3−2 functional groups represents a robust alternative for rapid, fast, and low-cost laboratory measurements of soil carbonates, and this high-throughput approach may be used in assays aiming to minimize carbonate interference in SOC estimations.

The objectives of this study were to evaluate several spectral indices for estimating soil tillage practices and determining crop residue cover using multispectral and Hyperspectral satellite image...

A study was conducted to examine the impact of synthetic calcium sulfite (CaSO3 · 0.5H2O) and calcium sulfate (CaSO4 · 2H2O, i.e., gypsum) from flue gas desulfurization (FGD) on soil enzyme activities, used as soil quality indicators, following land application. We used application rates of 0, 1.12 Mg ha−1 of FGD-CaSO3 (CaS-1.12T), 3.36 Mg ha−1 of FGD-CaSO3 (CaS-3.36T), and 1.12 Mg ha−1 of FGD-gypsum (Gyp-1.12T). The field experiments were conducted on a silt loam soil with 1.6 % total C and a pH of 6.5. No crop was grown during the experiment. The concentration of water soluble SO4 2−-S and the associated electrical conductivity (EC) significantly increased during the first 2 weeks following gypsum application but decreased later on (2–4 weeks) due to leaching. There was no significant difference between the Gyp-1.12T and CaS-1.12T treatment after approximately 2 weeks indicating all of the sulfite in the CaS-1.12T treatment was completely oxidized to sulfate. In contrast, the sulfite treated with highest application rate (CaS-3.36T) was not completely oxidized within the 12-week experimental period. Application of FGD-CaSO3 and FGD-CaSO4 did not negatively affect C and N mineralization because β-glucosidase and β-d-glucosaminidase activities did not show any negative change compared with the control. Arylsulfatase activity of the CaS-1.12T treatment markedly declined compared with Gyp-1.12T and control, but then increased after 2 weeks. The arylsulfatase activity reduction, however, persisted for the duration of the experiment in the CaS-3.36T treatment. These activity changes were similar to the observed oxidation pattern of sulfite to sulfate, which indicated that arylsulfatase activity could be used as an indicator for the extent of sulfite oxidation in soil.

Data mining from existing County soil surveys can improve the utility of maps for research, management and decision making. Aggregated soil series information in soil survey map units can be disaggregated by following a possibilistic decision tree approach to provide maps at the soil series level. The “overall map unit composition percentage” available in the soil survey tabular data can be treated as a possibility distribution of the prevalence of different soil series within all occurrences of the map unit in the survey area. A case study was conducted in Monroe County in southeastern Ohio. Three different learning approaches including C4.5 decision trees, nonspecificity based possibilistic decision trees and clustering based possibilistic decision trees were applied to the existent County survey, and the efficiency of predicting soils at the series level was assessed using an independent soil series point data set. The results showed an improvement in prediction accuracy by following the clustered possibilistic decision tree approach. The maps were useful in identifying the locations of component soil series within map unit associations, consociations, and complexes. Data mining from existent soil survey maps using the component information available in the tabular data can serve as a guide for disaggregating soil map units to create soil series maps, identifying misplacement of polygon boundaries, identifying presence of inclusions, and correcting mislabeled polygons, when updating soil surveys.

Synthetic gypsum, a by-product of electricity generation, is used as a soil amendment to overcome water ponding, improve soil and water quality, improve field conditions to support farm equipment, and reduce the variability of crop yield in no-till fields by improving hydrology. Gypsum is a source of soluble calcium (Ca) that improves physical properties of the soil by promoting clay aggregation, thereby increasing water infiltration rates and movement through the soil profile. Undisturbed soil samples from Brookston and Celina soils in Ohio, USA were collected to a depth of 75 cm in agricultural fields treated with gypsum for 0, 4, and 12 years to determine changes in chemical and physical properties. Gypsum applications increased exchangeable Ca and Ca: Mg ratios, and promoted clay flocculation and improved soil structure. Mean weight diameter of aggregates increased with gypsum treatment at most depths in both soils. Micromorphological analysis showed variations in porosity (ɸ), pore size distribution, pore shape and aggregate size related to gypsum treatment, soil, and soil depth. There were no consistent responses to years of gypsum application. Gypsum treated soils had higher porosity than untreated soils in all depths

Increased availability of flue gas desulfurization gypsum (FGDG) and the recognition of its potential to improve soil–water relationships have stimulated studies of its use on poorly drained soils with high seasonal rainfall, especially on no-tillage (NT) soils. Previous studies have focused on the effects of gypsum on hydrological properties at the soil surface (e.g. infiltration and soil crusting) but little information is available concerning the effects of surface-applied gypsum in profiles of non-sodic NT soils with contrasting drainage classes. To address this lack of information, undisturbed columns from two non-sodic NT soil profiles, characterized as being either moderately well drained or poorly drained, were collected from agricultural fields with historic information regarding number of years of gypsum application (i.e. 0, 4, and 12 yr). Alfalfa was planted in the columns and then plant growth, and soil chemical and hydrologic properties were measured. Higher Ca concentrations were found in both gypsum treated soils compared with control (CT) soils (P

Since the first documented soil survey in Tanzania by Milne (J Ecol 35:192–265, 1936), a number of other soil inventory exercises at different scales have been made. The main challenge has been the fragmented nature of the often outdated detailed soil maps and small-scale less-informative country-wide soil maps. Recent advances in information and computational technology have created vast potential to collect, map, harness, communicate and update soil information. These advances present favorable conditions to support the already popular shift from qualitative (conventional) to quantitative (digital) soil mapping (DSM). In this study, two decision tree machine learning algorithms, J48 and Random Forest (RF), were applied to digitally predict k-means numerically classified soil clusters to update a soil map produced in 1959. Predictors were derived from 1 arc SRTM digital elevation data and a 5 m RapidEye satellite image. J48 and RF predicted the soil units of the legacy maps with greater detail. However, RF showed superiority for predicting clusters J48 could not predict and for showing higher pixel contiguity. No significant difference (P = 0.05) was observed between the soil properties of the predicted soil clusters and the actual field validation points. Young soils (Entisols and Inceptisols) were found to occupy about 56 % of the study site’s 30,000 ha followed by Alfisols, Mollisols and Vertisols at 31, 9 and 4 %, respectively. This study demonstrates the usefulness of DSM techniques to update conventionally prepared legacy maps to offer soil information at improved detail to agricultural land use planners and decision makers of Tanzania to make evidence-based decisions for climate-resilient agriculture and other land uses.

Tree seedlings planted in containers along sidewalks in urban environments show restricted growth and development over time. This is the result of limited soil volume and soil compaction that hampers nutrient availability and water movement. Using tree species commonly used in urban forestry in Puerto Rico, this study was conducted to determine their growth response when planted in compacted soils. Seedlings of three ornamental tropical tree species, trumpet bush [Tecoma stans (L.) Juss. ex Kunth], bottle brush [Callistemon citrinus (Curtis) Skeels], and pink trumpet (Tabebuia rosea dc.) were transplanted into pots of sandy clay loam or clay soils at three levels of compaction: control (no compaction), 1.2 g/cm3 and 1.4 g/cm3 bulk density, respectively. Plant height, shoot diameter, leaf number and color, foliar area, and root, shoot, and leaf dry weights were measured on two plants every two months for six months. All species exhibited better growth in sandy clay loam at 1.2 g/cm3; after six months, all species showed a reduced root-shoot ratio. When planted in clay at 1.4 g/cm3, all trumpet bush seedlings died within two months. No leaf color differences were observed between species at different compaction levels. Bottle brush showed less growth suppression by increasing compaction level in both soils.

The use of high-sulfur coal for power generation in the United States requires the removal of sulfur dioxide (SO2) produced during burning in order to meet clean air regulations. If SO2 is removed from the flue gas using a wet scrubber without forced air oxidation, much of the S product created will be sulfite (SO3 2- ). Plants only take up S in the form of sulfate (SO4 2- ), and SO3 2- is toxic to plants and may cause damage to plant roots. For agricultural uses, SO3 2- in flue gas desulfurization (FGD) products must oxidize to SO4 2- in soils before crops are planted. However, there is little information about the oxidation of SO3 2- in FGD product to SO4 2- under field conditions. An FGDCaSO3 obtained from American Electric Power Company was applied at rates of 0, 1.12 and 3.36 Mg ha -1 to the surface of an agricultural soil (Wooster silt loam, Oxyaquic Fragiudalf). The SO4 2- in the surface soil (0-10 cm) was analyzed on days 3, 7, 17, 45, and 61. The distribution of SO4 2- and Ca in the 0-90 cm soil layer was also determined on day 61. Results indicated that SO3 2- in the FGD-CaSO3 rapidly oxidized to SO4 2- on the field surface during the first week and much of SO4 2- and Ca moved downward into the 0-50 cm soil layer during the experimental period of two months. It is safe for plants if FGD-CaSO3 is applied to the field surface one month before planting. However, application too long before planting might cause some of the S and Ca for plant uptake to be lost due to leaching or other soil processes.

The objective of this study was to predict and map SOC stocks at different depth intervals within the upper 1-m depth using profile depth distribution functions and ordinary kriging. These approaches were tested for the state of Indiana as a case study. A total of 464 pedons representing 204 soil series was obtained from the National Soil Survey Center database. Another 48 soil profile samples were collected to better represent the heterogeneity of the environmental variables. Two methods were used to model the depth distribution of the SOC stocks using negative exponential profile depth functions. In Procedure A, the functions to describe the depth distribution of volumetric C content for each soil profile were fitted using nonlinear least squares. In Procedure B, the exponential functions were fitted to describe the depth distribution of the cumulative SOC stocks. The parameters of the functions were interpolated for the entire study area using ordinary kriging on 81% of the data points (n = 414). The integral of the exponential function up to the desired depth was used to predict SOC stocks within the 0- to 1-, 0- to 0.5-, and 0.5- to 1-m depth intervals. These estimates were validated using the remaining 19% (n = 98) of the data. Procedure B showed a higher prediction accuracy for all depths, with higher r and lower RMSE values. The highest prediction accuracy (r = 0.75, RMSE = 2.89 kg m -2 ) was obtained for SOC stocks in the 0- to 0.5-m depth interval. Using Procedure B, SOC stocks within the top 1 m of Indiana soils were estimated to be 0.90 Pg C.

Knowledge of the spatial variability of soil physical and chemical properties is important for precision agriculture. Fuzzy classification and geostatistical interpolation are useful tools for the simultaneous handling of continuous variation in both attributes and location. The objectives of this study were: (i) to classify data to capture optimally in-field variability and (ii) to delineate contiguous areas for site-specific management. Several soil physical and chemical parameters for a loam soil were measured in the 0- to 15-cm depth of a 50- X 25-m grid at the experimental farm of the University of Agricultural Sciences, Vienna, Austria. Using Variowin and ArcGIS, models were fitted to variograms, and continuous maps of individual attributes were generated by spline smoothing. The coefficient of variation (CV) for soil bulk density (ρ b ), pH, and silt and clay contents was 35%. The nugget:sill ratio showed moderate spatial dependence for silt content, ρ b , SOC, K s , Φ m , EC, clay content, and TC (from 38% for silt content to 59% for TC) and weak spatial dependence for TN and pH (>75%). The nugget:sill ratio from covariograms showed moderate spatial dependence for K and TC, TC and clay content, ρ b and pH, TN and clay content, K s and Φ m , and ρ b and 0 (38 to 62%). Four classes captured total within-field variability optimally. Despite a low level of actual statistical variation in data, contiguous areas of each class were present in the field. The study demonstrated that by careful collection of data, site-specific management zones can be created for soil management within the field.

Spatial distribution information about soil organic C (SOC) pools at a proper scale is critical for developing feasible C sequestration programs. This study characterizes the spatial variation in SOC pools related to soil taxon, Major Land Resource Area (MLRA), and land use. Grouping data by the land uses associated with soil orders within each MLRA leads to a statewide average SOC pool of 10.2 ± 2.8 kg m -2 in the upper 1-m depth, ranging from 7.1 kg m -2 in Ultisols to 117 in Histosols (8.8,11.3,12.7, and 16.9 kg m -2 in Alfisols, Inceptisols, Entisols, and Mollisols, respectively), and geographically varying from 7.7 kg m -2 in MLRA 124 to 12.0 in both MLRA 99 and 111. These variations can be also partially attributed to the properties for sub-order differentiation. Moreover, land use effects are confounded by preferential selection of land for cropland use and site topographic features, resulting in a higher SOC pool in cropland (10.9 kg m -2 ) than in both forestland (9.5 kg in -2 ) and pastureland (8.4 kg m -2 ). The grand total SOC storage in Ohio ranges from 853 to 881 Tg (1 Tg = 10 12 g). The SOC pool is primarily related to landscape slope and soil drainage, and must be considered in interpretations for C sequestration potential among land uses for each soil taxon at the MLRA scale.

The PAD Trial is a prospective, multicenter, randomized clinical study testing whether volunteer, non-medical responders can improve survival from out-of-hospital cardiac arrest (OOH-CA) by using automated external defibrillators (AEDs). These lay volunteers, who have no traditional responsibility to respond to a medical emergency as part of their primary job description, will form part of a comprehensive, integrated community approach to the treatment of OOH-CA. The study is being conducted at 24 field centers in the United States and Canada. Approximately 1000 community units (e.g. apartment or office buildings, gated communities, sports facilities, senior centers, shopping malls, etc.) were randomized to treatment by trained laypersons who will provide either cardiopulmonary resuscitation (CPR) alone or CPR plus use of an AED, while awaiting arrival of the community's emergency medical services responders. The primary endpoint is the number of OOH-CA victims who survive to hospital discharge. Secondary endpoints include neurological status, health-related quality of life (HRQL), cost, and cost-effectiveness. Data collection will last approximately 15 months and is expected to be completed in September 2003.

Soil survey is a major component of forest land resource assessment. Conceptual and operational problems arise from employing the conventional methods of survey in forest lands, namely: implicit methods of landscape interpretation (lack of explicit procedures), transfer of data by analogy to unsampled landscapes by inferences which are scale-, and interpreter-dependent, variability of intuitive surveyor judgement, and poor expression of soil variation within map units. These issues are being addressed through the forestland resource assessment and modeling study (FRAMS). This study redefines the conceptual process of resource assessment, and applies soil–landscape modeling (developed here as regolith–terrain modeling) by developing explicit relationships between soil–landscape attributes within a digital, spatial geographic information system (GIS) framework. Soil survey (advanced here as regolith–terrain modeling) is the science and art of predicting soil attribute patterns in the 3D landscape. The FRAMS attempts to overcome some scale and procedural issues related to soil mapping in forest site assessment by adopting a multi-scale and explicit landscape modeling approach. The conceptual aspects of the method presented here aim to predict the ranges in variation of soil–geomorphic attributes that are relevant to forest plantation management. Soil–landscape analysis is adapted in this study to encompass regolith–terrain analysis (i.e. the complete regolith within an understanding of geomorphic systems) employed at three environmental scales: ‘hillslope’, ‘catenary’, and ‘landscape’. There is no linear relationship of data resolution and expression of regolith–terrain attributes between these scales. Each scale is a scale-dependent system linked by an explicit multi-scale method. When combined with geological and climatic data analysis the resultant model provides an advanced, stratified sampling scheme for subsequent field survey procedures in forestland resource assessment. The field analysis, remote-sensing and digital terrain model (DTM) analyses are managed in a raster GIS and can then be effectively classified, a posteriori, according to ‘fuzzy logic’ rules. In the FRAMS, we investigate the scale effect on both the regolith–terrain parameters and their notional relationships to forestland management by investigation at finer scales: hillslope and catenary scales (in southeast Queensland for planted native hoop pine (Araucaria cunninghamii)), and at a broader scale: the landscape scale (in north Queensland for native species reforestation). The study is still in the preliminary stages so the model is not yet fully functional nor have the components been validated so far.

Numerical classification methods may provide an alternative to manual landform delineation using aerial photographs, a subjective process that requires much knowledge of the landscape in question. Continuous classification (fuzzy set) methods and unsupervised (ISODATA) classification techniques were used to classify the landscape of a study area in southwestern Wisconsin, USA. Each pixel of a 10-m resolution digital elevation model (DEM) was grouped according to its membership in a continuous landform class. These classes were determined by the natural clustering of the data in attribute space. Attributes used for the classification were elevation, slope, profile and tangent (related to plan) curvature, compound topographic (wetness) index, and incident solar radiation. The ISODATA classification assigned pixels to one, and only one, landform class while the continuous classification allocated relative class memberships to each pixel. The resulting classifications roughly follow subjective manual delineation lines but give more detailed results. These classification methods may prove useful for statistical analyses and determination of sample schemes.

Summary Background The time to skill deterioration between primary training/retraining and further retraining in cardiopulmonary resuscitation (CPR) and automated external defibrillation (AED) for lay-persons is unclear. The Public Access Defibrillation (PAD) trial was a multi-center randomized controlled trial evaluating survival after CPR-only versus CPR + AED delivered by onsite non-medical volunteer responders in out-of-hospital cardiac arrest. Aims This sub-study evaluated the relationship of time between primary training/retraining and further retraining on volunteer performance during pretest AED and CPR skill evaluation. Methods Volunteers at 1260 facilities in 24 North American regions underwent training/retraining according to facility randomization, which included an initial session and a refresher session at approximately 6 months. Before the next retraining, a CPR and AED skill test was completed for 2729 volunteers. Primary outcome for the study was assessment of global competence of CPR or AED performance (adequate versus not adequate) using χ2-test for trends by time interval (3, 6, 9, and 12 months). Confirmatory (GEE) logistic regression analysis, adjusted for site and potential confounders was done. Results The proportion of volunteers judged to be competent did not diminish by interval (3, 6, 9, and 12 months) for either CPR or AED skills. After adjusting for site and potential confounders, longer intervals to further retraining was associated with a slightly lower likelihood of performing adequate CPR but not with AED scores. Conclusions After primary training/retraining, the CPR skills of targeted lay responders deteriorate nominally but 80% remain competent up to 1 year. AED skills do not deteriorate significantly and 90% of volunteers remain competent up to 1 year.

Background The Public Access Defibrillation (PAD) Trial was a randomized, controlled trial designed to measure survival to hospital discharge following out-of-hospital cardiac arrest (OOH-CA) in community facilities trained and equipped to provide PAD, compared with community facilities trained to provide cardiopulmonary resuscitation (CPR) without any capacity for defibrillation. Objectives To report the implementation of community-based lay responder emergency response programs in 1,260 participating facilities recruited for the PAD Trial in the United States and Canada. Methods This was a descriptive study of the characteristics of participating facilities, volunteers, and automated external defibrillator (AED) placements compiled by the PAD Trial, and a qualitative study of factors that facilitated or impeded implementation of emergency lay responder programs using focus groups of PAD Trial site coordinators. Results The PAD Trial enrolled 1,260 community facilities (14.8% residential), with 20,400 lay volunteers (mean +/- standard deviation = 13.4 +/- 10.7 per facility) trained to respond to OOH-CA. The 598 locations randomized to receive AEDs required 2.7 +/- 1.8 AEDs per facility. Volunteer attrition was high, 36% after two years. Barriers to recruitment and implementation included identification of appropriate "at-risk" facilities, lack of interest or fear of litigation by a facility key decision maker, lack of motivated potential volunteer responders, training and retraining resource requirements, and lack of an existing communication/response infrastructure. Conclusions These data indicate that implementation of community-based lay responder programs is feasible in many types of facilities, although these programs require substantial resources and commitment, and many barriers to implementation of effective PAD programs exist.

Soluble reactive phosphorus (SRP) in northwestern Ohio river water has declined over the past 20 yr in response to decreased applications of fertilizer P. Our objective was to evaluate changes in soluble P (Bray-1 P) levels in the soil over time as influenced by fertilizer P management, cultivation practice, soil properties, and landscape factors. Because soil is the intermediary between added P and SRP measured in river water, we examined the relationship between fertilizer P, soluble soil P, and SRP. Using historical soil survey sample sites as a baseline for original soluble P concentrations (P or ), we resampled Ap horizons to establish current levels of soluble P (P cu ). The P or baseline extended from 1953-1982 and P cu from 1996-1998. Thirty percent of the P cu values and 17% of the P or values were ≥40 mg kg -1 . Log-transformed means for P cu were significantly higher than for P or . The principal determining factors for P or were physiography, soil texture, and soil series. Current P is affected by present tillage practice and drainage class. Change in soluble P in the soil is not as responsive to fertilizer P sales as is SRP in river water. This suggests that as fertilizer P sales decline, a declining percentage of P added as fertilizer is annually dissolved and transported into the drainage system. Soluble P in soil is governed by a combination of fertilizer and tillage management, soil properties, and landscape factors interacting over time.