Incorporating Legacy Soil pH Databases into Digital Soil Maps

Soil data and reliable soil maps are imperative for environmental management. conservation and policy. Data from historical point surveys, e.g. experiment site data and farmers fields can serve this purpose. However, legacy soil information is not necessarily collected for spatial analysis and mapping such that the data may not have immediately useful geo-references. Methods are required to utilise these historical soil databases so that we can produce quantitative maps of soil propel-ties to assess spatial and temporal trends but also to assess where future sampling is required. This paper discusses two such databases: the Representative Soil Sampling Scheme which has monitored the agricultural soil in England and Wales from 1969 to 2003 (between 400 and 900 bulked soil samples were taken annually from different agricultural fields); and the former State Chemistry Laboratory, Victoria, Australia where between 1973 and 1994 approximately 80,000 soil samples were submitted for analysis by farmers. Previous statistical analyses have been performed using administrative regions (with sharp boundaries) for both databases, which are largely unrelated to natural features. For a more detailed spatial analysis that call be linked to climate and terrain attributes, gradual variation of these soil properties should be described. Geostatistical techniques such as ordinary kriging are suited to this. This paper describes the format of the databases and initial approaches as to how they can be used for digital soil mapping. For this paper we have selected soil pH to illustrate the analyses for both databases.

Sampling the soil in long-term forest plots: The implications of spatial variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.

Soil carbon in the Jena Experiment from intensive sampling campaigns (only block 2 Main Experiment up to 1 m depth, year 2007)

This data set contains soil carbon measurements (Organic carbon, inorganic carbon, and total carbon; all measured in dried soil samples) from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. Stratified soil sampling to a depth of 1 m was repeated in April 2007 (as had been done before sowing in April 2002). Three independent samples per plot were taken of all plots in block 2 using a motor-driven soil column cylinder (Cobra, Eijkelkamp, 8.3 cm in diameter). Soil samples were dried at 40°C and segmented to a depth resolution of 5 cm giving 20 depth subsamples per core. All samples were analyzed independently. All soil samples were passed through a sieve with a mesh size of 2 mm. Because of much higher proportions of roots in the soil, the samples in 2007 were further sieved to 1 mm according to common root removal methods. No additional mineral particles were removed by this procedure. Total carbon concentration was analyzed on ball-milled subsamples (time 4 min, frequency 30 s**-1) by an elemental analyzer at 1150°C (Elementaranalysator vario Max CN; Elementar Analysensysteme GmbH, Hanau, Germany). We measured inorganic carbon concentration by elemental analysis at 1150°C after removal of organic carbon for 16 h at 450°C in a muffle furnace. Organic carbon concentration was calculated as the difference between both measurements of total and inorganic carbon.

Soil carbon in the Jena Experiment from intensive sampling campaigns (only block 2 Main Experiment up to 1 m depth, year 2002)

This data set contains soil carbon measurements (Organic carbon, inorganic carbon, and total carbon; all measured in dried soil samples) from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. Stratified soil sampling to a depth of 1 m was performed before sowing in April 2002. Three independent samples per plot were taken of all plots in block 2 using a motor-driven soil column cylinder (Cobra, Eijkelkamp, 8.3 cm in diameter). Soil samples were dried at 40°C and segmented to a depth resolution of 5 cm giving 20 depth subsamples per core. All samples were analyzed independently. All soil samples were passed through a sieve with a mesh size of 2 mm. Rarely present visible plant remains were removed using tweezers. Total carbon concentration was analyzed on ball-milled subsamples (time 4 min, frequency 30 s**-1) by an elemental analyzer at 1150°C (Elementaranalysator vario Max CN; Elementar Analysensysteme GmbH, Hanau, Germany). We measured inorganic carbon concentration by elemental analysis at 1150°C after removal of organic carbon for 16 h at 450°C in a muffle furnace. Organic carbon concentration was calculated as the difference between both measurements of total and inorganic carbon.

A modular traffic sampling architecture for flexible network measurements

Dissertação de Mestrado (Programa Doutoral em Informática)

Building energy saving techniques and indoor air quality : a dilemma

In European countries and North America, people spend 80 to 90% of time inside buildings and thus breathe indoor air. In Switzerland, special attention has been devoted to the 16 stations of the national network of observation of atmospheric pollutants (NABEL). The results indicate a reduction in outdoor pollution over the last ten years. With such a decrease in pollution over these ten years the question becomes: how can we explain an increase of diseases? Indoor pollution can be the cause. Indoor contaminants that may create indoor air quality (IAQ) problems come from a variety of sources. These can include inadequate ventilation, temperature and humidity dysfunction, and volatile organic compounds (VOCs). The health effects from these contaminants are varied and can range from discomfort, irritation and respiratory diseases to cancer. Among such contaminants, environmental tobacco smoke (ETS) could be considered the most important in terms of both health effects and engineering controls of ventilation. To perform indoor pollution monitoring, several selected ETS tracers can be used including carbon monoxide (CO), carbon dioxide (CO2), respirable particles (RSP), condensate, nicotine, polycyclic aromatic hydrocarbons (PAHs), nitrosamines, etc. In this paper, some examples are presented of IAQ problems that have occurred following the renewal of buildings and energy saving concerns. Using industrial hygiene sampling techniques and focussing on selected priority pollutants used as tracers, various problems have been identified and solutions proposed. [Author]

Spatial patterns of preconsolidation pressure and soil moisture along transects in two directions under coffee

Information on the spatial structure of soil physical and structural properties is needed to evaluate the soil quality. The purpose of this study was to investigate the spatial behavior of preconsolidation pressure and soil moisture in six transects, three selected along and three across coffee rows, at three different sites under different tillage management systems. The study was carried out on a farm, in Patrocinio, state of Minas Gerais, in the Southeast of Brazil (18 º 59 ' 15 '' S; 46 º 56 ' 47 '' W; 934 m asl). The soil type is a typic dystrophic Red Latosol (Acrustox) and consists of 780 g kg-1 clay; 110 g kg-1 silt and 110 g kg-1 sand, with an average slope of 3 %. Undisturbed soil cores were sampled at a depth of 0.10-0.13 m, at three different points within the coffee plantation: (a) from under the wheel track, where equipment used in farm operations passes; (b) in - between tracks and (c) under the coffee canopy. Six linear transects were established in the experimental area: three transects along and three across the coffee rows. This way, 161 samples were collected in the transect across the coffee rows, from the three locations, while 117 samples were collected in the direction along the row. The shortest sampling distance in the transect across the row was 4 m, and 0.5 m for the transect along the row. No clear patterns of the preconsolidation pressure values were observed in the 200 m transect. The results of the semivariograms for both variables indicated a high nugget value and short range for the studied parameters of all transects. A cyclic pattern of the parameters was observed for the across-rows transect. An inverse relationship between preconsolidation pressure and soil moisture was clearly observed in the samples from under the track, in both directions.

Spatial relationships between soil attributes and corn yield in no-tillage system

Soil properties play an important role in spatial variability of crop yield. However, a low spatial correlation has generally been observed between maps of crop yield and of soil properties. The objectives of the present investigation were to assess the spatial pattern variability of soil properties and of corn yield at the same sampling intensity, and evaluate its cause-and-effect relationships. The experimental site was structured in a grid of 100 referenced points, spaced at 10 m intervals along four parallel 250 m long rows spaced 4.5 m apart. Thus, points formed a rectangle containing four columns and 25 rows. Therefore, each sampling cell encompassed an area of 45 m² and consisted of five 10 m long crop rows, in which the referenced points represented the center. Samples were taken from the layers 0-0.1 m and 0.1-0.2 m. Soil physical and chemical properties were evaluated. Statistical analyses consisted of data description and geostatistics. The spatial dependence of corn yield and soil properties was confirmed. The hypothesis of this study was confirmed, i.e., when sampling the soil to determine the values of soil characteristics at similar to sampling intensity as for crop yield assessments, correlations between the spatial distribution of soil characteristics and crop yield were observed. The spatial distribution pattern of soil properties explained 65 % of the spatial distribution pattern of corn yield. The spatial distribution pattern of clay content and percentage of soil base saturation explained most of the spatial distribution pattern of corn yield.

Characterization of soil chemical properties of strawberry fields using principal component analysis

One of the largest strawberry-producing municipalities of Rio Grande do Sul (RS) is Turuçu, in the South of the State. The strawberry production system adopted by farmers is similar to that used in other regions in Brazil and in the world. The main difference is related to the soil management, which can change the soil chemical properties during the strawberry cycle. This study had the objective of assessing the spatial and temporal distribution of soil fertility parameters using principal component analysis (PCA). Soil sampling was based on topography, dividing the field in three thirds: upper, middle and lower. From each of these thirds, five soil samples were randomly collected in the 0-0.20 m layer, to form a composite sample for each third. Four samples were taken during the strawberry cycle and the following properties were determined: soil organic matter (OM), soil total nitrogen (N), available phosphorus (P) and potassium (K), exchangeable calcium (Ca) and magnesium (Mg), soil pH (pH), cation exchange capacity (CEC) at pH 7.0, soil base (V%) and soil aluminum saturation(m%). No spatial variation was observed for any of the studied soil fertility parameters in the strawberry fields and temporal variation was only detected for available K. Phosphorus and K contents were always high or very high from the beginning of the strawberry cycle, while pH values ranged from very low to very high. Principal component analysis allowed the clustering of all strawberry fields based on variables related to soil acidity and organic matter content.

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

Variability of soil fertility properties in areas planted to sugarcane in the State of Goias, Brazil

Soil sampling should provide an accurate representation of a given area so that recommendations for amendments of soil acidity, fertilization and soil conservation may be drafted to increase yield and improve the use of inputs. The aim of this study was to evaluate the variability of soil fertility properties of Oxisols in areas planted to sugarcane in the State of Goias, Brazil. Two areas of approximately 8,100 m² each were selected, representing two fields of the Goiasa sugarcane mill in Goiatuba. The sugarcane crop had a row spacing of 1.5 m and subsamples were taken from 49 points in the row and 49 between the row with a Dutch auger at depths of 0.0-0.2 and 0.2-0.4 m, for a total of 196 subsamples for each area. The samples were individually subjected to chemical analyses of soil fertility (pH in CaCl2, potential acidity, organic matter, P, K, Ca and Mg) and particle size analysis. The number of subsamples required to compose a sample within the acceptable ranges of error of 5, 10, 20 and 40 % of each property were computed from the coefficients of variation and the Student t-value for 95 % confidence. The soil properties under analysis exhibited different variabilities: high (P and K), medium (potential acidity, Ca and Mg) and low (pH, organic matter and clay content). Most of the properties analyzed showed an error of less than 20 % for a group of 20 subsamples, except for P and K, which were capable of showing an error greater than 40 % around the mean. The extreme variability in phosphorus, particularly at the depth of 0.2-0.4 m, attributed to banded application of high rates of P fertilizers at planting, places limitations on assessment of its availability due to the high number of subsamples required for a composite sample.

ORGANIC PHOSPHORUS FRACTIONS IN SOIL FERTILIZED WITH CATTLE MANURE

Inorganic phosphorus (Pi) usually controls the P availability in tropical soils, but the contribution of organic P (Po) should not be neglected, mainly in systems with low P input or management systems that promote organic matter accumulation. The aims of this study were to evaluate the changes in the Po fractions over time in soil fertilized and not fertilized with cattle manure and to correlate Po forms with available P extracted by anion exchange resin. The experiment was carried out under field conditions, in a sandy-clay loam Haplustox. The experimental design was a 2 × 9 randomized complete block factorial design, in which the first factor was manure application (20 t ha-1) or absence, and the second the soil sampling times (3, 7, 14, 21, 28, 49, 70, 91, and 112 days) after manure incorporation. Labile, moderately labile and non-labile Po fractions were determined in the soil material of each sampling. Manure fertilization increased the Po levels in the moderately labile and non-labile fractions and the total organic P, but did not affect the Po fraction proportions in relation to total organic P. On average, 5.1 % of total Po was in the labile, 44.4 % in the moderately labile and 50.5 % in the non-labile fractions. Available P (resin P) was more affected by the manure soluble Pi rather than by the labile Po forms. The labile and non-labile Po fractions varied randomly with no defined trend in relation to the samplings; for this reason, the data did not fit any mathematical model.

SPATIAL CORRELATION BETWEEN PHYSICAL PROPERTIES OF SOIL AND WEEDS IN TWO MANAGEMENT SYSTEMS

The spatial correlation between soil properties and weeds is relevant in agronomic and environmental terms. The analysis of this correlation is crucial for the interpretation of its meaning, for influencing factors such as dispersal mechanisms, seed production and survival, and the range of influence of soil management techniques. This study aimed to evaluate the spatial correlation between the physical properties of soil and weeds in no-tillage (NT) and conventional tillage (CT) systems. The following physical properties of soil and weeds were analyzed: soil bulk density, macroporosity, microporosity, total porosity, aeration capacity of soil matrix, soil water content at field capacity, weed shoot biomass, weed density, Commelina benghalensis density, and Bidens pilosa density. Generally, the ranges of the spatial correlations were higher in NT than in CT. The cross-variograms showed that many variables have a structure of combined spatial variation and can therefore be mapped from one another by co-kriging. This combined variation also allows inferences about the physical and biological meanings of the study variables. Results also showed that soil management systems influence the spatial dependence structure significantly.

Changes in Soil Organic Carbon Fractions in Response to Cover Crops in an Orange Orchard

ABSTRACT The cultivation of cover crops intercropped with fruit trees is an alternative to maintain mulch cover between plant rows and increase soil organic carbon (C) stocks. The objective of this study was to evaluate changes in soil total organic C content and labile organic matter fractions in response to cover crop cultivation in an orange orchard. The experiment was performed in the state of Bahia, in a citrus orchard with cultivar ‘Pera’ orange (Citrus sinensis) at a spacing of 6 × 4 m. A randomized complete block design with three replications was used. The following species were used as cover crops: Brachiaria (Brachiaria decumbes) – BRAQ, pearl millet (Pennisetum glaucum) – MIL, jack bean (Canavalia ensiformis) – JB, blend (50 % each) of jack bean + millet (JB/MIL), and spontaneous vegetation (SPV). The cover crops were broadcast-seeded between the rows of orange trees and mechanically mowed after flowering. Soil sampling at depths of 0.00-0.10, 0.10-0.20, and 0.20-0.40 m was performed in small soil trenches. The total soil organic C (SOC) content, light fraction (LF), and the particulate organic C (POC), and oxidizable organic C fractions were estimated. Total soil organic C content was not significantly changed by the cover crops, indicating low sensitivity in reacting to recent changes in soil organic matter due to management practices. Grasses enabled a greater accumulation of SOC stocks in 0.00-0.40 m compared to all other treatments. Jack bean cultivation increased LF and the most labile oxidizable organic C fraction (F1) in the soil surface and the deepest layer tested. Cover crop cultivation increased labile C in the 0.00-0.10 m layer, which can enhance soil microbial activity and nutrient absorption by the citrus trees. The fractions LF and F1 may be suitable indicators for monitoring changes in soil organic matter content due to changes in soil management practices.

Soil Physical Quality and Soybean Yield as Affected by Chiseling and Subsoiling of a No-Till Soil

ABSTRACT The concept of soil physical quality (SPQ) is currently under discussion, and an agreement about which soil physical properties should be included in the SPQ characterization has not been reached. The objectives of this study were to evaluate the ability of SPQ indicators based on static and dynamic soil properties to assess the effects of two loosening treatments (chisel plowing to 0.20 m [ChT] and subsoiling to 0.35 m [DL]) on a soil under NT and to compare the performance of static- and dynamic-based SPQ indicators to define soil proper soil conditions for soybean yield. Soil sampling and field determinations were carried out after crop harvest. Soil water retention curve was determined using a tension table, and field infiltration was measured using a tension disc infiltrometer. Most dynamic SPQ indicators (field saturated hydraulic conductivity, K0, effective macroporosity, εma, total connectivity and macroporosity indexes [CwTP and Cwmac]) were affected by the studied treatments, and were greater for DL compared to NT and ChT (K0 values were 2.17, 2.55, and 4.37 cm h-1 for NT, ChT, and DL, respectively). However, static SPQ indicators (calculated from the water retention curve) were not capable of distinguishing effects among treatments. Crop yield was significantly lower for the DL treatment (NT: 2,400 kg ha-1; ChT: 2,358 kg ha-1; and DL: 2,105 kg ha1), in agreement with significantly higher values of the dynamic SPQ indicators, K0, εma, CwTP, and Cwmac, in this treatment. The results support the idea that SPQ indicators based on static properties are not capable of distinguishing tillage effects and predicting crop yield, whereas dynamic SPQ indicators are useful for distinguishing tillage effects and can explain differences in crop yield when used together with information on weather conditions. However, future studies, monitoring years with different weather conditions, would be useful for increasing knowledge on this topic.