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.

Collection of data on soil carbon (particulate and dissolved) in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains three time series of measurements of soil carbon (particular and dissolved) 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. 1. Particulate soil carbon: Stratified soil sampling was performed every two years since before sowing in April 2002 and was repeated in April 2004, 2006 and 2008 to a depth of 30 cm segmented to a depth resolution of 5 cm giving six depth subsamples per core. Total carbon concentration was analyzed on ball-milled subsamples by an elemental analyzer at 1150°C. Inorganic carbon concentration was measured 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. 2. Particulate soil carbon (high intensity sampling): In one block of the Jena Experiment soil samples were taken to a depth of 1 m (segmented to a depth resolution of 5 cm giving 20 depth subsamples per core) with three replicates per block ever 5 years starting before sowing in April 2002. Samples were processed as for the more frequent sampling. 3. Dissolved organic carbon: Suction plates installed on the field site in 10, 20, 30 and 60 cm depth were used to sample soil pore water. Cumulative soil solution was sampled biweekly and analyzed for dissolved organic carbon concentration by a high TOC elemental analyzer. Annual mean values of DOC are provided.

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.

Collection of data on particulate and dissolved soil nitrogen in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains four time series of particulate and dissolved soil nitrogen measurements 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. 1. Total nitrogen from solid phase: Stratified soil sampling was performed every two years since before sowing in April 2002 and was repeated in April 2004, 2006 and 2008 to a depth of 30 cm segmented to a depth resolution of 5 cm giving six depth subsamples per core. In 2002 five samples per plot were taken and analyzed independently. Averaged values per depth layer are reported. In later years, three samples per plot were taken, pooled in the field, and measured as a combined sample. Sampling locations were less than 30 cm apart from sampling locations in other years. All soil samples were passed through a sieve with a mesh size of 2 mm in 2002. In later years samples were further sieved to 1 mm. No additional mineral particles were removed by this procedure. Total nitrogen 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). 2. Total nitrogen from solid phase (high intensity sampling): In block 2 of the Jena Experiment, soil samples were taken to a depth of 1m (segmented to a depth resolution of 5 cm giving 20 depth subsamples per core) with three replicates per block ever 5 years starting before sowing in April 2002. Samples were processed as for the more frequent sampling but were always analyzed independently and never pooled. 3. Mineral nitrogen from KCl extractions: Five soil cores (diameter 0.01 m) were taken at a depth of 0 to 0.15 m (and between 2002 and 2004 also at a depth of 0.15 to 0.3 m) of the mineral soil from each of the experimental plots at various times over the years. In addition also plots of the management experiment, that altered mowing frequency and fertilized subplots (see further details below) were sampled in some later years. Samples of the soil cores per plot (subplots in case of the management experiment) were pooled during each sampling campaign. NO3-N and NH4-N concentrations were determined by extraction of soil samples with 1 M KCl solution and were measured in the soil extract with a Continuous Flow Analyzer (CFA, 2003-2005: Skalar, Breda, Netherlands; 2006-2007: AutoAnalyzer, Seal, Burgess Hill, United Kingdom). 4. Dissolved nitrogen in soil solution: Glass suction plates with a diameter of 12 cm, 1 cm thickness and a pore size of 1-1.6 µm (UMS GmbH, Munich, Germany) were installed in April 2002 in depths of 10, 20, 30 and 60 cm to collect soil solution. The sampling bottles were continuously evacuated to a negative pressure between 50 and 350 mbar, such that the suction pressure was about 50 mbar above the actual soil water tension. Thus, only the soil leachate was collected. Cumulative soil solution was sampled biweekly and analyzed for nitrate (NO3-), ammonium (NH4+) and total dissolved nitrogen concentrations with a continuous flow analyzer (CFA, Skalar, Breda, The Netherlands). Nitrate was analyzed photometrically after reduction to NO2- and reaction with sulfanilamide and naphthylethylenediamine-dihydrochloride to an azo-dye. Our NO3- concentrations contained an unknown contribution of NO2- that is expected to be small. Simultaneously to the NO3- analysis, NH4+ was determined photometrically as 5-aminosalicylate after a modified Berthelot reaction. The detection limits of NO3- and NH4+ were 0.02 and 0.03 mg N L-1, respectively. Total dissolved N in soil solution was analyzed by oxidation with K2S2O8 followed by reduction to NO2- as described above for NO3-. Dissolved organic N (DON) concentrations in soil solution were calculated as the difference between TDN and the sum of mineral N (NO3- + NH4+).

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.

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.

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.

Tomato yield and potassium concentrations in soil and in plant petioles as affected by potassium fertirrigation

Tomato (Lycopersicon esculentum Mill.) cv. Santa Clara was grown on a silt clay soil with 46 mg dm-3 Mehlich 1 extractable K, to evaluate the effects of trickle-applied K rates on fruit yield and to establish K critical concentrations in soil and in plant petioles. Six potassium rates (0, 48, 119, 189, 259 and 400 kg ha-1 K) were applied in a randomized complete block design with four replications. Soil and plant K critical levels were determined at two plant growth stages (at the beginning of the second and fourth cluster flowering). Total, marketable and weighted yields increased with K rates, reaching their maximum of 86.4, 73.4, and 54.9 ton ha-1 at 198, 194, and 125 kg ha-1 K , respectively. At the first soil sampling date K critical concentrations in the soil associated with K rates for maximum marketable and weighted yields were 92 and 68 mg dm-3, respectively. Potassium critical concentrations in the dry matter of the petioles sampled by the beginning of the second and fourth cluster flowering time, associated with maximum weighted yield, were 10.30 and 7.30 dag kg-1, respectively.

Soil compaction due to forest harvest operations

The objective of this work was to determine, through the use of the bearing capacity model, the traffic effects of the forest harvest operations on the preconsolidation pressure (sigmap), during one cycle of the eucalyptus plantation. The work was conducted using undisturbed soil samples, collected at the surface of the A horizon and in the top of the B horizon of an Udult (PA), Aquox (FX) and Udox (LA) soils. The undisturbed soil samples were used in the uniaxial compression tests. The soil sampling was done before and after the harvest operations. The operations performed with the Forwarder caused greater soil compaction than the ones done with the Feller Büncher and Harvester. The percentage of soil samples, in the region with additional soil compaction, indicated that the Udult was the soil class more susceptible to soil compaction, followed by the Aquox and Udox. Despite Udult is the more susceptible to soil compaction, the regeneration of the soil structure in this soil class was more efficient than in Aquox. The percentage of soil samples with sigmap values in the region with additional soil compaction in 1996, 1998 and 2004, after harvest operations, indicated a sustainable forest exploration in this period.

Correlation of physical and chemical attributes of soil with sugarcane yield

The objective of this work was to evaluate the correlation between sugarcane yield and some physical and chemical attributes of soil. For this, a 42‑ha test area in Araras, SP, Brazil, was used. Soil properties were determined from samples collected at the beginning of the 2003/2004 harvest season, using a regular 100x100 m grid. Yield assessment was done with a yield monitor (Simprocana). Correlation analyses were performed between sugarcane yield and the following soil properties: pH, pH CaCl2, N, C, cone index, clay content, soil organic matter, P, K, Ca, Mg, H+AL, cation exchange capacity, and base saturation. Correlation coefficients were respectively ‑0.05, ‑0.29, 0.33, 0.41, ‑0.27, 0.22, 0.44, ‑0.24, trace, ‑0.06, 0.01, 0.32, 0.14, and 0.04. Correlations of chemical and physical attributes of soil with sugarcane yield are weak, and, per se, they are not able to explain sugarcane yield variation, which suggests that other variables, besides soil attributes, should be analysed.