Routine Soil Research Articles

An investigation in laser diffraction soil particle size distribution analysis to obtain compatible results with sieve and pipette method

Recent studies have shown that soil particle size analyses using laser diffraction method (LDM) can give compatible results compared with traditional sedimentation based methods, if the clay-silt particle size cutoff is transformed. Additionally, procedures including separation of the sand fraction by wet sieving and running a well dispersed sample of only fractions smaller than sand during laser diffraction measurement, have given promising results. The main purpose of the present study was to test a combination of these approaches for determining cutoff transformed LDM values on 44 soil samples from agricultural sites spread over Sweden, including its compatibility with the sieve and pipette method (SPM). Furthermore, these results were compared with results of transformed LDM values based on pedotransfer functions between measured LDM and SPM. Also LDM related aspects concerning scattering parameters, repeatability and organic matter calculations were studied. To find the optimum clay-silt cutoff, Lin’s concordance correlation coefficient (Lin’s CCC) was calculated. The highest value (0.977) was found with the 3.409–3.905 µm bin (a refractive index of 1.52 and an absorption coefficient of 0.1 was used). The pedotransfer-transformed LDM approach showed equally high Lin´s CCC as the cutoff-transformed approach for the different soil particle fraction size classes. With the cutoff-transformed LDM approach, 36 out of 44 samples were assigned to the same texture class as SPM, and with the pedotransfer-transformed LDM, the corresponding number was similar (34 out of 44 samples). The results here are promising for application in routine soil analyses, but more specific transformed clay-silt cutoffs and pedotransfer functions for LDM versus SPM should ideally be established for different types of soils. For this, microscopy and image analysis methods to help understand and quantify the influence of particle shapes on obtained particle size distributions are useful.

Cover crops enhance resource availability for soil microorganisms in a pecan orchard

Currently, there are substantial knowledge gaps on the impacts of cover crops on soil resources in tree cropping systems, wherein they are typically planted in interrow alleys and maintained for multiple years. While cover crops uptake soil nutrients and water, they can also prevent soil water evaporative losses and return nutrients to soils via decomposition of plant residues and stimulation of microbial nutrient cycling. This field sampling study examined variances in soil conditions across 5- and 7-year-old, cover cropped pecan orchards. We collected soil beneath cover crops and beneath adjacent trees, where soil was kept bare, to evaluate impacts of cover on the soil biota, nutrients, carbon, and their dynamics across a production season. We employed routine soil chemical analyses, phospholipid fatty acid (PLFA) analysis, and high throughput sequencing of 16 S rRNA genes and ITS regions for soils collected at four time points. We revealed that the cover cropped alley soils contained higher relative abundances of microbes that use labile soil substrates in resource rich conditions than did the tree row soils. Soil chemical analyses provided additional evidence that the cover crops did not deplete soil nutrients and reduce soil moisture, but rather, enhanced soil nutrient and moisture contents during many of the sampling time points. Notably, cover crop plant species correlated with soil nutrients and plant beneficial microbes, which may warrant consideration when selecting cover crop species. The tree row and cover cropped alley soils had different proportions of plant-beneficial mycorrhizal fungi. The tree rows supported higher numbers of ectomycorrhizal (ECM) fungi and alleys had higher relative abundances of arbuscular mycorrhizal (AM) fungi, suggesting potential benefits for tree species like pecan, which support dual colonization by AM and ECM Fungi. Altogether, the cover crops enhanced soil carbon, nutrients, and microbial populations in a pecan orchard and these impacts were frequently larger in a 7-year-old versus 5-year-old orchard.

Combining different pre-processing and multivariate methods for prediction of soil organic matter by near infrared spectroscopy (NIRS) in Southern Brazil

In Brazil, most guidelines for soil nitrogen (N) fertilization are based on the soil organic matter (SOM) content, which is one of the most laborious and expensive parameters in routine soil analysis. Recently, several studies have been made to generate and evaluate alternative methods to estimate SOM content, such as those based on near infrared spectroscopy (NIRS). However, the accuracy of NIRS is highly affected by spectral pre-processing techniques and multivariate methods. Moreover, the models are highly site-specific, indicating the need to develop regional calibrations. The objective of this study was to evaluate the effect of pre-processing and multivariate methods in SOM prediction using NIRS. A total of 2388 soil samples from Southern Brazil were analyzed for SOM content by carbon oxidation with a sulfochromic solution, followed by spectrophotometric colorimeter determination (modified Walkley-Black method) and by a NIR-spectrometer (1200–2400 nm). Seven pre-processing techniques were tested, including: Savitzki-Golay derivative (SGD), continuum removal (CR), detrend (DET), binning (BIN), smoothing (SMO), and the standard normal variate (SNV). The pre-processing techniques were combined with four multivariate models including multiple linear regression (MLR), partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM), random forest (RF) and gaussian process regression (GPR). The performance of the predictions was evaluated by the coefficient of determination (R2) and the root mean squared error (RMSE). Moreover, SOM predictions obtained from the models were compared with the wet digestion method in order to evaluate the capability to correctly classify the SOM content for N fertilization. Overall, comparing the multivariate models, the best predictions according to R2 were found for SVM > GPR > PLSR > MLR > PCR > RF, in decreasing order. The best prediction was obtained with the combination of SVM model and SNV spectral pre-processing (R2 = 0.70, RMSE = 0.44, RPIQ = 2.26). For SVM and GPR, only DET, SNV, BIN and CR improved predictions when compared to raw spectra. When using the linear model MLR, the prediction of SOM was improved only with SNV, SMO and DET, compared to raw spectra. For the PLSR model, the prediction of SOM was improved with DET and SNV. Results estimated by NIRS reached 85% accuracy when compared to values of SOM obtained by the modified Walkley-Black method.

Soil organic matter, texture, and drying temperature effects on water content

AbstractA balance between soil water and air is needed to optimize soil microbial activity in laboratory incubations. Some fraction of water holding capacity (WHC) is often used to optimize water during controlled laboratory incubations, and yet this level may differ among soils. This study was undertaken to develop relationships among gravimetric and volumetric water content, WHC, and water‐filled pore space (WFPS) among a variety of soils, representing eight soil orders, 46 subgroups, total soil N from 0.46 to 4.68 g kg–1, and clay concentration from .072 to .491 kg kg–1 (5–95% range). Gravimetric soil water content was determined for each sample under the following sequence of conditions: (a) from dry storage, (b) when dried at 55 °C, (c) near saturation, and (d) when drained to WHC. Gravimetric water content was calculated at 50% WFPS (.50 m3 m–3). Like that of WFPS, WHC varied strongly with soil clay and total N concentrations. To achieve 50% WFPS, water content was 69 ± 10% of WHC and 58 ± 3% of near saturation. Although drying soil to only 55 °C for routine soil microbial processing led to overestimation of soil bulk density by 0.012 ± 0.006 Mg m–3, it was a bias. Soil water content calculations are important for obtaining best estimates of soil‐test biological activity and other soil C and N fractions to assess soil health, and this research demonstrated that using drying temperature of 55 °C was practical but created a small and consistent bias.

Calibration Spiking of MIR-DRIFTS Soil Spectra for Carbon Predictions Using PLSR Extensions and Log-Ratio Transformations

There is a need to minimize the usage of traditional laboratory reference methods in favor of spectroscopy for routine soil carbon monitoring, with potential cost savings existing especially for labile pools. Mid-infrared spectroscopy has been associated with accurate soil carbon predictions, but the method has not been researched extensively in connection to C lability. More studies are also needed on reducing the numbers of samples and on how to account for the compositional nature of C pools. This study compares performance of two classes of partial least squares regression models to predict soil carbon in a global (models trained to data from a spectral library), local (models trained to data from a target area), and calibration-spiking (spectral library augmented with target-area spectra) scheme. Topsoil samples were+ scanned with a Fourier-transform infrared spectrometer, total and hot-water extractable carbon determined, and isometric log-ratio coordinates derived from the latter measurements. The best RMSEP was estimated as 0.38 and 0.23 percentage points TC for the district and field scale, respectively—values sufficiently low to make only qualitative predictions according to the RPD and RPIQ criteria. Models estimating soil carbon lability performed unsatisfactorily, presumably due to low labile pool concentration. Traditional weighing of spiking samples by including multiple copies thereof in training data yielded better results than canonical partial least squares regression modeling with embedded weighing. Although local modeling was associated with the most accurate predictions, calibration spiking addressed better the trade-off between data acquisition costs and model quality. Calibration spiking with compositional data analysis is, therefore, recommended for routine monitoring.

Empower Innovations in Routine Soil Testing

Conventional soil tests are commonly used to assess single soil characteristics. Thus, many different tests are needed for a full soil fertility/soil quality assessment, which is laborious and expensive. New broad-spectrum soil tests offer the potential to assess many soil characteristics quickly, but often face challenges with calibration, validation, and acceptance in practice. Here, we describe the results of a 20 year research program aimed at overcoming the aforementioned challenges. A three-step approach was applied: (1) selecting and establishing two contrasting rapid broad-spectrum soil tests, (2) relating the results of these new tests to the results of conventional soil tests for a wide variety of soils, and (3) validating the results of the new soil tests through field trials and communicating the results. We selected Near Infrared Spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl2 extraction (1:10 soil to solution ratio; w/v) as broad-spectrum techniques. NIRS was extensively calibrated and validated for the physical, chemical, and biological characteristics of soil. The CaCl2 extraction technique was extensively calibrated and validated for ‘plant available’ nutrients, often in combination with the results of NIRS. The results indicate that the accuracy of NIRS determinations is high for SOM, clay, SOC, ECEC, Ca-CEC, N-total, sand, and inorganic-C (R2 ≥ 0.95) and good for pH, Mg-CEC, and S-total (R2 ≥ 0.90). The combination of the CaCl2 extraction technique and NIRS gave results that related well (R2 > 0.80) to the results of conventional soil tests for P, K, Mg, Na, Mn, Cu, Co, and pH. In conclusion, the three-step approach has revolutionized soil testing in The Netherlands. These two broad-spectrum soil tests have improved soil testing; have contributed to increased insights into the physical, chemical, and biological characteristics of soil; and have thereby led to more sustainable soil management and cropping systems.

Cover cropping and conservation tillage improve soil health in the southeastern United States

AbstractConservation agricultural systems have a long history of development and use in the southeastern United States, but they are still not the norm on the landscape. Literature from the region suggests that a diversity of soil properties can be improved with cover cropping and conservation tillage, but results are not always consistent. Our objectives were to (a) review recent research on conservation tillage and cover cropping in the region and (b) compile and synthesize recent data from a diversity of farms along a conventional to conservation gradient. As part of recent on‐farm trials focused on soil N availability, we analyzed soil organic C and N fractions and routine soil nutrient characteristics from a total of 196 fields in corn (Zea mays L.), wheat (Triticum aestivum L.), and barley (Hordeum vulgare L.) production in three physiographic regions of the Coastal Plain (n = 71 fields), Piedmont (n = 77 fields), and Appalachians (n = 48 fields) in North Carolina, South Carolina, Pennsylvania, and Virginia. These data allowed us to establish first estimates of threshold optima for soil organic C and N fractions and other inorganic nutrients. On‐farm data confirmed the literature review that conservation tillage resulted in greater stratification of soil organic C and N and nutrient concentrations. Soil health improvement could be inferred along the gradient from inversion tillage to no tillage without cover cropping to no tillage with cover cropping. This collection of recent literature and on‐farm data can be a useful starting point for establishing target limits and assessing soil health conditions on farms in the region.

Reappraisal of Fall-Cone Flow Curve for Soil Plasticity Determinations

Abstract Several attempts have been made to devise alternate plastic limit (PL) determination methods, targeting higher degrees of repeatability and reproducibility. Among these, empirical-type correlations linking the plasticity index (PI) to the flow index (FI)—the slope magnitude of the flow curve—seem to be gaining increased attention, particularly for the fall-cone (FC) approach, and hence demand further examination. To better understand the true potentials and limitations of this emerging practice for soil plasticity determination, this study presents a critical statistical appraisal of FI-based correlations—using a large and diverse database of 230 FC tests (for the 80 g–30° cone)—in estimating the PI (and hence the PL). It is demonstrated that the so-called “strong” correlation between the PI and FI reported in some literature, favoring the use of FI as a PI estimator, is an overlooked “statistical pitfall” originating from an over-reliance on the coefficient of determination (R2) statistic. Employing appropriate error-related statistics, it is shown that the PI predictions made by FI-based correlations are associated with high average errors of 22–33 %. Hence, such correlations, at best, can only provide a rough approximation of the actual PI (and hence PL). An attempt is also made to assess the validity of FI-based correlations in the context of soil classification using the Casagrande-style plasticity chart. The agreement level between the conventional classification approach and that performed using PI deduced from FI-based correlations was 75–80 %. This analysis, however, did not account for errors in the rolling-thread plastic limit data, maintaining a strong possibility that FI-based correlations may be suitable for routine soil classification purposes.

Estimating cation exchange capacity and clay content from agricultural soil testing data

Clay content and the ability to reversibly retain cations affect many essential chemical and physical properties of soil, such as pH buffering and carbon sequestration. Cation exchange capacity (CEC) and base saturation are also commonly used as criteria in soil classification. However, determination of CEC and particle-size distribution is laborious and not included in routine soil testing. In this study, pedotransfer functions including soil test cations (STCat; Ca2+ + Mg2+ + K+), pH and soil organic carbon (SOC, %) as explanatory variables were developed for estimating CEC, titratable acidity (TA; H+ + Al3+) and clay content (clay, %). In addition, reference values for potential CEC and its components were determined for Finnish mineral and organic soils. The mean of potential CEC extracted by 1 M ammonium acetate at pH 7.0 ranged from 14 (range 6.4−25) in coarse soils to 33 (21−45) cmol(+) kg-1 in heavy clay soils, and from 42 (24−82) in mull soils to 77 (25−138) cmol(+) kg-1 in peat soils. The average CEC of clay and SOC were 27 and 160 cmol(+) kg-1, respectively. Titratable acidity occupied 53% and around 40% of the CEC sites in organic and mineral soils, respectively, evidencing that it is a prominent component of the potential CEC in these predominantly acidic soils. STCat, pH and SOC explained 96% of the variation in potential CEC. STCat and pH can be used in estimating the clay content especially for soils containing over 30% clay. In coarse textured soils, in contrast, SOC hampers the STCat based estimation of clay content.

Soil organic matter characterisation using alkali and water extraction, and its relation to soil properties

The optical and fluorescence properties of dissolved organic matter are widely used to estimate the soil organic matter composition. To do so, various absorbance and fluorescence properties of soil extracts are investigated using various indicators such as the alkali-extracted fulvic acid (FA) fraction and the water-extractable organic matter (WEOM) content. Nevertheless, it is not yet clear whether different indicators from various matrixes are directly comparable. The present study compares the fluorescence and optical properties of the same soil under various tillage systems for fourteen years using the isolated FA and WEOM fractions. The soil is a haplic Luvisol under ploughing (PT) and conservation tillage (CT) in subhumid conditions. The extractions were characterised using UV–vis spectroscopy and the excitation emission matrix. Microbiome-related soil properties were also investigated. The microbial biomass carbon and cellulose decomposition did not register differences between the two tillage systems. The WEOM carbon content was related to the organic matter content of the soil (R2 = 0.87), whereas that of the FA was not. The WEOM indicated more humified composition under CT. In contrast, the FA did not show differences in humification between the two tillage systems, but revealed higher aromaticity under CT. Although the measured absorbance and fluorescence values were standardised by the extracted carbon content or the index calculations, the results differed significantly between the FA and WEOM fractions. Compared to the FA, in the present case, the WEOM is a better indicator of organic matter composition changes due to land use shift, providing an economical and fast method for routine soil organic matter characterisation

Estimating cation exchange capacity from agronomic soil tests: Comparing Mehlich‐3 and ammonium acetate sum of cations

AbstractSoil cation exchange capacity (CEC) measures the capability of soils to hold and release nutrients. It is an important measure for classifying soils and planning nutrient additions, but it is not included in routine soil tests offered to farmers. In this study, we compared CEC measured with standard laboratory practices (neutral ammonium acetate [AAc]) with CEC estimated from agronomic soil test data (Mehlich‐3 and AAc). We applied previously published estimation methods to a dataset of 48 soil samples. Based on the results, traditional methods underestimate measured cation exchange capacity by 36% for Mehlich‐3 and 24% for AAc. The estimation can be improved by including aluminium (12% overestimate) or by fitting a localized pedotransfer function (3% underestimate), which offers a possibility for informing land managers about their soil nutrient holding capacity without adding costs to the analysis.

APPLICATION OF FRACTAL THEORY IN QUANTIFYING SOIL AGGREGATE STABILITY AS INFLUENCED BY VARYING TILLAGE PRACTICES AND COVER CROPS IN NORTHERN GUINEA SAVANNA, NIGERIA

<p><strong>Background.</strong> Soil aggregate stability varies with management practices like tillage and soil organic matter management. <strong>Objective.</strong> The objectives of this study were to (i) Explore a fractal theory to investigate the extent of soil fragmentation in soil of Samaru, Northern Nigeria, subjected to different tillage practices and covers crops for the purpose of improving soil aggregate stability (ii) Establish a relationship between fractal dimension (D) and mean weight diameter (MWD). <strong>Methodology.</strong> A three years field trials was conducted with three tillage practices (no-till, reduced till and conventional till) as main treatments and four cover crops (<em>Centrosema pascuorum, Macrotyloma uniflorum, Cucurbita maxima</em> and <em>Glyine max</em>), and a bare/control (no cover crop) as sub treatments. The trial was laid out in randomized complete block design, split plot arrangement and replicated three times. Soil samples were collected at 0-15 cm prior to trial establishment for routine soil physical and chemical properties and at depths 0-5, 5-10, 10-15 and 15-20 cm at the end of each cropping season each year for soil aggregate stability test. <strong>Results. </strong>No-till soils had 12.58% better soil aggregate stability than soils under reduced and conventional tillage systems. Also, no-till soils had 2.40 % less fragmented soil aggregates than Reduced and Conventional till soil as indicated by the fractal dimension of soil aggregates. Soils under <em>Macrotyloma uniflorum</em> cover crop, were on the average 2.33% less fragmented than soils under <em>Centrosema pascuorum, Glycine max </em>and<em> Cucurbita maxima</em> but 4.56% less fragmented than soils with no cover crops. <strong>Implication, </strong>No-till and reduced till systems; and cover cropping better enhanced soil aggregate stability due to better accumulation of organic matter emanating from crop residues return to soil in these practices. <strong>Conclusion.</strong> Conservation tillage and the use of cover crops lowered soil aggregate fragmentation relative to conventionally tilled system and bare soil with no cover crop. The strong linear relationship established between MWD and fractal dimension showed over 80% dependency, suggesting that fractal dimension is another useful index for evaluating soil aggregate stability.</p><p>.</p>

Analysis of the relationship between leaf color spectrum and soil plant analysis development

Assessment nutrient content of maize leaf is particularly important in achieving higher grain yield. Characterization of leaf chlorophyll involves routine Soil Plant Analyzer Development (SPAD) reading particularly at critical stage of growth development. The objective of the study was to assess the color spectrum of maize leaf in relation to the chlorophyll content by using Random-forest modeling. genotypes of corn plants based on the characters of the ear and kernel using a logistic regression model. The research was conducted at IP2TP Bajeng in 2021 by planting maize varieties at various fertilizer level. RGB data of maize leaf was recorded by using Hamamatsu sensor (Hamamatsu, Japan), and converted to HIS, HSV and LAB color spectrum. The results indicated that Random-forest model with 20-fold validation indicated the highest accuracy as compared to the other fold-range. Among the tested model, integration of Random-forest model to LAB (Light, red/green coordinate, and the yellow/blue coordinate) color spectrum provided the best model performances with RMSE (4.77), MSE (22.76), MAE (3.80) and R2 (0.853). This value indicates that the use of Hamamatsu color sensor and converted into LAB color spectrum provided the best SPAD (Soil Plant Analyzer Development) reading with high accuracy and consistency of results. Thus, digital based model can be integrated with manual selection for fast and precise nutrient monitoring.

Soil rock fragments: Unquantified players in terrestrial carbon and nitrogen cycles

Soils with a significant proportion of rock fragments (RFs, with diameter >2 mm) are widely distributed in terrestrial ecosystems. However, a lack of quantitative information about RF characteristics restricts our ability to explain and predict the related soil carbon (C) and nitrogen (N) processes, and induces biases of C and N investigations in areas where soils contain significant RFs. In this paper, we reviewed the direct and indirect effects of RFs on soil C and N cycles, including via affecting soil C and N stocks, hydrology, temperature, and via chemical weathering that releases C, N, cations and anions, consumes CO2 and regulates pH. Based on this, we discussed current challenges and potential solutions in investigating C and N cycles of high RF content soils. First, we proposed a series of approaches to quantifying the effects of RFs on C and N stocks and soil hydrology across scales. Second, we emphasized the evolutions of soil RFs’ properties over time, and recommended including RF physicochemical properties in the routine soil inventory. Third, we suggested that both the effects of RFs and the evolutions of soil RFs over time should be considered in model simulation. Finally, we provided a framework by coupling monitoring, mapping and modelling to investigate the roles of RFs on soil C and N cycles from earth’s critical zone to global scale. This review will improve our understandings of RFs and terrestrial C and N cycles, and their responses to global change.

Comparison of laboratory methodologies to determine soil nitrogen mineralization from organic residues

Recycling organic waste for use as fertilizer requires prior knowledge of mineral nitrogen (N) availability for crops. Estimation of soil N release or potentially mineralizable N is an important tool for the design of fertilization strategies that aim to minimize the use of N fertilizer. The aerobic incubation method is considered a standard technique to measure soil potential to mineralize N. In this study, alternative methods of aerobic incubation were evaluated to help overcome its limitations (long time and equipment). In this regard, biological methods (anaerobic incubation at 7 and 14 days) and chemical extraction (hot KCl) procedures were examined. To determine potentially mineralizable N, a silty clay loam soil was fertilized with spent mushroom substrates and anaerobic digestates from different origins (C/N ratio of 4 to 38). Based on the results, chemical extraction emerges as a reliable alternative to the aerobic incubation method, particularly when the C/N ratio of the organic residues ranges from 12 to 15. Moreover, its implementation in routine soil laboratories is straightforward and faster, and it does not require any special equipment.

Which management practices influence soil health in Midwest organic corn systems?

AbstractFostering and maintaining soil health via holistic management is a central goal for most organic farmers. However, many questions remain regarding how different management practices influence soil health in farm fields. In this study, we used a mail‐in soil survey to assess how organic management practices such as crop diversity, perennials in rotation, tillage, manure use, and subscription to soil cation balancing influence soil biochemical health indicators in certified organic corn (Zea mays L.) fields. Organic farmers (n = 195) from the eastern Corn Belt mailed in soil along with a completed management survey to a research lab for analysis. Soils were analyzed for mineralizable carbon (C), permanganate oxidizable carbon (POXC), soil protein, texture, and routine soil nutrient analyses. Soil texture had the largest influence on soil biochemical health indicators, underscoring the need to consider soil type with soil health assessments. Crop diversity was negatively correlated with mineralizable C, soil C, and soil nitrogen (N) (r = –.19 to –.24) at p < .05 when perennials were in the rotation. This was attributed to tillage frequency increasing with crop diversity across all soil types. The presence of a perennial in rotation influenced soil biochemical health indicators, except for total C. Additionally, mineralizable C significantly increased when perennials were left in rotation for longer periods of time. This study demonstrates the effectiveness of incorporating management survey data with soil biochemical health analyses. We conclude that a main management driver for improved soil biochemical health in organic corn production systems is to reduce tillage intensity and incorporate perennials.

Elemental Concentration and Physicochemical Properties of Soils under Different Landuses in Gashua a Sahel Region of Nigeria

Soil elements assessment under various land uses is vital in knowing the status of the soils in terms of fertility and toxicity. Therefore, a study was conducted to investigate some elements concentrations of soils under three different land uses in Gashua, Yobe State, Nigeria. Samples were randomly taken at 0-15 and 15-30 cm soil depth intervals from land uses. The land uses are Dry upland (DU), Lowland (LL) and Residential area (RA). The selected elements were determined in the laboratory using Atomic Absorption Spectrophotometer (AAS), while some physicochemical properties were determined using routine soil analysis methods. The results indicated significantly higher concentrations of Iron (6.96 mg kg-1), Manganese (3.12 mg kg-1) and clay content (12.88%), except for Nickel which was higher in DU (2.36 mg kg-1) but at par with the content in LL (2.04 mg kg-1) land use. There was no significant difference within the following soil elements within the study location: Cadmium (Cd), Chromium (Cr), Copper (Cu) and Lead (Pb) were below detection limit within the soils of the chosen land use types. Generally the soils textural class is sandy loam with higher mean bulk density (1.62g cm-3) and a neutral soil pH (6.81). The physicochemical properties and elements investigated didn’t showed significant differences with soil depth. The basic elemental concentrations within the soils were mostly adequate for crop production, except Zn, with little variability within the landuse. They were generally below the utmost ecological risk permissible level set by WHO and FAO.

Identifying barriers to routine soil testing within beef and sheep farming systems

Soil testing in agriculture is associated with many economic and environmental benefits. However, previous studies have shown that a significant proportion of beef and sheep farmers in the UK do not carry out standard soil index testing (pH, available Mg, P and K); with much fewer again carrying out more extensive soil tests (e.g., organic matter, micronutrients). This study identifies barriers and motivations to soil testing amongst the beef and sheep sector, using a combination of farmer surveys, expert interviews, and a 5-year soil testing dataset from the largest commercial UK soil testing laboratory. Evidence for differences in the adoption of soil tests by beef and sheep farmers compared to the arable and dairy sectors is explained in relation to: (1) the extent of soil pH and nutrient imbalances, linking to the intensity of management in the different sectors; and (2) the extent to which farmers perceive links between their soils and their outputs (profits, yield, livestock health). We show a greater likelihood for farmers to engage with soil testing when the links to declining outputs are clearer. Our results showed that beef and sheep farmers who did engage with soil testing showed greater levels of innovation and were more likely to seek advisory support, most often associated with larger farm sizes. Our data also highlights the importance of an output-driven approach to initiate an interest in soil analyses amongst less engaged farmers. We argue that this avenue offers greater potential for enhancement of farmers’ knowledge of the soil system than a primarily regulatory-driven approach, where soil testing becomes a compulsory action but does not lead to subsequent improvements in farm management.

How Effective Are Existing Phosphorus Management Strategies in Mitigating Surface Water Quality Problems in the U.S.?

Phosphorus is an essential component of modern agriculture. Long-term land application of phosphorous-enriched fertilizers and animal manure leads to phosphorus accumulation in soil that may become susceptible to mobilization via erosion, surface runoff and subsurface leaching. Globally, highly water-soluble phosphorus fertilizers used in agriculture have contributed to eutrophication and hypoxia in surface waters. This paper provides an overview of the literature relevant to the advances in phosphorous management strategies and surface water quality problems in the U.S. Over the past several decades, significant advances have been made to control phosphorus discharge into surface water bodies of the U.S. However, the current use of phosphorus remains inefficient at various stages of its life cycle, and phosphorus continues to remain a widespread problem in many water bodies, including the Gulf of Mexico and Lake Erie. In particular, the Midwestern Corn Belt region of the U.S. is a hotspot of phosphorous fertilization that has resulted in a net positive soil phosphorous balance. The runoff of phosphorous has resulted in dense blooms of toxic, odor-causing phytoplankton that deteriorate water quality. In the past, considerable attention was focused on improving the water quality of freshwater bodies and estuaries by reducing inputs of phosphorus alone. However, new research suggests that strategies controlling the two main nutrients, phosphorus and nitrogen, are more effective in the management of eutrophication. There is no specific solution to solving phosphorus pollution of water resources; however, sustainable management of phosphorus requires an integrated approach combining at least a reduction in consumption levels, source management, more specific regime-based nutrient criteria, routine soil fertility evaluation and recommendations, transport management, as well as the development of extensive phosphorus recovery and recycling programs.

Guiding fall fertilization of cool‐season turfgrass lawns with verdure tissue‐N tests

AbstractObjective tests are not widely used to guide fall N fertilization of turfgrasses, and N is applied at predetermined rates regardless of plant‐N status. This study was conducted across 3 yr in Connecticut to determine if fall verdure concentrations of total N, dry‐tissue extractable nitrate (NO3)–N, and fresh sap NO3–N could guide fall N fertilization for a mixed‐species, cool‐season turfgrass lawn. Field experiments were set out with varying fall N rates and verdure samples were collected weekly in October and November. Verdure tissue‐N test concentrations in response to N rates were analyzed with polynomial (quadratic or cubic) and linear‐response and plateau (LRP) models. Models were significant (P < .0001) for all tissue concentrations in response to N rates and estimates were comparable between models. When referenced to expected responses from the commonly recommended fall N rates of 24.5, 49, and 98 kg ha−1, there would be no need for N fertilizer when concentrations of fall verdure total N are ≥22, ≥25, and ≥30 g kg−1; dry‐tissue extractable NO3–N are ≥119, ≥193, and ≥840 mg kg−1; and fresh sap NO3–N are ≥94, ≥115, and ≥171 mg L−1, respectively. Variability of the verdure tissue‐N tests was no greater than most routine soil and tissue tests currently used to monitor nutrient status in turfgrasses. The results suggest that verdure tissue‐N tests have potential in guiding fall N fertilization of cool‐season turfgrass lawns.