Intensive Soil Sampling Research Articles

Interpreting Relationships between Soil Variables and Soybean Iron Deficiency using Factor Analysis

Iron chlorosis in soybean [Glycine max (L.) Merr.] can be difficult to predict and often depends on various soil factors. The objective of this study was to determine the underlying soil factors that are conducive to Fe chlorosis in soybean using a statistical factor analysis. This study was conducted at seven locations in western Kansas with intensive soil sampling to investigate the relationships between soil variables and the incidence of Fe chlorosis. The soil variables measured were pH, P, Fe, organic matter (OM), Ca, Mg, electrical conductivity (EC), NO3–N, and calcium carbonate equivalent (CCE). Factor analysis was performed using the Varimax rotation and the Heywood convergence to obtain the best possible relationships. The factors were deemed significant if the Eigenvalues were >1. The factor analysis showed that two underlying factors can be selected to explain the incidence of Fe chlorosis in soybean. These factors are “plant chlorosis” (Factor 1) and “soil available Fe” (Factor 2). With regression analysis, these underlying factors were indicative of the chlorophyll meter (CM) readings at the V3 and V6 growth stages and in the grain yield (GY). However, soybean management practices, such as variety selection and the use of seed-applied Fe fertilizers were shown to affect the relationship between latent factors (from factor analysis) and soybean response. When seed-applied Fe fertilizers are used with tolerant and nontolerant soybean varieties, the overall effect of the undelaying soil factors seems irrelevant to soybean response.

Improving the precision of estimating nitrate (NO3 −) concentration in seepage water of forests by prestratification with soil samples

Nitrate (NO3−) concentrations in seepage water (\( {\text{SW}}_{{{\text{NO}}_{ 3} }} \)) of N saturated forests regularly show a high spatial variability. As seepage water sampling is expensive, most studies use small numbers of soil solution samplers. This implies high uncertainties of calculated concentration and flux data. At the N saturated Hoglwald site we investigated, whether a preselection of seepage water sampling places by a prior intensive soil sampling can improve the precision of the estimation of \( {\text{SW}}_{{{\text{NO}}_{ 3} }} \). We implemented 121 suction cups and analyzed the soil, removed with the soil auger during installation at the sampling depth of 40 cm, for water extractable NO3−. The NO3− content in soil (\( {\text{Soil}}_{{{\text{NO}}_{ 3} }} \)) was calculated and \( {\text{SW}}_{{{\text{NO}}_{ 3} }} \) was measured. With this data set we tested the correlation between \( {\text{Soil}}_{{{\text{NO}}_{ 3} }} \) and \( {\text{SW}}_{{{\text{NO}}_{ 3} }} \) and simulated a random selection of sampling places (RS) as well as a prestratified sampling based on \( {\text{Soil}}_{{{\text{NO}}_{ 3} }} \) (PS). With bootstrap statistics different numbers of replications (n) were tested and the 95% confidence interval (95% CI) was used to compare RS with PS. Highly significant correlations between \( {\text{Soil}}_{{{\text{NO}}_{ 3} }} \) and \( {\text{SW}}_{{{\text{NO}}_{ 3} }} \) were found (r = 0.99***). It was even possible to calculate \( {\text{SW}}_{{{\text{NO}}_{ 3} }} \) from \( {\text{Soil}}_{{{\text{NO}}_{ 3} }} \). Here best results where achieved when \( {\text{Soil}}_{{{\text{NO}}_{ 3} }} \) was determined from field moist soil samples. With PS the accuracy of the mean value of \( {\text{SW}}_{{{\text{NO}}_{ 3} }} \) improved considerably. To achieve a 95% CI lower than ±10%, 250 suction cups would be needed for RS compared to n = 20 for PS. Even 6 months after implementation, precision of mean \( {\text{SW}}_{{{\text{NO}}_{ 3} }} \) was higher for suction cup places selected with the PS method. A detailed description is given, how these findings may be applied in studies and monitoring programs focusing on NO3− leaching in forest ecosystems.

Diffusivity Models and Greenhouse Gases Fluxes from a Forest, Pasture, Grassland and Corn Field in Northern Hokkaido, Japan

Information on the most influential factors determining gas flux from soils is needed in predictive models for greenhouse gases emissions. We conducted an intensive soil and air sampling along a 2000 m transect extending from a forest, pasture, grassland and corn field in Shizunai, Hokkaido (Japan), measured CO 2, CH 4, N 2O and NO fluxes and calculated soil bulk density (ρ b), air-filled porosity ( f a) and total porosity (Φ). Using diffusivity models based on either f a alone or on a combination of f a and Φ, we predicted two pore space indices: the relative gas diffusion coefficient ( D s /D o) and the pore tortuosity factor (τ). The relationships between pore space indices ( D s/ D o and τ) and CO 2, CH 4, N 2O and NO fluxes were also studied. Results showed that the grassland had the highest ρ b while f a and Φ were the highest in the forest. CO 2, CH 4, N 2O and NO fluxes were the highest in the grassland while N 2O dominated in the corn field. Few correlations existed between f a, Φ, ρ b and gases fluxes while all models predicted that D s /D o and τ significantly correlated with CO 2 and CH 4 with correlation coefficient ( r) ranging from 0.20 to 0.80. Overall, diffusivity models based on f a alone gave higher D s /D o, lower τ, and higher R 2 and better explained the relationship between pore space indices ( D s /D o and τ) and gases fluxes. Inclusion of D s/ D o and τ in predictive models will improve our understanding of the dynamics of greenhouse gas fluxes from soils. D s /D o and τ can be easily obtained by measurements of soil air and water and existing diffusivity models.

Spatial variability of soil nutrients and GIS-based nutrient management in Yongji County, China

Integrated nutrient management is important for sustainable agricultural production and protecting environment quality and has been widely investigated around the world. In this article the spatial variability of soil nutrients was investigated and a regionalized nutrient management system was developed using geostatistics and geographic information system technologies. A total of 511 GPS-referenced soil samples were taken in Yongji County, Shanxi province, China, and analysed for major soil nutrients: soil total nitrogen (TN), Olsen extractable phosphorus (OLSENP) and extractable potassium (EXTK). Low concentrations of nitrogen (N) and phosphorus (P) were found and they are likely to be the main limiting nutrients for crop growth in this county. Within the county moderate spatial dependence was found for all three soil variables, but at different spatial scales. The spatial distributions of TN, OLSENP and EXTK were estimated by using kriging interpolation. The cropped areas of the county were divided into fertilizer management categories consisting of four classes of TN, three classes of OLSENP and two classes of EXTK. For the targeted crop yields, regionalized fertilization maps of N, P and K in the county were produced using geographic information system. In 3-year field verification trials in two villages the crop yields of the wheat–maize rotation system increased by 10–20%, and farmers' cash income increased by 1550–2610 RMB ha−1 year−1 where regional fertilization recommendations were implemented, in comparison with traditional farmers' practices. The regionalized maps are a practical alternative to site-specific soil nutrient management approaches in areas where it is not practical, because of small farm size or other constraints, to use intensive soil sampling and chemical analyses.

Mapping of soil micronutrients in an european atlantic agricultural landscape using ordinary kriging and indicator approach

Nutrient maps based on intensive soil sampling are useful to develop site-specific management practices. Geostatistical methods have been widely used to determine the spatial correlation and the range of spatial dependence at different sampling scales. If spatial dependence is detected, the modelled semivariograms can then be used to map the interested variable by kriging, an interpolation method that produces unbiased estimates with minimal estimation variance. The objectives of this paper were to examine and to map the spatial distribution of the micronutrients Cu, Zn, Fe and Mn on an agricultural area in Galicia, Spain, under European Atlantic climatic conditions. The ordinary kriging was first used to determine the values for the non-sampled locations, then the indicator approach was used to transform the micronutrient content values into binary values having the mean values of each nutrient as the threshold content. All four elements analyzed showed spatial dependence using the indicator semivariograms. The strength of spatial dependence was assessed using the values of nugget effect and range from the semivariogram, the fitted range values decreased in the order Mn >Fe >Zn >Cu. The spatial dependence of the combination of two or more of the studied micronutrients was also examined using indicator semivariograms. In opposition to spatial analysis of individual microelements, indicator semivariograms obtained for the binary coding of the variables showed a great nugget effect value or a low proportion of sill. The maps for each nutrient obtained using indicator kriging showed some similarity in the spatial distribution, suggesting the delimitation of uniform management areas.

No Differences in Soil Carbon Stocks Across the Tree Line in the Peruvian Andes

Reliable soil organic carbon (SOC) stock measurements of all major ecosystems are essential for predicting the influence of global warming on global soil carbon pools, but hardly any detailed soil survey data are available for tropical montane cloud forests (TMCF) and adjacent high elevation grasslands above (puna). TMCF are among the most threatened of ecosystems under current predicted global warming scenarios. We conducted an intensive soil sampling campaign extending 40 km along the tree line in the Peruvian Andes between 2994 and 3860 m asl to quantify SOC stocks of TMCF, puna grassland, and shrubland sites in the transition zone between the two habitats. SOC stocks from the soil surface down to the bedrock averaged (±standard error SE) 11.8 (±1.5, N = 24) kg C/m2 in TMCF, 14.7 (±1.4, N = 9) kg C/m2 in the shrublands and 11.9 (±0.8, N = 35) kg C/m2 in the grasslands and were not significantly different (P > 0.05 for all comparisons). However, soil profile analysis revealed distinct differences, with TMCF profiles showing a uniform SOC distribution with depth, shrublands a linear decrease, and puna sites an exponential decrease in SOC densities with soil depth. Organic soil layer thickness reached a maximum (~70 cm) at the upper limit of the TMCF and declined with increasing altitude toward puna sites. Within TMCF, no significant increase in SOC stocks with increasing altitude was observed, probably because of the large variations among SOC stocks at different sites, which in turn were correlated with spatial variation in soil depth.

Mehlich 3 extractable nutrients as determined by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) is a cost-effective and environmentally friendly technique of soil analysis that is particularly advantageous in intensive soil sampling and soil nutrient management as well. This study evaluated the potential of NIRS for predicting P, K, Ca, Mg, Cu, Zn, Mn, Fe, and Al extracted by Mehlich 3. We used 150 air-dried samples collected from a 15-ha site dominated by Orthic Humic Gleysol and Gleyed Dystric Brunisol soils. Calibration equations were developed using modified partial least squares regression. The accuracy of NIRS prediction was evaluated using the coefficient of determination (R2), the ratio of performance deviation (RPD), and the ratio of error range (RER). Reliable calibrations were found for Ca, Cu, and Mg (R2 ≥ 0.7, RPD ≥ 1.75, and RER ≥ 8). Less-reliable calibrations were found for Al, Fe, K, Mn, P, and Zn (R2 < 0.7, RPD < 1.75, and RER < 8). In the validation with independent samples, acceptable regression coefficients (i.e., 0.8 ≤ slope ≤ 1.2) were only found for Ca, Mg, and Mn. We presumed that the pH of the Mehlich 3 extractant (2.5 ± 0.1) may affect the solubility of most of these nutrients, regardless the soil texture and, consequently, the potential of NIRS to predict them. The more a nutrient was correlated to clay content, the more it was likely predictable by NIRS. The prediction models obtained for Al, Ca, Cu, Fe, K, Mg, and Mn could still be used for screening purposes in cases where high accuracy is not required. These NIRS prediction models should be validated across larger geographic areas of geological homogeneity. Key words: Soil analysis, Mehlich 3, near-infrared reflectance spectroscopy, calibration

Response to the ‘Comments on “No‐Tillage and Soil‐Profile Carbon Sequestration: An On‐Farm Assessment”’

Franzluebbers (2009) is right about the need for a more intensive soil sampling, “repeated sampling with time,” and “stratified sampling” as well as for the use of multiple fields and collection of larger number of pseudoreplicates to overcome the high field variability in soil organic carbon (SOC) pools within each Major Land Resource Area (MLRA). The selected fields were representative of each MLRA in terms of soil type, slope, and management, but it is correct that a single soil would not capture all the variability in soil and management for the whole MLRA. This study was not intended to relate the data from the single soil to the whole MLRA but rather to emphasize the differences in SOC sequestration rates among the three management systems within each soil.

Shrub (Prosopis velutina) encroachment in a semidesert grassland: spatial–temporal changes in soil organic carbon and nitrogen pools

AbstractRecent trends of increasing woody vegetation in arid and semiarid ecosystems may contribute substantially to the North American C sink. There is considerable uncertainty, however, in the extent to which woody encroachment alters dryland soil organic carbon (SOC) and total nitrogen (TN) pools. To date, studies assessing SOC and TN response to woody plant proliferation have not explicitly assessed the variability caused by shrub age or size and subcanopy spatial gradients. These factors were quantified via spatially intensive soil sampling around Prosopis velutina shrubs in a semidesert grassland, using shrub size as a proxy for age. We found that bulk density increased with distance from the bole (P < 0.005) and decreased with increasing shrub size (P= 0.056), while both SOC and TN increased with shrub size and decreased with distance from the bole (P < 0.001 for both). Significant (and predictable) spatial variation in bulk density suggests that use of generic values would generate unreliable estimates of SOC and TN mass, and subcanopy SOC pools could be overestimated by nearly 30% if intercanopy bulk density values were applied to subcanopy sites. Predictive models based on field‐documented spatial patterns were used to generate integrated estimates of under‐shrub SOC and TN pools, and these were compared with results obtained by typical area‐weighting protocols based on point samples obtained next to the bole or at a specified distance from the bole. Values obtained using traditional area‐weighting approaches generally overestimated SOC pools relative to those obtained using the spatially integrated approach, the discrepancy increasing with increasing shrub size and proximity of the point sample to the bole. These discrepancies were observed at the individual plant scale and for landscapes populated by various shrub size classes. Results suggest that sampling aimed at quantifying shrub encroachment impacts on SOC and TN pools will require area‐weighting algorithms that simultaneously account for shrub size (age) and subcanopy spatial patterns.

Predicting soil organic matter content in southwestern Ontario fields using imagery from high-resolution digital cameras

The spatial pattern of soil organic matter (SOM) content may provide information for variable-rate fertilizer nitrogen recommendations, but often requires intensive soil sampling to be properly characterized. This study evaluated whether imagery of bare soils obtained using a high-resolution digital camera system may be used to predict SOM content in southwestern Ontario fields. Using the camera system, image intensity was measured in near-infrared (0.70–0.80 µm) and visible red (0.60–0.70 µm), green (0.50–0.60 µm), and blue (0.40–0.50 µm) wavebands underboth controlled and field conditions for soil samples representative of the range in SOM typically found in southwestern Ontario fields. Under controlled conditions, SOM content did not relate well to image intensity measured in any waveband when multiple soil types were used (r2 ≤ 0.39). Without multiple soil types, image intensity in all wavebands related to SOM content for soil samples taken from two of the sites (r2 ≥ 0.53 for both sites). Image intensity measured under field conditions related to SOM content for only one site (r2 ≥ 0.54 for all wavebands). Variability between SOM content and image intensity shown in this study is most likely attributed to the relative variability in SOM content and confounding factors such as surface residue. Key words: Soil organic matter, soil reflectance, high-resolution digital camera, aerial imagery

Mapping Soil pH

The theoretical profitability of variable rate (VR) lime management has driven adoption of intensive soil sampling strategies used with complex statistical techniques without demonstration of approach efficacy. Our objective was to compare the accuracy of spatially continuous pH and lime requirement (LR) maps derived from commercially used approaches to sampling and LR prediction at unsampled locations. We evaluated point (P) sampling on 0.1‐, 0.4‐, and 1.0‐ha grids and area composite (AC) sampling by 1‐ha grids, soil type (ST), and whole field (WF). Inverse distance (ID) weighting and ordinary kriging were applied to water pH and LR data from 11 fields. Modeling of semivariance identified range parameters of ≤100 m. For intensive P sampling (0.1‐ or 0.4‐ha grids), kriging was occasionally more accurate than ID weighting but mean absolute error (MAE) differences were small (≤0.01 pH units and ≤0.13 Mg lime ha−1), suggesting little practical consequence to prediction method selection. One‐hectare point data were too sparse to produce variograms and applying ID weighting to these data found only small advantages over WF compositing. Lime use was either unaltered or minimally reduced (10%) by 1‐ha P as compared with WF compositing. When compared with WF composites, map prediction efficiencies (PEs) based on mean square error (MSE) analysis ranged from 7 to 51, −13 to 40, and −6 to 54% for ST compositing, 1‐ and 0.4‐ha P sampling, respectively. These results suggest ST compositing remains viable and cost‐effect for pH management, especially where ancillary information exists to verify distinct soil series boundaries.

Evaluating the use of digital terrain modelling for quantifying the spatial variability of 2,4-D sorption by soil within agricultural landscapes

The most sensitive input parameter in many herbicide fate and transport models is the sorption-partitioning coefficient (Kd), a measure of herbicide sorption by soil. Spatial analyses of Kd are traditionally performed using geostatistics, but this approach requires intensive soil sampling and herbicide analysis. This study examined the use of digital terrain modelling as an alternative tool for quantifying the distribution of herbicide sorption within agricultural fields. Soil samples from a conventional-till (CT) and zero-till (ZT) field were analysed for soil organic carbon (SOC), soil pH, clay content, and the 2,4-D ([(2,4-dichlorophenoxy)acetic acid]) soil-water partitioning coefficient. Digital terrain models were used to calculate topographical variables (elevation, slope gradient, slope aspect, horizontal curvature, vertical curvature, mean curvature, specific catchment area, topographic index and stream power index) for each sampling point. Results indicated that topographic variables were adequate predictors of all soil properties in ZT (R2 ranging from 0.64 to 0.76), and of SOC in CT (R2= 0.65, P < 0.001). For CT, 2,4-D sorption by soil was very well predicted with soil properties alone (R2= 0.82, P < 0.001) and with soil properties in combination with topographic variables (R2= 0.85, P < 0.001), but was less predicted by topographic variables alone (R2= 0.50, P < 0.001). For ZT, the level of prediction of 2,4-D sorption by soil was weak with soil properties alone (R2= 0.53, P < 0.001) or when topographical variables alone (R2= 0.56, P < 0.001) were used, but a substantial increase in the level of prediction was achieved when both soil properties and topographic variables were used (R2= 0.73, P < 0.001). We conclude that digital terrain modelling, in combination with soil properties data, is an appropriate approach for predicting the spatial distribution of 2,4-D sorption within undulating-to-hummocky glacial till landscapes in western Canada. Key words: herbicide sorption, topography, zero-tillage, conventional-tillage, digital terrain modelling.

Using Soil Electrical Conductivity to Improve Nutrient Management

While site‐specific nutrient management has the potential for improving crop yields, the cost of intensive soil sampling is usually greater than the benefits gained. Apparent soil electrical conductivity (ECa) has been used successfully to measure soil salinity, clay content, and, in the laboratory, nutrient concentrations. This study was initiated to determine if ECa could be used to measure nutrient concentrations in the field. Fifteen field sites with 12 different soil series were studied in three topographic areas of North Carolina in 1997 and 1999. Soil samples and ECa measurements were taken at the same locations in the field and analyzed for P, K, Ca, Mg, Mn, Zn, Cu, pH, cation exchange capacity (CEC), percentage humic matter (HM), and percentage sand, silt, and clay. Nutrient concentrations and soil properties were compared with ECa using correlation and principal components (PC)–stepwise regression analysis. Few significant direct correlations were found between ECa and the selected nutrient elements (R2 < 0.50). Correlations improved when soil series were analyzed separately within a field. The results indicated that salinity, soil texture, or soil moisture were masking the response of ECa to changing nutrient levels in the soil. While the PC–stepwise regression analysis found that ECa was often a key loading factor, changes in soil texture, CEC, and HM resulted in field‐specific relationships between ECa and nutrient concentrations. The primary value of ECa in measuring nutrient levels lies in its ability to identify small changes in soil texture, CEC, or HM that, in turn, indicate where differences in nutrient levels occur.

Predicting yield of barley across a landscape: a state-space modeling approach

Spatial crop yield prediction is an enigma that needs to be solved to avoid ecological and economical risks in agricultural crop production, that can result from local fertilizer surplus or deficiency. Current approaches for site-specific fertilizer distribution are based on patterns of soil properties and yield maps obtained from previous years. The aim of this study was to evaluate the quality of crop yield prediction in an arable field using two sets of variables in autoregressive (AR) state-space models. One set included detailed soil information (texture, organic carbon content) and yield data from the previous year at a high spatial resolution. In the other set, remotely sensed soil and crop information (vegetation index, crop nitrogen status, land surface elevation) was assembled, which is available under farm conditions without intensive soil sampling campaigns. Soil and remotely sensed variables were evaluated in bi- and multivariate autoregressive state-space analysis to predict spring barley grain yield. Remotely sensed variables showed to be better predictors for spatial grain yield estimation than soil variables. Transition coefficients determined from state-space analysis were applied in AR-equations with soil and remotely sensed information, but yet given only the initial value of the spatial yield series. Both sets of variables elicited similar prediction quality.

Yield and Early Growth Responses to Starter Fertilizer in No‐Till Corn Assessed with Precision Agriculture Technologies

Starter fertilization often is recommended to complement broadcast fertilization. This study evaluated no‐till corn (Zea mays L.) yield and early growth responses to starter fertilization using precision agriculture tools. Strip trials, yield monitors, intensive soil sampling, global positioning systems (GPS), and geographical information systems (GIS) were used to conduct 11 trials. Liquid N–P–K starter and no‐starter treatments were applied to the seed furrow in nine fields and beside and below the seeds in two fields. Grain yield and early plant growth data were analyzed with analyses of variance (ANOVA) with or without accounting for spatial correlation with nearest‐neighbor analysis (NNA). Use of NNA reduced standard errors of treatment means. Starter fertilization increased yield in seven fields, reduced yield in one field (−231 kg ha−1), and often increased early growth. Yield increases were large (200–671 kg ha−1) in field areas with low soil‐test P (STP) (<16 mg P kg−1, Bray‐P1). Responses in areas with high STP were small (80–194 kg ha−1) when the starter was applied in the furrow and larger (165–465 kg ha−1) when it was applied beside and below the seeds at higher N rates (16.3–27.2 kg N ha−1). Across fields, early growth response (32%) was linearly but poorly correlated (r = 0.44) with yield response (2.4%). The within‐field variation in yield and growth responses was not consistently related with starter treatments, soils, or soil tests other than STP. Large yield responses of no‐till corn to starter are likely when STP is below optimum or when preplant or sidedress N rates are deficient.

Using yield maps and intensive soil sampling to improve nitrogen fertiliser recommendations from a deterministic model in the Western Australian wheatbelt

A field-scale experiment was conducted to determine the ability of a deterministic model developed for Western Australian wheat farmers to guide site-specific applications of nitrogen fertiliser. The results indicated that site-specific information of achievable yield improved the prediction accuracy much more than information about soil nitrogen — even though the latter was more costly to acquire. When applied together, these sources of information improved the prediction accuracy of the model markedly, explaining about half of the variation of yield response to nitrogen. However, the model failed to explain a substantial portion of site-specific variation, even with this intensity of information. This failure indicates the difficulty of representing complex biological systems with simple functional models.

Method for Precision Nitrogen Management in Spring Wheat: I Fundamental Relationships

Wheat (Triticum aestivum L.) fields in the semi-arid Northern Great Plains are spatially variable in soil N fertility and crop productivity. Consequently, there is interest in applying variable, rather than uniform rates of fertilizer N across the landscape. Intensive soil sampling as a basis for variable-rate fertilizer management is too costly when compared to the value of wheat in this region. The objective of this research was to determine relationships between yield and protein, and protein and available N as needed to develop a cost-effective variable-rate N fertilizer strategy for spring wheat. A three-year study (1996–1998) was carried out at a site near Havre, Montana, USA (48°30′N, 109°22′W). Treatments consisted of three water regimes, four cultivars, and five fertilizer N levels per water regime in a randomized complete block design with four replicates. Scatter diagrams of relative yield vs. grain protein were consistent with earlier investigators, and indicated protein concentrations at harvest provided a method for indexing N nutrition adequacy (deficiency vs. sufficiency) in wheat. A critical protein concentration of 13.2% was defined using a graphical Cate-Nelson analysis. This value appeared to be consistent across the three water regimes and four cultivars as 159 (88%) of the 180 water×cultivar×N level episodes were in positive quadrants. No correlation could be found between relative yield and protein for episodes below the critical level (r2=0.1). Hence, grain protein concentrations could not be used to predict the magnitude of yield losses from N deficiency. Grain protein content would be useful for prescribing fertilizer recommendations where N deficiency (<13.2% protein) reduces grain yield under semi-arid conditions. Inverse slopes (dy/dx) of the protein-available N curves reveal that it takes 12–18 kg N/ha to change protein 1% (e.g., 12% vs. 13%) where wheat is under water stress during grain fill. The total N requirement could then be computed by summing the N required for raising protein and the N removed by the crop in the year when the grain was harvested.

Localized Soil Management in Fertilizer Injection Zone to Reduce Nitrate Leaching

AbstractNitrogen fertilization of row crops in humid regions can result in leaching of NO3, which represents an inefficient use of resources and may result in environmental degradation. A localized compaction and doming (LCD) fertilizer injector was developed to alter the physical properties of soil surrounding knife‐injected N. Injection by LCD includes smearing macropores below the injection slot, formation of a localized compacted soil layer over the injected N, and formation of a surface dome to cover the compacted soil layer and the fertilizer band. The LCD injector was tested, along with a conventional knife injector (without a covering disk), to evaluate its effect on leaching by determining NO3 and Br tracer redistribution after NO3 fertilizer injection. Chemical distributions were determined by intensive soil sampling to 0.8 m below the soil surface. In a second experiment, corn (Zea mays L.) yield response to both N injectors was evaluated. Four fertilization rates (67, 112, 157, and 202 kg N ha−1 of UAN [urea‐ammonium nitrate]) were used to define yield response. During seasons when rainfall was below average, neither NO3 redistribution nor crop yield showed a response to fertilizer injection technique. During a growing season with above‐average rainfall, 26 kg ha−1 more NO3 and 25 kg ha−1 more Br remained in the top 0.8 m of soil when LCD‐injected. LCD injection increased crop yield approximately 0.48 Mg ha−1 over injection by the conventional knife method during an above‐average rainfall season, indicating that one‐fifth of the conventional knife‐applied N was lost prior to crop uptake during the wet year. These findings suggest that the LCD injector may be effective at reducing leaching losses during growing seasons when rainfall is abundant.

Modeling the Transport of Water Stable Isotopes in Unsaturated Soils Under Natural Conditions: 2. Comparison With Field Experiments

The purpose of this work is to test a mechanistic model for predicting depth profiles of stable isotope values in unsaturated soil under natural climatic conditions. An experiment was conducted on a (20 × 50 m) bare field (27% clay, 62% silt, and 11% sand) at Montfavet (France) for a 50‐day period. The field was irrigated with a sprinkler (approximately 7 mm h−1) at the beginning of the experiment (July 9), after which evaporation operated under natural conditions (July 10). Three to five gravimetric water content profiles, down to 0.20 m, were measured daily. The field was equipped with tensiometers at 0.10‐m intervals from 0.15 to 1.55 m depth at three sites. Temperature probes were installed at intervals down to 1.00 m depth at one site, and soil‐temperature measurements were recorded every 15 min. Soil‐water was sampled for isotopic analysis about every 5 days. A micrometeorological mast was set up in the center of the field to measure wind speed, air temperature, and vapor pressure and to estimate the sensible heat flux (eddy correlation method). Unsaturated hydraulic conductivity and aerodynamic roughness length were estimated before running a full comparison with field data. Good agreement was achieved between observed and calculated hourly fluxes for net radiation, soil heat conduction, sensible heat flux, and actual soil evaporation. The model described well the variations of both moisture content and soil temperature. The isotopic enrichment in the top soil layers was qualitatively well described, although systematic differences appeared in the shape of the calculated and measured isotope profiles. The model results were highly sensitive to small changes in convective transport in the liquid phase and to the initial isotope profile. More intensive soil sampling would be useful for validating the model.

Rapid Salinity Mapping by Electromagnetic Induction for Determining Riparian Restoration Potential

AbstractThe feasibility of measuring soil salinity with electromagnetic induction (EM) for determining riparian restoration potential was investigated on a 28‐hectare plot at the Bosque del Apache National Wildlife Refuge in central New Mexico. The plot was cleared of exotic Tamarix chinensis (saltcedar), surveyed and gridded into 1370.2 hectare sections. Soil samples and EM measurements were taken at each section. We compared laboratory‐determined ECe values from the soil samples with ECa values calculated from the EM measurements using a model developed by Rhoades et al. (1990). Direct comparison of ECe values determined from the two methods yields a low correlation due to sample‐size differences but the calculated ECa was able to accurately predict whether the measured ECe would lie above or below some threshold value. An assessment of general site suitability for riparian restoration with electromagnetic induction has proven to be a rapid, accurate, and cost‐effective alternative to intensive soil sampling.