ASSESSMENT OF SAMPLING ERROR ASSOCIATED WITH SOIL MOISTURE ESTIMATION DESIGNS1

A spectral formalism was developed and applied to quantify the sampling errors due to spatial and/or temporal gaps in soil moisture measurements. A design filter was developed to compute the sampling errors for discrete measurements in space and time. This filter has as its advantage a general form applicable to various types of sampling design. The lack of temporal measurements of the two‐dimensional soil moisture field made it difficult to compute the spectra directly from observed records. Therefore, the wave number frequency spectra of soil moisture data derived from stochastic models of rainfall and soil moisture were used. Parameters for both models were estimated using data from the Southern Great Plains Hydrology Experiment (SGP97) and the Oklahoma Mesonet. The estimated sampling error of the spatial average soil moisture measurement by airborne L‐band microwave remote sensing during the SGP97 hydrology experiment is estimated to be 2.4 percent. Under the same climate conditions and soil properties as the SGP97 experiment, equally spaced ground probe networks at intervals of 25 and 50 km are expected to have about 16 percent and 27 percent sampling error, respectively. Satellite designs with temporal gaps of two and three days are expected to have about 6 percent and 9 percent sampling errors, respectively.     

Field-scale variation in microbial activity and soil properties in relation to mineralization and sorption of pesticides in a sandy soil

Pesticides applied to agricultural soils are subject to environmental concerns because leaching to groundwater reservoirs and aquatic habitats may occur. Knowledge of field variation of pesticide-related parameters is required to evaluate the vulnerability of pesticide leaching. The mineralization and sorption of the pesticides glyphosate and metribuzin and the pesticide degradation product triazinamin in a field were measured and compared with the field-scale variation of geochemical and microbiological parameters. We focused on the soil parameters clay and organic carbon (C) content and on soil respiratory and enzymatic processes and microbial biomass. These parameters were measured in soil samples taken at two depths (Ap and Bs horizon) in 51 sampling points from a 4-ha agricultural fine sandy soil field. The results indicated that the spatial variation of the soil parameters, and in particular the content of organic C, had a major influence on the variability of the microbial parameters and on sorption and pesticide mineralization in the soil. For glyphosate, with a co-metabolic pathway for degradation, the mineralization was increased in soils with high microbial activity. The spatial variability, expressed as the CV, was about five times higher in the Bs horizon than in the Ap horizon, and the local-scale variation within 100 m(2) areas were two to three times lower than the field-scale variation within the entire field of about 4 ha.     

Application of flow field-flow fractionation and laser sizing to characterize soil colloids in drained and undrained lysimeters

This paper reports the use of a new technique, flow field-flow fractionation (FlFFF), for the characterization of soil sampled under grassland. FlFFF can be used to determine the fine colloidal material in the <1 microm fraction obtained by gravitational settling of 1% m/v soil suspensions. The aim of this work was to determine the potential of FIFFF to characterize soil colloids in drained and undrained field lysimeters from soil cores sampled at different depths. Two different grassland lysimeter plots of 1 ha, one drained and one undrained, were investigated, and the soil was sampled at 20-m intervals along a single diagonal transect at three different depths (0-2, 10-12, and 30-32 cm). The results showed that there was a statistically significant (P = 0.05) increase in colloidal material at 30- to 32-cm depth along the transect under the drained lysimeter, which correlates with disturbance of the soil at this depth due to the installation of tile drains at 85-cm depth backfilled to 30-cm depth with gravel. Laser sizing was also used to determine the particles in the size range 1 to 2000 microm and complement the data obtained using FlFFF because laser sizing lacks resolution for the finer colloidal material (0.1-1.0 microm). The laser sizing data showed increased heterogeneity at 30- to 32-cm depth, particularly in the 50 to 250 microm size fraction. Therefore FIFFF characterized the finer material and laser sizing the coarser soil fraction (<2000 microm) at depth in drained and undrained grassland. This is of importance as colloidal material is more mobile than the larger material and consequently an important vector for contaminant transport from agricultural land to catchments.     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.     

Relationships between phosphorus levels in soil and in runoff from corn production systems

Phosphorus-enriched runoff from cropland can hasten eutrophication of surface waters. A soil P level exceeding crop needs due to long-term fertilizer and/or manure applications is one of several potential sources of increased P losses in runoff from agricultural systems. Field experiments were conducted at locations representative of three major soil regions in Wisconsin in corn (Zea mays L.) production systems to determine the effect of tillage, recent manure additions, soil P extraction method, and soil sampling depth (0-2, 0-5, and 0-15 cm) on the relationship between soil test P level and P concentrations in runoff. Runoff from simulated rainfall (75 mm h(-1)) was collected from 0.83-m2 areas for 1 h after rainfall initiation and analyzed for dissolved phosphorus (DP), total phosphorus (TP), and sediment. The DP fraction of the TP concentration in runoff ranged from 5 to 17% among sites with most of the variation in TP due to varying sediment concentration on the well-drained silt loam soils and to soil test P level on the poorly drained silty clay loam soil. In 213 observations across a range of soils and managements, good relationships occurred between soil test P level and DP concentration in runoff for most of the tests and sampling depths used. Recent manure additions and high levels of surface cover from corn residue sometimes masked this relationship. The slope of DP relative to soil test P level was markedly higher on the silty clay loam soil than on the silt loam soils possibly due to soil permeability-infiltration rate differences. Agronomic soil P tests were as effective as environmentally oriented soil P tests for predicting DP concentrations in runoff.     

Application of remote and in situ information to the management of wetlands in Poland

The protection and regeneration of wetlands has been of crucial importance as a goal in ecological research and in nature conservation for some time and is more important than ever now. Knowledge about the biophysical properties of wetlands' vegetation retrieved from satellite images enables us to improve the monitoring of these unique areas, which are otherwise very often impenetrable and therefore difficult to examine, analyze and assess by means of site visits. The Biebrza Wetlands are situated in the North-East part of Poland and are one of the largest areas made up of marshes and swamps in the entire EU. This is still one of the wildest areas and one of the least destroyed, damaged or changed by human impact. However, in the recent decades there have been attempts made to intensify and overexploit the natural resources of the region and implement new agriculture practices in the area. In this period, drainage canals have been built, and a good deal of the area has been drained. The area of this precious ecosystem covers 25 494 ha. This valuable area of peat with unique vegetation species and with very special birds is one of the most valuable areas in Europe and in 1995 was added to the list of Ramsar sites. The investigation of wetlands in the Biebrza River Valley has been carried out at ground level by taking measurements of soil moisture, evapotranspiration, Leaf Area Index, wet and dry biomass and the levels of ground water and meteorological parameters. Also examined were radiative temperature, detailed vegetation mapping, and APAR. For some years the deterioration of peat lands has been noticed due to the drying out of the area and the frequent outbreak of fires. The consequence is the succession of new vegetation and the appearance of new ecosystems. The Remote Sensing Centre in the Institute of Geodesy and Cartography has undertaken the investigation by applying ERS-2.SAR and ENVISAT ASAR of IS2 and IS4 and VV, HH, HV polarization for the purpose of modeling soil moisture and humidity changes of the area under investigation. The investigation also aimed at finding the best biophysical properties of wetlands' vegetation to characterize marshland habitats and its changes. At the same time as registering the microwave data, the optical data from Landsat ETM+, SPOT VEGETATION, ERS-2.ATSR, ENVISAT MERIS, and NOAA/AVHRR have been registered and information about the biomass and heat fluxes as sensible and latent heat has also been calculated. The vegetation indices are calculated from EO satellite data taking into account jointly the features of vegetation responsible for reflection in various bands and combining this information from several spectral bands. Also, the changes in the humidity of the area have been examined by extracting the backscattering coefficients from two SAR images that were taken at a similar period of the year but with a gap of 5 years. The information about soil moisture as retention, soil moisture changes, heat fluxes and evapotranspiration are all very important for estimates of CO(2) sequestration. The ENVISAT images have been obtained for the ESA AO-ID122 project. Also the SMOS and ALOS data will be applied for the Biebrza Wetlands in the future.     

IMPROVED RAINFALL/RUNOFF ESTIMATES USING REMOTELY SENSED SOIL MOISTURE1

ABSTRACT: Remotely sensed soil moisture data measured during the Southern Great Plains 1997 (SGP97) experiment in Oklahoma were used to characterize antecedent soil moisture conditions for the Soil Conservation Service (SCS) curve number method. The precipitation‐adjusted curve number and the soil moisture were strongly related (r²= 0.70). Remotely sensed soil moisture fields were used to adjust the curve numbers and the runoff estimates for five watersheds, in the Little Washita watershed; the results ranged from 2.8 km² to 601.6 km². The soil moisture data were applied at two spatial scales, a finer one (800 m) measuring spatial resolution and a coarser one (28 km). The root mean square error (RMSE) and the mean absolute error (MAE) of the runoff estimated by the standard SCS method was reduced by nearly 50 percent when the 800 m soil moisture data were used to adjust the curve number. The coarser scale soil moisture data also significantly reduced the error in the runoff predictions with 41 percent and 28 percent reductions in MAE and RMSE, respectively. The results suggest that remote sensing of soil moisture, when combined with the SCS method, can improve rainfall runoff predictions at a range of spatial scales.     

Diuron occurrence and distribution in soil and surface and ground water associated with grass seed production

Little is known about the occurrence and distribution of the herbicide diuron [3-(3,4-dichlorophenyl)-1,1-dimethyl urea] in soil, ground water, and surface water in areas affected by grass-seed production. A field study was designed to investigate the occurrence and distribution of diuron and its transformation products at a poorly drained field site located along an intermittent tributary of Lake Creek in the southern Willamette Valley of Oregon. The experimental sites consisted of a field under commercial grass seed production with a cultivated riparian zone and a second site that was part of the same grass seed field but with a noncultivated riparian zone. Diuron and its transformation product DCPMU [3-(3,4-dichlorophenyl)-1-methylurea] were the only significant residues detected in this study. Concentrations of diuron in surface water declined from a maximum of 28 microg/L immediately following application to low levels that persisted as long as flow was present. Diuron and DCPMU concentrations in shallow ground water (15-36 cm below ground surface) were highest (2-13 microg/L) in the zone immediately adjacent (0.5 m) to Lake Creek and indicated the influence of stream water on shallow ground water near the stream. Diuron and DCPMU detected in soil prior to the second season's application indicated the persistence of diuron and DCPMU from the previous year's application. Surface runoff during the rainy season removes only a very small percentage (<1%) of the applied herbicide. In addition, no evidence was obtained for the downward transport of diuron or its transformation products to deep ground water.     

Herbicide loading to shallow ground water beneath Nebraska's Management Systems Evaluation Area

Better management practices can counter deterioration of ground water quality. From 1991 through 1996 the influence of improved irrigation practices on ground water pesticide contamination was assessed at the Nebraska Management Systems Evaluation Area. Three 13.4-ha corn (Zea mays L.) fields were studied: a conventional furrow-irrigated field, a surge-irrigated field and a center pivot-irrigated field, and a center pivot-irrigated alfalfa (Medicago sativa L.) field. The corn fields received one identical banded application of Bicep (atrazine [6-chloro-N-ethyl-N'-(1-methylethyl)-1,3,5-triazine-2,4,-diamine] + metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl) acetamidel) annually; the alfalfa field was untreated. Ground water samples were collected three times annually from 16 depths of 31 multilevel samplers. Six years of sample data indicated that a greater than 50% reduction in irrigation water on the corn management fields lowered average atrazine concentrations in the upper 1.5 m of the aquifer downgradient of the corn fields from approximately 5.5 to <0.5 microg L(-1). Increases in deethylatrazine (DEA; 2-chloro-4-amino-6-isopropylamino-s-triazine) to atrazine molar ratios indicated that reducing water applications enhanced microbial degradation of atrazine in soil zones. The occurrence of peak herbicide loading in ground water was unpredictable but usually was associated with heavy precipitation within days of herbicide application. Focused recharge of storm runoff that ponded in the surge-irrigated field drainage ditch, in the upgradient road ditch, and at the downgradient end of the conventionally irrigated field was a major mechanism for vertical transport. Sprinkler irrigation technology limited areas for focused recharge and promoted significantly more soil microbial degradation of atrazine than furrow irrigation techniques and, thereby, improved ground water quality.     

Peat fire effects on some properties of an artificially drained peatland

The management of artificially drained organic soils is a very important issue, since the accelerated mineralization and sometimes peat fires alter physical and chemical properties of soils and the availability of plant nutrients. This study was performed to determine relatively short- and long-term effects of peat fires on some physical and chemical properties of soils in the artificially drained Gavur Lake Peatland of Turkey. To achieve this objective, measured properties of soils burned in 2001, burned in 1965, and unburned were compared. The results indicated that soil bulk density, pH, amounts of soluble salts, CaCO3, and concentrations of ammonium acetate-extractable (AAE) Ca, Mg, K, and Na were significantly higher for both sampling depths in the burned areas. The areas burned in 2001 had higher pH, soluble salts, and the concentrations of AAE Ca, Mg, and K compared with sites burned in 1965, and this was reasoned with leaching losses and plant uptake of these basic cations for four decades in the latter. Percent saturation and organic carbon contents of soils, however, were significantly lower in the burned areas for both sampling depths. Olsen P levels were not significantly different between the sites. This work clearly shows that alterations in soils properties with peat fires do not recover in the long term.     

PREPLANTING SOIL MOISTURE USING PASSIVE MICROWAVE SENSORS1

ABSTRACT: Accurate assessment of preplanting soil moisture conditions is necessary for good agricultural management, and can have a significant influence on crop yield in the Texas Panhandle region. The Texas High Plains Underground Water Conservation District invests considerable time and money in developing a soil moisture deficit map each year in the hopes of achieving optimal use of irrigation water. Microwave sensors are responsive to surface soil moisture and, if used in this application, can provide timely and detailed information on root zone soil moisture. For this reason, an experiment was conducted in 1984 to evaluate the potential of aircraft-mounted passive microwave sensors. Microwave radiometer data were collected over a 2700 km² area near Lubbock, Texas, with a processed resolution of 0.32 km². These data were ground registered and converted to estimates of soil moisture using an appropriate model and land cover and soil texture information. Analyses indicate that the system provides an efficient means for mapping variations in soil moisture over large areas.     

Multi-stage sampling for large scale natural resources surveys: a case study of rice and waterfowl

Large-scale sample surveys to estimate abundance and distribution of organisms and their habitats are increasingly important in ecological studies. Multi-stage sampling (MSS) is especially suited to large-scale surveys because of the natural clustering of resources. To illustrate an application, we: (1) designed a stratified MSS to estimate late autumn abundance (kg/ha) of rice seeds in harvested fields as food for waterfowl wintering in the Mississippi Alluvial Valley (MAV); (2) investigated options for improving the MSS design; and (3) compared statistical and cost efficiency of MSS to simulated simple random sampling (SRS). During 2000-2002, we sampled 25-35 landowners per year, 1 or 2 fields per landowner per year, and measured seed mass in 10 soil cores collected within each field. Analysis of variance components and costs for each stage of the survey design indicated that collecting 10 soil cores per field was near the optimum of 11-15, whereas sampling >1 field per landowner provided few benefits because data from fields within landowners were highly correlated. Coefficients of variation (CV) of annual estimates of rice abundance ranged from 0.23 to 0.31 and were limited by variation among landowners and the number of landowners sampled. Design effects representing the statistical efficiency of MSS relative to SRS ranged from 3.2 to 9.0, and simulations indicated SRS would cost, on average, 1.4 times more than MSS because clustering of sample units in MSS decreased travel costs. We recommend MSS as a potential sampling strategy for large-scale natural resource surveys and specifically for future surveys of the availability of rice as food for waterfowl in the MAV and similar areas.     

Modeling phosphorus concentrations in Irish rivers using land use, soil type, and soil phosphorus data

Modeling diffuse phosphorus (P) loss may indicate management strategies to minimize P loss from agricultural sources. An empirical model predicting flow-weighted phosphorus concentrations (MRP) was derived using data collected from 35 Irish river catchments. Monitoring records of riverine P and stream flow data were used to calculate MRP values averaged for the years 1991-1994. These data were modeled using land use, soil type, and soil P data. Soil type in catchments was described using soil survey classifications weighted according to their P desorption properties from laboratory results. Soil test P concentrations for the studied watersheds were obtained from a national database. Soil P levels were weighted based on the results of field experiments measuring P losses in overland flow from fields at different soil test P levels. The 35 catchments were statistically clustered into two populations (A and B) based on differences in soil type, specifically, soil hydrology. Catchments in Cluster A had predominantly poorly drained soils and comparatively higher MRP concentrations (0.03-0.17 mg L(-1)) than Cluster B areas (0.01-0.7 mg L(-1)) with mostly well-drained soils. Regression equations derived for A and B type catchments predicted MRP values with 68 and 62% of the variation explained in the models, respectively. Data extracted for the rest of the country were applied to the models to delineate areas at risk on a national scale. While the models were only moderately accurate they highlighted the influence of land management, specifically, high production grassland receiving high P inputs, in conjunction with the effect of soil type and soil hydrology on the transport of P to surface waters.     

Sampling and Mapping a Soil Erosion Cover Factor by Integrating Stratification,Model Updating and Cokriging with Images

Cost-efficient sample designs for collection of ground data and accurate mapping of variables are required to monitor natural resources and environmental and ecological systems. In this study, a sample design and mapping method was developed by integrating stratification, model updating, and cokriging with Landsat Thematic Mapper (TM) imagery. This method is based on the spatial autocorrelation of variables and the spatial cross-correlation among them. It can lead to sample designs with variable grid spacing, where sampling distances between plots vary depending on spatial variability of the variables from location to location. This has potential cost-efficiencies in terms of sample design and mapping. This method is also applicable for mapping in the case in which no ground data can be collected in some parts of a study area because of the high cost. The method was validated in a case study in which a ground and vegetation cover factor was sampled and mapped for monitoring soil erosion. The results showed that when the sample obtained with three strata using the developed method was used for sampling and mapping the cover factor, the sampling cost was greatly decreased, although the error of the map was slightly increased compared to that without stratification; that is, the sample cost-efficiency quantified by the product of cost and error was greatly increased. The increase of cost-efficiency was more obvious when the cover factor values of the plots within the no-significant-change stratum were updated by a model developed using the previous observations instead of remeasuring them in the field.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

Estimation of Nonpoint Source Nitrate Concentrations in Indiana Rivers Based on Agricultural Drainage in the Watershed

Subsurface tile‐drained agricultural fields are known to be important contributors to nitrate in surface water in the Midwest, but the effect of these fields on nitrate at the watershed scale is difficult to quantify. Data for 25 watersheds monitored by the Indiana Department of Environmental Management and located near a U.S. Geological Survey stream gage were used to investigate the relationship between flow‐weighted mean concentration (FWMC) of nitrate‐N and the subsurface tile‐drained area (DA) of the watershed. The tile DA was estimated from soil drainage class, land use, and slope. Nitrate loads from point sources were estimated based on reported flows of major permitted facilities with mean nitrate‐N concentrations from published sources. Linear regression models exhibited a statistically significant relationship between annual/monthly nonpoint source (NPS) nitrate‐N and DA percentage. The annual model explained 71% of the variation in FWMC of nitrate‐N. The annual and monthly models were tested in 10 additional watersheds, most with absolute errors within 1 mg/l in the predicted FWMC. These models can be used to estimate NPS nitrate for unmonitored watersheds in similar areas, especially for drained agricultural areas where model performance was strongest, and to predict the nitrate reduction when various tile drainage management techniques are employed.     

Comparison of Field Performance of Multiple Soil Moisture Sensors in a Semi‐Arid Rangeland1

Abstract:  Automated electronic soil moisture sensors, such as time domain reflectometry (TDR) and capacitance probes are being used extensively to monitor and measure soil moisture in a variety of scientific and land management applications. These sensors are often used for a wide range of soil moisture applications such as drought forage prediction or validation of large‐scale remote sensing instruments. The convergence of three different research projects facilitated the evaluation and comparison of three commercially available electronic soil moisture probes under field application conditions. The sensors are all installed in shallow soil profiles in a well instrumented small semi‐arid shrub covered subwatershed in Southeastern Arizona. The sensors use either a TDR or a capacitance technique; both of which indirectly measure the soil dielectric constant to determine the soil moisture content. Sensors are evaluated over a range of conditions during three seasons comparing responses to natural wetting and drying sequences and using water balance and infiltration simulation models. Each of the sensors responded to the majority of precipitation events; however, they varied greatly in response time and magnitude from each other. Measured profile soil moisture storage compared better to water balance estimates when soil moisture in deeper layers was accounted for in the calculations. No distinct or consistent trend was detected when comparing the responses from the sensors or the infiltration model to individual precipitation events. The results underscore the need to understand how the sensors respond under field application and recognize the limitations of soil moisture sensors and the factors that can affect their accuracy in predicting soil moisture in situ.     

Heavy metals in an urban watershed in southeastern Michigan

The occurrence of heavy metals in the soil was measured over a period of several years to determine background concentrations in a heavily urbanized watershed in southeastern Michigan. A spatially dispersed sample was collected to capture the inherent variability of the soils and historic land use. The analysis focused on 14 metals (antimony, arsenic, barium, beryllium, cadmium, chromium, copper, lead, mercury, nickel, selenium, silver, thallium, and zinc) that are part of the USEPA's list of the 129 most common pollutants. Metal concentrations were measured at three depths: near-surface (<0.5 m), shallow subsurface (0.5-10 m), and depths greater than 10 m across six soil units in glacial terrain. Additional analyses assessed the metal concentrations in each depth profile across three general land use categories: residential, commercial, and industrial. Metal concentrations were the highest in the near-surface with Pb present at concentrations averaging 15.5 times that of background in industrial areas and approximately 16 times background in residential areas. Cadmium, Hg, and Zn were also present in surface soils at levels of several times that of background. The highest concentrations of each of these metals were present in the clay-rich soils located in the eastern, more urbanized and industrialized part of the watershed. Metals detected at elevated concentrations decreased in concentration with increasing depth and distance from the urbanized and industrialized center of the watershed. Statistically significant differences in the concentrations of heavy metals were also noted between the land use categories, with Cd, Cr, Cu, Pb, Ni, and Zn observed within industrial areas at mean concentrations several times greater than background levels.     

ECONOMIC AND WATER QUALITY EFFECTS OF VARIABLE AND UNIFORM APPLICATION OF NITROGEN1

ABSTRACT: Nitrogen (N) fertilizer rates for achieving optimum crop yields often vary within a field due to spatial variability in soil moisture and nitrogen content and other crop growth factors. When there is substantial within-field variability in these factors, uniform application of N (UAN) may not be economically efficient in terms of maximizing net return because N is likely to be over-applied in some areas and under-applied in other areas of the field. In addition, over-application can adversely affect water quality. A sample of fields in a Midwestern agricultural watershed is used to test for statistically significant differences in N application rates, crop yields, surface and ground water quality and net returns between UAN and variable application of N (VAN) for four cropping systems. Profitability and water quality benefits of VAN are sensitive to the distribution of soil types within a field. Water quality effects and profitability of UAN and VAN vary with cropping systems. VAN is not uniformly superior to UAN in terms of increasing net returns and improving water quality for the farming systems and watershed evaluated in this study.     

Optimal spatial resolution for collection of ground data and multi-sensor image mapping of a soil erosion cover factor

Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.