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Research on nitrogen (N) mineralization from organic residues is important to understand N cycling in soils. Here we review research on factors controlling net N mineralization as well as research on laboratory and field modeling efforts, with the objective of highlighting areas with opportunities for additional research. Among the factors controlling net N mineralization are organic composition of the residue, soil temperature and water content, drying and rewetting events, and soil characteristics. Because C to N ratio of the residue cannot explain all the variability observed in N mineralization among residues, considerable effort has been dedicated to the identification of specific compounds that play critical roles in N mineralization. Spectroscopic techniques are promising tools to further identify these compounds. Many studies have evaluated the effect of temperature and soil water content on N mineralization, but most have concentrated on mineralization from soil organic matter, not from organic residues. Additional work should be conducted with different organic residues, paying particular attention to the interaction between soil temperature and water content. One- and two-pool exponential models have been used to model N mineralization under laboratory conditions, but some drawbacks make it difficult to identify definite pools of mineralizable N. Fixing rate constants has been used as a way to eliminate some of these drawbacks when modeling N mineralization from soil organic matter, and may be useful for modeling N mineralization from organic residues. Additional work with more complex simulation models is needed to simulate both gross N mineralization and immobilization to better estimate net N mineralized from organic residues.

The amount of N mineralized or immobilized during the decomposition of a crop residue will influence the amount of N available for crop uptake and will ultimately impact N‐management practices and groundwater quality. The objective of this work was to determine quantitative relationship(s) between a crop residue's chemical properties and the potential net amount of N that would mineralize in a season. Eight experiments (six from the literature and two conducted by the authors) were combined to determine general relationships between net N mineralized and residue chemical characteristics. Regression analysis indicated that 75 and 72% of the variability in the measured amounts of N mineralized in the eight experiments could be explained using either the C/N ratio or the square‐root transformation of N concentration of the residue, respectively. The break point between net N mineralization and net immobilization was calculated to be at a C/N ratio of 40, which corresponds to a N concentration of about 10 g N/kg (assuming residue C is 400 g/kg). Eighty percent of the variability in the amount of N mineralized could be explained by a regression equation that included N and the lignin‐to‐N ratio as independent variables. The fitted equations provide estimates of the maximum amount of N that potentially will mineralize in a season from incorporated crop residues of different N contents.

The surface soil organic C (SOC) concentration is a useful soil The dark color of soil is typically associated with high property to map soils, interpret soil properties, and guide fertilizer organic-matter concentration and high native fertility. and agricultural chemical applications. The objective of this study was Soils with thick, dark surface horizons are often sepato determine whether surface SOC concentrations could be predicted from remotely sensed imagery (an aerial photograph of bare surface rated from other soils at the highest categorical level in soil) of a 115-ha field located in Crisp County, Georgia. The surface many soil classification systems, reflecting the differ-SOC concentrations were determined for soil samples taken at 28 ences in the genesis of soils as well as the importance field locations. The statistical relationship between surface SOC conof these soils as a medium for plant growth and indepencentrations and image intensity values in the red, green, and blue dent natural bodies worthy of further study (Schulze bands was fit to a to a logarithm linear equation (R 2 ϭ 0.93). The et al., 1993). Research has been done concerning the distribution of the surface SOC concentrations was predicted with relationships between soil color and soil organic matter. two approaches. The first approach was to apply the relationship However, many of these studies were based on Munsell to individual pixels and then determine the distribution; the second color notations for specific soils at specific locations approach was to classify the image and then apply the relationship (Alexander, 1969; Steinhardt and Franzmeier, 1979; to determine the class boundaries and means. Eight levels of surface SOC concentrations were classified in both approaches, and there Schulze et al., 1993) or for the purpose of designing was good agreement between the two approaches with a probability spectral sensors (Pitts et al., 1983; Griffis, 1985; Smith value near one using a paired t-test. The predicted and measured et al., 1987). surface SOC concentrations, based on additional soil samples from There were attempts to quantify relationships be-31 field locations, were compared using linear regression (r 2 ϭ 0.97 tween soil color and organic matter concentrations by and r 2 ϭ 0.98 for the two approaches). The surface SOC concentrations Brown and O'Neal in the 1920's (Schulze et al., 1993). were correctly classified in 77.4 and 74.2% of cases for the two ap-Later, color charts or tables that described the relationproaches. The procedures tested were accurate enough to be used ships between soil color and organic-matter concentrafor precision farming applications in agricultural fields.

A system is needed to measure ammonia volatilization from N fertilizer applied under field conditions. This study was undertaken to develop a device for making these measurements and still maintain a field environment. The basic system developed consists of a vacuum pump, a chemical trap to capture ammonia, and the volatilization chamber.The volatilization chamber consists of a steel cylinder 21.8‐cm I.D. by 15‐cm long that is forced into the soil with its top flush with the soil surface to provide a microplot. The lid assembly attached to the cylinder includes a hinge and reversible electric motor that allows the lid to be automatically rotated from the microplot. The volatilization chamber was designed with the removable lid so that the lid is closed only for short intervals during the day while ammonia loss is measured. The total ammonia loss is calculated by integrating the rates of loss over time. Between measurements, the lid is open to allow normal environmental conditions of temperature, wind movement, etc., on the micro‐plot. The volatilization chamber has five air intake ports and one exhaust port. Ammonia loss was unaffected by air flow rate if air movement through the chamber exceeded 15 exchange volumes/min.Our complete system consisted of eight volatilization chambers with a system of multiple chemical traps that can operate automatically without attention for 24 hours. When tested in the field, duplicate measurements of ammonia loss from ammonium sulfate agreed quite closely, both in the rates of loss and the total ammonia loss. In another test, ammonia loss from ammonium chloride agreed reasonably well with other laboratory results.