Emission of N2O from soils is an extensively studied environmental process, given that N2O is ‘‘at the heart of debates’’ on several prevalent current issues.Approximately two-thirds of total global emission comes from soils; most of the emission from soils is in turn attributed to agriculture.The intrinsic soil properties most commonly mentioned in research studies and models as controlling emission of N2O are texture, pH, organic matter, and ability to supply inorganic nitrogen.Production of N2O in soil is generally attributed to microbiological processes, and therefore the factors that regulate the activity of N2O-producing microorganisms should be the same factors that regulate N2O production.These controlling factors are generally thought to be well recognized, but as research and related commentary on N2O emission from agricultural soils continue to accumulate, the possible role of iron is rarely considered.This is in spite of its known involvement in enzymatic reactions and non-enzymatic reactions that generate N2O.The connection between iron and N2O may have been neglected because iron has never figured prominently in routine evaluations of soil for agronomic research or practical management decisions.Unlike the other soil properties cited above, iron does not have a direct and immediate bearing on the growth of most crops or on the agricultural suitability of a soil from either a physical or a chemical point of view.When it is considered, this is in instances of suspected plant deficiency or toxicity, not in the context of its potential connection with the nitrogen cycle.In addition, compared to other intrinsic properties, soil iron does not dramatically affect the short-term changes in microbiological activity generally associated with N2O production.For these reasons,dutch bucket hydroponic once interest in N2O began to intensify, the previously reported connection with iron was already out of sight.The intent of our work was to reconsider the potential significance of iron in emission of N2O from agricultural soils.Soils were collected from the top 15 cm in 10 agricultural fields throughout California, and were sieved to 2 mm following collection.
Soil pH was measured in 1 M KCl.Percent clay, silt, and sand were determined by a modified pipet method.Total carbon and nitrogen were determined on ball-milled samples by combustion-GC.Just prior to setting up the experiment, inorganic nitrogen was extracted by 0.5 M K2SO4 and determined colorimetrically.Dissolved organic carbon was determined in the same extract by UV-persulfate digestion.We chose two commonly used, contrasting indices to characterize soil iron: that extractable by acid hydroxylamine , an index of reactive iron minerals; and that extractable by pyrophosphate , representing iron complexed with soilorganic matter.FeA was extracted by shaking 0.8 g soil for one hour with 40 ml 0.25 M hydroxylamine hydrochloride in 0.25 M HCl, followed by centrifugation for 30 minutes at 156006 G.FeP was extracted by shaking 1 g soil with 100 ml 0.1 M tetrasodium pyrophosphate for 16 hours, followed by centrifugation for 30 minutes at 156006 G; further centrifugation did not result in any difference in measured iron concentration, indicating that all fine iron colloids had been removed, an important consideration when using this extractant.The concentration of iron in all extracts was determined color imetrically; pyrophosphate extracts were neutralized by a small addition of HCl prior to this determination.All analyses of soil properties were performed in duplicate.These properties are reported in Table 1.As stated above, the properties most commonly believed to control emission of N2O from agricultural soil include texture, pH, organic matter, and the inherent ability of the soil to release inorganic nitrogen.These are intrinsic properties which are not abruptly altered by environmental conditions; in contrast, our treatments were designed to manipulate the most common temporary extrinsic changes that influence N2O production: water content, fertilization, and organic amendments.Since these can vary across a range of values, we necessarily limited our choice of treatments.Fertilizer and compost were either withheld or added at a rate typical of agriculture in California, and two water contents were chosen according to the range expected in agricultural soils.Field capacity, the amount of water a soil can retain against gravity, was chosen as the upper reference point.This is not uncommon, as soil moisture can temporarily exceed field capacity following irrigation or rainfall events.In practice we used water holding capacity to represent field capacity.As a contrasting treatment, we chose 50% WHC.This is near the permanent wilting point of most soils, and it is not likely that soil moisture will fall below this in the field except during unmanaged dry seasons.
Although many intermediate values could have been selected as treatments, we chose to use both ends of a typical spectrum of values in order to present a broad yet concise study.Prior to set-up, WHC was determined as follows: a soil sample was placed into a funnel lined with filter paper, which was then placed into a beaker of water such that just the tip of the funnel was always in contact with water; after the sample ceased to take up water, the sample was allowed to drain, and the moisture content measured.To begin the experiment, the equivalent of 50 g dry soil was placed into cups, which were themselves placed into larger jars containing a small amount of water to avoid desiccation.The larger jars were sealed with lids containing a small foam plug to allow gas exchange with the atmosphere.To imitate the timing typical of agricultural operations, 2 g finely ground finished green waste compost were mixed with the soils and incubated at 40% WHC for seven days.Treatments not receiving compost were similarly incubated.Following this preincubation, each soil received a fertilizer addition according to treatment: none, ammonium sulfate, or potassium nitrate.The amount of nitrogen added was 100 mg kg21 soil, corresponding approximately to a field rate of 150 kg ha21.Fertilizer solution was sprayed onto the soils to reach a water content of 50% or 100% WHC, depending on the treatment.For each soil there were three replicates per treatment.Samples were incubated for 14 days at 22 degrees C.Samples for N2O analysis were taken on days 0, 1, 2, 3, 5, 9, and 14 following addition of fertilizer.The jars containing the soil cups were closed with lids containing septa and allowed to stand for one hour.Gas samples were taken at 0, 30, and 60 minutes after closure and transferred to evacuated gas sampling vials.N2O concentration was determined by gas chromatography-ECD detection.At each sampling date, the rate of N2O emission was determined by linear interpolation of the 0, 30, and 60 minute measurements.Cumulative N2O emission over the course of the incubation was calculated using these data, taking the flux measured at a given date to be the average flux for the interval represented by that date.To identify the soil properties that most strongly explained N2O emission in each experimental treatment, we studied the data using partial least squares multivariate analysis, a form of structural equation modeling.This tool is particularly suitable when the number of predicting variables is greater than the number of observed variables, when multicolinearity is expected among predicting variables, and when multivariate normality can not be assumed.PLS ranks the predicting variables by importance based on linear regression models that project the predicting variables and the observed variables to a new, multivariate space.
Prior to subjecting the data to PLS analysis, predicting variables and the observed response were standardized by centering and scaling the data to have a mean of zero and a standard deviation of one.This ensures that the predicting variables are ranked based on how much of the variation is explained when all variables have the same weight.Although correlations among variables are possible, especially in studies that involve soil properties, this does not change the interpretation given by PLS, which depicts the relative importance of each variable separately, independently of intrinsic links between variables.Nevertheless, a correlation matrix is presented as an aid in understanding the relationships between the soil properties used in our study.Following the exploratory PLS analysis, linear regressions between iron and N2O emission were calculated using unweighted, untransformed data, and were considered significant enough to report at P,0.1.All statistical analyses were performed using JMP 10 software.The results of the PLS analysis are shown in Figure 1, where each soil property is ranked according to its ability to explain cumulative N2O emission across all soils.This ranking was performed for each of the 12 different treatments studied.In five of these treatments, iron ranked higher than any other measured soil characteristic in explaining observed emissions.In four additional treatments,dutch buckets system iron was among the top four predictors.As a complementary approach to further investigate the relationship between iron and N2O emission, simple linear regressions were calculated in which N2O data were compared against FeP and FeA.Whereas PLS was used to arrange a suite of soil properties according to their ability to explain N2O emission, regressions indicate, by the value of r2 , how much of the variability in N2O emission can be explained by a single property; regressions also indicate the direction of the effect and degree of importance of the effect.In most cases, a significant relationship between N2O emission and a given variable can be expected when that variable is ranked highly by PLS.In certain cases, however, a variable ranked highly by PLS may not necessarily yield a significant linear relationship when that variable is considered apart from the other variables; conversely, certain treatments in which a variable is not ranked highly by PLS may nonetheless yield a significant regression.The primary reason for this occasional discrepancy is the nature of the PLS procedure: by considering all predicting variables together, new predictors are generated which are composites of the original variables.Table 3 reports the results of the regressions for treatments that showed a significant relationship between N2O emission and either iron index.Despite a dataset of values for N2O emission which spanned more than three orders of magnitude across soils, several notable connections between iron and N2O emission emerged.FeP was significantly related to N2O emission in four treatments, in which it explained between 16 and 62 percent of the variability, with a positive slope in all cases.This influence was greatest under 100% WHC when ammonium was present and compost was absent.
Such a condition may be reasonably expected on occasion, since most fertilizers supply ammonium, and since this may occur close in time to irrigation or rainfall.In this treatment, an increase in FeP of 1 mg kg-1 corresponded to an increase in cumulative emission of 11.9 ng N2O-N g21 soil during the course of the incubation.Like FeP, the connection between FeA and N2O emission was also significant under several different conditions.Unlike FeP, however, which was positively related to N2O emission, FeA was always negatively related to N2O emission.There was no treatment in which both iron indices were significantly related to N2O emission.Considering that FeP and FeA bear almost no relationship to each other , this difference in behavior suggests that these two indices indeed reflect two forms of iron that differ in reactivity.Also notable in Table 3 is the effect of compost in fertilized treatments: the observed negative association between N2O emission and FeA occurred only in the presence of compost, while the stimulating effect of FeP was observed only without compost.The contrasting relationships of FeA and FeP with N2O emission could be due to differences in the reaction of either form of iron with nitrogen compounds in the soil matrix.For example, hydroxylamine is produced from biological oxidation of ammonia, and is known to generate N2O upon chemical reaction with iron.Reaction with FeP versus FeA, or locally high concentrations of either hydroxylamine or iron, could lead to more or less N2O compared to other reaction products.The ability of aerobic microorganisms to acquire iron can likewise depend on its chemical nature, consequently influencing the amount of reactive compounds produced or consumed through reactions that use iron-dependent enzymes.As soil water content increases, reducing conditions may develop, especially when the depletion of oxygen is accelerated by easily metabolized organic matter.The chemical nature of existing iron may determine the ease with which it is reduced to iron in anaerobic microsites.This will in turn control its participation in other reactions that produce N2O, such as chemodenitrification, which includes the abiotic reduction of nitrite to N2O by iron.Chemodenitrification can also produce other gases, and the relative amount of N2O released may be affected by the form of iron present.