Using soil temperature and moisture to predict forest soil nitrogen mineralization

Knoepp, Jennifer D.; Swank, Wayne T.

doi:10.1007/s00374-002-0536-7

Cited by 111 publications

(68 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Generated linear regression equations included into the leaves of the regression tree model RT125. 1998; Knoepp and Swank, 2002). Whereas maximum mineralization is reported for the forest soil temperature range between 20 and 28 • C (Nicolardot et al, 1994), the mineralization becomes strongly inhibited at temperatures below 10 • C (Knoepp and Swank, 2002).…”

Section: Interpretation Of the Model Results In The Light Of Domain Kmentioning

confidence: 96%

“…1998; Knoepp and Swank, 2002). Whereas maximum mineralization is reported for the forest soil temperature range between 20 and 28 • C (Nicolardot et al, 1994), the mineralization becomes strongly inhibited at temperatures below 10 • C (Knoepp and Swank, 2002). Presuming that air temperatures indirectly represent the temperature of the forest soils, the values of avgT 3 >10.1 • C and avgT 14 >11.6 • C, used for splitting in the regression tree model, define the periods of more effective mineralization, nitrification and nitrate accumulation in forest floors, which is in the periods of changed hydrological conditions available for hydrological mobilization.…”

Section: Interpretation Of the Model Results In The Light Of Domain Kmentioning

confidence: 99%

“…In temperate soils, mineralization displays a seasonal pattern; rates are greatest in summer or fall when soil temperatures are highest Hydrological events and associated streamwater nitrate concentrations included into the data mining process. (Strader et al, 1989;Reich et al, 1997;Knoepp and Swank, 2002). Furthermore, Clark et al (2004) used mean monthly air temperature to describe the mean monthly streamwater nitrate concentrations in forested watersheds, however, without a detailed consideration of the possible effect of changed hydrological conditions.…”

Section: Regression Treesmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of hydrological and seasonal controls over the nitrate flushing from a forested watershed using a data mining technique

Rusjan

Mikoš

2008

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract.A data mining, regression tree algorithm M5 was used to review the role of mutual hydrological and seasonal settings which control the streamwater nitrate flushing during hydrological events within a forested watershed in the southwestern part of Slovenia, characterized by distinctive flushing, almost torrential hydrological regime. The basis for the research was an extensive dataset of continuous, high frequency measurements of seasonal meteorological conditions, watershed hydrological responses and streamwater nitrate concentrations. The dataset contained 16 recorded hydrographs occurring in different seasonal and hydrological conditions. Based on predefined regression tree pruning criteria, a comprehensible regression tree model was obtained in the sense of the domain knowledge, which was able to adequately describe most of the streamwater nitrate concentration variations (RMSE=1.02 mg/l-N; r=0.91). The attributes which were found to be the most descriptive in the sense of streamwater nitrate concentrations were the antecedent precipitation index (API) and air temperatures in the preceding periods. The model was most successful in describing streamwater concentrations in the range 1-4 mg/l-N, covering large proportion of the dataset. The model performance was little worse in the periods of high streamwater nitrate concentration peaks during the summer hydrographs (up to 7 mg/l-N) but poor during the autumn hydrograph (up to 14 mg/l-N) related to highly variable hydrological conditions, which would require a less robust regression tree model based on the extended dataset.

show abstract

Section: Interpretation Of the Model Results In The Light Of Domain Kmentioning

confidence: 96%

Section: Interpretation Of the Model Results In The Light Of Domain Kmentioning

confidence: 99%

Section: Regression Treesmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of hydrological and seasonal controls over the nitrate flushing from a forested watershed using a data mining technique

Rusjan

Mikoš

2008

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…No interaction between air temperature and precipitation were found to significantly affect N leaching or N concentration, though. However, many studies have shown that such interaction is important for N mineralization (Knoepp and Swank, 2002;Cabrera et al, 2005), and likely also for N leaching. In our projections of N leaching, we did not account for such interactions, and the application of our regression models for extrapolating N leaching should therefore be interpreted with due caution.…”

Section: Climate Effectsmentioning

confidence: 99%

Effect of temperature and precipitation on nitrate leaching from organic cereal cropping systems in Denmark

Jabloun

Schelde

Tao

et al. 2015

European Journal of Agronomy

117

View full text Add to dashboard Cite

a b s t r a c tThe effect of variation in seasonal temperature and precipitation on soil water nitrate (NO 3 N) concentration and leaching from winter and spring cereals cropping systems was investigated over three consecutive four-year crop rotation cycles from 1997 to 2008 in an organic farming crop rotation experiment in Denmark. Three experimental sites, varying in climate and soil type from coarse sand to sandy loam, were investigated. The experiment included experimental treatments with different rotations, manure rate and cover crop, and soil nitrate concentrations was monitored using suction cups. The effects of climate, soil and management were examined in a linear mixed model, and only parameters with significant effect (P < 0.05) were included in the final model. The model explained 61% and 47% of the variation in the square root transform of flow-weighted annual NO 3 N concentration for winter and spring cereals, respectively, and 68% and 77% of the variation in the square root transform of annual NO 3 N leaching for winter and spring cereals, respectively. Nitrate concentration and leaching were shown to be site specific and driven by climatic factors and crop management. There were significant effects on annual N concentration and NO 3 N leaching of location, rotation, previous crop and crop cover during autumn and winter. The relative effects of temperature and precipitation differed between seasons and cropping systems. A sensitivity analysis revealed that the predicted N concentration and leaching increased with increases in temperature and precipitation.

show abstract

“…Nitrogen (N) mineralization is an important component of nitrogen supply for plant growth [14] and the nutrient dynamics is controlled by the rate of decomposition of organic matter [15]. N-mineralization is of prime importance in ecosystem productivity [12,16], hence for the ecosystem and nutrient cycling, soil nitrogen transformations are measured as indices of potential availability and ecosystem losses of nitrogen [17]. In forest and grassland ecosystems, N-mineralization is affected by the biological factors, such as soil animals, soil microorganisms and plants and the non-biological factors, such as environmental factors and anthropogenic disturbances [12,18,19].…”

Section: Introductionmentioning

confidence: 99%

Impact of Savannization on Nitrogen Mineralization in an Indian Tropical Forest

Tripathi¹,

Singh²

2012

Forest Res

View full text Add to dashboard Cite

The effects of conversion of an Indian dry tropical forest ecosystem into savanna, on mineral nitrogen (N), net N-mineralization rate and microbial biomass carbon (MBC), nitrogen (MBN) and phosphorus (MBP) in soil were studied for two years. There was a marked seasonal variation in all the above parameters at both (upper, 0-10 cm and lower, 10-20 cm) the soil depths of forest and savanna ecosystems. In forest ecosystems the mean annual values of mineral N, net nitrification rate, net N-mineralization rate, MBC, MBN and MBP at both depths were 17. 41 respectively. The soil microbial biomass was positively related to root biomass and total plant biomass (i.e., above-and below-ground biomass). Interestingly, seasonal soil moisture and temperature are reciprocally related to microbial biomass and mineral N and directly related to nitrification and N-mineralization. The microbial biomass, nitrification and N-mineralization are negatively related to clay content. Savannization caused significant loss of soil mean organic carbon (OC), total N (TN) total mean annual nitrification, N-mineralization, MBC, MBN and MBP by 40,42,27,27, 29 and 7%, respectively at upper soil depth and 18,21,42, 29 and 22%, respectively at lower soil depth. The reflectances of soil microbial biomass to OC were 1.22 and 1.06 folds at upper and lower soil depths, respectively.Thus, conversion of dry tropical forests into savanna affects remarkably the soil N transformation; microbial biomass and loss of soil organic C which adds to the environmental pollution.

show abstract

Using soil temperature and moisture to predict forest soil nitrogen mineralization

Cited by 111 publications

References 21 publications

Assessment of hydrological and seasonal controls over the nitrate flushing from a forested watershed using a data mining technique

Assessment of hydrological and seasonal controls over the nitrate flushing from a forested watershed using a data mining technique

Effect of temperature and precipitation on nitrate leaching from organic cereal cropping systems in Denmark

Impact of Savannization on Nitrogen Mineralization in an Indian Tropical Forest

Contact Info

Product

Resources

About