Soil carbon and nitrogen changes as influenced by tillage and cropping systems in some Iowa soils

Al‐Kaisi, Mahdi; Yin, Xinhua; Licht, Mark A.

doi:10.1016/j.agee.2004.08.002

Cited by 241 publications

(115 citation statements)

References 30 publications

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“…The calculated C-to-N ratios of the organic matter in soil under DS, RT and CT system reached values 17.8;18.4,and 20.7,respectively (Table 1). Also, Al-Kaisi et al (2005) reported that after 7 years tillage experiment no significant difference in C:N ratio was observed between no-tillage and chisel plowing soil. In our studies, the influence of the tillage system on soil environment was reflected stronger in the C-to-N ratio of the microbial biomass.…”

Section: Results and Disussionmentioning

confidence: 99%

Soil biological activity as affected by tillage intensity

Gajda

Przewloka²

2012

International Agrophysics

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Soil biological activity as affected by tillage intensityThe effect of tillage intensity on changes of microbiological activity and content of particulate organic matter in soil under winter wheat duirng 3 years was studied. Microbial response related to the tillage-induced changes in soil determined on the content of biomass C and N, the rate of CO2evolution, B/F ratio, the activity of dehydrogenases, acid and alkaline phosphatases, soil C/N ratio and microbial biomass C/N ratio confirmed the high sensitivity of soil microbial populations to the tillage system applied. After three year studies, the direct sowing system enhanced the increase of labile fraction of organic matter content in soil. There were no significant changes in the labile fraction quantity observed in soil under conventional tillage. Similar response related to the tillage intensity was observed in particulate organic matter quantities expressed as a percentage of total organic matter in soil. A high correlation coefficients calculated between contents of soil microbial biomass C and N, particulate organic matter and potentially mineralizable N, and the obtained yields of winter wheat grown on experimental fields indicated on a high importance of biological quality of status of soil for agricultural crop production.

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Section: Results and Disussionmentioning

confidence: 99%

Soil biological activity as affected by tillage intensity

Gajda

Przewloka²

2012

International Agrophysics

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“…It was reported that there are necessarily functional linkages among plant biomass, plant residues and SOM. SOM correlates closely with soil quality (Reeves, 1997;Al-Kaisi et al, 2005), which influences plant available nutrients and the ability of soil in absorbing plant nutrients (Chen et al, 2005;Ladoni et al, 2010). Both of them primarily determine plant photosynthetic capacity (Chapin et al, 2011;Janssens et al, 2001) and plant biomass over long term periods.…”

Section: Biological Basis Of the Rersmmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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a b s t r a c tEcosystem respiration (R e ) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R e (ReRSM) by assuming that one part of R e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO 2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R e agreed well with the observed R e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol C m −2 d −1 and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R e across five vegetation types, with the RMSE of 0.05 mol C m −2 d −1 and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in R e estimation and has the potential of estimating spatial R e . However, the performance of ReRSM in other vegetation types or regions still needs a further study.

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“…the world where properly implemented CA systems have improved soil quality and agronomic yields, including some from the US Central Great Plains (Mikha et al 2012) and Corn Belt (Al Kaisi et al 2005;Triplett and Dick 2008) region. Positive effects on soil physical quality (structure and water infiltration rate) are especially relevant to erosion control in erosive climate and erodible soils of the tropics (Lal 1976a, Lal 1976bChoudhury et al 2014) and elsewhere (Li et al 2011;Kheyrodin and Antoun 2011).…”

Section: Tablementioning

confidence: 99%