Carbon dioxide flux as affected by tillage and irrigation in soil converted from perennial forages to annual crops

Jabro, Jalal D.; Sainju, Upendra M.; Stevens, William B.; Evans, Robert G.

doi:10.1016/j.jenvman.2007.07.012

Cited by 136 publications

(74 citation statements)

References 25 publications

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“…Conventional tillage has influence on soil aggregate turnover, improves soil aeration, infiltration, water holding capacity, and increases the contact between soil and crop residues, which results in increased soil CO2 emissions compared to no-tillage. Lower soil C-CO2 emissions at no-tillage compared to conventional tillage have also been determined by many other authors [15,[18][19][20][21]. Similar results were reported by Franzluebbers et al [26], who found that CO2 emissions in some years were higher in no-tillage than in conventional tillage, but in other years, the tillage effect was not observed.…”

Section: Influence Of Tillage Treatment On Soil C-co2 Emissionssupporting

confidence: 84%

Influence of Tillage Practices and Crop Type on Soil CO2 Emissions

2016

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Nonsustainable agricultural practices often lead to soil carbon loss and increased soil carbon dioxide (CO 2 ) emissions into the atmosphere. A research study was conducted on arable fields in central lowland Croatia to measure soil respiration, its seasonal variability, and its response to agricultural practices. Soil C-CO 2 emissions were measured with the in situ static chamber method during corn (Zea mays L.) and winter wheat (Triticum aestivum L.) growing seasons (2012 and 2013, n = 288) in a field experiment with six different tillage treatments. During corn and winter wheat growing season, average monthly soil C-CO 2 emissions ranged, respectively, from 6.2-33.6 and 22.1-36.2 kg ha´1 day´1, and were decreasing, respectively, from summer > spring > autumn and summer > autumn > spring. The same tillage treatments except for black fallow differed significantly between studied years (crops) regarding soil CO 2 emissions. Significant differences in soil C-CO 2 emissions between different tillage treatments with crop presence were recorded during corn but not during winter wheat growing season. In these studied agroecological conditions, optimal tillage treatment regarding emitted C-CO 2 is plowing to 25 cm along the slope, but it should be noted that CO 2 emissions involve a complex interaction of several factors; thus, focusing on one factor, i.e., tillage, may result in a lack of consistency across studies.

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Section: Influence Of Tillage Treatment On Soil C-co2 Emissionssupporting

confidence: 84%

Influence of Tillage Practices and Crop Type on Soil CO2 Emissions

2016

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“…The effect of tillage and residue management practices on PR was observed to be non significant with slightly greater value under HS in SL soil. Similarly, Jabro et al (2008) observed higher PR under conservation tillage due to undisturbed or unloosened condition of soil. The water transmission characteristics also did not show any significant change due to tillage and residue management practices, the reason may be short period of imposing different tillage and residue management practices in experiment.…”

Section: Discussionmentioning

confidence: 81%

Soil Physical Characteristics and Crop Productivity as Affected by Tillage in Rice-Wheat System

Kahlon¹

2014

JAS

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Tillage practices significantly influences soil physical characteristics and crop productivity. The infiltration characteristics, bulk density, porosity, penetration resistance, grain yield of rice and wheat were measured under no-till without residue (NT), roto-tillage (RT), and no-till with residue i.e. happy seeder (HS) in rice-wheat cropping system in sandy loam (SL) and loamy sand (LS) soils. For surface soil layer (0-15 cm) highest soil bulk density (Mg m -3 ) was recorded in HS (1.58) followed by RT (1.56) and NT (1.55) in LS soil. Soil porosity ranged from 40.6 % to 41.2 % under HS and RT, respectively for 0-15 cm depth in SL soil. The PR (K Pa) varied from 2412 (NT) to 2539 (HS) in SL. In LS soil PR (K Pa) varied from 2433 (NT) to 2603 (RT). Final infiltration rate (FIR) varied from 0.1 to 0.3 cm hr -1 under RT and NT in SL soil, while, in LS soil FIR varied from 0.5 to 0.8 cm hr -1 in RT and HS, respectively. Maximum rice grain yield (t ha -1 ) was recorded in RT (8.0) followed by NT (7.5) and HS (7.0) in SL soil. The corresponding rice yield (t ha -1 ) in LS was 6.5, 5.8 and 5.5 in RT, NT and HS, respectively. Wheat straw yield (t ha -1 ) ranged from 4.9 (NT) to 5.9 (HS) in SL soil. The wheat grain yield (t ha -1 ) was observed to be maximum in HS (4.5), followed by RT (4.0) and NT (3.9) in SL soil, while in LS soil the wheat grain yield (t ha -1 ) values were observed to be 2.7, 3.0 and 3.6 in NT, RT and HS, respectively. No significant effect of tillage and residue management practices was observed on grain yield of rice and wheat, however, significant changes were observed on water transmission characteristics and straw yields.

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“…Incorporation of crop residue into the soil with rotavator helped in mitigating the CO 2 emissions by building up SOC which in turn improves the availability of nutrients such as nitrogen, phosphorus, sulfur and potassium and making them available to plant roots. Even reduced tillage would provide minimum soil disturbance and improve the soil aggregation through enhanced C sequestration (Jabro et al 2008). The rate of SOC sequestration was greater with regular application of organic matter; application of chemical fertilizers and conjunctive use of organic and mineral fertilizers significantly enhance SOC stock (Srinivasarao et al 2012).…”

Section: Improved Annual Crop Management Practicesmentioning

confidence: 99%

Assessing village-level carbon balance due to greenhouse gas mitigation interventions using EX-ACT model

Srinivasarao

Rani

Veni

et al. 2015

Int. J. Environ. Sci. Technol.

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Under National Initiative on Climate Resilient Agriculture (NICRA) project, a range of climate smart agricultural practices were evaluated with on-farm demonstrations during 2011-2013 in eight climatically vulnerable villages of Andhra Pradesh, India. Proven climate smart practices viz residue recycling, soil, water and nutrient management, afforestation and feeding ? breeding practices in livestock were implemented in annual and perennial crops, irrigated rice, horticulture, fodder, forestry and livestock. An EX-ante carbon-balance tool (EX-ACT) developed by the FAO was used with a combination of various climate smart interventions to know the mitigation potentials in eight climatically vulnerable villages of Andhra Pradesh, India. Based on our observations, EX-ACT model had shown that these practices were effective to mitigate CO 2 emissions apart from enhancing soil productivity. In Nacharam, Yagantipalli, Sirusuwada and Matsyapuri villages, climate smart practices implemented in annual crops along with crop residue recycling, crop and water management practices resulted in negative carbon (C) balance by -16,410, -8851, -7271 and -6125 t CO 2 e, respectively. The EX-ACT model predicted positive carbon balance with irrigated module in the rice-growing villages of Sirusuwada and Matsyapuri villages. The negative values suggest a sink, and positive values a source for CO 2 emissions. In Chamaluru village, although there were CO 2 emissions (source) due to livestock and nonforest and land use changes, there was a carbon sink due to other activities as predicted by the model. The results suggested that various climatic smart practices at the village level were successful in creating net sink of CO 2 emissions (t CO 2 e).

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Carbon dioxide flux as affected by tillage and irrigation in soil converted from perennial forages to annual crops

Cited by 136 publications

References 25 publications

Influence of Tillage Practices and Crop Type on Soil CO2 Emissions

Influence of Tillage Practices and Crop Type on Soil CO2 Emissions

Soil Physical Characteristics and Crop Productivity as Affected by Tillage in Rice-Wheat System

Assessing village-level carbon balance due to greenhouse gas mitigation interventions using EX-ACT model

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