Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-29T05:25:56.891Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of optimal straw incorporation characteristics based on quadratic orthogonal rotation combination design

Published online by Cambridge University Press:  30 April 2018

Guohua Rong ,
Yucui Ning ,
Xu Cao ,
Ye Su ,
Jing Li ,
Lei Li ,
Liyan Liu  and
Dongxing Zhou
Guohua Rong
Affiliation:
College of Resources and Environmental Science, Northeast Agricultural University, Harbin, China
Yucui Ning
Affiliation:
College of Resources and Environmental Science, Northeast Agricultural University, Harbin, China
Xu Cao
Affiliation:
Institute of Microbiology, Heilongjiang Academy of Sciences, Harbin, China
Ye Su
Affiliation:
College of Resources and Environmental Science, Northeast Agricultural University, Harbin, China
Jing Li
Affiliation:
College of Resources and Environmental Science, Northeast Agricultural University, Harbin, China
Lei Li
Affiliation:
College of Resources and Environmental Science, Northeast Agricultural University, Harbin, China
Liyan Liu
Affiliation:
Publicity and United Front Work Department, Northeast Agricultural University, Harbin, China
Dongxing Zhou*
Affiliation:
College of Resources and Environmental Science, Northeast Agricultural University, Harbin, China
*
Author for correspondence: Dongxing Zhou, E-mail: zhboshi@163.com
Get access Rights & Permissions [Opens in a new window]

Abstract

For straw incorporation, three crucial factors affect the soil microbial community and various enzyme activities: straw length, amount and burial depth. To analyse the individual and interactive effects of these three factors on the soil microbial community and various enzyme activities, 23 treatments with five levels of the three variables (straw length, amount and burial depth) were applied in a quadratic orthogonal rotation combination design. A comprehensive indicator was constructed that could represent soil microbial functional diversity and enzyme activity by determining the weights of measured indicators and using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results indicated that the soil microbiological indicators have a higher criteria weight than soil enzyme activity indicators. The final weight orders of indicators were as follows: Shannon–Weaver > invertase > Shannon evenness > urease > catalase > McIntosh index > Simpson diversity > phosphatase. The soil comprehensive values constructed by the TOPSIS method are reliable. The optimal combination for the improvement of soil microbial functional diversity and enzyme activity was a straw length of 13–24 cm, burial depth of 10–17 cm and straw amount of 370–650 g/m2.

Keywords

quadratic orthogonal rotation combination designsoil enzyme activitysoil microbial functional diversitystraw incorporation
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 3 , April 2018 , pp. 367 - 377
Check for updates
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allison, MF and Killham, K (1988) Response of soil microbial biomass to straw incorporation. European Journal of Soil Science 39, 237–242.CrossRefGoogle Scholar
Balota, EL, et al. (2004) Soil enzyme activities under long-term tillage and crop rotation systems in subtropical agro-ecosystems. Brazilian Journal of Microbiology 35, 300–306.Google Scholar
Bending, GD and Turner, MK (1999) Interaction of biochemical quality and particle size of crop residues and its effect on the microbial biomass and nitrogen dynamics following incorporation into soil. Biology and Fertility of Soils 29, 319–327.CrossRefGoogle Scholar
Box, GEP and Draper, NR (1986) Empirical Model-Building and Response Surfaces. New York, USA: John Wiley & Sons.Google Scholar
Dannehl, T, Leithold, G and Brock, C (2017) The effect of C:N ratios on the fate of carbon from straw and green manure in soil. European Journal of Soil Science 68, 988–998.CrossRefGoogle Scholar
Dhawane, SH, Kumar, T and Halder, G (2015) Central composite design approach towards optimization of flamboyant pods derived steam activated carbon for its use as heterogeneous catalyst in transesterification of Hevea brasiliensis oil. Energy Conversion and Management 100, 277–287.CrossRefGoogle Scholar
Dick, RP, Sandor, JA and Eash, NS (1994) Soil enzyme activities after 1500 years of terrace agriculture in the Colca Valley, Peru. Agriculture, Ecosystems & Environment 50, 123–131.Google Scholar
Dursun, E, Güner, M and Edogan, D (1999) Determination of burying ratio of moldboard and disc plough. Tarim Bilimleri Dergisi 5, 45–50.Google Scholar
Fagbote, EO, Olanipekun, EO and Uyi, HS (2014) Water quality index of the ground water of bitumen deposit impacted farm settlements using entropy weighted method. International Journal of Environmental Science and Technology 11, 127–138.CrossRefGoogle Scholar
Farid Eltom, AE, et al. (2015) Field investigation of a trash-board, tillage depth and low speed effect on the displacement and burial of straw. Catena 133, 385–393.CrossRefGoogle Scholar
Frank, DM and Sarkar, S (2010) Group decisions in biodiversity conservation: implications from game theory. PLoS ONE 5, e10688. https://doi.org/10.1371/journal.pone.0010688.Google Scholar
Frankenberger, WT and Dick, WA (1983) Relationships between enzyme activities and microbial growth and activity indices in soil. Soil Science Society of America Journal 47, 945–951.Google Scholar
Garland, JL and Mills, AL (1991) Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Applied and Environmental Microbiology 57, 2351–2359.CrossRefGoogle ScholarPubMed
Guan, SY (1986) Soil Enzymes and Study Method. Beijing, China: Agricultural Press.Google Scholar
Hadas, A, et al. (2004) Rates of decomposition of plant residues and available nitrogen in soil, related to residue composition through simulation of carbon and nitrogen turnover. Soil Biology and Biochemistry 36, 255–266.Google Scholar
Hassink, J (1997) The capacity of soils to preserve organic C and N by their association with clay and silt particles. Plant and Soil 191, 77–87.CrossRefGoogle Scholar
Heal, OW, Anderson, JM and Swift, MJ (1997) Plant litter quality and decomposition: an historical overview. In Cadisch, G and Giller, K (eds). Driven by Nature: Plant Litter Quality and Decomposition. Wallingford, UK: CABI, pp. 3–30.Google Scholar
Ji, B, et al. (2014) Effects of deep tillage and straw returning on soil microorganism and enzyme activities. The Scientific World Journal 2014, Article ID 451493. http://dx.doi.org/10.1155/2014/451493.Google Scholar
Jiao, XG, et al. (2011) Effect of long-term fertilization on soil enzyme activities under different hydrothermal conditions in Northeast China. Agricultural Sciences in China 10, 412–422.Google Scholar
Lai, C, et al. (2015) A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory. Natural Hazards 77, 1243–1259.Google Scholar
Lal, R (2005) World crop residues production and implications of its use as a biofuel. Environment International 31, 575–584.Google Scholar
Liang, X, et al. (2017) Responses of soil organic carbon decomposition and microbial community to the addition of plant residues with different C:N ratio. European Journal of Soil Biology 82, 50–55.Google Scholar
Liu, J, Chen, Y and Kushwaha, RL (2010) Effect of tillage speed and straw length on soil and straw movement by a sweep. Soil and Tillage Research 109, 9–17.CrossRefGoogle Scholar
Mari, IA, Tagar, AA and Huimin, F (2014) Performance and evaluation disc tillage tool forces action on straw incorporation soil. Pakistan Journal of Agricultural Sciences 51, 588–860.Google Scholar
May, PB and Douglas, LA (1976) Assay for soil urease activity. Plant and Soil 45, 301–305.CrossRefGoogle Scholar
Myers, RH and Montgomery, DC (1995) Response Surface Methodology. Process and Product Optimization Using Designed Experiments. New York, USA: John Wiley and Sons.Google Scholar
Roberge, M (1978) Methodology of enzymes determination and extraction. In Burns, RG (ed.), Soil Enzymes. New York, USA: Academic Press, pp. 341–373.Google Scholar
Roghanian, E, Rahimi, J and Ansari, A (2010) Comparison of first aggregation and last aggregation in fuzzy group TOPSIS. Applied Mathematical Modelling 34, 3754–3766.CrossRefGoogle Scholar
Rottmann, N, Dyckmans, J and Joergensen, RG (2010) Microbial use and decomposition of maize leaf straw incubated in packed soil columns at different depths. European Journal of Soil Biology 46, 27–33.Google Scholar
Russell, EJ and Appleyard, A (1917) The influence of soil conditions on the decomposition of organic matter in the soil. Journal of Agricultural Science, Cambridge 8, 385–417.Google Scholar
Saaty, TL (2008) Decision making with the analytic hierarchy process. International Journal of Services Sciences 1, 83–98.Google Scholar
Said-Pullicino, D, et al. (2014) Nitrogen immobilization in paddy soils as affected by redox conditions and rice straw incorporation. Geoderma 228–229, 44–53.Google Scholar
Shih, HS, Shyur, HJ and Lee, ES (2007) An extension of TOPSIS for group decision making. Mathematical and Computer Modelling 45, 801–813.Google Scholar
Silgram, M and Chambers, BJ (2002) Effects of long-term straw management and fertilizer nitrogen additions on soil nitrogen supply and crop yields at two sites in Eastern England. Journal of Agricultural Science, Cambridge 139, 115–127.CrossRefGoogle Scholar
Sindhu, S, Nehra, V and Luthra, S (2017) Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: case study of India. Renewable and Sustainable Energy Reviews 73, 496–511.Google Scholar
Sommer, R, et al. (2011) Effect of shallow tillage, moldboard plowing, straw management and compost addition on soil organic matter and nitrogen in a dryland barley/wheat-vetch rotation. Soil and Tillage Research 115, 39–46.Google Scholar
Son, J, Vavra, J and Forbes, VE (2015) Effects of water quality parameters on agglomeration and dissolution of copper oxide nanoparticles (CuO-NPs) using a central composite circumscribed design. Science of the Total Environment 521–522, 183–190.Google Scholar
Sun, H, Wang, S and Hao, X (2017) An improved analytic hierarchy process method for the evaluation of agricultural water management in irrigation districts of North China. Agricultural Water Management 179, 324–337.Google Scholar
Sun, JF, et al. (2016 a) An estimation of CO2 emission via agricultural crop residue open field burning in China from 1996 to 2013. Journal of Cleaner Production 112, 2625–2631.CrossRefGoogle Scholar
Sun, L, et al. (2016 b) An integrated decision-making model for transformer condition assessment using game theory and modified evidence combination extended by d numbers. Energies 9, 697, doi: 10.3390/en9090697.Google Scholar
Swift, MJ, Heal, OW and Anderson, JM (1979) Decomposition in Terrestrial Ecosystems. Berkeley, CA, USA: University of California Press.Google Scholar
Tabatabai, MA (1994) Soil enzymes. In Weaver, RW (ed.), Methods of Soil Analysis. Part 2: Microbiological and Biochemical Properties. Madison, WI, USA: Soil Science Society of America, pp. 775–833.Google Scholar
Utobo, E and Tewari, L (2015) Soil enzymes as bioindicators of soil ecosystem status. Applied Ecology and Environmental Research 13, 147–169.Google Scholar
Vigil, MF and Kissel, DE (1991) Equations for estimating the amount of nitrogen mineralized from crop residues. Soil Science Society of America Journal 55, 757–761.Google Scholar
Wang, X, et al. (2015) Effects of ditch-buried straw return on soil organic carbon and rice yields in a rice–wheat rotation system. Catena 127, 56–63.Google Scholar
Wang, X, et al. (2017) Comprehensive assessment of regional water usage efficiency control based on game theory weight and a matter-element model. Water 9, 113, doi: 10.3390/w902011.Google Scholar
Wei, T, et al. (2015) Effects of wheat straw incorporation on the availability of soil nutrients and enzyme activities in semiarid areas. PLoS ONE 10, e0120994. https://doi.org/10.1371/journal.pone.0120994.Google Scholar
Yang, H, et al. (2016) Long-term ditch-buried straw return alters soil water potential, temperature, and microbial communities in a rice-wheat rotation system. Soil and Tillage Research 163, 21–31.Google Scholar
Zak, JC, et al. (1994) Functional diversity of microbial communities: a quantitative approach. Soil Biology and Biochemistry 26, 1101–1108.Google Scholar
Zavadskas, EK, et al. (2016) Development of TOPSIS method to solve complicated decision-making problems-an overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making 15, 645–682.Google Scholar
Zhang, J, et al. (2014) Design and field experiment of power consumption measurement system for high stubble returning and tillage machine. Transactions of the Chinese Society of Agricultural Engineering 30, 38–46.Google Scholar
Zhang, B, et al. (2016 a) Long-term effect of residue return and fertilization on microbial biomass and community composition of a clay loam soil. Journal of Agricultural Science, Cambridge 154, 1051–1061.Google Scholar
Zhang, P, et al. (2016 b) Effects of straw incorporation on the soil nutrient contents, enzyme activities, and crop yield in a semiarid region of China. Soil and Tillage Research 160, 65–72.Google Scholar
Zhao, S, et al. (2016) Changes in soil microbial community, enzyme activities and organic matter fractions under long-term straw return in North-Central China. Agriculture, Ecosystems & Environment 216, 82–88.Google Scholar
Zou, ZH, Yun, Y and Sun, JN (2006) Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental Sciences 18, 1020–1023.Google Scholar
Zyoud, SH and Fuchs-Hanusch, D (2017) A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications 78, 158–181.Google Scholar