Aluminum-based winter wheat biomass and grain yield spatial variability in arable soils: concept and field test

• Autorzy: Diatta J. , Walkowiak R. , Grzebisz W. , Witczak R.

Warianty tytułu

PL

Wpływ glinu na przestrzenną zmienność biomasy i plonu ziarna przenicy ozimej na glebach uprawnych: koncepcja i badania terenowe

• Języki publikacji: EN

Abstrakty

EN

The paper outlines a concept related to selecting a site for experimental purposes. Selection of an experimental plot most frequently relies on performing visual evaluation of a given site, followed by the establishment of a field trial. In general, the question of geochemical variability is ‘intentionally’ postponed! Verification of this approach has been undertaken, testing such parameters as soil pH and exchangeable aluminum (Alex) versus spatial (investigated area, 12 672 m2) and downward (sampling depths, i.e., 0-20, 20-40 and 40-60 cm) distribution. Winter wheat biomass at tillering (BBCH29) and grain yield at harvest (BBCH99) were additionally considered. The results have revealed that pH values fluctuated between 3.6 and 4.4 with respective coefficients of variation (CV) ranging from 3.10 to 5.92%. The concentrations of Alex ranged from 38.0 to 144.9 mg kg–1, corresponding to CV within 28.34 and 44.03%. The variograms and geostatistical maps have demonstrated the spatial as well as downward variability of these parameters. The spatial distribution of plant biomass followed quite closely the exchangeable aluminum (Alex) levels, which implies that natural soil parameters such as Alex are not easily compensated for by agricultural practices, for instance nitrogen application. The spatial grain yield – Alex dependence which emerged at harvest confirmed the variability observed at tillering (BBCH29). Thus, the spatial variability of pH, Alex and wheat biomass as well as grain yields (BBCH99) verified the approach to selecting an experimental site. It was demonstrated that selection of a research site on the basis of its appearance and shape alone may lead to misinterpretation of experimental results.

PL

Praca przedstawia koncepcję związaną z wyborem stanowiska na cele doświadczalne. To podejście opiera się na częstej ocenie wzrokowej danego stanowiska, a dalej – założeniu doświadczenia polowego. Zmienność geochemiczna bywa generalnie „celowo” odłożona! Przedstawioną koncepcję zweryfikowano za pomocą parametrów, takich jak pH oraz zawartość glinu wymiennego (Alex), pod względem przestrzennym (pole badawcze 12 672 m2) i w głąb profilu glebowego (warstwy gleby: 0-20, 20-40 i 40-60 cm). Następnymi parametrami do opracowania przestrzennej zmienności były biomasa pszenicy ozimej w fazie krzewienia (BBCH29) oraz plon ziarna w fazie dojrzałości pełnej (BBCH99). W badaniach wykazano, że wartości pH wahały się między 3,6 a 4,4, a odpowiednie współczynniki zmienności (CV) wynosiły 3,10-5,92%. W przypadku Alex, jego zawartość zmieniła się w szerokich granicach (38,0-144,9 mg kg–1), co odpowiadało wartościom CV od 28,34 do 44,03%. Opracowane wariogramy oraz mapy geostatystyczne wyraźnie podkreślały zmienność zarówno przestrzenną, jak i w głąb profilu glebowego badanych parametrów. Przestrzenne rozmieszczenie biomasy roślinnej postępowało zgodnie z zawartością Alex. Oznacza to, że naturalne czynniki glebowe, jak Alex, nie są łatwo zrównoważone zabiegami agrotechnicznymi, np. nawożeniem azotowym. Przestrzenna zależność: plon ziarna – Alex, która ujawni ła się w fazie żniw, potwierdziła zmienność zaobserwowaną w fazie krzewienia (BBCH29). Zatem przestrzenna zmienność pH, Alex oraz zarówno biomasy pszenicy, jak i plonów ziarna (BBCH99) zweryfikowały koncepcję związaną z wyborem stanowisk na cele badawcze. Wykazano, że wybór stanowiska badawczego oparty tylko na wyglądzie terenu i jego kształcie może doprowadzić do błędnej interpretacji danych eksperymentalnych.

• Wydawca: -
• Czasopismo: Journal of Elementology
• Rocznik: 2012
• Tom: 17
• Numer: 2
• Opis fizyczny: p.215-229,fig.,ref.

Twórcy

autor

Diatta J.

• Chair of Agricultural Chemistry and Environmental Biogeochemistry, University of Life Sciences Poznan, Poland

autor

Walkowiak R.

autor

Grzebisz W.

autor

Witczak R.

Bibliografia

• Bocci S., Castrignane A., Fornaro F., Maggiore T. 2000. Application of factorial kriging for mapping soil variation at field scale. Europ. J. Agronomy, 13: 295-308.
• Boruvka L., Mladkova L., Drabek O. 2005. Factors controlling spatial distribution of soil acidification and Al forms in forest soils. J. Inorg. Biochem., 99(9): 1796-1806.
• Bouma J. 1997. Precision agriculture: introduction to the spatial and temporal variability of environmental quality. In: Precision agriculture: spatial and temporal variability of environmental quality. Lake J.V., Bock G.R., Goode J.A. (Eds.). Ciba Foundation Symposium 210, Wiley, Wageningen, The Netherlands, pp. 5-17.
• Brejda J.J., Moorman T.B., Smith J.L., Karlen D.L., Allan D.L., Dao T.H. 2000. Distribution and variability of surface soil properties at a regional scale. Soil Sci. Soc. Am. J., 64: 974-982.
• Briggs I.C. 1974. Machine contouring using minimum curvature. Geophysics, 39(1): 39-48.
• Buchter B., Aina P.O., Azari A.S., Nielsen D.R. 1991. Soil spatial variability among transects. Soil Technol., 4: 297-314.
• Casa R., Castrignane A. 2008. Analysis of spatial relationships between soil and crop variables in a durum wheat field using a multivariate geostatistical approach. Europ. J. Agronomy, 28: 331-342.
• Clarke N., Danielsson L-G., Sparen A. 1996. Analytical methodology for the determination of aluminum fractions in natural fresh waters. Pure Appl. Chem., 68(8): 1597-1638.
• Corvin D.L., Lesch S.M. 2005. Characterizing soil spatial variability with apparent soil electrical conductivity. Part II. Case study. Comp. Electron. Agric., 46: 135-152.
• Diiwu J.Y., Ridry R.P., Dickenson W.T., Wall G.J. 1998. Effect of tillage on the spatial variability of soil water properties. Can. Agri. Engin., 40: 1-8.
• Dolan M.S., Clapp C.E., Allmaras R.R., Baker J.M., Molina J.A.E. 2006. Soil organic carbon and nitrogen in Minnesota soils as related to tillage, residue and nitrogen management. Soil Till. Res., 89: 221-231.
• Dong X.W., Zhang X.K., Bao X.L., Wang J.K. 2009. Spatial distribution of soil nutrients after the establishment of sand-fixing shrubs on sand dune. Plant Soil Environ., 55: 288-294.
• Filipek T. 1999. Principles and impacts of chemical inputs into agroecosystems. Course Book. Ed. Filipek. Wyd. AR w Lublinie 242, pp. (in Polish)
• Florin M.J., McBratney A.B., Whelan B.M. 2009. Quantification and comparison of wheat yield variation across space and time. Europ. J. Agronomy, 30: 212-219.
• Fu W., Tunney H., Zhang C. 2010. Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application. Soil Till. Res., 106: 185-193.
• Goovaerts P. 1997. Geostatistics for natural resources evaluation. Oxford University Press, New York, USA.
• Huang X., Skidmore E.I., Tibke G. 2001. Spatial variability of soil properties along a transect of CRP and continuously cropped land. 10th Int. Soil Conservation Organization Meeting, pp. 641-647.
• Janik G. 2008. Spatial variability of soil moisture as information on variability of selected physical properties of soil. Int. Agroph., 22: 35-43.
• Joernsgaard B., Halmoe S. 2003. Intra-field yield variation over crops and years. Europ. J. Agronomy, 19: 23-33.
• Katyal J.C. 2003. Soil fertility management — a key to prevent desertification. J. Ind. Soc. Soil Sci., 51: 378-387.
• Kidd P.S., Proctor J. 2001. Why do plants grow poorly on very acid soils: are ecologist missing the obvious? J. Exp. Bot., 52(357): 791-799.
• Kirchmann H., Thorvaldsson G. 2000. Challenging targets for future agriculture. Europ. J. Agronomy, 12: 145-161.
• Kobierski M., Długosz J., Piotrowska A. 2011. Spatial variability of different magnesium forms in Fluvisols formed from glacial till. J. Elementol., 16(2): 205-214.
• Logan K.A.B., Floate M.J.S., Ironside A.D. 1985. Determination of exchangeable aluminum in hill soils. Part 2. Exchangeable aluminum. Commun. Soil Sci. Plant Anal., 16(3): 309-314.
• Maia S.M.F., Ogle M.S., Cerri C.C., Cerri, C.E.P. 2010. Changes in soil organic carbon storage under different agricultural management systems in the Southwest Amazon Region of Brasil. Soil Till. Res., 106: 177-184.
• Mercer W.B., Hall A.D. 1911. The experimental error in field trials. J. Agric. Sci., 4: 107-132.
• Mohammadi J. 2002. Spatial variability of soil fertility, wheat yield and weed density in one-hectare field in Shahre Kord. J. Agric. Sci. Technol., 4: 83-92.
• Newman S., Reddy K.R., Debusk W.F., Wang Y., Shih G., Fisher M.M. 1997. Spatial distribution of soil nutrients in a Northern Everglades Marsh: water conservation area 1. Soil Sci. Soc. Am. J., 61: 1275-1283.
• Panayiotopoulos K.P., Kostopoulou S., Hatjiyiannakis E. 2004. Variation of physical and mechanical properties with depth in Alfisols. Int. Agroph., 18: 55-63.
• Polish Standardisation Committee, ref. PrPN-ISO 10390 (E). 1994. Soil quality and pH determination. First edition. (in Polish)
• Puget P., Lal R. 2005. Soil organic carbon and nitrogen in a Mollisol in Central Ohio as affected by tillage and land use. Soil Till. Res., 80: 201-213.
• Röver M., Kaiser E.A. 1999. Spatial heterogeneity within the plough layer: low and moderate variability of soil properties. Soil Biol. Biochem., 31: 175-187.
• Sawyer J., Mallarino A., Killorn R. 2004. Take a good soil sample to help make good decisions. Iowa State University, University Extension. File Code: Agronomy 8-5.
• Stevenson F.J., Cole M.A. 1999. Cycles of soil. Carbon, nitrogen, phosphorus, sulphur, micronutrients. 2nd Ed. John Wiley & Sons, New York.
• Turgut B., Aksakal E.L., Oztas T. 2008. Assessment of spatial distribution patterns of soil properties in the EAARI-Experimental Station (Erzurum). Int. Meeting on Soil Fertility Land Management and Agroclimatology, Turkey, pp. 165-173.
• Verma V.K., Patel L.B., Toor G.S., Sharma P.K. 2005. Spatial distribution of macronutrients in soils of Arid Tract of Punjab. Ind. Int. J. Agri. Biol., 7(2): 295-297.
• Warrick A.W., Myers D.E., Nielsen D.R. 1986. Geostatistical methods applied to soil science. In: Methods of soil analyses. Part I. Physical and mineralogical methods. Klute A. (ed.). ASA and SSSA. Madison WI, pp. 53-73.
• Washmon C.N., Solie J.B., Raun W.R., Itenfisu D.D. 2002. Within field variability in wheat grain yields over nine years in Oklahoma. J. Plant Nutrit., 25(12): 2655-2662.
• Webster R., Oliver M.A. 2001. Geostatistics for environmental scientists. John Wiley & Sons, Chichester.
• Włodarczyk T., Stępniewski W., Brzezińska M., Przywara G. 2008. Impact of different aeration conditions on the content of extractable nutrients in soil. Int. Agroph., 22: 371-375.
• Yang J., Hammer R.D., Blanchar R.W. 1995. Microscale pH spatial distribution in the Ap horizon of Mexico Silt Loam. Soil Sci., 160(5): 371-375.
• Yang Y., Zhang S. 2008. Approach of developing spatial maps of soil nutrients. In: International Federation for Information Processing (IFIP), vol. 258; Computer and Computing Technologies in Agriculture. Vol. 1; Daoliang Li (Boston: Springer), pp. 565-571.

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