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2015 | 20 | 1 |

Tytuł artykułu

Attempt at an application of neural networks for assessment of the nitrogen content in meadow sward on the basis of long-term fertilizer experiments

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Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
On the basis of long-term fertilizer experiment, conducted since 1968, an attempt was made to verify the nitrogen content with the use of a neural network, in terms of yields from subsequent cuts. The experiment is located at Czarny Potok village near Krynica (20°54′ E; 49°24′ N) on the altitude of about 720 m a.s.l., at the foot of Jaworzyna Krynicka Mt., in the south-eastern Beskid Sądecki mountain range, on a slope with 7° inclination and NNE aspect. The experiment was set up in 1968 on a natural type of mountain meadow of mat-grass (Nardus stricta L.) and red fescue (Festuca rubra L.) with a large share of dicotyledonous plants. The soil was classified to acid brown soils developed from the Magura sandstone with the texture of light silt loam. Since autumn 1985, the experiment has been conducted in two series, with the same level of fertilization: without liming (0 Ca) and limed (+Ca). Liming was repeated in 1995. The first liming was conducted with a dose calculated on the basis of 0.5 Hh value, the second one was established according to the total hydrolytic acidity. Mineral fertilization was discontinued in 1974 - 1975 and in 1993 - 1994, when the experiment was limited to an assessment of the sward yield and its chemical composition. The experiment comprises 8 treatments with five replications, receiving either nitrogen or phosphorus fertilization (90 kg N or 39.24 kg P ha-1) and (39.24 kg P ha-1 and 124.5 kg K ha-1) against the PK background, nitrogen in two forms (ammonium nitrate and urea) and two doses (90 and 180 N ha-1). In 1968-1980, phosphorous and potassium fertilizers were sown in autumn and since 1981 – in spring. However, potassium (1/2 of the dose) was supplemented in summer after I cut. In 1968 - 1973, thermophosphate was applied, but triple superphosphate has been used since 1976. Over the whole period of the experiment, nitrogen fertilizers have been sown at two dates: 2/3 of the annual dose in the spring at the onset of plant growth and 1/3 of the dose several days after the first cut. A single regenerative treatment with copper (10 kg kg-1) and magnesium (8 kg ha-1) was applied once in 1994. Foliar nutrition (2 dm3 ha-1 applied twice) with the microelement fertilizer Mikrovit-1 has been used since 2000. The microelement fertilizer contains (per 1 dm3): 23.3 g Mg; 2.3 g Fe; 2.5 g Cu; 2.7 g Mn; 1.8 g Zn; 0.15 g B and 0.1 g Mo. The model was compared with a regression analysis. Statistical analysis was applied for two data sets: the whole data set, i.e. 43 years and 8 treatments, 2 cuts and 2 series – data of the 1st set (n = 1376), and a narrow data set, comprising exclusively fertilization 90 kg N ha-1, irrespective of the form against the background of PK – 43 years and 2 objects, 2 cuts and 2 series – data of the 2nd set (n = 344). A neural network can be applied in the assessment of the nitrogen content on the basis of yield including subsequent years of nitrogen fertilization and cuts. Neural networks including quantitative and qualitative features are useful for modelling the element content.

Słowa kluczowe

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-

Rocznik

Tom

20

Numer

1

Opis fizyczny

p.127-136,fig.,ref.

Twórcy

autor
  • Chair of Agricultural and Environmental Chemistry, University of Agriculture in Krakow, Krakow, Poland
autor
  • Chair of Agricultural and Environmental Chemistry, University of Agriculture in Krakow, Al.Mickiewicza 21, 31-120 Krakow, Poland

Bibliografia

  • Bakker J.P., Elzinga J.A., de Vries Y. 2002. Effects of long-term cutting in a grassland system: perspectives for restoration of plant communities on nutrient-poor soils. Appl. Vegetation Sci., 5: 107-120.
  • Bojarszczuk J., Staniak M., Harasim J. 2011. Productivity of pasture mixtures with legumes in organic forming. J. Res. Appl. Agric. Engineer., 56(3): 27-35. (in Polish)
  • Boniecki P. 2005. The use of artificial neuronal networks of the RBF type for prediction of yield of chosen cereal plants. J. Res. Appl. Agric. Engineer., 50(2): 15-19. (in Polish)
  • Grzebisz W., Wrońska M., Diatta J.B, Szczepaniak W. 2008. Effect of zinc foliar application at an elary stage of maize growth on patterns of nutrients and dry matter accumulation by the canopy. Part II. Nitrogen uptake and dry matter accumulation patterns. J. Elementol., 13(1): 29-39.
  • Grzebisz W., Przygocka-Cyna K., Szczepaniak W., Diatta J., Potarzycki J. 2010. Magnesium as a nutritional tool of nitrogen efficient management – plant production and environment. J. Elem., 15(4): 771-788.
  • Kacorzyk P., Kasperczyk M. 2006. Evaluation of natural fertilisation of a meadow in a submontane region. Part I. Botanical composition, dry matter yield and the content of total protein and simple sugars. Acta Agr. Silv. Ser. Agr., 48, 25-32.
  • Kopeć M. 2000. Dynamice of Fielding and quality changes of mountain meadow sward over 30 years of fertiliser experiment. Zesz. Nauk. AR w Krakowie, ser. Rozpr. 267, ss. 84. (in Polish)
  • Kopeć M., Mazur K. 2011. Field forming effects of cultivation measures in long-term fertilizer experiment of grass sward. Nawozy Nawożenie, 42: 65-77.
  • Kulik M. 2009. Effect of different factors on chemical composition of grass-legumes sward. J. Elementol., 14(1): 91-100.
  • Mazur K., Mazur T. 1972. Effect of mineral fertilisation on the yield, botanical composition and chemical content of the plant mass from a montane meadow. Acta Agr. Silv., ser. Agr., 12(1): 85-112.
  • Moghaddam P. A., Mohamm adali H. D., Mahrokh S. 2010. A new method in assessing sugar beet leaf nitrogen status through color image processing and artificial neural network. The research conducted within the theme number 3101 was financed from the fund granted by the Ministry of Science and Higher Education. Int. J. Food Agric. Environ., 8(1): 485-489.
  • Mutanga O., Skidmore A.K. 2004. Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sensing Environ., 90(1): 104-115.
  • Stastny J., Konecny V., Trenz O. 2011. Agricultural data prediction by means of neural network. Agric. Econ. – Czech, 57(7): 356-361.
  • StatSoft, Inc. 2009. Statistica (data analysis software system), version 9.0. www.statsoft.com.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-5490af87-6c02-4ef4-bef4-99d4f936c868
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