PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Mathematical models use to yield prognosis of perennials on marginal land according to fertilisers doses

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Models describe our beliefs about how the world functions. In mathematical modelling, we translate those beliefs into the language of mathematics. Mathematical models can yield prognose on the base of applied fertiliser dose. In this work results of finding yield mathematical model according to fertiliser (nitrogen) dose for perennials (willowleaf sunflower Helianthus salicifolious, cup plant Silphium perfoliatum and Jerusalem artichoke Helianthus tuberosus) on marginal land are presented. Models were described as normalised square equations for dependence between yield and fertiliser doses. Experiments were conducted in lisymeters and vases for willowleaf sunflower and cup plant. For Jerusalem artichoke experiments were done in vases only. All experiments have been doing during two years (2018 and 2019) for different fertilisers doses (45, 90 and 135 kg N∙ha-1) in three repetitions. From simulations maximal yield could be achieved for following fertiliser doses – willowleaf sunflower 104 kg N∙ha-1, cup plant 85 kg N∙ha-1 and Jerusalem artichoke 126 kg N∙ha-1.
Wydawca
Rocznik
Tom
Strony
233--242
Opis fizyczny
Bibliogr. 45 poz., fot., tab., wykr.
Twórcy
  • Institute of Technology and Life Sciences – National Research Institute, Falenty, Hrabska Av. 3, 09-090 Raszyn, Poland
  • Institute of Technology and Life Sciences – National Research Institute, Falenty, Hrabska Av. 3, 09-090 Raszyn, Poland
autor
  • Institute of Technology and Life Sciences – National Research Institute, Falenty, Hrabska Av. 3, 09-090 Raszyn, Poland
  • Institute of Technology and Life Sciences – National Research Institute, Falenty, Hrabska Av. 3, 09-090 Raszyn, Poland
  • Institute of Technology and Life Sciences – National Research Institute, Falenty, Hrabska Av. 3, 09-090 Raszyn, Poland
autor
  • Warsaw University of Life Sciences (SGGW), Institute of Wood Sciences and Furniture, Warszawa, Poland
Bibliografia
  • AMIS 2020. Yield forecasting [online]. Agricultural Market Information System. [Access 12.08.2020]. Available at: http://www.amis-outlook.org/technical/research/forecasts/en/
  • ASYLBAEV I., KHABIROV I., KHASANOV A., GABBASOVA I., GARIPOV T. 2020. Temporal change of soil chemical properties in the southern forest-steppe of the Ufa Region of the Republic of Bashkortostan, Russia. Journal of Water and Land Development. No. 44 p. 8–12. DOI 10.24425/jwld.2019.127039.
  • BASSO B., LIU L. 2019. Seasonal crop yield forecast: Methods, applications, and accuracies. Advances in Agronomy. Vol. 154 p. 201–255. DOI 10.1016/bs.agron.2018.11.002.
  • BLANCO-CANQUI H. 2016. Growing dedicated energy crops on marginal lands and ecosystem services. Soil Science Society of America Journal. Vol. 80(4) p. 845–858. DOI 10.2136/sssaj2016.03.0080.
  • BURY M., MOŻDŻER E., KITCZAK T., SIWEK H., WŁODARCZYK M. 2020. Yields, calorific value and chemical properties of cup plant Silphium perfoliatum L. biomass, depending on the method of establishing the plantation. Agronomy. Vol. 10(6), 851. DOI 10.3390/agronomy10060851.
  • BURZYŃSKA I. 2019. Monitoring of selected fertilizer nutrients in surface waters and soils of agricultural land in the river valley in Central Poland. Journal of Water and Land Development. No. 43 p. 41–48. DOI 10.2478/jwld-2019-0061.
  • CALLO-CONCHA D., JAENICKE H., SCHMITT C. B., DENICH M. 2020. Food and Non-food biomass production, processing and use in Sub-Saharan Africa: Towards a regional bioeconomy. Sustainability. Vol. 12(5). DOI 10.3390/su12052013.
  • DELINCE J. 2017. Recent practices and advances for AMIS crop yield forecasting at farm and parcel level: A review. Rome. FAO–AMIS Publication. ISBN 978-92-5-109779-3 pp. 51.
  • EU Science Hub 2020. Crop yield forecasting [online]. [Access 30.10.2020]. Available at: https://ec.europa.eu/jrc/en/research-topic/crop-yield-forecasting.
  • FAO 1973. Guide to the calibration of soil tests for fertilizer recommendations. FAO Soils Bulletin. No. 18 pp. 76.
  • HOCHMUTH G., HANLON E., OVERMAN A. 2011. Fertilizer experimentation, data analyses, and interpretation for developing fertilization recommendations – Examples with vegetable crop research. University of Florida IFAS pp. 9.
  • HOEFSLOOT P., INES A., VAN DAM J., DUVEILLER G., KAYITAKIRE F., HANSEN J. 2012. Combining crop models and remote sensing for yield prediction: Concepts, applications and challenges for heterogeneous smallholder environments. Report of CCFAS-JRC Workshop at Joint Research Centre. 13–14.07.2012 Ispra, Italy. Joint Research Center Technical Report. Luxembourg. Publications Office of the European Union. ISBN 978-92-79-27883-9 pp. 48.
  • IGUE A., BALOGOUN I., OGA A., SAIDOU A., EZUI G., YOUL S., MANDO A. 2018. Recommendations of fertilizer formulas for the maize production in Northern Benin. Advances in Crop Science and Technology. Vol. 6(3) p. 1–8. DOI 10.4172/2329-8863.1000359.
  • JADCZYSZYN T. 2021. Chemia rolna. System doradztwa nawozowego. Wiadomości wprowadzające [Agricultural chemistry. Fertilizer advisory system. Introduction information] [online]. [Access 12.05.2021]. Available at: https://e.sggw.pl/mod/page/view.php?id=20377
  • KARDAVUT U., PALTA C., KOKTEN K. 2010. Comparative study on some non-linear growth models for describing leaf growth of maize. International Journal of Agriculture & Biology. Vol. 12(2) p. 227–230.
  • KAYS S., NOTTINGHAM S. 2007. Biology and chemistry of Jerusalem artichoke. 1st ed. Boca Raton. CRC Press. ISBN 9781420044959 pp. 496.
  • KHAWAJA C., JANSSEN R. 2014. Sustainable supply of non-food biomass for a resource efficient bioeconomy. A review paper on the state-of-the-art. Munich, Germany. WIP – Renewable Energies pp. 55.
  • KOWALCZYK-JUŚKO A. 2010. Badania nad energetycznym wykorzystaniem wybranych gatunków roślin wieloletnich [Study on energetic utilization of selected species of the perennial crops]. Zeszyty Problemowe Postępów Nauk Rolniczych. Z. 556 p. 421–427.
  • KRZYŻANIAK M., STOLARSKI M. J., WARMIŃSKI K. 2020. Life cycle assessment of giant miscanthus: Production on marginal soil with various fertilisation treatments. Energies. Vol. 13(8), 1931. DOI 10.3390/en13081931.
  • KUŚ J., FABER A., STASIAK M., KAWALEC A. 2008. Plonowanie wybranych gatunków roślin uprawianych na cele energetyczne na różnych glebach [Yielding of the selected plant species cultivated for energy purposes on various soils]. Problemy Inżynierii Rolniczej. R. 16. Nr 1 p. 79–86.
  • Meteomodel.pl 2020. Średnie i sumy miesięczne – Wrocław [Average and monthly sums – Wroclaw] [online]. [Data from Institute of Meteorology and Water Management – National Research Institute (IMWM-NRI IMiGW)]. [Access 12.05.2020]. Available at: https://meteomodel.pl/dane/srednie-miesieczne/
  • MUDRYK K., WRÓBEL M. 2012. Słonecznik wierzbolistny Helianthus salicifolius A. Dietr. - na cele energetyczne [Willow-leaved sunflower Helianthus salicifolius A. Dietr. for energy purposes]. Inżynieria Rolnicza. R. 16. Nr 2 p. 249–256.
  • OLBA-ZIĘTY E., STOLARSKI M. J., KRZYŻANIAK M., GOŁASZEWSKI J. 2020. Environmental external cost of poplar wood chips sustainable production. Journal of Cleaner Production. Vol. 252, 119854. DOI 10.1016/j.jclepro.2019.119854.
  • PESHAWA J.M.A., FARAJ R.H. 2014. Data normalization and standardization: A technical report. Machine Learning Technical Reports. Vol. 1(1) p. 1–6. DOI 10.13140/RG.2.2.28948.04489.
  • PICHARD G. 2012. Management, production, and nutritional characteristics of cup-plant (Silphium perfoliatum) in temperate climates of Southern Chile. Ciencia e investigación agrarian. Vol. 39(1) p. 61–77. DOI 10.4067/S0718-16202012000100005.
  • SAWICKA B., SKIBA D. 2009. Zmienność ciemnienia miąższu bulw surowych i gotowanych słonecznika bulwiastego (Helianthus tuberosus L.) [Fluctuation of flesh darkening of raw and cooking tubers (Helianthus tuberosus L.)]. Annales UMCS, Agricultura. Vol. 64(2) p. 15–22. DOI 10.2478/v10081-009-0013-1.
  • SCARLAT N., DALLEMAND J.-F., MONFORTI-FERRARIO F., NITA V. 2015. The role of biomass and bioenergy in a future bioeconomy: Policies and facts. Environmental Development. Vol. 15 p. 3–34. DOI 10.1016/j.envdev.2015.03.006.
  • SCHITTENHELM S., SCHOO B., SCHROETTER S. 2016. Ertragsphysiologie von Biogaspflanzen: Vergleich von Durchwachsener Silphie, Mais und Luzernegras [Yield physiology of biogas plants: comparison of streaky Silphie, maize and alfalfa]. Journal für Kulturpflanzen Bd. 68(12) p. 378–384. DOI 10.5073/JFK.2016.12.06.
  • SEGHAL V., RAJAK D., CHAUDHARY K., DADHVAL V.K. 2002. Improved regional yield prediction by crop growth monitoring system using remote sensing derived crop phenology. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. 34(7) p. 329–334.
  • SERAFIN A., POGORZELEC M., BRONOWICKA-MIELNICZUK U. 2020. Effect of the quality of shallow groundwaters on the occurrence of selected relic plant species of peatlands in the Łęczna-Włodawa Lakeland. Journal of Water and Land Development. No. 45 p. 133–142. DOI 10.24425jwld.2020.133055.
  • STOLARSKI M. 2004. Produkcja oraz pozyskiwanie biomasy z wieloletnich upraw roślin energetycznych [Production and harvesting of biomass from perennial energy crops]. Problemy Inżynierii Rolniczej. R. 12. Nr 3 p. 47–56.
  • STOLARSKI M., KRZYŻANIAK M., WARMIŃSKI K., OLBA-ZIĘTY E., PENNI D., BORDIEAN A. 2019a. Energy efficiency indices for lignocellulosic biomass production: Short rotation coppices versus grasses and other herbaceous crops. Industrial Crops and Products. Vol. 135 p. 10–20. DOI 10.1016/j.indcrop.2019.04.022.
  • STOLARSKI M.J., KRZYŻANIAK M., WARMIŃSKI K., TWORKOWSKI J.,SZCZUKOWSKI S., OLBA-ZIĘTY E., GOŁASZEWSKI J. 2017. Energy efficiency of perennial herbaceous crops production depending on the type of digestate and mineral fertilizers. Energy. Vol. 134 p. 50–60. DOI 10.1016/j.energy.2017.05.195.
  • STOLARSKI M., SZCZUKOWSKI S., TWORKOWSKI J., KRZYŻANIAK M. 2019b. Extensive willow biomass productionon marginal land. Polish Journal of Environmental Studies. Vol. 28(6) p. 4359–4367. DOI 10.15244/pjoes/94812.
  • STOLARSKI M., ŚNIEG M., KRZYŻANIAK M., TWORKOWSKI J., SZCZUKOWSKI S., GRABAN Ł., LAJSZNER W. 2018. Short rotation coppices, grasses and other herbaceous crops: Biomass properties versus 26 genotypes and harvest time. Industrial Crops and Products. Vol. 119 p. 22–32. DOI 10.1016/j.indcrop.2018.03.064.
  • STOLARSKI M.J., WARMIŃSKI K., KRZYŻANIAK M. 2020. Energy value of yield and biomass quality of poplar grown in two consecutive 4-year harvest rotations in the North-East of Poland. Energies. Vol. 13(6), 1495 p. 1–13. DOI 10.3390/en13061495.
  • STOLARSKI M., WARMIŃSKI K., KRZYŻANIAK M., TYŚKIEWICZ K., OLBA-ZIĘTY E., GRABAN Ł., LAJSZNER W., ZAŁUSKI D., WIEJAK R., KAMIŃSKI P., RÓJ E. 2020a. How does extraction of biologically active substances with supercritical carbon dioxide affect lignocellulosic biomass properties? Wood Science and Technology. Vol. 54(3) p. 519–546. DOI 10.1007/s00226-020-01182-5.
  • SZPUNAR-KROK E., BOBRECKA-JAMRO D., GROCHOWSKA S., BUCZEK J. 2016. Yield of the aboveground parts and tubers of Jerusalem artichoke (Helianthus tuberosus L.) depending on plant density. Acta Scientiarum Polonorum. Agricultura. Vol. 15(3) p. 69–78.
  • TYCHON B., BUFFET D., DEHEM D., EEERENS H., OGER R. 2001. The Belgian crop growth monitoring system. 2nd International Symposium “Modelling Cropping Systems”. [16–18.07.2001 Florence, Italy].
  • VAN DER VELDE M., NISINI L. 2019. Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015. Agricultural Systems. Vol. 168 p. 203–212. DOI 10.1016/j.agsy.2018.06.009.
  • VARNERO C., URRUTIA M., IBACETA S. 2018. Bioenergy from perennial grasses. In: Advances in biofuels and bioenergy. Eds. M. Nageswara-Rao, J.R. Soneji. Rijeka. InTech. DOI 10.5772/intecho-pen.74014.
  • WALESIAK M. 2014. Przegląd formuł normalizacji wartości zmiennych oraz ich własności w statystycznej analizie wielowymiarowej [Data normalization in multivariate data analysis an overview and properties]. Przegląd Statystyczny. R. 61. Z. 4 p. 363–372.
  • WALESIAK M. 2019. The choice of normalization method and rankings of the set of objects based on composite indicator values. Statistics in Transition New Series. Vol. 19(4) p. 693–710. DOI 10.21307/stattrans-2018-036.
  • WEVER C., HÖLLER M., BECKER L., BIERTÜMPFEL A., KÖHLER J., VANI NGHELANDT D., WESTHOFF P., PUDER., PESTSOVA E. 2019. Towards high-biomass yielding bioenergy crop Silphium perfoliatum L.: Phenotypic and genotypic evaluation of five cultivated populations. Biomass and Bioenergy. Vol. 124 p. 102–113. DOI 10.1016/j.biombioe.2019.03.016.
  • WIDELSKA E., WALCZAK W. 2019. Restoration of ponds in the municipal park in Zduńska Wola, Poland. Journal of Water and Land Development. No. 44 p. 151–157. DOI 10.24425/jwld.2019.127056.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-900ee2e1-bfb2-43a0-a141-38888a082890
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.