Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network Approach
Predicting yield is essential for producers, stakeholders and international interchange demand. The majority of the divergence in yield and essential oil content is associated with environmental aspects, including weather conditions, soil variety and cultivation techniques. Therefore, aniseed produc...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/12/11/1722 |
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| author | Lato Pezo Biljana Lončar Olja Šovljanski Ana Tomić Vanja Travičić Milada Pezo Milica Aćimović |
| author_facet | Lato Pezo Biljana Lončar Olja Šovljanski Ana Tomić Vanja Travičić Milada Pezo Milica Aćimović |
| author_sort | Lato Pezo |
| collection | DOAJ |
| description | Predicting yield is essential for producers, stakeholders and international interchange demand. The majority of the divergence in yield and essential oil content is associated with environmental aspects, including weather conditions, soil variety and cultivation techniques. Therefore, aniseed production was examined in this study. The categorical input variables for artificial neural network modelling were growing year (two successive growing years), growing locality (three different locations in Vojvodina Province, Serbia) and fertilization type (six different treatments). The output variables were morphological and quality parameters, with agricultural importance such as plant height, umbel diameter, number of umbels, number of seeds per umbel, 1000-seed weight, seed yield per plant, plant weight, harvest index, yield per ha, essential oil (EO) yield, germination energy, total germination, EO content, as well as the share of EOs compounds, including limonene, <i>cis</i>-dihydro carvone, methyl chavicol, carvone, <i>cis</i>-anethole, <i>trans</i>-anethole, β-elemene, α-himachalene, <i>trans</i>-β-farnesene, γ-himachalene, <i>trans</i>-muurola-4(14),5-diene, α-zingiberene, β-himachalene, β-bisabolene, <i>trans</i>-pseudoisoeugenyl 2-methylbutyrate and epoxy-pseudoisoeugenyl 2-methylbutyrate. The ANN model predicted agricultural parameters accurately, showing r<sup>2</sup> values between 0.555 and 0.918, while r<sup>2</sup> values for the forecasting of essential oil content were between 0.379 and 0.908. According to global sensitivity analysis, the fertilization type was a more influential variable to agricultural parameters, while the location site was more influential to essential oils content. |
| first_indexed | 2024-03-09T18:56:16Z |
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| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-09T18:56:16Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
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| series | Life |
| spelling | doaj.art-f0fecd8d58cf46d2b7729cd79596bd262023-11-24T05:29:53ZengMDPI AGLife2075-17292022-10-011211172210.3390/life12111722Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network ApproachLato Pezo0Biljana Lončar1Olja Šovljanski2Ana Tomić3Vanja Travičić4Milada Pezo5Milica Aćimović6Institute of General and Physical Chemistry, University of Belgrade, Studentski trg 12, 11000 Belgrade, SerbiaFaculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, SerbiaFaculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, SerbiaFaculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, SerbiaFaculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, SerbiaDepartment of Thermal Engineering and Energy, “Vinča” Institute of Nuclear Sciences—National Institute of the Republic of Serbia, University of Belgrade, Studentski trg 12, 11000 Belgrade, SerbiaInstitute of Field and Vegetable Crops Novi Sad, Maksima Gorkog 30, 21000 Novi Sad, SerbiaPredicting yield is essential for producers, stakeholders and international interchange demand. The majority of the divergence in yield and essential oil content is associated with environmental aspects, including weather conditions, soil variety and cultivation techniques. Therefore, aniseed production was examined in this study. The categorical input variables for artificial neural network modelling were growing year (two successive growing years), growing locality (three different locations in Vojvodina Province, Serbia) and fertilization type (six different treatments). The output variables were morphological and quality parameters, with agricultural importance such as plant height, umbel diameter, number of umbels, number of seeds per umbel, 1000-seed weight, seed yield per plant, plant weight, harvest index, yield per ha, essential oil (EO) yield, germination energy, total germination, EO content, as well as the share of EOs compounds, including limonene, <i>cis</i>-dihydro carvone, methyl chavicol, carvone, <i>cis</i>-anethole, <i>trans</i>-anethole, β-elemene, α-himachalene, <i>trans</i>-β-farnesene, γ-himachalene, <i>trans</i>-muurola-4(14),5-diene, α-zingiberene, β-himachalene, β-bisabolene, <i>trans</i>-pseudoisoeugenyl 2-methylbutyrate and epoxy-pseudoisoeugenyl 2-methylbutyrate. The ANN model predicted agricultural parameters accurately, showing r<sup>2</sup> values between 0.555 and 0.918, while r<sup>2</sup> values for the forecasting of essential oil content were between 0.379 and 0.908. According to global sensitivity analysis, the fertilization type was a more influential variable to agricultural parameters, while the location site was more influential to essential oils content.https://www.mdpi.com/2075-1729/12/11/1722aniseedessential oilgrowing yearlocalityfertilizationartificial neural network |
| spellingShingle | Lato Pezo Biljana Lončar Olja Šovljanski Ana Tomić Vanja Travičić Milada Pezo Milica Aćimović Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network Approach Life aniseed essential oil growing year locality fertilization artificial neural network |
| title | Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network Approach |
| title_full | Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network Approach |
| title_fullStr | Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network Approach |
| title_full_unstemmed | Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network Approach |
| title_short | Agricultural Parameters and Essential Oil Content Composition Prediction of Aniseed, Based on Growing Year, Locality and Fertilization Type—An Artificial Neural Network Approach |
| title_sort | agricultural parameters and essential oil content composition prediction of aniseed based on growing year locality and fertilization type an artificial neural network approach |
| topic | aniseed essential oil growing year locality fertilization artificial neural network |
| url | https://www.mdpi.com/2075-1729/12/11/1722 |
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