Nutrient Diagnosis of Fertigated 'Prata' and 'Cavendish' Banana (Musa spp.) at Plot-Scale
| Autor: | Léon E. Parent, Vagner Alves Rodrigues Rodrigues Filho, José Aridiano Lima de Deus, William Natale, Antonio João de Lima Neto |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Fertigation Soil test Neural Network Plant Science 01 natural sciences Article clr index Nutrient Ecology Evolution Behavior and Systematics Mathematics Ecology Nutrient management compositional data analysis Botany 04 agricultural and veterinary sciences Cavendish banana machine learning Agronomy QK1-989 040103 agronomy & agriculture 0401 agriculture forestry and fisheries perturbation vector Scale (map) Plant nutrition 010606 plant biology & botany Log ratio |
| Zdroj: | Plants, Vol 9, Iss 1467, p 1467 (2020) Plants Volume 9 Issue 11 |
| ISSN: | 2223-7747 |
| Popis: | Fertigation management of banana plantations at a plot scale is expanding rapidly in Brazil. To guide nutrient management at such a small scale, genetic, environmental and managerial features should be well understood. Machine learning and compositional data analysis (CoDa) methods can measure the effects of feature combinations on banana yield and rank nutrients in the order of their limitation. Our objectives are to review ML and CoDa models for application at regional and local scales, and to customize nutrient diagnoses of fertigated banana at the plot scale. We documented 940 &ldquo Prata&rdquo and &ldquo Cavendish&rdquo plot units for tissue and soil tests, environmental and managerial features, and fruit yield. A Neural Network informed by soil tests, tissue tests and other features was the most proficient learner (AUC up to 0.827). Tissue nutrients were shown to have the greatest impact on model accuracy. Regional nutrient standards were elaborated as centered log ratio means and standard deviations of high-yield and nutritionally balanced specimens. Plot-scale diagnosis was customized using the closest successful factor-specific tissue compositions identified by the smallest Euclidean distance from the diagnosed composition using centered or isometric log ratios. Nutrient imbalance differed between regional and plot-scale diagnoses, indicating the profound influence of local factors on plant nutrition. However, plot-scale diagnoses require large, reliable datasets to customize nutrient management using ML and CoDa models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|