Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models.
Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the curren...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0268516 |
| _version_ | 1817970455698472960 |
|---|---|
| author | Leandro Hahn Léon-Étienne Parent Angela Cristina Paviani Anderson Luiz Feltrim Anderson Fernando Wamser Danilo Eduardo Rozane Marcos Matos Ender Douglas Luiz Grando Jean Michel Moura-Bueno Gustavo Brunetto |
| author_facet | Leandro Hahn Léon-Étienne Parent Angela Cristina Paviani Anderson Luiz Feltrim Anderson Fernando Wamser Danilo Eduardo Rozane Marcos Matos Ender Douglas Luiz Grando Jean Michel Moura-Bueno Gustavo Brunetto |
| author_sort | Leandro Hahn |
| collection | DOAJ |
| description | Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers' observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production. |
| first_indexed | 2024-04-13T20:34:15Z |
| format | Article |
| id | doaj.art-8e418122c3664c529cff47aa1b8c014e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T20:34:15Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-8e418122c3664c529cff47aa1b8c014e2022-12-22T02:31:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01175e026851610.1371/journal.pone.0268516Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models.Leandro HahnLéon-Étienne ParentAngela Cristina PavianiAnderson Luiz FeltrimAnderson Fernando WamserDanilo Eduardo RozaneMarcos Matos EnderDouglas Luiz GrandoJean Michel Moura-BuenoGustavo BrunettoBrazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers' observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production.https://doi.org/10.1371/journal.pone.0268516 |
| spellingShingle | Leandro Hahn Léon-Étienne Parent Angela Cristina Paviani Anderson Luiz Feltrim Anderson Fernando Wamser Danilo Eduardo Rozane Marcos Matos Ender Douglas Luiz Grando Jean Michel Moura-Bueno Gustavo Brunetto Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models. PLoS ONE |
| title | Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models. |
| title_full | Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models. |
| title_fullStr | Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models. |
| title_full_unstemmed | Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models. |
| title_short | Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models. |
| title_sort | garlic allium sativum feature specific nutrient dosage based on using machine learning models |
| url | https://doi.org/10.1371/journal.pone.0268516 |
| work_keys_str_mv | AT leandrohahn garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT leonetienneparent garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT angelacristinapaviani garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT andersonluizfeltrim garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT andersonfernandowamser garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT daniloeduardorozane garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT marcosmatosender garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT douglasluizgrando garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT jeanmichelmourabueno garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels AT gustavobrunetto garlicalliumsativumfeaturespecificnutrientdosagebasedonusingmachinelearningmodels |