Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica)
Plant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflict...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2013-11-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpls.2013.00449/full |
| _version_ | 1811278393142935552 |
|---|---|
| author | Serge-Étienne eParent Leon Etienne eParent Danilo-Eduardo eRozane William eNatale |
| author_facet | Serge-Étienne eParent Leon Etienne eParent Danilo-Eduardo eRozane William eNatale |
| author_sort | Serge-Étienne eParent |
| collection | DOAJ |
| description | Plant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflicting results and wrong inferences. Our objective was to present an unbiased statistical approach of plant nutrient diagnosis using a balance concept and mango (Mangifera indica) as test crop. We collected foliar samples at flowering stage in 175 mango orchards. The ionomes comprised 11 nutrients (S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe). Traditional multivariate methods were found to be numerically biased. Ionomes were thus represented by unbiased balances computed as isometric log ratios (ilr). Soil fertility attributes (pH and bioavailable nutrients) were transformed into balances to conduct discriminant analysis. The orchards differed more from genotype than soil nutrient signatures. A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalance and high/low-yielders. The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree-1 proved to be a fairly informative test (area under curve = 0.84-0.92). The [P | N,S] and [Mn | Cu,Zn] balances were found to be potential sources of misbalance in the less productive orchards, and should thus be further investigated in field experiments. We propose using a coherent pan balance diagnostic method with median ilr values of top yielders centered at fulcrums of a mobile and the critical Mahalanobis distance as a guide for global nutrient balance. Nutrient concentrations in weighing pans assisted appreciating nutrients as relative shortage, adequacy or excess in balances. |
| first_indexed | 2024-04-13T00:34:57Z |
| format | Article |
| id | doaj.art-d7604e14b9df40d39e85dcbe68b150cf |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-13T00:34:57Z |
| publishDate | 2013-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-d7604e14b9df40d39e85dcbe68b150cf2022-12-22T03:10:22ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2013-11-01410.3389/fpls.2013.0044963683Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica)Serge-Étienne eParent0Leon Etienne eParent1Danilo-Eduardo eRozane2William eNatale3Université LavalUniversité LavalUniversidade Estadual PaulistaUniversidade Estadual PaulistaPlant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflicting results and wrong inferences. Our objective was to present an unbiased statistical approach of plant nutrient diagnosis using a balance concept and mango (Mangifera indica) as test crop. We collected foliar samples at flowering stage in 175 mango orchards. The ionomes comprised 11 nutrients (S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe). Traditional multivariate methods were found to be numerically biased. Ionomes were thus represented by unbiased balances computed as isometric log ratios (ilr). Soil fertility attributes (pH and bioavailable nutrients) were transformed into balances to conduct discriminant analysis. The orchards differed more from genotype than soil nutrient signatures. A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalance and high/low-yielders. The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree-1 proved to be a fairly informative test (area under curve = 0.84-0.92). The [P | N,S] and [Mn | Cu,Zn] balances were found to be potential sources of misbalance in the less productive orchards, and should thus be further investigated in field experiments. We propose using a coherent pan balance diagnostic method with median ilr values of top yielders centered at fulcrums of a mobile and the critical Mahalanobis distance as a guide for global nutrient balance. Nutrient concentrations in weighing pans assisted appreciating nutrients as relative shortage, adequacy or excess in balances.http://journal.frontiersin.org/Journal/10.3389/fpls.2013.00449/fullionomicsPlant Nutritioncrop managementMangoCompositional data analysis |
| spellingShingle | Serge-Étienne eParent Leon Etienne eParent Danilo-Eduardo eRozane William eNatale Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) Frontiers in Plant Science ionomics Plant Nutrition crop management Mango Compositional data analysis |
| title | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
| title_full | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
| title_fullStr | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
| title_full_unstemmed | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
| title_short | Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica) |
| title_sort | plant ionome diagnosis using sound balances case study with mango mangifera indica |
| topic | ionomics Plant Nutrition crop management Mango Compositional data analysis |
| url | http://journal.frontiersin.org/Journal/10.3389/fpls.2013.00449/full |
| work_keys_str_mv | AT sergeetienneeparent plantionomediagnosisusingsoundbalancescasestudywithmangomangiferaindica AT leonetienneeparent plantionomediagnosisusingsoundbalancescasestudywithmangomangiferaindica AT daniloeduardoerozane plantionomediagnosisusingsoundbalancescasestudywithmangomangiferaindica AT williamenatale plantionomediagnosisusingsoundbalancescasestudywithmangomangiferaindica |