Strategies to Overcome Impediments to Adoption of Conservation Tillage

Carter, M. R.

doi:10.4324/9781315150529-1

Cited by 5 publications

(2 citation statements)

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“…Agroecosystems are human-made ecosystems that relate living organisms to their environment within arbitrarily delineated boundaries [27] at plot, farm or territory scales [28]. The success or failure of an agroecosystem depends on many physical, biological and socio-economic factors dominated by climate, soil and management [29,30]. Present guidelines for tillage were elaborated at a territorial scale [30].…”

Section: Definition Of Natural System and Subsystemsmentioning

confidence: 99%

“…The success or failure of an agroecosystem depends on many physical, biological and socio-economic factors dominated by climate, soil and management [29,30]. Present guidelines for tillage were elaborated at a territorial scale [30]. Assuming that all factors except for those being addressed are at equal or optimum levels at a territorial scale, a minimum dataset is selected to generate response curves and calibrate soil and tissue tests against crop yields in fertilizer trials [17,31], and to facilitate nutrient management [32].…”

Section: Definition Of Natural System and Subsystemsmentioning

confidence: 99%

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Nutrient Diagnosis of Fertigated “Prata” and “Cavendish” Banana (Musa spp.) at Plot-Scale

Neto

Deus

Filho³

et al. 2020

Plants

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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 “Prata” and “Cavendish” 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.

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