Utility of citizen science data: A case study in land-based shark fishing

Gibson, Kesley J.; Streich, Matthew K.; Topping, Tara S.; Stunz, Gregory W.

doi:10.1371/journal.pone.0226782

Cited by 27 publications

(25 citation statements)

References 30 publications

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“…There are a small number of experimental data from fertilizer trials compared to the large capacity of crop managers to acquire observational data at production sites. Growers can act as citizen scientists [83,84] by providing data reliably and ethically for use with machine learning models at factor-specific levels, and to adjust fertigation locally to reach high yields with high fertilizer-use efficiency. In the present banana dataset, the fertilization regime, meteorological data, composition of well water, solum thickness, profile stratification, soil texture and soil compaction at the field level were not quantified.…”

Section: Need For Big Datamentioning

confidence: 99%

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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Section: Need For Big Datamentioning

confidence: 99%

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

Neto

Deus

Filho³

et al. 2020

Plants

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“…Where low yield, DBH or plant vigor is observed and nutrient imbalance is suspected, the objective is to reach high nutrient-use efficiency by adopting reliable corrective measures already implemented in the successful neighborhood. To generate large, trustful, and informative data sets to conduct nutrient diagnoses at a local scale, a close and ethical collaboration is required between researchers and stakeholders [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

Nutrient Diagnosis of Eucalyptus at the Factor-Specific Level Using Machine Learning and Compositional Methods

Paula

Arruda

Parent

et al. 2020

Plants

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Brazil is home to 30% of the world’s Eucalyptus trees. The seedlings are fertilized at plantation to support biomass production until canopy closure. Thereafter, fertilization is guided by state standards that may not apply at the local scale where myriads of growth factors interact. Our objective was to customize the nutrient diagnosis of young Eucalyptus trees down to factor-specific levels. We collected 1861 observations across eight clones, 48 soil types, and 148 locations in southern Brazil. Cutoff diameter between low- and high-yielding specimens at breast height was set at 4.3 cm. The random forest classification model returned a relatively uninformative area under the curve (AUC) of 0.63 using tissue compositions only, and an informative AUC of 0.78 after adding local features. Compared to nutrient levels from quartile compatibility intervals of nutritionally balanced specimens at high-yield level, state guidelines appeared to be too high for Mg, B, Mn, and Fe and too low for Cu and Zn. Moreover, diagnosis using concentration ranges collapsed in the multivariate Euclidean hyper-space by denying nutrient interactions. Factor-specific diagnosis detected nutrient imbalance by computing the Euclidean distance between centered log-ratio transformed compositions of defective and successful neighbors at a local scale. Downscaling regional nutrient standards may thus fail to account for factor interactions at a local scale. Documenting factors at a local scale requires large datasets through close collaboration between stakeholders.

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“…There, the resources to tackle controllable factors can be used parsimoniously and efficiently to reach trustful yield targets. Because the number of successful factor combinations is limited by the size and diversity of datasets, a close collaboration is required between stakeholders to collect facts and document local knowledge trustfully [6,7,[90][91][92][93][94]. The decision to fix a yield target in classificaiton mode depends not only on growers' yield objective, but also on model precision and the number of true negative specimens available as close neighbors.…”

Section: Local Diagnosismentioning

confidence: 99%

Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition

Parent¹,

Natale²,

Brunetto³

2022

Soil Science - Emerging Technologies, Global Perspectives and Applications

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Soils, nutrients and other factors support human food production. The loss of high-quality soils and readily minable nutrient sources pose a great challenge to present-day agriculture. A comprehensive scheme is required to make wise decisions on system’s sustainability and minimize the risk of crop failure. Soil quality provides useful indicators of its chemical, physical and biological status. Tools of precision agriculture and high-throughput technologies allow acquiring numerous soil and plant data at affordable costs in the perspective of customizing recommendations. Large and diversified datasets must be acquired uniformly among stakeholders to diagnose soil quality and plant nutrition at local scale, compare side-by-side defective and successful cases, implement trustful practices and reach high resource-use efficiency. Machine learning methods can combine numerous edaphic, managerial and climatic yield-impacting factors to conduct nutrient diagnosis and manage nutrients at local scale where factors interact. Compositional data analysis are tools to run numerical analyses on interacting components. Fractal models can describe aggregate stability tied to soil conservation practices and return site-specific indicators for decomposition rates of organic matter in relation to soil tillage and management. This chapter reports on machine learning, compositional and fractal models to support wise decisions on crop fertilization and soil conservation practices.

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Utility of citizen science data: A case study in land-based shark fishing

Cited by 27 publications

References 30 publications

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

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

Nutrient Diagnosis of Eucalyptus at the Factor-Specific Level Using Machine Learning and Compositional Methods

Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition

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