Protective rain shields alter leaf microclimate and photosynthesis in organic apple production

Kjær, Katrine Heinsvig

doi:10.17660/actahortic.2016.1134.42

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…However, the relationship between LAI and spectral reflectance is also affected by other factors, such as the crop type, phenology, Sun illumination, local micro-climate, the type of soil, or the amount of precipitation (Kjaer et al, 2016;Din et al, 2017), and it may vary throughout the life cycle of the crop. These effects can be included in spatio-temporal models of LAI (Mustafa et al, 2014;Seyednasrollah et al, 2018;Jin et al, 2019;Qiu et al, 2020;Ji et al, 2021), which can be applied to data from aerial or satellite surveillance.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Hierarchical Models can Infer Interpretable Predictions of Leaf Area Index From Heterogeneous Datasets

Stojanović

Siegmann

Jarmer

et al. 2022

Front. Environ. Sci.

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Environmental scientists often face the challenge of predicting a complex phenomenon from a heterogeneous collection of datasets that exhibit systematic differences. Accounting for these differences usually requires including additional parameters in the predictive models, which increases the probability of overfitting, particularly on small datasets. We investigate how Bayesian hierarchical models can help mitigate this problem by allowing the practitioner to incorporate information about the structure of the dataset explicitly. To this end, we look at a typical application in remote sensing: the estimation of leaf area index of white winter wheat, an important indicator for agronomical modeling, using measurements of reflectance spectra collected at different locations and growth stages. Since the insights gained from such a model could be used to inform policy or business decisions, the interpretability of the model is a primary concern. We, therefore, focus on models that capture the association between leaf area index and the spectral reflectance at various wavelengths by spline-based kernel functions, which can be visually inspected and analyzed. We compare models with three different levels of hierarchy: a non-hierarchical baseline model, a model with hierarchical bias parameter, and a model in which bias and kernel parameters are hierarchically structured. We analyze them using Markov Chain Monte Carlo sampling diagnostics and an intervention-based measure of feature importance. The improved robustness and interpretability of this approach show that Bayesian hierarchical models are a versatile tool for the prediction of leaf area index, particularly in scenarios where the available data sources are heterogeneous.

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Section: Introductionmentioning

confidence: 99%

Bayesian Hierarchical Models can Infer Interpretable Predictions of Leaf Area Index From Heterogeneous Datasets

Stojanović

Siegmann

Jarmer

et al. 2022

Front. Environ. Sci.

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“…However, the spectral reflectance is also affected by countless other factors, such as the crop type, phenology, sun illumination, local micro-climate, the type of soil, or the amount of precipitation [19].…”

Section: Introductionmentioning

confidence: 99%

Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets

Stojanović

Siegmann

Jarmer

et al. 2021

Preprint

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Environmental scientists often have to predict a complex phenomenon from a heterogeneous collection of datasets. This is particularly challenging if there are systematic differences between them, as is often the case. Accounting for these differences requires a larger number of parameters and thus increases the risk of overfitting. We investigate how Bayesian hierarchical models can help mitigate this problem by allowing the practitioner to explicitly incorporate information about the dataset structure and general domain knowledge. To this end, we look at a typical application in remote sensing: the estimation of leaf area index (of white winter wheat), an important indicator for agronomical modeling, from measurements of reflectance spectra collected at different locations and growth stages. Since the insights gained from such a model could be used to inform policy or business decisions, the interpretability of the model is a primary concern. We, therefore, focus on models that capture the association between leaf area index and the spectral reflectance at various wavelengths by spline-based kernel functions, which can be visually inspected and analyzed. We compare models with three different levels of hierarchy: a non-hierarchical baseline model, a model with hierarchical bias parameter, and a model in which bias and kernel parameters are hierarchically structured. We analyze them using Markov Chain Monte Carlo sampling diagnostics and an intervention-based measure of feature importance. The improved robustness and interpretability of this approach lead us to recommend Bayesian hierarchical models as a versatile tool for environmental sciences and beyond, particularly in scenarios where the available data sources are heterogeneous.

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“…Given the positive results concerning insects and mites, in the current study we report on the impact of this netting system on various photosynthesis and fruit quality parameters, evaluated over six years. We included an additional treatment in the experiment (rainproof exclusion, i.e., netting with a polyethylene cover) to verify if diseases could be excluded more effectively without adverse effects on quality, as proposed in a European study [9]. The impact of netting on the fruit cuticle was also investigated because of its role in gaseous exchanges, sunscreen and as a barrier against pathogens [10].…”

Section: Introductionmentioning

confidence: 99%

Impact of Exclusion Netting Row Covers on ‘Honeycrisp’ Apple Trees Grown under Northeastern North American Conditions: Effects on Photosynthesis and Fruit Quality

Chouinard

Veilleux

Pelletier

et al. 2019

Insects

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Exclusion nets have been used successfully to protect fruit from insect pests of apples under various conditions, but the effect of each particular netting system on the plant itself has rarely been investigated. In this study, a complete exclusion system—in which the soil is also excluded—was used to grow ‘Honeycrisp’ apples for six years in southern Quebec, Canada. Abiotic conditions, as well as plant photosynthesis and fruit quality characteristics (colour, firmness, size, sugar content, number of seeds, ripeness and skin integrity) and yield were estimated yearly and compared in netted (either with or without a rainproof top) and unnetted row units. Although annual variations were high and results showed little or no difference between netted and unnetted rows for all measured variables, with the following exceptions; colour (increased red surface on fruits from unnetted rows some years), size (fruits from unnetted rows were smaller) and maturity (fruits from unnetted rows matured slightly faster). Fruits produced under nets had fewer microcracks at the surface than fruits produced without nets. Reduced cracking possibly helped decrease sooty blotch and flyspeck incidence and severity. Impacts for pest control and prospects for pesticide-free production are discussed.

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Protective rain shields alter leaf microclimate and photosynthesis in organic apple production

Cited by 6 publications

References 17 publications

Bayesian Hierarchical Models can Infer Interpretable Predictions of Leaf Area Index From Heterogeneous Datasets

Bayesian Hierarchical Models can Infer Interpretable Predictions of Leaf Area Index From Heterogeneous Datasets

Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets

Impact of Exclusion Netting Row Covers on ‘Honeycrisp’ Apple Trees Grown under Northeastern North American Conditions: Effects on Photosynthesis and Fruit Quality

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