Development of a dynamic model to estimate canopy par interception

Rojo, Francisco; Dhillon, Rajveer; Upadhyaya, Shrinivasa K.; Jenkins, B. M.

doi:10.1016/j.biosystemseng.2020.06.009

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…They divided the canopy into many cubic cells, and then calculated the probability that a beam would penetrate to any given cell without being intercepted by the foliage in the path, using an exponential extinction function. Another promising approach was to transform the 3D canopy light interception problem into a two-dimensional (2D) problem by using the shadow area and intensity on the ground at any given time (Rojo et al 2020).…”

Section: Canopy Development and Radiation Interceptionmentioning

confidence: 99%

Developing perennial fruit crop models in APSIM Next Generation using grapevine as an example

Zhu

Parker

Gou³

et al. 2021

In Silico Plants

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A new model for grapevines (Vitis vinifera L.) is the first perennial fruit crop model using the Agricultural Production System sIMulator (APSIM) Next Generation framework. Modules for phenology, light interception, carbohydrate allocation, yield formation and berry composition were adapted or added into APSIM Next Generation to represent the nature of fruit-bearing vines. The simulated grapevine phenological cycle starts with the dormancy phase triggered by a critical photoperiod in autumn, and then goes through the subsequent phenophases sequentially and finally returns to dormancy for a new cycle. The canopy microclimate module within APSIM Next Generation was extended to allow for row crop light interception. The carbohydrate arbitrator was enhanced to consider both sink strength and sink priority to reflect carbohydrate reserve as a concurrent competing sink. Weather conditions and source-sink ratio at critical developmental stages were used to determine potential grapevine yield components e.g., bunch number, berry number and berry fresh weight. The model was calibrated and tested extensively using four detailed datasets. The model captured the variations in the timing of measured budburst, flowering and véraison over 15 seasons across New Zealand for five different varieties. The calculated seasonal dynamics of light interception by the row and alley were consistent with field observations. The model also reproduced the dynamics of dry matter and carbohydrate reserve of different organs, and the wide variation in yield components caused by seasonal weather conditions and pruning regimes. The modelling framework developed in this work can also be used for other perennial fruit crops.

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Section: Canopy Development and Radiation Interceptionmentioning

confidence: 99%

Developing perennial fruit crop models in APSIM Next Generation using grapevine as an example

Zhu

Parker

Gou³

et al. 2021

In Silico Plants

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“…This result is interesting because, theoretically, lightbar estimated fPAR contains spatial information by considering the solar Zenith angle. Rojo et al (2020) showed a digitized image of fPAR intercepted by almond trees scanned by a lightbar platform, where the darker pixels represent greater light interception than gray-white pixels. The color gets lighter from the tree center to the edge (from the top view) due to the canopy density decrease.…”

Section: Results and Discussion Fpar Estimationmentioning

confidence: 99%

Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction

et al. 2021

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Canopy-intercepted light, or photosynthetically active radiation, is fundamentally crucial for quantifying crop biomass development and yield potential. Fractional photosynthetically active radiation (PAR) (fPAR) is conventionally obtained by measuring the PAR both below and above the canopy using a mobile lightbar platform to predict the potential yield of nut crops. This study proposed a feasible and low-cost method for accurately estimating the canopy fPAR using aerial photogrammetry-based canopy three-dimensional models. We tested up to eight different varieties in three experimental almond orchards, including California's leading variety of ‘Nonpareil’. To extract various canopy profile features, such as canopy cover and canopy volume index, we developed a complete data collection and processing pipeline called Virtual Orchard (VO) in Python environment. Canopy fPAR estimated by VO throughout the season was compared against midday canopy fPAR measured by a mobile lightbar platform in midseason, achieving a strong correlation (R2) of 0.96. A low root mean square error (RMSE) of 2% for ‘Nonpareil’. Furthermore, we developed regression models for predicting actual almond yield using both measures, where VO estimation of canopy fPAR, as a stronger indicator, achieved a much better prediction (R2 = 0.84 and RMSE = 195 lb acre−1) than the lightbar (R2 = 0.70 and RMSE = 266 lb acre−1) for ‘Nonpareil’. Eight different new models for estimating potential yield were also developed using temporal analysis from May to August in 2019 by adjusting the ratio between fPAR and dry kernel yield previously found using a lightbar. Finally, we compared the two measures at two different spatial precision levels: per-row and per-block. fPAR estimated by VO at the per-tree level was also assessed. Results showed that VO estimated canopy fPAR performed better at each precision level than lightbar with up to 0.13 higher R2. The findings in this study serve as a fundamental link between aerial-based canopy fPAR and the actual yield of almonds.

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“…These observations are consistent with findings from sensitivity analyses of forest radiative transfer models. According to these works, crown radius is among the most sensitive parameters for 3D crown models (Stadt and Lieffers 2000;Beaudet et al 2002;Piboule 2001;Gersonde et al 2004;Da Silva et al 2012;Ligot et al 2014a) while the crown shape plays a secondary role (e.g., West and Wells 1992;Larsen and Kershaw 1996;Brunner 1998;Piboule 2001;Piboule et al 2005;Rojo et al 2020). In our case, the crown representation is determined based on crown radius measurements for all modalities and slight differences appear only between the crown shape accounting or not for the crown asymmetry ("Ec," "Bc" vs "Ed," "Bd," "M").…”

Section: Discussionmentioning

confidence: 99%

Radiative transfer modeling in structurally complex stands: towards a better understanding of parametrization

André

Wergifosse

Coligny

et al. 2021

Annals of Forest Science

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Key messageThe best options to parametrize a radiative transfer model change according to the response variable used for fitting. To predict transmitted radiation, the turbid medium approach performs much better than the porous envelop, especially when accounting for the intra-specific variations in leaf area density but crown shape has limited effects. When fitting with tree growth data, the porous envelop approach combined with the more complex crown shape provides better results. When using a joint optimization with both variables, the better options are the turbid medium and the more detailed approach for describing crown shape and leaf area density.• Context Solar radiation transfer is a key process of tree growth dynamics in forest.• Aims Determining the best options to parametrize a forest radiative transfer model in heterogeneous oak and beech stands from Belgium.• Methods Calibration and evaluation of a forest radiative transfer module coupled to a spatially explicit tree growth model were repeated for different configuration options (i.e., turbid medium vs porous envelope to calculate light interception by trees, crown shapes of contrasting complexity to account for their asymmetry) and response variables used for fitting (transmitted radiation and/or tree growth data).• Results The turbid medium outperformed the porous envelope approach. The more complex crown shapes enabling to account for crown asymmetry improved performances when including growth data in the calibration.• Conclusion Our results provide insights on the options to select when parametrizing a forest radiative 3D-crown transfer model depending on the research or application objectives.

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Development of a dynamic model to estimate canopy par interception

Cited by 6 publications

References 27 publications

Developing perennial fruit crop models in APSIM Next Generation using grapevine as an example

Developing perennial fruit crop models in APSIM Next Generation using grapevine as an example

Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction

Radiative transfer modeling in structurally complex stands: towards a better understanding of parametrization

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