The grape cases, which had high anisotropy in both the leaf inclination and azimuthdistributions, did incur significant errors due leaf anisotropy for the 1D model. If leaf azimuth is uniformly distributed, this effectively reduces the impact of anisotropy in leaf inclination on the projected area fraction G. Since a leaf with a certain elevation angle could be parallel to the sun at one azimuth and perpendicular to the sun at another, an integration over all azimuths can smear out the effects of leaf inclination alone. As in the virtual canopies of this study, field measurements have shown that leaf inclination distributions are usually highly anisotropic. The azimuthal distribution of leaves may be strongly anisotropic within a single plant, but for relatively dense canopies, the azimuthal distribution is often fairly isotropic. In these cases, the assumption of leaf isotropy is likely to result in minimal errors. However, sparse, row-oriented crops such as vineyards may have highly anisotropic azimuthal distributions, in which case it may be necessary to explicitly calculate G based on measurements. These types of canopies are becoming increasingly prevalent in agricultural applications, due in part to the improved access to mechanical harvesters that a trellised or hedgerow canopy provides.Plant spacing and the resulting heterogeneity had the most pronounced effect on errors resulting from the use of Beer’s law. For the Grape N-S case, the assumption of heterogeneity resulted in an overestimation of the total daily absorbed radiation by 28%, 30%, and 36% on Julian days153, 232, and 305, respectively, with larger instantaneous over estimation near midday. For the Grape E-W case, round planter pot the assumption of heterogeneity also resulted in overestimating the total daily absorbed radiation by 74%, 51%, and 5% on Julian days 153, 232, and 305, respectively.

This was not simply an effect related to L, as was illustrated by the two potato cases. By simply rearranging the potato plants from a uniformly spaced into a row-oriented configuration, errors in the 1D model increased substantially. It is possible that the effect of horizontal heterogeneity can vary in the vertical direction, which appeared to be the case with the Corn canopy. This significantly altered the performance of the 1D model at any given height, although the canopy was dense enough overall that the 1D model performed well when predicting whole-canopy radiation absorption. This could have important implications if the radiation model is coupled with other biophysical models such as a photosynthesis model. The response of photosynthesis to light is nonlinear and asymptotic, so although whole-canopy absorption may be well-represented in some cases by a 1D horizontally homogeneous model, it is unclear if that will result in significant errors in total photosynthetic production given the non-linearity of its response to light. A limitation of this study is that results are only applicable under clear sky conditions. However, results can provide some insight regarding diffuse sky conditions by simultaneously considering all canopy geometries and simulated sun angles. Under a uniformly overcast sky, equal energy originates from all directions. A particular combination of sun angle and leaf orientation bias was required in order to observe a pronounced effect of leaf anisotropy. Thus, for diffuse solar conditions, it is speculated that the impact of leaf anisotropy will be decreased. Sun angle had an important effect on the instantaneous impact of leaf heterogeneity, and most commonly it was observed that low sun angles resulted in a decreased impact of heterogeneity. Therefore, it is likely that highly diffuse conditions will reduce the impact of heterogeneity near midday because a significant fraction of incoming radiation will originate from directions nearer to the horizon. Estimating light interception with Beer’s law is based on the assumption that canopies are homogeneous.

This inherently means that the rate of radiation attenuation along a given path is linearly related to the flux at that location. As the canopy becomes sparse, there are pathways for radiation propagation that allow radiation to penetrate the entire canopy without any probability of interception, which fundamentally violates the assumptions behind Beer’s law or a turbid medium. Therefore, the non-random leaf dispersion in canopies limits the ability of Beer’s law to link light interception to simple bulk measures of plant architecture. It is well-known that this heterogeneity or “clumping” of vegetation usually results in decreased radiation interception as compared with an equivalent homogeneous canopy. A common means of dealing with this problem without significantly increasing model complexity is to add a “clumping coefficient” W to the argument of the exponential function in Beer’s law. While this is a simple and practical means of reducing the amount of radiation attenuation predicted by Beer’s law, the challenge in applying the clumping coefficient approach is that W is a complex function of nearly every applicable variable, and thus is it is difficult to mechanistically specify. Another approach is to use a model that explicitly resolves plant-level heterogeneity, as it may not be necessary to explicitly resolve every leaf if within-plant heterogeneity is small. Row orientation played an important role when estimating light interception from Beer’s law, particularly when the rows were widely spaced. For sparse, row-oriented canopies, the effective path length of the sun’s rays through vegetation can change dramatically with changes in sun azimuth. For East-West rows, absorption is significantly reduced early and late in the day because the rows are close to parallel with the sun’s rays, whereas North-South rows are perpendicular to the sun at this time. As the day of year progresses further from the summer solstice, the sun spends more time closer to the horizon and thus the impact of heterogeneity in an East-West row orientation increased. For the East-West row configuration, G and light interception were surprisingly constant throughout much of the day, which resulted in 41% and 36% less absorption on Julian days 153 and 232, respectively, compared to North-South rows.

In some climates, it may be desirable to maximize sunlight interception, whereas in others it may be desirable to mitigate effects of excess sunlight to reduce temperatures and water use.Despite the simplified assumptions in Beer’s law regarding scattering, there was good agreement between predicted radiation interception using the 1D and 3D models in the PAR band. Scattering did not significantly influence light interception in this band because most of the incident radiation received by individual leaves was absorbed. However, in the NIR band, scattering introduced significant over estimation of absorption using the standard 1D model, since leaves are poor absorbers in this band. Using an ad hoc correction to account for reflection only reduced this over estimation of absorption. An additional correction to account for both reflection and transmission resulted in over correction, and a net under prediction of total radiation absorption.The objective of this work was to evaluate common assumptions used in estimating radiation absorption in plant canopies, namely assumptions of homogeneity or isotropy of vegetation. Our results demonstrated that for relatively dense canopies with azimuthally symmetric leaves, a 1D model that assumes homogeneity and isotropy of vegetation generally produced relatively small errors. As plant spacing became large, the assumptions of homogeneity break down and model errors became large. In the case of a vineyard with rows oriented in the East-West direction, errors in daily intercepted radiation were up to 70% due to heterogeneity alone, round pot for plants with much larger instantaneous errors occurring during the day. If leaves were highly anisotropic in the azimuthal direction, there was also the potential for large errors resulting from the assumption of vegetation isotropy which had the potential to increase errors above 100%. Day of year had an impact on model errors, which was that overall errors tended to decline with time from the summer solstice. In cases of canopies where the plant spacing starts to approach the plant height, it is likely necessary to use a plant-resolving radiation model in order to avoid substantial over prediction of absorbed radiative fluxes. Additionally, if vegetation is highly anisotropic in terms of both elevation and azimuthal angle distributions, it is also likely necessary to explicitly calculate the projected area fraction G based on measurements and the instantaneous position of the sun.Recent shifts in climatic patterns have influenced the frequency, timing, and severity of heat waves in many wine grape growing regions, which has introduced challenges for viticulturists. Growing the same varieties under these altered climatic conditions often requires mitigation strategies, but quantitative, generalized understanding of the impacts of such strategies can be difficult or time consuming to determine through field trials. This work developed and validated a detailed three-dimensional model of grape berry temperature that could fully resolve spatial and temporal heterogeneity in berry temperature, and ultimately predict the impacts of potential high berry temperature mitigation strategies such as the use of alternative trellis systems.

A novel experimental data set was generated in which the temperature of exposed grape berry clusters was measured with thermocouples at four field sites with different trellis systems, topography, and climate. Experimental measurements indicated that the temperature of shaded berries closely followed the ambient air temperature, but intermittent periods of direct solar radiation could generate berry temperatures in excess of 10◦C above ambient. Validation results indicated that by accurately representing the 3D vine structure, the model was able to closely replicate rapid spatial and temporal fluctuations in berry temperature. Including berry heat storage in the model reduced the errors by dampening extreme temporal swings in berry temperature.Increasing temperatures and temperature variability associated with a changing climate have become a major concern for grape producers due to the sensitivity of grape quality to climate, particularly in wine grape production. Short-term temperature extremes associated with heat waves, along with longer-term shifts in seasonal temperature patterns are known to create significant challenges in managing grape quality. Diurnal fluctuations in solar irradiance and air temperature have been shown to affect amino acid and phenylpropanoid berry metabolism at hourly time scales. Elevated temperatures during daily or weekly time periods have been shown to decrease anthocyanin concentration around veraison. Furthermore, the duration of the elevated temperatures not only has an effect on berry composition but also on berry skin appearance. Exposed berries can be damaged by sunburn, and even a few minutes of high temperature exposure can result in cellular damage. Moderate temperatures can also result in berry injury or death after long-term exposure. Grape producers have begun to implement a number of canopy design and management strategies in an attempt to mitigate the negative effects of elevated berry temperatures, including the use of shade cloth, trellis design, and cluster height. However, grape berry microclimate is complex and highly heterogeneous due to interactions between the vine architecture and the environment, making it difficult to understand and predict the integrated effects of mitigation efforts. Experimental field trials are complicated by the fact that measurement of light and temperature at the berry level is labor-intensive and expensive. Furthermore, the relatively slow development of grapevine systems means that field trials are costly and may require many years of data collection. Because it is not feasible to independently vary every parameter that determines berry temperature in field experiments , crop models provide a means for understanding, and ultimately optimizing, how grapevine design and management practices can be used to mitigate elevated berry temperatures. Previous process-based models have been developed to predict berry radiative fluxes and berry temperatures from environmental parameters. However, in these models the calculation of absorbed radiation and the parameters to represent specific geometrical canopy structure are often simplified. Therefore, the models cannot account for the vertical and horizontal variability within the cluster or canopy, making it difficult to represent different design or management choices such as using altered trellis designs or pruning practices. Previous work has developed models for individual grape and apple fruits, and the work of Saudreau et al. successfully developed a 3D model of apple fruit temperature. However, to the authors’ knowledge, previously developed 3D grapevine structural model have yet to be coupled with a physically-based berry temperature model. This work develops and tests a new 3D model for grape berry temperature based on the Helios modeling framework. The berry temperature model was validated using a unique data set that spans four different canopy geometries.