Technical Brief
Influence of Canopy Management Practices on Vineyard Microclimate: Definition of New Microclimatic Indices Alessandro Matese,1,2* Alfonso Crisci,1 Filippo Salvatore Di Gennaro,1 Edoardo Fiorillo,1 Jacopo Primicerio,1 Piero Toscano,1 Francesco Primo Vaccari,1 Stefano Di Blasi,3 and Lorenzo Genesio1 Abstract: Meteorological parameters have a crucial influence on grapevine (Vitis vinifera L.) production quantity and quality. Most of the commonly used bioclimatic indices are not appropriate to represent intravineyard micrometeorological variability, in particular the subdaily dynamics that are important in grape maturation processes. The aim of this research was to compile a new set of micrometeorological indices and evaluate their capacity to discriminate the differences in the microclimatic daily cycle induced by different canopy management techniques, based on a statistical data set of three years (2008, 2009, 2010) of hourly data of cluster internal temperature, canopy air temperature, and solar radiation intercepted by the cluster. Data was collected in four vineyards planted with Sangiovese and Cabernet Sauvignon, located in three climatic zones of Tuscany (Italy). Starting from this data set, some new micrometeorological indices were defined using two different criteria for subdaily time segmentation: static, based on fixed hourly intervals, and dynamic, based on solar height daytime segmentation. Results showed that indices based on subdaily data better provide a better representation of vineyard microclimate than daily indices and are able to highlight microclimatic differences induced by canopy management practices. The indices more sensitive to treatments are those related to the solar radiation intercepted by the cluster parameter and relative to the Broad Daylight Index, which represent the average of micrometeorological parameters in the middle hours of the day. The proposed indices enhance the characterization of micrometeorological conditions induced by different canopy management practices and, therefore, the assessment of the within-vineyard spatial variability of environmental parameters. Key words: canopy microclimate, climatic indicators, wireless sensor network (WSN), micrometeorology, grapevine
Meteorological variables play a crucial role in vegetativeproductive responses of the vine (Vitis vinifera L.) in terms of quantity and quality of grapes. Climate is a key parameter in the definition of terroir, driving basic cultivation choices such as the trellis system and pruning options. Many indices based on climate data (Winkler 1974, Branas 1974, Huglin
1978, Gladstone 1992, Fregoni et al. 2002) have been widely used in viticultural zoning studies, particularly with respect to the interaction between environmental parameters and wine quality, to describe and identify an area’s productive potential and risk and to provide a synthetic description of a single season. While these indices provide an appropriate representation of regional climate conditions, they tend to lose effectiveness in representing intravineyard spatial variability because of the poor representation of the dynamics of the daily meteorological cycle that is at the base of microscale climate variability. Indeed, vineyard microclimate is demonstrated to significantly affect grape quality and canopy microclimate is known to have a significant variability at the intravineyard scale (Nicholas et al. 2011). In particular, solar radiation and temperature are the meteorological parameters that mainly influence the composition and metabolism of grapes, as they are directly related to the synthesis of sugar and to the secondary metabolites that characterize grape quality (Spayd et al. 2002). In precision viticulture, the knowledge of variability within the vineyard and the factors underlying this variability are crucial in implementing best management practices and, in this sense, the understanding of how management practices affect canopy microclimate requires further investigation. The newer technologies for micrometeorological monitoring, such as wireless sensor networks (WSN), allowing high spatial and time
Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche (IBIMETCNR), Via Giovanni Caproni 8, 50145 Firenze, Italy; 2Dipartimento Colture Arboree, Università di Torino, Via Leonardo da Vinci 44, 10095 Grugliasco, Italy; and 3Consorzio Tuscania, Via Sangallo 43, località Sambuca, Tavarnelle Val di Pesa, Italy. *Corresponding author (email:
[email protected]; tel: +390553033711; fax+39055308910) Acknowledgments: The authors thank Francesco Sabatini, Lorenzo Albanese, Leandro Rocchi, and Tiziana De Filippis of IBIMET-CNR for their support, especially in the stages of collecting and processing data; and Alessandra Biondi Bartolini, Manuel Pieri, Marco Valentini, and the staff of the Consorzio Tuscania S.r.l., which provided technical and financial support for the work. The entire Consorzio Tuscania data set is available on request and accessible from the Tuscania GeoDB at www.consorziotuscania.it. Supplemental data is freely available with the online version of this article at www.ajevonline.org. Manuscript submitted Nov 2011, revised Apr 2012, accepted May 2012. Publication costs of this article defrayed in part by page fees. Copyright © 2012 by the American Society for Enology and Viticulture. All rights reserved. doi: 10.5344/ajev.2012.11117 1
424 Am. J. Enol. Vitic. 63:3 (2012)
Definition of New Microclimatic Indices – 425
resolution detection of parameters, enhance our understanding of vineyard microclimate (Pierce and Elliot 2008). The fundamental assumption is that canopy treatments will result in different amounts of light and heat at the canopy/cluster level. The availability of specifically designed micrometeorological indices could improve the characterization of those amounts and of their magnitude and dynamics, enabling a better assessment of the impact of canopy management practices on microclimate. The aim of this work was to compile and test a new set of micrometeorological indices able to correctly represent the daily micrometeorological cycle, evaluate their capacity to discriminate differences in microclimate induced by different canopy management strategies, and compare their performance with traditional indices. For this purpose, hourly micrometeorological parameters were monitored for three years (2008, 2009, 2010) in four vineyards planted with Sangiovese and Cabernet Sauvignon in three climatic zones of Tuscany.
Materials and Methods Experimental sites. The four vineyards were located in three viticulture domains of Tuscany (Supplemental Figure 1): Chianti Classico, Castello di Brolio, Brolio vineyard (BR); Bolgheri, Donna Olimpia 1898, Donna Olimpia vineyard (DO); and Monteregio di Massa Marittima, Tenuta Le Mortelle, in two vineyards, Cacciagrande (CA) and Cortigliano (CO). All vineyards had vine and row spacing of 0.8 and 2.00 m, respectively, and a single-curtain canopy. The main characteristics (climate, vineyard structure) for each experimental site are noted in Table 1. For Brolio, according to the 30-yr mean (1961–1990), the average temperatures of the coldest (January) and hottest (July) months were 5°C and 22.2°C, respectively. Average annual rainfall was distributed over 87 days on average, with a relative minimum in summer and a peak in autumn. Annual average relative humidity was 62.8%, with a minimum of 50% in July and a maximum of 76% in December. For Donna Olimpia, the coldest month was January (7.1°C avg) and the hottest was August (24.5°C avg). Mean annual rainfall was distributed over 77 days on average, with a minimum in summer, peaking in autumn and winter. For Cacciagrande and Cortigliano, the average temperature in January was 7.7°C, while in August, the hottest month, it was
23.6°C. Mean annual rainfall reached a minimum in summer (77 mm) and a peak in autumn (233 mm). During the study period (2008–2010), the climate features have been summarized by the Winkler Index (Winkler et al. 1974) with a base temperature of 10°C (Table 1). For all four experimental sites the hottest year was 2009, which was also the driest, and the wettest year was 2010. The coldest year for Donna Olimpia was 2008 and for the three other sites it was 2010. During the study period Brolio was the coldest site and Donna Olimpia the hottest. In terms of rainfall, the Cacciagrande and Cortigliano sites were the driest, but with values consistently above the climatological average. Rainfall was similar for Brolio and Donna Olimpia, with heavy rainfall in late autumn (November and December) at Brolio. Observational data on main phenological stages in the vineyards showed that Cacciagrande and Cortigliano were the earliest (budbreak DOY 80), followed by Donna Olimpia after one week (DOY 88), and lastly by Brolio (DOY 91). Veraison was reached earlier at Cacciagrande and Cortigliano (DOY 206) than at Donna Olimpia (DOY 215) and Brolio (DOY 217), while harvest date was on doy 258 at Cacciagrande and Cortigliano, DOY 265 at Donna Olimpia, and DOY 270 at Brolio. Experimental design. In each of the four vineyards, a two x two factorial experiment was conducted examining the micrometeorological effects of two different canopy management practices: pruning level (number of buds per vine, P) and leaf removal (L). Each management practice was applied at two treatment levels. For low pruning level, vines were pruned above the first nonbasal bud (P1) (4 to 5 buds per vine); for high, vines were pruned above the third bud (P3) (12 to 15 buds per vine). For the no leaf removal treatment (L0), the natural leaf load was retained; for the leaf removal treatment (L1), the leaves were removed by hand in the cluster area (i.e., in the first six basal nodes of the shoots) during the flowering-to-fruit interval; BBCH code (Biologische Bundesanstalt Bundessortenamt and Chemical Industry; Lorenz et al. 1994) 69 (end of flowering) to 71 (fruit set: young fruits begin to swell, remains of flowers lost). The four vineyards had the same general single-curtain configuration. The mean height of the trellis was ~1.8 to 2 m, with three (pairs) movable shoot wires, the first and second pair of wire at 0.2 to 0.3 m from the cordon wire and the
Table 1 Experimental sites and vineyard descriptions. Winkler Index and annual rainfall are referred to as mean and standard deviation for the study period, 2008–2010. Climatological mean annual rainfall and mean annual air temperatures are referred to the 30-year mean 1961–1990. Elevation (m asl)
Cultivar
Row Clone Rootstock orientation
Winkler index (GDD)
Annual rainfall (mm)
Mean Mean annual annual air rainfall (mm) temp (°C)
Site
Coordinates
Brolio (BR)
43°24’50” N 11°27’23” E
420
Sangiovese
R24
420A
E-W
1967 (± 129)
775 (± 79)
750
13.7
Donna Olimpia (DO)
43°12’32” N 10°34’07” E
8
Cabernet Sauvignon
191
101-14
E-W
2204 (± 102)
755 (± 109)
780
15.4
Cacciagrande (CA)
42°48’34” N 10°57’33” E
15
Cabernet Sauvignon
191
101-14
E-W
2169 (± 149)
755 (± 39)
632
15.1
Cortigliano (CO)
42°48’27” N 10°57’29” E
12
Sangiovese
R23
420A
N-S
2169 (± 149)
755 (± 39)
632
15.1
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third pair at 0.4 from the second. The height of cordon wire was ~0.7 from the soil, and all vineyards were spur-pruned. In each vineyard, the experimental design was defined by the combination of the two levels of canopy management practices (L0-P1, L0-P3, L1-P1, L1-P3); the four treatment combinations were applied to two replicate plots consisting of four rows of 48 vines each (192 vines per replicate treatment). Micrometeorological parameters were monitored on each combination of treatments, for every replicate. In order to minimize the environmental site-specific variability and to evaluate only that related to the different location of the four vineyards, the eight plots for each site were placed in homogeneous vigor areas as described elsewhere (Fiorillo et al. 2009). Briefly, at the beginning of the research (March 2007), for each vineyard, homogeneous vigor areas were identified by a preliminary airborne multispectral survey. Micrometeorological measurements. A custom system based on Wireless Sensor Network (WSN), called NAV (Advanced Network for Vineyard), was developed (Matese et al. 2009) and installed in each vineyard for the monitoring of micrometeorological parameters from 1 Feb to 30 Oct in 2008, 2009, and 2010. The NAV system included a base station (master unit, one for each vineyard for a total of four stations) with solar radiation, air temperature, humidity, and wind sensors, and a set of peripheral wireless nodes (slave unit) located in the vineyard (one for each monitored plot, eight per vineyard, for a total of 32 stations) with sensors for agrometeorological precision data monitoring. Each slave unit included a RISC 16-bit microprocessor board with a single impulsive channel and five analog input channels with 12-bit resolution. Three micrometeorological parameters were acquired by each slave unit: cluster internal temperature (CT), canopy air temperature (AT), and solar radiation intercepted by the cluster (CR). The two stations from each treatment replicate were averaged together and compared to the other vineyards. Data collected by the slave units at hourly intervals were sent directly via wireless connection to the master unit, which sent daily data to a remote central server designed for receiving, storing, and processing data. Temperatures were measured with thermocouples (type T [copper-constantan]). The cluster temperature sensor was applied on the rachis (not inside a berry) at the beginning of fruit set. The air temperature sensor was positioned on the second wire around the middle of the curtain shaded under a plastic multiplate shield. Intercepted solar radiation was measured with a custom-developed case sensor, composed of a silicon photodiode housed in a Teflon diffusing ball (an excellent UV-visible diffuser) and positioned immediately above the cluster to simulate an exposed berry surface. The sensor was a PerkinElmer Optoelectronics (St. Louis, MO) silicon photodiode VTP4085, with a spectral application range of 400 to 1100 nm and sensitivity 0.55 A/W. Further details regarding sensor specifications and system design are in Matese et al. (2009). Bioclimatic indices. A new set of micrometeorological indices was defined starting from the measured parameters (CT, AT, and CR) in order to describe canopy microclimate and its daily and seasonal dynamics within each canopy management
treatment (Table 2). The microclimatic indices were calculated by averaging the three years in the data set collected from 1 June to 30 Sept. 1 June corresponded to the average date of fruit set and therefore enabled the placement of the cluster sensors; 30 Sept was chosen as it was the last harvest date in the vineyards. These new indices were then compared with a set of “traditional” indices based on daily averages and summations (Avg, Sum, Range; Table 2). The choice of temporal resolution for the new indices was made with the two-fold aim of highlighting the daily cycle to identify the dynamics of the meteorological parameters and accounting for the evolution of the daily cycle over the season. The indices were calculated using two time-aggregation criteria: static and dynamic. The first followed a daytime segmentation using fixed intervals: the averaging of parameters at fixed hourly intervals (S criteria; Table 2). The second followed a daytime segmentation based on range of solar height (h), and the duration of each period therefore varied over the season (D criteri; Table 2). In order to eliminate the effect due to the changing solar height angles during the season and thus provide a standardization of the physical phenomenon (the direct solar radiation intercepted by the canopy/grape), we operated a dynamic daytime segmentation based on the value of solar angle during the year. This approach was applied by Ge et al. (2011) in estimating photosynthetically active radiation (PAR) for crop model calibration; their findings confirmed a substantial sensitivity of PAR-related variables to changes in solar zenith angle. This approach enables the comparison of indices calculated at different latitudes. Furthermore, in a row crop such as a vineyard, this method also enables daily time segmentation to be adapted according to the exposable leaf area (Carbonneau 1995) of the specific training system and row spacing (i.e., to the specific 3-D model of each vineyard). Dynamic daytime segmentation is structurally linked to the daily solar path in the sky, and obviously to seasonal daylight duration, and allows the horizontal and vertical shadow angle variation occurring in a real canopy during the season to be taken into account. Four ranges of solar height were considered: 30° (broad daylight),
