Delineating site-specific management zones for pH-induced iron chlorosis

Iron chlorosis can limit crop yield, especially on calcareous soil. Typical management for iron chlorosis includes the use of iron fertilizers or chlorosis tolerant cultivars. Calcareous and non-calcareous soil can be interspersed within fields. If chlorosis-prone areas within fields can be predicted accurately, site-specific use of iron fertilizers and chlorosis-tolerant cultivars might be more profitable than uniform management. In this study, the use of vegetation indices (VI) derived from aerial imagery, on-the-go measurement of soil pH and apparent soil electrical conductivity (EC_a) were evaluated for their potential to delineate chlorosis management zones. The study was conducted at six sites in 2004 and 2005. There was a significant statistical relationship between grain yield and selected properties at two sites (sites 1 (2005) and 3), moderate relationships at sites 2 and 4, and weak relationships at site 5. For sites 1 (2005) and 3, and generally across all sites, yield was predicted best with the combination of NDVI and deep EC_a. These two properties were used to delineate chlorosis management zones for all sites. Sites 1 and 3 showed a good relationship between delineated zones and the selected properties, and would be good candidates for site-specific chlorosis management. For site 5, differences in the properties between mapped zones were small, and the zones had weak relationships to yield. This site would be a poor candidate for site-specific chlorosis management. Based on this study, the delineation of chlorosis management zones from aerial imagery combined with soil EC_a appears to be a useful tool for the site-specific management of iron chlorosis.     

Evaluating unoccupied aerial systems (UAS) imagery as an alternative tool towards cotton-based management zones

Unoccupied aerial system (UAS) imagery may serve as an additional tool towards management zone delineation. This is because UAS data collection is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to preexisting tools (e.g. apparent soil electrical conductivity or EC_a). The purpose of this study, therefore, was to evaluate UAS imagery, relative to EC_a, in terms of their ability to: 1) predict cotton traits (i.e. height, seed cotton yield), and 2) define cotton management zones based on these traits. Single-season UAS images from multispectral/thermal sensors were collected and processed into Normalized Difference Vegetation Index (NDVI) and radiometric surface temperature (T_r), respectively. Management zones were also delineated using digital camera (RGB) imagery collected at periods before planting and near harvest. RGB management zones were delineated by a novel open boll mapping approach. In-season NDVI and T_r layers were significant (P?<?0.01) predictors of canopy height. Additionally, NDVI and T_r maps produced statistically different management zones during flowering and boll filling growth stages in terms of yield (P?=?0.001 or less). Open boll layers were all more accurate predictors of cotton seed yield than EC_a data—these two layers also produced statistically distinct management zones. ANOVA tests revealed that, given EC_a alone, adding UAS information via the RGB open boll map resulted in a significantly different yield prediction model (P?<?0.001). These results suggest that UAS imagery can offer valuable information for cotton management zone delineation that other techniques cannot.

     

Delineating productivity zones in a citrus grove using citrus production,tree growth and temporally stable soil data

The productivity of a citrus grove with variation in tree growth was mapped to delineate zones of productivity based on several indicator properties. These properties were fruit yield, ultrasonically measured tree canopy volume, normalized difference vegetation index (NDVI), elevation and apparent electrical conductivity (EC_a). The spatial patterns of soil series, soil color and EC_a, and their correspondence with the variation in yield emphasized the importance of variation in the soil in differentiating the productivity of the grove. Citrus fruit yield was positively correlated with canopy volume, NDVI and EC_a, and yield was negatively correlated with elevation. Although all the properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. Tree canopy volume was used to classify the citrus grove into five productivity zones termed as ‘very poor’, ‘poor’, ‘medium’, ‘good’ and ‘very good’ zones. The study showed that productivity of citrus groves can be mapped using various attributes that directly or indirectly affect citrus production. The productivity zones identified could be used successfully to plan soil sampling and characterize soil variation in new fields.     

Linking Microbial-Scale Findings to Farm-Scale Outcomes in a Dryland Cropping System

Soil biological response to management is best evaluated in field-scale experiments within the context of the soil environment and crop; however, cost-effective methods are lacking to relate these data which span multiple spatial scales. We hypothesized that zones of apparent electrical conductivity (EC_a) could be used to integrate soil properties (sampling-site scale), microbial-scale measures of vesicular-arbuscular mycorrhizal (VAM) fungi, and field-scale wheat yields from yield maps. An on-farm dryland experiment (250 ha) was established wherein two (32-ha) fields were assigned to each phase of a winter wheat (Triticum aestivum L.) – corn (Zea mays L.) – proso millet (Panicum miliaceum L.) – fallow rotation. Each field was mapped and classified into four zones (ranges) of EC_a. Soil samples were collected from geo-referenced sites within EC_a zones and analyzed for multiple soil properties associated with productivity (0–7.5 and/or 0–30 cm). Additionally, VAM fungi were assessed using C16:1(cis)11 fatty acid methyl ester biomarker (C16_vam), glomalin immunoassay, and wet-aggregate stability (WAS) techniques (1–2mm aggregates from 0- to 7.5-cm soil samples). Concentrations of C16_vam and WAS increased among cropping treatments as: fallow < wheat < corn < millet. Glomalin across crops and replicates, C16_vam and WAS in fallow (crop effect removed), soil properties associated with productivity, and wheat yields were negatively correlated with EC_a and different among EC_a zones (P 0.05). Zones of EC_a provide a point of reference for relating data collected at different scales. Monitoring cropping system parameters and profitability, over time, may allow linkage of microbial-scale processes to farm-scale economic and ecological outcomes.     

Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture

The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (EC_a) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the EC_a in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the EC_a patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the EC_a and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the EC_a data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.     

Apparent Electrical Conductivity,Soil Properties and Spatial Covariance in the U.S. Southern High Plains

Site-specific soil and crop management will require rapid low-cost sensors that can generate position-referenced data that measure important soil properties that impact crop yields. Apparent electrical conductivity (EC_a) is one such measure. Our main objective was to determine which commonly measured surface soil properties were related to EC_a at six sites in the Texas Southern High Plains, USA. We used the Veris 3100 and Geonics EM-38 EC mapping systems on 12 to 47 ha areas in six center-pivot irrigation sites. Soil samples were taken from 0–150 mm on a 0.1 to 0.8 ha grid and analyzed for routine nutrients and particle size distribution. At four of the six sites, shallow EC_a measured with the Veris 3100 (EC_a-sh) positively correlated to clay content. Clay content was negatively related with EC_a-sh at one site, possibly due to low bulk density of the shallow calcic horizon at that site. Other soil properties that were often correlated with EC_a included soil extractable Ca²⁺, Mg²⁺, Na⁺, CEC, silt and soluble salts. Extractable K⁺, NO₃^–, SO₄^–, Mehlich-3-P, and pH were not related to EC_a. Partial least squares regression (PLS) of seven soil properties explained an average of 61%, 51% and 37% of the variation in observed shallow EC_a-sh, deep EC_a with the Veris 3100 (EC_a-dp) and EC_a with the Geonics EM-38 (EC_a-em), respectively. Including nugget, range and sill parameters from a spherical semivariance model of the residuals from PLS regression improved the fit of mixed models in 15 of 18 cases. Apparent EC, therefore can provide useful information to land-users about key soil properties such as clay content and extractable Ca²⁺, but that spatial covariance in these relationships should not be ignored.     

Effect of Daily Soil Temperature Fluctuations on Soil Electrical Conductivity as Measured with the Geonics® EM-38

Soil electrical conductivity (EC_a) measured by electromagnetic induction (EM) using the EM-38 has shown promise as a soil survey tool. Soil temperature influences EC_a readings, and temperature can fluctuate considerably in the upper 10cm of the soil during a day. EC_a readings were taken in the horizontal and vertical dipole orientations once an hour from 8a.m. to 8p.m. at four sites on three separate days to determine if EC_a values were influenced by diurnal temperature variations. Soil temperature readings were taken at the same times at four depths. EM-38 readings remained steady at all four sites all 3days. Linear regression analysis when temperature in the upper 10cm was plotted against EC_a yielded low r ² values and slopes, indicating no correlation between soil temperature in the upper 10cm and EC_a values. Diurnal changes in soil temperature do not significantly influence soil EC_a readings obtained with the EM-38 under the conditions encountered during the study.     

Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features

A fuzzy inference system (FIS) was developed to generate recommendations for spatially variable applications of N fertilizer. Key soil and plant properties were identified based on experiments with rates ranging from 0 to 250 kg N ha⁻¹ conducted over three seasons (2005, 2006 and 2007) on fields with contrasting apparent soil electrical conductivity (EC_a), elevation (ELE) and slope (SLP) features. Mid-season growth was assessed from remotely sensed imagery at 1-m² resolution. Optimization of N rate by the FIS was defined against maximum corn growth in the weeks following in-season N application. The best mid-season growth was in areas of low EC_a, high ELE and low SLP. Under favourable soil conditions, maximum mid-season growth was obtained with low in-season N. Responses to N fertilizer application were better where soil conditions were naturally unfavourable to growth. The N sufficiency index (NSI) was used to judge plant N status just prior to in-season N application. Expert knowledge was formalized as a set of rules involving EC_a, ELE, SLP and NSI levels to deliver economically optimal N rates (EONRs). The resulting FIS was tested on an independent set of data (2008). A simulation revealed that using the FIS would have led to an average N saving of 41 kg N ha⁻¹ compared to the recommended uniform rate of 170 kg N ha⁻¹, without a loss of yield. The FIS therefore appears to be useful for incorporating expert knowledge into spatially variable N recommendations.     

A flexible approach to managing variability in grain yield and nitrate leaching at within-field to farm scales

We up-scaled the APSIM simulation model of crop growth, water and nitrogen dynamics to interpret and respond to spatial and temporal variations in soil, season and crop performance and improve yield and decrease nitrate leaching. Grain yields, drainage below the maximum root depth and nitrate leaching are strongly governed by interaction of plant available soil water storage capacity (PAWC), seasonal rainfall and nitrogen supply in the water-limited Mediterranean-type environment of Western Australia (WA). APSIM simulates the interaction of these key system parameters and the robustness of its simulations has been rigorously tested with the results of several field experiments covering a range of soil types and seasonal conditions in WA. We used yield maps, soil and weather data for farms at two locations in WA to determine spatial and temporal patterns of grain yield, drainage below the maximum root depth and nitrate leaching under a range of weather, soil and nitrogen management scenarios. On one farm, we up-scaled APSIM simulations across the whole farm using local weather and fertiliser use data and the average PAWC values of soil type polygons. On a 70 ha field on another farm, we used a linear regression of apparent soil electrical conductivity (EC_a) measured by EM38 against PAWC to transform an EC_a map of the field into a high resolution (5 m grid) PAWC map. We then used regressions of simulated yields, drainage below the maximum root depth and nitrate leaching on PAWC to upscale the APSIM simulations for a range of weather and fertiliser management scenarios. This continuous mapping approach overcame the weakness of the soil polygons approach, which assumed uniformity in soil properties and processes within soil type polygons. It identified areas at greatest financial and environmental risks across the field, which required focused management and simulated their response to management interventions. Splitting nitrogen applications increased simulated wheat yields at all sites across the field and decreased nitrate leaching particularly where the water storage capacity of the soil was small. Low water storage capacity resulted in both low wheat yields and large leaching loss. Another management option to decrease leaching may be to grow perennial vegetation that uses more water and loses less by drainage.Paper from the 5th European Conference on Precision Agriculture (5ECPA), Uppsala, Sweden, 2005     

The measurement,prediction, and development of soil management zones in low-relief sodic soils

Inexpensive, accurate, and rapid measurements of sodicity are required to identify the restoration options for degraded sites. This study determined the spatial variability of the percent of ammonium acetate extractable Na (%Na), apparent electrical conductivity (EC_a), pH_1:1, elevation and topographic wetness index, and used this information to create %Na management zones. In an 8.1 ha North Dakota field that contained Natraquolls and Calciaquolls, 1088 soil samples from the 0–0.3 and 0.3–0.6 m were collected from a 12.2 by 12.2 m geo-referenced grid. At each grid point, the elevation and ECa was determined using a differential corrected global positioning system and EM38m, respectively. Soil samples were analyzed for the %Na, EC_1:1, pH_1:1, and soil dispersion. Exponential semi-variogram models explained 96.7% of the ln-transformed %Na data in the 0–0.3 m soil depth, and %Na was correlated to EC_1:1 (r = 0.54), pH_1:1 (r = 0.68), clay dispersion (r = 0.68), ECav (r = 0.49), and ECah (r = 0.57). Forward stepwise regression models based on elevation, EC_1:1, pH_1:1, and ECah explained 64 and 74% of the %Na variability in the surface 0.3 m and subsurface 0.3–0.6 m, respectively. Management zones were identified that reduced the %Na variability up to 82%.     

Soil electrical conductivity as a function of soil water content and implications for soil mapping

Apparent soil electrical conductivity (EC_a) has shown promise as a soil survey tool in the Midwestern United States, with a share of this interest coming from the precision agriculture community. To fully utilize the potential of EC_a to map soils, a better understanding of temporal changes in EC_a is needed. Therefore, this study was undertaken to compare temporal changes in soil EC_a between different soils, to investigate the influence of changes in soil water content on soil EC_a, and to explore the impacts these EC_a changes might have on soil mapping applications. To this end, a 90 m long transect was established. Soil EC_a readings were taken in the vertical and horizontal dipoles at five points once every one to two weeks from June until October in 1999 and 2000. At the same time, soil samples were collected to a depth of 0.9 m for volumetric soil water content analysis. Soil EC_a readings were compared to soil water content. At four of the five sites linear regression analysis yielded r ² values of 0.70 or higher. Regression line slopes tended to be greater in lower landscape positions indicating greater EC_a changes with a given change in soil water content. Two of the soils had an EC_a relationship that changed as the soils became dry. This is an item of concern if EC_a is to be used in soil mapping. Results indicated that soil water content has a strong influence on the EC_a of these soils, and that EC_a has its greatest potential to differentiate between soils when the soils are moist. Soil water content is an important variable to know when conducting EC_a surveys and should be recorded as a part of any report on EC_a studies.     

Variograms of Ancillary Data to Aid Sampling for Soil Surveys

To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograms of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site. Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograms showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.     

A floating sensing system to evaluate soil and crop variability within flooded paddy rice fields

Continuous paddy rice cultivation requires fields to be flooded most of the time limiting seriously the collection of detailed soil information. So far, no appropriate soil sensor technology for identifying soil variability of flooded fields has been reported. Therefore, the primary objective was the development of a sensing system that can float, acquire and process detailed geo-referenced soil information within flooded fields. An additional objective was to determine whether the collected apparent electrical conductivity (EC_a) information could be used to support soil management at a within-field level. A floating sensing system (FloSSy) was built to record EC_a using the electromagnetic induction sensor EM38, which does not require physical contact with the soil. Its feasibility was tested in an alluvial paddy field of 2.7 ha located in the Brahmaputra floodplain of Bangladesh. The high-resolution (1 × 1 m) EC_a data were classified into three classes using the fuzzy k-means classification method. The variation among the classes could be attributed to differences in subsoil (0.15–0.30 m below soil surface) bulk density, with the smallest EC_a values representing the lowest bulk density. This effect was attributed to differences in compaction of the plough pan due to differential puddling. There was also a significant difference in rice yield among the EC_a classes, with the smallest EC_a values representing the lowest yield. It was concluded that the floating sensing system allowed the collection of relevant soil information, opening potential for precision agriculture practices in flooded crop fields.     

Evaluation of a Geonics EM31-3RT probe to delineate hydrologic regimes in a tile-drained field

A world-wide need to use water resources efficiently necessitates more effective approaches to study water and contaminant transport in soil. This study examined the effectiveness of a multi-receiver electromagnetic induction probe (Geonics EM31-3RT) and modeling software (EMIGMA) to delineate hydrological regimes at field scale. The site consisted of 20 (15 m × 15 m) tile-drained plots in Southern Ontario, Canada. Measurements of apparent soil electrical conductivity (EC_a) and magnetic susceptibility were obtained using the EM31-3RT in each plot at four distances (0, 2.25, 4.5 and 7.5 m) from the tile drain, and on three occasions (August 22, 26 and 29) in 2003. The EMIGMA was used to simulate a depth profile of electrical conductivity (EC_s) from EM31-3RT readings. The near-surface soil showed significantly (p < 0.01) smaller EC_a values than at greater depth. The EC_a measurements made directly over the tile drains were smaller than those observed further away due to the presence of the drains. Cluster analysis indicated that the largest EC_a values were at the lower elevations of the site related to the redistribution of moisture from higher elevations. The effect of tile drains and rainfall events on EC_a was simulated well by EMIGMA, with smaller EC_s values above the drains compared to further away, and showing an increase in EC_s in the near-surface soil after rain. This study suggests that EM31-3RT measurements combined with EMIGMA simulation of electrical conductivity can provide valuable information on depth profiles of EC_a and water dynamics in soil.     

Comparison of QuickBird Satellite Imagery and Airborne Imagery for Mapping Grain Sorghum Yield Patterns

Timely and accurate information on crop conditions obtained during the growing season is of vital importance for crop management. High spatial resolution satellite imagery has the potential for mapping crop growth variability and identifying problem areas within fields. The objectives of this study were to use QuickBird satellite imagery for mapping plant growth and yield patterns within grain sorghum fields as compared with airborne multispectral image data. A QuickBird 2.8-m four-band image covering a cropping area in south Texas, USA was acquired in the 2003 growing season. Airborne three-band imagery with submeter resolution was also collected from two grain sorghum fields within the satellite scene. Yield monitor data collected from the two fields were resampled to match the resolutions of the airborne imagery and the satellite imagery. The airborne imagery was related to yield at original submeter, 2.8 and 8.4 m resolutions and the QuickBird imagery was related to yield at 2.8 and 8.4 m resolutions. The extracted QuickBird images for the two fields were then classified into multiple zones using unsupervised classification and mean yields among the zones were compared. Results showed that grain yield was significantly related to both types of image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery at the 2.8 and 8.4 m resolutions. Moreover, the unsupervised classification maps effectively differentiated grain production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for determining plant growth and yield patterns for within-field crop management.     

Soil Test,Aerial Image and Yield Data as Inputs for Site-specific Fertility and Hybrid Management Under Maize

Several potential sources of information exist to support precision management of crop inputs. This study evaluated soil test data, bare-soil remote sensing imagery and yield monitor information for their potential contributions to precision management of maize (Zea mays L.). Data were collected from five farmer-managed fields in Central New York in 1999, 2000, and 2001. Geostatistical techniques were used to analyze the spatial structure of soil fertility (pH, P, K, NO₃ and organic matter content) and yield variables (yield, hybrid response and N fertilization response), while remote sensing imagery was processed using principal component analysis. Geographic information system (GIS) spatial data processing and correlation analyses were used to evaluate relationships in the data. Organic matter content, pH, P, and K were highly consistent over time and showed high to moderate levels of spatial autocorrelation, suggesting that grid soil sampling at 2.5–5.5ha scale may be used as a basis for defining fertility management zones. Soil nitrate levels were strongly influenced by seasonal weather conditions and showed low potential for site-specific N management. Aerial image data were correlated to soil organic matter content and in some cases to yield, mainly through the effect of drainage patterns. Aerial image data were not well correlated with soil fertility indicators, and therefore were not useful for defining fertility management zones. Yield response to hybrid selection and nitrogen fertilization rates were highly variable among years, and showed little justification for site-specific management. In conclusion, we recommend grid-based management of lime, P, and K, but no justification existed within our limited study area for site-specific N or hybrid management.     

Changes in yield classification in a soybean-rice rotation

For yield based site-specific management to be successful in fields with crop rotations, changes in management zones between crops must be determined. The study objectives were to determine if yield classes change between crops within a rotation and whether soil properties can predict the yield classes or the year-to-year changes. A percentile classification method was used to categorize yearly soybean (Glycine max) and rice (Oryza sativa) yield in two fields with soybean-rice-soybean rotations into low, medium and high yield classes. There was little agreement in yield classifications between years. Yield class based on soil properties was predicted accurately by linear discriminant analysis in Field 1 20–67% of the time and in Field 2 13–83% of the time. Predictions in Field 1 were based on soil available Mg and P, elevation and the deep soil apparent electrical conductivity (EC_a). Predictions in Field 2 were based on soil texture, soil available P, K and Mg, and pH. The linear discriminant analysis was also able to predict year-to-year changes in yield class. Changes in class in Field 1 could be predicted by total soil C and N, silt, and soil available Mg and P depending on the year. Soil texture, soil available P, K and Mg, total soil C and pH, elevation and deep soil EC_a predicted yield changes in Field 2 depending on the year. The results of this study indicate only limited success at management zone definition in a soybean-rice rotation. Further investigation is needed with other crop rotation sequences to verify the findings of this study.     

Relevance of sink-size estimation for within-field zone delineation in vineyards

Source to sink size ratio, i.e.: the relative abundance of photosynthetically active organs (leaves) with regards to photosynthate demanding organs (mainly bunches), is widely known to be one of the main drivers of grape oenological quality. However, due to the difficulty of remote sink size estimation, Precision Viticulture (PV) has been mainly based on within-field zone delineation using vegetation indices. This approach has given only moderately satisfactory results for discriminating zones with differential quality. The aim of this work was to investigate an approach to delineate within-vineyard quality zones that includes an estimator of sink size in the data-set. The study was carried out during two consecutive seasons on a 4.2 ha gobelet-trained cv. ‘Tempranillo’ vineyard. Zone delineation was performed using Normalized Difference Vegetation Index (NDVI), soil apparent electrical conductivity (EC_a) and bunch number (BN) data. These variables were considered separately, in pairs, or the three altogether, using fuzzy k-means cluster analysis for combinations. The zones delineated based on single variables did not allow a sufficient discrimination for grape composition at harvest, NDVI being the only variable that by itself resulted in zones that to some extent differed in grape composition. On the contrary, when two variables were combined, discrimination in terms of grape composition improved remarkably, provided the sink size estimation variable (BN) was included in the combination. Lastly, the combination of the three variables yielded the best discriminating zoning, improving slightly on those provided by NDVI + BN and EC_a + BN combinations. Thus, the relevance of including a variable related to sink size (in this case the number of bunches per plant) has been confirmed, which makes its consideration highly advisable for any PV work aiming at zone delineation for grape quality purposes.