^{ 1 }Tasmanian Institute of Agriculture, University of Tasmania, Private Bag 98, Hobart, TAS 7001, Australia

^{ 2 }CSIRO, Waite Campus, Glen Osmond, SA 5064, Australia

^{ 1 }

^{ 1 }

^{ 1 }

^{ * }corresponding author: xinxin.song@utas.edu.au

The main difficulties grapegrowers and consultants face in obtaining robust trial results include time and labour to collect data and land variability that confounds trial results. Spatial approaches that use whole-field designs, sensing technologies and geostatistical analysis enable more efficient data collection and account for the impact of spatial variation on crop responses while generating statistically robust results. However, the practical application of these approaches for vineyard trials requires affordable automation of measurements of viticultural variables and access to skills for geostatistics. A strip approach has been developed to simplify experimentation by allowing the farmer to use a single crop row to trial and analyse data in a spreadsheet. However, guidance is needed as to how to position trial strips in a vineyard block to reveal likely treatment effects across the entire block. Here, we investigated using a covariate to a response variable of interest to position a strip trial to infer treatment effects beyond the trial strip. Strip trials were simulated for two experiments: one comparing three treatments for vineyard floor management on grape yield and another comparing two spray programs for powdery mildew control. Useful covariates for yield or mildew severity were determined using correlation analyses. Trial results were analysed using a moving pairwise comparison of treatments and a moving average of the covariates. Simulated trial strips that incorporated a range of variation in a useful covariate close to that encountered in the whole block showed how yield or mildew severity varied with the covariates along the strips. Importantly, such results provided information about likely crop responses in other parts of the block according to variation in the covariates, thus contributing to better-informed decision-making. Compared to whole-field approaches, this strip approach is more efficient and simpler for growers to implement.

Keywords: on-farm experimentation, precision agriculture, spatial variation, vineyard trials, viticulture

On-farm experimentation (OFE) is commonly used by farmers to evaluate alternative farming practices under local contexts and to generate information to guide management decisions (

A potential means to account for the impact of spatial variation on trial results is conducting experiments at field scales using spatially distributed designs. Examples include those conducted for broadacre crops by

A practical design for vineyard experiments using a whole block is to apply replicated treatments to entire rows of vines—also known as “strips” in the context of OFE. The application of replicated strip designs in whole-of-block experimentation has been demonstrated by

To simplify experimentation further, the use of a single strip of field length or a section of field length has been explored (

The objective of this study was to investigate criteria and proxy indicators for positioning one or a few trial strips in a vineyard block to infer likely varying treatment responses in all locations of the block. Indicators of crop or land variability covarying with a response variable of interest, namely covariates, have been described previously (

Information regarding the trials used for this study is provided in Table 1. The investigation involved five main steps: (1) defining response variables and candidate covariates for each experiment; (2) sampling the datasets for subsequent processing; (3) using the sampled data to select useful covariates to the response variable of interest; (4) assessing the temporal stability of the relationship between the covariates and response variables; and (5) identifying potential locations for strip trials by examining the results of trials using simulated strips that have different ranges of spatial variation in a useful covariate.

titre du tableau
Description
Mid-row management experiment
Powdery mildew experiment
Objective
Investigating three different mid-row management strategies regarding their effects on improving grape yield.
Evaluating the effects of two ‘reduced sulphur’ fungicide programs for the control of powdery mildew.
Treatment
(1) Ryegrass supplemented by compost (RC) (2) Ryegrass supplemented by mulch (RM), or (3) Cereal or legume in alternating mid-rows (CL). The treatments were applied after harvest in 2004 in alternating strips comprising four mid-rows.
(1) One application (S1), or (2) Two applications (S2) of sulphur as Cosavet-DF® (Sulphur Mills Ltd, Mumbai, India). The treatments were applied in December 2005 in alternating strips comprising six rows and as part of a broader spray program for season-long mildew management.
Experimental site and region
A 4.8 ha block of Merlot in the Clare Valley, South Australia. Row and vine spacings were 3 m and 1.5 m.
A 4.5 ha block of Pinot noir in the Coal River Valley, Tasmania. Row and vine spacings were 2.5 m and 1.25 m.
Response variable and related data used in each study
Yield (t/ha) in 2004 from a yield map Yield (kg/m) of 377 target vines in 2006 and 2007 Yield (log-transformed) of RC, RM and CL in 2006 from yield maps
Severity of powdery mildew of 236 target vines expressed as transformed disease scores Mildew severity as transformed disease scores of S1 and S2 from disease maps
Candidate covariate in the current study
PCD* in 2004 Soil EC** (dS/m) in 2004
Elevation (m)
Reference

*PCD, plant cell density, a vegetation index of crop vigour (

The whole-of-block experiment of

The maps show spatial variation in the response variables (a) yield (t/ha) in vintage 2004, and log-transformed yield in vintage 2006 for treatments (b) ryegrass and compost (RC), (c) ryegrass and mulch (RM), and (d) cereal and legume (CL), and the candidate covariates (e) soil EC (dS/m) and (f) PCD at veraison in 2004 for the mid-row management experiment. For the powdery mildew experiment, they show the response variable transformed disease scores for treatments (g) S1 and (h) S2, and (i) the candidate covariate elevation (m). For ease of interpretation, the black lines in the Merlot block (a-f) represent every fourth simulated row or strip (from the western boundary) of 59 rows; those in the Pinot noir block (g-i) represent every 10th row (from the southern boundary) of 89 rows. See text for further explanation.

The maps show spatial variation in the response variables (a) yield (t/ha) in vintage 2004, and log-transformed yield in vintage 2006 for treatments (b) ryegrass and compost (RC), (c) ryegrass and mulch (RM), and (d) cereal and legume (CL), and the candidate covariates (e) soil EC (dS/m) and (f) PCD at veraison in 2004 for the mid-row management experiment. For the powdery mildew experiment, they show the response variable transformed disease scores for treatments (g) S1 and (h) S2, and (i) the candidate covariate elevation (m). For ease of interpretation, the black lines in the Merlot block (a-f) represent every fourth simulated row or strip (from the western boundary) of 59 rows; those in the Pinot noir block (g-i) represent every 10th row (from the southern boundary) of 89 rows. See text for further explanation.

In the current study, yield (t/ha, kg/m or transformed yield) was the response variable (Table 1). PCD was treated as a candidate covariate at this site because vine vigour has been found to be correlated with yield in spur-pruned vineyards (

The whole-of-block experiment of

In the current study, the mean severity of mildew (% per vine or transformed disease score) was the response variable (Table 1). Elevation (m) was treated as a candidate covariate because mildew severity at this site was observed to be greater at the upslope than downslope (

The raster data described above for the two experiments, including the DEM, PCD, soil EC and maps for response variables, were projected onto the same 2 m grid of the vineyard block by the authors of the original studies. Strips were simulated on the raster layers to represent vine rows, and points along the strips were used to represent vines in the blocks to enable data sampling.

To select useful covariates for the mid-row management experiment, values of PCD, soil EC (dS/m), and yield (t/ha) in 2004 were extracted from the locations of 377 target vines using the “Extract Pixel Statistics for Points” tool of PAT (

The sampling to simulate trial strips was a three-step process using QGIS. First, 59 north-south (NS) strips were created along the 59 vineyard rows (Figure 1a–f). Two rows at the west and east boundaries of the block were excluded to eliminate potential edge effects. Second, sample points were created at 2 m intervals along each of the 59 strips using the “Create Strip Trial Points” tool in PAT (

For the mildew experiment, 89 NS trial strips were similarly simulated along actual vine rows. However, these NS strips only encompassed small proportions of the range of spatial variation in elevation in the block, which prevented the evaluation of information generated by strips encompassing large proportions of the elevation range, assuming elevation would be a useful covariate. Therefore, 89 east-west (EW) trial strips were also simulated (Figure 1g–i), generating 8737 sample points. Note that the EW positioning of trial strips in a block with NS-oriented vine rows would be impractical for a real experiment. Here we use these simulated strips to demonstrate the potential of the proposed method. Two strips at the southern and northern boundaries were excluded to eliminate potential edge effects.

The selection of useful covariates consisted of two steps (Figure 2). The first step was analysing the correlation between a response variable and candidate covariates sampled at the target vines using Pearson's correlation coefficient (

A correlation is considered significant if

A correlation is considered significant if

The second step was to determine useful covariates by examining the correlation between values of the ‘row-to-block’ variation index (RBVI) for the response variable and values of the index for the candidate covariate. In other words, what this step seeks to do is, for each simulated row, calculate the proportion of variation in a variable of interest in the row relative to its variation in the entire block. The index was calculated as follows:

Thus, a row with a low value close to 0 for RBVI indicates that the row has much lower variation than the block as a whole, whereas a row with a high value close to 1 for RBVI might have almost as much variation as encountered in the whole block. The correlations between values of RBVI for response variables and candidate covariates for the simulated stirps were measured using uncorrected Spearman’s correlation because the associations were monotonic. If a correlation was significant (

For the mid-row management experiment, the relationship between a useful covariate and a response variable was considered to be temporally stable if there was a significant correlation between the covariate in 2004 and the yield (kg/m) of 377 vines in 2006 and 2007. Pearson’s correlation analysis with Dutilleul’s correction was applied after removing seven and five outliers in the 2006 and 2007 yield data. Prior to correlation analysis, data of PCD and yield (kg/m) in 2006 and 2007 were log- or square root-transformed as required for normality (

To identify potentially useful locations for strip trials in a block, threshold values of RBVI of > 0.80 and < 0.20 were used to compare and contrast the information generated by strip trials having relatively large and small values for the index. Note that the choice of RBVI values is subjective, and one can select other values as desired. Nonetheless, higher RBVI values of close to 1 for a useful covariate are suggested for selecting trial rows as the trial can then theoretically provide more information about treatment effects at the trial site.

Crop responses in adjacent strips were compared using a moving window average. Each window comprised a pair of 10 sample points of each treatment, equivalent to 20 m of a row. Using the mid-row management trial as an example, moving from the north end to the south, the mean of the first ten values of yield (kg/m) for the RC treatment was compared with that for RM or CL. The window was then moved by one sample point along the strip to compare the next pair of 10 values (from the 2nd to the 11th value) until the last pair on the strip was compared. In total,

To examine trial results in the context of grape production, the extracted values of sample points of selected strips for both trials were back-transformed to yield (kg/m) or back-matched to original values of mildew severity (%). The latter was done according to a transformation file which recorded the transformed disease scores and corresponding values of mildew severity. Where there was no original value for a disease score in the file, the severity was estimated as the mean of the values before and after it in the file.

PCD and soil EC (dS/m) were correlated to yield (t/ha) in 2004 based on effective sample sizes of 26 and 31 (Table 2). However, only PCD was considered a useful covariate for grape yield based on the significance of correlations between values of RBVI for grape yield and those for each candidate covariate (Table 2).

titre du tableau
Candidate covariate
Data of target vines
Row-to-block index
Pearson (
_{raw}
_{corrected}
n
_{effective}
Spearman (
PCD
0.83
***
***
26
0.67
***
Soil EC
−0.53
***
**
31
0.11
ns

***, ** and ns denote

PCD in 2004 was correlated to yield (kg/m) in 2006 and 2007. The correlation coefficients were 0.64 and 0.55 for effective sample sizes of 50 and 67 (

Simulated strips 57 and 9 with RBVI of 0.18 and 0.85 for PCD were selected to compare the information generated from strip trials with relatively large or small values of RBVI.

In strip 57 (low RBVI), the yield for CL was numerically higher than the yield for RC or RM, while there was little difference in yield between RC and RM (Figure 3a). These results may lead to the conclusion that applying CL to the whole block would be beneficial for improving grape yield. This conclusion, however, was not sufficiently informed since the strip provided little information about the effect of CL relative to RC or RM at other locations of the block with different values of PCD.

Treatments were cereal/legume (CL), ryegrass/compost (RC), and ryegrass/mulch (RM) applied in a 4.8 ha block of Merlot in South Australia.

Treatments were cereal/legume (CL), ryegrass/compost (RC), and ryegrass/mulch (RM) applied in a 4.8 ha block of Merlot in South Australia.

Similar to strip 57, the yield for CL in strip 9 was numerically higher than RC or RM in locations with relatively low or high PCD (Figure 3b). However, Figure 3b also shows that where PCD was higher than approximately 1.4, the yield difference between CL and RM was smaller than that at locations with lower values of PCD, with the difference decreasing with increasing PCD values. The performance of CL relative to RM, where PCD was > 1.4, represented about 20 % of the area of the block (Figure 1f). Therefore, the results of strip 9 demonstrated that the use of a strip with an RBVI > 0.80 for PCD could generate information about the likely performance of the treatments relative to each other across the entire block; that is, strip 9 gave results which were highly consistent with those obtained using

Analysis of spatial variation in crop responses was also conducted for other simulated strips. The strips with RBVI > 0.80 showed results of varying treatment effects similar to the results of strip 9, while strips with RBVI < 0.20 showed results similar to the results of strip 57 (data not presented).

Elevation (m) was correlated to powdery mildew severity based on the coefficient of 0.57 and effective sample size for target vines of 11 (

Simulated NS strips 70 and 15 and EW strips 40 and 75 with respective RBVI values of 0.07, 0.13, 0.97, and 0.87 for elevation were selected to compare the information generated from strip trials with relatively large or small values of RBVI. Here, trial results were interpreted based on the severity of powdery mildew tolerated by the wine business. We selected a threshold value of 3 % for the mean severity of mildew for parcels of grapes, above which there are known negative impacts on winemaking for some grape varieties (

The NS strip 70 showed that both S1 and S2 resulted in mildew severities below 3 % along the strip (Figure 4a), which may lead to the conclusion that both treatments could provide effective control of powdery mildew for vines in the entire block. However, in the whole-of-block experiment, S1 resulted in mildew severities greater than 3 % for almost one-third of the area of the block (areas of disease score > 0.8 in Figure 1g), as did S2 at some upslope sites. Similarly, the results of NS strip 15 (Figure 4b) did not reveal the relatively poor performance of S2 at higher elevations or the effective control of mildew that S1 provided at lower elevations. Overall, neither of the two strips with low RBVI values for elevation could support informative decision-making given the range of elevation in the block as a whole.

S1, S2 were one or two applications of sulphur applied in the 4.5 ha block of Pinot noir in the Coal Valley, Tasmania.

S1, S2 were one or two applications of sulphur applied in the 4.5 ha block of Pinot noir in the Coal Valley, Tasmania.

Compared to NS strips 15 and 70, EW strips 40 and 75 showed that the mildew severity of S1 and S2 varied with elevation along the strips, with severities being greater at higher elevations (Figure 4c, d). Moreover, in the two EW strips, S1 resulted in mildew severities greater than 3 % at elevations higher than approximately 94 m and S2 at 98 m, with both treatments providing effective control of mildew at lower elevations. These results demonstrated that the use of a strip with an RBVI > 0.80 for elevation could generate information about likely crop responses to the treatments across the entire block, results that were analogous to those obtained using the whole-of-block design (

Note that the analysis of spatial variation in crop responses along a strip was also conducted for other simulated strips. The strips with RBVI > 0.80 showed similar overall trends in spatial variation in mildew severity, while strips with RBVI < 0.20 also showed similar results (data not presented).

The results of this study demonstrate that a useful covariate, if available, can be used to assist with positioning a strip trial in a vineyard block that generates information about likely crop responses across the whole block. By selecting a strip with RBVI close to 1 for a useful covariate, the trial can indicate variation in treatment effects and the covariate along the strip, thereby informing how treatment effects will likely vary with the covariate across the block. Such information enables a grower to extrapolate the results of the strip trial to the rest of the block or possibly broader areas where the same covariate is available (

A key step of the strip trial approach is selecting available and useful covariates for a response variable of interest at a particular site, which requires data for the response variable and candidate covariates. In the demonstration of the approach, we used data from previous trials. However, we recognise that the data of a response variable of interest may not be available for identifying a useful covariate for a trial to be conducted. In this case, the grower can treat all available spatial data as candidate covariates to guide the positioning of trial strips in a block. To do so, we suggest using the map of each covariate with the associated RBVI value for each row in the block. Given that candidate covariates can have different spatial structures, multiple crop rows may be needed such that the trial has an RBVI of close to 1 for each of the covariates. However, a strip that has an RBVI of close to 1 for a covariate may only encompass the higher and lower values of the covariate in the block, meaning that it will be unlikely to generate useful information about treatment effects at locations with medium values of the covariate. Therefore, using the map of the covariate is recommended to help select a strip that encompasses the range of variation in the covariate through low, medium and high values. Measurements of the response variable can then be used to identify the useful covariates by modelling the relationship between the response variable and candidate covariates, estimating the map of the response variable for the whole block in GIS software and performing the two-step correlation analysis. Since growers often run a trial for multiple years (

In some cases, the crop rows in a block may have RBVI values much smaller than 1 for a particular covariate, such as the mildew experiment in which all the NS vine rows had an RBVI of < 0.2 for elevation. As such, at least three rows encompassing the low, medium and high ranges of variation in the covariate are recommended.

Currently, spatial data commonly used by grapegrowers in Australia are predominantly proxy measures of vine vigour and soil EC (

In addition to spatial data, selecting a useful covariate also involves simulating strips and conducting correlation analyses for a response variable and a candidate covariate and the ‘row-to-block' variation index. Currently, performing these analyses requires the use of statistical and GIS software, likely presenting a barrier for farmers and consultants. Automation of the analyses through tools such as PAT (

In this study, a

A concern that one might have about using a covariate to position trial strips is whether the correlation between a response variable and a useful covariate is temporally stable. In the mid-row management experiment, the correlation between yield and PCD remained significant from 2004 to 2007, although the strength of the correlation decreased. This might be attributed to a severe frost event and/or drought conditions that occurred during the experiment, which led to reduced water availability for irrigation for vintages in 2006 and 2007 (

In conclusion, the positioning of a trial strip such that the range of underlying variation in the strip is close to that encountered in the entire block can provide information about the likely variation in crop responses to a particular treatment in the block. The results of such a trial would therefore enable practical insights for a grower on whether to adopt the treatment and/or on opportunities for targeted management of the block according to spatial variation in treatment effects. While this approach has been deliberately discussed in terms of experiments in vineyards, it may also be adapted for those on other crops with distinct rows. How growers and their consultants perceive this strip approach and what support they will need to adopt or adapt it for their own trials, assuming they are interested in doing so, requires further investigation.

This work was co-funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Tasmanian Institute of Agriculture (TIA) at the University of Tasmania (UTAS) under the Australian Sustainable Agriculture Scholarship (ASAS). Wine Australia also contributed to operating funds.