Grape Heterogeneity Index: Assessment of Overall Grape Heterogeneity Using an Aggregation of Multiple Indicators

Uniform grape maturity can be sought by producers to minimise underripe and/or overripe proportions of fruit and limit any undesirable effects on wine quality. Considering that grape heterogeneity is a multifaceted phenomenon, a composite index summarising overall grape heterogeneity was developed to benefit vineyard management and harvest date decisions. A grape heterogeneity index (GHI) was constructed by aggregating the sum of absolute residuals multiplied by the range of values from measurements of total soluble solids, pH, fresh weight, total tannins, absorbance at 520 nm (red colour), 3-isobutyl-2-methoxypyrazine, and malic acid. Management of grape heterogeneity was also studied, using Cabernet Sauvignon grapes grown under four viticultural regimes (normal/low crop load, full/deficit irrigation) during the 2019/2020 and 2020/2021 seasons. Comparisons of GHI scores showed grape variability decreased throughout ripening in both vintages, then significantly increased at the harvest time point in 2020, but plateaued on sample dates nearing the harvest date in 2021. Irrigation and crop load had no effect on grape heterogeneity by the time of harvest in both vintages. Larger vine yield, leaf area index, and pruning weight significantly increased GHI score early in ripening, but no significant relationship was found at the time of harvest. Differences in the Ravaz index, normalised difference vegetation index, and soil electrical conductivity did not significantly change the GHI score.Claire E. J. Armstrong, Pietro Previtali, Paul K. Boss, Vinay Pagay, Robert G. V. Bramley, and David W. Jeffer

Proximal Soil Sensing For Precision Agriculture: Simultaneous Use Of Electromagnetic Induction And Gamma Radiometrics In Contrasting Soils

The use of high spatial resolution, on-the-go proximal soil sensing of apparent electrical conductivity (ECa) through electromagnetic induction (EMI) is increasingly common, in concert with yield mapping, to assist in the delineation of management zones for Precision Agriculture (PA). Less common, but gaining in popularity, is the use of gamma-radiometric (γ) soil sensing. Using contrasting sites in South Australia and Queensland, the specific objectives of the study were to assess for each site, region or all sites together, how well soil cation exchange capacity (CEC) and clay content may be predicted by EMI and γ sensing; to see whether the predictions were improved when both sensors were used, compared to a single sensor; and to evaluate the potential utility of the multi-sensor data in terms of understanding the variation in observed crop yield within sites. Of particular interest was evaluating a generic, as opposed to site-specific, approach to the simultaneous use and calibration of EMI and γ sensing at contrasting sites chosen across a dispersed geography and pedology.EMI and γ soil surveys were carried out at five sites across three cereal growing regions in South Australia, and at three sites in Queensland used for sugarcane production. Soil samples were also collected from each site for laboratory analysis. Data analysis comprised simple correlation analysis between soil sensor data and soil properties; fusion of sensor data by region and across all sites using weighted principal component analysis (PCA), with the data weighted on the basis of the two source sensors (weight of 0.5 assigned to ECa and the remaining weight divided equally amongst 238U, 232Th, 40K and 'total count' (CPS); weights of 0.125 to each). The output from the PCA was used to predict maps of CEC and clay using multiple regression.Simple correlation analysis showed the expected potential utility of both sensors for predicting soil properties by site and by region. The first three principal components (PCs) explained 98% of the data variation across regions and all sites. Models for the prediction of CEC and clay content, derived from the all sites PCs, were significant (p. <. 0.05) at five of the eight study sites. 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Australian Centre for Precision Agriculture, McMillan Building A05, the University of Sydney, NSW 2006, , http://sydney.edu.au/agriculture/pal/software/vesper.shtml, (accessed April 2013)Myers, D.B., Kitchen, N.R., Sudduth, K.A., Grunwald, S., Miles, R.J., Sadler, E.J., Udawatta, R.P., Combining proximal and penetrating soil electrical conductivity sensors for high-resolution digital soil mapping (2010) Proximal Soil Sensing, pp. 233-243. , Springer, Dordrecht, R.A. Viscarra Rossel, A.B. McBratney, B. Minasny (Eds.)Piikki, K., Söderström, M., Stenberg, B., Sensor data fusion for topsoil clay mapping of an agricultural field (2011) Proceedings of the Second Global Workshop on Proximal Soil Sensing, Canada, pp. 56-59. , http://adamchukpa.mcgill.ca/gwpss/materials.html, McGill University Press, Montreal, Canada, (Accessed March 2013)), V.I. Adamchuck, R.A. 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Did someone say “farmer-centric”? Digital tools for spatially distributed on-farm experimentation

On-farm experimentation (OFE) embeds the conduct of agronomic research within normal farm business operations such that experiments are driven by farmers’ needs for business improvement, albeit enabled and facilitated by collaborating ‘experts’ in a process of co-learning. Because experiments are laid down using the farmers’ own equipment in their own fields and at a scale that is consistent with the scale at which farm management decisions are made, it provides them with a salient, credible and legitimate means of creating knowledge for effective application that is valuable to the individual farmer in their field and farm, and potentially to neighbouring farmers in a region. Here, with a particular view to the potential application of OFE in Australian farming systems, we consider the synergies between OFE and the use of precision agriculture (PA) technologies such as yield monitors, crop and soil sensors, and variable rate application of inputs. Indeed, it is suggested that whilst the tools of PA greatly facilitate the conduct of OFE, it is arguably the case that OFE is an essential part of the optimal deployment of PA. We also address statistical issues associated with OFE conducted using PA, including the use of replication, randomization for experimental design, and concerns about spatial autocorrelation in data collected at the within-field scale. However, whilst farmers are generally disengaged from data analysis and place greater emphasis on the magnitude of gross effects and benefit:cost than on statistical significance, they nevertheless want robust and interpretable results. Accordingly, we identify some tools which facilitate simple assessment of alternative management actions across the range of variation in the production systems which farmers encounter. The need for farmer-trustworthy systems of data governance and data sharing amongst those engaged in OFE is also highlighted

Rootstock, Vine Vigor, and Light Mediate Methoxypyrazine Concentrations in the Grape Bunch Rachis of Vitis vinifera L. cv. Cabernet Sauvignon

Ramsey rootstock has previously been implicated in an approximate 8-fold increase of 3-isobutyl-2-methoxypyrazine (IBMP) levels in the rachis (grape bunch stem) of Vitis vinifera L. cv. Shiraz scions over own-rooted Shiraz vines at harvest. IBMP extracted from rachis during red wine fermentation can contribute potent “green” flavors. Methoxypyrazines (MPs) are normally present in Cabernet Sauvignon grapes, rachis, and wines, but it is unknown whether rootstocks can influence the MP concentration in the rachis. This study considered the effect of eight rootstocks including Ramsey and own roots on the concentrations of IBMP, 3- isopropyl-2-methoxypyrazine (IPMP), and 3-sec-butyl-2-methoxypyrazine (SBMP) in the rachis and grapes of Cabernet Sauvignon over two seasons. IBMP predominated, and its concentration in rachis and berries at harvest was significantly affected by rootstock and growing season. In the 2020 vintage, light exclusion, vine vigor, and spatial variation in vine vigor were shown to significantly affect MP concentrations in rachis.Ross D. Sanders, Paul K. Boss, Dimitra L. Capone, Catherine M. Kidman, Robert G.V. Bramley, Emily L. Nicholson, and David W. Jeffer