WINETECH Technical Yearbook 2019

(October until April for 2011/12 to 2015/16) at a spatial resolution of 20 m. • Crop production data – actual vineyard block production information (quality and quantity) for the above-mentioned five production seasons obtained through the industry. • Block boundaries – vineyard block field boundary information obtained through the industry, indicating the geographical locations and extent of the fields considered. Additional datasets were derived and all datasets were joined at block level to study the relationship between crop yield and RS derived FL data, using a combination of statistical modelling and ML. Initially the ML involved manually changing target variables, but subsequently a brute force approach was applied. This involved a near- exhaustive set of experiments, each using a different permutation of target variables, input variables and geographical areas. WHAT CONCLUSIONS CAN BE MADE FROM THIS STUDY? Although crop yield per block is frequently recorded, the associated hectares and other associated information are not regularly updated. The graphically delineating information (block boundaries) too are not readily available in electronic format. This study was a first attempt to model grape yield using RS variables. The initial

TABLE 1. Many variables were considered in this study. Spatial data FL weekly data from 2011/12 to 2015/16, including biomass production, actual evapotranspiration, evaporation deficit, water use efficiency, leaf area index, normalised difference vegetation index, plant nitrogen, nitrogen in top leaf layer and FL various derivatives also at monthly and seasonal time steps (made available by eLEAF and the Western Cape Department of Agriculture).

WHAT ARE THE CURRENT WINE GRAPE YIELD ESTIMATION CHALLENGES? S i mp l y pu t , t h e ye a r l y und e r - o r overestimation of vine yield by producers leads to under- or overestimated wine volumes which in turn could lead to a loss of money. It is estimated that the expected error could be up to 20% for forecasts based on bunch counts in spring, 10-15% for forecasts based on berry counts at fruit set and 5% on harvesting segments close to the harvest. The estimated yield has a direct effect on cellar capacity, chemical usage and marketing activities. For growers, yield directly impacts planning of the harvesting processes and the scheduling of labour and machinery. Current yield prediction largely relies on historical yield data and weather indices combined with manual vineyard measurements and sampling, but this approach is inaccurate and time consuming. WHAT MEASURING PARAMETERS AND SOURCES WERE USED IN THIS STUDY? Selected production areas with the required “big datasets” (block specific and remote sensing derived) were included in the study and extended over the Coastal, Breede and Olifants Wine of Origin regions of the Western Cape. Three main data sources were considered: • FruitLook data – weekly FL spatial datasets on crop growth, water use and nitrogen content for five production seasons

Block specific data

Grape yield in ton/ha or ton/block for 2 104 fields (obtained through industry).

Other block attributes including quality indicators (acid, pH and sugar), block number, block area (ha), cultivar, grape type, plant year, rootstock, trellis and irrigation method.

Field boundary data for 2 104 fields.

Geographical delineation of wine production wards, districts and regions and climatic zones.

Other data like production season.

regression analyses to investigate the strongest relationships between individual FL variables and yield generally yielded poor results (R 2 < 0.3), but the models improved when individual cultivars, specific regions and seasons were considered. For example, the 2014/15 season’s weekly (non-aggregated) FL variables yielded a strong (R 2 = 0.83) model for Pinotage in the Coastal region. Other ML findings, especially from the Olifants River region, were very encouraging. When RF (Random Forest), an artificial intelligence classifier, was applied to the 2015/16 seasonal (aggregated) FL

variables in this region, an overall accuracy of 85% was achieved when all the cultivars were considered. Similar results were also observed for the 2014/15, 2011/12 and 2013/14 seasons, confirming the consistently strong relationship between all the FL variables and yield for this region. More data and work is needed regarding a harvest date model. The data that was used was obtained from less than 30 blocks, but yielded promising results. Ultimately, based on the regression and ML experiments the following was concluded: