Climate-induced cyclical properties of regional wine production using a time-frequency approach in Douro and Minho Wine Regions

Abstract

The impact of climate on wine production (WP) temporal cycles in Douro (DR) and Vinhos Verdes (VVR) wine regions for a period of about 80 years, characterized by strong technological trend and climate variability, was modelled. The cyclical properties of WP, and which cycles are determined by spring temperature (ST) and soil water during summer (SW), were identified. It was achieved by applying a time-frequency approach, which is based on Kalman filter in the time domain. The time-varying autoregressive model can explain more than 67% (DR) and 95% (VVR) of the WP’ variability and the integration of the ST and mainly SW increase the models’ reliability. The results were then transferred into the frequency domain, and can show that WP in both regions is characterized by two cycles close to 5-6 and 2.5 years around the long run trend. The ST and SW showed great capacity to explain the cyclicality of WP in the studied regions being the coherence temporarily much more stable in VVR than in the DR, where a shift of the relative importance away from ST to SW can be recognized. This could be an indicator of lower impact of the foreseen hot and dry climate scenarios on WP in the regions with a maritime climate, such as the VVR, compared with hot and dry wine regions. Despite the marked differences in the two studied regions on ecological, viticulture practices and technological trend, the modelling approach based on time-frequency proved to be an efficient tool to infer the impact of climate on the dynamics of cyclical properties of regional WP, foreseeing its generalized use in other regions. This modelling approach can be an important tool for planning in the wine industry as well as for mitigation strategies facing the scenarios that combine technological progress and climate change.

Using NDVI, climate data and machine learning to estimate yield in the Douro wine region

“…The current model also performs well when referring to other pollenbased models (and without the limitations mentioned above) with estimations in line with the work of Cunha et al, (Cunha et al, 1999(Cunha et al, , 2003(Cunha et al, , 2015 for the DDR. The different studies for regional-scale applications identified in the authors' previous work (Barriguinha et al, 2021) have an overall average R 2 between 0,71 and 0,99.…”

“…Comparing our results with the works of Cunha et al (Cunha et al, 1999(Cunha et al, , 2003, that developed and used an estimation model in the same study area (DDR) and relied on the relationship between airborne pollen and yield (Table 14), we can state that our results are very satisfactory. The pollen-based model predicts wine production for the whole DDR with a minimum and maximum threshold, and ADVID has used it since 1992 with the predictions made yearly at the VER stage.…”

“…Different methodological approaches for regional vineyard yield prediction (Barriguinha et al, 2021). In-field Airborne pollen concentration, weather data R 2 = 0,96 (Cour); R 2 = 0,99 (Hirst) (Besselat, 1987) Pollen Based Pollen concentration data In-field Airborne pollen concentration R 2 < 0,98 (for yield) (Cunha et al, 2015) Pollen Based Airborne pollen trap Simulated Airborne pollen concentration 0,71 < R 2 < 0,86 (for annual wine production) (Cunha et al, 1999) Pollen Based Pollen concentration data In-field Airborne pollen concentration R 2 = 0,93 (for yield) (Cunha et al, 2003) Pollen achieved good results simulating the fruit load based on light interception derived gross assimilation and thermal and water limitations with R 2 = 0,96 (for yield in low-density canopies) and R 2 = 0,94 (for yield in high-density canopies). Sirsat et al (Sirsat et al, 2019) focused on grape yield predictive models for flowering, coloring, and harvest phenostages using ML techniques and climatic conditions, yield, phenological dates, fertilizer data, soil analysis, and maturation index data to construct the relational dataset.…”

“…The purpose of the present paper is two-fold: first, to evaluate a new methodology for estimating vineyard yield at the regional level, using the Douro Demarcated Wine Region as a study area with readily available data, and allowing transferability to other regions, to give decisionmakers, as far in advance as possible, a good estimation, not only for the total regional and sub-regional wine grape production areas but also at a more detailed scale, considering three sub-regions and 169 subadministrative regions where there are vineyards in production. Secondly, to cope with the limitations identified in the current model in use in the study area, based on the work of Cunha et al (Cunha et al, 1999(Cunha et al, , 2003, which relies on the relationship between airborne pollen and wine production, namely: predicting only for the entire region; predicting wine production instead of wine grape production; the need to maintain representative pollen sampling devices with high maintenance and operational costs, and complex laboratory process to treat the data; and a wide prediction interval.…”

Advances in Viticulture, Enology and Vitivinicultural Economy: Ciência e Técnica Vitivinícola 2020

“…An innovative methodology to modelling the impact of climate on the wine production temporal cycles was reported by Cunha and Richter (2020); the article also shows its applicability for a period of about 80 years in two world-renowned but considerably different wine-producing regions: Douro and Vinhos Verdes. This new tool can be of great significance for planning purposes as well as for mitigation strategies facing the scenarios that combine technological progress and climate change.…”