Dimitri Giovanna Maria, Trambusti Alberto
Dipartimento di Ingegneria dell'Informazione e Scienze Matematiche, Universitá di Siena, Via Roma 56, 53100 Siena, Italy.
Heliyon. 2024 May 21;10(11):e31648. doi: 10.1016/j.heliyon.2024.e31648. eCollection 2024 Jun 15.
The agricultural sector, in particular viticulture, is highly susceptible to variations in the environment, crop conditions, and operational factors. Effectively managing these variables in the field necessitates observation, measurement, and responsive actions. Leveraging new technologies within the realm of precision agriculture, vineyards can enhance their long-term efficiency, productivity, and profitability. In our work we propose a novel analysis of the impact of pedoclimatic factors on wine, with a case study focusing on the Denomination of Controlled and Guaranteed Origin Chianti Classico (DOCG), a prime wine-producing region located in Tuscany, between the provinces of Siena and Florence. We first collected a novel dataset, where geographic information as well as wine quality information were collected, using publicly available sources. Using such geographic information retrieved and an unsupervised machine learning approach, we conducted an in-depth examination of pedoclimatic and production data. To collect the whole set of possibly relevant features, we first assessed the region's morphological attributes, including altitude, exposure, and slopes, while pinpointing individual wineries. Subsequently we then calculated crucial viticultural indices such as the Winkler, Huglin, Fregoni, and Freshness Index by utilizing daily temperature records from Chianti Classico, and we further related them to an assessment of wine quality. In addition to this, we designed and distributed a survey conducted among a sample of wineries situated in the Chianti Classico area, obtaining valuable insights into local data. The primary goal of this study is to elucidate the interrelationships between various parameters associated with the region, considering influential factors such as the environment, viticulture, and field operations that significantly impact wine production. By doing so, wineries could potentially unlock the full potential of their resources. In fact, through the unsupervised and correlation analysis we could elucidate the relationships existing between the pedoclimatic parameters of the region, considering the most important factors such as viticulture and field operations, and relate them to wine quality as for instance using the survey data collected. This study represents an unprecedent in the literature, and it could pave the path for future studies focusing on the importance of climatic factors into production and quality of wines.
农业部门，尤其是葡萄栽培业，极易受到环境、作物状况和运营因素变化的影响。在田间有效管理这些变量需要进行观察、测量并采取相应行动。借助精准农业领域的新技术，葡萄园可以提高其长期效率、生产力和盈利能力。在我们的工作中，我们提出了一种关于土壤气候因素对葡萄酒影响的新颖分析方法，并以位于锡耶纳省和佛罗伦萨省之间的托斯卡纳优质葡萄酒产区基安蒂经典法定产区（DOCG）为例进行研究。我们首先使用公开可用来源收集了一个新颖的数据集，其中包括地理信息和葡萄酒质量信息。利用检索到的此类地理信息和无监督机器学习方法，我们对土壤气候和生产数据进行了深入研究。为了收集所有可能相关的特征，我们首先评估了该地区的形态属性，包括海拔、朝向和坡度，同时确定各个酒庄的位置。随后，我们利用基安蒂经典产区的每日温度记录计算了关键的葡萄栽培指数，如温克勒指数、胡格林指数、弗雷戈尼指数和新鲜度指数，并将它们与葡萄酒质量评估进一步关联起来。除此之外，我们设计并向基安蒂经典产区的一部分酒庄发放了一份调查问卷，从而获得了有关当地数据的宝贵见解。本研究的主要目标是阐明与该地区相关的各种参数之间的相互关系，同时考虑环境、葡萄栽培和田间作业等对葡萄酒生产有重大影响的因素。通过这样做，酒庄有可能充分发挥其资源的全部潜力。事实上，通过无监督和相关性分析，我们可以阐明该地区土壤气候参数之间存在的关系，同时考虑葡萄栽培和田间作业等最重要的因素，并将它们与葡萄酒质量联系起来，例如使用收集到的调查数据。这项研究在文献中尚无先例，它可能为未来关注气候因素对葡萄酒生产和质量重要性的研究铺平道路。
