Plants strongly interact with their environment making phenology a sensitive indicator of climatic variability. Exploring the possibility of early prediction of phenological timings of table grape cultivars has huge socioeconomic benefits for the growers. Various statistical, mechanistic, and theoretical approaches have been used for parameterization of phenology models mainly for wine grapes. The studies on application of process-based models such as STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) particularly for table grapes and non-traditional viticulture zones are rather limited. The objective of this study was to calibrate the STICS model and evaluate its performance to simulate phenology and yield of four table grape cultivars in Pothwar- a new emerging viticulture zone of south Asia. For this, phenological timings, growth and yield responses were evaluated at two locations (Islamabad and Chakwal) for two consecutive vintages (2019 and 2020). Daily weather data, soil characteristics, genotype and crop specific data were recorded for model calibration. The results indicated a high robustness of the model for simulating table grapes phenology and yield in all cases. The model simulations indicated a satisfactory to high model efficiency (ME) of 0.69 to 0.94 with a prediction variability up to 4 days for growth stages, whereas ME for yield and pruning weight was 0.84 and 0.90, respectively. The model provided an efficient decision support tool (DST) though crucial predictions for viticulture industry. For instance, early maturity (up to 10 days), lesser yield (19–34 %) and pruning weight (19–42 %) for the warmer location (Chakwal) than for the colder Islamabad were simulated by the model with a high skill. The proximity of predicted values to observed values indicates that STICS is powerful tool for predicting vine phenology and yield, thus it provides a valuable insight in timely planning vineyards operations at key growth stages and selecting cultivars with higher yield for sustainable viticulture.

中文翻译：

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采用基于过程的 STICS 模型来模拟鲜食葡萄的物候和产量 - 南亚新兴葡萄栽培区的高价值水果作物


植物与其环境相互作用强烈，使物候成为气候变化的敏感指标。探索早期预测鲜食葡萄品种物候时间的可能性对于种植者来说具有巨大的社会经济效益。各种统计、机械和理论方法已被用于主要针对酿酒葡萄的物候模型的参数化。基于过程的模型的应用研究，例如 STICS（多学科模拟器标准），尤其是鲜食葡萄和非传统葡萄栽培区的研究相当有限。本研究的目的是校准 STICS 模型并评估其模拟 Pothwar（南亚新兴葡萄栽培区）四个鲜食葡萄品种的物候和产量的性能。为此，我们在两个地点（伊斯兰堡和 Chakwal）连续两个年份（2019 年和 2020 年）评估了物候时间、生长和产量反应。记录每日天气数据、土壤特征、基因型和作物特定数据以用于模型校准。结果表明该模型在所有情况下模拟鲜食葡萄物候和产量的稳健性很高。模型模拟表明，模型效率 (ME) 令人满意，为 0.69 至 0.94，生长阶段的预测变异性长达 4 天，而产量和修剪重量的 ME 分别为 0.84 和 0.90。该模型通过对葡萄栽培行业的关键预测提供了有效的决策支持工具（DST）。例如，模型模拟了较冷的伊斯兰堡，较温暖的地区（Chakwal）的早熟（最多 10 天）、较低的产量（19-34%）和修剪重量（19-42%） 。 预测值与观测值的接近表明 STICS 是预测葡萄树物候和产量的强大工具，因此它为及时规划葡萄园关键生长阶段的运营以及选择产量较高的品种进行可持续葡萄栽培提供了宝贵的见解。