Assessing Global Climate Datasets for Small-Scale Agricultural Applications: The Case of Nemea, Greece

In recent years, extensive research has been conducted to evaluate various surface-, satellite-, and reanalysis-based gridded datasets of climatic variables on a global scale. However, a noticeable gap exists in understanding their effectiveness and accuracy in agricultural applications, particularly in very small-scale areas. While these datasets have proven valuable for assessing global climate patterns, their translation to on-the-ground impacts, especially in agricultural landscapes, remains a challenge. The complexities of agricultural systems, including irrigation management, farming practices, and responses to extreme weather events, demand a closer examination of the suitability and precision of existing climate datasets for informed decision-making in the agricultural sector.

This study seeks to address this gap by focusing on the wine-making region of Nemea, Greece, providing valuable insights into the utility of global climate datasets in agricultural applications and especially irrigation management to streamline precision irrigation management in regions where data scarcity prevails. The primary objective is to explore the applicability of diverse global climate datasets in small-scale areas, emphasizing the unique challenges posed by the very high spatial variability in regions characterized by complex landscapes, very steep relief, and very small farms. The study delves into the intricacies of irrigation management, and the impact of extreme temperatures on vine stress.

The methodology employed involves leveraging a variety of open-source global climate datasets, which are subsequently evaluated for accuracy through validation against local meteorological stations data. A network of 10 agrometeorological stations located throughout the wine-making region of Nemea will be used. The key variables under scrutiny include the variables related to irrigation and crop management, i.e. precipitation, air temperature, air humidity, wind velocity, and solar radiation. The applied methodology includes the assessment of the characteristics of the available grided datasets; the evaluation of the grided datasets accuracy in general and for specific conditions (e.g. heatwaves, frost days, storms etc.); and the comparison of optimum irrigation schedules compiled using detailed meteorological data obtained by local agrometeorological stations for a five-year period with the corresponding schedules compiled using the gridded datasets under evaluation. The effects of gridded datasets inaccuracies on crops development, crop stress, and crop yield quality and quantity are also evaluated.

The results demonstrate the clear influence of spatial resolution on data accuracy. The study underscores the significance of selecting datasets with an optimal spatial resolution to enhance the precision of climatic variables in large-scale areas. This insight contributes to the broader discourse on the practicality and limitations of employing global climate datasets in small scale agricultural applications in regions characterized by complex landscapes. Insights on relevant downscaling and correction methodologies are provided.