Hamidreza Kamali

Bio: Hamidreza Kamali is an academic researcher. The author has contributed to research in topics: Transpiration & Evapotranspiration. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Optimization of Vineyard Water Management: Challenges, Strategies, and Perspectives

Abstract: Water availability is endangering the production, quality, and economic viability of growing wine grapes worldwide. Climate change projections reveal warming and drying trends for the upcoming decades, constraining the sustainability of viticulture. In this context, a great research effort over the last years has been devoted to understanding the effects of water stress on grapevine performance. Moreover, irrigation scheduling and other management practices have been tested in order to alleviate the deleterious effects of water stress on wine production. The current manuscript provides a comprehensive overview of the advances in the research on optimizing water management in vineyards, including the use of novel technologies (modeling, remote sensing). In addition, methods for assessing vine water status are summarized. Moreover, the manuscript will focus on the interactions between grapevine water status and biotic stressors. Finally, future perspectives for research are provided. These include the performance of multifactorial studies accounting for the interrelations between water availability and other stressors, the development of a cost-effective and easy-to-use tool for assessing vine water status, and the study of less-known cultivars under different soil and climate conditions.

Improving CERES-Wheat Yield Forecasts by Assimilating Dynamic Landsat-Based Leaf Area Index: A Case Study in Iran

Abstract: In this study, we tried to address the applicability of using dynamic remotely sensed data into a static crop model to capture the yield spatiotemporal variability at the field scale. Taking the example of the crop environment resource synthesis for wheat (CERES-wheat), the model was calibrated, improved, and validated using three years of winter wheat field measurement data (growing seasons of 2017–2019). We assimilated the Landsat-based leaf area index (LAI) into the model using the particle filter approach. Four vegetation indices, including NDVI, SAVI, EVI, and EVI-2, were evaluated to identify winter wheat LAI’s best estimator. A linear regression of Landsat-EVI-2 was found to be the most accurate representation of LAI (LAI = 10.08 × EVI-2 − 0.53) with R2 = 0.87, and mean bias error = − 2.04. The higher LAI accuracy from EVI-2 was attributed to the soil and canopy background noise reduction and accounting for certain atmospheric conditions. Assimilating the LAI based on Landsat-EVI-2 into the CERES model improved the model’s overall performance, particularly for grain yield and biomass simulations. The default model predicted LAImax, grain yield, and biomass at 5.1 cm2 cm−2, 8.3 Mg ha−1, and 14.9 Mg ha−1 with RMSE of 1.44, 0.91 Mg ha−1, and 1.2 Mg ha−1, respectively, while the modified model (using the Landsat-EVI-2 data) predicated these values at 6.6 cm2 cm−2, 9.9 Mg ha−1, and 16.6 Mg ha−1 with RMSE of 0.81, 0.54 Mg ha−1, and 0.62 Mg ha−1, respectively.

A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning

Abstract: To estimate the irrigation volume required for agriculture and improve water resources utilization efficiency, it is essential to obtain an estimate of reference evapotranspiration (ET0) and its components (e.g., reference transpiration, T0 and reference soil evaporation, E0). This study updated a soil-plant-atmosphere continuum (SPAC) evapotranspiration model and its associated components to obtain a reference-based SPAC model of reference evapotranspiration (R-SPAC), and it applied the model to an agricultural ecosystem. Model simulations of mean hourly ET0 were benchmarked against those of the Penman-Monteith method by the Food and Agriculture Organization (FAO-PM) throughout the growing season. The resulting good correlation obtained (R2 = 0.96, agreement index, I = 0.98, root-mean-square deviation (RMSD) = 0.05 mm h−1) validated the accuracy of the R-SPAC model. Sensitivity analysis was used to explore uncertainties and errors for ET0, T0, and E0 caused by input variables. The results showed that net radiation and shortwave radiation at the study site were the main drivers of ET0 for both the FAO-PM and R-SPAC models. The study showed that the proposed R-SPAC model can be used for predicting ET0 and for exploring interactions between climate, crop type, and soil in determining evapotranspiration under various future environment conditions.