Luis Santos Pereira

Accurately simulating wheat production under water-limited conditions remains a major challenge for AquaCrop and other models used in irrigation management. In this study, the hypothesis of variable normalized water productivity (WP*) was evaluated for winter wheat under water-stress conditions. The original AquaCrop (AquaCrop-OS) model was applied across five irrigation treatments over two growing seasons. The AquaCrop-OS model successfully simulated canopy cover dynamics and soil water content, the latter with acceptable accuracy. However, consistently overestimated aboveground biomass and grain yield under moderate to severe water stress, with normalized root mean square error (NRMSE) values of 15.0% and 16.0%, respectively. The enhanced model version, AquaCrop-WM, which incorporates a water stress-responsive adjustment to WP^*, substantially reduces these discrepancies, lowering NRMSE to 7.9% for biomass and 8.7% for grain yield. In this improved model, a dimensionless coefficient (W_s) is used to control the sensitivity of water productivity to stomatal stress. The W_s value for winter wheat was estimated to equal 3.63, which is lower than the previously reported value for maize (5.32), indicating winter wheat’s moderate sensitivity to progressive water limitation.

The FAO Irrigation and Drainage Paper 56, which was first published in 1998, has been widely recognized as a comprehensive guidebook for estimating crop evapotranspiration and calculating crop water requirements under various conditions, supporting the efficient management of water resources in agriculture. Over the past twenty-eight years, science and technology have significantly evolved in agricultural productivity and water resource mobilization, use, and management, as well as in research advances, data availability and management, and modeling capabilities and uses. However, these improvements have come against a backdrop of increasingly pressing challenges, especially those posed by climate change and water scarcity. Thus, considering all recent advances in knowledge, an updated version (FAO56 Rev.1) of that guidebook was recently released. The current article summarizes and highlights the main features and innovations that the revision has incorporated.

Crop water use (ET_c) is typically estimated as the product of crop evapotranspiration (ET_o) and a crop coefficient (K_c). However, the estimation of ET_o requires various meteorological data, which are often unavailable or of poor quality, particularly in countries such as Guinea-Bissau, where the maintenance of weather stations is frequently inadequate. The present study aimed to assess alternative approaches, as outlined in the revised FAO56 guidelines, for estimating ET_o when only temperature data is available. These included the use of various predictors for the missing climatic variables, referred to as the Penman–Monteith temperature (PMT) approach. New approaches were developed, with a particular focus on optimizing the predictors at the cluster level. Furthermore, different gridded weather datasets (AgERA5 and MERRA-2 reanalysis) were evaluated for ET_o estimation to overcome the lack of ground-truth data and upscale ET_o estimates from point to regional and national levels, thereby supporting water management decision-making. The results demonstrate that the PMT is generally accurate, with RMSE not exceeding 26% of the average daily ET_o. With regard to shortwave radiation, using the temperature difference as a predictor in combination with cluster-focused multiple linear regression equations for estimating the radiation adjustment coefficient (k_Rs) yielded accurate results. ET_o estimates derived using raw (uncorrected) reanalysis data exhibit considerable bias and high RMSE (1.07–1.57 mm d⁻¹), indicating the need for bias correction. Various correction methods were tested, with the simple bias correction delivering the best overall performance, reducing RMSE to 0.99 mm d⁻¹ and 1.05 mm d⁻¹ for AgERA5 and MERRA-2, respectively, and achieving a normalized RMSE of about 22%. After implementing bias correction, the AgERA5 was found to be superior to the MERRA-2 for all the studied sites. Furthermore, the PMT outperformed the bias-corrected reanalysis in estimating ET_o. It was concluded that PMT-ET_o can be recommended for further application in countries with limited access to ground-truth meteorological data, as it requires only basic technical skills. It can also be used alongside reanalysis data, which demands more advanced expertise, particularly for data retrieval and processing.

The need for controlling salinity in arid zones is essential for sustainable agricultural production and irrigation water use. A case study performed for two years in Hetao, Inner Mongolia, China, is used herein to rethink the contradictory issues of arid lands represented by water saving and controlling soil and water salinity. Two sets of static lysimeters, where water table depths (WTDs) were fixed at 1.25, 150, 2.00, and 2.25 m, were continuously monitored, and soil water and solute data were used to calibrate and validate two models: the soil water balance model SIMDualKc and the deterministic soil water and salt dynamics model HYDRUS-1D. Once accurately calibrated, the models were used to simulate maize water use, percolation, and capillary rise, along with the observed variables for the actual WTD and the autumn irrigation applied. Simulation scenarios also considered agricultural system degradation and dynamic water table behavior. Results have shown that large leaching efficiencies (Lefs) were obtained for large irrigation depths in cases of shallow water tables, but higher Lefs corresponded to high application depths when the water table was deeper. Agricultural system degradation, particularly increased groundwater salinity, lowered Lef, regardless of WTD. Conversely, water savings were minimal and only achievable when considering the dynamic nature of groundwater. These results indicate that there is a need to define different WTDs based on soil characteristics that influence fluxes and root zone storage, as well as the impacts of newly installed drainage systems aimed at salt extraction.