The water requirements of the wheat crop are represented by the actual evapotranspiration, which depends on the meteorological data of the study area and the amount of water consumed during the season. Estimation of crop coefficients (KC) and evapotranspiration (ETc) using remote sensing data is...

Modeling achievable yield under water-limiting environments is an imperative goal in arid, semi-arid, and drought-prone locations. This study aimed to calibrate the AquaCrop model simulation based on experience gained at al-Hashimiya District, 20 km east of Babylon, Iraq. Wheat (Triticum aestivum) in 2016–2017 and 2017–2018 was grown using deficit irrigation systems (surface and sprinkler irrigation) and different tillage methods (deep, moderate, and zero tillage). A simulation was conducted on the systems, using climate data for a period of 10 years and specific data on the study area, crops grown, and irrigation types. Then, statistical analysis was conducted with the root-mean-square error (RMSE), which refers to compatibility between the results and predictions of the measured values. For 2016–2017 and 2017–2018, RMSE values were 0.764 and 0.643 for biomass, 0.473 and 0.419 for dry matter, 0.141 and 0.154 for water productivity, and 1.59 and 0.946 for harvest index. The R2 values were 0.917 and 0.956 for dry matter, 0.982 and 0.946 for biomass, 0.869 and 0.849 for harvest index, and 0.923 and 0.943 for water productivity from 2016–2017 and 2017–2018. Mean bias error (MBE) and mean absolute error (MAE) were also derived with comparable results. The model results indicated that there is an explicit agreement with the simulated values of the wheat crop. The AquaCrop model can be used to estimate water productivity, dry matter, and biomass to improve agricultural water management and as an input into future research of alternate forms of irrigation.

The rapidly increasing global demand for food security urgently calls for agricultural technologies that can boost productivity without contributing to environmental degradation. A group of fertilizers that combines nanotechnology with beneficial microorganisms – known as nano-biofertilizers – has shown tremendous potential to enhance plant growth, increase yields, and improve soil health. Controlled nutrient release, enhanced microbial activities, and improved nutrient-use efficiency are key benefits of these new fertilizers, collectively leading to higher crop productivity with reduced environmental impact. These advantages contribute to sustainable agriculture by reducing the use of chemical fertilizers, surface soil degradation, nutrient leaching, and greenhouse gas emissions. Recent research findings have also highlighted their role in increasing plant resilience to abiotic stress factors, enhancing soil microbial diversity, and supporting ecological balance. This paper presents a comprehensive analysis of the multifaceted roles of nano-biofertilizers in modern agriculture, focusing on their contributions to plant physiology, soil health, environmental sustainability, and long-term agricultural productivity. Through empirical integration, we argue that nano-biofertilizers could be a transformative tool in advancing sustainable farming and achieving global environmental conservation goals.

Crop models of simulation are utilised effectively to evaluate the management of irrigation strategies which help in managing the water use. The aim of this study was to verify the validity of the Aquacrop model of maize under the surface and sprinkler irrigation systems, and a cultivation system, borders and furrows, and for two varieties of Maze (Fajr and Drakma) at two different sites in Iraq, i.e. the Babylon and Al-Qadisiyah governorates. The current study conducted an experiment to evaluate the Aquacrop model capacity in simulating canopy cover (CC), biomass (B), dry yield, harvest index (HI), and water productivity (WP). The results of RMSE, R-2, MAE, d, NSE, CC, Pe indicated good results and high compatibility between the measured and simulated values. The highest achieved results were identical to the method of sprinkler irrigation due to the decrease in the amount of water consumed and the furrows cultivation method as the aerial roots were covered and the cultivar was Drakma. The highest values for the statistical data were R-2 (90 and 96%), RMSE (0.60, 0.73), MAE (0.5, 0.67), d (0.97, 0.97), NSE (0.87, 0.90), for the Babylon and Al-Qadisiyah sites, respectively. As for the CC values, they were very compatible with the values of R-2 and ranged between (92-99)%. The prediction error was Pe and minor errors were found. Thus, the Aquacrop model can be used reliably to evaluate the effectiveness of proposed irrigation management strategies for maize.