2 research outputs found
Growing Lettuce (Lactuca sativa L.) in Floating Disk Systems Under Variable and High Salinity Ranges in Water Enriched with Nanobubbles
No full textHydroponic systems, which use commercial hydroponics technologies, are cheaper and easier to maintain than traditional farming methods in soil. The objective of this study was to evaluate various salinity ranges (E.C.i from 1 dS/m to 14 dS/m) in water enriched with nanobubbles (NBs) for the growth and productivity of lettuce plants in a floating disk hydroponic system. This research study investigated how using floating disks in a greenhouse with a nanobubble (NB) generator may affect lettuce’s (Lactuca sativa L.) morphological and physiological responses to salt stress. The goal of this experiment was to examine the results of the influence of NB and non-NB treatments on agronomic traits and yield. The results indicated that the NB device is an innovative and very effective technology for sustainable lettuce production under a high-salinity nutrient solution. This device presents a valuable solution to the global issue of the increased salinity of irrigation water
Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton
No full textThis study introduces a novel methodology for predicting cotton yield by integrating machine learning (ML) with mechanistic soil modeling. This hybrid approach enhances yield prediction by combining data-driven ML techniques with soil process modeling. Using the developed yield model, yield curves for various nitrogen (N) levels can be constructed to identify the optimal N dose that maximizes yield. Estimating cotton N requirements is crucial, as growers often apply excessive N, exceeding the amount needed for maximum yield. By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). Additionally, the RF model exhibited high sensitivity to N fertilization, ranking N as the most influential variable in feature importance analysis. Furthermore, phosphorus (P) availability in the soil model was found to be a significant factor influencing the RF yield model, highlighting P’s crucial role in cotton growth and productivity
