Aerobic irrigation can effectively improve the oxygen environment in the root zone, and enhance crop quality and yield. However, how aerobic irrigation regulates root growth has not been elucidated. In this study, tomato plants were irrigated with three levels of oxygen (high, medium, and low) under underground drip irrigation. The morphology, activity, transcriptome, and hormone content of tomato roots under oxygen irrigation were analyzed. We found that the aeration irrigation significantly promoted root development. Notably, in the high-aeration irrigation treatment (HAI), the total root length, total surface area, total volume, and root activity were 12.41%, 43.2%, 79.1%, and 24.15% higher than in the non-aeration irrigation treatment (CK), respectively. The transcriptome of tomato roots under aeration irrigation was determined with a total of 272 differentially expressed genes (DEGs), including 131 up-regulated and 141 down-regulated genes. The Kyoto encyclopedia of genes and genomes (KEGG) analysis revealed that the DEGs were enriched mainly in the metabolic pathways and plant hormone signal transduction. Among the plant hormone signal transduction, 50% of DEGs belonged to IAA signal-related genes and were upregulated. LC-MS analysis showed that the content of auxin hormones in the tomato roots subjected to aeration irrigation was significantly higher than that in CK. The content of Indole-3-acetic acid (IAA), Indole-3-carboxylic acid (ICA) and Indole-3-carboxaldehyde (ICAld) were 2.3, 2.14 and 1.45 times higher than those of the CK, but insignificant effects were exerted on the contents of cytokinins, salicylic acid, jasmonic acid, abscisic acid, and ethylene. Meanwhile, the key enzyme of auxin synthesis flavin monooxygenase (YUCCA) was significantly up-regulated. The aforementioned results show that aeration irrigation may promote the growth and development of roots by auxin regulation.

Understanding the spatial and temporal distribution of irrigation water requirements is significant to realize the rational allocation of water resources and also serves as the basis for analyzing agricultural water-saving potential. This study refers to the standard irrigation regions in southwestern, northern, central, southern, and eastern Shandong province. The irrigation water requirements at 20 weather stations in Shandong Province from 1968 to 2016 were calculated, and the spatial and temporal distribution characteristics were analyzed. The results indicated the following: (a) The trend of the annual irrigation water requirements for summer maize and winter wheat showed an insignificant increase in the eastern Shandong irrigation region, a significant decline in northern and southwestern Shandong irrigation regions, and an insignificant decrease in the other irrigation regions. (b) The multi-year average irrigation water requirement for summer maize generally presents a spatial distribution characteristic which is less in the southwest, more in the northeast, less in the south, and more in the north, while the spatial distribution characteristic for winter wheat is less in the southeast, more in the northwest, less in the south, and more in the north. (c) The main meteorological factors affecting the irrigation water requirements for summer maize are precipitation and sunshine duration, while relative humidity is the main factor affecting winter wheat in Shandong Province.

Tomato (Jinglu 6335) was selected for assessing the impact of varying fertilizer (F:N-P₂O₅-K₂O) and aeration rates on crop quality, as well as water and fertilizer utilization efficiency during the cyclic aeration subsurface drip irrigation process. Four aeration treatments (O1, O2, O3, and S, representing aeration ratios of 16.25%, 14.58%, 11.79%, and non-aerated treatment, respectively) and three fertilizer applications (F1: 240–120–150 kg/hm², F2: 180–90–112.5 kg/hm², F3: 120–60–75 kg/hm²) were compared in a total of 12 treatments in this study. This study revealed that cyclic aerated drip irrigation improved the fruit quality. The aerated treatment resulted in increased accumulation of nitrogen, phosphorus, and potassium, with the level of aeration positively correlating with the increase in nutrient accumulation, reaching the highest values in the high aeration irrigation treatment. The highest nitrogen, phosphorus, potassium, and water use efficiency occurred under the medium fertilizer with high aeration treatment. The maximum partial productivity of the fertilizer occurred under the low fertilizer with high aeration treatment, while the minimum occurred in the high fertilizer with non-aerated treatment. Taking all factors into consideration, the high-aeration and medium-fertilizer treatment was the most effective combination for greenhouse tomatoes under the conditions in this experiment.

Solar radiation is the main source of energy on the Earth’s surface. It is very important for the environment and ecology, water cycle and crop growth. Therefore, it is very important to obtain accurate solar radiation data. In this study, we use the highest temperature T_max, lowest temperature T_min, average temperature T_avg, wind speed U, relative humidity RH, sunshine duration H and maximum sunshine duration H_max as input variables to construct a deep learning prediction model of solar radiation in the Yellow River Basin. It is compared with the recommended and corrected values of the widely used Å-P method. The results show that: (1) The correction results of the Å-P equation are better in the upstream and downstream of the Yellow River Basin but worse in the midstream. (2) The prediction result of the deep learning model in the Yellow River Basin is far better than that of the Å-P equation using the FAO-56 recommended value. It is the best in the downstream of the Yellow River Basin: R² increases from 0.894 to 0.934; MSE, RMSE and MAE decrease by 43.12%, 27.73% and 25.80%, respectively. The upstream prediction result comes in second: R² increases from 0.888 to 0.921; MSE, RMSE and MAE decrease by 33.27%, 20.02% and 19.04%, respectively. The midstream result is the worst: R² increases from 0.869 to 0.874; MSE, RMSE and MAE decrease by −0.50%, 0.07% and 3.82%, respectively. (3) The prediction results of the deep learning model in the upstream and downstream of the Yellow River Basin are far better than those of the Å-P equation using correction. The R² in the upstream of the Yellow River Basin increases from 0.889 to 0.921. MSE, RMSE and MAE decrease by 22.11%, 11.84% and 8.94%, respectively. R² in the downstream of the Yellow River Basin increases from 0.900 to 0.934, and MSE, RMSE and MAE decrease by 13.21%, 11.40% and 5.55%, respectively. In the midstream of the Yellow River Basin, the prediction results of the deep learning model are worse than those of the Å-P equation using correction: R² increases from 0.870 to 0.874, but MSE, RMSE and MAE decrease by −24.93%, −10.83% and −11.56%, respectively.

The construction of ecological irrigation districts is of great significance to protect the Yellow River ecology and achieve sustainable development of the local ecological economy. Taking the ecological irrigation district of Helan County as the study area, a health evaluation index system of the irrigation district was established, including three primary indexes of ecological environment, modernization level, and agricultural production and benefit, and 20 secondary indexes. Then, the Topsis method, entropy weight evaluation method, fuzzy pattern recognition model, and variable fuzzy model were used to evaluate the health of the Helan ecological irrigation district. In order to avoid the one-sidedness of the evaluation results of a single evaluation method, a combined evaluation method named deviation maximization combined evaluation method was used to combine each single evaluation result. The evaluation results by the combined evaluation method showed the following: (1) The ecological health of Helan irrigation district had a trend of becoming better from 2007 to 2016. (2) The grey correlation analysis showed that the soil salt content, groundwater depth, canal lining rate, ratio of efficient water-saving irrigation area, information level of the irrigation district, water productivity, agricultural unilateral aquatic output value, irrigation water consumption per mu, and coefficient of effective utilization of farmland irrigation water were closely related to the evaluation results. (3) In order to effectively improve the ecological health of Helan irrigation districts, it is necessary to reduce soil salt content and groundwater salinity, increase canal linings, promote water-saving irrigation measures, and agricultural information construction, etc.

Yellow River sediment is the potential resource for saline–sodic soil reclamation. Experiments of one-dimensional soil columns were conducted to investigate the upward and downward soil water transportation characteristics for saline–sodic soil mixed with different sediment addition (0, 10, 20 kg/m² in the top 20 cm layer). The saturated hydraulic conductivity, ratio of macroporosity, cumulative capillary adsorption and infiltration rate all increased with the increase in sediment addition. No significant differences were detected for both the initial capillary rise rate and the initial infiltration rate for the upward and downward water transportation treatments, respectively. The average adsorption and infiltration rates showed an increasing trend with the increased sediment addition. The initial and average infiltration rates were higher than the initial capillary rise rate and average adsorption rates. The Philip model seems the optimal choice for the dynamic simulation of both upward and downward soil water transportation. The results may provide useful information for soil salinization amelioration.