The irrigation districts of northern China face issues such as water scarcity, inability to effectively utilize flood resources, and groundwater overexploitation. In view of these challenges, this study proposes a new concept of deep storage irrigation through flood resources utilization. However, whether deep storage irrigation can recharge deep soil moisture and sustain crop production still requires further study. A two-year field experiment was conducted on summer maize in the Guanzhong Plain with five soil wetting layer depths (T1: 60 cm; T2: 90 cm; T3: 120 cm; T4: 150 cm; T5: 180 cm) and soil saturation moisture content as the irrigation upper limit. The results presented that the ranges of deep soil moisture recharge in the 100–200 cm soil profile (SMS_100–200) was 73.34–267.42 and 0–150.03 mm in 2021 (wet season) and 2022 (normal season). When the effective precipitation and irrigation exceeded 390 mm, the SMS_100–200 began to linearly increase. The highest grain yield (GY) were observed at T2 and T3 treatments in 2021 (11.44 t ha^-1) and 2022 (11.25 t ha^-1), respectively. The maize GY of T4 in 2021 and T5 in 2022 were only 3.9 and 5.7% lower than the maximize GY, respectively. However, the SMS_100–200 for T4 and T5 were 2.4 and 5.0 times that of T2 and T3 treatments in 2021 and 2022, respectively. Overall, the further increase in irrigation amounts induced only a slight decrease in grain yield, but it significantly increased deep soil moisture recharge. Therefore, the deep storage irrigation breaks through the traditional idea of water-saving irrigation with limited water resources, which can be utilized as an effective alternative to address the issues of water scarcity, low flood resources utilization, and groundwater level declines in the irrigation districts of northern China.

Environmental challenges can often stem from the inefficient use of water and fertilizers in vineyards located in the Guanzhong Plain of Shaanxi, China. It is highly required for effective irrigation and fertilizer management in the area. This study aims to optimize the irrigation and fertilizer, in order to balance the grape growth, nutrient retention, yield factors, and greenhouse gas emissions. A three-year field trial was conducted from 2019 to 2021. 'Hutai 8' was also taken as the test variety. A combination design was employed to feature three irrigation levels: W3 (100% of the irrigation quota, M), W2 (75% of M), and W1 (50% of M), along with four rates of fertilizer: F3 (648 kg/hm²), F2 (486 kg/hm²), F1 (324 kg/hm²), and F0 (0 kg/hm²). A systematic investigation was implemented to explore the impacts of irrigation and fertilization on grape growth, soil water and fertilizer distribution, greenhouse gas emissions, and yield components. The TOPSIS method was applied to identify the optimal irrigation and fertilizer amounts for grape cultivation. The results indicated that the fertilization shared a predominant impact on the leaf area index, SPAD value, leaf nitrogen content, leaf phosphorus content, and leaf potassium content, compared with the irrigation. These index values rose significantly, as the amount of fertilization increased from F0 to F2 treatments. By contrast, irrigation shared a notable influence on the soil moisture levels up to 60 cm deep in the soil. The content of residual nutrients in the F1 and F0 treatments declined each year, while there was an increase in the F3 treatment. Proper application of potassium fertilizer effectively reduced the nutrient levels of residual soil among the three types of soil. Compared with irrigation, fertilization was the main influencing factor on the cumulative greenhouse gas emissions from the soil. The cumulative emissions of N₂O rose significantly, as the fertilization increased, while the cumulative emissions of CO₂ declined gradually. Additionally, the cumulative emissions of CH₄ were mostly negative over the last two years. It infers that the soil absorbed CH₄ gas. The F2 treatment was achieved in the highest grape yield, water use efficiency, and fertilizer agronomic use efficiency. The best strategies of irrigation and fertilization were determined using the TOPSIS method. The grapevine growth, yield components, and soil conditions were also considered during optimization. The optimal combination was achieved in the W2F2 (225 m³/hm² of irrigation and 486 kg/hm² of fertilization) during wet years and W3F2 (465 m³/hm² of irrigation and 486 kg/hm² of fertilization) during dry years. A theoretical framework was offered to maximize the growth and yield of grapevines. The effective usage of water and fertilizer was also combined for the decision-making in the vineyards. A balance between yield and environmental advantages can be achieved during both wet and dry seasons

Timely and accurate forecasting of crop yields is critical for food management and trade. However, only limited research has explored the impact of integrating crop phenotypic parameters (CPPs) with unmanned aerial vehicle (UAV) data across different phenological stages on maize yield prediction. The extent to which multi-temporal data enhances the accuracy and reliability of yield projections compared to mono-temporal data has yet to be systematically investigated. To attain the proper balance between accuracy and cost in crop yield estimation, this study proposed a structured framework for identifying the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data. Three classical methods of custom mean decrease accuracy (C-MDA), optimal parameters-based geographical detector (OPGD), and grey relational analysis (GRA) were first used to sort and screen both the CPPs and vegetation indices (VIs) derived from UAV-based information over six growth stages. Ridge regression models based on multi-temporal data combinations and mono-temporal data were established separately, and their performance in yield prediction were compared to identify the optimal phenological stages and the corresponding key factors. Our results showed that C-MDA was much better at factor screening and ranking compared to OPGD and GRA. The green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), and normalized difference red edge index (NDRE) emerged as the top-performing VIs, while the leaf area index (LAI) and above ground biomass (AGB) proved to be the most effective CPPs. When predicting yield using only mono-temporal data, the dough stage delivered the highest predictive accuracy (R²=0.871, RMSE=0.407 t ha^–1), while the tasseling stage was the earliest that achieved yield estimates with acceptable precision (R²=0.810, RMSE=0.493 t ha^–1). In contrast, the integration of UAV data from different crop growth stages markedly enhanced the accuracy of yield estimation. Combinations of data from the tasseling, silking, and dough stages were recommended as the best option (R²=0.942, RMSE=0.291 t ha^–1). These findings indicate that the precise estimation of maize yields in smallholder fields may be attainable, and present both substantial theoretical insights and practical benefits for the advancement of precision agriculture.