【Objective】Ammonia oxidation is a key process in soil nitrogen cycling and is influenced by various biotic and abiotic factors. This study investigated the interactive effects of biochar application and drip irrigation on ammonia-oxidizing microorganisms in the rhizosphere.【Method】The experiment was conducted under field conditions and comprised two irrigation treatments: furrow irrigation (control) and surface drip irrigation, each combined with three biochar application rates: 0, 10 and 20 t/hm2. The abundances of ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) were determined using quantitative real-time PCR targeting the amoA functional gene. Soil enzymatic activities and physicochemical properties were also measured to elucidate the interactive effects of irrigation and biochar on ammonia-oxidizing microorganisms in the rhizosphere.【Result】Compared with furrow irrigation, drip irrigation significantly reduced rhizosphere NH4⁺-N concentration by 61.7%-69.8%, while increasing NO3--N concentration by 70%-300%. Drip irrigation reduced water use by 50% relative to furrow irrigation, while simultaneously enhancing soil ammonia oxidation, increasing AOB-amoA abundance by up to 200%, and decreasing AOA-amoA abundance by 30.2%-60.8%. Under drip irrigation, biochar application increased rhizosphere NH4⁺-N concentration while slightly decreasing NO3--N concentration. In contrast, under furrow irrigation, biochar application had no significant effect on AOA-amoA abundance but significantly increased AOB-amoA abundance. Compared with furrow irrigation without biochar application, drip irrigation combined with biochar application increased AOA-amoA and AOB-amoA abundances by 1.3-1.8-fold and 1.5-2.3-fold, respectively.【Conclusion】Drip irrigation combined with biochar application promoted rhizosphere nitrogen transformation, stimulated proliferation of ammonia-oxidizing microorganisms, particularly AOB, and enhanced soil ammonia oxidation. These findings highlight the potential of integrating drip irrigation with biochar amendment as an effective strategy for regulating rhizosphere nitrogen cycling and improving nitrogen bioavailability.
【Objective】The arid and semi-arid regions in Northwestern China are constrained by water scarcity, and improving irrigation and fertilization management is essential for sustaining agricultural production in these regions. This paper presents the results of an experimental study on the optimal drip fertigation regime for maize production under mulch-free conditions in these regions.【Method】The experiment was conducted in the Hetao Irrigation District, using the local variety ‘Xiongyu 581’ as the model plant. It consisted of six irrigation treatments: 2 250, 3 000, 3 750, 4 500, 5 250, and 6 000 m3/hm2. Water and fertilizer were applied at different growth stages via 12 drip irrigation events and 8 fertigation events, with the total fertilizer application in all treatments being 975 kg/hm2. During the experiment, we measured photosynthetic, growth and yield-related traits.【Result】①With increasing drip irrigation amounts, the net photosynthetic rate, transpiration rate, and intercellular CO2 concentration increased initially and then declined. The maximum net photosynthetic rate was 39.64 μmol/(m2·s), observed when the irrigation amount was 4 500 m3/hm2; the stomatal conductance was not significantly affected by irrigation amount (P＞0.05). ②Plant height, stem diameter, ear traits and grain yield varied quadratically with drip irrigation amount. When irrigation exceeded 3 750 m3/hm2, a further increase in irrigation amount reduced stem diameter and ear diameter, while increasing ear-tip barrenness. Irrigation exceeding 5 250 m3/hm2 significantly reduced plant height, single-ear weight and grain yield.【Conclusion】Under drip fertigation without mulch in the Hetao Irrigation District, the estimated irrigation amount associated with the maximum maize grain yield (18 714.45 kg/hm2) was 4 656.15 m3/hm2. These findings provide a quantitative basis for optimizing drip fertigation and improving maize production in the Hetao Irrigation District.
【Objective】Most arid and semi-arid regions depend heavily on groundwater to support agricultural production, making an understanding of groundwater dynamics and their driving factors essential for sustainable water-resource management. This study analyzed the spatiotemporal variation in groundwater depth and its major influencing factors in the Dengkou Irrigation District, Inner Mongolia, China.【Method】The analysis was based on groundwater and related hydroclimatic and irrigation data collected from 2018 to 2024. The Mann-Kendall trend test and spatial interpolation methods were used to characterize temporal trends and spatial patterns of groundwater depth, respectively, and to examine their relationships with irrigation water diversion, precipitation, and hydrogeological conditions.【Result】①From 2018 to 2024, groundwater depth ranged from 1.7 to 2.8 m and exhibited an annual increasing trend of approximately 0.2 m. Seasonally, surface water diverted from the Yellow River for spring and autumn irrigation accounted for approximately 15% and 40% of the annual irrigation water supply, respectively, and was associated with decreases in groundwater depth of 17% and 14%, respectively. Approximately 70% of annual precipitation occurred during rainy season, contributing to groundwater recharge and a shallower groundwater depth. ②Groundwater depth in all three irrigation sub-districts showed an increasing trend from 2018 to 2024, but the temporal patterns differed because of differences in Yellow River water diversion and hydrogeological conditions. The Main Canal Irrigation District experienced the largest increase in Yellow River water diversion, the deepest groundwater levels, and the fastest increase in groundwater depths. The Minsheng Canal Irrigation District showed a significant synchronous increase in water diversion and groundwater depth, together with relatively shallow groundwater and the steepest increasing trend in groundwater depth. The extensive distribution of water-rich zones contributed to pronounced fluctuations in groundwater depth. The Yuejin Canal Irrigation District was the only sub-district where Yellow River water diversion decreased; it had the shallowest groundwater depth and relatively stable groundwater dynamics. Its moderate groundwater abundance, slower runoff, and declining water diversion were associated with weaker groundwater recovery and smaller fluctuations in groundwater depth. ③Spatially, groundwater depth generally increased from the east to the west. Groundwater depth in the Main Canal Irrigation District ranged from 4.5 to 6.5 m, whereas that in the Minsheng and Yuejin Canal Irrigation Districts ranged from 1 to 3 m. Across all three sub-districts, groundwater was deeper in the west and shallower in the east, reflecting spatial differences in hydrogeological conditions and groundwater recharge and discharge processes.【Conclusion】From 2018 to 2024, groundwater depth in the Dengkou Irrigation District exhibited an overall increasing trend with considerable temporal fluctuations and was greater in the west than in the east. Groundwater dynamics were jointly influenced by precipitation, irrigation practices, Yellow River water diversion, and hydrogeological conditions. These findings provide a basis for improving integrated groundwater and irrigation-water management in the Dengkou Irrigation District and other arid irrigation regions.
【Objective】Choi sum is a vegetable often grown in fragmented smallholder plots in China. Accurately mapping its regional planting distribution is important for agricultural management but remains challenging. This study developed a remote-sensing-based method to address this challenge.【Method】The method was based on multi-temporal Sentinel-2 imagery and a two-layer, long short-term memory (LSTM) model. Sentinel-2 L2A time-series imagery, combined with field survey data, was used as the primary data source. A multi-feature time-series dataset covering the entire growth period of choy sum from March to December 2025 was constructed. The differences in time-series patterns of the normalized difference vegetation index (NDVI) between choy sum and other crops, including spring wheat, maize and rice, were analyzed. Based on these temporal differences, a two-layer LSTM-based model was developed to identify the spatial distribution of choy sum.【Result】Temporally, the time series of the NDVI of choy sum exhibited alternating peak-trough fluctuations, with peak values ranging from 0.7 to 0.9 and trough values ranging from 0.1 to 0.2. Such a pattern differed markedly from the single-peak pattern of single-season crops. Overall, the LSTM model achieved an accuracy of 97.39% for all crops, with a Kappa coefficient of 0.92. Its accuracy for choy sum and other crops was 0.93 and 0.98, respectively.【Conclusion】The proposed two-layer LSTM-based method can effectively map the spatial distribution of choy sum. It provides a useful approach for assessing spatial planting patterns and supporting water-resource management and precision agriculture for irrigation districts.
【Objective】Reservoirs are critical components of water-resource regulation; understanding their long-term dynamics and the underlying mechanisms is essential for sustainable water management and ecological security. This study analyzed the spatiotemporal dynamics of reservoir areas and the underlying natural and anthropogenic drivers in the Yellow River Basin.【Method】Spatiotemporal variations in water surface areas of 66 reservoirs across the basin were extracted from multi-source Landsat and Sentinel imagery acquired from 1990 to 2020 using the Google Earth Engine platform. Pearson correlation analysis and geographically and temporally weighted regression (GTWR) were employed to quantify the effects of natural and anthropogenic factors on reservoir area.【Result】In the past three decades, the total reservoir area in the basin increased at an average rate of 9.43 km2/year. The total reservoir area reached a maximum of 1 205.63 km2 in 2012, representing a 36.61% increase relative to 1990. Change in reservoir area varied across elevation gradients, with reservoirs at elevations above 3 000 m exhibiting the highest average annual increase, at a rate of 4.21 km2/year. The GTWR model revealed that mean annual temperature and precipitation, which varied spatially, were the dominant factors influencing reservoir areas. In high-elevation areas, precipitation was negatively correlated with reservoir areas, likely due to the lagged contribution of snowmelt to reservoir inflow. In low-elevation regions, human activities had a positive influence on reservoir areas.【Conclusion】These findings indicate that reservoirs in densely populated areas are strongly influenced by anthropogenic and socioeconomic factors, with reservoir dynamics primarily reflecting flood-control and water-supply demands. By integrating long-term remote sensing monitoring with spatiotemporal modeling, this study revealed the complex interactions between natural and socioeconomic factors in their influence on reservoir dynamics and provided scientific support for optimizing water-resource allocation and ecological protection in the Yellow River Basin.
