A field experiment was conducted for the purpose of examining the effects of different combinations of water and fertilizer applications on the water consumption and yields of alfalfa under subsurface drip irrigation (SDI). The results showed that the jointing and branching stages were the key stages for alfalfa water requirement. The water consumption had varied greatly (from 130 to 170 mm) during the growth period of each alfalfa crop. The water consumption during the whole growth period was approximately 500 mm, and the maximum water consumption intensity was 3.64 mm·d^-1. The overall changes in water consumption and yields during the growth period of the alfalfa displayed trends of first increasing and then decreasing. The sensitivities of the yields to water changes were much higher than that of fertilizer. The water use efficiency (WUE) of the alfalfa was determined to range from 1.68 to 3.20 kg·m^-3, and the rate of growth had ranged from 4.85% to 51.77%. The WUE and rate of growth of the alfalfa indicated the following trend: second crop > third crop > first crop. The results of frequency analysis based on the water‐nitrogen‐yield regression equation are the following: irrigation amounts of 142~165 mm and nitrogen application of 61~80 kg·hm^-2 have a 95% probability of obtaining a hay yield of alfalfa of more than 11903 kg·hm^-2. These results suggest that SDI is a promising irrigation method, which can increase the WUE and hay yield of alfalfa under the condition of SDI within an appropriate amount of water and nitrogen fertilizer, and too low or too high water and nitrogen fertilizer will adversely affect the WUE and hay yield of alfalfa.

Neither single nor discontinuous hydrological observation data can truly reflect periodic changes in soil moisture under natural conditions or interrelationships between various water sources. Therefore, in this study, precipitation pulse characteristics and variations in the soil water content (SWC) and actual evapotranspiration (ET_a) in relation to pulses are explored through a field multi-water continuous observation system set in desert steppe in Inner Mongolia, China. A comparison between precipitation events in the growing seasons of 2016 and 2017 shows that precipitation events that are greater than 10 mm are the main cause of dramatic interannual precipitation variations in this region. A single small precipitation event has a limited impact on SWC and provides no obvious increase in the SWC within the top 10 cm soil layer. The precipitation interval ratio (P/I) is suitable for comparing water stresses of different drying-wetting cycles, and correlations between soil layers are found to be closer in humid years than in dry years. In this study, three modes of interpulse ET_a in the desert steppe are discussed: a stable ET_a mode under a water-sufficient condition, an attenuation ET_a mode, and a stable ET_a mode under extreme drought conditions.

Currently, there is a limited supply of freshwater on a worldwide scale, and finding effective ways to use agricultural freshwater resources has become a widely discussed subject. To investigate the most suitable crops and the efficient use of water resources in dry regions, we performed a comparison study of water consumption between two common crops, maize and sunflowers, in the irrigation area located on the south bank of the Yellow River in Northwest China. Both sunflowers and maize have diverse water sources at various reproductive phases. We discovered that sunflower predominantly consumes 0–50 cm soil water throughout the reproductive cycle, whereas maize mostly utilizes 0–50 cm soil water in the early reproductive phase and 50–90 cm soil water in the late reproductive period. The comparison of yield sustainability between sunflowers and maize demonstrated that sunflowers exhibited more resilience than maize when subjected to the same level of water decrease. Sunflowers’ yield sustainability score stayed consistently over 0.95, while maize had a lowest score of 0.84. We observed via correlation analysis that it was the fraction of subsurface water contribution and the water contribution of the 50~70 cm soil layer that impacted the yield of sunflowers and maize, with coefficients of 0.88 or higher. Ultimately, sunflowers exhibited a lower level of responsiveness to water scarcity compared to maize. Sunflowers have greater drought tolerance compared to maize since they rely less on soil layers replenished by a limited water supply. Based on our findings, sunflowers are well suited to arid conditions and limited spaces that do not have access to irrigation, while maize is better suited to well-watered conditions and expansive cultivation areas.

The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p < 0.01), leaf area index (LAI) (p < 0.001), and SPAD (p < 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches—linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R² = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R² = 0.828; ANN: R² = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices.