Irrigation and fertilization are vital to increasing crop yield, but their application often exceeds crop requirements. Excessive fertilization under inefficient irrigation depletes the already scarce water resources while contributing to water pollution. To overcome these problems, the introduction of fertigation in combination with modern irrigation technologies has been promoted in the North China Plain (NCP), but farmers have been reluctant to adopt such technologies. To better understand the current situation and farmers' perceptions, we performed a case study in the People's Victory Canal Irrigation District (PVCID) of NCP. A field survey was carried out using a participatory approach, and field monitoring was conducted on a representative farm. We found that farmers are generally satisfied with their irrigation and fertilization practices, although they result in low application efficiency and distribution uniformity. In principle, the lack of knowledge about how to implement fertigation technology, the small‐scale farming conditions, and the high cost of developing advanced fertigation systems are the main obstacles for fertigation adoption. We further conclude that (i) to improve the on‐farm performance in terms of efficiency and uniformity of irrigation and fertilization, evidence‐based guidelines are required to help farmers to implement; (ii) for effective adoption of new technologies, consideration of farmers' situation and perspectives is critical; and (iii) surface fertigation might be a good start for fertigation promotion.

Water stress (drought and waterlogging) is one highly important factor affecting food security in China. Investigating the effects of soil moisture stress on the morphological and physiological characteristics of maize seedlings is crucial for ensuring food production. The use of spectral monitoring to observe crop phenotypic traits and assess crop health has become a focal point in field crop research. However, studies exploring the contribution of crop phenotypic and physiological data to the Normalized Difference Vegetation Index (NDVI) are still limited. In this study, a 35-day pot experiment was conducted with seven soil moisture gradients: 50%, 60%, 70%, 80% (control group, CK), 90%, 100%, and 110% treatment. In order to investigate the effects of soil moisture stress on seedling phenotypes, antioxidant enzyme activities, and NDVI, an ASD FieldSpec 4 Hi-Res NG portable spectrometer was used to collect spectral data from maize (Zea mays L. B73) leaves. The contributions of maize phenotypic and physiological traits to NDVI were also examined. The results indicated that (1) the 50% and 110% treatments significantly affected maize seedling phenotypes compared to the CK group; (2) the activities of superoxide dismutase (SOD) and peroxidase (POD) in the leaves increased under water stress, while the activities of glutathione peroxidase (GSH-PX) and ascorbate peroxidase (APX) decreased; (3) soil moisture stress (drought and waterlogging) reduced photosynthetic pigments, chlorophyll content (SPAD), and NDVI, with inhibitory effects intensifying as the stress level increased; (4) Redundancy analysis showed that antioxidant enzymes explained 69.87% of the variation in seedling height, leaf area, and NDVI. Soil moisture stress, chlorophyll, and SPAD explained 58.14% of the variation in these parameters. The results demonstrated that maize seedlings were highly sensitive to soil moisture changes, and the SPAD value contributed significantly to NDVI (p < 0.01). This study provides valuable insights for future research in precision agriculture management

Mulch drip irrigation (MDI) technology can effectively solve the problem of insufficient temperature accumulation during the pre-fertility period and facilitate the efficient supplementation of water and fertilizer during the fertility period in spring corn planting. Moreover, this local MDI technology also has impacts on the farmland environment. To investigate the effect of drip irrigation technology on the water and salt environment of farmland, a field study on corn cultivation was carried out at West Liaohe Plain. In addition, the water and salt dynamics of the farmland were simulated using HYDRUS-2D for mulch drip irrigation (MDI), shallowly buried drip irrigation (SBDI), and sprinkler irrigation (SI), with variable rainfall and initial salt content. The results showed that the distribution of and variation in water and salt in the soil were similar under MDI and SBDI. The change near the drip tape was mainly affected by irrigation, while the water and salt in the soil between drip tapes were correlated with irrigation and rainfall. The amount of salt in the topsoil (5 cm) increased with a decrease in rainfall. With an initial EC = 480 μs/cm (soil salt content 0.1%), the salinity of the topsoil under MDI was significantly higher than that under SBDI and SI within two years. The topsoil salinity was similar for all three irrigation technologies with increasing operating life, reaching a relatively stable state, and much lower than the salinity determination threshold of 480 μs/cm. Given the current conditions of rainfall, soil, buried depth, and mineralization in the West Liaohe Plain, the risk of secondary salinization is minimal if irrigation management is reasonable. This study provides data to support the application of drip irrigation technology in the Western Liaohe Plain.

Irrigation and fertilisation are often over-applied, which exceeds crop requirements. Surface fertigation, a technique of applying pre-dissolved fertilisers together with irrigation water, seems to be a viable way to improve the on-farm performance in the North China Plain (NCP). Thus, we conducted a field experiment based on farmers’ practices from 2017 to 2019. Moreover, we calibrated and validated SWAP-WOFOST-N, a seasonal integrated agro-hydrology and crop growth model, to assess the effects of different practices on yield, water and nitrogen use efficiency (WUE and NUE) and resource loss. Lastly, we developed various scenarios using the model to determine improved strategies. The results showed that the SWAP-WOFOST and extended Soil-N model offered satisfactory accuracy when compared with field measured data for the tested domain of the hydrological and nitrogen cycle; farmers’ current irrigation and fertilisation practices resulted in low WUE and NUE, but the practice of split top-dressing nitrogen did not show significant improvement in the surface irrigation system; WUE, NUE and nitrogen loss were closely related to irrigation practices. We further concluded that an optimised irrigation practice combined with an optimal fertigation scenario is the feasible strategy to achieve sustainable crop yield, high WUE and NUE and reduced nitrogen loss.

Soil structure, a critical indicator of soil quality, significantly influences agricultural productivity by impacting on the soil’s capacity to retain and deliver water, nutrients, and salts. Quantitative study of soil structure has always been a challenge because it involves complex spatial-temporal variability. This study employs multifractal analysis to assess the temporal variation in soil pore distribution, a pivotal factor in soil structure. Field observation data were collected in a sandy loam area of the People’s Victory Canal Irrigation scheme in Henan Province, China. A 200 m × 200 m test plot with five sampling points was used to collect soil samples at three depth layers (10–30 cm, 30–50 cm, and 50–70 cm) for soil water retention curve and particle size composition analysis, with a total of seven sampling events throughout the growing season. The results revealed that while soil particle-size distribution (Particle-SD) showed minor temporal changes, soil pore-size distribution (Pore-SD) experienced significant temporal fluctuations over a cropping season, both following a generalized power law, indicative of multifractal traits. Multifractal parameters of Pore-SD were significantly correlated with soil bulk density, with the strongest correlation in the topsoil layer (10–30 cm). The dynamic changes in soil pore structure suggest potential variations during saturation–unsaturation cycles, which could be crucial for soil water movement simulations using the Richards equation. The study concludes that incorporating time-varying parameters in simulating soil water transport can enhance the accuracy of predictions.

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.