Soil quality is an indicator of the ability to ensure ecological security and sustainable soil usage. The effects of long-term straw incorporation and different irrigation regimes on the yield and soil quality of paddy fields in cold regions remain unclear. This study established four treatments: controlled irrigation + continuous straw incorporation for 3 years (C3), controlled irrigation + continuous straw incorporation for 7 years (C7), flooded irrigation + continuous straw incorporation for 3 years (F3), and flooded irrigation + continuous straw incorporation for 7 years (F7). Analysis was conducted on the impact of various irrigation regimes and straw incorporation years on the physicochemical characteristics and quality of the soil. The soil quality index (SQI) for rice fields was computed using separate datasets for each treatment. The soil nitrate nitrogen, available phosphorus, soil organic carbon, and soil organic matter contents of the C7 were 93.51%, 5.80%, 8.90%, and 8.26% higher compared to C3, respectively. In addition, the yield of the C7 treatment was 5.18%, 4.89%, and 10.32% higher than those of F3, C3, and F7, respectively. The validity of the minimum data set (MDS) was verified by correlation, E_f and E_R, which indicated that the MDS of all treatments were able to provide a valid evaluation of soil quality. The MDS based SQI of C7 was 11.05%, 11.97%, and 27.71% higher than that of F3, C3, and F7, respectively. Overall, long-term straw incorporation combined with controlled irrigation increases yield and soil quality in paddy fields in cold regions. This study provides a thorough assessment of soil quality concerning irrigation regimes and straw incorporation years to preserve food security and the sustainability of agricultural output. Additionally, it offers a basis for soil quality diagnosis of paddy fields in the Northeast China.

Enhancing irrigation and nitrogen fertilizer application has become a vital strategy for ensuring food security in the face of population growth and resource scarcity. A 2-year experiment was conducted to determine to investigate the effects of different irrigation lower limits and nitrogen fertilizer application amounts on millet growth, yield, water use efficiency (WUE), N utilization, and inorganic nitrogen accumulation in the soil in 2021 and 2022. The experiment was designed with four irrigation lower limits, corresponding to 50%, 60%, 70%, and 80% of the field capacity (FC), referred to as I₅₀, I₆₀, I₇₀, and I₈₀. Four nitrogen fertilizer application were also included: 0, 50, 100, and 150 kg·hm⁻² (designated as F₀₀, F₅₀, F₁₀₀, and F₁₅₀), resulting in a total of 16 treatments. Binary quadratic regression equations were established to optimize the irrigation and nitrogen application. The results demonstrated that the plant height, stem diameter, leaf area index, aboveground biomass, yield, spike diameter, spike length, spike weight, WUE, and nitrogen agronomic efficiency for millet initially increased before subsequently decreasing as the irrigation lower limit and nitrogen fertilizer application increased. Their maximum values were observed in the I₇₀F₁₀₀. However, the nitrogen partial factor productivity (PFPN) exhibited a gradual decline with increasing nitrogen application, reaching its peak at F₅₀. Additionally, PFPN displayed a pattern of initial increase followed by a decrease with rising irrigation lower limits. The accumulation of NO₃⁻-N and NH₄⁺-N in the 0~60 cm soil layer increased with the increase of nitrogen fertilizer application in both years, while they tended to decrease as the irrigation lower limit increased. An optimal irrigation lower limit of 64% FC to 74% FC and nitrogen fertilizer application of 80 to 100 kg ha⁻¹ was recommended for millet based on the regression equation. The findings of this study offer a theoretical foundation and technical guidance for developing a drip irrigation and fertilizer application for millet cultivation in Northeast China.

Optimizing the coupling effect between irrigation and N fertilizer to balance mung bean (Vigna radiata L.) production and the effective utilization of water and fertilizer resources is an important challenge for sustainable agricultural production. In this study, a field drip irrigation experiment was conducted on Mollisols in Northeast China, and twelve treatments were performed: four levels of soil water content (W1, 80~100% of field capacity; W2, 70~90% of field capacity; W3, 60~80% of field capacity; W4, rainfed condition) and three N application treatments (40 (N1), 80 (N2), and 120 (N3) kg/ha). We analyzed the coupling effects of water and N levels on mung bean growth, yield and yield components, water consumption, water use efficiency (WUE) and N partial factor productivity (PFP) in 2021 and 2022 and screened the optimal water and N regulation by the TOPSIS method. The results showed that the amount of N application dominated the regulation of water and N. In the first year, plant height, stem diameter, number of seeds per pod, 100-seeds weight, yield, aboveground dry matter accumulation, WUE, and PFP in mung bean decreased with increasing N applications at the same irrigation treatment. Furthermore, except for WUE, all results of the W3N1 treatment reached the highest levels, at 79.14 cm, 13 mm, 12.4, 6.2 g, 1430.45 kg/ha, 79.27 g (the drumming stage), and 35.76 kg/kg, respectively. The second year, plant height, stem diameter, yield and WUE had an increasing trend with increasing N applications at the W1. Based on the TOPSIS method, the W3N1 treatment could obtain the optimal comprehensive benefits of yield, WUE and PFP. This study can provide a most suitable water and N regulation model for guiding mung bean cultivation in the Mollisols region of Northeast China.

Nitrogen (N) application rates and irrigation regimes are key factors determining rice yield and N use efficiency. To evaluate the effects of different irrigation regimes and N application rates on rice yield and N uptake, a four-year field experiment was conducted from 2021 to 2024 at the Qing’an National Irrigation Experimental Station in Heilongjiang Province. The experiment included two irrigation regimes (C: Controlled irrigation and F: Flooded irrigation) combined with four N application rates (N0: 0 kg N·ha⁻¹, N1: 82.5 kg N·ha⁻¹, N2: 110 kg N·ha⁻¹, and N3: 137.5 kg N·ha⁻¹). The results showed that, considering the same N application rate, C promoted dry matter accumulation by 3% to 9% and total N accumulation by 4.1% to 25.5% in the aboveground parts of rice compared to F. Under the same irrigation regime, total N accumulation in the aboveground parts of rice increased with N application rate and then plateaued. Regarding the distribution of N among organs, the proportion of panicle N relative to total N in the aboveground parts of rice followed an initial increase and subsequent decline as N input increased, resulting in the nitrogen harvest index (NHI) reaching its maximum under the moderate N treatment (N2). Overall, controlled irrigation significantly improved the NHI and AE, whereas the moderate N treatment (N2) further increased the NHI and promoted greater N allocation to panicles. Therefore, combining C with a moderate N application rate can enhance N use efficiency and markedly improve the internal N partitioning pattern.

Climate change poses significant challenges to global agricultural systems, exerting profound impacts on crop yields and water resource management, which are particularly pronounced in cold-region rice-growing systems. This study employed the AquaCrop model to assess the spatiotemporal distribution characteristics of rice yield, crop water requirement (ET_c), irrigation water requirement (Ir), and water use efficiency (WUE) in Heilongjiang Province under the RCP4.5 and RCP8.5 scenarios from 2021 to 2080. The results indicate that the average rice yield in Heilongjiang Province will increase by approximately 2% to 5%, with more significant gains observed in the western and southern regions. However, climate warming will cause ET_c to increase by 3–7%, leading to a rise in Ir of about 5–12%, which is particularly pronounced under the RCP8.5 scenario. Compared to RCP4.5, the yield under RCP8.5 will increase by 1–2%, but the increase in Ir will be more significant. Despite these changes, WUE remains within a relatively constrained range (approximately 1.55~1.75 kg·m⁻³), as yield increases are largely offset by corresponding rises in ET_c. Overall, the findings reveal a pronounced yield–water trade-off in cold-region rice systems under future climate scenarios, indicating that yield gains may be accompanied by heightened Ir.

PM_2.5 is a major air pollutant characterized by complex sources and strong spatiotemporal heterogeneity. However, accurately quantifying the relative contributions of different factors remains difficult due to the lack of long-term datasets and the strong correlations between meteorological factors and emissions. To address this problem, the study utilizes the China long-term particulate matter (CLPM) dataset developed in previous research to investigate the dominant drivers and regional disparities of PM_2.5 concentration variations from 1980 to 2022. The analysis employs Gaussian Convolution (GC) to model pollutant diffusion, Partial Least Squares (PLS) regression to address multicollinearity, and the Lindeman-Merenda-Gold (LMG) method to quantify the relative contributions of each driver. The results reveal that as the convolution scale increased from 0.25° to 10°, dominant PM_2.5 sources shifted from local anthropogenic emissions to regional biomass burning and large-scale dust transport, highlighting the scale-dependent transition of pollution drivers. Furthermore, PM_2.5 concentrations are predominantly explained by emissions, which account for over 60% of the total variance and exceed 80% in eastern China, while meteorological factors are associated with 12–26%. Among these, total precipitation and downward surface solar radiation have the strongest influences on pollutants. It is important to note that these results reflect the statistical explanatory power of emissions and meteorological variables within the regression model. Overall, this research provides a method for separating the statistical influences of emissions and meteorological factors, offering methods for multi-scale explanatory power of PM_2.5 and other atmospheric pollutants.