Saline-alkali soils lock phosphorus (P) availability and crop productivity, highlighting the need for irrigation strategies that improve nutrient acquisition. However, the microbial and enzymatic responses associated with CO₂ micro-nano bubble water (CO₂-MNBW) irrigation remain poorly understood. We evaluated cotton responses to CO₂-MNBW irrigation over three growing seasons (2021–2023) and examined associated soil nutrient and microbial responses in 2023. Under identical irrigation and fertilizer inputs, CO₂-MNBW increased aboveground dry matter by 10.6%–33.1% and seed-cotton yield by 4.1%–6.7% relative to conventional irrigation. These responses were accompanied by reduced soil electrical conductivity and increased rhizosphere nutrient availability. CO₂-MNBW also altered microbial community composition and increased nutrient-acquiring enzyme activities. Core microbial ASVs were associated with soil N and P fractions and enzyme activities, linking community variation with nutrient transformations. Higher alkaline phosphatase activity and lower NaOH-extractable organic P were consistent with enhanced organic P mineralization, while increased alkaline protease and glutaminase activities suggested greater potential for organic N transformation. Exploratory partial least squares path modeling using matched soil and crop observations from 2023 explained 76% of the variation in the rhizosphere cotton productivity construct, with a positive path coefficient from available P (β = 0.92). These findings highlight coordinated rhizosphere chemical and biological responses associated with improved cotton nutrient acquisition and productivity, supporting CO₂-MNBW as a promising irrigation strategy for saline–alkali cropland.
Ridge–furrow plastic-mulching systems are characterized by coexisting ridge and furrow microenvironments with contrasting soil hydrothermal conditions. However, whether these spatially adjacent positions exhibit different greenhouse gas (GHG) response relationships with environmental drivers remains insufficiently understood. Here, we investigated whether ridge and furrow positions within the same ridge–furrow system represent distinct observation units with different hydrothermal characteristics and gas-specific responses to soil moisture and temperature. We conducted repeated field measurements at 11 tobacco sites and quantified CO₂, CH₄, and N₂O fluxes from fixed ridge and furrow positions, together with soil temperature and volumetric water content at 5 cm depth. Linear mixed-effects models and paired-position analyses were used to evaluate positional contrasts and environmental response relationships. Ridge positions showed higher soil temperature than furrow positions, whereas volumetric water content did not differ significantly between positions. Differences in mean flux magnitude were most evident for CO₂, with higher ridge fluxes than furrow fluxes (model-estimated ridge–furrow difference: 71.44 mg CO₂ m⁻² h⁻¹), while CH₄ and N₂O positional contrasts were weaker and less consistent. In contrast, ridge and furrow positions exhibited contrasting moisture-response relationships for all three gases. CO₂ flux showed a significant positive response to soil moisture on ridges but no significant response in furrows. For CH₄, increasing soil moisture significantly weakened net CH₄ uptake in furrows, whereas no significant moisture response was detected on ridges. For N₂O, furrows showed a significant negative moisture-response relationship, whereas ridges showed a positive but non-significant response. Positional differences in temperature-response slopes were not statistically resolved across gases, whereas moisture-response contrasts were statistically supported. These results indicate that ridge–furrow plastic-mulching systems contain functionally distinct microenvironments in which GHG fluxes exhibit gas-specific moisture-response relationships. Ridge and furrow positions should therefore not be automatically treated as interchangeable observation units when monitoring, interpreting, and scaling GHG fluxes in ridge–furrow systems.
Irrigation water requirement (IWR) is a key parameter in agro-hydrological research and water resource optimization. Its forecast accuracy determines irrigation scheduling reliability, but it is constrained by meteorological errors, which propagate through the modeling chain. An optimal correction scheme was developed for short-term (1–3 days), medium-term (4–15 days), and long-term (16–40 days) forecasts of maximum temperature (T′max), minimum temperature (T′min), and precipitation (P′) based on 1–40-day public weather forecasts from 2019 to 2023. A regionally calibrated Hargreaves model was evaluated for estimating reference evapotranspiration (ETo), and an IWR forecast method driven by meteorological error correction was established. The results demonstrate that optimal correction methods varied by lead time: the decaying-average method based on autocorrelation function (ACF-DAM) was optimal for T′max at short lead times; genetic algorithm (GA)-optimized long short-term memory (LSTM) for T′max at medium-to-long lead times and T′min at short-to-medium lead times; stepwise regression (SR) for T′min at long lead times; and Bayesian optimization (BO)-optimized random forest (RF) for P′ across all lead times. ETo forecasts achieved accuracy exceeding 80% at 86.36% of stations across different lead times. ETo predictions ranged from 2.06 to 3.48 mm d−1, with root mean square error (RMSE) values of 1.22 mm d−1 (short-term), 0.87–0.99 mm d−1 (medium-term), and 1.25–1.32 mm d−1 (long-term). Spatially, the Yellow River Delta and northern plains performed best, while the central region showed larger errors. Across different lead times, IWR forecasts remained stable, with IWR ranging from 145.13 to 293.62 mm for winter wheat and from 31.34 to 239.99 mm for summer maize; station-mean values were 207.11–249.39 mm and 78.21–196.69 mm, respectively. Spatially, IWR was higher in the north and lower in the southwest. Correcting weather forecast errors can significantly reduce the prediction errors of ETo, ETc, and IWR, providing a scientific basis for hydrological regulation, water allocation, and agricultural water demand forecasting in irrigation districts.
Detecting ecosystem responses to rehydration in extensive grazing landscapes is challenging due to their large spatial scale and seasonal variability. We evaluated whether high-resolution Sentinel-2 multispectral imagery, combined with factorial statistical analysis, can support adaptive monitoring of rehydration interventions in arid and semi-arid pastoral systems of northern Australia. Using a replicated before–after, control–impact (BACI) design, we monitored three 75-ha treatment plots modified with water-spreading banks designed to slow overland flow and enhance infiltration, alongside three matched control plots located ~1.6 km away. From monthly time series, we derived total standing dry matter (TSDM), green and non-green biomass, and surface moisture/soil exposure indices—including the Normalised Difference Moisture Index (NDMI), Normalised Difference Water Index (NDWI), and Normalised Bare Soil Index (NBSI). Analyses were conducted across three monitoring phases: pre-rehydration (August–October 2022), rehydration implementation during the wet season (November 2022–April 2023), and post-rehydration monitoring (May–July 2023). A replicated Before–After–Control–Impact (BACI) framework integrating Sentinel-2-derived biomass estimates, terrain analysis and factorial ANOVA enabled treatment effects to be distinguished from the seasonal influence of rainfall. Sentinel-2-derived biomass responses differed significantly between rehydrated treatment and control sites across the monitoring periods, whereas NDMI, NDWI and NBSI remained largely governed by seasonal variability, demonstrating that biomass and moisture indices provide complementary indicators of ecosystem response. Terrain and predicted soil properties further explained spatial differences among treatment sites, highlighting the importance of local hydrological setting in determining restoration outcomes. Collectively, these findings provide a transferable remote-sensing framework for objectively evaluating landscape/rangeland rehydration interventions in dryland environments where climatic variability often obscures management effects.
Increasing hydrological and agricultural droughts in the Po River Basin in northern Italy, one of the most intensively irrigated agricultural regions in Europe, have raised growing concerns about water resources management and highlighted the need for accurate estimates of irrigation requirements (IR). This study presents AGRHYD, a two-compartment (topsoil–subsoil) soil-water balance model built on the FAO 56 dual crop coefficient approach to estimate crop actual evapotranspiration (ETc act), crop evapotranspiration (ETc) and IR at point and regional scales. The explicit topsoil compartment, responsible for soil evaporation, facilitates future reconciliation and integration with satellite-based soil moisture retrievals. Since IR is a theoretical construct and cannot be directly measured, the model’s daily ETc act estimates for wheat and vine, two of the typical rainfed crops in the region, were evaluated against two independent satellite-based evapotranspiration products, WaPOR and GLEAM yielding Pearson’s correlation coefficient (r) up to 0.88 and an RMSE as low as 0.91 mm day⁻¹ for wheat, and r up to 0.91 and an RMSE as low as 0.58 mm day⁻¹ for vine, with satisfactory reproduction of ETc act temporal dynamics at daily, seasonal, and annual scales. IR was subsequently estimated for maize and forages, two of the main irrigated crops in the region, over the period 1981–2022, revealing pronounced spatial heterogeneity with the highest demands concentrated in eastern Piedmont and Emilia-Romagna. Long-term trend analysis identified statistically significant increasing trends in annual IR across the basin with mean rates of 0.88 mm year⁻¹ for maize and 1.17 mm year⁻¹ for forages, driven primarily by trends in June and July. Concurrent declining precipitation and increasing crop reference evapotranspiration (ET₀) across the basin during these months broadly provide a physically consistent hydroclimatic explanation. A sensitivity analysis confirmed that the key trend findings are robust to uncertainty in crop calendar timing. The resulting IR dataset and long-term trend diagnostics constitute a valuable resource for irrigation planning and water allocation in the Po River Basin, while the model's reliable performance and modest data requirements make it transferable to other irrigated regions.
