Di Xu’s research while affiliated with China Institute of Water Resources and Hydropower Research and other places

Nonylphenol (NP) is one of the typical endocrine‐disrupting chemicals (EDCs), which can be harmful at very low concentrations. Municipal sewage and reclaimed water are its two main sources. EDCs can enter into the soil with reclaimed water irrigation and accumulate in plants, causing environmental and human health risks. The occurrence and migration of NP in soil‐crop systems were studied by pot experiments of lettuce and eggplants simulating long‐term irrigation with reclaimed water. The health risks were also evaluated. The experiments set treatments with different initial soil NP concentrations (0.28–6.42 mg/kg) and soil moisture (60%–90% field water capacity [FC]). After harvest, the NP in edible parts of lettuce and eggplants were 35.80–54.30 and 15.45–23.38 μg/kg, respectively. The residual amounts of NP in the soil‐lettuce system and soil‐eggplant system were limited with the residual rates of 0.9%–24.4% and 0.3%–14.5%, respectively. The results showed that the lettuce and eggplant tissues had the highest bioconcentration factors ( BCF s) in 75% FC and the translocation factors ( TF s) tended to decrease with the initial soil NP contents increased. The noncancer hazard quotients ( HQ ) for adults and children exposed to NP had the order of 10 ⁻⁵ , which showed little health risk for reclaimed water irrigation.

Remote Sensing-based two-source model is widely used to estimate crop evapotranspiration (ET), involving one key step of partitioning land surface temperature (LST) into canopy and soil temperatures (Tc and Ts). Leaf area index (LAI) plays a significant part in available energy allocation during this process. However, the asymptotic saturation problem makes the mismatch between vegetation index and LAI. In this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIred-edge) considering the hysteresis between them. Considering the distinct characteristics, modelling LAI by one-degree linear equations for sunflower (C3), linear and exponential functions for maize (C4) were presented in the distinguished grow-up and senescence periods. The two-source energy balance (TSEB) and hybrid dual-source scheme and trapezoid framework-based evapotranspiration (HTEM) models were selected to estimate Tc, Ts, ET, and its components contrastively. The established LAI models and other modified parameters were then integrated into the two models to improve the estimation of Tc, Ts, and ET (named the R-TSEB and R-HTEM models, respectively). Results demonstrated that the partitioned Tc & Ts became closer to the measurements after utilizing the presented LAI models. For daily ET, the R-TSEB and R-HTEM models alleviated the overestimation and underestimation existing in the original two models, respectively. At monthly and seasonal scales compared to the water balance results (ETwb), the ET of R-TSEB model had significant promotion, including the determination coefficient (R2), mean relative error (RE), root mean square error (RMSE), and model agreement index (d) with values of 0.87, 6.54%, 11.65 mm, and 0.95, from the according values of 0.80, 12.85%, 17.60 mm, and 0.90 for the TSEB model, respectively. The estimated ET by the R-HTEM model was more consistent with ETwb than the HTEM model. These results indicate that the established LAI models can enhance ET estimation and further advance water cycle understanding.

Soil moisture and salinization are key environmental factors affecting crop growth and yield in arid farming areas with intense irrigation. Quantifying their effects on crops is beneficial to understanding the principle of soil water-salt interactions. Due to the strong coupling relationship between them, the independent effect on field crops is hard to distinguish clearly. In this study, taking Yongji Sub-irrigation District (YJSID) in Inner Mongolia of China as case study, the sorting bins method and GeoDetector were employed to try to disentangle relative effects of soil moisture and salinization on crop development and yield during growing seasons in 2021–2022. The regional soil water content (SWC) and soil salt content (SSC) were firstly mapped and validated through the inverse density weighted method and random forest model. Separated effects of SWC and SSC on solar-induced chlorophyll fluorescence (SIF), gross primary productivity (GPP), and crop yield were then calculated and presented. Results showed that low SWC inhibited SIF and the ratio of GPP to SIF (GPP/SIF) in SSC bins, as their values declined among 52.989% and 71.801% of YJSID. In SWC bins, the increase of SIF and GPP/SIF covered 91.356% and 50.087% of the area, indicating that low SSC could bring out higher SIF and GPP/SIF for field crops. Relative importance assessment suggested that SSC posed greater impacts than SWC on SIF and GPP/SIF in YJSID with the area of 77.188% and 59.628%, respectively. To the SWC effects, it performed greater for the C3 crops (sunflower, wheat, interplant, others) than the C4 plant (maize) in SIF, while reversely in GPP/SIF. The q value calculated from GeoDetector also indicated that SSC had greater effects than SWC on crop yield. Increased SSC tended to reduce grain yield with linear relationships, and the severity was bigger for maize than sunflower. These results would contribute to further understanding of the key processes involved in soil water-salt interactions and dealing with them better in agricultural practice.

Increased frequency and severity of chilling damage events pose potential risks to crop performance and productivity due to climate change. Accurate and real‐time access to chilling damage is important for crop growth and yield stability based on field's actual environment. To precisely identify regional chilling events and evaluate the impacts on crops, this study presents a model to estimate field air temperature in view of field crop situations. Land surface temperature, enhanced vegetation index, solar‐induced chlorophyll fluorescence and solar declination were involved in the model. With field simultaneous continuous monitoring and multisource fused remote sensing data, the model was calibrated and validated in Jiefangzha Irrigation Area (JIA) and Changchun City (CC) in North China, accompanied by the determination coefficient ≥0.756, root mean square error ≤0.782°C, relative error ≤0.041 and consistency index ≥0.902. Meanwhile, sensitivities of the model factors were determined through path analysis, where the factors performed according to the order solar‐induced chlorophyll fluorescence >solar declination >land surface temperature > enhanced vegetation index. Using the validated model, chilling damage to maize was further detected in JIA and CC from 2010 to 2020. Results showed that the severity of chilling damage was greater in CC than in JIA, along with the sterile‐type occurring three events in JIA and seven in CC, while the delayed‐type only twice in JIA in 2012 and 2016, but five times in CC in 2013, 2014, 2016, 2017 and 2019, respectively, being consistent with local statistics. In response to chilling damage, enhanced vegetation index and solar‐induced chlorophyll fluorescence demonstrated the negative chilling effects on greenness and light use efficiency for fluorescence. Serious yield losses were caused, with yield‐reducing by 5.00% (Dehui, 2013), 19.00% (Jiutai, 2014), 21.65% (Suburban district, 2016), 8.83% (Shuangyang, 2017) and 2.19% (Jiutai, 2019) in CC. The linear relationship between yield and growing degree days was a bit weakened by chilling damage, with the determination coefficient varying from 0.614 to 0.531. The increasing rate of yield with growing degree days decreased from 20.365 kg/(°C·d) in non‐chilling damage years to 9.670 kg/(°C·d) in chilling damage years. These findings indicate that the presented model is especially adaptive for agricultural field environments, enabling rapid precision detection of chilling damage on crops at regional scales. It will provide references for gauging the impact of chilling damage on crops, finding efficient solutions to the stress and ensuring sustainable development of agriculture.

Early forecasting of crop yield from field to region is important for stabilizing markets and safeguarding food security. Producing a precise forecasting result with fewer inputs is an ongoing goal for the large-area yield evaluation. We present one approach of yield prediction for maize that was explored by incorporating remote-sensing-derived land surface temperature (LST) and field in-season data into a series of logistic models with only a few parameters. Continuous observation data of maize were utilized to calibrate and validate the corresponding logistic models for regional biomass estimating based on field temperatures (including crop canopy temperature (Tc)) and relative dry/fresh biomass accumulation. The LST maps from MOD11A1 products, which are considered to be matched as Tc in large irrigation districts, were assimilated into the validated models to estimate the biomass accumulation. It was found that the temporal-scale difference between the instantaneous LST and the daily average value of field-measured Tc was eliminated by data normalization method, indicating that the normalized LST could be input directly into the model as an approximation of the normalized Tc. Making one observed biomass in-season as the driving force, the maximum of dry/fresh biomass accumulation (DBA/FBA) at harvest could be estimated. Then, grain yield forecasting could be achieved according to the local harvest index of maize. Silage and grain yields were evaluated reasonably well compared with field observations based on the regional map of LST values obtained in 2017 in Changchun, Jilin Province, China. Here, satisfactory grain and silage yield forecasting was provided by assimilating once measured value of DBA/FBA at the middle growth period (early August) into the model in advance of harvest. Meanwhile, good results were obtained in the application of this approach using field data in 2016 to predict grain yield ahead of harvest in the Jiefangzha sub-irrigation district, Inner Mongolia, China. This study demonstrated that maize yield can be forecasted accurately prior to harvest by assimilating remote-sensing-derived LST and field data into the logistic models at a regional scale considering the spatio-temporal scale extension of ground information and crop dynamic growth in real time.

Early forecasting of crop yield from field to region is important for stabilizing markets and safeguarding food security. Producing a precise forecasting result with fewer inputs is an ongoing goal for the large-area yield evaluation. We present one approach of yield prediction for maize that was explored by incorporating remote-sensing-derived land surface temperature (LST) and field in-season data into a series of logistic models with only a few parameters. Continuous observation data of maize were utilized to calibrate and validate the corresponding logistic models for regional biomass estimating based on field temperatures (including crop canopy temperature (Tc)) and relative dry/fresh biomass accumulation. The LST maps from MOD11A1 products, which are considered to be matched as Tc in large irrigation districts, were assimilated into the validated models to estimate the biomass accumulation. It was found that the temporal-scale difference between the instantaneous LST and the daily average value of field-measured Tc was eliminated by data normalization method, indicating that the normalized LST could be input directly into the model as an approximation of the normalized Tc. Making one observed biomass in-season as the driving force, the maximum of dry/fresh biomass accumulation (DBA/FBA) at harvest could be estimated. Then, grain yield forecasting could be achieved according to the local harvest index of maize. Silage and grain yields were evaluated reasonably well compared with field observations based on the regional map of LST values obtained in 2017 in Changchun, Jilin Province, China. Here, satisfactory grain and silage yield forecasting was provided by assimilating once measured value of DBA/FBA at the middle growth period (early August) into the model in advance of harvest. Meanwhile, good results were obtained in the application of this approach using field data in 2016 to predict grain yield ahead of harvest in the Jiefangzha sub-irrigation district, Inner Mongolia, China. This study demonstrated that maize yield can be forecasted accurately prior to harvest by assimilating remote-sensing-derived LST and field data into the logistic models at a regional scale considering the spatio-temporal scale extension of ground information and crop dynamic growth in real time.

Context Leaves at different heights in a canopy have differential roles on photosynthetic characteristics and yield but have not been compared systematically under plastic film mulching with drip irrigation. Aims To determine the temporal and spatial variation of morpho-physiological characteristics in relation to the benefit of mulched drip irrigation in spring maize growth. Methods Field experiments were conducted in northeastern China during 2017 and 2018 that included mulched drip irrigation (MD), non-mulched drip irrigation (ND), and traditional non-mulched rain-fed (CK) treatments. Key results MD significantly increased lower leaf area by 13.1–62.3%, upper leaf N content (Nmass) by 6.3–13.0%, and upper leaf photosynthetic capacity (Amax) and maximum carboxylation rate (Vcmax) by 13.4–42.3% and by 4.7–11.6%, respectively. There were close correlations between leaf physiological parameters (Nmass, carbon isotope discrimination (Δ), Amax, and Vcmax), and also between morphological parameters (leaf area (LA) with leaf mass per area (LMA), and LMA with leaf dry matter content (LDMC). As for time scale, leaf morphological parameters (LA, LMA, and LDMC) in the reproductive stage (R-stage) were higher than those in the vegetative stage (V-stage), while physiological parameters (Nmass, Amax, and Vcmax) were higher in the V-stage. This study indicated that MD treatment increased the photosynthetic area of lower leaves and the photosynthetic capacity of upper and middle leaves compared with non-mulched rainfed CK. In addition, an increase of net radiation absorbed by the canopy in MD was likely to correspond to a higher net photosynthetic rate, which was beneficial to yield accumulation in the treatment. Conclusions This study provided relevant information for the simulation of water and carbon flux under mulched drip irrigation. Implications The research explained that the morpho-physiological characteristics of leaves at different canopy heights played different role on affecting maize yields under plastic film mulched drip irrigation.

In North China Plain, straw mulching is increasingly applied to the succeeding crops with winter wheat-summer maize double cropping system. However, research results on the effect of maize straw mulching on the yield of winter wheat are inconsistent, and the underlying mechanism of the effect remains unclear. Such information is helpful to guide the field water management for higher yield. In this study, soil moisture, plant growth, photosynthesis and yield of winter wheat field treated with no-till and straw mulching (SM) and non- mulching (N) and three irrigation amounts (high, middle and low irrigation, HI, MI and LI) in four growing seasons in 2013–2016. The results showed that straw mulching improved soil moisture by reducing the days of soil moisture less than 60% field capacity for 2–10 days. Straw mulching increased net photosynthetic rate, stomatal conductance, maximum carboxylation rate (Vcmax), and the maximum rate of photosynthetic electron transport (Jmax) of flag leaves, especially at the post-anthesis measurements, during which, the above parameters increased by 20.6%, 21.9%, 28.7% and 25.2%, respectively. Compared with pre-anthesis measurements, the post-anthesis Vcmax and Jmax values decreased, but the decrease percentage of the SM treatments (10.8–25.7% for Vcmax and 22.0%−49.6% for Jmax) was less than those of N treatments (19.6%−41.4% for Vcmax and 29.3%−61.3% for Jmax). Vcmax and Jmax were significantly linear correlating to leaf nitrogen content and the relationships between them were not affected by straw mulching. The yield was not affected by straw mulching. However, straw mulching significantly increased the intercept of the linear relationship between yield and canopy potential photosynthetic capacity (the productions of leaf area index and Vcmax, or Jmax), indicating that straw mulching may improve the harvest index of winter wheat. The overall net effect of straw mulching was a favorable environment that stimulated plant photosynthesis, which compensated the lower LAI and tiller density, in turn, maintained wheat yield. This study elucidated the physiological basis of straw mulching and no-till effect on yield and provided photosynthetic parameters of winter wheat under such soil management.

Reclaimed water (RW) irrigation provides an effective method to alleviate freshwater resources shortage. However, the residual endocrine disrupting chemicals in RW may cause potential risks to the environment and human body. Pot experiments were conducted to study the occurrence and environmental behavior of nonylphenol (NP) in soil-celery system simulating long-term RW irrigation, and exposure to NP was assessed to identify human health risks. Celery was grown on soil with different initial NP concentrations (0.126–22.9 mg·kg⁻¹) to simulate the different irrigation years. After harvest, the NP concentrations in roots, stems and leaves were 0.26–1.51, 0.21–0.45 and 0.33–0.74 mg·kg⁻¹, respectively. The NP concentrations in soil at depths of 5, 10 and 15 cm were 0.047–1.75, 0.088–1.77 and 0.048–1.07 mg·kg⁻¹, respectively. The results showed a limited NP enrichment capacity of celery and low residual rates of NP in soil-celery system, which were between 6.33% and 26.3%. Both the bioconcentration factors (BCFs) and the residual rates of NP decreased with the initial NP concentrations in soil. The total noncancer hazard quotients (HQs) for human exposure to NP from celery and soil had the order of 10⁻⁴–10⁻³, which was lower than the acceptable risk level of 1 and showed safe conditions under long-term RW irrigation.