Returning biogas slurry to fields is an important approach for the resource utilization of organic waste. It enhances soil fertility and crop growth, but its long-term effects on soil ecosystems need evaluation. A meta-analysis approach was employed to comprehensively assess the influence of biogas slurry application from multiple perspectives (soil enzyme activities, heavy-metal accumulation, microbial communities, and fundamental nutrients) across different time scales: short-term (<1 year), medium-term (1–3 years), and long-term (>3 years). The results demonstrate that the ecological effects of biogas slurry application exhibited a pronounced time-dependency. Specifically, short-term application (less than 1 year) significantly increased soil urease (23.0%) and sucrase activity (22.1%), along with organic matter and available nutrients. However, it also resulted in the rapid accumulation of heavy metals, including Hg and As. Under medium-term application, sucrase activity further surged (108.1%). Meanwhile, phosphatase activity and total potassium content decreased, and heavy-metal accumulation showed declining trends. Following long-term application, microbial richness substantially improved, but certain heavy metals (Cu, Zn and Cd) continued to accumulate. In summary, this study reveals distinct temporal-scale mechanisms by which field biogas slurry application regulates soil functions.

Climate change threatens nitrogen cycling in agricultural ecosystems. Optimizing sowing dates and nitrogen management for maize-soybean intercropping is critical for sustainable production in the North China Plain (NCP). Using a calibrated Agricultural Production Systems Simulator (APSIM) model driven by three representative global climate models (GCMs) selected from 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, we evaluated management strategies under two Shared Socioeconomic Pathway scenarios (SSP2-4.5 and SSP5-8.5) across three climatic zones for near-term (2030-2059) and long-term (2070-2099) periods. Under SSP5-8.5, warming was 1.8-2.2 times greater than under SSP2-4.5, nitrate nitrogen (NO3--N) leaching increased by 12.1%, and nitrate storage in the 100-150 cm soil layer rose by 53.4% in Zone III. Biological nitrogen fixation contributed 20.1-29.1% of soybean nitrogen uptake under low nitrogen and 14.9-23.4% under medium nitrogen. Optimal strategies were identified: sowing on 7 June (S3) with medium nitrogen (220.8 kg N ha-1) under SSP2-4.5, and advancing sowing to 28 May (S2) with medium nitrogen under SSP5-8.5 to alleviate heat stress. This study reveals a climate-driven "earlier supply-shortened demand-concentrated leaching" mismatch, providing adaptive management guidance for maize-soybean intercropping systems in the NCP.

This study evaluates the impact of using biogas slurry (BS) instead of nitrogen fertilizer (NF) on wheat soil, and aims to provide an optimized fertilization strategy for green wheat production. Five fertilization modes were tested: basal fertilizer only (CK), NF at the full-bearing stage (CF), BS at the jointing stage + NF at the grouting period (S1), NF at the jointing stage + BS at the grouting period (S2), and BS at the full-bearing stage (S3). Wheat yield in S3 treatment was not significantly different from CF (9632.57 kg·ha−1), but significantly increased starch content by 23.39% (p < 0.05). Analysis of soil nutrient content showed that S3 treatment elevated ammonium nitrogen (AN) content by 98.30% during the harvest period and maintained the highest urease activity (686.45 μg·g−1·d−1). Microbial community analysis showed that the bacterial Shannon index under S3 treatment reached 7.09, and the abundance of Actinomycetes reached 39.40%. The fungal Simpson index was 0.02, lower than that of other treatments (p < 0.01). A comprehensive evaluation led to the conclusion that a complete replacement of BS with NF synergistically improves soil quick-acting nutrient levels, enhances soil enzyme activities, and sustains high microbial diversity, whilst maintaining wheat yield.

Introduction:The nitrogen nutrition index (NNI) of winter wheat decreased under water deficit conditions, primarily due to an increase in the critical nitrogen concentration (%Nc) associated with a reduction in shoot biomass (SB). However, the effect of plant nitrogen concentration (PNC) on NNI under water deficit conditions remains unclear. This study aimed to: (1) determine whether significant differences in PNC and leaf nitrogen concentration (LNC) of winter wheat exist among different water treatments under controlled conditions; (2) analyze the reasons for changes in PNC and LNC under water deficit conditions; and (3) assess the stability of relationships between PNC and LNC, as well as between plant nitrogen accumulation (NAp) and leaf area index (LAI), across different water treatments. Methods:To address the above mentioned objectives, a series of rainout shelter experiments were conducted during the winter wheat growing seasons from 2018 to 2021. Results and discussion:The results indicated that water deficit treatments limited PNC and LNC values at specific growth stages of winter wheat under controlled conditions. However, such severe water deficits are unlikely to occur in typical field conditions; thus, PNC was not identified as the primary factor affecting NNI in field environments experiencing water deficit. Component analysis clarified the causes behind the decline in PNC and LNC. The decline in specific leaf area (SLA) and leaf biomass fraction (LBF) contributed to the decrease in PNC, with SLA accounting for more variation than LBF. Similarly, declines in both SLA and specific leaf nitrogen (SLN) led to reduced LNC, with SLN explaining more variation in LNC than SLA across different water treatments. LNC was jointly controlled by both PNC and the ratio of SLN to LBF. Furthermore, water deficit did not alter the proportional linear relationship between NAp and LAI, suggesting that the impact of water deficit on PNC and LNC is limited, which helps a better understanding of the factors contributing to the declination of NNI.

This study explored how top-dressed biogas slurry at winter wheat’s (Triticum aestivum L.) jointing stage (JS) and grain-filling period (GP) affects soil enzyme–microbe interactions, aiming to address nutrient supply–crop demand mismatches. A field experiment with five treatments (water [CK], chemical fertilizer [CF], and three biogas slurry topdressing regimes [S1–S3]) was conducted. Soil samples (0–20 cm) were collected at JS, flowering stage (FS), GP, and reaping period (RP) to analyze soil properties (total nitrogen [TN], available phosphorus [AP], available potassium [AK], soil organic matter [SOM], ammonium nitrogen [AN], pH), enzyme activities (urease [UE], neutral phosphatase [NP], sucrase [SC], catalase [CAT]), and microbial community abundance (via Illumina NovaSeq sequencing). Results showed biogas slurry altered enzyme activities, microbial structure (e.g., Actinomycetota, Ascomycota), and their interactions by regulating soil properties. JS application boosted Pseudomonadota and UE activity, GP application increased Ascomycota and CAT activity, and S3 had the most complex enzyme–microbe network, enhancing nutrient cycling. The analysis indicated that UE activity was strongly and positively correlated with several bacterial phyla (e.g., Planctomycetota, Verrucomicrobiota) (p < 0.01) and fungal phyla (e.g., Ascomycota) (p < 0.01).

Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model’s ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that the accuracy and F1 score of the REM-ShuffleNetV2 model on the self-constructed field crop leaf disease dataset are 96.72% and 96.62%, which are 3.88% and 4.37% higher than that of the ShuffleNetV2 model; and the number of model parameters is 4.40M, which is 9.65% less than that of the original model. Compared with classic networks such as DenseNet121, EfficientNet, and MobileNetV3, the REM-ShuffleNetV2 model not only has higher recognition accuracy but also has fewer model parameters. The REM-ShuffleNetV2 model proposed in this study can achieve accurate identification of crop leaf disease in complex field backgrounds, and the model is small, which is convenient to deploy to the mobile end, and provides a reference for intelligent diagnosis of crop leaf disease.

Crop evapotranspiration is a key parameter influencing water-saving irrigation and water resources management of agriculture. However, current models for estimating maize evapotranspiration primarily rely on meteorological data and empirical coefficients, and the estimated evapotranspiration contains uncertainties. In this study, the evapotranspiration data of summer maize were collected from typical stations in Northern China (Yucheng Station), and a back-propagation neural network (BP) model for predicting maize evapotranspiration was constructed based on meteorological data, soil data, and crop data. To further improve its accuracy, the maize evapotranspiration model was optimized using three bionic optimization algorithms, namely the sand cat swarm optimization (SCSO) algorithms, hunter-prey optimizer (HPO) algorithm, and golden jackal optimization (GJO) algorithm. The results showed that the fusion of meteorological, soil moisture, and crop data can effectively improve the accuracy of the maize evapotranspiration model. The model showed higher accuracy with the hybrid optimization model SCSO-BP compared to the stand-alone BP neural network model, with improvements of 2.7-4.8%, 17.2-25.5%, 13.9-26.8%, and 3.3-5.6% in terms of R2, RMSE, MAE, and NSE, respectively. Comprehensively compared with existing maize evapotranspiration models, the SCSO-BP model presented the highest accuracy, with R2 = 0.842, RMSE = 0.433 mm/day, MAE = 0.316 mm/day, NSE = 0.840, and overall global evaluation index (GPI) ranking the first. The results have reference value for the calculation of daily evapotranspiration of maize in similar areas of northern China.

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGO-XGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R2), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 mm d−1, and −0.254–0.062 mm d−1, respectively. Furthermore, the accuracy of the NGO-XGBoost model in estimating ETo varied across different seasons, which was more significantly affected by humidity and wind speed in winter. When the target station data was insufficient, the NGO-XGBoost model was trained by using the historical data from neighboring stations and still maintained a high precision. Overall, this study recommends a reliable method for estimating ETo, which provides a reference for accurately calculating ETo in the North China Plain in the absence of meteorological data.

Land use types have a significant impact on river ecosystems. The Yiluo River is the largest tributary below Xiaolangdi Reservoir in the middle reaches of the Yellow River, and is one of the important water conservation areas in the Yellow River Basin. Studying the ecological status of the Yiluo River under varied land use types in this basin is crucial for both ecological protection and the high-quality development of the Yellow River Basin. This study investigated the impacts of land use types on the macroinvertebrate community and functional structure in the Yiluo River Basin and introduced the concept of the land use health index (LUI). During the survey period, a total of 11,894 macroinvertebrates were collected, and 143 species were identified, belonging to 4 phyla, 7 orders, 22 families, and 75 families. The results showed that LUI had the most significant impact on macroinvertebrate community structure, with substrate type, dry plant weight, total phosphorus, turbidity, and attached algae biomass also playing significant roles in affecting macroinvertebrate communities. The species richness, the Shannon-Wiener index, and the Margalef richness index exhibited a nonlinear positive correlation with LUI of the sampling site, increasing as LUI enhancing and eventually reaching a plateau. Functional richness showed a linear and positive correlation with LUI, increasing with its enhancement, while functional evenness and functional divergence exhibited a nonlinear correlation with LUI. Functional evenness initially increased and then decreased with the enhancement of LUI, while functional divergence decreased with LUI enhancement. This study can provide a scientific reference for river ecological management under various land use scenarios.The Yiluo River is the largest tributary below Xiaolangdi Reservoir in the middle reaches of the Yellow River, and is one of the important water conservation areas in the Yellow River Basin. Studying the ecological status of the Yiluo River under varied land use types in this basin is crucial for both ecological protection and the high-quality development of the Yellow River Basin. This study investigated the impacts of land use types on the macroinvertebrate community and functional structure in the Yiluo River Basin and introduced the concept of the land use health index (LUI). During the survey period, a total of 11,894 macroinvertebrates were collected, and 143 species were identified, belonging to 4 phyla, 7 orders, 22 families, and 75 families. The results showed that LUI had the most significant impact on macroinvertebrate community structure, with substrate type, dry plant weight, total phosphorus, turbidity, and attached algae biomass also playing significant roles in affecting macroinvertebrate communities. The species richness, the Shannon-Wiener index, and the Margalef richness index exhibited a nonlinear positive correlation with LUI of the sampling site, increasing as LUI enhancing and eventually reaching a plateau. Functional richness showed a linear and positive correlation with LUI, increasing with its enhancement, while functional evenness and functional divergence exhibited a nonlinear correlation with LUI. Functional evenness initially increased and then decreased with the enhancement of LUI, while functional divergence decreased with LUI enhancement. This study can provide a scientific reference for river ecological management under various land use scenarios.

针对干旱胁迫对玉米生长发育带来不利影响的问题,通过分根水培试验,研究充分供水处理(CK)、局部水分胁迫处理(CP)和水分胁迫后局部复水处理(SP)对玉米生长和内源化学信号的影响.结果表明:CP 在6～12 d时地上部干质量增长速率为 117.92%,显著大于 CK的 90.85%,SP 在 0～3 d 时的地上部干质量增长速率为 137.65%,显著大于CK的 104.15%;SP 的根系导水率与根系表面积(R2 分别为 0.832 和 0.825)、根系导水率与叶面积(R2 分别为 0.904 和 0.937)和地上部干质量与叶面积(R2 为 0.913)之间成显著正相关,CP各指标的相关性系数显著大于 SP;CP 和 SP 有利于叶片中脱落酸(ABA)的累积,抑制 CKs 和 NO3- 分泌,1 d时即可发生明显的反应,1～3 d时 SP 影响程度显著大于 CP,之后 CP 的影响程度显著大于 SP.CP 和 SP 均可起到强化补偿效应的目的,但 ABA、CKs和NO3- 三者共同作用导致了CP 补偿效应较SP 更持久.该研究结果揭示了玉米响应非均匀水分胁迫的补偿生长机制,将局部水分胁迫与农业应用有效结合,将是提高玉米生产和水分高效利用的关键策略.

Green pepper (Capsicum annuum L.) is one of the major vegetables cultivated in sub-tropical monsoon climate regions. However, with the unreasonable use of water and nitrogen (N) fertilizer, efficient water and N fertilizer management systems need to be identified. The goal of this project was to investigate the coupling effects of different amounts of water and N on green pepper yield, water use efficiency (WUE), as well as N use efficiency (NUE) in sub-tropical monsoon climate regions. The optimum combination of water and N inputs was determined for multi-objective optimization through the multiple regression analysis and the combinations of likelihood functions. The pot experiment was conducted during the green pepper growing seasons (May–September) of 2019 and 2020 in a greenhouse at Nanchang, Jiangxi of China that included three water deficit levels, i.e., mild deficit (W1: 95~80%θFC, %θ field capacity simplified as %θFC), moderate deficit (W2: 80~65%θFC), and severe deficit (W3: 65~50%θFC), and four levels of nitrogen application (Napp) rate, i.e., 6.0 (N1), 3.0 (N2), 1.5 (N3), and 0.0 g plant−1 (N4), for a total of twelve treatments (i.e., 3 × 4) with six replications. Results show that water levels have an extremely significant effect (p < 0.01) on green pepper yield and WUE, but no effect on NUE (p > 0.05). N treatments have significant effects on green pepper yield, WUE, and NUE. Meanwhile, the effects of water levels and N treatment interaction on WUE and NUE were extremely significant (p < 0.01), but varied on yield between the two years. The maximum yields (576.26 and 619.00 g plant−1) occurred when the water level and Napp rate were 80~65%θFC and 6.0 g plant−1. While the water level and Napp rate were 80~65%θFC and 3.0 g plant−1, the WUEs and NUEs reached the maximum, which were 20.14 and 17.71 g L−1, 76.54, and 77.73 g−1 in 2019 and 2020. The dualistic and quadric regression equations of irrigation amount and Napp rate indicated that the yield, WUE and NUE cannot reach the maximum at the same time. By establishing a multiobjective optimization model using combinations of likelihood functions, it was concluded that the water level shall be controlled in 80~65%θFC and the Napp rate is 3.78 g plant−1, which can be used as the suitable strategy of water and N management for the maximum comprehensive benefits of yield, WUE, and NUE for green pepper. The obtained optimum combination of water and N inputs can provide a scientific basis for irrigation and fertilization optimization and management in sub-tropical monsoon climate regions.

China has the largest apple-growing area and fresh fruit production in the world; however, water shortages and low fertilizer utilization rates have restricted agricultural development. It is a major challenge to obtain scientific and reasonable irrigation and fertilization systems for young apple trees in semi-arid regions of northern China. A 2-year field bucket experiment with four irrigation levels of W1 (75–90% Fs, where Fs is the field water holding capacity), W2 (65–80% Fs), W3 (55–70% Fs), and W4 (45–60% Fs), and three fertilizer levels of F1 (27-9-9 g N-P2O5-K2O), F2 (18-9-9 g N-P2O5-K2O), and F3 (9-9-9 g N-P2O5-K2O) was conducted in 2019 and 2020, so as to explore the effects of different water and fertilizer treatments on the growth and physiological characteristics of young apple trees. The results showed that the plant growth, leaf area, and dry matter of young apple trees at each growing period reached maximum values under F1W2, and they showed a positive linear relationship with relative chlorophyll content (SPAD), net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), water consumption, and water use efficiency (WUE). With the growth of young apple trees, water-fertilizer coupling could significantly increase the leaf SPAD of young apple trees. Pn, Tr, and Gs reached the maximum value under F1W1, and although they decreased under F1W2, the water use efficiency increased by 2.3–25.7% and 4.0–23.8% under F1W2 compared with other treatments in two years, respectively. The water consumption of young apple trees increased with the increase of irrigation and fertilizer, and both dry matter and water productivity reached the maximum value under F1W2, which increased by 0.8%, 14.6% in 2019, and 0.6%, 11.1% in 2020 compared with F1W1, while water consumption decreased by 12.2% and 9.4% in both years. In conclusion, F1W2 treatment (soil moisture was controlled at 65–80% of field water holding capacity, and N-P2O5-K2O was controlled at 27-9-9 g) was the best coupling mode of water and fertilizer for young apple trees in semi-arid areas of northern China.

【Objective】 The synthetic cytokinin 6-benzyladenine (6-BA) is an exogenous regulator to promote plant growth, and the purpose of this paper is to study the effect of 6-BA on growth of maize seedlings watered by different irrigations. 【Method】 We compared two irrigation treatments each having two 6-BA treatments (with and without adding 6-BA): partial water deficit irrigation following a uniform sufficient irrigation (PD for without 6-BA, and PDT for with 6-BA), and partial sufficient irrigation after a uniform deficit irrigation (PR for without 6-BA, PRT for with 6-BA). Sufficient irrigation without 6-BA addition was taken as the control (CK). In all treatments, the concentration of 6-BA was 10 mg/L. For each treatment, we measured the physiological traits of the crop. 【Result】 Compared with CK, PD significantly increased dry root weight on the irrigated side, dry weight of the above-ground parts and the leaf area, but it did not have a noticeable impact on dry root weight on the not-irrigated side; it increased the contents of zeatin (ZA), nitrate nitrogen (NO3-) and soluble protein (Cpr) in the leaves, xylem sap and roots on the irrigated side, but reduced the content of abscisic acid (ABA). PR significantly reduced the weight of dry roots on both sides, weight of dry aboveground parts and leaf area, as well as the contents of ZA, NO3- and Cpr in leaves, xylem sap and roots on both sides, but it increased the content of ABA. Compared with PD and PR, PDT and PRT increased dry weight of the roots on both sides, dry weight of shoots and the leaf areas, as well as the contents of ZA, NO3- and Cpr in the plant, but reduced ABA thereby facilitating compensatory growth of the crop. 【Conclusion】 Partial water deficit irrigation followed a sufficient uniform irrigation is optimal for maize seedling growth, and combined with praying 6-BA, it can further boost compensatory growth of the crop.

Aerated drip irrigation can increase the soil oxygen content and improve the utilization efficiency of fertilizer. However, limited information is available on how soluble potassium fertilizer with different aeration levels affects plant yield and quality. Specifically, the underlying mechanism associated with microbial community level has not been reported so far. In this study, a micro-nano bubble water (MNBW) drip irrigation experiment was conducted in a strawberry plantation greenhouse, with three ratios of MNBW and two potassium application amounts and a control treatment (without MNBW). The results showed that compared with the CK (no aeration, 100% traditional potassium fertilizer application, 144 kg hm(-2) of potassium sulfate), MNBW irrigation improved the soil oxygen content and changed the structure and functionality of the soil microbial community in the strawberry rhizosphere. The bacterial diversity was reduced, and the microbial co-occurrence patterns were altered. The soil respiration intensity increased by 1.32%-14.22%. The soil temperature increased, and the decomposition of organic matter was enhanced. It was indicated that the increase of soil oxygen had a positive impact on soil fertility, strawberry yield, and quality under the condition of reducing potassium application. The yield and quality of strawberries in MNBW treatments were significantly higher than those treated with CK (p < 0.05), but the carbon dioxide emissions significantly increased. High potassium fertilizer application amount and MNBW ratio did not mean high yield and quality of strawberries. Thus, O20K70 (20% of irrigation water with micro-nano bubbles, 70% of traditional drip irrigation potassium fertilizer level, 101 kg hm(-2) of potassium sulfate) and O60K70 (60% of irrigation water with micro-nano bubbles, 70% of traditional drip irrigation potassium fertilizer level, 101 kg hm(-2) of potassium sulfate) could be chosen for strawberry plantations with MNBW.

【Objective】 Reducing water and fertilizer application is critical to developing sustainable agriculture worldwide. This paper investigates the optimal water-saving drip irrigation and nitrogen-reducing fertilization for cultivation of young apple in arid and semi-arid regions in northwestern China. 【Method】 The two-year experiment consisted of three irrigation treatments by keeping soil water content at 75%~90% (W1), 60%~75% (W2) and 45%~60% (W3) of the field capacity. For each treatment, there were four fertilizations by applying N-P2O5-K2O to each plant at 18-12-6 g (F1), 15-12-6 g (F2), 12-12-6 g (F3), and 9-12-6 g (F4). For each treatment, we measured the growth, photosynthetic traits, and water use efficiency of the trees. 【Result】 F2+W2 combination gave the highest plant growth, basal stem growth, and leaf area, demonstrating that a moderate water deficit and fertilization reduction was beneficial to the tree growth. Leaf SPAD started to increase from the budding and flowering stage, with the impact of fertilization on it ranked in the order of F1 > F2 > F3 > F4, and the influence of irrigation ranked in the order of W1 > W2 > W3 at the fruit-set and ripening stages. The F1+W1 combination gave the highest leaf SPAD. Photosynthesis and transpiration increased with the increase in fertilization and irrigation amount. Maximum water use efficiency was achieved in F1+W2 and F2+W2, with its value being 5.16 and 4.81 μmol/mmol, respectively, which were 8.6% and 5.3% more than those in F1+W1. Irrigation amount affected tree growth more than fertilization, and strong correlations were found between growth, leaf area, SPAD, and photosynthetic traits of the trees. 【Conclusion】 The comparative results show that F2+W2 is optimal for young apple tree under drip irrigation. It indicates that a moderate reduction in irrigation and fertilization did not result in a noticeable effect on tree growth and can be used as an improved agronomic practice for apple production in arid and semi-arid regions in Northern China and areas with similar climates.

To evaluate the physiological responses of Korshinsk peashrub (Caragana korshinskii Kom.) to water deficit, photosynthetic gas exchange, chlorophyll fluorescence, and the levels of superoxide anion (O2•−), hydrogen peroxide (H2O2), malondialdehyde (MDA), antioxidant enzymes, and endogenous hormones in its leaves were investigated under different irrigation strategies during the entire growth period. The results showed that leaf growth-promoting hormones were maintained at a higher level during the stages of leaf expansion and vigorous growth, and zeatin riboside (ZR) and gibberellic acid (GA) gradually decreased with an increase in water deficit. At the leaf-shedding stage, the concentration of abscisic acid (ABA) dramatically increased, and the ratio of ABA to growth-promoting hormones increased to a high level, which indicated that the rate of leaf senescence and shedding was accelerated. At the stages of leaf expansion and vigorous growth, the actual efficiency of photosystem II (PSII) (ΦPSii) was downregulated with an increment in non-photochemical quenching (NPQ) under moderate water deficit. Excess excitation energy was dissipated, and the maximal efficiency of PSII (Fv/Fm) was maintained. However, with progressive water stress, the photo-protective mechanism was inadequate to avoid photo-damage; Fv/Fm was decreased and photosynthesis was subject to non-stomatal inhibition under severe water deficit. At the leaf-shedding stage, non-stomatal factors became the major factors in limiting photosynthesis under moderate and severe water deficits. In addition, the generation of O2•− and H2O2 in the leaves of Caragana was accelerated under moderate and severe water deficits, which caused an enhancement of antioxidant enzyme activities to maintain the oxidation–reduction balance. However, when the protective enzymes were insufficient in eliminating excessive reactive oxygen species (ROS), the activity of catalase (CAT) was reduced at the leaf-shedding stage. Taken all together, Caragana has strong drought resistance at the leaf expansion and vigorous growth stages, but weak drought resistance at the leaf-shedding stage.

Land use/cover change (LUCC) is one of global environmental change hot issues. The study of land-use changes in the Yiluo River Basin is significant to ecological protection and high-quality development of the Yellow River Basin. Based on long-term LandsatTM satellite remote sensing images, R language string diagram visualization model and linear model redundancy analysis(RDA analysis), this paper analyzed the temporal and spatial change characteristics of land use, land cover flow rate, directionand internal driving factors in the Yiluo River Basin from 1990 to 2020. The results showed: (1) From 1990 to 2020, the land change in the Yiluo River Basin showed a trend of change in forest land firstly decreasing and then increasing, arable land first increasing and then decreasing, construction land increasing as a whole, and water area decreasing as a whole. (2) In terms of quantity Every 10 years from 1990 to 2020, the total amount of change in cultivated land and forest land is the largest, followed by construction land, and the amount of change in water, grassland and unused land are very small. (3) 1990-2000, 2000-2010, 2010-2020 The mutual conversion activity of land use types and the degree of land-use change showed an upward trend, reaching the highest in 2010-2020. (4) From 1990 to 2020, the center of gravity of forest land shifts to the northeast as a whole, and the center of gravity of cultivated land migrates to the south. The change in the center of gravity of forest land and cultivated land is related to the policy of returning farmland to the forest. The construction land is generally centered around the main urban area of Luoyang City, which is related to the direction of social and economic development and urban development; (5) In terms of the driving force, rapid economic development is the main driving force for the change of land use area in the Yiluo River Basin. The promulgation and implementation of the policy of returning farmland to forest is the main reason for the change of forest land and cultivated land area. The results of this study can provide a scientific basis for the ecological protection and sustainable development of the Yellow River Basin.

从TRIZ创新理论视角出发,分析了农业机械化专业产教融合协同育人现状.针对学生创新思维和创新能力不足的问题,鼓励学生打破惯性思维,敢于提出问题,利用TRIZ创新理论的关键方法和具体流程,引导和启发学生分析农机实践中遇到的问题,提出具有初步可行性的解决方案,激发学生能动性和创新性,从而提升产教融合协同育人效果,提高人才培养质量.

作为水利类专业一门基础必修课,"水文地质与工程地质"课程涉及学科的专业基础知识点,天然具有丰富思政元素,包括工程伦理、法制教育、爱国情怀等.提出了"水文地质与工程地质"课程思政的必要性,并从修订培养大纲、改进教学方法、丰富教学手段和精心设计教学环节4个方面阐明了落实课程思政的方法和措施,并以地面沉降和地裂缝作为教学案例,详细介绍了如何实现知识点学习与思政教育有机融合,为提升水利类专业学生的思想道德修养和培养地质学创新人才作出贡献,落实高校立德树人任务,将社会主义核心价值观贯穿教育教学全过程.