Representative Phoebe species (Lauraceae), particularly those classified as rare and endangered, encompass critical ecological functions and cultural-economic values, yet face severe threats from habitat loss and climate change. This study aims to synthesize global research trends and identify critical knowledge gaps to guide future conservation strategies for Phoebe species. We conducted a bibliometric analysis of 226 publications from the Web of Science Core Collection (1973-2025) using Bibliometrix, CiteSpace, and VOSviewer. Key findings reveal a 30-fold increase in annual publications compared to the 1990s, with China, the USA, and Malaysia as primary contributors. Research has evolved from taxonomic descriptions to genomics-driven stress tolerance and sustainable forest management. However, systemic gaps in germplasm conservation, hybrid germplasm management, pathogen dynamics, and climate resilience demand urgent transdisciplinary interventions. By integrating ecological insights and technological innovations, we propose the Integrated Genetic-Ecological-Socioeconomic Framework (IGESF), linking genetic safeguarding, stress resilience, sustainable value chains, and policy integration to advance the ecology and sustainability of threatened tree species.

Context and research question: Water and nitrogen (N) are primary limiting factors for maize productivity in arid and semi-arid regions. However, the interactive effects of water and N availability on the structure-function relationships governing plant hydraulics and photosynthesis remain poorly understood. Methods: A three-year field experiment was conducted to investigate how water-N interactions modify maize stem and leaf anatomy, regulate hydraulic and photosynthetic functions, and ultimately determine grain yield. The experiment comprised three irrigation lower limits (W85, W70, and W55) and four N application rates (N0, N90, N180, and N270). Results: Severe drought (W55) induced a conservative anatomical phenotype, characterized by narrower stem xylem vessels, reduced vessel numbers, and decreased leaf vein density, alongside a significant increase in vascular bundle density. Under adequate moisture (W85 and W70), N supplementation expanded the waterconducting tissues, substantially enhancing both stem-specific hydraulic conductivity and leaf hydraulic conductance. This efficient hydraulic network sustained rapid water transport and high stomatal conductance, thereby maximizing photosynthetic rates. However, under severe drought, the compensatory effect of N was nullified. Specifically, high N application (N270) under W55 stimulated transpirational demand that exceeded the restricted hydraulic supply, exacerbating physiological drought. This led to a sharp decline in midday leaf water potential (below -1.8 MPa) and carbon assimilation capacity, decoupling the positive relationship between hydraulic conductance and photosynthesis. Partial least squares structural equation modeling (PLS-SEM) confirmed that hydraulic conductivity acted as a central mediator, directly driving photosynthetic capacity (beta= 0.981) and grain yield (beta = 0.866). Conclusions: Adequate moisture combined with moderate N application (W85/W70 + N180) optimally balances hydraulic efficiency and transport safety, representing an effective water-N management strategy to maximize maize yield while preventing hydraulic failure under drought stress.

Introduction Balanced source-sink relations are essential for achieving high maize yield and water productivity, and maintaining post-silking green leaf area is critical for dry matter accumulation and yield formation in maize (Zea mays L.). However, the mechanisms by which nitrogen (N) rate and planting density affect yield formation via leaf senescence and source-sink regulation remain unclear. This study aimed to elucidate the respective contributions of post-silking leaf functional decline and source-sink balance to grain yield and water productivity of drip-irrigated maize.Methods A two-year field experiment was conducted in northwest China with three planting densities (LD: 80,000; MD: 100,000; HD: 120,000 plants ha-1) and four N rates (N0: 0; N1: 120; N2: 180; N3: 240 kg N ha-1). Leaf area duration (LAD), post-silking leaf functional decline, source-sink traits, grain yield, and water productivity were evaluated, and relationships among key variables were analyzed using PLS-SEM.Results Nitrogen application alleviated stress-induced premature leaf functional decline after silking, whereas increasing planting density accelerated the loss of effective leaf function. Both higher planting density and higher N rate significantly increased LAD. Compared with LD, MD and HD increased source growth by 23.0% and 19.4%, sink capacity by 23.9% and 15.2%, sink growth rate by 23.7% and 15.8%, and source-sink difference by 16.2% and 18.2%, respectively, indicating that sink limitation constrained further yield increases at higher densities. PLS-SEM showed that N mitigated premature leaf functional decline, while planting density negatively affected leaf functional maintenance. Planting density indirectly affected LAD via leaf functional decline (63.7% of the effect), whereas N rate and planting density directly influenced LAD (75.6% of the effect). LAD strongly affected source growth parameters (90.7%), which increased grain yield (89.0%) and source-sink parameters (73.4%), ultimately contributing to direct increases in grain yield (81.8%) and water productivity (74.3%). D2N3 achieved the highest grain yield, followed by D2N2, which significantly improved water productivity and irrigation water productivity.Discussion Considering source-sink balance, water saving, and stable yield, D2N2 is recommended. These results improve understanding of how to achieve effective dense planting and high-yield maize cultivation under water-scarce conditions.

Crop evapotranspiration (ET) and its partitioning into soil evaporation (E) and plant transpiration (T) are essential for understanding surface-atmosphere interaction, designing irrigation scheduling, and managing water resources. Nevertheless, direct measurement of ET and its components is time-consuming, labor-intensive and costly, which highlights the imperative of establishing accurate estimation models with readily available data. Two novel hybrid deep learning models, i.e. convolutional neural network-long short-term memory (CNN-LSTM) and transformer neural network-LSTM (Transformer-LSTM) models were proposed to estimate daily maize ET and its partitioning (especially E), and their performances were compared with SVM, XGBoost, DNN and standalone LSTM models. Meteorological (reference crop evapotranspiration, ET0), soil (soil water content, SWC; soil temperature, Ts) and crop (leaf area index, LAI; plant height, H) variables were grouped into eight input combinations to explore their effects on estimation accuracy. The percentage of the data used for training and testing was 4:1. The results showed that the inclusion of Ts and H can effectively enhance the model's accuracy in estimating daily ET and its components, and it is necessary to take all variables into account while using the machine learning models to estimate daily maize ET and its components. The Transformer-LSTM exhibited satisfactory accuracy (R2 >= 0.841 and NRMSE <= 0.326) with only ET0. Crop variables, especially LAI, exerted greater influence than soil variables. The Transformer-LSTM model slightly outperformed CNN-LSTM, followed by LSTM, DNN, SVM and XGBoost almost under all input combinations, showing the best estimation accuracy of R2 = 0.894, MAE = 0.166 mm d- 1, RMSE = 0.217 mm d- 1, NRMSE = 0.266, and |MBE| = 0.000 mm d- 1 for E estimation with ET0, LAI and H, R2 = 0.955, MAE = 0.221 mm d- 1, RMSE = 0.296 mm d- 1, NRMSE = 0.147, and |MBE| = 0.017 mm d- 1 for T estimation and R2 = 0.955, MAE = 0.287 mm d- 1, RMSE = 0.379 mm d- 1, NRMSE = 0.134, and |MBE| = 0.021 mm d- 1 for ET estimation with all variables. This study highlighted the potential of hybrid LSTM models and the importance of meteorological and crop variables for accurate estimation of ET and its components in maize fields.

Synergistic water-nitrogen (N) management is vital for high maize (Zea mays L.) yields, but the integrated physiological mechanisms driving yield formation remain unclear. A 2-year field study with 3 irrigation levels and 4 N rates revealed that high maize yields were maintained under mild drought combined with medium-to-high N via distinct pathways. Water-N synergy enhanced leaf antioxidant capacity, with N increasing peroxidase (POD) activity and reducing malondialdehyde, thereby mitigating oxidative stress, delaying chlorophyll and photosynthesis (An) decline, and sustaining assimilates such as soluble sugars (SS) and free amino acids (FAA). In grains, mild drought raised SS by 3.0% but reduced sucrose synthase (SuSy) and ADP-glucose pyrophosphorylase (AGPase) activities by 13.3% and 20.7%, respectively, lowering starch (ST) by 9.7%. Severe drought drastically reduced assimilate input, enzyme activities, and ST (-37.3%). N metabolism was also impaired, with lower FAA and protein (PRO) linked to lower glutamine synthetase and glutamate synthase activities. Hormonal balance was critical: zeatin + zeatin riboside (Z + ZR) and indole-3-acetic acid (IAA) promoted grain weight and correlated positively with carbon-metabolizing enzymes, while severe drought increased gibberellin A3 (GA3). In a multivariate analysis, SuSy, AGPase, IAA, Z + ZR, and GA3 explained 82.32% of ST variation, and the interaction between N metabolism enzymes and hormonal ratios explained 92.0% of PRO variation. Carbohydrate metabolism, N metabolism, and hormone balance accounted for 44%, 19%, and 7% of the variation in 100-grain weight, respectively, while their interactions explained an additional 19%. This study establishes a physiological network of water-N synergy, highlighting antioxidant enhancement and hormone-metabolism interactions, that provides a theoretical basis for precision water-N management in maize production.

The global demand for food necessitates an increase in dryland agricultural production. Sustainable dryland crop production largely relies on integrated soil and crop management practices. The impact of long-term soil and crop management practices on winter wheat-summer maize (WM) rotation systems was explored, and the key resources that contribute to grain yield (GY) and quality for sustainable production were identified. A five-season field experiment of WM rotation seasons was conducted in the drylands of the Loess Plateau of China. Varied soil mulch patterns [non-mulched flat cultivation (NF), straw-mulched flat cultivation (SF), and transparent filmmulched ridge and bare furrow cultivation (TR)] and different amount of nitrogen applicant [winter wheat: 0, 100, and 200; summer maize: 0, 90, and 180 kg ha-1 (N0, N1 and N2, respectively)]. The 'grain heat energy yield' concept was proposed to evaluate the contributions of different resources to GY and protein production of WM rotation seasons. Compared to N0 treatment, N1 and N2 treatment increased GY by 28.6% and 54.7% and grain protein content by 6.0% and 14.3% in summer maize, while they increased GY by 45.4% and 81.8% and grain protein content by 13.7% and 26.1% in winter wheat, respectively, ultimately increasing the net income of rotation system by 58.9-127.1%. Compared to NF treatment, SF and TR treatment increased GY by 16.2% and 21.9% and grain protein content by 1.6% and 3.1% in summer maize, while they increased GY by 10.1% and 11.3% and grain protein content by 13.7% and 26.1% in winter wheat, respectively, ultimately increasing the net income of rotation system by 1.0-12.0%. Summer maize exhibited a greater harvest index of biomass (48.9%) than winter wheat (38.6%), but winter wheat had a greater nitrogen harvest index (71.8%) than summer maize (56.2%). These strategies also improved resource use efficiency of the rotation system. Based on grain heat energy yield, summer maize exhibited higher efficiency than winter wheat, and radiation use efficiency contributed most to the productivity of rotation system. In conclusion, the accumulation of soil hydrothermal resources, improvement of canopy growth and light interception facilitated the accumulation of aboveground biomass and plant nitrogen in wheat and maize plants, which enhanced the productivity, stability, resource use efficiency, and finally profitability of rotation system. This study identifies the link between resource utilization and crop productivity, advancing sustainable agriculture in drylands and contributing to regional food security.

Intercropping is widely recognized as an effective strategy for enhancing resource use efficiency and promoting agricultural sustainability. However, the synergistic effects of row configuration and nitrogen application on system productivity and resource use efficiency in maize/soybean intercropping system remain poorly understood. A two-year field experiment (2022-2023) was conducted to evaluate plant growth, grain yield, water, nitrogen and land use efficiencies, as well as economic profits of maize/soybean strip intercropping system in response to various row configurations (M2S2: two maize rows with two soybean rows, M2S4: two maize rows with four soybean rows, M4S4: four maize rows with four soybean rows, MM: maize monocropping, S: soybean monocropping) and maize nitrogen application levels (N0: 0 kg center dot ha- 1, N1: 150 kg center dot ha- 1, N2: 250 kg center dot ha- 1). The results showed that M2S4 configuration effectively alleviated the shading stress on soybean and optimized canopy structure, while N1 enhanced biomass accumulation, stem strength, and lodging resistance, together maximizing intercropping advantages. Across all intercropping treatments, system yields increased by 7.16 %- 23.36 % compared with monocropping. The highest yield was obtained under N1 + M2S2 (7647.66 kg center dot ha- 1) in 2022, with N1 + M2S4 producing a slightly lower yield (7274.50 kg center dot ha- 1), whereas N1 + M2S4 achieved the highest system yield in 2023 (7704.24 kg center dot ha- 1). Although intercropping generally reduced water productivity relative to monocropping, optimization of row configuration effectively mitigated this effect. All intercropping patterns exhibited land equivalent ratios and water equivalent ratios greater than 1. Among treatments, N1 + M2S4 consistently performed best in terms of system yield, water productivity, nitrogen use efficiency, land equivalent ratio, and economic returns, indicating a strong synergy between spatial arrangement and nutrient management. Economically, N1 + M2S2 yielded the highest profit in 2022 (11,970.6 CNY center dot ha- 1), only 4.4 % higher than that of N1 + M2S4, whereas N1 + M2S4 generated the maximum profit in 2023 (13,395.58 CNY center dot ha- 1), demonstrating its stable economic advantage. Overall, considering productivity, resource use efficiency, economic returns and mechanization feasibility, N1 + M2S4 was identified as the optimal strategy for sustainable maize/soybean intercropping production on the Loess Plateau of China.

Context: Maize-soybean intercropping, recognized as a sustainable agricultural practice, improves land productivity and resource use efficiency. However, the patterns of dry matter and nitrogen redistribution, which are critical for yield advantage, remain inadequately quantified under film-mulched drip fertigation. Objective: This study aimed to quantify the post-flowering translocation and accumulation of dry matter and nitrogen of intercropped maize and soybean under film-mulched drip-fertigation, and identify the optimal configuration that maximizes the intercropping advantage. Methods: A two-year field experiment was carried out in 2022 and 2023, including eight maize-soybean intercropping row configurations, with monocultures of maize and soybean as controls. Key plant organs were sampled at critical growth stages to determine their dry matter and nitrogen content, which facilitated the subsequent calculation of translocation parameters. The land equivalent ratio, actual yield loss index, and nitrogen equivalent ratio were utilized to evaluate the advantages of the intercropping systems. Results: Intercropping significantly enhanced post-flowering dry matter translocation in maize by 23.5 % but reduced it in soybean by 34.5 %. Conversely, post-flowering nitrogen translocation was reduced in both maize by 39.9 % and soybean by 29.4 %. Dry matter accumulation after flowering was the primary source of grain yield, contributing 94.2 % in maize and 85.0 % in soybean, significantly outweighing the contribution from translocation (5.8 % for maize and 15.0 % for soybean). A similar trend was observed for nitrogen source. Among the configurations, two rows of maize alternating with four rows of soybean (M2S4) achieved the highest land equivalent ratio (1.52), actual yield loss index (1.29), intercropping advantage index (3.24) and nitrogen equivalent ratio (1.70). Conclusion: The M2S4 configuration effectively coordinated dry matter and nitrogen translocation and accumulation, leading to enhanced resource complementarity and yield advantage in drip-fertigated maize-soybean intercropping. This finding provides effective strategy for improving productivity and nitrogen use efficiency in maize-soybean strip intercropping systems.

Optimizing water-fertilizer regime in maize-soybean strip intercropping systems is critical for enhancing crop productivity, profitability and resource use efficiency. A two-year field experiment was conducted under drip fertigation in northwest China, with three irrigation levels (W100:100%ETc, W80: 80%ETc and W60: 60%ETc) and four nitrogen rates (N250: 250 kg N ha(-1), N200: 200 kg N ha(-1), N150: 150 kg N ha(-1) and N0: 0 kg N ha(-1) for maize; 60 kg N ha(-1) for soybean). Energy yield (EY) was introduced to integrate system productivity, and nitrogen use efficiency (NUEe) and water productivity (WPe) were assessed at the system level. The results showed that nitrogen rate, irrigation level, and their interaction significantly affected crop growth, nitrogen uptake, evapotranspiration, grain yield, EY, economic return, NUEe, and WPe (p < 0.05). The MSN200 + 60W80 achieved the highest grain yield (maize: 17,046.4 kg ha(-1), soybean: 3488.9 kg ha(-1)), EY (35.5 MJ m(-2)), net income (4297.8 $ ha(-1)), NUEe (747.8 MJ kg(-1)), and WPe (591.4 MJ ha(-1) mm(-1)). Compared to MSN250 + 60W100, MSN200 + 60W80 increased energy yield by 11.9%, net income by 28.1%, NUEe by 6.5%, and WPe by 17.3%. The response surface modeling found that the optimal nitrogen rate ranged between 237.3 and 288.1 kg N ha(-)& sup1; (177.3-228.1 kg ha(-)& sup1; for maize), with irrigation at 75-90% ETc maximizing system performance. This study optimized water-nitrogen regimes to improve crop productivity and profitability in maize-soybean strip intercropping systems in arid regions.

Fast and precise estimation of leaf/canopy-scale information is essential for winter wheat field management. This study aimed to improve the estimation accuracy of winter wheat leaf area, photosynthetic pigment and nitrogen contents by integrating UAV remote sensing data with bio-inspired optimized machine learning models. Data collections were at different growth stages during the 2020-2021 and 2021-2022 winter wheat growing seasons. This study extracted 17 Vegetation indices (VIs), eight texture features (TFs), and three color features (CFs) from UAV images, and their relationships with winter wheat parameters were systematically analyzed. The performance of multiple machine learning models on winter wheat leaf/canopy-scale information were evaluated, including traditional machine learning models and bio-inspired optimization models, using two types of input features (the optimally selected VIs alone and combined VIs+TFs+CFs). The results showed that multi-features fusion (VIs+TFs+CFs) significantly improved estimation accuracy compared with using VIs alone. Among the evaluated models, back propagation neural network (BPNN) generally outperformed linear (multiple stepwise regression, MSR) and kernel-based models (support vector machine, SVM). And bio-inspired optimization further enhanced model performance, with Northern Goshawk Optimization (NGO)-based optimization showing the greater improvement than Genetic Algorithm (GA). Across all parameters, canopy chlorophyll b was highest (R2 = 0.811, RMSE = 0.152), while the estimation accuracy of leaf nitrogen content was lowest (R2 = 0.670, RMSE = 0.193). Overall, the integration of UAV multi-features fusion and bio-inspired algorithm optimized machine learning model (NGO-BPNN_VIs+TFs+CFs model) significantly improved the inversion accuracy of leaf area, photosynthetic pigment and nitrogen contents. This provided a reliable and effective method for precision monitoring of winter wheat leaf/canopy information.

Understanding the coordination between aboveground and belowground plant organs is crucial for improving crop productivity in resource-limited environments. However, the integrative mechanisms by which water and nitrogen (N) interactions regulate the covariation of maize leaf and root functional traits frameworks remains poorly understood. A three-year field experiment was conducted to quantitatively evaluate the effects of varying lower irrigation limits (full irrigation: 85% theta f, mild deficit: 70% theta f, severe deficit: 55% theta f, where theta f is the field capacity) and nitrogen application rates (0, 90, 180, and 270 kg ha-1) on maize leaf photosynthetic physiology, leaf morphology, and root architectural traits, so as to elucidate the adaptive trade-offs of the leaf-root system between acquisitive and conservative resource use strategies and their consequences for grain yield (GY) and water productivity (WP). The results indicated that root absorptive traits (total root length density, root surface area) were significantly correlated with leaf photosynthetic traits (net photosynthetic rate, specific leaf area). Under conditions of full irrigation or mild deficit combined with 180 kg N ha-1, maize adopted a resource-acquisitive strategy. This was characterized by a tightly connected trait network where enhanced root foraging capability supported high leaf physiological activity, creating a positive feedback loop that maximized carbon assimilation and biomass accumulation, thereby achieving high GY and WP. Conversely, severe water stress drove the system toward a conservative strategy, reducing network connectivity and prioritizing survival over production, which diminished the yield-promoting effects of N fertilization. Conclusively, mild deficit irrigation with 180 kg N ha-1 optimized the coordination of leaf-root traits, balancing resource capture with efficiency. These findings deepen our understanding of the physiological mechanisms underlying maize yield formation from a whole-plant perspective and provide a theoretical basis for precision water and nitrogen management in arid agricultural systems.

Ridge-furrow planting with plastic film mulch has been widely applied for improving maize production in sub-arid and sub-humid areas around the world, but the effects of various ridge-furrow patterns and film colors on nitrogen fertilizer utilization, net ecosystem carbon budget, and net greenhouse gas emission intensity remain poorly understood. Field trials were undertaken during 2023-2024 to explore the impacts of six mulching cultivation practices, including flat planting without mulch (FN), flat planting with straw mulch (FS), ridge-furrow planting with transparent film mulch on the ridge (RP), ridge-furrow planting with transparent film mulch on continuous ridges (RFCt), ridge-furrow planting with silver-black film mulch on continuous ridges (RFCs), and ridge-furrow planting with black film mulch on continuous ridges (RFCb) on grain yield, nitrogen use efficiency, and soil carbon sequestration of dryland summer maize in the Loess Plateau of China. Relative to FN, the five mulching practices significantly improved grain yield, nitrogen use efficiency, and net ecosystem economic budget, with RFCb exhibiting the largest increases by 51.1% in grain yield, 96.5% in plant nitrogen uptake, 96.5% in nitrogen uptake efficiency, 51.8% in partial factor productivity, 26.5% in nitrogen harvest index, 87.7% in net ecosystem economic budget, and a decrease of 45.9% in soil nitrate residue compared to FN. Compared to FN, FS and RFCb increased net ecosystem carbon budget by 248.9% and 233.3%, decreased net greenhouse gas budget by 637.5% and 286.3%, and reduced net greenhouse gas emission intensity by 639.7% and 179.8%, respectively. Among mulching practices, RFCb was environmentally friendly while obtaining the greatest grain yield, nitrogen use efficiency, and net ecosystem economic budget, and the lowest soil nitrate residue, with its soil carbon sequestration second only to that of FS, which was considered a preferred planting practice for dryland summer maize production in northwest China.

Drought is a serious abiotic factor that impacts plant productivity and threatens global food security. Various pretreatments have been proven to be effective in counteracting the detrimental effects of drought on plants. These include chemical priming agents, exogenous substances and soil amendments/conditioners, which can mitigate drought stress in plants by modulating various morphological, biochemical, and molecular mechanisms, such as stomatal opening, hormone production, osmotic adjustment, drought-responsive gene expression, and the accumulation of both enzymatic and non-enzymatic antioxidants. This literature review summarizes the various pretreatment approaches used to alleviate drought adverse effects in plants and discusses the associated mechanisms. The findings delivered here highlight numerous solutions for mitigating drought and pave the way for future research into developing effective formulations against drought stress in field conditions. (c) 2025 SAAB. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Wheat is a staple crop widely sown in Northwest China, and understanding and modelling evapotranspiration (ET) during the wheat-growing stage is important for irrigation scheduling and the efficient use of agricultural water resources. In this study, a four-year observation was conducted on a spring wheat field with border irrigation (BI) treatment and drip irrigation (DI) treatment, based on two Bowen ratio energy balance (BREB) systems. The results showed that the average ET across the whole growing stage scale was 512.0 mm for the BI treatment and 446.9 mm for the DI treatment, and the DI treatment reduced ET by 65.1 mm across the growing stage scale. The driving factors of the changes in ET in the two treatments were investigated using partial correlation analysis after understanding the changing pattern of ET. Net radiation (Rn), soil water content (SWC), and leaf area index (LAI) were the main meteorological, soil, and crop factors leading to the changes in ET in the two treatments. In terms of ET simulation, the SWAP model and different types of machine learning algorithms were used in this study to numerically simulate ET at a daily scale. The total ET values simulated by the SWAP model at the interannual scale were 11.0–14.2% lower than the observed values of ET, and the simulation accuracy varied at different growing stages. In terms of the machine learning simulation of ET, this study is the first to apply five machine learning algorithms to simulate a typical irrigated wheat field in the arid region of Northwest China. It was found that the Stacking algorithm as well as the SWAP model had the optimal simulation among all machine learning algorithms. These findings can provide a scientific basis for irrigation management and the efficient use of agricultural water resources in spring wheat fields in arid regions.

The planting density regulation and nitrogen fertilizer application have been widely utilized in maize (Zea mays L.) production. Improving the canopy structure can enhance the light energy interception and photosynthetic capacity of maize. However, the mechanisms by which canopy structure influences maize yield under varying planting densities and nitrogen rates remain poorly understood. A two-year field experiment was conducted in northwest China, with three planting densities (LD: 80,000 plants ha-1; MD: 100,000 plants ha-1; HD: 120,000 plants ha-1) and four nitrogen rates (N0: 0 kg N ha-1; N1: 120 kg N ha-1; N2: 180 kg N ha-1; N3: 240 N kg ha-1). Increasing planting density increased SDLA by 29.6 % in upper layer, 20.4 % in middle layer, and 12.7 % in lower layer compared to LD. Compared to N0, nitrogen application increased SDLA in middle layer by 30.0 %, in upper layer by 17.0 %, in lower layer by 11.6 %, separately. Compared to N0, the leaf base angle of the upper layer showed a significant increase by 6.4 % in N1, by 8.3 % in N2, and by 13.3 % in N3. Leaf base angle in N3 exhibited significantly increase by 10.5 % in middle layer and 11.1 % in lower layer. Compared to LD, the aboveground dry matter accumulation in the middle layer showed an increase of 23.0 % in MD, with no significant difference between MD and HD. In comparison to the aboveground dry matter accumulation of each layer under N0, the aboveground dry matter accumulation increased by 18.7 % in N1, 31.2 % in N2, and 33.3 % in N3, respectively. Increasing planting density led to a 10.8 % increase in IPAR in MD and a 17.1 % increase in HD over the entire reproductive period compared to LD, respectively. The MDN3 and MDN2 produced the highest grain yields in both years, at 17644 kg ha-1 and 16217 kg ha-1, respectively. Structural equation modeling indicated that nitrogen fertilization and planting density affected the intercepted light energy and ultimately dry matter accumulation and yield by influencing leaf basal angle and SDLA. This study contributes to a deeper understanding of the mechanisms behind yield variations under different planting densities and nitrogen rates, providing a scientific foundation for developing high-yield planting strategies.

Intercropping greatly affects canopy structure compared to monocropping, which in turn leads to changes in light distribution and subsequently crop yields. However, the light distribution, interception and use efficiency of maize-soybean strip intercropping systems with various row configurations, and especially the relationships between border row proportion or band width proportion and light utilisation and grain yield are still poorly understood. A two-season (2022 and 2023) field experiment was performed on maize and soybean under drip fertigation in the arid northwest China, with eight intercropping patterns and two controls of monocropping maize and soybean. Plant growth, grain yield and canopy photosynthetically active radiation were measured, and light interception fraction was simulated using a strip crop structure model. The results showed that intercropping reduced cumulative light interception of maize and soybean by 11.2% and 81.0% on average, respectively. The cumulative light interception of intercropping system was 13.0% smaller than that of monocropping soybean, but 13.7% greater than that of monocropping maize. Intercropping decreased light use efficiency of maize by 10.2%, but increased light use efficiency of soybean by 138.8% compared to monocropping. The spatial light distribution in intercropping varied greatly in the morning, midday, and afternoon compared to that of monocropping, especially for soybean. The band width proportion was significantly correlated with cumulative light interception of both maize and soybean as well as light use efficiency and grain yield of soybean. The border row proportion was significantly correlated with aboveground biomass and grain yield of both maize and soybean as well as light use efficiency of maize. When the border row proportion of maize was high and border row proportion of soybean was moderate (i.e., two rows of maize alternating with four rows of soybean), the grain yield of intercropping system was maximised. This study provides important information for improving intercropping models and optimising light distribution in maize-soybean strip intercropping systems.

Leaf photosynthesis plays an important role in maize growth and yield components due to its involvement in dry matter partitioning and organ formation. Nevertheless, how varying planting patterns affect maize leaf photosynthesis, chlorophyll fluorescence and subsequently maize yield remains poorly understood, particularly at various nitrogen rates. A two-season field experiment was performed on rainfed maize in 2021 and 2022 to explore the responses of photosynthetic physiological characteristics, leaf N and chlorophyll contents, chlorophyll fluorescence parameters, grain yield and water productivity to various planting patterns and N rates. The experiment included six planting patterns, i.e., flat planting without mulching (CK), flat planting with straw mulching (SM), ridge mulched with transparent film and furrow without mulching (RP1), flat planting with full transparent film mulching (FM1), ridge mulched with black film and furrow without mulching (RP2), and flat planting with full black film mulching (FM2). Additionally, there were two nitrogen rates, i.e., 0 kg N ha−1 (N0) and 180 kg N ha−1. The results showed that nitrogen application significantly improved leaf physiological characteristics. Under various planting patterns, leaf photosynthetic pigments, leaf area duration, leaf nitrogen content, QYmax and ΦPSII ranked as RP2 > RP1(FM2) > FM1 > SM(CK) in 2021, and RP2(RP1) > FM1(FM2) > SM(CK) in 2022. No significant variations were observed in water productivity (WP) among different film colors, with overall performance of RP2(FM2) > RP1(FM1) > SM > CK. WP significantly improved by 36.14% and 25.15% under N1 compared to N0 in 2021 and 2022, respectively. This pattern paralleled the fluctuation in water consumption intensity. Compared to CK, RP significantly increased leaf nitrogen content (29.3%), total Chl content (16.0%), QYmax (6.39%), ΦPSII (32.01%), and net photosynthesis rate (14.2%), thereby significantly improving grain yield (46.35%) and WP (27.69%), while reducing evapotranspiration (6.84%). Yield performance ranked as RP2 > (RP1 and FM2) > FM1 > SM > CK in 2021 and RP2 > RP1 > (FM1 and FM2) > SM > CK in 2022. Overall, RP2N1 obtained the highest principal component scores in both years, suggesting great potential to improve leaf photosynthetic physiological characteristics, thereby increasing grain production and ensuring food security in rainfed maize cultivation areas.

Improved soil and crop management practices are essential for enhancing soybean production, but a comprehensive evaluation of agronomic performance, economic benefit and environmental sustainability associated with various soil and crop management practices is still missing. Field experiments were performed on soybean using a split-split plot design on the Loess Plateau of China in 2019 and 2020, with soil water management practice as the main plot (NM: flat cultivation; RF: ridge-furrow cultivation with film mulch; RW: ridge-furrow cultivation with film mulch and supplemental irrigation), nitrogen rate as the sub-plot (N-30: 30 kg N ha(-1) and N-60: 60 kg N ha(-1)) and seeding rate as the sub-sub plot (D-16: 160,000 plants ha(-1) and D-32: 320,000 plants ha(-1)). A multi-level multi-objective fuzzy comprehensive evaluation model was established for analysis. The results indicated that the soil water-nitrogen management practice and seeding rate (p < 0.01) as well as the interaction between nitrogen rate and seeding rate significantly (p < 0.01) affected the growth, photosynthetic capacity, grain yield and its components, seed quality, economic benefits and resource use efficiency of soybean. The RWN30D32 treatment achieved the highest ranking for multi-objective optimization in both growing seasons. Compared with the local management strategy (NMN60D16), RWN30D32 significantly (p < 0.05) increased the canopy photosynthetic capacity, biological yield, grain yield, seed protein yield, seed oil yield, net income, ratio of output over input costs, crop water productivity, partial factor productivity of nitrogen and radiation use efficiency by 22.8 %, 66.7 %, 35.9 %, 20.1 %, 23.9 %, 69.8 %, 28.4 %, 29.0 %, 171.8 % and 18.5 %, while it decreased the hundred-grain weight, number of pods, seed protein content, seed oil content, energy use efficiency and carbon productivity by 5.4 %, 34.2 %, 11.6 %, 8.8 %, 20.5 % and 69.0 %, respectively. In conclusion, the optimized management practice exhibited significant improvements agronomic performance, economic benefits and resources use efficiency, which was recommended for sustainable soybean production on the Loess Plateau of China.