Search Results (1,861)

The quality of rice, one of the most important food crops in the world, is directly related to people’s dietary experience and nutritional health. With the improvement in living standards, consumer requirements for the taste quality of rice are becoming increasingly strict. Japonica rice occupies an important position in rice production due to its rich genetic diversity and excellent agronomic characteristics. In this study, LJ433, JY653, LJ218, LJ177, LY66, and LX21, which are mainly popularized in northern China and have different taste values, were selected as the experimental subjects, and YJ219, which won the gold award in the third China high-quality rice variety taste quality evaluation, was taken as the control (CK). Low-field nuclear magnetic resonance and spectral analysis were adopted as the main detection techniques. The effects of free water (peak area increased by 13.24–86.68% when p < 0.05), bound water, appearance characteristics (such as chalkiness, which decreased by 18.48–86.48%), and chemical composition (amylose content decreased by 3.76–26.47%) on the taste value of rice were systematically analyzed, and a multi-dimensional “appearance–palatability–nutrition” evaluation system was constructed. The experimental results indicated that increasing the free water content, reducing the chalkiness and chemical component content could significantly improve the taste value of rice (p < 0.05). The results of this research provide a theoretical basis for breeding new high-yield and high-quality rice varieties and have guiding significance for the practice of rice planting and processing. Full article

Choosing appropriate tillage methods and nitrogen application are important steps in the management of wheat production for obtaining high-yield and high-quality products, as well as managing the level of weed infestation. The aim of this research was to examine the impacts of three different tillage practices (conventional tillage—CT, mulch tillage—MT, and no tillage—NT), and two top dressing fertilization nitrogen levels (rational—60 kg ha⁻¹ and high—120 kg ha⁻¹) on the grain yield and quality of winter wheat, as well as on weed infestation. The present study was carried out in field experiments on chernozem luvic type soil at the Faculty of Agriculture Belgrade-Zemun Experimental field trial “Radmilovac”, in the growing seasons of 2020/2021–2022/2023. The C/N ratio in the soil was also assessed on all plots. The results showed that the number of weeds and their fresh and air-dry weights were higher on the MT and NT plots, compared to the CT plots. Therefore, the CT system has better effects on the yield (5.91 and 5.36 t ha⁻¹) and the protein content (13.3 and 13.1%). Furthermore, the grain weight per spike and the 1000-grain weight were higher in the wheat from the CT system (41.83 and 42.75 g) than from the MT (40.34 and 41.49 g) and NT (40.26 and 41.08 g) systems. Also, the crops from the CT system had higher values of grain density and grain uniformity compared to the crop from the MT and NT systems. Fertilization with a high nitrogen level (120 kg ha⁻¹) causes higher grain yield and more weediness compared with the rational level (60 kg ha⁻¹). Top dressing fertilization in each tillage system resulted in an increase in the number of weeds, but, at the same time, it also resulted in stronger competitive ability of the wheat crop against weeds. The most favorable C/N ratio occurred on the NT plots, and the least beneficial one on the CT ones. A correlation analysis showed strong negative correlations of number (r = −0.82) and fresh weed mass (r = −0.72) with yield. It is concluded that the conventional tillage practice with a low nitrogen dose manifests its superior performance in minimizing weed infestation and maximizing crop productivity. Full article

Straw mulching is an important strategy for regulating soil moisture, nutrient availability, and thermal conditions in agricultural systems. However, the mechanisms by which the mulching period, thickness, and planting density interact to influence yield formation in wheat–soybean rotation systems remain insufficiently understood. In this study, we systematically examined the combined effects of straw mulching at the seedling and jointing stages of winter wheat, as well as varying mulching thicknesses and soybean planting densities, on soil properties and crop yields through field experiments. The experimental design included straw mulching treatments during the seedling stage (T₁) and the jointing stage (T₂) of winter wheat, with soybean planting densities classified as low (D₁, 1.8 × 10⁵ plants·ha⁻¹) and high (D₂, 3.6 × 10⁵ plants·ha⁻¹). Mulching thicknesses were set at low (S₁, 2830.19 kg·ha⁻¹), medium (S₂, 8490.57 kg·ha⁻¹), and high (S₃, 14,150.95 kg·ha⁻¹), in addition to a no-mulch control (CK) for each treatment. The results demonstrated that (1) straw mulching significantly increased soil water content in the order S₃ > S₂ > S₁ > CK and exerted a temperature-buffering effect. This resulted in increases in soil organic carbon, available phosphorus, and available potassium by 1.88−71.95%, 1.36−165.8%, and 1.92−36.34%, respectively, while decreasing available nitrogen content by 1.42−17.98%. (2) The T₁ treatments increased wheat yields by 1.22% compared to the control, while the T₂ treatments resulted in a 23.83% yield increase. Soybean yields increased by 23.99% under D₁ and by 36.22% under D₂ treatments. (3) Structural equation modeling indicated that straw mulching influenced yields by modifying interactions among soil organic carbon, available nitrogen, available phosphorus, available potassium, bulk density, soil temperature, and soil water content. Wheat yields were primarily regulated by the synergistic effects of soil temperature, water content, and available potassium, whereas soybean yields were determined by the dynamic balance between organic carbon and available potassium. This study provides empirical evidence to inform the optimization of straw return practices in wheat–soybean rotation systems. Full article

A field experiment in growing spring wheat (Triticum aestivum L.—cv. ‘Monsun’) under organic, integrated and conventional farming systems was conducted over the period of 2020–2022 at the Czesławice Experimental Farm (Lubelskie Voivodeship, Poland). The first experimental factor analyzed was the farming system: A. organic system (control)—without the use of chemical plant protection products and NPK mineral fertilization; B. conventional system—the use of plant protection products and NPK fertilization in the range and doses recommended for spring wheat; C. integrated system—use of plant protection products and NPK fertilization in an “economical” way—doses reduced by 50%. The second experimental factor was irrigation strategy: 1. no irrigation—control; 2. double irrigation; 3. multiple irrigation The aim of the research was to determine the physical, chemical, and enzymatic properties of loess soil under spring wheat crops as influenced by the factors listed above. The highest organic C content of the soil (1.11%) was determined in the integrated system with multiple irrigation of spring wheat, whereas the lowest one (0.77%)—in the conventional system without irrigation. In the conventional system, the highest contents of total N (0.15%), P (131.4 mg kg⁻¹), and K (269.6 mg kg⁻¹) in the soil were determined under conditions of multiple irrigation. In turn, the organic system facilitated the highest contents of Mg, B, Cu, Mn, and Zn in the soil, especially upon multiple irrigation of crops. It also had the most beneficial effect on the evaluated physical parameters of the soil. In each farming system, the multiple irrigation of spring wheat significantly increased moisture content, density, and compaction of the soil and also improved its total sorption capacity (particularly in the integrated system). The highest count of beneficial fungi, the lowest population number of pathogenic fungi, and the highest count of actinobacteria were recorded in the soil from the organic system. Activity of soil enzymes was the highest in the integrated system, followed by the organic system—particularly upon multiple irrigation of crops. Summing up, the present study results demonstrate varied effects of the farming systems on the quality and health of loess soil. From a scientific point of view, the integrated farming system ensures the most stable and balanced physicochemical and biological parameters of the soil due to the sufficient amount of nutrients supplied to the soil and the minimized impact of chemical plant protection products on the soil. The multiple irrigation of crops resulting from indications of soil moisture sensors mounted on plots (indicating the real need for irrigation) contributed to the improvement of almost all analyzed soil quality indices. Multiple irrigation generated high costs, but in combination with fertilization and chemical crop protection (conventional and integrated system), it influenced the high productivity of spring wheat and compensated for the incurred costs (the greatest profit). Full article

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts. Full article

Excessive chemical fertilizers degrade soil and increase greenhouse gas (GHG) emissions. Organic substitution of nitrogen fertilizers is recognized as a sustainable agricultural-management practice, yet its dual role in carbon sequestration and emissions renders the net GHG balance (NGHGB) uncertain. To assess the GHG mitigation potential of organic substitution strategies, this study analyzed GHG fluxes, soil organic carbon (SOC) dynamics, indirect GHG emissions, and Net Primary Productivity (NPP) based on a long-term field positioning experiment initiated in 2016. Six fertilizer regimes were systematically compared: no fertilizer control (CK); only phosphorus and potassium fertilizer (PK); total chemical fertilizer (NPK); 1/3 chemical N substituted with sheep manure (OF1); dual substitution protocol with 1/6 chemical N substituted by sheep manure and 1/6 substituted by straw-derived N (OF2); complete chemical N substitution with sheep manure (OF3). The results showed that OF1 and OF2 maintained crop yields similar to those under NPK, whereas OF3 reduced yield by over 10%; relative to NPK, OF1, OF2, and OF3 significantly increased SOC sequestration rates by 50.70–149.20%, reduced CH₄ uptake by 7.9–70.63%, increased CO₂ emissions by 1.4–23.9%, decreased N₂O fluxes by 3.6–56.2%, and mitigated indirect GHG emissions from farm inputs by 24.02–63.95%. The NGHGB was highest under OF1, 9.44–23.99% greater than under NPK. These findings demonstrate that partial organic substitution increased carbon sequestration, maintained crop yields, whereas high substitution rates increase the risk of carbon emissions. The study results indicate that substituting 1/3 of chemical nitrogen with sheep manure in maize cropping systems represents an effective fertilizer management approach to simultaneously balance productivity and ecological sustainability. Full article

Potato (Solanum tuberosum L.) is a chlorine-sensitive crop. When soil Cl⁻ concentrations exceed optimal thresholds, the yield and quality of potatoes are limited. Consequently, chloride-containing fertilizers are rarely used in actual agricultural production. Therefore, two years of field experiments under natural rainfall regimes with three chlorine application levels (37.5 kg ha⁻¹/20 mg kg⁻¹, 75 kg ha⁻¹/40 mg kg⁻¹, and 112.5 kg ha⁻¹/60 mg kg⁻¹) were conducted to investigate the leaching characteristics of Cl⁻ in field soils with two typical textures for Northeast China (loam and sandy loam soils). In this study, the reliability of Cl⁻ residual estimation models across different soil types was evaluated, providing critical references for safe chlorine-containing fertilizer application in rain-fed potato production systems in Northeast China. The results indicated that the leaching efficiency of Cl⁻ was significantly positively correlated with both the rainfall amount and the chlorine application rate (p < 0.01). The Cl⁻ migration rate in sandy loam soil was significantly greater than that in loam soil. However, the influence of soil texture on the Cl⁻ leaching efficiency was only observed at lower rainfall levels. When the rainfall level exceeded 270 mm, the Cl⁻ content in all the soil layers became independent of the rainfall amount, soil texture, and chlorine application rate. Under rain-fed conditions, KCl application at 80–250 kg ha⁻¹ did not induce Cl⁻ accumulation in the primary potato root zone (15–30 cm), suggesting a low risk of toxicity. In loam soil, the safe application range for KCl was determined to be 115–164 kg ha⁻¹, while in sandy loam soil, the safe KCl application range was 214–237 kg ha⁻¹. Furthermore, a predictive model for estimating Cl⁻ residuals in loam and sandy loam soils was validated on the basis of rainfall amount, soil clay content, and chlorine application rate. The model validation results demonstrated an exceptional goodness-of-fit between the predicted and measured values, with R² > 0.9 and NRMSE < 0.1, providing science-based recommendations for Cl-containing fertilizer application to chlorine-sensitive crops, supporting both agronomic performance and environmental sustainability in rain-fed systems. Full article

Compound drought and heatwave (CDHW) events have shown a marked increase under global warming, posing significant challenges to crop productivity. This review systematically categorizes key input and output datasets utilized across diverse research frameworks that investigate the impacts of CDHW stress on crops. The data are organized across multiple spatial scales—from site-specific and field-level measurements to regional and global assessments—and span various temporal dimensions, including historical records, present conditions, and future projections. These datasets include laboratory experiments, field trials, Earth system observations, statistical records, and model simulations. By employing a structured and integrative approach, this review aims to facilitate efficient data access and utilization for researchers. Ultimately, it supports improved data integration, cross-study comparability, and cross-scale synthesis, thereby advancing the assessment of climate change impacts on agricultural systems. Full article

Herbicide phytotoxicity represented a critical constraint on crop safety in soybean–corn intercropping systems, where early detection of herbicide stress is essential for implementing timely mitigation strategies to preserve yield potential. Current methodologies lack rapid, non-invasive approaches for early-stage prediction of herbicide-induced stress. To develop and validate a spectral-feature-based prediction model for herbicide concentration classification, we conducted a controlled experiment exposing three-leaf-stage vegetable soybean (Glycine max L.) seedlings to aqueous solutions containing three concentrations of nicosulfuron herbicide (0.5, 1, and 2 mL/L) alongside a water control. Hyperspectral imaging of randomly selected seedling leaves was systematically performed at 1, 3, 5, and 7 days post-treatment. We developed predictive models for herbicide phytotoxicity through advanced machine learning and deep learning frameworks. Key findings revealed that the ResNet-18 deep learning model achieved exceptional classification performance when analyzing the 386–1004 nm spectral range at day 7 post-treatment: 100% accuracy in binary classification (herbicide-treated vs. water control), 93.02% accuracy in three-class differentiation (water control, low/high concentration), and 86.53% accuracy in four-class discrimination across specific concentration gradients (0, 0.5, 1, 2 mL/L). Spectral analysis identified significant reflectance alterations between 518 and 690 nm through normalized reflectance and first-derivative transformations. Subsequent model optimization using this diagnostic spectral subrange maintained 100% binary classification accuracy while achieving 94.12% and 82.11% accuracy for three- and four-class recognition tasks, respectively. This investigation demonstrated the synergistic potential of hyperspectral imaging and deep learning for early herbicide stress detection in vegetable soybeans. Our findings established a novel methodological framework for pre-symptomatic stress diagnostics while demonstrating the technical feasibility of employing targeted spectral regions (518–690 nm) in field-ready real-time crop surveillance systems. Furthermore, these innovations offer significant potential for advancing precision agriculture in intercropping systems, specifically through refined herbicide application protocols and yield preservation via early-stage phytotoxicity mitigation. Full article

Greenhouse horticulture plays an important role globally by producing nutritious fruits and vegetables with high resource use efficiency. Modern greenhouses are large-scale high-tech production factories that are increasingly data-driven, and where climate and irrigation control are gradually becoming more autonomous. This is enabled by technological developments and driven by shortages in skilled labor and the demand for improved resource use efficiency. In the Autonomous Greenhouse Challenge, it has been shown that controlling greenhouse cultivation can be done efficiently with intelligent algorithms. For an optimal strategy, however, it is essential that control algorithms properly account for crop responses, which requires appropriate sensors, reliable data, and accurate models. This paper presents the results of the 4th Autonomous Greenhouse Challenge, in which international teams developed six intelligent algorithms that fully controlled a dwarf tomato cultivation, a crop that is well-suited for robotic harvesting, but for which little prior cultivation data exists. Nevertheless, the analysis of the experiment showed that all teams managed to obtain a profitable strategy, and the best algorithm resulted a production equivalent to 45 kg/m²/year, higher than in the commercial practice of high-wire cherry tomato growing. The predominant factor was found to be the much higher plant density that can be achieved in the applied growing system. More difficult challenges were found to be related to measuring crop status to determine the harvest moment. Finally, this experiment shows the potential for novel greenhouse cultivation systems that are inherently well-suited for autonomous control, and results in a unique and rich dataset to support future research. Full article

The rising demand for plant proteins and climate change highligth the need for adaptable legume crops. A three-year field experiment examined forage and seed production, as well as nitrogen (N) and phosphorus (P) accumulation in an indeterminate field bean (Vicia faba L. var. minor Beck) variety, as affected by two fertilization rates (0 and 120 kg N ha⁻¹, i.e., N0 and N120) and two sowing rates (60 and 100 seeds m⁻², i.e., S60 and S100), along with their interaction with climatic variability. Forage yield ranged from 11.1 Mg ha⁻¹ in Year I (S100) to 6.8 Mg ha⁻¹ in Year III (S60 and S100), and seed yield dropped from 4.1 Mg ha⁻¹ in Year II to 1.9 Mg ha⁻¹ in Year III, due to fewer seeds per pod and lower seed weight unaffected by fertilization and sowing rate. Nitrogen fertilization increased forage by 20% but had no effect on seed production. Field bean showed good adaptability to variable climatic conditions, compensating for lower stem number with more pods per stem. The possibility to obtain either forage or seed yield makes field bean a valuable source of plant proteins in a changing environment, contributing to the sustainability of cropping systems. Full article

Peanut productivity is severely restricted by soil salinization and associated nutrient deficiency in saline soil. The quinoa–peanut relay intercrop pattern (IP) is a promising planting system that utilizes the biological advantages of quinoa to improve soil ecological functions and productivity. However, the effects of IP on soil physicochemical and biological properties and the yield formation of the combined peanut crop are still unclear. Two-year field experiments in coastal saline soil were conducted to explore the effects of IP on peanut growth and pod yield, soil physicochemical properties, and microbial community characterization at different growth stages of peanut based on the traditional monocrop pattern (MP). The results show that IP promoted peanut pod yield, although there was the disadvantage of plant growth at an early stage. Soil water content, electrical conductivity (EC), and Na⁺ content in the peanut rhizosphere were lower, whereas K⁺, NH₄⁺, and total organic carbon (TOC) contents were higher in IP systems at both the vegetative and reproductive stages. The pod yield of peanut was significantly negatively correlated with soil EC and Na⁺ contents at the vegetative stage, but positively correlated with K⁺, NO₃⁻, NH₄⁺, PO₄³⁻, and TOC contents at the reproductive stage. IP rebuilt the composition of the soil bacterial community in the peanut rhizosphere and increased the abundance of the beneficial bacterial community, which were positively correlated with soil TOC, K⁺, NH₄⁺, NO₃⁻, and PO₄³⁻ contents. These findings suggest that IP can increase peanut pod yield through optimizing soil physicochemical properties and microbial community composition, and it is a promising planting system for improving agricultural production in coastal saline lands. Full article

As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5–70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R²) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51–56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques’ data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems. Full article

Vertical farming systems offer an efficient solution for sustainable food production in urban areas. However, managing nitrate (NO₃⁻) levels remains a significant challenge for improving crop yield, quality, and safety. This study evaluated the effects of nitrate availability on growth performance, nutrient uptake, and water use efficiency in a vertical hydroponic system that intercropped lettuce (Lactuca sativa) with alfalfa (Medicago sativa). The experiment was conducted in a controlled vertical hydroponic system using Nutrient Film Technique (NFT) channels, with nitrogen levels set at 0, 33, 66, 100, and 133% of the standard concentration. The results indicated that the intercropping treatment with 66% nitrate (IC-N_66%) improved water use efficiency by 38% and slightly increased leaf area compared to the other intercropping treatments. However, the control group, which consisted of a monoculture with full nitrate supply, achieved the highest overall biomass. Ion concentrations, including nitrate, calcium, magnesium, and micronutrients, were moderately affected by the intercropping strategy and nitrate levels. These findings suggest that moderate nitrate input, combined with nitrogen-fixing legumes, can enhance resource efficiency in hydroponic systems without significantly compromising yield. These findings offer a promising framework for incorporating legumes into hydroponic systems, minimizing the need for synthetic inputs while maintaining yield. These results support the use of agroecological intensification strategies in highly efficient soilless systems. Full article

Efficient weed detection in dryland spring wheat fields is crucial for sustainable agriculture, as it enables targeted interventions that reduce herbicide use, minimize environmental impact, and optimize resource allocation in water-limited farming systems. This paper presents HSG-Net, a novel lightweight object detection model based on YOLOv8 for weed identification in dryland spring wheat fields. The proposed architecture integrates three key innovations: an HGNetv2 backbone for efficient feature extraction, C2f-S modules with star-shaped attention mechanisms for enhanced feature representation, and Group Head detection heads for parameter-efficient prediction. Experiments on a dataset of eight common weed species in dryland spring wheat fields show that HSG-Net improves detection accuracy while cutting computational costs, outperforming modern deep learning approaches. The model effectively addresses the unique challenges of weed detection in dryland agriculture, including visual similarity between crops and weeds, variable illumination conditions, and complex backgrounds. Ablation studies confirm the complementary contributions of each architectural component, with the full HSG-Net model achieving an optimal balance between accuracy and resource efficiency. The lightweight nature of HSG-Net makes it particularly suitable for deployment on resource-constrained devices used in precision agriculture, enabling real-time weed detection and targeted intervention in field conditions. This work represents an important advancement in developing practical deep learning solutions for sustainable weed management in dryland farming systems. Full article