Entire Growth Cycle Research Articles

Nutrient Stoichiometry and Tree Development: Insights from a 5-Year Study on Catalpa bungei Fertilization

Short-term fertilization may provide limited improvements in tree growth and demonstrate suboptimal fertilizer efficiency; however, its benefits often fall short of expectations. Unfortunately, research addressing the sustained impacts of prolonged fertilization (e.g., beyond five years) on trees’ developmental dynamics and productivity remains relatively scarce. This study focused on a 7-year-old Catalpa bungei plantation located in Jinan City, Shandong Province, China. The study employed two fertilization techniques: hole fertilization (HF) and integrated water and fertilizer application (WF), with a no-fertilization treatment serving as the control (CK). The findings revealed that the WF significantly enhanced stand productivity. When comparing the different treatments, the productivity of WF stands demonstrated a remarkable increase of 39.7% compared to HF stands and 55.1% compared to CK stands. After five years of fertilization, the stands treated with WF exhibited a significant increase in volume accumulation, reaching 112.36 m3·hm−2. Additionally, the productivity of these WF-fertilized stands achieved an impressive 41.75 m3·hm−2·a−1. Fertilization notably enhanced the nitrogen content in the leaves and fine roots of C. bungei, as well as the potassium content in the coarse roots. These nutrients were found to be more concentrated in the corresponding organs within the WF stands. Over the entire growth cycle, there was a substantial consumption of key nutrients, with leaf nitrogen, phosphorus, and potassium contents decreasing by 30.5%, 18.8%, and 47.3%, respectively. Similarly, the coarse root potassium and fine root phosphorus content decreased by 24.7% and 24.4%, respectively. The enhancement in leaf nitrogen content following fertilization significantly contributed to increases in tree height, breast height diameter (DBH), and individual tree volume. Similarly, the enrichment of potassium in the branches and coarse roots was associated with improvements in DBH and tree volume. To maximize forest stand productivity, the WF fertilization method demonstrated superior results compared to HF. Therefore, WF should be prioritized in future fertilization experiments for C. bungei.

From Spectral Characteristics to Index Bands: Utilizing UAV Hyperspectral Index Optimization on Algorithms for Estimating Canopy Nitrogen Concentration in Carya Cathayensis Sarg

Employing drones and hyperspectral imagers for large-scale, precise evaluation of nitrogen (N) concentration in Carya cathayensis Sarg canopies is crucial for accurately managing nitrogen fertilization in C. cathayensis Sarg cultivation. This study gathered five sets of hyperspectral imagery data from C. cathayensis Sarg plantations across four distinct locations with varying environmental stresses using drones. The research assessed the canopy nitrogen concentration of C. cathayensis Sarg trees both during singular growth periods and throughout their entire growth cycles. The objective was to explore the influence of band combinations and spectral index formula configurations on the predictive capability of the hyperspectral indices (HIs) for canopy N concentration (CNC), optimize the performance between HIs and machine learning approaches, and validate the efficacy of optimized HI algorithms. The findings revealed the following: (i) Optimized HIs demonstrated optimal predictive performance during both singular growth periods and the full growth cycles of C. cathayensis Sarg. The most effective HI model for singular growth periods was the optimized–modified–normalized difference vegetation index (opt-mNDVI), achieving an adjusted coefficient of determination (R2) of 0.96 and a root mean square error (RMSE) of 0.71. For the entire growth cycle, the HI model, also opt-mNDVI, attained an R2 of 0.75 and an RMSE of 2.11; (ii) optimized band combinations substantially enhanced HIs’ predictive performance by 16% to 71%, while the choice between three-band and two-band combinations influenced the predictive capacity of optimized HIs by 4% to 46%. Hence, utilizing optimized HIs combined with Unmanned Aerial Vehicle (UAV) hyperspectral imaging to evaluate nitrogen concentration in C. cathayensis Sarg trees under complex field conditions offers significant practical value.

Optimization of a Boom Height Ultrasonic Detecting Model for the Whole Growth Cycle of Wheat: Affected by the Oscillation of the Three-Section Boom of the Sprayer

In the dynamic operation of a boom sprayer, the boom oscillation will cause the detection value of the boom height to fluctuate greatly, resulting in failures of the control system. Based on the previously developed static boom height detection model for the entire wheat growth cycle, this study aimed to optimize the model to reduce the impact of boom oscillation on the accuracy of boom height detection. Three ultrasonic sensors were installed on each section boom of a three-section boom sprayer, and dynamic boom height detection tests were conducted at vehicle speeds of 4 to 8 km/h across six growth stages of winter wheat in Beijing, a total detection area within a single fixed operational row of approximately 14 ha. The test results showed that as vehicle speed increased, boom oscillations intensified across all sections. By setting the boom oscillation correction parameters, the detecting value of each section of boom height is corrected. The results show that the fluctuation and deviation degree of the boom height-detecting value are obviously reduced, and the correction effect is obvious. Further analysis of the detecting value of the boom height after the correction shows that the previously established detection model still maintains high detection accuracy under dynamic conditions; that is, the detection position of the ultrasonic sensor does not downward shift. This paper provides a low-cost technical method that can be directly applied to the dynamic detection of boom height.

Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields.

Transcriptome-level analysis of gene expressions in different tissues of tomato and key gene identifications during seed germination

Exploring the spatiotemporal expression patterns of genes is the foundation for studying their functions and molecular mechanisms. Genes with stable spatiotemporal expression patterns are often used as reference genes for quantitative real-time polymerase chain reaction (qRT-PCR) and genes are significantly highly expressed in a specific tissue or stage often represent their unique biological functions. Tomato is an important economic crop and a model plant for scientific researches. However, the existing reference genes in tomatoes are not stable enough and researches on tissue-specific genes have been more focused on flowers and fruits, with less emphasis on studying the seed germination process. In this study, a total of 26 tissue/organ samples from the entire growth cycle of tomatoes were collected from Micro-Tom (Solanum lycopersicum cv. Micro-Tom). After RNA sequencing, a transcriptome database of the entire growth cycle of tomatoes was constructed. Solyc01g107870.3 gene was identified as a novel reference gene through systematic analysis and validated by qRT-PCR. Moreover, tissue-specific genes in seed and 8 days past germination (DPG) seedlings are screened. Among them, SlLEA16 gene belongs to late embryogenesis abundant protein (LEA) gene family, having subcellular localization in the nucleus and cytoplasm, and playing important roles in seed germination initiation. The above experimental results provide data support for studying tomato gene spatiotemporal expression patterns and can also serve as an experimental basis for investigating the deeper mechanisms of tomato seed germination processes.

Research on Estimating Potato Fraction Vegetation Coverage (FVC) Based on the Vegetation Index Intersection Method

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure the accurate extraction of coverage information throughout the crop’s entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top six vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2 = 0.9116, RMSE = 5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2 = 0.8987, RMSE = 5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality.

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

Quantitative Assessment of UAV Assisted Particle Spraying Distribution in Agriculture

The overall well-being and productivity of crops rely on a series of interconnected processes throughout their entire growth cycle. Among these processes, the quality of spraying plays a pivotal role in maintaining crop health and ensuring increased productivity. An Unmanned Aerial Vehicles assisted particle spraying system in agriculture involves the use of Unmanned Aerial Vehicles equipped with specialized equipment to distribute particles such as plant protection products, fertilizers, or other agricultural inputs over crops. This technology offers several advantages over traditional ground-based methods, including increased efficiency, precision, and reduced environmental impact. The effectiveness of spraying, in turn, hinges on various factors, one of which is the even distribution of spraying droplets. Consequently, there exists a need for a reliable, consistent, precise, and accurate automated method to assess the parameters governing this distribution. In this study, a methodology is introduced for evaluating the quality of plant spraying, and the results of this method's testing are presented. Data is gathered by employing water-sensitive papers positioned on the crops, which are then scanned using an industrial-grade camera. Subsequently, this data undergoes processing through image analysis algorithms using Matlab. The outcomes of the research demonstrate the robustness of the proposed methodology in obtaining the essential data required for determining spraying distribution. Compared to existing solutions, the presented approach offers increased reliability, consistency, precision, and automation, thereby addressing the need for a more reliable and accurate method of assessing spraying quality in agriculture.

Low-Cost Imaging to Quantify Germination Rate and Seedling Vigor across Lettuce Cultivars.

The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce (Lactuca sativa) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the 'Muir' cultivar to have a larger canopy size and better germination, though 'Sparx' and 'Crispino' surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests (R2 = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.

Environmental efficacy of polyethylene microplastics: Enhancing the solidification of CuO nanoparticles and reducing the physiological toxicity to peanuts

Microplastics and metal-based nanoparticles (NPs) are environmental pollutants that have attracted significant attention. However, there have been relatively few studies on the combined pollution of these substances in the soil-plant system. To investigate the environmental impact and interaction mechanisms of these two pollutants, a pot experiment was conducted to examine the effects of soil exposure on peanut growth. The experiment results revealed that polyethylene (PE) had a minimal effect on peanut growth, while CuO NPs significantly inhibited peanut growth. Peanut biomass decreased by over 50 % in all Cu treatments. The presence of PE significantly impacted the dissolution and absorption of CuO NPs. When 0.5 % PE was present, the dissolution and transformation of CuO NPs were limited, resulting in a total Cu concentration of 458 mg/kg. Conversely, when 5 % PE was present, the dissolution and transformation of CuO NPs were promoted, leading to a DTPA-Cu concentration of 141 mg/kg, the highest level observed. The distribution of trace elements in peanut stems also responded to the differences in Cu concentration. Both pollutants significantly disrupted soil bacteria, with CuO NPs having a more pronounced effect than PE. Throughout the entire growth cycle of peanuts, no chemical adsorption occurred between PE and CuO NPs, and CuO NPs had no significant impact on the aging rate of PE. In summary, this study provides insights into the environmental impact and transport mechanisms of composite pollution involving microplastics and metal-based nanoparticles in the soil-peanut system.

Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.

Fluctuations in Radiation Use Efficiency Throughout the Growth Cycle in Diploid Potato Crop

The capture of incoming solar radiation under unlimited light, water, and nutrient conditions by plant canopies and converting it into biomass has been described as radiation use efficiency (RUE). RUE has been computed as a function of biomass accumulation and intercepted photosynthetically active radiation without considering the loss of photoassimilates due to respiratory processes. This study evaluated the RUE in diploid potato crop (Solanum phureja Juz. et Buk.) across six experiments in Colombia. Total biomass was measured during the crop season from the early vegetative stage through maturity. However, this proposal involves not only the total biomass accumulated concerning the amount of photosynthetically active radiation intercepted but also took into account the losses by respiration, following Thornley respiration approach. This research demonstrates that the RUE is not a constant value as the respiration process leads to RUE values being variable in a non-linear way over time. The daily RUE simulation, conducted through an interpolation process, revealed significant variation from emergence to the end of the cycle. This indicates an error in assuming a constant RUE throughout the entire growth period, particularly in assessing its physiological impact across the entire growth and development crop cycle.

Dhurrin content and biomass yield in sorghum hybrids throughout plant growth cycle

AbstractIn recent years, the use of sorghum [Sorghum bicolor (L.) Moench] in crop rotation as a green manure to decrease nematode infestation and promote the growth of subsequent crops has strongly increased. The reason why sorghum is effective in biofumigation against root‐knot nematodes is because it releases dhurrin, a cyanogenic glycoside found in epidermal leaf tissue. Currently, there is little information regarding dhurrin production deriving from dhurrin concentration and biomass produced. A field study was conducted in northeastern Italy to investigate the change in dhurrin content and biomass production of three commercial sorghum‐sudangrass cultivars (Hay Day, Mataco, and Ruzrok), over their entire growth cycle under seeding dates (May and July). Regardless of the growing period, Ruzrok displayed a higher dhurrin production. During the early growth stages, Ruzrok did not exhibit a higher yield compared with Mataco and Hay Day, hence its higher potential as a biofumigant is due to the higher concentration of dhurrin in plant tissues. During spring conditions, all tested cultivars showed higher dhurrin production up to a height of 100 cm, while the amount of dhurrin did not significantly change during the entire growing period in summer.

An integrated method for phenotypic analysis of wheat based on multi-view image sequences: from seedling to grain filling stages.

Wheat exhibits complex characteristics during its growth, such as extensive tillering, slender and soft leaves, and severe organ cross-obscuration, posing a considerable challenge in full-cycle phenotypic monitoring. To address this, this study presents a synthesized method based on SFM-MVS (Structure-from-Motion, Multi-View Stereo) processing for handling and segmenting wheat point clouds, covering the entire growth cycle from seedling to grain filling stages. First, a multi-view image acquisition platform was constructed to capture image sequences of wheat plants, and dense point clouds were generated using SFM-MVS technology. High-quality dense point clouds were produced by implementing improved Euclidean clustering combined with centroids, color filtering, and statistical filtering methods. Subsequently, the segmentation of wheat plant stems and leaves was performed using the region growth segmentation algorithm. Although segmentation performance was suboptimal during the tillering, jointing, and booting stages due to the glut leaves and severe overlap, there was a salient improvement in wheat leaf segmentation efficiency over the entire growth cycle. Finally, phenotypic parameters were analyzed across different growth stages, comparing automated measurements of plant height, leaf length, and leaf width with actual measurements. The results demonstrated coefficients of determination ( ) of 0.9979, 0.9977, and 0.995; root mean square errors (RMSE) of 1.0773 cm, 0.2612 cm, and 0.0335 cm; and relative root mean square errors (RRMSE) of 2.1858%, 1.7483%, and 2.8462%, respectively. These results validate the reliability and accuracy of our proposed workflow in processing wheat point clouds and automatically extracting plant height, leaf length, and leaf width, indicating that our 3D reconstructed wheat model achieves high precision and can quickly, accurately, and non-destructively extract phenotypic parameters. Additionally, plant height, convex hull volume, plant surface area, and Crown area were extracted, providing a detailed analysis of dynamic changes in wheat throughout its growth cycle. ANOVA was conducted across different cultivars, accurately revealing significant differences at various growth stages. This study proposes a convenient, rapid, and quantitative analysis method, offering crucial technical support for wheat plant phenotypic analysis and growth dynamics monitoring, applicable for precise full-cycle phenotypic monitoring of wheat.

Effects of water and fertilizer regulation on soil physicochemical properties, bacterial diversity and community structure of Panax notoginseng

Improper irrigation and fertilization can result in soil compaction, solidification, and acidification, consequently disrupting the equilibrium of the soil microbial community. The objective was to enhance the microbial environment in Panax notoginseng soil, augment bacterial diversity, increase community abundance, and establish a scientific and theoretical foundation for Panax notoginseng cultivation. Panax notoginseng field planting experiment was carried out from 2019 to 2021, involving two controlled variables: irrigation (W) and fertilizer application (F). Three irrigation levels were established, denoted as W1 (0.6 FC), W2 (0.75 FC), and W3 (0.9 FC). A total of 1440 kg•hm−2 of soluble organic fertilizer was applied throughout the entire growth cycle, with varying proportions allocated to each growth stage. Four fertilization levels were designated as F1 (rooting stage: seedling stage: flowering stage: fruiting stage = 25 %: 25 %: 25 %: 25 %), F2 (20 %: 25 %: 30 %: 25 %), F3 (15 %: 30 %: 30 %: 25 %), and F4 (10 %: 40 %: 20 %: 30 %), total 12 treatments. Non-irrigated and non-fertilized CK treatments were employed as control. Soil physicochemical attributes, bacterial diversity, community structure, and functional characteristics across various water and fertilizer treatments were comprehensively assessed utilizing the quotient-weighted TOPSIS method. The results showed that coupled irrigation and fertilization treatments had a significant impact on various soil parameters of Panax notoginseng, including total nitrogen, nitrate nitrogen, ammonium nitrogen, organic carbon, pH, water content, and bacterial alpha diversity (P < 0.05). Moreover, coupled irrigation and fertilization treatments had a highly significant influence on soil water content during the rooting stage, seedling stage, and fruiting stage (P < 0.01). Total nitrogen, nitrate nitrogen, ammonium nitrogen, water content, and pH emerged as the primary environmental factors significantly shaping the structure of the soil bacterial community. In the W2F4 treatment, soil total nitrogen, organic carbon content, pH, Chao1, Shannon index, and functional abundance in categories such as chemoheterotrophy, aerobic chemoheterotrophy, nitrogen fixation, nitrate reduction, nitrogen respiration, and nitrate respiration were notably higher compared to other water-fertilized treatments. Furthermore, the W2F4 treatment ranked first in the TOPSIS comprehensive evaluation. Consequently, the W2F4 treatment emerged as the optimal irrigation-fertilization strategy for enhancing soil microbial activity and fertility in Panax notoginseng cultivation. This study establishes a theoretical foundation for enhancing the microbiological environment of Panax notoginseng soils, augmenting soil bacterial diversity, increasing community abundance, and ultimately elevating soil fertility for the efficient and high-quality cultivation of Panax notoginseng.

Effects of Different Tillage and Fertilization Methods on the Yield and Nitrogen Leaching of Fragrant Rice

Conservation tillage and deep-side fertilization both hold the potential to reduce nitrogen leaching and improve grain yield and nitrogen use efficiency in fragrant rice cultivation practices. However, the combined impact of different tillage practices with deep-side fertilization on nitrogen leaching remains uncertain. Therefore, this study conducted on-site experiments for four rice-growing seasons in both early and late seasons in 2018 and 2019 using the fragrant rice varieties “Meixiangzhan 2” (MX) and “Xiangyaxiangzhan” (XY). The four experimental treatments included the following: conventional tillage with regular fertilization (T1), conventional tillage with simultaneous deep fertilization (T2), reduced tillage with simultaneous deep fertilization (T3), and no-tillage with simultaneous deep fertilization (T4). Our results indicate that the T4 treatment exhibited higher nitrogen leaching rates and potential nitrogen losses throughout the entire rice growth cycle, with a 4.51% increase in total mineral nitrogen leaching (TMNL) and a 1.86% increase in potential nitrogen leaching compared to T1 treatment. In contrast, the T2 treatment demonstrated the lowest nitrogen leaching rate, resulting in a 6.01% reduction in TMNL and a 9.57% decrease in potential nitrogen leaching compared to T1, demonstrating the most optimal performance. It is important to note that a reduction in nitrogen leaching does not directly translate into an increase in rice yield. Our study involved the cultivation of two fragrant rice varieties, ‘Meixiangzhan2’ (MX) and ‘Xiangyaxiangzhan’ (XY), and the results revealed some interesting insights. For MX, the T1 treatment resulted in lower daily grain outputs compared to the other treatments, with disparities ranging from 5.35% to 9.94%. Similarly, for XY, the T1 treatment yielded significantly lower daily grain outputs compared to the other treatments, with discrepancies ranging from 6.26% to 10.81% during the late season of 2019. Therefore, this study suggests that conventional tillage combined with deep fertilizer application can be considered as an effective agricultural strategy to reduce nitrogen leaching and enhance fragrant rice yields.

Farmland Carbon and Water Exchange and Its Response to Environmental Factors in Arid Northwest China

Carbon neutrality is an important target in China’s efforts to combat the climate crisis. The implementation of carbon neutrality requires high crop yields in farmland ecosystems of arid regions. However, the responses of farmland ecosystems to environmental changes and their effects on the conversion and intensity of carbon sources/sinks within farmlands in arid regions remain unclear, which limits carbon sequestration. In this study, we used a set of eddy covariance systems to observe carbon and water fluxes in cotton and spring maize, two typical crops in arid regions of Northern Xinjiang in China. The carbon and water exchange and water use efficiency (WUE) of cotton and spring maize were evaluated over the entire growth cycle with respect to changes in the environment. Our results show that the carbon sequestration capacity of farmland ecosystems in arid regions is undeniable and is strongly influenced by the growth and development of plants. Spring maize, as a representative of C4 plants, exhibited a 58.4% higher carbon sequestration efficiency than cotton, a C3 plant, and they both reached their carbon sequestration efficiency peak in July. Throughout the growth period, temperature, net surface radiation, and saturated vapor pressure differences (VPD) significantly affected the carbon sequestration capacity and WUE of both crops. Optimal temperatures can maximize the carbon sequestration efficiency of cotton and spring maize; for cotton, they are 20–25 °C, and for spring maize, they are 22–27 °C, respectively. In addition, it is recommended that spring maize be harvested at the end of July when it meets the harvesting standards for silage feed and achieves the maximum carbon sequestration. Afterward, winter crops should be planted to maximize the yield and improve the carbon sequestration capacity of farmlands.

Study on the effectiveness and mechanism of a sustainable dual slow-release model to improve N utilization efficiency and reduce N pollution in black soil

Long-term intensive cultivation has led to serious N loss and low N fertilizer utilization efficiency (NUE) in black soil areas. The lost N is not only a waste of resources but also a serious pollution threat to the environment, leading to the decline in water quality and food safety and the greenhouse effect. In the present study, a stable dual slow-release model, CPCS-Urea, was prepared by in situ polymerization using nitrapyrin, urea and melamine-formaldehyde resin as raw materials. The effect of the dual slow-release model was systematically evaluated using two consecutive years of field experiments. Five treatments were established in the field experiment: no N fertilizer (N0), urea (N180), 1 % CPEC-Urea, 0.5 % CPCS-Urea, and 1 % CPCS-Urea. The results showed that the new dual slow-release CPCS-Urea model outperformed both the use of urea and the traditional slow-release CPEC-Urea model in reducing N losses and improving NUE. The application of CPCS-Urea reduced nitrate (NO3−) leaching by 28.2 %–47.2 % and N2O emissions by 36.5 %–42.4 % and increased NUE by 20.7 %–28.5 % compared to urea application. The CPCS-Urea model modulated the activity of ammonia-oxidizing bacteria (AOB) and dissimilatory nitrate reduction to ammonium (DNRA) bacteria in soil, showing a significant decrease in AOB activity and an increase in DNRA activity. This results in a lower soil NO3−-N yield and a 53.1 %–72.0 % increase in NH4+-N content, providing sufficient N for the entire growth and development cycle of maize. In short, the dual slow-release CPCS-Urea model has great application prospects for promoting agricultural development in black soil areas.

Glutathione Concentration in Dunaliella salina: A Growth-Phase-Dependent Study

The microalga Dunaliella salina can produce antioxidants such as glutathione, which is an essential and powerful regulator of major cell functions. Changes in the glutathione concentration occur due to a microalga’s response to oxidative stress, which usually occurs when cells are exposed to environmental stressors or reach senescence. This study represents one of the few examples where changes in the glutathione concentration were tracked over the entire growth cycle of an alga. We found significant differences in the glutathione concentration depending on the growth stage. During the early lag growth phase, D. salina had relatively low levels of glutathione (190–280 µmol/1012 cell), which gradually increased as it entered the log phase (280–500 µmol/1012 cell) but then decreased as it entered the stationary phase (320–370 µmol/1012 cell). We also observed that the ratio between the reduced form of glutathione (GSH) and the oxidized form (GSSG) decreased with time, probably as a result of senescence or a lack of nutrients.

A deep learning framework for crop mapping with reconstructed Sentinel-2 time series images

Timely and accurate access to regional scale crop plant area and spatial distribution is essential for regional agricultural production and food security, especially in the context of global population growth and climate change. Deep learning has become prevalent in crop mapping under complex conditions due to its powerful feature extraction and nonlinear ability. This study proposes a time-series image classification framework using Attention-based Bidirectional Gated Recurrent Unit (A-BiGRU) to map rice, maize, and soybean in Fujin, China, from reconstructed Sentinel-2 time-series images. Firstly, the reconstructed Sentinel-2 time-series images with 10 spectral dimensions and 22 temporal dimensions were obtained by linear interpolation and Savitzky-Golay (SG) filter. Then, a neural network, the A-BiGRU was developed to identify different crops by taking advantage of their unique growth patterns. The attention mechanism enables temporal neural networks to focus on the critical growth periods of crops. Additionally, the structure of GRU is simpler than that of long short-term memory (LSTM) and simple recurrent neural network (SRNN), which reduces the number of parameters and alleviates overfitting. Compared to GRU, BiGRU can fully uses the time-series information of the entire crop growth cycle. To assess the effectiveness of the proposed method, we compared two deep learning methods (LSTM and SRNN) and three widely used non-deep learning classifiers (Spectral Angle Mapping (SAM), Support Vector Machine (SVM)) and eXtreme Gradient Boosting (XGBoost). The results demonstrate that A-BiGRU achieved the highest accuracy, with an overall accuracy of 0.9804, a macro F1 score of 0.9788 and a kappa score of 0.9714. We also selected four typical regions and compared the classification results with optical images, which showed that the proposed method has a good recognition effect. Therefore, the A-BiGRU method is capable of achieving high-precision crop mapping.