Maomao Hou

Reclaiming coastal saline–alkali land is important for food security, yet little is known about how irrigation and biochar jointly affect the fate of fertilizer nitrogen in these soils. Using ¹⁵N isotope tracing, this field experiment tracked the distribution and recovery of labeled fertilizer in a soil–sunflower system under three irrigation quotas (8, 16, and 24 mm per event, applied every 10 days) and four biochar rates (0, 3, 5, and 7 t·ha⁻¹). After harvest, approximately 73% of residual ¹⁵N remained in the 0–40 cm topsoil, with organic-bound N as the dominant fraction (72–74%). Mineral ¹⁵N increased with soil depth, indicating downward movement with water flow. Within sunflower plants, labeled N accumulation followed the order flower head > stem > leaf > root, with heads containing 11–12 times more ¹⁵N than roots, confirming active transport to reproductive organs. Overall ¹⁵N use efficiency ranged from 18.8% to 24.9% across treatments. Increasing biochar rate enhanced ¹⁵NUE by up to 28.2% under the same irrigation regime, whereas raising irrigation from 16 mm to 24 mm reduced ¹⁵NUE by 3.2–3.8%. Mass balance analysis showed that moderate irrigation (16 mm) combined with high biochar (7 t·ha⁻¹) achieved the highest plant ¹⁵N recovery (24.9%), maintained 70.0% of labeled N in soil, and limited unaccounted ¹⁵N to only 5.1%. These findings demonstrate that integrated water–biochar management can optimize fertilizer N retention and crop uptake in coastal saline–alkali soils, providing a scientific basis for precision fertilization in these degraded lands.

For many countries, the reclamation of coastal saline–alkali land is of great significance for ensuring food security. However, systematic research on the effects of leaching irrigation and biochar application on the “soil–crop” system in coastal areas remains scarce. This study investigated the effects of three irrigation quotas (8, 16, and 24 mm per application, applied once every 10 days) and four biochar rates (0, 3, 5, 7 t·ha⁻¹, from composite biomass) on soil salinity, available nutrients, and yield and quality of sunflower in coastal saline–alkali soils of Jiangsu, China. The experiment used a completely randomized block design with three replications. The results showed that soil salinity under all treatments exhibited a consistent decline–rebound–decline pattern over the growing season, with the lowest values occurring around 55 days after sowing and peaking around 95 days. The combined application of water and biochar enhanced salt reduction, with the highest irrigation and biochar levels achieving the greatest desalination effect. Biochar application increased soil available phosphorus and potassium contents, with average increases of 19.6% and 13.3% under the highest biochar rate compared with the control, while available nitrogen showed no clear response to any treatment. Crop yield responded nonlinearly to water and biochar inputs: under low irrigation, yield increased continuously with biochar addition, whereas under medium or high irrigation, yield plateaued at the moderate biochar rate (5 t·ha⁻¹), with no further gains from additional biochar or water. Regarding seed quality, linoleic acid content generally increased with higher water and biochar levels, oleic acid decreased, and crude fat remained unaffected. Based on a balanced consideration of yield, quality, salt reduction, and water conservation, the combination of 16 mm irrigation per application and 5 t·ha⁻¹ biochar is recommended as the optimal amelioration strategy for coastal saline–alkali land.

Early and accurate identification of tomato leaf diseases constitutes a key safeguard for mitigating economic losses in tomato production. Conventional tomato leaf disease detection methodologies are constrained by inherent limitations, such as low operational efficiency, inadequate detection precision, and limited adaptability to environmental fluctuations. In contrast, the integration of deep learning techniques has yielded improvements in this research domain. Consequently, the development of deep learning-based approaches for the rapid and precise detection of tomato leaf diseases holds considerable theoretical significance and practical application value. To improve the detection accuracy of tomato leaf diseases, this study proposes a transfer learning-based DenseNet disease recognition model named DenseNet-BiFPN-ECA Fusion Network. The bidirectional feature pyramid network (BiFPN) is introduced at the terminal of DenseNet121 to achieve multi-scale feature fusion, while the efficient channel attention (ECA) mechanism is applied to enhance the discriminative capacity of fused features. Classification is ultimately completed via a global average pooling layer and a fully connected layer. The experimental results demonstrate that the improved model achieves an accuracy of 90.63% on the small-sample tomato leaf dataset collected from complex greenhouse environments, representing an improvement of 20.32 percentage points over the original DenseNet121 model. On the large-scale open-source Plant Village dataset, the model attains an accuracy of 98.47%, significantly outperforming the baseline models. Furthermore, a comparative analysis shows that the highest accuracy achieved by DenseNet, ResNet101, and VGG16 models on the same dataset is only 83.59% (within ±0.5%). This result validates the effectiveness of DenseNet-BiFPN-ECA Fusion Network in disease recognition tasks. The model provides a reliable technical reference for the intelligent diagnosis of tomato leaf diseases.

1-deoxy-D-xylulose-5-phosphate synthase (DXS) is a rate-limiting enzyme in terpene synthesis that can affect the accumulation of secondary metabolites in plants. In this study, three DXS gene family members were identified in the tomato genome-wide database. Using bioinformatics methods, we analyzed the gene structure, evolutionary affinities, and cis-acting elements of the SlDXS gene family members. Promoters of SlDXS genes contain plant hormone-responsive elements such as the CGTCA-motif, TGACG-motif, ABRE, TCA-element, TGA-element, ERE, CAT-box, and AACA-motif, which suggested that the SlDXS gene family may play an important role in hormone response. The RT-qPCR analysis showed that the tomato DXS2 gene was able to respond upon exposure to methyl jasmonate (MeJA). The construction of a virus-induced gene silencing (VIGS) vector for the SlDXS gene showed that the SlDXS2 gene was also able to respond to MeJA in silenced plants, but the induction level was lower relative to that of wild-type plants. The SlDXS1 gene is associated with the synthesis of photosynthetic pigments. This study provides a reference for the further elucidation of the DXS gene’s biological function in the terpenoid synthesis pathway in tomatoes.

When optimizing irrigation methods, much consideration is given to crop growth indicators while less attention has been paid to soil’s gaseous carbon (C) and nitrogen (N) emission indicators. Therefore, adopting an irrigation practice that can reduce emissions while maintaining crop yield and quality is of great interest. Thus, open-field experiments were conducted from September 2020 to January 2022 using a single-factor randomized block design with three replications. The lettuce plants (“Feiqiao Lettuce No.1”) were grown using four different irrigation methods established by setting the lower limit of drip irrigation to 75%, 65%, and 55% of soil water content at field capacity corresponding to DR1, DR2, and DR3, respectively. Furrow irrigation (FI) was used as a control. Crop growth indicators and soil gas emissions were observed. Results showed that the mean lettuce yield under DR1 (64,500 kg/ha) was the highest, and it was lower under DR3 and FI. The lettuces under DR3 showed greater concentrations of crude fiber, vitamin C, and soluble sugar, and a greater nitrate concentration. Compared with FI, the DR treatments were more conducive to improving the comprehensive quality of lettuce, including the measured appearance and nutritional quality. Among all the irrigation methods, FI had the maximum cracking rate of lettuce, reaching 25.3%, 24.6%, and 22.7%, respectively, for the three continuous seasons. The stem cracking rates under DR2 were the lowest—only 10.1%, 14.4%, and 8.2%, respectively, which were decreased to nearly half compared with FI. The entropy model detected that the weight coefficient evaluation value of DR2 was the greatest, reaching 0.93, indicating that the DR2 method has the optimal benefits under comprehensive consideration of water saving, yield increase, quality improvement, and emission reduction.

Lettuce is a water-sensitive stem-used plant, and its rapid growth process causes significant disturbances to the soil. Few studies have focused on the gaseous carbon emissions from lettuce fields under different irrigation methods. Therefore, this study investigated the effect of different drip-irrigation lower limits and methods (drip and furrow irrigation) on greenhouse gas (CO₂, CH₄) emissions from lettuce fields. Thus, drip irrigation (DI) was implemented using three different lower limits of irrigation corresponding to 75%, 65%, and 55% of the field capacity, and named DR1, DR2, and DR3, respectively. Furrow irrigation (FI) was used as a control treatment. The CO₂ and CH₄ emission fluxes, soil temperature, and soil enzyme activities were detected. The results showed that the cumulative CO₂ emission was highest under DR3 and relatively lower under DR1. For the FI treatment, the cumulative CO₂ emission (382.7 g C m⁻²) was higher than that under DR1 but 20.2% lower than that under DR2. The cumulative CH₄ emissions under FI (0.012 g C m⁻²) were the greatest in the whole lettuce growth period, while DR2 and DR3 treatments emitted lower amounts of CH₄. The irrigation method considerably enhanced the activity of urease and catalase, meanwhile promoting CO₂ emission. The low irrigation amount each time combined with high irrigation frequency reduced soil CO₂ emission while increasing CH₄ emission. From the perspective of the total reduction of gaseous carbon, DR1 is the optimal drip irrigation method among all the irrigation lower limits and methods.

Superoxide dismutase (SOD) is an important antioxidant metalloenzyme present in plants that plays a vital role in plant growth and development, but studies on the SOD gene family in Pakchoi are lacking. In this study, we identified 13 SOD genes from pakchoi, including three MnSODs, five Cu/ZnSODs, and five FeSODs. Through structural analysis, the gene structures and motif patterns in the three subfamilies showed a high degree of conservation. From an evolutionary point of view, gene duplication is an important pathway driving the evolutionary development of the SOD gene family. In addition, by analyzing the structure and function of BchSOD proteins, most of the genes were shown to be involved in different developmental stages of pakchoi, and their expression was shown to be regulated by external conditions such as light, phytohormones, and abiotic stress. qPCR results revealed that BchSODs were expressed in different parts of pakchoi, and most of the genes were expressed in response to abiotic stresses (salt and drought) and hormones (GA and MeJA). In addition, the BchFSD2 gene was studied in depth, and subcellular localization confirmed that the BchFSD2 gene was expressed in plant chloroplasts. Overexpression of BchFSD2 promoted salt tolerance, limited superoxide anion and MDA production, and increased antioxidant enzyme activities in Arabidopsis. In summary, the BchSOD gene family was comprehensively analyzed in this study to provide new insights for a better understanding of BchSOD function and to improve salt tolerance in pakchoi.

Organic fertilizer applications and subsurface drainage are two important measures for improving coastal saline soil; however, nitrous oxide (N₂O) emissions from saline soil under a combination of these two measures are seldom evaluated. In this study, saline soil cultivated with sunflowers (Helianthus annuus L.) was employed as an experimental system. Prior to the experiment, the saline soils were buried with three different spacings (10 m (S1), 15 m (S2), and 20 m (S3)) of subsurface drainage pipes. The nitrogen nutrients that are needed by sunflowers came from two different nitrogen sources (organic and inorganic fertilizer), including six application schemes of either 100% organic fertilizer (100%OF), 75% organic fertilizer combined with 25% inorganic fertilizer (75%OF), 50% organic fertilizer (50%OF), 25% organic fertilizer (25%OF), 0% organic fertilizer (0%OF), and no fertilizer (CK). The results show that the cumulative N₂O emissions from the treatments under S1, S2, and S3 throughout the entire growth period were 8.9–15.8, 9.5–17.5, and 10.1–17.6 kg ha⁻¹, respectively. A smaller spacing between adjacent drainage pipes or a higher replacement proportion of organic fertilizer reduced the accumulative N₂O emissions. The increased replacement of organic fertilizer decreased the soil salinity, whereas it increased the C/N ratio and total carbon content. The fertilization treatments significantly increased the nitrogen uptake of sunflower plants, with increase ranges of 18.1–47.2%, 8.6–40.5%, and 8.8–34.5% under S1, S2, and S3, respectively, compared with CK. The highest yield of sunflowers was achieved under S2 combined with 25%OF, reaching 3.82 t ha⁻¹. Correlation analysis showed that the N₂O emission flux was positively correlated with the soil salinity, crop yield, and crop nitrogen uptake, whereas it was negatively correlated with the total carbon, C/N ratio, and organic carbon content. We concluded that using 25% organic fertilizer instead of inorganic fertilizer was beneficial for reducing N₂O emissions while maintaining the crop yield under subsurface drainage.

Soil residual N is a potential factor threatening the environment, but it is also an N fertilizer resource. Few studies have evaluated the fate of soil residual N under agronomic practice. The objective of this study was to investigate the distribution of residual N and its possible influencing factors with different irrigation regimes. Under three N residual situations created by the previous season using the ¹⁵N labeled urea, we employed lettuce as the plant material and three lower limits of drip irrigation including 75% (DR1), 65% (DR2), and 55% (DR3) accounting for the field water capacity as experimental treatments. A furrow irrigation treatment (FI) with the same irrigation regime as DR2 was used as control. Results showed that 2.1–4.8% of the residual ¹⁵N from the previous season was absorbed by the succeeding lettuce, 78.0–84.4% was still remained in the 0–80 cm soil, and 10.9–20.0% was unaccounted for. After harvest of succeeding lettuces, the soil residual ¹⁵N mainly existed in the mineral form. Moreover, the lettuce reuse efficiency for¹⁵N was positively correlated with the total residual ¹⁵N amount (p < 0.01) and the mineral ¹⁵N amount (p < 0.01). The overall results indicated that an appropriate irrigation regime (DR2) was conducive to promoting absorption of residual N by succeeding crop.

Fertilizer nitrogen (N) is a main pollutant in the agricultural ecosystem, while the fate of fertilizer N influenced by different irrigation modes is not well comparatively investigated. In this study, the distribution of fertilizer N in soil layers and tomato organs as well as its loss under drip, spray and flood irrigation with different quotas of 140, 180 and 220 m³ ha⁻¹ were evaluated quantitatively by using nitrogen-15 (¹⁵N) labeled urea (abundance of 19.6%) as fertilizer source. The results showed that the plant ¹⁵N, soil ¹⁵N and ¹⁵N loss accounted for 27.9–47.8%, 38.8–54.0% and 10.3–21.9% of the total applied ¹⁵N, respectively. The amount of ¹⁵N absorbed by plants was significantly (p < 0.05) higher under drip and spray irrigation in comparison to flood irrigation with the same irrigation quota. The maximum ¹⁵N use efficiency and the minimum ¹⁵N residual were detected under drip irrigation with quota of 180 m³ ha⁻¹, indicating that the supply and demand of urea-¹⁵N was more synchronized under such an irrigation mode. The ¹⁵N loss increased obviously as irrigation quota increased. Moreover, the correlation analysis between ¹⁵N loss and the possible impact factors indicated that the soil mineral ¹⁵N content after irrigation was one important factor influencing the ¹⁵N loss. Among the three irrigation modes, spray irrigation caused the lowest ¹⁵N loss of 10.3–13.1% when using the same irrigation quota. It was concluded that the irrigation modes have profound impacts on the fate of urea-¹⁵N. Irrigation could be used as a regulation pathway of plant N absorption and agricultural N output.