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Soil salinization and/or alkalization is a major constraint to crop production worldwide. Approximately 60% of the cultivated land is affected by salt, over half of which is alkalized. Alkaline soils are characterized by high alkalinity and typically high salinity, which creates a complex saline-alkaline (SA) stress that affects plant growth. Rice cultivation has been accepted as an important strategy for effective utilization of SA land if water is available for irrigation. Nevertheless, as a salt-sensitive plant, rice plants suffer severe SA-induced damage, which results in poor plant growth and grain yield. Various approaches have been employed to improve rice productivity in SA land. Among them, the priming technique has emerged as a powerful method for enhancing SA tolerance in rice plants. In this review, we summarized how SA stress damages rice plants, and then presented how priming treatment can mitigate such damage.

Remote sensing estimation of maize residue cover (MRC) can rapidly acquire large-scale data on MRC, crucial for monitoring and promoting conservation tillage. Herein, seven tillage indices derived from Sentinel-2 satellite imagery were analyzed alongside measured MRC data to assess their correlation with MRC. MRC estimation models were built using six machine learning algorithms, including back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), Stacking1, and Stacking2. Model performance was compared using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The potential for conservation tillage was explored. The results showed that the R2 values of the seven tillage indices in the study area exceeded 0.5, with particularly high correlations for NDTI and STI, with R2 values of 0.755 and 0.751, respectively. When using machine learning algorithms to construct models, the Stacking2 model exhibited the highest estimation accuracy, with an R2 of 0.923, RMSE of 3.32%, and MAE of 0.025, while Stacking1 also demonstrated robust performance, with an R2 of 0.910, RMSE of 3.45%, and MAE of 0.029. Among the base models, XGBoost achieved the highest estimation performance and the lowest error, with R2, RMSE, and MAE values of 0.884, 4.77%, and 0.031, respectively. The R2 values of RF, SVR, and BPNN were 0.865, 0.859, and 0.842, respectively, with RMSE values of 4.06%, 4.76%, and 5.91%, and MAE values of 0.039, 0.047, and 0.059, respectively. These results indicate that the Stacking2 model demonstrates a significant advantage in prediction accuracy. Geostatistical analysis of the inversion results of the Stacking2 model revealed that the proportions of farmland with MRC values exceeding 30% in Changchun, Songyuan, and Siping were 81.90%, 77.96%, and 83.58%, respectively. This indicates that Changchun and Siping have greater potential for implementing conservation tillage. This study demonstrates that the stacking ensemble learning model significantly improves the predictive performance by leveraging the strengths of multiple base models and accurately monitoring the spatial distribution of MRC. [ABSTRACT FROM AUTHOR]

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson's correlation coefficient (r) = 0.8875, R2 = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R2 = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R2 = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km2 and 1336 km2, and NT = 559 km2 and 585 km2, respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km2 and NT was 1265 km2). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems. [ABSTRACT FROM AUTHOR]

The improvement of saline-alkali land plays a key role in ensuring food security and promoting agricultural development. Saline soils modifies the response of the soil microbial community, but research is still limited. The effects of applying phosphogypsum with rice cultivation (PRC) on soil physicochemical properties and bacterial community in soda saline-alkali paddy fields in Songnen Plain, China were studied. The results showed that the PRC significantly improved the physicochemical properties of soil, significantly reduced the salinity, increased the utilization efficiency of carbon, nitrogen, and phosphorus, and significantly increased the activities of urease and phosphatase. The activities of urease and phosphatase were significantly correlated with the contents of total organic carbon and total carbon. A redundancy analysis showed that pH, AP, ESP, HCO3−, and Na+ were dominant factors in determining the bacterial community structure. The results showed that PRC could improve soil quality and enhance the ecosystem functionality of soda saline-alkali paddy fields by increasing nutrient content, stimulating soil enzyme activity, and regulating bacterial community improvement. After many years of PRC, the soda-alkali soil paddy field still develops continuously and healthily, which will provide a new idea for sustainable land use management and agricultural development. [ABSTRACT FROM AUTHOR]

Alkaline stress with high pH levels could significantly influence plant growth and survival. The enzyme 9-cis-epoxycarotenoid dioxygenase (NCED) serves as a critical bottleneck in the biosynthesis of abscisic acid (ABA), making it essential for regulating stress tolerance. Here, we show that OsNCED3-overexpressing rice lines have increased ABA content by up to 50.90% and improved transcription levels of numerous genes involved in stress responses that significantly enhance seedling survival rates. Overexpression of OsNCED3 increased the dry weight contents of the total chlorophyll, proline, soluble sugar, starch, and the activities of antioxidant enzymes of rice seedlings, while reducing the contents of O2·−, H2O2, and malondialdehyde under hydroponic alkaline stress conditions simulated by 10, 15, and 20 mmol L−1 of Na2CO3. Additionally, the OsNCED3-overexpressing rice lines exhibited a notable increase in the expression of OsNCED3; ABA response-related genes OsSalT and OsWsi18; ion homeostasis-related genes OsAKT1, OsHKT1;5, OsSOS1, and OsNHX5; and ROS scavenging-related genes OsCu/Zn-SOD, OsFe-SOD, OsPOX1, OsCATA, OsCATB, and OsAPX1 in rice seedling leaves. The results of these findings suggest that overexpression of OsNCED3 upregulates endogenous ABA levels and the expression of stress response genes, which represents an innovative molecular approach for enhancing the alkaline tolerance of rice seedlings. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max) is the main oilseed crop that provides vegetable oil for human nutrition. The main objective of its breeding research is to increase the total oil content. In the Kennedy pathway, Diacylglycerol acyltransferase (DGAT) is a rate-limiting enzyme that converts diacylglycerol (DAG) to triacylglycerol (TAG). Here, the AhDGAT3 gene was cloned from peanut and overexpressed in the wild-type (WT) Arabidopsis. The total fatty acid content in T3AhDGAT3 transgenic Arabidopsis seeds was 1.1 times higher on average than that of the WT. Therefore, AhDGAT3 was transferred into the WT (JACK), and four T3 transgenic soybean lines were obtained, which proved to be positive using molecular biological detection. Specific T-DNA insertion region location information was also obtained via genome re-sequencing. The results of high-performance gas chromatography showed that the contents of oleic acid (18:1) composition and total fatty acids in transgenic soybean plants were significantly higher than that of the WT. However, linoleic acid (18:2) was much lower compared to the WT. The agronomic trait survey showed that the quantitative and yield traits of AhDGAT3 transgenic soybean were better than those of the WT. These results suggest that fatty acids in transgenic soybeans, especially oleic acid and total fatty acid, are enhanced by the over-expression of AhDGAT3. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) is a crucial information technology that promotes the transformation and upgrading of the construction industry. It has been widely used in various stages of construction projects, including design, construction, and operation. However, BIM technology still faces numerous obstacles in practice. From the perspective of construction practitioners, this study constructs a structural equation model to explore the obstacles encountered by construction practitioners in the process of applying BIM technology. Task–technology fit, effort expectancy, performance expectancy, user trust, and facilitating conditions can significantly improve practitioners' behavioral intention, with task–technology fit having the most significant impact on behavioral intention. Facilitating conditions and behavioral intention significantly affect usage behavior, while perceived cost does not significantly affect behavioral intention. The multiple-group analysis found that in the path of performance expectancy on behavioral intention, males have a significant effect while females do not; in the path of facilitating conditions on behavioral intention, higher education levels have a significant effect while lower education levels do not; in the path of facilitating conditions on behavioral behavior, lower usage time has a significant effect while higher usage time does not. Suggestions for promoting the application of BIM technology are proposed in this article to improve its utilization rate. This study provides more perspectives and ideas for future research on BIM diffusion. [ABSTRACT FROM AUTHOR]

The minerals in the hydrothermal and cold seep system form at different temperatures and show responses to the laser power to varying degrees. Here, we focus on the heat-induced by laser to study thermal transformations of the chalcopyrite, covellite, pyrite, barite, and aragonite based on Raman spectroscopy. Chalcopyrite mainly transforms into hematite, and covellite mainly transforms into chalcocite with the increase of laser power. Interestingly, comparing with the previous study, the pyrite can transform to the marcasite firstly, and form hematite finally. We also find that high-temperature opaque chalcopyrite is more likely to occur thermal transformations due to the smaller absolute energy difference (|ΔE1|) based on the frontier orbital theory. In contrast, the oxysalt minerals won't transform into new components under high laser power. However, the structure of the barite has been destroyed by the high laser power, while the more transparent aragonite is not affected by the high laser power due to the laser penetrates through the transparent aragonite crystal and causes little heat absorption. Finally, we established the minimum laser power densities for thermal transformations of these minerals formed under different environments. The above study provides a simple way to study the thermal transformations of minerals by the local heat-induced by laser and also enlightens us to identify the minerals phases precisely. [ABSTRACT FROM AUTHOR]