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China's Yellow River Basin (YRB) has large watershed but scarce water resources. More importantly, most parts of the YRB are located in semi-arid areas of Northwest China, where the ecology and environments are fragile. So, ecological restoration and agricultural production always are the key research topics of the YRB. However, the specific research interest of YRB changed over time and was always closely related to the implementation of government policies. Hence, we conducted a comprehensive analysis of YRB's research topics based on the methods of bibliometrics. The results showed that the number of papers about YRB's research experienced a change from slowly increasing (1998–2010, 83 papers) to rapidly increasing (2011–2015, 128 papers), and then to exponentially rising (2016–2020, 369 papers). Secondly, the main research fields of the YRB included farming, crops, water, soil, environment, and etc. The journal of Agriculture Water Management had the highest global total citations and H-index, even local cited references were the highest among all of the reference papers about the YRB. Through summarizing the most cited papers and references, we found the most important research hotspots about the YRB were: the impacts of climate change and human activities on the amount of sediment in the YRB, the management of soil erosion and vegetation restoration in the YRB, and the relationship between crops and environment and management in the Loess Plateau of China. In addition, " Loess Plateau " was the most frequent keyword in the past ten years and the popularity of " climate change " rose sharply in the past five years. For YRB's research in near future, how to effectively control carbon emissions, greenhouse gas (GHG) emissions, and carbon surplus is becoming an important implication for YRB's agricultural production and ecological restoration in the future. In general, this research is expected to promote a comprehensive and quantitative understanding of the past, present and future of YRB's research. • The research of Yellow River Basin (YRB) has exponentially increase in 2016–2020. • The research interest was closely related to the implementation of government policies. • The main research fields of the YRB included farming, crops, water, soil, environment. • The keyword of "Loess Plateau" was the most frequent keyword in the past ten years. • The carbon emissions, and carbon surplus is becoming an important research direction for YRB. [ABSTRACT FROM AUTHOR]

Frost exposure and poor pollination are great challenges for the cultivation of perennial orchard systems such as apples, whose adaptive capacity to climate impacts is limited. Unfortunately, such climatic risks remained largely unexplored due to the complexity in future climate predictions, uncertainties in apple phenological simulations, and difficulties in quantifications of risks of late-spring frost and poor pollination. Here, focusing on the main apple production Sub-regions I–IV (i.e., the Loess Plateau, the Bohai Bay, the Southwest Cool Highland, and Xinjiang) in China, we used an ensemble of seven phenological models to estimate changes in apple first flowering and fruit-setting. Based on phenological sensitivity window proposed, we evaluated the late-spring frost risk and climatic risk of poor pollination based on two individual indices developed for these two kinds of meteorological risks. The phenology model ensemble was driven by climatic data derived from an ensemble of 27 global climate models (GCMs) from CMIP6 to simulate apple phenology in two periods of 2021 − 2060 (Near Future) and 2061 − 2100 (Far Future) under two climate scenarios of SSP245 and SSP585. Model ensemble could largely improve phenology prediction accuracy. Our results also confirmed the general earlier occurrence of apple phenological stages and the shortening of phenological sensitive windows. The first flowering advanced about 0.19 − 0.32 d∙y-1 and fruit-setting advanced about 0.24 − 0.47 d∙y-1. Although the shortening of phenological sensitivity window might decrease frost frequency in some regions and reduce the general risk of late-spring frost (e.g. in Sub-region II), the increase of frost intensity could offset this kind of alleviation and even exacerbate frost risks in Sub-regions I and IV. Finally, except for Sub-region III and a few sites in Sub-regions I and IV, shifts in climatic risks of poor pollination in spatial contrasts were lower than late-spring frost risk. This highlighted late-spring frosts would be more possibly the reason for apple yield or quality losses than the unfavorable climatic pollination conditions under climate change. Model ensemble provides a realistic assessment for quantifying future risks of late spring frost and poor pollination in apple production systems, and helps to identify urgent meteorological risks and provide options for apple production systems in response to climate change. [Display omitted] • Model ensemble approach improved the accuracies in apple phenophase prediction. • Apple phenophase is advanced while phenological sensitive window is shortened. • Warming may not reduce frost due to discrepancies in frost intensity & frequency. • Change of pollination risk is small in most regions of China under climate change. [ABSTRACT FROM AUTHOR]

Along with rapid urbanization and intensive human activities, polycyclic aromatic hydrocarbon (PAH) pollution in the Pearl River Estuary (PRE) and its effects on the microbial community have attracted extensive attention. However, the potential and mechanism of microbial degradation of PAHs across water and sediment habitats remain obscure. Herein, the estuarine microbial community structure, function, assembly process and co-occurrence patterns impacted by PAHs were comprehensively analyzed using environmental DNA-based approaches. The contamination and distribution of PAHs were jointly affected by anthropogenic and natural factors. Some of the keystone taxa were identified as PAH-degrading bacteria (i.e., genera Defluviimonas , Mycobacterium , families 67–14 , Rhodobacteraceae, Microbacteriaceae and order Gaiellales in water) or biomarkers (i.e., Gaiellales in sediment) that were significantly correlated with PAH levels. The proportion of deterministic process in the high PAH-polluted water (76%) was much higher than that in the low pollution area (7%), confirming the significant effect of PAHs on the microbial community assembly. In sediment, the communities with high phylogenetic diversity demonstrated a great extent of niche differentiation, exhibited a stronger response to environmental variables and were strongly influenced by deterministic processes (40%). Overall, deterministic and stochastic processes are closely related to the distribution and mass transfer of pollutants, and substantially affect the biological aggregation and interspecies interaction within communities in the habitats. [Display omitted] • Pollution and distribution of PAH are affected by anthropogenic and natural factors. • Significant effect of PAHs on the microbial community assembly was confirmed. • The order Gaiellales is identified as a biomarker in sediment habitats. • Stochastic processes impact the distribution of PAH-degrading species in water. • Deterministic processes play an important role in shaping the microbial community. [ABSTRACT FROM AUTHOR]

As a vital cotton producing region, the arid and semi-arid region of northwest China has faced increasing scarcity of water resources, and the associated decline in irrigation capacity has necessitated the adoption of more efficient irrigation methods in this region. A two-year field experiment was carried out in Northern Xinjiang of China in 2020 and 2021 to explore the effects of various soil water potential (SWP) thresholds (−10, −20, −30 and −40 kPa) for drip irrigation on soil salinity, soil temperature, cotton growth, seed cotton yield, harvest index and water productivity. The results showed that with the decrease of SWP threshold, soil moisture in the 0–20 cm soil layer decreased gradually, while soil salinity decreased first and then increased. Soil salt content was lowest under − 20 kPa, and soil salinity was controlled below the root zone. SWP thresholds of −10 and −20 kPa could well control soil temperature during the whole growing season of cotton to reduce heat stress. Higher SMP was beneficial to plant height, leaf area and stem diameter, but it did not significantly improve seed cotton yield. The effects of soil moisture, heat and salt on growth indexes and seed cotton yield were quantitatively evaluated based on the path analysis. The number of cotton boll was the most influential growth variable on seed cotton yield, and soil salt content had the most significant influence on boll number. High-frequency irrigation (−10 kPa) was not conducive to reducing soil salinity, increasing boll number and increasing seed cotton yield. Drip irrigation with SWP threshold of −20 kPa was recommended for drip-irrigated cotton under film mulch in Northern Xinjiang. This study can provide scientific basis for irrigation management of cotton in saline-alkali regions. • With decreasing soil water potential, surface soil moisture decreased, but soil salinity decreased and then increased. • High-frequency irrigation (−10 kPa) was not conducive to reducing soil salinity and increasing seed cotton yield. • Soil water potential threshold of −20 kPa is more conducive to cotton yield improvement and soil salinity control. [ABSTRACT FROM AUTHOR]

• The minimum temperature in greenhouse (T imin) is essential for greenhouse management. • Local weather data could be used to predict T imin. • Different machine learning including deep learning models were used to predict T imin. • The RF , SVM and MLR models generally underestimated T imin values. • The GRU model was more suitable for T imin prediction in lacking input variable. Obtaining the minimum temperature of greenhouse (T imin) in advance can determine the lower limit of crop growth. Some research thought that was feasible to install sensors in the greenhouse so that can obtain the input variables for predicting T imin. However, lots of expenditures would be spent for intensive greenhouse parks. Local weather data can be easily and economically obtained so that considered it as an input variable for predicting T imin. First, the Pearson correlation coefficients were used to select the relevant input meteorological variables and eight different input combinations were consequently constructed. Then, three generalized machine learning models (i.e. Random forest, RF ; Support vector machine, SVM; and Multiple linear regression, MLR) and two deep learning models (i.e. Long-short term memory, LSTM ; Gated recurrent unit, GRU) were used to predict T imin based on the eight different input combinations. The results showed that the RF and GRU model had the best prediction performance among the generalized machine learning and deep learning models, respectively. Deep learning models were not sensitive to the number of input variables. In the absence of sufficient meteorological factors as input variables, the GRU model generally had better prediction performance than the other models. The prediction ability of deep learning models was obviously superior to the generalized machine learning models when T imin > 28.5 °C or T imin < 13.9 °C, particularly for the GRU model. Most of the differences between predicted and observed value (D i) of deep learning models distributed between −1 °C and 1 °C. And most of the predicted T imin by RF , SVM and MLR models were lower than the actual T imin , while the deep learning models were relatively stable. Finally, consider the prediction accuracy in terms of lacking input variables and the stability of the model, we recommended deep learning models to predict T imin , especially the GRU model. [ABSTRACT FROM AUTHOR]