Simulating the effects of optimizing sowing date and variety shift on maize production at finer scale in northeast China under future climate.

BACKGROUND: Global warming and the rising occurrences of climate extremes have become formidable challenges for maize production in northeast China. The optimization of sowing date and variety choice stand out as two economic approaches for maize to enhance its resilience to climate change. Nevertheless, assessment of the potential of optimizing sowing date and variety shift on maize yield at finer scale remains underexamined. This study investigated the implications of optimizing sowing date and implementing variety shift on maize yield from a regional perspective. RESULTS: Compared to the reference period (1986-2005), climate change would decrease by 11.5-34.6% (the range describes the differences among climate scenarios and agro-ecological regions) maize yield in the 2050s (2040-2059) if no adaption measure were to be implemented. The combined adaption (optimizing sowing date and variety shift) can improve maize yield by 38.8 ± 11.3%, 42.7 ± 9.7% and 33.9 ± 7.6% under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively. The current sowing window typically falls within the projected optimal sowing window, defined as the period capable of achieving 90% of the maximum yield within the potential sowing window under future climate conditions. Consequently, the potential of the effect of optimizing sowing window on maize yield is limited. In contrast, variety shift results in higher yield improvement, as temperature rise creates favorable conditions for transplanting varieties with an extended growth period, particularly in high latitudes and mountainous regions. Under future climate, cumulative precipitation and compound drought and hot days during maize growing seasons are two key factors influencing maize production. CONCLUSIONS: The optimization of sowing date and variety choice can improve maize yield in northeast China. In addition, maize production should consider varieties with longer growth period and drought and heat tolerance to adapt to climate change. © 2023 Society of Chemical Industry.

Effects of extreme drought events on vegetation activity from the perspectives of meteorological and soil droughts in southwestern China.

Under climate warming, extreme drought events (EDEs) in southwestern China have become more frequent and severe and have had significant impacts on vegetation growth. Clarifying the influence of soil and meteorological droughts on the vegetation photosynthetic rate (PHR) and respiration rate (RER) can help policymakers to anticipate the impacts of drought on vegetation and take measures to reduce losses. In this study, the frequency and features of EDEs from 1990 to 2021 were analyzed using the standardized precipitation evapotranspiration index, and the longest-lasting and most severe EDE was chosen to assess the effects of drought on vegetation activity. Then, a land surface model was used to simulate the vegetation PHR and RER. Finally, the effects of the EDE on the vegetation PHR and RER were analyzed from the perspectives of soil and meteorological droughts. The results revealed that from 1990 to 2021, a total of 11 EDEs were observed in southwestern China, and the longest-lasting and most severe EDE occurred in 2009-2010 (EDE2009/2010). EDE2009/2010 significantly reduced the monthly mean PHR and RER by 9.82 g C m-2 month-1 and 0.80 g C m-2 month-1, respectively, causing a cumulative reduction of approximately 5.61 × 1013 g C. Soil and meteorological droughts had a driving force of 39 % on the PHR changes and an explanatory force of 42 % on the RER reduction. In particular, the soil drought had an average explanatory force of 25 % on the PHR and made a contribution of 24 % to the RER. The drought affected different types of vegetation differently, and crops were more susceptible than grassland and forests on the monthly time scale. The vegetation exhibited resilience to drought, returning to normal PHR and RER levels 2 months after the end of EDE2009/2010. This research contributes to understanding and predicting the impact of EDEs on vegetation growth in southwestern China.

Refined revealing the chain path of multiple ecosystem services under diverse environmental factor gradients.

A fragile karst ecosystem presents ecosystem services affected by the environment and interactions between ecosystem services. However, the ecosystem in the karst area is characterized by low environmental capacity and low resistance to force disturbance. Current research mainly focuses on the trade-off/synergy of ecosystem services in the karst area yet it lacks inductive analysis of the multiple chain path among ecosystem services. This paper quantitatively identified dominant factors influencing spatial differentiation of surface runoff, soil moisture, sediment yield, and net primary productivity (NPP) and determined the chain path. The chain paths of surface runoff-soil moisture-NPP and NPP-surface runoff-sediment yield were analyzed. The results showed that land use and soil type were the dominant factors, and chain effects of ecosystem services were diverse under the various dominant factor gradients. The mediation effects of paddy soil (97.21 %) and mountain meadow soil (55.56 %) were high, and surface runoff had a greater impact on NPP by affecting soil moisture. Among the diverse land use types, the mediation effect of surface runoff on NPP affecting sediment yield varied greatly (from 5 to 100 %). In addition, its variation trend was consistent with that of the soil moisture as the mediation variable. The mediation effect of surface runoff on construction land was the highest (99.43 %). This study provides the scientific basis for selecting more effective water and soil conservation measures by analyzing the chain relationships of multiple ecosystem services under different environmental factor gradients.

A comprehensive meta-analysis of the impacts of intensified drought and elevated CO2 on forage growth.

Forage crops are used worldwide as key feed sources for dairy systems. However, their productivity and quality are limited due to intensified drought events, elevated carbon dioxide (CO2), and their interaction with climate change, with consequences for the security of animal husbandry and the agricultural economy. Although studies have quantified the impacts of such stresses on forage growth, these impacts have been less systematically investigated in a global context due to differences among various forage groups, regional microclimates, and environmental factors. Herein we employed nine forage growth-related variables involving three perspectives, i.e., photosynthetic parameters, production, and quality, from research articles published between 1990 and 2021 via a meta-analysis. A linear mixed-effect model was then used to explore the quantitative relationship between these factors in a restricted dataset. Decreasing trends in all four photosynthetic parameters were detected across different eco-geographical regions with increasing drought stress. The maximum decrease in DMY occurred in the Mediterranean, with 52.8% under drought conditions. Globally, eCO2 significantly increased photosynthetic rate (Pn) and instantaneous water use efficiency (WUEi) by 40.8% and 62.1%, respectively, which also had positive effects on forage dry matter yield (DMY) (+25.1%), especially for forage in Northern Europe. However, this stress would significantly decrease forage quality by decreasing crude protein (CP) (-19.7%) and nitrogen content (N content) (-13.5%). These negative impacts would be aggravated under the co-occurrence of drought and eCO2, including a significant increase in WUEi (+111.1%) and a decrease in DMY (-12.3%). Gramineae showed a more sensitive response to drought stress in photosynthetic parameters and DMY than Leguminosae, but the latter exhibited a better response in photosynthetic parameters and production under eCO2. Our analysis provides a consensus concerning how the growth parameters of forage have changed under environmental stresses.

Spatial Distribution of Soil Heavy Metals and Associated Environmental Risks near Major Roads in Southern Tibet, China.

Soil heavy metal pollution is becoming an increasingly serious environmental problem. This study was performed to investigate the contents of surface soil heavy metals (Cu, Zn, Pb, Cd) near six roads in the southern part of the Tibetan Plateau. Multivariate statistics, geoaccumulation index, potential ecological risk, and a human health assessment model were used to study the spatial pollution pattern and identify the main pollutants and regions of concern. The mean Igeo was ranked in the order Cd > Cu > Zn > Pb, with the average concentrations of Cd, Zn, and Cu exceeding their corresponding background levels 4.36-, 1.00-, and 1.8-fold, respectively. Soil Cd level was classified as posing a considerable potential risk near national highways and a high potential risk near non-national highways, whereas soil Cu, Zn, and Pb were associated with a low potential ecological risk for each class of roads. Furthermore, the non-carcinogenic risk due to soil heavy metals for each class of roads was within the acceptable risk level for three exposure pathways for both adults and children, but the carcinogenic risk attributable to soil Pb exceeded the threshold for children near highways G318, G562, and G219 and for adults near highway G318. Our work not only underscores the importance of assessing potential threats to ecological and human health due to soil heavy metal pollution on road surfaces but also provides quantitative guidance for remediation actions.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China.

Understanding the spatiotemporal characteristics of PM2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing-Tianjin-Hebei region during 2014-2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 µg m-3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.

Impact of spring phenology variation on GPP and its lag feedback for winter wheat over the North China Plain.

Spring green-up date (GUD) is a sensitive indicator of climate change, and of great significance to winter wheat production. However, our knowledge of the chain relationships among them is relatively weak. In this study, based on 8-day Enhanced Vegetation Index (EVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2015, we first assessed the performance of four algorithms for extracting winter wheat GUD in the North China Plain (NCP). A multiple linear regression model was then established to quantitatively determine the contributions of the time lag effects of hydrothermal variation on GUD. We further investigated the interactions between GUD and gross primary production (GPP) comprehensively. Our results showed that the rate of change in curvature algorithm (RCCmax) had better performance in capturing the spatiotemporal variation of winter wheat GUD relative to the other three methods (Kmax, CRmax, and cumCRmax). Regarding the non-identical lag time effects of hydrothermal factors, hydrothermal variations could explain winter wheat GUD variations for 82.05% of all pixels, 36.78% higher than that without considering the time lag effects. Variation in GUD negatively correlated with winter wheat GPP after green up in most parts of the NCP, significantly in 35.75% of all pixels with a mean rate of 1.89 g C m-2 yr-1 day-1. Meanwhile, winter wheat GPP exerted a strongly positive feedback on GUD in >82.42% of all pixels (significant in 28.01% of all pixels), characterized by a humped-shape pattern along the long-term average plant productivity. This finding highlights the complex interaction between spring phenology and plant productivity, and also suggests the importance of preseason climate factors on spring phenology.

Climatic determinants impacting the distribution of greenness in China: regional differentiation and spatial variability.

This study investigated climatic determinants for regional greenness in China and spatially variable correlations between climatic determinants and vegetation in specific regions using the geographical detector and geographically weighted regression (GWR) methodologies. The analyses were based on normalized difference vegetation index (NDVI) and interpolations of climatic determinants from 652 Chinese meteorological stations. The study period (1982-2013) was divided into two stages (T1-T2) before and after the inflection year identified by the accumulative anomaly of NDVI. Three typical regions (R1-R3) were then selected according to the same NDVI variation trend as China in the two periods. Precipitation was the dominant climatic factor of NDVI in China, and the effect of temperature on greenness increased with warming from T1 to T2. In a relatively arid region (R1), the effect of precipitation in T2 was further strengthened compared to T1. Meanwhile, the effect of minimum temperature in T2 also increased compared with T1 in a relatively humid region (R2), becoming the major climatic determinant. In addition to the regional differentiation, spatial variability was investigated by comparing normalized coefficients of GWR for climatic determinants; this showed significant spatial heterogeneity within each region. Temperature impact areas also existed within precipitation-dominated regions (R1 and R3), where areas of precipitation impact expanded from T1 to T2. Furthermore, regression coefficients between NDVI dynamics and climate variability revealed relationships between regional differentiation and spatial variability. For example, the increasing precipitation rate could mediate the adverse impacts on greenness caused by the higher warming rate in relatively arid regions (R1).

Spatial gradient and quantitative attribution of karst soil erosion in Southwest China.

Soil erosion estimation has attracted considerable attention from the scientific community and governments because of its importance to sustainable regional development. In karst areas, the heterogeneous environment and rocky desertification create difficulties in determining the influencing factors and spatial patterns of soil erosion. A quantitative analysis of karst soil erosion distribution was conducted by applying the revised soil loss equation model and the geographical detector method of attribution identification, which was based on spatial variance analysis. The results show that soil erosion was most severe in areas with an elevation of 1200-1800 m and intense anthropogenic activity. When the vegetation coverage was below 0.5-0.6, soil erosion showed characteristics of a source-limited regime and increased with the increasing vegetation coverage. When the vegetation coverage was higher than 0.5-0.6, soil erosion followed a transport-limited regime and decreased with the increasing vegetation coverage. The factor detector showed land use to be the dominant factor, explaining 51% of soil erosion distribution. Among various land use types, dry land had the greatest vulnerability to soil erosion. Slope served as a controlling factor at large scales, especially when combined with annual precipitation exceeding 1500 mm, and in dry and grassland areas. From the attribution analysis of multiple factors, the combination of land use and slope was the controlling interaction factor explaining 68% of soil erosion distribution. The methods and results of this research could serve as scientific references for decision makers and researchers exploring the characteristics of soil erosion to develop effective measures for its control.

[Evaluation of ecosystem resilience in the regions across Qinghai-Tibet railway based on GIS].

Based on GIS technique and the methods of mean-squared deviation weight decision and catastrophe progression, a more clear definition and associated evaluation for ecosystem resilience were given, with a case study in the regions across Qinghai-Tibet railway by using the indices of plant community coverage, species diversity, and biomass. It was shown that the areas with high ecosystem resilience were mainly located in the Qilian Mountain meadow grassland, Huangshui Valley needle-leaved and deciduous broad-leaved forest, and south Tanggula Mountain kobresia swamp meadow, while those with the lowest resilience were in the central part of Qaidam Basin, and the Kunlun Mountains. Most areas in the regions had higher or medium ecosystem resilience, with a trend of that in the south of Kunlun Mountains, the resilience in the north of the railway was lower, while in the east of Qaidam Basin (especially in the Qinghai Lake area), the resilience was lower in the south than in the north of the railway. Through the evaluation of ecosystem resilience, the key issues in the process of ecological resilience could be found, and corresponding effective measures would be pointed out to manage alpine ecosystems. Moreover, combining with the evaluation of vulnerability, scientific basis for regional development could be provided to avoid or mitigate the negative effects of human activities on eco-environment.