Crop Leaf Phenotypic Parameter Measurement Based on the RKM-D Point Cloud Method.

Crop leaf length, perimeter, and area serve as vital phenotypic indicators of crop growth status, the measurement of which is important for crop monitoring and yield estimation. However, processing a leaf point cloud is often challenging due to cluttered, fluctuating, and uncertain points, which culminate in inaccurate measurements of leaf phenotypic parameters. To tackle this issue, the RKM-D point cloud method for measuring leaf phenotypic parameters is proposed, which is based on the fusion of improved Random Sample Consensus with a ground point removal (R) algorithm, the K-means clustering (K) algorithm, the Moving Least Squares (M) method, and the Euclidean distance (D) algorithm. Pepper leaves were obtained from three growth periods on the 14th, 28th, and 42nd days as experimental subjects, and a stereo camera was employed to capture point clouds. The experimental results reveal that the RKM-D point cloud method delivers high precision in measuring leaf phenotypic parameters. (i) For leaf length, the coefficient of determination (R2) surpasses 0.81, the mean absolute error (MAE) is less than 3.50 mm, the mean relative error (MRE) is less than 5.93%, and the root mean square error (RMSE) is less than 3.73 mm. (ii) For leaf perimeter, the R2 surpasses 0.82, the MAE is less than 7.30 mm, the MRE is less than 4.50%, and the RMSE is less than 8.37 mm. (iii) For leaf area, the R2 surpasses 0.97, the MAE is less than 64.66 mm2, the MRE is less than 4.96%, and the RMSE is less than 73.06 mm2. The results show that the proposed RKM-D point cloud method offers a robust solution for the precise measurement of crop leaf phenotypic parameters.

Enhancing salt tolerance and crop growth in agricultural systems: the impact of magnetized-ionized water irrigation on soil properties, microbial communities, and cotton growth.

BACKGROUND: The development of agricultural practices requires an understanding of the improvement of salt tolerance and crop growth in agricultural systems through magnetized-ionized water irrigation. METHOD: This study examined the impacts of fresh water (F), brackish water (B), magnetized-ionized fresh water (MIF), and magnetized-ionized brackish water (MIB) on soil properties and the growth of cotton seedlings through microbial analysis during the cotton seedling period. RESULTS: The results revealed that magnetized-ionized water irrigation improved soil water retention and promoted salt leaching. In comparison with F irrigation, plant height, leaf area index (LAI), dry matter accumulation (DM), and chlorophyll content (SPAD) levels increased by 3.61%, 4.07%, 5.76%, and 1.33%, respectively, under MIF irrigation. Similarly, when compared with B irrigation, LAI, DM, and SPAD increased by 5.13%, 6.12%, and 3.12% under MIB irrigation. Magnetized-ionized water irrigation also led to a notable rise in the relative abundance of beneficial soil bacterial communities, particularly Pseudomonas and Azoarcus, as well as fungal communities like Trichoderma, while reducing the prevalence of pathogenic fungi, such as Lasionectria, Gibberella, and Alternaria. Notably, this irrigation approach induced alterations in soil properties, and partial least squares path modeling revealed significant links between soil properties and both cotton growth and fungal community structure (with path coefficients of -0.884 and 0.693, respectively). CONCLUSION: This study elucidated the distinct effects of soil properties and growth indices on cotton yield during the seedling period, providing a crucial scientific foundation for enhancing future agricultural production through the use of magnetized-ionized water irrigation. © 2024 Society of Chemical Industry.

Comprehensive assessment of combined inorganic and organic fertilization strategies on cotton cultivation: implications for sustainable agriculture.

BACKGROUND: The integration of inorganic and organic fertilizers is increasingly being recommended to address the demand for sustainable cotton cultivation and to mitigate the ecological impacts of reliance on inorganic fertilizers. However, the nuanced effects of this combined fertilization approach on soil quality, cotton growth, yield, and their interaction mechanisms, remain unclear. METHOD: To elucidate this, a 2-year field trial (2022-2023) was conducted, incorporating five fertilization treatments: low inorganic fertilizer (BI1), high inorganic fertilizer (BI2), organic fertilizer (BO), combined low inorganic and organic fertilizer (BIO1), and combined high inorganic and organic fertilizer (BIO2). This study aimed to evaluate the influence of these treatments on soil quality, cotton growth, and yield. RESULTS: The results indicate that the BO treatment significantly enhanced plant height growth rate, and BIO1 treatment increased leaf area index and dry matter accumulation growth rate. Critical soil parameters such as alkali-hydrolyzed nitrogen and available potassium emerged as pivotal determinants of soil quality over the trial period, corresponding to soil quality index (SQI) values of 0.482 and 0.478, and yields of 7506.19 kg ha-1 and 6788.02 kg ha-1, respectively. Water productivity reached optimum levels at SQI values of 0.461 and 0.462, with corresponding efficiencies of 13.31 kg (ha mm)-1 and 12.16 kg (ha mm)-1. Partial least squares path modeling revealed that integrating organic fertilizer with reduced inorganic fertilizer usage significantly boosts cotton yield by enhancing soil quality (path coefficient: 0.842). CONCLUSION: In conclusion, this integrated fertilization strategy not only improves soil health but also increases agricultural productivity. It presents a promising approach for optimizing crop yields while fostering sustainable agricultural practices. © 2024 Society of Chemical Industry.

Spatiotemporal patterns and evolutionary trends of eco-environmental quality in arid regions of Northwest China.

The arid regions of northwest China suffer from water shortages, low land quality, and a fragile ecological environment, while social and economic development has increased the ecological and environmental load. The spatiotemporal pattern and evolutionary trend of ecological environmental quality were investigated by constructing a remote sensing-based ecological environmental index (EQI) evaluation model incorporating four indicators: drought index (DI), soil erosion index (SEI), greenness index (GI), and carbon exchange index (CEI). The study found that between 2001 and 2020, the DI, the SEI, and the CEI in the northwest arid region exhibited a downward trend with reduction rates of - 3e-05, -0.0006, and -0.0018, respectively. However, the GI demonstrated an upward trend, with a growth rate of 0.002. The average EQI in 2020 was 0.315, indicating a fair grade, with only 11.56% falling above the medium level. A general increasing trend was observed throughout the study period in EQI, with an incremental rate of 0.0002. Areas with future improvements in EQI accounted for 57.547% and were principally located in the eastern part of Inner Mongolia, Qinghai, and the northern and southern portions of Xinjiang. Notably, land use was significantly correlated with EQI (p < 0.01), with a hierarchy of effects that ran: forest land (0.678) > cultivated land (0.422) > grassland (0.382) > wasteland (0.138). The highly robust findings presented here offer innovative methods for ecological and environmental monitoring in the arid region of the northwest, with potential implications at an international scale.

Ecosystem health evaluation based on land use change-case study of the riparian zone of the Yangtze River in Jiangsu Province, China.

Evaluating the ecosystem health of riparian zones is helpful for decision-makers to formulate appropriate management measures. However, there are few methods for such evaluation which account for both the human requirements and ecological aspects of riparian zones. To address this, we created a Pressure-State(Vigor-Organization-Resilience)-Response framework for evaluating the ecosystem health of the riparian zone of the Yangtze River in Jiangsu Province, a region experiencing intense land use changes. Evaluation indicators, including land use change and ecosystem services, were selected. The comprehensive index method was used to calculate the evaluation indicators of ecosystem health, namely pressure, state, and response, and the comprehensive evaluation indicator itself. Using the cold and hot spot analysis, we also analyzed the spatial heterogeneity of ecosystem health in the riparian zone, constructed an ecological management pattern, and proposed corresponding management and protection measures. The results show that (1) from 2010 to 2020, construction land in the study area increased by more than 20%, and all studied land types underwent some degree of conversion to construction land, with cultivated land and water bodies being the main focus of conversion. (2) In 2020, the average ecosystem health in the riparian zone was normal, with a spatial distribution characterized by "high dispersion and low clustering"; and (3) according to the results of the ecosystem health evaluation and cold and hot spot analysis, key areas for stronger ecological protection were identified and, based on this, a number of management recommendations were proposed.

Identification and prediction of climate factors based on factor analysis and a grey prediction model in China.

Identifying and predicting the impacts of climate change are crucial for various purposes, such as maintaining biodiversity, agricultural production, ecological security, and environmental conservation in different regions. In this paper, we used the surface pressure (SP), surface temperature (ST), 2-m air temperature (AT), 2-m dewpoint temperature (DT), 10-m wind speed (WS), precipitation (PRE), relative humidity (RH), actual evapotranspiration (ETa), potential evapotranspiration (ETP), total solar radiation (TRs), net solar radiation (NRs), UV intensity (UVI), sunshine duration (SD), convective available potential energy (CAPE) as factors in our climate modeling. The spatiotemporal distribution characteristics of the climate factors were analyzed and identified based on historical data for China from 1950 to 2020 using factor analysis and a grey model (GM (1,1)), and their future change characteristics were predicted. The results show that there is a strong correlation between climate factors. ST, AT, DT, PRE, RH, and ETa are the main factors that have the potential to cause heavy rain, thunderstorms, and other severe weather. Meanwhile, PRE, RH, TRs, NRs, UVI, and SD are among the major factors linked to climate change. Specifically, SP, ST, AT, and WS are among the minor factors in most areas. The top ten provinces in terms of combined factor scores are Heilongjiang, Neimenggu, Qinghai, Beijing, Shandong, Xizang, Shanxi, Tianjin, Guangdong, and Henan. The trend of climate factors in China is expected to remain relatively stable over the next 30 years, with a noteworthy decrease observed in CAPE compared to the past 71 years. Our findings can help to better mitigate the risks associated with climate change and enhance resilience; they also provide a scientific basis for environmental, ecological, and agricultural systems to cope with climate change.

A novel multi-model fusion framework diagnoses the complex variation characteristics of ecological indicators and quantitatively reveals their driving mechanism.

Systematic analysis of the change law and driving mechanism of ecological indicators (GPP, ET, WUE), as well as the study of maximum threshold of water resources benefit changing with ecological benefit, are important prerequisites for realizing the scientific allocation and efficient utilization of water resources in desert riparian forests. However, previous studies have defects in the detailed description of the change characteristics of ecological indicators. How to accurately diagnose the characteristics of a site, mutation year, pattern (linear, exponential, logarithmic, etc.), duration of change, future change trends of ecological indicators in a desert riparian environment, as well as quantitatively revealing their driving mechanisms, are major scientific problems that need to be solved urgently. In this regard, an ensemble function coupling a logistic function and an asymmetric Gaussian function was creatively adopted, a novel framework was created to integrate the time-series trajectory fitting method and the sensitivity analysis method, and the arid and ecologically fragile Tarim River Basin was taken as a typical area. The results showed that with enhanced water resource management in the Tarim River Basin, GPP, ET, and WUE all showed patterns of increasing change and could be expected to continue to rise or to remain at a high-level stable state. The longest continuous period of GPP change was 15 years, showing that ecological restoration is a long-term process. The years of GPP mutation were consistent with the implementation periods of major measures in the Tarim River Basin (1990, 2001, and 2011), indicating the reliability of this framework. More importantly, when GPP increased to 216.44 gCm-2, the maximum WUE threshold of 0.93 gCm-2mm-1 occurred. This threshold can be used as a reference criterion for efficient utilization of ecological water in the basin. Among the ecological indicators studied, GPP was the most sensitive to environmental change, but GPP, with 80.60% of pixel area, showed a weak memory effect（α < 0.4). Besides, GPP was the most sensitive to the leaf area index (LAI) and had the strongest correlation with it (p < 0.001). Therefore, LAI can be used as the main control factor for judging plant growth. This research can provide important scientific guidance and reference for the analysis of ecological indicator changes and the sustainable utilization of water resources in arid areas.

Experimental and Numerical Investigations on Thermal-Hydraulic Performance of Three-Dimensional Overall Jagged Internal Finned Tubes.

To satisfy the demand for efficient heat transfer, a novel three-dimensional overall jagged internal finned tube (3D-OJIFT) was fabricated, using the rolling-ploughing/extruding method. The thermal performance of the 3D-OJIFT were studied and compared in experiments and three-dimensional numerical simulations. The RNG k-ε turbulence model is well verified with the experimental results. By analyzing the distributions of velocity, temperature, and turbulence kinetic energy, it was found that the 3D-OJIFT destroyed the development of the velocity and thermal boundary layers, increased the turbulence disturbance, and reduced the temperature gradient, thus improving the heat transfer. The influences of the jagged height and jagged spiral angle of the 3D-OJIFT are discussed. The Nu and f increased as the jagged height of the 3D-OJIFT increased. The Nusselt number of the 3D-OJIFT was 1.67-2.04 times the value for the smooth tube. In addition, the comprehensive heat transfer performance of the 3D-OJIFT improved after increasing the jagged spiral angle. Compared with conventional internal helical-finned tubes and other reinforcement structures reported in the literature, the 3D-OJIFT demonstrated better comprehensive heat transfer performance. Finally, empirical correlations of the 3D-OJIFT were obtained.

Response of cotton growth, yield, and water and nitrogen use efficiency to nitrogen application rate and ionized brackish water irrigation under film-mulched drip fertigation.

Introduction: The presence of brackish water resources is significant in addressing the scarcity of freshwater resources, particularly in the Xinjiang region. Studies focused on reducing adverse effect of brackish water irrigation based on using ionized brackish water, as well as on investigating its effects on fibre and oil plant production processes, remain incipient in the literature. Some benefits of this technique are the optimization of the quality and quantity of irrigation water, economy of water absorbed by the plants, improvement in the vegetative growth and productivity compared to irrigation using conventional brackish water. Thus, the aim of the current study is to assess the effect of different nitrogen application rates on soil water and salinity, cotton growth and water and nitrogen use efficiency. Methods: The experimental design consisted of completely randomized design with two water types (ionized and non-ionized) and six nitrogen application rates with four replications. Results: Irrigation conducted with ionized brackish water and different nitrogen application rates had significant effect on the plant height, leaf area index, shoot dry matter, boll number per plant and chlorophyll content. The study also demonstrated significant effects of ionized brackish water on soil water content and soil salinity accumulation. The highest cotton production was achieved with the use of 350 kg·ha-1 of ionized brackish water for irrigation, resulting in an average increase of 11.5% compared to the use of non-ionized brackish water. The nitrogen application exhibits a quadratic relationship with nitrogen agronomic use efficiency and apparent nitrogen use efficiency, while it shows a liner relationship with nitrogen physiological use efficiency and nitrogen partial productivity. After taking into account soil salinity, cotton yield, water and nitrogen use efficiency, the optimal nitrogen application rate for ionized brackish water was determined to be 300 kg·ha-1. Discussion: It is hoped that this study can contribute to improving water management, reducing the environmental impact without implying great costs for the producer.

Effects of Organic Fertilizer on Photosynthesis, Yield, and Quality of Pakchoi under Different Irrigation Conditions.

Water scarcity and the overuse of chemical fertilizers present significant challenges to modern agriculture, critically affecting crop photosynthesis, yield, quality, and productivity sustainability. This research assesses the impact of organic fertilizer on the photosynthetic attributes, yield, and quality of pakchoi under varying irrigation water conditions, including fresh water and brackish water. Findings reveal that the modified rectangular hyperbolic model most accurately captures the photosynthetic reaction to organic fertilization, outperforming other evaluated models. The maximum net photosynthesis rate (Pnmax), yield, soluble sugar (SS), and soluble protein content (SP) all exhibited a downward-opening quadratic parabolic trend with increasing amounts of organic fertilizer application. Specifically, under fresh-water irrigation, the optimal Pnmax, yield, SS, and SP were obtained at organic fertilizer rates of 65.77, 74.63, 45.33, and 40.79 kg/ha, respectively, achieving peak values of 20.71 µmol/(m2·s), 50,832 kg/ha, 35.63 g/kg, and 6.25 g/kg. This investigation provides a foundational basis for further research into the intricate relationship between water salinity stress and nutrient management, with the goal of crafting more sophisticated and sustainable farming methodologies. The insights gained could significantly influence organic fertilizer practices, promoting not only higher yields but also superior quality in agricultural outputs.

The mechanism of using magnetized-ionized water in combination with organic fertilizer to enhance soil health and cotton yield.

Addressing critical challenges in sustainable agriculture, notably water scarcity and soil degradation, necessitates innovative irrigation and fertilization methods. This investigation thoroughly assessed the effects of combining inorganic and organic fertilizers under brackish water irrigation, particularly focusing on magnetized-ionized brackish water-a promising solution for these challenges. The study shows that the integration of inorganic and organic fertilizers notably enhances soil water retention and salt leaching when applied with magnetized-ionized brackish water irrigation (MIBIO treatment), with water storage rate and salt accumulation rate observed at -0.454 and -0.075, respectively. Additionally, soil microbial diversity and uniformity witnessed significant improvement, positively influencing cotton growth rates, particularly noting a dry matter accumulation rate of 9.3262 kg·(ha·°C)-1. Transcriptomic analysis revealed that the MIBIO treatment elevated gene expression during the boll period, with notable enrichment in pathways such as the MAPK signaling pathway-plant and amino sugar and nucleotide sugar metabolism. Furthermore, the partial least squares path modeling indicated that soil alkali-hydrolyzed nitrogen (AN) and available potassium (AK) positively impact cotton leaf transcription and yield, with path coefficients of 0.613 and 0.428, respectively. Specifically, AN and AK contribute to enhancing cotton growth and affect the expression of metabolism genes in cotton leaves, thereby increasing cotton yield. Our study highlights the crucial role of irrigation and fertilization in influencing the soil environment and cotton growth. We recommend the use of magnetized-ionized water irrigation in combination with organic fertilizers as a strategy to boost agricultural productivity. Through the development of these strategies, our goal is to offer farmers practical guidance that can be readily implemented to enhance crop production efficiency, reduce environmental impact, and adhere to the principles of sustainable agriculture.

The Synergistic Production Effect of Water and Nitrogen on Winter Wheat in Southern Xinjiang.

Water and nitrogen management are crucial for food security and the efficient use of water and fertilizer, especially in arid regions. Three irrigation levels, namely, 80% crop water requirement (ETC) (W1), 100% ETC (W2), and 120% ETC (W3), and three nitrogen application levels, namely, 0 kg/ha (N1), 207 kg/ha (N2), and 276 kg/ha (N3), were used as the experimental treatments, and a control group, denoted as CK, was created. The results show that the maximum height achieved was 82.16 cm under W3N3. There was a single-peak variation trend throughout the growth stages of SPAD. It peaked at 58.44 under W3N3 and then at 27.9 under W2N2. The net photosynthetic and transpiration rates displayed bimodal peaks and the phenomenon of a "photosynthetic midday depression". And the prominent peaks in leaf water use efficiency occurred at 14:00 and 18:00, alongside noteworthy enhancements observed under the W3 treatment. Water and nitrogen and their interactions significantly affected the dry matter (DM) of winter wheat, with the spike accounting for the highest percentage. The W2N2 treatment demonstrated superior effectiveness in enhancing winter wheat water use efficiency, offering the potential to decrease irrigation requirements by 20% and nitrogen application by 25%. Moreover, the maximum PFPN attained under W2N2 reached 60.13, representing a noteworthy 35.25% increase compared to the control group (CK), but the HI of the W2N2 treatment only reached 0.56. The highest HI was achieved with W3N2 (0.73), and the nitrogen application of 207 kg/ha was more conducive to obtaining a higher HI. The highest yield was achieved under W3N3 (13.599 t/ha), followed by W2N2 (12.447 t/ha), and the spike proportion exceeded 60% with W2N2, and its production cost and economic benefit ratio of under 0.31 were superior to those for other treatments. Multiple regression analysis revealed that the maximum yield reached 12.944 t/ha with an irrigation amount of 3420.1 m3/ha and a nitrogen application of 251.92 kg/ha. Overall, our study suggests using an optimal water-nitrogen combination, specifically an irrigation level of 2829 m3/ha and a nitrogen application rate of 207 kg/ha, leading to increased winter wheat yields and economic benefits. These research results provide a pragmatic technique for improving winter wheat production in southern Xinjiang.

Spatiotemporal variation of irrigation water requirements for grain crops under climate change in Northwest China.

Clarifying the spatiotemporal variation of crop irrigation water requirement (IWR) under the background of climate change is an essential basis for water resource management, determining the irrigation quota and adjusting the planting structure. Using 61 years of climate data from 205 stations in Northwest China, this study investigated the spatiotemporal variations of climatic factors and IWR during the growth period of five main grain crops (spring wheat, winter wheat, spring maize, summer maize, and rice) and explored the dominant climatic driving factors of IWR variation. Results showed that (1) the IWR of grain crops showed distinct differences. Rice was the highest water consumption crop (mean of 753.78 mm), and summer maize was the lowest (mean of 452.90 mm). (2) The variation trends and average values of IWR of different grain crops have spatial heterogeneity across Northwest China. For most crops, high values and increasing trends of IWR were mainly located in eastern Xinjiang, western Gansu, and western Inner Mongolia. (3) Tmax (maximum temperature), Tmin (minimum temperature), and Peff (effective precipitation) showed an increasing trend during the growth period of each grain crop, while U10 (wind speed at 10 m height), SD (solar radiation), and RH (relative humidity) presented decreasing trends. (4) SD, Tmax, and U10 promoted the increase of grain crops' IWR, while Peff and RH inhibited it. The impacts of climatic factors on the grain crop IWR differed among different regions. Peff was the most influential factor to the IWR of all grain crops in most areas. Therefore, under the premise of a significant increase in T and uncertain precipitation mode in the future, it is urgent to take effective water-saving measures according to the irrigation needs of the region. To cope with the adverse impact of climate change on the sustainable development of agriculture in the northwest dry area, to ensure regional and national food security.

A new regional cotton growth model based on reference crop evapotranspiration for predicting growth processes.

Meteorological conditions and irrigation amounts are key factors that affect crop growth processes. Typically, crop growth and development are modeled as a function of time or growing degree days (GDD). Although the most important component of GDD is temperature, it can vary significantly year to year while also gradually shifting due to climate changes. However, cotton is highly sensitive to various meteorological factors, and reference crop evapotranspiration (ETO) integrates the primary meteorological factors responsible for global dryland extension and aridity changes. This paper constructs a cotton growth model using ETO, which improves the accuracy of crop growth simulation. Two cotton growth models based on the logistic model established using GDD or ETO as independent factors are evaluated in this paper. Additionally, this paper examines mathematical models that relate irrigation amount and irrigation water utilization efficiency (IWUE) to the maximum leaf area index (LAImax) and cotton yield, revealing some key findings. First, the model using cumulative reference crop evapotranspiration (CETO) as the independent variable is more accurate than the one using cumulative growing degree days. To better reflect the effects of meteorological conditions on cotton growth, this paper recommends using CETO as the independent variable to establish cotton growth models. Secondly, the maximum cotton yield is 7171.7 kg/ha when LAImax is 6.043 cm2/cm2, the corresponding required irrigation amount is 518.793 mm, and IWUE is 21.153 kg/(ha·mm). Future studies should consider multiple associated meteorological factors and use ETO crop growth models to simulate and predict crop growth and yield.

Spatiotemporal variations and its driving factors of NDVI in Northwest China during 2000-2021.

Northwest China (WTL) is an essential ecological barrier zone of China, an important node of the "Silk Road Economic Belt," and a crucial bearing area for China's execution of the "One Road and One Belt" and "Going Global" strategies. However, its ecology is exceedingly fragile and particularly vulnerable to climate change and human interference. This study explored the spatiotemporal evolution characteristics of vegetation in WTL using NDVI data and investigated its drive mechanisms by geodetector, partial correlation analysis, and residual trend analysis methods. As well as forecasting the trend for vegetation changes. The findings demonstrated that (1) the change in NDVI manifested an overall improvement trend and the distribution in space of NDVI rose from the center to the periphery. 57.07% of the area had a sparse cover of vegetation (NDVI between 0 and 0.2). In addition, about 49% of regions had deterioration tendencies, which were mainly aggregated in HX, QCXDB, QCXDN, and the eastern of QCXQN and QCXXB. (2) The NDVI's shifting trend was unsustainability, and the region of uncertain future accounted for 57.45% of the total, with apparent unsustainability features. (3) The key parameters influencing NDVI spatial distribution were Pre (precipitation), vegetation type, land use type, and soil type. The interaction between two factors enhanced the influence of any single element, which appeared as bivariate and nonlinear enhancements. (4) Both climate variations and human activities have been recognized as key variables affecting NDVI growth. NDVI variance in 73.02% of areas was influenced by the combined effects of climate variations and human activities. However, human activities were the most influential element in NDVI growth, with the relative contributions of 80.28% (19.72% of which was caused by climate variations). These results can be conducive to deepening insights into the local vegetation status, identifying the mechanisms driving vegetation change, and providing scientific recommendations for WTL's ecosystem restoration measures based on actual situations.

Past and projected future patterns of fractional vegetation coverage in China.

With the intensifying climate change and the strengthening ecosystem management, quantifying the past and predicting the future influence of these two factors on vegetation change patterns in China need to be analyzed urgently. By constructing a framework model to accurately identify fractional vegetation coverage (FVC) change patterns, we found that FVC in China from 1982 to 2018 mainly showed linear increase (29.5 %) or Gaussian decrease (27.4 %). FVC variation was mainly affected by soil moisture in the Qi-North region and by vapor pressure deficit in other regions. The influence of environmental change on FVC, except for Yang-Qi region in the southwest (-2.0 %), played a positive role, and weakened from the middle (Hu-Yang region: 2.7 %) to the northwest (Qi-North region: 2.4 %) to the east (Hu-East region: 0.8 %). Based on five machine learning algorithms, it was predicted that under four Shared Socioeconomic Pathways (SSPs, including SSP126、SSP245、SSP370、SSP585) from 2019 to 2060, FVC would maintain an upward trend, except for the east, where FVC would rapidly decline after 2039. FVC in the eastern region experienced a transition from past growth to future decline, suggesting that the focus of future ecosystem management should be on this region.

Spatiotemporal pattern of reference crop evapotranspiration and its response to meteorological factors in Northwest China over years 2000-2019.

Agricultural irrigation water in Northwest China accounts for more than 80% of total local water consumption, which is 1.23 times that of China. However, Northwest China is the most water-scarce place in China. Water scarcity in restricts crop growth and production. Reference crop evapotranspiration (ET0) is important for agricultural water management. Understanding the reason for ET0 change is helpful to provide a basis for rational planning of agricultural irrigation systems to conserve water. This study investigated the temporal and spatial variation characteristics of ET0 at 181 meteorological stations in Northwest China from 2000 to 2019. And the sensitive factors and dominant factors affecting ET0 change were quantitatively identified based on sensitivity analysis and contribution rate evaluation. Results showed that (1) a significant increase in maximum and minimum temperature (Tmax and Tmin), a significant decrease in sunshine duration (SD) and relative humidity (RH), and a slight increase in wind speed at 10 m height (U10) were observed. (2) Annual ET0 had an insignificant increasing trend. Spring and autumn ET0 contributed greatly to the growth of annual ET0, especially in March, May, September, October, and November. ET0 in HH (Yellow River Basin area) had decreased at annual scale, while other subregions were the opposite trend. Significant differences in monthly and seasonal changes in the spatial distribution of ET0. (3) U10 was the dominating contribution factor related to annual ET0 variability, followed by Tmin, RH, Tmax, and SD. In seasonal time scale, Tmin, SD, U10, and RH were the most dominant factors in spring, summer, autumn, and winter respectively. (4) Spatial distribution for contribution rates of various meteorological factors showed significant diversity among various subregions. The positive contribution of U10 was the major cause of the increase in ET0 in semi-arid grassland area (BGH), the southwest of "Qice line" (QCXXN), and the southeast of "Qice line" (QCXDN); the significant increase in Tmin contributed most in Qaidam Basin (CDM), Hexi inland river basin (HX), the northeast of "Qice line" (QCXDB), and the northwest of "Qice line" (QCXXB), while the contribution of decreasing SD offsets the positive effects of other factors, leading to the decrease in ET0 in HH. Our work illustrates that water management measures should be different at different spatial and temporal scales. The effect of U10 can be offset by covering, to reduce evaporation and maintain water in BGH, QCXXN, and QCXDN. And high-temperature resistant varieties are planted to adapt to temperature growth in CDM, HX, QCXDB, and QCXXB. Agricultural water management strategies should be formulated and selected according to local conditions.

Shift in the migration trajectory of the green biomass loss barycenter in Central Asia.

Revealing the vegetation response law under drought stress has become a hot issue in global climate change research. Against the background of human beings actively responding to climate change, quantitatively revealing the change and migration laws of green biomass loss (GBL) caused by drought in historical and future periods is insufficient. In this regard, we innovatively constructed a joint kNDVI-SPEI (kernel normalized difference vegetation index and standardized precipitation evapotranspiration index) distribution based on copula theory to accurately capture GBL dynamic under various drought scenarios unlike previous studies conducted in a deterministic way. Taking the drought-sensitive and ecologically vulnerable Central Asia (CA) as a typical region, we verified that an average 94.4 % of region showed greater vegetation vulnerability in times of water shortage from May to October, which exhibited the greatest probability of GBL under different drought scenarios, mainly in Kazakhstan and Uzbekistan. Significantly intensified drought due to high emissions will cause an 18.16 percentage-point increase in GBL probability in the far future (FFP, 2061-2100) compared to the near future (NFP, 2019-2060), which is much higher than in the lower-emission (0.38 %) and moderate-emission scenarios (9.82 %). In the NFP, the GBL barycenter will shift from Kazakhstan to Xinjiang, China; in the FFP, it will shift back to Kazakhstan due to the measures taken by the Chinese government to conserve energy and reduce emissions. Results illustrate that against the background of worsening drought, active climate change coping strategies can reverse the migration trajectory of the GBL barycenter caused by drought, which provides a new idea for vegetation protection research in response to global climate change.

Spatiotemporal variations of agricultural water footprint and its economic benefits in Xinjiang, northwestern China.

Agriculture is the largest water user and is the main driving force behind water stress in Xinjiang, northwestern China. In this study, the water footprint (WF) (blue, green and gray WF) of main crop production and their temporal and spatial characteristics in Xinjiang were estimated in 2006, 2010, 2014 and 2018. The blue water footprint deficit (BWFd) was conducted and food productivity and economic benefits of WF were also analyzed via the water consumption per output value (food productivity and economic benefits). The results reveal that the WF increased from 22.75 to 44.16 billion m3 during 2006-2018 in Xinjiang, of which cotton, corn and wheat are main contributors of WF. In terms of different regions, corn has the largest WF in north Xinjiang and cotton has the largest WF in south and east Xinjiang. The BWFd broadened from - 11.51 to + 13.26 billion m3 in Xinjiang with the largest increased BWFd in Kashgar (from - 3.35 to 1.40 billion m3) and Aksu (from - 2.92 to 2.23 billion m3) of south Xinjiang and in Shihezi (from - 0.11 to 2.90 billion m3) of north Xinjiang. In addition, the water footprint food productivity does not well correspond with the water footprint economic benefits in prefectures of Xinjiang. It means we should consider the food yields priority and economic benefits priority to formulate a scientific and effective supervisor mode to realize the sustainable management of agricultural water in prefectures of Xinjiang.

Estimation of regional irrigation water requirements and water balance in Xinjiang, China during 1995-2017.

Estimating water requirements and water balance for irrigated agricultural areas are important and will facilitate the efficient allocation of water resources for agriculture while minimizing the impact on natural ecosystems in arid regions. Based on the Penman-Monteith formula and GIS technology, the irrigation water requirements (IWR) of three main crops (cotton, corn and wheat) during the growing season were estimated and their spatio-temporal changes over the past 23 years (1995-2017) were analyzed in Xinjiang province, China. Our results indicated a dramatic increase in IWR from 14.12 billion m3 in 1995 to 38.99 billion m3 in 2017 due to the rapid cropland expansion of approximately 2.58 × 104 km2 in this period. Monthly IWR usually peaked in summer from May to July and varied in different basins. From the perspective of crops, cotton was identified to have consumed the largest amount of water, reaching 26.39 billion m3 in 2017, accounting for 67.68% of total water consumption. Spatially, the fastest increasing rate of IWR was Tarim Basin, which was attributable to the increase in water requirement of cotton. By comparing IWR and actual irrigation of Xinjiang in 2014, the amount of water scarcity had reached -15.01 billion m3 (-9.80 billion m3 in Tarim Basin and -6.58 billion m3 in Junggar Basin). The planting areas of three main crops (wheat, corn and cotton) were more sensitive to IWR than rising temperature indicated by our model. This study is of great significance for the scientific allocation of water resources in the irrigated areas of the different prefectures of Xinjiang.