Green Index Research Articles

A novel soybean mapping index within the global optimal time window

Efficient soybean mapping is critical for agricultural production and yield prediction. However, current sample-driven soybean mapping methods heavily rely on large representative sample datasets, limiting the interpretability of physical mechanisms. Besides, sample-free methods failed to exploit key features that differentiate soybean from other crops, especially Chlorophyll content. Misclassification errors persist and spatiotemporal generalization remains limited. Therefore, this study develops a novel Soybean Mapping Composite Index (SMCI) within a precise Global Optimal Time Window (GOTW). It integrates unique features of soybean Chlorophyll content, canopy water content, and canopy greenness by coupling three red-edge bands (RE2, RE3, and RE4), one near-infrared band, one shortwave infrared band, and two feature indices (Enhanced Vegetation Index and Green Chlorophyll Vegetation Index). The novel index was applied to soybean mapping at six sites in four major soybean producing countries (China, Argentina, Brazil, and the United States) from 2019 to 2021, using an optimal threshold of 3.25. Within the GOTW, the index responds better to spectral features and improves soybean separability. The average overall accuracy (OA: 91%) and average Kappa coefficient (Kappa: 0.83) for the novel index at all sites outperformed the traditional sample-driven Random Forest (RF) method (OA: 84%, Kappa: 0.70) and the existing sample-free index-based Greenness and Water Content Composite Index (GWCCI) (OA: 81%, Kappa: 0.64). Furthermore, interannual transfer experiments consistently showed high accuracy, demonstrating robust spatiotemporal transferability. The proposed SMCI index meets the need for a lightweight and stable soybean mapping tool and serves as a valuable reference for efficient global crop mapping.

Accelerated solvent extraction of apomorphine from Nymphaea caerulea (Blue Water Lily) products: A proof‐of‐concept Green extraction for plant materials

AbstractNymphaea caerulea (Blue water lily) is an esthetically pleasing aquatic plant which is widely located across India and Africa. The blue water lily contains an alkaloid called apomorphine which is said to be a sedative, and a nonselective dopamine agonist and is now available in the local and online market in the form of powders and oils for various applications such as sleeping aid, anxiety reliever and sexual performance enhancer. These properties are abused by the consumption of Nymphaea caerulea to achieve a state of “high” which has led the categorization of the same as a novel psychoactive substance. In this paper, a rapid mass spectral analysis was performed for the preliminary screening of commercially available blue water lily products using the Waters Radian as soon as possible instrument, followed by the high performance liquid chromatography‐photo diode array method development and validation of the samples for the qualitative and quantitative analysis of apomorphine. Accelerated solvent extraction as a green alternative to the conventional soxhlet extraction was used in the extraction of the plant material. The method was finally screened for its greenness using the Complex green analytical procedure index method. The method was validated with a linearity of 0.9973; limit of detection and limit of quantitation of 0.02 and 0.18 µg/mL, respectively. The method was able to detect and quantitate apomorphine in two samples from the commercially available natural products of Nymphaea caerulea.

Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

ABSTRACT Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring.

Exploring environmental sustainability through dimension reduction algorithms: A cost-effective UV spectroscopic method for simultaneous determination of veterinary binary mixtures of doxycycline hydrochloride and tylosin tartrate

This study presents a cost-effective and environmentally friendly approach for the simultaneous determination of a veterinary binary mixture comprising doxycycline hydrochloride (DOX) and tylosin tartrate (TYZ) utilizing UV spectroscopy alongside dimension reduction algorithms (DRAs). Seventeen DRAs were evaluated, and their performances were compared based on four metrics: mean squared error (MSE), mean absolute error (MAE), median absolute error (MedAE), and coefficient of determination (R2). Based on the performance indices, Isomap algorithm demonstrated the highest predictive capacity among all DRAs. MSE, MAE, MedAE and R2 values of (0.38, 0.28, 0.19 and 0.999) and (0.08, 0.26, 0.22 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 4.67–30 μg mL−1 for DOX. MSE, MAE, MedAE and R2 values of (0.54, 0.34, 0.19 and 0.994) and (0.07, 0.19, 0.07 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 3.51–24 μg mL−1 for TYZ. The developed method underwent validation utilizing the accuracy profile approach. An ecological impact assessment was carried out employing six greenness evaluation tools: The Green Solvent Selection Tool (GSST), National Environmental Methods Index (NEMI), the Assessment of Green Profile (AGP), carbon footprint analysis, Analytical Greenness Calculator (AGREE), and Complementary Green Analytical Procedure Index (Complex GAPI). Additionally, we applied blueness and whiteness assessments using Blue Applicability Grade Index (BAGI) and Red-Green-Blue 12 (RGB 12) algorithms, respectively. The proposed method demonstrated higher GSST scores, a more "green" profile in NEMI, a superior AGP profile, and better environmental sustainability in Complex GAPI. The calculated carbon footprint value was 0.0002 kg CO2 equivalent per sample, The AGREE score was 0.87, BAGI was assessed at 72.5, and the whiteness assessment by the RGB12 algorithm was 89.6. Statistical comparison of the proposed method with a previously reported HPLC method for dosage form analysis revealed no significant differences at a 95% confidence level. This study highlights the novelty of combining UV spectroscopy with dimension reduction algorithms, offering significant advancements over traditional UV and chemometric methods for the analysis of these drugs. This approach not only enhances the efficiency and accuracy of determining active ingredients in pharmaceutical dosage forms but also contributes to environmental sustainability.

Combining OBIA, CNN, and UAV imagery for automated detection and mapping of individual olive trees

The identification of individual trees is an important research topic in forestry, remote sensing and computer vision. It represents a tool for effectively and efficiently managing and maintaining forests and orchards. However, this task is not as simple as it seems; tree detection and counting can be time consuming, cost-prohibitive and accuracy-limited, especially if performed manually on a large scale.The availability of very high-resolution UAV imagery with remote sensing can make the counting process easier, faster and more precise. With the development of technology, this process can be made more automated by using intelligent algorithms such as CNN.This work presents an OBIA-CNN (Object Based Image Analysis-Convolution Neural Network) approach that combines CNNs with OBIA to automatically detect and count olive trees from Phantom4 advanced drone imagery. Initially, The CNN-based classifier was created, trained, validated, and applied to generate the Olive trees probability maps on the ortho-photo. The post-classification refinement based on OBIA was then conducted. A super-pixel segmentation and the Excess Green index were performed and a detailed accuracy analysis has been carried out to establish the suitability of the proposed method.The application to a RGB ortho-mosaic of an olive grove, in the east region of Morocco was successful using a manually elaborated training dataset of 4500 images of 24×24 pixels. Finally, the CNN detected and counted 2934 olive trees on the ortho-photo, achieving an overall accuracy of 97 % and 99 % after the OBIA refinement. The results of the proposed OBIA-CNN method were also compared with the classification results of using the Template matching technique, CNN method alone, and OBIA analysis alone to evaluate the performance of the approach. Our findings suggest the use of very high resolution images with object-based deep learning is promising for automatic detection and counting of olive trees to support the accurate and sustainable agricultural monitoring.

Evaluation of the Development Level of Green Transportation in National Central Cities

Green transportation is the core embodiment of ecological civilization and the concept of green development within the field of transportation, and it is an important strategic choice for sustainable urban development. National central cities represent the highest level in China’s urban system planning. This paper aims to evaluate the level of green transportation development in national central cities. It established a set of 29 specific evaluation indicators from five dimensions: basic indicators, green transportation infrastructure, traffic environmental protection, traffic travel, and traffic safety. It constructed an evaluation index system for the development level of green transportation. The entropy weight TOPSIS method was utilized to evaluate the development levels of green transportation in nine national central cities from 2020 to 2022. An obstacle degree model was constructed to identify key obstacle factors at both the criterion and indicator layers of the green transportation development level evaluation index system for national central cities. Suggestions were proposed from five aspects: establishing a comprehensive policy framework, promoting regional collaborative development, accelerating infrastructure construction, improving transportation service quality, and fostering the green upgrading of industries. The results showed that the comprehensive ranking of green transportation development levels among the national central cities from high to low for the years 2020–2022 was as follows: Shanghai, Chongqing, Chengdu, Beijing, Guangzhou, Tianjin, Wuhan, Xi’an, Zhengzhou. In terms of the regional spatial layout, the green transportation development levels of the nine national central cities generally exhibited a “high on the periphery, low in the center” distribution characteristic. The comprehensive ranking of the obstacle degree in the criterion layer was as follows: basic indicators, traffic travel, green transportation infrastructure, traffic environmental protection, traffic safety. After screening the criteria level where the obstacle degree calculation results are above 15%, traffic safety is eliminated. The nine cities, which were located in different regions, generally maintained consistent internal obstacle factors and their order. The top five indicators with the highest frequency of obstacle degrees at the indicator layer were as follows: total passenger transport volume, number of taxis, new energy vehicle production, expenditure for transportation, and total freight transport volume. The specific key obstacle factors at the indicator level were different in the nine cities.

Sodium salicylate as a green fluorescent probe for ultrasensitive determination of vonoprazan fumarate via fluorescence switch off strategy; greenness assessment

A green, simple and sensitive spectrofluorometric approach for determining vonoprazan fumarate in bulk and pharmaceutical dosage form by turning off the fluorescence of sodium salicylate is developed. The addition of vonoprazan fumarate reduced linearly the fluorescence intensity of 0.4 mM sodium salicylate at λem 408 nm and at λex 330 nm. The approach was found to be linear in the 50.0–3000.0 ng/mL range. The limits of detection and quantification were 10.97 and 33.23 ng/mL, respectively. The presented method proved its suitability in determination of vonoprazan fumarate in its pure and pharmaceutical dosage form. This method employs water as the exclusive solvent and utilizes safe reagents, evaluated using the Analytical Eco Scale, Green Analytical Procedure Index (GAPI), and carbon footprint. In contrast, previous methods relied on toxic reagents and required extended heating times, resulting in higher environmental impact. The novel method not only enhances analytical efficiency but also aligns with green chemistry principles, offering a sustainable solution for routine pharmaceutical analysis.

Risk assessment based on spectrophotometric signals used in eco-friendly analytical scenarios for estimation of carvedilol and hydrochlorothiazide in pharmaceutical formulation

Special attention is given to the pharmacological treatment of combined medication of Carvedilol and hydrochlorothiazide which is the most effective and the most beneficial therapy for hypertensive patients with diabetes and various metabolic comorbidities. This work represents spectrophotometric platform scenarios based on factorized spectrum (FS) using interpoint data difference resolution scenarios (IDDRS) coupled with spectrum subtraction method (SS) for the concurrent quantification of carvedilol (CAR) and hydrochlorothiazide (HCT) when present together in a combination without the need for any initial physical separation steps. This IDD resolution scenario based on manipulating the zero-order spectra (D0) of both drugs in the mixture with various spectral features at different wavelength regions (200–400 nm), region I (220–250 nm), region II (240–300 nm) and region III (270–320 nm) via absorbance resolution (AR) and induced absorbance resolution (IAR) methods coupled with corresponding spectrum subtraction (SS). The calibration curves were established across the linearity ranges of 2.0–12.0 µg/mL at 242.50 nm and 4.0–40.0 µg/mL at 285.5 nm for CAR and 1.0–11.0 µg/mL at 226.10 nm and 2.0–20.0 µg/mL at 270.5 nm for HCT. Moreover, methods’ validation was confirmed via ICH guidelines. A Multicenter comparison between sensitivity, specificity in respect resolution sequence were applied using different wavelength regions with various concentration ranges was applied and finally spectral resolution recommendation is issued and cumulative validation score (CVS) is calculated as an indicator in the risk analysis. In quality control laboratories, the studied approaches are applicable for conducting analysis on the mentioned drugs. In addition, the selection of spectrophotometry aligns with the principles of green analytical chemistry, an approach that resonates with the overarching theme of minimizing environmental impact. Via four metric tools named: analytical greenness (AGREE), green analytical procedure index (GAPI), analytical eco-scale, and national environmental method index (NEMI), methods’ greenness profile was guaranteed.

Bermudagrass fairway responses to various combinations of a soil surfactant and nitrogen

AbstractSurfactants are commonly employed in sand‐based turfgrass systems to address soil water repellency, which often results in the formation of preferential flow channels leading to non‐uniform wetting patterns. While the influence of soil moisture on nutrient dynamics is well documented, the potential of surfactants to improve N uptake remains poorly understood. A 2‐year study was conducted on ‘Celebration’ bermudagrass [Cynodon dactylon (L.)] fairway to better understand the interaction between fertility programs (296 or 586 kg N ha−1 year−1 from ammonium sulfate and 564 kg N ha−1 year−1 primarily from a controlled‐release N fertilizer) and surfactant rate and frequency (3.2 L ha−1 every 14 or 28 days, or at 6.4 L ha−1 every 28 days) on turfgrass performance, leaf N content, and soil moisture. Overall, no significant interaction was observed between surfactant treatment and fertility program. During the dry season (November to May), fertility programs influenced turfgrass performance parameters with higher N rates consistently leading to improvements in visual quality, normalized difference vegetation index, percent green cover, and dark green color index; however, no differences were observed during the rainy season (June to October). Surfactants showed no effect on turfgrass performance but led to minimal increase in leaf N content from 3.72% to 3.85%. All surfactant treatments reduced water drop penetration time at 0 cm. Moreover, surfactant treatments applied every 28 days regardless of the rate exhibited a higher volumetric water content compared to those applied every 14 days. Surfactant applications might not be an economically feasible strategy to improve leaf N content in South Florida well‐watered bermudagrass fairways.

GREEN BOND INDEX PRICE FORECASTING: COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS

Today, green financial products have garnered recognition and are consequently regarded as alternative assets. Green bonds exclusively allocate their funds to environmentally sustainable initiatives. Green bonds facilitate companies in enhancing both their financial and environmental performance by promoting innovations stemming from green initiatives and long-term green investments. This study focused on predicting the price of the green bond index in Japan. The input factors for price prediction in Japan include Nikkei225, USD/JPY, and crude oil price assets, which are seen as alternative investment options for Japanese investors. The study utilized a dataset spanning 693 days, from 06.05.2021 to 02.05.2024. The acquired data is partitioned into two distinct sets: one for training and one for testing. 80% of the data was allocated for training purposes, while the remaining 20% was designated for testing. The study utilized various prediction approaches including RF, MLP, GBR, XGBoost, LSTM, SVR, Catboost, and Linear Regression. The performance of these models was compared using evaluation metrics such as MSE, RMSE, MAE, MAPE, and R2 values. The research revealed that the GBR model exhibited the highest performance on the training data set, whereas the XGBoost and RF models yielded superior prediction results on the test data set.

Research on Urban Street Spatial Quality Based on Street View Image Segmentation

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage.

Monitoring invasive exotic grass species in ecological restoration areas of the Brazilian savanna using UAV images

Identifying and monitoring invasive exotic grasses (IEG) is critical for the ecological restoration of grasslands and savannas, as they are the main barrier to the successful recovery of native grasslands and savannas. The integration of high-resolution remote sensing data, acquired through UAVs (Unmanned Aerial Vehicles), with machine learning algorithms is advancing restoration monitoring. The present study aimed to estimate IEG cover and identify plots with different invasive species dominance in ecological restoration areas in the Brazilian savanna. For ground truth, species cover estimates were carried out in plots through point-line intercept sampling. Then, the areas were classified according to the dominance of each invasive species (>40% vegetation cover) or of a mix of native species. A multispectral camera onboard a UAV was used to acquire images in the visible to near-infrared spectrum. From the images, vegetation indices and texture metrics were derived as predictor variables. The Random Forest (RF) algorithm was used to estimate the percentage of invasive species cover and to classify plots in terms of species dominance. The final RF regression for invasive species cover percentage presented an R2 of 0.71 and selected the blue band, NIR and Ratio Vegetation Index (RVI) as the most important variables. The overall accuracy of plot classification according to species dominance was 84%. The most prominent predictors were the Green Chlorophyll Index (GCI), the atmospherically resistant vegetation index (ARVI), and the RVI. The structural and photosynthetic characteristics of exotic and native species influenced the spectral responses. In conclusion, multispectral images acquired with UAV can be used to estimate the proportion of invasion in restoration sites and to map areas dominated by different invasive grass species in grasslands and savannas. This is a useful tool for evaluating restoration success and can help indicate areas that require management interventions.

Building running-friendly cities: effects of streetscapes on running using 9.73 million fitness tracker data in Shanghai, China

The association between built environment and physical activity has been recognized. However, how and to what extent microscale streetscapes are related to running activity remains underexplored, partly due to the lack of running data in large urban areas. Moreover, few studies have examined the interactive effects of macroscale built environment and microscale streetscapes. This study examines the main and interactive effects of the two-level environments on running intensity, using 9.73 million fitness tracker data from Keep in Shanghai, China. Results of spatial error model showed that: 1) the explanatory power of microscale streetscapes was higher than that of macroscale built environment with R2 of 0.245 and 0.240, respectively, which is different from the prior finding that R2 is greater for macroscale built environment than for microscale streetscape; 2) sky and green view indexes were positively associated with running intensity, whereas visual crowdedness had a negative effect; 3) there were negative interactions of land use Herfindahl–Hirschman index with sky and green view indexes, while a positive interaction was observed for visual crowdedness. To conclude, greener, more open and less visually crowded streetscapes, can promote running behavior and enhance the benefits of land use mix as well. The findings highlight the importance of streetscapes in promoting running behavior, instead of a supplement to macroscale built environment.

Combining proximal and remote sensing to assess 'Calatina' olive water status.

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R2 values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.

A Sustainable Approach to Valuable Polyphenol and Iridoid Antioxidants from Medicinal Plant By-Products.

Supply chain waste gives rise to significant challenges in terms of disposal, making upcycling a promising and sustainable alternative for the recovery of bioactive compounds from by-products. Lignocellulosic by-products like STF231, which are derived from the medicinal plant extract industry, offer valuable compounds such as polyphenols and iridoids that can be recovered through upcycling. In an unprecedented study, we explored and compared conventional hydroethanolic extraction, ultrasound hydroethanolic extraction, and natural deep eutectic solvents-ultrasound extraction methods on STF231 to obtain extracts with antioxidant activity. The extraction profile of total polyphenols (TPCs) was measured using the Folin-Ciocalteu test and the antioxidant capacity of the extracts was tested with FRAP and DPPH assays. HPLC-UV was employed to quantify the phenolic and iridoid markers in the extracts. Additionally, the sustainability profile of the process was assessed using the green analytical procedure index (GAPI), AGREEprep, and analytical GREEnness metric approach (AGREE) frameworks. Our findings indicate that a choline chloride and lactic acid mixture at a 1:5 ratio, under optimal extraction conditions, resulted in extracts with higher TPC and similar antioxidant activity compared with conventional hydroethanolic extracts. The innovative aspect of this study lies in the potential application of sustainable upcycling protocols to a previously unexamined matrix, resulting in extracts with potential health applications.

Water Quality and Vegetation Health in Urban Conservation Areas: A Case Study of Paragem Municipal Natural Park

The Paragem Municipal Natural Park (PMNP), an integral protection conservation unit, plays a crucial role in the conservation and recovery of ecosystems located in the city of Dourados/MS. Located in an urban area, it faces challenges related to human influence, which can affect its terrestrial and aquatic communities. This study aimed to analyze the vegetation index and diagnose the water quality of a tributary of the Paragem stream located within the PMNP. The research involved examining the Green Leaf Index, physical-chemical and microbiological parameters of the water, as well as ecotoxicological tests covering three trophic levels. In addition, the genotoxic effect of the water on a piscean model was investigated. The results revealed low vegetation cover and degradation of the park’s water quality, evidenced by microbial contamination, toxicity to primary producers, and genetic damage to fish. Therefore, these results indicate the need for monitoring and environmental education measures, ecological restoration, and water resource recovery and conservation management in the PMNP to favor the recovery and conservation of its environmental resources.

The Impact of Land Transfer on Sustainable Agricultural Development from the Perspective of Green Total Factor Productivity

China’s agricultural sector is transitioning from extensive management to intensive management, and land transfer brings about changes in land use and management methods, which may encourage the agricultural sector to enter a sustainable development track, but this mechanism has not been effectively proven. Using the SBM-GML index to construct a green total factor productivity index to measure the level of sustainable agricultural development in each province (or autonomous region or municipality directly under the central government) and provincial panel data from 2010 to 2022, we applied a panel interactive fixed-effects model to empirically test the impact of land transfer on sustainable agricultural development, with a focus on analyzing the heterogeneity and related mechanisms of this impact. The results indicate that (1) land transfer significantly promotes sustainable agricultural development, and this conclusion still held true after robustness tests such as controlling for regional omitted variables, replacing dependent variables, changing the sample size, IV estimation, and GMM estimation. (2) The mechanism testing found that land transfer mainly promotes sustainable agricultural development by increasing the desirable output, and has no significant effect on reducing non-point source pollution. At the same time, land transfer mainly improves the desirable output through factor allocation effects rather than scale operation effects, thereby promoting sustainable agricultural development. (3) The heterogeneity analysis found that the higher the quantile of agricultural development level is, the weaker the role of land transfer in promoting sustainable agricultural development, indicating that land transfer has a greater impact on areas with poor agricultural development foundations, and areas with poor agricultural development foundations are more likely to obtain sustainable development space through land transfer. The impact of different land transfer methods and land transfer objects on sustainable agricultural development was heterogeneous. Compared with non-market transfer methods such as exchange and transfer, market-oriented transfer methods such as leasing and equity had a more significant impact on sustainable agricultural development. Compared to transferring land to ordinary farmers, transferring land to new business entities such as family farms, professional cooperatives, and enterprises can significantly promote sustainable agricultural development.

Mapping winter wheat crop traits dynamic change and growth performance for variable rate application using Sentinel-1 and Sentinel-2

Site specific crop management for variable rate application is extensively recognized as a method for distributing agricultural input unevenly across a field, tailored to the diverse requirement of different areas. From the previous study, this approach proven to reduce agricultural input expenses by 10 % without impacting yield and ensure environmental sustainability. This study presents a new approach to delineate management zones for precision agriculture using crop biophysical property variability assessment within winter wheat fields. A multivariate random forest framework was developed to estimate winter wheat’s biophysical properties within fields from surface reflectance and backscatters of Sentinel-1 and Sentinel-2. Combining Sentinel-1 and Sentinel-2 data resulted in more precise estimation of the green area index (R²=0.98), aboveground dry biomass (R²=0.90), plant height (R²=0.94), and leaf nitrogen content (R²=0.78). Sentinel-2 alone was particularly effective in estimating shoot density (R²=0.94). These estimates were then used to create management zones for precision agriculture, classified based on agronomic performance benchmarks. The fuzzy c-mean clustering algorithm helped generate homogeneous management zones, considering the biophysical variations within fields.The ultimate goal is to integrate these biophysical property maps and management zones into crop management workflows. This integration will assist farmers in recognizing field variability and understanding its causes. Moreover, the spatial distribution of these zones supports variable rate application, guiding farmers towards more efficient, profitable, and sustainable crop management practices.

Construction of green logistics evaluation index system based on big data

Construction of green logistics evaluation index system based on big data

Application of a novel deep eutectic solvent as an eco-friendly medium for green extraction of cadmium from herbal medicines

In this research, hydrophobic DESs were successfully prepared using components with hydrophobic and sulfydryl group, which were applied to develop a green and eco-friendly method to extract and detect the cadmium (Cd) in herbal medicine (HM). The basic properties of the DESs were studied, which showed good hydrophobicity, low density and sufficient absorption capacity. In extraction process, the extraction efficiency (EE) of Cd employing proposed process ranged from 89.12 %∼94.59 % and the maximal enrichment factor (EF) was 46.85. The DES based method demonstrated a good linear response in the range of 1 ∼ 50 µg·L-1 with a detection limit (LOD) of 0.29 μg L-1, which was detected by Inductively coupled plasma mass spectrometry (ICP-MS). Additionally, the accuracy of method (as recoveries %) was obtained with the recoveries ranging from 98.38 %∼105.48 %. The extraction process was also compared with microwave digestion (MD) method and showed basically equivalent effect with good propensity to enrichment. The mechanism was explored by DFT calculation to reveal the interaction of extraction process. The greenness of the proposed method was also evaluated using the Green Analytical Procedure Index (GAPI) and Analytical Eco-Scale (AES), which indicated excellent environmental protection. This study successfully prepared novel DESs and established an eco-friendly and effective analytical process integrating adsorption, collection, separation and detection for the detection of heavy metals in HM, and the DESs were contributed to the first three parts as a multifunctional role. The absorption mechanism related to the formation of molecular complexes in quantum chemistry perspective, which were attribute to the electrostatic attraction and H+ exchange between Cd and adsorbent. This method showed vast potential in heavy metal research involving complex matrices such as HM and its processed products, and the proposed process provides the way for a greener alternative for Cd extraction rather than conventional polluting processes.