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Agricultural cropping structure is related to the quality of the agricultural supply system and is a key element of the structural reform of the agricultural supply side. Based on China's provincial panel data from 2000 to 2021, this paper empirically examines the impact and mechanism of rural population aging on the planting structure of food and cash crops by using a two-way fixed-effects model, which fills the gap in the research on the impact mechanism of the rural population aging on agricultural planting structure. The conclusions of the study show that: as the aging of the rural population deepens, the proportion of food crops planted will further increase, while the proportion of cash crops planted will decrease; agricultural mechanization will promote the further increase of the proportion of food crops planted while the proportion of cash crops planted will decrease in the deepening of the aging of rural population; the aging of the rural population has a more significant impact on the structure of agricultural planting in the eastern region and does not have a significant impact on the central and western provinces. The aging of the rural population has a significant impact on the agricultural planting structure in the eastern region, but not in the central and western provinces. This paper argues that we should fully respect the willingness of agricultural management subjects to choose planting varieties, increase the research, development, and promotion of agricultural machinery, continuously improve the level of farmers' human capital, and further enhance the degree of organization, scale, and specialization of agricultural production. [ABSTRACT FROM AUTHOR]

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors—including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of crop planting units plays a critical role in crop yield estimation, soil management, food supplies, and disaster warnings. However, precisely mapping crop types is challenging in smallholder farming systems due to heterogeneous and mixed pixels, where field sizes are small, and crop types are very diverse. In this paper, the crop type system of Huocheng County in northwest China as an example area, crop classification feature variables are constructed using Sentinel-2 remote sensing images, and combining the ReliefF algorithm, three feature selection classification models are established by 3092 crop type sampling points data. The crop planting units are extracted using pixel-based and object-based classification methods, respectively, and unmanned aerial vehicle (UAV) data assess the accuracy as ground truth. The Sentinel-2 image based smallholder crops classification results indicate that: The effective combination of optimal input feature variables selection and classification models significantly improves crop classification accuracy. This is particularly evident when integrating this approach into pixel-based classification, addressing challenges such as low pixel classification accuracy in regions of this type. The validation results from UAV data also confirm these findings. At validation points 2, 3, and 4, pixel-based classification demonstrates significantly higher accuracy compared to object-based classification, with pixel-based classification accuracy improving by 9.16%, 7.83%, and 31.83%, respectively. Although the UAV validation accuracy does not reach a high level, pixel-based classification remains the optimal choice for smallholder crop classification. This method offers new insights and references for research related to the classification of smallholder crops with complex planting structures. [ABSTRACT FROM AUTHOR]

Some subtle features of planting structures in irrigation areas could only be visible on high-resolution panchromatic spectral images. However, low spatial resolution multispectral image makes it hard to recognize them. It is challenging to accurately obtain crop planting structure when using traditional methods. This paper proposes an extraction method of crop planting structure based on image fusion and U-Net depth semantic segmentation network, which can automatically and accurately extract multi-category crop planting structure information. This method takes Landsat8 commercial multispectral satellite data set as an example, chooses RGB pseudo-color synthetic image which highlights vegetation characteristics, and uses HLS(Hue, Luminance, Saturation), NND(Nearest-Neighbor Diffusion) and G-S(Gram-Schmidt) methods to fuse panchromatic band to obtain 15m high-resolution fusion image to obtain training set and test set, six types of land features including cities and rivers were labeled by manual to obtain the verification set. The training and validation sets are cut and enhanced to train the U-Net semantic segmentation network. Taking the Xiaokaihe irrigation area in Binzhou City, Shandong Province, China, as an example, the planting structure was classified, and the overall accuracy was 87.7%, 91.2%, and 91.3%, respectively. The accuracy of crop planting structures (wheat, cotton, woodland) was 74.2%, 82.5%, 82.3%, and the Kappa coefficient was 0.832, 0.880, and 0.881, respectively. The results showed that the NND-UNet method was suitable for large-scale continuous crop types (wheat, cotton), and the GS-UNet method had a better classification effect in discrete areas of cash crops (Jujube and many kinds of fruit trees). [ABSTRACT FROM AUTHOR]

The crop planting structure in the world has shown a trend of "non-grain", which will shake the foundations of global food security in the long run. As a basic and important production factor, changes in land will have an impact on farmers' crop planting decisions. In this paper, we take China, a country that is experiencing land transfer, "non-grain" production, and farmer differentiation, as the research area, use the household survey data at the national level, and adopt the methods of Propensity Score Matching (PSM) and multiple regression models to reveal the impact of land transfer-in on the crop planting structure and its heterogeneity among farmers. The results showed that land transfer-in can drive the crop planting structure to tend to be "non-grain" in China. The research conclusion was still valid after the robustness tests of expanding the sample size, increasing the number of control variables, and introducing endogenous problem management. The heterogeneity analysis indicated that the negative impact of land transfer-in on the planting of grain crops mainly exists for large-scale farmers and farmers with agriculture as the main source of income. Based on these findings, the Chinese government should formulate targeted policies to prevent the "non-grain" tendency of crop planting structure after land transfer-in. [ABSTRACT FROM AUTHOR]

China tends to be a dominant figure in the literature on global land grabbing. It is either cast as a major land grabber in distant places such as Africa, or as a key player in crop booms elsewhere because it provides for massive market demand, such as for soya from South America. These are all important issues and are well covered in the literature. However, the crop booms inside China that involve transnational capital and investors - and have provoked conflict around land politics - have been overlooked. Spotlighting the issue of land grabbing inside China reminds us that capital accumulation is principally interested in geographies and settings where it can generate profit - regardless of nationalities, boundaries, structural or institutional conditions. This paper hopes to contribute towards a more refined picture of global land grabbing. [ABSTRACT FROM AUTHOR]

Morus alba L. (Mulberry), one of industrial crops, have been widely cultivated in China, mulberry leaves (Folium Mori), one of the main products of Morus alba L., is high yield and rich in nitrogen element, high-value-utilization of mulberry leaves is very important for agricultural economic development. Carbon quantum dots have been attracting extensive attention because of their excellent properties including tunable fluorescence, bio-compatibility, non-toxicity, high water-solubility, and ease of functionalization. Heteroatoms doping is an effective way to improve quantum yield and fluorescence performance of carbon quantum dots. In this work, the self-nitrogen-doped carbon quantum dots was prepared from mulberry leaves, a nitrogen-rich, economic value-high and eco-friendly crops products, via a facile one-step hydrothermal method. The as-prepared self-nitrogen-doped carbon quantum dots exhibited certain stability, solubility and fluorescence intensity (quantum yield is 9.3%). With the introduction of tetracycline, the efficiency fluorescence quenching was observed, and an obvious color change can also be identified through naked eye. The fluorescence quenching mechanism is induced by internal filtering effect and static quenching. Based on this phenomenon, a rapid, naked-eye, and dual-readout assay for tetracycline detection was constructed and prepared as a fluorescent test paper. The linear range and detection limit of tetracycline were 0.158–40 μM and 158 nM, respectively. Other types of antibiotics, comment ions and chemicals show little influence on tetracycline detection. Furthermore, the constructed dual-readout assay was employed to detect tetracycline in river water with recovery range of 98.6–102.2%, relative standard deviation less than 2.54%. The constructed fluorescence probe show potential application to tetracycline detection in the real water sample. [Display omitted] • The self-N-doped carbon dots were prepared from mulberry leaves by hydrothermal method. • The self-N-doped carbon dots show good linear response to tetracycline. • The quenching mechanism is internal filtering effect and static quenching. • The fluorescent test paper based on the self-N-doped carbon dots was prepared. • The fluorescent test paper can detect tetracycline visually and quickly. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Timely and accurate extraction of crop planting structure information is of great importance for food security and sustainable agricultural development. However, long time series data with high spatial resolution have a much larger data volume, which seriously limits the quality and efficiency of the application of remote sensing to agriculture in complex crop rotation areas. To address this problem, this paper takes Lujiang County, a typical complex crop rotation region in the middle and lower reaches of the Yangtze River in China as an example, and proposes utilizing the Google Earth Engine (GEE) platform to extract the Normalized Difference Vegetation Index (NDVI), Normalized Difference Yellowness Index (NDYI) and Vertical-Horizontal Polarization (VH) time series sets of the whole planting year, and combining the Simple Non-Iterative Clustering (SNIC) multi-scale segmentation with the Support Vector Machine (SVM) and Random Forest (RF) algorithms to realize the fast and high-quality planting information of the main crop rotation patterns in the complex rotation region. The results show that by combining time series and object-oriented methods, SVM leads to better improvement than RF, with its overall accuracy and Kappa coefficient increasing by 4.44% and 0.0612, respectively, but RF is more suitable for extracting the planting structure in complex crop rotation areas. The RF algorithm combined with time series object-oriented extraction (OB + T + RF) achieved the highest accuracy, with an overall accuracy and Kappa coefficient of 98.93% and 0.9854, respectively. When compared to the pixel-oriented approach combined with the Support Vector Machine algorithm based on multi-temporal data (PB + M + SVM), the proposed method effectively reduces the presence of salt-and-pepper noise in the results, resulting in an improvement of 6.14% in overall accuracy and 0.0846 in Kappa coefficient. The research results can provide a new idea and a reliable reference method for obtaining crop planting structure information efficiently and accurately in complex crop rotation areas. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) is a non-essential, highly phytotoxic metal and damages ramie plant growth and development even at low concentrations. Ramie is one of the most significant crops in China, with excellent fiber quality and immense industrial importance. Planting Cd-tolerant ramie cultivars can prevent yield loss on contaminated soil. Previously, significant efforts have been made to develop Cd tolerance in ramie. However, the Cd tolerance mechanism is still not fully understood; hence, breeding industrial crops is critical to tackling the ongoing challenges. Cd tolerance is a complex genetic mechanism requiring high-level molecular studies to clarify the genes network. Genetic studies have identified several Cd-tolerant genes in ramie, which led to the development of several ramie cultivars suitable to grow on toxic soils; however, due to the continuous rise in Cd toxicity, potent molecular tools are critical in modern-day breeding programs. Genetic engineering, and transcriptome analysis have been used to develop abiotic stress tolerance in ramie, but QTL mapping and clustered regularly interspaced short palindromic repeats (CRISPR) are rarely studied. However, studies are still limited in addressing this issue. This review critically elaborated on using QTL mapping, transcriptomes, transcription factors, CRISPR/Cas9, and genetic engineering to enhance Cd tolerance in ramie. These genes/QTL should be transferred or edited into sensitive cultivars using genetic engineering or CRISPR/Cas9. CRISPR/Cas9 is highly recommended because it provides targeted gene editing in ramie, its use is limited and can address the research gaps, and it would revolutionize the field of agriculture. Limitations, gaps, and future potential are briefly discussed. This review paper presents new clues to help future researchers comprehensively understand Cd tolerance in ramie and develop tolerant cultivars for industrial purposes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a complex agricultural region, determine the appropriate crop for each plot of land to maximize the expected total profit is the key problem in cultivation management. However, many factors such as cost, yield, and selling price are typically uncertain, which causes an exact programming method impractical. In this paper, we present a problem of crop cultivation planning, where the uncertain factors are estimated as fuzzy parameters. We adapt an efficient evolutionary algorithm, water wave optimization (WWO), to solve this problem, where each solution is evaluated based on three metrics including the expected, optimistic and pessimistic values, the combination of which enables the algorithm to search credible solutions under uncertain conditions. Test results on a set of agricultural regions in East China showed that the solutions of our fuzzy optimization approach obtained significantly higher profits than those of non-fuzzy optimization methods based on only the expected values. [ABSTRACT FROM AUTHOR]

China is issuing a warning regarding the 'unprecedented' hurdles it faces in safeguarding its food security. The substantial conversion of farmland for non-grain cultivation is exacerbating the situation, necessitating urgent investigation into the underlying causes. Nonetheless, previous studies have revealed a dearth of village-level analysis and have been marred by a bias stemming from econometric methodologies. To remedy this, the present paper employs a sophisticated approach combining econometric modelling and machine learning techniques to unravel the intricate driving forces at the village level. The study yields three principal findings. Firstly, the availability of farmland, poverty incidence, and the age of village leaders exert a detrimental influence on non-grain use of farmland (NGUF). Secondly, a conglomerate of factors, such as the aging rural workforce, income levels, positively impact the planting of non-grain crops, with remarkably increasing effects. Finally, it is pivotal to acknowledge that the influencing factors display sensitivity to the spatial scale examined. Village-level research provides a comprehensive overview, transcending individual household decisions. Collectively, this study underscores the indispensability of incorporating the underlying principles into policy formulation. Township and county authorities should craft guidelines, policies, and regulations in accordance with the ascertained village-level drivers, rather than relying solely on household-level determinants. [ABSTRACT FROM AUTHOR]

GM crops, as a widely researched and applied biotechnology, hold significant strategic and practical implications for promoting the commercialization of GM crops in China, driving agricultural industry upgrading, and fostering economic and social development. However, despite their potential benefits, the commercialization of GM crops in China has been consistently delayed. Therefore, this study aims to explore the trust relationship between the government and the public in the field of genetically modified organisms and the heterogeneous impacts generated at the production and consumption ends. We primarily focus on insect-resistant cotton and genetically modified papaya as representatives and base our research on survey data from Xinjiang and Guangdong. By employing factor analysis and establishing multiple Probit models with government trust, crop purposes, and farmer expectations as independent variables, and the commercialization of GM crops as the dependent variable, we conduct two sets of empirical analyses. The study finds that government trust has a more significant impact on consumer concerns about whether to consume GM products than on producers, who prioritize farmers' desire for agricultural product profitability. Age and education levels also influence the public's acceptance of planting GM crops, but the effects are not as significant as the core variables mentioned earlier. Based on this, we conclude that consumers and farmers have different positions and contradictions in the specific scenario of delayed GM commercialization in China. In this context, this paper argues that diversified strategies should be adopted to address the issue of commercialization of GM crops in China. [ABSTRACT FROM AUTHOR]

Based on the data of 2015 China Household Finance Survey (CHFS), this paper applies the intermediary effect model to identify the intermediate transmission mechanism in which farmland transfers affect agricultural production efficiency. The results show that the output value of households that have transferred farmland is increased by about 27% more than those that have not. In addition, the transfer of farmland has a more significant impact on the agricultural production efficiency of young and middle --aged families. The transfer of farmland from grain production areas has an increased tendency to household output value. The mechanism analysis shows that the transfer of farmland affects agricultural production efficiency in the following two ways. On the one hand, the transfer of farmland can help realize the effective replacement of labor by machinery, increase the mechanization degree of agricultural production, and realize the scale effect of farmland. On the other hand, based on the maximization of their own interests, farmers prefer to plant cash crops and improve agricultural production efficiency through the selection effect. By comparing the above two effects, we found that the planting selection behavior of compacting grain and expanding economic growth is more prominent. Therefore, this article proposes to promote the development of the farmland transfer market and make use of scale advantage. Meanwhile, the self-selection behavior of transferred farmers in planting crops should be valued and young people with advanced concepts to engage in agricultural production activities should be introduced, which inject "fresh blood" into agricultural modernization. However, we should also keep the red line of farmland and alert the phenomenon of "non-agricultural" and "non-grain" in the transfer of farmland. [ABSTRACT FROM AUTHOR]

The original intention of virtual water trade (VWT) is to help water-scarce areas adjust the crop trade structure to alleviate the water shortage problem. However, China's existing virtual water trade (VWT) must effectively alleviate the problem. This paper's structural features and stability of the VWT network (VWTN) in China's major crop trade between 2000 and 2017 were characterized using complex network theory. The results in terms of time scale showed that the total content of VW in China's major crops increased by ~23.6% for 18 years. Trade relations among most regions remained stable, whereas a few areas changed: Jilin, Henan, and Heilongjiang played essential roles in the output network, and so did Guangdong, Shanghai, and Fujian in the input network. Attributed to policy adjustment, Henan and Jilin became more prominent in the output network with the gradual decline of Jiangsu's position. Regarding spatial scale, the cumulative distribution of degrees consistent with the power–law relationship showed high variability and vulnerability of China's VWTN, especially when nodes were weighted. Since areas with more VWT partners/content provided connectivity to those with fewer partners/content, it is worthwhile to focus on developing protection policies for critical areas. The virtual water trade from North China (water-poor) to South China (water-rich) is contrary to the distribution of water resources. A similar situation showed in a global world that the long-term supply relationship would aggravate the water resources shortage and food security. Furthermore, we suggest combining network theory and VWT to lay the foundation for the invulnerability research of VWTN and the optimal regulation of crops. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Chemistry is the property of Chinese Journal of Applied Chemistry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Growing populations and rapid urbanization have put tremendous pressure on the food supply. The rural hinterland around cities is an important source of the urban food supply chain. Facing the constraints of China's land stock, reasonable use of land space and optimization of agricultural crop structure is crucial to meet the food demand. Von Thünen Model, which is fronted by a 19th-century German economist, outlines a rural landscape of commercial farmers growing agricultural products for local markets while proposing basic patterns and principles of land use in agriculture. Using data from China's OVOP ("One village, One product"), this paper analyzes the agricultural location and crop choices around two levels of cities (provincial capital cities, and county-level cities) by using Thünen's theory. The results showed that crop density did decrease as the distance to urban increased. Crop rings are present in the vicinity of both metropolitan and county-level cities, distributed according to crop intensity. Evidence from China suggests that agricultural location and crop selection still follow the basic principles of the Thünen model. Planners and policymakers should refocus on the Von Thünen model to utilize land space and optimize agricultural production scientifically and efficiently. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

China implemented the Action Plan for the Zero Increase of Fertilizer Use in 2015, which led to a decrease in fertilizer use. However, Will fertilizer use continue to reduce? With data obtained from 2006 to 2017, the paper used the logarithmic mean Divisia index (LMDI) method to analyze the scale effect, intensity effect and structural effect of fertilizer use change in China from three aspects: crops, regions and fertilizer types. Our finding suggests that (1) The intensity effect was the most critical factor affecting the decline in fertilizer use in China. (2) The sowing scale and fertilization intensity of grain, vegetables and fruits had the most significant driving effect on fertilizer reduction. (3) The three effects of each region were different in space, and the eastern region contributed most to the fertilizer decrement. (4) Nitrogen fertilizer and compound fertilizer had the most considerable influence on fertilizer reduction, especially in the sowing scale and fertilization intensity since 2009. The government should establish a fertilizer reduction management system, which includes scale control, intensity reduction, structural adjustment and other measures. [ABSTRACT FROM AUTHOR]

Agricultural intensification has boosted land productivity, but it has also created new sustainability issues. As one of the most important human habitations and agricultural farming areas in arid areas, the Cele Oasis has a very developed agricultural system. This paper studies the long-term effects of different types of agricultural intensification strategies on groundwater level fluctuations in the Cele Oasis. A soil water flow (HYDRUS-1D) and aquifer simulation (MODFLOW) coupling model were used to construct the geometric structures of the vadose zone and saturated zone in the Cele Oasis and to analyze the recharge and discharge mechanism of the oasis. The results showed that HYDRUS-1D accurately simulated soil moisture transport in the Cele Oasis, providing reliable data for calibration of the MODFLOW model. The groundwater level simulated by MODFLOW was in good agreement with the observed value. The results of the R2, RMSE, and NSE were ranges of 0.77–0.90, 0.45–0.74 m, and 0.76–0.87, respectively. The errors were acceptable limits. The coupling model predicted the responses of different agricultural types and cropping scenarios to groundwater. Predictions showed that the groundwater level in the Cele Oasis remained stable under the current cropping scenario (100% cropping intensity), and that the groundwater level decreased slightly under the cropping scenario (110% cropping intensity and 130% cropping intensity). When the cropping scenario was at 170% cropping intensity, the groundwater level decreased rapidly, and the maximum drawdown value was 7 m. Therefore, the maximum cropping intensity of the Cele Oasis in the future should be 130% of the current cropping intensity. [ABSTRACT FROM AUTHOR]

Degradation of microplastics (MPs) by both physicochemical and biological processes in the natural environment is determined by the enzymes inside the soil, and which was severely influenced by crop growth and straw amendment (SA). However, it is still unclear how crop growth and SA influence degradation of MPs in soils. In this study, both catalase and sucrase were measured, and the stereomicroscope combined with microscopic infrared spectroscopy and scanning electron microscope (SEM) was used to detect the morphology and quantity of low-density polyethylene microplastic (LDPE-MP) and low-density polypropylene microplastic (LDPP-MP), after crop growth (maize and soybean, with and without SA, 1 and 2% MP) in an outdoor pot experiment, in the Mollisols. The results showed that the growth of the crops changed the morphology, functional groups (e.g., methylene, carbonyl), total mass, and abundance ratio of MPs of different sizes. These were possibly caused by enzymes that were significantly influenced by crop types, abundance, and types of MPs in the soils. Maize growth decreased the mass of LDPE-MP and LDPP-MP by 28.7 and 32.7%, respectively, and 2% (w/w) of LDPP-MP addition in soil decreased mass of 9%, which was higher than that in 1% (w/w) LDPP-MP addition in soil. Soybean growth with SA decreased the mass of LDPE-MP and LDPP-MP by 36.6 and 20.7%, respectively, than the control treatment (CK). Compared with CK, both crop growth and SA changed the abundance of MPs of different sizes and decreased the mean size of MPs. The LDPE-MP could be more easily degraded by enzymes in the soils compared to LDPP-MP when the MP size was smaller with surface roughness. Generally, both maize and soybean growth can accelerate MP change in soils, and MP change process was mainly determined by SA, MP types, and the dose effect of MP. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a theoretical model to illustrate the relationship between the scale of land transfer-in and farmers' crop selection behaviour. We use a semiparametric instrumental variable regression model to test this relationship. Data come from a face-to-face interview survey of 2,677 farmers located in nine provinces of China collected in 2014–2015. Results show that the size of land transfer-in has a U-shaped impact on the probability of planting food crops. To guarantee food crop self-sufficiency, the Chinese government should encourage land transfer, foster the mobility of agricultural labourers, and enhance the capacity of the rural financial market. [ABSTRACT FROM AUTHOR]

Soil rill erodibility (Kr) and critical shear stress (tc) are important parameters in some physical soil erosion models. In the present study, the temporal variations in Kr and tc by overland flow were investigated using undisturbed topsoil samples collected from three cropped plots (ryegrass, lucerne, and corn) and a fallow plot. In this study, the potential factors leading to these changes were examined from March to September 2022 in western Sichuan Province, China. The results revealed significant seasonal variations in Kr, tc, soil bulk density (SD), soil cohesion (SC), and soil water-stable aggregate (WSA). The Kr values were significantly lower, whereas tc values were slightly higher for cropped plots when compared to that for the fallow plot. The mean Kr values for cropped plots were 4.51~17.26 times lower than that for the fallow plot. In contrast, the mean tc values for the cropped plots were 1.03~1.08 times higher than that for the fallow plots. The results also indicated a negative correlation of Kr with SD, SC, WSA, soil organic matter content (SOC), and root weight density (RWD), while a positive correlation of tc with SD, SC, WSA, and RWD. Furthermore, an exponential decrease in Kr was observed with an increase in SD, SC, WSA, SOC, and RWD. An increase in SD, SC, WSA, and RWD of the experimental plots led to a further increase in tc. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Pesticide Science / Nongyaoxue Xuebao is the property of Chinese Journal of Pesticide Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An agrovoltaic system combines agricultural crop production and energy production in the same place, emphasizing the dual use of land. This article provides a bibliometric analysis of agrivoltaic topics based on publications indexed in SCOPUS, in which either economic assessments of agrivoltaics, agrivoltaic systems for crops and livestock animals, photovoltaic greenhouse and agrivoltaics with open field are discussed, or its ideas are used to analyze certain locations. A bibliometric analysis was conducted using the SCOPUS database. Multiple bibliometric tools, such as R Studio and Biblioshiny, were applied to analyze data for this study. Finally, 121 relevant articles were obtained and reviewed. The results show that the focus topic is a brand-new research area, with the majority of relevant scientific publications concentrated in the last three years, and with much ongoing research. This is why AV-specialized scientific conferences might be the best place to get relevant and up-to-date information, with the highest number being offered in the USA and China. A typical trend in recent years has been researched, focusing on different agricultural aspects. The research results show that scientific publications in recent years mainly focus on short-term predictions, there is no recognized evaluation standard for various prediction analyses, and it is difficult to evaluate various prediction methods so far. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Natural Resources / Ziran Ziyuan Xuebao is the property of Journal of Natural Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Variable-rate fertilization is crucial in the implementation of precision agriculture and for ensuring reasonable and efficient fertilizer application and nutrient management that is tailored to local conditions. The overall goal of these technologies is to maximize grain output and minimize fertilizer input and, thus, achieve the optimal input–output production ratio. As the main form of variable-rate fertilization, real-time variable-rate control technology adjusts fertilizer application according to the growth status and nutrient information of crops and, as such, its effective application relies on the stable and accurate acquisition of crop phenotypic information. Results: Due to the relationship between crop phenotype and real-time fertilizer demand, phenotypic information has been increasingly applied in these contexts in recent years. Here, the establishment and characteristics of inversion models between crop phenotypic information and nutritional status are reviewed. The principles of real-time monitoring applications, the key technologies relating to crop phenotypic biological parameters, and the existing challenges for real-time variable-rate fertilization technology are also evaluated. Future research directions are then discussed in the specific context of the need for sustainable development of modern agriculture in China. Conclusion: This paper provides a theoretical reference for the construction of scientific management technology systems aimed at reducing fertilizer application and maximizing output, and for the development of relevant technologies in the specific context of China. [ABSTRACT FROM AUTHOR]

Leaf vegetables serve as an important food for the local residents in China. This paper focuses on the uptake, accumulation, transfer, and mercury (Hg) sensitivity of leafy vegetables. Two types of soil (an alkaline Cambosol and an acid Ferrosol) and eleven species of leafy vegetable, namely, Spinach, Tung choy, Leek, Fennel, Coriander, Chinese flowering cabbage, Wuta-tsai, Pakchoi, Chicory, Crown daisy, and Lettuce, were selected to investigate their sensitivity to Hg accumulation in a greenhouse pot experiment. Three Hg concentration treatments were carried out as control (background values), low concentration (1.5 times standard value), and high concentration (2 times standard value) as adjusted by the soil pH. Hg concentrations of more than half vegetable samples grown in Cambosol (collected from Shandong Province) reached or exceeded the maximum permissible food safety levels (10 μg kg-1) according to the General Standard of Contaminants in Food in China (GB 2762-2012), while only about 15% in Ferrosol (collected from Jiangxi Province). Meanwhile, Hg bio-concentration factors (BCF) in all treatments were < 1, while Hg translocation factors (TF) in most treatments were < 1. Correlation analysis among soil, root, and edible plant parts revealed that the principal source of Hg in leafy vegetables was most likely from Hg-contaminated soils. Species sensitivity distribution (SSD) models were constructed and their simulated curves indicated that sensitivity to Hg was highest in Pakchoi in low Hg-contaminated soils, and Chicory in highly Hg-contaminated soils. Therefore, Hg concentration is mostly accumulated in roots of leafy crops, which reduces the risk of Hg bioaccumulation in edible portion of vegetables, and (2) Brassicaceae vegetables are mostly less sensitive to soil Hg contamination. Our results provide effective guidance for the selection of leafy vegetables for cultivation and daily consumption that minimizes health risk. [ABSTRACT FROM AUTHOR]

Moisture content is one of the important indicators in grain harvesting, trading, storage and processing. High moisture content will lead to a series of problems of grain such as mildew, deterioration, shortened storage time, lower transaction price and higher processing cost. With the development of precision agriculture, accurate detection of grain moisture content can also accurately assess the growing situation of crops, and then provide guidance for the next season’s crop planting. Compared with the drying method, the capacitance method and the electric resistance method, the microwave method is easy to realize the non-destructive measurement of the grain moisture content, which is an important direction of moisture content detection. In the field of on-line detection of moisture content of rice and wheat in combine harvesters, foreign related research started earlier, and enterprises have commercialized, but this technology is still in the research stage in China. Aiming at the problem that the rice-wheat combine harvester is difficult to accurately measure the moisture content of wheat and rice during harvesting operation, this paper studied a non-destructive on-line detection method of grain moisture content based on microwave reflection method, established the grain moisture content measurement model, designed a non-destructive on-line detection device for grain moisture content used on rice-wheat combine harvesters. The device used a 450 MHz microwave measurement module to measure the moisture content of rice and wheat non-destructively. The voltage conversion circuit was designed to convert microwave parameters into voltage signals, and the average filtering algorithm was used for signal filtering to reduce the impact of interference factors. Finally, the moisture content detection model of rice and wheat, which was established by the calibration experiment, was used to calculate the moisture content. The calculation result was displayed on the display in real time via CAN bus communication. Due to the insufficient installation space in rice-wheat combine harvester, the structure of the detecting device was designed and optimized, and the circuit portion and the sampling mechanism were integrated in a compact structure. Based on the above theoretical research, technology development and structural design, this paper carried out laboratory static experiments and field harvesting experiments. The laboratory static experiment used rice and wheat to illustrate the establishment process of the grain moisture content detection model: 30 groups of rice and 30 groups of wheat samples were prepared, moisture contents and voltages calibration experiment was completed, the rice and wheat moisture content detection models was established and the experimental results were analyzed. The field harvesting experiment was verified the practicability and reliability of the grain moisture content detecting device with rice as the test object. The experiment results show that the performance standard deviations of the laboratory static experiment and the field harvest experiment are 0.458 3 and 1.078 0, respectively, and the relative error are around 2.5% and 5%, respectively. Moisture content measuring range of the detection device for rice and wheat is 14%-34%, which meets the project requirements. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Precise large-area crop mapping products are the foundation for cropland monitoring, market policy decision-making, and subsequent agricultural applications. However, owing to sharp discrepancies among the temporal-spectral attributes of crops from different areas, crop mapping across phenological regions faces significant challenges. Given the inadequacy of generalizing mapping frameworks from the source to target regions, research areas are often limited, and trained models cannot be reused beyond a particular study area. In this paper, we propose a novel framework called the Phenology Alignment Network (PAN) to address the cross-phenological-region (CPR) crop-mapping problem using deep recurrent networks and unsupervised domain adaptation. Our PAN adopts a Siamese structure consisting of two identical deep models called the Temporal Spectral Network (TSNet), which serves as an adaptive multilevel phenological feature extractor for crops under various planting conditions. Specifically, the two branches of PAN accept crop samples from the source and target regions, and aim to encode them into similar deep phenological features. By constraining the distribution of hidden states from the two branches, the deep temporal-spectral features are aligned, and regional discrepancies between the source and target regions are alleviated. Consequently, the PAN adapts the deep model pretrained on the source region to the target area, improving the mapping accuracy without using additional target label information. To verify the effectiveness of our model, crop samples were collected from three plains of China, and a winter crop dataset based on Sentinel-2 time-series imagery containing over two million pixels was annotated. Experiments conducted on this dataset indicate that PAN serves as a practical CPR crop mapping framework and achieves significant improvement in the overall accuracy as well as the macro-average F1 score compared to other state-of-the-art methods. Further visual interpretation illustrates the capability of PAN to correct inaccurate predictions ascribed to phenological diversity and suggests the potential of PAN for large-area crop mapping applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fire and explosion hazards pose significant safety concerns in the processing and storage of biomass particles, warranting the safe utilization of these particles. This study employed scanning electron microscopy, thermogravimetric analysis, and cone calorimetry to investigate the thermal hazards and toxicity of raw biomass particles from four prevalent agricultural crops in China: rice, sorghum, corn, and reed. Among the samples, corn exhibited the highest heat output of 8006.82 J/g throughout the thermal decomposition process. The quantitative evaluation of critical heat flux, heat release rate intensity, fire growth rate index (FIGRA), post-ignition fire acceleration (PIFA) and flashover potential (X) revealed a substantial fire risk inherent to all the examined straw samples. Notably, corn displayed the lowest FIGRA value of 8.30 kW/m2 s, while rice demonstrated the minimum PIFA value of 16.11 kW/m2 s. Moreover, the X values for all four biomass particle types exceeded 10 under varying external heat flux levels, indicating their high propensity for fire hazards. Analysis of CO and CO2 emissions during combustion showed all four biomass samples exhibited high concentrations throughout, from the initial stages to the end. The present study offers crucial insights for formulating comprehensive fire safety guidelines tailored to the storage and processing of biomass particles. [ABSTRACT FROM AUTHOR]

Cropland reactive nitrogen losses (Nr) are of the greatest challenges facing sustainable agricultural intensification to meet the increases in food demand. The environmental impacts of Nr losses and their yield responses to the mitigation strategies were not completely evaluated. We assessed the environmental impacts of Nr losses in China and coupled the efficiency of mitigation actions with yield responses. Datasets about Nr losses in China were collected, converted into potentials of acidification (AP), global warming (GWP), and aquatic eutrophication (AEP), and analyzed by a meta-analysis program. Results showed that producing 1 Mg of rice grains had the highest AP (153 kg acid equiv.), while wheat had the highest GWP and AEP (74 kg CO2 equiv. and 0.37 kg PO4 equiv., respectively). Using the conventional rates (averagely, 200, 230, and 215 kg N ha−1) of urea as a surface application to produce 131.4, 257.2, and 212.1 Tg of wheat, maize, and rice resulted in 17–33 Tg, 7–10 Tg, and 6–87 Gg of AP, GWP, and AEP, respectively. For their balanced effect on reducing AP, GWP, and AEP while maximizing yields, inhibitors, and subsurface application could be set as the best mitigation strategies in wheat production. Inhibitors usage and biochar are strongly recommended strategies for sustainable production of maize. None of the investigated strategies had a balanced effect on rice yield and the environment, thus new mitigation technologies should be developed. [ABSTRACT FROM AUTHOR]

Sweetcane (Erianthus arundinaceus [Retzius] Jeswiet) is an ecologically dominant warm‐season perennial grass native to southern China. It traditionally plays an important role in sugarcane breeding due to its excellent biological traits and genetic relatedness to sugarcane. Recent studies have shown that sweetcane has a great potential in bioenergy and environmental remediation. The objective of this paper is to review the current research on sweetcane biology, phenology, biogeography, agronomy, and conversion technology, in order to explore its development as a bioenergy crop with environmental remediation potential. Sweetcane is resistant to a variety of stressors and can adapt to different growth environments. It can be used for ecological restoration, soil and water conservation, contaminated land repairing, nonpoint source pollutants barriers in buffer strips along surface waters, and as an ornamental and remediation plant on roadsides and in wetlands. Sweetcane exhibits higher biomass yield, calorific value and cellulose content than other bioenergy crops under the same growth conditions, thereby indicating its superior potential in second‐generation biofuel production. However, research on sweetcane as a bioenergy plant is still in its infancy. More works need be conducted on breeding, cultivation, genetic transformation, and energy conversion technologies. Sweetcane is an ecologically dominant warm‐season perennial grass with excellent biological traits and genetic relatedness to sugarcane. It not only plays an important role in sugarcane breeding, but also has great potential in environmental remediation and as a biofuel feedstock. It can be planted on many types of marginal land in southern China for biofuel feedstock, environmental remediation, or both. More works need be conducted on its breeding, cultivation, genetic transformation, and energy conversion technologies. [ABSTRACT FROM AUTHOR]

Multiple cropping index (MCI) is a very important indicator in crop production and agricultural intensification, which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land. The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS (Moderate-Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index, and the method of no additional authentication data is independent and reliable. The result was accurate and stable, the slope of linear regression of the multiple cropping index between the statistical results and the remote sensing results was 1.0136 (R²=0.779). The precision of sample areas validation was 97.91%. Suggesting that the time series MODIS-EVI which after Savitzky-Golay filtering processed, could provide an effective way to extract spatial information of multiple cropping index for management department of agriculture. [ABSTRACT FROM AUTHOR]

Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution. Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN). Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to extract fine crop areas. The CNN comprises a feature extractor and a classifier. The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features. A linear model is used to fuse features of different levels, and the fusion results are up-sampled to obtain a feature map consistent with the structure of the input image. This feature map is used by the classifier to perform pixel-by-pixel classification. In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment. Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maintaining crop outputs to feed its large population with limited resources while simultaneously mitigating carbon emissions are great challenges for China. Improving the efficiency of resource use in crop production is important in reducing carbon emissions. This paper constructs a methodological framework combining emergy-based indicator accounting and a nonseparable undesirable output slack-based measurement (SBM) data envelopment analysis (DEA) model. This framework is used to explore the efficiency of inputs and outputs and the greenhouse gas (GHG) emissions reduction potential for crop production systems, using Zhejiang province, China, as a case study. It is found that an emergy synthesis and a nonseparable undesirable output SBM-DEA framework is compatible with the case study. Crop production in Zhejiang province has relied heavily on an increase in agrochemical inputs to maintain agricultural output. Energy and chemical fertilizer use are determined as the province’s major carbon emissions sources. Although carbon emissions per unit of monetary output has decreased sharply, the carbon emissions per unit emergy output has increased, demonstrating a high carbon intensity reality. The DEA highlighted the differences in crop production efficiency, resource factor redundancy and carbon mitigation potential in the different prefectures of the province. To conclude this research, policies to support low carbon agriculture development, including subsidizing low carbon agriculture technology development and expansion and the cancellation of subsidies to high carbon production factors, such as chemical fertilizer production and sales, are discussed to conclude the research. [ABSTRACT FROM AUTHOR]

Accurate and reliable farmland crop mapping is an important foundation for relevant departments to carry out agricultural management, crop planting structure adjustment and ecological assessment. The current crop identification work mainly focuses on conventional crops, and there are few studies on parcel-level mapping of horticultural crops in complex mountainous areas. Using Miaohou Town, China, as the research area, we developed a parcel-level method for the precise mapping of horticultural crops in complex mountainous areas using very-high-resolution (VHR) optical images and Sentinel-2 optical time-series images. First, based on the VHR images with a spatial resolution of 0.55 m, the complex mountainous areas were divided into subregions with their own independent characteristics according to a zoning and hierarchical strategy. The parcels in the different study areas were then divided into plain, greenhouse, slope and terrace parcels according to their corresponding parcel characteristics. The edge-based model RCF and texture-based model DABNet were subsequently used to extract the parcels according to the characteristics of different regions. Then, Sentinel-2 images were used to construct the time-series characteristics of different crops, and an LSTM algorithm was used to classify crop types. We then designed a parcel filling strategy to determine the categories of parcels based on the classification results of the time-series data, and accurate parcel-level mapping of a horticultural crop orchard in a complex mountainous area was finally achieved. Based on visual inspection, this method appears to effectively extract farmland parcels from VHR images of complex mountainous areas. The classification accuracy reached 93.01%, and the Kappa coefficient was 0.9015. This method thus serves as a methodological reference for parcel-level horticultural crop mapping and can be applied to the development of local precision agriculture. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drought is a natural disaster caused by long-term water deficit. Because the growth of crops and vegetation is closely related to soil moisture environment, it is of great significance to study the soil drought and vegetation response. In this paper, the soil moisture availability index (SMAI) was developed for quantifying soil drought conditions. The effectiveness and the ability of SMAI to recognize drought events were analyzed, while the vegetation condition index (VCI) was used to characterize the vegetation status. Temporal and spatial variations of soil drought and vegetation condition as well as the impacts of drought on vegetation in North China during 2001–2015 were comprehensively examined. We firstly concluded that SMAI related well with standardized precipitation evapotranspiration index (SPEI), and drought events can be detected by SMAI. Next, the mean value of SMAI in North China showed a decreasing trend in recent 15 years. Finally, the SMAI positively correlated with VCI in most areas of North China, and the response of four types of vegetation to SMAI differed over time. The results of SMAI on vegetation would assist drought research and application in North China. [ABSTRACT FROM AUTHOR]