Spatio-Temporal Distributions and Environmental Safety Threshold of Cropland Fertilization of Jiangsu Province, China

Liu, Qin Pu; Giesy, John P.; Li, Zhao Hui

doi:10.4028/www.scientific.net/amr.962-965.2110

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…Its predominantly plain topography and dense population have made Jiangsu a nationally renowned region for intensive high-yield agriculture. With advancements in modernization, Jiangsu's agriculture has experienced a shift in its production mode, with capital (including chemical fertilizer inputs) replacing labor; there was also once an unsustainable trend of intensive high yields [32], exemplified by the fact that the intensity of fertilizer use in Jiangsu was once much greater than the national average [2,33]. As the only remaining grain-producing province in the developed southeastern coastal region of China, Jiangsu not only contributes financial resources but also grains to the nation.…”

Section: Study Regionmentioning

confidence: 99%

Chinese Agriculture for “Green and Grain” Productivity Growth: Evidence from Jiangsu Province

Zhao,

Jin,

Qin

et al. 2023

Sustainability

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China is striving to leverage the power of science and technology to green its agriculture and simultaneously enhance grain productivity. To assess the performance dynamics of the dual agricultural mission, this study presents the green growth rate of agriculture using the DEA-based Green Total Factor Productivity (GTFP) indicator, together with the growth rate of grain yields, and applies it to the case of Jiangsu, a major grain-producing province with a well-developed economy. It is found that Jiangsu’s agriculture has generally performed well during the implementation of the two major national strategies addressing green development and grain security, especially in northern Jiangsu, which is a major grain-producing area. In contrast, the phased fluctuations in green growth in agriculture in southern Jiangsu are more pronounced, with negative green growth even occurring during the green agriculture movement. Much of the volatility in agricultural green growth at the provincial, subregional and municipal levels is generated by the effects of the movement of the green technology frontier, which is led by the best practitioners. Accordingly, the possibility of improving the weak state of the catch-up effect on green growth is explored from the perspective of the Chinese government-led agricultural science and innovation system; it also traces the green agricultural initiatives in the main grain-marketing areas that have failed to deliver the expected green growth, and recommends a review of such policies and a refinement of the GTFP Index tool for assessing sustainable green growth.

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Section: Study Regionmentioning

confidence: 99%

Chinese Agriculture for “Green and Grain” Productivity Growth: Evidence from Jiangsu Province

Zhao,

Jin,

Qin

et al. 2023

Sustainability

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“…Nitrogen (N), as a nutrient element with the largest demands, plays a vital role in the growth and development of maize, and is also the key to characterizing growth conditions and photosynthesis of maize plants [1,2]. Nitrogen deficiency leads to short plant height and slow growth of maize, whereas excessive nitrogen causes lodging and heat injuries in maize plants.…”

Section: Introductionmentioning

confidence: 99%

Automatic Modeling Prediction Method of Nitrogen Content in Maize Leaves Based on Machine Vision and CNN

Sun,

Yang,

Wang

et al. 2024

Agronomy

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Existing maize production is grappling with the hurdles of not applying nitrogen fertilizer accurately due to subpar detection accuracy and responsiveness. This situation presents a significant challenge, as it has the potential to impact the optimal yield of maize and ultimately, the profit margins associated with its cultivation. In this study, an automatic modeling prediction method for nitrogen content in maize leaves was proposed based on machine vision and convolutional neural network. We developed a program designed to streamline the image preprocessing workflow. This program can process multiple images in batches, automatically carrying out the necessary preprocessing steps. Additionally, it integrates an automated training and modeling system that correlates the images with nitrogen content values. The primary objective of this program is to enhance the accuracy of the models by leveraging a larger dataset of image samples. Secondly, the fully connected layer of the convolutional neural network was reconstructed to transform the optimization goal from classification based on 0–1 tags into regression prediction, so that the model can output numerical values of nitrogen content. Furthermore, the prediction model of nitrogen content in maize leaves was gained by training many samples, and samples were collected in three key additional fertilizing stages throughout the growth period of maize (i.e., jointing stage, bell mouth stage, and tasseling stage). In addition, the proposed method was compared with the spectral detection method under full-wave band and characteristic wavelengths. It was verified that our machine vision and CNN (Convolutional Neural Network)-based method offers a high prediction accuracy rate that is not only consistently better—by approximately 5% to 45%—than spectral detection approaches but also features the benefits of easy operation and low cost. This technology can significantly contribute to the implementation of more precise fertilization practices in maize production, leading to potential yield optimization and increased profitability.

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“…Other threats to the stability of river ecosystems include changes in land use, toxic and domestic waste and climate change [22,23]. These individual changes in aquatic ecosystems could impair the natural functioning, and could also modify the structure and function of biotic communities [24,25]. The decline in biodiversity is also caused by overexploitation, urban development, invasion and disease, system modification, human disturbance, transport and energy production [26].…”

Section: Introductionmentioning

confidence: 99%

Diversity of Rotifers in Small Rivers Affected by Human Activity

et al. 2022

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The rivers flowing through Upper Silesia and the adjacent areas (Southern Poland) are affected by various anthropogenic pressures including urbanisation, agriculture and animal husbandry, as well as industry (e.g., mining), which are reflected in the measured physical and chemical water parameters. The species composition of rotifers relative to a variety of microhabitats was studied in eight small rivers of this region in 2017. Our research is a comprehensive and up-to-date analysis that focuses on the rotifers in small rivers and shows the diversity of rotifers relative to the microhabitats and environmental variables. The diversity of rotifers ranged from 0 to 23 taxa in individual samples. In the studied rivers, 129 taxa of rotifers were found. Notommata groenlandica, a species that has not been recorded in the country for 100 years, was found in two rivers. The Kruskal–Wallis one-way ANOVA and Dunn’s multiple comparison post hoc tests revealed statistically significant differences in the median number of rotifer taxa between the abiotic types of rivers, rivers, sampling sites, microhabitats and seasons. A multiple regression analysis revealed a significant relationship (correlation) between the number of rotifer taxa, and the concentration of nitrites, total dissolved solids and dissolved oxygen in the water.

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Spatio-Temporal Distributions and Environmental Safety Threshold of Cropland Fertilization of Jiangsu Province, China

Cited by 9 publications

References 3 publications

Chinese Agriculture for “Green and Grain” Productivity Growth: Evidence from Jiangsu Province

Chinese Agriculture for “Green and Grain” Productivity Growth: Evidence from Jiangsu Province

Automatic Modeling Prediction Method of Nitrogen Content in Maize Leaves Based on Machine Vision and CNN

Diversity of Rotifers in Small Rivers Affected by Human Activity

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