Functional characterization of DnSIZ1, a SIZ/PIAS-type SUMO E3 ligase from Dendrobium.

BACKGROUND: SUMOylation is an important post-translational modification of eukaryotic proteins that involves the reversible conjugation of a small ubiquitin-related modifier (SUMO) polypeptide to its specific protein substrates, thereby regulating numerous complex cellular processes. The PIAS (protein inhibitor of activated signal transducers and activators of transcription [STAT]) and SIZ (scaffold attachment factor A/B/acinus/PIAS [SAP] and MIZ) proteins are SUMO E3 ligases that modulate SUMO conjugation. The characteristic features and SUMOylation mechanisms of SIZ1 protein in monocotyledon are poorly understood. Here, we examined the functions of a homolog of Arabidopsis SIZ1, a functional SIZ/PIAS-type SUMO E3 ligase from Dendrobium. RESULTS: In Dendrobium, the predicted DnSIZ1 protein has domains that are highly conserved among SIZ/PIAS-type proteins. DnSIZ1 is widely expressed in Dendrobium organs and has a up-regulated trend by treatment with cold, high temperature and wounding. The DnSIZ1 protein localizes to the nucleus and shows SUMO E3 ligase activity when expressed in an Escherichia coli reconstitution system. Moreover, ectopic expression of DnSIZ1 in the Arabidopsis siz1-2 mutant partially complements several phenotypes and results in enhanced levels of SUMO conjugates in plants exposed to heat shock conditions. We observed that DnSIZ1 acts as a negative regulator of flowering transition which may be via a vernalization-induced pathway. In addition, ABA-hypersensitivity of siz1-2 seed germination can be partially suppressed by DnSIZ1. CONCLUSIONS: Our results suggest that DnSIZ1 is a functional homolog of the Arabidopsis SIZ1 with SUMO E3 ligase activity and may play an important role in the regulation of Dendrobium stress responses, flowering and development.

Reducing the carbon emission from agricultural production in China: do land transfer and urbanization matter?

Urbanization and land transfer have triggered a profound reform of the Chinese agricultural sector since reform and opening, leading to a continuous rise in agricultural carbon emissions. However, the impact of urbanization and land transfer on agricultural carbon emissions is not widely understood. Therefore, based on the panel data covering 30 provinces (cities) in China from 2005 to 2019, we adopted a panel autoregressive distributed lag model and a vector autoregressive model to empirically explore the causal relationship between land transfer, urbanization, and agricultural carbon emissions. The main conclusions are as follows: (1) Land transfer can significantly reduce carbon emissions from agricultural production in the long run, while urbanization has a positive effect on agricultural carbon emissions. (2) In the short run, land transfer has a significant positive impact on agricultural carbon emissions, and urbanization also has a positive impact on the carbon emissions of agricultural production, but in insignificant ways. (3) There is two-way causality between land transfer and agricultural carbon emission, and between urbanization and land transfer is the same, but urbanization is the one-way Granger cause of agricultural carbon emissions. Finally, some suggestions are provided for low-carbon agriculture development: the government should encourage the transfer of land management rights and guide high-quality resources to gather in green agriculture.

Scale of Operation, Financial Support, and Agricultural Green Total Factor Productivity: Evidence from China.

Large-scale agricultural operations number among the ways to promote the green development of the agricultural sector, which can not only encourage farmers to adopt green innovative technology, reduce the input of chemical fertilizers and pesticides, and achieve environmental protection, but it also enables production with a high efficiency through an economy of scale and an improvement in farmers' income. Based on the agricultural panel data of 30 provincial administrative regions in China from 2000 to 2019, the panel autoregressive distribution lag model was used to explore the dynamic relationship between a business' scale, financial support, and agricultural green total factor productivity (AGTFP). The empirical outcomes indicate that there is a significant cross-sectional dependence, cointegration relationship, and long-run relationship between the scale of agricultural operations, financial support for agriculture, and AGTFP. Strengthening the intensity of financial support for agriculture is not conducive to improving AGTFP. On the contrary, increasing the scale of agricultural operations could promote AGTFP. In addition, the panel Granger causality test results indicate that financial support for agriculture has a unidirectional causal relationship with the scale of agricultural operations and AGTFP. The impulse response results demonstrate that reducing part of the financial support for agriculture or increasing the scale of operation can promote AGTFP. These conclusions have a long-term practical significance for agricultural departments and decision-making regarding financial distribution.

Financial Support for Agriculture, Chemical Fertilizer Use, and Carbon Emissions from Agricultural Production in China.

In the past 15 years, China has emitted the most carbon dioxide globally. The overuse of chemical fertilizer is an essential reason for agricultural carbon emissions. In recent years, China has paid more and more attention to financial support for agriculture. Therefore, understanding the relationship between chemical fertilizer use, financial support for agriculture, and agricultural carbon emissions will benefit sustainable agricultural production. To achieve the goal of our research, we selected the panel data of 30 provinces (cities) in China from 2000 to 2019 and employed a series of methods in this research. The results demonstrate that: the effect of chemical fertilizer consumption on agricultural carbon emissions is positive. Moreover, financial support for agriculture has a significantly positive impact on reducing carbon emissions from agricultural production. In addition, the results of causality tests testify to one-way causality from financial support for agriculture to carbon emissions from agricultural production, the bidirectional causal relationship between chemical fertilizer use and financial support for agriculture, and two-way causality between chemical fertilizer use and agricultural carbon emissions. Furthermore, the results of variance decomposition analysis represent that financial support for agriculture will significantly affect chemical fertilizer use and carbon emissions in the agricultural sector over the next decade. Finally, we provide several policy suggestions to promote low-carbon agricultural production based on the results of this study. The government should uphold the concept of sustainable agriculture, increase financial support for environmental-friendly agriculture, and encourage the research and use of cleaner agricultural production technologies and chemical fertilizer substitutes.

Dynamic Linkage between Aging, Mechanizations and Carbon Emissions from Agricultural Production.

The trend of aging is intensifying and has become a prominent population phenomenon worldwide. The aging population has an important impact on carbon emissions, but at present, there is little research on its ecological consequences, especially the relationship with agricultural carbon emissions. For a long time, China has been dominated by a scattered small-scale peasant economy. Currently, the aging population also means that the agricultural labor force will gradually become scarce, and the agricultural production will face reform. This article is intended to find the long-term impact of aging and mechanization on agricultural carbon emissions and construct a more comprehensive policy framework for sustainable development, hoping to contribute to environmental and ecological protection. The research sample in this article is from 2000 to 2019, covering 30 provinces (cities, autonomous regions) in China. We adopted methods and models including Fully Modified General Least Squares (FMOLS), Dynamic General Least Squares (DOLS), Panel Vector Autoregression (PVAR) model, etc., and used the Granger causality test to determine the causal relationship between variables. Results show that aging is the Granger cause of agricultural carbon emissions and agricultural mechanization. Agricultural carbon emissions and agricultural mechanization have a bidirectional causal relationship. In the short term, agricultural mechanization and aging both have made a great contribution to carbon dioxide emissions from agricultural production. However, in the long term, the impact of aging on agricultural mechanization is significantly negative. Therefore, it is generally beneficial to improve the environmental problems of agricultural production. Our research focuses on the latest background of population trends and global climate issues and finally provides suggestions and a theoretical basis for the formulation of government agricultural policies according to the research conclusions.

Forecasting Carbon Price in China: A Multimodel Comparison.

With the global concern for carbon dioxide, the carbon emission trading market is becoming more and more important. An accurate forecast of carbon price plays a significant role in understanding the dynamics of the carbon trading market and achieving national emission reduction targets. Carbon prices are influenced by many factors, which makes carbon price forecasting a complicated problem. In recent years, deep learning models are widely used in price forecasting, because they have high forecasting accuracy when dealing with nonlinear time series data. In this paper, Multivariate Long Short-Term Memory (LSTM) in deep learning is used to forecast carbon prices in China, which takes into account the factors affecting the carbon price. The historical time series data of carbon prices in Hubei (HBEA) and Guangdong (GDEA) and three traditional energy prices affecting carbon prices from 5 May 2014 to 22 July 2021 are collected to form two data sets. To prove the forecast effect of our model, this paper not only uses Multivariate LSTM, Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Recurrent Neural Network (RNN) to forecast the same data, but also compares the forecast results of Multivariate LSTM with the existing research on HBEA and GDEA forecast based on deep learning recently. The results show that the MAE, MSE, and RMSE obtained by the Multivariate LSTM are all smaller than other prediction models, which proves that the model is more suitable for carbon price forecast and offers a new approach to carbon prices forecast. This research conclusion also provides some policy implications.

Assessing the Relationship among Land Transfer, Fertilizer Usage, and PM2.5 Pollution: Evidence from Rural China.

Concern for environmental issues is a crucial component in achieving the goal of sustainable development of humankind. Different countries face various challenges and difficulties in this process, which require unique solutions. This study investigated the relationship between land transfer, fertilizer usage, and PM2.5 pollution in rural China from 2000 to 2019, considering their essential roles in agricultural development and overall national welfare. A cross section dependence test, unit root test, and cointegration test, among other methods, were used to test the panel data. A Granger causality test was used to determine the causal relationship between variables, and an empirical analysis of the impulse response and variance decomposition was carried out. The results show that the use of chemical fertilizers had a significant positive impact on PM2.5 pollution, but the impact of land transfer on PM2.5 pollution was negative. In addition, land transfer can reduce the use of chemical fertilizers through economies of scale, thus reducing air pollution. More specifically, for every 1% increase in fertilizer usage, PM2.5 increased by 0.17%, and for every 1% increase in land transfer rate, PM2.5 decreased by about 0.07%. The study on the causal relationship between land transfer, fertilizer usage, and PM2.5 pollution in this paper is helpful for exploring environmental change-they are supplements and innovations which are based on previous studies and provide policy-makers with a basis and inspiration for decision-making.

The LEA protein, ABR, is regulated by ABI5 and involved in dark-induced leaf senescence in Arabidopsis thaliana.

The phytohormone abscisic acid (ABA) modulates plant growth and developmental processes such as leaf senescence. In this study, we investigated the role of the Arabidopsis late embryogenesis abundant (LEA) protein ABR (ABA-response protein) in delaying dark-induced leaf senescence. The ABR gene was up-regulated by treatment with ABA, NaCl and mannitol, as well as by light deprivation. In the dark, abr mutant plants displayed a premature leaf senescence phenotype, and various senescence-associated indicators, such as an increase in chlorophyll degradation and membrane leakiness, were enhanced, whereas 35S:ABR/abr transgenic lines showed a marked delay in dark-induced leaf senescence phenotypes. In vitro and in vivo assays showed that ABI5 bind to the ABR promoter, indicating that ABI5 directly regulates the expression of ABR. The disruption of ABI5 function in abr abi5-1 plants abolished the senescence-accelerating phenotype of the abr mutant, demonstrating that ABI5 is epistatic to ABR. In summary, these results highlight the important role that ABR, which is negatively regulated by ABI5, plays in delaying dark-induced leaf senescence.