Search

The frequency of chilling injury during the soybean growing season in Heilongjiang province was higher, and the impact was severe. The index for delayed-type chilling injury on soybean in Heilongjiang province was calculated. It was important to prevent and mitigate disasters and ensure safe production of soybeans. In this paper, the soybean yield data of 73 cities and counties in Heilongjiang province from 1961 to 2020, and the daily temperature data of 73 weather stations in the research area were used. Anomalies value of the sum of monthly average temperatures from May to September were taken as hazard factors, respectively. The threshold was determined by regression, which used hazard factors and yield variability to calculate the threshold, which passed the test, and then established an index for delayed chilling injury on soybeans in Heilongjiang province. The results showed that, total monthly average temperature anomaly indicators for delayed chilling injury of soybeans in the study area from May to September correspond to five planting areas, the mild chilling injury thresholds were −1.8 ℃, −1.9℃, −1.9℃, −2.0℃, −2.0℃, moderate chilling injury thresholds were −2.0℃, −2.2℃, −2.3℃, −2.4℃, −2.6℃, and severe chilling injury thresholds were −2.2℃, −2.5℃, −2.7℃, −2.9℃, −3.1℃. After testing, the average accuracy rate of identifying the occurrence trend of delayed-type chilling injury in soybean was 83%, the average accuracy rate of identifying the level of cold damage occurrence was 62%, and the average accuracy rate of identifying cold damage in typical cold damage years was 93%. In summary, the definition and evaluation of the delayed chill injury indicator for the soybean growing season in Heilongjiang province has a high accuracy rate, easy access to the indicator, and strong practicality. [ABSTRACT FROM AUTHOR]

Meteorological disasters are one of the major risks to agricultural production, causing huge losses to agriculture. Agricultural insurance is an important part of the agricultural security system and can effectively spread agricultural risks and losses. Weather index insurance, which emerged in the late 1990s, has received a lot of attention, research, and applications due to its objectivity, standardization, and robust liquidity. In this paper, authors analyzed the advantages and disadvantages of weather index insurance compared to traditional agricultural insurance. The development of weather index insurance technology at home and abroad was reviewed. Authors introduced the development and practical application of weather index insurance model for Anhui grain crops (winter wheat, one season rice, summer corn), and discussed and prospected the future development of weather index insurance. [ABSTRACT FROM AUTHOR]

The extensive use of antibiotics in the medical and aquaculture industries has led to antibiotic resistance, be a global public health concern. Compared with soil and water, there are few reports on antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the air. To provide theoretical support for scientific and effective response to the cross-media migration of microbial resistance, this paper comprehensively reviewed the occurrence status, influencing factors and transmission mechanism of ARB and ARGs in different types of ambient air at home and abroad. The results showed that the abundance of ARB and ARGs in the air was affected by environmental conditions and sampling factors. The pollution was more serious in typical polluted areas such as animal farms, sewage treatment plants and hospitals. The dominant types of ARGs in the above environment were tetracyclines, sulfonamides and β-lactams. Among them, the abundance of ARGs in the air of animal farms was about 2-3 orders of magnitude higher than that in other environments. The highest abundance was found in pig farm compared to other animal houses. At present, there was still no standard for the sampling requirements of ARB and ARGs in the air. The sampling season, duration, particle size and coexisting pollutants all affected their detection. Air pollution, soil water content, rainfall and air mass movement were proved to play a certain role in affecting the spread of ARB and ARGs in the air. Based on it, the research on the migration mechanism of ARB and ARGs at the soil-air interface needs to be further intensified. The review aimed to comprehensively reveal the occurrence characteristics of ARB and ARGs in the air, and provide theoretical support for the scientific and effective response to the cross-media migration of microbial resistance. [ABSTRACT FROM AUTHOR]

Droughts are increasing in frequency and intensity as the global climate changes. Risk assessment for agricultural drought disasters is an important approach for disaster prevention and mitigation. Based on the formation mechanism of natural disaster risk, the index system model of agricultural drought risk assessment in North China was constructed, and the robustness of the established risk assessment model was tested by using the internal test method. Then based on the characteristics of agricultural drought risk formation in North China, the paper proposes corresponding risk prevention recommendations. The conclusions were as follows: when adjusting the indices and methods in the risk assessment model, the range of changes in the agricultural drought risk ranking for each city was mostly 1-2.5 places, and the model was relatively robust. With the exception of Beijing and Tianjin, which had the highest disaster prevention and mitigation capacity, central and northern China had the highest agricultural drought risk, which was previously linked to the high risk and vulnerability of these regions. The agricultural drought risk in northern Henan and southern Shandong is low, mainly due to the low risk. The 34 cities in North China can be divided into 6 agricultural drought risk clusters, which are not dispersed in space and cross-city distribution, and have good aggregation. Cluster 4, located in the northernmost mountainous areas, has the highest risk of drought, which is associated with high environmental vulnerability and low capacity for disaster prevention and mitigation, so agricultural areas should be reduced and agricultural input should be increased. Clusters 2, 5 and 6, located in the central and southern regions, have higher grain acreage and yields due to drought, so agricultural drought risk can be further reduced by increasing agricultural input and improving irrigation guarantee rate. [ABSTRACT FROM AUTHOR]

As one of the low carbon emission resource technologies, anaerobic digestion technology still faces the challenges of low methane production, unstable system operation, and residue refractory pollutants. Biochar had been widely concerned in the application of improving anaerobic digestion due to its excellent characteristics. In this study, CiteSpace software was used to conduct a quantitative analysis of the research trends of biochar application in the field of anaerobic digestion, and to explore the research status, hot spots and development trends in this field. The results showed that from 2010 to 2022, the number of papers published on the application of biochar in the field of anaerobic digestion kept increasing year by year, and China and the United States were the countries with the largest number of papers. The effects of the physical and chemical properties of different biochar on its application in anaerobic digestion, the mechanisms of improving the efficiency of anaerobic digestion by biochar, and the removal of pollutants such as heavy metals, polycyclic aromatic hydrocarbons and resistant genes by biochar in anaerobic digestion have gradually become hot spots. The research frontiers in this field include quantifying the contribution of different mechanisms of biochar in anaerobic digestion, further developing modification methods to improve the removal of refractory pollutants (heavy metals, polycyclic aromatic hydrocarbons, antibiotics, resistant genes, microplastics, etc.) during anaerobic digestion. [ABSTRACT FROM AUTHOR]

In this paper, the reliability and accuracy of the FY-3 and MODIS NDVI data in Xinjiang from 2014 to 2020 was analyzed by using numerical comparison, correlation analysis and trend consistency analysis, and the reliability and accuracy of FY-3 and MODIS were studied. Based on FY-3 NDVI data, the vegetation change in Xinjiang from 2014 to 2020 was studied by using coefficient of variation analysis method. The results show that the MODIS NDVI monthly mean from 2014 to 2020 were generally lower than those of FY-3, however, the change rules of the two sets of data were consistent, but there were certain differences in NDVI between the two data in sandland, grassland, woodland, cultivated land, spring 2017, summer 2016 and 2018, autumn 2015 and 2019, and winter 2019, the difference areas were mainly located in Altai mountain district, Ili river valley and the eastern section of Tianshan mountains; in 2020, the average NDVI data for each land use type and season in FY-3 was greater than the average MODIS NDVI data, and the FY-3 NDVI data were closer to the true value. At the same time, the NDVI data R2, RMSE, MAD, RE, θ for each land use type and season in FY-3 were lower than the MODIS NDVI data, indicating that the FY-3 data tends to be concentrated, with higher stability than the MODIS data, the NDVI data of FY-3 superior to MODIS NDVI. In Xinjiang, the internal stability of vegetation varies greatly seasonally and spatially, with small fluctuations in summer and large fluctuations in spring and autumn on a seasonal scale. Spatially, regions with better vegetation coverage, such as grasslands, forests, and oasis farming areas, have stronger internal stability of vegetation, and vice versa. [ABSTRACT FROM AUTHOR]

In order to grasp the evolutionary trend of drought and vegetation in various regions of North China, and to analyze the degree of response of vegetation to different drought types, it is of guiding significance for drought mitigation work. In the paper, data from 90 meteorological stations in North China were used to construct the standardized precipitation evapotranspiration index (SPEI), and the normalized vegetation index (NDVI) was selected to quantify the vegetation coverage. Then, the Sen's slope estimator, Mann-Kandall test, correlation analysis, and Copula-Bayesian conditional probability formula were used to analyze the drought sensitivity of vegetation in North China. The results show that: (1) the SPEI value in most parts of North China showed a extremely significant increase, the SPEI value in some parts of North China showed a extremely significant decrease, the NDVI value in the western and northern regions of North China showed an extremely significant increase, and the rest of the regions showed an extremely significant decline. (2) Drought and vegetation in North China were dominated by insignificant spatial clustering, with high and low values of drought and vegetation clustering in small areas. (3) In North China, the correlation between annual scale SPEI and NDVI was good, the response rate of vegetation to drought was slower and the sensitivity was low, the correlation between month-scale SPEI and NDVI was better in some parts of Henan, and the vegetation was more sensitive to drought, and the joint distribution function of NDVI and SPEI was the most consistent with the Clayton Copula function. (4) The likelihood of lowest vegetation coverage occurring in North China was decreasing with decreasing drought, the likelihood of lower vegetation coverage occurring was increasing from extreme to moderate drought and decreasing from moderate to light drought, and the likelihood of medium, higher and highest vegetation coverage all increase with decreasing drought. [ABSTRACT FROM AUTHOR]

Net Primary Productivity (NPP) is the net carbon sequestration of terrestrial vegetation, which is of great importance for the study of global carbon estimation. The rocky mountainous areas of North China is a typical arid and semi-arid climatic zone, and the accurate estimation of the NPP and its variation characteristics in this area is of great significance for the construction of China's ecological forestry engineering. Based on the climate data from 1980 to 2020 from Henan Xiaolangdi Forest Ecosystem National Observation and Research Station, this paper used 5 climate productivity models, including the Miami model, Thornthwaite Memorial model, Chikugo model, Zhu Zhihui model and Zhou Guangsheng model, to estimate and analyze the changing trend of NPP. The random forest algorithm was used to explore the influencing factors of NPP, and the regional MODIS NPP data was used as the standard to evaluate the suitable climate productivity model for estimating NPP in this area. The results showed that: (1) the annual average temperature, annual average maximum temperature, annual average minimum temperature, and annual precipitation in the rocky mountainous areas of North China showed an upward trend, and the rates were 0.05℃y-1,0.04℃y-1,0.05℃y-1 and 1.58mmy-1, respectively. The annual average solar radiation and annual average relative humidity showed a downward trend, with rates of 0.46MJm-2y-1 and 0.17 percent pointsy-1, respectively. (2) The NPP calculated by 5 models showed an upward trend, but the NPP values were different, ranging from 739.35gCm-2y-1 to 958.48gCm-2y-1, with an average of 862.19gCm-2y-1. Among them, the Miami model had the maximum estimated value (958.48gCm-2y-1), and the Zhou Guangsheng model had the minimum estimated value (739.35gCm-2y-1). (3) The random forest algorithm showed that precipitation was the predominant factor affecting NPP in the region. The applicability analysis indicated that the estimated value of the Zhou Guangsheng model was the closest to MODIS NPP, with the relative error, RMSE, and MAE of 1.45%, 451.05gCm-2y-1, and 446.03gCm-2y-1, respectively, and the correlation coefficient was the highest (0.49). This study showed that the Zhou Guangsheng model was more suitable for NPP estimation in this area, and priority should be given to the Zhou Guangsheng model when using the climate productivity model to estimate NPP in the rocky mountainous areas of North China. [ABSTRACT FROM AUTHOR]

Based on two major types of carbon sources of agricultural materials and crop cultivation in Henan province and 18 cities from 2001 to 2021, this paper constructed a planting carbon emission measurement system to assess the planting carbon emissions during the study period and analyzed the spatial pattern. Then the Tapio decoupling model was used to explore the interactions between the planting carbon emissions and the economic development of Henan province, and the LMDI model was applied to probe the main driving factors of the planting carbon emissions. The results showed that: (1) carbon emissions from the planting industry in Henan province showed an increasing and then decreasing trend, and the total carbon emission intensity decreased year by year. (2) Agricultural fertilizer is the most important source of carbon emissions from the planting industry. (3) Carbon emissions from the planting industry in Henan province gradually showed the characteristics of "high in the south and low in the north". Meanwhile, the carbon emission intensity of the planting industry in all regions and cities showed a decreasing trend, and the spatial differences gradually narrowed. (4) Only four types of decoupling, namely strong negative decoupling, weak decoupling, expanding negative decoupling and strong decoupling, occurred during the study period. (5) Economic growth and urbanization level had a positive impact on carbon emissions from planting. Production efficiency, production structure and labor force size had negative impacts. In the future, innovation and leadership should be strengthened, classification and optimization of planting structure should be pursued in order to realize the low-carbon and high-quality development of planting industry in Henan province. [ABSTRACT FROM AUTHOR]

Warming of the climate and changes in precipitation patterns are major manifestations of climate change and abiotic factors affecting soil respiration. Authors presents a systematic analysis of recent research advances on the effects and mechanisms of temperature and moisture on soil respiration. The results show that:（1）there is positive feedback between soil respiration and climate warming, but the temperature adaptation weakens this positive feedback. The effect of temperature on soil respiration varies spatially and temporally due to the different duration of warming and soil carbon storage. The main mechanisms of soil respiration adaptation to temperature include soil microbial adaptation, substrate depletion and soil mineral activation（. 2）The effect of precipitation on soil respiration depends on the initial soil water content. When soil water content is lower than the wilting factor, precipitation not only increases soil water content but also promotes soil respiration, reaching a maximum when soil water content is close to the field holding capacity, while soil respiration is inhibited when soil water content reaches saturation value. The main mechanisms by which water affects soil respiration are substitution and blocking effects, substrate supply, microbial stress and root response. （3）The coupling of soil respiration with soil temperature and moisture depends on the ratio of soil water and heat factors. When soil temperature becomes a stress factor, the stimulating effect of increasing soil water content induced by precipitation on soil respiration is suppressed by the negative effect of low temperature. When soil moisture becomes a stress factor, the promoting effect of increased soil temperature due to climate warming on soil respiration is counteracted by the negative impact of drought. The interaction between soil temperature and moisture should be fully considered when studying soil respiration. In order to understand the disturbance factors of soil carbon emissions in terrestrial ecosystems, this paper proposes that future research on the relationship between soil respiration and the environment under climate change. Firstly, strengthen the research on the effects of multi-factor interaction on soil respiration and quantify the soil respiration components. Secondly, continue to pay attention to the characteristics of soil respiration in response to initial soil temperature and temperature fluctuations, and to explore the effects of biodiversity or community structure composition on soil respiration. [ABSTRACT FROM AUTHOR]

Agroecosystems can provide many ecosystem services in addition to providing agricultural products for humans. It is a realistic and far-reaching impact on the sustainable development of farmland that to accurately evaluate the importance of ecosystem services provided by agroecosystem. In order to investigate the difference of different methods to evaluate the ecosystem service functions, this paper constructed the evaluation index system for agroecosystem service function and determined the weighted value of ecosystem service indicators of dryland and paddy field in Jiangsu province, using analytic hierarchy process and equivalent factor method based on agricultural statistic data and meteorological data from 1991 to 2020 by taking the agroecosystem in Jiangsu province as the research area. The results showed that the weighted values of paddy fields and dryland in the criterion layer B are in the following order: provisioning services, regulation and maintenance services, and cultural services by an analytical hierarchical process. In the index layer C, food production, gas regulation and raw material production showed higher weighted values for both paddy fields and dryland ecosystem. By the equivalent factor method, the weighted values of paddy fields in the criterion layer B are in the following order: regulation and maintenance services, provisioning services and cultural services. The weighted values of dryland are: regulation and maintenance services, provisioning services and cultural services. In the index layer C, the weighted values of hydrological regulation, food production and gas regulation of paddy fields were higher than other indicators, while the weighted values of food production, gas regulation and raw material production of dryland were higher than others. There are certain differences between the results of the two methods. The analytical hierarchical process relies on the experience of experts to some extent. In contrast, the equivalent factor method was influenced by the base equivalent factor table of ecosystem services. Therefore, to overcome the bias caused by single evaluation approach, different evaluation methods could be used for comprehensive analysis. [ABSTRACT FROM AUTHOR]

Machine learning, as a new technique combining statistics and computer science, has been widely used in crop information acquisition tasks in recent years. Traditional methods for obtaining crop information mainly rely on chemical detection methods, which is time-consuming and labor-intensive. Based on machine learning algorithms and hyperspectral remote sensing techniques, crop appearance and internal physical and chemical parameters can be quickly sensed in a non-destructive way, which has obvious application advantages and development prospects. First, the researches related to the hyperspectral remote sensing of crop information were systematically reviewed in this paper. Second, the application, advantages and disadvantages and uncertainties of different machine learning algorithms in hyperspectral sensing crop information were summarized. Finally, it was pointed out that the future development trends of hyperspectral sensing crop information were as follows: (1) complementary crop information acquisition methods could be realized through multi-source remote sensing collaborative observations. (2) The assimilation technologies of hyperspectral remote sensing and crop model as well as the deep integration technologies of hyperspectral remote sensing and artificial intelligence could be developed. (3) The intelligent acquisition of key information oriented to the whole growth period of crops and decision-making could be realized. [ABSTRACT FROM AUTHOR]

Mulch film mulching cultivation technology not only increases agricultural production and income, but also causes serious pollution problems because of the long-term use of polyolefin mulch film and low recovery rates. In areas with serious mulch film residue, the soil structure is seriously damaged, the quality of cultivated land is reduced, agricultural operations are blocked, and crop emergence, nutrient absorption and root growth and development are restricted. Biodegradable mulch film can be degraded by microorganisms such as bacteria, fungi and actinomycetes in the natural environment, and finally decomposed into CO2 and H2O, which not only has no pollution to soil but also promotes the growth and development of crops, and has become one of the effective ways to solve the problem of white pollution. With the deepening of the research on biodegradable mulch film, people found that biodegradable mulch film has different effects on soil environment and crop yield, and these effects are quite different in different regions and crops. It is not possible to directly draw the conclusion that biodegradable mulch film is better than PE mulch film through one or several experiments. In this paper, the effects of biodegradable mulch film on soil environment, crop growth and yield in recent years were summarized, and the experimental effects of biodegradable mulch film (BM) and mulch mulch film (PE) were compared, and their advantages and disadvantages were analyzed, and suggestions were put forward to improve the performance of biodegradable mulch film, so as to continuously improve the performance of biodegradable mulch film and realize the sustainable development of agricultural science and technology. Summary and analysis show that: (1) Biodegradable mulch film can increase soil temperature and humidity, meet the needs of crops in the early stage of growth, accelerate the emergence of seedlings, shorten the growth cycle, and have beneficial effects on soil organic matter, available nitrogen and soil enzyme activity, and improve soil nutrient content. In terms of microorganisms, biodegradable mulch film can promote the growth of soil microbial content and soil respiration rate. At the same time, biodegradable mulch film has better weed control ability than PE mulch film, among which black biodegradable mulch film has the best effect, which can effectively reduce the number of weeds in the field and ensure the supply of nutrients needed by crops. (2) In terms of crops, biodegradable mulch film can promote corn growth, shorten the growth period and increase the yield in the early and middle stages of corn growth. There was no significant difference between the yield of cotton seed cotton covered with biodegradable mulch film and PE mulch film, and the yield of cotton seed cotton covered with mulch film is significantly higher than that of bare land. The potato treated with biodegradable mulch film germinated faster in the early growth stage because of the increase of soil temperature, which significantly shortened the growth cycle and brought it to market earlier, and significantly increased the yield compared with PE mulch film and bare land, among which the black biodegradable mulch film had the most obvious effect. For millet crops, there was no significant difference in yield between the treatment with biodegradable mulch film and the treatment with PE mulch film, and the yield of the treatment with mulch film was significantly improved compared with the treatment with bare land. For vegetable crops such as tomato, eggplant and beet with short growth cycle, biodegradable mulch film can play the role of heat preservation and moisture increase for a long time, and promote the rapid growth of crops. The final yield is not significantly different from that of PE mulch film mulching treatment, even slightly improved, and significantly improved compared with bare land treatment. (3) Put forward the influence of different components of biodegradable mulch film on soil and crops, controllable degradation and cost problems, such as the difficult control of degradation speed, environmental problems caused by incomplete degradation of biodegradable mulch film, low technical maturity and high price, and put forward suggestions for future research and development, so as to modify and innovate biodegradable raw materials, reduce costs and regulate the degradation mechanism of biodegradable mulch film. Strengthen the research on raw materials, formula and production technology of biodegradable mulch film, and develop new biodegradable mulch film with high performance and multifunction, which can meet the regional applicability and crop applicability at the same time, and lay a theoretical foundation for the popularization and application of biodegradable mulch film to more regions and more crop varieties. [ABSTRACT FROM AUTHOR]

As an ideal 'indicator' for directly monitoring plant photosynthesis, solar-induced chlorophyll fluorescence(SIF) has a rapid response to variations in plant physiological status. SIF is becoming one of the most active research direction in vegetation remote sensing. This study conducted a systematic literature review on topics of relevance to SIF. To do so, there are 786 SIF-related papers published during 1982-2021 were collected from the Scopus database. Based on VOSviewer, the visual bibliometric analysis was applied to analyze these papers from the perspective of country, institution, author, journal and keyword. The results show that the number of SIF articles published per year is rising rapidly. Among all the countries, United States, China and Germany published 285, 263, and 166, making them the top three countries that have the most SIF papers during the study period. Germany has the highest citations per publication (56.2) and the largest number of cooperative countries(36). Nanjing University, California Institute of Technology, and University of Valencia have made outstanding contributions to the development of this field as they have not only the large number of publications but also the high h-indices Among the scholars in this area, Guanter L(Polytechnic University of Valencia), Zhang Y(Nanjing University), Frankenberg C(California Institute of Technology), Liu L(Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences), and Rascher U(Jülich Research Centre), are currently the most productive, having published more than 40 papers. The Journal Remote Sensing of Environment has the highest number of SIF articles (92) and the highest H-index (40), and Global Change Biology has the highest citations per publication (65.6) in this field. The keyword analysis shows that SIF is a highly multidisciplinary area mainly involving geoscience, ecology, agronomy, plant physiology across a range of spatial and temporal scales. Currently, the most active research topics are the monitor of plant physiological status and stress condition, the acquisition methods of SIF measurements, and the applications of SIF in remote sensing such as terrestrial carbon cycle. China leads the world of overall research focus in this field, which, however, still should have a further improvement of its influence. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence technology, especially the emergence of deep learning, has promoted new developments of agriculture, and is regarded as a new direction of modern agricultural production. Deep learning has the advantages of strong learning ability, wide coverage, strong adaptability, and great portability. Considering that its development of simulated datasets can solve real-world problems, it is more and more widely used in monitoring, forecasting and risk assessment of agricultural drought. This paper used the method of literature review to summarize the development and application of monitoring, forecasting and risk assessment of agricultural drought, and summarized the principles, advantages and disadvantages of the deep learning model. The practical applications of depth learning model in monitoring, prediction and risk assessment of agricultural drought were systematically summarized. The existing problems of large dataset requirements, long data preprocessing time, narrow predefined category range, and complex remote sensing images were discussed, and the future research directions were prospected. The results showed that in recent years, the technologies of monitoring, prediction and risk assessment of agricultural drought had made important progress. However, due to the nonlinearity of agricultural system and the complexity of disasters, existing technologies were still difficult to meet the needs of actual agricultural production in the new situation in terms of applicable regions, objects and accuracies. The deep learning technology provided a new means for agricultural drought research. However, the deep learning model could not accurately express the specific process and mechanism of crop growth, so coupling of crop growth model with deep learning model could ensure the interpretability of deep learning model. For correcting the prediction sequence, coupling models based on general circulation model and depth learning model could be established to further improve the prediction ability of deep learning model for medium and long-term agricultural drought. Aiming at the problem of limited disaster sample size, strengthening the research on agricultural drought monitoring and evaluation based on migration learning could further improve the precisions in fine monitoring and evaluation of agricultural drought. In view of the fact that the factors affecting agricultural drought formation was characterized by large amount of data, diverse types and nonlinearity, the method of combining deep learning and information fusion was adopted to further improve the accuracies in regional monitoring, prediction and risk assessment of agricultural drought. Therefore, the coupling of deep learning models and crop growth models, agricultural drought prediction by integrating deep learning models and general circulation models, fine monitoring and evaluation of agricultural drought based on deep learning and migration learning, regional monitoring, prediction and risk assessment of agricultural drought based on deep learning and information fusion were considered as the development trends of applicating deep learning technologies in monitoring, prediction and risk assessment of agricultural drought in the future. [ABSTRACT FROM AUTHOR]

Biochar has been recognized as a new exogenous organic substrate and is often used as an important organic resource for carbon reduction because of its stability, adsorption and carbon nutrient richness. The study was conducted in a typical indica rice area of Nanning from 2021 to 2022, against the background of having high early indica rice yields, simultaneous rain and heat, and unique cropping system. In this paper, authors set three treatments: Control treatment (CK): no fertilizer. Inorganic N input (T1, chemical fertilizer) treatment: chemical fertilizer application at conventional fertilizer level, compound fertilizer 800kgha-1, urea 260.87kgha-1, potassium 193.55kgha-1. Inorganic N with organic N (T2, biochar + chemical fertilizer) treatment: biochar 4000kgha-1, compound fertilizer 738.67kgha-1, urea 146.09kgha-1, potassium 34.19kgha-1. The cumulative greenhouse gas emissions, emission equivalents, rice yield traits and the effect of isonitrogen substitution of biochar application on greenhouse gas emissions and rice yield in early southern rice fields were analyzed by regular monitoring of soil greenhouse gas emissions in rice fields during the rice reproductive period using a split static box-meteorological chromatography method 5d after rice transplanting, this study provide a basis for optimizing intensive early rice low-carbon cultivation and reduce fertilizer and increase efficiency. The results showed that: (1) biochar can reduce CH4 and CO2 emissions from paddy soils, and reduce the combined emission equivalent by slowing down CH4 emissions. The application of fertilizer with biochar can mitigate the increase of greenhouse gas carbon emissions caused by fertilizer application alone, and its delayed effect of mitigating CO2 emissions is more obvious. In biochar treatment (T2), compared with the chemical fertilizer treatment (T1), the maximum CH4 emission flux in 2021 was reduced by 41.38% and the cumulative emission was reduced by 31.25%, and the maximum emission flux in 2022 was reduced by 50.50% and the cumulative emission was significantly reduced by 50%, and the combined emission equivalents of 2 years were significantly lower than those of the T1 treatment. The maximum CO2 emission flux and cumulative emission in 2021 were reduced by 57.38% and 37.68%, respectively, compared with the T1 treatment, and the corresponding reduction in 2022 was 16.06% and 35.52% compared to the T1 treatment. (2) Biochar can suppress N2O emissions, significantly reduce cumulative emissions, and reduce nitrogen source emission equivalents. Compared to the T1 treatment, the maximum N2O emission flux was reduced by 5.43% and the cumulative emission was significantly reduced by 33.53% in 2021 in T2 treatment; the maximum emission flux was reduced by 73.75% and the cumulative emission was significantly reduced by 54.33% in 2022, and there was no significant change with the CK treatment. (3) Biochar facilitates the optimization of intensive early indica rice cultivation structure and enhances the productivity of early indica rice. After biochar was put into the paddy field for 2 years, the effect of increasing yield became more and more obvious, and the theoretical yield of T2 treatment was 1.02-1.33 times that of T1 treatment, while the actual yield was 1.06-1.32 times that of T1 treatment. Fertilizer with biochar reduced greenhouse gas emissions and increased rice yield in early indica rice fields, which can be used as an optimization model for low-carbon production of intensive early indica rice in the south. [ABSTRACT FROM AUTHOR]

Accurate monitoring of crop spatial distribution in topographically complex areas is of great significance for guiding agricultural management production and reasonable allocation of resources. However, scattered crop distribution and high spatial heterogeneity pose challenges for precise classification. The objective of this study was to explore a method for simultaneous precise identification of multiple crops with multi-temporal high-resolution satellite data under complex terrain conditions, thus further providing a theoretical and practical basis for crop identification in Chongqing city area and the complex low hills area in southwest China. The western Chongqing was selected as the study area. First, a multi-scale segmentation algorithm was adopted to construct homogeneous units from fields of the same feature type. This approach helped to achieve high accuracy classification by avoiding overly fragmented classification results based on pixel classification. Secondly, the multi-temporal Sentinel-2/MSI remote sensing images were used to explore the weathering patterns and characteristic parameters during the fertility period of major food crops. The spectral indices, such as NDVI, RVI and NDWI, were constructed to compare the differences in remote sensing spectral information of different crop types at various fertility periods based on ground sample points. Such information was then combined with the topographic features to determine the optimal combination for crop identification. Finally, an object-oriented decision tree logical classification rule set was established to extract major grain crop growing areas. The results showed that, (1) the multi-scale segmentation method was an effective approach that can make crop identification on a field basis. In addition, the vegetation edge segmentation was optimal at a segmentation scale of 30 and a compactness and shape factor of 0.5. (2) The target feature discrimination threshold was established by combining the spectral feature indices of NDWI in April, RVI in June, NDVI in August and NDVI in August with the topographic features such as altitude and slope. The overall accuracy of the classification reached 90.8%, being 85.7%, 83.3% and 80.7%, for paddy rice, maize, and sweet potato, respectively. This paper showed that the crop planting area identification method based on multiple spectral indices combined with topographic features can achieve high recognition accuracy and has practical significance. [ABSTRACT FROM AUTHOR]

Evapotranspiration(ET) is an important component of farmland water cycle, and its accurate estimation is of great significance for precision irrigation and water-saving agriculture. The Penman-Monteith (P-M) model is one of the most commonly used estimation methods, but reliable representation of canopy resistance (rs) has been a difficult problem in applying the P-M. In this paper, seven commonly used rs models were selected to assess if their simulated rs could be used with P-M to directly estimate winter wheat ET. The P-M simulated ET was compared with measured values by Bowen ratio energy balance (BREB) system in Shunyi, Beijing for two years (2020 and 2021), and the main factors affecting wheat rs were analyzed. The results showed that the seven models generally underestimated wheat canopy resistance and overestimated evapotranspiration. Overall, the Todorovic model (TD) performed the best, and the R² for simulated rs and ET were >0.605, mean bias error (MBE) being -82.8s·m-1 and 10.4W·m-2, respectively, with root mean square error (RMSE) of 254.4s·m-1and 33.5W·m-2; the other six models performed poor, and the R² for simulated rs was between 0.113-0.241, MBE and RMSE, were between -236.4 to -61.3s·m-1 and 277.2 to 373.8s·m-1, respectively. The R² for simulated ET was between 0.046-0.184, MBE and RMSE were between 44.5-97.4W·m-2 and 81.4-147.9W·m-2, respectively. On basis of RMSE, the performing order was TD>FAO56-PM>Katerji-Perrier (KP)>Garcıá-Santos (GA)>idso (IS)>Jarvis (JA)>CO. The correlation between canopy resistance and various factors suggested that net radiation (Rn) affected the most on wheat rs, while air temperature (Ta) and canopy temperature (Tc) affected the least with the following specific order: Rn>leaf area index (LAI)>relative humidity (RH)>vapor pressure deficit (VPD)>soil moisture (θ)> canopy-air temperature difference (ΔT)>Tc>Ta. This results better explained the good performance of TD model, and it considers the key factors affecting canopy resistance such as Rn, VPD and ΔT. In addition, it has no parameters to be calibrated, which makes it easy to use. The results of this paper provided a scientific basis for applying one-step approach to calculate the water consumption of winter wheat. [ABSTRACT FROM AUTHOR]

The aims of this paper is to illustrate the importance of grain production in China's food security strategy based on the three Northeastern provinces (NEP) in the new period, which quantitative assessment of climate change under the background of the loss caused by meteorological disasters of regional food production is clarify. In this paper, using grain planting area, yield and agricultural disaster statistics from 1981 to 2020, comparison analysis grain production and disaster characteristics between NEP and the whole country respectively. The disaster-yield assessment model was used to estimate the disaster-yield loss and final yield of the NEP by inputting disaster data in recent 10 years, and the sensitivity and stability of the disaster-yield assessment model were re-tested. The results showed that :(1) from 1981 to 2020, the grain planting area and total output in NEP increased significantly, and the proportion of the total output of NEP increased steadily, and the proportion of the total output of the three northeast provinces reached 1/5 of the total output of the whole country by 2020. (2) The disaster situation in NEP showed a significant trend of first increasing and then decreasing. The incrasing of average grain yield during the past 40 years was 65.96kg⋅ha-1 and 252.5kg⋅ha-1 per year for NEP and the whole country, while the incrasing of average grain yield during the past 10 years was 52.6kg⋅ha-1 per year for NEP significantly. (3) From 2011 to 2020, the average affected area and affected area in China were 23704.5x10³ha and 11204.7x10³ha, respectively, and 3899.1x10³disasters of the whole country and the three northeastern provinces were significantly lower than those of the previous three years, making it the decade with the least severe disasters in 40 years. (4) The simulation accuracy of the disaster-yield assessment model was high. The linear regression coefficients (R²) of simulated grain yield and measured grain yield in Heilongjiang, Jilin and Liaoning provinces were 0.98, 0.90 and 0.88, respectively. The slopes were 1.05, 1.02 and 0.98 (P < 0.01), respectively. The loss rate of grain yield due to agro-disaster was 10.4%, 17.9% and 18.0% respectively in the three province, while which was more than 8.0%, 17.0% and 16.0% for 20a.years. (5) The model slightly overestimated the grain yield of Jilin and Liaoning in recent 10 years due to the overall light disaster situation. Based on the data from 1981 to 2010, the regional grain disaster loss assessment model was proved to be able to evaluate the loss of grain yield caused by meteorological disasters well, and had the performance of predicting grain yield, and has the feasibility of operational application. The impact of meteorological disasters on the grain output in northeast China is higher than the average level of disasters in the whole country. Considering that the grain output in Northeast China accounts for a high proportion of the national grain output, preventing the risk of agro-meteorological disasters in Northeast China in the new period is of great importance to guarantee the national food security. [ABSTRACT FROM AUTHOR]

The real disaster information is an important reference for preventing and reducing heavy rainfall disaster losses effectively. Taking process rainfall intensity as the index, this paper constructs a heavy rainfall disaster event database matching the multi-source meteorological disasters and disaster-causing processes in Hebei Province during 1984-2020. After manual quality control, 2305 groups of real disaster information and 263 groups of false information are obtained. In this study, correlation analysis was used to determine and select the rainfall eigenfactors that are significantly related to the disaster degree. Based on the One-class support vector machine (OCSVM) and 10 folds cross-validation method, 10 samples were randomly selected to establish the meteorological factor disaster discriminant model and test and optimize, so as to explore the intelligent and easy-to-use intelligent discriminant method of multi-source heavy rainfall disaster credibility. The results showed that: (1) there are eleven rainfall eigenfactors related to disaster degree at 0.01 significant level, which are maximum rainfall, minimum rainfall, average process rainfall, average daily rainfall, average hourly rainfall intensity, hourly maximum rainfall, 3-hour maximum rainfall, 6-hour maximum rainfall, 12-hour maximum rainfall, 24-hour maximum rainfall and rainfall in the first 10 days. (2) Ten models (M1-M10) were established using 11 rainfall eigenfactors. According to the identification accuracy of the real disaster, the optimal model was determined to be M9, with the authenticity rate of 96.4% and the falsification rate of 67.6%, which indicated that the model is one-sided in determining the authenticity of the disaster situation and should be further optimized. (3) Through the autocorrelation test, maximum rainfall, average hourly rainfall intensity, hourly maximum rainfall and rainfall in the first 10 days were taken as input factors. Ten models (M11-M20) were reconstructed, and the optimal model is M20 with 96.2% proof rate and 82.9% false rate. Based on comprehensive analysis, the model established by 4 factors is more reliable than the model established by 11 factors. [ABSTRACT FROM AUTHOR]

Carbon emission from crop production is one of the important sources of greenhouse gas emission in China, and it is a hot issue on agricultural ecological environment in recent years. Based on the statistical data of agricultural production input, crop yield, cultivated land area and crop sowing area in Hebei province from 2010 to 2018, this paper studied the dynamic changes of carbon emission of crop production inputs, and analyzed the proportion of carbon emission for each different crop production inputs, so as to provide a theoretical basis for energy conservation and emission reduction in agricultural production, and in the end to realize the low-carbon agriculture. The results showed that from 2010 to 2018, the carbon emission from crop production in Hebei province took 2015 as the turning point, showing a trend of increasing first and then decreasing. From 2010 to 2018, the total carbon emissions from crop production, carbon emissions per unit yield and carbon emissions per unit cultivated land area in Hebei province were 22.19-104-26.74x104tCO2 eq, 0.21-0.30tCO2eq·t-1 and 3.40-4.10tCO2eq·ha-1, respectively. The total carbon emission of crop production, carbon emission per unit yield and carbon emission per unit cultivated land area in 2018 were the lowest, decreasing by 17.0%, 30.0% and 17.0% respectively compared with the highest value. The 9-year average carbon emission of chemical fertilizer, pesticide, agricultural film, diesel fuel and irrigation electricity accounted for 38.6%, 2.1%, 11.5%, 34.7% and 13.1%, respectively. Chemical fertilizer and diesel fuel were the main sources of agricultural carbon emission, accounting for more than 30%. The dynamic change of carbon emission of crop production inputs was obviously affected by the national policies. It is suggested that the carbon emissions from agricultural production should be guided by national policies, and measures such as formulating mature agricultural management system, strengthening agricultural technology training and promoting the application of advanced cultivation techniques should be taken to reduce the carbon emissions from agricultural sources, so as to promote the green development of agriculture. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess the loss of grain production caused by agrometeorological disasters in the Inner Mongolia Autonomous Region (IMAR), which is an important grain production base of China. The disaster-yield assessment model was formed and verified using grain production, yield, planting area and agrometeorological disaster conditions data of the IMAR from 1981 to 2020, and the loss of grain yield caused by meteorological disasters was assessed by this model. The results showed that grain production, yield and planting area observed an escalating trend from 1981 to 2020, and the rate of increase was 78.85×104t·y-1, 100.97kg·ha-1·y-1 and 74.48×10³ha-1·y-1. The covered and affected area of agrometeorological disasters for the IMAR and National both increased and then decreased during the 1981-2020 period. Drought was the most important agrometeorological disaster in the IMAR, and the covered and affected area was 64.10% and 62.45%, respectively, accounting the total covered and affected area of all kinds of disasters in 1981-2020. The grey correlation analysis showed that drought had the highest correlation with grain yield at the level of disaster covered and affected rate, and hail had the highest correlation with grain yield at the level of disaster destory rate. The disaster-yield assessment model constructed by this study was of high simulation accuracy, and the linear regression coefficients (R²) and the slop between the simulated and measured grain yield was 0.99 and 0.98, respectively (P < 0.01). The average relative simulation yield was 0.20% and relative simulation error of the following year grain yield was 2.49%. Grain yield loss rate due to disaster in the IMAR decreased (R²=0.77，P<0.01) from 1981 to 2020 with a rate of 0.48 percentage points·y-1, and average grain yield loss rate was 14.79% and there were 68.42% of years which grain yield loss rate above 10%. The disaster-yield assessment model of the IMAR formulated in this study can simulate and predict grain yield, and assess the loss of grain yield due to disasters, which can meet the needs of agrometeorological services. [ABSTRACT FROM AUTHOR]

When the daily average water temperature exceeds 32℃, the hatching rate and feeding rate of some warm water fish decrease with the increase of temperature, grow slowly or stop growing, and are easy to induce diseases. In summer, the water temperature in the fish pond is not always in the high temperature period. In this paper, based on the water and air temperature data of Fushun Aquaculture Meteorological Service Test Base from 2017 to 2019, the variation characteristics of water temperature in each layer of fish pond in summer were analyzed, and the prediction models of average and maximum water temperature on the sunny days were established by stepwise regression analysis. Results showed that the daily max. water temperature in each layer in summer occurs from 18:00 to 19:00, which lagged behind the temperature by 1-3 hours. The period when the water temperature ≥32℃ was mainly concentrated from 16:00 to 22:00, and the hourly average water temperature of each layer in this period was 32.9-33.1℃, 32.6-32.7℃ and 32.1-32.3℃, respectively. The water temperature of each layer fluctuated and rose with the temperature in summer, especially from mid-June to early-July, while the maximum values of temperature and water temperature appeared in late-July. The average number of days with water temperature ≥32℃ in summer was 23 days, accounting for a quarter of the whole summer, mainly from late July to mid-August. During this period, the daily average water temperature of each layer was 28.5-34.4℃, 28.2-34.1℃ and 27.9-33.9℃, respectively. The back substitution test in 2017-2018 and the forecast test in 2019 showed that the average absolute error of the model was 0.3-0.8℃, and the average relative error was within 2.6%, indicating that the model has good prediction effect and can be better applied to the prediction of fish pond water temperature in summer. [ABSTRACT FROM AUTHOR]

Crop model remote sensing and play important roles in agricultural production monitoring, evaluation, and future prediction with their unique advantages. The integration technologies of crop model and remote sensing information have obvious application advantages and broad development prospects in monitoring, evaluation and prediction of large-scale and high-precision agricultural production. In order to promote the wider applications of these technologies in crop yield prediction, impact assessments of agrometeorological disaster, and agricultural decision-making to deal with climate change on a regional scale, the method of literature review were adopted in this paper. The development and application of crop models in Europe, United States, Australia and China were systematically summarized. The principle, characteristics and shortcomings of the current mainstream data integration methods were concluded. The practical applications of integration technologies of crop model and remote sensing information were summarized. The existing problems in improving the accuracy of data integration were discussed, and the future research direction was prospected. The results showed that the research and application of crop model and its integration with remote sensing data were extensive and intensive at home and abroad. The assimilation method could effectively improve the simulation accuracies of crop model, providing technical support for crop growth and yield evaluation on regional scales, impacts of climate change on yield, farmland management decision-making, etc. The uncertainties from crop model simulation results and remote sensing inversion data, diversities of data assimilation strategies, and scale effects were the limiting factors to further improve the accuracy and efficiency of integrated systems. Therefore, multi-source fusion of remote sensing data, multivariable cooperation in assimilation process, multi-type coupling of crop models, and high-performance parallel computing of data were the development trends of integrating crop models and remote sensing research in the future. [ABSTRACT FROM AUTHOR]

The effects of irrigation on winter wheat yield and water use efficiency (WUE) were mostly explored based on site-based experiments in North China Plain (NCP) region. However, due to the influences of contrasting environmental factors (e.g. climate and soil properties), these results cannot be accurately cross-compared among different studies, resulting in the difficulty to get the generalizable pattern at regional scale. Here meta-analysis was conducted to comprehensively evaluate the effects of irrigation on winter wheat yield and WUE in NCP region, with the observation data of 1876 pairs from 186 field experimental papers totally. The differences of the irrigation effects in different regions, precipitation year types, soil texture, and nitrogen (N) application levels were explored, and the corresponding optimal irrigation amounts for reaching high yield and WUE were ascertained. Results showed that compared with non-irrigation during the winter wheat growing period, irrigation increased the yield of winter wheat in NCP by 32.0%-38.3%, and reduced the WUE by 27.1%-30.1%. The yield increment due to irrigation in the northwest of NCP (39.6%-53.5%) with total precipitation during the winter wheat growing period below 150mm was higher than that in the southeast of NCP (24.3%-27.1%) with total precipitation during the winter wheat growing period higher than 150mm, while the decrease of WUE due to irrigation in northwest of NCP (32.4%-37.5%) was higher than that in the southeast of NCP (22.0%-24.3%). The optimum irrigation amount for high yield and WUE of winter wheat was 150-180mm in the northwest and 120-150mm in the southeast. Specifically, for different precipitation year types, the optimum irrigation amounts were 120-150mm with 2 times during jointing and flowering stages for dry year, during jointing and heading stages for normal year, and during jointing and booting stages for wet year. For different soil texture, irrigation under loamy soil had the highest increases in yield, while clay soil had the lowest decreases in WUE; the optimum irrigation amounts of winter wheat with four different soil texture of sandy soil, loam, clay loam and clay were 60-90mm, 120-150mm, 180-210mm, and 150-180mm, respectively. For different N application levels, N application of 120-240kg.ha-1 led to the highest yield and WUE under the irrigation amounts of 80-140mm, of which the higher yield was obtained under irrigation amounts of 110-140mm, and the higher WUE was obtained under the irrigation amounts of 80-110mm. Collectively, the NCP region can achieve the goals of high yield and WUE when irrigation amount was 120-150mm with 2 times but at different stages during different precipitation year types (i.e. dry year, normal year and wet year). Meanwhile, the combination of loam conditions with the N application of 120-240kg.ha-1 can synergistically improve the yield and water use efficiency of winter wheat. [ABSTRACT FROM AUTHOR]

Excessive anthropogenic emissions of greenhouse gas have led to global climate change in recently years. Methane is an important greenhouse gas after carbon dioxide. Methane emissions increase with the global temperature, which further intensifies global warming. Soil is a huge source and sink of methane, and methanotrophic bacteria play a key role in reducing methane emissions. Exploring the effect of temperature on soil methane-oxidizing ability has been a research hotspot in recent years. This paper reviewed the effects of temperature on soil methane oxidation and methanotrophic bacteria; analyzes and compares the soil methane oxidation process and methanotrophic bacteria in various ecosystems at different temperatures, and the growth temperature range of methanotrophic bacteria strains. The results showed that various ecosystems have different temperature ranges for methane oxidation; the rate of methane oxidation increases with increasing temperature in their particular temperature ranges. When the cultivation temperature is closer to the in-situ temperature, the methane oxidation response will be faster. Similar to the effect of temperature on methane oxidation, the diversity and abundance of methanotrophic bacteria also increase with increasing temperature and are closely related to the magnitude of temperature increase and the adaptation temperature of dominant bacteria. Type II methanotrophic bacteria in the soil are more sensitive to temperature. As the temperature rises, the abundance of type II methanotrophs increases. Therefore, temperature affects the abundance and community structure of methanotrophs, thereby affecting the rate of methane oxidation and methane emissions. However, whether temperature can only change the methane oxidation capacity of the soil by regulating the replacement of dominant bacteria is still unconfirmed. This review discussed the response of methane oxidation to temperature and its microbial mechanism, in order to provides a comprehensive analysis for methane oxidation capacity under the context of future climate change. [ABSTRACT FROM AUTHOR]

Ammonia is the only alkaline gas in the atmosphere. Reducing ammonia emissions plays an important role in controlling atmospheric pollution. Livestock and poultry breeding industry is an important source of ammonia emissions. Scientific assessment of ammonia emissions and reduction potential in different links is of great significance. In this paper, the ammonia emission factors of different livestock and poultry breeding houses in China and abroad, the main emission reduction technologies and efficiency were summarized. Combined with the number of mechanical ventilation farms and the animal population of major livestock and poultry obtained from the second national pollution source census, the ammonia emission reduction potential of exhaust air from mechanical ventilation livestock farms in China was analyzed. The results show that the reduction potential of ammonia emission from mechanically ventilated livestock houses in China is 265.6 thousand tons; pigs, laying hens and broilers were the three main sources of livestock and poultry, and their emission reduction potential were 101.8 thousand tons, 77.3 thousand tons and 67.5 thousand tons, respectively. The emission reduction potential of the three livestock and poultry types accounted for 92.8 %. Regionally, the main implementing regions are East China, South Central and North China, with ammonia emission reduction potential accounting for 35.1%, 26.9% and 14.9%, respectively. It is proposed to carry out the construction of ammonia emission reduction facilities in mechanical ventilation barn of pig, laying hens and broiler, which can provide guarantee for the national ammonia emission reduction and the improvement of atmospheric environmental quality. [ABSTRACT FROM AUTHOR]

The cultivation of apples plays an important role in China's fruit production. Climate change has brought about more frequent and severe drought and waterlogging disasters, which have negatively impacted apple yield quantity and quality. Through the summary of previous research studies done on the impact of drought and waterlogging disasters and its effect on apple production, methods for disaster prevention and mitigation can be provided to reduce the impact of these disasters to the industry. In this paper, the methods of classification and systematic analysis combined with studying actual disaster cases were used to review the latest research into the effects of droughts and waterlogging disasters on apple production in China and identify the next steps for future research and development into this subject. The intensity and impacts of droughts and waterlogging disasters on apple production are determined by weather, regional climate, topography, soil composition, and several other factors. Adopting the morphological indicators to study these disasters, most existing research into this area only focuses on meteorological factors and rarely consider agrometeorological factors such as soil composition, current crop conditions, and more. The distribution of apple drought disasters in China decreases from north to south. The prevention measures of apple drought and waterlogging included irrigation, strengthening orchard soil management, implementing the integration of soil and fertilizer, etc. More of the current research done into this subject takes place in the Northern parts of China and often lack the necessary information to be put into use in disaster monitoring and early warning applications. In the future, research into apple droughts and waterlogging disasters should focus on increasing in orchard observation data collection, constructing a disaster risk monitoring and early warning model, and implementing the model into apple growing. [ABSTRACT FROM AUTHOR]

Three major production areas of Fuji apple, Linyi (in Shanxi province), Luochuan (in Shaanxi province) and Qixia (in Shandong province) were selected as the study region, based on 1 km gridded meteorological data, actual first flowering date data, and Chilling Hour Model (CHM) and Growing Degree Hour (GDH) data of the survey sample points in 2019-2020, the optimal chill/heat requirement at the first flowering date of apple was obtained using the gridded search method. Then, the daily air temperature characteristic values (Tmax, Tmin and Tavg) were divided into seven daily air temperature characteristic factor combinations, including single factor, double factors and triple factors, and the random forest algorithm (RF) was used to establish three regional daily chill/heat accumulation models with different daily air temperature characteristic factor combinations to select the optimal daily air temperature characteristic factor. On the basis of which, forecasting model of apple first flowering date was established based on the optimal daily air temperature characteristic factor by using RF algorithm, and the accuracy of the forecasting model was evaluated by independent actual first flowering date data. The results showed that: (1) the optimal chill/heat requirement at the first flowering date for apple in three regions were 730CH and 7350GDH in Linyi, 345CH and 4950GDH in Luochuan, and 520CH and 4450GDH in Qixia. (2) Among the seven combinations of daily air temperature characteristics, the three regional daily chill/heat accumulation models with the combination of Tmax, Tmin and Tavg had high accuracy in estimating daily chill/heat accumulation, and the RMSE between the estimated daily chill accumulation and the daily chill accumulation obtained from the CHM model was 0.97-2.50CH, and the RMSE between the estimated daily heat accumulation and the daily heat accumulation obtained from the GDH model was 1.73-15.76GDH. (3) When the daily chill/heat accumulation was estimated by forecast model of apple first flowering date, the RMSE between the estimated daily chill accumulation and the daily chill accumulation based on the CHM model ranged from 1.08 to 1.14CH, and the RMSE between the estimated daily heat accumulation and the daily heat accumulation based on the GDH model ranged from 2.03 to 3.74GDH. When the model was used to forecast first flowering date of apple, R² between the predicted and actual first flowering date was 0.92, and RMSE was 3.44d. The accuracy of the predicted first flowering date based on daily air temperature characteristic values was in overall agreement with that based on real hourly air temperature data, it indicated that the forecast model of apple flowering date established in this paper could effectively convert the input air temperature data from hourly scale to daily scale, which will have good application value and potential in the subsequent work on apple first flowering date forecasting. [ABSTRACT FROM AUTHOR]

In order to reduce the spring frost damage to tea plant in Anhui province, based on the daily meteorological observation data froml981 to 2017 of 45 stations, as well as garden area and tea production of 45 counties in tea plantation district from 1998 to 2017, the spring frost risk index was built by weighted index sum method. The index included comprehensive frequency of spring frost, sensitivity index of slope aspect, tea area and variation coefficient of yield reduction. The spring frost risk zoning was analyzed using the ANUSPLIN and geographic information system. The results showed that the areas of high-risk region, medium-risk region and low-risk region werell8xl04ha, 3 37x104ha, 353x10-, and take 14.6%, 41.7%, 43.7% of the gross area of tea plantation district respectively in Anhui province. The high-risk region was mainly distributed in Jinzhai county, Huoshan county, Yuexi county, Qianshan county in Dabieshan tea area and the mountain areas with altitude above 600m in 五angnan tea area, and scattered in the windward slope regions in Jiangbei tea area. The medium-risk region was mainly distributed in mountain areas with altitude below 600m in Dabieshan tea area and Jiangnan tea area, as well as hilly and low mountains in Jiangbei tea area. The low-risk region was mainly concentrated in the region along the Yangtze river, and scattered in low mountain areas in Jiangnan tea area. The spring frost risk index constructed in this paper can objectively reflect the risk level of spring frost damage to tea plant. This study provides a scientific foundation for tea disaster prevention and disaster management. [ABSTRACT FROM AUTHOR]

Soil moisture is an important variable for the surface hydrological cycle. High-precision soil moisture products, which have consistent time and space consistency, play a vital role in studying hydrological-driven climate and vegetation growth. In this paper, several soil moisture products were evaluated, including Fengyun satellite soil moisture products(F¥-3B/3C) and Chinese Land Data Assimilation System Version 2.0(CLDAS-V2.0), aiming to clarify their accuracy characteristics of the time and space sequence. This study is expected to provide a reference for improving accuracy and inversion models of soil moisture products. The verification experiment was carried out in the Naqu region of the Qinghai-Tibet Plateau, based on the daily observed soil moisture data of 0-5cm, 0-1 Ocm and 10-40cm at 1 ° xl °; 0.3° xO.3Q and 0.1 ° xO.l ° scale sparse observation networks. In comparison, the Global Land Data Assimilation System Vision 1.0(GLDAS-l) Noah soil moisture products were added to conduct the evaluate as a reference. The results showed that: (1) in terms of time sequence, CLDAS-V2.0 soil moisture products showed good temporal and spatial continuity. The null values of FY series soil moisture products were common existed, especially in freezing periods. FY and CLDAS-V2.0 soil moisture products usually overestimated the in-situ measurements, especially after the precipitation and in the vegetation growth period. (2) During the multi-scale evaluation, compared with the large-scale sparse observation station, the results were more stable and excellent on the small-scale sparse observation network. Correlation coefficients between soil moisture products and in-situ data at small scale sparse observation network were higher than those at large and middle scale sparse observation network, but Bias, RMSE and ubRMSD were lower. For example, correlation coefficient of CLDAS-V2.0 soil moisture product of 0-5cm at three(large, middle and small) scale sparse observation networks were 0.77, 0.78 and 0.79. (3) At multi-scale sparse observation networks, the results of validation were consistent. At 0-5cm, the quality of CLDAS-V2.0 soil moisture products was better than that of FY series soil moisture products on the whole. The daytime retrieved FY series soil moisture products were more accurate than the night observed data. At 0-1 Ocm, GLDAS-1 Noah soil moisture products estimated closely soil moisture in Naqu region. However, R between GLDAS-1 Noah soil moisture products and in-situ data at multi-scale sparse observation networks were less than CLDAS-V2.0. At 10-40cm5 CLDAS-V2.0 soil m&sture products performed better than GLDAS-1 soil moisture products. The spatial statistical indices(Bias, RMSE and ubRMSD) of CLDAS-V2.0 and GLDAS-1 soil moisture products were closed, but correlation coefficients of CLDAS-V2.0 soil moisture products were much higher than those of GLDAS-1 soil moisture products. It showed that the quality of FY-3B/3C and CLDAS soil moisture products was relatively stable and reliable. [ABSTRACT FROM AUTHOR]

The hottest and coldest month-long periods' temperatures are common indicators of Agro-climatical division. Commonly the hottest/coldest period is replaced by the full month of July/January directly, but this fixed full month is not an accurate reflection of the actual hottest/coldest period. The hottest/coldest period of the year changes in temperatures and starting to end dates every year. In this paper, the length of the month-long period was set to be 31 days, as the common hottest and coldest months, July and January, are 31 days long. By using the daily temperature data from 1951 to 2018, collected at Changde weather station in Hunan province, the temperature of the hottest/coldest 31-day period was calculated using moving average approaches. The results showed that, (1)the hottest period spans from late June to early September, and the average hottest 31-day period was from mid-July to mid-August. Comparing the average hottest 31-day period and the full months of July and August with the actual highest 31-day period temperatures, the average temperature error was 0.5℃, 0.9℃, and 1.7℃ colder, respectively. (2) The coldest period spanned from early December to mid-March, and the average coldest 31-day period is from early January to early February. Compared to the actual coldest 31-day period, the temperatures of the average coldest 31-day period and the full months of January and February were 1.0℃, 1.1℃, and 2.9℃ warmer than the temperature of the actual month-long period, respectively. (3) With a temperature error within 1.0℃ considered to be acceptable, the average hottest 31-day period' s average temperature was 90% accurate in calculating the actual hottest 31-day period' s average temperature while using July' s temperature is only 61.2% accurate, which demonstrated that the average hottest 31-day period was more accurate than July. (4) With a temperature error within 2.0℃ and temperature accuracy above 80% considered to be acceptable, the effect during the average coldest 31-day was slightly better than January. In summary, the temperature error of the average 31-day hottest/coldest period was less than these of July/January, and the accuracy is the opposite. Therefore, it is recommended that when estimating the temperature of the hottest/coldest month-long period to not use the fixed full month of July/January but instead use the average hottest/coldest 31-day period while still taking into consideration in the adjustments from the actual temperature. [ABSTRACT FROM AUTHOR]

The concepts of accumulated temperature and heat resources have been widely presented in domestic literature and teaching materials, and accumulated temperature is usually as the main form of representation of heat resource. But its traditional definition obviously violates the principles of physics, and the measurement units also have not been unified. It urgently needs to be given an accurate scientific interpretation and further standardized. Based on the origin and development history of the concepts of accumulated temperature and heat resources, the problems in its practical application are reviewed in this paper, and the application frequency, geographical distribution characteristics of the term of accumulated temperature and its synonyms through Web of Science related literature are statistically analyzed and compared. The results show that, although accumulated temperature is the main indicator of traditional term of agricultural heat resources and is widely used, however, in fact, plants and cold-blooded animals do not need a certain amount of heat energy to complete a certain stage of growth and development, but require suitable temperature conditions and necessary duration. Excessive heat or too high temperature will cause damage to organisms, and some species even require relatively lower temperatures. On the other hand, literature search and statistics show that the term "accumulated temperature" has been rarer and rarer used in the international academic communities now, and the term "heat resource" is also rarely used in authoritative agricultural meteorological literature. In order to promote a more accurate and scientific term, it is recommended to stop use of the term "accumulated temperature" in Chinese Scientific Journals and to change into "integrated temperature" or "thermal time". And the unit should be unified into "℃·d" or "℃·h" and not "℃". The term of "heat resource" is also changed to "temperature resource" and broadened, i.e. the synthesis of temperature conditions and their duration conducive to growth and development of plants and cold-blood animals. It will give full scientific significance without affecting the large number of applications and achievements of the current accumulated temperature theory, and will promote further development of agricultural meteorology and related disciplines. [ABSTRACT FROM AUTHOR]

Vitex negundo is the native shrub species which was very popular in low mountain area of Taihang Mountains. It is widely used in such aspects as mountain greening, soil and water conservation, and ecological restoration due to its characteristics of strong resistance, and tolerance to barrenness. Studying on the water consumption of Vitex negundowas could not only reveal its water consumption dynamics and influencing mechanism, but also provide theoretical basis for local vegetation restoration and management. The water consumption of Vitex negundo and meteorological factors were measured with large weighing lysimeter and the automatic weather station, respectively. At the same time, reference evapotranspiration (ET0) was calculated by the Penman-Monteith equation recommended by FAO-56. The objective of the paper was to study the water consumption of Vitex negundo on different time scales, to find the adaption capacity to the arid environment, to reveal the relationship with ET0, and to provide empirical model for estimating the water consumption of Vitex negundo under the conditions of lacking measured data. The results showed that: (1) diurnal variation of water consumption of Vitex negundo showed single peak curve on typical days, which was consistent with solar radiation, air temperature, and vapor pressure deficit, but contrary to the relative humidity. Meanwhile, the peak time of water consumption of Vitex negundo. was 2 hours behind that of solar radiation, and time lag was found between them. Water consumption trendy of Vitex negundo on daily time scale showed low water consumption in the early and later growth period, and high in the middle growth period, and the value was 1.50mm, 2.00mm, and 4.00mm per day, respectively. Maximum and minimum of daily water consumption was found on 29th in August and 9th in October, with the value of 6.38mm and 0.20mm, respectively. The water consumption of Vitex negundo on monthly time scale showed the tendency of August >July > September > October> June > May. The total water consumption of Vitex negundo during the growth period was 513.5mm, and the rainfall was 526.6mm. The precipitation could meet the needs of water consumption Vitex negundo from the perspective of water balance. However, seasonal drought existed from May to June due to the less rainfall. (2) Vitex negundo has the feature of environmental adaptability and ecological plasticity. In rainy seasons, Vitex negundo was in prosperous growth period, the water transmission rate was high, and the water consumption was large. Adaptive mechanism started in arid season to reduce water consumption. (3) Good logarithmic relationship was found between daily water consumption of Vitex negundo and reference evapotranspiration in rainy seasons (July to October), while poor correlation was discovered between them in dry seasons (May to June). The estimated value of water consumption was calculated with the fitting equation, and was compared with the measured valued by the lysimeter. The good consistency and small error were found between the estimated and measured values, which conformed the precision of the fitting equation. It was indicated the fitting equation was accurate to estimate water consumption of Vitex negundo in rainy seasons of the local area, but inaccurate in dry seasons. [ABSTRACT FROM AUTHOR]

This paper is based on the meteorological observation data of the five meteorological stations (Shiquanhe, Gerze, Xianza, Bangoin and Amdo) near Qiangtang National Nature Reserve, the climate of Qiangtang National Nature Reserve from 1960 to 1990 and the estimated grid data in the future issued by IPCC5. The Miami model, Thornthwaite Memorial model, and tendency rate were used. The statistical analysis methods were used to study the spatiotemporal evolution of Net Primary Productivity in Qiangtang National Nature Reserve and its response to climate change. The results showed that: (1) the average annual temperature decreases from the west to the east, and the annual precipitation increases from the west to the east. The annual mean temperature(T) showed a significant increasing trend, while the annual precipitation(R) and annual evapotranspiration(E) showed a slight increasing trend. NPP of Temperature(NPPt) and NPP of precipitation(NPPr) showed a slight increasing trend at all stations, and NPP of evapotranspiration(NPPe) and standard NPP(NPPb) showed a slight increasing trend at most stations. (2) NPPt gradually decreased from the west to the east, NPPr gradually increased from the west to the east, NPPe and NPPb first increased and then decreased from the west to the east. The limiting factor of NPP was precipitation in Shiquanhe station and Gerze station, evapotranspiration in Xianza station and Bangoin station, and evapotranspiration or air temperature in Amdo station. (3) The NPPt, NPPr, NPPe and NPPb in different periods of the 21st century were significantly higher than those in all periods of the 20th century. The NPPt, NPPr, NPPe and NPPb were significantly increased in the future under different emission scenarios compared with those from 1960 to 1990. Under the trend of "warming and wetness" in the past 48 years and in the future, the NPP of vegetation increased, and the increase was larger in the cold and humid area in the southeast, but less in the cold and arid area in the northwest. The future climate is conducive to the improvement of local vegetation NPP and the improvement of local ecological environment. Assessing the possible impact of future climate change on the ecological environment of Qiangtang Plateau. The results not only provide a basic method for studying the Net Primary Productivity of vegetation in no man's land at regional scale, but also have important reference value for estimating ecological security research and ecological function planning. [ABSTRACT FROM AUTHOR]

The global solar radiation(Rs) is of great significance in the fields of agricultural production system, hydrometeorological research and clean energy development. Due to the complicated topography and various climate in Sichuan province, the distribution of global solar radiation is uneven. In order to explore the most suitable Rs estimation models for different zones of Sichuan, Sichuan was divided into three solar radiation zones in this paper (zone I: western Sichuan plateau, zone II: eastern Sichuan basin, and zone III: southwest Sichuan mountain) for research. Zone I(Ganzi and Hongyuan stations) has low temperature, sufficient sunshine and strong solar radiation; zone II(Chengdu, Mianyang and Luzhou stations) has high relative humidity, short sunshine duration and low radiation; zone III(Emeishan and Panzhihua stations) is abundant in solar radiation resources, but the air temperature and humidity vary greatly within the zone. In three solar radiation zones, based on the meteorological data from 1994 to 2016, the applicability of six Rs estimation models were evaluated under three weather types(sunny, partially cloudy and cloudy), and the simulation effect of combined models based on weather types in different zones were analyzed. The results showed that: (1) the empirical models performed well in Sichuan province(the coefficient of determination R2 ranged from 0.554 to 0.934, P<0.001). The most accurate simulation model in zone I was sunshine-based model A-P with mean absolute error (MAE) 2.210±0.714MJ-m_2-d_1. The hybrid model Chen was the best in zone II (MAE was 1.510±0.027MJ-m_2-d_1) and zone III (MAE was 1.510±0.027MJ-m_2-d_1). (2) The statistical performance of the six models under three weather types in Sichuan showed sunny>partially cloudy>cloudy. The sunshine-based models(A_P and Ba models) could better simulate the Rs in sunny days, while the hybrid modelsfChen and Ab models) had higher accuracy in simulating partially cloudy and cloudy days. The A_P(the global performance indicator GPI = 0.850), Ab(1.294) and Ba(0.862) models were the best models to simulate Rs of zone I in sunny, partially cloudy and cloudy, respectively. The A-P(GPI = 0.381), Chen(1.358) and Chen(1.742) models were the best models to simulate Rs of zone II under three weather types, respectively. The Chen model was the best model to simulate Rs of zone III in sunny, partially cloudy and cloudy with GPI 0.204, 0.857 and 0.526, respectively. (3) The Rs combined models(Mnew) based on weather types had the best simulation accuracy in each zone(GPI were 0.558, 0.582 and 0.134 in 3 zones, respectively). Therefore, it is recommended to use the Rs combined models based on weather types to estimate Rs in Sichuan province. [ABSTRACT FROM AUTHOR]