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Affected by global warming, the frequency of crop pests and diseases have increased, causing huge losses to agricultural production. To better grasp the development and trends of research on the effects of climate change on crop pests and diseases, the literature on the impact of climate change on crop pests and diseases published from 1990 to 2021 in the Web of Science (WOS) core collection database was used. This study explores the literature characteristics and hotspot evolution through the bibliometric visualization analysis software COOC, VOSviewer, and CiteSpace, with a view to identifying the changing characteristics and trends of research changes in this field. The results showed that the number of literature on the impact of climate change on crop pests and diseases increased rapidly. The main fields involved include environmental sciences, ecology, and agronomy. Papers in these fields mainly published in journals, such as PLos One, Forest Ecology and Management, and Frontiers in Plant Science. The country with the highest number of publications was the United States, followed by China and Australia. The most prolific authors in the top 20 are research scholars from China. The first author of the top 20 highly cited papers was from the United States. It was found that that current research on the impact of climate change on crop pests and diseases mainly focuses on agricultural production and food safety. Modelling and crop growth has maintained steady development. At present, research in this field mainly focuses on pest management strategies under the impact of climate change, the response of single species, and the complex ecological mechanisms behind the response. This study provides unique insights into the research field of the impact of climate change on crop pests and diseases and provides a reference direction for future research development in this field. [ABSTRACT FROM AUTHOR]

In the context of the Chinese government's policy guidance, there is black soil protection and ecological environment protection. The purpose of this paper is to solve the problem that the soil ecology of the black soil in Northeast China is changing year by year, and it is necessary to explore the sowing and fertilization strategy under the new situation; most Chinese growers rely excessively on their personal experience in the process of soybean sowing and fertilization. In this study, we used "Heihe 43" soybeans and used regression experimental design methods to analyze the effects of planting density, nitrogen, phosphorus, and potassium fertilizer application on soybean yield and to determine the optimal planting density and fertilizer ratios. The study reveals that the optimal soybean planting density in Northeast China is 45.37 × 104 plants/ha, with nitrogen at 98.4 kg/ha, phosphorus at 218.96 kg/ha, and potash at 47.62 kg/ha. Under these conditions, soybean yields can reach 3816.67 kg/ha. This study can provide a theoretical method for decision-making to obtain the optimal planting density and fertilizer ratio for different regions of the farming system. [ABSTRACT FROM AUTHOR]

Improving agricultural energy efficiency is essential in reducing energy consumption and achieving agricultural sustainable development. This paper aims to measure the agricultural total-factor energy efficiency in China rather than the partial-factor energy efficiency while taking full account of regional heterogeneity and to investigate the driving factors of agricultural total-factor energy efficiency. The empirical results showed that the average value of agricultural total-factor energy efficiency is 0.814 in China, and the technological gap ratio is 0.853. The regional difference in agricultural total-factor energy efficiency was quite obvious. Higher agricultural energy inputs are associated with higher agricultural total-factor productivity. The total value of potential agricultural energy savings in 30 provinces of China reached 1704.41 billion tons of standard coal. In terms of the absolute amount of agricultural energy saving, the amount was largest in the low-energy-input area, which was 113.87 million tons of standard coal, accounting for 66.81% of the total potential saving amount. Furthermore, we used the Tobit model to analyze the influencing factors of agricultural total-factor energy efficiency. We found that the proportion of agriculture to GDP has a positive impact on agricultural total-factor energy efficiency, while the per capita income of farmers, fiscal support for agriculture, the illiteracy rate of farmers, agricultural labor input, and agricultural capital stock have a negative impact on agricultural total-factor energy efficiency. Finally, we proposed policy implications in terms of agricultural technological progress, agricultural infrastructure, technical training, etc. [ABSTRACT FROM AUTHOR]

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing. [ABSTRACT FROM AUTHOR]

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%. [ABSTRACT FROM AUTHOR]

Agriculture is the foundation of every country's survival and development. This paper analyzes the interaction between the business environment, agricultural opening-up and high-quality agricultural economy from the perspective of China's provinces. According to the panel data of 31 provinces and municipalities in China from 2009 to 2019, the empirical analysis was carried out based on the panel vector autoregression (PVAR) model. The results show that there is a quantitative interaction between China's business environment, agricultural opening-up and high-quality agricultural development, and the long-term time effect is very obvious. Specifically, (1) the impact of the business environment and the high-quality development of agriculture on itself is decreasing year by year. The agriculture opening-up itself has a strong impact and has always maintained strong stability. (2) The high-quality development of the agricultural economy has little impact on the business environment and the opening of agriculture to the outside world, but it has a certain stable and continuous effect. It is particularly important that the agricultural opening-up has an increasingly strong effect on the business environment and the high-quality development of the agricultural economy as time progresses. Therefore, we must always adhere to the agriculture opening-up. The regional business environment and the agriculture opening-up can jointly promote the high-quality development of the agricultural economy. The research results can provide a basis and reference for the central and local governments to formulate relevant agricultural development policies and provide a reference for relevant agricultural economic entities and enterprises. [ABSTRACT FROM AUTHOR]

Weedy rice (Oryza sativa f. spontanea) is a noxious weed infesting rice fields worldwide and causes great yield losses for cultivated rice. Effective management of this weed is essential for the world's rice production. Yet, the management of weedy rice is challenging. One of the reasons is that shattered weedy rice seeds stored in soil often trigger great weed proliferation in the succeeding crop seasons. To study the survival of weedy rice seeds in soil seedbanks, we conducted 90-day soil burial experiments at different soil depths from 0–25 cm, using weedy rice seeds from Jiangsu Province in China. Results from two independent experiments under the rice field and laboratory conditions indicated significant differences in seed death ratios (SDRs) and induced seed dormancy ratios (ISDRs) of weedy rice at different soil burial depths. Weedy rice seeds exposed to the soil surface (0 cm burial treatment) had the highest SDRs and lowest ISDRs. An evident pattern of quickly declining SDRs with increased soil burial depths was identified from this study, suggesting rapid losses of seed viability on the surface and in shallow layers of soil. Our findings provide a useful guide for designing strategies to effectively control weedy rice by maintaining shattered seeds on the surface or in shallow layers of soil. The practices can easily be achieved through adopting the no-till farming system, which can substantially minimize viable weedy rice seeds as an important component in comprehensive weed management strategies. [ABSTRACT FROM AUTHOR]

Microplastic pollution in agricultural soil threatens soil quality and human health which has attracted extensive attention worldwide. However, there is no uniform standard for microplastic extraction methods and the identification of microplastic component in different typical agricultural soils. In this study, an artificial simulation adding experiment was used in eight typical agricultural soil samples in China. The aim of the study was to use different methods for extraction, comparing the extraction rates of four microplastics and their influence on polymer identification using ATR- FTIR. The two separate methods were oil extraction (water + oil and saturated NaCl solution + oil), and density method (saturated NaCl solution). The four types of microplastics include polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and polyethylene (PE). We found that the oil extraction method effectively extracted four types of microplastics in agricultural soils, which varied from 83.33% to 100.00%. However, the extraction rate of PET under the oil extraction method and PP under the density method from Southern laterite area was lower than other soils. The presence of iron and aluminum ions influenced the extraction rates of microplastics in the Southwest laterite area. With the increase in microplastic density, the extraction rates of the density method decrease. The oil extraction methods with the cleaning of residual oil were recommended for the higher density microplastics. The density method was recommended for the lighter microplastics in agricultural soils. However, these two extraction methods were not ideal to extract the microplastics from the Southern laterite area and the appropriate extraction methods for laterite need to be further studied in the future. Our results can provide technical support for the extraction treatment and scientific microplastic pollution control of typical agricultural soils with different erosion areas. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) is the most serious heavy metal pollutant in the agricultural soil of China and can transfer and accumulate through the food chain and affect the growth, development and physiological processes of phytophagous insect. Glyphodes pyloalis Walker (G. pyloalis) is one of the most important pests of mulberry, and there are few studies on the adverse effects of heavy metals on insects, especially mulberry pests. To understand the toxicology of Cd exposure on G. pyloalis, we investigated the effects of three different concentrations of Cd (0, 3.89 and 51.69 mg/kg, labeled as control check (CK), low dose (LD) and high dose (HD)) on the development and detoxification mechanism of G. pyloalis and explored the molecular mechanism of Cd on G. pyloalis larvae using RNA-seq technology. Transcriptome analysis showed that compared with the CK, a total of 63 differentially expressed genes (DEGs) were identified in LD exposure, including 24 upregulated and 39 downregulated candidates. In CK versus HD groups, 395 upregulated DEGs and 436 downregulated DEGs were identified and the expression patterns of 12 genes related to detoxification and metabolism were verified using qPCR. These DEGs were relevant to multiple specific peroxisome and drug metabolism-cytochrome P450 pathways. Gene annotation and quantitative real-time PCR revealed that a high concentration of Cd significantly stimulated the expression of metabolic detoxification enzyme genes. The results revealed that Cd exposure changed the pupal weight, adult emergence rate and the activities of AKP, ACP and GST in G. pyloalis and induced notable adverse effects at the molecular level on detoxification and metabolism such as concentration and time differences. In addition, we silenced CYP12A2 by RNA interference. Bioassays showed that after silencing CYP12A2, the survival rate of G. pyloalis under Cd exposure was lower than that of the control group. [ABSTRACT FROM AUTHOR]

Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R2) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R2 = 0.85 and R2 = 0.77, followed by STI, R2 = 0.82 and R2 = 0.80 and NDRI, R2 = 0.69 and R2 = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region. [ABSTRACT FROM AUTHOR]

Agricultural ammonia (NH3) emissions (including farmland, livestock and poultry) are China's main sources of NH3 emissions. China's government has proposed a national strategic goal to reduce NH3 emissions. Excessive protein feeds, unreasonable manure treatments and agricultural fertilizer applications result in large emissions of NH3. Agricultural activities such as the breeding of livestock and fertilization in farmlands are the main sources of atmospheric NH3 emissions. This article discussed the progress and characteristics of typical NH3 emission inventory, calculated the nationwide NH3 emissions and analyzed the NH3 emission control strategy in the past 40 years in China. There was also an integration analysis of national documents on emission reduction technologies (including government reports) in China. The results showed that there existed single calculation methods and insufficient localization of emission factors in the estimation of domestic NH3 emissions. NH3 emission inventories varied greatly influenced by meteorology, planting structure and breeding pattern. The control strategy of NH3 emission in China has transformed from emission standards to technical guidelines to national strategic control, and it involves the coordination and cooperation of the Ministry of Agriculture and Rural Affairs departments. Current domestic NH3 emission management strategy needs scientific emission verification specification, multi-department and inter-provincial regional coordination mechanisms, and suggestions for further improvement have been put forward. It is urgent to evaluate precise NH3 emission inventories at different regional scales, followed by intensive NH3 emission controls in the key regions (such as North China). Government, agriculture, and breeding industries should vigorously promote low-protein feeds, large-scale livestock farming (including poultry), and pilot projects on closed negative pressure farming. Also, agriculture-related NH3 emission reduction measures should be fully implemented by providing technical support for NH3 emission control in domestic agricultural farms. [ABSTRACT FROM AUTHOR]

Agricultural soils originating from carbonate rocks within karst regions exhibit inherent high concentrations of potentially toxic elements (PTEs) due to geogenic processes. However, the influencing factors of the elevated levels of PTEs in the naturally contaminated karst regions remain inadequately comprehended. This research investigates the chemical compositions of 278 soils derived from carbonate rocks. Descriptive statistics, stepwise multiple regression, and the random forest (RF) method were applied to screen the significant factors that affect the distribution, migration, and enrichment of the PTEs in soils. Cadmium (Cd) and arsenic (As) are the most highly contaminated PTEs in the soils of the study area, and the average contents of Cd and As in soils are 11.5 and 2.92 times the national soil background value, respectively. The pollution risk ranking of PTEs is Cd > As > Cr > Zn > Pb > Cu > Ni > Hg, using the proportion exceeding the risk screening thresholds of agricultural land as the standard. Soil Fe2O3 and Mn contents, soil pH and total organic carbon (TOC) values, and weathering intensity (characterized using the chemical index of alternation, CIA) are the most important factors influencing the PTE levels in agricultural soils. [ABSTRACT FROM AUTHOR]

The Peiligang culture (ca. 9000–7000 cal. BP) represents the first Neolithic settlements in the middle Yellow River Valley, marking the beginning of millet and rice farming in the region. While previous studies have focused primarily on identifying cultivated cereals, less attention has been given to plant harvesting and processing practices or environmental conditions. To address this gap, we present new phytolith data from the Peiligang site (8000–7600 cal. BP) to make three key contributions. First, we show that the Peiligang people utilized two microhabitats: hillslopes for dryland millet cultivation and alluvial plain for wetland resources. Second, we combine our findings with other archaeological evidence to reconstruct the environmental conditions of the Peiligang site, suggesting that it was a water-rich habitat. Finally, by analyzing phytolith remains of plant processing waste in middens, we reconstruct how people harvested and processed millets and rice at the site. This study sheds light on the plant-based subsistence strategies employed by the Peiligang people and offers insights into the environmental factors that contributed to the development of early farming in the middle Yellow River Valley. [ABSTRACT FROM AUTHOR]

Agricultural advancements have significantly impacted people's lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop's conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China's National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance. [ABSTRACT FROM AUTHOR]

Crop straw retention in the rice-based rotation cropland has been widely accepted as an effective method to improve soil quality in China. Rice–wheat rotation cropland is one the most prevalent rice-based rotation patterns, where it only exploits a small proportion of the total agricultural land yet feeds the majority of the Chinese population. Previous studies indicated that the incorporation of fore-rotating crop straw can effectively facilitate soil carbon sequestration in rice paddy fields. However, the application of crop straw may increase methane (CH4) emissions from rice paddies due to the anaerobic soil condition. To mitigate CH4 emissions from rice paddies while still preserving their soil carbon sequestration ability, a field experiment was conducted in the 2012–2016 rice growing seasons to determine the optimal low-carbon crop straw retention strategy. Five treatments with different wheat straw retention strategies were employed in this study, including non-fertilization and non-straw (Control), conventional fertilization without straw incorporation (CF), conventional fertilization with wheat straw incorporation (FS), slow-release fertilizer combined with wheat straw (SFS), and conventional fertilization with wheat-straw-derived biochar (FB). The results indicated that FS, SFS, and FB treatments significantly increased soil carbon sequestration in comparison with CF treatment. However, the increment of soil carbon sequestration was offset by raw wheat straw induced excess CH4 emissions under FS and SFS treatments. In contrast, the application of wheat-straw-derived biochar significantly promoted soil carbon sequestration, but showed no significant effect on CH4 emissions. Collectively, to the farmers, who aim to achieve agricultural carbon neutrality, the application of straw-derived biochar is worthy of consideration in rice cultivation processes. [ABSTRACT FROM AUTHOR]

Rice is the most important crop for worldwide consumers. The water utilization of rice planting is more than 50% of agricultural water in China, and it is necessary to breed water-saving and drought-resistant rice. The rice variety Dianheyou 615 can be planted in the paddy and upland fields, which satisfies rice production farmers in mountainous regions of Yunnan. We aimed to explore the variations in Magnaporthe oryzae isolates collected from Dianheyou 615 planted in paddy or upland fields. Through pathogenicity tests, we found that most isolates had the highest pathogenicities, but there were no significant differences between the paddy and upland isolates. By a combination of monogenetic and elite rice lines, with a further resistance assessment, the monogenetic lines with Pi9, Diantun 506, and Lvhan 1 displayed better resistances. Moreover, we re-sequenced 15 isolates to explore their genetic variations. Our results showed that the source of the upland isolates may have been the offspring of the paddy isolates, but there were many genes with specifically found SNPs in two populations that would develop subdivisions after long-time planting. Overall, we compared the pathogenicities and genetic variations in blast isolates from the planting of Dianheyou 615 in paddy and upland fields, which provided references for the influence of the planting environment on population subdivisions. [ABSTRACT FROM AUTHOR]

Direct-seeded rice (DSR) has received much attention because of its advantages in having low labor costs compared to the traditional transplanted rice (TPR). Investigating the differences in evapotranspiration (ET) partitioning and crop coefficients (Kc) between DSR and TPR is essential in understanding how agricultural water demand is affected by crop rotation. In this study, the water fluxes of two-year (2017–2018) growing seasons were collected from a pair of eddy covariance (EC) towers for DSR and TPR in the Poyang Lake Basin, Southern China. This study aims to compare the seasonal characteristics of the ET components (evaporation, E, and transpiration, T) and dual crop coefficients (basal crop coefficient, Ks·Kcb, and soil/water crop coefficient, Ke) of DSR with those of TPR. The ET values for the 2017 and 2018 growing seasons were 374 mm and 436 mm for the DSR, respectively, and 309 mm and 342 mm for the TPR. The seasonal T/ET values in 2017 and 2018 were 0.40 and 0.46 for the DSR, respectively, and 0.49 and 0.52 for the TPR, indicating that the higher ET values for the DSR can be mostly attributed to E. The DSR had overall higher Kc values than the TPR because of free water evaporation during the initial stage and a higher plant density. Our results enrich the Kc dataset for DSR and have great implications for the sustainable irrigation of the Poyang Lake Basin in the future. [ABSTRACT FROM AUTHOR]

The separation between planting and breeding results in an unbalanced distribution of the regional livestock and poultry manure (RLM) industry, and it has raised great concerns. A holistic analysis and problem-solving scheme using 72 townships as the research point was developed in this study. On the basis of a survey from a typical agricultural region in China, the local characteristics of manure discharge, land use, and crop cultivation were analyzed. The assessment of land carrying capacity and environmental risk assessment was conducted by simulating the nitrogen cycle. Afterwards, optimized livestock breeding strategies and inter-regional transfer and flow scheme of manure nutrients were proposed. The spatial distribution of RLM in terms of pig manure equivalent showed an imbalance of high north–south and low middle, and the nitrogen requirement of crops showed a decreasing trend from north to south. In some townships, the environmental risks were higher than level I, which indicated that pollution existed around large construction sites and water areas in the northwest. The land carrying capacity index calculated at 50% nutrient ratio displayed no overloaded risk, whereas 10–20% nutrient ratio exhibited overloaded risk. Assessments showed that the residual RLM and its nitrogen volume were 151,700 and 3574.64 tons per year, respectively. More than 80% of the study area could be used as a nitrogen nutrient sink area, and only six townships are nitrogen nutrient sources. Therefore, optimizing the allocation of manure nutrients is expected to avoid agricultural contamination from livestock manure. [ABSTRACT FROM AUTHOR]

Obtaining complete and consistent spectral images of wheat ears in the visible areas of in situ wheat canopies poses a significant challenge due to the varying growth posture of wheat. Nevertheless, detecting the presence and degree of wheat Fusarium head blight (FHB) in situ is critical for formulating measures that ensure stable grain production and supply while promoting green development in agriculture. In this study, a spectral quantitative analysis model was developed to evaluate the infection degree of FHB in an in situ wheat canopy's visible areas. To achieve this, a spectral acquisition method was used to evaluate the infection degree of FHB in a wheat canopy's visible areas. Hyperspectral images were utilized to obtain spectral data from healthy and mildly, moderately, and severely infected wheat ear canopies. The spectral data were preprocessed, and characteristic wavelengths were extracted using twelve types of spectral preprocessing methods and four types of characteristic wavelength extraction methods. Subsequently, sixty-five spectral quantitative prediction models for the infection degree of FHB in the in situ wheat canopy visible areas were established using the PLSR method, based on the original spectral data, preprocessed spectral data, original spectral characteristic wavelengths extracted data, and preprocessed spectral characteristic wavelengths extracted data. Comparative analysis of the models indicated that the MMS + CARS + PLSR model exhibited the best prediction effect and could serve as the spectral quantitative analysis model for the evaluation of the infection degree of FHB in an in situ wheat canopy's visible areas. The model extracted thirty-five characteristic wavelengths, with a modeling set coefficient of determination (R2) of 0.9490 and a root-mean-square error (RMSE) of 0.2384. The testing set of the coefficient of determination (R2) was 0.9312, with a root-mean-square error (RMSE) of 0.2588. The model can facilitate the spectral quantitative analysis of the infection degree of FHB in the in situ wheat canopy visible areas, thereby aiding in the implementation of China's targeted poverty alleviation and agricultural power strategy. [ABSTRACT FROM AUTHOR]

Solanum nigrum is a noxious weed in agricultural ecosystem that limits many crops' production. The aim of the present study was to understand the level of genetic diversity and population structure of S. nigrum in China. A specific-locus amplified fragment (SLAF) sequencing method was conducted to detect single-nucleotide polymorphisms (SNPs) in the genomes of S. nigrum from 66 populations in China. A total of 616,533 high-quality SNPs were identified from 189,840 SLAFs, with an average sequencing depth of 10.59× fold and a Q30 value of 93% and a GC content of 42.78%. It showed a considerable amount of genetic diversity and genetic variability of S. nigrum among samples. The genetic differentiation of S. nigrum indicated that there was a low level of genetic differentiation (Fst < 0.1000) among geographical populations. A cluster analysis showed that populations of S. nigrum were divided into two subgroups, with some samples from adjacent position roughly clustered together, which showed some correlation between geographic origins. A population structure analysis suggested the 66 S. nigrum samples could have originated from three different genetic clusters. The Xinjiang site was the only location where all genetic clusters were found, which suggested these populations were genetically diverse. These results showed that there was a high degree of genetic diversity and low difference among the different geographical populations of S. nigrum. The results from the genetic structure of the SNP markers indicated that wide genetic variability exists among the population of S. nigrum in China, which may contribute to the adaptation and infestation of this weed species. [ABSTRACT FROM AUTHOR]

Over-fertilization has a significant impact on soil microbial properties and its ecological environment. However, the effects of long-term fertilization on microbial properties on a large scale are still vague. This meta-analysis collected 6211 data points from 109 long-term experimental sites in China to evaluate the effects of fertilizer type and fertilization duration, as well as soil and climate conditions, on the effect sizes on various microbial properties and indices. The organic fertilizers combined with straw (NPKS) and manure (NPKM) had the highest effect sizes, while the chemical fertilizers N (sole N fertilizer) and NPK (NPK fertilizer) had the lowest. When compared with the control, NPKM treatment had the highest effect size, while N treatment had the lowest effect size on MBN (111% vs. 19%), PLFA (110% vs. −7%), fungi (88% vs. 43%), Actinomycetes (97% vs. 44%), urease (77% vs. 25%), catalase (15% vs. −11%), and phosphatase (58% vs. 4%). NPKM treatment had the highest while NPK treatment had the lowest effect size on bacteria (123% vs. 33%). NPKS treatment had the highest while N treatment had the lowest effect sizes on MBC (77% vs. 8%) and invertase (59% vs. 0.2%). NPKS treatment had the highest while NPK treatment had the lowest effect size on the Shannon index (5% vs. 1%). The effect sizes of NPKM treatment were the highest predominantly in arid regions because of the naturally low organic carbon in soils of these regions. The effect sizes on various microbial properties were also highly dependent on soil texture. In coarse-textured soils the effect sizes on MBC and MBN peaked sooner compared with those of clayey or silty soils, although various enzymes were most active in silty soils during the first 10 years of fertilization. Effect sizes on microbial properties were generally higher under NPKM and NPKS treatments than under NPK or N treatments, with considerable effects due to climate conditions. The optimal field fertilizer regime could be determined based on the effects of fertilizer type on soil microorganisms under various climate conditions and soil textures. This will contribute to the microbial biodiversity and soil health of agricultural land. Such controls should be used for adaptation of fertilization strategies to global changes. [ABSTRACT FROM AUTHOR]

Pesticide treatment dramatically reduces crop loss and enhances agricultural productivity, promoting global food security and economic growth. However, owing to high accrual and persistent tendency, pesticides could create significant ecological consequences when used often. Lately, the perspective has transitioned to implementing biological material, environmentally sustainable, and economical strategies via bioremediation approaches to eradicate pesticides contaminations. Microalgae were regarded as a prominent option for the detoxification of such hazardous contaminants. Sustainable application and remediation strategies of pesticides pollutants in the agriculture system by microalgae from the past studies, and recent advancements were integrated into this review. Bibliometric strategies to enhance the research advancements in pesticide bioremediation by microalgae between 2010 and 2020 were implemented through critical comparative analysis of documents from Scopus and PubMed databases. As a result, this study identified a growing annual research trend from 1994 to 2020 (nScopus > nPubMed). Global production of pesticide remediation by microalgae demonstrated significant contributions from India (23.8%) and China (16.7%). The author's keyword clustering was visualized using bibliometric software (VOSviewer), which revealed the strongest network formed by "microalgae", "bioremediation", "biodegradation", "cyanobacteria", "wastewater", and "pesticide" as significant to the research topic. Hence, this bibliometric review will facilitate the future roadmap for many scholars and authors who were drawing attention to the burgeoning research on bioremediation of pesticides to counteract environmental impacts while maintaining food sustainability. [ABSTRACT FROM AUTHOR]

Technification in agriculture has resulted in the inclusion of more efficient companies that have evolved into a more complex sector focused on production and quality. Artificial intelligence, one of the relevant areas of technology, is transforming the agriculture sector by reducing the consumption and use of resources. This research uses a bibliometric methodology and a fractional counting method of clustering to analyze the scientific literature on the topic, reviewing 2629 related documents recorded on the Web of Science and Scopus databases. The study found significant results regarding the most relevant and prolific authors (Hoogenboom), supporting research organizations (National Natural Science Foundation of China) and countries (U.S., China, India, or Iran). The identification of leaders in this field gives researchers new possibilities for new lines of research based on previous studies. An in-depth examination of authors' keywords identified different clusters and trends linking Artificial Intelligence and green economy, sustainable development, climate change, and the environment. [ABSTRACT FROM AUTHOR]