Planting Area Research Articles

Reduction of uncertainties in rice yield response to elevated CO2 by experiment-model integration: A case study in East China

Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors, of which the most important one is the increasing carbon dioxide (CO2) concentrations. Estimates of CO2 fertilization effect (CFE) on rice, however, still had large uncertainties. Therefore, using the rice planting areas in East China as the study area, we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models, and found that the CFE predicted by these models had significant differences. We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province. Using CFE measurements from a field experiment as benchmark, we have developed an experiment–model integration approach aiming to reduce this variation. This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty, which is beneficial for the accurate prediction of future global rice yield in the context of climate change.

Two invasive bark beetles Phloeosinus armatus Reitter and Xylosandrus compactus (Eichhoff) (Coleoptera, Curculionidae: Scolytinae) newly recorded in Russia

AbstractIn 2023, two bark beetle species were recorded in Russia for the first time based on findings in Sochi: the cypress bark beetle, Phloeosinus armatus, on Mediterranean cypress (Cupressus sempervirens) and the black twig borer, Xylosandrus compactus, on southern magnolia (Magnolia grandiflora), noble laurel (Laurus nobilis), and cherry laurel (Prunus laurocerasus). P. armatus is a Mediterranean pest attacking exclusively Cupressaceae. In Sochi, it was recorded on cypress trees in several locations, including on plants for planting imported from Italy. X. compactus is a highly polyphagous pest from East Asia. In Sochi, it has established populations in ornamental planted areas. The paper provides species descriptions with identification keys. One specimen of X. compactus from Sochi was DNA‐barcoded and compared with 31 specimens deposited in the BOLD and GenBank under the name of X. compactus. High genetic variability (>7%) was detected highlighting the difficulty of identifying ‘true’ X. compactus using DNA data. Genetically, the specimen of X. compactus from Sochi was identical to those from Italy, Spain and France. An accidental introduction with ornamental plants for planting from Italy is considered the most probable pathway of invasion of both bark beetle species into the Russian Black Sea coast of the Caucasus.

Preferencia alimenticia y de oviposición de Gonipterus platensis(Coleoptera: Curculionidae) en tres especies comerciales de Eucalyptus en Colombia

Gonipterus platensis (Marelli, 1926), is a beetle that belongs to the family Curculionidae, a phytophagous species within the genus Eucalyptus. It is native to Australia, where it is not considered a pest due to the presence of biological controllers. In Colombia, eucalyptus trees are the second exotic species with the largest planted area, and because in 2016 was recorded the presence of G. platensis in the country, efforts have been made to study and determine the potential damage that this species can cause in plantations through studies of its feeding preferences and oviposition, among others. The present study aims to determine the preference of G. platensis for the three most planted commercial eucalyptus species in the country: Eucalyptus grandis W.Hill ex Maiden, E. urophylla S.T. Blake, and the hybrid E. grandis W.Hill ex Maiden × E. urophylla S.T. Blake (E. urograndis). An experiment was conducted using a multiple-choice design to assess feeding and oviposition preference. Two assays were carried out: one with seedlings and the other with an olfactometer. The present study reveals that G. platensis has a feeding preference for E. grandis, followed by E. urophylla, and to a lesser extent, the hybrid species E. urograndis. Furthermore, G. platensis is capable of ovipositing in all three eucalyptus species studied without making any differentiation.

Annual time-series 1 km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850–2021

Abstract. Agricultural activities have been recognized as an important driver of land cover and land use change (LCLUC) and have significantly impacted the ecosystem feedback to climate by altering land surface properties. A reliable historical cropland distribution dataset is crucial for understanding and quantifying the legacy effects of agriculture-related LCLUC. While several LCLUC datasets have the potential to depict cropland patterns in the conterminous US, there remains a dearth of a relatively high-resolution datasets with crop type details over a long period. To address this gap, we reconstructed historical cropland density and crop type maps from 1850 to 2021 at a resolution of 1 km × 1 km by integrating county-level crop-specific inventory datasets, census data, and gridded LCLUC products. Different from other databases, we tracked the planting area dynamics of all crops in the US, excluding idle and fallow farm land and cropland pasture. The results showed that the crop acreages for nine major crops derived from our map products are highly consistent with the county-level inventory data, with a residual less than 0.2×103 ha (0.2 kha) in most counties (>75 %) during the entire study period. Temporally, the US total crop acreage has increased by 118×106 ha (118 Mha) from 1850 to 2021, primarily driven by corn (30 Mha) and soybean (35 Mha). Spatially, the hot spots of cropland distribution shifted from the Eastern US to the Midwest and the Great Plains, and the dominant crop types (corn and soybean) expanded northwestward. Moreover, we found that the US cropping diversity experienced a significant increase from the 1850s to the 1960s, followed by a dramatic decline in the recent 6 decades under intensified agriculture. Generally, this newly developed dataset could facilitate spatial data development, with respect to delineating crop-specific management practices, and enable the quantification of cropland change impacts on the environment. Annual cropland density and crop type maps are available at https://doi.org/10.6084/m9.figshare.22822838.v2 (Ye et al., 2023).

Spatiotemporal Evolution of Winter Wheat Planting Area and Meteorology-Driven Effects on Yield under Climate Change in Henan Province of China.

This study examines the impact of climate change on winter wheat production in Henan Province. The analysis, under the utilization of GLASS LAI data, focuses on shifts in the planting areas of winter wheat. In addition, a comprehensive assessment of the spatiotemporal trends in meteorological factors during the winter wheat growth period has also been conducted. The findings reveal a fluctuating increase in accumulated temperature across Henan Province, ranging from 3145 °C to 3424 °C and exhibiting a gradual rise from north to south. In particular, precipitation patterns from 1980 to 2019 showed limited significant trends, while notable abrupt changes were observed in 1983, 2004, 2009, and 2016. Geographically, southwestern Henan Province experiences greater precipitation than the northeast. Moreover, a fluctuating downward trend in sunshine hours has been observed, gradually decreasing from north to south. The study further highlights an increase in winter wheat planting frequency in the northwestern region of Luoyang and the northeastern part of Zhumadian, contrasted by a decrease in Zhengzhou and Kaifeng. Accumulated temperature is positively correlated with the expansion of winter wheat planting areas (R2 = 0.685), while sunshine hours exert a suppressive effect (R2 = 0.637). Among meteorological factors, accumulated temperature emerges as the most crucial determinant, followed by precipitation, with sunshine hours having a relatively minor influence. Yield demonstrates a positive association with accumulated temperature (R2 = 0.765) and a negative correlation with sunshine hours (R2 = -0.614). This finding is consistent with the impact of meteorological factors on winter wheat production. The results of this study enhance the understanding of how the underlying mechanisms of climate change impact crop yields.

Improving Mapping Accuracy of Smallholder Potato Planting Areas by Embedding Prior Knowledge into a Novel Multi-temporal Deep Learning Network

Improving Mapping Accuracy of Smallholder Potato Planting Areas by Embedding Prior Knowledge into a Novel Multi-temporal Deep Learning Network

Biomass Resources and Emission Reduction Potential of Agricultural and Livestock Residues in Mainland China from 2013 to 2022

Controlling carbon emissions is a global goal, and China is actively implementing carbon reduction measures. As a major agricultural nation, China has considerable potential for developing agricultural residues as renewable and environmentally friendly biomass energy. In this study, we obtained data on crop yields, crop-to-grain ratios, and livestock excretion coefficients to calculate the biomass resources of agricultural and livestock residues in Chinese provinces from 2013 to 2022. Crop residue biomass resources showed a distribution pattern with higher levels in the north than in the south and the east than in the west. Henan and Heilongjiang provinces consistently had the highest resource levels, exceeding 35 million tons annually for 10 years. The biomass resources from livestock residues were relatively abundant in Sichuan, Henan, Yunnan, Shandong, Hunan, and Inner Mongolia. Inner Mongolia, Sichuan, Shandong, and Henan had the greatest potential for CO2 emission reductions, primarily located in regions abundant in biomass resources and with high traditional energy consumption levels. ArcGIS was used to apply natural break classification to categorize the potential for emission reductions from agricultural and livestock residues across China from 2013 to 2022 into five classes. Based on factors such as crop planting area and livestock numbers, the spatiotemporal distribution of factors influencing the quantity of biomass resources was examined using Geographically and Temporally Weighted Regression. A tailored and integrated approach should be used for biomass, and the development of biomass energy should be promoted through policy support and technological innovation.

Ecological Evaluation of Land Resources in the Yangtze River Delta Region by Remote Sensing Observation

The evaluation of land ecological security (LES) evaluates how human activity and land use affect land ecosystems. Its ultimate objective is to provide guidance and assistance for decision making in order to preserve and restore the efficacy and health of terrestrial ecosystems. The assessment model presented in this article is comprehensive and integrates the advantages of both subjective and objective weighting techniques. This study extends the “Pressure–State–Response” (PSR) model to “Driver–Pressure–State-Impact–Response” (DPSIR) and combines it with TOPSISI to determine the weights of each contributing component. Furthermore, the geographical and temporal distribution patterns of regional land ecological security levels were investigated using GIS geostatistical approaches. According to this study, (1) the Yangtze River Delta region’s LES index, with a mean value in the fairly safe range, is generally safe. The year 2019 marks an inflection point for the index, with the highest level of ecological safety on land. The primary element is the modification of environmental policies that are enacted by the government. (2) The LES status is divided into two stages during the course of this study. The Yangtze River Delta region’s LES quickly develops throughout the first stage (2012–2019), which sees a shift in the safety rating from IV to II. The second stage (2019–2023) sees a progressive improvement in the LES index and a shift in the safety category from Class II to Class I. (3) Important variables influencing the geographical distribution of LES in the Yangtze River Delta region include barrier elements, including soil and water erosion areas, flood disaster areas, grain planting areas, urban green covering areas, and effective irrigation areas of farmland.

Energy assessment and heat integration of biofuel production from bio-oil produced through fast pyrolysis of sugarcane straw, and its upgrading via hydrotreatment

Sugarcane processing is one of the most important economic activities in Brazil, producing ethanol and sugar for domestic and international markets. Utilising the lignocellulosic residues of this industry, specifically bagasse and straw, would increase the second-generation biofuel production without increasing the sugarcane planted area. Among the processes available, the fast pyrolysis is a thermochemical conversion process that produces mainly bio-oil, a liquid with several advantages over solid biomass, regarding transportation, pumping, storage, and handling. Moreover, the bio-oil can be upgraded to obtain biofuels of higher added-value. Among feasible upgrading technologies, the hydrotreatment is one of the most promising methods for eliminating the reactive functionalities of the bio-oil by removing oxygen or cracking large molecules in the presence of hydrogen; however, this comes at the expense of a significant hydrogen consumption. Thus, the aim of this study is to evaluate the biofuel production through the fast pyrolysis of sugarcane straw, followed by the upgrading of the produced bio-oil via hydrotreatment. The evaluation is carried out by way of energy and mass balances of the processes, performed using the software Aspen Plus. Furthermore, the possibilities of integrating the bio-oil production and upgrading into the conventional ethanol and sugar production process will also be assessed through heat integration, applying the Pinch analysis. Results showed the potential for renewable gasoline and diesel production with yields of 0.086 kg/kg of dry straw and 0.08 kg/kg of dry straw, respectively. Additionally, integrating the pyrolysis process, including the bio-oil upgrading, into the ethanol production process can achieve a liquid fuel production (ethanol, gasoline and diesel) of 2289.8 MJ/t cane, which represents an increase of 29 %, while the surplus electricity increases in 5.3 %, when compared to the conventional ethanol production plant.

Prediction of potential suitable distribution for sweet cherry (Prunus avium) based on the MaxEnt model.

The sweet cherry (Prunus avium) is among deciduous fruit trees with high economic value and its planting area is gradually expanding. However, little was known about its accurately suitable area in China. Herein, the potential distributions were modeled based on the MaxEnt model under the current conditions. Its performance was excellent, with AUCs >0.9 for model training and testing. The key environmental factors were the thermal factors (minimum temperature of the coldest month (bio06) from -14.5 to 4.5°C, the mean temperature of the warmest quarter (bio10) from 21.0 to 28.0°C), followed by the water factor (the annual precipitation (bio12) from 500 to 1200 mm), indicating that it is not resistant to cold and heat, nor is it resistant to drought or floods. The suitable area in China mainly is found in seven geographical regions including southwest China (eastern Sichuan, northeast and main urban areas of Chongqing, mid-western Guizhou and mid-northern Yunnan), northwest China (mid-southern Shaanxi, southern Ningxia mid-southern and eastern Gansu), northeast China (Coastal region of Liaoning), central China (most of Henan, mid-northern Hubei and central Hunan), north China (Beijing, Tianjing, mid-southern Shanxi), east China (Shanghai, Jiangsu, Shandong, central Zhejiang, central and northern Anhui and eastern Jiangxi) and south China (western Guangxi). Based on statistical analysis, these fourteen provinces or cities, namely, Shaanxi, Beijing, Tianjing, Shanxi, Hebei, Henan, Shanghai, Jiangsu, Shandong, Sichuan, Guizhou, Yunnan, Liaoning and Hubei were the main regions for current development and utilization while for the twelve provinces with higher moderate suitable areas, namely, Chongqing, Guizhou, Yunnan, Shaanxi, Ningxia, Liaoning, Hubei, Hunan, Zhejiang, Anhui, Jiangxi and Guangxi, we should supplement the appropriate irrigation and winter insulation facilities etc. Additionally, Hubei, Hunan, Anhui, also have been identified to have some potentially suitable areas. These information will help avoid the loss of human labor, material, and financial resources and provide a scientific basis for its current introduction, cultivation, and management.

Mapping Ratoon Rice Fields Based on SAR Time Series and Phenology Data in Cloudy Regions

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally.

Research on fruit picking test of blueberry harvesting machinery under transmission clearance

Since China possesses the vast territory and a large blueberry planting area, blueberry harvesting with hand is laborious and time consuming. As the blueberry planting agronomy in China is different from other countries, it is pretty significant to develop blueberry harvesting machinery applicable to the domestic planting agronomy to achieve mechanized harvesting of blueberries. In blueberry harvesting operation, the harvesting system as the core component of the machine is the key technology of the harvesting machinery. The previous study found that: in terms of harvesting machinery, most of the literature has studied the dynamic characteristics, while few articles have been published to study the transmission clearance. But the clearance collision force generated by the transmission clearance of the harvesting system directly affected the output load moment and harvesting force of the machine developed by acting on the plant, and then affected the picking efficiency and the quality of picked fruit. Therefore, this paper focuses on the study of the transmission clearance of the blueberry harvesting machine. Firstly, the MLSD modeling method was applied to establish the transmission clearance model of the harvesting device, and the corresponding mechanical analysis of the transmission clearance was carried out. Secondly, after programming in MATLAB software and simulation in ADAMS software, the correctness of the transmission clearance model of the harvesting device was verified in different environments. The multi-body dynamics analysis software ADAMS was used for building the transmission clearance model of the harvesting device and perform mechanical simulation to analyze the clearance collision force and the output load moment of the harvesting device. Pro/E, ADAMS and ANSYS software were integrated to establish the flexible body of blueberry plant. Then the flexible body was combined with the mechanical model of harvesting device for rigid-flexible coupling simulation analysis to study the fruit harvesting force under different transmission clearances. Finally, the orthogonal method was used to conduct field test on blueberry harvesting to study the influence of the transmission clearance on the quality of picked fruit and picking efficiency of the machinery. Therefore, the best combination of the transmission clearance of the harvester was obtained as follows: cam clearance (the clearance joints of cam) was 0.25 mm, the slider clearance was 0.2 mm, left connecting rod clearance pair was 0.1 mm, right connecting rod clearance pair was 0.2 mm; the field picking test was conducted to obtain the machine's picking efficiency was 3.93 kg min−1, the shedding rate of unripe fruit was 3.1 %, and the damage rate of picked fruit was 2.8 %. The research findings of this paper can offer referenced basis and theoretical support for berry harvesting machinery, and also can provide guidance for the design and improvement of other agricultural and forestry harvesting machinery.

The Impact of Factor Price Change on China’s Cotton Production Pattern Evolution: Mediation and Spillover Effects

Cotton is an important agricultural crop and strategic resource. China is currently the country with the largest global cotton production and consumption, but few studies have systematically analyzed the dynamic relationship between factor price change (FPC) and cotton production pattern evolution (CPPE). Based on provincial panel data from the main cotton planting areas from 1985 to 2021, this paper used spatial econometric models to empirically analyze the impact of FPC including labor price, production material cost, and mechanical cost on CPPE from the perspective of mechanical substitution difficulty. The findings are that (1) FPC significantly affected CPPE, specifically the rise of labor price induced the demand for mechanical substitution, resulting in a significant cotton agglomeration effect; (2) spatial econometric analysis found that FPC had a significant spatial spillover effect on CPPE in adjacent regions; and (3) the transfer analysis found that higher mechanical substitution difficulty exerted an inhibitory effect on cotton planting, leading to a gradual shift of the main cotton planting areas from the Yangtze River Basin and Yellow River Basin with high labor prices to the Northwest Inland region with lower labor prices. Updating the production technology and improving the efficiency of cotton specialization are effective strategies. The results are valuable for policy making related to the development of China’s cotton industry.

Genome-wide identification of SIMILAR to RCD ONE (SRO) gene family in rapeseed (Brassica napus L.) reveals their role in drought stress response

ABSTRACT Rapeseed (Brassica napus L.) is an important oilseed crop widely cultivated worldwide, and drought is the main environmental factor limiting its yield enhancement and the expansion of planted areas. SIMILAR TO RCD ONE (SRO) is a plant-specific small gene family that plays a crucial role in plant growth, development, and responses to abiotic stresses such as drought. However, the functional role of SROs in rapeseed remains poorly understood. In this study, 19 BnaSROs were identified from the rapeseed genome, with 9, 10, 10, 18, and 20 members identified from the genomes of Brassica rapa, Brassica nigra, Brassica oleracea, Brassica juncea, and Brassica carinata, respectively. We then analyzed their sequence characteristics, phylogenetic relationships, gene structures, and conserved domains, and explored the collinearity relationships of the SRO members in Brassica napus and Brassica juncea. Next, we focused on the analysis of tissue expression and stress-responsive expression patterns of rapeseed SRO members and examined their expression profiles under ABA, MeJA and water-deficit drought treatments using qPCR. Transcriptome data analysis and qPCR detection indicated that BnaSROs exhibit multiple stress-responsive expression patterns. BnaSRO1 and BnaSRO11, which are likely to function through interactions with NAC transcription factors, were screened as major drought-regulated members. Our results provide a solid foundation for functional analysis of the role of the SRO gene family in abiotic stress responses, especially drought stress responses, in rapeseed.

Winter Wheat Mapping Method Based on Pseudo-Labels and U-Net Model for Training Sample Shortage

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for major crops in China, and the visual interpretation process is usually time-consuming and laborious. The sample size also makes it difficult to support the model to learn enough ground features, resulting in poor generalisation ability of the model, which in turn makes the model difficult to apply in fine extraction tasks of large-area crops. In this study, a method to establish a pseudo-label sample set based on the random forest algorithm to train a semantic segmentation model (U-Net) was proposed to perform winter wheat extraction. With the help of the GEE platform, Winter Wheat Canopy Index (WCI) indicators were employed in this method to initially extract winter wheat, and training samples (i.e., pseudo labels) were built for the semantic segmentation model through the iterative process of “generating random sample points—random forest model training—winter wheat extraction”; on this basis, the U-net model was trained with multi-time series remote sensing images; finally, the U-Net model was employed to obtain the spatial distribution map of winter wheat in Henan Province in 2022. The results illustrated that: (1) Pseudo-label data were constructed using the random forest model in typical regions, achieving an overall accuracy of 97.53% under validation with manual samples, proving that its accuracy meets the requirements for U-Net model training. (2) Utilizing the U-Net model, U-Net++ model, and random forest model constructed based on pseudo-label data for 2022, winter wheat mapping was conducted in Henan Province. The extraction accuracy of the three models is in the order of U-Net model > U-Net++ model > random forest model. (3) Using the U-Net model to predict the winter wheat planting areas in Henan Province in 2019, although the extraction accuracy decreased compared to 2022, it still exceeded that of the random forest model. Additionally, the U-Net++ model did not achieve higher classification accuracy. (4) Experimental results demonstrate that deep learning models constructed based on pseudo-labels exhibit higher classification accuracy. Compared to traditional machine learning models like random forest, they have higher spatiotemporal adaptability and robustness, further validating the scientific and practical feasibility of pseudo-labels and their generation strategies, which are expected to provide a feasible technical pathway for intelligent extraction of winter wheat spatial distribution information in the future.

Enhancing Solar Power Plant Location Selection Through Multicriteria Decision-Making with the Analytic Hierarchy Process

Renewable energy, particularly solar power, is clean, cost-effective, and virtually limitless, making it an essential source of energy for developing countries. In the Kurdistan region, however, dependence on fossil fuels remains high, with 85 percent of electricity generated from these sources, whereas solar power contributes less than 1 percent. This study addresses this disparity by employing Boolean logical analytical hierarchy process techniques and a comprehensive set of criteria encompassing technical, ecological, and economic factors to identify suitable sites for solar power plants in Duhok Governorate. The results indicate that 68.5 percent of the governorate’s area is deemed suitable for solar power station construction, with the most promising zone comprising 132.2 km2 and capable of generating more than 8,000 megawatts of solar energy. Notably, most suitable areas are concentrated in the south and southwest, particularly in the districts of Bardarash, Shekhan, and Semel. This study paves the way for a significant shift toward renewable energy in northern Iraq. Identifying vast suitable areas for solar power plants offers a practical roadmap to reduce dependence on fossil fuels, provide a clean energy source, and enable a large-scale solar power project. This study empowers policymakers, energy companies, and investors to make informed decisions toward realizing this renewable energy potential, ultimately transforming the energy landscape in northern Iraq.

Effects of exogenous copper on microbial metabolic function and carbon use efficiency of Panax notoginseng planting soil.

Soil copper (Cu) pollution is a serious environmental risk in the Panax notoginseng planting area. However, the effect of Cu on soil microbial metabolism and nutrient cycling in this area remains unknown. Therefore, Biolog ECO-plate and enzyme stoichiometry methods were utilized in this study to investigate the impact of exogenous Cu (control: 0 mg·kg-1; Cu100: 100 mg·kg-1; Cu400: 400 mg·kg-1; and Cu600: 600 mg·kg-1) on the metabolic function of soil microbial and nutrient limitation in the P. notoginseng soil. The results indicated that Cu100 significantly increased soil organic carbon (SOC), total phosphorus (TP), soil C:N, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) 9.89%, 15.65%, 17.91%, 61.87%, and 90.56% higher than the control, respectively. Moreover, the carbon source utilization ratio of carbohydrates, amino acids, and amphiphilic compounds of Cu100 also increased by 7.16%, 25.47%, and 84.68%, respectively, compared with the control. The activities of β-1,4-glucosidase, cellobiohyrolase, leucine amino peptidase, β-1,4-N-acetylglucosaminidase, and phosphatase significantly decreased with increasing Cu concentration. Soil enzyme stoichiometry showed that all treatments were limited by nitrogen (vector angle < 45°; 19.045-22.081). Cu600 led to the lowest carbon limitation (1.798) and highest carbon use efficiency (CUE:0.267). The PLS-SEM model also showed that MBC, MBN, MBP, and microbial diversity positively affected carbon and nitrogen limitation (0.654 and 0.424). Soil carbon, nitrogen, phosphorus, stoichiometric ratio, MBC, MBN, and MBP positively affected CUE (0.527 and 0.589). The microbial diversity index significantly negatively affected CUE (-1.490). Multiple linear stepwise regression analyses showed that CUE was mainly influenced by MBC, AP, C:P, and LAP. Thus, P. notoginseng soil can benefit soil microbial carbon and nitrogen limitations at low Cu concentrations. Clarifying the metabolic activity and nutritional status of microorganisms under Cu stress can provide some theoretical basis for realizing China's comprehensive and effective management and control policies for environmental risks from metals by 2035.

Evaluation of landscape ecological security pattern via the “pattern-function-stability” framework in the Guanzhong Plain Urban Agglomeration of China

Most studies have assessed and constructed landscape ecological security patterns (LESPs) primarily from the perspective of landscape morphology by employing morphological spatial pattern analysis, with little attention paid to fundamental ecosystem attributes (such as ecosystem functions and stability). Thus, this study proposed a comprehensive evaluation and optimization framework for LESPs from the perspective of “mode function stability” by integrating assessment landscape pattern, ecological function, and landscape stability. A comprehensive evaluation and optimization of the LESPs was conducted using the Minimum Cumulative Resistance (MCR) model in the Guanzhong Plain Urban Agglomeration (GPUA) as the research area. The results showed that: (1) Generally, the landscape quality in urban agglomerations was relatively low because of the large area of agricultural vegetation, high human interference, and landscape fragmentation, which caused low levels of landscape pattern, function, and stability. (2) LESPs were mainly determined by landscape stability in urban agglomerations due to low-quality ecosystems, rapid landscape transformation, and severe anthropogenic disturbances. (3) There was a large variation in the landscape function and stability of a certain landscape type due to their different structures and human interference across space in urban agglomerations. According to assessment by “pattern-function-stability” framework, the ecological sources and corridors covered with natural forest were the crucial cornerstone to constructing LESPs, and the rivers in crop planting areas were the inevitably selected corridors, but their ecological function and stability needed further improvement in construction. (4) The framework can assess spatial patterns and ecological functions to further the stability and sustainability of the landscape in maintaining ecological security and facilitating the identification of improvement objectives or measures of individual ecological sources or corridors in regional ecological infrastructure construction practices.

The Isolation and Identification of a New Pathogen Causing Sunflower Disk Rot in China

Sunflower (Helianthus annuus) is an important oil crop, ranking behind soybean, peanut, and rapeseed in terms of planting area in China. Throughout its cultivation, sunflower is susceptible to various diseases that can significantly reduce its seed yield. Among them, Fusarium species pose a major threat to numerous crops. The accurate identification of Fusarium species responsible for specific diseases is crucial for developing effective control measures. In Inner Mongolia, sunflower disk rot (SDR) has been observed in various sunflower fields, with an average infection rate of approximately 8.50%. The infection rate can reach up to 11.67% in certain highly susceptible cultivars. Samples of diseased sunflower receptacles were collected from different locations, and Koch’s postulates were employed to identify the causal agent. The results confirmed Fusarium verticillioides as the pathogen responsible for SDR. Fungicide toxicity tests were conducted, screening six fungicides for efficacy against F. verticillioides. Fludioxonil and Flutolanil were identified as the most effective, with EC50 values of 0.05 µg/mL (R = 0.9825) and 0.96 µg/mL (R = 0.9964), respectively. This is the first report of SDR caused by F. verticillioides, and it will alert sunflower researchers to include SDR in the disease list, so as to control sunflower diseases with integrated management strategies successfully.

Differences in Soil Fertility and Bacterial Community Structure Between Carbon Inputs such as Biochar and Organic Fertilizer and Their Relationship

The potato planting area of Guizhou Province ranks second in China. However, due to factors such as climatic conditions and unbalanced fertilization, soil organic matter in potato fields is consumed rapidly and has a large deficit, which affects soil biological function and soil fertility. Biochar and organic fertilizer are effective ways to supplement foreign aid organic matter to improve soil quality. However, the differences in soil fertility and microbial community structure and their relationships under the conditions of organic fertilizer or biochar combined with chemical fertilizer are not clear. In this study, three treatments of conventional fertilization （NPK）, increased application of biochar （NPKB）, and increased application of organic fertilizer （NPKO） were set up to investigate the characteristics of potato rhizosphere soil, bacterial community composition, and diversity； to analyze the effects of these factors on the soil integrated fertility index； and to explore the direct and indirect effects of IFI on soil fertility and bacterial community structure differences between treatments and their driving factors. The results showed that soil pH, available phosphorus （AP）, available potassium （AK）, total nitrogen （TN）, organic carbon （SOC）, and C/N ratio were significantly higher in the NPKB and NPKO treatments than in the NPK treatment （P<0.05）. Soil IFI was greatest for NPKO, followed by NPKB and least for the NPK treatment. A total of 8 214 ASVs were obtained from all the soil samples, belonging to 26 phyla, 75 classes, 165 orders, 176 families, and 251 genera （excluding unidentified fungi）. Proteobacteria, Actinobacteria, and Chloroflexi were the dominant phyla, accounting for 54.85% of all ASVs. Compared to that in the NPK and NPKB treatments, the NPKO treatment had the highest bacterial diversity and number of significantly different taxa, and soil AN, AP, AK, SOC, TN, and IFI were significant correlates of bacterial diversity index （P<0.05）. Additionally, pH, TN, and SOC were significant influencers of bacterial taxa differences （P<0.05）, with importance ranked as TN （70.59%） &gt; SOC （49.42%） &gt; pH （27.08%）. Structural equations suggested that pH-related soil properties and bacterial community diversity were the direct pathways influencing IFI, and soil pH-related soil characteristics could also indirectly affect IFI by affecting bacterial Shannon diversity. These results indicate that soil fertility and bacterial community structure were significantly different and correlated between the biochar and organic fertilizer addition treatments and that pH and bacterial community diversity were the key factors influencing IFI, with the NPKO treatment in particular having the best effect on improving IFI. Considering the effect of soil fertilization and the functional group of bacteria, NPKO is the recommended combination for the best synergistic effect of soil fertilization, that is, N 150 kg·hm-2+P2O5 135 kg·hm-2+K2O 135 kg·hm-2+organic fertilizer 6.6 t·hm-2.