Major Production Regions Research Articles

A Lightweight Cotton Field Weed Detection Model Enhanced with EfficientNet and Attention Mechanisms

Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments.

Integrating mechanical and cultural methods for weed control in organic chickpea

Abstract The Northern Great Plains of the United States is a major production region for organic pulse crops that are prone to yield losses due to weeds. Weed management in organic systems relies on the integration of several tactics to stack additive effects and for redundancy to deal with variable efficacy of individual weed management practices. To address the need for effective, integrated weed management, we conducted a 2-yr trial that evaluated the effects of planting date, seeding rate, and preemergent weed control practices (shallow tillage and flame weeding) on weed biomass, crop density, and yield in organically managed chickpea (Cicer arietinum L.) in Montana. Stacking weed management practices increased yields. Early planting had the largest effects on yields, increasing them by 1.8- to 3.6-fold compared with planting 10 to 14 d later. Increasing the seeding rate from the standard rate (43 viable seeds m−2) by 50% increased yields by 47% from 889 to 1,304 kg ha−1. Both preemergent weed control practices increased yields by 40% to 50 % relative to the non-weeded controls. By integrating all three practices, yields of organic chickpea increased greater than 6-fold from 318 kg ha−1 in the controls to 2,006 kg ha−1. The effects of weed control treatments on midseason weed biomass were complex and variable. Although efficacy of the cultural (seeding rate and planting date) and physical (preemergent) treatments on weed biomass varied between years and when combined with other treatments, their full integration, that is, early planted, at higher seeding rate, and preemergent weed control, produced consistently lower weed biomass (84% reduction on average) compared with the standard grower practice (later planting, standard seeding rate, no preemergent weed control). The results lend support to the concept of integrating multiple weed management practices to achieve weed control and high yields in organically managed crops.

Widely Targeted Metabolomics Analysis Revealed the Component Differences of Hemerocallis citrina Borani in Different Production Areas of Datong.

Hemerocallis citrina Borani (H. citrina) has garnered significant attention due to its abundant nutritional quality. Datong, located in Shanxi Province, is recognized as one of the four major production regions for high-quality H. citrina. While Datong boasts multiple production areas, the nutritional composition of daylilies varies across regions due to environmental factors and planting patterns, which remain unclear. This study focuses on the total polyphenol and flavonoid contents (TPCs and TFCs) and protein content of H. citrina from three areas in Datong: Sanshilipu (DTSSLP), Dangliuzhuang (DTDLZ), and Jijiazhuang (DTJJZ). Additionally, a widely targeted metabolomics analysis was used to analyze the metabolite composition of H. citrina from these three areas. The results showed that H. citrina in DTSSLP had the highest contents of protein and amino acids, as well as TPCs and TFCs. A total of 798 differential metabolites were identified in H. citrina across the areas, with DTSSLP showing the highest levels of different classifications of metabolites, indicating its enhanced health benefits and physiological activities. Nine metabolic pathways were related with the different characteristics among DTSSLP, DTDLZ, and DTJJZ. This study provides theoretical support for distinguish H. citrina from different producing regions and elucidates the mechanisms underlying its metabolic pathways.

Distinct pollinator communities persist among co-flowering specialty crops in Indiana

Diverse bee communities pollinate fruits and vegetables, and the composition of these communities has been described for many specialty crops in major production regions. However, pollinator communities in landscapes dominated by agronomic crops may differ in species composition and the contribution of wild bees. With over 4 million ha of maize and soybean, Indiana presents a novel landscape to compare pollinator communities among specialty crops that differ in their use of managed bees and bloom phenology, with potential implications for the composition of wild bee communities and their contributions to flower visitation. We sampled pollinator communities with flower observations and pan traps in spring-blooming apples and blueberries and summer-blooming tomatoes and watermelons, allowing us to compare communities between specialty crops with overlapping and distinct bloom times. Apples, blueberries, and watermelons were stocked with honey bees, and watermelons additionally had managed bumble bees. Across two years of sampling, we observed 1,651 flower visits by 13 taxa, collected 1,967 bees, and identified 84 taxa from collected specimens. Apples and blueberries hosted the richest pollinator communities (34 and 51 taxa, respectively) followed by watermelons (22 taxa), and tomatoes (19 taxa). While 70% of flower visits in apples and blueberries were attributed to honey bees, wild bees accounted for 82% and 99% of flower visits in watermelons and tomatoes, respectively. Our results indicate that distinct pollinator communities persist among co-flowering specialty crops in a landscape dominated by maize and soybean production and underscore the contribution of wild bees for specialty crop pollination, particularly in watermelons and tomatoes.

A one health study on phylogenetics and risk of pathogenic intestinal parasites at a ranch in Inner Mongolia

Cryptosporidium, Giardia duodenalis, and Enterocytozoon bieneusi are widespread zoonotic pathogens causing gastrointestinal diseases in humans and various animal species. Inner Mongolia, a major beef production region in China, there is a notable absence of comprehensive research on intestinal parasitism. Thus, timely and comprehensive diagnosis is essential to mitigate disease spread and minimize economic losses in the livestock industry. In this study, we collected fecal samples from cattle and humans, as well as soil and water samples, and all samples were tested for pathogenic intestinal protozoa at the Simmental cattle ranch in Wengniute, Chifeng City, Inner Mongolia. Among the 393 samples tested, 76/371 (20.5 %) cattle, 6/11 (54.5 %) ranch workers, 1/7 (14.3 %) water, and 2/4 (50 %) soil samples were positive. Factors affecting the infection rate of intestinal protozoa were examined. Results showed that the infection rate was higher in June than in January, higher in calves than in adults, and higher in diarrheal calves than in healthy calves. Additionally, the infection rate of intestinal protozoa was higher in pathogen-contaminated water source sheds than in uncontaminated sheds. Genetic and evolutionary analyses revealed that the prevalent E. bieneusi subtypes are predominantly J, I, and BEB4, while the G. duodenalis subtypes are assemblages B and E. The Cryptosporidium species identified were C. bovis, C. andersoni, C. parvum, C. ryanae, and C. suis, with C. parvum being a notable zoonotic pathogen. The pathogen sequences from humans, cattle, water, and soil showed 99–100 % similarity, suggesting possible transmission or contamination between animals and the environment. This study contributes to the One Health approach by addressing the gap in research on intestinal protozoa in Inner Mongolia. It provides important data for other ranches in the region to understand the prevalence of such pathogens and develop effective control measures. Using the concept of One Health to analyze the spatiotemporal distribution of intestinal protozoa in pastures is of great significance for maintaining public health.

Soil health of bananas cultivated in Ribeira River Valley – the major producing region of Brazil

Soil health of bananas cultivated in Ribeira River Valley – the major producing region of Brazil

Geographical Distribution and Abundance of Species of the Pycreus Genus in Rice Production Regions in Rio Grande do Sul

Theoretical Reference: Identifying, tracking, and analyzing weed infestation levels in rice crops are essential for developing more effective control strategies, ultimately enhancing efficiency, reducing costs, and minimizing environmental impact. This study focuses on the geographic distribution and abundance of species within the Pycreus genus (family Cyperaceae) in rice fields in Rio Grande do Sul (RS), Brazil. Method: During the 2005/06 growing season, collections and abundance assessments of Pycreus species were conducted across three irrigated rice fields located in three municipalities within five major rice production regions of RS. Plants were collected during vegetative or reproductive stages, dried, and identified using specialized literature. Specimens were then deposited as vouchers in the PEL Herbarium at the Department of Botany, Federal University of Pelotas. Results and Conclusion: The study revealed the presence of Pycreus species in rice fields across RS, with P. polystachyos (Rottb.) P. Beauv. being the most abundant and frequently observed species. P. macrostachyos (Lam.) J. Raynal and P. lanceolatus (Poir.) C.B. Clarke were also identified, though less commonly. The highest abundance of Pycreus species was recorded in the coastal physiographic region, particularly within the Internal Coastal Plain of Lagoa dos Patos and southern rice production areas. Implications of Research: These findings emphasize the need for targeted weed management strategies in rice cultivation, particularly in regions where Pycreus species are most prevalent, to enhance crop productivity and sustainability. Originality/Value: This study provides valuable insights into the distribution and abundance of Pycreus species in RS rice fields, contributing to the understanding of weed dynamics in rice production and offering a basis for the development of more effective control measures.

Meloidogyne hapla dominates plant‐parasitic nematode communities associated with kiwifruit orchards in Portugal

AbstractKiwifruit, Actinidia spp., is an important crop worldwide and its cultivation has been increasing in Portugal. Kiwifruit plants are susceptible to plant‐parasitic nematodes (PPNs), including Meloidogyne spp. M. hapla and M. incognita have been reported to affect kiwifruit in major production regions worldwide, although information is lacking on the distribution and identity of PPNs associated with kiwifruit orchards in Portugal. Moreover, research is scarce on the drivers of PPN communities in this crop. Our primary objective was to investigate the diversity, density and distribution of PPNs within kiwifruit orchards of Portugal and to elucidate the influence of environmental factors on their community composition. Additionally, we aimed to evaluate population densities and identity of Meloidogyne spp. Nematodes were extracted from composite soil and root samples collected from orchards. Analysis of soil nematode communities suggested bacterial‐driven decomposition and high nutrient enrichment in these soils. Sixteen different genera of PPNs were detected, with Meloidogyne spp. as the dominant genus, mostly represented by a single species, M. hapla. PPN communities exhibited low richness, and β‐diversity analysis indicated a high degree of similarity among them. Additionally, their composition was significantly influenced by soil and climatic parameters. We found that 37 out of 40 kiwifruit orchards in Portugal were infested by root‐knot nematodes. The unexpectedly low species diversity, dominated by M. hapla, merits further research to establish underlying factors for its high incidence in this crop and to assess the impact of these nematodes on kiwifruit performance and productivity.

Levels of heavy metals and physicochemical properties of honey from four selected areas of Ethiopia

In this work, the qualities of honey samples were evaluated by assessment of some physicochemical parameters and levels of heavy metals (Cd, Pb, Cr, Ni, Zn and Cu). The honey samples were collected from the major producing regions of Ethiopia; Gojjam, Gondar, Jimma and Tigray. The pH and electrical conductivity (EC) of the samples were determined using digital pH-meter and conductivity meter, respectively. The metal contents were determined by flame atomic absorption spectrometry (FAAS). The validity of the method employed was evaluated by spiking experiments with recoveries ranging from 90.0 to 95.8%. The FAAS results show higher concentration of Cu and Zn compared to other metals. The amount of Cu and Zn were in the range 6.2–18 μg/g and 4.2–10 μg/g. The heavy metals (Cd, Pb, Cr and Ni) were below detection limit in all the honey samples. Moreover, the result obtained regarding the physicochemical properties showed that the pH of all the honey samples were acidic (pH of 3.84–4.00), the EC 0.09–0.34 mScm-1 and the ash content 0.13–0.33%. The results obtained were in agreement with European standard and Codex Alimentarius, indicating the honey samples studied are of good quality and suitable for human consumption. KEY WORDS: Honey, Heavy metals, Physicochemical properties, FAAS, Ethiopia Bull. Chem. Soc. Ethiop. 2024, 38(6), 1521-1531. DOI: https://dx.doi.org/10.4314/bcse.v38i6.2

A Review of Fluoride Removal from Phosphorous Gypsum: A Quantitative Analysis via a Machine Learning Approach.

This review comprehensively explores fluoride removal from phosphogypsum, focusing on its composition, fluorine-containing compounds, characterization methods, and defluorination techniques. It initially outlines the elemental composition of phosphogypsum prevalent in major production regions and infers the presence of fluorine compounds based on these constituents. The study highlights X-ray photoelectron spectroscopy (XPS) as a pivotal method for characterizing fluorine compounds, emphasizing its capability to determine precise binding energies essential for identifying various fluorine species. Additionally, the first-principle density functional theory (DFT) is employed to estimate binding energies of different fluorine-containing compounds. Significant correlations are observed between the total atomic energy of binary fluorides (e.g., of alkali metals, earth metals, and boron group metals) and XPS binding energies. However, for complex compounds like calcium fluorophosphate, correlations with the calculated average atomic total energy are less direct. The review categorizes defluorination methods applied to phosphogypsum as physical, chemical, thermal, and thermal-combined processes, respectively. It introduces neural network machine learning (ML) technology to quantitatively analyze and optimize reported defluorination strategies. Simulation results indicate potential optimizations based on quantitative analyses of process conditions reported in the literature. This review provides a systematic approach to understanding the phosphogypsum composition, fluorine speciation, analytical methodologies, and effective defluorination strategies. The attempts of adopting DFT simulation and quantitative analysis using ML in optimization underscore its potential and feasibility in advancing the industrial phosphogypsum defluorination process.

Impact of Fruit Maturity on Internal Disorders in Vapor Heat Treated Mango Cv. ‘B74’

UN Sustainable Development Goal 12 (SDG 12) aims to reduce food losses in production and postharvest stages within supply chains. Identifying and addressing contributors to such losses is crucial to their reduction and to overall supply chain sustainability. Internal disorders (IDs) often contribute to postharvest losses and waste of highly perishable fruits like mangoes. Understanding and addressing influencers of susceptibility is limited but essential. Factors potentially associated with the expression of IDs in ‘B74’ mango commercial supply chains were investigated. Over three fruiting seasons (2020/21, 2021/22, and 2022/23), 43 export supply chains in Australia were monitored from two major production regions, the Northern Territory and North Queensland. Prior to export, the mangoes were subject to a mandatory phytosanitary vapor heat treatment (VHT) in which they were heated with saturated water vapor to a core temperature 46 °C maintained for 15 min and were then assessed for IDs at the end of their shelf life. The predominant IDs observed in the ‘B74’ fruit were flesh cavity with white patches (FCWP) and flesh browning (FB). VHT-induced FCWP, but not FB. Harvest maturity was identified as a predisposing factor. FB was generally positively correlated and FCWP was typically negatively correlated with fruit maturity at harvest. Relatively more-mature fruit was prone to FB irrespective of VHT, and relatively less-mature fruit was susceptible to FCWP post-VHT. Therefore, selective harvesting and/or sorting for optimum maturity after harvest can be practiced minimizing the incidence and severity of these two IDs in ‘B74’ fruit. Thus, dry matter (DM) sorting can contribute to postharvest loss reduction and the general sustainability of mango supply chains.

Deciphering the key factors affecting pesticide residue risk in vegetable ecosystem

Soil contamination, particularly from pesticide residues, presents a significant challenge to the sustainable development of agricultural ecosystems. Identifying the key factors influencing soil pesticide residue risk and implementing effective measures to mitigate their risks at the source are essential. Here, we collected soil samples and conducted a comprehensive survey among local farmers in the Three Gorges Reserve Area, a major agricultural production region in Southwest China. Subsequently, employing a dual analytical approach combining structural equation modeling (SEM) and random forest modeling (RFM), we examined the effects of various factors on pesticide residue accumulation in vegetable ecosystems. Our SEM analysis revealed that soil characteristics (path coefficient 0.85) and cultivation factor (path coefficient 0.84) had the most significant effect on pesticide residue risk, while the farmer factors indirectly influenced pesticide residues by impacting both cultivation factors and soil characteristics. Further exploration using RFM identified the three most influential factors contributing to pesticide residue risk as cation exchange capacity (CEC) (account for 18.84%), cultivation area (account for 14.12%), and clay content (account for 13.01%). Based on these findings, we carried out experimental trials utilizing Integrated Pest Management (IPM) technology, resulting in a significant reduction in soil pesticide residues and notable improvements in crop yields. Therefore, it is recommended that governmental efforts should prioritize enhanced training for vegetable farmers, promotion of eco-friendly plant protection methods, and regulation of agricultural environments to ensure sustainable development.

Virulent Fusarium isolates with diverse morphologies show similar invasion and colonization strategies in alfalfa.

Root rot is a major disease that causes decline of alfalfa production, and Fusarium is a major pathogen associated with root rot. In this study, 13 Fusarium isolates were obtained from alfalfa with root rot in Gansu Province, the major alfalfa production region in China. The isolates were characterized by molecular genotyping (ITS, TEF 1-α and RPB2 fragments) and identified as six species, which included the F. acuminatum, F. incarnatum, F. oxysporum, F. proliferatum, F. redolens, and F. solani. We found that their morphology varied significantly at both the macro- and micro-levels, even for those from the same species. We developed a low cost and fast pathogenicity test and revealed that all isolates were pathogenic to alfalfa with typical root rot symptoms such as leaf yellowing and brown lesions on the root and stem. However, the virulence of the isolates differed. We also found that the conidia of all isolates germinated as early as 24 hours post inoculation (hpi), while hyphae colonized the root extensively and invaded the xylem vessel by 48 hpi. Together our results reveal that different virulent Fusarium isolates use a similar invasion strategy in alfalfa. This natural plant-fungus pathosystem is intriguing and warrants further examination, particularly with regard to efforts aimed at mitigating the impact of multiple similar vascular pathogens on infected alfalfa plants.

Pathogenicity, Host Resistance, and Genetic Diversity of Fusarium Species under Controlled Conditions from Soybean in Canada.

Fusarium spp. are commonly associated with the root rot complex of soybean (Glycine max). Previous surveys identified six common Fusarium species from Manitoba, including F. oxysporum, F. redolens, F. graminearum, F. solani, F. avenaceum, and F. acuminatum. This study aimed to determine their pathogenicity, assess host resistance, and evaluate the genetic diversity of Fusarium spp. isolated from Canada. The pathogenicity of these species was tested on two soybean cultivars, 'Akras' (moderately resistant) and 'B150Y1' (susceptible), under greenhouse conditions. The aggressiveness of the fungal isolates varied, with root rot severities ranging from 1.5 to 3.3 on a 0-4 scale. Subsequently, the six species were used to screen a panel of 20 Canadian soybean cultivars for resistance in a greenhouse. Cluster and principal component analyses were conducted based on the same traits used in the pathogenicity study. Two cultivars, 'P15T46R2' and 'B150Y1', were consistently found to be tolerant to F. oxysporum, F. redolens, F. graminearum, and F. solani. To investigate the incidence and prevalence of Fusarium spp. in Canada, fungi were isolated from 106 soybean fields surveyed across Manitoba, Saskatchewan, Ontario, and Quebec. Eighty-three Fusarium isolates were evaluated based on morphology and with multiple PCR primers, and phylogenetic analyses indicated their diversity across the major soybean production regions of Canada. Overall, this study contributes valuable insights into host resistance and the pathogenicity and genetic diversity of Fusarium spp. in Canadian soybean fields.

APSIM-based modeling approach to understand sorghum production environments in Mali

Sorghum production system in the semi-arid region of Africa is characterized by low yields which are generally attributed to high rainfall variability, poor soil fertility, and biotic factors. Production constraints must be well understood and quantified to design effective sorghum-system improvements. This study uses the state-of-the-art in silico methods and focuses on characterizing the sorghum production regions in Mali for drought occurrence and its effects on sorghum productivity. For this purpose, we adapted the APSIM-sorghum module to reproduce two cultivated photoperiod-sensitive sorghum types across a latitude of major sorghum production regions in Western Africa. We used the simulation outputs to characterize drought stress scenarios. We identified three main drought scenarios: (i) no-stress; (ii) early pre-flowering drought stress; and (iii) drought stress onset around flowering. The frequency of drought stress scenarios experienced by the two sorghum types across rainfall zones and soil types differed. As expected, the early pre-flowering and flowering drought stress occurred more frequently in isohyets < 600 mm, for the photoperiod-sensitive, late-flowering sorghum type. In isohyets above 600 mm, the frequency of drought stress was very low for both cultivars. We quantified the consequences of these drought scenarios on grain and biomass productivity. The yields of the highly-photoperiod-sensitive sorghum type were quite stable across the higher rainfall zones > 600 mm, but was affected by the drought stress in the lower rainfall zones < 600 mm. Comparatively, the less photoperiod-sensitive cultivar had notable yield gain in the driest regions < 600 mm. The results suggest that, at least for the tested crop types, drought stress might not be the major constraint to sorghum production in isohyets > 600 mm. The findings from this study provide the entry point for further quantitative testing of the Genotype × Environment × Management options required to optimize sorghum production in Mali.

The Modelling of the Evapotranspiration Portion of the Water Footprint: A Global Sensitivity Analysis in the Brazilian Serra Gaúcha

Water footprints have been widely used to illustrate the consumption of water in many situations, for instance, in products, processes, or regions of interest. In this work, we analyzed—using a sensitivity analysis approach—the effect of some variables in the calculation of the water footprint in the viticulture in the Brazilian Serra Gaúcha (the major producing region of Brazilian wine). The classical Penman–Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering a fraction of the vineyard, maximum temperatures for some months, the altitudes and latitudes of the site). A sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to evapotranspiration is more sensitive to the values of the mulch-covered fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

Registration of ‘Top Shelf’ barley: The first glycosidic nitrile‐null, winter malting cultivar to be released in North America

Abstract‘Top Shelf’ (Reg. no. CV‐380, PI 704478), experimental designation DH162310, is a two‐row winter barley (Hordeum vulgare L.) released by Oregon Agricultural Experiment Station in 2023. It is a malting cultivar that is known to be a non‐producer of glycosidic nitrile (GN0). GN0 is the precursor to a regulated compound (ethyl carbamate) in distilled spirits and thus, the GN0 trait is becoming a requisite for malting cultivars geared toward distilling. While GN0 spring cultivars have been available for over 20 years, Top Shelf is the first winter GN0 cultivar to be released in North America, and only the second globally. In addition to its GN0 status it exceeds contemporary agronomic and malt quality expectations. Over 5 years of field trials in Oregon it performed equal to or better than the check entries. It shows high yields, plump grains, and test weights. It also has good resistance to scald (caused by Rhynchosporium commune) and preliminary data suggest resistance to barley stripe rust (Puccinia striiformis f. sp. hordei). Results were confirmed in field trials in southern Idaho, a major malting barley production region, and a target environment for this cultivar. Under standard malting protocols Top Shelf meets the guidelines for malt and all‐malt and/or grain distilling, and has high extract, enzymatic activity, and free amino nitrogen. When malted under optimized conditions, mimicking commercial distilling malt production, it produced even better results. As one of only a handful of GN0 cultivars available in the United States with potential for mainstream production, Top Shelf provides an important option for the distilling supply chain.

Niche differentiation and biogeography of Bathyarchaeia in paddy soil ecosystems: a case study in eastern China

Bathyarchaeia (formerly Bathyarchaeota) is a group of highly abundant archaeal communities that play important roles in global biogeochemical cycling. Bathyarchaeia is predominantly found in sediments and hot springs. However, their presence in arable soils is relatively limited. In this study, we aimed to investigate the spatial distributions and diversity of Bathyarchaeia in paddy soils across eastern China, which is a major rice production region. The relative abundance of Bathyarchaeia among total archaea ranged from 3 to 68% in paddy soils, and Bathy-6 was the dominant subgroup among the Bathyarchaeia (70–80% of all sequences). Bathyarchaeia showed higher migration ability and wider niche width based on the neutral and null model simulations. Bathy-6 was primarily assembled by deterministic processes. Soil pH and C/N ratio were identified as key factors influencing the Bathyarchaeia composition, whereas C/N ratio and mean annual temperature influenced the relative abundance of Bathyarchaeia. Network analysis showed that specific Bathyarchaeia taxa occupied keystone positions in the archaeal community and co-occurred with some methanogenic archaea, including Methanosarcina and Methanobacteria, and ammonia-oxidizing archaea belonging to Nitrososphaeria. This study provides important insights into the biogeography and niche differentiation of Bathyarchaeia particularly in paddy soil ecosystems.

Method development, validation, and risk assessment of multiple pesticide residues of fruits in China.

In this study, a muti-residue analysis method of 40 pesticides in five different categories of fruits in China was developed based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Five hundred real samples were analyzed and assessed for the dietary exposure risk. The sample treatment method was optimized by comparing four clean-up methods. The matrix effects of different fruits were evaluated. The analytical method was validated in terms of linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, and precision. The results showed that the optimal method was the treatment by clean up with ODS (octadecylsilane) and MgSO4. The matrix effect was the strongest in orange and weakest in apple. The LOD and LOQ of pesticides were 0.04-5.9 μg kg-1 and 0.13-19.5 μg kg-1, respectively. The recoveries at three spiked levels were ranged from 71.2 to 115.2% with the RSDs from 0.1 to 19.6%. Twenty-two pesticides were detected in 500 fruit samples fromthe major production regions of China, with concentrations ranging from 0.1 to 1930 μg kg-1. A total of 13, 10, 9, 8, and 4 pesticides were detected in peach, orange, grape, apple, and strawberry. Both the acceptable daily intake (ADI) and acute reference dose (ARfD) for all the detected pesticides were lower than 100%, indicating that the dietary intake risks are acceptable and would not pose potential health risks.

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations.