Farming Areas Research Articles

95-95-95 HIV indicators among children younger than 15 years in South Africa: results from the 2017 national HIV prevalence, incidence, behaviour, and communication survey

BackgroundEarly detection and initiation of care is crucial to the survival and long-term well-being of children living with HIV (CLHIV). However, there remain challenges regarding early testing and linking of CLHIV for early treatment. This study examines the progress made towards achieving the 95-95-95 HIV indicators and associated factors among CLHIV < 15 years in South Africa.MethodsThe data was collected as part of the 2017 cross-sectional, multistage cluster randomized population-based household National HIV survey. Age-appropriate structured questionnaires were utilized to gather sociodemographic data, HIV-related knowledge, risk behaviours, and health-related information. Blood samples were collected to test for HIV serology, viral load suppression, and antiretroviral usage. Backward stepwise multivariable generalized linear regression models were fitted to identify factors associated with the 95-95-95 HIV indicators. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) are shown, and p < 0.05 indicates statistical significance.ResultsA total of 12,237 CLHIV < 15 years were included (median 8 years, interquartile range 4–11 years). HIV prevalence was 2.8% (95% CI: 2.4–3.3). Overall, 40.0% of the CLHIV were tested and knew their status (first 95%), and among these, 72.6% (95% CI: 61.7–81.3) were on antiretroviral therapy (ART) (second 95%), and 95.0% (95% CI: 88.4–97.9) of these were virally suppressed (third 95%). Among CLHIV, the odds of testing and knowing the HIV-positive status were significantly higher among children whose health was rated as fair/poor than excellent/good [AOR = 1.32 (95%CI: 1.05–1.67), p = 0.022], and were significantly lower among females than males [AOR = 0.82 (95% CI: 0.71–0.95), p = 0.009], and were significantly lower among those attending private healthcare facilities than public health facilities [AOR = 0.64 (95% CI:0.57–0.74), p < 0.001]. Among those who knew their HIV-positive status, the odds of being on ART were significantly higher among children residing in farm areas than urban areas [AOR = 1.40 (95% CI:1.05–1.86), p = 0.017], and were significantly lower among children attending private healthcare facilities [AOR = 0.44 (95% CI:0.36–0.54), p < 0.001].ConclusionsAwareness of HIV status and initiation of treatment in children was low. The findings highlight the need to improve HIV status awareness and disclosure to children. The findings underscore the need for targeted interventions and programs tailored for CLHIV in urban areas.

Effects of Climate Shocks on the Dynamism of Agricultural Production in Burkina Faso

ABSTRACTFloods constitute climate shocks that cause both tangible and intangible damage to the agricultural sector. An immediate effect of flooding on the farming sector is production loss. We analyze the effects of flooding on the dynamism of the production of Burkina Faso farmers. To this end, we apply the difference‐in‐differences method to balanced panel data covering the agricultural seasons from 2008 to 2012. Our case study involves farming areas that flooded in 2009. The results indicate that the effects of flooding persist for several agricultural seasons.

Land-derived metals impact the survival and settlement of larval mussels.

Global declines in wild mussel populations and production have been linked to the impacts of climate change and pollution. Summer die-offs of mussels (Perna canaliculus), spat retention issues, and a severe decline in mussel spat settlement have been reported in the Marlborough Sounds, an important area for mussel farming in New Zealand. Preliminary evidence suggests that naturally occurring contaminants and changing land use in the surrounding areas, could contribute to the decline of this species. The aim of this study was to determine whether trace metals affect the larval survival and settlement of mussels. Concentrations of trace elements in surface waters, mussel gill tissues and sediments were characterized across the Marlborough Sounds. Over a two-year period, diffusive gradients in thin film (DGT) passive samplers were deployed at three locations, to estimate trace metal levels in the water column. Mussel gills and sediment were also collected to complement metal monitoring. Lead and zinc were the most abundant bioavailable metals detected during in-situ monitoring, with strong temporal variability. Ecotoxicity thresholds affecting P. canaliculus embryo-larval development and settlement were determined for these metals and overlapped with levels of lead and zinc found in the field during the reproductive season of mussels. Furthermore, high concentrations of zinc and lead in waters were strongly correlated with rain events, suggesting land runoff in the surrounding catchments as their main source. We hypothesize that vulnerable early life stages of mussels are exposed to these metals, especially following summer rainfalls, delivering pulses of these metals into the environment while triggering mussel spawning events. Climate-associated stressors (intense storms, marine heatwaves, freshwater and sedimentation inputs, acidification) are likely to exacerbate any toxic effects from land-derived inputs; a better understanding of these complex interactions is essential to prevent further P. canaliculus recruitment declines occurring in NZ, and in other mussel species globally.

Documentation of ethnoveterinary knowledge and alternative practices for cattle tick control in Sekhukhune District, Limpopo Province, South Africa

IntroductionThe integration of traditional plant-based methods for controlling ectoparasites in the primary healthcare of livestock is progressively emerging as a crucial intervention to enhance livestock productivity in regions with limited resources, particularly in smallholder farming areas facing resource constraints. In Sekhukhune District, where livestock plays a vital role in rural livelihoods, cattle ticks present a significant challenge to cattle farming. This study aimed to document the ethnoveterinary practices employed by local communities to control cattle ticks, highlighting the use of alternative methods rooted in indigenous knowledge (IK).MethodsData were collected using a purposive sampling method to select traditional livestock keepers, herders, and community elders to uncover the plant-based treatments and management strategies used in tick control. In addition, a semi-structured questionnaire and a guided field survey were employed to collect data.ResultsA total of 250 participants, with an age range from 18 to over 60 years, were recruited. The result revealed that the elder participants, over 60 years of age, were more knowledgeable compared to the youth and adults. Furthermore, 28 plant species with potential acaricidal properties and other methods aimed at controlling cattle tick infestations were documented. Cissus quadrangularis was the most frequently cited species, which was widely distributed throughout the district.ConclusionIn addition, these results are framed within the larger context of sustainability, promoting eco-friendly cattle farming practices in Sekhukhune District while reducing reliance on conventional acaricides. By documenting this ethnoveterinary knowledge, the study contributes to the preservation of indigenous knowledge while advocating for sustainable approaches to livestock health management in rural areas. The study concludes with valuable insights into the selected local community’s traditional methods of managing tick infestations. Furthermore, the study underscores the significance of preserving and understanding indigenous knowledge in livestock health management, particularly in regions where conventional veterinary approaches may face challenges.

Evaluation of metals in surface sediments from shrimp farming areas in the estuarine region of the Baixo São Francisco, in Northeastern Brazil

Evaluation of metals in surface sediments from shrimp farming areas in the estuarine region of the Baixo São Francisco, in Northeastern Brazil

Validation of the Ensemble Generalized Analog Regression Downscaling (En-GARD) Model to Downscale Near-Surface Wind Speed for the Northeast United States

Abstract Wind, often overlooked in climate change impact assessments, is now a crucial atmospheric variable due to our imminent reliance on renewable energy sources like wind energy. Therefore, establishing a wind speed database in higher temporal and spatial resolution is of great importance to assess wind power in historical periods and future projections. This study focuses on enhancing wind data representation and reliability of statistically downscaled model outputs by refining grid spacing from coarser to a desirable finer scale. Employing the Ensemble Generalized Analog Regression Downscaling (En-GARD) technique, we downscaled near-surface wind speed across a northeast (NE) U.S. domain that combines onshore and offshore wind farm areas. We tested multiple atmospheric variable combinations and identified near-surface wind speed (wind speed at 10 m), longitudinal and latitudinal wind at 10 m, and atmospheric temperature at 2 m, as the set that effectively guides the selection of analog days for the analog regression downscaling approach. Validation involved using ERA5 atmospheric reanalysis products (31 km) and high-resolution (4 km) Weather Research and Forecasting (WRF) Model simulations. Downscaling was conducted using a leave-one-year-out approach over 12 years (2001–12). Statistical analysis of the downscaled output over this period demonstrated a significant correlation and low error metrics (less than 20% for normalized RMSE and 16% for normalized MAE), indicating the reliability of the method and paving the way for its future application to downscale climate projection outputs. Significance Statement Wind speed has become a significant atmospheric variable due to our society’s imminent reliance on renewable energy resources like wind power. Often overlooked in climate change assessments, wind speed at a local scale over wind farm locations plays a crucial role in assessing and planning wind power resources in a future climate. We are providing the successful validation of a statistical downscaling methodology for near-surface wind speed at 4-km grid spacing using reanalysis data. The validity of the methodology gives confidence to downscale climate projection models and assess changes in wind speed for future climate scenarios in a follow-up study.

Soil fertility dynamics in the Northeastern agroclimatic zone of Tamil Nadu: a targeted analysis of Tiruvannamalai district

Soil fertility degradation due to poor nutrient management is a growing concern in many farming areas. This study focuses on assessing the soil health of Tiruvannamalai district, Tamil Nadu, by examining key factors like pH, organic carbon (OC), electrical conductivity (EC) and the availability of essential nutrients (nitrogen, phosphorus, potassium) and micronutrients (zinc, iron, copper, manganese, boron). A systematic soil sampling method was employed, collecting 129 samples from 18 blocks using a 5 × 5 km grid at 0-15 cm depth. The results showed that the soils were primarily neutral to slightly alkaline, with pH values ranging from 7.15 to 7.41. Electrical conductivity was low (&lt;0.26 dS/m), indicating minimal salinity. Organic carbon levels were also low, with more than half of the samples (53.06%) falling below optimal levels, ranging from 0.29% to 0.64%. The analysis revealed widespread nutrient deficiencies, with 65% of the soils low in nitrogen, 63.06% lacking phosphorus and 79.44% deficient in potassium. Additionally, significant micronutrient shortages were detected, with zinc (83.89%), iron (83.06%), copper (76.67%), manganese (88.89%) and boron (75.28%) all showing depletion. These results highlight the urgent need for improved soil management practices, including targeted fertilization and nutrient restoration efforts, to enhance soil fertility and ensure sustainable agricultural productivity in the region.

Management of soil organic matter regime in biologization of agriculture

All related research was conducted from 2010 to 2022 in the Belgorod region, located in the south-west of the Central Chernozem region (CCR). The goal of this study was to analyze the effects of introducing agriculturally biologized elements into arable soils on the organic matter content of those same soils. It was determined, that compared to their virgin counterparts, unwashed arable chernozems, typical and leached, had lost about 39.8–42.0% of their organic matter due to long-term agricultural use. In comparison to 2010–2014, the level of organic fertilizer input had doubled to 9,6 t/ha in 2019–2022, while the area of green manure cultures had increased by a factor of 2.56 to 317 thousand ha/year. Concurrently to the aforementioned years, the by-product output increased by 18.8–37.6% (depending on the crop), reaching levels between 2.07–10.3 t/ha, due to an increase in agricultural yield. The area of ley farms decreased by a factor of 2.73 down to 47.1 thousand ha/year. Additionally, a wide percentage of the Belgorod region have implemented low tillage technology usage, including no-till and executed complex anti-erosion activities. As a result of that, in 2019–2022, the average content of organic matter in the arable layer of soils went up by 0.3% (to 5.3%), while reserve by 9 t/ha (to 159 t/ha). Each hectare of the cultivated areas on average accumulated 19 carboxylic units, thereby making it worth about 19 thousand rubles. The percentage of soils, which contain 6.1–8.0% of organic matter, rose to 20.0%, while those containing 2.1–4.0%, were reduced to 10.9%.

Assessing the Contribution of Participatory Mapping Data Collection Methods in Detecting and Mitigating Land Boundary Conflicts on Farm Areas: A Methodological Framework

Land conflict is a serious and sensitive issue in Africa. Disputes about land occur at all levels, which are: Conflicts between neighbors about field boundaries; between pastoralists and farmers; between states and indigenous peoples; etc (Wardell &amp; Lund, 2006). These conflicts are a serious threat to agricultural and land productivity. Now our days there exist several methods of land data collection which facilitates land conflict mitigation, and each of these methods are been explained in various papers. However, none of these papers have compared these methods to bring out their unique contribution as a means to assist practitioners in identifying land conflicts prone areas. Our research is centered around the question: what is the distinctive contribution of each land data collection methods in identifying land conflict prone areas during participatory mapping? Using a case study of three villages in Mbalmayo (Cameroon), our research adopts an approach that combines farmland data from interviews, questionnaires, consultative meetings, field visits, demarcation of farm boundaries; recording of Ground Corner Points using Catalyst DA2, UAV flights, and participatory mapping. In this paper, we discuss the usefulness and limitation of each method, highlighting their unique contribution to the holistic understanding of the context of Mbalmayo and the mitigation of land conflicts. The study was conducted from June to October 2023. The results of our analysis shows that amongst the eight methods used to collect farmland data, six of these methods had a distinctive contribution that facilitated the mitigation process by precisely identifying the land boundary conflicts zones. The ideas provided by this paper can serve as guidelines for researchers and practitioners interested in mitigating land conflicts through participatory mapping approaches.

Improving Satellite-Based Retrieval of Maize Leaf Chlorophyll Content by Joint Observation with UAV Hyperspectral Data

While satellite-based remote sensing offers a promising avenue for large-scale LCC estimations, the accuracy of evaluations is often decreased by mixed pixels, attributable to distinct farming practices and diverse soil conditions. To overcome these challenges and to account for maize intercropping with soybeans at different growth stages combined with varying soil backgrounds, a hyperspectral database for maize was set up using a random linear mixed model applied to hyperspectral data recorded by an unmanned aerial vehicle (UAV). Four methods, namely, Euclidean distance, Minkowski distance, Manhattan distance, and Cosine similarity, were used to compare vegetation spectra from Sentinel-2A with the newly constructed database. In a next step, widely used vegetation indices such as NDVI, NAOC, and CAI were tested to find the optimum method for LCC retrieval, validated by field measurements. The results show that the NAOC had the strongest correlation with ground sampling information (R2 = 0.83, RMSE = 0.94 μg/cm2, and MAE = 0.67 μg/cm2). Additional field measurements sampled at other farming areas were applied to validate the method’s transferability and generalization. Here too, validation results showed a highly precise LCC estimation (R2 = 0.93, RMSE = 1.10 μg/cm2, and MAE = 1.09 μg/cm2), demonstrating that integrating UAV hyperspectral data with a random linear mixed model significantly improves satellite-based LCC retrievals.

Genetic Characterization of Japanese Encephalitis Virus Isolates Circulating in Mosquitoes from Pig and Sheep Farms in Shanghai, China.

Japanese encephalitis virus (JEV) is regarded as an emerging and reemerging pathogen that is a primary cause of viral encephalitis in humans. However, there is a scarcity of research on the prevalence of JEV genotypes across the different mosquito species in Shanghai. In this study, the diversity of mosquito species and prevalence of JEV in pig and sheep farms in Shanghai were surveyed in 2022. A total of 24,073 mosquitoes, belonging to four genera and seven species, were collected from pig and sheep enclosures in the Chongming, Jiading, Pudong, Fengxian, and Jinshan Districts of Shanghai. Culex tritaeniorhynchus was identified as the predominant species (87.09%, 20,965/24,073) with the highest JEV detection rate. Six strains of JEV were isolated in this study, and genetic analysis revealed that five strains (SH22-M5, SH22-M9, SH22-M14, SH22-M41, and SH22-M52) belonged to genotype I (GI), while one strain (SH22-M1) was classified as genotype III (GIII). The sequence homology was highest between SH22-M9 and SD-1 (99.87%) and between SH22-M14 and SD12 (99.53%). SH22-M5, SH22-M41, and SH22-M52 shared the highest sequence homology with the HEN07011 strain (99.73-99.93%). SH22-M1 was most closely related to SH18, with a sequence homology of 99.8%. Additionally, for the first time, the GI JEV strain was isolated from mosquitoes in sheep sties in this study. The findings highlight the necessity of enhancing the surveillance of JEV in pigs and other livestock farms, including sheep, as well as monitoring the mosquitoes present in these environments. It is recommended that livestock farming areas be kept separate from human habitation to reduce the risk of JEV infections in humans.

Proposed Business Strategy to Increase Profitability, Case Study: Majafreshindo Green Farm

This study examines the declining profitability of Majafresh Indo Green Farm, a leading hydroponic farming company in Majalengka, Indonesia. Using qualitative methods, the research identifies key issues, including a focus on operational repairs and insufficient marketing efforts, which have resulted in underutilized production capacity. The study addresses these challenges using analytical tools such as SWOT, IE Matrix, and TOWS Matrix. These tools reveal strengths like expertise in hydroponic farming and sustainable practices and areas for improvement, such as limited marketing capabilities and reliance on a few large clients. Opportunities include government support, health-conscious market trends, and technological advancements, while threats involve rising costs and increased competition. The proposed strategies focus on leveraging strengths in quality control and sustainability. These include diversifying the customer base, enhancing market penetration, and expanding product offerings through agri-tourism and educational programs. By adopting advanced technologies and securing government incentives, Majafresh can improve operational efficiency, broaden its market, and achieve sustainable growth. This case study provides valuable insights for stakeholders in the sustainable agriculture sector.

Swiftlet Farming Industry—A White Gold in the Malaysian Housing Market?

The swiftlet farming industry is fast-growing and contributes to Malaysia’s economic growth. Nonetheless, dirty and noisy surroundings caused by bird droppings and audio systems are unattractive and may adversely impact the housing market. Hence, this study seeks to ascertain the price impacts of the swiftlet farming industry. The first objective defines the hedonic model by reviewing its definition, theoretical framework, methodological process, and house price applications. The second objective explores factors influencing house prices, including the animal farming industry, through a literature review. The third objective evaluates the impact of the swiftlet farming industry on house prices. Ten-year house transaction data and spatial data provided by the Valuation and Property Services Department and Municipal District of Bentong are used to construct the Hedonic model. The result is evaluated based on its logical, statistical and predictive performance. The study found that swiftlet farming in Bentong has a negative impact on house prices, with an inverse price impact that increases with distance from the farming area. Buyers are reluctant to pay for houses near farming due to noise pollution and obstruction. This study supports international and national agendas relating to environment and health sustainability.

TREE SPECIES DIVERSITY AND MANAGEMENT IN AGROFORESTRY POHPOHAN (Pilea melastomoides)

Article Highlights- Pohpohan (Pilea melastomoides) is the main commodity cultivated by using traditional agroforestry in the traditional zone of Gunung Halimun Salak National Park as a fresh vegetable.- There is a limited database of plant species diversity in the traditional zones, which is dynamic and needs further research.- This study found 54 tree species from 37 families with the dominant family being Rubiaceae.- Most farmers cultivated Pohpohan by conducting plant spacing, maintenance, and harvesting. Their income is USD 154 – 256/month. AbstractGunung Halimun Salak National Park (GHSNP) is Java's largest tropical mountain forest. GHSNP is still facilitating community grounds around GHSNP with the existence of a traditional zone. Pohpohan (Pilea melastomoides) is the primary commodity in the traditional zone cultivated by the local people around GHSNP. Pohpohan is planted using traditional agroforestry planting patterns. The traditional zone has a limited plant species diversity and management database, so further study is necessary. This study was conducted in the traditional zone of GHSNP, specifically at the agroforestry system in Tamansari Village, Bogor Regency. Data collection of tree diversity was conducted by establishing 25 plots. This study also interviewed 30 local farmers belonging to the Mandiri farmer group. There are 54 species from 37 families. Rubiaceae, Malvaceae, Fabaceae, and Phyllanthaceae families are the most common families. Pinus merkusii, Agathis dammara, and Maesopsis eminii have the highest important value index (IVI) values. Understorey and seedlings have a low category on H' due to the dominant planting of Pohpohan. Most respondents are people aged 35-55 (70%) with a farming area of 1000 m2. They cultivate Pohpohan by conducting plant spacing, plant maintenance (e.g., soil cultivation, weeding, fertilization), and harvesting. Most farmers conduct fertilization 9 times/year. Farmers produce 30.000-50.000 bunches/harvest. The price of a bunch of Pohpohan is 0,00054 USD. Their income is about 154 USD – 256 USD/month.

Potential use of microbial product to degrade Chlorpyrifos in tea cultivation soil

Pesticides containing organophosphate (OP) compounds in general and Chlorpyrifos (CPF) in particular are used to control insects and pests. However, they have been proven to be highly toxic not only to some insects and wild animals but also have negative impacts on the environment and human health. This study provides an assessment of the effectiveness of using microbial product (MP) to degrade CPF in a large- and small-scale tea cultivation environment. The research results show that in some farming areas, residual CPF exists both in the soil and in agricultural products. When using the MP, the CPF content in tea decreased by 77.66%; the efficacy of CPF treatment in the soil reached 72-76% in the small scale . Additionally, the use of MP also increased the total microbial density in the net house scale from 106 to 107 CFU/g and beneficial microorganisms in the soil, stimulating growth and productivity for tea plants.

Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India.

Paddy crop mapping is essential for agricultural monitoring, ensuring food security, and enhancing resource allocation. This study observes the Cauvery Delta Zone (CDZ), recognized as the rice bowl of Tamil Nadu and a crucial area for paddy farming in India. The study seeks to elucidate rice-growing trends over three years (2021-2023) by examining the regional variability of the Radar Vegetation Index (RVI) throughout a paddy crop growing season (June to September). A temporal examination of the RVI and the cross-polarization ratio (VH/VV) demonstrates a good correlation of 0.79, enhancing the comprehension of paddy crop dynamics. Additionally, machine learning algorithms such as random forest (RF), support vector machine (SVM), and decision tree (DT) are utilized on radar data in both VV and VH polarizations to improve the classification of paddy fields. The accuracy evaluation indicates that the RF algorithm exhibits superior performance, achieving accuracies of 86.72% in VH mode and 86.42% in VV mode. The results underscore the efficacy of integrating radar-based indices with machine learning methodologies for proficient agricultural surveillance. These findings offer essential assistance for enhancing crop yield, optimizing resource management, and enabling informed decision-making in the Cauvery Delta Zone.

Investigating the Impact of Agricultural Credit on Wheat Farming: Evidence from Pakistan

Increasing food production is crucial for attaining the Sustainable Development Goal (SDG#2) by 2030 through supplying agricultural credit and improved inputs. Thus, the present study examined the roles of agricultural credit, applications of fertilizer, usage of pesticides, farming area, and agricultural employment in enhancing wheat production in the developing economy (Pakistan) from 1990 to 2020. This study applied the autoregressive distributed lag (ARDL) regression method for the empirical analysis. The empirical findings from the ARDL regression technique revealed that agricultural credit can significantly enhance wheat production, while improved inputs, including fertilizer and pesticide usage, also positively contribute to wheat production. Notably, agriculture credit plays a vital role in assisting farmers to purchase advanced farm inputs and indirectly increases the wheat yield. Consequently, this study suggests that the banking sector should supply agricultural credit to farmers under flexible lending conditions; therefore, farmers can easily access credit and adopt modern technologies to enhance food production and ensure food security.

Perbandingan Fuzzy Time Series Chen dan Cheng untuk Peramalan Harga Beras di Kabupaten Banyumas

Indonesia is mostly an agricultural country. Most people here make a living from farming. Rice is a major crop in Indonesia. The price of rice is very important to the economy, especially in farming areas like Banyumas. Fluctuating rice prices can affect the economic lives of both farmers and consumers in the region. The rapid fluctuation in rice prices and the uncertainty of future prices demand the need for rice price forecasting. This study uses fuzzy time series to forecast rice prices. The prediction models used are the Chen model and the Cheng model. To calculate the accuracy of the models, MAPE calculations are employed. Based on the results, the MAPE value for the Chen model is 0.957539%, and for the Cheng model, it is 0.477921%. The results indicate that the accuracy of the Cheng model is higher than that of the Chen model, meaning that the Cheng model is better suited for forecasting rice prices in Banyumas Regency.

Abundance, Sex and Size Distribution of European Green Crabs, Carcinus maenas (Linnaeus, 1785), at Natural and Aquacultured Oyster Areas in Great Bay Estuary, NH

ABSTRACTGreat Bay Estuary (GBE) is a complex estuarine system at the convergence of seven rivers, ocean and land. The GBE ecosystem supports typical estuarine species but, like many estuaries, has been a target of both invasive and range‐expanding species. The goal of this study was to compare abundance, sex and size distribution of green crabs between oyster farm sites and non‐farmed sites within GBE in 2021 and 2022. Overall catch was higher in 2021 compared to 2022. The highest population of green crabs was estimated to be in the farm area around Cedar Point, and natural/restoration area near Moody Point had the smallest European green crab population. In both years, the majority of green crabs captured (98%) were adult males, and few were juveniles captured. At the last sampling date of 2021 and 2022, most crabs were large (68 ± 0.5 mm and 63 ± 2 mm carapace width, respectively). Shellfish growers, restoration and management teams could use these data in mitigation strategies to sustain industries and enhance GBE health. This information is important for oyster restoration initiatives and oyster growers as they may want to: consider putting traps around their restoration reefs and farms to help reduce green crab infestation/predation in their growing areas; make decisions regarding best management practices and optimal growing locations when applying for permits; and work with management teams with gear improvement, such as floating gear, to reduce green crab intrusion into cages. High‐priority locations for trapping should be areas such as oyster farms, oyster reefs and restoration sites.

Principle of fault direction discrimination in near area of AC grid-connected doubly-fed wind farm

Abstract With a large number of new energy sources, the current characteristics of doubly-fed fans under the circumstances of AC grid connection short-circuit faults have become the focus of research. After crowbar resistance is put in, the attributes of short-circuit current become complicated. By calculating the correlation coefficient between the voltage of the AC system and the current of the doubly-fed fan, the fault direction can be accurately judged. In this paper, the research work for new energy power system fault analysis and protection provides a new thought and method. The current characteristics of the doubly-fed fan under fault state are analyzed deeply, and the principle of fault direction discrimination based on waveform correlation coefficient is proposed. The advantage of this method is that it is independent of fault types, and can be applied to all kinds of fault situations, with high reliability and practicability. Simulation results unveil that the principle put forward can judge the fault direction accurately, reliably, and sensitively when various types of faults occur in areas near an AC grid-connected doubly-fed wind farm.