Farm Operations Research Articles

Perceived phytosanitary impacts of climate change and climate change adaptation among Cocoa farmers in Cameroon

Cocoa productivity is dependent on favourable weather variables. In Cameroon, climate change presents some phytosanitary challenges to cocoa farmers due to weed and cocoa pest problems. This paper analyzed the perceived phytosanitary impacts of climate change and the determinants of selected mitigation options in Cameroon. The data were collected using stratified random sampling from 303 farmers in Centre Cameroon. The data were analyzed with Probit regression. The results showed that cocoa farming operations were affected by climate change through weed control problems (51.48%), more incidence of black pod disease (65.02%), more incidence of malaria (68.98%), more cocoa pests (72.94%), death of cocoa trees (74.26%) and general reduction in cocoa yields (78.88%). The Probit regression results revealed that some adaptation methods were significantly influenced by perceived phytosanitary impacts of climate change such as perception of more pests, difficulties in weed control, death of cocoa trees, and black pod diseases. Other impacts such as malaria, pneumonia, lack of drinking water, inability to effective spray cocoa pod and dry the beans also showed some statistical significances with different impacts on adaptation. Cocoa farmers’ demographic characteristics such as education, gender, farming experience and primary occupation also influenced some adaptation methods. It was concluded that climate change presents significant constraints to cocoa production, and efforts to address the problem should integrate proper education on perception of the phytosanitary impacts and relevant mitigation with sensitivity on farmers’ gender, education level and the nature of primary occupation. Res. Agric. Livest. Fish. Vol. 11, No. 2, August 2024: 215-230

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Offshore wind farm operation contributed to a slight improvement in seawater quality along the Jiangsu Coast, China

The rapid growth of offshore wind farms (OWFs) is driven by concerns for energy security and climate change mitigation. However, their impact on marine environments remains poorly understood due to limited research. This study analyzes the effects of an OWF along China's Jiangsu Coast on seawater quality using data from different development phases. Results show the major pollutants were different across phases. Heavy metal pollution reached alert levels during construction compared to the safe levels observed in the pre-construction and operational phases, mainly due to increases in Pb, Cd, and Hg concentrations. Eutrophication was mild throughout all periods but exhibited a continuous decrease, primarily attributed to reductions in PH and COD concentrations. As a result, the comprehensive pollution level during construction was increased, but it was improved to a clean level during the operational phase. Besides, significant variations were observed in the spatial distribution patterns of major pollutant indices across different scenarios. These changes may stem from a combination effect of land-based pollution, aquaculture, OWF-induced disturbances to atmosphere and hydrodynamics, OWF-related drain and leakage contamination, and marine management policies. Understanding these effects informs OWF optimization, rational wind resource utilization, and marine ecology protection.

Recent Development Trends in Plant Protection UAVs: A Journey from Conventional Practices to Cutting-Edge Technologies—A Comprehensive Review

Uncrewed aerial vehicles (UAVs) for plant protection play a vital role in modern agricultural operations. In recent years, advancements in UAVs and pest control technologies have significantly enhanced operational efficiency. These innovations have addressed historical challenges in agricultural practices by improving automation and precision in managing insect pests, diseases, and weeds. UAVs offer high operational efficiency, wide adaptability to different terrain, and safe applications. The development and demand for these technologies have increased to boost agricultural production. In agricultural settings where conventional machinery struggles to carry out farming operations, UAVs have transformed farming practices by providing high operational efficiency and significant profitability. The integration of UAVs and other smart technologies has driven advancements. The UAV sector has received substantial attention as a convergence of production, service, and delivery, introducing synergy through the presence of several developing areas. The market for this technology is expected to grow in the future. In this comprehensive review, we analyzed an overview of historical research, diverse techniques, the transition from conventional to advanced application, development trends, and operational milestones across diverse cropping systems. We also discussed adoption and subsidy policies. In order to properly understand UAV operational efficiency, we also analyzed and discussed smart atomization systems, spray drift, droplet deposition detection technologies, and the capabilities of related technologies. Additionally, we reviewed the role of software programs, data-driven tools, biodegradable materials, payloads, batteries, sensing technologies, weather, and operational and spraying factors. Regulatory limitations, operating and farmer’s training, economic effects, and guidelines were also acknowledged in this review. This review highlights deficiencies and provides essential knowledge of the use of UAVs for agriculture tasks in different regions. Finally, we examine the urgency of UAV technology implementations in the agricultural sector. In conclusion, we summarize the integration of UAVs and their related technologies with applications and future research prospects, offering directions for follow-up research on the key technologies of UAVs and encouraging the enhancement of agricultural production management in terms of efficiency, accuracy, and sustainability.

Carbon footprint of global rice production and consumption

Rice paddies are not only sources of staple food for half of the global population but also account for nearly half of the anthropogenic greenhouse gas emissions (GHGs) of croplands. Carbon footprint (CF) is a key tool for identifying and weighting sources of the GHGs along the food supply chain, promoting the efforts to curb these emissions within the targets of the climatic change protocol of the Paris Agreement. We introduced a comprehensive global quantification of rice CF, including direct and indirect emissions and sinks of the GHGs of inputs production, packaging, transportation, and application, soil and plant systems, farm operations, and uses of the produced biomass until the end of life. Globally, the rice CF was 2430 kg CO2eq. Mg−1 grain in 2020, of which 46% and 42% were sourced from the Gate and Grave stages, respectively, after excluding 3265 kg CO2eq. Mg−1 grain, that is the assimilated C in plant biomass. Net GHGs emissions of soil, biomass mulching and burning, and farm operations accounted for 20, 17, and 63%, respectively of the Gate stage CF. Meanwhile, food consumption contributed to the Grave stage CF by 92%. The rice CF ranged between 14 and 4854 kg CO2eq. Mg−1 grain among countries, wherein, for example, the rice CF values in Indonesia, India, Vietnam, and Russia represented 9, 50, 97, and 122% of the global average. Southeast, South, and East Asia were the major contributors (35, 34 and 18%, respectively) to the atmospheric CO2 load (2.4 Pg CO2eq.) of global rice production and consumption. This CO2 load will increase to 3.1 Pg CO2eq. in 2100, driven by a 32% growth in rice consumption. Here, we suggested an optimistic strategy (green energy use, hybridization, improving use efficiency of the inputs, and reducing food losses) to reduce the CO2 load by 60%.

Impacts of Climate Variability on Agricultural Activities and Availability of Agroforestry Practices in Southeast Nigeria

Climate variability poses a significant challenge for smallholder farmers globally, including those in Southeast Nigeria. The purpose of this study is to explore farmers’ perceptions of the impact of climate variability and agroforestry practices available in the region. Specifically, the study evaluates the availability of agroforestry practices for adoption by farmers and the extent of climate variability effects on agricultural activities in Southeast Nigeria. The study employed primary data, which were collected from 350 registered farmers in the study region using a validated and well-structured questionnaire. The study employed a structured questionnaire to gather data on the availability of agroforestry practices among farmers in Southeast Nigeria as well as utilized a seven-item scale to assess the perceived impacts of climate variability on agricultural activities in Southeast Nigeria. Findings revealed that home gardening emerged as the most prevalent agroforestry practice among respondents, while multipurpose wood lots were the least common. The mean ratings for all thirteen agroforestry practices were below the critical threshold of 3.00, indicating a lack of widespread availability. Thus, there is a limited adoption of these practices among farmers in the region. Irregular rainfall and rising temperatures were identified as the most significant climate variability impacts on farming operations. However, extreme events like storms, hailstorms, and thunderstorms had the least impact according to respondents, indicating that farmers perceive these impacts as significant and pressing concerns. The study concludes that there is a high level of climate variability impacts on agricultural practices in Southeast Nigeria, coupled with a low availability of agroforestry practices for farmers to mitigate these impacts. The study suggests enhancing agricultural extension services, developing tailored extension programs, advocating for governmental support and investment, providing technical support and capacity-building assistance to farmers, investing in research and innovation, and fostering collaboration among stakeholders to promote the adoption of agroforestry practices as a climate-resilient agricultural approach

Usability and Impact of Agromet Advisory Services for Kharif season in Jagtial, Telangana

The access to real-time weather information supports the farmers in deciding their day-to-day field operations efficiently. Agromet Field Unit (AMFU) utilizes the medium-range weather forecast of the India Meteorological Department (IMD) for issuing agromet advisories at district level and disseminates them using a multimedia approach to reach a maximum number of farmers at every corner of the village in India. During Kharif 2023, a study was conducted to analyse the extent of impact of Agromet Advisory Services (AAS) that benefitted the farming community of Jagtial district in an economic way provided through AMFU, RARS, Jagtial. The rainfall forecast accuracy of Jagtial district was verified both in qualitative and quantitative terms for the Kharif season.The study for a survey on AAS used interview scheduling to collect real-time feedback information from the farmers of four villages of four different mandals namely Allipur, Pormalla, Shankarraopet and Polasa villages of Raikal, Medipalle, Gollapalle and Jagtial mandals respectively of Jagtial district to verify the usefulness of agromet advisories provided using the medium-range weather forecast and the significance of ICT tools in their dissemination. A survey was conducted with a total of 120 farmers on the basis of the village, usefulness of the weather forecasting service, its accuracy, their application in agricultural operations, and satisfaction to the agromet advisories. Tabular analysis, quantitative and qualitative verifications methods were used to derive the valid conclusions.The results revealed that there is moderate level (60.66 %) of rainfall accuracy for Jagtial district during southwest monsoon 2023 andthe AAS were highly useful to majority 70 % of the farmers for their day to day farm operations and whatsapp being 55 % as the best source of medium in receiving agromet advisories. The weather forecast was highly useful in performing farm operations at all stages of the crop, for pesticides spraying and harvesting to 44.2 %, 20% and 15.8 % of the farmers respectively. Majorly, 38.6 % and 28% of the farmers saved an amount of upto Rs. 1000 and Rs. 2000 in paddy cultivation in Kharif season 2023 by adopting the agromet advisory services.

Exploring Critical Success Factors in Agritourism: A Mixed-Methods Approach

Agritourism is an alternative approach to distributing economic and social advantages and fostering sustainable development in rural regions. This research aims to identify critical success factors of agritourism to comprehend the obstacles and problems associated with Thailand and examine the essential factors of achieving success. The research adopts a mixed-method approach to collect, analyze, and interpret qualitative and quantitative aspects. The data was collected by reviewing documents, conducting in-depth interviews, conducting questionnaires, making onsite observations, and conducting focus group discussions. The questionnaires were sent to a panel of experts from educational institutions, the Ministry of Agriculture and Cooperatives, the Ministry of Tourism and Sports, and the tour operators. The data analysis involved paired t-tests and factor analysis to provide a comprehensive analytical output. The findings revealed four critical success factors in the Thai agritourism industry: geographical location, the entrepreneurial mindset of the farm operators, the quality of products and stories, and social media and publicity strategies. Agritourism sites in famous areas were found to have more potential for success, and an entrepreneurial mindset allows farmers to create attractions and diversify products. Therefore, knowing how to use new communication channels helps farmers deal with customers and better understand customer needs. Doi: 10.28991/HEF-2024-05-03-08 Full Text: PDF

Analysis of Antibiotic Resistance Genes (ARGs) across Diverse Bacterial Species in Shrimp Aquaculture.

There is little information available on antibiotic resistance (ABR) within shrimp aquaculture environments. The aim of this study was to investigate the presence of antibiotic resistance genes (ARGs) in shrimp farming operations in Atacames, Ecuador. Water samples (n = 162) and shrimp samples (n = 54) were collected from three shrimp farming operations. Samples were cultured and a subset of isolates that grew in the presence of ceftriaxone, a third-generation cephalosporin, were analyzed using whole-genome sequencing (WGS). Among the sequenced isolates (n = 44), 73% of the isolates contained at least one ARG and the average number of ARGs per isolate was two, with a median of 3.5 ARGs. Antibiotic resistance genes that confer resistance to the β-lactam class of antibiotics were observed in 65% of the sequenced isolates from water (20/31) and 54% of the isolates from shrimp (7/13). We identified 61 different ARGs across the 44 sequenced isolates, which conferred resistance to nine antibiotic classes. Over half of all sequenced isolates (59%, n = 26) carried ARGs that confer resistance to more than one class of antibiotics. ARGs for certain antibiotic classes were more common, including beta-lactams (26 ARGs); aminoglycosides (11 ARGs); chloramphenicol (three ARGs); and trimethoprim (four ARGs). Sequenced isolates consisted of a diverse array of bacterial orders and species, including Escherichia coli (48%), Klebsiella pneumoniae (7%), Aeromonadales (7%), Pseudomonadales (16%), Enterobacter cloacae (2%), and Citrobacter freundii (2%). Many ARGs were shared across diverse species, underscoring the risk of horizontal gene transfer in these environments. This study indicated the widespread presence of extended-spectrum β-lactamase (ESBL) genes in shrimp aquaculture, including blaCTX-M, blaSHV, and blaTEM genes. Increased antibiotic resistance surveillance of shrimp farms and identification of aquaculture operation-level risk factors, such as antibiotic use, will likely be important for mitigating the spread of ARGs of clinical significance.

Dual benefits of biofouling reduction in non-fed aquaculture: On-site heat treatment enhances oyster production and mitigates local environmental impacts

We experimentally evaluated the biofouling reduction by on-site heat treatment in enhancing oyster aquaculture production and mitigating its environmental impacts. Specifically, we compared oyster growth, settling flux of particulate organic matter (POM), and its oxygen consumption rate (OCR) between heat-treated (HT) and non-treated (N) sections within suspension oyster farms in a temperate bay. HT demonstrated a substantial reduction of 81–99% in fouling mussel biomass and approximately 20–30% increases in cultivated oysters’ growth and condition index. The settling flux of POM was 10–57% smaller in HT mainly due to the reduction in biodeposition from fouling mussels. However, OCR of settling POM at a unit mass was 6–62% higher in HT than in N; overall, the short-term oxygen consumption by settling POM did not significantly differ between HT and N. The increase in OCR of settling POM in HT may be attributable to the decrease in biodeposits from fouling organisms and increase in the relative contribution of fresh POM entering from unfarmed areas. Meanwhile, from the longer-term perspective, the substantial reduction in settling POM by the heat treatment lower the potential for oxygen consumption in the bottom layers over the half-year farming operation by 31%. In conclusion, on-site heat treatment is a simple tactic for controlling nutrient and POM flows at production sites, simultaneously enhancing oyster production and mitigating the local environmental impact of non-fed oyster aquaculture. Furthermore, this approach exemplifies an effective tactic to enhance the sustainability of primary food production processes.

A short-term wind speed prediction method based on the IDBO-BPNN

Wind energy is known for its uncertainty and volatility, necessitating accurate wind speed prediction for stable wind farm operations. To enhance wind speed prediction accuracy, this study proposes a BP neural network (BPNN) short-term wind speed prediction model based on the Improved Dung Beetle Optimization (IDBO) algorithm. Addressing the issue of local optimization and reduced accuracy in the BPNN optimized by the Dung Beetle Optimization (DBO) algorithm, the circle chaotic mapping is utilized for population initialization to achieve a more uniform initial distribution. The improved sine-cosine algorithm, triangle wandering strategy, and adaptive weight coefficient are then employed to optimize dung beetle positions, balancing global exploration and local development capabilities and improving the algorithm’s search performance. Finally, the improved DBO algorithm optimizes the weights and thresholds of the BPNN, and the IDBO-BPNN prediction model was constructed. Simulation experiments were conducted based on wind speed data from a wind farm in Ohio, USA. The IDBO-BPNN model was compared with other prediction models, and error evaluation indexes were introduced to evaluate the experimental results. The findings demonstrate that the suggested model yields the most accurate predictions and achieves the optimal error evaluation indexes. MAE, MSE, RMSE, NSE and R2 of dataset 1 are 0.42247, 0.28775, 0.53642, 88.8785%, 89.161%, those of dataset 2 are 0.28283, 0.14952, 0.38668, 85.7383%, 86.577%, and those of dataset 3 are 0.45406, 0.39268, 0.62664, 84.3859%, 84.931%. In particular, compared with BPNN model, the five evaluation indexes of the IDBO-BPNN model promoted by 41.53%, 57.38%, 34.71%, 24.91%, and 11.44%, respectively in dataset 3. Therefore, that the proposed IDBO-BPNN model exhibits higher accuracy in short-term wind speed prediction, indicating its feasibility and superiority in the realm of wind energy.

Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI).

The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies into modern agriculture has profound implications on data collection, management, and decision-making processes. However, ensuring the security of agricultural data has consistently posed a significant challenge. This study presents a novel evaluation metric titled Latency Aware Accuracy Index (LAAI) for the purpose of optimizing data security in the agricultural sector. The LAAI uses the combined capacities of the IoT and AI in addition to the latency aspect. The use of IoT tools for data collection and AI algorithms for analysis makes farming operation more productive. The LAAI metric is a more holistic way to determine data accuracy while considering latency limitations. This ensures that farmers and other end-users are fed trustworthy information in a timely manner. This unified measure not only makes the data more secure but gives farmers the information that helps them to make smart decisions and, thus, drives healthier farming and food security.

"They Can't Possibly Understand What I'm Going Through": Female Farmers' Perspectives on Barriers to Care in Georgia.

The purpose of this study was to explore female farmers' perspectives on barriers to engaging with resources for physical and mental healthcare faced by agriculture producers in the state of Georgia. In-depth interviews were conducted with female farm owners and managers (n = 16) across the state. Interviews were recorded and transcribed, and researchers coded interviews separately before thematic analysis was used to identify common themes. Three primary themes were identified: (i) formal healthcare challenges, (ii) stigma, and (iii) cultural norms. Formal healthcare challenges included time constraints, healthcare costs, and a lack of cultural competence from healthcare providers. Both community and self-stigma were identified as barriers to engaging with mental health resources. Cultural norms that acted as a barrier to care included the prioritization of farm operations, self-reliance, pride, and the minimization of health concerns. Interviewees identified gender differences in the impact of stigma and cultural norms, reporting that these sociocultural barriers were more prominent among older, male producers. Central to many of these barriers is the concept of 'farm identity', where farmers' commitment to their operations consistently trumped concerns about physical or mental health. Future efforts to improve health outcomes among farmers should utilize the concept of farm identity as a guide for tailoring interventions and improving cultural competence among rural healthcare providers.

Evaluation and Optimization of Hybrid Renewable Energy Systems for Sustainable Farm Machinery Operations in Northern Nigeria

This study aimed to assess and optimize hybrid renewable energy systems (HRES) integrating solar and wind energy for sustainable farm machinery operations in Northern Nigeria. A purposive sampling technique was used to select representative farms and agricultural sites within the Kaduna region. Qualitative data were collected through interviews and surveys with farm owners and operators to gain insights into energy use practices, challenges, and perceptions regarding renewable energy adoption. The comparative analysis of different HRES configurations revealed that Configuration C, with the highest solar (20 kW) and wind capacities (15 kW), achieved the highest renewable energy fraction of 90% and the lowest annual cost of $14,800. Configuration B, with increased capacities (15 kW solar and 10 kW wind) and a battery capacity of 200 kWh, achieved an 85% renewable fraction and reduced the annual cost to $16,200. The cost-benefit analysis confirmed that HRES are economically viable and offer significant environmental advantages compared to conventional diesel generators. These findings underscore the potential for optimizing energy resources to achieve higher renewable energy fractions and lower operational costs. The study provides practical recommendations for promoting HRES adoption, highlighting the necessity for financial support, technical training, and robust policy frameworks to facilitate the transition towards sustainable energy solutions in agriculture.

Applying DMAIC Methodology to Reduce Egg Breakage Rates in Small-Scale Production Enterprise in Mandalay Region

This study employed the DMAIC methodology (Define, Measure, Analyze, Improve, Control) to investigate and minimize Unusable egg breakage (Completely broken eggs) at Golden Yolk farm. Data was collected using a triangulated approach that included direct observations of egg-handling processes, interviews with experienced farm personnel, and a review of historical breakage records in order to ensure reliability. Standardized checklists were also used, and initial analysis revealed a total defect rate of 0.74%, with Unusable breakage being the highest contributor at 0.47% among all types of defects. Qualitative tools like Pareto charts, FMEA, and Fishbone diagrams identified the main culprits of inadequate handling techniques and the absence of appropriate equipment. Subsequent interventions focused on implementing farm personnel training, standardized handling procedures, and introducing relevant equipment, significantly reducing Unusable egg breakage from 0.47% to 0.39%. This approach demonstrates the effectiveness of the DMAIC methodology for addressing Unusable egg breakage in the egg farm. These findings offer valuable insights for improving farm operations, ensuring consistent, high-quality egg production, and minimizing associated losses.

Unlocking the Potential of Zero-tillage Farming: Challenges, Opportunities, and Key Influences on Adoption

Zero tillage (ZT) is a crucial agricultural strategy that prioritizes little soil disturbance. This research investigates the future possibilities of ZT, concentrating on three critical dimensions: technology developments, climate change considerations, and possible rise in adoption rates. Precision agriculture, robotics, artificial intelligence, and biotechnology have been shown to play a critical role in improving ZT efficiency and sustainability. These advancements enable more intelligent and focused methods, lowering waste and harmonizing farming operations with overall sustainability goals. Climate change is also a crucial factor in influencing ZT's future. ZT's intrinsic qualities of soil moisture conservation, decreased erosion, and carbon sequestration make it an effective technique for climate mitigation and adaptation. According to the report, the worldwide need to address climate change may serve as a stimulus for ZT expansion, linking it with crucial agricultural initiatives in the future. The possible rise in ZT adoption rates is investigated in light of these technological and environmental aspects. The findings indicate that technology's role in eliminating obstacles and increasing efficiency, together with government and organizational backing, might encourage widespread ZT adoption, particularly in developing nations. Collaborative efforts among multiple stakeholders, including researchers, policymakers, farmers, and industry, are emphasized as critical to optimizing ZT for different situations and requirements. Zero Tillage's future possibilities are broad and varied, characterized by technical innovation, compatibility with climate goals, and a clear route to widespread implementation. The combination of these variables offers a favorable scenario for ZT, establishing it as a key technique in developing sustainable agriculture in the future. This study adds to our understanding of ZT's future trajectory and provides insights that can help direct its ongoing evolution and effect in the agriculture sector.

Technology Development with Zero Tillage and Stubble Residue Management for Sustainable Soil Health and System Productivity in Wheat-maize Cropping Pattern

Conventional farm operations comprising traditional soil tillage and burning stubble residues become the cause of concern for soil sickness threatening sustainable system productivity. Wheat and maize constituting one of the predominant cropping systems strive with the commitment of substantial contribution to the world food security front. A better understanding of these alarming issues insisted on us to explore ‘Conservation Agriculture’ practices designing a field experiment with tillage and crop residue management. The study from 2019 to 2022 derived phenomenal achievement with Zero Tillage (ZT) and Minimal Tillage (MT) in compliance with crop residue retention (+ R); while, the eventuality of stubble burning (- R) deteriorated soil health, especially with Conventional Tillage (CT). Results illustrated significantly higher system productivity (11.60 - 12.0 t ha-1) in terms of wheat equivalent yield at (ZT+R) stands followed by those (11.34-11.64 t ha-1) at (MT + R), significantly higher than those (10.52 - 10.59 t ha-1) at conventional (CT-R) stands and those (10.63-10.78 t ha-1) at (CT+R) stands. Soil health also significantly improved at (ZT + R) stands accounting for higher soil porosity (39.45%), pH (7.64), electrical conductivity (0.370 dS m-1), hydraulic conductivity (10.56 mm h-1), soil organic carbon (0.458%), and N, P and K contents (272.5, 18.36 and 254.8 kg ha-1) than at conventional (CT -R) stands. Therefore, the study could develop a viable cutting-edge agro-technology fostering sustainable maize-wheat production in a system perspective mode. Nonetheless, the stewardship of zero tillage along with stubble residues could also be envisaged for the mitigation of soil sickness, too.

IoT-enabled fire detection for sustainable agriculture: A real-time system using flask and embedded technologies

In modern agriculture, the threat of fire poses significant economic and environmental risks. Additionally, traditional fire detection methods are inadequate and poorly integrated with advanced technologies. It is crucial to develop a more efficient and reliable fire detection system. Also, the lack of real-time and accurate monitoring in current systems compromises the resilience and sustainability of farming operations. This article details developing and implementing a real-time fire detection system tailored for smart agriculture. It integrates cutting-edge technologies, including the Internet of Things (IoT), embedded systems, and a Flask-based web application, fortified by cybersecurity measures such as login authentication and secure HTTP protocols. The system's primary aim is to monitor environmental conditions continuously in agricultural fields to detect signs of smoke or flame swiftly, facilitating preventive actions to safeguard crops and infrastructure. The architecture employs sensors distributed across fields to monitor conditions, a Raspberry Pi 3 B+ as the central processing unit for data acquisition and transmission, and a web interface developed using Flask, HTML, and CSS for secure and real-time visualization of detection data. Critical components like the MCP3208 analog-to-digital converter ensure the system's reliability and accuracy. Experimental results confirm the system's efficacy in early fire detection and real-time data visualization via the web browser. Enhanced security features, such as login authentication, protect sensitive information, while regular updates maintain data relevance. This study advances fire prevention and farm preservation efforts, offering a high-performance technological solution for proactive monitoring and quick response to fire risks, thereby supporting food security and sustainable agricultural practices.

Monopile-induced turbulence and sediment redistribution form visible wakes in offshore wind farms

Offshore wind farms are becoming an increasingly common feature in the marine environment as a renewable energy source. There is a growing body of evidence on the effects of wind farms on the seabed and its organisms. However, an important and understudied aspect of site development is the interaction of turbine foundations on the surrounding marine environment. Structures exert significant disturbance on tides, waves and currents; these are visible as optically-distinct, elongate wakes at the sea surface with elevated suspended particulate matter. Despite this, there is uncertainty on the mechanisms that lead to the visible manifestation of wakes at turbine foundations, primarily due to a lack of direct measurements. Here, in situ measurements along with a 15-year time series of satellite images of the Thanet offshore wind farm, located within the Thames Estuary, were used to investigate the formation of visible monopile wakes, and the effects these have on the surrounding water column. We show the optically distinct wakes are near-constant at Thanet; visible in >90% of all satellite images, yet no regional change in sea surface turbidity could be attributed to wind farm construction or operation. Monopile wake in situ water samples and acoustic Doppler current profiler (ADCP) backscatter measurements demonstrated colour change related to elevated sea surface sediment concentration. However, averaged water column measurements of suspended sediment within wakes, and upstream of monopiles, remained consistent. These measurements demonstrate that sediment was redistributed towards surface waters, rather than additional sediment becoming suspended in the wake. ADCP velocity measurements supported a mechanism of sediment lofting towards the surface, with enhanced vertically upwards flow recorded in wakes.

Drone-Assisted Climate Smart Agriculture (DACSA): The design of the groundwork flow data for drone operations

The success of precision farming hinges on effective ground support and workflow. In pursuit of this, we undertook a thorough requirement study of the system necessary for precision farming and developed a precision farming data flow model in ground support. The prototype hardware ground support and conceptual data flow provided valuable guidance in the successful realization of Drone-Assisted Climate Smart Agriculture (DACSA). Using open-source software to accommodate a range of data processing algorithms becomes crucial in operationalizing ground support for precision farming. This study has culminated in a comprehensive prototype model for precision farming operations that can be executed with confidence. The management system of flow data for precision farming has been drawn, this platform is specifically crafted to streamline agriculture operations by transforming diverse inputs into useful spatial data. To maintain the growth of the database, it is necessary to incorporate it in the entire crop cycle. The integration of this database can significantly enhance the precision of predicting plant performance. While this innovative approach is still in progress, it has already demonstrated its potential in supporting informed decision-making. For the next, it is imperative that we prioritize research aimed at creating decision-support algorithms that can effectively gather and blend information pertaining to soil, crops, and weather into actionable maps. These maps must incorporate location-specific data and be utilized by agricultural professionals for on-site decision-making. Moreover, they must be well-suited for drone usage in tasks such as monitoring, mapping, or spraying.