The potential benefits of bioconversion of commercial broiler poultry litter (PL) to vermicompost (with Eudrilus eugeniae) were studied by optimizing the carbon-nitrogen ratio (C/N) using coconut (Cocos nucifera) coir pith (CP) and farm yard manure (FM) as co-substrate. In this experiment, after optimizing the C/N at the levels of 25, 30, and 35, the pre-composting of the PL followed by vermicomposting was done, and the PL and FM alone were used as the control group. After pre-composting, the earthworms (Eudrilus eugeniae) were introduced, and the vermicomposting process was continued for 90 days and the vermicompost samples were analyzed on the 45th and 90th days. The study revealed a significantly (P

In agriculture, the intricate relationship between innovation, productivity, and entrepreneurship is underexplored. Despite the widely recognised role of innovation in driving productivity, concrete indicators and comprehensive farm-level studies are lacking. This research aims to unravel this complexity by exploring the impact of innovation, specifically in agricultural entrepreneurship, on transformative changes in farm productivity. Using a two-stage analysis with the Malmquist Index (MI) of total factor productivity (TFP) on cereal farms from the Farm Business Survey (FBS) over 11 years, the study identifies innovators and, it assesses changes in productivity, technical efficiency, and economic efficiency. The second stage decomposes the MI to understand factors enabling innovation, including changes in technology scale. Objectives include a literature review to map innovation variables, integrating FBS data for validation and assessing productivity impacts from asset enhancements and changes in farming scale. Results reveal significant productivity variation and a moderate overall improvement. Future research directions emphasize expanded data collection on managerial behaviours and technology investments' role in efficiency frontier shifts. The study concludes by emphasising nuanced agricultural policies that leverage farmers' knowledge for innovation through enhanced management efficiency. It advocates for a departure from the 'bigger is better' mentality, proposing educational programs and support services to encourage informed decision-making. This forward-looking approach aims to inform future policies and enhance understanding of the intricate dynamics between agricultural innovation, productivity, and entrepreneurship.

Highly diverse agroecosystems are increasingly of interest as the realization of farms' invaluable ecosystem services grows. Simultaneously there has been an increased use of uncrewed aerial systems (UAS) in remote sensing as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UAS and the attention on agroecosystems, there exists an opportunity to assess UAS practicality in highly biodiverse settings.  In this study, we utilized UAS to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and assembled accuracy assessments for each classification. To bolster our understanding of the classifications, we interviewed farmers to understand their thoughts on how these maps may be best used to support their land management. The average overall accuracy (53.9%), though relatively low, was expected for such a diverse landscape with fine-resolution data. After sharing imagery and land cover classifications with farmers, we found that while the prints were often a point of pride or curiosity for farmers, integrating the maps into farm management was perceived as impractical. These findings highlight that while remote sensing of diverse agroecosystems may provide a detailed way of estimating land cover classes and ecosystem services for researchers and government agencies for example these maps may be of limited use to land managers without additional interpretation.

Bovine mastitis (BM) has been causing great economic and financial losses in the dairy industry worldwide and one of its major pathogenic agents is S. aureus. BM treatment still relies on antibiotics. The extensive use of antimicrobials gives rise to bacterial strains such as methicillin-resistant S. aureus (MRSA) and mupirocin-resistant S. aureus (MuRSA). This study aimed to investigate and compare the prevalence of MRSA and MuRSA and antimicrobial susceptibility in S. aureus isolates from bovine mastitis milk in conventional and organic dairy farms. Milk samples were taken from mastitis-infected cattle in 6 conventional and 7 organic dairy farms from South Korea. According to the Food Codex, 163 suspected S. aureus colonies were isolated from the pooled milk samples from each farm. Further rapid coagulase test confirmed 11 out of 74 isolates from 4 conventional farms (CF1, CF2, CF3, CF4) while 17 out of 89 isolates from 3 organic farms (OF1, OF2, OF3) exhibited coagulase activity. Multiplex PCR amplification showed that the nuc gene marker for S. aureus was found in all coagulase-positive isolates from OF 1 and at least one isolate from CF1, CF2, CF3, and CF4. Conversely, only 2 isolates from CF2 contained mecA gene for MRSA while none had the mupA gene for MuRSA. Antimicrobial susceptibility testing revealed that conventional farm isolates were more resistant to antibiotics particularly, against ampicillin and tetracycline, and, in turn, could imply a potential to develop multidrug resistance if stringent measures to control antimicrobial use in dairy farms is not implemented properly.

Indirect calorimetry (IC) is a widely used method to study animal energy metabolism by measuring gas exchange. The accuracy of IC depends on detecting variations in signals reflecting the metabolic response, which can be challenging due to measurement noise and external factors. In this study, we propose a methodology to validate IC systems, including an easy-to-use spreadsheet for data computing, to verify accuracy and detect whole-system leaks. We conducted a re-covery test using a simulation of CO2 dynamics in MS Excel and injecting a known CO2 concentration into four respirometry chambers. The clearance rate of CO2 was observed and compared to the expected clearance rate from the simulation. The results from the recovery test in our system show that the proposed methodology is accurate and precise. The proposed methodology and recovery test can be used to standardize the validation of IC systems, providing accurate measurements of animal energy metabolism in different environmental conditions and energy utilization from feeds.

Based on data from the Yunnan Province farm household survey, we examine the effect of rural land rights policy on farmers’ income. The regression results show that right significantly raises the total income of farmers, with farm income serving as the primary source of total income. After performing numerous robustness tests, using instrumental variables to handle endogeneity and arriving at the same conclusion, the result is still valid. According to the heterogeneity analysis, in the sample of households with long-term migrant workers, the confirmation of rural land rights significantly increases total and nonfarm income while decreasing farm income. Furthermore, total income includes nonfarm income, which reflects the effect of different farmers' optimal labor allocation based on the external market environment. According to the impact mechanism anal-ysis, right can increase farmers' total income by promoting land transfer, and farmers in less developed areas are more willing to increase their income by land transfer out.

Scientists, politicians, and practitioners are debating the current structure of pig farms in Lithuania, as medium and small farms have almost disappeared over the past decade. The debated problem is whether the revitalization of medium and small pig farms would sustainably contribute to self-sufficiency in pork? Therefore, this research aims to determine which farms in terms of size could be the most perspective. In order to achieve this aim the multi-criteria evaluation method TOPSIS was used. The economic, production and environmental indicators of Lithuanian pig farms and pig farming sector in Lithuania and selected EU countries: Latvia, Estonia, Poland, Germany, and Denmark were analyzed in this research. The multi-criteria evaluation led to the conclusion that Danish pig farms were the best managed. Germany occupied the second place. The industrial large farms were found as dominated in those countries. Large pig farms (approximately two thousand sows) appeared most perspective in Lithuania: they took the first place in the years examined (2016–2021). The criterion estimate of their assessed indicators was much higher than of the medium (100 sows) and small (20 sows) farms. The main reasons: significantly higher labour productivity, lower cost, lower price, and better production indicators.

Wind energy is one of the most attractive renewable energy sources because of its low operating, maintenance, and production costs as well as its low environmental impact. The goal of this study is to discover the best locations in Bangladesh where wind farms can be built and operated efficiently. This study applied the GIS and AHP methodologies to examine the eight important parameters upon which the suitability of locations is highly dependent. This analysis finds that Bangladesh has large regions appropriate for wind farm installation, with 3718.76 km² and 16631.14 km² classified as "very high" and "high" suitability, respectively. It was also observed that wind speed, land slope, and elevation each had a height-weighted criterion of 32 %, 27 %, and 12 %, respectively, when picking suitable locations. However, the viability of this study in identifying suitable sites has been evaluated based on ROC and AUC techniques and found satisfactory as per AUC value. The knowledge gained from this study will help the sustainable and renewable energy development authority (SREDA), Bangladesh to expedite the renewable energy investment process and will ensure the great certainty. The findings of this research can be considered as baseline information in the wind energy sector.

Livestock marketplaces consist of infrastructure and rules through which information is exchanged and transactions are completed. Livestock trading incurs significant transaction costs, beyond just commission fees paid to marketplace owners (e.g., se-lecting, loading and transporting animals). In addition, animals being transported, held and transacted in a market setting increases the likelihood of injury or disease. The Covid-19 pandemic contributed to large-scale changes across typically physical marketplaces as stay-at-home orders and travel restrictions were invoked. Livestock marketplaces pivoted online rapidly, facilitated by the deployment of video feeds, al-lowing buyers to view/purchase animals via mobile phone. This shift offered the promise of mitigating many of the transaction costs associated with a fully physical marketplace. This study evaluated how computer vision (CV) approaches can con-tribute to the evolution of livestock marketplaces. It used livestock video data for 240 cattle (bulls) weighing 320-740 Kg from 16 different breeds to elicit additional trade-relevant data (livestock weight) and can be considered as an intermediary step to better understand how video data can be used to further digitalize livestock trade, price formation and transaction completion via technology solutions. The dataset comes from three sales held on different dates in a single mart location, which allows a consistent camera setup throughout. CV technologies were employed to solve the problem of cattle weight estimation and a distinctive achievement lies in curating a dataset cap-turing cattle in motion within the context of a cattle auction thereby injecting realism and complexity into the CV weight estimation task. Employing advanced object detection algorithms, the study created an intricate data pipeline to generate an appropriate modelling dataset and devised new multi-channel architectures for deeper analysis of diverse image inputs. The significance of sizable datasets and the challenge of man-aging inherent input variability for accurate CV regression is highlighted. Top-down view predictions demonstrate comparatively better performance, although failing to achieve benchmark precision. Challenges arise from camera angle variations, roaming behaviors, and breed diversity. Likewise, side-view predictions face challenges due to increased variability and lack of standardization. Despite this, these predictions offer insights into the value of incorporating Region of Interest (ROI) coordinates and data augmentation for enhanced performance. The study concludes with a multi-input ar-chitecture combining top-down and side-view predictions, demonstrating moderate success highlighting potential synergy between both perspectives. The central con-clusion resonates strongly – using standardized and multi-view images encompassing controlled movement and consistent poses could bolster accurate CV weight estima-tion.

Poultry farming in Slovakia has a tradition and an irreplaceable position to ensure rational nutrition for the population. In terms of a nutritional value, poultrymeat (Galliformes) contains a high amount of proteins, essential amino acids, essential unsaturated fatty acids, minerals, vitamins and a low fat content. Poultrymeat production has a relatively short reproductive cycle and thus allows regulating the demand and supply. The poultry market also influences the development of domestic consumption. According to the Statistical Office of the Slovak Republic, the consumption of meat has witnessed a persistent increase over the last 10 years. [1] According to the 2023 AVEC (The Voice of European Meat Sector) annual report, the consumption of poultrymeat in Slovakia was 20.0 kg per person per year. The average consumption in the EU-27 (2021) was 23.4 kg, with the countries with the highest consumption being Portugal (37.8 kg), Ireland (36.0 kg), Hungary (33.5 kg) and Spain (29.9 kg). On the other hand, the countries with the lowest consumption of poultrymeat, apart from Slovakia, also include Austria and Greece with a consumption of 21.5, resp. 21.0 kg [2]. The breeding of broiler chickens, as well as the production and placing on the market of poultrymeat must comply with the requirements laid down by legislation, while the compliance is regularly checked by the competent authorities.

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

Yaw misalignment can make a wake steer, which is an effective method to increase the power of wind farms, but it also increases the fatigue load of the turbines. In this paper, the method of combining yaw misalignment and pitch control (CYMP) is proposed to investigate the potential to mitigate wake interference, the wind velocity distribution and turbulence distribution in the wake, as well as the power and load changes of the wind turbine are studied, focusing on power in-crement and fatigue damage reduction. Although some studies have found that the method of CYMP are beneficial to increase power generation and reduce fatigue loads, there are no papers that have made a detailed analysis of the effects of wake turbulence, wind speed distribution, power, especially fatigue damage under this control. Simulation tool FAST.Farm is used to model and analyze wind farms, obtain the variation details of wake and provide simulation data for calculating power and fatigue load. The pitch control brings bias to the CYMP optimization results was neglected, the case studies show that the method of CYMP can reduce the fatigue load by 10.29% and increase the total power by 1.7% compared with only wake steering. The Model Predictive Control collaborative method based on CYMP is proposed, which can increase power by more than 2% and reduce thrust by more than 4% under 10m/s turbulent wind.

In recent years, farmers and policymakers have faced ample challenges and have struggled to support the sustainability of the agricultural sector. Sustainable agriculture encompasses multiple concepts, and its performance produces extensive debate about data requirements, appropriate indicators, evaluation methods, and tools. Under the European Union (EU) financed project FLINT (Farm Level Indicators for New Topics in policy evaluation), detailed data have been collected at the farm level to provide broader coverage of sustainability indicators on a wide range of relevant topics to facilitate the assessment of sustainability performance. The approach has been applied in a pilot network of representative farms at the EU level, considering the heterogeneity of the EU farming sector to provide data infrastructure with up to date information for sustainability indicators. This study aims to assess sustainability performance at the farm level in Greece. Representative and dominant agricultural systems, such as permanent crops, olive trees, arable crops, and livestock (sheep) farms, comprise the Greek sample. It uses the analytical hierarchy process (AHP) methodology and attempts to gain insights into the sustainability performance of agricultural systems. The outcome of the sustainability assessment reveals knowledge and develops support for strategic farm choices in order to support both farmers and policymakers towards more sustainable development plans. The results indicate that three typical Mediterranean farming systems, like permanent crops, olive trees, and extensive livestock systems (sheep farms), are more sustainable in contrast to intensive and arable crop farms.

This manuscript presented an autonomous home farm for predicting metrological characteristics that can not only automate the process of growing crops but also, due to a neural network, significantly increase the productivity of the farm. The developed farm monitors and manages the following indicators: illumination, soil PH, air temperature, earth temperature, air humidity, CO2 concentration, and soil moisture. The presented farm can also be considered as a device for testing various weather conditions to determine the optimal temperature characteristics for different crops. This farm as a result is completely autonomous grows tomatoes at home.

The use of wind energy has been developing fast over the last years. The global cumulative wind power capacity increased by 10.5% in 2019, most of which comes from onshore wind farms. One of the consequences of this continuous increase is the use of land for onshore wind farms. There are already cases worldwide where lack of well-established plans and strategies have caused delays in projects. The need for efficiently using land for wind farms will be mandatory in the short term. In this work, we present a numerical analysis to evaluate wind farm land-use. By defining the ratio between mechanical output power over an area as a parameter called land-use ratio, this work focused on comparing several cases of aligned and staggered layouts. Mechanical output power was estimated using a validated code based on Blade Element Momentum code, and the wake velocities and wake interaction effects were estimated using a validated wind turbine CFD model. In terms of output power, staggered designs are more efficient than aligned designs. However, the results showed that even though staggered designs produced higher output power, aligned farms with tight lateral spacing could be as efficient as staggered ones in terms of land-use but using fewer turbines. In summary, tightly aligned designs should be a tendency in the future towards efficient use of land in wind farms.

Rhizobia are biological nitrogen fixing non-pathogenic micro-organisms that make inert nitrogen available to legumes establishing symbiosis and living within their nodules and promoting their growth. Livestock farmers use antibiotics without prescriptions to prevent losses, death and economic wastes, resulting in indiscriminate release of excess, un-utilized antibiotics into the environment causing detrimental effects to soil microbes particularly rhizobia.This study evaluates the susceptibility pattern of selected rhizobia strains. The intrinsic antibiotic resistance (IAR) ability of common rhizobia strains including FA3, B574, RS15, USDA136, USDA9032, R25B, 532C, CC511, RANI22, RAUG1 were investigated. The antibiotics used included Ceftazamine , Florfenicol , Cefpodoxime, Sulphamethoxazole, Ciprofloxacin, Gentamycin , Kanamycin, Ertapenem, Tetracycline , Meropenem and Carbapenem. Results showed different resistance patterns among isolates where high resistance was shown against Carbapenem and least resistance was shown against Ciprofloxacin (40mm), Tetracycline (33mm), kanamycin (45mm) and Gentamycin (50mm). Rani 22 showed most resistant. Most strains showed no resistance to these antibiotics, but it is significant to note that those with resistance can be used for the recovery of antibiotics contaminated sites and may provide potential solutions for futuristic problems associated with AMR.This study suggests that using a consortium of symbiotic rhizobia strains is a strategy to achieve optimal yields in antibiotic contaminated fields as the strains showed differences in their susceptibility pattern. Also, the antibiotics produced or contained active compounds and substances that are detrimental for the growth of rhizobia and may therefore be a hindrance to these plant growth promoting bacteria when they are applied to aid plant growth used during agricultural practises.

The detection of Fasciola eggs in ruminant fecal samples typically occurs around 15 weeks post-infection during patent infections. The recent introduction of coproantigen ELISA (cELISA) diagnostics, which capture antigenic enzymes in fecal material, holds promise for monitoring Fasciola fecal egg count (FFEC) to implement control programs, particularly targeted treatments. This study compared two study groups, those with positive FFEC (FFEC+ve) and negative FFEC (FFEC-ve), to assess the sensitivity of Flukefinder sedimentation and a cELISA kit. This study reports that the coproantigen ELISA detection limit was 4.5 eggs per gram (epg) for a 100% positivity rate. Additionally, a moderate, statistically significant positive correlation (r²=0.716, p-value < 0.01) and an odds ratio of 1.96 (p-value < 0.01) were observed between FFEC and Fasciola coproantigen from cELISA. This implies that for every single unit increase in FFEC, there is a 96% likelihood of obtaining a higher optical density (OD) reading from the cELISA test. The results suggest that cELISA can complement fecal sedimentation tests, serving as a valuable tool for detecting cattle with high FFEC. This capability facilitates targeted treatment strategies, thereby aiding in the control and prevention of the spread of fasciolosis within the studied area.

Shrinking farm size and fragile farm resources pose a significant challenge to the sustainability of small-scale farms. Efficient resource utilization in small-scale farms is crucial to achieving farm sustainability through endogenous mechanisms. However, the precise mechanisms to integrate physical resources to achieve farm sustainability are not very clear yet. By capturing the interaction among farm resources as a network phenomenon, we identify the discrete resource interactions (RIs) in different types of small-scale farms of Indian Sundarbans, which are associated with higher farm sustainability. Thirty-two linkages, 11 reciprocal linkages, 22 triads, and three ‘core elements’ that occurred and cooccurred on highly sustainable farms are found to be critical in achieving farm sustainability. Using the properties of resource interaction networks as explanators of farm sustainability, we anticipate that sustainability in small-scale farms can be achieved by strategically creating new RIs on the farm. However, there may be limitations to such achievement depending on the nature of RI and type of farm. The analytical approach helps to understand the structural basis of sustainability in small-scale farms, and this approach can be used to achieve farm sustainability through the strategic integration of existing farm resources in the smallholder systems.

We present an automated system for nutrient solution management. Prior arts usually measure only pH and EC of the nutrient solutions for maintenance. When EC drops, they just simply add concentrated nutrient to the horticulture bed. Such approach can maintain the density of nutrient solution but cannot maintain the rates of individual ion particles. To prevent nutrition related disorders, fertilization methods with ion selective electrodes are widely introduced. This trend measures individual ion concentration of nutrient solution to maintain appropriate nutrient composition by supplying only insufficient ions. Many researchers have suggested ISE based automated fertilization systems. However, they failed to control a chemical artifact called ion interference effect, which becomes greater at higher density. Our system measures individual concentration of multiple ions and add only deficient nutrients, while handling the ion interference effect issue. To ensure the performance of ion selective electrodes, the system also performs fully automated 3-point calibration 24 times a day. A machine learning algorithm is applied on the sensory parts to remove ion interference effect which make measurement of complex solution with ISE almost impossible. With automated calibration and signal processing technology, the system robustly and continuously maintains nutrient condition for plants. We suggest applying this system on closed hydroponic systems such as smart farms or plant factory, to reduce water consumption and to provide more appropriate environment for the crops.

A study was conducted to determine the estimated parasitic infestation and associated risk factors (Water quality, Farm management practices and External factors such as Intermediate hosts and Wild fish entry) of farmed Nile tilapia in Pond grow-out, Cage grow-out (lake), Cage grow-out (reservoir) farms and Hatcheries in the Lake Victoria Crescent, Uganda. Sixteen parasite genera and 65% (418/640) infestation rate were obtained. However, Pond grow-out farms and Hatcheries did not only constitute 81% and 63% of the parasite genera, but also had the highest mean number of parasite genera per farm of 1 to 7 and 4 to 8 respectively. Cage grow-out (reservoir) farms and Hatcheries had the highest mean prevalence of >0.7. Water quality parameters, farm management practices and external factors varied across the 4 farming systems, with Cage grow-out farms (lake) with the best water quality parameters, farm management practices and control over intermediate hosts and wild fish entry. Using Spatial Areal Unit Modelling with Conditional Autoregressive Priors, out of 16 risk factors, only Intermediate hosts had a significant effect on estimated parasitic infestation. Estimated parasitic infestation of 0.28 (low) or 0.55 (high) prevalence was obtained. All Cage grow-out (reservoir) farms and Hatcheries, and 78% (14/18) of Pond grow-out farms had high estimated parasitic infestation despite their locations, while 75% (3/4) of Pond grow-out farms that had low estimated parasitic infestation were located in Masaka (an area with many minor and less polluted rivers). Fifty-six percent (5/9) of Cage grow-out (lake) had low estimated parasitic infestation and were located in the Southern part of Wakiso, Southern part of Mukono and Southern part of Jinja but far from the Napoleon Gulf. The Cage grow-out farms with high estimated parasitic infestation were found in the Southeastern part of Buikwe (an area with sugar cane plantations and factories) and Southern part of Jinja in the polluted Napoleon Gulf. Therefore, the type of farming system and its location (spatial component) need to be given at-most importance when coming up with management and sanitary control strategies to encounter parasite infestation.

Using LSMS-ISA panel data, we analyzed determinants of diversification into Rural Non-Farm Enterprises (RNFEs) and dynamics of the sector by studying the transition between farming and both low- and high-return RNFEs. We found that diversification into RNFE is determined by household characteristics mainly age and gender of the household head, whereas initial capital endowments such as assets, human capital, land and livestock significantly affect level of participation than entry into RNFE. Furthermore, analysing dynamics of RNFE separately for the youth and matured households, our study finds that continuous participation in RNFE is higher among the youth than among matured households. Similarly, initial capital endowments mainly educational level and access to credit are important factors for the youth to transit to high return RNFE, implying that entrepreneurial skills development and access to finance that enhance investments and gradual accumulation of capital are vital for the youth to benefit from RNFEs. Moreover, shocks that affect agricultural productivity are significant in increasing the probability of transition out of high-return RNFE signifying the need for social protection measures to ensure growth of RNFEs.

Wind energy is a rapidly expanding renewable energy technique. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind-farm performance. In this research, we investigate the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind-turbine array on various rough topographies. First, we evaluated our LES method through mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyze the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results demonstrate that the ground roughness element’s flow resistance boosts large wind-farm power production by almost 68% in fully aerodynamic rough surface compared to flat terrain. Dispersive stress analysis revealed that the primary degree of spatial heterogeneity in the wind farm is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress is almost 45% to the overall drag. We also observed that the power performance of the wind farm in complex terrain outperforms the drag. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.

The present work proposed a low-cost portable device as an enabling technology for Smart Farms using multispectral imaging and Machine Learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed an good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

The exponential growth of population and the consistent food demand has compelled humanity to seek alternatives to traditional farming and innovative technologies to increase production. Exploring offshore for natural resources and alleviating pressure on land has been an ongoing research field, especially in the energy and aquaculture sector. However, the idea of floating farming is still in its infancy and requires significant innovations. The work presented here shed further light on this area by proposing a comprehensive model of ‘Integrated, multicultural, Multileveled Floating Farm (MFF).’ Various aspects of planning, design, constructions and operations of such MFF are discussed. An integrated waste management system is proposed to improve sustainability. The conceptual design and associated financial analysis demonstrated that such integration of various modes of farming could be profitable and sustainable at the same time. The cost estimation and profit analysis are presented in the context of Singapore, and a conservative approach is followed for the calculation. However, the model can easily be extended for application in other countries.

Amplitude modulation (AM) is a characteristic feature of wind farm noise and has the potential to contribute to annoyance and sleep disturbance. This study aimed to develop an AM detection method using a random forest approach. The method was developed and validated on 6,000 10-second samples of wind farm noise manually classified by a scorer via a listening experiment. Comparison between the random forest method and other widely-used methods showed that the proposed method consistently demonstrated superior performance. This study also found that a combination of low-frequency content features and other unique characteristics of wind farm noise play an important role in enhancing AM detection performance. Taken together, these findings support that using machine learning-based detection of AM is well suited and effective for in-depth exploration of large wind farm noise data sets for potential legislative and research purposes.

Safety and health issues are growing concerns in the agricultural sector among farm-workers in South Africa. The current health pandemic arising from the corona virus has thrown these issues into the spotlight, and this study explored the perceived usefulness and perceived ease of using personal protective materials among farm-workers in the banana sector. Using a case study of 10 large farms in the lower south coast of South Africa, we utilized descriptive and inferential analysis to identify the demographic composition of farm-workers in the study area, examine their perception of specific personal protective materials, and determine the relationship between demographic characteristics and perception of personal protective materials. Farm-workers in the study area were found to be predominantly single black males aged between 36-55years, with no more than a primary education, with work experience of between 6-10 years and employed as unskilled farm-labourers. Perceived usefulness (83%) and perceived ease of use (79%) for personal protective materials was high. Respondents gender (p=0.012), marital status (p=0.029), level of education (p=0.035) and farm-work experience (p=0.008) were significant, while their age (p=0.057), population group (p=0.160) and work classification (p=0.203) were not found significant in determining perceived usefulness or perceived ease of use. Our study makes valuable contribution to the existing body of knowledge regarding farm-worker safety issues by exploring perception of personal protective materials.

In an age of market liberalization, globalization, and expanding agribusiness, there is a danger that small-scale farmers will find difficulty in fully participating in the market economy. One of the solutions to solve the above situation is to strengthen linkages between farmers and firms through contract farming. The purpose of this study is to assess the factors affecting the level of linkages between businesses and farmers in crop development in Thai Binh province, Vietnam. The two main research methods used were exploratory factor analysis (EFA) and regression analysis with survey data which was conducted from 300 farmers, 10 firms, and 15 factors related to contract-farming. The results indicated that there are 5 factors that significantly affect the level of participation of farmers in the linking process, the order of impacts from strong to weak is as follows: (1) Commitment and sharing of benefits and risks between farmers and firms in the linking process, (2) Issues related to management skills and enterprises capacity of link firms, (3) Price issues that farmers are interested in in the process of implementing links with firms, (4) Policy environment - Institutions related to farmer contracts between firms and farmers, (5) Household awareness about the benefits brought about by farmer contracts with firms. Based on results, a number of specific solutions have been proposed to enhance the efficiency of farming contracts between firms and farmers in cultivation production.

The objective of this research is to show the family farms as an energy consumers and producers from renewable sources. It is a possibility for them to reduce operating costs. Time scope of the study covers the period 2014-2022. The data source is the Farm Accountancy Data Network. On the basis of the value of energy output and energy costs the rankings were prepared. After the ranks were awarded, the strong position of the Netherlands and Germany was indicated. It has been shown that the production and consumption of energy depend on the economic size of the farm, and do not depend on the type of production. As such, panel models were built for energy output and costs. The determinants that affect them were indicated. Energy output depends on total inputs – the higher they are, the higher the output. The energy costs are related to utilized agricultural area, total output and family farm income. The results obtained are discussed within the framework of recommendations of the European Commission.

This paper proposes the GD (Geometric Distribution) algorithm, a novel approach to enhance the default Adaptive Data Rate (ADR) mechanism in the Long Range Wide Area Network (LoRaWAN). By leveraging the Probability Mass Function (PMF) of the GD model, the GD algorithm effectively addresses massive device distribution challenges in real-life scenarios. To evaluate the algorithm's performance, the LoRaWAN simulations were conducted using the fixed node pattern derived from actual locations of dairy farms in Ratchaburi province, Thailand. The research established scenarios for assessing the network's performance namely, DER (Data Extraction Rate) and SF (Spreading Factor) assignment. Comparative analyses were performed against the uniform random node pattern and established algorithms, including the default ADR scheme, EXPLoRa, Quantile Classification of Variance from the Mean (QCVM), and Standard Deviation (SD). The GD algorithm demonstrated significant improvements over existing methodologies for both fixed and uniform random patterns. The fixed pattern exhibited an enhancement of 14.3%, while the uniform random pattern showed 4.8% enhancement over the default ADR scheme. Further assessments covered the coverage area, payload size, and energy consumption. The GD algorithm consistently achieved the optimal DER values with a coverage area and payload size, albeit often at the expense of increased energy consumption.

The article presents a methodology for modeling the impact of factors of both on-farm activity and environmental factors on the resulting indicators of an international company. The dataset includes the company's profit, revenue, valuation, share price, and market share from 2012 through 2022. This empirical period is optimal for such a type of modeling. Modeling the impact of carbon emissions factors on the resulting indicators is considered based on complex systems theory. Endogenous model variables include indicators of the development of the international company. Predefined model variables reflect the influence of both the external environment and internal factors on the development indicators of an international company. An approach of picture fuzzy rough sets based on time series of endogenous and exogenous variables can provide an opportunity to analyze and consider the consequences of feedback changes in the systems of which they are a part. Based on the results of the analysis of the model construction, it is concluded that picture fuzzy rough sets can be an excellent way to model interdependent social processes.

This article presents the suitability analysis of onshore wind farm sites using an AHP (Hierarchical Analysis Process) multi-criteria decision-making (MCDM) approach based on geographic information system modeling (GIS). This analysis is based on different technical aspects (wind speed, elevation, slope), economic (proximity to the electricity network, proximity to roads) and social (proximity to residential areas, population density). The introduction of the factor “population density” and the constraint “required area” rarely used in previous studies makes it possible to refine the results. The data is classified into several categories in a gradual manner, namely unsuitable (0); less suitable (1); suitable (2); highly suitable (3) and most suitable (4). The results of the study show us that the largest percentage relating to spatial coverage is observed in the “suitable” range with 50.89% and the corresponding TWPP (theoretical wind power potential) is 1377.926 GW; it is followed by “highly suitable” 5.67% and a TWPP of 153.652 GW; then “less suitable” with 1.04% and a TWPP of 28.163GW. No areas were identified in “most suitable” and finally the “unsuitable” range with 42.4%. The results show that the most favorable areas are located in the part of the far north of the country corresponding to the strong wind zone. The sensitivity analysis was carried out based on scenario tests (technical, economic and equal-weight) in order to give greater visibility of choice to the different stakeholders who would like to invest in onshore wind energy in Cameroon. This study is useful in more than one way because the results obtained can help investors, the government and other stakeholders to identify potential areas for the deployment of wind energy systems in Cameroon. Furthermore, a study of hybrid solar-wind systems could improve the efficiency of electricity parks in Cameroon.

Farm fragmentation is the occurrence of numerous and often discontinuous land parcels associated with a single farm. Farm fragmentation is considered to be a defining feature of Northern Ireland’s (NI) agricultural landscape, influencing agricultural efficiency, productivity, and the spread of livestock diseases. Despite this, the full extent of farm fragmentation in cattle farms is not well understood, and little is known of how farm fragmentation either influences, or is influenced by, different animal production types. This study describes and quantifies farm fragmentation metrics for cattle enterprises in NI, presented separately for dairy and non-dairy production types. We find that 35% of farms consist of five or more fragments, with larger farms associated with greater levels of farm fragmentation, fragment dispersal and contact with contiguous farms. Moreover, this was particularly evident in dairy farms, which were over twice the size of farms associated with non-dairy production types, with twice as many individual land parcels and twice as many fragments. We hypothesise that the difference in farm fragmentation and farm size between dairy and non-dairy production types is associated with the recent expansion of dairy farms after the abolition of the milk quota system in 2015, which may have driven the expansion of dairy farms via the acquisition of land. The high levels of land fragmentation, fragment dispersal and contiguous contact observed in NI cattle farms may also have important implications for agricultural productivity and epidemiology alike. Whilst highly connected pastures could facilitate the dissemination of disease, highly fragmented and parcellised land could also hamper productivity via diseconomies of scale, such as preventing the increase of herd sizes or additionally, adding to farm costs by increasing the complexity of herd management.

Protected areas have been used worldwide to conserve natural resources. Nevertheless, economic activities to provide income for communities living inside and surrounding conservation areas remain an issue. This study aimed to assess the sustainability of a Deuterodon iguape hatchery, placed inside an Atlantic Rainforest Park, to leverage this small native fish's grow-out farming, affording income and food security for local families. We have used a set of indicators of economic, social, and environmental sustainability. The initial investment is about US$ 40,000, which returned in ~2 years. The internal rate of return is close to 50%, including the externality costs, which is attractive for public and private investments. The hatchery generated few direct jobs, but the workforce can be recruited in the community, and hatchery can enable the establishment of several small grow-out farms, leveraging the development of indirect jobs and self-employments. The system had a low environmental impact, showing minor release of pollutants, low risk for biodiversity, and absorption of 18 g of CO2 equivalent per thousand post-larvae produced, contributing to the struggle against climate change. Therefore, D. iguape hatchery demonstrates the potential of combining biodiversity conservation and income generation, meeting the Sustainable Development Goals of Agenda 2030.

The work is devoted to assessing changes in the water regime of the Lan River in modern conditions and the near future with the aim of developing a strategy for the rational use of the water potential of small rivers in the Belarusian Polesie region. There was a tendency towards an increase in annual runoff at the Loktyshi and Mokrovo gauging stations during the study period from 1948 to 2015, which was caused by anthropogenic factors in the form of amelioration measures. Testing the hypothesis about the homogeneity of the runoff series under consideration for periods with different averaging intervals has showed heterogeneity for some types of runoff. This was caused by intensive economic activity, which significantly disrupts the natural hydrological regime. A runoff forecast was made for the Lan River at the Mokrovo gauging station for the period up to 2035 based on archive of meteorological data using a multi-model ensemble of four CMIP5 scenarios. A slight decrease in runoff was predicted, caused by additional evaporation from the water surface of the reservoir due to increased air temperature, as well as a slight shift in the peak of the spring flood to March. Results of calculating of permissible withdrawal of surface water from the Lan River for the needs of the Loktyshi fish farm have shown great influence of the fish farm on the river runoff volume that requires strict adherence to scientifically based regimes for managing the water regime of the Loktyshi Reservoir and the Loktyshi fish farm.

Green hydrogen is likely to play an important role in meeting the net zero targets of countries around the globe. Hence, producing green hydrogen cheaply and effectively is an important area of research. One potential option for green hydrogen production is to run electrolysers directly from offshore wind turbines, with no grid connection and hence no expensive cabling to shore. The removal of the grid presents an unusual integration challenge. The variable nature of wind turbines and farms results in a power output that can fluctuate more quickly than the electrolyser’s ability to respond without significantly stressing the electrolyser. Thus, the use of a battery, with the wind farm, becomes essential to even out some of the power variations that the electrolyser cannot deal with. In this work, a proof of concept of a wind farm control methodology designed to reduce variability in wind farm active power output is presented. Smoothing the power supplied by the wind farm to the battery reduces the size and number of battery charge cycles and helps to increase battery lifetime. Considering off-grid wind farms which exclusively power an electrolyser, this work quantifies the impact of the wind farm control method on battery lifetime for wind farms of 1, 4, 9 and 16 wind turbines. This is achieved using suitable wind farm, battery and electrolyser models. As an example, for the largest wind farm studied, consisting of 16 x 5 MW wind turbines, batteries with a lifetime of 15 years have approximately a 30 % reduction in required capacity (reduced from 14 MWh to 10 MWh) compared to operating without wind farm control. It is found that reducing the variability of the active power output of wind farms through the wind farm control methodology presented can have a significant impact on battery degradation and hence on battery lifetime. Hence, wind farm control can reduce the required battery capacity for a given lifetime or it can increase the lifetime of a given battery capacity. The work presented shows that wind farm control could play a critical role in reducing the levelised cost of green hydrogen produced from wind farms with no grid connection and paves the way for the design and testing of a full implementation of the wind farm control.

Market channel choice makes important contributions to the incomes and other livelihood attributes among smallholder farmers in developing countries. Often considered from a number of perspectives, the dominant view articulated suggests an advantageous integration into formal market channels. This position is questioned as it has implications for smallholder farmers’ food security and rural incomes. Using a mixed methods approach, the study collected primary data from 174 smallholder farmers and applied both a descriptive and multinomial logit regression model to analyze factors contributing to cocoyam production and market channel choices among respondents in the study area. Findings indicate that financial returns and available markets were key factors in cocoyam production, while amount received was a driver of market channel choice among 89% of respondents who sold directly at farm-gate. Farmers’ age (p=0.044), household size (p=0.043), distance to market (p=0.021), additional income (p=0.017) and amount received (p=0.014) were significant variables (p<.05) in the determinants of market channel choice. The study recommended improving market information provision and strengthening farmer associations which will enable smallholder farmers in rural communities to make informed choices with respect to produce price, access other markets and consolidate their collective market bargaining position.

Dairy calves are susceptible to a great range of welfare and health issues up to the age of weaning. The aim of the current pilot study was to evaluate incidence of the main health disorders affecting dairy calves up to one year of age. The study was carried out at the Experimental Farm of the Research and Development Institute for Bovine Balotesti, Romania, were health data was collected for two consecutive years, between November 2017 and October 2019, from a number of 176 purebred Romanian Black and White calves. Diarrhea had the highest incidence in un-weaned calves (0-3months old), of 29.55±0.34%, significantly higher (p≤0.001) compared to 3-6 months (2.16±1.24) and 6-12 months age groups (5.88±2.17), respectively. Coccidiosis incidence was on average of 39.20±0.36% in un-weaned calves, 1.44±1.01% in the 3-6 months group and of 5.04±2.01% in the 6-12 months group, significantly higher (p≤0.001) in the 0-3 months age group, compared to older calves.

Botulinum neurotoxins (BoNTs) are emerging as multipurpose therapeutic compounds for the treatment of several different syndromes involving peripheral and central nervous systems, and muscular and musculoskeletal disorders both in human and veterinary medicine. Therefore, the studies of BoNTs are rapidly developing and identifying newly produced BoNT variants. Efforts should be made to clarify the biological and pharmacological characteristics of these novel BoNTs as well as the natural ones. The high potential of BoNTs as a therapeutic compound for medical syndromes lies on its ability to reach a specific cell type bypassing other cells, thus having mild or no side effects. In this paper the recent developments on BoNTs are reviewed with the aim to analyze the current knowledge on BoNTs biological mechanisms of action, immunogenicity, formulations, and therapeutic applications in the veterinary field, highlighting advantages and drawbacks and identifying the gaps to be filled in order to address research priorities.

This study aimed to quantify the extent of heavy metal, non-metal and metalloid level of Campomanesia adamantium pulp obtained from area crossed by road of the high large vehicle traffic and intensive agriculture modern farm, and for monitoring the health risks associated with pulp human consumption. For this purpose, three spots located between this area, ripe fruits were collected in roadside, bush and margin-farm. Pulp samples were digested by microwave-assisted equipment, and mineral elements were quantified by ICP OES. The mineral elements average demonstrated no statistical difference observed between this pulp (p > 0.05). The heavy metals and metalloid concentrations that exceeded FAO/WHO standards are ordered Pb > As > Mo > Co > Ni > Mn > Cr. Therefore, among these metalloid and heavy metals, As, Pb and Cr were found higher in farm-margin > roadside > bush (1.5 × 10^-3, 1.1 × 10^-3 and 6.2 × 10^-4) respectively. Therefore, As is the most important metalloid with higher levels in farm-margin, roadside and bush (1.5 × 10^-3, 1.1 × 10^-3 and 6.2 × 10^-4 > 10^-6–10^-4 and 3.33, 2.30 and 1.34 > 1) respectively, to total cancer risk and hazard quotient, if 100 g daily of pulp are consumed.

Recently, there has been renewed interest in the performance, functionality, and sustainability of traditional small-scale storage interventions (check dams, farm bunds and tanks) used across India for the improvement of local water security. The Central Groundwater Board of India is en-couraging the construction of such interventions for the alleviation of water scarcity. It is of critical importance to understand the hydrological effect of these interventions at basin scales to maximise their effectiveness. The quantification of small-scale interventions in hydrological modelling is often neglected, especially in large-scale modelling exercises. A bespoke version of the GWAVA model was developed to assess the impact of interventions on the water balance of the Cauvery Basin and two smaller sub-catchments. Model results demonstrate that farm bunds appear to have a negligible effect on the estimated average annual simulated streamflow at the outlets of the two sub-catchments and the basin whereas tanks and check dams have a more significant effect. In-terventions generally were found to increase evaporation losses across the catchment. The model adaption used in this study provides a step-change in the conceptualisation and quantification of the consequences of small-scale storage interventions in large- or basin-scale hydrological models.

The objective of this work was to investigate the use of remotely sensed vegetation indices to improve the quality of yield maps. The method was applied to the yield data of twelve cornfields from the Data Intensive Farm Management project. The results revealed the need to time shift the yield values up to three seconds to better match the sensor readings with the geographic coordinates. The residuals of the yield prediction model were used to identify points with unlikely yield values for that location, as an alternative to traditional approaches using local spatial statistics, without any assumption of spatial dependence or stationarity. The temporal and spatial distribution of the standardized coefficients for each experimental unit highlighted the presence of trends in the data. At least five out of the twelve fields presented trends that could have been induced by data collection.

To address global warming, there is a worldwide effort to establish carbon-neutral energy plans. Given the characteristics of renewable energy generation, a diverse 'Energy Mix' consisting of various energy sources such as wind and solar power is crucial. In particular, the wind power is expected to continue expanding globally due to its sustainable nature. Furthermore, the expan-sion of offshore wind farm is making essential and a rapidly growing industry sector to be achieving carbon neutrality with limited land for construction. To facilitate the construction and operation of offshore wind farms, specialized vessels with dynamic positioning (DP) systems are essential such as Wind Turbine Installation Vessels (WTIVs) and Service Operating Vessels (SOVs). The purpose of this study is to predict and analyze the scale and structural changes in the fleet capacity of DP vessel, which is essential for offshore wind power construction as the offshore wind power industry develops using System Dynamics (SD) modeling. Specifically, this study aims to construct a demand forecasting model for DP vessel used in offshore wind farm con-struction based on SD methodology. The study aims to identify influencing factors by analyzing dynamic changes in system configurations over time and establishing causal relationships based on feedback structures. Instead of traditional time series analysis methods, causal relation-ship-based simulations were conducted for quantitative analysis. The results provide insights into the changes in the DP vessel market structure due to the emergence of the new industry and highlight areas that require improvement throughout the industry.

Contrary to pig farming in developed western countries, in a large part of the world, pigs are still traditionally kept, in small backyard farms, usually for family needs. Their main characteristics are low biosecurity, swill feeding, natural breeding and uncontrolled trade. Given the high number of backyard farms in Serbia and the risk they are thought to pose to intensive pig farming, the main aim of this study was to evaluate the prevalence of major viral diseases of swine among traditionally kept pigs in small holdings with low biosecurity. For this investigation, 222 serum samples from 69 backyard holdings were randomly selected and tested for PRRS, Aujeszky`s disease, PPV infection and Swine influenza by ELISA. The herd-level seroprevalence of PRRS, Aujeszky`s disease and PPV infection was 2.9%, 27.5% and 37.7%, respectively. Swine influenza seroconversion was not confirmed in any of the tested holdings. Despite widely distributed PPV and AD in backyard farms in Serbia, almost 50% of them are still negative for the all-tested disease. The backyard farms must be monitored, and owners educated as their role in eradication programs and obtaining country-free status may be crucial.

Rising sensitivity of the loads with respect to power quality has grown-up the consideration of power system study and power quality enhancement systems. The fluctuation of voltage outside the normal working range due to faults may lead to unsuitable interruption of wind turbines. This thesis present 1.5MW grid connected Adama Ⅰ Wind Farm in Ethiopia with the objectives to capture the optimal power from the wind and ensure voltage source for sensitive load. This paper deals with the effective voltage sag mitigation and Harmonic distortion effectively met IEEE 519-1992 standard under all Phase to ground fault compensation by using Dynamic Voltage Restorer (DVR), to regulate the terminal voltage of the wind farm and safe operation of sensitive load. The DVR utilizes a feed forward control-based algorithm to generate PWM. The simulation results are going to be carried out by MATLAB-Simulink to verify the operation and effectiveness of DVR during balanced voltage sag and swell conditions.

Enteropathogens, namely Salmonella and Campylobacter, are a concern in global public health and have been attributed in numerous risk assessments to a poultry source. During the last decade a large body of research addressing this problem has been published. The literature reviewed contains review articles on certain aspects of poultry production chain, however in the past decade there hasn’t been a review on the through production chain, farm to fork, production of poultry. This review, a pool of 514 articles were selected for relevance via a systematic screening process (from >7500 original search articles). These studies identified a diversity of management and intervention strategies for the elimination or reduction of enteropathogens in poultry production. Many studies were laboratory or limited field trials with implementation in true commercial operations being problematic. Entities considering using commercial anti-enteropathogen products and interventions are advised to perform an internal validation and fit for purpose trial as Salmonella and Campylobacter serovars and biovars may have regional diversity. Future research should focus on non-chemical application within the processing plant and how synergistic through chain intervention may contribute to reducing the overall carcass burden of enteropathogen, coupled with increased consumer education on safe handling and cooking of poultry.

Nations of the world have seen unprecedented changes in climate variables in recent decades. But it is unclear to what extent climate change has impacted and will impact food systems in some developing regions, and how policymakers can frame an approach to encouraging adaptation and advancing climate-smart agriculture. Many studies attempting to link agroecology to climate change adaptation do so without understanding the potential of Agroecology not only to mitigate climate change – which is the weak response – but to reverse its impact and ‘climate proof’ our food systems. By modeling the near and far future impacts of climate change on crop production, we showed how climate will impact crop production under two crop production systems (agroecology and non-agroecology production systems). The overarching aim is to derive sustainable development strategies and lessons for policymakers and climate researchers - essential components of environment and Agricultural development. Using case studies from Nigeria, we observed that transitioning to agroecology, even at the farm level also transforms farm designs, thereby affecting their overall food and nutrition status. The result showed that the use of agroecology management practices not only reduces the impact of climate change in the near future but will also lead to increased crop yield in the future. The finding suggests that to feed the over 400 million projected population of Nigeria by 2050, the use of agroecological practices will be a better alternative to the conventional farming methods. To advance the use of agroecological farming methods, governments at every level in Nigeria need to mainstream organic agriculture in national government policies. This is important as it will not only address climate change impacts but also hunger and poverty.

The maintenance of traditional agricultural practices in protected small islands is closely linked to the preservation of the land, the protection of natural resources, and the conservation of local traditions. For this purpose, the Tuscan Archipelago National Park promoted the census of ancient horticultural varieties preserved by some farmers on the island of Elba. The survey revealed a deep-rooted historical, cultural, social, and economic link between this germplasm and the island’s territory. A scientific characterization of the landrace under ethnobotanical, morphological, and genetic aspects was carried out. Among these local varieties, two landraces of the Brassica species and three landraces of the Phaseolus species were identified and analyzed in this study. To ascertain their distinguishability and uniqueness, similar varieties were also identified based on morpho-physiological characteristics or territorial proximity and compared with the Elban landraces. Morpho-phenological data were collected, and molecular analyses were carried out using Simple Sequence Repeats (SSR) markers. Ten different SSR loci were considered for the Phaseolus species and nine for the Brassica species. The Tuscan Archipelago landraces showed a high level of internal variability but were clearly distinct from each other and from the analyzed varieties, as shown by AMOVA (Analysis of Molecular Variance). Their distinctness was further highlighted by the UPGMA dendrogram obtained from the genetic distance matrix. The same results were confirmed by Principal Coordinate Analysis (PCoA). This study confirms the peculiarity of the Elban landraces, their biodiversity is a heritage to be safeguarded, which can also be used in future breeding programs. These ancient Elban landraces were allocated to local farmers (most were young farmers) to protect them from extinction and to promote more resilient varieties cultivation compared to commercial hybrids widely spread in the area. Local farmhouses also play a strategic role in safeguarding the germplasm and dissemination of sustainable agricultural practices. There is high demand for ancient Elban landraces, this has led to the development of a new local economy balanced between agricultural tradition and innovation.

In the context of globalization One Health enables and concurs to the achievement of Global Health. One Health approach involves wide knowledge and understanding of its critical factors, next to an adequate implementation, also in farm animal sector. Moreover, animal farming and breeding shows particularities depending on country. Therefore, well-defined and specific strategies are still needed to be developed and adapted to the current conditions found in various countries for ensuring a well implementation, thus driving to better and healthier global outcomes and security. This paper will explore the One Health status in Romania, highlighting the current progress registered until now and the future perspectives based on its potential, in the context of the livestock farming framework and challenges. Our findings may help to disclose the challenges of putting in practice One Health in Romania, alongside to the pathways showing potential to enhance sustainability in livestock farming.

This paper investigates the hydroelastic responses of offshore floating solar photovoltaic farms (OFPVs) to minimize structural motion. The OFPV usually occupies a large sea space in the order of hectares and due to its huge structural length-to-thickness ratio, the structural deformation under wave action has to be taken into consideration. The flexible deformation of the structure under hydrodynamic loading is termed the hydroelastic response. Due to the relatively long structural length with respect to the encountering wavelength, the diffraction and radiation of waves have to be taken into account to accurately represent the hydrodynamic loadings on the floating platform. The numerical model is first validated by comparing the eigenvalues and eigenvectors of the OFPV obtained from the proposed numerical scheme with their counterparts obtained from an established finite element software. This is followed by the investigation of the hydroelastic response of various OFPVs designed in varying layout configurations. The various layout configurations are obtained by altering the floating modular units’ dimensions as well as the spacing of the OFPVs when deployed adjacent to each other. The optimal configuration that gives the best performance in terms of the overall smallest response, known as compliance, is then suggested. The results suggest that a longish OFPV layout has a lower hydroelastic response and the motion could be further reduced by increasing the global flexural stiffness via layout arrangement.

Precision agriculture and open-source data repositories provide a plethora of field-specific ecological data about agroecosystems, but few mechanisms have been developed to turn that information into management recommendations for crop production. The On-Farm Precision Experiments (OFPE) framework is an agroecological model-based methodology to improve crop manager’s abilities to make field-scale agronomic input decisions. This work evaluates the use of field-specific experiments that employ open-source data and the data emanating from precision agriculture technologies to gain local knowledge of the spatial and temporal variability in agroeconomic performance at the sub-field scale. Quantification of the temporal variability in crop response to inputs (e.g., crop seeding rates, crop rotations, fertilizers, other soil amendments, pesticides, etc.) allows for estimation of the probability that a future management scenario will outcompete another, in terms of crop yield, crop quality, farmer net return, or environmental quality. The challenge is to integrate OFPE into applied management with minimal disruption of stakeholder practices while drawing on historic knowledge about the field and economic constraints. OFPE is the basis of a decision support system that includes a six-step cyclical process that harnesses precision agriculture technology to apply experiments and gather field-specific data, incorporates modern data management and analytical approaches, and generates management recommendations as probabilities of outcomes. The OFPE framework allows field managers to assess the tradeoffs in agronomic input management between the maximization of crop production, quality and profits from production while considering environmental effects.

Bovine tuberculosis (bTB) remains a challenging endemic pathogen of cattle in many parts of the globe. Spatial clustering of Mycoacterium bovis molecular types in cattle suggests that local factors are the primary drivers of spread. Northern Ireland’s agricultural landscape is comprised of highly fragmented farms, distributed across spatially discontinuous land parcels, and these highly fragmented farming structures are thought to facilitate localised spread. We conducted a matched case control study to quantify the risks of bTB breakdown with farm area, farm fragmentation, fragment dispersal, and contact with neighbouring herds. Whilst our results show small but significant increases in breakdown risk associated with each of farm fragmentation, farm area, fragment dispersal, and contact with neighbouring herds, these relationships were strongly confounded with the number of contiguous neighbours with bTB. Our key finding was that every infected neighbour led to an increase in the odds of breakdown by 40% to 50%, and that highly fragmented farms were almost twice as likely to have a bTB positive neighbour compared to non-fragmented farms. Our results suggest that after controlling for herd size, herd type, spatial and temporal factors, farm fragmentation increasingly exposes herds to infection originating from first order spatial neighbours. Given NI’s particularly fragmented landscape, and reliance on short-term leases, our data supports the hypothesis that between-herd contiguous spread is a particularly important component of NI’s bTB disease system.

In the last decades farm animals kept in confined and mechanically ventilated livestock buildings are increasingly confronted with heat stress (HS) due to global warming. These adverse conditions cause a depression of animal health and welfare and a reduction of the performance up to an increase of the mortality. To facilitate sound management decisions, livestock farmers need relevant arguments, which quantify the expected economic risk and the corresponding uncertainty. The economic risk was determined for the pig fattening sector based on the probability of HS and the calculated decrease in the gross margin. The model calculation for confined livestock buildings showed, that HS indices calculated by easily available meteorological parameters can be used for assessment quantification of indoor HS, which is so far difficult to determine. These weather-related HS indices can be applied not only for an economic risk assessment but also for a weather-index based insurance for livestock farms. Based on the temporal trend between 1981 and 2017, a simple model was derived to assess the likelihood of HS for 2020 and 2030. Due to global warming, the return period for a 90-percentile HS index is reduced from 10 years in 2020 to 3-4 years in 2030. The economic impact of HS on livestock farms was calculated by the relationship between an HS index based on the temperature-humidity index (THI) and the reduction of the gross margin. From the likelihood of the HS and this economic impact function, the probability of the economic risk could be determined. The reduction of the gross margin for a 10 year return period was determined for 1980 with 0.27 € per year and animal place and increased by the 20-fold to 5.13 € per year and animal place in 2030.

This paper aims in studying the effect of vermicompost on soil and growth of the plant Sesamum indicum L. by measuring the its various growth and yield components. For this purpose, a mixture of textile mill sludge, cow dung and saw dust have been mixed in different ratios to produce vermicompost by using the earthworm Perionyx excavates and was compared with inorganic (NPK) and organic fertilizer (FYM). The results of soil quality revealed that the porosity, water holding capacity (WHC), cation exchange capacity (CEC) and occurrence of macronutrients were significantly increased and the particle density were decreased in treatment with 100% vermicompost (VC) followed by soil treated with 50% VC + 50% NPK, on contrary, reduction in porosity, WHC, CEC were noted in NPK treated plots. The effect of vermicompost on plant growth components (root, shoot, leaf area index, branch, DMP) and yield components (pod number, weight, length, seed weight, number of seed, seed yield) were significantly higher in the plots treated with 50% VC + 50% NPK followed by 100% vermicompost than the plots treated only with FYM and NPK. The significant growth upon using vermicompost was accounted by its nutrients composition over other fertilizers.

To curb the impacts arising from the agricultural sector the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint (EF) 3.0 life cycle impact assessment (LCIA) method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is constructed utilizing neighborhood interactions, through which a parameter known as farmer's green consciousness (GC) is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e. when farmers’ GC is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as EF “single score”) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried using the EF single score impact in the objective function, though this results in an 8% reduction in profitability over ten years.

Infections with multi-drug-resistant (MDR) pathogens in food-animals threaten public health and food security. However, the epidemiology and factors associated with MDR Escherichia coli on Ugandan farms are not well known. This study investigated the prevalence, resistance patterns and factors associated with MDR E. coli carriage on chicken farms. A total of 158 E. coli strains were isolated from 158 chicken farms. The disc diffusion method for susceptibility testing was performed. Clinical breakpoints were interpreted according to Clinical and Laboratory Standards Institute guidelines. MDR was defined as resistance to three or more classes of antimicrobials. MDR E. coli prevalence on chicken farms was 62.7% (95% CI: 55.0 – 70.3). High resistance was observed against ampicillin 79.8% (95% CI: 72.7–85.4), tetracycline 72.8% (95% CI: 65.2–79.2), cotrimoxazole 55.7% (95% CI: 47.8–63.3), and ciprofloxacin 38% (95% CI: 30.7–45.9). Farmers who were males (APR = 0.72, CI: 0.55, 0.93), and attained at least secondary education (APR=0.64, CI: 0.46, 0.88) and administration of recommended antibiotic doses (APR = 0.76, CI: 0.59–0.96) posed a lower risk of MDR E. coli carriage. Farms without footbaths posed a heighten risk (APR=1.48, CI: 1.16–1.88). MDR E. coli were highly prevalent on chicken farms. This study underscores the need for antimicrobial stewardship and improved infection prevention strategies on chicken farms.

Plant biotechnology is dependent today on accessing broader plant genetic resources for food and agriculture (PGRFA) diversity for ensuring food security for our future. Heterogenous agricultural lands are considered today as hotspot for biodiversity, and they also exist in the hilly mountain areas of Romanian’s Carpathians. The scope of this review is to describe traditional home-hardens related to the land use of households and cultivated species from Moșna commune, Sibiu County, Romania. The survey was realized with the support of authorities and local stakeholders from 2010 up to 2019. The results of this survey revealed that maintenance of crop species structure and land use of traditional households is longer than 100 years. Consequently at least 15 traditional Saxon home-gardens of 120 taken into the study may be defined. Today these home-garden covers a media of 26% of the total area of a household and should be part of a surface ranging between 5,000 and 1,500 m2. The first Local Council Decision in Romania in 2019 supporting the recognition of landraces and home-gardens as heritage values was officially adopted supporting the need to more access bottom-up approaches for supporting biodiversity conservation.

The electricity generated from fossil fuel and nuclear energy has negative impacts on the environment and has shown a need to search for and promote the use of clean, renewable and sustainable energy resources such as wind, solar and geothermal resources. Nowadays, wind energy resource has emerged as the fastest-growing source of energy on an overall scale. However, the selection of the best location for constructing a wind farm is a vital issue that can be considered a site selection problem, which involves various conflicting factors. Therefore, it is classified as a multi-criteria decision-making (MCDM) problem. In this research, the site selection of wind power plants is conducted in Burundi country. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weigh the criteria considering their relative importance. The main factors affecting optimal wind farm location considered in this study are wind speed, slope, distance from the grid network, distance to roads, and land use/land cover (LULC). Furthermore, a geographic information system (GIS) is utilized to generate the final suitability wind farm locations map. The obtained results show that 20.91% of the total study area is suitable; nevertheless, only 1.96% is highly suitable for wind farm placement. The western part of Burundi is concluded to be the most suitable area for wind farm construction and the most area is situated in Lake Tanganyika.

For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). This paper proposes a refined equivalent modeling method for mixed WFs based on artificial intelligence technology. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the main factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on sample data, to explore the nonlinear relationship between the dependent variables and the independent variables. Finally, the electromechanical transient simulations of the power systems with a mixed WF is carried out by using the MATLAB platform. Simulation results show that the proposed model can reflect the external characteristics of the mixed WF in different wind scenarios and voltage dips.

Working farm dogs are essential to many livestock farmers. Little is known about factors that influence dogs’ risk of being lost from work. This paper explores risk factors for farm dogs being lost through death, euthanasia and retirement. All enrolled dogs were working and minimum 18 months old. Five data collection rounds were done over four years. Data about dogs were collected from owners and dogs were given physical examinations by veterinarians. Dogs that were lost from work were counted and owner-reported reasons for loss were recorded. Multivariable logistic regression modelling was used to investigate risk factors for loss. Of 589 dogs, 81 were lost from work. Of these, 59 dogs died or were euthanized and 22 were retired. Farm dogs tended to reach high ages, with 38% being 10 years or older when last examined. Acute injury or illness was the most commonly owner-reported reason for loss. Age group (P &lt; 0.0001) and lameness (P = 0.04, OR = 1.8) significantly affected dogs’ risk being lost. These results expand our knowledge about factors that affect health, welfare and work in farm dogs. Further investigation into reasons for lameness may help improve health and welfare in working farm dogs.

The high dimensions and governing non linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non linearities. The work here presented applies the ideas motivated by non linear dynamical systems theory - the Koopman Operator - to an innovative algorithm in the context of wind farm systems - Input Output Dynamic Mode Decomposition - to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm.

This study measures the communication ability of wineries in two extreme territories of Southern Italy, Mount Etna and the island of Pantelleria. The evaluation of four dimensions of web communication was carried out by the AGIL Scheme (i.e. adaptation, goal-attainment, integration, latent pattern maintenance). The study provides a generalizable model to be applied in other similar studies. Also, focus groups of experts were carried out. The method proved to be suitable to measure the communication effectiveness of wineries through websites. Extreme territories may add value to the wine, regardless of the brand. The heroic wines may become the symbol of these territories helping environmental safeguard and contrasting territory abandonment by rural communities. The findings highlight that effective communication of heroic viticulture may be used to reposition these wines and increase their competitive advantage in foreign markets. The study generates new ideas for reflection on new types of web communication.

This study identifies the optimal management policy of a given energy storage system (ESS) installed in a grid-connected wind farm for maximizing the monetary benefits and provides guidelines for defining the economic value of the ESS under the optimal management policy and selecting the optimal size of the ESS based on the economic value. Considering stochastic models for wind power and electricity price, we develop a finite-horizon periodic-review Markov decision process (MDP) model to seek the optimal management policy. We also use a simple optimization model to find the optimal storage capacity and charging/discharging capacity of the ESS. By applying our analytic approach to a real-world grid-connected wind farm located in South Korea, we verify the usefulness of this study. Our numerical study shows that the economic value of the ESS is highly dependent on the management policy, wind electricity variability, and the electricity price variability. Thus, the optimal size of ESS should be carefully determined based on the locational characteristics and management policy even with limited investments. Furthermore, this study provides a meaningful policy implication on how much a subsidy the government should provide for installing ESS in a wind farm.

Balancing the levels of ammonia, in poultry facilities plays a role in enhancing the welfare and productivity of animals. Various environmental factors such as temperature, humidity, ammonia concentrations, carbon dioxide concentration and exposure to light have an impact on the wellbeing and efficiency of poultry. It is essential to monitor these elements to achieve outcomes necessitating practical, reliable and cost-effective solutions. Leveraging the Internet of Things (IoT) in conjunction with LoRa-WAN (Long Range Wide Area Network) technology presents an opportunity in the industry by enhancing connectivity among devices and facilitating real time data transmission. This research introduces a hardware and software setup for monitoring conditions at poultry farms through LoRa WAN and IoT technologies. More-over researchers employed Random Forest Machine Learning prediction models to tune the concentration of Ammonia (NH3) in poultry facilities across a wide area or multiple Poultry houses. Comparative assessments were conducted to evaluate the proposed system against devices revealing a correlation exceeding 0.9830 between the sensors and standard equipment while costing 28% less than traditional tools. The prototype displayed outcomes underscoring its potential, for managing poultry facilities across multiple farms with centralized oversight.

In the context of energy decarbonization, wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The Low Voltage Ride Through (LVRT) characteristic implemented in type IV wind turbine inverters makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1-2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rise in the load for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. The paper presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, or upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes.

Given the increasing public interest in how ingredients are processed and the growing demand for organic food products, it is critical to understand consumers’ expectations about the process-related quality of organic products. Consumers perceive organic food to be nutritious, healthy and either natural or less processed, as they are afraid of the loss of nutrients and other natural properties of the food products. However, emerging food processing technologies might generate healthy and safe food options with nutritional quality properties. Simplified communication schemes might help to overcome this barrier. The main objective of this paper is to propose a working definition of "careful processing" for organic products and test its consistency while being used in scoring different processing methods by consumers. Results show that the proposed definition allows to consistently rate alternative processing methods. Consumers tend to score novel processing methods such as pulsed electric fields and microwave as less careful, supporting the idea that organic consumers want the least man-made interference with their food products. Results show that a simple but effective definition of careful processing may help consumers to distinguish further organic food products from conventional ones, no matter which communication scheme is used.

We describe a recently developed approach to quantify welfare loss in animals, the Cumulative Pain metric. It combines the two most relevant dimensions of negative affective experiences: intensity and duration. The metric enables estimating the time individuals spend in negative affective states of a physical or psychological nature (operationally referred to simply as ‘pain’) of different intensities as the result of one or more challenges (e.g., diseases, injuries, deprivations). A new notation protocol (the Pain-Track) is used in which the duration of the experience is represented along the horizontal axis and intensity is represented by four categories in the vertical axis. Pain experiences are partitioned into temporal segments, where hypotheses for the experienced duration and intensity are proposed based on existing welfare indicators (e.g., neurophysiological, behavioral, anatomical, evolutionary). This structure forces transparency about assumptions and uncertainties, highlights knowledge gaps, and enables estimates to be continuously adjusted. Because the Cumulative Pain metric is based on parameters with a broadly common biological meaning, it provides the much needed interoperability among assessments of animal welfare. It enables comparing the impact of practices and living conditions, policies and interventions, and the calculation of welfare footprints of animal-sourced products using a universal measurement unit.

Besides the increase in global energy demand, access to clean energy, reduction in greenhouse gas emissions caused by conventional power generation sources, energy security, and availability of electricity in remote villages in emerging nations are some of the factors that foster the use of renewable energy sources (RESs) in generating electricity. One of the aims of initiating microgrids (MGs) is to maximize the benefits of RES and alleviate the associated grid integration issues. Microgrids are made up of RES connected to electrical loads within clearly delineated electrical limits that operate as individual controllable units on the electrical network. It can operate in-dependently as well as in connection with the grid. The paper presents an overview of microgrids and investigates the system's performance when connected to and disconnected from the grid. Furthermore, in both modes of operation, the functioning and behavior of system components such as the bidirectional DC-DC converter and energy storage system (ESS) were evaluated. The architecture of the proposed microgrid consists of a small hydropower plant, a wind farm, and a battery energy storage system (BESS). The microgrid under investigation is modeled and simu-lated using MATLAB and Simulink.

With the rapid development of global offshore wind power, the demand for offshore wind power operation and maintenance is also increasing. Wisdomization of offshore wind farms is a practical need to improve the operation level and benefit of offshore wind farms. This paper first introduces the current development situation and characteristics of global offshore wind power, and expounds the current situation and main challenges of offshore wind power operation and maintenance market. Therefore, our paper discusses the innovation of offshore wind power operation and maintenance from the aspects of operation and maintenance management of offshore wind power, monitoring and analysis technology of units, far-reaching wind field monitoring and operation and maintenance risks. Then, combined with information technology and lean management concept, a smart operation and maintenance management platform for wind farms in far-reaching sea areas is built to explore centralized and intelligent operation and maintenance management mode, improve operation and maintenance efficiency of wind farms in far-reaching sea areas, and minimize operation and maintenance costs. Finally, through the research on the characteristics of 5G technology, combined with the practical experience of operation and maintenance, and in view of the characteristics of offshore wind farms, we analyze and propose several typical application scenarios of 5G technology in the intelligent operation and maintenance of offshore wind farms, which provides a new solution for the efficient operation and maintenance of offshore wind farms.

Economic risks for livestock production are caused by volatile commodities and market conditions, but also by environmental drivers like increasing uncertainties due to weather anomalies and global warming. These risks impact the gross margin of farmers and can stimulated investment decisions. For confined pig and poultry production, farmers can reduce the environmental impact by implementing specific adaptation measures to reduce heat stress. A simulation model driven by meteorological data was used to calculate heat stress impact as a projection for 2030. For a business-as-usual livestock building, the indoor climate for several adaptation measures was calculated. The weather-related value-at risk quantified the economic risks caused by global warming and the stochastic component of the weather. The results show that only energy-saving adaptation measures to reduce the inlet air temperature are appropriate to reduce the economic risk to the level of the year 1980. The efficiency of other adaptation measures to reduce heat stress is distinctly lower. The results in this study can support the decision making of farmers concerning adaptation management and investments. It can inform agricultural policy design as well as technological development.

The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

This study focuses on the design, installation, and performance evaluation of a photovoltaic farm situated in Tulcea County, Romania, connected to a 20 kV distribution network. The farm, with a peak capacity of approximately 2800 kWp, benefits from Romania’s favorable climate conditions and supportive renewable energy policies. The analysis encompasses both the photovoltaic panel array and the electrical energy evacuation system. The substation at the park is equipped with technology that adheres to European standards and incorporates advanced features typical of modern electricity distribution networks. The assessment includes an evaluation of the park’s operational efficiency, with detailed calculations of the performance ratio and various power losses attributed to factors like temperature fluctuations, fouling, inverter efficiency, grid-related challenges, and availability issues. The farm contributes around 3620 MWh of electricity to the grid annually, demonstrating the effectiveness of photovoltaic installations in promoting sustainable energy solutions within regional energy networks.

Despite the known benefits of precision agriculture, the adoption is challenging due to the cost of investment and the farm sizes. Therefore, profitability is an important aspect to consider. This study aimed to evaluate the net returns, profitability, and investment efficiencies of PA by different economic farm sizes. The study was based on data retrieved from FADN and Eurostat. The study examined four countries (Poland, Germany, France, and Romania) under field crop farming using an investment cost of €35 941 - €71 883, and a 20% and 15% reduction in the cost of crop protection and fertilizer usage respectively without compromising productivity. There is a positive relationship between the adoption of PA and farm returns for larger-scale farms. The result of the profitability and analysis of investment efficiency using NPV showed a positive value for economic farm sizes of €100 000 and above. Hence, it is not economically advisable that all farmers use PA technologies with the hope that they will be profitable but with public support (subsidies) more farms will be able to use PA and be profitable. Also, an opportunity to meet the goal of the European Union Green Deal of minimizing emissions that cause climate change.

Smart farm is a technology that greatly helps improve the productivity and quality of crops and is being actively introduced into indoor environments such as green houses. However, it has not yet been distributed to field-grown crops. The main reason is that, unlike indoor environments, it is very difficult to collect sensor data for monitoring the growth environment of crops in open fields where weather conditions change significantly and power supply is difficult. Additionally, because various sensors are used, the data formats of the devices are different, making it difficult to process. This paper presents a field crop growth environment monitoring and big data analysis system. The proposed system first solves power supply and data communication problems using solar power generation and LPWA technology. Additionally, based on the oneM2M architecture, data from various sensors is transmitted to the server using standardized technology. The transmitted data is stored and managed on the server as big data, and can be used to predict the production and quality of field-grown crops and take appropriate measures. The proposed system is expected to create an environment optimized for the growth of field crops and help prevent and manage diseases.

To investigate the optimum layouts of small vertical axis wind turbines, a two-dimensional analysis of dynamic fluid body interaction is performed via computational fluid dynamics for a rotor pair in various configurations. The rotational speed of each turbine rotor (diameter: D = 50 mm) varies based on the equation of motion. First, the dependence of rotor performance on the gap distance (gap) between two rotors is investigated. For parallel layouts, counter-down (CD) layouts with blades moving downwind in the gap region yield a higher mean power than counter-up (CU) layouts with blades moving upwind in the gap region. CD layouts with gap/D = 0.5–1.0 yield a maximum average power that is 23% higher than that of an isolated single rotor. Assuming isotropic bidirectional wind speed, co-rotating (CO) layouts with the same rotational direction are superior to the combination of CD and CU layouts regardless of the gap distance. For tandem layouts, the inverse-rotating configuration (IR) shows an earlier wake recovery than the CO configuration. For 16-wind-direction layouts, both the IR and CO configurations indicate similar power distribution at gap/D = 2.0. For the first time, this study demonstrates the phase synchronization of two rotors via numerical simulation.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.

There are close dynamic relationships among the livelihood, well-being, and ecological environment of farm households. It is of great significance to scientifically clarify the impact of the grain for green policy on the livelihoods and well-being of farm households in mountainous areas. Based on data from a survey of 392 farm households in Zhangbei County, a system of indicators for farm household livelihood assets and farm household well-being was constructed, drawing on a sustainable livelihood framework (SLF). The livelihood assets and well-being levels of different types of farm households were measured, and a multiple linear regression model was used to analyze the impact of the grain for green policy implementation on the well-being levels of farm households. The three main results are as follows: (1) The level of natural assets among the total average livelihood assets of farm households in Zhangbei County is the highest at 0.374, while the level of physical assets is the lowest at 0.018. The level of livelihood assets of returned farmland households (0.948) is lower than that of nonreturned farmland households (1.117). (2) The level of well-being of all farm households in Zhangbei County is 0.517, with the level of wealth contributing the most to the well-being of farm households at 40.20% and the quality of the ecological environment contributing the least at 11.99%. The level of well-being of returned farmland households (0.518) was slightly higher than that of nonreturned farmland households (0.514). (3) The degree of influence of each influence factor on the level of well-being of farm households varies significantly. Household size was the strongest driver, at 0.366, while educational attainment of household members, household labor capacity, annual household expenditure, livelihood diversity, number of large production tools, and total value of livestock were also important drivers of household well-being, and area of arable land is negatively associated with household well-being. There are also differences in the factors influencing the level of well-being of different types of farming households.

Current global commitments to reduce emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfill its obligations. However, no scoping study has yet been carried out. The methodology presented here addresses this research need. It employs Analytical Hierarchy Process and pairwise comparison methods in a Geographical Information Systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked, then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint, to deliver maximum turbine number. Ten favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/yr of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the paper, giving an initial plan for installation in these locations.

The purposes of this study are based upon the theory of planned behavior (TPB) to examine the impact of past experiences of contract farming on selecting a specific type of contract farming in the future and then compare different psychological factors in the TPB for different potential contract farmer statuses. These statuses include homesteaders, farmers from cooperative farms, farmers from production and sales teams, professional farmers, and brokers. The impact of factors in the TPB for a particular contract type on potential contract farmers is further to compute. To this end, data are collected in three major sweet potato production areas in Taiwan. The results show that the farmers’ past contract farming experience does not influence the selection of the contract in the future. As for the selection of contract type, strengthening the perception and motivating the behavioral intention of contract farming for cooperative farms will increase the probability of selecting an unclassified sweet potato size contract. On the other hand, enhancing perceived behavioral control factors and behavioral intention factors for professional farmers and brokers is apt to have a relatively high probability of selecting those types involving the highest amount or the best price to obtain the best deal.

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines Particle Swarm Optimisation (PSO) with a turbulence intensity based Jensen wake model (TI-JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ($C_P$) or deflecting wakes by applying yaw-offsets for maximising net farm production. First, TI-JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimized strategies are evaluated using simulations based on TI-JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 seconds for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

The Regulation (EU) 2016/429 establishes new extensive guidelines for managing transmissible animal diseases, including zoonoses. The Commission Implementing Regulation 2018/1882 classifies diseases into five categories based on their prevalence and the required response, ranging from eradication to surveillance. Key aspects of the regulations concern emphasis on animal traceability, biosecurity, wildlife pathogen control, sustainable farming, and the reduction of the impact of diseases on both public and animal health, as well as on the environment. These align with the European Green Deal, the Farm to Fork Strategy and the One Health approach. In Italian inland areas—similarly to other European regions that are fragile, remote from major service centers, and often neglected—agropastoral and agri-food activities are beneficial for tourism, commercialization of typical foods, and recreational activities. In these areas including the Molise Region, the activation of optional programmes within the framework of the aforementioned regulations could help control infectious animal diseases that are not subject to mandatory eradication but cause income loss and trade restrictions, such as infectious bovine rhinotracheitis and paratuberculosis, a suspected zoonosis potentially linked to Crohn's disease in humans. These programmes could boost the economic income of local extensive breeding, counteract depopulation, and protect human health and the environment.