Farm Operations Research Articles

The Changes in Grassland Animal Husbandry and Herdsmen’s Life in the Qinghai Pastoral Area of China Based on the Perspective of Changes in the Grassland Property Rights System

Based on the historical background of the changes made to the grassland property rights system, the first part of this study restores the changes in property rights systems in six major autonomous counties of grassland animal husbandry production in Qinghai region, while the second part qualitatively analyzes the changes in grassland animal husbandry operations and herdsmen’s life under the changes in property rights. The results show that, with the reform of the property rights system, the range of livestock grazing has changed from large-scale nomadic grazing to regional rotational grazing. The herds are mainly yaks and Tibetan sheep, accounting for more than 90%, with the proportion of Tibetan sheep being higher than that of yaks. The numbers of total livestock and those on the market first rose and then declined, showing a dynamic balance in recent 10 years, while the number of breeding female animals has increased year by year. Artificial grass planting has gradually become popularized in pastoral areas. The time span of livestock supplementary feeding has increased and is more scientific, and the output value of animal husbandry has increased year by year, showing a strong peak associated with intensification. The proportion of the population engaged in animal husbandry has decreased gradually with increasing population in the region. The income sources of herdsmen have become more and more diversified and are increasing year by year, and the Engel coefficient presents a downward trend. The human environment in pastoral areas has improved. Based on the above analysis, suggestions for the sustainable and high-quality production of animal husbandry under the current grassland property rights system are put forward, as well as for the construction of green organic agricultural and livestock product export land in Qinghai Province.

A review on way towards mechanized castor cultivation

Castor (Ricinus communis L.) is an important non edible oil seed crop which is known for its oil content (48–60 percent) and it accounts for 0.15% of the global production of vegetable oil. In recent years, farmers have preferred castor cultivation due to its suitability for both rainfed and irrigated conditions. But the farmers face many challenges, which includes labour scarcity, pest incidence, high input and labour costs, inadequate market information, etc. Due to a significant labour shortage, the area under this crop is gradually decreasing. The high reliance on human labour which leads to the incompletion of various agronomic operations on time. In these circumstances, the cultivation of castor by adopting various mechanization techniques for critical operations such as field preparation, sowing, fertilizer application, weeding, irrigation, crop protection measures and harvesting will be the solution for getting higher productivity and profitability by spending minimum expenses. Adapting mechanization is crucial for achieving sustainable development goals by enhancing production through timely farm operations, minimizing losses and reducing operational costs through efficient management of expensive inputs.

Agroforestry across the United States: Results of the 2022 Census of Agriculture

Agroforestry offers producers climate mitigation and adaptation benefits and the opportunity to diversify income and enhance ecosystem services. Though these benefits are well-researched, few studies document agroforestry implementation through time in the United States using consistent national data sources. To fill this knowledge gap, we investigated changes in the number of farm operations with agroforestry across the United States using data from the 2017 and 2022 United States Census of Agriculture. Through this analysis, we showed that the number of farm operations with agroforestry increased by 6% nationally between 2017 (30,853) and 2022 (32,717). States with the highest percent of total farms reporting at least one type of agroforestry were Vermont (7.8%), Maine (6.0%), and New Hampshire (5.6%), while states with the lowest percent were Arizona (0.3%), Utah (0.4%), and North Dakota (0.4%). States on the eastern half of the country tended to see increases in the number of farm operations with agroforestry between census years, while states on the western half of the country saw the largest decreases. Reasons for these changes are complex and require additional analysis. Additionally, we identified potential factors that may decrease the accuracy of agroforestry census data, along with recommendations on how to improve overall data quality going forward. We outlined how national agroforestry census data can inform decision-making related to farm policies, funding, programs, research, and extension delivery. With an online supplement, we also provided detailed maps of the extent of agroforestry in all 50 states.

Automating Agriculture: The Impact of AI and IoT on Farming

The agriculture industry is under tremendous pressure to boost food grain productivity and production due to the effects of climate change and population growth. Agriculture automation is now the only viable choice and a pressing necessity in the majority of nations where agricultural growth is just not feasible. Artificial intelligence and the Internet of Things have already begun to make inroads into every sector, including agriculture. The development of these digital technologies has revolutionized agriculture by enabling intelligent systems that are as intelligent as human experts and are capable of real-time monitoring, control, and visualization of a variety of farm operations. The study discusses the possible uses of AI and IoT in the establishment of smart farm equipment, irrigation systems, greenhouse culture, storage buildings, weed and pest control, manure application, drones for crop monitoring, plant health monitoring, etc. This study's main objectives are to give a summary of new studies on agriculture powered by digital technology and to list the most well-known applications of artificial intelligence and the internet of things in agriculture engineering. Scientific databases like Web of Science, PubMed and Scopus have been used to examine research conducted in the fields over the past ten years. It has been found that the utilization of IoT and IoT, from its initial conceptual stage to its implementation stage, the effort to automate agriculture has advanced. The technical features of artificial intelligence and the Internet of Things are also covered, along with the challenges associated with putting these digital technologies into practice. It would aid people in understanding how digitalization may be incorporated into agricultural processes to make it easier for farmers to implement AI and IoT-based solutions.

Effect of Workload and Pedalling Rate on Physiological Response for Male Worker

Until about two and half centuries ago, muscle power was the prime source of energy for performing all the physical activities on our earth, and much of this power had been from human muscles. Because of the socio-economic conditions of farmers in developing countries including India, human muscle power is going to contribute energy requirements for performing many farm activities for the next two decades. Pedalling is the most efficient way of utilising power from human muscles. Pedal power enables a person to drive devices at the same or higher rate as that achieved by hand cranking, but with far less effort and fatigue. However, the use of pedal power for occupational work such as stationary farm operations has got scant attention in the past. Keeping these points into consideration a study was planned for Effect of workload and pedalling rate on physiological response for male worker. Physiological responses of 20 male subjects were studied on a computerised bicycle ergometer at four levels of power output (60, 70, 80, 90 W) four levels of pedalling rates (40, 50, 60, 70 rev/min). Analysis of data indicated that physiological responses were significantly affected with power output as well as pedalling rate. Increases in physiological responses (heart rate and oxygen consumption rate) over rest were significantly higher when pedalling frequency was 50rev/min and above 60rev/min. There was no significant difference between physiological responses at 50 and 60 rev/min. Physiological responses increased linearly with power output and were significantly different at different power outputs. The average value of physiological responses at 60W power output at male agriculture worker at 50rev/min pedalling rate were within acceptable limits for continuous pedalling work.

Development and ergonomic evaluation of hand held vegetable seedling transplanter

Efficient vegetable production is essential to meeting growing food demands, requiring innovative solutions to reduce labor intensity and enhance productivity. Vegetable cultivation, especially for small farmers, demands innovative and affordable solutions to enhance productivity and reduce labor-intensive practices. The mechanization of agricultural processes has become increasingly important in reducing the physical strain and time-intensive nature of traditional farming operations, paving the way for innovative tools that enhance efficiency and user comfort. Transplanting of seedlings, a highly drudgery-oriented operation, is mainly performed by women. A hand held seedling transplanter has been designed and developed for transplanting vegetables seedlings based on the anthropometric dimensions of the user population. The number of seedlings transplanted per min using the handheld seedling transplanter ranged from 19 to 21, compared to 16 to18 using the conventional method. By using the hand held seedling transplanter, a significant reduction in the cost of operation was achieved, with 11.44 to 16.52% savings in cost reported. The average working heart rate while transplanting using the seedling transplanter was 103 bpm, compared to 120 bpm for conventional transplanting. The average energy expenditure while transplanting the seedlings using the seedling transplanter was 7.72 kJ min-1, compared to 10.94 kJ min-1. The performance evaluation of the designed transplanted indicted an increase in human productivity and a decrease in musculoskeletal discomfort during the transplanting process. The introduction of ergonomic and cost-effective solutions, such as the handheld seedling transplanter, not only improves productivity and reduces operational costs but also promotes sustainable practices while prioritizing the well-being of farmers.

NANOTECHNOLOGY IN AGRICULTURAL YIELD EXPANSION: A COMPEHENSIVE REVIEW

This comprehensive review explores the burgeoning field of nanotechnology and its transformative impact on agricultural yield expansion. Nanotechnology offers unprecedented opportunities to address key challenges facing global agriculture, including soil degradation, resource inefficiency, pest and disease management, and climate change adaptation. By manipulating materials at the nanoscale, researchers have developed innovative solutions to enhance soil fertility, improve crop protection, optimize resource utilization, and promote sustainable farming practices. This paper examines recent advancements in nanofertilisers, nanopesticides, nanosensors, and precision agriculture technologies, providing insights into their mechanisms of action and potential benefits for crop productivity. Nanotechnology enables the development of nanorobots and nanoscale devices for automated farming operations and precision agriculture applications. Furthermore, environmental and safety considerations, as well as regulatory frameworks governing nanotechnology in agriculture, are discussed to ensure responsible and sustainable deployment of these technologies. Through case studies and practical examples, the review underscores the transformative potential of nanotechnology in revolutionizing agricultural practices and contributing to global food security. Finally, challenges, future perspectives, and recommendations for further research and implementation are outlined to guide the continued development and adoption of nanotechnology for agricultural yield expansion.

Determination of the Pathogenicity of the Variant UPM 1432/2019 IBDV in SPF Chicken Eggs and Chicken Fibroblast Cell Line

The infectious bursal disease virus (IBDV) is a nuisance to chicken productivity in the global poultry industry due to its high pathogenicity. In this study, the pathogenicity of the UPM 1432/2019 variant was evaluated using specific pathogen-free (SPF) chicken eggs and fibroblast cell lines. This viral strain was isolated from chickens displaying symptoms of watery diarrhoea and ruffled feathers. Following inoculation, the SPF chicken eggs, and cell lines were meticulously monitored for indicators of viral replication and the onset of cytopathic effects (CPE). The results showed that the virus could replicate in the SPF eggs and cell lines due to the presence of CPE in some cells. In addition, virus-induced weight loss in infected embryonated chicken eggs was evaluated. The UPM 1432/2019 and UPM 1219/2019 strains demonstrated similar mortality rates and CPE features. These findings indicate that the UPM 1432/2019 variant is pathogenic in SPF chicken eggs and fibroblast cell lines. Furthermore, the new UPM 1432/2019 variant caused IBD with pathogenic characteristics similar to the traditional UPM 1219/2019 variant. The emergence of the UPM 1432/2019 variant poses a risk to the economy and poultry production and is potentially resistant to the existing bursal disease vaccines. Consequently, the UPM 1432/2019 variant may significantly contribute to economic losses for chicken farm operators. Moreover, this study provides essential information on the potential virulence of the UPM 1432/2019 variant of IBDV and its potential impact on the poultry industry.

Concept Drift Early Fault Detection in Wind Turbine Based on Distance Metric: A Systematic Literature Review

The Supervisory Control and Data Acquisition (SCADA) system in wind turbines generates substantial data that remains underutilized in terms of wind farm operation and maintenance (O&M). Numerous fault detection methods leveraging SCADA data are being extensively researched to reduce O&M costs. The detection methods are revolutionizing wind farm O&M strategies, shifting from scheduled passive detection to predictive active detection, with the potential to significantly reduce spare parts and labor costs. This paper presents a systematic review of wind turbine fault detection methods based on concept drift and distance metrics, employing the PRISMA methodology. The selected literature is analyzed from three perspectives: fault components, modeling methods, and data sources. Additionally, this review addresses research questions related to current trends, concept drift applications, and distance metric utilization in wind turbine fault detection. Lastly, it provides valuable insights for researchers and industry practitioners in wind energy engineering to explore future research and development in fault detection techniques for enhancing the reliability and efficiency of wind turbine operations.

Deep mining of megawatt large wind turbine actual operating data: exploration of Bayesian parameter fusion modeling

Abstract Real-time operation data analysis and condition monitoring of large wind turbines are crucial for efficient and safe operation of wind farms. In response to this, an accurate prediction model architecture based on the multivariate linear regression algorithm is proposed for a deeper understanding of the actual operation of large wind turbines. Comparing different prediction variables, we have confirmed that the average wind direction and average wind speed play a core role in predicting active power. The display has significantly better approximation than the traditional multiple linear regression (MLR), especially in the peak region of period-like variation. The quasi-periodic peak variation characteristics of the active power near the observed series 2000, 2500 and 3000 are realized, and the peak prediction results of B-MLR are significantly better than those of traditional MLR. Meanwhile, for RMSE and R-squared, B-MLR is significantly better than the traditional MLR algorithm in the active power prediction of wind turbines. However, the data complexity in actual applications poses a challenge to the effectiveness of the Bayesian fusion algorithm. Further optimization of the algorithm is needed to cope with the complex and variable real data environment. This study provides scientific evidence for the efficient operation, precise maintenance, and environmentally friendly design of wind turbines, promoting the continuous progress and development of wind power generation technology.

Modeling and Simulation on the Hybrid Solution of Static Var Generator and Synchronous Condenser for Unlocking Power Output Limitation of Wind Farms Integrated into Weak Grid

The issues of low inertia, overvoltage, and wide-frequency oscillations in high-proportion renewable energy systems have become prominent, posing major challenges to renewable energy integration and threatening grid stability. Currently, many wind-rich areas ensure grid safety and stability by reducing wind farm output. To enhance the active power delivery capability of wind farms, this paper proposes a hybrid solution of a small synchronous condenser (SC) and static var generator (SVG) within wind farm stations to optimize reactive power and voltage at the point of grid connection. First, it was analyzed that the low short-circuit ratio (SCR) is a key factor affecting the stable operation of wind farms, and the sub-transient reactance of the SC can increase the SCR. Based on this, a method for configuring the capacity of the SC was developed. Next, simulation models for both the SC and the SVG were established, and their reactive power compensation capabilities were verified. The hybrid control approach combined the advantages of both devices, providing comprehensive voltage support across sub-transient, transient, and steady-state conditions for renewable energy stations. Furthermore, based on a practical 50.5 MW wind farm, which has been operating with a power delivery consistently limited to 60% of its capacity, a simulation model and scenarios were set up. A comparison of the simulation results shows that, with only the SVG in operation, the wind farm is prone to oscillations after a grid fault. However, after adopting the hybrid control of the SC and SVG, the wind farm operates stably. Therefore, installing a small SC within wind farms can effectively address the limitations of voltage stability and a low short-circuit ratio, thereby supporting higher levels of renewable energy integration.

Does multiple job holding raise labour use efficiency of the farm operators? Evidence from rural India

ABSTRACT Amid various agro-climatic shocks when farm income becomes volatile, farmers often diversify income by holding multiple non-farm jobs. While the impact of multiple jobholding on overall farm efficiency is documented in literature, very little is known about the impact of multiple job holding on farm labour use efficiency. This is important as participation in non-farm activities alters farmers’ labour allocation decisions between farm and non-farm activities. Using a rich household level panel data from eight Indian states for the period 2010–2014 from Village Dynamics in South Asia (VDSA) Project by International Crops Research Institute for the Semi-Arid Tropics and adopting Data Envelopment Analysis, we estimate labour use efficiency. Our Instrumental Variable Tobit estimation shows that multiple job holding increases farm operators’ labour use efficiency significantly. However, the heterogeneity analysis using sub samples demonstrates that the positive impact of multiple non-farm jobs is enjoyed mostly by medium and large farmers. Also, farmers engaged in wage and salary based non-farm employment experience higher labour efficiency once they hold multiple jobs. Self-employed farmers experience higher labour efficiency only if they earn a threshold level of non-farm income. Since multiple job holding improves farm labour use efficiency, more avenues for non-farm employment to be created for agricultural and rural development.

AGRO-Cloud Model and Smart Algorithm to Increase Agriculture Production to Improve Agriculture Quality

Smart Agriculture is a revolutionary approach to farming that aims to increase crop yields, optimize resource usage, and reduce costs, through the use of technology the design and implementation of an AGRO-Cloud Model for crop yield prediction using hybrid deep learning. The proposed system aims to improve crop yield prediction accuracy and facilitate decision-making for farmers. The system utilizes a hybrid deep learning approach that associates the (CNNs) convolutional neural networks and (LSTM) long short-term memory networks to process multi-sensor data, including soil, moisture data, weather data, and data of crop growth. LSTMs are used to capture temporal dependencies in the input data, while CNNs are utilized to extract spatial features. The system is implemented on a cloud platform, allowing farmers to access the system from anywhere using a web-based interface. The system provides real-time crop yield prediction and alerts farmers to potential risks such as pests, disease, and adverse weather conditions. The system also provides data visualization tools that enable farmers to monitor the growth of their crops and make informed decisions about fertilization, crop management practices, and irrigation. Experimental results show that the proposed hybrid deep learning approach outperforms traditional machine learning methods for crop yield prediction, achieving a prediction accuracy of over 90%. The proposed model can increase agriculture production to improve the quality and profitability of farming operations and contribute to sustainable agriculture practices.

Assessment of the relationship between body weight and body measurements among Morkaraman, Tushin and Awassi sheeps with different ages

This study aimed to identify morphological predictors of body weight in Morkaraman, Tushin, and Awassi sheep across different age groups, providing valuable insights for effective livestock management and breeding programs. Data were collected from 260 Morkaraman, 109 Tushin, and 96 Awassi sheep, with body measurements including body length (BL), wither height (WH), chest girth (CG), chest width (CW), chest depth (CD), rump width (RW), rump height (RH), ear length (EL), head length (HL), forehead width (FW), cannon forelimb circumference (CFC), and cannon hindlimb circumference (CHC). Pearson correlation analysis was conducted to assess relationships between body weight and these morphological traits. A multiple linear regression model was developed using the backward stepwise elimination method to estimate body weight based on the collected measurements. The results revealed that the Morkaraman breed and the 3-year-old age group had the highest average body weights. Among the morphological traits, CG (r = 0.702), RH (r = 0.694), BL (r = 0.678), and WH (r = 0.677) exhibited the strongest correlations with body weight. The final regression model identified CG, RW, RH, and HL as the most significant predictors, collectively explaining approximately 78.3% of the variation in body weight. These findings underscore the importance of selecting specific morphological traits for accurate body weight estimation in sheep. The results of this study have significant implications for sheep farming, particularly in enhancing breeding, management, and selection strategies. By focusing on key morphological predictors, livestock producers can improve decision-making processes, optimize resource allocation, and promote the overall productivity and sustainability of sheep farming operations.

Development of blocking ELISA for detection anti-PRRSV antibodies and serological investigation of PRRSV in China.

Porcine reproductive and respiratory syndrome virus (PRRSV) demonstrates a significantly high prevalence among swine populations. Monoclonal antibodies (mAbs) with high affinity for conserved epitopes of PRRSV can facilitate the development of a broad-spectrum detection method for this virus. This study identified two PRRSV-specific mAbs, designated 2B1 and 2C6, which recognized two conformation-dependent epitopes through indirect immunofluorescence assay (IFA) and Western blot analysis. Further investigation via immunoprecipitation and eukaryotic expression studies confirmed that both mAbs specifically target the Nucleocapsid (N) protein of PRRSV. Importantly, these two epitopes exhibit high conservation across PRRSV isolates, including DV, CH-1a, HuN4, NADC30-like strains, NADC34-like strains and VR2332. The mAb 2C6 was effectively blocked by sera from pigs positive for PRRSV-1 and PRRSV-2. Consequently, a blocking enzyme-linked immunosorbent assay (b-ELISA) based on 2C6 was developed to detect anti-PRRSV antibodies, achieving enhanced sensitivity and specificity. The results obtained using this method demonstrated a higher concordance rate compared to those derived from commercial kit. Additionally, a total of 451 animals from various provinces in China were sampled, revealing an overall IgG antibody seropositivity against PRRSV of 77.38 % (349/451), with nursery pigs at 33.48 % (151), growing pigs at 15.96 % (72), fattening pigs at 39.69 % (179) and sows at 10.86 % (49). Collectively, the established b-ELISA represents an optimal method for large-scale serological investigations into PRRSV antibodies within farming operations.

Machine learning-based identification of animal feeding operations in the United States on a parcel-scale.

The increasing global demand for meat and dairy products, fueled by rapid industrialization, has led to the expansion of Animal Feeding Operations (AFOs) in the United States (US). These operations, often found in clusters, generate large amounts of manure, posing a considerable risk to water quality due to the concentrated waste streams they produce. Accurately mapping AFOs is essential for effective environmental and disease management, yet many facilities remain undocumented due to variations in federal and state regulations. Current techniques for mapping AFOs in the US rely on a mix of manual digitization, aerial imaging, and image processing. By applying a machine learning-based random forest (RF) classification method to a socio-environmental dataset that excluded aerial images in this work, we overcame some of the limitations associated with aerial image-based approaches, enhancing mapping accuracy to 87%. We used publicly available environmental, nutrient-focused, and socioeconomic data downscaled to the parcel level, which more accurately reflects farm boundaries and operations than previous methods. Our study incorporates 58 variables, with canopy cover, surrounding vegetation, day and nighttime land surface temperatures, and phosphorus from animals identified as key predictors of AFO presence. The relevance of these variables varies across states, influenced by whether the dominant land covers are human-induced, like croplands, or natural, such as savannas and grasslands. Thus, our public-data based approach, easily replicable, not only improves the precision of AFO detection, but also facilitates the monitoring of nutrient flows at the parcel level-critical for nutrient budgeting and recovery, water quality management, and disease risk assessment and tracing.

Profile and constraints faced by rural women in performing activities related to farm operation

Profile and constraints faced by rural women in performing activities related to farm operation

IPond: An Internet of Things–Based Water Quality Monitoring System for Fishpond

Abstract The cultivation of aquatic species faces a significant challenge due to water quality degradation, which impacts farming operations and poses a threat to livelihoods. To contribute to advancing aquaculture informatics and enabling more precise and convenient monitoring of aquaculture ponds, this study introduces an IoT-based Water Quality Monitoring System for Fishponds. This system is designed to measure pH and salinity values in real-time, aiming to address the pressing need for continuous monitoring and management of water quality parameters for cultivating milkfish (Chanos chanos). Users can remotely access and control measurements by integrating the prototype with the Blynk IoT system, ensuring that water quality remains within optimal thresholds or 6.5 to 8.5 pH for acidity and 18 to 29 ppt for salinity. In addition, the system is equipped with an alert system to notify end-users when the parameters exceed predetermined limits promptly. The prototype received an average functionality score of 4.325 and an efficiency score of 4.13. The error percentages recorded were 0.027% for acidity, 0.187% for salinity, and 4.36% for overall effectiveness. This system enhances cultivation security, enabling farmers to make informed decisions and take timely actions to maintain optimal water quality conditions in fishponds.

The Rise of AI and Autonomous Systems: Transforming Industries and Navigating Ethical Challenges

The integration of artificial intelligence (AI) with autonomous systems represents one of the most transformative technological advancements of the modern era. This study explores the evolution, applications, and challenges of these systems across key sectors, including transportation, healthcare, manufacturing, agriculture, and defense. Autonomous systems leverage AI algorithms, such as machine learning and deep learning, to perform tasks with minimal human intervention by processing data, making decisions, and executing actions. Their applications range from self-driving vehicles and robotic surgical systems to precision farming and military operations. These innovations promise enhanced efficiency, accuracy, and scalability, but they also raise significant ethical, societal, and regulatory challenges. Issues such as algorithmic bias, accountability in case of failures, job displacement, and potential misuse in defense highlight the complexities of widespread adoption. To address these challenges, the study emphasizes the need for robust regulatory frameworks, ethical guidelines, and interdisciplinary collaboration to ensure the safe and equitable deployment of AI and autonomous systems. Through a thematic analysis of recent research, this article provides insights into the transformative potential and the multifaceted concerns of these technologies, paving the way for future advancements and responsible innovation.

Analysis of economic efficiency of wind farm and photovoltaic installation operation in a cable pooling system in Poland

Analysis of economic efficiency of wind farm and photovoltaic installation operation in a cable pooling system in Poland