Large Farms Research Articles

Determination for the need for analgesics in day old calves following caustic paste disbudding

Caustic paste disbudding is practiced on 16.3% of U.S. opera­tions and 32.5% of calves, with a greater percentage usage as­sociated with larger farms. Only 5.6% of operations provide an­algesics/anesthetics for caustic paste disbudding heifers (USDA NAHMS, 2018). One standard operating procedure for caustic paste disbudding advises applying paste before calves are 2 days of age, and immediately after feeding to avoid scratching/ rubbing behavior. The author of one such protocol is adamant that no anesthetics or analgesics are needed with this protocol (Villarroel, 2011). There is increasing scientific evidence of pain experienced when caustic paste is used and supporting multi­modal analgesic protocols in association with this procedure. However, this data was not accumulated on calves < 24 h of age at disbudding. The objective of this study is to examine effects of a multimodal analgesia protocol on nociceptive thresholds, behavior, and physiologic responses when caustic paste is ap­plied to calves < 24 h of age. The following specific objectives were investigated.

Comparison of two methods for collecting antimicrobial usage in large dairy farms in Ohio and California

Quantification of on-farm antimicrobial use (AMU) is critical to correlate with patterns of antimicrobial resistance, surveil­lance programs and formulate interventions to optimize its use. Prior studies have used the empty drug container (EDC) method to quantify AMU; however, the method is labor-intensive and time-consuming. Therefore, this study aimed to compare it against farm treatment records (FTR) as a reliable method to collect on-farm AMU. We hypothesized a low level of agreement in on-farm treatment incidence (TI) obtained between these 2 different collection methods.

The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects

Abstract. The growth in the number and size of wind energy projects in the last decade has revealed structural limitations in the current approach adopted by the wind industry to assess potential wind farm sites. These limitations are the result of neglecting the mutual interaction of large wind farms and the thermally stratified atmospheric boundary layer. While currently available analytical models are sufficiently accurate to conduct site assessments for isolated rotors or small wind turbine clusters, the wind farm's interaction with the atmosphere cannot be neglected for large-size arrays. Specifically, the wind farm displaces the boundary layer vertically, triggering atmospheric gravity waves that induce large-scale horizontal pressure gradients. These perturbations in pressure alter the velocity field at the turbine locations, ultimately affecting global wind farm power production. The implication of such dynamics can also produce an extended blockage region upstream of the first turbines and a favorable pressure gradient inside the wind farm. In this paper, we present the multi-scale coupled (MSC) model, a novel approach that allows the simultaneous prediction of micro-scale effects occurring at the wind turbine scale, such as individual wake interactions and rotor induction, and meso-scale phenomena occurring at the wind farm scale and larger, such as atmospheric gravity waves. This is achieved by evaluating wake models on a spatially heterogeneous background velocity field obtained from a reduced-order meso-scale model. Verification of the MSC model is performed against two large-eddy simulations (LESs) with similar average inflow velocity profiles and a different capping inversion strength, so that two distinct interfacial gravity wave regimes are produced, i.e. subcritical and supercritical. Interfacial waves can produce high blockage in the first case, as they are allowed to propagate upstream. On the other hand, in the supercritical regime their propagation speed is less than their advection velocity, and upstream blockage is only operated by internal waves. The MSC model not only proves to successfully capture both local induction and global blockage effects in the two analyzed regimes, but also captures the interaction between the wind farm and gravity waves, underestimating wind farm power by about only 2 % compared with the LES results. Conversely, wake models alone cannot distinguish between differences in thermal stratification, even if combined with a local induction model. Specifically, they are affected by a first-row overprediction bias that leads to an overestimation of the wind farm power by 13 % to 20 % for the modeled regimes.

Risk Categorization in On-Farm Welfare in Different-Sized Dairy Sheep Flocks.

The objective of the present work was to categorize the risks in the on-farm welfare of dairy sheep in semi-extensive systems in order to highlight if risks in welfare vary according to the farm size. To the best of our knowledge, this study constitutes one of the few categorizations of the risks in the welfare of dairy sheep reared semi-extensively. The survey was conducted on 12 semi-extensive dairy sheep farms in Tuscany (Central Italy), which were classified according to flock size: <500, from 500 to 1000, and >1000. The results showed an adequate rating for all the farms included in the study. The most critical issues concerned the ratio between the number of stockpersons and the number of animals within the farms, the small decubitus areas, the lack of udder cleaning procedures during milking operations, and, in terms of biosecurity, the lack of prevention, control, and eradication plans for the most important infectious ovine diseases. The results show that different items in the five areas evaluated, including the assessments of management of the flock, the number of daily inspections, and the hygiene of the water supplies are associated with the farm size. In large farms, the assessment of management of the flock was better, but the number of daily inspections and the hygiene of the water supplies were worse.

Spillover effects of medium- and large-scale farms on smallholder farmers in Tanzania: Evidence from the National Sample Census of Agriculture 2019/20

Tanzania has implemented a number of policies and initiatives to promote the growth of the agricultural sector. Among these are efforts geared toward fostering large-scale farm investments, which is partly premised on the notion that smallholder farmers will also benefit through improved access to factor markets and output markets, better prices, and knowledge transfers. Nevertheless, evidence on such spillover effects is far from clear. To fill this gap, we draw on the 2019/20 National Sample Census of Agriculture to estimate the spillover effects from large and medium-scale farms to smallholder farmers in Tanzania. Using logistic and linear regression models, we find evidence consistent with positive effects from medium-scale farms to smallholder farmers around adoption of improved technologies—particularly the use of improved seeds, organic fertilizer, irrigation, and mechanization—and crop yield. These relationships are more evident for maize farms. We also find evidence consistent with spillover effects from large-scale farms, specifically around the adoption of improved crop technologies and livestock vaccinations. These results add an argument in favor of policies that foster the coexistence of large-, medium-, and small-scale farms.

Changing Sea Conditions as a Threat to Our Underwater Cultural Heritage

Changing Sea Conditions as a Threat to Our Underwater Cultural Heritage

Interpreting Different Narratives about Land Services and Land Use Economics of Common Agricultural Policy

Since 2023, a new format of the Common Agricultural Policy (CAP) has been implemented in Europe. Market forces alone cannot guarantee land services, which can be described as flows of goods and services from ecosystems to human systems as functions of nature. Market forces also cannot ensure food security everywhere and at all times, so deliberate measures should incentivize farmers to adopt sustainable agricultural practices and maintain necessary skills and resources. This study identifies, quantifies, and interprets four narratives that are typical in the approach to food security and the public debate about the land services in the CAP Strategic Plans: (A) provision of land services within the Pillar 1 economics; (B) small vs. large farms; (C) direct payments in comparison with rural development; and (D) choices by the old and new member states. Participatory processes, descriptive statistics, and partial component analysis were used in terms of the methodology. The key finding is that the importance attached to eco-schemes varies among member states, although a majority dedicate approximately 25% of the Pillar 1 budget to them. We showed that small-scale farming countries move resources from Pillar 2 to strengthen direct payments. In contrast, affluent countries with robust agricultural structures can afford to reinforce Pillar 2 rural development through transfers from direct payments. To support small-scale and medium farms, appropriate support requires a combination of several measures, including the sizable hectare payment in Pillar 1 and farmer-oriented agri-environmental measures.

Study on the Evaluation of the Profitability, Efficiency and Investment Trends of Different Poultry Farming Systems

Over the past 20 years, the Indian poultry sector has transformed into a dynamic entity with more structured operations. Following all these practices almost at every level, poultry industry is now achieving new heights day by day. The entire livestock sector's contribution to India's GDP is greatly aided by the poultry industry in the country. India's poultry sector ranks second and third in the world in terms of meat and egg output, respectively, with 3.4 million tonnes of meat and 65 million tons of eggs produced (FAO). Significant advancements in the industry have resulted from management initiatives. Most of India's organized structure has contributed to the effective use of resources and the right implementation of policies, which have improved firm financial positions and produced the intended results. The data was collected from stakeholders with the help of pretested interview schedule. Separate interview schedule was prepared for broiler farmers (100 samples), breeder farmers (10 samples) and processing cum distribution centers (20 samples). A combination of tools viz. average, percentage, cost and return analysis. To get better understanding of the study, the sample farmers were classified into different supply chains based on their mode of working and business stages performed. Feeding management practices were followed aptly by most of the farmers. Almost all farmers were following suitable health management practice by vaccinating their birds. Based on number of birds reared poultry farms were classified as small, medium and large poultry farms. Small farms were in only in Supply Chain II and III. Fixed investment they did was `583010 and `46733 out of which nearly 85 per cent building charge. In medium sized poultry farms category, poultry farms from Supply Chain II, III and IV were present. Fixed investment they made was `12,22,096, `15,94,147 and `13,27,778 respectively of which more than 90 per cent was spent on farm buildings construction purpose. In large poultry farms, Supply Chain IV farmers had made quite huge fixed investment. For all the farms, of the total investment, 90 per cent was in buildings and 10 per cent was in equipments.

Advanced Swine Management: Infrared Imaging for Precise Localization of Reproductive Organs in Livestock Monitoring

Traditional methods for predicting sow reproductive cycles are not only costly but also demand a larger workforce, exposing workers to respiratory toxins, repetitive stress injuries, and chronic pain. This occupational hazard can even lead to mental health issues due to repeated exposure to violence. Managing health and welfare issues becomes pivotal in group-housed animal settings, where individual care is challenging on large farms with limited staff. The necessity for computer vision systems to analyze sow behavior and detect deviations indicative of health problems is apparent. Beyond observing changes in behavior and physical traits, computer vision can accurately detect estrus based on vulva characteristics and analyze thermal imagery for temperature changes, which are crucial indicators of estrus. By automating estrus detection, farms can significantly enhance breeding efficiency, ensuring optimal timing for insemination. These systems work continuously, promptly alerting staff to anomalies for early intervention. In this research, we propose part of the solution by utilizing an image segmentation model to localize the vulva. We created our technique to identify vulvae on pig farms using infrared imagery. To accomplish this, we initially isolate the vulva region by enclosing it within a red rectangle and then generate vulva masks by applying a threshold to the red area. The system is trained using U-Net semantic segmentation, where the input for the system consists of grayscale images and their corresponding masks. We utilize U-Net semantic segmentation to find the vulva in the input image, making it lightweight, simple, and robust enough to be tested on many images. To evaluate the performance of our model, we employ the intersection over union (IOU) metric, which is a suitable indicator for determining the model’s robustness. For the segmentation model, a prediction is generally considered ‘good’ when the intersection over union score surpasses 0.5. Our model achieved this criterion with a score of 0.58, surpassing the scores of alternative methods such as the SVM with Gabor (0.515) and YOLOv3 (0.52).

Straw management adopted by large farms sustains grain yield but mitigates greenhouse gas emissions

ContextStraw incorporation is a globally recommended strategy for enhancing soil fertility. The methods for straw incorporation adopted by large farms usually differ from those used by small households, primarily because of variations in cost affordability. However, the effects of straw management on rice yields and greenhouse gas (GHG) emissions have not been well explored at different farming scales. ObjectivesThis study aims to compare the grain yield and greenhouse gas emissions under different straw management adopted by large farms than small households. MethodsA three-year field experiment using split-plot design, with straw management as the main plot and preceding tillage as the sub-plot, was conducted in a wheat-rice rotation system. Four treatments were given as rotary tillage with straw removal (RT-S), rotary tillage with straw incorporation (RT+S; small households), ploughing tillage with straw removal (PT-S), and ploughing tillage with straw incorporation (PT+S, large farms) in the preceding wheat season. Soil tillage in the rice season was the same, which was rotary tillage after irrigation. ResultsStraw incorporation significantly increased methane (CH4) and nitrous oxide (N2O) emissions, and 60%-62% of CH4 was emitted during the transplanting to jointing stages while 45–73% of N2O was emitted during the jointing to the booting stage. The stimulation of CH4 and N2O emissions by straw incorporation was closely correlated to enhanced soil organic carbon (SOC) and denitrification enzyme activity, respectively. In the last year, PT+S significantly reduced the global warming potential (GWP) compared to RT+S in the whole rice season, which can be attributed to the dramatically decline of CH4 emission, even though N2O emission was increased. Additionally, the decreased CH4 emission was mainly due to lower methanogenic activity, and the increased N2O emission was mainly resulted from lower nosZ abundance under PT+S compared to RT+S. Straw incorporation increased rice yield by 15.73% on average in the last two years, and rice yield of PT+S in the last year was significantly enhanced by 27.50%, resulting in a lower greenhouse gas intensity (GHGI) by 25.54% compared to RT+S. ConclusionStraw management practices adopted by large farms (PT+S) have the potential to synchronize high rice yields with lower GHG emissions compared to small households (RT+S). ImplicationsOur findings provide some evidence that large-scale agricultural organization may be helpful for sustainable rice production in China.

Occurrence of ultrasonographic assessed placental abnormalities, treatments, pregnancy outcome, and subsequent fertility on a large warmblood stud farm: A retrospective field study

Little is known about the incidence and outcome of high-risk pregnancies in equine practice and clinical studies on spontaneous occurring placentitis cases and treatments are missing. Therefore, the aims of this retrospective field study were to (1) describe the incidence and severity of ultrasonographic assessed placental abnormalities (UPA) in 4,192 pregnancies on a large commercial warmblood stud farm in 2017 – 2019 and (2) characterize these UPA cases and their pregnancy outcome. UPA severity (Placental abnormality score (PSc) 1-3; low to high), nine treatment regimens (TM1-9) used in UPA cases and treatment duration as well as subsequent fertility were analyzed in the group of UPA mares.The proportion of pregnancies affected by UPA was 4.2 % (n=177/4192). Placental abnormality severity was scored as PSc1 (51.4 %), PSc2 (32.8 %) and PSc3 (15.8 %).The generalized mixed model revealed PSc was affected by mare age and mare status (own pregnancy (OP) or embryo transfer recipient (ER)) (P=0.035) with ER mares having increased PSc compared with mares having their own pregnancy. Abortion occurred in 17/177 (9.6 %) UPA pregnancies.Overall, at the end of the next season, 61.1 % of UPA mares were pregnant, 32.0 % barren, and 6.9 % open (n=175). Pregnancy was established in 62/91(68.1 %) of mares with PSc1, 31/58 (53.4 %) with PSc2 and 14/26 (53.8 %) with PSc3. Most pregnancies were achieved in the first 81/107 (75.7 %) or second 18/107 (16.8 %) inseminated cycle.In conclusion, early detection and treatment of ultrasonographic assessed placental abnormalities can save high-risk pregnancies in > 90 % of cases with a satisfying subsequent fertility.

Constraints and suggestions of small, marginal and large farmers towards crop diversification in Gadwal district of Telangana state

Constraints and suggestions of small, marginal and large farmers towards crop diversification in Gadwal district of Telangana state

Variation in partial direct costs of dry cow therapy on 37 large dairy herds

The objective of this observational study was to estimate partial direct costs of dry cow antibiotic therapy (DCT) protocols used on 37 large dairy herds in Wisconsin and to estimate the potential monetary savings and reduction in antimicrobial usage (AMU) if selective DCT was used. Partial direct costs of DCT were calculated using costs of intramammary (IMM) antimicrobials and teat sealants. Data were retrospectively collected on 37 large dairy farms for a period of one year and included the total number of cows dried-off, types of IMM antimicrobial used, and product prices (obtained from farm invoices). A single farm visit was performed to verify data. Clinical mastitis (CM) and SCC history across lactation were used as the criteria to identify cows eligible to receive only teat sealants (no antibiotic DCT) based on adoption of a hypothetical selective DCT program. Descriptive statistics were performed using PROC MEANS to summarize continuous herd and cow characteristics. Differences in costs among IMM antibiotic products and eligibility to not receive antibiotics at dry off by parity were analyzed using ANOVA. Differences in milk yield at the last test day and DIM at dry-off based on eligibility for selective DCT were analyzed using PROC MIXED. A total of 35,691 cows were dried-off across all herds (n = 37) and most of the herds used IMM antibiotic DCT at dry-off in most of the cows. Teat sealant was used as part of the dry-off protocol in all but 3 herds. Of the enrolled farms, 30 used blanket antibiotic DCT in all quarters of all cows and 7 herds used selective DCT with no administration of antibiotics given to 0.8% to 58% of the dry cows within herd. Across all farms, the average cost of products given per dried cow was $19.57 ± 0.64 and ranged from $8.72 to $24.04. As expected, the cost per dried cow was greater ($23.45 ± 0.38) in herds that used higher-cost intramammary antibiotics as compared with herds that used lower-cost intramammary antibiotics ($16.64 ± 0.40). When using an algorithm based on udder health records to hypothetically select cows that would be eligible to receive teat sealants only (no antibiotic DCT), eligibility ranged from 27.3% to 93.3% within-herds and varied by parity, milk yield at last test day, and DIM at dry-off. If a selective DCT program based on udder health records was used, an overall reduction of $5.37 (27% of total costs) per dry cow would be expected. Likewise, adoption of selective DCT based on udder health records would reduce AMU at dry off by approximately 51%. However, neither of these estimates included potential adverse health complications (such as increased clinical mastitis) that can occur if selective DCT programs are not effectively performed. Variations in partial direct costs at dry-off were observed among herds based on treatment protocols. In herds that have good udder health management and contagious mastitis pathogens controlled, adoption of selective DCT based on SCC and history of clinical mastitis can reduce AMU and partial direct costs of dry cow management.

Plant disease detection with modified deep joint segmentation and combined GoogleNet‐IRNN

AbstractProductivity in agriculture plays a major role in economic expansion. Because plant disease is a widespread occurrence, plant disease detection is an important problem in the world of agriculture. Plants do suffer a significant consequence if the required care is not taken at the beginning, which affects the amount, quality or productivity of the relevant products. Because it can detect disease symptoms at the earliest stage and reduces the labour required for large crop farm tracking, the automated plant disease detection system is more advantageous. In order to detect plant diseases, this paper proposes a novel, four‐step methodology that consists of improved deep joint image segmentation, feature extraction (which includes LGXP, MBP, colour feature and hierarchy of skeleton feature extraction) and detection via hybrid DL classifier, specifically improved RNN with the transfer learning process and GoogleNet. By averaging the classifiers' results scores, the final detection result is calculated. In terms of several performance metrics, the suggested work's effectiveness is verified in comparison to the traditional models. In contrast to the SVM (79.5597), KNN (59.2767), LSTM (78.1446), GoogleNet (79.4025), CNN (77.6729), and CAE + CNN (80.1886), the F‐measure of the IRNN‐TL is 91.1949.

Political Mobilization of Farmers in India: A Case Study of Bharti Kisan Union (Ekta Ugrahan) in Punjab

Growing agrarian contradictions and peasant protests globally are seen as outcomes of the commercialization of agricultural production systems. The Indian Punjab is one such site that has also witnessed a high degree of agricultural commercialization, and farmers’ movements and protests are attributed to growing discontentment among peasantry due to the emergence of agrarian crises. This has led to massive farmers’ movements and protests through their unionization over time. These protests received global limelight during the introduction of three farm laws by the union government of India in 2020, which were repealed in 2021. This necessitates understanding the organizational structure and functioning of unions; their social composition, mobilization strategies and role of gender. This article examines the evolution and strategies of Bharti Kisan Union (Ekta Ugrahan, ekta meaning united), which is the largest farmers’ union in Punjab and one of the largest in India. Using a mixed-method approach of research, which included interviewing union member farmers and agricultural workers through a semi-structured schedule and conducting focused group discussions with block presidents and executive committee members, the study finds that the unionization of farmers has accelerated the process of rural democratization. However, active persuasion of social reforms along with other agrarian issues in rural society by the unions is needed to bring social inclusiveness to make rural development process pro-poor and inclusive.

Early adopter insights on physical impact mill technology for harvest weed seed control in Canada

The evolution and spread of herbicide resistance among the weed community has increased interest in alternative weed management strategies such as harvest weed seed control. Western Canadian producers have begun adopting physical impact mills as an additional weed management strategy. A survey of early adopters of physical impact mill technology in Canada was conducted to better understand the motivations behind producers adopting, initial experiences, and research needs. Ten producers responded to the survey, accounting for 18 out of an estimated 30 impact mills in use in Canada, believed to be located primarily in the Canadian Prairies. These producers were mainly from larger farms (&gt;4000 ha), equipped the majority of their combines (75% average) and used the mills in essentially all crops grown. The majority of respondents were located in Saskatchewan, with two mills being used in Alberta. Wild oat ( Avena fatua L.) (60%) and kochia ( Bassia scoparia (L.) A.J. Scott) (50%) were the weeds most frequently mentioned as specific motivators of impact mill adoption. Average increased fuel cost from the mill was estimated at CAD$3.46 ha−1, with average annual maintenance costs of about $1500 per impact mill. Producers relied on information from mill companies and other early-adopting farmers primarily, followed by extension talks and social media. Research needs were also identified by producers that could inform the future direction of harvest weed seed control research in Canada. Future research should focus on confirming efficacy, optimizing combine settings, and looking at integrated systems with precision agriculture technologies.

CattNIS: Novel identification system of cattle with retinal images based on feature matching method

Small and large farm animals are frequently identified using techniques like microchipping or tagging. However, these techniques may have a detrimental impact on animal welfare and may result in theft and fraud. Modern and sophisticated identification procedures using biometrics, including retina, face, and iris recognition, are available and have less of a detrimental effect on animal welfare. Because biometric markers are unchangeable and unaffected, it is feasible to stop situations of theft and fraud. This study uses retina images to identify animals based on each animal's different and specific retinal patterns. To the best of our knowledge, there isn't a publicly accessible dataset for images of animal retinas. The three main goals of this study are to: 1) produce a novel dataset of Cattle retinal images and make it publicly available; 2) create a successful image processing-based system for retinal identification and recognition; and 3) create a graphical user interface (GUI) that enables querying the database to ascertain whether a specific Cattle retina is present. To accomplish these goals, a dataset comprising 2430 images from the left and right eyes of 300 cattle was produced. After the dataset was created, the animal retinal images were segmented using image processing methods such as scaling, color transformations, image sharpening, contrast enhancements, noise filtering, and histogram equalization. By using the segmented images with the SURF, FAST, BRISK, and HARRIS techniques, distinctive retinal features were found. During the identification phase, the animal whose retinal characteristic matched the sought animal's retina the closest was recognized. Within the developed CattNIS system, the SURF approach has the highest accuracy rate of 92.25 percent. The results of this study show that the identification of cattle using retinal vascular patterns is highly successful. The availability of the collected cattle retinal images from this study is expected to significantly contribute to the advancement of future research in this field, both in terms of quantity and quality.

Assessment of the Carbon Footprint of Large Yellow Croaker Farming on the Aquaculture Vessel in Deep Sea in China

The present study conducted a Life Cycle Assessment (LCA) to evaluate the carbon emissions associated with large yellow croaker farming on Aquaculture Vessel “Conson No. 1”. The functional unit considered was 1 kg of fresh large yellow croakers delivered to a wholesaler. The life cycle of large yellow croaker farming on the aquaculture vessel was divided into five processes: feed production (FP), ship construction (SC), fingerling breeding (FB), adult fish farming (AF), and fish distribution (FD). Results showed that the carbon footprint (CF, kgCO2e/kg LW) for the complete life cycle amounted to 6.2170 kgCO2e/kg LW, while the CF per unit economic value of “Conson No. 1” large yellow croaker was estimated at 31 gCO2e/CNY. Among all processes, AF and FP had the highest CF contribution rates at 69.30% and 24.86%, respectively. Notably, energy consumption by aquaculture equipment on board emerged as the primary contributor across all sources of CF comparative analysis demonstrated that the CF of marine fish farming on the aquaculture vessel was lower than that of closed aquaculture systems’ average level and it was a viable option for implementing low-carbon aquaculture in the deep sea. In order to reduce energy consumption and promote a low-carbon economy in aquaculture vessels, several suggestions were proposed, including adjusting energy structure, enhancing energy efficiency, improving feed ratio, and optimizing feeding methods.

OVERVIEW OF MECHANIZED CARE EQUIPMENT BY THE HOOVES OF CATTLE

The current conditions of keeping cattle in specialized industrial complexes and large farms often lead to traumatism of the hooves, which can ultimately lead to pathology of the limbs. This negatively affects the productivity of animals and, as a result, causes significant economic losses in the livestock sector. Several reasons have been identified that cause trauma to cattle limbs and lead to their pathology. Yes, hard surfaces, slotted floors, and conveyor-type conveyors for removing manure have a negative effect on the animals' hooves. Up to 40% of animals suffer from limb diseases when cows are tethered, while up to 90% in monoblock cowsheds of the untethered type. In dairy enterprises, among all mechanical injuries to cows, from 60% to 80% are limb diseases caused by trauma to the hooves and toes. In order to prevent and successfully treat horn diseases in cattle on specialized farms and dairy complexes, it is necessary to improve treatment methods, animal care rules, and methods of cleaning and trimming horn growth of hooves. Trimming and cleaning of hooves are quite time-consuming operations, which are among the mandatory measures for the prevention of diseases of the limbs of animals. This operation is performed 2-3 times a year. At the same time, the excess layer of horny growth is cut off, and the legs are given an optimal shape. This ensures uniform distribution of the load over the entire support surface of the limb. The used mechanical means for trimming hooves have some disadvantages. Such a large mass and the lack of regulation of cutting modes depending on the physical and mechanical properties of the horn growth do not contribute to high-quality processing of hooves. The review of systems and equipment for cutting hoof horn growth in cattle, especially cows, shows that this direction of research is relevant and promising. To increase the productivity of trimming hooves and ensure their natural geometric shape and the necessary roughness of the treated surface, it is advisable to conduct further research on the justification of structural and technological parameters and modes of operation of mechanical equipment for effective care of cattle hooves.

Insights into Information Perception in Poultry Farming Systems

Indian poultry industry has found to be more structured and evolved as a dynamic organization in last two decades. Planning, organizing, directing, monitoring and controlling can profoundly be seen. Indian poultry industry contributes significantly to total livestock sectors contribution to India's GDP. India's poultry meat production is 3.4 million tons and its eggs production is sixty-five million tons and industry ranks second and third in worlds ranking in meat and eggs production respectively (FAO). The data was collected from stakeholders with the help of pretested interview schedule. The separate interview schedule was prepared for broiler farmers (100 samples), breeder farmers (10 samples) and processing cum distribution centers (20 samples). The study revealed that the majority of the poultry farmers, 63 per cent were aged between 35 to 54 years. Young generation farmers, aged between 20 to 34 years, 37 per cent, seemed lesser interested in poultry farming. In addition, women farmers were on only 3 per cent out of total sample farmers surveyed. It is indicative of the need for the promotion of poultry farming as a source of women empowerment. Almost 81 per cent farmers stated that poultry farming is their main occupation and major source of income where they have fulltime business commitment. Among the sample farmers, majority of, 52 per cent, were new entrant. The majority of farmers, 54 per cent, were rearing 5000 to 9999 birds at their farm and they were classified under medium-sized poultry farms. The average area of small farms was 2785.71 sq. ft., medium farm was 5886.89 sq. ft. and large farms was 18403 sq. ft. All the sample farmers had permanent housing (100 per cent) with pucca flooring (90 per cent). Almost 81 per cent farmers had their poultry shades in agriculture farms.