Veterinary Science Collected Works - Research Publications
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ItemAn empirical analysis of the use of agricultural mobile applications among smallholder farmers in Myanmar(WILEY, 2021-03)Abstract Mobile phone applications (apps) designed to assist smallholder farmers improve decision‐making have been revolutionizing the agriculture sector. These apps offer solutions to farmer information needs by providing weather information, crop market trends, pest and disease damage identification, and advice on pesticide and fertilizer use. They also facilitate interaction with fellow farmers, extension workers and other stakeholders in the value chain who are interested in information exchange. Much previous research has investigated the contribution of mobile apps to agricultural production. This study explored the agricultural mobile apps available in Myanmar, analyzed factors affecting their use and assessed the potential for farm‐based decision support. Our findings indicate that when introducing mobile‐based tools, focus should be given to younger, more educated farmers growing more specialized crops. The main constraints to adopt agricultural apps are lack of access to smartphone and/or internet (63%) and lack of digital knowledge (20%). However, smallholder farmers in Myanmar were optimistic and positive toward agricultural apps for effective utilization. We also found that majority of the surveyed farmers were familiar with information received through Facebook groups. Incorporating useful information and functions from an agricultural mobile app to a Facebook Page could have a more useful and sustainable impact.
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ItemPrevalence and associated risk factors of Bovine mastitis in dairy cows in and around Assosa town, Benishangul-Gumuz Regional State, Western Ethiopia(WILEY, 2021-07)BACKGROUND: Mastitis, a complex disease of multifactorial aetiology, is one of the most costly diseases in the dairy industry worldwide. It can be categorized as clinical and subclinical type relying on the clinical sign. The objectives of the study were to determine the prevalence of mastitis and to identify its intrinsic and extrinsic risk factors in dairy cows in and around Assosa town, Western Ethiopia. METHODS: A cross-sectional study design was followed to address the objectives of the study. A total of 367 lactating cows were selected using simple random and systematic sampling techniques. Thorough clinical examination and California Mastitis Test (CMT) were deployed for detection of both clinical and subclinical mastitis, respectively. RESULTS: Based on CMT result and clinical examination the cow level prevalence of mastitis was 40.3% (n = 148), of which 11.99% (n = 44) and 28.34% (n = 104) were clinical and subclinical mastitis respectively. The corresponding quarter-level prevalence was determined to be 26.9% (n = 394), comprising 11.99% (n = 176) clinical and 14.85% (n = 218) subclinical mastitis. The Chi-square analysis of intrinsic risk factors revealed statistically significant differences (p <.05) in the prevalence of mastitis among breed, stage of lactation and body condition score. Likewise, production system, previous mastitis exposure, hygiene practice and type of floor were extrinsic risk factors significantly associated with the occurrence of mastitis. CONCLUSIONS: In general, this study revealed a high prevalence of bovine mastitis in the study area. Thus, the current study shows the need for applying feasible mastitis intervention strategy with special emphasis on sub-clinical mastitis and associated risk factors.
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ItemNo Preview AvailableChemical pollution: A growing peril and potential catastrophic risk to humanity(PERGAMON-ELSEVIER SCIENCE LTD, 2021-11)Anthropogenic chemical pollution has the potential to pose one of the largest environmental threats to humanity, but global understanding of the issue remains fragmented. This article presents a comprehensive perspective of the threat of chemical pollution to humanity, emphasising male fertility, cognitive health and food security. There are serious gaps in our understanding of the scale of the threat and the risks posed by the dispersal, mixture and recombination of chemicals in the wider environment. Although some pollution control measures exist they are often not being adopted at the rate needed to avoid chronic and acute effects on human health now and in coming decades. There is an urgent need for enhanced global awareness and scientific scrutiny of the overall scale of risk posed by chemical usage, dispersal and disposal.
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ItemNo Preview AvailableAbsence of high priority critically important antimicrobial resistance in Salmonella sp. isolated from Australian commercial egg layer environments(ELSEVIER, 2021-02-16)The development of antimicrobial resistance in foodborne pathogens is a growing public health concern. This study was undertaken to determine the antimicrobial susceptibility of Salmonella enterica subspecies enterica isolated from the Australian commercial egg layer industry. S. enterica subspecies enterica (n=307) isolated from Australian commercial layer flock environments (2015-2018) were obtained from reference, research and State Government laboratories from six Australian states. All Salmonella isolates were serotyped. Antimicrobial susceptibility testing (AST) for 16 antimicrobial agents was performed by broth microdilution. Antimicrobial resistance genes and sequence types (STs) were identified in significant isolates by whole genome sequencing (WGS). Three main serotypes were detected, S. Typhimurium (n=61, 19.9%), S. Senftenburg (n=45, 14.7%) and S. Agona (n=37, 12.1%). AST showed 293/307 (95.4%) isolates were susceptible to all tested antimicrobial agents and all isolates were susceptible to amoxicillin-clavulanate, azithromycin, ceftiofur, ceftriaxone, ciprofloxacin, colistin, florfenicol, gentamicin, kanamycin and trimethoprim-sulfamethoxazole. Low levels of non-susceptibility were observed to streptomycin (2.3%, n=7), sulfisoxazole (2.0%, n=6), chloramphenicol (1.3%, n=4) and tetracycline (1.0%, n=3). Very low levels of non-susceptibility were observed to ampicillin (2/307; 0.7%) and cefoxitin (2/307; 0.7%). Two isolates (S. Havana and S. Montevideo), exhibited multidrug-resistant phenotypes to streptomycin, sulfisoxazole and tetracycline and possessed corresponding antimicrobial resistance genes (aadA4, aac(6')-Iaa, sul1, tetB). One S. Typhimurium isolate was resistant to ampicillin and tetracycline, and possessed both tetA and blaTEM-1B. WGS also identified these isolates as belonging to ST4 (S. Montevideo), ST578 (S. Havana) and ST19 (S. Typhimurium). The absence of resistance to highest priority critically important antimicrobials as well as the extremely low level of AMR generally among Australian commercial egg layer Salmonella isolates likely reflect Australia's conservative antimicrobial registration policy in food-producing animals and low rates of antimicrobial use within the industry.
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ItemCBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements.(MDPI AG, 2021-12-29)The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.
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ItemA pilot study comparing the pharmacokinetics of injectable cyanocobalamin and hydroxocobalamin associated with a trace mineral injection in cattle(WILEY, 2021-05)Injectable vitamin B12 (cobalamin) is traditionally used to prevent or treat vitamin B12 deficiencies in ruminants. Sheep and human studies have demonstrated the superiority of a single dose of hydroxocobalamin (OHB12) over cyanocobalamin (CNB12) in maintaining high levels of cobalamin in plasma and liver. However, limited data are available for cattle. The purpose of this study was to compare the pharmacokinetics of two forms of cobalamin-OHB12 and CNB12-as a single subcutaneous injection of 28 µg/kg BW at the same time of a trace mineral injection in six non-cobalt/B12 -deficient Holstein-Friesian steers. Plasma and liver samples were obtained to determine cobalamin concentration after treatment. Cyanocobalamin had lower retention in plasma and liver than OHB12 (p < .05). Cobalamin levels peaked in plasma by 8 h after treatment in both groups. However, OHB12 reached a higher peak compared to CNB12. Levels of cobalamin in plasma dropped closer to baseline levels 24 h after CNB12 treatment while OHB12 maintained higher concentrations. Hydroxocobalamin increased significantly hepatic concentration of cobalamin 28 days after treatment, while CNB12 did not increase liver levels relative to pre-treatment (p < .05). These results confirm that a single subcutaneous OHB12 injection increases the level of cobalamin in the blood in the first 24 hours, and this increase is maintained in the liver for at least 28 days.
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ItemActive and Passive Electro-Optical Sensors for Health Assessment in Food Crops(MDPI, 2021-01)In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.
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ItemCarbon (δ13C) dynamics in agroecosystems under traditional and minimum tillage systems: a review(CSIRO PUBLISHING, 2021)Following cultivation, substantial loss of soil organic matter occurs in surface soil layers. No-till is an agronomic practice to reverse or slow the loss of soil organic matter. We reviewed 95 research papers that used 13C natural abundance of soils to quantify the impact of tillage on the C dynamics of cropping systems. New C (from current cropping systems) accumulated in the surface soil under no-till, whereas the most extreme cultivation (mouldboard ploughing) mixed new C throughout the soil. There was a decline in soil C with years of cultivation. Compared with land that had been tilled, no-till generally had little impact on the accumulation on soil organic C. Tillage and residue retention caused stratification in C stocks that depended on tillage depth, with the highest C concentrations and stocks found in the surface under no-till. Shifts in the δ13C signature indicated significant exchange of ‘new’ C for the original (old) C. Tillage methods had no impact on the size and δ13C signature of the microbial biomass pool. Change in δ13C indicates that microbial biomass rapidly incorporates new carbon. The largest change in the δ13C values (Δ13C) was observed in the coarse sand fraction, whereas the smallest change occurred in the clay fraction. Comparison of conventional vs no-till showed inconsistent results on the effect of tillage on C in the different particle size fractions. Natural 13C abundance data show that no-till cropping systems do not result in increases in soil organic C in the top 0.30 m of soil.
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ItemDual Pathway Model of Responses Between Climate Change and Livestock Production(Springer International Publishing, 2021)Abstract This chapter was aimed at evaluating the responses of livestock to fluctuations in climate and the debilitating effect of livestock production on the environment. Survey of livestock stakeholders (farmers, researchers, marketers, and traders) was carried out in Sahel, Sudan, Northern Guinea Savannah, Southern Guinea Savannah, and Derived Savannah zones of Nigeria. In total, 362 respondents were interviewed between April and June 2020. The distribution of the respondents was 22 in Sahel, 57 in Sudan, 61 in Northern Guinea Savannah, 80 in Southern Guinea Savannah, and 106 in Derived Savannah. The respondents were purposively interviewed based on their engagement in livestock production, research or trading activities. Thirty-eight years’ climate data from 1982 to 2019 were obtained from Nigerian Metrological Agency, Abuja. Ilela, Kiyawa, and Sabon Gari were chosen to represent Sahel, Sudan, and Northern Guinea Savannah zone of Nigeria, respectively. The data contained precipitation, relative humidity, and minimum and maximum temperature. The temperature humidity index (THI) was calculated using the formula: THI = 0.8*T + RH*(T-14.4) + 46.4, where T = ambient or dry-bulb temperature in °C and RH=relative humidity expressed as a proportion. Three Machine Learning model were built to predict the monthly minimum temperature, maximum temperature, and relative humidity respectively based on information from the previous 11 months. The methodology adopted is to treat each prediction task as a supervised learning problem. This involves transforming the time series data into a feature-target dataset using autoregressive (AR) technique. The major component of the activities of livestock that was known to cause injury to the environment as depicted in this chapter was the production of greenhouse gases. From the respondents in this chapter, some adaptive measures were stated as having controlling and mitigating effect at reducing the effect of activities of livestock on the climate and the environment. The environment and climate on the other side of the dual pathway is also known to induce stress on livestock. The concept of crop-livestock integration system is advocated in this chapter as beneficial to livestock and environment in the short and long run. Based on the predictive model developed for temperature and relative humidity in a sample location (Ilela) using Machine Learning in this chapter, there is need for development of a web or standalone application that will be useable by Nigerian farmers, meteorological agencies, and extension organizations as climate fluctuation early warning system. Development of this predictive model needs to be expanded and made functional.
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ItemMachine learning regression analysis for estimation of crop emergence using multispectral uav imagery(MDPI AG, 2021-08-01)Optimal crop emergence is an important trait in crop breeding for genotypic screening and for achieving potential growth and yield. Emergence is conventionally quantified manually by counting the sub-sections of field plots or scoring; these are less reliable, laborious and inefficient. Remote sensing technology is being increasingly used for high-throughput estimation of agronomic traits in field crops. This study developed a method for estimating wheat seedlings using multispectral images captured from an unmanned aerial vehicle. A machine learning regression (MLR) analysis was used by combining spectral and morphological information extracted from the multispectral images. The approach was tested on diverse wheat genotypes varying in seedling emergence. In this study, three supervised MLR models including regression trees, support vector regression and Gaussian process regression (GPR) were evaluated for estimating wheat seedling emergence. The GPR model was the most effective compared to the other methods, with R2 = 0.86, RMSE = 4.07 and MAE = 3.21 when correlated to the manual seedling count. In addition, imagery data collected at multiple flight altitudes and different wheat growth stages suggested that 10 m altitude and 20 days after sowing were desirable for optimal spatial resolution and image analysis. The method is deployable on larger field trials and other crops for effective and reliable seedling emergence estimates.