Inflammation of dairy cow mammary glands, or mastitis, is one of the most important diseases in dairy production. Costs related to veterinary service, labor, loss of saleable milk, reduced milk production, and culling make it one of the most costly dairy diseases. In conventional milking systems, detection of clinical mastitis (CM) is straight forward by identification of abnormal milk or a swollen quarter, but subclinical mastitis (SCM) is only identified by a somatic cell count (SCC) ≥ 200,000 cells/mL. Accurate and timely detection of SCM has the potential to improve milk quality and farm economics. We propose to develop predictive models to accurately detect clinical mastitis (CM) and subclinical mastitis (SCM) in dairy cows milked with automated milking systems. These systems automatically provide hundreds of data points from each cow at milking. We will collect quarter level data points, such as milk yield, milking time, duration between milking visits, kick-offs, incomplete milkings, and conductivity, and use them to develop an algorithm for accurate identification of cows at the onset of CM and SCM. Not only will this provide a substantial economic benefit by reducing the costs associated with mastitis, but it will also improve animal welfare. While automated milking systems are still rare in the dairy industry, there is a projected annual increase of 20 to 30% in the coming years, in part as a way of addressing the current labor challenges in the dairy industry. New York State is seen as a leader in the dairy industry both locally, regionally, and globally and development of an algorithm for accurate identification of cows with CM or SCM will keep New York State on the leading edge of adoption of agriculture technology.
Microbial food spoilage is a significant economic, environmental and societal problem: 40% of food in the US is reported to go to waste, with 2/3 of this spoilage being estimated to result from unwanted microbial growth. The goal of this project is to develop a computational model of microbiome interactions and perturbations during processing, transportation and retail for predicting shelf life of fresh spinach. Prediction of food spoilage in the food industry to date is typically based on limited laboratory experiments and shelf-life studies conducted under a single or very few conditions. Actual product however is produced and distributed under a range of very different conditions throughout the supply chain. Hence, there is a need for transformative solutions to reduce food waste using a systems approach, in which innovative technologies are integrated across each stage of the supply chain to reduce the volume of food wasted. In this study, we will construct computational models and decision support tools to predict shelf life using both classical microbiological and metagenomics data. This work will serve as a basis to later develop and pilot transformational strategies to reduce food waste through more accurate shelf-life prediction.
Deoxynivalenol (DON) is a secondary metabolite or mycotoxin produced by Fusarium molds. DON contamination in maize poses a major threat to food and feed safety. Conventional detection of mycotoxins involves time and resource-intensive chemical assays. Inexpensive and non-destructive methods for detecting DON based on the spectral properties of grain have shown promising success at various prediction tasks. Grain-based models, however, may not be effective for assessing silage, which consists of mixed maize tissues. Available evidence suggests that the maize rachis can harbor much more DON than grain, but rachis tissues have not been included in spectral models for DON detection. Models that leverage spectral data from both grain and rachis tissue could enable cost-effective DON surveillance in maize silage. To assess the extent of this applicability, we will acquire diverse silage samples from local dairy farms. This model’s performance will suggest how specific our rachis and grain wavelengths are to DON in general, and in turn, how applicable they may be to silage samples.
Multi-drug resistant (MDR) Salmonella dublin is an emerging zoonotic pathogen capable of causing severe disease in humans and is also a major concern to dairy cattle operations worldwide, including in New York State. Current gaps in the understanding of the transmission dynamics of MDR S. dublin and the economic feasibility of different mitigation strategies severely challenge the ability of dairy farmers to make informed decisions about cost-effective approaches to safeguard their production. This project proposes to develop a decision support tool that assesses MDR S. dublin control strategies based on integrated mathematical modeling and economic analysis approaches. Based on the results, a web-based interface will be designed to provide model simulation and planning of S. dublin mitigation strategies.
Extension specialists recommend that vineyard managers sample for leaf blade nutrients by collecting a large, random sample in each block each season. However due to the time and effort required, this task is either completely ignored or a biased sample is collected. The goal of this project is to use free, readily available, remotely-sensed images such as Normalized Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) images to assess vineyard block variability and guide efficient sampling practices. Guided sampling pathways will reduce the labour and fuel required for nutrient sampling, resulting in reduced production costs for NYS grape growers. An additional benefit may be that growers are more likely to sample, resulting in improved environmental sustainability due to more targeted fertilizer applications.
This research will investigate the mechanism of how an elastic leaf interacts with an impacting raindrop, having potential implications for characterizing the water/nutrient condition of plants from the drop-leaf interaction. When the drop hits on a leaf, the leaf flutters differently depending on the properties like stiffness and shape. Based on this fact, we are trying to reveal the functional relation between the leaf motion and its water/nutrient stress. High-speed cameras will quantify the shape deformation of the leaf and sensors will measure vibration. Using a piezo-electric material instead of a leaf, the project aims to develop an innovative engineered system based on this raindrop-leaf interaction to record and store mechanical vibrations subject to drop impact leading to an innovative energy-harvesting device from raindrops.
Insect pollinators are vital for agriculture. Unfortunately, recent pollinator declines threaten food security, biodiversity, and the agricultural economy. Pollinator management typically focuses on honey bees (Apis mellifera), but native bees are often more efficient pollinators. Attracting native bees to agricultural areas is an efficient and sustainable solution to our escalating pollinator crisis. Recent research from collaborators in Cornell Entomology showed that wild bumble bee queens are highly attracted to the nests of other bumble bees, which they try to usurp. I aim to develop a tool that exploits this attraction, encouraging bees to nest and pollinate in orchards. This summer, I will determine what factors nest-searching bumble bees use to locate and select nest sites. The results will inform the design of this tool and fill a gap in our knowledge of this ecologically important species. With this knowledge and integrated grower feedback, we will prototype design for 3D printed artificial nest cavities to simulate natural burrows.
Soils harbor a tremendous diversity of bacteria that serve as reservoirs for natural products beneficial to agriculture. The majority of soil bacteria are not cultivable under standard laboratory conditions using petri plates and liquid broth cultures. Two decades ago, the rise of metagenomics liberated researchers interested in natural product discovery from the constraints of microbial cultivation but innovative methods to identify bacteria capable of small molecule production are still lacking. We propose a proof-of-concept study on the application of ultrasonic chip imaging for high throughput natural product discovery based on screening of a 100kb soil metagenomic library.
This research project aims to tackle challenges related to greenhouse climate control by developing a smart and efficient AI-based control framework for greenhouse climate that minimizes costs while maintaining a suitable greenhouse climate for crop growth. In addition, this research will optimize use of resources such as energy and water which are of critical importance, particularly in the era of climate change, further highlighting the importance of efficient greenhouse climate control.
Digital agronomy has the potential to significantly improve both crop productivity and environmental stewardship by optimizing several aspects of crop management including input placement, resource use, and timing of operations. Curating management data on the farm for digital agronomy applications requires significant time, skill, and attention from the farmer, which explains in multiple instances the limited availability of that data. Without a way to collect farm data systematically and reliably, small and medium farmers may lag behind in the digital revolution happening in agriculture, hindering their ability to remain competitive and meet increasing environmental standards. This project proposes a novel approach to both simplify and improve crop management data collection process with a novel human-machine interface solution. An interactive voice system in the tractor’s cabin can be used to exhaustively collect farm management data more reliably and efficiently than current crop management data recording processes and provide tactical answers to farmers.
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