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Data Analytics for Production Ag
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Authors
Acconcia Dias, M
Alves de Lima, J.D
Aryal, B
Asci, S
Balbinot, A
Barai, K
Barbosa, M
Basir, M.S
Bello, N
Bhandari, M
Buckmaster, D
Campos, S
Carcedo, A
Ciampitti, I
Ciampitti, I
Craker, B
Culman, S
Da Silva, M.L
Deri Setiyono, T
Dhillon, R
Dhiman, V
Eldefrawy, M
Fernandez, O
Fernando, H
Ferreyra, R
Ferreyra, R
Fuller, H.D
Gimenez, V
Ha, T
Hernandez, C
Hillyer, C.C
Hodeghatta, U.R
KC, K
Ketterings, Q
Khanal, S
Krogmeier, J
Kulhandjian, H
Landivar, J
Landivar-Scoot, J.L
Lee, S
Lehmann, J
Lingua, L.N
Maddonni, G
Magalhaes Cisdeli, P
Marcaida, M
Molin, J.P
Nagle, M
Nketia, K
Nocera Santiago, G.N
Peiretti, J
Pereira de Souza, F
Shajahan, S
Sharda, A
Sharma, V
Shiratsuchi, L
Shirtliffe, S
Srinivasagan, S
Swinton, S.M
Takoo, G
Trang, T
Valencia Ramirez, P
Watanabe, K
Wilson, J.A
Zhang, X
Zhang, Y
Zhang, Y
Zhao, L
tao, H
van Steenbergen, S
Topics
Data Analytics for Production Ag
Type
Poster
Oral
Year
2024
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Filter results17 paper(s) found.

1. Comparing Profitability of Variable Rate Nitrogen Prescription Methods

Variable rate nitrogen (VRN) prescriptions have been field-tested against uniform N application for over 25 years.  VRN prescription algorithms vary in the type and cost of information they require.  To date, few studies have compared the benefits and costs of alternative VRN prescription methods. VRN prescriptions draw on diverse information, including soil and tissue N sampling, yield history (YH), and remotely sensed spectral reflectance (such as the Normalized Differen... S. Lee, S.M. Swinton

2. Yield Analysis in Sugarcane Harvesters Using Design of Experiments (DoE) Methodology

The sugarcane crop is highlighted in national agribusiness, Brazil is the world’s largest producer of the plant, and the prospection of specialists is of strong growth for the next years. However, in order to increase productivity, technological interventions through of precision agriculture must be implemented. Among them, the management of inputs guided by yield spatial variability for otmizing production and income. This project approaches the implementation of the methodology of ana... M.L. Da silva, J. . Alves de lima, A. Balbinot, J.P. Molin

3. Interoperability As an Enabler for Principled Decision-making in Irrigation: the Precision Agriculture Irrigation Language (PAIL)

Fresh water is a scarce resource, and agriculture consumes a high fraction of it worldwide. As climate change increases the likelihood of high temperatures and droughts, irrigation becomes an increasingly attractive option for managing crop production risks. Unfortunately, and despite decades of efforts by professional associations to promote the use of a principled, data-driven approach to irrigation scheduling often called scientific irrigation scheduling (SIS), the fraction of far... R. Ferreyra, C.C. Hillyer, H.D. Fuller, B. Craker, K. Watanabe

4. Standards for Data-driven Agrifood Systems, One Year After the ISO Strategic Advisory Group for Smart Farming

The lack of data interoperability is a major obstacle for the data-driven, principled multi-objective decision-making required for modern agrifood systems to help meet the UN Sustainable Development Goals. Aware of this, the International Organization for Standardization (ISO) chartered a Strategic Advisory Group for Smart Farming (SAG-SF) to survey the existing standardization landscape of the domain within ISO, to identify gaps where additional standardization is needed, and to provide a st... R. Ferreyra, J. Lehmann, J.A. Wilson

5. Digital Agriculture Driven by Big Data Analytics: a Focus on Spatio-temporal Crop Yield Stability and Land Productivity

In the ever-evolving landscape of agriculture, the adoption of digital technologies and big data analytics has ushered in a transformative era known as digital agriculture. This paradigm shift is primarily motivated by the pressing imperative to address the growing global population's food requirements, mitigate the adverse effects of climate change, and promote sustainable land management. Canada, a significant player in global food production, has made a substantial commitment to reduci... K. Nketia, T. Ha, H. Fernando, S. Shirtliffe, S. Van steenbergen

6. Assessing Plant Spacing Inequality and Its Impact on Crop Yield Using Lorenz Curves and Gini Index

Plant spacing is the distance between individual plants in a crop field. It is vital for proper crop establishment as it can influence the spatial and temporal variation in plant emergence. These variations alter how plants interact for light, water, and nutrient resource needs, which, in turn, impact an individual plant's growth conditions and crop yield. Alternatively, studies have associated uniformity in plant spacing with higher yields and increased weed suppression. Modern precision... B. Aryal, A. Sharda, J. Peiretti

7. Almonds and Pistachios: Sustaining Legacy, Innovations, and Nutritional Advancements in California

California's unique Mediterranean climate has made it the global epicenter for tree nut production, providing nearly 99 percent of the nation’s almond and pistachio supply. The California tree nut industry is characterized by its deep-rooted heritage, with 90% of its farms being family-owned and operated, often spanning multiple generations. These farmers have been at the forefront of agricultural innovation, investing approximately millions of dollars annually in scientific researc... H. Kulhandjian, S. Asci

8. Predicting Water Potentials of Wild Blueberries During Drought Treatment Using Hyperspectral Sensor and Machine Learning

Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) us... Y. Zhang, U.R. Hodeghatta, V. Dhiman, K. Barai, T. Trang

9. Machine Learning Approach to Study the Effect of Weather and Proposed Climate Change Scenarios on Variability in the Ohio Corn and Soybean Yield

Climate is one of the primary factors that affects agricultural production.  Climate change and extreme weather events have raised concerns about its effect on crop yields. Climate change patterns affect the crop yield in many ways including the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days. It is important to analyze the effect of climate change on yield variability for a better understanding of the effec... R. Dhillon, G. Takoo

10. Environmental Characterization for Rainfed Maize Production in the US Great Plains Region

Identifying regions with similar productivity and yield-limiting climatic factors enables the design of tailored strategies for rainfed maize (Zea mays L.) production in vulnerable environments. Within the United States (US) Great Plains region, rainfed maize production in Kansas is susceptible to weather fluctuations. This study aims to delimit environmental regions with similar crop growth conditions and to identify the main climatic factors limiting rainfed maize yield, using the ... L.N. Lingua, A. Carcedo, V. Gimenez, G. Maddonni, I. Ciampitti

Showing 1 to 10 of 17 entries