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| Filter results7 paper(s) found. |
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1. Cotton Precision Farming Adoption In The Southern United States: Findings From A 2009 SurveyThe objectives of this study were 1) to determine the status of precision farming technology adoption by cotton producers in 12 states and 2) to evaluate changes in cotton precision farming technology adoption between 2000 and 2008. A mail survey of cotton producers located in Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina, Tennessee, Texas and Virginia was conducted in February and March of 2009 to establish the use of precision farming technologies... M. Velandia, D.F. Mooney, R.K. Roberts, B.C. English, J.A. Larson, D.M. Lambert, S.L. Larkin, M.C. Marra, R. Rejesus, S.W. Martin, K.W. Paxton, A. Mishra, C. Wang, E. Segarra, J.M. Reeves |
2. Evaluation Of A Controlled Release N-P Fertilizer Using A Modified Drill For Variable Rate FertilizationBase NP or NPK fertilization is a common practice in cereal production in Chile. Usually, a physical NPK blend is band applied with the seed at planting with the drill. Normal fertilizer rates vary from 400 to 500 kg ha-1; however, there is a tendency in the market to move from physical blend towards chemical blends (monogranule) and, more recently, to controlled release fertilizers (CRF). The CRF are usually recommended at very low rates, varying from 70 to 120 kg ha-1, however this rates are... R.A. Ortega, J.F. Reyes, W. Esquivel, J. Orellana |
3. Yield Maps, Soil Maps, and Technical Efficiency: Evidence from U.S. Corn FieldsYield maps and GPS-based soil maps have been increasingly used in U.S. agriculture but little research has explored the economic relationship between mapping technologies and agricultural productivity. Research on this relationship is lacking, perhaps because maps are information inputs that do not directly enter the production function in a comparable way to conventional inputs. A stochastic frontier model was used to evaluate one potential avenue through which mapping technologies may influence... J. Mcfadden, A. Rosburg |
4. Global Adoption of Precision Agriculture: an Update on Trends and Emerging TechnologiesThe adoption of precision agriculture (PA) has been mixed. Some technologies (e.g., Global Navigation Satellite System (GNSS) guidance) have been adopted rapidly worldwide wherever there is mechanized agriculture. Adoption of some of the original PA technologies introduced in the 1990s has been modest almost everywhere (e.g., variable rate fertilizer). New and more advanced technologies based on robotics, uncrewed aerial vehicles (UAVs), machine vision, co-robotic automation, and artificial intelligence... J. Mcfadden, B. Erickson, J. Lowenberg-deboer, G. Milics |
5. Predicting, Mapping, and Understanding the Drivers of Grain Protein Content Variability – Utilising John Deere’s New Harvestlab 3000 Grain Sensing SystemGrain protein content (GPC) is a key determinant of the prices that grain growers receive, and the rising cost of production is shifting management focus towards optimising this to maximise return on investment. In 2023, John Deere released the HarvestLab 3000TM Grain Sensing system in Australia for real-time, on-the-go measurement of protein, starch, and oil values for wheat, barley, and canola. However, while the uptake of these sensors is increasing, GPC maps are not available for... M.J. Tilse, P. Filippi, T. Bishop |
6. Are Pulses Really More Variable Than Cereals? a Country-wide Analysis of Within-field VariabilityIn Australia, pulses are underutilised by growers relative to cereal crops. There is significant global interest in growing pulses to provide more plant protein, and they also provide a string of agronomic and environmental benefits, such as their ability to fix nitrogen, and provide a pest and disease break for cereal crops. Many studies attribute this underutilisation to pulses exhibiting greater within-field yield variability than cereals. However, this has never been comprehensively examined... P. Filippi, T. Bishop, D. Al-shammari, T. Mcpherson |
7. On Data-driven Crop Yield Modelling, Predicting, and Forecasting and the Common Flaws in Published StudiesThere has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), and abundance of machine learning modelling approaches. This is a particular problem in the field of Precision Agriculture, where many studies will take a crop yield map (or a small number), create... P. Filippi, T. Bishop, S. Han, I. Rund |