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| Filter results12 paper(s) found. |
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1. The Adoption of Information Technologies and Subsequent Changes in Input Use in Cotton ProductionThe use of precision farming has become increasingly important in cotton production. It allows farmers to take advantage of knowledge about infield variability by applying expensive inputs at levels appropriate to crop needs. Essential to the success of the precision... N.M. Thompson, J.A. Larson, B.C. English, D.M. Lambert, R.K. Roberts, M. Velandia, C. Wang |
2. A Computer Decision Aid For The Cotton Precision Agriculture Investment DecisionThis article introduces the Cotton Precision Agriculture Investment Decision Aid (CPAIDA), a software decision tool for analyzing the precision agriculture investment decision. CPAIDA was developed to provide improved educational information about precision farming equipment ownership costs, and the required returns to pay for their investment. The partial budgeting and breakeven analysis framework is documented along with use of the decision aid. With care in specifying values, program users... J.A. Larson, D.F. Mooney, R.K. Roberts, B.C. English |
3. 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 |
4. Adoption And Perceived Usefulness Of Precision Soil Sampling Information In Cotton ProductionSoil testing assists farmers in identifying nutrient variability to optimize input placement and timing. Anecdotal evidence suggests that soil test information has a useful life of 3–4 years. However, perceived usefulness may depend on a variety of factors, including field variability, farmer experience and education, farm size, Extension, and factors indirectly related to farming. In 2009, a survey of cotton farmers in 12 Southeastern states collected information... D.C. Harper, D.M. Lambert, B.C. English, J.A. Larson, R.K. Roberts, M. Velandia, D.F. Mooney, S.L. Larkin |
5. Spectral Vegetation Indices to Quantify In-field Soil Moisture VariabilityAgriculture is the largest consumer of water globally. As pressure on available water resources increases, the need to exploit technology in order to produce more food with less water becomes crucial. The technological hardware requisite for precise water delivery methods such as variable rate irrigation is commercially available. Despite that, techniques to formulate a timely, accurate prescription for those systems are inadequate. Spectral vegetation indices, especially Normalized Difference... J. Siegfried, R. Khosla, L. Longchamps |
6. Net Returns and Production Use Efficiency for Optical Sensing and Variable Rate Nitrogen Technologies in Cotton ProductionThis research evaluated the profitability and N use efficiency of real time on-the-go optical sensing measurements (OPM) and variable-rate technologies (VRT) to manage spatial variability in cotton production in the Mississippi River Basin states of Louisiana, Mississippi, Missouri, and Tennessee. Two forms of OPM and VRT and the existing farmer practice (FP) were used to determine N fertilizer rates applied to cotton on farm fields in the four states. Changes in yields and N rates due to OPM... J.A. Larson, M. Stefanini, D.M. Lambert, X. Yin, C.N. Boyer, J.J. Varco, P.C. Scharf , B.S. Tubaña, D. Dunn, H.J. Savoy, M.J. Buschermohle, D.D. Tyler |
7. Enhancing Spatial Resolution of Maize Grain Yield DataGrain yield data is frequently used for precision agriculture management purposes and as a parameter for evaluating agronomy experiments, but unexpected challenges sometimes interfere with harvest plans or cause total losses. The spatial detail of modern grain yield monitoring data is also limited by combine header width, which could be nearly 14 m in some crops. Remote sensing data, such as multispectral imagery collected via satellite and unmanned aerial systems (UAS), could be used to... J. Siegfried, R. Khosla, D. Mandal, W. Yilma |
8. How Digital is Agriculture in South America? Adoption and LimitationsA rapidly growing population in a context of land and water scarcity, and climate change has driven an increase in healthy, nutritious, and affordable food demand while maintaining the current cropping area. Digital agriculture (DA) can contribute solutions to meet the demands in an efficient and sustainable way. South America (SA) is one of the main grain and protein producers in the world but the status of DA in the region is unknown. This article presents the results from a systematic review... G. Balboa, L. Puntel, R. Melchiori, R. Ortega, G. Tiscornia, E. Bolfe, A. Roel, F. Scaramuzza, S. Best, A. Berger, D. Hansel, D. Palacios |
9. Overcoming Educational Barriers for Precision Agriculture Adoption: a University Diploma in Precision Agriculture in ArgentinaThe lack of educational programs in Precision Agriculture (PA) has been reported as one of the barriers for adoption. Our goal was to improve professional competence in PA through education in crop variability, management, and effective practices of PA in real cases. In the last 20 years different efforts has been made in Argentina to increase adoption of PA. The Universidad Nacional de Rio Cuarto (UNRC) launched in 2021 the first University Diploma in PA, a 9-month program to train agronomist... G. Balboa, A. Degioanni, R. Bongiovanni, R. Melchiori, C. Cerliani, F. Scaramuzza, M. Bongiovanni, J. Gonzalez, M. Balzarini, H. Videla, S. Amin, G. Esposito |
10. Data Gator: a Provisionless Network Solution for Collecting Data from Wired and Wireless SensorsAdvances in wireless sensor technology and data collection in precision agriculture enable farmers and researchers to understand operational and environmental dynamics. These advances allow the tracking of water usage, temperature variation, soil pH, humidity, sunlight penetration, and other factors which are crucial for trend prediction and analysis. Capitalizing on this advancement, however, requires data collection infrastructure using large and varied sensor networks. Adoption and implementation... G. Wells, J. Shovic, M. Everett |
11. Recovery Mechanism for Real-time Precision Agriculture Sensor Networks: a Case StudyVariable rate technologies are lagging behind other precision agriculture technologies in terms of farmer adoption, and sensor networks have been identified as a necessary step to implement these improvements. However, sensor networks face many issues in terms of cost, flexibility, and reliability. In rugged outdoor environments, it cannot be assumed that a sensor network will maintain constant connectivity to a monitoring interface, even if data is still being collected onsite. This paper presents... L. Hunt, M. Everett, J. Shovic |
12. Remote and Proximal Sensing for Sustainable Water Use in Almond Orchards in Southeast Spain in a Digital Farming ContextThe increasing expansion of irrigated almond orchards in regions of southeast Spain, facing water scarcity, underscores the need for a more effective and precise monitoring of the crop water status to optimize irrigation scheduling and improve crop water use efficiency. Remote and proximal sensing, combining visible, multispectral and thermal capabilities at different scales allows to estimate water needs, detect and quantify crop water stress, or identify different productivity zones within an... |