Proceedings
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| Filter results6 paper(s) found. |
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1. 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 |
2. 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 |
3. 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 |
4. 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 |
5. Predicting Corn Emergence Uniformity with On-the-go Furrow Sensing TechnologyIntegration of proximal soil sensors into commercial row-crop planter components have allowed for a dense quantification of within-field soil spatial variability. These technologies have potential to guide real-time management decisions, such as on-the-go variable seeding rate or depth. However, little is known about the performance of these systems. Therefore, research was conducted in central Missouri, USA to determine the relationship between planter sensor metrics, and corn (Zea mays L.) ... L.S. Conway, C. Vong, N.R. Kitchen, K.A. Sudduth, S.H. Anderson |
6. Soil, Landscape, and Weather Affect Spatial Distributions of Corn Population and YieldAs more planters are equipped with the technology to vary seeding rate, evaluation of the within-field relationships between plant stand density (or population) and yield is needed. One aspect of this evaluation is determining how stand loss and yield are related to soil and landscape factors, and how these relationships vary with different weather conditions. Therefore, this research examined nine site-years of mapped corn yield, harvest population, and soil and landscape data obtained for a... K.A. Sudduth, N.R. Kitchen, L.S. Conway |