Authors
| Filter results4 paper(s) found. |
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1. Spatial Econometric Approaches to Develop Site-Specific Nematode Management Strategies in Cotton ProductionRoot-knot nematode infestations tend to be spatially clustered within agricultural... Z. Liu, T. Griffin, T. Kirkpatrick, S. Monfort |
2. Utilizing GPS Technology and Science to Improve Digital Literacy Among Students in Australia and the United States of AmericaA key issue facing regional, rural and remote communities, in both Australia and the United States of America (USA), is the low level of digital literacy among some cohorts of students. This is particularly the case for students involved in agricultural studies where it is commonly perceived that digital literacy is not relevant to their future occupation. However, this perception is far from the truth, as the reality of farming today means students who intend on entering the agricultural workforce... C.W. Knight, A. Cosby, M. Trotter |
3. Spatial Decision Support System: Controlled Tile Drainage – Calculate Your BenefitsClimate projection studies suggest that extreme heat waves and floods will become more frequent, affecting future crop yields by 20%-30%, globally. Managing vulnerability and risk begins at the farm level where best management practices can reduce the impacts associated with extreme weather events. A practice that can assist in mitigating the impact of some extreme events is controlled tile drainage (CTD). With CTD, producers use water flow control structures to manage the drainage of water from... A. Kross, G. Kaur, D. Callegari, D. Lapen, M. Sunohara, H. Mcnairn, H. Rudy, L. Van vliet |
4. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean YieldThe ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van vliet |