Authors
| Filter results3 paper(s) found. |
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1. Constraint of Data Availability on the Predictive Ability of Crop Response Models Developed from On-farm ExperimentationDue to the variability between fields and across years, on-farm experimentation combined with crop response modeling are crucial aspects of decision support systems to make accurate predictions of yield and grain protein content in upcoming years for a given field. To maximize accuracy of models, models fit using environmental covariate and experimental data gathered up to the point that crop responses (yield/grain protein) are fit repeatedly over time until the model can predict future crop responses... P. Hegedus, B. Maxwell |
2. Generation of Site-specific Nitrogen Response Curves for Winter Wheat Using Deep LearningNitrogen response (N-response) curves are tools used to support farm management decisions. Conventionally, the N-response curve is modeled as an exponential function that aims to identify an important threshold for a given field: the economic optimum point. This is useful to determine the nitrogen rate beyond which there is no actual profit for the farmers. In this work, we show that N-response curves are not only field-specific but also site-specific and, as such, economic optimum points should... G. Morales, J.W. Sheppard, A. Peerlinck, P. Hegedus, B. Maxwell |
3. Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat ProductionField-specific fertilizer rate optimization is known to be beneficial for improving farming profit, and profits can be further improved by dividing the field into smaller plots and applying site-specific rates across the field. Finding optimal rates for these plots is often based on data gathered from said plots, which is used to determine a yield response curve, telling us how much fertilizer needs to be applied to maximize yield. In related work, we use a Convolutional Neural Network, known... A. Peerlinck, J. Sheppard, G.L. Morales luna, P. Hegedus, B. Maxwell |