Predicting crop yield response to management and environmental variables is a crucial step
towards nutrient management optimization. With the increase in the amount of data …

There are growing evidence that nitrogen (N) recommendation based on the expected yield
concept developed by Stanford in 1973 lacks in agronomic principles, despite it widespread …

The Data‐Intensive Farm Management (DIFM) project works with participating farmers,
using precision technology to inexpensively design and run randomized agronomic field …

Straw mulching increases soil water availability, but the influence of tillage practices with
straw mulching on soil water, soil nitrogen, root distribution, and the physiological response …

Within-field variability of crop yield levels has been extensively investigated, but the spatial
variability of crop yield responses to agronomic treatments is less understood. On-farm …

The varying influence of the environment on N supply and demand dictates the need for
annually updated fertilizer N recommendations. Currently, crop yield goal (CYG) methods …

In-season sensing can account for field variability and improve nitrogen (N) management;
however, opportunities exist for refinement. The purpose of this study was to compare …

Variable-rate technology (VRT) may reduce input costs, increase crop productivity and
quality, and help to protect the environment. The present study was conducted to evaluate …

The site-specific nutrient management (SSNM) strategy provides guidelines for effective
nitrogen, phosphorus and potassium management to help farmers make better decisions on …

Fertilizing maize at an optimum nitrogen rate is imperative to maximize productivity and
sustainability. Using a combination of long-term (n= 379) and short-term (n= 176) …