Evaluation Of Sensor-Based Technologies and Nitrogen Sources For Improved Spring Wheat Recommendations.

Precision agriculture tools such as sensor-based technologies allow us to accurately access the crop’s nutrient status and account for spatial and temporal variability. This enables adjusting fertilizer application rates according to site-specific conditions which results in more efficient, profitable, and sustainable crop production. Remote sensing is a precision agriculture technique that quantitatively measures vegetation indices such as the Normalized Difference Vegetation Index (NDVI). The feasibility of various sensor-based systems must be evaluated before a recommendation can be made as to what system is more efficient and appropriate for Montana conditions. The major objectives of this study are: 1. To evaluate two sensors – GreenSeeker, and Pocket Sensor – for developing NDVI-based topdress fertilizer N recommendations for dryland and irrigated spring wheat production in Montana, and 2. To determine whether sensor-based recommendations have to be adjusted depending on what N fertilizer source (liquid urea ammonium nitrate (UAN), or granular urea) is used. Three experiments: two dryland studies – at Western Triangle Agricultural Research Center, Conrad, MT, and in a producer’s field (Pat Wheeler, Valier, Pondera County), and one irrigated study at WARC will be established using the spring wheat variety Choteau. There will be a total of 9 treatments, each replicated 4 times. The preplant N rates of 0, 22, 45, 67, and 90 kg N ha^-1 were applied as broadcasted urea. There were two reference treatments at each site – the unfertilized check plot (0 kg N ha^-1), and the non-limiting N-rich reference (247 kg N ha^-1). The NDVI readings from each treatment were collected at Feekes 5 growth stage. Topdress N rates (applied as urea, broadcasted or as UAN, foliar sprayed) were prescribed using NDVI values and three algorithms experimentally developed for spring wheat. Spring wheat grain yield, grain protein content, N uptake and N use efficiency (NUE) data will be analyzed to determine whether there were statistically significant differences depending on what sensor was used to make fertilizer N recommendations. Many of the new precision sensing technologies currently on the market are very expensive and represent a costly investment for crop producers that wish to adopt them. Thus, this study will provide valuable information on feasibility of sensor-based technologies for Montana wheat varieties and growing conditions.