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  • 1
    Publication Date: 2018-09-28
    Description: In water-scarce regions, high yield and improved water use efficiency (WUE) of crops can be obtained if water and nitrogen (N) are properly applied. While water and N have been the subject of research worldwide, studies are needed to advance our understanding on the complexity of their interaction. A field experiment was conducted at the University of Wyoming Powell Research and Extension Center in 2014 and 2015 growing seasons to determine the effect of irrigation water and N on growth, dry matter (DM) yield, and WUE of silage corn (Zea mays L.) grown under on-surface drip irrigation (ODI). The experiment was laid out as a randomized complete block design in split-plot arrangement with three replications. Irrigation was the main treatment and included 100ETc (100% crop evapotranspiration), 80ETc, and 60ETc. Nitrogen was the sub-treatment and included 0, 90, 180, 270, and 360 kg N ha−1 as urea-ammonium-nitrate solution Results showed that irrigation water, N, and application timing significantly affected growth and DM yield, especially at late vegetative and mid reproductive growth stages. At harvest (R4), no significant difference was observed between 180 kg N ha−1 and 270 kg N ha−1 on DM yield and WUE. However, significant differences of DM yield were observed between irrigation treatments, and 100ETc and 80ETc did not differ in WUE. Our findings suggest that 100ETc and 180 kg N ha−1 is the best combination for high yielding corn for silage grown in a semi-arid climate under ODI.
    Electronic ISSN: 2073-4395
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition , Economics
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  • 2
    Publication Date: 2018-07-19
    Description: Water and nitrogen (N) play an important role in closing the yield gap of crops by reducing associated stresses and yield variability. Field research data coupled to the CSM-CERES-Maize model of Decision Support System Agrotechnology Transfer were used to advance our understanding of the effect of water and N on silage corn growth and yield. The objectives of the study were to determine: (i) the best combination of irrigation water and N for optimum biomass yield, and (ii) the yield gap of silage corn grown at different locations in Wyoming, USA. Field experiments were conducted under sub-surface drip irrigation using a randomized complete block design in a split-plot arrangement with four replications. The main plot was irrigation and consisted of 100% crop evapotranspiration (100ETc), 80% (80ETc), and 60% (60ETc), and the sub-plot was N rates, including 0, 90, 180, 270, and 360 kg N ha−1 as urea-ammonium-nitrate. The simulated results indicated full irrigation and at least 150 kg N ha−1 as the best combination for silage corn production in Wyoming. Our observed and simulated results show the potential to increase the biomass and reduce the yield gap of silage corn in the region if irrigation water and N are properly managed.
    Electronic ISSN: 2071-1050
    Topics: Energy, Environment Protection, Nuclear Power Engineering
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  • 3
    Publication Date: 2019-11-06
    Description: Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach.
    Electronic ISSN: 2073-4395
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition , Economics
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