Location: Northwest Sustainable Agroecosystems Research
Title: A classification system for describing N-fertilizer performance in dryland wheat crops of the inland Pacific NorthwestAuthor
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Casanova, Joaquin |
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Phillips, Claire |
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Huggins, David |
Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 2/22/2025 Publication Date: 3/25/2025 Citation: Casanova, J.J., Phillips, C.L., Huggins, D.R. 2025. A classification system for describing N-fertilizer performance in dryland wheat crops of the inland Pacific Northwest. Journal of Environmental Quality. 2025. Article 70017. https://doi.org/10.1002/jeq2.70017. DOI: https://doi.org/10.1002/jeq2.70017 Interpretive Summary: Nitrogen fertilizer is an important input in wheat crops. Excess nitrogen can lead to leaching, reduced air quality, and wasted money. Under fertilizing can lead to poor crop growth and low protein, leading to reduced income. Most fertilizer recommendations focus only on yield and neglect protein and efficient use of nitrogen. This paper describes a sytem to grade wheat performance based on all three factors, so that farmers can see where and how they can improve nitrogen use, through changing application amounts, timing, or crop rotations. A model is developed to make estimates of performace where data might be limited. Such a system is useful for growers and researchers. Technical Abstract: Wheat crops in the inland Pacific Northwest demand nitrogen fertilizers at high levels to achieve yield and grain protein objectives. Inefficiencies in N use can accelerate soil acidification, lower air quality and result in unnecessary input costs. More precise applications, using wheat performance maps at the field-scale, could lead to increased N use efficiency but requires a multidimensional assessment of performance including grain protein, yield, and nitrogen efficiency. In this paper, we use over 20 years of harvest data from the Cook Agronomy Farm (CAF) Long-Term Agroecosystem Research (LTAR) site to assess spatial and temporal patterns in wheat performance and develop a discrete six class evaluation system. To highlight underlying spatial patterns, a multinomial model is fitted on the data, which assesses the probabilities of each performance class at 619 geo-referenced field locations. The model includes remote sensing data, topography, cropping history, soil properties, nitrogen, weather, and cropping history as predictors. The full model has a weighted F1 score in estimating classes of 0.519. Overall, we notice some patterns in the field in performance, but there is a large amount of unexplained variability. |