Sapana Pokhrel, Wade E. Thomason, Amy L. Shober, Rory O. Maguire
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The average observed N-factor (16.0 kg Mg<sup>−1</sup>) was not statistically different from YG-N factor (17.9 kg Mg<sup>−1</sup>); yet, the observed N-factor ranged from 5.9 to 37.3 kg Mg<sup>−1</sup>; 11 of 24 sites would have been over-fertilized, while seven would have been under-fertilized if YG-N factor was applied at these sites (90% confidence interval). Attempted adjustments of the YG-N factor using single parameters (e.g., cover crop C:N ratio, soil N, and soil nitrate) were insufficient. In contrast, we evaluated N recommendations generated by two open-source decision support tools (available through PennState Extension and precision sustainable agriculture) that account for cover crops, soil organic matter, and weather parameters. These tools provided more site-specific, quantitative N recommendations compared to a YG-based approach, as demonstrated by statistically significant linear relationships between tool-derived N factors and the observed N factor from our field trials. However, further validation of these open-source decision support tools is needed to adapt them to regions with different climates and soil conditions.</p>","PeriodicalId":7567,"journal":{"name":"Agrosystems, Geosciences & Environment","volume":"8 2","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/agg2.70150","citationCount":"0","resultStr":"{\"title\":\"Evaluating adequacy of corn nitrogen recommendations in long term cover cropping systems\",\"authors\":\"Sapana Pokhrel, Wade E. Thomason, Amy L. Shober, Rory O. 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The average observed N-factor (16.0 kg Mg<sup>−1</sup>) was not statistically different from YG-N factor (17.9 kg Mg<sup>−1</sup>); yet, the observed N-factor ranged from 5.9 to 37.3 kg Mg<sup>−1</sup>; 11 of 24 sites would have been over-fertilized, while seven would have been under-fertilized if YG-N factor was applied at these sites (90% confidence interval). Attempted adjustments of the YG-N factor using single parameters (e.g., cover crop C:N ratio, soil N, and soil nitrate) were insufficient. In contrast, we evaluated N recommendations generated by two open-source decision support tools (available through PennState Extension and precision sustainable agriculture) that account for cover crops, soil organic matter, and weather parameters. 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引用次数: 0
摘要
在玉米(Zea mays L.)生产中实现最佳氮(N)利用效率是具有挑战性的。我们质疑弗吉尼亚州基于产量目标(YG)的氮肥推荐量(N因子= 17.9 kg Mg - 1,即每单位玉米产量的氮肥需要量)是否充分解释了包括覆盖作物在内的保护措施下氮肥利用效率的提高。在24个覆盖作物≥3年且免耕的试验点确定最佳氮素用量和最佳产量,计算观测氮因子,并与产量目标氮(YG-N)因子进行比较。平均观察到的n因子(16.0 kg Mg−1)与YG-N因子(17.9 kg Mg−1)无统计学差异;然而,观察到的n因子范围为5.9 ~ 37.3 kg Mg−1;如果在这些地点施加YG-N因子,24个地点中有11个将会过度受精,而7个将会受精不足(90%置信区间)。尝试使用单一参数(如覆盖作物碳氮比、土壤氮和土壤硝酸盐)调整YG-N因子是不够的。相比之下,我们评估了两个开源决策支持工具(通过PennState Extension和精准可持续农业提供)生成的N建议,这些工具考虑了覆盖作物、土壤有机质和天气参数。与基于yg的方法相比,这些工具提供了更多针对特定地点的定量氮素建议,正如我们在现场试验中观察到的N因子与工具衍生的N因子之间的统计显着线性关系所证明的那样。然而,需要进一步验证这些开源决策支持工具,使其适应不同气候和土壤条件的地区。
Evaluating adequacy of corn nitrogen recommendations in long term cover cropping systems
Achieving optimum nitrogen (N) use efficiency in corn (Zea mays L.) production is challenging. We questioned whether Virginia's yield goal (YG)-based N recommendation (N factor = 17.9 kg Mg−1, the N fertilizer requirement per unit of corn grain yield) adequately accounted for improved N use efficiency under conservation practices including cover crops. We determined the agronomic optimum nitrogen rate and optimum yield at 24 field sites with ≥3 years of cover crops and no-till to calculate an observed N factor, which was then compared to the yield goal nitrogen (YG-N) factor. The average observed N-factor (16.0 kg Mg−1) was not statistically different from YG-N factor (17.9 kg Mg−1); yet, the observed N-factor ranged from 5.9 to 37.3 kg Mg−1; 11 of 24 sites would have been over-fertilized, while seven would have been under-fertilized if YG-N factor was applied at these sites (90% confidence interval). Attempted adjustments of the YG-N factor using single parameters (e.g., cover crop C:N ratio, soil N, and soil nitrate) were insufficient. In contrast, we evaluated N recommendations generated by two open-source decision support tools (available through PennState Extension and precision sustainable agriculture) that account for cover crops, soil organic matter, and weather parameters. These tools provided more site-specific, quantitative N recommendations compared to a YG-based approach, as demonstrated by statistically significant linear relationships between tool-derived N factors and the observed N factor from our field trials. However, further validation of these open-source decision support tools is needed to adapt them to regions with different climates and soil conditions.