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Prediction of leaf nitrogen from early season samples and development of field sampling protocols for nitrogen management in Almond (Prunus dulcis [Mill.] DA Webb)

机译:从早期季节样品中预测叶片氮含量并开发用于杏仁氮管理的野外采样方案(李子[Mill。] DA Webb)

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摘要

Protocols for leaf sampling in deciduous tree crops are commonly executed too late in the season and do not adequately consider field variability to be effectively used to guide N management. The goal of this study was to develop improved sampling strategies to optimize nitrogen management in deciduous tree crops.Leaf nutrient concentration from individual trees in four mature commercial orchards was collected (n =1148) for three consecutive seasons to develop nitrogen prediction models and to estimate the distribution of N values in orchards in July. Spatial variance analysis was used to determine optimal sampling strategies.Leaf nitrogen concentration in summer can be predicted (r(2) = 0.9) from the leaf N and B concentration in spring with the sum of K, Ca, and Mg equivalents. Mean field leaf nutrient concentration can be obtained by collecting one pooled sample per management zone composed of 30 trees each of which are at least 30 m apart. Using these methods the percentage of trees with leaf N above the recommended July critical value can be predicted accurately.Optimized methods for sample collection and models to predict mid-season leaf N from early season samples can be used to improve N management in deciduous tree crops
机译:落叶乔木叶片采样方案通常在该季节执行得太晚,并且没有充分考虑到田间变异性来有效地指导氮素管理。这项研究的目的是开发改进的采样策略,以优化落叶乔木的氮素管理。连续三个季节收集四个成熟商业果园(n = 1148)中单个树木的叶片养分浓度,以开发氮素预测模型并估算7月果园中N值的分布。使用空间方差分析确定最佳的采样策略。夏季的氮含量可以通过春季叶片的氮和硼浓度(含K,Ca和Mg的总和)来预测(r(2)= 0.9)。通过在每个管理区收集30个树木,每个树木至少相距30 m的区域收集一个样本,即可获得平均田间叶子养分浓度。使用这些方法可以准确地预测叶片N高于建议的7月临界值的树木的百分比。可以使用优化的采样方法和模型来预测早季样品的季节中叶N,以改善落叶乔木的氮素管理。

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