用于估算肥料推荐支持工具关键土壤测试值的模型和充分性解释

N. A. Slaton, A. Pearce, L. Gatiboni, D. Osmond, C. Bolster, F. Miquez, J. Clark, J. Dhillon, B. Farmaha, D. Kaiser, S. Lyons, A. Margenot, A. Moore, D. Ruiz Diaz, D. Sotomayor, J. Spackman, J. Spargo, M. Yost
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引用次数: 0

摘要

土壤测试相关性决定了土壤测试是否可用于根据临界土壤测试值 (CSTV) 预测施肥需求。我们的目标是比较相关模型和产量充分性解释的五种组合估算出的临界土壤测试值,并选择一种使用肥料推荐支持工具(FRST)进行土壤测试相关性分析的方法。对土壤测试 P 或 K 与作物相对产量之间存在强(Mehlich-1 K)、中(Mehlich-3 K)或弱(Olsen P)相关性的三个数据集拟合了四个模型。我们测试了弧线-对数校准曲线 (ALCC)、指数 (EXP)、线性高原 (LP) 和二次高原 (QP) 模型。对于 ALCC 和 EXP 方法,CSTV 被定义为最大预测产量的 95%;对于 LP,CSTV 被定义为连接点;对于 QP,CSTV 被定义为连接点和最大预测产量的 95%。Mehlich-1 K 数据集的五个 CSTV 为 46 至 66 毫克/千克,Mehlich-3 K 数据集为 115 至 165 毫克/千克,Olsen P 数据集为 7 至 16 毫克/千克。十次成对比较显示,估计的 CSTV 在数值上,有时在统计上受到模型和充足水平解释的影响。尽管 CSTV 之间存在差异,但各种方法之间出现高于或低于预测 CSTV 的显著产量反应的频率基本相当,在给定数据集中,0%-18% 的地点出现假阴性误差。在 FRST 的建模方法中,选择了 CSTV 为预测最大值 95% 的 QP 模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Models and sufficiency interpretation for estimating critical soil test values for the Fertilizer Recommendation Support Tool

Models and sufficiency interpretation for estimating critical soil test values for the Fertilizer Recommendation Support Tool

Soil test correlation determines whether a soil test can be used to predict the need for fertilization based on the critical soil test value (CSTV). Our objectives were to compare the CSTV estimated from five combinations of correlation models and yield sufficiency interpretations and to select one method for soil test correlation performed with the Fertilizer Recommendation Support Tool (FRST). Four models were fit to three datasets with strong (Mehlich-1 K), moderate (Mehlich-3 K), or weak (Olsen P) correlations between soil test P or K and crop relative yield. We tested the arcsine-log calibration curve (ALCC), exponential (EXP), linear plateau (LP), and quadratic plateau (QP) models. The CSTV was defined as 95% of the maximum predicted yield for the ALCC and EXP methods, the join point for LP, and both the join point and 95% of the maximum for the QP providing five CSTV predictions. The five CSTVs ranged from 46 to 66 mg kg−1 for the Mehlich-1 K dataset, 115 to 165 mg kg−1 for the Mehlich-3 K dataset, and 7 to 16 mg kg−1 for the Olsen P dataset. Ten pairwise comparisons showed the estimated CSTV was numerically and sometimes statistically influenced by the model and sufficiency level interpretation. Despite differences among CSTVs, the frequency of significant yield responses above and below the predicted CSTV was generally comparable among the methods, with false-negative errors occurring at 0%–18% of sites for a given dataset. The QP model with a CSTV at 95% of the predicted maximum was selected as the modeling approach for FRST.

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