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Soil phosphorus testing for intensive vegetable cropping
Journal of Plant Nutrition and Soil Science ( IF 2.6 ) Pub Date : 2020-04-17 , DOI: 10.1002/jpln.201900512
Stany Vandermoere 1 , Tomas Van De Sande 2 , Greet Tavernier 3 , Lore Lauwers 3 , Ellen Goovaerts 4 , Joris De Nies 4 , Stefaan De Neve 1
Affiliation  

Environmental concerns and rapidly decreasing phosphorus (P) resources caused a renewed interest in improving soil P tests for a more efficient P fertilization. This led to the development of better P fertilizer recommendation systems for major arable crops and grass. Nevertheless, these P fertilizer recommendation systems seem to fail for intensive vegetable crops, with often a very short growing season and limited rooting system. This leads to low P use efficiencies in the horticultural sector. In order to address this problem we set up a study to answer following questions: (1) which soil P test predicts the plant available P content for intensive vegetable crops the best and (2) can new insights, such as combining different soil P tests, improve P fertilizer recommendations for intensive vegetable crops? To this end, bulk samples of 41 soils with very different P status (based on ammonium lactate extractable P) were collected. The plant available P content of these soils was determined using six commonly used soil P tests (P‐CaCl2, P‐water, P‐Olsen, P‐acetate, P‐lactate, and P‐oxalate) and a P fertilizer pot experiment with endive (a very P sensitive vegetable crop) was conducted. Six pots of each soil were planted with endive. Three of these pots received no P fertilization (0P) and three pots received ammonium polyphosphate equivalent to 24 kg P ha−1 (24P). All other factors were kept constant. Relative crop yield of the 0P fertilized plants compared to the 24P fertilized plants was determined. Plotting these relative yields against the P status of the soil per soil P test allowed to fit a Mitscherlich curve through the data. Also the combination of two different soil P tests to predict the relative yield with a Mitscherlich equation was evaluated. The coefficients of variation of the soil P tests, the R2 values and the relative standard errors of the parameter estimates revealed that P‐acetate and P‐water predicted the relative yield of the 0P plants the best and that combining two different soil P tests gave no extra predictive power. This finding may form the basis for the development of a new P fertilizer recommendation system for intensive vegetable crops, leading to an improved P use efficiency in horticulture. In order to develop this new system more data relating soil P test values with RY of intensive vegetable crops should be collected.

中文翻译:

集约化蔬菜种植的土壤磷检测

环境问题和快速减少的磷 (P) 资源引起了人们对改善土壤 P 测试以更有效地施肥的兴趣。这导致为主要可耕作物和草开发了更好的磷肥推荐系统。然而,这些磷肥推荐系统似乎不适用于集约化蔬菜作物,通常生长季节很短,生根系统有限。这导致园艺部门的磷利用率低。为了解决这个问题,我们建立了一项研究来回答以下问题:(1)哪种土壤 P 测试最能预测集约化蔬菜作物的植物有效磷含量;(2)可以有新的见解,例如结合不同的土壤 P 测试,提高集约型蔬菜作物磷肥的建议?为此,收集了 41 种具有非常不同 P 状态(基于乳酸铵可提取 P)的土壤的大容量样品。这些土壤的植物有效磷含量是通过六种常用的土壤 P 测试(P-CaCl2、P-水、P-Olsen、P-乙酸盐、P-乳酸和 P-草酸盐)和 P 肥料盆栽试验确定的进行了菊苣(一种对磷非常敏感的蔬菜作物)。每种土壤种植六盆菊苣。其中三个盆没有施肥 (0P),三个盆接受相当于 24 kg P ha-1 (24P) 的多磷酸铵。所有其他因素保持不变。确定了 0P 受精植物与 24P 受精植物相比的相对作物产量。根据每个土壤 P 测试的土壤 P 状态绘制这些相对产量,允许通过数据拟合 Mitscherlich 曲线。还评估了两种不同土壤 P 测试的组合,以使用 Mitscherlich 方程预测相对产量。土壤 P 测试的变异系数、R2 值和参数估计的相对标准误差表明,P-乙酸盐和 P-水最能预测 0P 植物的相对产量,并且结合两种不同的土壤 P 测试给出没有额外的预测能力。这一发现可能成为为集约化蔬菜作物开发新的磷肥推荐系统的基础,从而提高园艺中磷的利用效率。为了开发这个新系统,应该收集更多有关土壤 P 测试值与集约化蔬菜作物的 RY 的数据。土壤 P 测试的变异系数、R2 值和参数估计的相对标准误差表明,P-乙酸盐和 P-水最能预测 0P 植物的相对产量,并且结合两种不同的土壤 P 测试给出没有额外的预测能力。这一发现可能成为为集约化蔬菜作物开发新的磷肥推荐系统的基础,从而提高园艺中磷的利用效率。为了开发这个新系统,应该收集更多有关土壤 P 测试值与集约化蔬菜作物的 RY 的数据。土壤 P 测试的变异系数、R2 值和参数估计的相对标准误差表明,P-乙酸盐和 P-水最能预测 0P 植物的相对产量,并且结合两种不同的土壤 P 测试给出没有额外的预测能力。这一发现可能成为为集约化蔬菜作物开发新的磷肥推荐系统的基础,从而提高园艺中磷的利用效率。为了开发这个新系统,应该收集更多有关土壤 P 测试值与集约化蔬菜作物的 RY 的数据。R2 值和参数估计的相对标准误差表明,P-乙酸盐和 P-水最能预测 0P 植物的相对产量,并且结合两种不同的土壤 P 测试没有给出额外的预测能力。这一发现可能成为为集约化蔬菜作物开发新的磷肥推荐系统的基础,从而提高园艺中磷的利用效率。为了开发这个新系统,应该收集更多有关土壤 P 测试值与集约化蔬菜作物的 RY 的数据。R2 值和参数估计的相对标准误差表明,P-乙酸盐和 P-水最能预测 0P 植物的相对产量,并且结合两种不同的土壤 P 测试没有给出额外的预测能力。这一发现可能成为为集约化蔬菜作物开发新的磷肥推荐系统的基础,从而提高园艺中磷的利用效率。为了开发这个新系统,应该收集更多有关土壤 P 测试值与集约化蔬菜作物的 RY 的数据。
更新日期:2020-04-17
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