the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global patterns and drivers of phosphorus pools in natural soils
Xianjin He
Laurent Augusto
Bruno Ringeval
Ying-Ping Wang
Julian Helfenstein
Yuanyuan Huang
Enqing Hou
Abstract. Most phosphorus (P) in soils is unavailable for direct biological uptake as it is locked within primary or secondary mineral particles, adsorbed to mineral surfaces, or immobilized inside of organic material. Deciphering the composition of different P pools in soil is critical for understanding P bioavailability and its underlying dynamics. However, widely used global estimates of different soil P pools are based on a dataset containing few measurements in which many regions or soil types are unrepresented. This poses a major source of uncertainty in assessments that rely on these estimates to quantify soil P constraints on biological activity controlling global food production and terrestrial carbon balance. To address this issue, we consolidated a database of six major soil P pools containing 1857 entries from globally distributed (semi-)natural soils and 11 related environmental variables. The P pools (labile inorganic P (Pi), labile organic P (Po), moderately labile Pi, moderately labile Po, primary mineral P, and occluded P) were measured using a sequential P fractionation method. Using the database, we trained random forest regression models for each of the P pools and captured observed variation with R2 higher than 60 %. We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH. When expressed in relative concentrations (i.e., as a proportion of total P), the model showed that soil pH is the most important predictor for proportions of all soil P pools, except for labile Pi proportion, which is primarily controlled by soil depth. Using the trained random forest models, we predicted soil P pools’ distributions in natural systems at a resolution of 0.5° × 0.5°. Our global maps of different P pools in soils as well as the pools’ underlying drivers can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.
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Xianjin He et al.
Status: open (until 11 Apr 2023)
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RC1: 'Comment on bg-2023-22', Anonymous Referee #1, 07 Mar 2023
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General comments
The manuscript submitted by He et al. compiled a large database of soil P pools by the Hedley’s chemical extraction method along with 11 environmental variables in global (semi-)natural ecosystems to reveal the global patterns of soil P pools and their drivers. The authors found that soil TP and pH as the main predictors for soil P pool concentrations, but soil pH and soil depth explained the variations in soil P pool proportions. Moreover, the authors presented the advantages of this database over previous one and highlighted the limitations and uncertainty of this database.
In general, this study is very interesting and important for biogeochemists to elevate the understanding of soil P cycling at the global scale. The authors did a great job to collect such a large database and take a systematic analysis of the data. Moreover, the manuscript was generally well organized with a smooth language. I think it is suitable for publication in the journal. Before recommending accepting the manuscript, I have several major concerns and some specific comments for the authors to improve the paper.
Major concerns
>> My main concern is that there are very different drivers for the concentrations (TP for most P fractions but soil pH for primary mineral P) vs. proportions (soil pH for most P fractions but soil depth for labile Pi) of soil P pools. Meanwhile, these factors were separately discussed in the paper (i.e., in Lines 322-341). This is beyond the common thoughts that the proportion of each P fraction is closely related to TP, because the calculation of soil P proportion in a soil or an ecosystem is based on the TP. More importantly, the authors even found the opposite trends of soil P concentrations to P pool proportions (Lines 241-243). Taking the pH (as the authors discussed) as example, soil pH can regulate all the processes of soil P pool concentrations (Lines 333-341), but why only proportions showed the close relationship? I think this difference is probably associated with the data extraction and calculation methods (Line 205-210). The proportions of each P pool were obtained by the predicted P pool concentrations rather than the measured data. But for the measured data (also the authors mentioned the limitations, Lines 246-247), it is clear that the numbers of P pool concentrations and proportions were not uniform (Table 2).
>> For the drivers, the authors highlighted the importance of edaphic properties and climatic factors, but the effects of climate on soil P pools were not discussed like other factors such as soil pH and development (Lines 296-298). I think the main issue lies in that many statistical methods or models were used in this study, and some may give the similar (e.g., methods in Fig. 5 and Table 3) but a little difference in the results, which results in the complex explanations for each P pools or proportion. I suggest to simplifying the methods (i.e., combining the relative importance analysis with the correlation analysis, one was used to find the main relationships between variables with positive or negative correlation, and another give the relative importance) to extract the key factors. Meanwhile, I do not think that only the first is the key factor, and the following one or two with the high explanation degree is also the key one(s). This, to some extent, will exhibit the roles of climatic factors or even plants in soil P pools.
Still, for the drivers, I do not find how soil depth affected the P pools in this study. First, you did not give specific data or figures/tables to show the difference in soil P pools despite of concentrations or proportions. Second, how depth determined P pools was not analyzed well like other drivers. The discussion now can be realized without this work. My suggestion is that soil depth can be discussed along with soil development, both of which change uniformly and jointly mediate the variations in soil P pools.
>> There are 26 tables and figures (11 in the main text and 15 in the supplementary materials) in the paper, which makes the readers difficult to quickly catch the story in this study. More importantly, some figures (e.g., Fig. S9 and S10) were shown, but they were not introduced in the main text. And, the sequence of some figures was even wrong (e.g., Line 97, 207, 221). In addition, the introduction of the contents in each figure should be continuously. For example, in Lines 249-252, when you described the contents in Fig. 4, the content in Fig. 4A should be first but not those in Fig. 4C. Similarly, the Figs 5, 6 were not introduced, but the Figs. 7, 8 were shown first. All these make the reading very jumping and will not help readers to have a good reading.
Specific comments
Line 50: Delete “limited”
Lines 53-63: The advantages of the Hedley’ extraction were not well introduced, which may lead to the suspicion why this work use it. Additionally, I suggest that the contents in Lines 97-101 can be put in this paragraph, which to some extent gives the better reasons for the use of the method.
Lines 65-75: Delete the references in the brackets when you tell the authors’ name and publishing year.
Line 80: Why did you say “only one set of global estimates”? In the last paragraph, you illustrated several global databases of P pools.
Lines 109-110: The OH-Po can be also associated with soil organic matters or mineral-organic complex.
Lines 119-120: Did you check the extraction efficiency of P in different soils? Although it is not the main objective in this work, I think this way may give the data quality for a sample.
Line 154: Biome types are fine, but why not use the productivity index (e.g., NDVI)? That may be better close to soil P pools.
Lines 172-173: Yes, as you mentioned in Lines 374-377, I think this is an important reason why your model sometimes only explained 48%~60% of the variance (Line 296).
Lines 178 &180: In Table 2, the largest P concentration shocked me. I think you are right not to consider it in your model. But, you should show the data between 1% and 99% in the table.
Line 214: For the soil depth, how to understand the 0 cm? In Line 220, how is the 450 cm from? You only gave the range of 0-100 cm (see Lines 213-215).
Lines 221-227: Do not repeat the data (which has shown in the table) in the results, and just show their characteristics.
Line 238: The sub-title is not closely related to the contents as following (introduce the drivers of soil P pools). Maybe, it is better to only highlight the drivers of soil P pools.
Lines 241-248 & 253-258: I suggest not to discussing the data here, and only show the main results or findings.
Lines 159 & 267: I do not know the difference in these two methods simultaneously using here. As I see, the correlation analysis tells us more information than that of partial dependence analysis.
Line 313: What results support this conclusion?
Citation: https://doi.org/10.5194/bg-2023-22-RC1
Xianjin He et al.
Data sets
Global patterns and drivers of phosphorus pools in natural soils Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Ying-Ping Wang, Julian Helfenstein, Yuanyuan Huang, Enqing Hou https://doi.org/10.6084/m9.figshare.16988029
Xianjin He et al.
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