the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantification of the effects of long-term straw return on soil organic matter spatiotemporal variation: A case study in typical black soil region
Yang Yan
Wenjun Ji
Baoguo Li
Guiman Wang
Songchao Chen
Dehai Zhu
Zhong Liu
Abstract. The straw return practice is essential to soil organic matter (SOM) accumulation in the black soil area with high carbon sequestration potential. However, due to lacking accurate spatial distribution of straw return, few studies took straw return as a variable to carry out rigorous research on the impact of straw return on SOM variation on a regional scale. Based on soil samples and 16 environmental covariates including a 10-meter-resolution straw return amount, the study mapped the spatial distributions of SOM in 2006 and 2018 by random forest (RF) and evaluated the effects of the interaction of soil properties, land use and straw return on SOM spatial-temporal variation. The results show that in the context of the straw returning, the mean SOM content increased from 18.93 g kg−1 to 20.84 g kg−1 during 2006–2018. And 74.49 % of the region had a significant increase (maximum: 24.41 g kg−1) of SOM. The severest SOM loss occurred in the northwest due to the light texture and the transition from paddy fields to dryland. Nevertheless, for areas from paddy fields to dryland, the SOM loss decreased with the increased amount of straw return. The SOM even increased by 1.84 g kg−1 when the straw return amount reached 60–100 %. In addition, soil with higher initial SOM and sand content had a lower response to straw return. The study revealed that straw return is beneficial to carbon sink in farmland and is a better way to prevent a carbon source caused by the change of paddy field to dryland.
Yang Yan et al.
Status: final response (author comments only)
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CC1: 'Comment on egusphere-2022-963', Shuo Li, 05 Dec 2022
GENERAL COMMENTS
Within a backdrop of prolonged and in places severe soil degradation in the black soil region, the article presents the SOM temporal variations from 2006 to 2018 using an optimized RF model and further explored the effects of straw returning on temporal variations under diverse soil properties and land-use change by creatively considering the spatial distribution of straw returning amount as a variable. The topic is well-focused and will be interesting to the readers of this journal. The datasets used in this work are comprehensive, the models are functional, and the comparisons between different soil types and land use types are valuable. Overall, the manuscript is well-written and presented interesting results. Therefore, I recommend acceptance of the manuscript for its scientific value and importance.
There are a number of minor issues that the authors should clarify to strengthen their manuscript. For example, the introduction can and should be expanded by some discussion on the mechanism of the influence of straw return on soil organic matter, it is necessary to include more references. Also, there are more than 6 soil types in the study area. but this paper only compared SOM results in 6 soil types in the section "Results and discussion".
Specific comments:
- Line 47-48, references should be added
- Line 53, remove "of three folds" and change to "3-fold"
- Line 54, add the full name of “RF”
- Line 90, land-use types. This item needs more description.
- Line 145, needs an explanation of “RF-XY”
- Figure 2: Missing text of X-axis
- In Figure 6,7,8,9,10, please explained “***”
- Table 1 If the year is not marked, are the variables used in two years?
- A little more discussion n section 3.3 could also help readers.
- Unified “straw return” or “straw returning”
Citation: https://doi.org/10.5194/egusphere-2022-963-CC1 -
AC2: 'Reply on CC1', Wenjun Ji, 17 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-963/egusphere-2022-963-AC2-supplement.pdf
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RC1: 'Comment on egusphere-2022-963', Anonymous Referee #1, 13 Dec 2022
Investigations into the effects of conservation agriculture, by means of straw return in this case, on soil organic carbon sequestration has increasingly been a subject of interest. This manuscript by Yan et al. has made attempts to include the amount of straw return as an explanatory variable for predictive modeling, mapping, and spatiotemporal analysis of SOM. From this perspective, this study certainly has the potential to contribute to a better understanding of how increasing organic inputs could help improve SOM content at regional scale.
However, I must say that, in my opinion, the approach adopted in this study is not of sufficient scientific soundness for it to be considered for publication in SOIL. My major concerns are summarized as:
General comments:
1. As outlined in the title, the main novelty of this manuscript would be the incorporation of straw return amount into the Digital Soil Mapping framework. But descriptions on how the straw return amount (the CRC factor) is quantified and mapped are lacking. The authors did give a reference (Huang et al., 2020; Liu et al. 2020 in Table 1 is not listed in the References) to the data source (I did not manage to get access to the paper), but a more detailed explanation should still be added into the paper. For example, what was the approach used for CRC mapping? What was the sampling design and sample size for CRC field quantification, at what time of year? Without this information, it is difficult to assess the data quality and thus the overall modeling approach.
2. As far as I understood, the authors only used the CRC data in 2018 to evaluate the effect of straw return on SOM content in the same year. I think this is problematic because the effect of organic inputs on SOM dynamics is a mid-to-long term process, so linking spatial variability of SOM to CRC at one timepoint seems a bit farfetched to me. I would suggest the authors to look into the cumulative effect of straw return on SOM over a longer time period.
3. For the RF model, the authors included the CRC factor for the year 2018, but the relative importance of CRC appears to be low. This again questions the validity of the approach and the CRC data. At the very least, the authors should compare the predictive performances of models with and without CRC, so as to demonstrate whether incorporating CRC improves the model performance.
4. The predictive performances (CC and RMSE) for the 2006 and 2018 RF models were not exceptional (Figure 2) – the accuracy was actually worse in 2018 after the addition of CRC. This means that the predicted SOM values are associated with large prediction errors and uncertainties, thus weakening the obtained results from direct comparisons for the purpose of SOM monitoring.
Specific comments:
1. In the Abstract, one should briefly mention the size and characteristics of the study area.
2. Line 35-40, what is the difference between conventional mapping and DSM? Doesn’t DSM also comprise the procedures you outlined in the first sentence of the paragraph?
3. Line 70-75, there is no mention of the sampling designs in 2006 and 2018. Also, what are the sample sizes?
4. Line 85, I suggest the authors to specify how NDVI and EVI were calculated? Annual mean or based on images from a specific month?
5. The authors should specify the statistical method used for significance tests for all the boxplots. Otherwise, it is difficult to evaluate the appropriateness of the comparisons on changes in SOM with varying straw return amount.
6. The entire Results and discussion section was more focused on the interpretation of the results. An in-depth discussion on the strengthens and weaknesses of the methodology is missing.
7. Overall, the writing of the manuscript should be improved.
Citation: https://doi.org/10.5194/egusphere-2022-963-RC1 -
AC1: 'Reply on RC1', Wenjun Ji, 16 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-963/egusphere-2022-963-AC1-supplement.pdf
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AC1: 'Reply on RC1', Wenjun Ji, 16 Dec 2022
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RC2: 'Comment on egusphere-2022-963', Anonymous Referee #2, 09 Mar 2023
The manuscript addresses an important topic: the effect of straw return on soil organic matter. However, the introduction is rather short and does not fully explain the spatio-temporal modelling of soil properties. Furthermore, the materials and methods lack precision and the protocols deviate from current practices. Why was the soil sieved at 0.25 mm, while the fine earth is generally defined as < 2 mm (line 78). There is also a confusion between SOM and SOC. The wet oxidation protocol should be explained more carefully, because these analyses determine the SOC and NOT the SOM content. the effect of straw return (section 3.5) is difficult to evaluate. First of all, the term is not clearly defined. According to the materials and methods section it is the residue cover and not the percentage of the residue produced by the crop. Second, the statistical analysis of the effect (see Fig. 6) is poorly explained. If Fig. 6 displays the SOM content of the pixels in each class, these observations are not independent and therefore cannot be pair wise compared using a statistical test (ANOVA or t test). This remark also holds for figures 7-10.
Lines 35-36 Would not it be better to express the functions of Jenny and SCORPAN with the dependent variable ‘soil property’ rather than ‘soil’. After all, ‘soil’ is a broad concept that cannot be quantified and you mention ‘soil properties ‘ in line 38.
Lines 40-43 You have explained (not in great detail) the role of DSM for quantifying the spatial variation in soil properties. Here you also include the temporal component. This has to be explained in more detail.
Lines 74 and 75 The sampling design and use of legacy data is not discussed, so it is difficult to interpret their effects on ‘prediction error’.
Line 82 Please provide the reference for the ‘Resource and environment data cloud platform’
Line 87 Although spectral indices such as NDVBI and EVI are well-known, this is much less the case for the NDTI and STI. Please specify these indices.
Line 89 If I understand correctly, you use the CRC of 2018 for all fields between 2007 and 2018? This is a strong assumption as it does not take differences in crop performance or crop type into account. Please describe more clearly that the CRC is not used as a co-variate, but you compare two datasets (with and without residue).
Section 2.4.2 The technique of geographical detector is not as widely known as e.g. random forest. The principles will have to explained in a couple of sentences.
Table 3 Please include a column with the number of samples.
Section 3.2 Please explain the abbreviations e.g. ‘(all-Y)’. As it stands the reader has to look them up in the figure caption. It is not clear either whether these statistics apply to the calibration or the validation data set.
Line 180 and further on. Please define what you mean by ‘straw return content’. As far as I can see it is the straw cover and not necessarily the percentage of residues produced.
Figure 6 Please explain how the significance was calculated and what it means. Why did you not try to fit a regression and analyse the significance of the regression?
Citation: https://doi.org/10.5194/egusphere-2022-963-RC2 -
CC2: 'Comment on egusphere-2022-963', Bifeng Hu, 16 Mar 2023
SOM is closely related to the hot issue like climate change mitigation and sustainable agriculture development. In this study, the author aimed to mapped the spatial distributions of SOM in Northeast of China by random forest (RF) and evaluated the effects of the interaction of soil properties, land use and straw return on SOM spatial-temporal variation. Generally, the author reported a very interesting and valuable study. The manuscript is well organized and presented. The introduction provide sufficient background and the research design appropriate. The conclusions they reported can also well supported by the results. And I think the study is interest and valuable to the readers. However, there still some minor issues need deal with before the manuscript could be accepted for publication in Soil. Therefore I would like to suggest acceptance after minor modification. The detailed comment is as follow:
- Line 26, you can only say SOM instead of SOM/SOC.
- Line 52-53, please revise this sentence since it is confused. You can consider replace it with several simple sentence to make it more clear to the readers.
- I would like suggest the author to explain the differences between GE and RF for quantifying the relative importance.
- Line 105, I would like suggest the authors to provide more detailed and essential information of RF method.
- The caption for the figures such as figure 4 need modification to make it more logical and grammar correct
- For the part of 3.5.1, it more like the Effects of soil types under the different the straw return on SOM variation
- Line 248-249, please modify this sentence
- Line 243, please revise this sentence
- I would like suggest the authors to introduce the meaning and value of their study more clearly in the end of Conclusion.
- It should be better if the authors could provide more reasons for the ST variation of SOM in the survey region.
Citation: https://doi.org/10.5194/egusphere-2022-963-CC2
Yang Yan et al.
Yang Yan et al.
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