Trade-Off between Energy Wood and Grain Production in Temperate Alley-Cropping Systems: An Empirical and Simulation-Based Derivation of Land Equivalent Ratio

Round  1

Reviewer 1 Report

To Authors,

The study by Seserman et al., (Agriculture_523536) looked at the trade-off relationship between mono-cropping and alley-cropping systems in eastern Germany over a period of two seasons. In addition to empirical data collected  the authors ran a simulation of Yield-SAFE model to calculate Land Equivalent Ratios (LER) for two cropping systems. Overall, the study results do not provide concrete evidence in favour of either cropping systems as one study site showed a higher LER, while other study site had a lower LER values. But, established the tree:crop density to be considered for design and adoption of alley-cropping systems.

The study builds on existing known knowledge and scientific evidence, thus confirming the previous findings. 

Minor edits:

Table1: Please provide month and year of planting and harvesting dates.

Figure 4 & 5: Consider merging Fig 4 and Fig5 (stacking one above other). Thus, permitting readers to visualise both on a single panel.

Table A1: replace “Tree Leaf Behaviour” with “Tree Phenology.

Author Response

Thank you so much for your time and review of our manuscript titled “Trade-Off between Energy Wood and Grain Production in Temperate Alley-Cropping Systems: An Empirical and Simulation-Based Derivation of Land Equivalent Ratio” (ID agriculture-523536). 

Regarding the addressed points, we have considered your suggestions and have changed accordingly. The planting and harvesting dates are now mentioned in the calibration section of our manuscript, sending to the Annexed Tables. Additionally, for clarity purposes, the Annexed Tables are now separated into Tree Parameters, Crop Parameters, and Soil Parameters.

Reviewer 2 Report

See file below 

Comments for author File: Comments.pdf

Author Response

Thank you for your time and review of our manuscript titled “Trade-Off between Energy Wood and Grain Production in Temperate Alley-Cropping Systems: An Empirical and Simulation-Based Derivation of Land Equivalent Ratio” (ID agriculture-523536).

Point 1: L47: Production of woody biomass for what purpose?

Response Point 1: Thank you very much, we have considered your question and have introduced this to our paper.

Point 2: L52: Would trees also compete for available light, thus reducing photosynthetic activity in the crops?

Response Point 2: More or less, depending on the latitude and longitude of the considered site and orientation of the tree strips, one of the practitioner’s concerns could come from this reason.

Point 3: L93: Did the four Aps act as replications? Therefore four replications? L97-99: If the above question was answered with “yes”, would dividing the plots into two sets of two leeward and windward plots change the design of the APs to an extent that they would no longer be replicated? Shouldn’t the four replications be designed in the same manner? L152: So back to my question above, how many true reps do you have now that you are comparing leeward and windward plots?

Response Point 3: Initially, distinctions were made between the four replicates, two set leeward and two windward. However, since the woody biomass measurements showed no significant differences between the two lee- and two windward plots, the woody biomass was analyzed considering four replicates. For clarity purposes, a sentence was supplemented to the manuscript.

Point 4: L122: Did you examine grain quality as well, or only weight?

Response Point 4: Not in this study.

Point 5: L147-152: Please provide more detail on parameters. T-tests were performed? What did you test? Please provide more details. Which statistical software did you use?

Response Point 5: Thank you very much, we missed explaining the reasoning and software behind our approach. More information was supplemented to the revised manuscript.

Point 6: L323: Were any of these modeled values compared statistically? If not, I suggest doing so. L358: Again, statistical analyses would help provide evidence for your claims.

Response Point 6: The performance of the model was assessed visually, as well as by the NRMSE, bias function, and R². Additionally, upon the request of another reviewer, we have employed a more common way of assessing the performance of a model (i.e. with the help of the concordance correlation coefficient and simulation bias from observations).

Point 7: L442: Be careful when making statements about being “less” or “more” productive, since these values were not compared statistically.

Response Point 7: Yes, you are right. Thanks to your comment, uncertainties given by this generalization were addressed by stating the time frame.

Reviewer 3 Report

General comments

This paper aims to assess the tree- and crop yields and to derive the LER and gross energy yield for two temperate ACSs in Germany under different design scenarios, i.e. tree arrangements (lee- or windward) and ratios of tree area to crop area.

This paper provides interesting experimental and modelling predictions in a mixed-system using different biophysical indicators (yield, energy, LER). Overall the rationale, design, quality of the language and research conducted are suitable for publication (with the reservations spelt out below). The results are good, but there is a lack of connection between these and the Discussion section, mainly related with the link between weather conditions during the experiments and results. I suggest providing objective information (e.g. water and solar radiation balance) to improve the support of your statements in the Discussion section. Likewise, the missing information about the N cycle in this experiment is something need to be discussed.

On the other hand, information about model inputs (crop management, soil data, etc.) and model functionality need to be provided. For the readers is important to understand the structure of the model and how the interactions (water use, solar radiation use, nutrients use, etc) between crops and trees are considered in the model processes.

Specific comments

Introduction

Overall, well written.

Line 65: please write ‘yield predictions’ instead of ‘probable yields’.

Materials and Methods

Figure 1. This figure is not easy to read. Please explain what means the blue lines between the top blue line and the bottom red line. Otherwise, consider showing minimum and maximum temperatures with lines and monthly rainfall with bar plot. This is a standard way to show the temporal variability of these variables.  You should also add letter to each panel to identify years and locations.

Lines 109 and 110: Please write Triticum aestivum and Hordeum vulgare in italics.

Line 81: Is the selected buffer zone (1.5 m) enough to isolate competition for natural resources (e.g. solar radiation, water, N) between the trees and the crop? Why did you select this distance and no others?

Line 180: please write global solar radiation instead global radiation.

Line 181: The nutrient availability is not considered to be a yield-limiting factor in this version of the model.

Line 192: “In this paper, two sets of data were reserved, one for calibration of the Yield-SAFE model one for validation of the model output”. How did you decided which dataset will be used for calibration and for validation? Whish were the assumptions for this?

Line 193: “As there could not be any distinction between leeward and windward rows in the model, we averaged between the Aps”. It means that the field experiment was not created thinking in the model functionality? This part of the M&M needs further explanation.

Line 204: In order to provide an objective assessment of the model performance, I suggest following the model performance classification described by Ojeda et al., 2017 and Ojeda et al., 2018 and based on the CCC (concordance correlation coefficient). The CCC integrates precision through Pearson’s correlation coefficient, which represents the proportion of the total variance in the observed data that can be explained by the model, and accuracy by bias which indicates how far the regression line deviates from the concordance (y = x) line.

Ojeda, J.J., Volenec, J.J., Brouder, S.M., Caviglia, O.P., Agnusdei, M.G., 2017.Evaluation of Agricultural Production Systems Simulator (APSIM) as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments. GCB Bioenergy 9, 796–816.

Ojeda, J. J., Caviglia, O. P., Volenec, J. J., Brouder, S. M., & Agnusdei, M. G. (2018). Modelling stover and grain yields, and subsurface artificial drainage from long-term corn rotations using APSIM. Agricultural Water Management, 195, 154-171.

Line 205: I suggest talking about crop yield instead plant yield. In my eyes, the organisational level assessed here is cropping systems rather than a single plant.

Line 208: please write sections instead parts.

Results

Line 235: I suggest providing a quantification of this ‘was significantly lower’… or ‘was slightly higher’…

Figure 4: I suggest adding SD (calculated using row data) to the observed data. I also recommend adding a new x-y figure using the complete observed and simulated yields. You can move Figs 4 and 5 to appendix and just keep the new figure in the manuscript.

Figures 6 and 7: I recommend converting the y axis to energy. This will provide a better understanding about what combination of systems is more efficient. In the current figures is difficult to compare which will be the best combination.

Line 368: ‘The gross energy yield values suggested that all ratios of tree area to crop area above 75:25 would be advantageous at Wendhausen, but only from 75:25 to 80:20 at Neu Sacro, since the values decreased thereafter’. This info is in Table A3. I suggest to put this in the body of the document (at least the energy values).

Discussion

Line 381: Please specify the literature.

Line 388: The author suggested: ‘which we would attribute to the weather conditions, but also to waterlogging during the winter months, extreme heterogeneity in water content, and soil texture…’. These are possible causes, but I would like to see more support data for these assumptions. For example, could you show a water balance during the experimental period? Soil texture data? Etc…

Line 393: what is an unfavourable growing condition? With respect to what? Which are the requirements for these mixed-systems to growth in potential conditions?

Line 399: what is a good range? You should explain which the main processes are affected by temperature.

Line 402: please talk about crop not plant.

Line 406: did you measure the leaf litter deposition for this experiment?

Lines 410-417: is it assumption based in a water balance conducted in the experiments? Otherwise it is just a hypothesis. Probably the main differences between treatments were based on competition between water and solar radiation. However, you did not measure it. So, it is difficult to conclude without these data. One possible option should be use the model to calculate the water and radiation balance, i.e. using the model inputs (rainfall and solar radiation) and the model outputs (evapotranspiration and solar radiation intercepted). Please check the following paper and the approach to assess resource capture and its effect on yields:

Ojeda, J. J., Caviglia, O. P., Agnusdei, M. G., & Errecart, P. M. (2018). Forage yield, water-and solar radiation-productivities of perennial pastures and annual crops sequences in the south-eastern Pampas of Argentina. Field crops research, 221, 19-31.

Author Response

Thank you for your time and review of our manuscript titled “Trade-Off between Energy Wood and Grain Production in Temperate Alley-Cropping Systems: An Empirical and Simulation-Based Derivation of Land Equivalent Ratio” (ID agriculture-523536).

Point 1: On the other hand, information about model inputs (crop management, soil data, etc.) and model functionality need to be provided. For the readers is important to understand the structure of the model and how the interactions (water use, solar radiation use, nutrients use, etc) between crops and trees are considered in the model processes.

Response Point 1: Thank you very much, the model inputs were mentioned in the Calibration section of our manuscript, sending to the Annexed Tables. They are now prominently and clearly stated in the Calibration section of our manuscript. Additionally, the Annexed Tables are now separated into Tree Parameters, Crop Parameters, and Soil Parameters. For the conciseness of the manuscript, we have referred to reports and guide manuals of Yield-SAFE describing the model processes.

Point 2: Figure 1. This figure is not easy to read. Please explain what means the blue lines between the top blue line and the bottom red line. Otherwise, consider showing minimum and maximum temperatures with lines and monthly rainfall with bar plot. This is a standard way to show the temporal variability of these variables.  You should also add letter to each panel to identify years and locations.

Response Point 2: Thank you very much for addressing this. A sentence clearly describing the graphs was supplemented to the caption of Figure A1. However, we would like to keep the Walter-Lieth graphs since they already show temperature trends, minimum and maximum temperature values, precipitation trends, and frost months.

Point 3: Line 81: Is the selected buffer zone (1.5 m) enough to isolate competition for natural resources (e.g. solar radiation, water, N) between the trees and the crop? Why did you select this distance and no others?

Response Point 3: The buffer zone comes mainly as a consequence of machinery as well as harvesting related traffic by tractor and trailer on the crop alley and not so much from us wanting to isolate competition for natural resources.

Point 4: Line 192: “In this paper, two sets of data were reserved, one for calibration of the Yield-SAFE model one for validation of the model output”. How did you decided which dataset will be used for calibration and for validation? Whish were the assumptions for this?

Response Point 4: Thank you for bringing this to our attention. Information regarding the calibration and validation data sets were supplemented to the manuscript.

Point 5: Line 193: “As there could not be any distinction between leeward and windward rows in the model, we averaged between the Aps”. It means that the field experiment was not created thinking in the model functionality? This part of the M&M needs further explanation.

Response Point 5: Initially, distinctions were made between the four replicates, two set leeward and two windward. However, since the woody biomass measurements showed no significant differences between the two lee- and two windward plots, the woody biomass was analyzed considering four replicates. Thank you for your comment, this is explicitly mentioned in the revised manuscript.

Point 6: Line 204: In order to provide an objective assessment of the model performance, I suggest following the model performance classification described by Ojeda et al., 2017 and Ojeda et al., 2018 and based on the CCC (concordance correlation coefficient). The CCC integrates precision through Pearson’s correlation coefficient, which represents the proportion of the total variance in the observed data that can be explained by the model, and accuracy by bias which indicates how far the regression line deviates from the concordance (y = x) line.

Ojeda, J.J., Volenec, J.J., Brouder, S.M., Caviglia, O.P., Agnusdei, M.G., 2017.Evaluation of Agricultural Production Systems Simulator (APSIM) as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments. GCB Bioenergy 9, 796–816.

Ojeda, J. J., Caviglia, O. P., Volenec, J. J., Brouder, S. M., & Agnusdei, M. G. (2018). Modelling stover and grain yields, and subsurface artificial drainage from long-term corn rotations using APSIM. Agricultural Water Management, 195, 154-171.

Response Point 6: Thank you very much, your suggestions were taken into consideration and supplemented to this manuscript.

Point 7: Figure 4: I suggest adding SD (calculated using row data) to the observed data. I also recommend adding a new x-y figure using the complete observed and simulated yields. You can move Figs 4 and 5 to appendix and just keep the new figure in the manuscript.

Response Point 7: Since the manuscript was about modelling the tree and crop yields for different years, we would like to keep Figure 4, as they show the modelled tree and crop yields in comparison to the measured ones. Regarding the x-y figure proposal using the complete data, unfortunately, in our case, the scarce amount of available data would not justify such a figure.

Point 8: Figures 6 and 7: I recommend converting the y axis to energy. This will provide a better understanding about what combination of systems is more efficient. In the current figures is difficult to compare which will be the best combination.

Response Point 8: Thank you very much, an additional figure showing the gross energy yield depending on the ratio of tree area to crop area has been supplemented to the manuscript.

Point 9: Line 368: ‘The gross energy yield values suggested that all ratios of tree area to crop area above 75:25 would be advantageous at Wendhausen, but only from 75:25 to 80:20 at Neu Sacro, since the values decreased thereafter’. This info is in Table A3. I suggest to put this in the body of the document (at least the energy values).

Response Point 9: Thank you for your recommendation. Since the new figure is added, we will refer to it when describing our results.

Point 10: Line 388: The author suggested: ‘which we would attribute to the weather conditions, but also to waterlogging during the winter months, extreme heterogeneity in water content, and soil texture…’. These are possible causes, but I would like to see more support data for these assumptions. For example, could you show a water balance during the experimental period? Soil texture data? Etc…

Response Point 10: These statements were investigated and reported prior in the publications we referred to in the text. For clarity purposes, these references are now mentioned next to the statements in the manuscript. Graphs showing the precipitation, evaporation, flux to trees and crops, and flow to groundwater level (irrigation was neither considered on the field, nor in the modelling approach) could be provided, but for the conciseness of the manuscript, we would like to focus on our objective, i.e. to assess the tree- and crop yields and to derive the LER and gross energy yield.

Point 11: Line 393: what is an unfavourable growing condition? With respect to what? Which are the requirements for these mixed-systems to growth in potential conditions?

Response Point 11: By unfavorable growing conditions for trees and crops in 2016, compared to 2017, it is meant that the rainfall in 2016 was subjected to a drastic decline precisely in those months essential for tree and crop growth (further mentioned in the text), accounting for an overall of 314 mm less precipitation in 2016, as compared to 2017 (Lines 396-403).

Point 12: Line 399: what is a good range? You should explain which the main processes are affected by temperature.

Response Point 12: Thank you for addressing this. The main process affected by each climatic input were analyzed through a sensitivity analysis, as well as with climate scenarios in Seserman et al. [44]. For clarity purposes, we have rewritten the sentence and have cited this study.

Point 13: Line 406: did you measure the leaf litter deposition for this experiment?

Response Point 13: Yes, it was measured and reported at Wendhausen by Swieter et al. [20].

Point 14: Lines 410-417: is it assumption based in a water balance conducted in the experiments? Otherwise it is just a hypothesis. Probably the main differences between treatments were based on competition between water and solar radiation. However, you did not measure it. So, it is difficult to conclude without these data. One possible option should be use the model to calculate the water and radiation balance, i.e. using the model inputs (rainfall and solar radiation) and the model outputs (evapotranspiration and solar radiation intercepted). Please check the following paper and the approach to assess resource capture and its effect on yields:

Ojeda, J. J., Caviglia, O. P., Agnusdei, M. G., & Errecart, P. M. (2018). Forage yield, water-and solar radiation-productivities of perennial pastures and annual crops sequences in the south-eastern Pampas of Argentina. Field crops research, 221, 19-31.

Response Point 14: Thank you for your recommendation. The reasoning behind obtaining such low, contrasting crop yields directed towards the “extreme heterogeneity in water content” which was empirically investigated and reported at Neu Sacro by Kanzler and Böhm, [21]  (and at Wendhausen by Lamerre et al. [23]). However, we allowed for uncertainties by changing “was” to “might”.

Point 15: Specific comments:

Line 65: please write ‘yield predictions’ instead of ‘probable yields’.

Lines 109 and 110: Please write Triticum aestivum and Hordeum vulgare in italics.

Line 180: please write global solar radiation instead global radiation.

Line 205: I suggest talking about crop yield instead plant yield. In my eyes, the organisational level assessed here is cropping systems rather than a single plant.

Line 208: please write sections instead parts.

Line 235: I suggest providing a quantification of this ‘was significantly lower’… or ‘was slightly higher’…

Line 381: Please specify the literature.

Line 402: please talk about crop not plant.

Response Point 15: Thank you very much, we have considered your suggestions and have changed accordingly.