Simulated Biomass, Climate Change Impacts, and Nitrogen Management to Achieve Switchgrass Biofuel Production at Diverse Sites in U.S.

Round 1

Reviewer 1 Report

This is a report of a simulation exercise to predict switchgrass performance across a range of environments.  This is generally a well-written article, and the authors have done an excellent job of collating a number of published works and conducted some detailed modelling on these datasets in relationship to climate change scenarios.    But I believe this article has some major conceptual flaws.  Therefore, I cannot accept this paper in current form at this time.

The major difficulty I have with this article is the use of ‘longitude’ without a full discussion of why this is important.  The research sites at southeastern Minnesota and western Louisiana are approximately at the same longitude, but that certainly is not the major defining factor of their similarity.  Or differences. Not by a long shot.  (latitude is obviously more interesting, but the authors ignored this).  The reasons that crops yield more or less or respond to N are not very clearly related to longitude, but more related to temperature, soil type and moisture availability.   So why are the authors modelling performance based upon longitude?  Unless the authors can offer a cogent argument that longitude is of strong interest biologically, this paper should be rejected.    

Some additional comments:

1. Yield analysis.  The authors should make sure they compare upland and lowland types correctly.  Upland types were ‘as high as’ 29.3 Mg/ha (actually chart says 40).  What was the highest Upland type?  Average between the two types?  Not clearly written. Authors pick out ‘Kansas Native’ but what were the highest yielding lowland and upland types AVERAGED across studies?  With this nice collection of data – should summarize more clearly.
2. Authors should clarify that this modelling exercise pertains only to rain-fed areas of the eastern US, not irrigated regions of the Western US – there is data from irrigated regions that the authors ignored – this is fine, except this should be clarified.   Also, it’s not clear why only relegated to single-cut systems.  There are several 2- and 3- cut systems that may work fine and improve yields.
3. I find the analysis of longitude to be very curious and unsatisfactory, since longitude appears to have very little relationship to biological factors (soil type or growth factors) – unless it is closely related to rainfall (which is likely, but not explained). However, the authors don’t address this factor in in relationship to biological growth factors.  Why is it important?  Unless authors can make an argument that longitude is important biologically, this section should be omitted.  It’s not surprising that they find little relationship of factors to longitude in their modelling.  Latitude is much more interesting, since daylength and temperature factors are more closely related to latitude than longitude.  Average rainfall is related to longitude – but that should be considered with data on moisture.   
4. The weakness of the discussion of longitude is related to the presentation on further simulation – which goes into great depth about what might happen at different longitudes, without really explaining why that might be (biological mechanisms) and how this might be important in terms of management.   Figure 5 shows simulated yields across a range of longitudes.  However, after staring at these at some length, I cannot see that they are at all enlightening. 
5. Conclusions: The conclusion that “overall, switchgrass response to longitude varied in response varied according to nitrogen application rates. For example yields of switchgrass lowland responded well to variation in longitude with low nutrient application, while upland cultivars appeared to suffer from a nutrient deficiency across locations”  - I don’t see the data which supports these statements.  Switchgrass is known to respond to nitrogen under higher yielding environments when N is limiting in the soil (which is virtually everywhere).  Don’t see how this would differ according to longitude.
6. The title talks about ‘best management’ to achieve switchgrass biofuel production – but offer essentially no recommendations on best management practices that might be important for different areas. Several important factors come to mind, such as freezing tolerance of some varieties, methods of production (harvest methods), stand establishment methods, nutrients other than N.  The N recommendations also appear also to be shallow.  If I live in Texas or Wisconsin - how much N would your recommend?

Author Response

Dear Reviewer,

We thank you for the comments on the manuscript, which we have addressed in revised manuscript as discuss below. Throughout, reviewer comments are in blue font and italic type, and our response in regular type. The significant additions and rewrites have been to the sections dealing with relationship between yield and longitude. From original study, we have found that critical relationship between yield and longitude through additive regression model which was developed using historical yield data. Additive regression model is a kind of machine learning model.

Comments: This is a report of a simulation exercise to predict switchgrass performance across a range of environments.  This is generally a well-written article, and the authors have done an excellent job of collating a number of published works and conducted some detailed modelling on these datasets in relationship to climate change scenarios.    But I believe this article has some major conceptual flaws.  Therefore, I cannot accept this paper in current form at this time.

The major difficulty I have with this article is the use of ‘longitude’ without a full discussion of why this is important.  The research sites at southeastern Minnesota and western Louisiana are approximately at the same longitude, but that certainly is not the major defining factor of their similarity.  Or differences. Not by a long shot.  (latitude is obviously more interesting, but the authors ignored this).  The reasons that crops yield more or less or respond to N are not very clearly related to longitude, but more related to temperature, soil type and moisture availability.   So why are the authors modelling performance based upon longitude?  Unless the authors can offer a cogent argument that longitude is of strong interest biologically, this paper should be rejected.   

Some additional comments:

Yield analysis.  The authors should make sure they compare upland and lowland types correctly.  Upland types were ‘as high as’ 29.3 Mg/ha (actually chart says 40).  What was the highest Upland type?  Average between the two types?  Not clearly written. Authors pick out ‘Kansas Native’ but what were the highest yielding lowland and upland types AVERAGED across studies?  With this nice collection of data – should summarize more clearly.

• In manuscript, we stated overall average values for lowland and upland types across locations in line 325-326, “ The mean dry yields of lowland and upland switchgrass across locations, management scenarios, cultivars, and years were 14.0 Mg ha^-1 and 7.4 Mg ha^-1, respectively.”
• We made the following statement on the yield analysis staring on line 330.

“Among lowland switchgrass cultivars, Alamo had the highest mean value of 15.9 Mg ha-1 (Fig. S1) and produced as high as 39.3 Mg ha-1 with higher N fertilizer inputs at Texas [72]. When rainfall was low in 1996, among lowland switchgrass cultivars, Kanlow produced the lowest yield of 1.9 Mg ha-1 yr-1 with 134 kg ha-1 yr-1 N fertilizer inputs at Dallas in Texas. NL931 produced the lowest mean yield of 9.15 Mg ha-1 yr-1 among other lowland cultivars (Fig. S1).  Among upland switchgrass cultivars, Cave-In-Rock can be produced as high as 26.1 Mg ha-1 yr-1 with 200 kg N fertilizer inputs in 2009 at Western in Ohio[49]. However, based on 108 Cave-In-Rock data values, the mean yield value for this cultivar was 8.97 Mg ha-1 yr-1. Carthage cultivar produced the lowest mean yield of 2.5 Mg ha-1 yr-1 with no nitrogen input between 2009-2011 at Hobet in West Virginia[73]. Among upland cultivars that are commercially available, Shelter and Caddo cultivars produced the highest mean yields near 9.7 Mg ha-1 yr-1 (Fig. S1).”

Authors should clarify that this modelling exercise pertains only to rain-fed areas of the eastern US, not irrigated regions of the Western US – there is data from irrigated regions that the authors ignored – this is fine, except this should be clarified.   Also, it’s not clear why only relegated to single-cut systems.  There are several 2- and 3- cut systems that may work fine and improve yields.

• We added statement in line 211, In simulation, to determine the effects of environmental condition on yields, switchgrass plants were produced only in rainfed areas.
• Due to limited studies on 2- and 3 cut systems, we only considered a single cut study to develop the model. Considering multiple cut system will be great for the further study.

I find the analysis of longitude to be very curious and unsatisfactory, since longitude appears to have very little relationship to biological factors (soil type or growth factors) – unless it is closely related to rainfall (which is likely, but not explained). However, the authors don’t address this factor in in relationship to biological growth factors.  Why is it important?  Unless authors can make an argument that longitude is important biologically, this section should be omitted.  It’s not surprising that they find little relationship of factors to longitude in their modelling.  Latitude is much more interesting, since daylength and temperature factors are more closely related to latitude than longitude.  Average rainfall is related to longitude – but that should be considered with data on moisture.  

• We added the regression model analysis section in the manuscript which was not included in the original paper. Based on the additive regression model analysis, which was developed based on the collected data, longitude was most significant factor that influences yields.

We added the following section in method part starting on page 4 line 116.

2.2. Analysis of factors determining switchgrass yield

Switchgrass growth and development is affected by numerous factors including temperature[42], rainfall [43], fertilizer inputs [44], and field location (e.g. soil type and latitude)[34, 45]. To determine which variable explains most of the variation in switchgrass yields, in first phase, correlation and importance of 16 variables for yield in switchgrass were analyzed. The correlation score for the 16 variables were switchgrass ecotype (-0.09), state name (-0.06), city name (-0.09), latitude (-0.40), longitude (0.37), soil type (0.06), components of sand (-0.05), silt (-0.05), and clay (0.18), GDD (0.29), total precipitation from April to September (0.36), average precipitation (0.36), minimum temperature (0.37), maximum temperature (0.22), average temperature (0.31), and N fertilizer rate (0.35). GDD is the accumulated growing degree days or heat units throughout growing season (April 1st - July 15th) at each study location. Variables with absolute correlation score less than 0.3 have been removed so that only 8 variables were chosen at the first phase. At the second phase, since a high correlation between predictor variables decreases precision of the estimated regression coefficient, the highly correlated predictor variables were removed from the model. To be more specific, minimum temperature is correlated with other selected factors such as avg. temperature (0.96) and maximum temperature (0.86). Among the three factors, the minimum temperature that has the highest correlation score with the switchgrass crop yield is selected. Similarly, total precipitation is selected and its correlated factor (i.e., avg. precipitation (0.99)) was removed.

Five predictor variables including N fertilizer rate (X_1), latitude (X_2), longitude (X_3), minimum temperature (X_4), and total precipitation (X_5) from April to September were used to establish the switchgrass yield estimation additive regression model (ARM). The values of these variables were standardized prior to regression (i.e. data point subtracted from the mean and divided by the standard deviation of the distribution).

ARM is expressed as follow [46]:

 (1)

where  and .

In Equation (1), Y is the response based on the five predictors variables and  is the weight for the jth predictor with lth order, where l=1, 2, …, L. The weight in each term represents significance of each variable on determining a value of Y (switchgrass yield). Error term  follows the normal distribution with mean zero and variance . Thus, the ARM can be illustrated as the multivariate normal distribution shown below:

(2)

where  and . Once the maximum order L is given, the maximum likelihood approach can be used to find the weight , where l = 1, …, L.

However, since L is unknown, structure learning approach with the 10-fold cross validation (CV) is utilized. In other words, the CV test will find a structure of  in equation (1) with the minimum LOOCV error [47].

(3)

where  is a vector involving observed values of Y in test set i and  is a vector involving estimated values of Y via Eq. (1).

The weakness of the discussion of longitude is related to the presentation on further simulation – which goes into great depth about what might happen at different longitudes, without really explaining why that might be (biological mechanisms) and how this might be important in terms of management.   Figure 5 shows simulated yields across a range of longitudes.  However, after staring at these at some length, I cannot see that they are at all enlightening.

• In discussion, we added the following results from regression model and listed the references that supported the results. In new section starting on page 8 line 346,

3.2. Analysis of factors influencing yield variability of lowland and upland ecotypes

Using latitude, longitude, climatic, and management dataset collected from total 66 study sites, ARM has been developed separately for lowland and upland cultivars. The ARM is:

For lowland cultivars,

  (8)

For upland cultivars,

 (9)

In equations (8) and (9), an absolute coefficient in each term represents significance of each variable on the potential yield estimation of switchgrass. In other words, a larger value of the absolute coefficient refers to higher correlation between a predictor variable and a response variable. In Eq. (8) and (9), because longitude X_₃  has the highest total coefficient of 1.658 (=0.827+0.424+0.406) and 43.614 (=2.136+16.102+25.376) for lowland and upland cultivars, respectively, we can intuitively understand that it is the most critical variable to determine the potential yield estimation of switchgrass. A similar result has been observed in Casler et al. [74]  who has reported that few switchgrass cultivars (e.g. Pathfinder, Blackwell, CIR, WS98-IP) had significant linear relation to longitude of the study locations because their heading dates were changed by longitude. However, most of previous studies have reported weak relationship among switchgrass yield and longitude[45, 75-77]. This may be because most of the previous studies were carried out between 95 and 100 °W.

According to the equation (8), significant factors on the yields of lowland cultivars are as follows: longitude (1.658), latitude (0.659), N fertilizer rate (0.576), minimum temperature (0.452), and total precipitation (0.006). The total precipitation had the least effect on the yields of lowland cultivars. This result is supported by Abdullahi et al. [78] who reported that the dry yield of upland cultivar was significantly decreased by decreasing irrigation frequency, while the irrigation regimes had a negligible impact effect on dry yield of lowland cultivar. Moreover, Sanderson and Reed [79] reported that the growth of switchgrass lowland cultivar in Central Texas did not respond to additional water. In Eq. (9), significance of predictor variables for the yields of upland cultivars is as follows: longitude (43.614), minimum temperature (0.868), total precipitation (0.685), latitude (0.463), and N fertilizer rate (0.031). Unlike lowland cultivars, N fertilizer rate had the least effect on the yields of upland cultivars, which means that upland yields are more influenced by regional adaptation, rather than N rate. According to Fike et al. [80], upland cultivars had lower yields in poorly drained site and did not response to fertilizer rate under low rainfall condition. However, upland cultivars produced higher yields and responded noticeably to N fertilizer rate from moderately well drained soil and under higher rainfall condition.  

        To evaluate the performance of ARM simulation, the ARM simulated yields were compared with the measured yields. In overall, ARM simulated switchgrass yields agreed moderately well with the measured yields across locations while showing the model failed to identify catchments with higher and lower yields for both ecotypes (Fig. 3). Regression analysis for ARM simulated and measured yields of lowland and upland cultivars including all data from total 66 study sites revealed R² =0.30 and 0.37, respectively (Fig. 3). Since ARM was developed based on the few key parameters, R² was relatively low. However, the simulated yields and measured yields for both lowland and upland cultivars were not significantly different (Both P ≥ 0.78) (Fig. 3).

Figure 3. Relation between measured and ARM simulated yields for lowland and upland switchgrass cultivars. The grey solid line represents the measured yields, and black solid line represents the ARM solid lines. Measured and simulated yields were statistically compared using t-test at α = 0.05.

Conclusions: The conclusion that “overall, switchgrass response to longitude varied in response varied according to nitrogen application rates. For example yields of switchgrass lowland responded well to variation in longitude with low nutrient application, while upland cultivars appeared to suffer from a nutrient deficiency across locations”  - I don’t see the data which supports these statements.  Switchgrass is known to respond to nitrogen under higher yielding environments when N is limiting in the soil (which is virtually everywhere).  Don’t see how this would differ according to longitude.

• In figure 5, switchgrass simulated yields in reference period (1976-2005), lowland under lower nitrogen treatment responded to longitude and upland under higher nitrogen treatment increased its yield from -100 to -80 degree of longitude. Also, according to regression model which is developed based on reported values, switchgrass yield was highly influenced by longitude.

The title talks about ‘best management’ to achieve switchgrass biofuel production – but offer essentially no recommendations on best management practices that might be important for different areas. Several important factors come to mind, such as freezing tolerance of some varieties, methods of production (harvest methods), stand establishment methods, nutrients other than N.  The N recommendations also appear also to be shallow.  If I live in Texas or Wisconsin - how much N would your recommend?

• That is great point. There are many other factors, beside N, that are important to switchgrass yields. That would be good further study for application of our developed model. However, in this study, we focus on impacts of climate change on yield and how switchgrass responses to nitrogen fertilizer application under various climate change scenarios. And we found that switchgrass ecotypes responded differently to the nitrogen between different climate change scenarios.
• We changed title to “Simulated biomass, climate change impacts, and nitrogen management to achieve switchgrass biofuel production at diverse sites in U.S.”
• we developed two parameters for lowland and upland ecotypes because we do not have enough data for developing plant parameters for individual cultivar.
• As a result, in overall, switchgrass yields increased as N input increases. However, under high nitrogen input, more simulated yield increases in upland ecotype appeared between -90 and -80 degree. So, if we grow upland ecotype in Texas (-98°), we do not recommend applying high nitrogen because switchgrass will not respond to nitrogen, while we might recommend to higher nitrogen application amount in Wisconsin (-89°) because switchgrass can respond to high nitrogen application in that area.

Author Response File: Author Response.docx

Reviewer 2 Report

The manuscript is relevant and generally well written and the research is designed well.

Still, there is a need to check the writing of certain sentences and there is a need to better explanation or discussion of the results linking the results to the agronomical processes.

Specific comments:

The results of the model calibration are presented in great detail and the results of the Almanac model yield predictions under different climate scenarios are presented. This work seems to be executed convincingly.

In the discussion of the results I do sometimes miss some critical review of the quality of the input data and on the correlations found. For example:

Page 3: last paragraph: “ Only biomass yields harvested.......... “

It says that yield were used of switchgrass harvested after a killing frost or in early spring.

I would expect yields to be higher when biomass is harvested after a killing frost compared to harvesting in early spring because some biomass will be lost over winter. Is this difference considered in the model?

I would be good to discuss this to qualify your results.

Yields of switchgrass generally take 1 to 3 years to develop and will reduce after some 10 to 15 years depending on management. How is this dealt with in the model? The model gives one yield per hectare though yield also depends on the age of the stand. This should be addressed.

On page 9 it is stated that “The model underestimated upland switchgrass yields at high soil organic carbon and high total N. For example, high soil organic carbon and high total N were observed in the Northwest site, OH site (Table 1).” I miss a critical discussion on why the model underestimates the upland switchgrass yields at high soil organic carbon.

The authors found that the yield is correlated with longitude especially at low N treatments. There is no discussion on the reason for this correlation. Page 10 last paragraph, does mention that “ management (N treatment?) may be a more important determinant yield factor than longitude”

Can more N deposition form the air (at eastern latitudes) explain this or a difference in precipitation from east to west? In western Europe no switchgrass nitrogen yield response has been found over a period of more than 20 years. This is generally attributed to N deposition, and spring harvest which removes only small amounts of N each year (see work at Rothamsted by Christian et al).

In general the article is well written though regularly there are small mistakes in sentences which could be fixed by having an editor review the text. For example:

Page 2: “in“ is missing:

“ This shortfall will lead to repeated waivers of the mandated volume of cellulosic ethanol production, resulting increasing ? uncertainty about the future policy for advanced biofuels [23].”

Page 3: remove “ can have” were?

“ Model simulations can have conducted with sufficient detail in space and time to quantify long-term changes in soil carbon, nutrients (N & P) cycling, water resources, and plant production across diverse environmental settings.”

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 3 Report

Please see both the word document and commented PDF for complete comments.

Comments for author File: Comments.rar

Author Response

Dear Reviewer,

We thank you for the comments on the manuscript, which we have addressed in revised manuscript as discuss below. Throughout, reviewer comments are in blue font and italic type, and our response in regular type.

Two of the biggest points I believe the paper needs to yet emphasize are as follows: 1) Why switchgrass merits further interest if it is not a cost-effective competitor with other lignocellulosic feedstocks for cellulosic biofuels production.

à In the introduction, in line 67-71, we added sentences why switchgrass merits further interest. And added two references that support the idea.

“Many studies have proved that switchgrass can produce relatively high, reliable yield across a wide geographical range with low water and nutrient inputs [16]  Moreover, this species has high tolerances to multiple abiotic stresses, which makes it capable of maintaining high productivity on marginal soils unsuitable for traditional agricultural crops [17]. For these reasons, switchgrass has been given high research priority by many research institutions [16].”

L.D. Quinn, K.C. Straker, J. Guo, S. Kim, S. Thapa, G. Kling, D.K. Lee, T.B. Voigt, Stress-tolerant feedstocks for sustainable bioenergy production on marginalland, BioEnergy Res. 8 (3) (2015) 1081–1100.

Mehmood, Muhammad & Rashid, Umer & Nawaz, Muhammad & Ali, Shafaqat & Hussain, Athar & Gull, Munazza. (2017). Biomass production for bioenergy using marginal lands. Sustainable Production and Consumption. 9. 3-21. 10.1016/j.spc.2016.08.003.

2) how will expected geographic trends in climate change impact regional switchgrass management efforts?

In discussion, we have explained how climate change effects on the switchgrass yield in regional scale.

Lowand and upland ecotypes responded differently to climate change under low and high nitrogen treatments. For upland ecotype, there is no response to climate change in both nitrogen treatments while for lowland ecotype, warmer temperature limits its production even though at higher nitrogen treatment.

We added more sentences for analysis in discussion and in collusion.

In line 499-502, “As shown in Figure S3, RCP 8.5 pathway showed higher mean of the daily maximum and minimum temperatures than RCP 4.5 scenarios. These warmer temperatures may limit switchgrass production increases achieved through the high nitrogen application across all study regions.”

In line 562-568, “On the basis of the overall simulation results, yields of lowland and upland switchgrass increased with increasing nitrogen application. However, lowland and upland ecotypes responded differently to climate variability.  While upland cultivars appeared to be showing no responses from climate change, the warmer temperatures may limit productions of lowland cultivars even though high nitrogen was applied to the plots. This paper shows that the fertilizer management may be the most important factor for upland cultivars, which will control switchgrass yield under increasing temperature.”

The following are comments for consideration in addition to the comments contained within the PDF document.

Switchgrass (Panicum virgatum L.) is a C4 warm season perennial native grass that has been strongly recommended as an ideal biofuel feedstock.

We rephrased to

“Switchgrass (Panicum virgatum L.)  is a C₄ warm season perennial native grass that has been strongly recommended as an ideal biofuel feedstock.”

The ALMANAC model consists of functional relationships that provide a better understanding of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrient), giving realistic predictions in different climate conditions.

We rephrased to

“The ALMANAC model consists of functional relationships that provide a better understanding of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrient) giving realistic predictions in different climate conditions.”

This sentence is unclear: Simulated lowland switchgrass would have more yield increases between Illinois and Ohio in future period (2021-2050) with low N fertilizer inputs than high N fertilizer inputs. (with climate change?)

We rephrased to

“Simulated lowland switchgrass would have more yield increases between Illinois and Ohio in future period (2021-2050) under both RCP 4.5 and 8.5 pathways with low N fertilizer inputs than high N fertilizer inputs.”

No significant effect of climate variability on upland yields (is predicted?), which means that N fertilization is a key factor in controlling upland switchgrass yields under future climate conditions.

We added simulated in the sentence.

“There was no significant effect of climate variability on upland simulated yields, which means that N fertilization is a key factor in controlling upland switchgrass yields under future climate conditions.”

 Remove the spaces between numbers and the percentage symbol, i.e., “In 2015, 80%”

We removed the space.

Consistently maintain a space between the final word in a sentence and the numbered citation, i.e.,  “sector [4]”

We made space between last words and reference throughout the manuscript.

Only one space should separate sentences

We made one space between sentences throughout the manuscript.

This is still a small fraction of the total fuel consumed? and will not noticeably reduce carbon dioxide emission.

In line 52, We added consumed in the sentence.

This sentence is confusing as written.  Do you mean to say: Corn ethanol production has not? reached the EPA fuel mandate threshold because corn is also needed for food and feed supplies [11].

We rephrased to

“Corn ethanol production has reached the EPA corn ethanol mandate threshold because corn is also needed for food and feed supplies”

Lignocellulosic biomass resources, otherwise known as second generation biomass feedstocks, represent promising feedstocks for biofuels (ethanol and biodiesel) production in the U.S.A. (Check with the journal standard regarding U.S. or U.S.A.)

We could not find the information from journal website, so we kept as U.S. but we added the before U.S. in line 60.

Many genetic and agronomic studies on switchgrass have been conducted in the Midwest and Great Plains in the U.S.A., which have increased our understanding of the adaptation of switchgrass cultivars, production practices, and environmental benefits [13-15].

We rephrased to the sentence to

“Many genetic and agronomic studies on switchgrass have been conducted in the Midwest and Great Plains in the U.S., which have increased our understanding of the wide geographic adaptation of switchgrass cultivars, production practices, and environmental benefits [13-15].”

And we addressed all your comments on manuscripts and left comments on the pdf file.

Thank you

Author Response File: Author Response.pdf

Reviewer 4 Report

I am not familiar with ALMANAC as a crop simulation model. So from the perspective of a naive reader, I found there was a lack of information on how the switchgrass plant was parametrised. Was it based on sorghum or another C4 and modified. Was it created new? It would be helpful to have a table of parameters in the supporting information. 

English need a lot of refinement. Many sentences in the manuscript that are missing key words for them to make sense.

No line numbers in manuscript made it difficult to referee. I have copied and pasted sentences below with my comment proceeding the -

Many genetic and agronomic studies on switchgrass have been conducted in the Midwest and Great Plains in the U.S. and has increased our understanding of the adaptation of switchgrass cultivars, production practices, and environmental benefits [13-15] – adaptation to what?

896 kg N ha yr? This is either a typo or an experiment that applied a ridiculous amount of nitrogen. If the latter, why was this included? There would be no N response.

Move table 1 to supplementary materials.

I don’t see the value in rating things as good or unsatisfactory as per Moriasi et al. This is a quantative modelling paper and the values of the error estimates should be adequate. Also do not see the value in having five different error estimates. RMSE should be fine alone. Unless there is some point to having 5 as a comparison, but that is not the point of the manuscript.

Units need to be consistent throughout. No t/ha if reporting in Mg/ha

According to ALAMANAC simulation results, switchgrass yield response to longitude varied by crop management, which means that the management may be more important determinant yield factor than longitude. – I do not think there is enough evidence to support this statement. Some kind of statistical analysis showing a significant interaction between longitude and management is required. If there is a significant interaction then this conclusion could possibly be drawn.

Figure 4 shows a regression line but there is no descriptuion of the methods used to derive the line. And there are no reports of error terms.

Unlike lowland cultivars, climate variability barely affected production of upland cultivars. – Considering this is a major part of the study, this statement needs to be addressed by some kind of analysis. Why is this the case? Is it due to the model outputs? Model assumptions? Or is it some real biological effect for example N limitation? Some detailed insight into this needs to be provided prior to publication in my opinion. 

Author Response

Please see the attachment.

Author Response File: Author Response.docx