Forage quality declines with rising temperatures, with implications for livestock production and methane emissions

Lee, Mark A.; Davis, Aaron P.; Chagunda, Mizeck G. G.; Manning, Pete

doi:https://doi.org/10.5194/bg-14-1403-2017

Articles | Volume 14, issue 6

https://doi.org/10.5194/bg-14-1403-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/bg-14-1403-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 14, issue 6

Research article

|

26 Mar 2017

Research article |

| 26 Mar 2017

Forage quality declines with rising temperatures, with implications for livestock production and methane emissions

Mark A. Lee, Aaron P. Davis, Mizeck G. G. Chagunda, and Pete Manning

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Final revised paper (published on 26 Mar 2017)
Supplement to the final revised paper
Preprint (discussion started on 22 Sep 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Referee Comment Summary', Anonymous Referee #1, 25 Oct 2016
RC2: 'review Biogeosciences Discuss. Bg-2016,389, 2016', Anonymous Referee #2, 30 Oct 2016
AC1: 'Author response to both reviewers’', Mark Lee, 25 Nov 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (20 Dec 2016) by Paul Stoy

My apologies for the delay in response. Two qualified referees had the opportunity to review this manuscript and their decision is split. Referee #1 had a number of insightful comments on important details of the analysis. Referee #2 recommends that the manuscript not be published in the present form, largely because the statistical basis for extrapolating future methane emissions was adjudged to not be sufficiently robust. The recommendation instead is to use a plant physiological model to quantify the mechanisms by which temperature impacts forage quality. The challenge is that livestock are critical in the climate system and must be studied posthaste, yet only basic studies of ruminant physiology oftentimes exist. (I'm running into similar challenges with bison, where it appears that only one calorimeter study on a one-year-old female cow has been published to date.)

My recommendation is to reconsider this manuscript after major revisions. It is timely and well-written, an important gap in the literature has been noted, and projections were qualified as 'coarse', which I assume to mean 'approximate'. At the same time, shifting the focus of the manuscript to important uncertainties in plant and ruminant physiology while noting that the proposed feedback exists when extrapolating the results of current understanding would result in a more conservative analysis that creates a clear path for future studies. 'Coarse' is qualitative, and I'm not sure what it means either. Using an 'if/then' framework in which global projections hold if current understanding is robust would be a more nuanced way to communicate this potentially quite important feedback to the scientific community.

Please submit a revised version of the manuscript that addresses referee comments and adjusts the tone of the manuscript to focus more on gaps in the literature and a bit less on global extrapolations and I would be happy to reconsider it for publication in Biogeosciences.

Sincerely,
Paul C. Stoy

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AR by Mark Lee on behalf of the Authors (27 Jan 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (28 Jan 2017) by Paul Stoy

RR by Anonymous Referee #2 (10 Feb 2017)

Suggestions for revision or reasons for rejection

General comments
The authors have addressed most of the comments by the reviewers. However, some weaknesses remain unresolved. I am not convinced the authors present compelling evidence for a new climate change reinforcing feedback, because of the nature of the data, and the sort of analysis that the data allowed. I do see however opportunities for future improvements in this field, and therefore I would recommend the authors revise the paper, tune down their claims a bit and resubmit the manuscript to be considered for publication.

Abstract
L17 Not clear what the authors call ‘species turnover’
L18 models were used to ‘estimate’ not to project
L19 the analyses ‘suggest’, not revealed
L20 …and correspondingly ‘may’ increase methane production
Please bear in mind that livestock do selective grazing, so at low stocking density, there may be a compensation for lower quality due to increased temperatures
L26 …[the suggested changes] ‘may’ reduce this additional source of …

Introduction
L81-82 Please re-phrase: I don’t understand here the use of ‘where possible’ “Published statistical models were used to explore the effect of increased temperatures due to climate change to grass nutritive value and cattle methane production”

Methods
L110. This database only includes 1 dataset from the tropics (Brazil). However, the analysis of methane prediction is done at the global scale (Fig 5)

L175. The use of assumed constant nutritive values reduces the validity of models C, D and E (Table 1) to estimate future methane production. I wonder whether these models should have been excluded from the ‘global’ analysis

L232-236, were the ranges (Fig.2 up to 30 degrees) used in fitting models for NDF respected when making projections using future temperatures? It is not clear from Figures 3 and 4 whether there were ceilings to the temperature change.

L251-260. I wonder how useful it is to use models with more feed quality variables, when only NDF changes with temperature.

L275-281. These ‘projections’ are the weakest part of the manuscript, given all the assumptions and unbalanced nature of the datasets used in the statistical models, with the tropics and Asia heavily under-represented.

L330. What do the authors mean with turnover in species identity? I suspect the authors refer to a change in the composition of the plant community drive by increase in temperature – a sort of adaptation mechanism?

L338. I am not convinced the predictions are robust. The authors find a statistical significant relationship, not a causal relationship.

L349. I suggest to replace Projections by Explorations

L358. Please make sure that the 5 degrees’ increase doesn’t place the ‘predictions’ outside the range of validity of the statistical models.

I wonder whether the authors have seen the paper by Caro et al 2016 Mitigation of enteric methane emissions from global livestock systems through nutrition strategies Climatic Change August 2016, Volume 137, Issue 3, pp 467–480 – which is similar to their study

L403. It seems the authors don’t know the literature well. The mechanistic cattle model (RUMINANT) by Herrero et al 2013 (paper cited by the authors) simulates methane emissions for individual animals, of different breeds and for different regions if feeds are known. The LINGRA model maintained by the Plant Production Systems group of Wageningen University (see http://models.pps.wur.nl/node/958) simulates grass quality, and is driven by temperature, rainfall and radiation. It is likely that the combination of both mechanistic models could produce more robust estimates than the ones presented here by the authors.

L419 Conclusions should be tuned down in ambition. Preliminary evidence is presented here

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RR by Anonymous Referee #1 (10 Feb 2017)

Suggestions for revision or reasons for rejection

Thanks for the opportunity to take a look at the revised version of this paper. I feel that the revisions have greatly clarified and improved this manuscript. The level of understanding (and sources of uncertainty) of the potential methane temperature feedback from cattle is improved by the inclusion of multiple models. In addition the manuscript covers the implications and limitations of the study more clearly without going beyond their findings. I have made some suggestions for clarity and to explicitly cover assumptions from the RCPs used in this study.

Line 93 – Do you include data from sites that are in non-cattle producing regions (such as those you exclude in figure 5)? Would be good to clarify this point whether it was an a priori decision (in methods) or just where the data fell out (in the results).

Line 188 – Why do you choose this combination? Do you have a reference or explanation for why this is a valid measure to weight these models?

Line 191 – This section needs some minor clarification on the use of RCPs. RCP 2.6 includes a reduction in livestock as part of the way to achieve targets. You are keeping livestock constant in your modeled projections. This seems fine if stated, since you use the RCPs to represent temperature increases and relate to current livestock, but the assumption should be stated (and implications discussed). Likewise, RCP 8.5 includes an increase in cattle as part of the inputs into climate models.

Line 200 – 213 – This section can be shortened. The paper is about overall trends independent of species so keep the species specific detail to the appendix (with the exception being Lolium perenne which you model). Maybe just cover the general patterns of NDF and CP from the data which relate to your discussion points (I could see some expansion of this in the appendix to better describe the patterns in NDF and CD).

Line 230 – Include some description of model fit and/or confidence intervals

Line 281 – See comment on line 191. This should say “do not represent changes to the global cattle industry as projected in the RCPs.”

Line 350 – See comment on line 191. It is important to convey what your results mean for the assumptions in the RCPs. If the GHGs from RCP2.6 are calculated using fewer cattle (which encompasses a multitude of forcings that change, including methane production) your results demonstrate that temperature increases may increase the methane production offsetting some of the methane reductions assumed to come from fewer cattle. Likewise in RCP 8.5, which includes increases in cattle, your results show methane may be underrepresented since the scenario includes methane from additional cattle, but not a potential increase per head from higher temperatures and forage quality.

Line 700 – See line 230

Line 732 – Values do not include projected changes to the global cattle inventory (up or down relative to RCP assumptions and your other references).

Line 737 – See earlier comments on line 200. Since your study shows a larger range of CP and NDF then prior compilations (an interesting finding in its own right), exploring the data’s distribution could warrant more coverage here.

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ED: Publish subject to minor revisions (Editor review) (17 Feb 2017) by Paul Stoy

AR by Mark Lee on behalf of the Authors (01 Mar 2017) Author's response Manuscript

ED: Publish as is (04 Mar 2017) by Paul Stoy

AR by Mark Lee on behalf of the Authors (09 Mar 2017)

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Short summary

We gathered data from published sources to assess the effects of growing conditions on the nutritive quality of grasses which feed livestock. Nutritive quality is important for livestock productivity and methane production. We found that forage nutritive quality was reduced at higher temperatures. We estimate that cattle methane production may increase in future due to temperature-driven reductions in forage quality. This is a positive climate feedback that further increases global temperatures.