Variability of Water Use Efficiency Associated with Climate Change in the Extreme West of Bahia
, Humberto Alves Barbosa 2,3, Washington Luiz Félix Correia Filho 4, José Francisco de Oliveira-Júnior 3,5, Franklin Paredes-Trejo 2,6 and Catarina de Oliveira Buriti 7Round 1
Reviewer 1 Report
In this manuscript, the authors examine the Water Use Efficiency (WUE) in an agricultural region in Brasil, and examines the relationship with land use strategies and climatic events.
The paper is adequately referenced and based on solid data. The region is nicely described, in a way that allows one to understand the limits and reach of the analysis. However, it has also some problems:
The introduction summarizes a good number of factors related to WUE in agricultural landscapes. However, they do not follow a proper order: structural factors are equated to circumstantial ones, but changes in human population and land use are not comparable to atmospheric phenomena. It should also be clarified whether it makes reference to natural ecosystems or only crops.
Moreover, the coherence between the introduction and the rest of the parts should be ensured: land use has only a reduced presence in the introduction and the results, but a prominent role in the discussion.
The most important concern relates to the interaction between rain and temperatures. In line 191, WUE is related to precipitation, while temperature also varies between rainy and dry seasons. The patterns in data even suggest an interaction between both factors, which could be tested by applying ANOVA or adjusting a GLM. Moreover, it is not so evident to establish a causal link between rain and temperature, as is done in l.230.
I also have concerns with the interpretation of data, that seem to suggest the WUE is maximum in dry conditions. However, it cannot be excluded that the relationship WUE - available water is not lineal: when water is scarce, all the available water is used; however, when abundant the two factors are decoupled, the water in "excess" reducing WUE when it goes over a threshold. In this sense, WUE (a relative value) cannot be used alone to explain productivity, but might require also an absolute reference: in dry conditions, plants might be most efficient in the use of water, but still have a much lower performance, well below its potential limit, in terms of productivity. In this sense, the objective of agricultural management seems to be to increase WUE (line 271), but considering it is maximized under dry conditions, this cannot be the ultimate objective.
A third, minor concern, relates to statistics: Pearson's correlation is a parametric test, to be used with data that follow a normal distribution; given the presence of some extremely low values, it should be checked whether the distribution is normal, and otherwise use Spearman's correlation.
Last, and central to the relevance of the article, is a question about the utility of this information. WUE can be measured, but if it depends mainly on temperature and humidity, it cannot be modified to maximize GPP. It would be advisable to include some practical implications for agricultural management or natural ecosystems.
Last and least relevant, there are some incoherences in the format of the article.
Author Response
A1 – In this initial study, we analyze the interaction between variables to see if there is an association; once we know which variables have interaction, we will apply regression models, in this case multiple regression; to quantify these variables in relation to the WUE, we will use the GAM, but that will come later.
A2 – It's not a suggestion. In this study, the largest WUE values were found after the calculations and processing associated with the dry periods and higher temperatures. This finding is not widespread. In this study, we looked at the interaction between WUE, precipitation, and LST in different ENSO years, as well as how WUE behaves in dry and rainy periods. None of this implies that other studies may use different or the same variables (precipitation and LST) and obtain different results.
A3 - In this case, we used Pearson's correlation from the prior analysis of the data. It was found that the data series of WUE presented behavior close to a normal distribution; moreover, it stands out that the values relative to the coefficients of variation were less than 20%, i.e., it has low variability motivated by low amplitude, with variation between 0 and 7 gC/mm.m2.
A4 - In this regard, the article's significance is to highlight the WUE behavior, which seeks to associate precipitation and temperature patterns with the process of water management in agriculturally based areas, as well as to monitor the behavior of natural ecosystems.
A5 – The Journal does not require a standard format for the submission. The own body Journal Edition is responsible for this, not the authors.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The paper deals with mapping the WUE in order to study the spatial and temporal variability considering climate change. The paper is written in correct English and form. The objectives are clear and the authors adopted a sound scientific approach. However, some issues must be dealt with and some clarifications need to be added to strengthen the current version.
1/- The climate change factor is weak in this study and the authors presented a summary statistical description of the in table 3 of rainfall and LST. Why these were the only two factors to consider? How do the authors explain or highlight the climate change effect? In my opinion, it is more of seasonal variations of the two mentioned parameters.
2/- The authors must add the localization of the measurement points on the map. What geostatistical model they used to get the spatial interpolation and specify the parameters?
3/- In figure 4, R² coefficients are pretty low, please explain?
4/- In figure 6, from 2009 to 2016 the WUE values are the same for Agriculture, Savanna, Pasture and Forests? Please explain? Is this the main key to the climate change effect?
5/- In the abstract please add one or two sentences about the problematic of the study first.
6/- L92: How can we “quatify” the spatio-temporal distribution?
Author Response
A1-In this study appointed the positive responses in the WUE variability based on the LST and precipitation variations. In this way, it was proposed to investigate the influence of this variables in the area of the extreme westmost baiano, where there are agricultural areas. The vegetation is easily altered according to changes in the environment, be it alteration by precipitation and temperature, or even related to the Land Use and Cover. Thus, changes in climate cause impacts on the environment that later modify the carbon-water cycle. When these impacts occur on the balance sheet, the WUE, i.e., the way in which vegetation promotes carbon uptake (GPP) by water loss (ET), occurs. Table 3 shows the variation of these variables over the dry and rainy periods, which makes it possible to understand that the variation in temperature and precipitation has a direct influence on the WUE.
A2-The map with data was entered (Figure 4a). No interpolation method for obtaining the products has been used. For the correlations, random points were extracted as described in the article. The complement "POINT SAMPLING TOOL" installed in the QGIS software was used to obtain one-off values, which were then correlated.
A3-Low R2 values may be associated with the influence of other factors such as elevation and vegetation in addition to the randomness of the one-off values, affecting the variability of the explained variables.
A4-No. There have been small differences between classes between 2009 and 2016, but due to variation in agriculture being higher among classes, it appears that both have the same values. Savanna [2006 (2.12 gC/mm.m2), 2007 (2.48 gC/mm.m2), 2009 (1.88 gC/mm.m2)]; pasture [2006 (2.28 gC/mm.m2), 2008 (2.23 gC/mm.m2), 2009 (1.98 gC/mm.m2), 2007 (2.19 gC/mm.m2), 2008 (1.94 gC/mm.m2), and 2009 (1.96 gC/mm.m2).
A5 – The information was included in the text, and it was highlighted in yellow.
A6- It was not clear this question. However, we believe that the article presents good responses related to the distribution of WUE associated with changes in the environment. It was included the R2 result.
Author Response File: Author Response.pdf
Reviewer 3 Report
This manuscript deals with a relevant issue: Water Use Efficiency Associated With Climate Change.
The study mapped and quantified the spatio-temporal distribution of Water Use Efficiency (WUE) based on its interactions with environmental changes in the Westernmost region of Bahia, Northeast Brazil (NEB).
The research issue and objectives are clear and coherently stated.
The method and material are coherent with the research objectives.
The research results are clear and soundly presented.
Some points are worth discussing a little more:
1 - In the Abstract, it seems relevant to add the main public policy and managerial implications that can be envisagend from the research results;
2 - In the Abstract is stated that "The results obtained indicated that the highest values of GPP (580 gC/m2), ET (3000 mm), and WUE (3.5 gC/mm.m2) occurred in agricultural areas, associated with cultural treatments and insertion of irrigation, which helped in the higher WUE values, and consequently, increased agricultural productivity in the study region. ". It seems relevant to be a little more specific about the impact of irrigation on WUE, ET, and PPG values. What are the levels of irrigation in the studied region? Into what extent irrigation impacted the results/values presented in Fugure 2, Figure 5 and Figure 6?;
3 - The Conclusions are generic in nature. It seems relevant to add the main results and the main public policy and managerial implications that can be envisagend from the research results.
Author Response
A1 – This information was inserted in the abstract. “This information about how vegetation (native or agricultural) responds to interactions with the environment aids in water management decision-making, allowing for lower losses or agricultural damage from a lack of water.”
A2 – In this study, it did not evaluate the irrigation levels, only the vegetation-less-WUE vegetation ratio. The irrigation insertion was ancillary, assuming that because the region is one of the largest agricultural producers with irrigation in Northeast Brazil, the influence of irrigation promotes an improvement in the WUE, seen in the water availability for the carbon-water balance. According to Guimarães (2014), 2,792 central pivots in the State of Bahia, occupying an irrigated area of 192,223.48 ha (0.34% of the state area). Pivols were found in 82 (19.66%) of the state's municipalities, accounting for 90% of the areas irrigated by central pivots in 2013. They were concentrated in the microregions of Barreiras (49.11%, 94,400.39 ha), Seabra (24.17%, 46,455.43 ha), and Santa Maria da Vitória (16.11%, 30,963.03 ha), with the first and third located in the extreme westmost Baiano mesoregion.
A3 – The conclusions were rewritten with the purpose of exposing further results and information relating to public policies.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The autors have modified the article according to the reviewers' comments, or otherwise have properly justified their approach.
Reviewer 2 Report
The authors corrected the manuscript according the mentioned recommendations.
