the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2025
Warmer growing seasons improve cereal yields in Northern Europe only with increasing precipitation
Abstract. Crop yields depend on climatic conditions such as precipitation and temperature and their timing before and during the growing season. At high latitudes, climate change could lengthen the growing season and provide more suitable temperatures, but also expose crops to more frequent damaging conditions. We quantified the response of regionally-averaged 1965–2020 winter and spring cereal yields in Sweden to a wide set of descriptors of climatic conditions. With statistical models, we explored the role of both short-term and average conditions over physiologically relevant developmental stages, as well as of a proxy of water availability during the period prior to the main growing season. Temperature and precipitation or dry spell lengths for the entire growing season explained 75–85 % of yield variability, performing better than short-term potentially damaging conditions. Low precipitation or extended dry spells combined with high temperatures and, conversely, high precipitation sums with cool temperatures decreased yields for all crops. Our findings suggest that under climate change crop yields will be reduced in Sweden, unless warming is accompanied by increase in precipitation during the main growing season. With unaltered or reduced growing season precipitation, benefiting from warmer temperatures caused by climate change will require adaptation measures.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1982', Anonymous Referee #1, 16 Aug 2025
- AC1: 'Reply on RC1', Faranak Tootoonchi, 19 Sep 2025
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CC1: 'Comment on egusphere-2025-1982', Martin Skoglund, 24 Oct 2025
General comments
This study addresses a highly relevant and timely topic, and I find the authors’ attempt to integrate short-, medium-, and long-term climatic influences on crop yields both commendable and innovative. The analysis represents a significant step forward compared to previous work by systematically evaluating multiple climatic indicators across different time-scales and their interactions across different crop types and periods.
However, I have several concerns regarding the model specification, which may substantially affect the results and their interpretation. These issues primarily concern how the models account for spatial heterogeneity in climatic responses across Sweden. In addition to this, I have some other smaller concerns regarding the interpretation of the results when using relative yield indicators as well as the formulations of the model equations.
- Model specification
My main concern is that the models do not properly account for the well-known spatial differences in the temperature–precipitation dependency of crop yields between northern (N) and central/southern (C/S) Sweden. These regional contrasts were already formally identified by Wallén (1917, 1918) and have been confirmed in subsequent studies dealing with both historical and recent periods (e.g., Edvinsson et al. 2009; Skoglund 2022, 2023; Sjulgård et al. 2023).
In short:
- In northern Sweden (Norrland and Dalarna), higher summer temperatures are positively associated with yields, whereas increased precipitation often has a negative, albeit small, effect.
- In central/southern Sweden (counties south of Dalarna/Gävleborg), the relationship is generally the opposite: higher temperatures tend to reduce yields, while greater precipitation tends to increase them.
These contrasting relationships imply that aggregating N and C/S counties into a single model (this article) or time-series (see Holopainen et al. 2012), without explicitly accounting for these systematic differences, introduces biases and will underestimate the true climatic sensitivity of year-to-year yield variability.
This issue is especially relevant for spring-sown crops, such as barley, which are cultivated across the entire country, but also potentially for winter crops and wheat to a lesser extent (see Sjulgård et al. 2023). For winter crops and wheat, the shift from positive (negative) or negative (positive) relationships with temperature (precipitation) tend to occur only in the southernmost counties, if at all.
In the Discussion, the authors write:
“The explanatory power of climatic conditions were lower for oats and spring barley yields compared with spring and winter wheat … A possible explanation is that wheat yield data refer to southern Sweden only, whereas spring barley and oats are grown under a wider range of latitudes and hence climatic conditions …”
Here the authors themselves imply that there is a possible aggregation bias, that lowers the explanatory power for oats and barley. The issue is less relevant for (spring and winter) wheat that is mainly grown in C./S. Sweden with a more homogenous climatic signal (see also the results for barley and wheat yields in Holopainen et al. 2012 where the same type of aggregation error is made). In an analysis where relationships are estimated at the county-level, where the systematic difference between N. and C./S. is largely accounted for (except perhaps when considering border counties such as Dalarna/Värmland/Gävleborg), it can clearly be seen that year-to-year climatic fluctuations have a much greater explanatory power in N. Sweden compared to C./S. Sweden (Sjulgård et al. 2023; Skoglund, 2022; 2023). In your model, this is mainly introduced as a random effect, which brings the oat and barley models to similar levels of explanatory power as the wheat models. However, because the random effects model assumes that group-level differences are uncorrelated with the explanatory variables, this treatment is inappropriate when the between-county differences are themselves driven by climate–yield dependencies.
Possible alternatives that address the aggregation bias:
- Including an interaction between region (N, C, and S or N and C/S) and key climatic variables.
- Fit separate models for N, C and S or N and C/S.
- A random-slope model that allows the effects of temperature and precipitation to vary by county.
Relevance of results: Another issue, that is related to the model specification but only becomes an issue in regards to the interpretation of the results is that since you are treating yield as a relative variable (i.e., tonnes per hectare), instead of absolute production levels, the lumping together of N. and C./S. Sweden also obfuscates the social relevance of the results as the overwhelming majority of grain production occurs in C./S. Sweden.
Equations: As a previous reviewer mentioned, in your model equations (eq. 1–3), you describe yield as, Y, when it should in fact be indexed as time- and location-variant, Yit. Furthermore, all explanatory variables should also include it since they describe a given variable at time t and location i. I do not believe the choice to make it only Y makes it clearer as the authors suggests as we now are several reviewers who had the same objection.
In summary, this paper makes a valuable contribution and offers promising methodological advances, but the current model specification overlooks a key structural feature of Swedish agroclimatic variability. Addressing these well-known north–south contrasts in yield–climate relationships is essential to obtain unbiased estimates and to strengthen both the statistical validity and applied relevance of the findings.
References
Holopainen, J., Rickard, I. J., & Helama, S. (2012). Climatic signatures in crops and grain prices in 19th-century Sweden. The Holocene, 22, 939–945.
Sjulgård, H., Keller, T., Garland, G., & Colombi, T. (2023). Relationships between weather and yield anomalies vary with crop type and latitude in Sweden. Agricultural Systems, 211, 103757.
Skoglund, M. K. (2022). Climate variability and grain production in Scania, 1702–1911. Climate of the Past, 18, 405–433.
Skoglund, M. K. (2023b). Farming at the margin: Climatic impacts on harvest yields and agricultural practices in Central Scandinavia, c. 1560–1920. Agricultural History Review, 71, 203–233.
Wallén, A. (1917). Sur la corrélation entre les récoltes et les variations de la température et de l’eau tombée en Suéde. Kungl. Svenska vetenskapsakademiens handlingar, 57, 1–87.
Wallén, A. (1918). Sambandet mellan klimat och skörd i Sverige. Ymer, 1, 1–23.
Citation: https://doi.org/10.5194/egusphere-2025-1982-CC1 -
AC2: 'Reply on CC1', Faranak Tootoonchi, 03 Dec 2025
Responses can be found in the attached pdf.
-
CC2: 'Reply on AC2', Martin Skoglund, 04 Dec 2025
-
CC3: 'Reply on CC2', Martin Skoglund, 04 Dec 2025
References (were missing in the attached PDF)
Edvinsson, R., Leijonhufvud, L., & S¨oderberg, J. (2009). V¨ader, sk¨ordar och priser i Sverige. In B. Liljewall, I. A. Flygare, U. Lange, L. Ljunggren, & J. S¨oderberg (Eds.), Agrarhistoria på
många sätt: 28 studier om m¨anniskan och jorden. Festskrift till Janken Myrdal på hans 60- årsdag (pp. 115–136). The Royal Swedish Academy of Agriculture and Forestry, Stockholm.Sjulgård, H., Keller, T., Garland, G., & Colombi, T. (2023). Relationships between weather and yield anomalies vary with crop type and latitude in Sweden. Agricultural Systems, 211, 103757.
Skoglund, M. K. (2022). Climate variability and grain production in Scania, 1702–1911. Climate of the Past, 18, 405–433.
Skoglund, M. K. (2023b). Farming at the margin: Climatic impacts on harvest yields and agricultural practices in Central Scandinavia, c. 1560–1920. Agricultural History Review, 71, 203–233.
Wallén, A. (1917). Sur la corrélation entre les récoltes et les variations de la température et de l’eau tombée en Suéde. Kungl. Svenska vetenskapsakademiens handlingar, 57, 1–87.
Citation: https://doi.org/10.5194/egusphere-2025-1982-CC3 - AC3: 'Reply on CC3', Faranak Tootoonchi, 13 Jan 2026
-
CC3: 'Reply on CC2', Martin Skoglund, 04 Dec 2025
-
CC2: 'Reply on AC2', Martin Skoglund, 04 Dec 2025
-
RC2: 'Comment on egusphere-2025-1982', Anonymous Referee #2, 29 Dec 2025
This paper explores the meteorological drivers of observed variability in crop yields in Sweden, investigating statistical relationships between key weather / climate indices and the yields of the four most commonly grown crops in the country over 55 years (1965-2020). Given the common assertion that anthropogenic climate change is beneficial for crops in higher latitudes, the conclusion that the potential benefits of warming may be limited by lack of increased precipitation is a very important one. There are also a number of extremely useful insights in the manuscript regarding the relative explanatory power of different variables, so overall I am supportive of this work.
I note that the manuscript has already received a review from a referee and also a community review, to which the authors have already responded. Here I will focus on two additional points that appear to have been overlooked, which I recommend are addressed in a revised manuscript.
- As the authors note, climate change is expected to increase the length of the growing season, and indeed this has already happened. eg. in southern Sweden, the meteorologically-defined growing season length has been starting earlier by between 2 and 4 days per decade between 1950 and 2022 (Miś and Tomczyk, 2025 https://doi.org/10.1007/s00704-025-05382-6). So, over the period analysed in the current study, the growing season may have lengthened by over 20 days in some places. However, the authors conduct their analysis using an average growing season length over the study period. Is there a risk that the results may have been affected by including events or averaging periods outside of the growing season in the earlier part of the study period but excluding some within the growing season in the later part? This seems particularly pertinent in the context of the authors remark at lines 317-320: “We surmise this low performance is in part due to the infrequent occurrence of short-term potentially damaging conditions (e.g., few occurrences of days with average temperatures 320 above 25 °C, or frost during sensitive developmental stages)”. I would be reassured if the authors could demonstrate that their conclusions are not sensitive to the use of an average growing season length over a period when the length has changed by a non-trivial amount.
- The authors do not mention the potential impact of rising atmospheric CO2 concentrations on photosynthesis, transpiration and yield - see, for example, Rezaie et al (2023 https://doi.org/10.1038/s43017-023-00491-0) for a recent review. If this is already having an influence then it will be included within the continuous variable representing the combined effects of time elapsed in since 1965 (as described in lines 197-200) so I don’t think it will alter the findings regarding the relative importance of the different meteorological drivers during the period of observations. However, given the potential non-linear effects of CO2 effects in the future, especially in how they may affect crop responses to drought and high temperatures, the existence of these influences and their implications should be highlighted as an outstanding uncertainty in relation to the results here. Eg. it should be mentioned in line 199 alongside climate change and technological improvements, and also discussed in section 4.4 (Implications under climate change).
Citation: https://doi.org/10.5194/egusphere-2025-1982-RC2 - AC4: 'Reply on RC2', Faranak Tootoonchi, 13 Jan 2026
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1982', Anonymous Referee #1, 16 Aug 2025
This manuscript systematically evaluated the effects of different climate factors (temperature, precipitation) on crop yield before and during the growing season, focusing on winter and spring cereal yields in Sweden from 1965 to 2020. Using county-level yield data and a range of physiologically relevant climate indictors for growth stages, the study found that warmer temperatures only benefit yields if accompanied by increased growing-season precipitation. This study, aligned with the focus of Biogeosciences on climate-ecosystem interactions, and provides recommendations for yield management in the context of climate warming in Sweden's high latitudes. However, the manuscript requires further improvement in its variable selection strategy, model specification, and reproducibility of the results. The article's structure and language should also be refined.
Specific Comments:
- Table 1: Nearly all the selected climate indicators represent frequency of short-term or extreme events, omitting intensity metrics that are known to influence crop yields. Please justify this choice or include intensity-based indicators in Section 2.2.1.
- Model formulation:
- Why the Best formula can only include two main climate variables?
- Pre-growing: why only consider DI, what about other indicators?
- Rather than fitting pre- and post-flowering variables separately, perform LASSO selection on the full set of seasonal indicators simultaneously (as iZhu et al. The critical benefits of snowpack insulation and snowmelt for winter wheat productivity. Nat. Clim. Chang. 12, 485–490 (2022)), then interpret the selected variables. Also you can try to put the average & short-term indicators together.
- Many climate-crop studies use log-transformed yield as the dependent variable. Please clarify why raw yield Y was chosen. Will the result change if you use log(Y)?
- The potential role of snowfall on soil moisture and yield is also important in high latitudes regions. Please discuss or analyze snowfall indicators.
- Section 2.2.2 Periods of interest: how to estimate the date of flowering and maturity. Now you only explain the beginning of growing-season. Uncertainty analyses of estimated phenological dates are also needed (e.g. shift the phenological dates forward or backward by equal intervals of days)
- Line300: here you indicated the yield maximizing DI, but for people who are not familiar with DI definition, they do not know what’s the meaning of this DI value. What’s the threshold of DI that represents drought? It should be clear in Methods.
- The current Discussion part repeats to many results and lacks logical flow. Please rewrite this part following a more clear structure and only remain main results.
Technical Corrections:
- Figure 1b-c: The spatial map in panels b and c do not march the extent of panels a and d. The geographical boundaries should be the same as others.
- Figure 2: the inclusion of too many indicators obscures the main information. Consider focusing on the most influential metrics. And the Spearman correlation coefficients were calculated from variables in which period? Please specify the period.
- Figure 3: add the explanation of each indicator in the caption (like what’s short-term).
- Table 2: relocate it to the Supplement and reference it appropriately in the main text.
- The current equations now only include fixed effects, random effects (e.g. site, year) also should be shown in the equation.
- Line275-280: “Yields increased between 0.2 ton·ha-1 per decade for spring crops and 0.5 ton·ha-1 per decade for winter wheat (Table 2), i.e., 7% to 10% of the long-term average”. The location of this paragraph is a bit abrupt.
Citation: https://doi.org/10.5194/egusphere-2025-1982-RC1 - AC1: 'Reply on RC1', Faranak Tootoonchi, 19 Sep 2025
-
CC1: 'Comment on egusphere-2025-1982', Martin Skoglund, 24 Oct 2025
General comments
This study addresses a highly relevant and timely topic, and I find the authors’ attempt to integrate short-, medium-, and long-term climatic influences on crop yields both commendable and innovative. The analysis represents a significant step forward compared to previous work by systematically evaluating multiple climatic indicators across different time-scales and their interactions across different crop types and periods.
However, I have several concerns regarding the model specification, which may substantially affect the results and their interpretation. These issues primarily concern how the models account for spatial heterogeneity in climatic responses across Sweden. In addition to this, I have some other smaller concerns regarding the interpretation of the results when using relative yield indicators as well as the formulations of the model equations.
- Model specification
My main concern is that the models do not properly account for the well-known spatial differences in the temperature–precipitation dependency of crop yields between northern (N) and central/southern (C/S) Sweden. These regional contrasts were already formally identified by Wallén (1917, 1918) and have been confirmed in subsequent studies dealing with both historical and recent periods (e.g., Edvinsson et al. 2009; Skoglund 2022, 2023; Sjulgård et al. 2023).
In short:
- In northern Sweden (Norrland and Dalarna), higher summer temperatures are positively associated with yields, whereas increased precipitation often has a negative, albeit small, effect.
- In central/southern Sweden (counties south of Dalarna/Gävleborg), the relationship is generally the opposite: higher temperatures tend to reduce yields, while greater precipitation tends to increase them.
These contrasting relationships imply that aggregating N and C/S counties into a single model (this article) or time-series (see Holopainen et al. 2012), without explicitly accounting for these systematic differences, introduces biases and will underestimate the true climatic sensitivity of year-to-year yield variability.
This issue is especially relevant for spring-sown crops, such as barley, which are cultivated across the entire country, but also potentially for winter crops and wheat to a lesser extent (see Sjulgård et al. 2023). For winter crops and wheat, the shift from positive (negative) or negative (positive) relationships with temperature (precipitation) tend to occur only in the southernmost counties, if at all.
In the Discussion, the authors write:
“The explanatory power of climatic conditions were lower for oats and spring barley yields compared with spring and winter wheat … A possible explanation is that wheat yield data refer to southern Sweden only, whereas spring barley and oats are grown under a wider range of latitudes and hence climatic conditions …”
Here the authors themselves imply that there is a possible aggregation bias, that lowers the explanatory power for oats and barley. The issue is less relevant for (spring and winter) wheat that is mainly grown in C./S. Sweden with a more homogenous climatic signal (see also the results for barley and wheat yields in Holopainen et al. 2012 where the same type of aggregation error is made). In an analysis where relationships are estimated at the county-level, where the systematic difference between N. and C./S. is largely accounted for (except perhaps when considering border counties such as Dalarna/Värmland/Gävleborg), it can clearly be seen that year-to-year climatic fluctuations have a much greater explanatory power in N. Sweden compared to C./S. Sweden (Sjulgård et al. 2023; Skoglund, 2022; 2023). In your model, this is mainly introduced as a random effect, which brings the oat and barley models to similar levels of explanatory power as the wheat models. However, because the random effects model assumes that group-level differences are uncorrelated with the explanatory variables, this treatment is inappropriate when the between-county differences are themselves driven by climate–yield dependencies.
Possible alternatives that address the aggregation bias:
- Including an interaction between region (N, C, and S or N and C/S) and key climatic variables.
- Fit separate models for N, C and S or N and C/S.
- A random-slope model that allows the effects of temperature and precipitation to vary by county.
Relevance of results: Another issue, that is related to the model specification but only becomes an issue in regards to the interpretation of the results is that since you are treating yield as a relative variable (i.e., tonnes per hectare), instead of absolute production levels, the lumping together of N. and C./S. Sweden also obfuscates the social relevance of the results as the overwhelming majority of grain production occurs in C./S. Sweden.
Equations: As a previous reviewer mentioned, in your model equations (eq. 1–3), you describe yield as, Y, when it should in fact be indexed as time- and location-variant, Yit. Furthermore, all explanatory variables should also include it since they describe a given variable at time t and location i. I do not believe the choice to make it only Y makes it clearer as the authors suggests as we now are several reviewers who had the same objection.
In summary, this paper makes a valuable contribution and offers promising methodological advances, but the current model specification overlooks a key structural feature of Swedish agroclimatic variability. Addressing these well-known north–south contrasts in yield–climate relationships is essential to obtain unbiased estimates and to strengthen both the statistical validity and applied relevance of the findings.
References
Holopainen, J., Rickard, I. J., & Helama, S. (2012). Climatic signatures in crops and grain prices in 19th-century Sweden. The Holocene, 22, 939–945.
Sjulgård, H., Keller, T., Garland, G., & Colombi, T. (2023). Relationships between weather and yield anomalies vary with crop type and latitude in Sweden. Agricultural Systems, 211, 103757.
Skoglund, M. K. (2022). Climate variability and grain production in Scania, 1702–1911. Climate of the Past, 18, 405–433.
Skoglund, M. K. (2023b). Farming at the margin: Climatic impacts on harvest yields and agricultural practices in Central Scandinavia, c. 1560–1920. Agricultural History Review, 71, 203–233.
Wallén, A. (1917). Sur la corrélation entre les récoltes et les variations de la température et de l’eau tombée en Suéde. Kungl. Svenska vetenskapsakademiens handlingar, 57, 1–87.
Wallén, A. (1918). Sambandet mellan klimat och skörd i Sverige. Ymer, 1, 1–23.
Citation: https://doi.org/10.5194/egusphere-2025-1982-CC1 -
AC2: 'Reply on CC1', Faranak Tootoonchi, 03 Dec 2025
Responses can be found in the attached pdf.
-
CC2: 'Reply on AC2', Martin Skoglund, 04 Dec 2025
-
CC3: 'Reply on CC2', Martin Skoglund, 04 Dec 2025
References (were missing in the attached PDF)
Edvinsson, R., Leijonhufvud, L., & S¨oderberg, J. (2009). V¨ader, sk¨ordar och priser i Sverige. In B. Liljewall, I. A. Flygare, U. Lange, L. Ljunggren, & J. S¨oderberg (Eds.), Agrarhistoria på
många sätt: 28 studier om m¨anniskan och jorden. Festskrift till Janken Myrdal på hans 60- årsdag (pp. 115–136). The Royal Swedish Academy of Agriculture and Forestry, Stockholm.Sjulgård, H., Keller, T., Garland, G., & Colombi, T. (2023). Relationships between weather and yield anomalies vary with crop type and latitude in Sweden. Agricultural Systems, 211, 103757.
Skoglund, M. K. (2022). Climate variability and grain production in Scania, 1702–1911. Climate of the Past, 18, 405–433.
Skoglund, M. K. (2023b). Farming at the margin: Climatic impacts on harvest yields and agricultural practices in Central Scandinavia, c. 1560–1920. Agricultural History Review, 71, 203–233.
Wallén, A. (1917). Sur la corrélation entre les récoltes et les variations de la température et de l’eau tombée en Suéde. Kungl. Svenska vetenskapsakademiens handlingar, 57, 1–87.
Citation: https://doi.org/10.5194/egusphere-2025-1982-CC3 - AC3: 'Reply on CC3', Faranak Tootoonchi, 13 Jan 2026
-
CC3: 'Reply on CC2', Martin Skoglund, 04 Dec 2025
-
CC2: 'Reply on AC2', Martin Skoglund, 04 Dec 2025
-
RC2: 'Comment on egusphere-2025-1982', Anonymous Referee #2, 29 Dec 2025
This paper explores the meteorological drivers of observed variability in crop yields in Sweden, investigating statistical relationships between key weather / climate indices and the yields of the four most commonly grown crops in the country over 55 years (1965-2020). Given the common assertion that anthropogenic climate change is beneficial for crops in higher latitudes, the conclusion that the potential benefits of warming may be limited by lack of increased precipitation is a very important one. There are also a number of extremely useful insights in the manuscript regarding the relative explanatory power of different variables, so overall I am supportive of this work.
I note that the manuscript has already received a review from a referee and also a community review, to which the authors have already responded. Here I will focus on two additional points that appear to have been overlooked, which I recommend are addressed in a revised manuscript.
- As the authors note, climate change is expected to increase the length of the growing season, and indeed this has already happened. eg. in southern Sweden, the meteorologically-defined growing season length has been starting earlier by between 2 and 4 days per decade between 1950 and 2022 (Miś and Tomczyk, 2025 https://doi.org/10.1007/s00704-025-05382-6). So, over the period analysed in the current study, the growing season may have lengthened by over 20 days in some places. However, the authors conduct their analysis using an average growing season length over the study period. Is there a risk that the results may have been affected by including events or averaging periods outside of the growing season in the earlier part of the study period but excluding some within the growing season in the later part? This seems particularly pertinent in the context of the authors remark at lines 317-320: “We surmise this low performance is in part due to the infrequent occurrence of short-term potentially damaging conditions (e.g., few occurrences of days with average temperatures 320 above 25 °C, or frost during sensitive developmental stages)”. I would be reassured if the authors could demonstrate that their conclusions are not sensitive to the use of an average growing season length over a period when the length has changed by a non-trivial amount.
- The authors do not mention the potential impact of rising atmospheric CO2 concentrations on photosynthesis, transpiration and yield - see, for example, Rezaie et al (2023 https://doi.org/10.1038/s43017-023-00491-0) for a recent review. If this is already having an influence then it will be included within the continuous variable representing the combined effects of time elapsed in since 1965 (as described in lines 197-200) so I don’t think it will alter the findings regarding the relative importance of the different meteorological drivers during the period of observations. However, given the potential non-linear effects of CO2 effects in the future, especially in how they may affect crop responses to drought and high temperatures, the existence of these influences and their implications should be highlighted as an outstanding uncertainty in relation to the results here. Eg. it should be mentioned in line 199 alongside climate change and technological improvements, and also discussed in section 4.4 (Implications under climate change).
Citation: https://doi.org/10.5194/egusphere-2025-1982-RC2 - AC4: 'Reply on RC2', Faranak Tootoonchi, 13 Jan 2026
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Faranak Tootoonchi
Göran Bergkvist
Giulia Vico
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1542 KB) - Metadata XML
-
Supplement
(1718 KB) - BibTeX
- EndNote
- Final revised paper
This manuscript systematically evaluated the effects of different climate factors (temperature, precipitation) on crop yield before and during the growing season, focusing on winter and spring cereal yields in Sweden from 1965 to 2020. Using county-level yield data and a range of physiologically relevant climate indictors for growth stages, the study found that warmer temperatures only benefit yields if accompanied by increased growing-season precipitation. This study, aligned with the focus of Biogeosciences on climate-ecosystem interactions, and provides recommendations for yield management in the context of climate warming in Sweden's high latitudes. However, the manuscript requires further improvement in its variable selection strategy, model specification, and reproducibility of the results. The article's structure and language should also be refined.
Specific Comments:
Technical Corrections: