the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Sep 2024
The effects of upstream water abstraction for commercial export farming on drought risk and impact of agropastoral communities in the drylands of Kenya
Abstract. In the Horn of Africa Drylands (HAD) conflict over water and vegetation is prominent. Additionally, large-scale land acquisitions (LSLAs) are increasing the competition of water, putting local communities at greater risk. A key impact of increasing LSLA's is the decrease in water and land availability for vulnerable agropastoral communities. Despite recent studies, there is still a lack of research that includes the influence of upstream-downstream dynamics on drought risk and impacts of agropastoralists. Therefore, this study further develops an agent-based model (ADOPT-AP) to investigate how upstream large scale commercial farms influence downstream drought risk and impact of agropastoralists in the Upper Ewaso Ng’iro catchment in Kenya. After the model has been calibrated and validated, we assess how commercial exporting farms affect drought risk and impact of downstream communities by simulating different scenarios where the farms are replaced by agropastoral communities or forests. Our results show how both drought hazard characteristics and impacts differ among these scenarios. The analysis shows that in the scenarios where these farms are replaced by forests or communities, drought conditions are alleviated by increasing soil moisture, streamflow, and groundwater tables. These improvements are linked to reduced water abstraction and increased infiltration, benefiting downstream communities by decreasing the distance to household water, and increasing crop and milk production in times of dry periods. Policy interventions should prioritize equitable water distribution, regulation of water use, and promotion of sustainable agricultural practices to mitigate long-term impacts on water resources and community resilience.
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RC1: 'Comment on egusphere-2024-2382', Anonymous Referee #1, 31 Oct 2024
General comments
Kenya is increasingly growing agricultural products for export. The water needed for commercial export farms competes directly with the water needed for agropastoral communities in Kenya. The authors did study this water competition. They developed a model that integrates a hydrological model with a decision model that simulates adaptation decisions by agropastoralists. After model calibration, they used the model to study the effect of upstream abstraction by commercial export farms on drought risk and its impact on agropastoral communities. The results show that these effects are relatively small compared to the effect of drought periods themselves.
The developed tool in this study is novel due to integrating a hydrological model with a human decision-making model. The upstream-downstream interactions and competition occur in many parts of the world and are therefore very relevant for the EGUsphere readers. The current increase in commercial export farms in different African countries makes the study very topical.
The setup of the study and the main results seem sound. However, the current manuscript raises a large number of questions and comments, which are listed below.
Major comments
Eq. 2: The water demand plays an important role in your study. I suppose that DRYP calculates the volumetric water content θ. If so, mention that here. It is not clear how extensive the irrigation is. Probably most agricultural fields are rainfed. Clarify how you determine the area that is irrigated.
Line 246: The sentence “Greenhouses are modelled as ‘closed systems’ and irrigation water is not added to the model while there is evaporation.” is unclear. Do you assume evaporation at the greenhouse locations?
Line 273: You mention that you list only the relative factors of the hydrology-related parameters in Table 2. Therefore all the factors are dimensionless. However, in this way the reader does not have any information on the actual values of the hydrology-related parameters. Therefore include in Table 2 a column in which you specify the reference values, such as Table S2 in the Supplement.
Line 295: In general de term “model validation” is reserved for the application of a model to different circumstances: a region or period for which the model was not calibrated. Am I right that the simulation results which you show in Figure 4 were derived after model calibration? In that case, you cannot call this “validation of the model”.
Figure 4: Milk production is one of the criteria you use to quantify the impact of water extraction. The simulated milk production does, despite the calibration, strongly deviate from the measured milk production. Especially in the dry periods, which are the focus of your study, the milk production is grossly underestimated by the model. Discuss how this may impact your results and conclusions.
Figure 6: What is the unit of soil moisture in the top graph? Earlier you used volumetric water content (-), which can never exceed 1.0.
Figure 6: This figure shows only the differences of scenarios 2 and 3 with the baseline scenario. In order to put these changes in perspective, I recommend to show also the time series in time, as depicted in Figure S6 of the Supplement.
Line 364: You discuss here where soil moisture is decreased. However, looking at the values in Figure 7, this decrease is always < 0.01. You should mention this here.
Lines 415-417: Here you discuss serious reductions (22 and 36%) of stream flows as a result of your study. However, you did not show these reductions in your paper. In Figure 7 you only mention absolute reductions (m3/d). You might add these % reductions in the Supplement as a function of time and refer to this information here.
Lines 482-483: “it should be noted that not all factors are included in this study and more factors may influence the adoption of drought measures”. Can you mention some of these factors?
Line 485: In line 59 you write: “The main goal of this paper is, therefore, to develop a coupled hydrological and agent-based model (ADOPT-AP) to investigate the influence of upstream large scale commercial export farms on downstream drought risk and adaptation by agropastoralists.” In this Conclusion section you discuss the influence of commercial export farms. However, how do you evaluate the performance of ADOPT-AP? Which parts of the model framework perform well and which parts need further development?
Lines 21-22 (abstract) + 489-490: You state: “The analysis shows that in the scenarios where these farms are replaced by forests or communities, drought conditions are alleviated by increasing soil moisture, streamflow, and groundwater tables.” Your results show that the simulated increases in soil moisture, stream flow and groundwater depth are very small and have a minor effect on crop production, milk production and distance to water. In my view, the current statements are too firm and should be put more in perspective.
Minor comments
Line 55: The phrase “minimum water availability” is unclear. Replace by “minimum river flows” (based on Lanari et al. (2018)).
Line 203: Equation 4 should be equation 2.
Line 206: Change “θ is the water content (–) and θfc is the water content at field capacity (–)” to “θ is the volumetric water content (–) and θfc is the volumetric water content at field capacity (–)”
Line 268: Table 3 should be Table 2.
Line 272: Do you mean Table 2 with “Table 5”?
Lines 366, 381 and 387: Check figure references.
Line 388: produciton should be production
Line 397: resuling should be resulting
Citation: https://doi.org/10.5194/egusphere-2024-2382-RC1 -
AC1: 'Reply on RC1', Ileen Streefkerk, 18 Nov 2024
Response to referee #1
General comments
Kenya is increasingly growing agricultural products for export. The water needed for commercial export farms competes directly with the water needed for agropastoral communities in Kenya. The authors did study this water competition. They developed a model that integrates a hydrological model with a decision model that simulates adaptation decisions by agropastoralists. After model calibration, they used the model to study the effect of upstream abstraction by commercial export farms on drought risk and its impact on agropastoral communities. The results show that these effects are relatively small compared to the effect of drought periods themselves.
The developed tool in this study is novel due to integrating a hydrological model with a human decision-making model. The upstream-downstream interactions and competition occur in many parts of the world and are therefore very relevant for the EGUsphere readers. The current increase in commercial export farms in different African countries makes the study very topical.
The setup of the study and the main results seem sound. However, the current manuscript raises a large number of questions and comments, which are listed below.
>> Thank you for your review and your kind words on the relevance of the paper. Your suggestions and remarks are very much appreciated. We have addressed your comments in the table below.
Major commentsComment
Reply
Eq. 2: The water demand plays an important role in your study. I suppose that DRYP calculates the volumetric water content θ. If so, mention that here. It is not clear how extensive the irrigation is. Probably most agricultural fields are rainfed. Clarify how you determine the area that is irrigated.
Thank you for your remark. Yes, the volumetric content is calculated by DRYP. From the household surveys we obtained the distribution of agricultural field sizes and their irrigation status. This statistical input is used to generate farms across the model domain. We will add this explanation to the revised version of the paper. For your reference, around 8% of households in semi-arid zones, and 17% for semi-humid areas have irrigated lands.
Line 246: The sentence “Greenhouses are modelled as ‘closed systems’ and irrigation water is not added to the model while there is evaporation.” is unclear. Do you assume evaporation at the greenhouse locations?
We understand this is not clear and we will revise the text accordingly. We assume that the flowers evaporate/transpire the full water demand of 40 m3/hectare/day. This means that in the model irrigation and evaporation cancel each other out and we model the greenhouse as a closed system. So, irrigation is not added to the model and there is no evaporation at the greenhouses. We realise that this is not the case for the entire grid cell because the greenhouses do not occupy the whole grid cell, so we updated the evaporation (crop factor) of the model based on the percentage of greenhouses in a grid cell (assuming crop factor of 0 at the greenhouse and 1 at the rest of the cell). The sentence will be re-formulated as:
“Greenhouses are modelled as ‘closed systems’. We assume all water added as irrigation is transpired by the flowers, so irrigation and transpiration cancel each other out. However, as the greenhouses do not cover the entire grid cells, we take into account the percentage of coverage of a grid cell and calculate evaporation and transpiration for the part of the grid cell not occupied by greenhouses.”
Line 273: You mention that you list only the relative factors of the hydrology-related parameters in Table 2. Therefore all the factors are dimensionless. However, in this way the reader does not have any information on the actual values of the hydrology-related parameters. Therefore include in Table 2 a column in which you specify the reference values, such as Table S2 in the Supplement.
Thank you for your suggestion. We will incorporate the actual values as an extra column in the table in the revised version of the manuscript.
Line 295: In general de term “model validation” is reserved for the application of a model to different circumstances: a region or period for which the model was not calibrated. Am I right that the simulation results which you show in Figure 4 were derived after model calibration? In that case, you cannot call this “validation of the model”.
Yes the results were derived after model calibration. We agree with you that we should not call this section validation, but model performance instead.
Figure 4: Milk production is one of the criteria you use to quantify the impact of water extraction. The simulated milk production does, despite the calibration, strongly deviate from the measured milk production. Especially in the dry periods, which are the focus of your study, the milk production is grossly underestimated by the model. Discuss how this may impact your results and conclusions.
Thank you for these valid points. The model indeed underestimated the milk production compared to observed data. This might indicate that milk production is not just influenced by environmental stressors, but that responses of organisations have decreased the impact of drought on milk production (e.g. people receiving cash transfers through humanitarian aid). However, we should also note that the observed data might not be accurate. Milk production does not go down as much as one would expect from the severity of drought impact reported on food security (Reliefweb, 2023) (assuming high milk production is strongly correlated with low food insecurity; Jodlowski et al., 2016). We will add this to the discussion of the paper.
Jodlowski, M., Winter-Nelson, A., Baylis, K., & Goldsmith, P. D. (2016). Milk in the data: food security impacts from a livestock field experiment in Zambia. World Development, 77, 99-114.
ReliefWeb. (2023). Kenya 2022 Drought Response in Review—Kenya | ReliefWeb. https://reliefweb.int/report/kenya/kenya-2022-drought-response-review
Figure 6: What is the unit of soil moisture in the top graph? Earlier you used volumetric water content (-), which can never exceed 1.0.
Please note that 1e*-5 is on top of the x-axis. However, we understand that this might be hard to see and we will change the location.
Figure 6: This figure shows only the differences of scenarios 2 and 3 with the baseline scenario. In order to put these changes in perspective, I recommend to show also the time series in time, as depicted in Figure S6 of the Supplement.
We indeed thought about doing this, but as the changes are relatively small it is really hard to see the differences compared to the variation in time.
Line 364: You discuss here where soil moisture is decreased. However, looking at the values in Figure 7, this decrease is always < 0.01. You should mention this here.
Thank you for your suggestion, we will incorporate the values here.
Lines 415-417: Here you discuss serious reductions (22 and 36%) of stream flows as a result of your study. However, you did not show these reductions in your paper. In Figure 7 you only mention absolute reductions (m3/d). You might add these % reductions in the Supplement as a function of time and refer to this information here.
That is a good suggestion, we will incorporate the % reductions in the supplement and refer to it in the main text.
Lines 482-483: “it should be noted that not all factors are included in this study and more factors may influence the adoption of drought measures”. Can you mention some of these factors?
Other factors include for example the risk and time preferences, included in economic theories (Expected Utility Theory and Rank Depended Utility theory) (Schrieks et al., 2023). We will specify this a bit more in the revised version by adding ‘such as variables which are included in economic theories (e.g. risk and time preference).’
Line 485: In line 59 you write: “The main goal of this paper is, therefore, to develop a coupled hydrological and agent-based model (ADOPT-AP) to investigate the influence of upstream large scale commercial export farms on downstream drought risk and adaptation by agropastoralists.” In this Conclusion section you discuss the influence of commercial export farms. However, how do you evaluate the performance of ADOPT-AP? Which parts of the model framework perform well and which parts need further development?
Thank you for your reflection, we should indeed incorporate some results of the model performance in conclusion as well. We will mention that the model underestimates milk production during dry periods. The model framework as a whole is tested with performance metrics (BR, KGE). However, the human-decision part of the model needs more validation, but that is currently not possible due to lack of longitudinal data (as mentioned in the discussion).
Lines 21-22 (abstract) + 489-490: You state: “The analysis shows that in the scenarios where these farms are replaced by forests or communities, drought conditions are alleviated by increasing soil moisture, streamflow, and groundwater tables.” Your results show that the simulated increases in soil moisture, stream flow and groundwater depth are very small and have a minor effect on crop production, milk production and distance to water. In my view, the current statements are too firm and should be put more in perspective.
We agree the wording is too firm, and we will put this sentence in perspective. We will also add; “However, compared with the impact of drought hazard itself this change is very small.”
Minor comments
Line 55: The phrase “minimum water availability” is unclear. Replace by “minimum river flows” (based on Lanari et al. (2018)).
Line 203: Equation 4 should be equation 2.
Line 206: Change “θ is the water content (–) and θfc is the water content at field capacity (–)” to “θ is the volumetric water content (–) and θfc is the volumetric water content at field capacity (–)”
Line 268: Table 3 should be Table 2.
Line 272: Do you mean Table 2 with “Table 5”?
Lines 366, 381 and 387: Check figure references.
Line 388: produciton should be production
Line 397: resuling should be resulting
>> Thank for providing minor comments on spelling and wording, they will be incorporated in the revised version.Citation: https://doi.org/10.5194/egusphere-2024-2382-AC1
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AC1: 'Reply on RC1', Ileen Streefkerk, 18 Nov 2024
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RC2: 'Comment on egusphere-2024-2382', Maurits Ertsen, 07 Feb 2025
This is an interesting paper, which presents both a tricky setting with loads of water-related interactions and a fascinating methodology to study the setting. Having said that, I am afraid that to me the paper is less convincing than it should be on both aspects. My main reason for this assessment is that the paper focuses rather strongly on the "what" (model choices, data inputs) and tends to ignore the "why" (are certain choices allowed, what are consequences of choices?). I cannot determine whether model results are not a direct result from the modelling setup. Let me rephrase that: I am convinced that the model results are the direct result of the modelling setup, but cannot easily determine to what extent the agency of model agents is "free" enough to escape modelling setups. As far as I can judge, the ABM setup is quite deterministic. I do not have problems as such, as some of the ABMs I have worked on have deterministic procedures too. I do have problems with this text because the choices made are not clearly explained. I will not present detailed comments on all parts of the text, as what needs to be done first in my opinion is a clearer discussion on the model setup. Below, I share some feedback on selected parts of the text to illustrate my assessment.
Both in the abstract and the introduction, the lack of studies on the topic is connected to the ABM setup with "therefore". Why "therefore"? I can imagine very useful studies to be developed on the topic without ABM. In fact, the strong hydrological base of the ABM setup suggests that agency may be less required to study flow-related feedbacks. What does agency add?
The text moves between "drivers", "behavior" and "actions" when describing aspects that I think are very close if not the same. As soon as "actions" are "driven", where is the choice? To me, "behavior" reflects a longer-term pattern, whereas "actions" are shorter term. As soon as hydrological effects are produced by "actions", can they be an external "driver"?
The ODD protocol and other materials in the supplement are obviously more detailed compared to the text on model setup. This is to be expected, but I think some elements from the supplements need to be clearer in the main text.
- Household characteristics: does the framing on the best fit mean all households start the same?
- The same for self-efficacy and how household properties like gender influence decisions
- The PMT theory is from 1983. I would like to read more about why such an old one can still be used.
- How can a grid cell represent multiple household as one agropast agent when apparently the density can differ?
- My main concern is the time step. Decisions are annual according to the text. The supplement mentions that annual decisions on crops and livestock are at other moments, right? This would mean that decisions are not annual, or should not be assuming that decisions on crops and livestock should be related. After all, there is only so much one can do in the time given to an agent. With the hydromodel running on shorter time steps, why can agents not act more often? or does the hydromodel actually represent many decisions of human agents, but keeps them hidden and unrelated to the "real" decisions?
I find figure 4 not easy to understand, as the middle and lower panel do not seem to have a legend. This remark stand symbol for almost all of the figures, which i find not easy to understand. I do know how hard making useful figures can be...
The Discussion is extremely general and is not going into much detail on model results in relation to the setup or in relation to other literature. Paragraph 5.1 does not seem to need ABM anyway to make its points. 5.2 is a nice overview of some literature, but does not reflect back on the ABM proposed in the text beyond claims that this model is "valuable" in "capturing" and "understanding" things. I would agree on the "capturing", even when I would still need to know more about why model choices make sense. I take issue with the "understanding", as copying processes is not necessarily the same as finding out why these processes happen.
I will be totally clear: when reading the conclusion, my first response was a simple "wow". It was not an enthusiastic "wow, though, as I find the conclusion frustratingly brief and shallow. Apart from the numbers mentioned (why only those?), I could have written the same conclusion without any deeper analysis of the setting. Again, to be totally clear: this conclusion was almost enough reason for me to suggest a "reject" for this text. If this is all one should take from the study, it can be easily missed in the academic world. There must be more to share. As already said, I propose that sharing much more on reasons for model choices and on implications of these choices for results would be required. Only with these aspects clarified could one think about consequences of both for understanding and follow-up actions (research and policy).
Citation: https://doi.org/10.5194/egusphere-2024-2382-RC2 -
AC2: 'Reply on RC2', Ileen Streefkerk, 27 Feb 2025
Dear Maurits,
Thank you for taking the time to review this paper and raising your concerns. Below you can find a response to your comments.
We are agree with you that the ‘why’ and the agency of the model can be better addressed in the paper. Thank you for pointing this out. While our explanation referred to the previous paper, we understand that some of the assumptions made in the model should be made clearer such that this paper can also be understood as a standalone paper, Therefore, we will incorporate this feedback into the next iteration.
We understand that the model might come across as deterministic, and might therefore raise the question of agency. We are bringing in agency in the human decision-making part of the model as the communities are affected differently by drought impacts and respond differently to these impacts due their different vulnerabilities, social network, water/grass availability, household characteristics etc. This behavior is stochastic due to the agents heterogenous characteristics (household size, off-farm income etc.), and randomly created elements, such as, cost perceptions, social network, groundwater points, etc. To see what the effect is of commercial farming activities on (different groups of) communities, we use the agent-based setup. The commercial farms follow deterministic water allocation rules to fulfil their water demand requirements. We will clarify this further in section 3.3.4 of the manuscript. However, in the way we present the paper it might indeed come across deterministic as we needed to set the model ‘fixed’ to be able to compare results among the three scenarios. For this reason, we can run the model multiple times (with different initialisation/randomness), so you can better see the results of the model among different runs.
About the use of "drivers", "behavior" and "actions". We looked at the text for inconsistencies, but we do not use the word ‘driver’ in the text. We see actions as the options people can take to adapt to drought, which is a result of behavior – although can be used interchangeable indeed. Behavior might indicate that actions are taken because of a high risk or coping appraisal for example. We will revise the text, especially the second paragraph of the introduction, to make sure the terms ‘actions’ and ‘behavior’ are used in a more consistent way.
Thank you for your specific questions. Please see a response to your points below. We will make this clearer in the main text.
- Household characteristics: does the framing on the best fit mean all households start the same?
It means that a statistical fit is used to generate a random distribution of the (heterogeneous) characteristics of the agents – with a unique set of characteristics. The distribution is based on the household survey. We will emphasize in the next iteration that the agents all start with a unique set of characteristics.
- The same for self-efficacy and how household properties like gender influence decisions
A regression analysis is used to define the relationship between household properties and self-efficacy. We use those relations – but the self-efficacy is unique for every household as a consequence of the unique set of household characteristics. Self-efficacy is also dependent on knowledge, which is dependent on the social network. We parameterized the relationships between self-efficacy and household characteristics based on regression analysis of a recent household survey study (Schrieks et al. 2024).
- The PMT theory is from 1983. I would like to read more about why such an old one can still be used.
The protection motivation theory is indeed originally from 1983, but is still state of the art theory in psychology and decision making under risk. Many recent studies on drought risk adaptation behavior use protection motivation theory and recent household survey studies with farmers and pastoralists in East-Africa show that PMT is a suitable theory to explain adaptation behavior under drought risk conditions (Wens et al. 2021; Schrieks et al. 2024; Gebrehiwot and Van der Veen, 2021). PMT is also used in many ABM studies on natural hazard management (Heliegiorgis et al. 2018; Wens et al. 2020; Michaelis et al. 2020; Moradzadeh and Ahmadi, 2024). Advantages of using such an established psychological theory are that the theory is supported by a lot of empirical evidence and that it increases comparability with other studies and replicability of the study (Schrieks et al. 2021)
- How can a grid cell represent multiple household as one agropast agent when apparently the density can differ?
The size of the representative agent can also vary through differences in household size and number of households in a cell. The total “size” of the representative agents depends on a density map (CIESIN, 2022). We will clarify this in the next iteration.
Thank you for your question about the timestep. Yes, it is indeed possible to include multiple decisions or decisions that are made multiple times a year. In principle is the model running on a daily timestep in which people use water for domestic, livestock and irrigation purposes (based on water availability and demand). We will make this clearer in Section 3.3 ‘Socio-hydrology’ in the revised version. The adaptation decisions are made at the point in the season people also make these decisions; the crop-related decisions are made before the rainy season, and the livestock-related decisions are made before the dry season. These are the adaptation decisions that are based on the household survey and the decision-making process is based on the PMT. We will make the timesteps of the model clearer in the general overview of the model in the ‘Data and Methods’ section. We will add more information on the timing of the adaption decisions in 3.2 ‘Human-decision making’.
Thank you for pointing out your remark on the readability of the figure - it is indeed hard to make clear figures. We will add the legend also to the other panels for all figures if that is what makes it easier to understand.
About the discussion, we do agree with the points raised and that some aspects, especially concerning the impact on communities and the assumption here should be made more clear in the Methods section and elaborated on in the discussion. We agree that, in the way section 5.1 is currently written, the results generated might not need an ABM - as we only discuss the hydrological implications. But in our opinion this does not mean we cannot discuss these results – these quantitative results are new and have practical implications for water management. We do need an ABM when we talk about community impacts, and we can elaborate more on the heterogeneity of the results in the discussion. We will do so in the next iteration. About section 5.2, we believe modelling processes close to reality - and in this sense copying a system - is valuable to generate alternative scenarios. In this case, we see value in quantifying the impact of commercial farming on hazard and impact of communities. ABM’s in general can give a greater understanding of the system as a whole (including the human-water interactions therein) by quantifying these processes and accessing the implications of various scenarios. We can reformulate this in the discussion.
After carefully re-reading the conclusions, we agree the conclusions can be more specific and to focus more on the results rather than implications. Please see the changes made in the new version.
References:
CIESIN (2022). Gridded Population of the World (GPW), v3. Retrieved from https://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density/datadownload
Hailegiorgis, A., Crooks, A., and Cioffi-Revilla, C. (2018). An agent-based model of rural households' adaptation to climate change. J. Artific. Soc. Soc. Simul. 21:3812. doi: 10.18564/jasss.3812
Wens, M., Veldkamp, T. I. E., Mwangi, M., Johnson, J. M., Lasage, R., Haer, T., et al. (2020). Simulating small-scale agricultural adaptation decisions in response to drought risk: an empirical agent-based model for Semi-Arid Kenya. Front. Water 2, 1–21. doi: 10.3389/frwa.2020.00015
Michaelis, T., Brandimarte, L., & Mazzoleni, M. (2020). Capturing flood-risk dynamics with a coupled agent-based and hydraulic modelling framework. Hydrological Sciences Journal, 65(9), 1458-1473.
Moradzadeh, M., & Ahmadi, M. (2024). Unraveling the interplay of human decisions and flood risk: An agent-based modeling approach. International Journal of Disaster Risk Reduction, 107, 104486.
Gebrehiwot, T., & van der Veen, A. (2021). Farmers’ drought experience, risk perceptions, and behavioural intentions for adaptation: Evidence from Ethiopia. Climate and Development, 13(6), 493-502.
Schrieks, T., Botzen, W. W., Haer, T., Wasonga, O. V., & Aerts, J. C. (2024). Assessing key behavioural theories of drought risk adaptation: Evidence from rural Kenya. Risk Analysis, 44(7), 1681-1699.
Wens, M. L., Mwangi, M. N., van Loon, A. F., & Aerts, J. C. (2021). Complexities of drought adaptive behaviour: Linking theory to data on smallholder farmer adaptation decisions. International Journal of Disaster Risk Reduction, 63, 102435.
Citation: https://doi.org/10.5194/egusphere-2024-2382-AC2
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