From Risk to Resilience: Willingness-to-Pay for Crop Insurance Among Paddy Farmers in the Kurunegala District, Sri Lanka

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

 

I am pleased to provide my review of the manuscript, “From Risk to Resilience: Willingness-to-Pay for Crop Insurance among Paddy Farmers in the Kurunegala District, Sri Lanka " which aimed to investigate the perceptions and Willingness to Pay for crop insurance schemes among the paddy farmers in Kurunegala district.  This paper identified as the major limitations in existing crop insurance schemes and offers suggestions to strengthen farmers' resilience, sustain agricultural livelihoods, and support climate-smart adaptation strategies in Sri Lanka. My assessment is that this manuscript holds strong potential for publication in Sustainability with some revisions, which I detail in the following sections to further enhance its impact and clarity.

 

Abstract:

Lines 23-26: potential rewording suggestion: Agriculture is one of the many sectors facing significant risks from climate change. To manage potential crop losses—whether climate-related or not—farmers widely rely on crop insurance to increase their resilience.

Introduction:

Lines 58-61: potential rewording suggestion: Climate change is expected to cause notably large production losses in South Asia and Sub-Saharan Africa. This threat makes farmers in these regions particularly vulnerable, driving up their risk perceptions and making investments in high-value crops less appealing. Such pressure could lead to severe financial strain on farmers, potentially driving them into poverty traps.

Line 81: practiced

Line 92-93: why provide %ag on only one quarter?

Lines 113-15: explain what no claim bonuses or no-hazard return (NHR) benefits are.

Line 139: remove a superfluous space.

Lines 140-142: what do you mean by “free agricultural insurance”. Do you mean that the government subsidizes the insurance? What is the coverage level?

Line 147: how can there be financial constraints if insurance is provided for free?

 Line 150: how can premium affordability be a problem if insurance is provided for free?

Line 157: explain what a no-hazard return option is.

Materials and Methods

Lines 194-198: suggested rewording: Decision-makers' views on different attributes can typically be assessed using two approaches: Stated Preference and Revealed Preference methods [26]. This study specifically employed the Discrete Choice Experiment (DCE), a widely used Stated Preference technique. The DCE involves presenting participants with choice cards that feature predefined key attributes and their associated levels to explore how the study population perceives these factors.

Line 232-236: Explain the process more in depth: what database did you draw your sample from? What type of information about the fields was available? Why did you choose to stratify on the irrigated nature of the fields?

Line 242: “farming”

Line 244: “insurance”

Line 264-265: please provide more details on the “if no hazard” option.

Line 270: Please describe the choice experiment protocol and what data was collected for each participant for the choice experiment.

Equation 2: Did you try other approaches to modelling your data, with other interactions for example?

Line 279: Define the variables “positive attitude towards crop insurance” and “status quo option”.

Results

Lines 288-31: are these summary statistics similar to the general population you are trying to extrapolate your results to (paddy farmers in the Kurunegala district)?

Discussion

Line 449: in apparent contradiction though, 85.6% disagree with the statement that “Only large farmers can afford crop insurance schemes” (Table 4).

Line 522: “are not willing”.

Author Response

Response to the Reviewer Comments (Sustainability-3866605)

 

Authors are thankful for the valuable comments of the reviewers that improved the overall quality of the manuscript. All the revisions suggested have been addressed in the revised version of the manuscript. In addition to these, authors are willing to comply with any additional revisions suggested by the editorial board to further improve the manuscript.

 

• Reviewer 1
• Comment 1

Abstract: Lines 23-26: potential rewording suggestion: Agriculture is one of the many sectors facing significant risks from climate change. To manage potential crop losses—whether climate-related or not—farmers widely rely on crop insurance to increase their resilience.

 

Response

The authors are thankful for the suggestion. The relevant section was revised as suggested.

 

Introduction

• Comment 2

Lines 58-61: potential rewording suggestion: Climate change is expected to cause notably large production losses in South Asia and Sub-Saharan Africa. This threat makes farmers in these regions particularly vulnerable, driving up their risk perceptions and making investments in high-value crops less appealing. Such pressure could lead to severe financial strain on farmers, potentially driving them into poverty traps.

 

Response

The authors are thankful for the suggestion. The relevant section was reworded as suggested.

 

• Comment 3

Line 81: practiced.

 

Response

Revised as suggested.

 

• Comment 4

Line 92-93: why provide % on only one quarter?

 

Response

The authors are thankful for the suggestion. We updated the value as follows.

 

“According to the Central Bank, the agriculture and fisheries sector contributed approximately 7.6% to Sri Lanka's Gross Domestic Product (GDP) in the first quarter of 2025 [13].

 

• Comment 5

Lines 113-15: explain what no claim bonuses or no-hazard return (NHR) benefits are.

 

Response

The authors are thankful for the suggestion. The relevant section was expanded as follows.

 

“Meanwhile, offering no-claim bonuses or No-Hazard Return (NHR) benefits, which aims to provide farmers with a partial refund or rollover of premiums in seasons without crop damage, has been proposed as an incentive to foster loyalty and mitigate the perceived loss of premiums during hazard-free years [20, 21].”

 

• Comment 6

Line 139: remove a superfluous space.

 

Response

The authors are thankful for pointing this. The superfluous space was removed.

 

• Comment 7

Lines 140-142: what do you mean by “free agricultural insurance”. Do you mean that the government subsidizes the insurance? What is the coverage level?

 

Response

The term “free agricultural insurance” refers to the fully government-subsidized crop insurance scheme implemented by the Agricultural and Agrarian Insurance Board (AAIB) of Sri Lanka. Under this programme, farmers are not required to pay any premium, as the entire cost is borne by the government. The scheme provides coverage of LKR 40,000 per acre for six major crops (paddy, onion, potato, maize, soybean, and chilli).

 

The relevant section was modified as follows to enhance the clarity.

 

“The government of Sri Lanka introduced a fully subsidized agricultural insurance programme in the 2018 Yala season, under which farmers are exempted from paying premiums. This scheme, which is administered by the Agricultural and Agrarian Insurance Board (AAIB), covers six major crops: paddy, onion, potato, maize, soybean, and chilli with a compensation level of LKR 40,000 per acre in the event of crop damage [15].”

 

• Comment 8

Line 147: how can there be financial constraints if insurance is provided for free?

 

Response

The authors are thankful for pointing this. The statement on financial constraints (Line 147) refers to situations outside the scope of the fully subsidized government scheme. Although the government provides free coverage for six selected crops under the AAIB scheme, many farmers cultivate multiple crops, including those not covered by the free insurance programme. Moreover, private insurance providers and index-based schemes still require premium payments. Therefore, financial constraints remain a key limitation for farmers who cultivate non-subsidized crops or seek additional coverage beyond the government programme. We have revised the text to clarify this distinction.

 

“Previous studies have indicated several reasons for this, including financial constraints—particularly among farmers cultivating crops not covered by the fully subsidized government insurance scheme or those seeking additional coverage through private insurance providers—as well as limited familiarity with insurance, low financial literacy, and a lack of trust in insurance institutions [14].”

 

• Comment 9

 Line 150: how can premium affordability be a problem if insurance is provided for free?

 

Response

The authors are thankful for pointing this. The concern about premium affordability arises primarily in relation to non-subsidized or privately provided crop insurance schemes, and for crops not included in the government’s free insurance coverage. We have clarified this earlier in line 147.

 

“As highlighted by Heenkenda [23], the affordability of insurance premiums, especially for farmers cultivating crops not covered by the fully subsidized government insurance scheme or those seeking additional coverage through private insurance providers, remains a major challenge in Sri Lanka. In addition, the limited range of crop coverage and the complexities involved in the claiming process have also been identified as significant challenges to the adoption of Sri Lankan crop insurance schemes.”

 

• Comment 10

Line 157: explain what a no-hazard return option is.

 

Response

We explained this earlier in the text as a response to a valuable suggestion of the reviewer (lines 114-117).

“Meanwhile, offering no-claim bonuses or No-Hazard Return (NHR) benefits, which aims to provide farmers with a partial refund or rollover of premiums in seasons without crop damage, has been proposed as an incentive to foster loyalty and mitigate the perceived loss of premiums during hazard-free years [20, 21].”

 

Materials and Methods

• Comment 11

Lines 194-198: suggested rewording: “Decision-makers' views on different attributes can typically be assessed using two approaches: Stated Preference and Revealed Preference methods [26]. This study specifically employed the Discrete Choice Experiment (DCE), a widely used Stated Preference technique. The DCE involves presenting participants with choice cards that feature predefined key attributes and their associated levels to explore how the study population perceives these factors.”

 

Response

The authors welcome the suggestion. The relevant section was reworded as suggested.

 

• Comment 12

Line 232-236: Explain the process more in depth: what database did you draw your sample from? What type of information about the fields was available? Why did you choose to stratify on the irrigated nature of the fields?

 

Response

The authors welcome the suggestion. The relevant section was revised as follows.

 

“2.3. Sample Size and Data Collection

This study was conducted from March to December 2024. A total of 250 paddy farmers representing eight Divisional Secretariat Divisions in the Kurunegala District were selected as the sample based on the Cochran’s formula with finite population correction [34]. The sampling frame was derived from the farmer registry of the Department of Agrarian Development (DAD), which contains information on land extent, ownership, and irrigation source for all registered paddy farmers. A stratified random sampling approach was employed based on the irrigated nature of the paddy fields (major and minor irrigation schemes), as irrigation type is a key determinant of drought exposure, productivity, and insurance participation. This stratification ensured proportional representation of farmers from both irrigation systems within the study sample.”

 

• Comment 13

Line 242: “farming”.

 

Response

The authors are thankful for pointing out this typo. The typo was corrected.

 

• Comment 14

Line 244: “insurance”.

 

Response

The authors are thankful for pointing out this typo. The typo was corrected.

 

• Comment 15

Line 264-265: please provide more details on the “if no hazard” option.

 

Response

The authors are grateful for the suggestion. The relevant sentence was revised as follows.

 

“The fourth attribute, ‘No Hazard Return (NHR)’, represented the benefit a farmer would receive if no crop damage occurred during the insured season. Under this option, a proportion of the insured amount either 50% or 75% would be refunded or rolled over to the next season, allowing farmers to gain a partial benefit even in hazard-free years.”

 

• Comment 16

Line 270: Please describe the choice experiment protocol and what data was collected for each participant for the choice experiment.

 

Response

The authors are thankful for the reviewer for highlighting this. The relevant sections were modified as follows.

 

“Four choice cards were developed, each comprising two alternative crop-insurance schemes and one opt-out (‘no-insurance’) option. The number of cards was intentionally limited to four to reduce cognitive burden and respondent fatigue, while ensuring adequate variation across the attribute levels. The order of presentation was randomized among respondents. Prior to the survey, a pilot test confirmed that this number of choice sets was appropriate for maintaining respondent engagement and comprehension.

 

2.6. Data Collection                                                                                                                                          

Data were collected through face-to-face interviews conducted by trained enumerators using a structured questionnaire (described under section 2.4) and choice cards. The choice sets were randomized across questionnaires to minimize order bias. Informed written consent was obtained from all respondents prior to data collection. For each participant, responses to the Discrete Choice Experiment (DCE) were recorded alongside the information obtained through the questionnaire.”

 

• Comment 17

Equation 2: Did you try other approaches to modelling your data, with other interactions for example?

 

Response

In the current study, the Conditional Logit Model (CLM) was employed primarily because the experimental design focused on attribute-level variations rather than respondent specific heterogeneity. This decision was taken based on the nature of the collected data set, literature (similar studies published in SCI journals) and the expertise of the authors.

 

• Comment 18

Line 316: Define the variables “positive attitude towards crop insurance” and “status quo option”.

 

Response

The authors welcome the suggestion. The relevant section was modified as follows.

 

“Where Vᵢⱼ represents the utility that respondent i derives from alternative j. Each Z variable corresponds to a dummy-coded attribute. The term ATIᵢ captures the individual's attitude toward crop insurance. SQᵢⱼ is a dummy for the status quo option. Respondents who exhibited a positive attitude towards crop insurance were more likely to choose non-status quo options. The status quo option in each choice card represented the no-insurance alternative, reflecting the decision to remain uninsured under real-life conditions.”

 

Results

• Comment 19

Lines 288-31: Are these summary statistics similar to the general population you are trying to extrapolate your results to (paddy farmers in the Kurunegala district)?

 

Response

The authors welcome the suggestion. Yes, we attempted our level best to maintain the representativeness, which allowed the generalization of the findings.

 

Discussion

• Comment 20

Line 449: In apparent contradiction though, 85.6% disagree with the statement that “Only large farmers can afford crop insurance schemes” (Table 4).

 

Response

The authors welcome the suggestion. The original line had stated “The discrepancy between favourable perception and limited adoption underscores a significant implementation gap.” As presented in Table 4, this “favourable perception” was mentioned due to a number of perception statements, as indicated in the results section as below.

 

The majority of the farmers accepted that,

• crop insurance helps to alleviate financial stress during periods of crop failure or low yields (80%)
• farmers participating in crop insurance schemes are more resilient to the impacts of climate change (75.6%)
• crop insurance schemes promote long-term sustainability and viability of farming communities (79.2%)
• crop insurance schemes should be mandatory for farmers (62.4% agreed and 37.6% strongly agreed).

 

In addition, the majority of farmers disagreed (85.6%) with the fact that insurance schemes are only affordable by large scale farmers. This indicates a favourable perception towards adoption of crop insurance schemes. As understood by the authors, this statement in line 449 is justified by the above findings mentioned in the results section (section 3.4). The authors are willing to make any revision to this statement, if the reviewer can point out the specific contradiction.

 

• Comment 21

Line 522: “are not willing”.

 

Response

The humbly disagree with the suggestion. This sentence reflects an ideal crop insurance scenario expected by the farmers, which was derived based on the outcomes of the CLM. It was mentioned by us as a recommendation. Therefore, the original form of the sentence is correct.

 

“The positive coefficients associated with the 'if no hazard' attribute indicated that farmers find value in having insurance coverage even when there is no immediate hazard (e.g., during normal growing conditions). This indicates a moderately demanded return on investment, even in years when there was no risk of a hazard. Implementing a no claim return or rollover benefit can notably alleviate dropout levels, increase retention, and reduce perceptions of wasted premiums, particularly during minimal-loss years. This feature is comparatively unexplored within the context of Sri Lankan schemes and thus provides a useful design innovation to enhance uptake. Local farmers are willing to pay more for this kind of assurance, which ensures continuity and encourages long-term risk management in the given protection schemes.”

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1. All wordings such as: Willingness to Pay (WTP), Conditional Logit Model (CLM) should be first written in all and then preented in bracets as the abbreviations but everytime written in the same way. Look at Willingnes to Pay, then PTP, then in the Keywords as Willingness-to-Pay.
2. Some sentences need quotation/s, f. ex. (...) Therefore, understanding the farmers' behaviors and attitudes towards crop insurance and the potential of such programmes in emerging climate-induced vulnerabilities is important to enhance the welfare of the local farming communities (quotation/s).
3. In the part Materials and Methods: why this research district has been chosen? 
4. In the part Questionnaire Design and Data Collection: how the sampling has been chosen? With the use of which formulas?
5. In the part Development of Choice Sets: in the table 1 it is advisable to enrichen this table with the description of the attributes and description of permissible levels of used indicies/measures.
6. The abbreviations in the table 4 (SD, D, N, A, SA) must be explained in the text. 
7. In the Figure 1: are there any other options than option 1, option 2 and no option?
8. The information on the insurance, its definition/s, clasiffication/s, pros and cons should be placed at the beginning of the manuscript.
9. What are the limitations of the research.
10. How the limitations can be decreased?
11. What are the recommendations from the research?
12. Where are the hypothesis?
13. Are the hypothesis confirmed / rejected? Why yes / why no?
14. The titles of the tables and charts have to be checked and their sources have to be listed below them.
15. What do I gain as a reader from the conclusion? Is there any linkage, is not? Between which variables? Why? Explain, etc.
16. The Conclusion is too short.    
17. The titles of the tables and charts have to be checked and their sources have to be listed.

Positives:

The article tackles with a very important and interesting subject!

Author Response

Response to the Reviewer Comments (Sustainability-3866605)

 

Authors are thankful for the valuable comments of the reviewers that improved the overall quality of the manuscript. All the revisions suggested have been addressed in the revised version of the manuscript. In addition to these, authors are willing to comply with any additional revisions suggested by the editorial board to further improve the manuscript.

 

• Reviewer 2
• Comment 1

All wordings such as: Willingness to Pay (WTP), Conditional Logit Model (CLM) should be first written in all and then presented in brackets as the abbreviations but every time written in the same way. Look at Willingness to Pay, then PTP, then in the Keywords as Willingness-to-Pay.

 

Response

The authors are grateful for pointing this out. All such instances were carefully corrected by the authors.

 

• Comment 2

Some sentences need quotation/s, f. ex. (...) Therefore, understanding the farmers' behaviors and attitudes towards crop insurance and the potential of such programmes in emerging climate-induced vulnerabilities is important to enhance the welfare of the local farming communities (quotation/s).

 

Response

The authors welcome the suggestion. We have come across many research articles that do not use “…”, in presenting results similar to these statements. Therefore, the author believe that quotations are not essential for these types of statements, since direct quotations of farmers are not presented. Further, both the editor and other reviewer have not pointed out the necessity of quotation marks. However, respecting the valuable comment of the reviewer, we subjected the manuscript to English Language correction and proofreading by a professional editor to address such instances.

 

If the reviewer can specifically point out such instances, we are happy to address such issues.

 

• Comment 3

In the part Materials and Methods: why this research district has been chosen? 

 

Response

The authors welcome the suggestion. The following sections in the introduction and methodology justifies the selection of the study area.

 

In the introduction,

“In theory, when farmers perceive higher climatic risk and if index triggers are well-designed and trusted, the acceptance of crop insurance schemes should increase, as insurance reduces vulnerability and supports resilience-building investments [5, 12]. However, empirical research indicates that while farmers globally express interest in insurance, the actual adoption rate remains low, particularly in developing countries. Similar to many developing countries in the region, agriculture serves as a major pillar of the Sri Lankan economy. According to the Central Bank, the agriculture and fisheries sector contributed approximately 7.7% to Sri Lanka's Gross Domestic Product (GDP) in the first quarter of 2024 [13]. Paddy production is one of the main productions and a staple food in Sri Lanka. Paddy is cultivated in all districts of Sri Lanka, primarily during the two monsoon seasons, namely the Yala and Maha seasons [14]. It is estimated that about 708,000 ha of land are under paddy cultivation [15]. Farmers have experienced substantial damage and losses in paddy production due to severe climate shocks, such as droughts and floods, over recent years [16]. According to recent reports from the Agricultural and Agrarian Insurance Board (AAIB), droughts have damaged approximately 58,766 acres of paddy, with the highest damage reported in the Kurunegala district. Meanwhile, extensive floods have damaged approximately 47,000 acres of paddy fields, directly impacting 20,064 farmers during the 2023 Maha season [15]. Therefore, Sri Lanka's paddy cultivation urgently needs sustainable adaptation strategies to mitigate the adverse effects of CC and to sustain food security [16,17].”

 

In the methodology,

“The Kurunegala District, located in the North Western Province of Sri Lanka, has a population of 1,760,829 and spans an area of 4,816 square kilometers. Around 625 km² of the Kurunegala district remains under paddy cultivation [15, 31]. It spans three agro-ecological zones: DL₁, IL_1, and IL₃, encompassing both the dry and intermediate zones of Sri Lanka. Regions belonging to the dry zone receive an average rainfall <1750 mm, while the intermediate zone is characterized by a mean annual rainfall of 1750 mm to 2500 mm [31]. Kurunegala District has long been recognized as one of Sri Lanka's most significant paddy-producing regions [24]. Its agro-ecological conditions, combined with an extensive network of both major and minor irrigation schemes, contribute to its paddy production. According to the Department of Census and Statistics, Kurunegala produced approximately 210,243 metric tons of paddy from 56,697 hectares during the Yala 2024 season, along with 268,234 metric tons of paddy during the Maha 2023/24 season. Therefore, the Kurunegala District often accounts for around 10% of Sri Lanka's total paddy production [32, 33]. These figures highlight the pivotal role of the Kurunegala District in ensuring national paddy production. Recent studies have shown that the Kurunegala District is a highly vulnerable area to climate change, particularly in terms of droughts [15, 27].”

 

• Comment 4

In the part Questionnaire Design and Data Collection: how the sampling has been chosen? With the use of which formulas?

 

Response

The authors welcome the suggestion. The relevant section was modified as follows to improve the clarity.

 

“2.3. Sample Size and Data Collection

This study was conducted from March to December 2024. A total of 250 paddy farmers representing eight Divisional Secretariat Divisions in the Kurunegala District were selected as the sample based on the Cochran’s formula with finite population correction [34]. The sampling frame was derived from the farmer registry of the Department of Agrarian Development (DAD), which contains information on land extent, ownership, and irrigation source for all registered paddy farmers. A stratified random sampling approach was employed based on the irrigated nature of the paddy fields (major and minor irrigation schemes), as irrigation type is a key determinant of drought exposure, productivity, and insurance participation. This stratification ensured proportional representation of farmers from both irrigation systems within the study sample.”

 

• Comment 5

In the part Development of Choice Sets: in the table 1 it is advisable to enrichen this table with the description of the attributes and description of permissible levels of used indices/measures.

 

Response

The authors welcome the suggestion. This information is already available in the manuscript, as mentioned below.

 

“Flood and drought represented the two most significant hazards experienced in paddy production. Meanwhile, the 'assessment method' referred to the method used to assess crop damage after a hazard occurred. This included two options: index-based assessment and on-field assessment (physical verification of the crop damage). The third attribute' premium' referred to the amount of premium that is charged from the farmer per acre of paddy per season, which was assigned with Sri Lankan Rupees (LKR): LKR800 and LKR1000 as the levels in the monetary attribute. The fourth attribute, ‘No Hazard Return (NHR)’, represented the benefit a farmer would receive if no crop damage occurred during the insured season. Under this option, a proportion of the insured amount either 50% or 75% would be refunded or rolled over to the next season, allowing farmers to gain a partial benefit even in hazard-free years.

 

Four choice cards were developed, each comprising two alternative crop-insurance schemes and one opt-out (‘no-insurance’) option. The number of cards was intentionally limited to four to reduce cognitive burden and respondent fatigue, while ensuring adequate variation across the attribute levels. The order of presentation was randomized among respondents. Prior to the survey, a pilot test confirmed that this number of choice sets was appropriate for maintaining respondent engagement and comprehension.”

 

• Comment 6

The abbreviations in the table 4 (SD, D, N, A, SA) must be explained in the text. 

 

Response

Please note that abbreviations have been already mentioned under each table (Tables 4, 5 and 6) as a table note, adhering to journal guidelines.

 

“Note: SD=Strongly Disagree; D=Disagree; N=Neutral; A=Agree; SA=Strongly Agree; RII=Relative Importance Index.”

 

• Comment 7

In the Figure 1: are there any other options than option 1, option 2 and no option?

 

Response

The authors welcome the suggestion. The following paragraph was added into the manuscript to clarify this.

 

“Four choice cards were developed, each comprising two alternative crop-insurance schemes and one opt-out (‘no-insurance’) option. The number of cards was intentionally limited to four to reduce cognitive burden and respondent fatigue, while ensuring adequate variation across the attribute levels. The order of presentation was randomized among respondents. Prior to the survey, a pilot test confirmed that this number of choice sets was appropriate for maintaining respondent engagement and comprehension.”

 

• Comment 8

The information on the insurance, its definition/s, classification/s, pros and cons should be placed at the beginning of the manuscript.

 

Response

The authors welcome the suggestion. This information is already available in the introduction, as indicated below.

 

“Agricultural insurance is a widely utilized risk management tool that supports farmers in dealing with variations in yield and climate-related shocks. It functions as a risk-transfer mechanism that can smooth consumption and incomes aftershocks, reduce downside risk, and encourage technology adoption, thereby enhancing resilience by lowering the probability of distress sales and debt traps [11, 12]. Currently, crop insurance is practiced in more than 70 countries in the world, supporting the stabilisation of income, reducing volatility, and strengthening against CC impacts [12]. Often, crop insurance schemes operate in the form of indemnity-based insurance, where the loss is paid, or index-based insurance, where the payment is based on the weather or yield indices [11]. One of the main shortcomings of index-based schemes is that their payments can diverge from real losses, a phenomenon known as basis risk [12].

 

In theory, when farmers perceive higher climatic risk and if index triggers are well-designed and trusted, the acceptance of crop insurance schemes should increase, as insurance reduces vulnerability and supports resilience-building investments [5, 12].”

 

• Comment 9

What are the limitations of the research?

 

Response

The limitations of the study have been already mentioned at the end of the discussion section as follows. Please note that these additions were approved during the response to editorial comments in round 1.

 

“Despite providing paddy farmer's acceptance of crop insurance and driving factors behind the adoption rate, this study faced several limitations. Only the paddy farmers in the Kurunegala District were considered in this study, which could limit the generalizability of the findings to other regions, crops, or socio-economic contexts. In addition, reliance on self-reported responses in the choice modeling could introduce hypothetical or social desirability biases. Although the conditional logit model effectively identified key attributes influencing insurance decisions, unobserved factors such as informal coping strategies, individual risk perceptions, and institutional support may influence the farmer intention to accept crop insurance schemes.”

 

• Comment 10

How the limitations can be decreased?

 

Response

The authors had included the suggestion to restrict the limitations in the same paragraph as follows. Please note that these additions were approved during the response to editorial comments in round 1.

 

“Therefore, further studies with expansions in the geographic scope are recommended, which incorporate longitudinal data, qualitative insights to comprehensively understand the farmer decision-making and strengthen the evidence base for policy design.”

 

• Comment 11

What are the recommendations from the research?

 

Response

The recommendations from this study have been already mentioned in the manuscript as follows. Please note that these additions were added earlier in response to editorial comments and thereafter approved during the round 1.

 

“Addressing these limitations, the findings of this study underscore the necessity of designing crop insurance schemes that balance affordability, comprehensive coverage, along with transparent claiming and damage assessment procedures. Simplifying claiming procedures, improving damage assessment methods, offering field-level verifications, and incorporating tangible benefits such as no-hazard returns could further enhance trust and participation of paddy farmers in crop insurance. Tailored premium structures and targeted awareness programmes for small and medium scale farmers would further improve adoption of crop insurance, ensuring inclusivity and equity. Development of context-specific insurance products that reduce financial barriers, improve credibility, and provide reliable risk coverage, policymakers and insurers can strengthen farmers' resilience, sustain agricultural livelihoods, and support climate-smart adaptation strategies in Sri Lanka and similar agricultural settings [39, 41].”

 

• Comment 12

Where are the hypothesis?

 

Response

The authors welcome the comment. Indicating the hypothesis separately is not mandatory in a research article, since the objectives and the statistical analysis indirectly covers these aspects. Some journal formats require explicit indication of hypotheses in the research article, which is subjective. In this journal indication of the hypothesis was not mandatory.

 

However, the hypothesis of the current study was “The Willingness-to-Pay for crop insurance schemes among the paddy farmers in the Kurunegala District is significantly influenced by their perceptions, socio-economic factors and operational factors of the existing crop insurance schemes”.” 

 

• Comment 13

Is the hypothesis confirmed / rejected? Why yes / why no?

 

Response

As comprehensively described in the results sections, the findings have denoted that the Willingness-to-Pay for crop insurance schemes among the paddy farmers is significantly influenced by several factors such as their positive attitude toward crop insurance, nature of the damage assessment method, higher premiums and provision of tangible benefits such as no-hazard returns.

 

• Comment 14

The titles of the tables and charts have to be checked and their sources have to be listed below them.

 

Response

The authors welcome the suggestion. Captions/titles of all tables and figures have been appropriately mentioned. All the tables and figures have been developed by the authors, exclusively for this research article. According to the journal guidelines, mentioning the source of self-developed figures is not essential.

 

• Comment 15

What do I gain as a reader from the conclusion? Is there any linkage, is not? Between which variables? Why? Explain, etc.

 

Response

We sincerely thank the reviewer for this valuable comment. In response, the conclusion has been revised to clearly emphasize the linkages identified through the conditional logit model between the key variables influencing farmers’ WTP for crop insurance. The revised section now explains how specific attributes—climate hazards, premium levels, compensation during hazard-free periods, and assessment methods—collectively shape the farmers’ intention to adopt crop insurance schemes.

 

To strengthen the reader’s understanding, the concluding section was also expanded to present the policy implications of these linkages. It now discusses how affordability, trust, and inclusivity must be jointly addressed to enhance the effectiveness of crop insurance schemes.

 

“Despite being recognized as a key tool for managing risk, a higher fraction of paddy farmers in the Kurunegala district were reluctant to purchase agricultural insurance schemes. Complications experienced in the claim form filling process, limited coverage of crops and poor service quality were identified as the major limitations in existing crop insurance schemes. The results further revealed that climate hazards, premium levels, and compensation for 'if no hazard' periods were the most influential attributes determining farmers’ WTP. Among these, the positive significance of the hazard attribute clearly indicated farmers’ higher preference for insurance products that protect against flood and drought events, while the negative coefficient for premium levels emphasized cost sensitivity and affordability concerns. Furthermore, the positive response to compensation in hazard-free periods suggested that farmers value continuity and fairness in returns, revealing their preference for insurance schemes that provide year-round assurance rather than seasonal risk coverage. The high value placed on continuity of coverage, even during hazard-free periods, underscores the importance of designing schemes that provide tangible benefits across all conditions. Conversely, concerns over assessment methods can reduce willingness to participate, emphasizing the need for transparent and credible claims procedures.

 

Despite the introduction of new agricultural insurance schemes over time, aforementioned inconsistencies and operational challenges have limited their effectiveness. Therefore, many farmers continue to rely on ad hoc relief measures to manage crop risks, underscoring the need for reliable, transparent, and tailored crop insurance schemes to meet the practical needs of paddy farmers. Developing responsive, context-specific crop insurance programmes that reduce financial barriers, enhance trust, and provide reliable risk coverage is essential to strengthen farmers' resilience, sustain agricultural livelihoods, and support climate-smart adaptation strategies in Sri Lanka.

 

From a policy perspective, strengthening crop insurance adoption requires a multi-pronged approach that integrates institutional reform, technological innovation, and capacity building. Policymakers should prioritize the development of affordable premium structures, supported by targeted subsidies or risk-sharing mechanisms for smallholder farmers. Simplifying claim procedures through digital platforms, mobile-based applications, and automated weather-indexed payout systems can substantially improve efficiency and reduce transaction delays. Enhancing transparency in damage assessments through community-based verification or independent audit mechanisms would play a pivotal role in rebuilding farmer confidence in insurance providers. In parallel, continuous awareness and financial literacy programmes are needed to enable farmers to better understand insurance principles, coverage options, and long-term benefits. Establishing stronger coordination among government institutions, private insurers, and local agricultural officers will ensure inclusivity, reduce administrative bottlenecks, and expand coverage to marginalized farming communities. By adopting such comprehensive reforms, Sri Lanka can transition from a reactive disaster-compensation model toward a proactive, sustainable, and climate-resilient agricultural insurance framework.”

 

• Comment 16

The Conclusion is too short.    

 

Response

The authors welcome the suggestion. The conclusion section was expanded as follows.

 

“Despite being recognized as a key tool for managing risk, a higher fraction of paddy farmers in the Kurunegala district were reluctant to purchase agricultural insurance schemes. Complications experienced in the claim form filling process, limited coverage of crops and poor service quality were identified as the major limitations in existing crop insurance schemes. The results further revealed that climate hazards, premium levels, and compensation for 'if no hazard' periods were the most influential attributes determining farmers’ WTP. Among these, the positive significance of the hazard attribute clearly indicated farmers’ higher preference for insurance products that protect against flood and drought events, while the negative coefficient for premium levels emphasized cost sensitivity and affordability concerns. Furthermore, the positive response to compensation in hazard-free periods suggested that farmers value continuity and fairness in returns, revealing their preference for insurance schemes that provide year-round assurance rather than seasonal risk coverage. The high value placed on continuity of coverage, even during hazard-free periods, underscores the importance of designing schemes that provide tangible benefits across all conditions. Conversely, concerns over assessment methods can reduce willingness to participate, emphasizing the need for transparent and credible claims procedures.

 

Despite the introduction of new agricultural insurance schemes over time, aforementioned inconsistencies and operational challenges have limited their effectiveness. Therefore, many farmers continue to rely on ad hoc relief measures to manage crop risks, underscoring the need for reliable, transparent, and tailored crop insurance schemes to meet the practical needs of paddy farmers. Developing responsive, context-specific crop insurance programmes that reduce financial barriers, enhance trust, and provide reliable risk coverage is essential to strengthen farmers' resilience, sustain agricultural livelihoods, and support climate-smart adaptation strategies in Sri Lanka.

 

From a policy perspective, strengthening crop insurance adoption requires a multi-pronged approach that integrates institutional reform, technological innovation, and capacity building. Policymakers should prioritize the development of affordable premium structures, supported by targeted subsidies or risk-sharing mechanisms for smallholder farmers. Simplifying claim procedures through digital platforms, mobile-based applications, and automated weather-indexed payout systems can substantially improve efficiency and reduce transaction delays. Enhancing transparency in damage assessments through community-based verification or independent audit mechanisms would play a pivotal role in rebuilding farmer confidence in insurance providers. In parallel, continuous awareness and financial literacy programmes are needed to enable farmers to better understand insurance principles, coverage options, and long-term benefits. Establishing stronger coordination among government institutions, private insurers, and local agricultural officers will ensure inclusivity, reduce administrative bottlenecks, and expand coverage to marginalized farming communities. By adopting such comprehensive reforms, Sri Lanka can transition from a reactive disaster-compensation model toward a proactive, sustainable, and climate-resilient agricultural insurance framework.”

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Overall Evaluation:

The paper is on a timely and relevant topic related to climate risk and agricultural resilience. It applies an appropriate empirical method—in this case, the Discrete Choice Experiment and Conditional Logit Model (DCE CLM)—to identify the predictors of farmers’ willingness to pay (WTP) for crop insurance. Although the article is a solid contribution to the field of study, it requires substantial revision to enhance analytical complexity, structural coherence, and clarity.

Advantages/Strengths:

• Justification:The topic is timely and relevant to climate change adaptation and agriculture.
• Methodological:The choice of DCE and CLM is well-reasoned and well-explained.
• Background:The review and context provided a rich overview of the global and local issues.
• Empirical:The study provides original data on farmers’ perceptions, behaviors, and WTP in insurance coverage.
• Policy:The recommendations are actionable and applicable for crop insurance development in Sri Lanka.

Disadvantages/Limitations:

• General:The study has limited generalizability due to its focus on one district and a single crop.
• Sample:The sample size (248 farmers) is small for the region and likely not very representative.
• Analysis:The pseudo R² of 0.051 is extremely low and under-discussed.
• Biases:The study does not sufficiently address hypothetical and social desirability biases inherent in the stated preference approaches.
• Length:The text is excessive and repetitive in several places.
• Theory:There is little theoretical integration when the empirical sections are linked back to RUT or the Lancaster model.
• Presentation:Some tables and figures are cluttered and under-explained.
• Language:Some minor grammar and language irregularities remain in the manuscript.

Overall Problems:

• Statistical:The statistical robustness is weak in the pseudo R² and the lack of checks for robustness or sensitivity.
• Generalizability:The external validity beyond the region and crop is ambiguous from the conclusion.
• Assumptions:The discussion of uncertainty about the survey and other factors is generalized.
• Length:There is unnecessary repetition in several sections.
• Theory:The separation of theory and the empirical does not provide a bridge for understanding.

Recommendations:         

• Statistical:Add in robustness checks, interactions, mixed models, etc.
• Length:Condense both the introduction and discussion.
• Theory:Develop more on the biases and limitations of the survey.
• Presentation:Simplify the tables and figures for the readers.
• Proof:Check the grammar and formatting.

Overall Recommendation:

The paper has a relevant context and a consistent methodology; however, the analytical rigor and theoretical explanation can be enhanced, along with the presented material.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Some minor grammar and language irregularities remain in the manuscript.

Author Response

The Authors are thankful for the valuable comments of the reviewers that improved the overall quality of the manuscript. All the revisions suggested have been addressed in the revised version of the manuscript. In addition to these, authors are willing to comply with any additional revisions suggested by the editorial board to further improve the manuscript.

 

• Reviewer 3

The authors are thankful for highlighting the advantages and limitations of the manuscript. Since the limitations of the study have been repeatedly highlighted as “overall problems and recommendations”, we have addressed those issues raised by the reviewer in a comprehensive manner.

 

• Comment 1

The study has limited generalizability due to its focus on one district and a single crop.

 

Response

The authors welcome the suggestion. Reason for selecting the Kurunegala District of Sri Lanka and paddy has been already justified within the manuscript. Paddy remain as the major staple food in Sri Lanka, similar to many developing countries. Therefore, paddy cultivation plays a key role in Sri Lankan agriculture sector and a large number of farmers are engaged in paddy cultivation. Kurunegala District is one of the major paddy cultivating districts in Sri Lanka, which is prone to numerous ill impacts of climate change.

 

The following sections in the introduction and methodology justifies the selection of the study area and the crop.

 

In the introduction,

“In theory, when farmers perceive higher climatic risk and if index triggers are well-designed and trusted, the acceptance of crop insurance schemes should increase, as insurance reduces vulnerability and supports resilience-building investments [5, 12]. However, empirical research indicates that while farmers globally express interest in insurance, the actual adoption rate remains low, particularly in developing countries. Similar to many developing countries in the region, agriculture serves as a major pillar of the Sri Lankan economy. According to the Central Bank, the agriculture and fisheries sector contributed approximately 7.7% to Sri Lanka's Gross Domestic Product (GDP) in the first quarter of 2024 [13]. Paddy production is one of the main productions and a staple food in Sri Lanka. Paddy is cultivated in all districts of Sri Lanka, primarily during the two monsoon seasons, namely the Yala and Maha seasons [14]. It is estimated that about 708,000 ha of land are under paddy cultivation [15]. Farmers have experienced substantial damage and losses in paddy production due to severe climate shocks, such as droughts and floods, over recent years [16]. According to recent reports from the Agricultural and Agrarian Insurance Board (AAIB), droughts have damaged approximately 58,766 acres of paddy, with the highest damage reported in the Kurunegala district. Meanwhile, extensive floods have damaged approximately 47,000 acres of paddy fields, directly impacting 20,064 farmers during the 2023 Maha season [15]. Therefore, Sri Lanka's paddy cultivation urgently needs sustainable adaptation strategies to mitigate the adverse effects of CC and to sustain food security [16,17].”

 

In the methodology,

“The Kurunegala District, located in the North Western Province of Sri Lanka, has a population of 1,760,829 and spans an area of 4,816 square kilometers. Around 625 km² of the Kurunegala district remains under paddy cultivation [15, 31]. It spans three agro-ecological zones: DL₁, IL_1, and IL₃, encompassing both the dry and intermediate zones of Sri Lanka. Regions belonging to the dry zone receive an average rainfall <1750 mm, while the intermediate zone is characterized by a mean annual rainfall of 1750 mm to 2500 mm [31]. Kurunegala District has long been recognized as one of Sri Lanka's most significant paddy-producing regions [24]. Its agro-ecological conditions, combined with an extensive network of both major and minor irrigation schemes, contribute to its paddy production. According to the Department of Census and Statistics, Kurunegala produced approximately 210,243 metric tons of paddy from 56,697 hectares during the Yala 2024 season, along with 268,234 metric tons of paddy during the Maha 2023/24 season. Therefore, the Kurunegala District often accounts for around 10% of Sri Lanka's total paddy production [32, 33]. These figures highlight the pivotal role of the Kurunegala District in ensuring national paddy production. Recent studies have shown that the Kurunegala District is a highly vulnerable area to climate change, particularly in terms of droughts [15, 27].”

 

• Comment 2

The sample size (248 farmers) is small for the region and likely not very representative.

 

Response

The authors welcome the suggestion. The sample size for this survey was calculated using the Cochran’s formula with finite population correction. Kindly note that none of the other three reviewers haven’t raised any similar concern regarding the sample size. The relevant section was revised as follows to provide the details of the sample size calculation.

 

“2.3. Sample Size and Data Collection

This study was conducted from March to December 2024. A total of 250 paddy farmers representing eight Divisional Secretariat Divisions in the Kurunegala District were selected as the sample based on the Cochran’s formula with finite population correction [34]. The sampling frame was derived from the farmer registry of the Department of Agrarian Development (DAD), which contains information on land extent, ownership, and irrigation source for all registered paddy farmers. A stratified random sampling approach was employed based on the irrigated nature of the paddy fields (major and minor irrigation schemes), as irrigation type is a key determinant of drought exposure, productivity, and insurance participation. This stratification ensured proportional representation of farmers from both irrigation systems within the study sample.”

 

• Comment 3

The statistical robustness is weak in the pseudo R² and the lack of checks for robustness or sensitivity.

 

Response

The authors welcome the comment. Usually for CLM the goodness of fit and the robustness of the model are tested using pseudo R² and Wald chi-square statistics. In our case we have presented both values in the text, as follows.

 

“The CLM demonstrated a good overall fit to the data. The pseudo R² was calculated at 0.051, which is acceptable in discrete choice modeling, where lower pseudo R² values are common due to the complexity of human decision-making. Furthermore, the Wald chi-square test was highly significant (χ² = 34.57, p < 0.001), confirming that the set of explanatory variables included in the model significantly improves the model fit.”

 

It has been well proven that in discrete choice modelling, pseudo-R² values are typically lower than general R² values in linear regression, because they measure improvement over a null (equal probability) model rather than explained variance. As indicated by McFadden (1974), low pseudo-R² values are considered indicative of good model fit in Conditional Logit models. The pseudo-R² in our model (0.051) falls within this acceptable range for stated-preference experiments in agricultural insurance contexts (especially with social sciences or consumer behaviour, a low Pseudo-R² is common because a wide variety of factors influence human behaviour). Further, the Wald-chi square statistics are also satisfactory. Therefore, the authors believe that robustness or sensitivity of the fitted CLM model is acceptable and satisfactory. Kindly note that we consulted few experts in statistics regarding this matter.

 

Reference

McFadden, D. Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). New York: Academic Press, 1974.

 

• Comment 4

The external validity beyond the region and crop is ambiguous from the conclusion.

 

Response

The authors are grateful for highlighting this concern. As highlighted in the response for comment 1, paddy act as a major staple food in Sri Lanka and many neighbouring countries in South-east Asia. The revised conclusion now acknowledges that the analysis was conducted among paddy farmers in the Kurunegala District and recognizes this as a contextual limitation when generalizing the results. However, the discussion and conclusion have been strengthened to reflect that the behavioural patterns and influencing attributes, such as the sensitivity to premium levels, preference for hazard coverage, and trust in assessment mechanisms, are consistent with the findings reported from other developing-country contexts, including India, Bangladesh, and Ghana. This alignment supports the broader applicability of the study outcomes beyond the specific region and crop.

 

“Although the present study was based on paddy farmers in the Kurunegala District, the identified behavioural patterns, such as sensitivity to premiums, preference for multi-hazard coverage, and trust-related concerns, reflect broader dynamics observed among smallholder farmers in others districts of Sri Lanka and many developing-country contexts. Hence, these insights extend beyond the specific regional and crop setting, offering implications for similar agricultural systems exposed to comparable climatic and institutional challenges.”

 

Furthermore, the conclusion highlights that the identified linkages between key variables and farmers’ WTP provide transferable insights for designing and refining agricultural insurance schemes targeting smallholder farming systems in similar socio-economic and climatic settings. Hence, while the study remains region-specific in scope, the underlying behavioural responses and policy implications offer a useful foundation for scaling and adaptation across comparable agricultural regions.

 

• Comment 5

The discussion of uncertainty about the survey and other factors is generalized.

 

Response

The authors are grateful for the comment. We have revised the section on limitations to explicitly elaborate on the sources of uncertainty related to the survey, modeling, and contextual factors. Specifically, we highlighted the potential for response biases, seasonal effects, and the influence of prior experiences on farmers’ stated preferences. We also clarified the assumptions and limitations of the CLM, including homogeneous preferences and the IIA assumption, and note that unobserved institutional, as indicated below.

 

“Despite providing paddy farmer's acceptance of crop insurance and driving factors behind the adoption rate, this study faced several limitations. Only the paddy farmers in the Kurunegala District were considered in this study, which could limit the generalizability of the findings to other regions, crops, or socio-economic contexts. In addition, reliance on self-reported responses in the choice modeling could introduce hypothetical or social desirability biases. Even though, face-to-face interviews were conducted to minimize misunderstanding of choice cards, some respondents may still have interpreted hypothetical scenarios differently depending on their prior experience with climate hazards or insurance participation. This is a common limitation in similar studies. Seasonal variations and timing of data collection of this study could also have influenced risk perception and WTP, as farmers’ attitudes often vary before and after major cultivation cycles.

 

Although the conditional logit model effectively identified key attributes influencing insurance decisions, unobserved factors such as informal coping strategies, individual risk perceptions, and institutional support may influence the farmer intention to accept crop insurance schemes. Moreover, the model assumed homogeneous preferences and independence of irrelevant alternatives, which may overlook variations in farmer heterogeneity and local-level risk behaviour. Future studies could apply mixed logit or latent class approaches to capture these differences more accurately and provide deeper behavioral insights. Therefore, further studies with expansions in the geographic scope are recommended, which incorporate longitudinal data, qualitative insights to comprehensively understand the farmer decision-making and strengthen the evidence base for policy design. Consideration of institutional factors such as government relief policies, market incentives, and administrative delays would also enhance the robustness and external validity of future analyses.

 

Addressing these limitations, the findings of this study underscore the necessity of designing crop insurance schemes that balance affordability, comprehensive coverage, along with transparent claiming and damage assessment procedures. Simplifying claiming procedures, improving damage assessment methods, offering field-level verifications, and incorporating tangible benefits such as no-hazard returns could further enhance trust and participation of paddy farmers in crop insurance. Tailored premium structures and targeted awareness programmes for small and medium scale farmers would further improve adoption of crop insurance, ensuring inclusivity and equity. Development of context-specific insurance products that reduce financial barriers, improve credibility, and provide reliable risk coverage, policymakers and insurers can strengthen farmers' resilience, sustain agricultural livelihoods, and support climate-smart adaptation strategies in Sri Lanka and similar agricultural settings [40, 42].”

 

We believe that these additions will provide a more detailed and transparent account of uncertainty, while maintaining the relevance and interpretability of the study findings, and offer guidance for future research to strengthen robustness and external validity.

 

• Comment 6

There is unnecessary repetition in several sections.

 

Response

The authors are thankful to the comment. To our understanding, the most essential facts have been presented and certain facts have been further highlighted in response to editorial comments. None of the other three reviewers and the editors have not highlighted any repetitions in the manuscript. If the respected reviewer can point out such repetitions, we are happy to address such instances.

 

• Comment 7

The separation of theory and the empirical does not provide a bridge for understanding.

 

Response

We agree that highlighting the conceptual link between the theoretical framework and the empirical design can further strengthen the manuscript. Accordingly, we have added a bridging paragraph in the manuscript under methodology section (section 2.1), as follows.

 

“In the context of this study, Lancaster’s Consumer Theory implies that farmers derive utility not from ‘crop insurance’ as a whole, but from its attributes, such as the extent of hazard coverage, premium level, damage assessment procedure, and no‐hazard return benefits. Random Utility Theory further suggests that farmers choose the insurance alternative that provides the highest perceived utility, relative to remaining uninsured (status quo). Therefore, the empirical framework of this study employed a Discrete Choice Experiment to present farmers with alternative insurance profiles that vary systematically across these attributes. The resulting choices are modelled using the Conditional Logit Model, allowing estimation of the marginal utility associated with each attribute and computing corresponding MWTP values.”

 

• Comment 8

Simplify the tables and figures for the readers.

 

Response

The comment is welcome. We have prepared the figures and tables in a simpler and self-explanatory manner, which can be easily understood by the readers. Further, a similar way of presentation is being used in the majority of research articles published in the same journal and other reputed journals. Please note that no similar concerns have been raised by the other three reviewers or the editor under their comments. Therefore, the authors humbly disagree with this recommendation.

 

• Comment 9

Check the grammar and formatting.

 

Response

The comment is welcome. All the other three reviewers and the editor (in the round one) have approved the grammar and formatting to be fine. However, respecting the valuable reviewer, the manuscript was again edited by a professional English language editor to address any grammatical concerns.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

This is an interesting paper which explores the willingness to pay for crop insurance among paddy farmers in the Kurunegala District of Sri Lanka, using a Discrete Choice Experiment and Conditional Logit Model. The study contributes to the literature on climate-resilient agricultural practices in developing economies.

I believe that the topic of this research paper should be accepted after minor revision. The following suggestions may improve the paper publishable here.

1. Originality: The topic is relevant to climate change adaptation and agricultural sustainability. The research paper offers empirical evidence from Sri Lanka, filling a significant gap in South Asian crop insurance literature.
2. Relationship to Literature: The paper demonstrates an adequate understanding of the relevant literature in the field. Even though the objectives are implied, the authors could add a clear statement of research objectives at the end of the introduction.
3. Methodology/Data: The paper follows a systematic and theoretically grounded approach, employing Lancaster’s consumer theory and Random Utility Theory. The Conditional Logit Model is appropriately chosen to model farmer choice behavior. The sample size (248 farmers) is adequate, the stratified random sampling enhances representativeness, and data collection instruments are well described.
4. Results/Discussion: Τhe results are well presented and analyzed. Discussion section ties the results together the other elements of the paper/other similar research papers.
5. Conclusion/Implications: The conclusions are consistent with the evidence presented. However, the paper could include a stronger statement on how policymakers and insurance companies could operate the paper’s recommendations.

Author Response

Response to the Reviewer Comments (Sustainability-3866605)

 

Authors are thankful for the valuable comments of the reviewers that improved the overall quality of the manuscript. All the revisions suggested have been addressed in the revised version of the manuscript. In addition to these, authors are willing to comply with any additional revisions suggested by the editorial board to further improve the manuscript.

 

• Reviewer 4

This is an interesting paper which explores the willingness to pay for crop insurance among paddy farmers in the Kurunegala District of Sri Lanka, using a Discrete Choice Experiment and Conditional Logit Model. The study contributes to the literature on climate-resilient agricultural practices in developing economies.

 

I believe that the topic of this research paper should be accepted after minor revision. The following suggestions may improve the paper publishable here.

 

Originality: The topic is relevant to climate change adaptation and agricultural sustainability. The research paper offers empirical evidence from Sri Lanka, filling a significant gap in South Asian crop insurance literature.

 

Methodology/Data: The paper follows a systematic and theoretically grounded approach, employing Lancaster’s consumer theory and Random Utility Theory. The Conditional Logit Model is appropriately chosen to model farmer choice behaviour. The sample size (248 farmers) is adequate, the stratified random sampling enhances representativeness, and data collection instruments are well described.

 

Results/Discussion: Τhe results are well presented and analysed. Discussion section ties the results together the other elements of the paper/other similar research papers.

 

Conclusion/Implications: The conclusions are consistent with the evidence presented. However, the paper could include a stronger statement on how policymakers and insurance companies could operate the paper’s recommendations.

 

Response

The authors are thankful for the valuable comments of the reviewer.

 

• Comment 2

Relationship to Literature: The paper demonstrates an adequate understanding of the relevant literature in the field. Even though the objectives are implied, the authors could add a clear statement of research objectives at the end of the introduction.

 

Response

The authors are thankful for the suggestion. The relevant section was revised as suggested.

 

“Paddy farming remains a key pillar in the Sri Lankan agricultural sector, which is highly affected by the floods and droughts [21, 24]. Therefore, the current study aimed to employ a Discrete Choice Experiment (DCE) based approach to estimate the WTP of paddy farmers towards crop insurance schemes, with a special focus on their perceptions of specific insurance characteristics that shape their enrollment into crop insurance schemes. Under this, the impact of four major attributes of crop insurance — hazard coverage, assessment method, premium, and no-hazard return — on the willingness of paddy farmers to adopt crop insurance schemes was investigated. The outcomes of this study would be essential for policymakers and insurance companies to address the inconsistencies and operational challenges of the existing crop insurance schemes and design reliable, transparent, and tailored crop insurance schemes to meet the practical needs of paddy farmers in Sri Lanka and other similar developing countries.”

 

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors imporved the quality of the manuscript, following my suggestions.